{ "nbformat": 4, "nbformat_minor": 0, "metadata": { "accelerator": "GPU", "colab": { "name": "Kr_changing_point.ipynb", "provenance": [], "collapsed_sections": [], "toc_visible": true }, "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.10" } }, "cells": [ { "cell_type": "markdown", "metadata": { "id": "kqXl71t9suiG" }, "source": [ "# Estimating the Date of COVID-19 Changes\n", "\n", "https://nbviewer.jupyter.org/github/jramkiss/jramkiss.github.io/blob/master/_posts/notebooks/covid19-changes.ipynb " ] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "gFnvD8OysuiI", "outputId": "8e99c305-6766-4f63-ab44-d6edb6d3f1d3" }, "source": [ "import pandas as pd\n", "import numpy as np\n", "\n", "import seaborn as sns; sns.set()\n", "import matplotlib.pyplot as plt\n", "import matplotlib.dates as mdates\n", "\n", "\n", "from sklearn.linear_model import LinearRegression\n", "\n", "from scipy import stats\n", "import statsmodels.api as sm\n", "import pylab\n", "\n", "# from google.colab import files\n", "# from io import StringIO\n", "# uploaded = files.upload()\n", "\n", "url = 'https://raw.githubusercontent.com/assemzh/ProbProg-COVID-19/master/full_grouped.csv'\n", "data = pd.read_csv(url)\n", "\n", "data.Date = pd.to_datetime(data.Date)\n", "\n", "# for fancy python printing\n", "from IPython.display import Markdown, display\n", "def printmd(string):\n", " display(Markdown(string))\n", " \n", "import warnings\n", "warnings.filterwarnings('ignore')\n", "\n", "import matplotlib as mpl\n", "mpl.rcParams['figure.dpi'] = 250" ], "execution_count": 1, "outputs": [ { "output_type": "stream", "text": [ "/usr/local/lib/python3.7/dist-packages/statsmodels/tools/_testing.py:19: FutureWarning: pandas.util.testing is deprecated. Use the functions in the public API at pandas.testing instead.\n", " import pandas.util.testing as tm\n" ], "name": "stderr" } ] }, { "cell_type": "markdown", "metadata": { "id": "hzvPpvVvphTD" }, "source": [ "## Create country\n" ] }, { "cell_type": "code", "metadata": { "id": "koX5yGHrsuib" }, "source": [ "# function to make the time series of confirmed and daily confirmed cases for a specific country\n", "def create_country (country, end_date, state = False) : \n", " if state :\n", " df = data.loc[data[\"Province/State\"] == country, [\"Province/State\", \"Date\", \"Confirmed\", \"Deaths\", \"Recovered\"]]\n", " else : \n", " df = data.loc[data[\"Country/Region\"] == country, [\"Country/Region\", \"Date\", \"Confirmed\", \"Deaths\", \"Recovered\"]]\n", " df.columns = [\"country\", \"date\", \"confirmed\", \"deaths\", \"recovered\"]\n", "\n", " # group by country and date, sum(confirmed, deaths, recovered). do this because countries have multiple cities \n", " df = df.groupby(['country','date'])['confirmed', 'deaths', 'recovered'].sum().reset_index()\n", "\n", " # convert date string to datetime\n", " std_dateparser = lambda x: str(x)[5:10]\n", " df.date = pd.to_datetime(df.date)\n", " df['date_only'] = df.date.apply(std_dateparser)\n", " df = df.sort_values(by = \"date\")\n", " df = df[df.date <= end_date]\n", "\n", "\n", " # make new confirmed cases every day:\n", " cases_shifted = np.array([0] + list(df.confirmed[:-1]))\n", " daily_confirmed = np.array(df.confirmed) - cases_shifted\n", " df[\"daily_confirmed\"] = daily_confirmed \n", "\n", " fig, ax = plt.subplots(nrows=1, ncols=1, figsize=(7, 6))\n", " ax = [ax]\n", " sns.lineplot(x = df.date, \n", " y = df.daily_confirmed, \n", " ax = ax[0])\n", "\n", " ax[0].set(ylabel='Daily Confirmed Cases')\n", "\n", " ax[0].axvline(pd.to_datetime('2020-02-25'), \n", " linestyle = '--', linewidth = 1.5,\n", " label = \"Start of Contact tracing and Quarantine: Feb 25, 2020\" ,\n", " color = \"red\") \n", "\n", " \n", " ax[0].xaxis.get_label().set_fontsize(16)\n", " ax[0].yaxis.get_label().set_fontsize(16)\n", " ax[0].title.set_fontsize(20)\n", " ax[0].tick_params(labelsize=16)\n", " myFmt = mdates.DateFormatter('%b %-d')\n", " ax[0].xaxis.set_major_formatter(myFmt)\n", "\n", " ax[0].set(ylabel='Daily Confirmed Cases', xlabel='');\n", " ax[0].legend(loc = \"bottom right\", fontsize=12.8)\n", " ax[0].xaxis.set_major_locator(mdates.MonthLocator(interval=1)) #to get a tick every month\n", "\n", " sns.set_style(\"ticks\")\n", " plt.tight_layout()\n", " sns.despine()\n", " plt.savefig('/content/sample_data/kr_daily.pdf')\n", " print(df.tail())\n", " return df\n", "\n", "\n", "def summary(samples):\n", " site_stats = {}\n", " for k, v in samples.items():\n", " site_stats[k] = {\n", " \"mean\": torch.mean(v, 0),\n", " \"std\": torch.std(v, 0),\n", " \"5%\": v.kthvalue(int(len(v) * 0.05), dim=0)[0],\n", " \"95%\": v.kthvalue(int(len(v) * 0.95), dim=0)[0],\n", " }\n", " return site_stats" ], "execution_count": 12, "outputs": [] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 577 }, "id": "w_A0fd4Zsuiw", "outputId": "d5c73e36-4989-474c-ecb3-b89fb56eb521" }, "source": [ "cad = create_country(\"South Korea\", end_date = \"2020-05-31\")" ], "execution_count": 13, "outputs": [ { "output_type": "stream", "text": [ " country date confirmed ... recovered date_only daily_confirmed\n", "126 South Korea 2020-05-27 11344 ... 10340 05-27 79\n", "127 South Korea 2020-05-28 11402 ... 10363 05-28 58\n", "128 South Korea 2020-05-29 11441 ... 10398 05-29 39\n", "129 South Korea 2020-05-30 11468 ... 10405 05-30 27\n", "130 South Korea 2020-05-31 11503 ... 10422 05-31 35\n", "\n", "[5 rows x 7 columns]\n" ], "name": "stdout" }, { "output_type": "display_data", "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "tags": [] } } ] }, { "cell_type": "code", "metadata": { "id": "UR0BM7TysujG" }, "source": [ "cad_start = \"2020-02-20\" # 13 confirmed cases\n", "cad = cad[cad.date >= cad_start].reset_index(drop = True)\n", "cad[\"days_since_start\"] = np.arange(cad.shape[0]) + 1" ], "execution_count": 14, "outputs": [] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "OaTHo6I2sujp", "outputId": "b66bde1f-8b0b-4498-fc5c-5f21526080cc" }, "source": [ "cad.shape\n", "cad_tmp = cad[cad.date < \"2020-04-15\"]\n", "cad_tmp.shape" ], "execution_count": 15, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "(55, 8)" ] }, "metadata": { "tags": [] }, "execution_count": 15 } ] }, { "cell_type": "markdown", "metadata": { "id": "loi3CtjSsuoz" }, "source": [ "## Data for Regression" ] }, { "cell_type": "code", "metadata": { "id": "Os_M7r4Tsuo4" }, "source": [ "# variable for data to easily swap it out:\n", "country_ = \"South Korea (Before April 15th)\"\n", "reg_data = cad_tmp.copy()" ], "execution_count": 16, "outputs": [] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 204 }, "id": "3RjDFEbA91X-", "outputId": "e45b7125-bdec-41c2-e1ee-8fc8b4129d0a" }, "source": [ "reg_data.head()" ], "execution_count": 17, "outputs": [ { "output_type": "execute_result", "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
countrydateconfirmeddeathsrecovereddate_onlydaily_confirmeddays_since_start
0South Korea2020-02-2010411602-20731
1South Korea2020-02-2120421602-211002
2South Korea2020-02-2243321602-222293
3South Korea2020-02-2360261802-231694
4South Korea2020-02-2483381802-242315
\n", "
" ], "text/plain": [ " country date ... daily_confirmed days_since_start\n", "0 South Korea 2020-02-20 ... 73 1\n", "1 South Korea 2020-02-21 ... 100 2\n", "2 South Korea 2020-02-22 ... 229 3\n", "3 South Korea 2020-02-23 ... 169 4\n", "4 South Korea 2020-02-24 ... 231 5\n", "\n", "[5 rows x 8 columns]" ] }, "metadata": { "tags": [] }, "execution_count": 17 } ] }, { "cell_type": "markdown", "metadata": { "id": "JkO0Z8M0supC" }, "source": [ "## Change Point Estimation in Pyro" ] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "aIUed4Ny3-oq", "outputId": "5f433c69-8987-434e-fa13-dc66a6d30ce6" }, "source": [ "!pip install pyro-ppl\n", "!pip install numpyro" ], "execution_count": 18, "outputs": [ { "output_type": "stream", "text": [ "Collecting pyro-ppl\n", "\u001b[?25l Downloading https://files.pythonhosted.org/packages/aa/7a/fbab572fd385154a0c07b0fa138683aa52e14603bb83d37b198e5f9269b1/pyro_ppl-1.6.0-py3-none-any.whl (634kB)\n", "\r\u001b[K |▌ | 10kB 18.0MB/s eta 0:00:01\r\u001b[K |█ | 20kB 17.7MB/s eta 0:00:01\r\u001b[K |█▌ | 30kB 14.7MB/s eta 0:00:01\r\u001b[K |██ | 40kB 13.9MB/s eta 0:00:01\r\u001b[K |██▋ | 51kB 11.3MB/s eta 0:00:01\r\u001b[K |███ | 61kB 12.8MB/s eta 0:00:01\r\u001b[K |███▋ | 71kB 11.6MB/s eta 0:00:01\r\u001b[K |████▏ | 81kB 12.6MB/s eta 0:00:01\r\u001b[K |████▋ | 92kB 11.6MB/s eta 0:00:01\r\u001b[K |█████▏ | 102kB 10.8MB/s eta 0:00:01\r\u001b[K |█████▊ | 112kB 10.8MB/s eta 0:00:01\r\u001b[K |██████▏ | 122kB 10.8MB/s eta 0:00:01\r\u001b[K |██████▊ | 133kB 10.8MB/s eta 0:00:01\r\u001b[K |███████▎ | 143kB 10.8MB/s eta 0:00:01\r\u001b[K |███████▊ | 153kB 10.8MB/s eta 0:00:01\r\u001b[K |████████▎ | 163kB 10.8MB/s eta 0:00:01\r\u001b[K |████████▉ | 174kB 10.8MB/s eta 0:00:01\r\u001b[K |█████████▎ | 184kB 10.8MB/s eta 0:00:01\r\u001b[K |█████████▉ | 194kB 10.8MB/s eta 0:00:01\r\u001b[K |██████████▎ | 204kB 10.8MB/s eta 0:00:01\r\u001b[K |██████████▉ | 215kB 10.8MB/s eta 0:00:01\r\u001b[K |███████████▍ | 225kB 10.8MB/s eta 0:00:01\r\u001b[K |███████████▉ | 235kB 10.8MB/s eta 0:00:01\r\u001b[K |████████████▍ | 245kB 10.8MB/s eta 0:00:01\r\u001b[K |█████████████ | 256kB 10.8MB/s eta 0:00:01\r\u001b[K |█████████████▍ | 266kB 10.8MB/s eta 0:00:01\r\u001b[K |██████████████ | 276kB 10.8MB/s eta 0:00:01\r\u001b[K |██████████████▌ | 286kB 10.8MB/s eta 0:00:01\r\u001b[K |███████████████ | 296kB 10.8MB/s eta 0:00:01\r\u001b[K |███████████████▌ | 307kB 10.8MB/s eta 0:00:01\r\u001b[K |████████████████ | 317kB 10.8MB/s eta 0:00:01\r\u001b[K |████████████████▌ | 327kB 10.8MB/s eta 0:00:01\r\u001b[K |█████████████████ | 337kB 10.8MB/s eta 0:00:01\r\u001b[K |█████████████████▋ | 348kB 10.8MB/s eta 0:00:01\r\u001b[K |██████████████████ | 358kB 10.8MB/s eta 0:00:01\r\u001b[K |██████████████████▋ | 368kB 10.8MB/s eta 0:00:01\r\u001b[K |███████████████████ | 378kB 10.8MB/s eta 0:00:01\r\u001b[K |███████████████████▋ | 389kB 10.8MB/s eta 0:00:01\r\u001b[K |████████████████████▏ | 399kB 10.8MB/s eta 0:00:01\r\u001b[K |████████████████████▋ | 409kB 10.8MB/s eta 0:00:01\r\u001b[K |█████████████████████▏ | 419kB 10.8MB/s eta 0:00:01\r\u001b[K |█████████████████████▊ | 430kB 10.8MB/s eta 0:00:01\r\u001b[K |██████████████████████▏ | 440kB 10.8MB/s eta 0:00:01\r\u001b[K |██████████████████████▊ | 450kB 10.8MB/s eta 0:00:01\r\u001b[K |███████████████████████▎ | 460kB 10.8MB/s eta 0:00:01\r\u001b[K |███████████████████████▊ | 471kB 10.8MB/s eta 0:00:01\r\u001b[K |████████████████████████▎ | 481kB 10.8MB/s eta 0:00:01\r\u001b[K |████████████████████████▉ | 491kB 10.8MB/s eta 0:00:01\r\u001b[K |█████████████████████████▎ | 501kB 10.8MB/s eta 0:00:01\r\u001b[K |█████████████████████████▉ | 512kB 10.8MB/s eta 0:00:01\r\u001b[K |██████████████████████████▍ | 522kB 10.8MB/s eta 0:00:01\r\u001b[K |██████████████████████████▉ | 532kB 10.8MB/s eta 0:00:01\r\u001b[K |███████████████████████████▍ | 542kB 10.8MB/s eta 0:00:01\r\u001b[K |███████████████████████████▉ | 552kB 10.8MB/s eta 0:00:01\r\u001b[K |████████████████████████████▍ | 563kB 10.8MB/s eta 0:00:01\r\u001b[K |█████████████████████████████ | 573kB 10.8MB/s eta 0:00:01\r\u001b[K |█████████████████████████████▍ | 583kB 10.8MB/s eta 0:00:01\r\u001b[K |██████████████████████████████ | 593kB 10.8MB/s eta 0:00:01\r\u001b[K |██████████████████████████████▌ | 604kB 10.8MB/s eta 0:00:01\r\u001b[K |███████████████████████████████ | 614kB 10.8MB/s eta 0:00:01\r\u001b[K |███████████████████████████████▌| 624kB 10.8MB/s eta 0:00:01\r\u001b[K |████████████████████████████████| 634kB 10.8MB/s \n", "\u001b[?25hRequirement already satisfied: torch>=1.8.0 in /usr/local/lib/python3.7/dist-packages (from pyro-ppl) (1.8.1+cu101)\n", "Requirement already satisfied: opt-einsum>=2.3.2 in /usr/local/lib/python3.7/dist-packages (from pyro-ppl) (3.3.0)\n", "Requirement already satisfied: tqdm>=4.36 in /usr/local/lib/python3.7/dist-packages (from pyro-ppl) (4.41.1)\n", "Collecting pyro-api>=0.1.1\n", " Downloading https://files.pythonhosted.org/packages/fc/81/957ae78e6398460a7230b0eb9b8f1cb954c5e913e868e48d89324c68cec7/pyro_api-0.1.2-py3-none-any.whl\n", "Requirement already satisfied: numpy>=1.7 in /usr/local/lib/python3.7/dist-packages (from pyro-ppl) (1.19.5)\n", "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.7/dist-packages (from torch>=1.8.0->pyro-ppl) (3.7.4.3)\n", "Installing collected packages: pyro-api, pyro-ppl\n", "Successfully installed pyro-api-0.1.2 pyro-ppl-1.6.0\n", "Collecting numpyro\n", "\u001b[?25l Downloading https://files.pythonhosted.org/packages/00/a6/064eedcec968207259acf06cf156c0ea9a6534328bdf7da0e768cfdb3239/numpyro-0.6.0-py3-none-any.whl (218kB)\n", "\u001b[K |████████████████████████████████| 225kB 11.2MB/s \n", "\u001b[?25hRequirement already satisfied: tqdm in /usr/local/lib/python3.7/dist-packages (from numpyro) (4.41.1)\n", "Collecting jax==0.2.10\n", "\u001b[?25l Downloading https://files.pythonhosted.org/packages/88/9d/2862825b5eddd0df64c78b22cc0b897f0128b1c6494bf39e4849e9e0fade/jax-0.2.10.tar.gz (589kB)\n", "\u001b[K |████████████████████████████████| 593kB 18.6MB/s \n", "\u001b[?25hCollecting jaxlib==0.1.62\n", "\u001b[?25l Downloading https://files.pythonhosted.org/packages/7e/75/30f1c643b7edb1309b6d748809042241737fe43127cb41754266eca79250/jaxlib-0.1.62-cp37-none-manylinux2010_x86_64.whl (35.7MB)\n", "\u001b[K |████████████████████████████████| 35.7MB 88kB/s \n", "\u001b[?25hRequirement already satisfied: numpy>=1.12 in /usr/local/lib/python3.7/dist-packages (from jax==0.2.10->numpyro) (1.19.5)\n", "Requirement already satisfied: absl-py in /usr/local/lib/python3.7/dist-packages (from jax==0.2.10->numpyro) (0.12.0)\n", "Requirement already satisfied: opt_einsum in /usr/local/lib/python3.7/dist-packages (from jax==0.2.10->numpyro) (3.3.0)\n", "Requirement already satisfied: flatbuffers in /usr/local/lib/python3.7/dist-packages (from jaxlib==0.1.62->numpyro) (1.12)\n", "Requirement already satisfied: scipy in /usr/local/lib/python3.7/dist-packages (from jaxlib==0.1.62->numpyro) (1.4.1)\n", "Requirement already satisfied: six in /usr/local/lib/python3.7/dist-packages (from absl-py->jax==0.2.10->numpyro) (1.15.0)\n", "Building wheels for collected packages: jax\n", " Building wheel for jax (setup.py) ... \u001b[?25l\u001b[?25hdone\n", " Created wheel for jax: filename=jax-0.2.10-cp37-none-any.whl size=679776 sha256=511b156e79c1476901deca3c1aa27170da2422b9715e25ff6ec768c9e6c1f136\n", " Stored in directory: /root/.cache/pip/wheels/44/ea/ac/3be3bc19ee3b62f6fe1561eb6df1199284bb6bab819c1befa4\n", "Successfully built jax\n", "Installing collected packages: jax, jaxlib, numpyro\n", " Found existing installation: jax 0.2.12\n", " Uninstalling jax-0.2.12:\n", " Successfully uninstalled jax-0.2.12\n", " Found existing installation: jaxlib 0.1.65+cuda110\n", " Uninstalling jaxlib-0.1.65+cuda110:\n", " Successfully uninstalled jaxlib-0.1.65+cuda110\n", "Successfully installed jax-0.2.10 jaxlib-0.1.62 numpyro-0.6.0\n" ], "name": "stdout" } ] }, { "cell_type": "code", "metadata": { "id": "3ZS9fTPxsupD" }, "source": [ "import torch\n", "\n", "import pyro\n", "import pyro.distributions as dist\n", "from torch import nn\n", "from pyro.nn import PyroModule, PyroSample\n", "\n", "from pyro.infer import MCMC, NUTS, HMC\n", "from pyro.infer.autoguide import AutoGuide, AutoDiagonalNormal\n", "\n", "from pyro.infer import SVI, Trace_ELBO\n", "from pyro.infer import Predictive" ], "execution_count": 19, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "9gPrEaEJsupP" }, "source": [ "# we should be able to have an empirical estimate for the mean of the prior for the 2nd regression bias term\n", "# this will be something like b = log(max(daily_confirmed))\n", "\n", "# might be able to have 1 regression model but change the data so that we have new terms for (tau < t) \n", "# like an interaction term\n", "\n", "class COVID_change(PyroModule):\n", " def __init__(self, in_features, out_features, b1_mu, b2_mu):\n", " super().__init__()\n", " self.linear1 = PyroModule[nn.Linear](in_features, out_features, bias = False)\n", " self.linear1.weight = PyroSample(dist.Normal(0.5, 0.25).expand([1, 1]).to_event(1))\n", " self.linear1.bias = PyroSample(dist.Normal(b1_mu, 1.))\n", " \n", " # could possibly have stronger priors for the 2nd regression line, because we wont have as much data\n", " self.linear2 = PyroModule[nn.Linear](in_features, out_features, bias = False)\n", " self.linear2.weight = PyroSample(dist.Normal(0., 0.25).expand([1, 1])) #.to_event(1))\n", " self.linear2.bias = PyroSample(dist.Normal(b2_mu, b2_mu/4))\n", "\n", " def forward(self, x, y=None):\n", " tau = pyro.sample(\"tau\", dist.Beta(4, 3))\n", " sigma = pyro.sample(\"sigma\", dist.Uniform(0., 3.))\n", " # fit lm's to data based on tau\n", " sep = int(np.ceil(tau.detach().numpy() * len(x)))\n", " mean1 = self.linear1(x[:sep]).squeeze(-1)\n", " mean2 = self.linear2(x[sep:]).squeeze(-1)\n", " mean = torch.cat((mean1, mean2))\n", " obs = pyro.sample(\"obs\", dist.StudentT(2, mean, sigma), obs=y)\n", " return mean" ], "execution_count": 20, "outputs": [] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "if0toOMysupU", "outputId": "1b1a1ea9-d7d6-4598-e67f-19825fcba4ed" }, "source": [ "tensor_data = torch.tensor(reg_data[[\"confirmed\", \"days_since_start\", \"daily_confirmed\"]].values, dtype=torch.float)\n", "x_data = tensor_data[:, 1].unsqueeze_(1)\n", "y_data = np.log(tensor_data[:, 0])\n", "y_data_daily = np.log(tensor_data[:, 2])\n", "# prior hyper params\n", "# take log of the average of the 1st quartile to get the prior mean for the bias of the 2nd regression line\n", "q1 = np.quantile(y_data, q = 0.25)\n", "bias_1_mean = np.mean(y_data.numpy()[y_data <= q1])\n", "print(\"Prior mean for Bias 1: \", bias_1_mean)\n", "\n", "# take log of the average of the 4th quartile to get the prior mean for the bias of the 2nd regression line\n", "q4 = np.quantile(y_data, q = 0.75)\n", "bias_2_mean = np.mean(y_data.numpy()[y_data >= q4])\n", "print(\"Prior mean for Bias 2: \", bias_2_mean)" ], "execution_count": 21, "outputs": [ { "output_type": "stream", "text": [ "Prior mean for Bias 1: 7.161369\n", "Prior mean for Bias 2: 9.24027\n" ], "name": "stdout" } ] }, { "cell_type": "markdown", "metadata": { "id": "nrm8RrFasupc" }, "source": [ "## Approximate Inference with Stochastic Variational Inference" ] }, { "cell_type": "markdown", "metadata": { "id": "x0nDLSPisupm" }, "source": [ "# HMC with NUTS" ] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "X1rSXXtKsupm", "outputId": "4226153a-d009-4422-8f44-178e0437c595" }, "source": [ "model = COVID_change(1, 1, \n", " b1_mu = bias_1_mean,\n", " b2_mu = bias_2_mean)\n", "# need more than 400 samples/chain if we want to use a flat prior on b_2 and w_2\n", "num_samples = 400 \n", "# mcmc \n", "nuts_kernel = NUTS(model)\n", "mcmc = MCMC(nuts_kernel, \n", " num_samples=num_samples,\n", " warmup_steps = 200, \n", " num_chains = 1)\n", "mcmc.run(x_data, y_data)\n", "samples = mcmc.get_samples()" ], "execution_count": 22, "outputs": [ { "output_type": "stream", "text": [ "Sample: 100%|██████████| 600/600 [33:58, 3.40s/it, step size=9.46e-05, acc. prob=0.860]\n" ], "name": "stderr" } ] }, { "cell_type": "code", "metadata": { "id": "_-lXw8vBJsAT" }, "source": [ "# Save the model:\n", "import dill\n", "with open('korea.pkl', 'wb') as f:\n", "\tdill.dump(mcmc, f)\n", "# with open('canada.pkl', 'rb') as f:\n", "# \tmcmc = dill.load(f)\n", " \n", "samples = mcmc.get_samples()" ], "execution_count": 24, "outputs": [] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "7Z968a5xsupv", "outputId": "fdea6e62-8291-4672-c8df-b8fabcb9d1d9" }, "source": [ "# extract individual posteriors\n", "weight_1_post = samples[\"linear1.weight\"].detach().numpy()\n", "weight_2_post = samples[\"linear2.weight\"].detach().numpy()\n", "bias_1_post = samples[\"linear1.bias\"].detach().numpy()\n", "bias_2_post = samples[\"linear2.bias\"].detach().numpy()\n", "tau_post = samples[\"tau\"].detach().numpy()\n", "sigma_post = samples[\"sigma\"].detach().numpy()\n", "\n", "# build likelihood distribution:\n", "tau_days = list(map(int, np.ceil(tau_post * len(x_data))))\n", "mean_ = torch.zeros(len(tau_days), len(x_data))\n", "obs_ = torch.zeros(len(tau_days), len(x_data))\n", "for i in range(len(tau_days)) : \n", " mean_[i, :] = torch.cat((x_data[:tau_days[i]] * weight_1_post[i] + bias_1_post[i],\n", " x_data[tau_days[i]:] * weight_2_post[i] + bias_2_post[i])).reshape(len(x_data))\n", " obs_[i, :] = dist.Normal(mean_[i, :], sigma_post[i]).sample()\n", "samples[\"_RETURN\"] = mean_\n", "samples[\"obs\"] = obs_\n", "pred_summary = summary(samples)\n", "mu = pred_summary[\"_RETURN\"] # mean\n", "y = pred_summary[\"obs\"] # samples from likelihood: mu + sigma\n", "y_shift = np.exp(y[\"mean\"]) - np.exp(torch.cat((y[\"mean\"][0:1], y[\"mean\"][:-1])))\n", "print(y_shift)\n", "predictions = pd.DataFrame({\n", " \"days_since_start\": x_data[:, 0],\n", " \"mu_mean\": mu[\"mean\"], # mean of likelihood\n", " \"mu_perc_5\": mu[\"5%\"],\n", " \"mu_perc_95\": mu[\"95%\"],\n", " \"y_mean\": y[\"mean\"], # mean of likelihood + noise\n", " \"y_perc_5\": y[\"5%\"],\n", " \"y_perc_95\": y[\"95%\"],\n", " \"true_confirmed\": y_data,\n", " \"true_daily_confirmed\": y_data_daily,\n", " \"y_daily_mean\": y_shift\n", "})\n", "\n", "w1_ = pred_summary[\"linear1.weight\"]\n", "w2_ = pred_summary[\"linear2.weight\"]\n", "\n", "b1_ = pred_summary[\"linear1.bias\"]\n", "b2_ = pred_summary[\"linear2.bias\"]\n", "\n", "tau_ = pred_summary[\"tau\"]\n", "sigma_ = pred_summary[\"sigma\"]\n", "\n", "ind = int(np.ceil(tau_[\"mean\"] * len(x_data)))" ], "execution_count": 25, "outputs": [ { "output_type": "stream", "text": [ "tensor([ 0.0000, 82.0016, 102.8954, 138.0626, 177.0313, 233.4354,\n", " 292.8321, 382.7584, 494.3716, 653.0012, 845.9763, 1071.9526,\n", " 1464.6138, 1130.1460, -6.6099, 61.6411, 75.4199, 73.4316,\n", " 56.1016, 97.3462, 101.0249, 61.6816, 97.2871, 83.4458,\n", " 64.8203, 88.0947, 114.5322, 62.6924, 112.9365, 89.4297,\n", " 80.8291, 102.3701, 85.1367, 107.2764, 59.3027, 139.7627,\n", " 96.3291, 81.1426, 90.6025, 138.5977, 63.8389, 111.0518,\n", " 109.2520, 112.5029, 100.8203, 86.5762, 126.0703, 100.6436,\n", " 118.2803, 117.6064, 111.0918, 96.0430, 121.1953, 112.4150,\n", " 144.9268])\n" ], "name": "stdout" } ] }, { "cell_type": "markdown", "metadata": { "id": "KegzMbLOsuqC" }, "source": [ "## Model Diagnostics\n", "\n", "- Residual plots: Should these be samples from the likelihood compared with the actual data? Or just the mean of the likelihood?\n", "- $\\hat{R}$: The factor that the scale of the current distribution will be reduced by if we were to run the simulations forever. As n tends to $\\inf$, $\\hat{R}$ tends to 1. So we want values close to 1.\n", "- Mixing and Stationarity: I sampled 4 chains. Do I then take these chains, split them in half and plot them. If they converge to the same stationary distribution, does that mean the MCMC converged? What do I do with more sampled chains?" ] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "QUh6fBjtsuqV", "outputId": "ef6a7336-6a48-4196-b5f0-970b79a66d6b" }, "source": [ "mcmc.summary()\n", "diag = mcmc.diagnostics()" ], "execution_count": 26, "outputs": [ { "output_type": "stream", "text": [ "\n", " mean std median 5.0% 95.0% n_eff r_hat\n", " tau 0.23 0.01 0.23 0.21 0.25 23.21 1.02\n", " sigma 0.03 0.00 0.03 0.03 0.03 7.24 1.10\n", "linear1.weight[0,0] 0.26 0.01 0.26 0.24 0.27 8.12 1.28\n", " linear1.bias 5.36 0.06 5.36 5.26 5.44 9.64 1.23\n", "linear2.weight[0,0] 0.01 0.00 0.01 0.01 0.01 359.67 1.00\n", " linear2.bias 8.74 0.02 8.74 8.71 8.77 327.63 1.00\n", "\n", "Number of divergences: 0\n" ], "name": "stdout" } ] }, { "cell_type": "markdown", "metadata": { "id": "LJ2a6Epnsuqf" }, "source": [ "## Posterior Plots" ] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 331 }, "id": "bPpET7b6suqg", "outputId": "fd647545-8b8b-40cd-c763-37c67e46755e" }, "source": [ "print(ind)\n", "print(reg_data.date[ind])\n", "\n", "sns.distplot(weight_1_post, \n", " kde_kws = {\"label\": \"Weight posterior before CP\"}, \n", " color = \"red\",\n", " norm_hist = True,\n", " kde = True)\n", "plt.axvline(x = w1_[\"mean\"], linestyle = '--',label = \"Mean weight before CP\" ,\n", " color = \"red\")\n", "\n", "sns.distplot(weight_2_post, \n", " kde_kws = {\"label\": \"Weight posterior after CP\"}, \n", " color = \"teal\",\n", " norm_hist = True,\n", " kde = True)\n", "plt.axvline(x = w2_[\"mean\"], linestyle = '--',label = \"Mean weight after CP\" ,\n", " color = \"teal\")\n", "\n", "legend = plt.legend(loc='upper right')\n", "legend.get_frame().set_alpha(1)\n", "sns.set_style(\"ticks\")\n", "plt.tight_layout()\n", "sns.despine()\n", "plt.savefig('/content/sample_data/kr_weights.pdf')" ], "execution_count": 27, "outputs": [ { "output_type": "stream", "text": [ "13\n", "2020-03-04 00:00:00\n" ], "name": "stdout" }, { "output_type": "display_data", "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "tags": [] } } ] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 395 }, "id": "C-eH-TrJKKa2", "outputId": "58114938-ca8a-4498-dae1-cd626684afc1" }, "source": [ "start_date_ = str(reg_data.date[0]).split(' ')[0]\n", "change_date_ = str(reg_data.date[ind]).split(' ')[0]\n", "print(\"Date of change for {}: {}\".format(country_, change_date_))\n", "import seaborn as sns\n", "\n", "# plot data:\n", "fig, ax = plt.subplots(nrows=1, ncols=1, figsize=(7, 5))\n", "ax = [ax]\n", "# log regression model\n", "ax[0].scatter(y = np.exp(y_data[:ind]), x = x_data[:ind], s = 15);\n", "ax[0].scatter(y = np.exp(y_data[ind:]), x = x_data[ind:], s = 15, color = \"red\");\n", "\n", "ax[0].plot(predictions[\"days_since_start\"],\n", " np.exp(predictions[\"y_mean\"]), \n", " color = \"green\",\n", " label = \"Fitted line by MCMC-NUTS model\") \n", "ax[0].axvline(5, \n", " linestyle = '--', linewidth = 1.5,\n", " label = \"Start of Policy: Feb 25, 2020\" ,\n", " color = \"red\")\n", "\n", "ax[0].axvline(ind, \n", " linestyle = '--', linewidth = 1.5,\n", " label = \"Date of Change: Mar 4, 2020\",\n", " color = \"black\")\n", "\n", "ax[0].fill_between(predictions[\"days_since_start\"], \n", " np.exp(predictions[\"y_perc_5\"]), \n", " np.exp(predictions[\"y_perc_95\"]), \n", " alpha = 0.25,\n", " label = \"90% CI of predictions\",\n", " color = \"teal\");\n", "ax[0].fill_betweenx([0, 1], \n", " tau_[\"5%\"] * len(x_data), \n", " tau_[\"95%\"] * len(x_data), \n", " alpha = 0.25,\n", " label = \"90% CI of changing point\",\n", " color = \"lightcoral\",\n", " transform=ax[0].get_xaxis_transform());\n", "ax[0].set(ylabel = \"Total Cases\",)\n", "\n", "ax[0].legend(loc = \"lower right\", fontsize=12.8)\n", "ax[0].set_ylim([100,150000])\n", "ax[0].xaxis.get_label().set_fontsize(16)\n", "ax[0].yaxis.get_label().set_fontsize(16)\n", "ax[0].title.set_fontsize(20)\n", "ax[0].tick_params(labelsize=16)\n", "\n", "plt.xticks(ticks=[5,13,34,50], labels=[\"Feb 25\",\n", " \"Mar 4\",\n", " \"Mar 25\",\n", " \"Apr 10\"], fontsize=15)\n", "ax[0].set_yscale('log')\n", "plt.setp(ax[0].get_xticklabels(), rotation=0, horizontalalignment='center')\n", "print(reg_data.columns)\n", "myFmt = mdates.DateFormatter('%m-%d')\n", "sns.set_style(\"ticks\")\n", "sns.despine()\n", "plt.savefig('/content/sample_data/kr_cp.pdf')\n" ], "execution_count": 39, "outputs": [ { "output_type": "stream", "text": [ "Date of change for South Korea (Before April 15th): 2020-03-04\n", "Index(['country', 'date', 'confirmed', 'deaths', 'recovered', 'date_only',\n", " 'daily_confirmed', 'days_since_start'],\n", " dtype='object')\n" ], "name": "stdout" }, { "output_type": "display_data", "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "tags": [] } } ] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 470 }, "id": "HrWADTLYsurS", "outputId": "bc71fc61-03c2-4f22-d74e-1fecc2e43bf0" }, "source": [ "fig, ax = plt.subplots(1,3, figsize=(15, 6))\n", "\n", "#plt.figure(figsize=(11, 5))\n", "sns.lineplot(x=\"date\", \n", " y=\"confirmed\", \n", " data= reg_data,\n", " ax = ax[0]\n", " ).set_title(\"Confirmed COVID-19 Cases in %s\" % country_)\n", "ax[0].axvline(reg_data.date[ind], color=\"red\", linestyle=\"--\")\n", "ax[1].scatter(y = reg_data.confirmed[:ind], x = x_data[:ind], s = 15);\n", "ax[1].scatter(y = reg_data.confirmed[ind:], x = x_data[ind:], s = 15, color = \"red\");\n", "\n", "ax[1].plot(predictions[\"days_since_start\"],\n", " np.exp(predictions[\"y_mean\"]), \n", " color = \"green\",\n", " label = \"Mean\") \n", "ax[1].axvline(ind, linestyle = '--', \n", " linewidth = 1,\n", " label = \"Day of Change\")\n", "ax[1].legend(loc = \"upper left\")\n", "ax[1].set(ylabel = \"Confirmed Cases\", \n", " xlabel = \"Days since %s\" % start_date_,\n", " title = \"Confirmed Cases: %s\" % country_);\n", "\n", "\n", "ax[2].scatter(y = reg_data.daily_confirmed[:ind], x = x_data[:ind], s = 15);\n", "ax[2].scatter(y = reg_data.daily_confirmed[ind:], x = x_data[ind:], s = 15, color = \"red\");\n", "\n", "ax[2].plot(predictions[\"days_since_start\"],\n", " predictions[\"y_daily_mean\"], \n", " color = \"green\",\n", " label = \"Mean\") \n", "\n", "ax[2].axvline(ind, linestyle = '--', \n", " linewidth = 1,\n", " label = \"Day of Change\")\n", "ax[2].legend(loc = \"upper left\")\n", "ax[2].set(ylabel = \"Daily Confirmed Cases\", \n", " xlabel = \"Days since %s\" % start_date_,\n", " title = \"Daily Confirmed Cases: %s\" % country_);\n", "printmd(\"**Date of change for {}: {}**\".format(country_, change_date_));\n", "\n", "import matplotlib.dates as mdates\n", "myFmt = mdates.DateFormatter('%m-%d')\n", "ax[0].xaxis.set_major_formatter(myFmt)\n", "\n", "# ax[0].set_xticklabels(reg_data.date, rotation = 45, fontsize=\"10\", va=\"center\")\n", "plt.setp(ax[0].get_xticklabels(), rotation=30, horizontalalignment='right')\n", "ax[0].set(ylabel='Confirmed Cases', xlabel='Date');\n", "\n", "plt.tight_layout()\n", "plt.savefig('/content/sample_data/kr_mean.pdf')" ], "execution_count": 40, "outputs": [ { "output_type": "display_data", "data": { "text/markdown": "**Date of change for South Korea (Before April 15th): 2020-03-04**", "text/plain": [ "" ] }, "metadata": { "tags": [] } }, { "output_type": "display_data", "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "tags": [] } } ] }, { "cell_type": "code", "metadata": { "colab": { "background_save": true }, "id": "P-RF4xjKoz9k" }, "source": [ "" ], "execution_count": null, "outputs": [] } ] }