{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Gl9SFGlaIgFk"
      },
      "source": [
        "https://tinyurl.com/zxyxa9ww Copy the notebook to your GDrive to edit."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "vKjNPo57FTJ8"
      },
      "outputs": [],
      "source": [
        "%reload_ext autoreload\n",
        "%autoreload 2\n",
        "%matplotlib inline"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "1vac2ktq2ljA",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "71e866e2-235b-43ad-f94b-d543ac6a04dc"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting transformers\n",
            "  Downloading transformers-4.33.2-py3-none-any.whl (7.6 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m7.6/7.6 MB\u001b[0m \u001b[31m42.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from transformers) (3.12.2)\n",
            "Collecting huggingface-hub<1.0,>=0.15.1 (from transformers)\n",
            "  Downloading huggingface_hub-0.17.2-py3-none-any.whl (294 kB)\n",
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            "Collecting tokenizers!=0.11.3,<0.14,>=0.11.1 (from transformers)\n",
            "  Downloading tokenizers-0.13.3-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (7.8 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m7.8/7.8 MB\u001b[0m \u001b[31m84.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting safetensors>=0.3.1 (from transformers)\n",
            "  Downloading safetensors-0.3.3-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (1.3 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.3/1.3 MB\u001b[0m \u001b[31m65.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: tqdm>=4.27 in /usr/local/lib/python3.10/dist-packages (from transformers) (4.66.1)\n",
            "Requirement already satisfied: fsspec in /usr/local/lib/python3.10/dist-packages (from huggingface-hub<1.0,>=0.15.1->transformers) (2023.6.0)\n",
            "Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub<1.0,>=0.15.1->transformers) (4.5.0)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (3.2.0)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (3.4)\n",
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            "Installing collected packages: tokenizers, safetensors, huggingface-hub, transformers\n",
            "Successfully installed huggingface-hub-0.17.2 safetensors-0.3.3 tokenizers-0.13.3 transformers-4.33.2\n"
          ]
        }
      ],
      "source": [
        "!pip3 install transformers"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "y7aI_mECQqhS"
      },
      "outputs": [],
      "source": [
        "import torch\n",
        "import random\n",
        "import numpy as np\n",
        "\n",
        "from transformers import AutoTokenizer, BertForMaskedLM"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "JkmVtpi_MxEc"
      },
      "outputs": [],
      "source": [
        "def enforce_reproducibility(seed=42):\n",
        "    # Sets seed manually for both CPU and CUDA\n",
        "    torch.manual_seed(seed)\n",
        "    torch.cuda.manual_seed_all(seed)\n",
        "    # For atomic operations there is currently\n",
        "    # no simple way to enforce determinism, as\n",
        "    # the order of parallel operations is not known.\n",
        "    # CUDNN\n",
        "    torch.backends.cudnn.deterministic = True\n",
        "    torch.backends.cudnn.benchmark = False\n",
        "    # System based\n",
        "    random.seed(seed)\n",
        "    np.random.seed(seed)\n",
        "enforce_reproducibility()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zrPfTMLb2ljC",
        "outputId": "5b9dacf4-ac39-48ce-8495-379dedfd4b4c"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "device(type='cuda')"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ],
      "source": [
        "device = torch.device(\"cpu\")\n",
        "if torch.cuda.is_available():\n",
        "    device = torch.device(\"cuda\")\n",
        "device"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MUeAr_bm2uL1"
      },
      "source": [
        "# Error Analysis and Explainability"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tnVZ0TB62-xP"
      },
      "source": [
        "Error analysis involves **examining the errors made by a system and developing a classification of them**. (This is typically best done over dev data, to avoid compromising held-out test sets.) At a superficial level, this can involve breaking things down by input length, token frequency or looking at confusion matrices. But we should not limit ourselves to examining only labels (rather than input linguistic forms) as with confusion matrices, or superficial properties of the linguistic signal. Languages are, after all, complex systems and linguistic forms are structured. So a deeper error analysis involves examining those linguistic forms and looking for patterns.\n",
        "\n",
        "A good error analysis tells us something about **why method X is effective or ineffective for problem Y**. This in turn provides a much richer starting point for further research, allowing us to go beyond throwing learning algorithms at the wall of tasks and seeing which stick, while allowing us to also discover which are the harder parts of a problem.\n",
        "\n",
        "Error analysis: Does the project provide a thoughtful error analysis, which looks for **linguistic patterns** in the **types of errors** made by the system(s) evaluated and sheds light on either **avenues for future work or the source of the strengths/weaknesses** of the systems?\n",
        "\n",
        "Source : http://coling2018.org/error-analysis-in-research-and-writing/"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1DFhmqqO4Hnv"
      },
      "source": [
        "Error analysis — the attempt to *analyze when, how, and why machine-learning models fail* — is a crucial part of the development cycle: **Researchers use it to suggest directions for future improvement, and practitioners make deployment decisions based on it. Since error analysis profoundly determines the direction of subsequent actions, we cannot afford it to be biased or incomplete.**\n",
        "\n",
        "\n",
        "\n",
        "![title](https://miro.medium.com/max/1400/1*r4RwXFD0vnNl5VNLin_4Zw.png)\n",
        "\n",
        "Source https://medium.com/@uwdata/errudite-55d5fbf3232e"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "z8hc6tVF5wA-"
      },
      "source": [
        "Error Analysis can include **everything that helps you understand how the model behaves**, what are it's strengths and weaknesses.\n",
        "\n",
        "When having **access to the model's decisions**, e.g. weights for each n-gram, and they are easily understandable, we say that the model is **interpretable by design** ([Chapter 4, Interpretable Models](https://christophm.github.io/interpretable-ml-book/simple.html)) For such models, we can explore what the model chose as important features and use that in our analysis."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7qG2wDMu2ljF"
      },
      "source": [
        "## Motivation (Examples)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "f5hJog3b2ljF"
      },
      "source": [
        "**Language Modeling:** Predict the likelihood of a sentence P(x)\n",
        "\n",
        "P(x) is <span style=\"color:green\">**high**</span>: &emsp; Barack Obama served as the 44th President of the UnitedStates.\n",
        "\n",
        "P(x) is <span style=\"color:red\">**low**</span>: &emsp; 44th the of the President United States served Barack Obama as. *(syntax)*\n",
        "\n",
        "P(x) is <span style=\"color:red\">**low**</span>: &emsp; Barack Obama barked as the 44th President of the kennel. *(semantics)*\n",
        "\n",
        "P(x) is <span style=\"color:red\">**low**</span>: &emsp; Barack Obama served as the 44th President of the UnitedStates. *(facts)*\n",
        "\n",
        "P(x) is <span style=\"color:red\">**low**</span>: &emsp; Barack Obama reached a height of 50 feet tall. *(common sense)*\n",
        "    \n",
        "Source https://www.youtube.com/watch?v=Oh2StnRQ3qE&ab_channel=3Blue1Brown"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 251,
          "referenced_widgets": [
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        "id": "Q850xbjE2ljG",
        "outputId": "ad8fb2c0-3ee6-4c44-9515-a142e8b31774"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)okenizer_config.json:   0%|          | 0.00/29.0 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "a4e1db1d2cce488e9fe64e943fc5368f"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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              "Downloading (…)lve/main/config.json:   0%|          | 0.00/570 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "ce88debdc45243e58ac28e5072ccc8fa"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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              "Downloading (…)solve/main/vocab.txt:   0%|          | 0.00/213k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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              "model_id": "3fd8c46fee3a498f8358c875c2fd6008"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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              "Downloading (…)/main/tokenizer.json:   0%|          | 0.00/436k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "033720271239482ab11925287f7bd263"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading model.safetensors:   0%|          | 0.00/436M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "b099d9cb06724a0f824e99098d18f7ce"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Some weights of the model checkpoint at bert-base-cased were not used when initializing BertForMaskedLM: ['cls.seq_relationship.bias', 'bert.pooler.dense.weight', 'cls.seq_relationship.weight', 'bert.pooler.dense.bias']\n",
            "- This IS expected if you are initializing BertForMaskedLM from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
            "- This IS NOT expected if you are initializing BertForMaskedLM from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n"
          ]
        }
      ],
      "source": [
        "tokenizer = AutoTokenizer.from_pretrained(\"bert-base-cased\")\n",
        "bert = BertForMaskedLM.from_pretrained(\"bert-base-cased\")\n",
        "\n",
        "def get_probs(sentence, word_idx):\n",
        "    input_ids = tokenizer.encode(sentence)\n",
        "    input_ids = torch.tensor([input_ids])\n",
        "    logits = bert(input_ids)[0][0]\n",
        "    sorted_probs = logits[word_idx].sort(descending=True)\n",
        "\n",
        "    return sorted_probs"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GBQTQO3i2ljG",
        "outputId": "97ee362e-02de-4217-f888-8d9bb0ed9688"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Denmark: 18.916263580322266\n",
            "Sweden: 12.336536407470703\n",
            "Europe: 11.822348594665527\n",
            "Copenhagen: 11.669337272644043\n",
            "Danish: 11.636178970336914\n",
            "Scandinavia: 11.140522003173828\n",
            "Latvia: 11.128833770751953\n",
            "Norway: 10.961599349975586\n",
            "Zealand: 10.413444519042969\n",
            "Greenland: 10.187922477722168\n"
          ]
        }
      ],
      "source": [
        "sentence = f\"Copenhagen is the capital of {tokenizer.mask_token}.\"\n",
        "sorted_probs = get_probs(sentence, 6)\n",
        "for i in range(10):\n",
        "    token = tokenizer._convert_id_to_token(sorted_probs.indices[i].numpy().tolist())\n",
        "    conf = sorted_probs.values[i].detach().numpy().tolist()\n",
        "    print(f\"{token}: {conf}\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "luBFuu5B2ljH"
      },
      "source": [
        "However, since language models were never trained to solve the exact tasks that we're asking them to solve, they are:\n",
        "* Very **sensitive to the wording** that we use to prompt them\n",
        "* Will return an answer **even when they have no idea**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PDsocvVT2ljH",
        "outputId": "13e96f8e-16d8-4f2b-a817-e37f030674ef"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "her: 16.38549041748047\n",
            "the: 12.918560981750488\n",
            "his: 11.123321533203125\n",
            "its: 10.893871307373047\n",
            "their: 10.13268756866455\n",
            "some: 9.505374908447266\n",
            "all: 9.481547355651855\n",
            "to: 9.095525741577148\n",
            "up: 8.563324928283691\n",
            "my: 8.469130516052246\n"
          ]
        }
      ],
      "source": [
        "sentence = f\"The nurse finished {tokenizer.mask_token} work.\"\n",
        "sorted_probs = get_probs(sentence, 4)\n",
        "for i in range(10):\n",
        "    token = tokenizer._convert_id_to_token(sorted_probs.indices[i].numpy().tolist())\n",
        "    conf = sorted_probs.values[i].detach().numpy().tolist()\n",
        "    print(f\"{token}: {conf}\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8rtKS3sQuW9A"
      },
      "source": [
        "Further reading on bias:\n",
        "\n",
        "https://aclanthology.org/N18-2003.pdf\n",
        "\n",
        "https://arxiv.org/pdf/2004.09456.pdf"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AVYZ81tw2ljH"
      },
      "source": [
        "## Error Analysis for sentiment classification"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "agbjVo8_2ljI"
      },
      "source": [
        "**Starting point for an error analysis:** If my model hits 90% accuracy, why are the remaining 10% misclassified? Are there any patterns?"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "id": "JrqzI-o45dQQ"
      },
      "outputs": [],
      "source": [
        "import spacy\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.feature_extraction.text import CountVectorizer\n",
        "from sklearn.feature_extraction.text import TfidfVectorizer\n",
        "from sklearn.model_selection import RandomizedSearchCV\n",
        "from sklearn.metrics import classification_report"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0o-H8Su982AB"
      },
      "source": [
        "### Upload the dataset\n",
        "We'll use two basic logistic regression classifiers for sentiment analysis from lab 2 with TF-IDF features and BPE. We use the dataset for fact-checking -- [Liar dataset](https://www.cs.ucsb.edu/~william/data/liar_dataset.zip).  "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 198
        },
        "id": "HJKFOvdZPqb_",
        "outputId": "63744f21-d6a5-4a1b-ef19-15e86509fb10"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-21bc7ec7-a5bb-4cb1-b9b1-ff9dbbf9505c\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-21bc7ec7-a5bb-4cb1-b9b1-ff9dbbf9505c\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script>// Copyright 2017 Google LLC\n",
              "//\n",
              "// Licensed under the Apache License, Version 2.0 (the \"License\");\n",
              "// you may not use this file except in compliance with the License.\n",
              "// You may obtain a copy of the License at\n",
              "//\n",
              "//      http://www.apache.org/licenses/LICENSE-2.0\n",
              "//\n",
              "// Unless required by applicable law or agreed to in writing, software\n",
              "// distributed under the License is distributed on an \"AS IS\" BASIS,\n",
              "// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
              "// See the License for the specific language governing permissions and\n",
              "// limitations under the License.\n",
              "\n",
              "/**\n",
              " * @fileoverview Helpers for google.colab Python module.\n",
              " */\n",
              "(function(scope) {\n",
              "function span(text, styleAttributes = {}) {\n",
              "  const element = document.createElement('span');\n",
              "  element.textContent = text;\n",
              "  for (const key of Object.keys(styleAttributes)) {\n",
              "    element.style[key] = styleAttributes[key];\n",
              "  }\n",
              "  return element;\n",
              "}\n",
              "\n",
              "// Max number of bytes which will be uploaded at a time.\n",
              "const MAX_PAYLOAD_SIZE = 100 * 1024;\n",
              "\n",
              "function _uploadFiles(inputId, outputId) {\n",
              "  const steps = uploadFilesStep(inputId, outputId);\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  // Cache steps on the outputElement to make it available for the next call\n",
              "  // to uploadFilesContinue from Python.\n",
              "  outputElement.steps = steps;\n",
              "\n",
              "  return _uploadFilesContinue(outputId);\n",
              "}\n",
              "\n",
              "// This is roughly an async generator (not supported in the browser yet),\n",
              "// where there are multiple asynchronous steps and the Python side is going\n",
              "// to poll for completion of each step.\n",
              "// This uses a Promise to block the python side on completion of each step,\n",
              "// then passes the result of the previous step as the input to the next step.\n",
              "function _uploadFilesContinue(outputId) {\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  const steps = outputElement.steps;\n",
              "\n",
              "  const next = steps.next(outputElement.lastPromiseValue);\n",
              "  return Promise.resolve(next.value.promise).then((value) => {\n",
              "    // Cache the last promise value to make it available to the next\n",
              "    // step of the generator.\n",
              "    outputElement.lastPromiseValue = value;\n",
              "    return next.value.response;\n",
              "  });\n",
              "}\n",
              "\n",
              "/**\n",
              " * Generator function which is called between each async step of the upload\n",
              " * process.\n",
              " * @param {string} inputId Element ID of the input file picker element.\n",
              " * @param {string} outputId Element ID of the output display.\n",
              " * @return {!Iterable<!Object>} Iterable of next steps.\n",
              " */\n",
              "function* uploadFilesStep(inputId, outputId) {\n",
              "  const inputElement = document.getElementById(inputId);\n",
              "  inputElement.disabled = false;\n",
              "\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  outputElement.innerHTML = '';\n",
              "\n",
              "  const pickedPromise = new Promise((resolve) => {\n",
              "    inputElement.addEventListener('change', (e) => {\n",
              "      resolve(e.target.files);\n",
              "    });\n",
              "  });\n",
              "\n",
              "  const cancel = document.createElement('button');\n",
              "  inputElement.parentElement.appendChild(cancel);\n",
              "  cancel.textContent = 'Cancel upload';\n",
              "  const cancelPromise = new Promise((resolve) => {\n",
              "    cancel.onclick = () => {\n",
              "      resolve(null);\n",
              "    };\n",
              "  });\n",
              "\n",
              "  // Wait for the user to pick the files.\n",
              "  const files = yield {\n",
              "    promise: Promise.race([pickedPromise, cancelPromise]),\n",
              "    response: {\n",
              "      action: 'starting',\n",
              "    }\n",
              "  };\n",
              "\n",
              "  cancel.remove();\n",
              "\n",
              "  // Disable the input element since further picks are not allowed.\n",
              "  inputElement.disabled = true;\n",
              "\n",
              "  if (!files) {\n",
              "    return {\n",
              "      response: {\n",
              "        action: 'complete',\n",
              "      }\n",
              "    };\n",
              "  }\n",
              "\n",
              "  for (const file of files) {\n",
              "    const li = document.createElement('li');\n",
              "    li.append(span(file.name, {fontWeight: 'bold'}));\n",
              "    li.append(span(\n",
              "        `(${file.type || 'n/a'}) - ${file.size} bytes, ` +\n",
              "        `last modified: ${\n",
              "            file.lastModifiedDate ? file.lastModifiedDate.toLocaleDateString() :\n",
              "                                    'n/a'} - `));\n",
              "    const percent = span('0% done');\n",
              "    li.appendChild(percent);\n",
              "\n",
              "    outputElement.appendChild(li);\n",
              "\n",
              "    const fileDataPromise = new Promise((resolve) => {\n",
              "      const reader = new FileReader();\n",
              "      reader.onload = (e) => {\n",
              "        resolve(e.target.result);\n",
              "      };\n",
              "      reader.readAsArrayBuffer(file);\n",
              "    });\n",
              "    // Wait for the data to be ready.\n",
              "    let fileData = yield {\n",
              "      promise: fileDataPromise,\n",
              "      response: {\n",
              "        action: 'continue',\n",
              "      }\n",
              "    };\n",
              "\n",
              "    // Use a chunked sending to avoid message size limits. See b/62115660.\n",
              "    let position = 0;\n",
              "    do {\n",
              "      const length = Math.min(fileData.byteLength - position, MAX_PAYLOAD_SIZE);\n",
              "      const chunk = new Uint8Array(fileData, position, length);\n",
              "      position += length;\n",
              "\n",
              "      const base64 = btoa(String.fromCharCode.apply(null, chunk));\n",
              "      yield {\n",
              "        response: {\n",
              "          action: 'append',\n",
              "          file: file.name,\n",
              "          data: base64,\n",
              "        },\n",
              "      };\n",
              "\n",
              "      let percentDone = fileData.byteLength === 0 ?\n",
              "          100 :\n",
              "          Math.round((position / fileData.byteLength) * 100);\n",
              "      percent.textContent = `${percentDone}% done`;\n",
              "\n",
              "    } while (position < fileData.byteLength);\n",
              "  }\n",
              "\n",
              "  // All done.\n",
              "  yield {\n",
              "    response: {\n",
              "      action: 'complete',\n",
              "    }\n",
              "  };\n",
              "}\n",
              "\n",
              "scope.google = scope.google || {};\n",
              "scope.google.colab = scope.google.colab || {};\n",
              "scope.google.colab._files = {\n",
              "  _uploadFiles,\n",
              "  _uploadFilesContinue,\n",
              "};\n",
              "})(self);\n",
              "</script> "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Saving test.tsv to test.tsv\n",
            "Saving valid.tsv to valid.tsv\n",
            "Saving train.tsv to train.tsv\n",
            "User uploaded file \"test.tsv\" with length 301118 bytes\n",
            "User uploaded file \"valid.tsv\" with length 301556 bytes\n",
            "User uploaded file \"train.tsv\" with length 2408165 bytes\n"
          ]
        }
      ],
      "source": [
        "from google.colab import files\n",
        "\n",
        "uploaded = files.upload()\n",
        "\n",
        "for fn in uploaded.keys():\n",
        "  print('User uploaded file \"{name}\" with length {length} bytes'.format(\n",
        "      name=fn, length=len(uploaded[fn])))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Eqr1sIwdPLh8"
      },
      "source": [
        "# Read in the data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "id": "asJF7gERQalI"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import numpy as np"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 293
        },
        "id": "JfJpXd03_59W",
        "outputId": "77e4e3dc-2e2b-4f92-fa03-da505c1ca2f0"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "           0            1                                                  2   \\\n",
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              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
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              "      border-radius: 50%;\n",
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              "    .colab-df-convert:hover {\n",
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              "    .colab-df-buttons div {\n",
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              "    [theme=dark] .colab-df-convert:hover {\n",
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              "        document.querySelector('#df-8ffc1ed2-2291-4764-9c78-ed767e8c345a button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-8ffc1ed2-2291-4764-9c78-ed767e8c345a');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
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              "\n",
              "\n",
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              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
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              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
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              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-543925e1-ec27-49d3-8823-ac8267452c01 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ],
      "source": [
        "train_data = pd.read_csv('./train.tsv', sep='\\t', header=None).fillna('')\n",
        "valid_data = pd.read_csv('./valid.tsv', sep='\\t', header=None).fillna('')\n",
        "test_data = pd.read_csv('./test.tsv', sep='\\t', header=None).fillna('')\n",
        "train_data.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "R8I44E81-pZP"
      },
      "source": [
        "## Model 1: TF-IDF classifier"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 30,
      "metadata": {
        "id": "39Ycg392I2b0"
      },
      "outputs": [],
      "source": [
        "from sklearn.feature_extraction.text import TfidfVectorizer\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.metrics import classification_report, confusion_matrix, ConfusionMatrixDisplay\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "i8BLPp-TVzp2",
        "outputId": "367be906-da41-4a1a-e065-2a70063f7f63"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['veterans' 'you' 'really' 'community' 'know' 'department' 'your' 'like'\n",
            " 'them' 'there' 'out' 'to' 'of' 'our' 'are' 'has' 'the' 'equal'\n",
            " 'employees' 'entire' 'enough' 'energy' 'estimated' 'end' 'even' 'for'\n",
            " 'employee' 'else' 'every' 'elections' 'election' 'elected' 'either'\n",
            " 'eight' 'effect' 'education' 'economy' 'economic' 'earth' 'earned' 'earn'\n",
            " 'ever' 'fact' 'executive' 'feingold' 'food' 'florida' 'five' 'fiscal'\n",
            " 'first' 'fire' 'find' 'financial' 'fight' 'fewer' 'few' 'force'\n",
            " 'existing' 'fees' 'federal' 'fbi' 'favor' 'fastest' 'far' 'family'\n",
            " 'earmarks' 'failed' 'experience' 'expansion' 'families' 'due' 'early'\n",
            " 'currently' 'decades' 'decade' 'debt' 'debate' 'death' 'deal' 'days'\n",
            " 'day' 'david' 'data' 'cutting' 'cuts' 'cut' 'current' 'each' 'cruz'\n",
            " 'crist' 'crisis' 'criminal' 'crimes' 'crime' 'credit' 'creation'\n",
            " 'creating' 'created' 'create' 'coverage']\n",
            "['private' 'prison' 'primary' 'prices' 'previous' 'presidents' 'rape'\n",
            " 'rates' 'president' 'rating' 'residents' 'research' 'required' 'require'\n",
            " 'republicans' 'republican' 'representatives' 'report' 'rep' 'released'\n",
            " 'regulations' 'registered' 'refused' 'refugees' 'reform' 'reduced'\n",
            " 'reduce' 'recovery' 'record' 'recently' 'recent' 'received' 'receive'\n",
            " 'really' 'real' 'reagan' 're' 'presidential' 'premiums' 'officers' 'paul'\n",
            " 'passed' 'pass' 'party' 'part' 'parents' 'parenthood' 'paid' 'own'\n",
            " 'overseas' 'over' 'out' 'our' 'other' 'oregon' 'order' 'or' 'opposes'\n",
            " 'opposed' 'opponent' 'open' 'only' 'one' 'once' 'old' 'oil' 'ohio'\n",
            " 'officials' 'past' 'pay' 'power' 'paying' 'poverty' 'position'\n",
            " 'population' 'popular' 'poor' 'polls' 'poll' 'policy' 'policies' 'police'\n",
            " 'points' 'point' 'plant' 'plans' 'planned' 'plan' 'place' 'personal'\n",
            " 'person' 'perry' 'percentage' 'percent' 'per' 'people' 'pension' 'pelosi'\n",
            " 'pays' '000']\n"
          ]
        }
      ],
      "source": [
        "n = 100\n",
        "\n",
        "vectorizer = TfidfVectorizer(max_features=1000)\n",
        "features = vectorizer.fit_transform(train_data.values[:, 2])\n",
        "# the raw/textual n-grams the vectorizer is using\n",
        "feature_array = np.array(vectorizer.get_feature_names_out())\n",
        "# TF-IDF scores of the words in each instance of the input datasets\n",
        "tfidf_sorting = np.argsort(features.toarray()).flatten()[::-1]\n",
        "\n",
        "# With a TF-IDF Vecotrizer, we can already tell the words that have high TF-IDF scores\n",
        "# Even before feeding them to the model:\n",
        "# Look-up of the top-n words at the indices with 1) highest and 2) lowest scores\n",
        "print(feature_array[tfidf_sorting][:n])\n",
        "print(feature_array[tfidf_sorting][-n:])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 75
        },
        "id": "J6vmdr7fRKoG",
        "outputId": "3c3edcdc-18e7-415c-ecea-12bf1153d85f"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LogisticRegression(max_iter=1000, multi_class='multinomial')"
            ],
            "text/html": [
              "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression(max_iter=1000, multi_class=&#x27;multinomial&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression(max_iter=1000, multi_class=&#x27;multinomial&#x27;)</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ],
      "source": [
        "vectorizer = TfidfVectorizer(ngram_range=(1,2), max_features=1000)\n",
        "features = vectorizer.fit_transform(train_data.values[:, 2])\n",
        "# training a linear model, which is interpretable by design\n",
        "lr = LogisticRegression(penalty='l2', max_iter=1000, multi_class='multinomial')\n",
        "lr.fit(features, train_data.values[:, 1])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "id": "eB1Pw8D6I9ic"
      },
      "outputs": [],
      "source": [
        "features_test = vectorizer.fit_transform(test_data.values[:, 2])\n",
        "preds_tfidf = lr.predict(features_test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "id": "-dS5-7oxTNBI"
      },
      "outputs": [],
      "source": [
        "features_valid = vectorizer.transform(valid_data.values[:, 2])\n",
        "preds_valid_tfidf = lr.predict(features_valid)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KrBXWEjWHUTz"
      },
      "source": [
        "## Model 2: BPE"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "id": "GnM5tGthHTpJ"
      },
      "outputs": [],
      "source": [
        "from tqdm import tqdm\n",
        "import nltk"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dVPzS7_lh41G",
        "outputId": "1b6110c4-6430-4ecf-ae55-c671c4b4d759"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting bpemb\n",
            "  Downloading bpemb-0.3.4-py3-none-any.whl (19 kB)\n",
            "Requirement already satisfied: gensim in /usr/local/lib/python3.10/dist-packages (from bpemb) (4.3.2)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (from bpemb) (1.23.5)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from bpemb) (2.31.0)\n",
            "Collecting sentencepiece (from bpemb)\n",
            "  Downloading sentencepiece-0.1.99-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (1.3 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.3/1.3 MB\u001b[0m \u001b[31m26.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: tqdm in /usr/local/lib/python3.10/dist-packages (from bpemb) (4.66.1)\n",
            "Requirement already satisfied: scipy>=1.7.0 in /usr/local/lib/python3.10/dist-packages (from gensim->bpemb) (1.11.2)\n",
            "Requirement already satisfied: smart-open>=1.8.1 in /usr/local/lib/python3.10/dist-packages (from gensim->bpemb) (6.4.0)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->bpemb) (3.2.0)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->bpemb) (3.4)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->bpemb) (2.0.4)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->bpemb) (2023.7.22)\n",
            "Installing collected packages: sentencepiece, bpemb\n",
            "Successfully installed bpemb-0.3.4 sentencepiece-0.1.99\n"
          ]
        }
      ],
      "source": [
        "!pip install bpemb"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "id": "OEpReyRzh1Jy",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ac6d5604-4ffa-4289-dd2f-d2edd21eb453"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "downloading https://nlp.h-its.org/bpemb/en/en.wiki.bpe.vs25000.model\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 661443/661443 [00:00<00:00, 851095.50B/s] \n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "downloading https://nlp.h-its.org/bpemb/en/en.wiki.bpe.vs25000.d100.w2v.bin.tar.gz\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 9477142/9477142 [00:01<00:00, 5895942.60B/s] \n"
          ]
        }
      ],
      "source": [
        "from bpemb import BPEmb\n",
        "\n",
        "# Load english model with 25k word-pieces\n",
        "bpemb_en = BPEmb(lang='en', dim=100, vs=25000)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "id": "G7kfbxF4RoCC"
      },
      "outputs": [],
      "source": [
        "def get_bpemb_features(dataset, bpemb):\n",
        "  # With bpemb we can tokenize and embed an entire document using .embed(x)\n",
        "  X = [bpemb.embed(x).mean(0) for x in tqdm(dataset[:,2])]\n",
        "  y = dataset[:,1]\n",
        "\n",
        "  return X,y"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 128
        },
        "id": "BMkRi8Y9KKSh",
        "outputId": "873eea5f-f2e0-4396-900e-20221a234eb4"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 10240/10240 [00:01<00:00, 10114.39it/s]\n",
            "100%|██████████| 1284/1284 [00:00<00:00, 10021.84it/s]\n",
            "100%|██████████| 1267/1267 [00:00<00:00, 9311.02it/s] \n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LogisticRegression(max_iter=1000, multi_class='multinomial')"
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              "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression(max_iter=1000, multi_class=&#x27;multinomial&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression(max_iter=1000, multi_class=&#x27;multinomial&#x27;)</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ],
      "source": [
        "X_train,y_train = get_bpemb_features(train_data.values, bpemb_en)\n",
        "X_valid,y_valid = get_bpemb_features(valid_data.values, bpemb_en)\n",
        "X_test,y_test = get_bpemb_features(test_data.values, bpemb_en)\n",
        "lr_bpemb = LogisticRegression(penalty='l2', max_iter=1000, multi_class='multinomial')\n",
        "lr_bpemb.fit(X_train, y_train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "id": "qxkXejsbUQce"
      },
      "outputs": [],
      "source": [
        "preds_bpemb = lr_bpemb.predict(X_test)\n",
        "preds_valid_bpemb = lr_bpemb.predict(X_valid)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VKpLXN3WOncZ"
      },
      "source": [
        "## Classification Performance Comparison\n",
        "\n",
        "Looking at the **classification report and the confusion matrix** is the most basic step of performing error analysis - you can find which classes are confused with which other classes most often and compare the performance of the different classes. Differences in the performance allow for a founded model choice."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 332
        },
        "id": "-91N1CFZOc4M",
        "outputId": "a3c6e51c-9163-4bd3-9d77-6861b295c8bd"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "              precision    recall  f1-score      support\n",
              "barely-true    0.166667  0.099057  0.124260   212.000000\n",
              "false          0.230986  0.329317  0.271523   249.000000\n",
              "half-true      0.221198  0.181132  0.199170   265.000000\n",
              "mostly-true    0.215152  0.294606  0.248687   241.000000\n",
              "pants-fire     0.090909  0.021739  0.035088    92.000000\n",
              "true           0.184332  0.192308  0.188235   208.000000\n",
              "accuracy       0.208366  0.208366  0.208366     0.208366\n",
              "macro avg      0.184874  0.186360  0.177827  1267.000000\n",
              "weighted avg   0.197334  0.208366  0.196564  1267.000000"
            ],
            "text/html": [
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>precision</th>\n",
              "      <th>recall</th>\n",
              "      <th>f1-score</th>\n",
              "      <th>support</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>barely-true</th>\n",
              "      <td>0.166667</td>\n",
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              "      <td>212.000000</td>\n",
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              "      <th>false</th>\n",
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              "      <td>0.271523</td>\n",
              "      <td>249.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>half-true</th>\n",
              "      <td>0.221198</td>\n",
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              "      <td>241.000000</td>\n",
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              "      <th>true</th>\n",
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              "      <td>0.188235</td>\n",
              "      <td>208.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>accuracy</th>\n",
              "      <td>0.208366</td>\n",
              "      <td>0.208366</td>\n",
              "      <td>0.208366</td>\n",
              "      <td>0.208366</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>macro avg</th>\n",
              "      <td>0.184874</td>\n",
              "      <td>0.186360</td>\n",
              "      <td>0.177827</td>\n",
              "      <td>1267.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>weighted avg</th>\n",
              "      <td>0.197334</td>\n",
              "      <td>0.208366</td>\n",
              "      <td>0.196564</td>\n",
              "      <td>1267.000000</td>\n",
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              "</div>\n",
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              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
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              "\n",
              "    .colab-df-convert {\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
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              "\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-c8059ccc-faaf-4553-b2f7-767db3c061f0 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-c8059ccc-faaf-4553-b2f7-767db3c061f0');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
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              "            title=\"Suggest charts.\"\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
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              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
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              "    border-radius: 50%;\n",
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              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-e93a5460-18a2-4875-b2a2-37c1f757f94c button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ],
      "source": [
        "# TF-IDF model\n",
        "report = classification_report(y_test, preds_tfidf, output_dict=True)\n",
        "pd.DataFrame(report).transpose()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 45,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 332
        },
        "id": "RhoTZeiwL2TO",
        "outputId": "5d204acd-703d-4a87-deb9-89d87eca761b"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "              precision    recall  f1-score      support\n",
              "barely-true    0.174312  0.089623  0.118380   212.000000\n",
              "false          0.241758  0.353414  0.287113   249.000000\n",
              "half-true      0.234604  0.301887  0.264026   265.000000\n",
              "mostly-true    0.251656  0.315353  0.279926   241.000000\n",
              "pants-fire     0.451613  0.152174  0.227642    92.000000\n",
              "true           0.233333  0.134615  0.170732   208.000000\n",
              "accuracy       0.240726  0.240726  0.240726     0.240726\n",
              "macro avg      0.264546  0.224511  0.224637  1267.000000\n",
              "weighted avg   0.244714  0.240726  0.229260  1267.000000"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-85e44654-96e7-46ec-a576-a5b481bdbbe8\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
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              "\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>precision</th>\n",
              "      <th>recall</th>\n",
              "      <th>f1-score</th>\n",
              "      <th>support</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>barely-true</th>\n",
              "      <td>0.174312</td>\n",
              "      <td>0.089623</td>\n",
              "      <td>0.118380</td>\n",
              "      <td>212.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>false</th>\n",
              "      <td>0.241758</td>\n",
              "      <td>0.353414</td>\n",
              "      <td>0.287113</td>\n",
              "      <td>249.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>half-true</th>\n",
              "      <td>0.234604</td>\n",
              "      <td>0.301887</td>\n",
              "      <td>0.264026</td>\n",
              "      <td>265.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mostly-true</th>\n",
              "      <td>0.251656</td>\n",
              "      <td>0.315353</td>\n",
              "      <td>0.279926</td>\n",
              "      <td>241.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>pants-fire</th>\n",
              "      <td>0.451613</td>\n",
              "      <td>0.152174</td>\n",
              "      <td>0.227642</td>\n",
              "      <td>92.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>true</th>\n",
              "      <td>0.233333</td>\n",
              "      <td>0.134615</td>\n",
              "      <td>0.170732</td>\n",
              "      <td>208.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>accuracy</th>\n",
              "      <td>0.240726</td>\n",
              "      <td>0.240726</td>\n",
              "      <td>0.240726</td>\n",
              "      <td>0.240726</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>macro avg</th>\n",
              "      <td>0.264546</td>\n",
              "      <td>0.224511</td>\n",
              "      <td>0.224637</td>\n",
              "      <td>1267.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>weighted avg</th>\n",
              "      <td>0.244714</td>\n",
              "      <td>0.240726</td>\n",
              "      <td>0.229260</td>\n",
              "      <td>1267.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-85e44654-96e7-46ec-a576-a5b481bdbbe8')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-85e44654-96e7-46ec-a576-a5b481bdbbe8 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-85e44654-96e7-46ec-a576-a5b481bdbbe8');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-20688852-5633-41f9-b4b0-970c2f28b4c5\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-20688852-5633-41f9-b4b0-970c2f28b4c5')\"\n",
              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-20688852-5633-41f9-b4b0-970c2f28b4c5 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 45
        }
      ],
      "source": [
        "# BPEmb model\n",
        "report = classification_report(y_test, preds_bpemb, output_dict=True)\n",
        "pd.DataFrame(report).transpose()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "izVjBuRJUhVN"
      },
      "source": [
        "### Confusion Matrix Comparison"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 32,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KPoifWNVOUC_",
        "outputId": "734134b1-6516-48db-8c14-8df395f3901f"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[19, 67, 42, 67,  2, 40],\n",
              "       [32, 73, 53, 64,  4, 37],\n",
              "       [19, 67, 50, 66,  0, 46],\n",
              "       [25, 52, 54, 67,  7, 46],\n",
              "       [ 8, 36, 25, 20,  2, 25],\n",
              "       [17, 32, 43, 52,  2, 23]])"
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ],
      "source": [
        "cm_tfidf = confusion_matrix(valid_data.values[:, 1], preds_valid_tfidf)\n",
        "cm_tfidf"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 48,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qSUNUmNiUpgb",
        "outputId": "240fda55-1b11-4d35-f5dc-67220fdedb07"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[20, 83, 74, 40,  5, 15],\n",
              "       [28, 98, 67, 37, 12, 21],\n",
              "       [17, 63, 86, 60,  4, 18],\n",
              "       [16, 70, 53, 76,  2, 34],\n",
              "       [13, 48, 23,  9, 14,  9],\n",
              "       [10, 37, 39, 53,  1, 29]])"
            ]
          },
          "metadata": {},
          "execution_count": 48
        }
      ],
      "source": [
        "cm_bpemb = confusion_matrix(valid_data.values[:, 1], preds_valid_bpemb)\n",
        "cm_bpemb"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### You can visualize the confusion matrices using either ConfusionMatrixDisplay from sklearn or (as shown here) a heatmap from seaborn."
      ],
      "metadata": {
        "id": "ufbvjh0ZZIWF"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# adjust font size as label names are quite long\n",
        "sns.set(font_scale=0.8)\n",
        "\n",
        "# extract the names of the six different labels from classification report\n",
        "label_names = list(report.keys())[0:6]"
      ],
      "metadata": {
        "id": "1LwuQkPeX0np"
      },
      "execution_count": 53,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# visualize CM for tfidf\n",
        "\n",
        "ax= plt.subplot()\n",
        "sns.heatmap(cm_tfidf, annot=True, ax=ax); # annot=True annotates cells (shows numbers)\n",
        "\n",
        "ax.set_xlabel('Predicted labels');ax.set_ylabel('True labels');\n",
        "ax.set_title('Confusion Matrix');\n",
        "ax.xaxis.set_ticklabels(label_names); ax.yaxis.set_ticklabels(label_names);"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 469
        },
        "id": "fpxDjWAiZ8K-",
        "outputId": "36d83c86-9932-42be-a1ea-4f792059310a"
      },
      "execution_count": 59,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# visualize CM for bpemb\n",
        "\n",
        "ax= plt.subplot()\n",
        "sns.heatmap(cm_bpemb, annot=True, ax=ax); # annot=True annotates cells (shows numbers)\n",
        "\n",
        "ax.set_xlabel('Predicted labels');ax.set_ylabel('True labels');\n",
        "ax.set_title('Confusion Matrix');\n",
        "ax.xaxis.set_ticklabels(label_names); ax.yaxis.set_ticklabels(label_names);"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 469
        },
        "id": "cra9u_KZZcZZ",
        "outputId": "af71ff50-0c0e-4be5-bfc6-6489adb9610c"
      },
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "grH1DgiuAO_s"
      },
      "source": [
        "Can you spot any insights?"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "viHHXjfS_EsC"
      },
      "source": [
        "## Model's Feature Weights\n",
        "\n",
        "We can now see what are the most importand words for each class by looking at the weights the model assigned to each feature in the input."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 62,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "pd_1JUgsPmL5",
        "outputId": "7aecf1bd-5ec3-430b-99bb-6ee545234f50"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "barely-true\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x500 with 1 Axes>"
            ],
            "image/png": 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xZKpXr14qW7asbty4obx586pDhw6aOHGi+vTpY8nZFD4+Pvrpp5/UsmVLbd++XTNmzNC2bdss2YthGM4viL/++qs6d+6sKlWqqGTJkho9erSaNWumcePG6fbt2yZXGnGxY8dWlSpV1KZNG9WpU0dPnjzR3Llz5e/vr8WLF6tq1ar67rvvNGPGDH355ZeSrLVOWerUqXXz5k0dOHBAHh4eztrLlSun48ePW3KQfuvWLcWOHVvly5eXJP3444/q3r27JGnz5s2WGaD/VfLkyRUUFKT79+9r+PDhyp49u8qVK6c7d+5o06ZNr3yWWck333yjKlWqaMOGDfLx8ZGHh4dy5sypc+fOWWpfIf3vve/r66u1a9eqW7duWrhwoX7//Xfn55gV31MOvr6+zvfShAkT9MMPP8gwDE2bNk0BAQEmVxdxfn5+On36tCpXrizDMDR48GBlzZpVCxYs0Pvvv69ly5ZZcvxnGIY8PT11/fp1zZo1SyNGjNC0adPk4+OjlClTysPDw7KzQ7y8vOTj46MePXqoXbt2Spo0qUaPHq0KFSrojz/+sNwSAY6x0vz587Vq1SqFh4erTp06evbsmZYvX67nz59bMnSRXgRlpUqVkp+fn3r37q3g4GCNGDFCFStWVMKECS0x688RKPft21dbt25VunTpVL9+fU2aNEkpU6bU3LlztXbtWvn4+Gj8+PHq2bOnyRVHzMWLF185CJUuXToVKFBA3377rbp166Y4ceJo1KhRWrFihR48eKB79+6ZWG3kOT6DZ8yYodu3b2v69Ony9fXV+fPnlSVLFj1+/Nh5FkhM4GV2AcDreHp6Kk2aNBozZoyyZcum/PnzS5K8vb1Vt25dBQUF6dixY7Lb7c4PMyt4/vy5vvrqKwUEBChr1qyqU6eOpBc7wq+++ko1atSQj4+PpP+tiRWTOQYJt2/fVvbs2VW4cGFJ0qhRozR8+HCVK1dOJ0+eVKZMmZQiRQozS42wl3/vDx48UMaMGdW9e3dVqVJFP//8s2bPnq0cOXKoefPmypAhg8nVRpyjJ7vdrnTp0un48eNq3Lix87aPPvpIv//+uy5fvqzUqVObWWqEOULaWLFiqU6dOsqXL5+2bt2qsLAweXp6ytPTUxkzZpS3t7dzUBXT9xdhYWHy8vLSkydP5OPjo2LFimn9+vWKHTu20qdPr+TJk2vTpk0qXbq0JQfpadKkUbx48XTgwAHdvn1buXLl0nvvvafg4GAtWbLEkl9+JSlDhgx69913NW3aNC1fvlzLli2T9OJUifLly1syJHPMnG3WrJkkacGCBerRo4ekF2sBZs2aVfXq1TOtvshyvPfXrFmj7t27K1euXFq4cKFy5syp0qVLa9u2bTp79qxatWpluVmndrtd2bJl06RJkxQQEKD169dr3LhxzoNV/v7+ypYtm9llRkj27NlVpkwZ9e7dWzabTZkyZVK7du0kSXnz5tW9e/d04cIFS4yRXuaodcuWLSpTpozeeecd7dq1S/7+/poxY4ZCQ0O1YMECtWnTRu+8847J1UZey5YtlS9fPj158kRFihSRJM2bN09hYWGqUqWKydVF3tmzZ/XNN99IenFBoMqVKyt27NiaPXu29u/fr++++84yr9Nf3yvVqlVTnDhx5O3trezZsytBggRau3atAgIC1LBhQxMrjRjH7EUvLy9t3LhRw4YNU8OGDTVixAidPn1avXv3VvPmzZ37cavM0pw4caLz8zYoKEgJEyZUlSpV5OfnpyJFiqh06dKSXhwEiRUrluXGFTabTYZh6MyZM+ratatsNpuGDh2qsmXLKleuXFq3bp0OHDigfv36xYjPYEIyxFjdu3eXzWbTd999p1KlSqlhw4ZKmTKlpBdH7U+fPh3jv/D+lWEYGjRokH799VcNGDBAR48eVa9evRQ3blxJUtGiRZ33jcmDv2XLluncuXPq16+fpBczXhxHak6ePKmsWbOqXLlyevLkiQ4fPqxu3bqZWG3EOUKX27dva/bs2Vq/fr0KFCigrFmz6osvvtDEiRO1dOlSbdq0yTJB0vr16xUWFuYMZG02mypUqKAffvhBK1euVMmSJZU6dWrdv3/fGWhageO1OnHihGbPnq02bdooT548ypIli/Lnz689e/bot99+0+nTp9W/f/8Y/X5yhP23b99W6tSpdfDgQf3www+aOXOmmjdvrtGjR2v48OGKFy+eHj9+rPjx4ztP67MSR5916tTRqFGjdOfOHS1cuFCBgYH66quvVLlyZWXPnt3sMiPkr186YsWKpQ8++ECjRo1SsmTJtHjxYl24cEFPnjzR119/bWKlb88RRBw7dkynTp1S2rRpVbNmTT179ky//vqrZs6caXaJbyVt2rQKCAjQs2fPNGnSJE2ePFnSi/DCiqdlSy/26x06dNC1a9f08OFDDRkyRHnz5tWlS5d06NAhffvtt2aXGGGZMmXSzz//rP3792vy5MnOINax70uVKpV27NgRo/fp/yZ37tzaunWrpBfLirRt21aenp5asmSJzpw5Y4ng5eX9340bN3TixAnlypXLOYYNDQ3Vpk2bnDPmrChXrlz67rvvtHPnTiVNmlQeHh4qUKCA/P39derUKUvNOnW8Vnv27NG5c+cUHh6uggULqmjRorLb7fLz89PMmTPVt29fSf87WBdTOS5AEBAQoCFDhqhatWoqWrSoli1bphUrVmj48OGaO3euRo8ereLFi1tmXzF48GAlTZpUz58/18SJE1WyZElVq1ZNNWrUkPRiH+jn56dVq1ZZ9oCih4eHUqVKpU2bNildunRau3atc6mXVatWKUeOHDHmM9jDsEq8CrcQFBSkDRs2aM+ePUqTJo1sNpsSJUqk69evKyAgQHnz5lWGDBk0c+ZMde7c2RJHPF4nLCxMW7du1ezZs3Xr1i01a9ZMLVu2NLusCDtw4IAyZcqk1KlTa+3atcqRI4dzBtytW7fUpk0bJU6cWIZhKFOmTBo1apTJFUdO69atlTZtWnXq1ElTpkzRoUOHtHbtWoWGhipevHh6/vy5YsWKZXaZb2QYhqZPn66aNWsqQ4YMOnr0qNKlS6dUqVJpypQp2rdvnzJkyKDLly8rduzYypkzpzP4tIp69eqpdu3azpkGDs+ePdOOHTuUMWNG5cqVK8bPOn3w4IHef/991a5dW3v27FHLli2dM/0kadOmTXr8+LFSpUolHx8fy8zMfFloaKi8vLxks9m0bds2/frrrzp16pSyZs2qrFmz6ssvv1Ts2LHNLvONXj7NfOPGjbp06ZLeeecdFS5cWEmSJNGyZctkt9uVIkUKFSxY0JKv1TfffCN/f3/VqVNHixYt0unTp7VkyRLlz59fvXr1cq5jaEXbtm3TihUr9PTpU3l7e2vSpEm6dOmSGjdurLVr11rmAIjDjz/+qIIFC6pkyZKS/rd9njlzRv369VPlypXVpUsXk6t8M8f4b/fu3UqbNq3effddvffee3+7X/Xq1dWhQwfVr1///77I/+iPP/6Qh4eHWrVqpbCwMHl7e2vdunW6c+eOPvzwQ3399deqVKmS2WW+kWMbmzt3rpYsWaI4ceLo/PnzatSokbp06aKECRPq3r17Onv2rMqUKWN2uRHmGCdcuXJFadOmlbe3t5YuXarr16+rQYMGzoOIx48fV4ECBUyuNmIcr9XSpUs1f/58ZciQQenTp3eekpg5c2bduXNHJ06cUOXKlc0uN1IOHjyoQ4cO6bPPPtPTp0+dEw4kqV+/fqpQoYIzYIrpHNue3W7X7du3NWjQID19+lQFChRQhQoVlDdvXufr6O3t/cr4MCZ7+WIljtfo+PHjmj59ui5fvqwSJUroq6++0rZt29S/f3/t2LEjxozVCckQo7Rv315x4sRR7NixFStWLB06dEgJEiRQyZIl9c4772jdunVKmjSpSpYsqUaNGpldbqQEBAToypUrSp06tVKnTq0UKVLo0aNHzkX7x40bZ6nBhPTiqMaAAQN05coVVa1aVS1btlS8ePF08OBBrVu3TkmSJFGnTp0sdVrYuXPn1LVrV23YsEGS1KhRIzVo0EBNmzbV7NmzVahQIRUqVMjcIt/CnTt31K9fP2XKlEk1atRQ8eLFdfjwYZ0+fVr3799X0aJFnVO5rWLfvn0aPny4Vq1aJel/R7cfPnyoP//8Uzly5DC5woh78uSJTp06pb59++rOnTsaOHDgK6fD2mw25/R7Kxo/fryuXbumy5cvq0mTJvrwww8lvQjVQ0NDLTOD8WVr1qzR2LFjlSpVKnl7eytVqlSqUKGCKlWqZNnXSXoRZk6YMEFVq1ZV4cKFNX36dO3atUuBgYFKmDChEiVKpJEjRypBggRml/pWnjx5orFjx2rlypVq1KiR7ty5o6CgIOXPn1+dO3c2u7xI2bdvn7p166YZM2Yob968Cg0N1YMHDxQnThxdunRJa9eu1YABA8wuM0Ic4784ceLI09NT27ZtU7Zs2dS2bVtVqFBBjx490sSJE3XixAktWbLE7HIjzBFSrF27Vtu2bdPo0aN19+5djRkzRqtWrVLlypX1/PlzZc2a1RIHqRyfs0FBQapcubImTJigMmXKqFOnTtq2bZu8vb3VvXt3NW/e3OxS30pISIhzWZRq1aqpRIkSmjlzps6fP68+ffpYLkhyqFq1qkaMGKFixYppzJgxOnPmjGbOnKmdO3cqW7ZszlP3rHIa8759+9SiRQulSZNGS5YsUcqUKRUeHq7w8PAYMxPpv/L19XXu68qWLasKFSooa9asJlcVOaGhoc7Xo2nTpmrfvr0qVKigWbNmadGiRSpUqJDOnz+v1KlTq1atWqpdu7bJFf8PIRlijJMnT6pjx47avn27JOn+/fsaMGCALl++rHfeeUeNGzdWxYoVY/w04Nfx9fXV9OnTdf36deXLl0+pUqVSpUqVVK5cOcWJE8d5mpUVOD5AHbuOgIAAHTp0SL///rtCQkL00UcfOU/ts6Lbt2+ra9eumjt3rlasWKFffvlFq1evlvRiHYfRo0dbJiRzhCuGYcgwDC1fvlwHDhzQw4cPlTdvXtWuXdtyH7gv8/f318CBAzVlyhTnqdiSdPPmTQ0ZMkQjR45U0qRJTawwcgzDUIcOHZQ2bVodPnxYNptNPXv2VNmyZRUaGqomTZpo5syZSpIkidmlRsr27dvVq1cv9e7dW4GBgfrll1+UMGFCffXVV87ZL1bg5+enESNGaPLkycqdO7c6duyoDz74QO+9955OnTql5cuX68qVK8qQIYOKFSumGjVqxJgjopExcuRI+fr6qnTp0ho6dKjsdruuXr2qJ0+eyG63K0+ePJb5EuIIKO7evavdu3crTpw4ypEjh7JkyaKDBw9q//79ih07tooXL+5c+9RK6tevr+bNm6thw4batWuXFi9erNu3b6t27dpq3rz5K7MeY7LXjf8GDhyoc+fOKUuWLGratKlKlSql7du3K3fu3MqYMaPJFb/ZnTt3Xvlc2rhxo3777TdNmzZNhmEoNDRUV65c0fHjx1WuXDklS5bMMu8r6cW6SAEBAZo8ebLOnj2rFi1aaO3aterVq5f27t2r/v3769NPPzW7zEgxDEOPHz/WsWPH9OjRI82cOVMpUqRQ1qxZtXjxYueaZDlz5jS71Ei5ePGiBg8erFmzZunBgweqVauW5s2bJx8fH/Xt21elSpWy1PqS0ovXatmyZZo6dapsNpuGDBmiUqVKSXo1mLGil/fboaGhmjdvnrZs2SJPT0998803ltj/OUyZMkW//vqr6tevr+XLl2vHjh3On/3xxx86cuSIkidPrmzZsr2yv4wJrJU0wKWFhoYqT548zi/2yZIlU4cOHTRz5kylTZtWM2bMUIkSJSxxOs5fDR8+XL1791adOnXUu3dvbdu2TdevX9fhw4dVvnx5547dCl4OyWw2m7Jnz67s2bOrYMGC8vX11YIFC7Rs2TL16tVLefPmNbvcCHGcPhkaGqpkyZIpVapUmjRpkhYvXqwffvhBkjRixAilT5/eMgGZ9L91KDw8POTh4aGGDRuqUqVKWrt2rfbu3atLly4pV65cqlOnjuUWAJVerC1ks9m0aNEi1a1bV5kzZ5b0Yp2XuHHjKmnSpJY5Kiq9eJ2mTZsm6UX4vHTpUn311VfKnDmzYsWKpQwZMlguIJOk8+fPq3fv3mrcuLHCw8NVq1YtzZo1Sx06dFDevHk1c+ZMSwxoCxUqpAIFCqhx48aqUqWKfHx8nK9H3rx5lTdvXm3dulWLFi3SrVu3LBmQSVLHjh31/Plz+fn5afbs2apTp46yZctmmffRyxxfNL744gsZhqE7d+4oZ86cKly4sGrXrq2OHTuaXOHb++OPPxQnThylTJlS169fV//+/dW6dWvdv39fmzdvVrVq1Sxz8O1147/PPvtMs2bNUtq0aTV79myVLl1aVatWtcR2+OzZMw0ePFjZs2fXRx99pHTp0ilv3ryaNm2anjx5ovjx4ytOnDjKmTOn7Ha70qRJY5nFxR2KFSvmXEPy+++/18cff6wUKVKoatWq+vjjj1W9enWTK4w4x3bn4eGhBAkSqHjx4ooVK5Zq1qypY8eO6c6dO0qWLJm2bt1quYBMkjJnzqzQ0FBt3bpVfn5+qlOnjnx8fHT27Flt3bpVX331ldklRppjTFurVi2NHTtWbdu2Vbly5TRgwABLhUiv4/jcstvt8vb2VuvWrfXee+9p06ZNluutXr16stvt+uGHH5Q3b149fPjQOW5Kly6d0qVLF2MP5jCTDDHG7du31bhxY9WqVUtffvmlnj59qn79+ilDhgzq1auXPvroI40aNUpZsmQxu9RIWbNmjX777TfNnj1bT5480XvvvacffvhBixcv1s6dO9W9e3d98MEHZpcZIS8HDuPGjdPJkydVsGBBFSpUSCVLlpTNZtPBgwe1fPlytWnTxlKDicePH2vatGn64osvdOTIEf3www+6d++eatWqpbt37+rAgQP64YcfLPMB9fK6aatXr9bevXtVoUIFVahQQbFjx1ZAQIBWrVqlo0ePatiwYZbp66/8/Pw0Z84cZciQQc+ePVPs2LF16NAhLV26VAkSJIjxa5G97K+B3rNnz3T+/HmtWLHCeQqLFcKklz1+/FhHjhz520DcMAydOHFCe/fuVfv27U2sMPJOnDihCRMmaNeuXapWrZomTZr0ys+fPHkiT09PSx7QeVlAQIAGDBigWLFi6eOPP1axYsVi3JHeiDh8+LAGDx6sVatW6cGDB1q6dKn27NmjZMmSqUCBApYKk/7qu+++09atW52zXRzr2NStW1fz5s2zTF9vGv99+OGHGj16tGXGf/fu3dPChQt16dIlhYSEqGzZsvroo4/Uo0cPNWnSRKGhoTp27Jj27dunu3fvat26dZYI//4qJCREdrtdX375pd599121bNlSVatWVffu3VWzZk2zy4u0+fPn6+TJk/Ly8lLu3Ln18ccfW/J1eZkjgFi+fLkmTJig27dva/369TIMQwMGDFD58uXVoUOHGBtUODjGciEhITp16pTOnj0rT09PffTRR5KkS5cuaciQITp69Kj27dtn2c/fv45ZX17Ty6r27NmjsWPHKnfu3PLz89P777+vHj16KF68eBo0aJAyZ86s1q1bm13m3xCSIUY5ePCgvv76a92+fVt58uRReHi4pk+frvjx46tRo0bq2LGj5dYDOHjwoG7cuKF69epp2LBh+vPPPzVp0iTt2bNH69evV//+/RUnThxLzHhx1Dh+/Hht375dFStW1MWLFxUSEqKcOXPqvffeU/78+f+2gGZM1qZNG/Xq1Uu//PKLHj165Jw5dvHiRa1Zs0Y2m03x48dX+fLllS1bNpOrjZiXp5qPGTNGfn5+8vHx0f79+5UjRw61adNGFSpUkPTii7BV+nK4e/eu7t+/r0SJEilNmjQ6e/asNm7cqKCgIKVNm9b5WsX0Qd9fWWEfEBnPnj1TkyZNdPfuXUlSjRo1VLduXcssePxXjsFrQECA0qVLpx07dmjEiBHy8PDQV199pSpVqphdYpQzDENbt25Vr1691LFjR7Vp08bskiLk5ffS9evXtXjxYnXr1s25P7hw4YIWL16s06dPa9y4cZYJk/7q1q1bOnLkiDJkyKDs2bMrbty46tChgxIkSKAxY8aYXV6kuMr4Lzg42HmA5vDhw9qxY4dOnTqlZMmSaf/+/TIMQz4+PvLx8VHevHlVuHBhpU+f3uyy38ix1ElISIhzTbLkyZNLkubNm6fFixcrbty4ztndVuE4oLh06VL99NNPqlixohIlSqSFCxcqZ86catGihcqXL292mZHy8tjn/v37ihs3rry8vLR9+3atW7dOhw4dUu7cuZUzZ07n1edj+vjDUd/AgQN14cIFxYsXT3fu3NHdu3c1ZMgQVa1aVdKLq61a4f3kENHfe0x/fd7Esf/4/fffNXfuXF28eFHVq1fXqlWrtGbNGqVJk8bsEv+GkAwxztOnT3XgwAHFjRtXOXPmVKJEibRixQqNHTv2lXOZreTu3btKkSKFxo8fr/jx46t9+/Zq0qSJqlevrlatWllitsvLV14ZPny4PvzwQ+XMmVNPnjzRypUrtXv3boWFhalEiRL69NNPY3w/0ottbcyYMVqyZIm8vLz066+/Oq/S6WC1D6bVq1frp59+0hdffKHq1aurffv26tmzp3LmzKlnz55pxIgR8vPzU4kSJdSuXTvlypXL7JIjxDHoO3TokCZNmqTjx4+rRIkSypYtm+rUqWOZPl7meE+9af0Mq22DDoMGDdKjR4/Uo0cP7dy5U0eOHNGTJ09UqFAh1ahRwxKzF1++2lnmzJl14sQJffPNN5o/f75ixYqlZ8+eafr06Zo5c6ayZs2qCRMmWPIiBG8SGhqqp0+fKnHixGaXEiGO/cXmzZt14MABLV68WJMnT1bZsmVfuZ8VDxI8ffpUZ8+e1bVr1xQ3blxVrlxZnp6eevDggXx9ffXLL79o6dKlljlQ9TJXGP999tlnatGihfNCOE+fPtXu3bu1Z88eHT16VPHixVPPnj1VsGBBkyuNuJc/g7p3766rV68qa9asypEjh+rUqaMkSZJo06ZNSpAggfLkyWPJ0LlSpUoaOnSoypUrpzFjxmj37t3KkyePtm3bpqJFi2r06NGWm8k9dOhQXb58WRcvXlTx4sXVuHFjFSxYUI8fP5aHh4fztLeY/h3Esf0dPXpUX3zxhbZv3+4Ma+fNm6e1a9fqm2++Ub58+cwuNVIcv/dnz55pw4YNztN5mzVrZumL/0j/e83++OMPXbt2TcmTJ1eyZMmULFkyPXr0SL6+vjpy5IgqVaqkatWqmV3uaxGSIcYLCAjQyJEj1aRJk9deDjymCgsL04MHD145PWXZsmXq37+/ihQpovv372v9+vUmVvh2Ro0apVOnTqlJkyavTKe/fv265s6dqwIFCqhWrVomVhh5H330kW7duqU7d+7o008/Vd++fZ0/GzdunJo2bWqZQd+xY8e0efNm7d+/X8mTJ5enp6d69+79SiBx9uxZ9e3bV40aNVKzZs1MrDbyatSooUaNGqlVq1bq16+ftm/frvz586tkyZKWXXx73LhxKleunIoWLer8ImK1WXB/FRwcrB9//FHVqlVzvianT5/Wjh07dOjQIaVJk0ZDhw41ucqIefz4sYoXL66KFSvqypUrqlOnjj777LNXLiJz8+ZNDR06VPXq1bPMJeddlWNwfufOHZUrV06tWrXS9evXtXnzZtWrV0/du3dXqlSpzC7zrX355Ze6fv26wsPDFStWLD18+FBdu3bVe++9p7179ypevHiWna35V1Yc/x08eFBFixaVJC1fvlxly5ZVypQpdfv2be3YsUOHDx/WnTt3VKBAAX322WcxOngxDEPHjh1zrsU6atQonThxQk2bNtWFCxd0/vx52Ww2vf/++6pQoYLixIljbsERZLfbtWHDBhUtWlSpUqXSqVOn9PPPP2vcuHG6f/++atasqUWLFunevXuaOnWqGjRoYJlxrSN4mTNnjpYsWaKpU6fq0aNHatSokVavXq3MmTPH6G3u38ybN0/79+/XpEmTnJ+/gYGB6tq1q6pVq6aPP/7Y7BIjxfFaffnll7pw4YLKlSun5cuXK168eGrWrJkaNWpkydNGHePXkydPatiwYbp48aLy5cun9OnTq0KFCipTpow1DuIYQAwXHh5unD592uwyIq1v375G8+bNjdKlSxuzZs1y3n769Glj7dq1xh9//GEYhmE8f/7cpAojLiwszDAMwwgJCTEWLlxoFC9e3KhVq5Zx+fJlkyv7b8LDww3DMIybN28ahmEYW7duNapWrWqUKlXKWLJkiTF16lSjXLlyZpb4Vu7fv29s3brVGDp0qFGkSBHj888/N+7du2d2Wf/Z77//bjRo0MD59woVKhgrV640OnbsaJQuXdqYN2+eidVFjuM99eOPPxqNGjUyHjx4YBiGYQQFBRkhISEmVhY1vvzyS+Pdd981+vTp4+zNMF685/z8/IyTJ0+aV9xbCA4ONurWrWvkypXLGD9+vBEYGGgYxv/231evXjWCgoLMLBH/n91uNwzDMJYvX26MGzfOefvp06eNRo0aGUWLFjXGjRvn3P9bya5du4wKFSoYhmEYDx8+NK5evWpMmzbNaN26tXHr1i1zi4sGVh3/GYZhnDhxwihSpIjRvHlzw9fX13j69KlhGIbh7+9vjB8/3pgzZ47JFb7Z77//buTMmdPo3bu3ceXKFWP06NHG2bNnDcN48Rm2a9cuY/To0UarVq2MQYMGWWI8axiGceXKFaNIkSJG165djV27dhm3bt0yjh07ZhiGYcyZM8do27atYRiGERAQYHz66aeW+0y22+1G8+bNjX379hmGYRjjx483WrVqZRiGYaxcudKYPXu2Jfd/J0+eNOrWrev87uHo4dtvvzVGjBhhZmmR5vicOnPmjFGyZEnn/qF169ZG27ZtjUKFChkNGjQwDhw4YGaZ/0n9+vWNyZMnG4ZhGKNHjzYKFy5stGjRwhg9erSxdetWk6t7s5g7txL4/2w2m3Lnzm12GZGybNky7du3T127dlW3bt00Y8YMVa5cWZs2bVLu3LlVs2ZN59UEHTMRYjLHjJaPPvpIefLk0YoVK+Tj46Pq1avr66+/VmhoqMkVvh2bzabg4GAFBQXp+vXrqlChgn7//Xe1adNGEyZM0MGDBzVlyhSzy4ww4/9PDE6aNKkqVKigdu3aaeDAgUqYMKE6duyomTNnWu4KWi/XmyBBAueRwvHjxytr1qyqW7eumjVrpvLlyzuP9FqhR09PT4WEhGjmzJn6+uuvlSRJEi1YsED9+/dXgQIFtGrVKrNL/E8+//xz1ahRQ/v27dP06dN19uxZPXv2TDabTZUqVbLMlW+lF7OC48ePrxIlSqh79+5av3693n//fS1dulReXl4KDg5W586d9fDhQ7NLhV4scHzp0iUtWLBAjx8/dt6eO3duLVmyRAMGDNDevXtj9OlF/+TEiRMqXry4pBf7w4wZM6p27dq6f/++9uzZY3J1Uc9q4z+73e78c758+bR7926VKVNGw4cPV//+/XX06FH5+Pioc+fO+uSTT0ysNGKqVq2qtWvX6vr166pbt6527dqlLVu2SHrxGVa6dGl9/vnnqlWrlqpVq2aJ8awkZcqUSXv37lWRIkU0cOBAzZ4923nV9pw5c8rDw0OrVq1Sp06dVKxYMcvNvAoLC1OqVKnk7++vmzdvasGCBRoyZIgkydfXVw8ePLDc/u/ChQvKkSOHMmTIoA8//FArVqzQpUuXtGXLFi1fvlwNGjQwu8RIcZw1sHHjRlWpUkVx4sTR6tWrFRAQoOnTp+vTTz/V8+fPnWu6WoVj/H306FGFh4friy++kCT9/vvv+uabb5QjRw799ttvunDhgpllRginWwLRYPbs2UqZMqXzS7vdbtekSZP0448/qlChQpozZ45lPnQd04GPHz+uvn37at26dc6fHThwQKNHj9aJEyc0ceJEy1zy++Wr/axZs0YPHjzQn3/+qZIlS+qbb75xTgN+8OCBkiZNanK1EeeYfn727Flt375df/zxhzw8PJQ2bVolTpxYW7Zs0Z07dzRgwAC9++67ZpcbIY7Xat26dSpdurSCg4OVIUMGjRo1ShkyZFDTpk3VqVMn5cmTR506dbLU+l2nT5/WhAkTNGTIEF24cEE9evTQ6NGjdfLkSV29elXDhw+3zH7in2zZskU//PCDwsLC9MEHH6hixYqWWIvsTWbPnq0JEyYoW7ZsSpAggeLGjatp06aZXRb+v927d2vGjBk6duyYWrdurdatW1vj9I5/8fz5cx0+fFgjR47U/PnzFT9+fOfn86BBg5QqVSp16tTJ7DLdmuP1mD9/vvz9/fXo0SN16NBBqVKl0siRI3XkyBGVKVNGPXr0UKJEicwu918ZhiG73e48SLp582Z99913unfvnoYMGaLq1atbLmh5ncDAQI0YMUKnTp1SgwYNlD9/fk2ePFlx48ZVsmTJNHLkSLNLjJC/LtGwYsUK7d27V1euXFHevHn11Vdfaf/+/erSpYu2b98ub29vy4yX/Pz8tGzZMg0bNkxJkiTRjBkzNH36dKVIkULp0qVTmTJl1LJlS7PLfCuBgYE6deqUSpUqpS+++ELly5fXhx9+qOnTpyt9+vSWukrsy9vT/Pnz5e3trcaNG2vmzJny9fXV4sWL5e/vrxkzZqhXr14xftkDQjIgit29e1e7du3S3r17//bheuvWLe3YsUONGze2zIeTw+zZs3XlyhX17dtXsWLFemVwtHDhQhUtWvRvi97HRI7f+8OHD1WtWjWNGTNGKVKk0PPnzzV27Fg9f/5cI0eOVNasWc0uNVJe3p7ee+89VaxYUc+ePVPChAnl7++vIkWKqEiRItq0aZOaN29uicXFHT1duXJF1atX165du165mtawYcNUrVo1HThwQNu2bbPUoE96cdWpFi1a6NGjR8qbN6+KFCmitm3batu2bRo3bpxWrFhhmV4cbty4oVu3bunatWuqUaOGc42aOXPmaNKkSfr888/Vrl07k6uMPMcXEMeV0KQXi9lPnz5dqVKlUu3atS0fwriS8PBw3bhxQ9u3b9emTZsUHh6uJk2aWGZdob9yXHSlTZs2GjRokC5cuKA+ffqobNmyOnr0qPr27avffvvNEvt1V+X47Dl+/LiaN2+utm3b6sGDB1q+fLkqVqyoIUOG6PLly/rpp58sNUP9r+HLlClTNG3aNBUuXFh9+vSx5DqgrxMQEKD+/fsrderUatCggdKmTavs2bNbZnacw7Bhw9SwYUNlzJhRgwcP1rZt29S8eXNduHBBQUFBzrW7Xl5TM6ZyvKdOnDihH3/8UWFhYRo7dqzix48vSTp58qRy5szp/Ey2MrvdrhEjRujq1atq1aqVPv/8c82aNcsyB7Sl/71ew4cP14kTJzRr1izFjRtXP//8swIDA9W9e3f17NlTyZIl04ABA8wu940IyYAodOPGDTVp0kRx48bV48ePVaRIEbVo0UJFihT5232t9GX++PHj+vDDDyW9WGTcSkc2/smcOXO0Z8+eV2Z//Pnnn+rSpYtat24dY6+28iY//fST9uzZo1mzZkl6Ecw6pqOPHz9eKVOmtNzspG3btmn//v3q3bv3K7evX79eN2/eVKlSpZQ7d25LDPr+KiQkRCtXrlTp0qWVIUMGSVLNmjXVtGlTNW3a1OTqIicwMFCtWrVSWFiYkidPrlu3bqlFixb66KOPJElBQUGy2WzOAa6V/HV/bcVtzZW9/Po8e/bMGc7a7XadOXNGmzdv1o4dO5Q8eXJNnDjRcoshT5o0SefPn9fkyZMVFham2bNn6+eff5a3t7dy5cqlUqVKWXYmhav5/vvvFT9+fLVs2VLPnz/XpUuXNHHiRO3atUuffvqpevToYXaJb+XlfV5wcLAGDRqktWvXql+/fmrRooXJ1UUNwzC0efNm9e3bVx06dFCbNm3MLilSwsLC1LdvX8WLF895YZx169bpypUrih8/vooXL26ZK4H/9TP38ePHGjBggC5evKi+ffs6rx77uvvGZI7Q+fz583r06JECAwNVokQJxY8fX0eOHNH48eMVEhKid999V19++aXZ5UbYy1drHz9+vKpWrap3331Xdrtdfn5++uKLL1S8eHFdvHhRa9eutcSVsgnJgCjUs2dPpUmTRq1bt9axY8e0bds2nTt3ToULF9Ynn3xi6dOMQkNDNXz4cC1evFi1atVS9+7dnV/qreLly1yfO3dOgwcP1pQpU5Q8eXLnDJExY8YoPDz8lStcWsmcOXN09epVDRw40NmvYRhq37696tatqzp16phdYqQcOXJEPXv2lCRNnDjR0ketHYOjwMBAXb16Vffu3VOFChWcP79x44amTJmiixcvavHixSZW+na6du2qePHiaeTIkdqxY4c6dOigNGnSKH369GrdurUqVKgQ4y81HxlWGpi7Osd7a/Xq1dq4caOuXLmiunXrqnTp0sqZM6dCQkK0d+9ePX361HIHeR4+fKixY8eqePHif9t/Hz9+XLly5bLcgQ9X49j+jP9/Ncjt27erS5cuzp8/f/5c27Zt09atWzVs2DATK/1vDMNQeHi4Myw7ceKEkiVLpvTp05tcWdQKDQ3V06dPLfFF/q9OnTqlgQMHqlChQvr666/NLuc/cRwcbdGihVKmTKn06dPr5MmTunTpklq2bKnMmTObXWKkOMY/d+7c0UcffaT48eMrVapU8vb2Vp06dVSzZk0FBwfLw8NDceLEseQVzkeOHKkNGzaodOnSGjBggBIkSCDpxay/U6dOqXDhwsqRI4fJVUYMIRkQRe7cuaOJEyeqefPmypkzpyTpypUrOnLkiNasWaPkyZNr1KhRJlf5du7fv69kyZJJejElvV+/fgoICNCHH36oHj16WG6q87Bhw5QtWzYtWLBAmTNn1vDhw5UwYULZ7XbVqVNHnTp1Uu3atc0u8634+fmpV69eGjt2rCpVquS8/eOPP9YHH3ygxo0bm1hd5IWFhWny5MlavXq18uXLp6ZNmypv3rzOD16reDlQadWqlZ48eaKLFy8qbdq06tq1q6pUqSLpxZfeJEmSWO60qWvXrqlVq1aaP3++UqdOrY8++khly5ZV7ty51bdvX2XJkkW//fab2WVGmmNB9Fy5cilBggRKnTq12SXhLxxfPK5evaoPPvhAvXr1Unh4uCZPniwfHx/VqFFDFStWtOwX+Z9//lm//PKL4saNq2HDhqlo0aJ/uw+BrXle/t23adNG58+fV2hoqJo2bap69eq9si+32uu0b9++137e2u12GYbhDAat1JMr+uvBpzNnzmjAgAGqVq2a2rRp4wxbrHaAasOGDerXr59Kly6tsmXL6rffftOjR4907949Zc6cWQsWLFC8ePHMLjPSBgwYoLhx46pv377at2+f9u7dq2PHjiljxoyqV6+eihcvbtn31aNHjzRx4kRt2bJFzZs3V9WqVZU6dWpLHsghJAOiyIABA7Rx40aVKlVKvXv3ds6yCgsL0+nTp50LTFphJoXjqOilS5e0evVqHTt2TEFBQWrfvr3ee+89SdKaNWs0b948LVq0yORqIycoKEgjRoxQihQp1Lp1a/Xr1087d+7Uu+++q9DQUKVKlUqTJk0yu8z/ZMaMGVq1apWyZMmismXL6ty5c9q1a5c2bNhgdmlv7fLly/ruu+909uxZ1ahRQ++//75y5sxpmVPeHO+pSZMmadeuXfr555/l4eGhIkWKyGazqXLlymrXrp0KFixodqlvxREmVa1aVYcOHdLgwYPl6+ur0NBQDRs2TB07dlSaNGnMLjNCHKcVLV++XEuWLFF4eLju3bunnDlzatiwYZa6mIc76dy5s9KnT6++ffvq/Pnz+vTTT9WgQQMtXLhQJUqUUP/+/S0XPksvLiCzdetW+fr6Kk6cOCpcuLAqVapkyV5ckePLrOMiRp06ddLhw4d17tw52Ww2lS5dWjVq1FDChAnNLvVfOfo4d+6cEiZMqOfPn6tt27b67bffLDmjyp2EhITo8uXLSpIkiWw2m1KmTKk1a9Zo1apV+uKLL1SgQAGzS4ywv35HWrp0qcaOHat27dqpadOmWrdunU6ePCkvLy9LnfHxcl9+fn7OK2ZLLyZZHDlyRL///rsk6bvvvjOtzqgSEBCgAQMGKFasWPr4449VtGjRGL9Q/18RkgFRJCAgQCtXrtTu3btVqFAhlStXTgUKFLDcF6qXF2n94IMPlCtXLhUsWFCXL1/WL7/8oqpVq2r06NGWPCrgEBAQoI4dOypfvnwaOnSo/P39dfToURUrVkyZMmWK8VedepPnz5/r4MGDWr9+vQ4dOqRatWqpQoUKyps3r9mlvZFjIBEeHq6bN2/q7Nmz8vHxcU6r37ZtmyZMmCC73a5FixZZarH0J0+eqHHjxvr222+VP39+ffnll4odO7ZatGihevXqycPDQ3v37rXcml3Hjx/XgwcP5OPjo3Tp0mn9+vVasWKFhg4dqpUrV8rPz89yYbphGCpbtqyGDBmi9957TwMHDtSff/6pH3/8UZcuXbLchT1c3YMHDzR+/Hg1bdpUOXPmVJ06ddSsWTN99NFH6tGjh5InT26JhYL/as2aNSpWrJhSp06t8+fPa/Xq1bpw4YKSJk2qIkWKqF69epY8JcdVvLwOz7hx41SrVi3lz59fdrtd+/fv165du3TmzBmlT59eX3/9dYx/rcLCwtS+fXulSpXKeWqUY20rxFzLli1T//79Vb58eQUGBipt2rQqXbq0xowZo3jx4mnUqFEqUaLE3y7CEFOFhoZq9erVqlKlipIkSaKTJ09q/fr1Kl68uCpUqKCQkBB5enpa5iDpy1asWKFff/1VcePG1Zdffqns2bM7w7MLFy4ofvz4Sps2rclVRg3DMLR161b16tVLHTt2tNwaf4RkwH8UHh6uy5cvK1u2bJKkXbt2adGiRXr06JGKFSumsmXLqnDhwiZXGXmbN2/WlClTtHz5cudt169fV4cOHfTFF1+oevXqJlYXOa+btnzr1i198803KlWqlJo1a/av97Uyq/Xj+NIxZswYbd++XYkTJ9adO3dUo0YNffbZZ86p9UePHlWhQoUsMTPTISgoSPPnz1e1atUUK1YsNW3aVCtXrlTSpEn1/fffq3LlysqdO7fZZUZapUqV1LNnT9WqVUseHh46ffq0fvjhB0kv1pSbNm2a5daSO3jwoH788UdNnz5dN2/eVIMGDbRgwQK988476tKli5o0afLKwsEw36VLlxQ3blw9ffpUAwYM0IIFCyRJLVq00KBBg/TOO++YXGHkTJw4UQcPHtTYsWNfOQK/f/9+LVq0SAUKFGCx/hji22+/1e+//64KFSqoZ8+eztMTg4ODtXHjRqVJk0alSpUyucqIuX79ukaOHKnNmzerRYsWqlSpknLlyqUkSZJIenEqX9KkSS0zM9hdBAYG6vTp0/Ly8tKaNWsUN25cBQUFadOmTSpXrpxGjx5tmbHg0aNH1a5dO2XPnl3vvPOOwsPD5eHh4bwieOnSpS01tnWMU2fPnq1ffvlFBQsW1NmzZ5UnTx5VrVpVhQoVcun3k1XX+CMkA/6jIUOG6MGDBxo0aNArs8bWrl2radOmqU2bNqpfv755BUbQsmXLFBoaqiZNmkh6MWNn6tSpmjJlipIlS6bw8HDFihVLI0eOlKenp3r37m2ZDyjpxWB169atqlKlip48eaLkyZNr27Zt6tOnj7p06WK5Kwm6IsdA4syZM2rRooXmzZunNGnSqHnz5nr48KHixImjTz/91PKvlWOm3/fff6+ffvpJx44d05dffqnt27ebXVqkjRw5Uv7+/po1a5aCg4O1YMECHT9+XFeuXFHbtm2VLVs25cuXz+wyI+3q1atq3bq1fvjhB40fP17p0qXTwIEDdfz4cXXp0kW+vr6Wu0Kiq3HMijh58qRSpEjh/JJx584dNW3aVAUKFFCsWLEUEBCgX3/91eRqIycoKEhVq1bVL7/84lzk+OVF00NDQyXJ0jO6XYljHZ5t27apefPmqlKlitKkSWO59VodwcPChQv16NEjnTlzRg8fPnSuCeXj46NGjRpp6NChKlSokNnlQi/GExcuXFDGjBkVHh7uDCIcV/m9fv26+vbtq4wZM2rYsGGWmEnmsGfPHl29elWXL1/W6tWrdffuXaVLl05+fn5mlxZhL4d5vXv31ocffqhixYrp2rVr+umnn3T27FkVKFBAlStXVunSpS31vcrVWW+eIhCDXLhwQWvWrHHOBtmzZ4/OnTunFClSqHbt2ipTpozzAysmH/UIDQ3VyJEjtWTJEkkv1hfy8fGRzWbTli1bVL9+fee05nv37ilVqlQxtpd/8vvvv+vHH3/UyJEjVbx4cQUHB6tWrVoqW7asJk6cqJIlSzpnA8IcjhlhM2bMUMOGDZUjRw5t3LhRjx490ty5c9WuXTt9++23ypw5s8qWLWtytW/28ns+PDxcf/75p/OLU4ECBeTt7a0PP/xQwcHB6t69u8nVRl5oaKguX76sTp06SXoxAAwNDVWBAgXk5eWlokWLWnax9EyZMqlmzZoaN26cjh07psGDB8tut2vIkCFq2rQpAZnJ7Ha7PD099ezZM3Xs2FH9+/dXlSpVFCtWLKVMmVIDBw7UTz/9pMyZM2vy5Mlmlxtp586dU8GCBf92FbCQkBAtWrRIderUsdxReVeWOHFiff311851ePz8/NSkSRNLrcPz8ufVxx9/7Lx9/vz58vX11aFDh/Tw4UOlTJmSgMxkjnUzt27dqqVLl2rnzp3KmTOn8ufPrwoVKqhs2bLy9vZWWFiYMmTIoMGDB+vLL79UcHCwJfYbjgOmpUqVcs7A7N69uzZv3mzZMcX8+fNls9n09OlTSVLGjBn1zTffaO/evZowYYKyZMmiMmXKmFwlXkZIBvwH8+bNU61atZQuXTpt3bpV3377rVKnTq2bN28qffr0Kly4sByTNWNyqHT58mWlS5dODx48UHh4uMaOHasJEyaofv36+vbbb7V582blyZNHt27dcl6t02rq1KmjDz74QMeOHdOlS5f06NEj/frrr0qSJImyZs1KQBZDhIWFqUqVKs4vFtOmTVOvXr2UKVMmVatWTYUKFbJEQCb97z2/adMmzZ07VzabTcmSJVOtWrVUuXJlDRgwQOfOnVP8+PFVoUIFk6uNPG9vb2XPnl3NmjVTjRo19ODBAy1YsEA2m00ffPCBzp8/b5kBrWNW0tWrV/Xnn3+qUKFCatSokaZMmaLEiRNr5MiRun37tnLnzq127dqZXa7bc7y3RowYoWLFiqlGjRqv/Lx06dIqV66c88uk1aRNm1b+/v5avXq1atWqJZvNJg8PD+3evVtz5859ZYkAxBzZsmXTwoULLbcOjyOUCAsL0549e3Ts2DFlyJBB9evXV9OmTVWtWjVt3LhRqVOnVpEiRcwu1+059mmDBg1Sly5dNGTIEB06dEgbNmzQL7/8ojx58ihZsmTOA4979uxR0qRJLRGQSf87YOoIbu12u7y9vfX++++bXFnkOeo/deqUVq5cqT/++EPFihVzrqdbsmRJLVq0SHa73eRK8Vecbgn8B8uWLdPq1auVM2dO7du3T82bN9cHH3ygAQMGKHPmzGrfvr3ZJUaIYRiaNGmSrl69qhMnTihPnjyaMGGCJOnu3buaNGmSQkJClCVLFpUqVcqljiI+ePBA3t7ellss3ZU9f/5coaGhCg4OVr9+/dSsWTOVKFFC1apV0/Tp05UnT54YPTPz3Llz8vLycgavZcuWVatWrRQ3blzduHFDhw8fVs6cOdWqVSvnBQmsKjQ0VL/++qs8PDxUvnx5ZcyYUevWrdOYMWO0ZcsWs8uLtAYNGqhJkyaqU6eOc/27gIAABQUFKXHixEqXLh2zyGKI4OBgde3aVZ06ddK7777rXLfGZrNp+/btypQpk7JkyWJ2mW9typQpOn/+vGrXrq1UqVIpduzY6tatm9q3b68PPvjA7PLwBlZah8cRkn3zzTfav3+/cuTIoV27dilLlizq2rWrZdZTcweO12r79u368ccfNX/+fOfPnj59qk8//VQlS5ZUz549nbcHBwdLknOtPCv463qzMXnMF1EnTpzQN998o3PnzumLL7545YCbK/TnagjJgP/g2rVrmjZtmry9vZUnTx41btxYhmGodOnSmjRpkooVK2aZHV9ISIgGDx7sPHJdtGhRlSxZUhkzZpT04sp8ji+NrsBKC767upevEBYaGqqgoCDn1X2GDh2qEydOyG63K0OGDJo4cWKMf+2aNm2qx48fq3Xr1kqaNKlWrlzpvKT3rVu3dPjwYW3fvl1HjhxRnz59VKVKFZMrjhqGYejgwYPq3bu3+vfvr2rVqpldUoQ4tqeffvpJ69at04oVK2QYhlauXKl9+/apUKFC+uijj8wuE6/x9ddfK0GCBOrTp88rt9eoUUM9evSwzDbocPnyZe3atUvBwcHKli2bVq1apVu3bjnXIStTpowlT81GzOXY/124cEFNmzbVunXrlDx5crVq1Up3797VxYsXVbFiRQ0ePFgpU6Y0u1y39fDhQ+fFEwzD0MWLF9WmTRuNHTv2ldl969at0+bNmzV69Gh5eno6v4PE1O8iL4/nnj17JpvNJk9PT0utnfY6fx2nOtaIk15c4XLChAkKDg7W1KlTVaxYMbPKxL+w3hx0IAbw8/PTkydPVLt2bQ0YMMAZHj148ECjRo1S/vz5LRGQbd68Wbt379bAgQMVO3ZsZcmSRZ07d9aTJ0+0efNmnT59WiVLllTZsmVdbqZVTA5Z3IHjvREaGipvb289fPhQw4cP19mzZ5UyZUrnrIkvvvhCq1evVpYsWSwzkJg3b56+//57TZgwQTly5NDdu3d1+vRp5cmTR2nSpFHNmjXl4+OjI0eOuNQaFM+fP1fs2LHVqVOnGB9OvPzFwWazyW63a8+ePerXr5+kF6ex+Pv765133tHq1atVsWJFpU6d2uSq8VcFCxbUqFGjlDFjRn388ce6ffu2fH195enpGeO3wdf5/PPP5ePjo7t372rLli2qWLGiypYtq1y5cilbtmzOU3SAqOIYCy1dulRVq1ZV8uTJtWnTJl24cEE7duxQz549tXPnTu3Zs0d169Y1uVr39OjRI/Xr108lS5ZUw4YNlSBBAmXLlk3lypXTL7/8otu3b6t8+fJKkCCBFi5cqGLFir0SkEkxc8kXR5AUHBysWbNmaeXKlcqTJ49Sp06t6tWrq2jRomaX+NYc76s5c+bo4MGDypAhg9555x29//77ql+/vmrWrKnRo0eLuUoxFzPJgEgKDAxU9erVNXr0aJUpU0Y2m033799XsmTJtH//fq1bt06dO3dW8uTJnevcxFRHjhxRSEiISpYsqTNnzih37tzOn61fv17bt2/X48ePlTJlSn3yySes24Uo8+DBAwUFBSlTpkySpNatWytFihTONSe2bNkif39/DR48WLly5TKz1Lf2xx9/6IcffpCvr69q1aqljz76SFmzZnUeTYzp+4e3FdMPDkgvTj+5du2acufOLbvdrvDwcI0YMUKbNm1SjRo1tHv3bs2aNUupUqVSs2bN1KZNG1WqVMnssvEaGzdu1MCBA5UoUSIlSJBASZIkUbdu3VSgQAGzS4uUMWPG6OTJk5ozZ47CwsKUL18+5c+fX2FhYSpXrpw++eQT5xU8gah28eJF3b59W6VKlVLbtm313nvvqUmTJpoxY4bSpUunmjVrml2i27p06ZIWL16sGzduyNvbW9WrV1e1atV0+/ZtTZ06Vbdu3VJAQIBz/zB37lxJMf+z2FFfly5dFCtWLNWoUUM3b97U2bNnFRISoj59+ljy4JRjbDdjxgytXr1a5cuXV0hIiPz8/FSwYEHVrVvXkuvQuhtCMiCSevbsKZvNpjFjxuj69euaP3++Nm/erFKlSmnIkCF69OiREidOHONPCQsKClLChAklvVgsvVmzZrp9+7a6deumevXqSXoRZKxevVoHDhzQ0KFDlTRpUjNLhguZOXOmRo8erRYtWqhDhw6aNGmSunfv7twmb9++rdGjRytOnDgaPny4ydX+NwcPHtS4ceMUFBSkhg0bqlKlSkqfPr0lFxR3FQsWLNCFCxf09ddfO/fVjx490tixY5UoUSJVq1ZNBQoU0MGDB9WlSxft3LkzRu/P3YHji8euXbvk5+enK1euKGfOnKpYsaKKFSumTZs2KXny5MqUKZOSJ09udrmR8vTpU/Xo0UOfffaZChUqpM6dOytNmjQaMGCA6tWrp5QpU+r7779nPTxEu9DQUA0aNEiJEiXShx9+qCZNmuj7779X8eLFzS7NrT158kT79u3Trl27dPXqVWXMmFGffPKJsmTJopMnT+r+/fvy9vZW/vz5lShRIsschLt27ZqaN2+uVatWKVGiRJJehIJ9+/ZVlixZNGrUKJMrfDshISGqVKmSfvjhBxUqVEhff/21Tp8+rfTp0+vkyZMqU6aMvvrqK3l7e5tdKv4BIRkQCY6BbPv27VW4cGE1btxYPj4+KliwoNauXasRI0YoQ4YMZpf5Rk+fPtXw4cNVsGBBVa5cWcmTJ9etW7e0fPlyLVmyROnTp1fv3r2dR+IdM+WAqLRnzx59++23unr1qlKnTq0OHTqoXr16zqOLJ0+e1LBhwzR58mSXWAtlyZIlmjVrlmLHjq3p06crRYoUZpfktp48eaK4cePKw8NDPXv2VObMmdWlSxfnz8PDw+Xv76+uXbuqRYsWatq0qYnVwhFkPnz4UNWrV1fXrl0VK1YsTZ06VY0aNVLHjh0t86XwdZ4/f67t27crS5Ysihs3rlq1aqXFixcrSZIk+vrrr1WnTh3LnG4Oa3Bc+fX27dt69uyZnj59quzZs8vLy0urVq3S7Nmz5eHhoRw5cmjkyJFml+vWXp4RduvWLe3du1c7d+7UgwcPVLJkSdWtW9eSM66kF2utde7cWV999ZVy5szpvP3UqVOaOnWqRo0aZcnlXvbv36+VK1dq+PDhunTpkj7++GNt3rxZJ06c0Pfff6+2bdsymyyG4zA2EAmxY8dWunTp9PHHH6ty5cpKnDixRowYIUn65ZdfdOvWLUuEZLdv31ZISIi2bdumc+fOqXz58ipVqpQ6dOigSpUqac6cOfriiy9UtGhRDR48mIAM0aJUqVJasWKFli5dqp9++knjxo1TypQpVbp0aRmGoZ07d+r58+cuEZBJUuPGjVWnTh2tWbOGgMxkjnUkQ0NDVbhwYS1evFibNm1Sly5d9N5778nT01P37t1T7dq1CchiAMcsvu+//14VKlTQJ598omvXrmnUqFFq3ry5/vzzT+3bt0+VK1e25BeqWLFiqUqVKjIMQzdv3lTixIl17NgxPXjwQH5+fho6dKjZJcKF2O12eXl56eHDh+rQoYMePnwoHx8fpU+fXjVr1lTdunWVI0cOxY8fX6lSpTK7XLf214X306RJo/r166tMmTJau3atDh48qCNHjqhs2bL65JNPzC43UgIDA5UkSRLFjx9fgwYNUvfu3VWiRAlJ0q5du3Tv3j1L7s8lKXv27GrQoIEMw9C6detUpkwZxY8fX15eXkqYMKHKly9vdol4A2aSAW9h3bp1ihs3rgoXLqwkSZLo559/1po1a7R8+XKzS4uw8PBw+fr6asuWLXr8+LFy5cqlypUrK1++fJKkHTt2aPz48frss89UvXp1k6uFq3v06JGmTJmiBQsWKHv27EqaNKkKFy6sWrVqKXv27JaeJYKYLTw8XDdv3tTixYu1atUq5c6d23mqR0w/bd7dzJo1S2FhYWrXrp2aNGmi4sWLq0ePHlq2bJlWrFihX375xewSo8Tw4cO1cOFC5c6dWx07dmQ9PESJ+/fva9WqVWrSpInixImjnj17Kk6cOOrUqZN2796tQ4cO6cGDBypcuLDee+89ZcuWLcava+XqHGMfX19f+fr66vHjx0qVKpWqVaumcuXK6dixY1q1apVy5MgRo6/C7Pgsffkqj3369FH//v0VFhamqVOn6tKlSzIMQxkyZNDu3bs1YcIE5c+f3+TK/ztfX1/Nnj1bbdu21dChQ9W+fXsOvlkAIRkQQf7+/s5FF1OlSqVs2bLp+fPn2rRpk4YPH67vv/9eBQsWtMSX+Ze/+N27d0+rVq3S4cOH5enpqXfffVdVqlRR+vTpX/kwA6La6wbfAQEB+vHHH7Vq1SqNHj1adevWZZCOKOXY/4WEhCgkJESxYsVSnDhx5OHhoRMnTuiXX37R6tWrNWrUKOf6jIgZNm7cqPHjx6tcuXLavHmzNm3aJEmqXr26OnTooPr165tbYBS6ceOGJCl9+vQmVwJXYBiGDh48qObNmytHjhzq2rWrAgICVLFiReXIkUPSi8/fHTt2aM+ePUqTJo2GDBlictXuzTH2uXbtmmrVqqVevXopKChIjx8/1pkzZ1S7dm01bNhQgYGBih8//t+uaBkT9evXT5UrV9bWrVt17do154GNK1eu6MyZMzp9+rS8vb1Vvnx5y1x8xTAMPX/+XN7e3vrjjz+0b98+3b17V6lSpVK+fPmUNm1affbZZwoLC1OePHk0cOBAs0tGBBCSAREwadIkHThwQLFixdLTp08VN25cTZo0Sd7e3rpx44YCAgL03nvvWWrWwV8/SM+dO6e1a9fK399fyZIlU/HixVW3bl3L9ANriOgAbufOnSpatKjixIkT4wd9sA7HPjooKEijRo3S1q1bVbx4ceXJk0cVK1ZU9uzZFRYWpm3btqlYsWLOhYRhDse6SY8fP1ZwcLBu376tw4cPy9fXVylSpFClSpV0+vRp+fv7u8wsMiC6jRo1SnPmzFGsWLH04YcfasCAAa/8fN++fUqePLmyZ89uUoV42Zo1a3TmzBn17t1bknT37l2tWbNGixYt0k8//eS8SnhMZhiGDMPQhAkTtGbNGt26dUsjRoxQ/fr1LfXd6a8c3wkdqlevLh8fH926dUtp06ZVrFix1KZNG+XNm1f3799XokSJuGiTRRCSAW9w79491axZU/PmzZOPj48+++wz5xG2EydOKGfOnM6rk1jhy7xjptuZM2d04sQJnT9/Xi1atHCupbZnzx79+uuvKlCggFq1amVytXBV+/fvV8GCBd94tTYrvKdgHY7BuGOR/rZt22rFihVat26dSpQooUqVKqlkyZJKkyaNyZXiZe3atdPDhw/18OFD3bx5U+XLl1eJEiXk5+en+vXrq3Tp0pZduBr4vxIaGuocrz58+FDDhg3TmjVr1LJlS7Vv3571Z2Og48ePa/DgwcqVK5dzDWSHNm3aqGHDhqpZs6ZJ1b2doUOH6uDBgwoJCVG5cuXUsGFD50VLhg0bptq1a6tQoUJmlxkhn3/+ufLmzavOnTtr27ZtmjFjhubOnStJOnLkiFasWCF/f39NmzZNiRMnNrlaRIY1Y1vg/5C/v79KlCghHx8fHTt2TMeOHVOfPn0kvZhhdvz4ced9Y/qXebvdLk9PTz18+FCdOnXSli1bFBAQoJo1a2rkyJG6d++eSpUqpVGjRnG+PKKM3W6XJJ09e1aStH37do0fP/6NAZkU899TsBabzabz58/r8OHD+vbbb1WgQAGdP39e1apVU1hYmL799lstWrTI7DLdmt1u17p163T79m1J0nfffafw8HCNHDlSixcvVq9evbR3717du3dPc+bMUYMGDQjIgH/g+Px99uyZMyDr2bOnvLy89N1332nOnDnasmWL6tSpo0WLFik0NNTMcvEXz549c15xdN68ea/87MSJE0qaNKmkFwcUraJPnz5atWqVRowYodOnT6tPnz6aP3++Ro0apQ0bNlgmIHvw4IHeffddHTt2TJ9++ql8fX2dFx6w2+0qXLiwevTooWfPnunIkSMmV4vIYr4f8Ab58uXT/v37dfDgQY0aNUodOnRQ/PjxtX79el27dk1FixY1u8QIcwQOQ4cOValSpTR8+HDdvXtXlSpV0tatW7Vx40Y1adJE7du3t9QHLmI2m82m+/fvq23btqpYsaJ27Nihdu3amV0W3NSdO3fUpEkTxYsXT+vXr9cff/yhuXPn6uTJk5oyZYpq1Khhdolu7fr16/r6669VpkwZNW7cWE+fPlWnTp2UPXt2GYahli1bKlWqVFq4cKGePHnivFIpgL9znMY2ZMgQ51pQd+7cUYIECWS321WiRAn5+vpq/vz5Gjx4sO7fv6+OHTuaXDUcs+iLFCmiadOmafny5Zo5c6bmzp2rQoUKKWnSpKpfv75KlSplibWQX+ZY67hIkSJasGCBlixZopUrVypDhgwaM2aMydVFXNKkSdW8eXOVLFlS27dv15YtW7Rx40aVLVvWGfQlTpxYXl5eunPnjrnFItI43RL4Fz179nQe8di5c6cuXbqk+fPnK1asWPr000/VrVs31axZ01IfULdv31bbtm01adIkZc2aVY0bN9b777+vDz/8UI0aNZKHh4fWr19vdplwQdevX1fnzp11/vx5denSRVWqVFHGjBmdR7ePHz+ujBkzOo+MAlHFsbbVhQsXlDp1at24cUO5cuXS5MmTZRiGunTpop9++klXr17VsGHDzC7X7YWFhWnhwoXOdZPy5s2rr7/+2rlGXGhoqOrWravhw4erSJEiJlcLxFxvWgvKbrc710h6eQFymMOxJIBhGHr8+LGePXumBAkSKE6cOLp69ap+/vln+fr6KmPGjBo1apTeeecdSdZfmiIsLEyhoaGWPehx9+5dnTt3TosXL9b58+dVrFgxVaxYUTdv3tTChQu1Zs0as0tEJBGSAf9gy5Yt+vbbb+Xr66vr169r/vz58vf3d14NrUiRIvriiy/MLvOtHDx4UGnTptX9+/fVv39/rV69WpI0YsQItW/fXilSpDC5QrgaR5A8evRoJUuWTOvWrZPNZlOTJk1UuXJlPX36VJ9//rlmzZrF9odokytXLs2fP98ZrCxbtkxff/212rZtq9mzZ2vOnDkqWLCgyVXCISgoSOPHj9emTZvUrFkzVa5cWUmSJHHO7N6yZYvZJQKW8bq1oN555x3Fjh1bQ4YMUcOGDZUvXz6zy3RrjrHSlClT5Ovrq0SJEilPnjwqV66cypcvL+nFGH7u3Lk6cuSIatWqpS+//NLkquEQEhIiX19fLVy4UCdOnFD+/Pk1ZMgQ5xVkYR2EZMBrGIbhXBOldevWztvPnj2rpEmTytvb2znbxQpXZXHMotiwYYP27NmjPn36KH78+Dp+/LiGDBmiLl266PTp01q9erXWrVtndrlwMf90hHPKlClasWKFsmbNqrt37ypfvnz65ptvLPGegnU4tqcjR45o/vz5+u677175+ffff68rV66oXLlyqlOnjklV4t8EBARowIABCgwM1LNnz1SuXDm9//77KlmypNmlAZbx7NkzxYkTR4cOHdLYsWMVFBSkevXq6d69e1q9erV27txpdomQdPnyZTVo0EDTp0/XuXPndO7cOd25c0c5cuRQ1apVlS9fPj169Ei7du3S+PHj1aVLFz67/o85vlf5+/vLz89PV65cUcKECVWpUiWVKlVKf/zxh1asWKHw8HB17tzZ7HLxFgjJgNdYuXKlvvzyS6VMmVIrVqxQ8uTJzS7prb0cUJQtW1Z9+/ZV9erVFStWLIWGhmrEiBE6fvy4nj59qrFjxypPnjwmVwxX4jgqevv2ba1du1anTp1SsWLF1KRJE0nSn3/+qZUrVypLliyqUqWKbDYbIRmi3P3799WvXz/dunVLw4YNU/78+c0uCZFkGIb27Nmjjh07qkuXLq8cwAIQeS+vBVWvXj2VKlXK7JIg6fTp09qyZYs6deokSfrjjz+0adMmHTt2TPfu3dPAgQOVLVs2GYahu3fvKmXKlCZX7F5e/l713nvvqWLFigoNDVXs2LF1+vRpvf/++2rWrJmCgoLk6elp2VNI3R0hGfAPdu3apbFjx+rGjRvq3bu3GjVqJMkaM8deZ968efLz89PMmTNlt9vl4eEhDw8PHTt2TJ6ensqYMSOXJ/5/7d15UJXn+f/xN4sglUUYEqwWJIhLQzBREcUFHWNU3OoSNzRGHbW1MXGp0Srj0iRVcYnBJdFKxAqxKkYIomgE0agRqYoVK1oX1FQILohTRZbDOb8/LOcbm6RJf0l8wPN5/cdzDjOfGThz7ud67uu65SczfPhw6tSpQ2BgIOnp6bi4uPCHP/zBuiivXnTU9rkaUvNYLBZKSkqYP38+2dnZdO/enZEjR9KkSRPN3qmFKioqePDggb6vRH4EtX0W1JOiemfSrVu3yM7OJiEhgbfffpuAgADrmuj06dPk5eUxbNgwg9MKwJ/+9CeOHj1KXFwc8HDmc2ZmJlu2bOG9997D39/f2IDyg9S+O32Rn9i+ffsoKiqiY8eO7Nixg0mTJrF48WIGDhxIdnZ2rSyQAbi6uuLl5WX9uby8HICSkhLi4uJwc3MzKpo8oaqPnj9z5gxOTk7Ex8czf/58UlJS6NKlC7/5zW8YPnw4d+7csS4CVSCTH5udnR2enp6sXLmS1atXk5+fz8yZM9m8eTPXrl2z/p9K7eDk5KQCmciPxNHRUQWyGqD68IRx48bxl7/8hZMnT7JixQpycnIoLS0FoGXLltYCmb63jOfs7Gw9OMFsNuPj48OwYcPw8fEhNzfX4HTyQ9XOu32Rn8j169dJSEhg4cKFpKWlUVFRwZgxY0hPTycgIICFCxcaHfH/W4MGDfjss8/IyMjA3t7eegTzunXraNq0aa0t/knNVf0/lZWVhYeHh7Uw6+npSVRUFNu3b8fR0ZGrV68aGVNsSEhICB999BGjR49m27ZtTJkyheLiYqNjiYiIjapu6kpNTcXZ2Zn4+HgyMjKws7Nj6tSprFmzhn/84x+YTCbr72jNbjxfX1+SkpLIzMy0/j3s7OysszOldlO7pchXLFiwgEuXLlFYWIifnx9+fn707duXkJAQAO7fv0+9evWs26Jrm40bN/Lxxx/j5+dHaGgo586dIy8vj+TkZKOjyRPq7NmzLFq0iLy8PCIjIxk4cCDPPPPM196nNkt53MrKykhNTbW20ouIiBihrKyMTZs2Ub9+fYYOHWq9/vnnn7No0SLMZjMff/yx9QG31AyxsbGkpKTg7+9Pp06dOH/+PEeOHGHPnj1GR5MfSEUykX+LjY0lPT2dzZs3c/PmTQ4dOsT+/fspLCyke/fuREREWLfV1lYmk4njx4+TlpbG2bNn6d+/P2FhYQQGBhodTZ5gV65c4dNPP+X48eN4e3sTGhpKeHj4I+2/IiIiIrYoNTWVGTNm4O/vT0pKytfmZebm5hIcHFxr5yI/qSorK633VSdOnKBPnz506dKFoKAgo6PJD6QimQgPd7FMnz6dli1bMnbsWOv1o0ePEhUVRatWrXBzc+ONN97Qjb3Id6jeFVZRUcH9+/dxc3PD0dGRM2fOsH37dm7evIm7uzuTJk3Cz8/P6LgiIiIihrl79y45OTmsWbOGwsJCxo8fz5gxY4yOJf8DdUQ8WVSKFuFhD3lQUBB79+595HpYWBht2rShZcuWnDhxgjVr1hiUUKR2qKqqws7OjkuXLvH222/Tt29fZs+ezbx582jevDlRUVH07NmTevXqqUAmIiIiNs/FxYWuXbuyZcsWJk2axPbt2xk0aBDp6elGR5PvSQWyJ4t2kon8W3FxMZMmTcLOzo4xY8bQqVMn0tPTWbp0KUeOHOGzzz4jISGB5cuX6yRIke8wePBgOnfuzIgRI4iJiSEvL4+kpCSKi4vx8vKioqICJycntQ6IiIiITamebZybm8uBAwesBxhNmTIFX19frl69SlxcHMXFxaxcudLgtCK2R3cmYvOqqqooKirCy8uL999/n9DQUJYtW8ZLL71ESkoK06ZNA+CLL77g1q1bKpCJfIOvPm/JycmhoqKCqVOn4uPjw4kTJ5gwYQIAO3bs4PTp09Z5GyqQiYiIiK2wWCzWw7+mT5/O7du36dWrF8ePHyc6OhqAxo0bs2DBApYsWQI8vFcRkcen9h3PJ/IjOnLkCFu3buXKlSv4+/szd+5cpk+fztSpU8nJySE4OBgnJycKCwuJiYlh8eLFRkcWqZGqt5mbzWYaNmyIu7s78PBADE9PT3r37s29e/fYvHkzXbt2NTCpiIiIiDGq10vx8fH4+fmxYMEC7t69S2VlJVOmTOHevXukpqYSERGBh4cHAA4ODkZGFrE5eoQvNi0qKgpfX18mT56M2WwmPDycxYsXY29vT5s2bXBycqK4uJisrCxGjx5Nt27djI4sUuO8++67ZGdnAw93hrm7u1O3bl1mz57NqlWrWLp0KQDvvPMOwcHBBAYGYjabjYwsIiIiYqhGjRoBMHfuXNq3b0/Tpk05d+4cqampODs7G5xOxHZpJ5nYrM8//5x27drx5ptvAtCjRw8OHjzIihUraNGiBZs2bSI0NBQvLy/69OmjgYwi3+Ds2bNcvHiRy5cvk52dTZ8+fXjmmWd48803iYmJwd/fnw0bNmA2mzl//jwbN240OrKIiIiIoQIDA0lOTiYjI4NTp06RmZkJwLJly+jUqRN169bV3FYRg2hwv9iksrIyoqOjOXnyJEuXLqVZs2bW18rLy0lMTKR37954eXnpSF+R73DhwgUOHz7MhQsXuHv3Lr169aJnz56UlJRw4MABbty4gY+PDx06dKBRo0ZUVVWpdUBERERs2rx580hMTCQ4OJglS5awZcsWsrKySE5OBtA9iIhBVCQTm3T58mXWrl1Lbm4uL7zwAj169KBjx47WYeLV9OUk8t9VF7wuXLjAunXryMjIwNfXl6CgIAYMGEC7du2MjigiIiJSY3z1/iIzM5P169fj4OBAeHg4nTt3pkWLFnqgKGIgFcnEZlVVVXHo0CF27drF/fv3efbZZwkLC6NNmzZGRxOpdQYMGMCQIUNo06YN586d49ixY+Tn59OuXTvCw8P1uRIRERH5Fv/6179wc3MzOoaIoCKZ2KAHDx6Ql5dH48aNqVevHlVVVXzyySdkZWXx4MED5s6di5+fn9ExRWq0r87JOHnyJDNmzGD//v3W1wsLC5k5cyb5+fmMHj2aiRMnGhVVREREpEap3immrhWRmkeD+8Wm7Nu3jx07dpCdnU1AQABBQUH069ePyMhIWrZsSV5engpkIt+hvLz8kVOX/P398fT05OTJk7Ru3RqAn//857z++uskJiYydOhQAA2gFREREZvy1SKYyWTCbDbj5ORkbaVUgUyk5tHditiUt956i4iICA4ePMjkyZMpLS1l5cqVFBUV8dxzzzFkyBDg4ReaiHxdRUUFAwcOpKioyHrNy8uL5s2bM336dLZs2WK9npCQgLe3N/Xr18disahAJiIiIjajqqoKOzs7bty4QUxMDH379iU6Opply5ZRUlJidDwR+RZqtxSbcfDgQWJjY4mPj7deq6ys5NVXX6V169bMmDHDwHQitcO9e/fIzc0lLCyMoqIi/va3v9GjRw8A1q9fT1paGl9++SWBgYHcvn2b7du34+LionYCERERsUljxozBz8+Pvn37kpSUxJkzZ9i5cyfFxcV4eHhoQL9IDaN2S7EZzZo1Iz8/n6ysLNq3bw9AnTp1GDlyJOnp6WoFE/keXF1dCQsLA+DYsWNERUURFhbG73//eyZMmMCgQYPIzs7G1dWVX/7yl7i4uOiEJhEREbFJZ86c4fr162zcuBGAhQsXMmLECAAyMjJ4/vnnadasmYEJReQ/qSIgNiEzMxM3Nzf69evH5s2bSUlJsW5zjo+Pp2nTptjb22M2m40NKlLDfXXzcf/+/dm8eTP169dn1KhRLFy4EBcXFyIiIujcuTPe3t4AKpCJiIiITfLw8LDOO05MTMRkMhEZGUlVVRWxsbGUl5cbnFBE/pPaLeWJl5WVxZw5c1i8eDH169dn27ZtFBYWkpeXx9NPP029evX48MMPAdQSJvI97d69m9atW9OgQQPu3btHdnY2sbGxFBYWMmLECJ1mKSIiIjapujulvLwci8XCyJEjCQkJISkpibi4OIKCgoiOjub8+fNs2LDB6Lgi8h9UJJMn3oABA3jllVcYPHgwAA8ePCA3NxcHBwdcXV1p1KgRrq6uagkT+Q4mkwlHR0c+/PBDDh06xMKFC2nYsKH19crKSuLi4rh58yZRUVEGJhURERF5fKoLY18tkMXGxvLaa69x/Phx1q5dy/Xr1xkwYAAmk4mdO3eyfv16fH19dQ8iUsOoSCZPtIsXLzJnzhzWrVuHu7s7ZrOZOnXq8ODBA3bt2kW3bt3w8vIyOqZIrVFWVkZ4eDjr1q2jVatWHDx4kKysLC5evMg777yDj4+PdbGnOX8iIiJiS+bMmcP06dNZtGgRlZWVrFy5EoDjx4/z17/+lYKCAho0aECXLl147rnntFYSqYE0uF+eaP7+/tSpU4eTJ0/y4osvWp/SVFRUsGrVKjp06GBwQpHaJTMzk5CQEFq1akVubi6zZ88mMjKSu3fvsmPHDiZNmmT9nGnRJyIiIrbAbDZTWlrKtWvX6NatG/b29iQnJ1tfDwkJISQk5Gu/p7WSSM2jT6U8sSwWC46OjrRu3Zp58+YRHx8PQFFREXPnziUsLIyGDRtqWL/I/8DX15djx47x61//mvfee4/hw4czefJkhgwZwpkzZ4yOJyIiIvLY2dvb4+rqSkJCAkFBQTg7OxMZGUl8fDwVFRXW973//vsUFhYamFREvovaLeWJZ7FYSEhI4JNPPuGLL74gMDAQd3d3YmJicHJy0jZnkf9RZmYm+/bto1evXoSHhwPQt29fIiMjiYyM1GdKREREbE5FRQVOTk6cOHGCNm3a8NFHH7Fs2TIaN27MrFmzyMrKYs+ePezdu9foqCLyX6hIJjahqqqKu3fvcv78eTw9PWncuDEuLi4alCnyHao/I2VlZVy6dAmABg0a4OHhgaOjIzdu3OCDDz7g1KlTJCUlGZxWRERExFh37tzB09PT+vO8efPIzMykWbNmvP7667zwwgu6BxGpwVQkExGRb2SxWLCzswNg/PjxlJeXU1xcjJubG6NHj6Z3797cvn2bXbt20a5dO5o3b65Fn4iIiNiU6rXPnj172LlzJ5WVlbi7uzN48GDCwsIAKC4upqqqiqeeeuqR9ZWI1DwqkomIyDeqbptcvXo1hw8fZsuWLRQUFNCtWzd2795NgwYNKC0txdvb2+ioIiIiIo9d9Vrpzp079OrViwkTJtCwYUPWr19PaWkpbdu2ZezYsTRp0sToqCLyPWlojIiIfCN7e3tMJhN5eXlMmzYNgDVr1tCjRw8CAgLIzMxkw4YNmEwmg5OKiIiIPH7VM1iXLVtGeHg448ePp2nTply7do1x48Zx4MABxo4dS3Z2tsFJReT7cjQ6gIiI1FyOjo489dRT7N27F3d3dzIyMvj0008BSExMJDQ0FEdHR7UOiIiIiE0qKyvDw8ODrl27AvDWW28xceJEhg0bRk5ODsHBwYSGhhobUkS+N+0kExGRR1RVVQHw97//nbS0NIYOHUpxcTFTp06lf//+uLu7k5aWxpUrV/jtb39rcFoRERGRx89sNgNQt25dXn31VQICAigoKKC0tJTu3bsDkJ+fT2Bg4CPvF5GaTTvJRETkEdWD9+fOnUvPnj2JiIggKCiIf/7zn9y7d4+IiAiaNGlCVFQUACaTCUdHfZ2IiIjIk6165/yNGzd4+umn2bZtGzk5OSxatAh4uCb6xS9+wZ///Gfu379PeXk57dq1A/6vNVNEajYN7hcREavqgldBQQEpKSkMHToULy8vAM6dO8etW7ews7MjODgYd3d3g9OKiIiIPF75+flMnjyZX/3qV2zatIkVK1bQtm1bKioqcHJyIjk5maSkJJo3b86wYcNo0qSJTv8WqUX06F9ERKyqd4SNGjWKgoICXF1dGTVqFAAtWrQwMpqIiIiI4RwdHRk3bhwrVqzAZDJRVlYGgJOTExaLhQEDBtCnTx/q1KkDPNx9pgKZSO2hPZ8iIvI1MTExvPTSSyxatIhVq1ZZr1ssFs3UEBEREZvl6+vLiy++iL+/P7169WLt2rW88cYbnDp1Cjs7Oy5dusSYMWOsp3/rYCOR2kXtliIi8q2nU+7evZslS5YAMHPmTHr37v24o4mIiIjUCGazGXt7e7788kucnZ1xc3Pj0KFD7N+/n7Nnz9KsWTMuXrxI27ZtmTlzpvX9IlJ7qEgmIiLWIll2djb5+flcvnyZl19+maZNmwKwevVqVq9ezbvvvqtCmYiIiNic6rliJ06cYOvWrUycONF6cmVJSQlHjx7lyJEj/OxnP2POnDnAtz+EFJGaS0UyEREbV73oy8rKYtasWTz77LNYLBYOHDhAnz59WL58OQC3bt3C29vb4LQiIiIixunduzcDBw5kwoQJ3Llzh6tXr3L//n06duz4yPs0rF+kdlKRTEREAHjllVcYNGgQAwcOBODSpUv87ne/4/nnn2f+/PlqFxARERGblpaWxrp160hOTgYerp1cXFw4e/Yss2bNol+/fsYGFJEfTHc8IiI2rHoI/4ULFwgICMDX19f6WpMmTXjttde4fPkyJSUlBiUUERERqRkcHBxwdXUlOzubOXPm4ODgQHR0NCNGjODcuXNo/4lI7acimYiIDaveHbZgwQK2bt1KXFwcBQUF1uJZSEgI+fn5VFZWGhlTRERExHAdOnTA2dmZadOm4erqSnR0NJ6enuTl5WEymTR/TOQJoHZLERHBYrEQHx/Pxo0bad68OT179sRisXDkyBGcnZ354x//iMlkwtHR0eioIiIiIoYqKirC29sbBwcHdu/ezYIFCzh06BDOzs4a1i9Sy6lIJiIiVoWFhSxfvpzDhw9TUVHBtGnTGDRoEPXq1TM6moiIiEiNcuHCBT744APat2/P0KFD9UBR5AmgIpmIiHzN6dOnWb58OSUlJQwaNIguXbrg5+en4f0iIiIi/2Yymbh9+zY+Pj5GRxGRH4mKZCIi8q0SExOJi4vD2dmZ9evX4+3tbXQkERERERGRn4SKZCIi8l+VlZWRmprKyy+/bHQUERERERGRn4yKZCIiIiIiIiIiYvM0XEZERERERERERGyeimQiIiIiIiIiImLzVCQTERERERERERGbpyKZiIiIiIiIiIjYPBXJRERERERERETE5qlIJiIiIiIiIiIiNk9FMhERERERERERsXkqkomIiIiIiIiIiM1TkUxERERERERERGze/wOXXxVRsD548wAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "false\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "half-true\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "mostly-true\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "pants-fire\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x500 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "true\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "top_features = 10\n",
        "# get the model's weights: n_classes x n_features - (? , ?)\n",
        "all_class_coef = lr.coef_\n",
        "\n",
        "for i, cls in enumerate(lr.classes_): # for each of the classes\n",
        "  print(cls)\n",
        "  # get the weights for the class\n",
        "  coef = all_class_coef[i]\n",
        "  # find the top negative and positive features for the class\n",
        "  top_positive_coefficients = np.argsort(coef)[-top_features:]\n",
        "  top_negative_coefficients = np.argsort(coef)[:top_features]\n",
        "  # combine them in one array\n",
        "  top_coefficients = np.hstack([top_negative_coefficients, top_positive_coefficients])\n",
        "  # create plot - humans tend to understand better plot visualizations\n",
        "  feature_names = vectorizer.get_feature_names_out()\n",
        "  plt.figure(figsize=(15, 5))\n",
        "  colors = ['red' if c < 0 else 'blue' for c in coef[top_coefficients]]\n",
        "  plt.bar(np.arange(2 * top_features), coef[top_coefficients], color=colors)\n",
        "  feature_names = np.array(feature_names)\n",
        "  plt.xticks(np.arange(1, 1 + 2 * top_features), feature_names[top_coefficients], rotation=60, ha='right')\n",
        "  plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DhvQand9AaQP"
      },
      "source": [
        "## Error classification\n",
        "\n",
        "We can now look at the predictions of the model to try and spot any problems or particular features of the model.\n",
        "\n",
        "We will look at instances the model classifies **in/correctly**, which in turn can be broken down to: **instances with high/ low/ medium confidence**. To do that, we'll use the **probability/confidence** of the model's prediction, which is available for all ML models."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 63,
      "metadata": {
        "id": "FTvP0zDQaX8m"
      },
      "outputs": [],
      "source": [
        "# get the probability of the model\n",
        "valid_pred_prob = lr_bpemb.predict_proba(X_valid)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 64,
      "metadata": {
        "id": "D8K6YMkTXsn6"
      },
      "outputs": [],
      "source": [
        "from collections import defaultdict\n",
        "\n",
        "# collect correct and wrong predictions, keeping the confidence of the prediction\n",
        "errors = defaultdict(lambda: [])\n",
        "correct_preds = defaultdict(lambda: [])\n",
        "\n",
        "for (i, instance), pred, pred_score in zip(valid_data.iterrows(), preds_valid_bpemb, valid_pred_prob):\n",
        "  # get the index/id of the gold class in the probability array (n_classes x 1)\n",
        "  index_of_class = np.where(lr_bpemb.classes_ == instance[1])\n",
        "  # get the index/id of the predicted class in the probability array (n_classes x 1)\n",
        "  index_of_pred_class =  np.where(lr_bpemb.classes_ == pred)\n",
        "  # depending on whether the prediction is correct, collect the instances as errors or correct predictions\n",
        "  if pred != instance[1]:\n",
        "    errors[instance[1]].append((instance[2], pred_score[index_of_class], pred_score[index_of_pred_class], pred))\n",
        "  else:\n",
        "    correct_preds[instance[1]].append((instance[2], pred_score[index_of_class], pred_score, pred))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 65,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YmWrLxFkbWpM",
        "outputId": "176dd891-f486-4f2f-dd1f-44e0df2760f1"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "barely-true\n",
            "High probability for correct class\n",
            "[('Just like Donald Trump, David Jolly wants to outlaw a womans right to '\n",
            "  'choose.',\n",
            "  array([0.2224716]),\n",
            "  array([0.2338382]),\n",
            "  'false'),\n",
            " ('Stimulus dollars paid for windmills from China.',\n",
            "  array([0.22531648]),\n",
            "  array([0.26168529]),\n",
            "  'half-true'),\n",
            " ('Obamacare is costing 2 million jobs.',\n",
            "  array([0.22574249]),\n",
            "  array([0.24798076]),\n",
            "  'half-true'),\n",
            " ('Gerry Connolly and his fellow Democrats went on a spending spree and now '\n",
            "  'their credit card is maxed out.',\n",
            "  array([0.22602134]),\n",
            "  array([0.24113175]),\n",
            "  'half-true'),\n",
            " ('Says Hillary Clinton has been saying lately that she thinks that I am, not '\n",
            "  'qualifiedto be president.',\n",
            "  array([0.23416318]),\n",
            "  array([0.24779808]),\n",
            "  'false'),\n",
            " ('Rick Scotts prison plan would cut Floridas prison budget in half, close '\n",
            "  'prisons, and release tens of thousands of prisoners early -- murderers, '\n",
            "  'rapists, sex offenders, armed robbers, drug dealers.',\n",
            "  array([0.23463377]),\n",
            "  array([0.23873547]),\n",
            "  'half-true'),\n",
            " ('Originally, Democrats promised that if you liked your health care plan, you '\n",
            "  'could keep it. One year later we know that you need a waiver to keep your '\n",
            "  'plan.',\n",
            "  array([0.23797386]),\n",
            "  array([0.24247701]),\n",
            "  'false'),\n",
            " ('DNC chair Debbie Wasserman Schultz denied unemployment went up under Obama.',\n",
            "  array([0.24116873]),\n",
            "  array([0.24555612]),\n",
            "  'false'),\n",
            " ('Says Gina Raimondos venture capital firm secured a secret no-bid contract '\n",
            "  'funded by taxpayers.',\n",
            "  array([0.24321609]),\n",
            "  array([0.30550704]),\n",
            "  'false'),\n",
            " ('Says his plan to end the toll on Ga. 400 fulfills his campaign promise to '\n",
            "  'commuters.',\n",
            "  array([0.2601096]),\n",
            "  array([0.27781512]),\n",
            "  'false')]\n",
            "Low probability for correct class\n",
            "[('Rhode Island is one of the worst states for income equality.',\n",
            "  array([0.05067526]),\n",
            "  array([0.28483852]),\n",
            "  'true'),\n",
            " ('Veterans can now download their electronic medical records with a click of '\n",
            "  'the mouse.',\n",
            "  array([0.05353415]),\n",
            "  array([0.25986546]),\n",
            "  'true'),\n",
            " ('Voted the best specialty plate in America',\n",
            "  array([0.07670447]),\n",
            "  array([0.46597907]),\n",
            "  'mostly-true'),\n",
            " ('The U.S. \"only ranks 25th worldwide on defense spending as a percentage of '\n",
            "  'GDP.\"',\n",
            "  array([0.07744584]),\n",
            "  array([0.32521434]),\n",
            "  'mostly-true'),\n",
            " ('In 2013, the United States accepted 67 percent of the worlds refugees.',\n",
            "  array([0.07826611]),\n",
            "  array([0.28746261]),\n",
            "  'mostly-true'),\n",
            " ('Nearly 25 percent of all automobile accidents are caused by texting while '\n",
            "  'driving.',\n",
            "  array([0.07844603]),\n",
            "  array([0.28717691]),\n",
            "  'mostly-true'),\n",
            " ('The Wisconsin Economic Development Corp. cannot account for creating one '\n",
            "  'single job.',\n",
            "  array([0.084891]),\n",
            "  array([0.31330716]),\n",
            "  'false'),\n",
            " ('There are over 100 pipelines between the United States and Canada right '\n",
            "  'now.',\n",
            "  array([0.08661863]),\n",
            "  array([0.29832183]),\n",
            "  'true'),\n",
            " ('We have less Americans working now than in the 70s.',\n",
            "  array([0.08869422]),\n",
            "  array([0.33833124]),\n",
            "  'mostly-true'),\n",
            " ('Financed the largest parking expansion program without a rate increase.',\n",
            "  array([0.09098913]),\n",
            "  array([0.35662085]),\n",
            "  'half-true')]\n",
            "false\n",
            "High probability for correct class\n",
            "[('During the Monica Lewinsky scandal, the Clintons brought the Rev. Jeremiah '\n",
            "  'Wright to the White House for \"spiritual counseling.\"',\n",
            "  array([0.23549227]),\n",
            "  array([0.23917458]),\n",
            "  'pants-fire'),\n",
            " ('Says Charlie Bass forfeits right to equal cost for TV ads under FCC rules',\n",
            "  array([0.2360216]),\n",
            "  array([0.25691944]),\n",
            "  'half-true'),\n",
            " ('By advocating new requirements for voters to show ID cards at the polls, '\n",
            "  'Republicans want to literally drag us all the way back to Jim Crow laws.',\n",
            "  array([0.23792375]),\n",
            "  array([0.2497029]),\n",
            "  'half-true'),\n",
            " ('Says Bill White is for gay marriage.',\n",
            "  array([0.24090115]),\n",
            "  array([0.24448802]),\n",
            "  'pants-fire'),\n",
            " ('Says Robert Hurt supports the tax loopholes that send American jobs '\n",
            "  'overseas.',\n",
            "  array([0.2430813]),\n",
            "  array([0.25987152]),\n",
            "  'half-true'),\n",
            " ('Says U.S. Rep. Tom Price is sending letters both supporting and opposing '\n",
            "  'the small-business killing Internet Tax Mandate.',\n",
            "  array([0.24395242]),\n",
            "  array([0.25934095]),\n",
            "  'half-true'),\n",
            " ('Obama Makes Huge Move to BAN Social Security Recipients From Owning Guns',\n",
            "  array([0.24782217]),\n",
            "  array([0.3183808]),\n",
            "  'half-true'),\n",
            " ('The (State Board of Administration) transparency issue got a great airing '\n",
            "  'the last legislative session.',\n",
            "  array([0.25824734]),\n",
            "  array([0.26974639]),\n",
            "  'true'),\n",
            " ('Social Security is a Ponzi scheme.',\n",
            "  array([0.2821064]),\n",
            "  array([0.32357105]),\n",
            "  'barely-true'),\n",
            " ('On expanding Medicaid as part of the health care law',\n",
            "  array([0.28620467]),\n",
            "  array([0.3012631]),\n",
            "  'half-true')]\n",
            "Low probability for correct class\n",
            "[('The Atlanta Beltline paid nearly $3.5 million for less than a quarter-acre.',\n",
            "  array([0.05160424]),\n",
            "  array([0.40122912]),\n",
            "  'mostly-true'),\n",
            " ('Rhode Island has the highest percentage of lawyers per capita in the '\n",
            "  'country.',\n",
            "  array([0.06372822]),\n",
            "  array([0.35213794]),\n",
            "  'mostly-true'),\n",
            " ('Says most Texas schools spend 45 out of 180 school days in mandated '\n",
            "  'testing.',\n",
            "  array([0.07270685]),\n",
            "  array([0.29759242]),\n",
            "  'mostly-true'),\n",
            " ('More than 7,000 Americans lost their lives to climate change-fueled events '\n",
            "  'last year.',\n",
            "  array([0.08234235]),\n",
            "  array([0.32273905]),\n",
            "  'mostly-true'),\n",
            " ('The average federal employee makes $120,000 a year. The average private '\n",
            "  'employee makes $60,000 a year.',\n",
            "  array([0.08436855]),\n",
            "  array([0.36238402]),\n",
            "  'mostly-true'),\n",
            " ('Students at the University of Texas in Austin have been advised not to wear '\n",
            "  'cowboy boots or cowboy hats on Halloween.',\n",
            "  array([0.08583635]),\n",
            "  array([0.35866011]),\n",
            "  'pants-fire'),\n",
            " ('State employees in Wisconsin earn about 8 percent less than if they worked '\n",
            "  'in the private sector.',\n",
            "  array([0.09197875]),\n",
            "  array([0.33049575]),\n",
            "  'mostly-true'),\n",
            " ('My opponent supported policies that increased tuition by 18 percent.',\n",
            "  array([0.09414829]),\n",
            "  array([0.41768377]),\n",
            "  'half-true'),\n",
            " ('We have the highest tax rate anywhere in the world.',\n",
            "  array([0.09900748]),\n",
            "  array([0.35879851]),\n",
            "  'mostly-true'),\n",
            " ('Says the jurisdictions with the strictest gun control laws, almost without '\n",
            "  'exception have the highest crime rates and the highest murder rates.',\n",
            "  array([0.10685495]),\n",
            "  array([0.33985154]),\n",
            "  'mostly-true')]\n",
            "half-true\n",
            "High probability for correct class\n",
            "[('He voted twice for a budget resolution that increases the taxes on '\n",
            "  'individuals making $42,000 a year.',\n",
            "  array([0.25027129]),\n",
            "  array([0.31948301]),\n",
            "  'mostly-true'),\n",
            " (\"''Both Democrats and Republicans are advocating for the use of student test \"\n",
            "  \"scores to measure teacher effectiveness.''\",\n",
            "  array([0.2521953]),\n",
            "  array([0.26462514]),\n",
            "  'mostly-true'),\n",
            " ('We lowered the business tax from 4 percent down to 1 percent.',\n",
            "  array([0.25333808]),\n",
            "  array([0.33573192]),\n",
            "  'mostly-true'),\n",
            " ('The violent crime rate in America is the same as it was in 1968, yet our '\n",
            "  'prison system has grown by over 500 percent.',\n",
            "  array([0.25488597]),\n",
            "  array([0.27667876]),\n",
            "  'mostly-true'),\n",
            " ('One out of two Americans are living either in or near poverty. That means '\n",
            "  '150 million Americans, half of us.',\n",
            "  array([0.26282003]),\n",
            "  array([0.28622885]),\n",
            "  'mostly-true'),\n",
            " ('When I was governor, not only did test scores improve we also narrowed the '\n",
            "  'achievement gap.',\n",
            "  array([0.26803264]),\n",
            "  array([0.26911791]),\n",
            "  'mostly-true'),\n",
            " ('Georgias high school graduation rate topped 80 percent in 2010.',\n",
            "  array([0.27014331]),\n",
            "  array([0.33123163]),\n",
            "  'mostly-true'),\n",
            " ('The accuracy of the Obama tax calculator',\n",
            "  array([0.27461074]),\n",
            "  array([0.31755994]),\n",
            "  'false'),\n",
            " ('The lieutenant governor has the power to be an economic ambassador and '\n",
            "  'negotiate on economic development',\n",
            "  array([0.28263438]),\n",
            "  array([0.33892291]),\n",
            "  'false'),\n",
            " ('Violent crime is up since the last year of Sharpe James administration. '\n",
            "  'This year its higher. The unemployment rate is almost 15 percent. The high '\n",
            "  'school dropout rate is over 50 percent.',\n",
            "  array([0.28395398]),\n",
            "  array([0.29556668]),\n",
            "  'mostly-true')]\n",
            "Low probability for correct class\n",
            "[('President Obama is shrinking our military.',\n",
            "  array([0.07734614]),\n",
            "  array([0.68473211]),\n",
            "  'pants-fire'),\n",
            " ('New Jerseys once-broken pension system is now solvent.',\n",
            "  array([0.07984389]),\n",
            "  array([0.27840403]),\n",
            "  'false'),\n",
            " ('Ken Lanci is a lifelong Clevelander',\n",
            "  array([0.0833397]),\n",
            "  array([0.38498939]),\n",
            "  'pants-fire'),\n",
            " ('New Hampshire is currently the only state in the nation that does not have '\n",
            "  'a full-service veterans hospital or equivalent access.',\n",
            "  array([0.106968]),\n",
            "  array([0.23695573]),\n",
            "  'true'),\n",
            " ('Limberbutt McCubbins (a five-year-old cat) is a candidate in the 2016 '\n",
            "  'presidential election.',\n",
            "  array([0.11245468]),\n",
            "  array([0.22598993]),\n",
            "  'false'),\n",
            " ('I waited until (the Trans-Pacific Partnership trade agreement) had actually '\n",
            "  'been negotiated before deciding whether to endorse it.',\n",
            "  array([0.12167538]),\n",
            "  array([0.37495339]),\n",
            "  'false'),\n",
            " ('If you dont buy cigarettes at your local supermarket, your grocery bill '\n",
            "  'wont go up a dime. The same is true of the sugary drink tax. If passed, you '\n",
            "  'can avoid paying the tax by not buying sugary drinks.',\n",
            "  array([0.12248952]),\n",
            "  array([0.26148264]),\n",
            "  'barely-true'),\n",
            " ('The new Ukrainian government introduced a law abolishing the use of '\n",
            "  'languages other than Ukrainian in official circumstances.',\n",
            "  array([0.12479265]),\n",
            "  array([0.24843353]),\n",
            "  'false'),\n",
            " ('Henry Kissinger \"said that we should meet with Iran guess what without '\n",
            "  'precondition.\"',\n",
            "  array([0.12793469]),\n",
            "  array([0.28153066]),\n",
            "  'false'),\n",
            " ('Each U.S. House member who voted to overhaul Social Security in 1983 was '\n",
            "  're-elected.',\n",
            "  array([0.1337608]),\n",
            "  array([0.27319248]),\n",
            "  'false')]\n",
            "mostly-true\n",
            "High probability for correct class\n",
            "[('Half the lottery directors across the country had not run a lottery before '\n",
            "  'they were hired.',\n",
            "  array([0.2413534]),\n",
            "  array([0.26181165]),\n",
            "  'true'),\n",
            " ('Texas had the worst voter participation in the country in the November 2014 '\n",
            "  'elections.',\n",
            "  array([0.24295152]),\n",
            "  array([0.29149397]),\n",
            "  'true'),\n",
            " ('The port provides more than 297,000 jobs directly to the state of Georgia.',\n",
            "  array([0.24363798]),\n",
            "  array([0.3019998]),\n",
            "  'half-true'),\n",
            " ('Says Marco Rubio has a 98 percent voting record with the Koch brothers.',\n",
            "  array([0.24370107]),\n",
            "  array([0.25281379]),\n",
            "  'true'),\n",
            " ('Says we have now more Border Patrol officers than weve had at any time in '\n",
            "  'our history.',\n",
            "  array([0.24475734]),\n",
            "  array([0.26872983]),\n",
            "  'true'),\n",
            " ('House Democrats have a two-to-one advantage money-wise against House '\n",
            "  'Republicans.',\n",
            "  array([0.24727238]),\n",
            "  array([0.27099114]),\n",
            "  'barely-true'),\n",
            " ('I think with the exception of the last year or maybe the last two years, we '\n",
            "  'were at 100 percent when it came to contributing to the Providence pension '\n",
            "  'fund.',\n",
            "  array([0.25467988]),\n",
            "  array([0.29153121]),\n",
            "  'true'),\n",
            " ('Says roughly two-thirds of (state) corporations didnt pay any income tax in '\n",
            "  'Virginia.',\n",
            "  array([0.26654209]),\n",
            "  array([0.30937665]),\n",
            "  'true'),\n",
            " ('Says 92 percent of Texas counties had no abortion provider in 2008.',\n",
            "  array([0.26791968]),\n",
            "  array([0.28343422]),\n",
            "  'true'),\n",
            " ('Says Texas has the fourth-lowest debt per capita of any state in the '\n",
            "  'nation, and we are the lowest of any of the big states.',\n",
            "  array([0.27783938]),\n",
            "  array([0.3070326]),\n",
            "  'true')]\n",
            "Low probability for correct class\n",
            "[('Barack Obama refuses to acknowledge Jerusalem as the capital of Israel.',\n",
            "  array([0.06173208]),\n",
            "  array([0.31783371]),\n",
            "  'false'),\n",
            " ('Says Nelson Mandela was a communist.',\n",
            "  array([0.06901594]),\n",
            "  array([0.27806835]),\n",
            "  'false'),\n",
            " ('A proposed ban on hollow-point bullets and bullets that expand upon impact '\n",
            "  'essentially bans deer hunting.',\n",
            "  array([0.09090985]),\n",
            "  array([0.28464093]),\n",
            "  'false'),\n",
            " ('Says Rick Perry supported a guest worker program to help people who would '\n",
            "  'otherwise be illegal aliens.',\n",
            "  array([0.09576496]),\n",
            "  array([0.25811281]),\n",
            "  'barely-true'),\n",
            " ('Says Ohios economic recovery started in February 2010.',\n",
            "  array([0.09599952]),\n",
            "  array([0.40835288]),\n",
            "  'false'),\n",
            " ('Zika mosquitoes cant catch me.',\n",
            "  array([0.09629752]),\n",
            "  array([0.31177874]),\n",
            "  'false'),\n",
            " ('Congress will begin its recess without having allocated one penny to fight '\n",
            "  'Zika.',\n",
            "  array([0.09882178]),\n",
            "  array([0.27860261]),\n",
            "  'false'),\n",
            " ('Says that President Obama said in 2008 that his proposed greenhouse gas '\n",
            "  'regulations will bankrupt anyone who wants to build a new coal-fired power '\n",
            "  'plant.',\n",
            "  array([0.10854213]),\n",
            "  array([0.284121]),\n",
            "  'false'),\n",
            " ('Says Barack Obama has pension investments that include Chinese firms, and '\n",
            "  'investments through a Caymans trust.',\n",
            "  array([0.11132525]),\n",
            "  array([0.22395059]),\n",
            "  'false'),\n",
            " ('Access for 12,000 women to use Planned Parenthood -- not for the right to '\n",
            "  'choose, but for basic health care -- was taken away by Wisconsin Gov. Scott '\n",
            "  'Walker and Lt. Gov. Rebecca Kleefisch.',\n",
            "  array([0.11281461]),\n",
            "  array([0.26626312]),\n",
            "  'false')]\n",
            "pants-fire\n",
            "High probability for correct class\n",
            "[('Blue Cross Blue Shield cancelled all their individual (health care) '\n",
            "  'policies in the state of Texas, effective Dec. 31.',\n",
            "  array([0.18449604]),\n",
            "  array([0.20870304]),\n",
            "  'half-true'),\n",
            " ('Says Ted Cruz said, There is no place for gays or atheists in my America. '\n",
            "  'None. Our Constitution makes that clear.',\n",
            "  array([0.19099055]),\n",
            "  array([0.22498345]),\n",
            "  'false'),\n",
            " ('Never mind that no red light camera, no speed camera, nor any radar gun has '\n",
            "  'ever stopped one accident from occurring.',\n",
            "  array([0.19394779]),\n",
            "  array([0.25780012]),\n",
            "  'false'),\n",
            " (\"Page 992 of the health care bill will establish school-based 'health' \"\n",
            "  'clinics. Your children will be indoctrinated and your grandchildren may be '\n",
            "  'aborted!',\n",
            "  array([0.19927584]),\n",
            "  array([0.23326156]),\n",
            "  'false'),\n",
            " ('Says Denzel Washington supports Donald Trump and speaks out against Barack '\n",
            "  'Obama.',\n",
            "  array([0.19951676]),\n",
            "  array([0.28611697]),\n",
            "  'barely-true'),\n",
            " ('Democratic Sens. Ed Markey, Al Franken and Jeanne Shaheen took Bribes From '\n",
            "  'Iran They Back Insane NUKE Deal.',\n",
            "  array([0.20419717]),\n",
            "  array([0.26711951]),\n",
            "  'barely-true'),\n",
            " ('Reporters rehearse questions with White House press (secretary).',\n",
            "  array([0.20722583]),\n",
            "  array([0.23711809]),\n",
            "  'true'),\n",
            " ('From Obama\\'s book: \"I found a solace in nursing a pervasive sense of '\n",
            "  'grievance and animosity against my mother\\'s race.\"',\n",
            "  array([0.21709643]),\n",
            "  array([0.21842319]),\n",
            "  'half-true'),\n",
            " ('Barack Obama is the first president to file lawsuits against the states he '\n",
            "  'swore an oath to protect.',\n",
            "  array([0.23673495]),\n",
            "  array([0.24841758]),\n",
            "  'false'),\n",
            " ('Michelle Nunns own plan says she funded organizations linked to terrorists.',\n",
            "  array([0.26219644]),\n",
            "  array([0.30105473]),\n",
            "  'barely-true')]\n",
            "Low probability for correct class\n",
            "[('In the past two years, Democrats have spent more money than this country '\n",
            "  'has spent in the last 200 years combined.',\n",
            "  array([0.00941243]),\n",
            "  array([0.30350381]),\n",
            "  'mostly-true'),\n",
            " ('The number of illegal immigrants in the United States is 30 million, it '\n",
            "  'could be 34 million.',\n",
            "  array([0.02549005]),\n",
            "  array([0.30403413]),\n",
            "  'mostly-true'),\n",
            " ('I never supported a state income tax for Texas.',\n",
            "  array([0.02933594]),\n",
            "  array([0.22730975]),\n",
            "  'mostly-true'),\n",
            " ('CNN did a poll recently where Obama and I are statistically tied.',\n",
            "  array([0.03527709]),\n",
            "  array([0.2921933]),\n",
            "  'mostly-true'),\n",
            " ('The last quarter, it was just announced, our gross domestic product was '\n",
            "  'below zero. Who ever heard of this? Its never below zero.',\n",
            "  array([0.036928]),\n",
            "  array([0.24535844]),\n",
            "  'true'),\n",
            " ('The United States stood alone in the war in Iraq.',\n",
            "  array([0.03705107]),\n",
            "  array([0.2881047]),\n",
            "  'mostly-true'),\n",
            " ('Says President Barack Obama doubled the national debt, which had taken more '\n",
            "  'than two centuries to accumulate, in one year.',\n",
            "  array([0.03879358]),\n",
            "  array([0.25173513]),\n",
            "  'half-true'),\n",
            " ('Gov. Deal has the worst record on education in the history of this state.',\n",
            "  array([0.04273579]),\n",
            "  array([0.24011644]),\n",
            "  'true'),\n",
            " ('Medicare insurance premiums will be rising to $120.20 per month in 2013 and '\n",
            "  '$247.00 per month in 2014.',\n",
            "  array([0.04410272]),\n",
            "  array([0.26869633]),\n",
            "  'mostly-true'),\n",
            " ('Out of 67 counties (in Florida), I won 66, which is unprecedented. Its '\n",
            "  'never happened before.',\n",
            "  array([0.0443458]),\n",
            "  array([0.29162912]),\n",
            "  'mostly-true')]\n",
            "true\n",
            "High probability for correct class\n",
            "[('In 1950, the average American lived for 68 years and 16 workers supported '\n",
            "  'one retiree. Today, the average life expectancy is 78 and three workers '\n",
            "  'support one retiree.',\n",
            "  array([0.24254727]),\n",
            "  array([0.30746761]),\n",
            "  'mostly-true'),\n",
            " ('The commission form of government is definitely losing favor in the United '\n",
            "  'States.',\n",
            "  array([0.24281209]),\n",
            "  array([0.25082104]),\n",
            "  'false'),\n",
            " ('Latinos are 17 percent of our countrys population but hold only 2 percent '\n",
            "  'of its wealth.',\n",
            "  array([0.2442968]),\n",
            "  array([0.29030381]),\n",
            "  'mostly-true'),\n",
            " ('Only five states, including Georgia, do not have a hate crimes law.',\n",
            "  array([0.2467326]),\n",
            "  array([0.27117288]),\n",
            "  'mostly-true'),\n",
            " ('Wisconsin still ranks first among the 50 states in manufacturing jobs per '\n",
            "  'capita.',\n",
            "  array([0.26235194]),\n",
            "  array([0.36065724]),\n",
            "  'mostly-true'),\n",
            " ('Georgia has had ʺmore bank failures than any other state.ʺ',\n",
            "  array([0.2658132]),\n",
            "  array([0.26697797]),\n",
            "  'mostly-true'),\n",
            " ('We spend more per student than almost any other major country in the world.',\n",
            "  array([0.26713864]),\n",
            "  array([0.34744133]),\n",
            "  'mostly-true'),\n",
            " ('In the last 50 years, (the federal government has) only balanced the budget '\n",
            "  'five times.',\n",
            "  array([0.26998255]),\n",
            "  array([0.31497805]),\n",
            "  'mostly-true'),\n",
            " ('More than half of all black children live in single-parent households, a '\n",
            "  'number that has doubled doubled since we were children.',\n",
            "  array([0.28642289]),\n",
            "  array([0.29443745]),\n",
            "  'mostly-true'),\n",
            " ('Says, Oregon has the third largest class size in the nation.',\n",
            "  array([0.29814782]),\n",
            "  array([0.31718089]),\n",
            "  'mostly-true')]\n",
            "Low probability for correct class\n",
            "[('Ronald Reagan did amnesty.',\n",
            "  array([0.06368239]),\n",
            "  array([0.36511345]),\n",
            "  'pants-fire'),\n",
            " ('Says Apple CEO Steve Jobs told President Obama that the company moved '\n",
            "  'factories to China because it needed 30,000 engineers.',\n",
            "  array([0.07741306]),\n",
            "  array([0.25269475]),\n",
            "  'half-true'),\n",
            " ('The Obama administration is allowing state waivers from welfare work '\n",
            "  'requirements only if they had a credible plan to increase employment by 20 '\n",
            "  'percent.',\n",
            "  array([0.07838654]),\n",
            "  array([0.23908168]),\n",
            "  'false'),\n",
            " ('Says Jeb Bush -- not Charlie Crist -- signed legislation that let Duke '\n",
            "  'Energy collect money for nuclear projects.',\n",
            "  array([0.08508295]),\n",
            "  array([0.32297679]),\n",
            "  'false'),\n",
            " ('John McCain and George Bush have \"absolutely no plan for universal health '\n",
            "  'care.\"',\n",
            "  array([0.08516684]),\n",
            "  array([0.30571375]),\n",
            "  'false'),\n",
            " ('Our trade deficit in goods reached nearly $800 billion last year alone.',\n",
            "  array([0.08606185]),\n",
            "  array([0.36138182]),\n",
            "  'mostly-true'),\n",
            " ('McCain \"said he was \\'stumped\\' when asked whether contraceptives help stop '\n",
            "  'the spread of HIV.\"',\n",
            "  array([0.08707506]),\n",
            "  array([0.29375062]),\n",
            "  'false'),\n",
            " ('Weare picking up oil with shovels and paper and plastic bags.',\n",
            "  array([0.08978534]),\n",
            "  array([0.29173293]),\n",
            "  'barely-true'),\n",
            " ('The House-passed budget proposal could cut funding for programs that help '\n",
            "  'keep local neighborhoods safe, slash more than $1.7 million in anti-terror '\n",
            "  'funds for Ohio.',\n",
            "  array([0.09584124]),\n",
            "  array([0.23427828]),\n",
            "  'half-true'),\n",
            " ('The Israelisgave up 1,000 terrorists in return for one sergeant.',\n",
            "  array([0.09603398]),\n",
            "  array([0.25522786]),\n",
            "  'half-true')]\n"
          ]
        }
      ],
      "source": [
        "import pprint\n",
        "\n",
        "for cls in lr_bpemb.classes_:\n",
        "  print(cls)\n",
        "  print('High probability for correct class')\n",
        "  # sort the errors by the probability of the gold class and look at the:\n",
        "  # 1. instances where the gold class had a high probability\n",
        "  pprint.pprint(sorted(errors[cls], key=lambda x: x[1])[-10:])\n",
        "  print('Low probability for correct class')\n",
        "  # 1. instances where the gold class had a low probability\n",
        "  pprint.pprint(sorted(errors[cls], key=lambda x: x[1])[:10])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 66,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IdWWxWdKcI9x",
        "outputId": "de42eb54-6833-4d25-f401-743066f9593d"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "  (0, 414)\t1.0\n",
            "['is']\n"
          ]
        }
      ],
      "source": [
        "# We can check for some instances what were their TF-IDF scores (the input for the model)\n",
        "text = 'Ken Lanci is a lifelong Clevelander'\n",
        "print(vectorizer.transform([text]))\n",
        "text = 'Ken Lanci is a lifelong Clevelander'\n",
        "print([word for word in text.lower().split() if word in vectorizer.vocabulary_])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 67,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1lYCXmr2fPYn",
        "outputId": "8fc3c6c6-2b5c-47ae-bd30-2503865dd570"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "barely-true\n",
            "High probability for correct class\n",
            "[('Austin politicians want to cram more kids into classrooms so they dont have '\n",
            "  'to make the tough decisions to balance the budget.',\n",
            "  array([0.26293873]),\n",
            "  array([0.26293873, 0.1877495 , 0.24022847, 0.153028  , 0.05467419,\n",
            "       0.10138111]),\n",
            "  'barely-true'),\n",
            " ('Rosalyn Dance voted against President Obamas Medicaid expansion.',\n",
            "  array([0.27698818]),\n",
            "  array([0.27698818, 0.27214867, 0.18223261, 0.09537736, 0.11163232,\n",
            "       0.06162086]),\n",
            "  'barely-true'),\n",
            " ('Says Hillary Clinton was asked repeatedly to provide security in Benghazi '\n",
            "  'on several occasions, including direct cables.',\n",
            "  array([0.28205543]),\n",
            "  array([0.28205543, 0.20777169, 0.16144232, 0.11440078, 0.10087247,\n",
            "       0.13345732]),\n",
            "  'barely-true'),\n",
            " ('Says Elizabeth Warren lied when she says I want to abolish the Federal '\n",
            "  'Minimum Wage.',\n",
            "  array([0.28351333]),\n",
            "  array([0.28351333, 0.2133466 , 0.18479076, 0.15160966, 0.05889967,\n",
            "       0.10783998]),\n",
            "  'barely-true'),\n",
            " ('Michelle Nunn has praised the Occupy movement.',\n",
            "  array([0.28630741]),\n",
            "  array([0.28630741, 0.17684571, 0.22940848, 0.13750899, 0.08333322,\n",
            "       0.0865962 ]),\n",
            "  'barely-true'),\n",
            " ('The federal government now tells us which light bulbs to buy.',\n",
            "  array([0.28637803]),\n",
            "  array([0.28637803, 0.21264737, 0.15412711, 0.10253004, 0.15452223,\n",
            "       0.08979521]),\n",
            "  'barely-true'),\n",
            " ('Says Georgia Democratic Senate hopeful Michelle Nunn supports higher taxes.',\n",
            "  array([0.29157936]),\n",
            "  array([0.29157936, 0.19429771, 0.16500747, 0.15866035, 0.11713223,\n",
            "       0.07332288]),\n",
            "  'barely-true'),\n",
            " ('Says Congressman Jon Runyan has a plan to raise Medicare costs $6,400 a '\n",
            "  'year.',\n",
            "  array([0.31597034]),\n",
            "  array([0.31597034, 0.19357217, 0.17844332, 0.14204934, 0.07728688,\n",
            "       0.09267795]),\n",
            "  'barely-true'),\n",
            " ('Says Russ Feingold voted to raise taxes on Social Security benefits for '\n",
            "  'seniors, he even tried to give Social Security benefits to illegal '\n",
            "  'immigrants.',\n",
            "  array([0.33623735]),\n",
            "  array([0.33623735, 0.21847523, 0.18641956, 0.11327064, 0.08041168,\n",
            "       0.06518553]),\n",
            "  'barely-true'),\n",
            " ('Brendan Doherty wants to repeal Obamacare, increasing drug prices for '\n",
            "  'seniors.',\n",
            "  array([0.42820342]),\n",
            "  array([0.42820342, 0.19570451, 0.17076141, 0.08438515, 0.07755998,\n",
            "       0.04338554]),\n",
            "  'barely-true')]\n",
            "Low probability for correct class\n",
            "[('In the last 24 months, 10 rural Texas hospitals have been forced to shut '\n",
            "  'their doors because state leaders chose not to expand Medicaid.',\n",
            "  array([0.1999132]),\n",
            "  array([0.1999132 , 0.19613836, 0.18807865, 0.19878478, 0.06443655,\n",
            "       0.15264846]),\n",
            "  'barely-true'),\n",
            " ('The brutal fact is, when it comes to education, America is slipping behind '\n",
            "  'other nations.',\n",
            "  array([0.20647071]),\n",
            "  array([0.20647071, 0.1888914 , 0.20047352, 0.17322145, 0.1218735 ,\n",
            "       0.10906942]),\n",
            "  'barely-true'),\n",
            " ('Says that President Obama promised that with the stimulus plan, '\n",
            "  'unemployment would never go above 8 percent. He even said it would be 6 '\n",
            "  'percent by now.',\n",
            "  array([0.21524374]),\n",
            "  array([0.21524374, 0.1825488 , 0.21455465, 0.19833121, 0.06832788,\n",
            "       0.12099372]),\n",
            "  'barely-true'),\n",
            " ('Says there are a half a trillion dollars in cuts to Medicare that are going '\n",
            "  'to go in place as a result of health care reform.',\n",
            "  array([0.22303722]),\n",
            "  array([0.22303722, 0.20622217, 0.17934072, 0.1984027 , 0.06507109,\n",
            "       0.12792609]),\n",
            "  'barely-true'),\n",
            " ('John Boehner said the jobs of teachers and nurses and police officers and '\n",
            "  'firefighters are government jobs that werent worth saving.',\n",
            "  array([0.22526601]),\n",
            "  array([0.22526601, 0.19346821, 0.22202639, 0.13779129, 0.08112572,\n",
            "       0.14032239]),\n",
            "  'barely-true'),\n",
            " ('The U.S. Army had a training program that put evangelical Christians, '\n",
            "  'Catholics and Mormons in the same category of religious extremism as we do '\n",
            "  'al-Qaida.',\n",
            "  array([0.22976294]),\n",
            "  array([0.22976294, 0.18342206, 0.19070291, 0.15856462, 0.13606787,\n",
            "       0.1014796 ]),\n",
            "  'barely-true'),\n",
            " ('Some even advocate wiping out 401(k)s entirely and replacing them with '\n",
            "  'government-run accounts.',\n",
            "  array([0.23123338]),\n",
            "  array([0.23123338, 0.20248718, 0.16603279, 0.17088941, 0.10733469,\n",
            "       0.12202255]),\n",
            "  'barely-true'),\n",
            " ('When Peppers tax was finally voted down, Pepper laid off cops and closed '\n",
            "  'jails and let criminals run free in the streets.',\n",
            "  array([0.24631789]),\n",
            "  array([0.24631789, 0.187     , 0.23150291, 0.13769644, 0.06517579,\n",
            "       0.13230697]),\n",
            "  'barely-true'),\n",
            " ('Says Donald Trump has given more money to Democratic candidates than '\n",
            "  'Republican candidates.',\n",
            "  array([0.25234214]),\n",
            "  array([0.25234214, 0.12311495, 0.21799388, 0.223318  , 0.03756651,\n",
            "       0.1456645 ]),\n",
            "  'barely-true'),\n",
            " ('Says Gwen Graham was a Washington lobbyist.',\n",
            "  array([0.25439619]),\n",
            "  array([0.25439619, 0.19731169, 0.10561918, 0.18593198, 0.16735846,\n",
            "       0.0893825 ]),\n",
            "  'barely-true')]\n",
            "false\n",
            "High probability for correct class\n",
            "[('Signs letter saying Consumer Product Safety Commission is acting without '\n",
            "  'consultation or input from the company to stop the sale of Buckyballs.',\n",
            "  array([0.31720201]),\n",
            "  array([0.13324369, 0.31720201, 0.1442478 , 0.11359783, 0.10239731,\n",
            "       0.18931136]),\n",
            "  'false'),\n",
            " (\"Obama nominee Dawn Johnsen called motherhood 'involuntary servitude.'\",\n",
            "  array([0.31845312]),\n",
            "  array([0.1571139 , 0.31845312, 0.12322398, 0.05432333, 0.26800495,\n",
            "       0.07888072]),\n",
            "  'false'),\n",
            " ('On support for Trade Promotion Authority, calledfast-track',\n",
            "  array([0.33003408]),\n",
            "  array([0.15449452, 0.33003408, 0.27052611, 0.09879782, 0.03091002,\n",
            "       0.11523744]),\n",
            "  'false'),\n",
            " ('No one questioned that she (Judge Sotomayor) was out of the mainstream.',\n",
            "  array([0.3304491]),\n",
            "  array([0.15801922, 0.3304491 , 0.17684553, 0.11784661, 0.12961763,\n",
            "       0.08722191]),\n",
            "  'false'),\n",
            " ('One of the main functions of the Department of Homeland Securitys SAVE '\n",
            "  'database is checking voter registration citizenship status.',\n",
            "  array([0.34393229]),\n",
            "  array([0.1012623 , 0.34393229, 0.1261279 , 0.12807412, 0.13399513,\n",
            "       0.16660826]),\n",
            "  'false'),\n",
            " ('Minnick voted to let the government fund abortion under Obamacare.',\n",
            "  array([0.35519009]),\n",
            "  array([0.21334239, 0.35519009, 0.1339369 , 0.07694845, 0.15884411,\n",
            "       0.06173806]),\n",
            "  'false'),\n",
            " ('On support of Gov. John Kasichs drilling tax plan',\n",
            "  array([0.35587732]),\n",
            "  array([0.16156201, 0.35587732, 0.20954705, 0.06895204, 0.10582437,\n",
            "       0.09823722]),\n",
            "  'false'),\n",
            " ('Ed Gillespie supports a personhood amendment.',\n",
            "  array([0.37241536]),\n",
            "  array([0.10626476, 0.37241536, 0.19261131, 0.12482822, 0.09529761,\n",
            "       0.10858274]),\n",
            "  'false'),\n",
            " ('Says Democratic Party created Planned Parenthood',\n",
            "  array([0.38118479]),\n",
            "  array([0.19668943, 0.38118479, 0.07339025, 0.03326489, 0.20429198,\n",
            "       0.11117866]),\n",
            "  'false'),\n",
            " ('On birthright citizenship for illegal immigrants.',\n",
            "  array([0.44841535]),\n",
            "  array([0.07034228, 0.44841535, 0.18420592, 0.09194252, 0.08798092,\n",
            "       0.117113  ]),\n",
            "  'false')]\n",
            "Low probability for correct class\n",
            "[(\"The Z-visa that was offered in that Senate bill let everybody who's here \"\n",
            "  'illegally, other than criminals, stay here for the rest of their lives.',\n",
            "  array([0.19681947]),\n",
            "  array([0.16659854, 0.19681947, 0.18906547, 0.16523017, 0.09468548,\n",
            "       0.18760088]),\n",
            "  'false'),\n",
            " ('A major part of the climate change bill sponsored by Sens. John Kerry and '\n",
            "  'Joe Lieberman was essentially written by BP.',\n",
            "  array([0.19733178]),\n",
            "  array([0.18670565, 0.19733178, 0.16276415, 0.16625966, 0.15876149,\n",
            "       0.12817728]),\n",
            "  'false'),\n",
            " ('Every person on death row was a foster kid.',\n",
            "  array([0.20263354]),\n",
            "  array([0.11878895, 0.20263354, 0.15934273, 0.15784724, 0.16025584,\n",
            "       0.2011317 ]),\n",
            "  'false'),\n",
            " ('The ammunition used in the Orlando shooting is banned by Geneva Convention. '\n",
            "  'It enters the body, spins explodes.',\n",
            "  array([0.20282439]),\n",
            "  array([0.11071877, 0.20282439, 0.16058566, 0.1424955 , 0.19283358,\n",
            "       0.1905421 ]),\n",
            "  'false'),\n",
            " ('Liberals have figured out a Facebook algorithm and all the people getting '\n",
            "  'banned from Facebook are somehow conservatives.',\n",
            "  array([0.20584631]),\n",
            "  array([0.19653844, 0.20584631, 0.17825952, 0.12369032, 0.14747565,\n",
            "       0.14818976]),\n",
            "  'false'),\n",
            " ('Reporters have uncovered another Scott company accused of criminal acts. '\n",
            "  'But Scott wont come clean.',\n",
            "  array([0.20630829]),\n",
            "  array([0.17973437, 0.20630829, 0.18834082, 0.17973266, 0.10025849,\n",
            "       0.14562538]),\n",
            "  'false'),\n",
            " ('We dont allow filming inside of the City Hall unless there is a specific '\n",
            "  'reason.',\n",
            "  array([0.20829401]),\n",
            "  array([0.18328997, 0.20829401, 0.13048927, 0.15941864, 0.15917919,\n",
            "       0.15932891]),\n",
            "  'false'),\n",
            " ('The number of Americans who receive means-tested government benefits -- '\n",
            "  'welfare -- now outnumbers those who are year-round full-time workers.',\n",
            "  array([0.20970762]),\n",
            "  array([0.20290485, 0.20970762, 0.15929638, 0.20310989, 0.05344864,\n",
            "       0.17153262]),\n",
            "  'false'),\n",
            " ('In Mexico, they dont have birth certificates... they dont have registration '\n",
            "  'cards for voters. They have one national ID.',\n",
            "  array([0.21029113]),\n",
            "  array([0.18482881, 0.21029113, 0.1381361 , 0.18960394, 0.11069533,\n",
            "       0.1664447 ]),\n",
            "  'false'),\n",
            " ('In his first meeting with University of Wisconsin System officials, '\n",
            "  'Republican Governor elect Scott Walker told them to prepare for cuts.',\n",
            "  array([0.21126352]),\n",
            "  array([0.20254151, 0.21126352, 0.18220883, 0.13630361, 0.12540161,\n",
            "       0.14228092]),\n",
            "  'false')]\n",
            "half-true\n",
            "High probability for correct class\n",
            "[('The planned expansion of Savannahs port is a jobs creating project.',\n",
            "  array([0.32714544]),\n",
            "  array([0.13431196, 0.27510154, 0.32714544, 0.12050378, 0.04394304,\n",
            "       0.09899423]),\n",
            "  'half-true'),\n",
            " ('Banks paid Hillary Clinton over $1 million and are contributing millions '\n",
            "  'more to elect her.',\n",
            "  array([0.33253286]),\n",
            "  array([0.17660581, 0.14266354, 0.33253286, 0.18350199, 0.02792594,\n",
            "       0.13676987]),\n",
            "  'half-true'),\n",
            " ('Every dollar we invested in high-quality, early education programs can save '\n",
            "  'more than $7 later on by boosting graduation rates, reducing teen '\n",
            "  'pregnancy, even reducing crime.',\n",
            "  array([0.33279613]),\n",
            "  array([0.18105138, 0.13529897, 0.33279613, 0.19593191, 0.02834225,\n",
            "       0.12657937]),\n",
            "  'half-true'),\n",
            " ('After losing 750,000 jobs a month before this administration, the U.S. '\n",
            "  'economy under Barack Obama has had 20 straight months of growth, has added '\n",
            "  '2.8 million jobs in the private sector and added millions of jobs in '\n",
            "  'manufacturing.',\n",
            "  array([0.33867912]),\n",
            "  array([0.14709196, 0.13689745, 0.33867912, 0.22487747, 0.02996626,\n",
            "       0.12248775]),\n",
            "  'half-true'),\n",
            " ('Says Texas unemployment rate has doubled on Rick Perrys watch.',\n",
            "  array([0.35566374]),\n",
            "  array([0.12508323, 0.17917294, 0.35566374, 0.14690756, 0.07856006,\n",
            "       0.11461247]),\n",
            "  'half-true'),\n",
            " ('Bill Clinton cut the military drastically.',\n",
            "  array([0.3591215]),\n",
            "  array([0.17261316, 0.17554368, 0.3591215 , 0.12179091, 0.0650811 ,\n",
            "       0.10584966]),\n",
            "  'half-true'),\n",
            " ('For each $1 billion in infrastructure investment, 42,000 jobs are created.',\n",
            "  array([0.36020607]),\n",
            "  array([0.16894378, 0.1055936 , 0.36020607, 0.25191564, 0.01830174,\n",
            "       0.09503918]),\n",
            "  'half-true'),\n",
            " ('Georgia can substantially increase its funding for education by going after '\n",
            "  '$2.5 billion in uncollected taxes.',\n",
            "  array([0.36468449]),\n",
            "  array([0.12985271, 0.12992273, 0.36468449, 0.25854372, 0.03320435,\n",
            "       0.08379201]),\n",
            "  'half-true'),\n",
            " ('Over half of science, technology, engineering and mathematics students '\n",
            "  'receiving advanced degrees are not citizens of the United States of '\n",
            "  'America.',\n",
            "  array([0.41270304]),\n",
            "  array([0.08239281, 0.06829849, 0.41270304, 0.30946874, 0.02648523,\n",
            "       0.10065169]),\n",
            "  'half-true'),\n",
            " ('David Perdue led efforts to ship thousands of jobs overseas.',\n",
            "  array([0.45951399]),\n",
            "  array([0.10625974, 0.18108695, 0.45951399, 0.13173895, 0.05534087,\n",
            "       0.0660595 ]),\n",
            "  'half-true')]\n",
            "Low probability for correct class\n",
            "[('I created the school choice program.',\n",
            "  array([0.21026457]),\n",
            "  array([0.17497173, 0.19126807, 0.21026457, 0.20475272, 0.09644771,\n",
            "       0.1222952 ]),\n",
            "  'half-true'),\n",
            " ('If Question 1 were to pass here in Nevada, we would have more restrictive '\n",
            "  'gun laws here in Nevada dealing with the transfer of firearms than they do '\n",
            "  'in California.',\n",
            "  array([0.21335601]),\n",
            "  array([0.14631344, 0.19717144, 0.21335601, 0.2031234 , 0.04493708,\n",
            "       0.19509864]),\n",
            "  'half-true'),\n",
            " ('Under (Obamacare), you cant reward a person for better behavior. You cant '\n",
            "  'have incentives to be healthier.',\n",
            "  array([0.21466005]),\n",
            "  array([0.21141402, 0.20820573, 0.21466005, 0.16168682, 0.09430993,\n",
            "       0.10972345]),\n",
            "  'half-true'),\n",
            " ('Marco Rubio \"supported $800,000 for AstroTurf for a field where he played '\n",
            "  'flag football.\"',\n",
            "  array([0.21505192]),\n",
            "  array([0.15784155, 0.16773003, 0.21505192, 0.18410935, 0.08304913,\n",
            "       0.19221802]),\n",
            "  'half-true'),\n",
            " ('We caught (the Texas Commission on Environmental Quality) lying to us about '\n",
            "  'the results of air quality studies in the Barnett Shale.',\n",
            "  array([0.2156791]),\n",
            "  array([0.11738483, 0.18508329, 0.2156791 , 0.19622835, 0.09394117,\n",
            "       0.19168326]),\n",
            "  'half-true'),\n",
            " (\"He's leading by example, refusing contributions from PACs and Washington \"\n",
            "  'lobbyists.',\n",
            "  array([0.21900082]),\n",
            "  array([0.18381263, 0.15535622, 0.21900082, 0.17223029, 0.10082142,\n",
            "       0.16877862]),\n",
            "  'half-true'),\n",
            " ('The same federal government that offers some money for a program is walking '\n",
            "  'away from another health care program.',\n",
            "  array([0.2198002]),\n",
            "  array([0.21723728, 0.21392384, 0.2198002 , 0.17311733, 0.06794281,\n",
            "       0.10797854]),\n",
            "  'half-true'),\n",
            " ('Americans who get their insurance through the workplace, cost savings could '\n",
            "  'be as much as $3,000 less per employee than if we do nothing.',\n",
            "  array([0.22053915]),\n",
            "  array([0.21732522, 0.1427825 , 0.22053915, 0.21875234, 0.03845791,\n",
            "       0.16214289]),\n",
            "  'half-true'),\n",
            " ('40 percent of illegal immigrants had a visa and then became illegal, mostly '\n",
            "  'because they changed jobs.',\n",
            "  array([0.22058522]),\n",
            "  array([0.14857569, 0.21146228, 0.22058522, 0.21579054, 0.04317115,\n",
            "       0.16041511]),\n",
            "  'half-true'),\n",
            " ('Were talking right now about a $12 billion hole in our current, so-called '\n",
            "  'balanced state budget.',\n",
            "  array([0.22207548]),\n",
            "  array([0.1400181 , 0.2202107 , 0.22207548, 0.19940998, 0.04716496,\n",
            "       0.17112078]),\n",
            "  'half-true')]\n",
            "mostly-true\n",
            "High probability for correct class\n",
            "[('We admit more than 100,000 lifetime migrants from the Middle East each '\n",
            "  'year.',\n",
            "  array([0.35194042]),\n",
            "  array([0.12082531, 0.0938232 , 0.22650509, 0.35194042, 0.01833319,\n",
            "       0.18857279]),\n",
            "  'mostly-true'),\n",
            " ('More than 70 percent of American adults have committed a crime that could '\n",
            "  'lead to imprisonment.',\n",
            "  array([0.352048]),\n",
            "  array([0.09485383, 0.06716016, 0.2956823 , 0.352048  , 0.01684138,\n",
            "       0.17341433]),\n",
            "  'mostly-true'),\n",
            " ('Georgia has more than 700 law enforcement agencies, and fewer than 20 '\n",
            "  'percent of them are state-certified.',\n",
            "  array([0.35916133]),\n",
            "  array([0.09896792, 0.06590845, 0.24561501, 0.35916133, 0.01683034,\n",
            "       0.21351694]),\n",
            "  'mostly-true'),\n",
            " ('We borrow a million dollars every minute.',\n",
            "  array([0.36447256]),\n",
            "  array([0.14570687, 0.08220682, 0.18839914, 0.36447256, 0.01224463,\n",
            "       0.20696998]),\n",
            "  'mostly-true'),\n",
            " ('Worldwide credit card transactions, the credit card fraud rate is 0.04 '\n",
            "  'percent, compared to almost 8 percent, 9 percent, 10 percent of Medicare '\n",
            "  'fraud.',\n",
            "  array([0.36820382]),\n",
            "  array([0.0966385 , 0.08400634, 0.26475349, 0.36820382, 0.01358332,\n",
            "       0.17281454]),\n",
            "  'mostly-true'),\n",
            " ('94 percent of winning candidates in 2010 had more money than their '\n",
            "  'opponents.',\n",
            "  array([0.36925275]),\n",
            "  array([0.10137774, 0.06151373, 0.26134473, 0.36925275, 0.00772976,\n",
            "       0.19878128]),\n",
            "  'mostly-true'),\n",
            " ('University of Texas undergraduate student debt is less than $21,000 '\n",
            "  'probably one of the lowest debts across the nation.',\n",
            "  array([0.36996614]),\n",
            "  array([0.07163589, 0.05843757, 0.21439903, 0.36996614, 0.01503513,\n",
            "       0.27052624]),\n",
            "  'mostly-true'),\n",
            " ('Ohios electricity rates are 10 percent below the national average.',\n",
            "  array([0.39153095]),\n",
            "  array([0.0695742 , 0.08307404, 0.18609563, 0.39153095, 0.01037135,\n",
            "       0.25935383]),\n",
            "  'mostly-true'),\n",
            " ('Americans work way more than an average of industrialized countries around '\n",
            "  'the world.',\n",
            "  array([0.39787982]),\n",
            "  array([0.08614868, 0.07672599, 0.31682581, 0.39787982, 0.01349392,\n",
            "       0.10892578]),\n",
            "  'mostly-true'),\n",
            " ('Russia has an economy the size of Italy.',\n",
            "  array([0.46377348]),\n",
            "  array([0.05621894, 0.12196212, 0.19434527, 0.46377348, 0.0259744 ,\n",
            "       0.1377258 ]),\n",
            "  'mostly-true')]\n",
            "Low probability for correct class\n",
            "[('Unlike virtually every other campaign, we dont have a super PAC.',\n",
            "  array([0.20862218]),\n",
            "  array([0.20601494, 0.15023724, 0.15127562, 0.20862218, 0.12666929,\n",
            "       0.15718072]),\n",
            "  'mostly-true'),\n",
            " ('Says an illegal immigrant fraudulently claimed children who actually lived '\n",
            "  'in Mexico on income tax forms to collect more than $29,000.',\n",
            "  array([0.21277675]),\n",
            "  array([0.17604594, 0.18543051, 0.18085869, 0.21277675, 0.09103976,\n",
            "       0.15384836]),\n",
            "  'mostly-true'),\n",
            " ('In a poll, 53 percent of young Republican voters . . . under age 35 said '\n",
            "  'that they would describe a climate [change] denier as ignorant, out of '\n",
            "  'touch or crazy.',\n",
            "  array([0.22162576]),\n",
            "  array([0.17223686, 0.1529058 , 0.20275739, 0.22162576, 0.07268065,\n",
            "       0.17779353]),\n",
            "  'mostly-true'),\n",
            " ('Barack Obama will somehow manage to add more than $8 trillion to the '\n",
            "  'national debt, which is more debt than the 43 presidents who held office '\n",
            "  'before him compiled together.',\n",
            "  array([0.22259806]),\n",
            "  array([0.16173838, 0.13842912, 0.21822372, 0.22259806, 0.04478754,\n",
            "       0.21422319]),\n",
            "  'mostly-true'),\n",
            " ('Between the year 2000 and 2006, (insurance) premiums in this country '\n",
            "  'doubled.',\n",
            "  array([0.22355591]),\n",
            "  array([0.14331681, 0.18784638, 0.16313595, 0.22355591, 0.08176917,\n",
            "       0.20037577]),\n",
            "  'mostly-true'),\n",
            " ('When I talk about (raising the) minimum wage ... half of Republicansagree '\n",
            "  'with it.',\n",
            "  array([0.22355806]),\n",
            "  array([0.20055943, 0.14026573, 0.19705574, 0.22355806, 0.02696989,\n",
            "       0.21159114]),\n",
            "  'mostly-true'),\n",
            " ('Says legislation pending in the House would effectively limit or eliminate '\n",
            "  'time-and-a-half for people who work overtime.',\n",
            "  array([0.22622218]),\n",
            "  array([0.18879636, 0.20121305, 0.19496073, 0.22622218, 0.02485575,\n",
            "       0.16395193]),\n",
            "  'mostly-true'),\n",
            " (\"And he's the only candidate who will fight for a national catastrophe fund \"\n",
            "  'to reduce insurance rates.',\n",
            "  array([0.22733935]),\n",
            "  array([0.17381065, 0.20249776, 0.1714165 , 0.22733935, 0.08640976,\n",
            "       0.13852598]),\n",
            "  'mostly-true'),\n",
            " ('Says Donald Trump won more counties than any candidate on our side since '\n",
            "  'Ronald Reagan.',\n",
            "  array([0.2313412]),\n",
            "  array([0.19338642, 0.15753922, 0.20409432, 0.2313412 , 0.05841282,\n",
            "       0.15522602]),\n",
            "  'mostly-true'),\n",
            " ('Says out of 588 school districts, we give 31 (former Abbott) districts 70 '\n",
            "  'percent of the aid.',\n",
            "  array([0.2324646]),\n",
            "  array([0.18722171, 0.13656086, 0.18530392, 0.2324646 , 0.06036494,\n",
            "       0.19808397]),\n",
            "  'mostly-true')]\n",
            "pants-fire\n",
            "High probability for correct class\n",
            "[('The president is brain-dead.',\n",
            "  array([0.26851736]),\n",
            "  array([0.15395241, 0.25665638, 0.14720798, 0.08090402, 0.26851736,\n",
            "       0.09276185]),\n",
            "  'pants-fire'),\n",
            " ('Nobody covered the remarks of Patricia Smith, the mother of a Benghazi '\n",
            "  'victim, live, but almost everybody covered KhizrKhans, Mr. Khans remarks '\n",
            "  'live.',\n",
            "  array([0.26945889]),\n",
            "  array([0.21126791, 0.17599086, 0.10573813, 0.11360373, 0.26945889,\n",
            "       0.12394048]),\n",
            "  'pants-fire'),\n",
            " ('President Obama went around the world and apologized for America.',\n",
            "  array([0.27440441]),\n",
            "  array([0.13692635, 0.17161122, 0.20155381, 0.12947367, 0.27440441,\n",
            "       0.08603054]),\n",
            "  'pants-fire'),\n",
            " ('A friends sister died from Obamacare becauseBlue Shield completely just '\n",
            "  'pulled out of California.',\n",
            "  array([0.2787945]),\n",
            "  array([0.12851609, 0.19809158, 0.13659396, 0.11327219, 0.2787945 ,\n",
            "       0.14473169]),\n",
            "  'pants-fire'),\n",
            " ('Says a rape kit can be used to clean out women, basically like dilation and '\n",
            "  'curettage.',\n",
            "  array([0.29759947]),\n",
            "  array([0.20908732, 0.17669767, 0.10785709, 0.13020548, 0.29759947,\n",
            "       0.07855296]),\n",
            "  'pants-fire'),\n",
            " ('Fidel Castro endorses Obama.',\n",
            "  array([0.29788084]),\n",
            "  array([0.29580727, 0.11494005, 0.22610661, 0.01980294, 0.29788084,\n",
            "       0.04546228]),\n",
            "  'pants-fire'),\n",
            " ('Sheldon Whitehouse [got] a secret closed-door briefing, warning of the '\n",
            "  '[2008 economic] crash.',\n",
            "  array([0.31676172]),\n",
            "  array([0.18418605, 0.21534665, 0.08779014, 0.09088848, 0.31676172,\n",
            "       0.10502696]),\n",
            "  'pants-fire'),\n",
            " ('Says President Barack Obama has said that everybody should hate the police.',\n",
            "  array([0.32944527]),\n",
            "  array([0.16973381, 0.2240644 , 0.11531421, 0.0663904 , 0.32944527,\n",
            "       0.09505192]),\n",
            "  'pants-fire'),\n",
            " ('Says Barack Obama is a Muslim.',\n",
            "  array([0.36353397]),\n",
            "  array([0.17929422, 0.16203119, 0.11740091, 0.10130973, 0.36353397,\n",
            "       0.07642998]),\n",
            "  'pants-fire'),\n",
            " ('His true name is Barak Hussein Muhammed Obama.',\n",
            "  array([0.41119171]),\n",
            "  array([0.08693351, 0.23777338, 0.08982556, 0.02367973, 0.41119171,\n",
            "       0.15059611]),\n",
            "  'pants-fire')]\n",
            "Low probability for correct class\n",
            "[(\"Clinton's former pastor convicted of child molestation.\",\n",
            "  array([0.21297145]),\n",
            "  array([0.13311789, 0.20969212, 0.18048638, 0.08171352, 0.21297145,\n",
            "       0.18201865]),\n",
            "  'pants-fire'),\n",
            " ('You cannot build a little guy up by tearing a big guy down -- Abraham '\n",
            "  'Lincoln said it.',\n",
            "  array([0.2214424]),\n",
            "  array([0.16455891, 0.18921558, 0.20657837, 0.11336819, 0.2214424 ,\n",
            "       0.10483655]),\n",
            "  'pants-fire'),\n",
            " ('President Barack Obamas latest executive order mandates the apprehension '\n",
            "  'and detention of Americans who merely show signs of respiratory illness.',\n",
            "  array([0.26461359]),\n",
            "  array([0.15541586, 0.21082723, 0.16791438, 0.08753156, 0.26461359,\n",
            "       0.11369739]),\n",
            "  'pants-fire'),\n",
            " ('Says the Democrats told the Catholic Church that theyll use federal powers '\n",
            "  'to shut down church charities and hospitals if the church doesnt change its '\n",
            "  'beliefs.',\n",
            "  array([0.2670547]),\n",
            "  array([0.17623461, 0.24011483, 0.11012406, 0.11439733, 0.2670547 ,\n",
            "       0.09207446]),\n",
            "  'pants-fire'),\n",
            " ('The president is brain-dead.',\n",
            "  array([0.26851736]),\n",
            "  array([0.15395241, 0.25665638, 0.14720798, 0.08090402, 0.26851736,\n",
            "       0.09276185]),\n",
            "  'pants-fire'),\n",
            " ('Nobody covered the remarks of Patricia Smith, the mother of a Benghazi '\n",
            "  'victim, live, but almost everybody covered KhizrKhans, Mr. Khans remarks '\n",
            "  'live.',\n",
            "  array([0.26945889]),\n",
            "  array([0.21126791, 0.17599086, 0.10573813, 0.11360373, 0.26945889,\n",
            "       0.12394048]),\n",
            "  'pants-fire'),\n",
            " ('President Obama went around the world and apologized for America.',\n",
            "  array([0.27440441]),\n",
            "  array([0.13692635, 0.17161122, 0.20155381, 0.12947367, 0.27440441,\n",
            "       0.08603054]),\n",
            "  'pants-fire'),\n",
            " ('A friends sister died from Obamacare becauseBlue Shield completely just '\n",
            "  'pulled out of California.',\n",
            "  array([0.2787945]),\n",
            "  array([0.12851609, 0.19809158, 0.13659396, 0.11327219, 0.2787945 ,\n",
            "       0.14473169]),\n",
            "  'pants-fire'),\n",
            " ('Says a rape kit can be used to clean out women, basically like dilation and '\n",
            "  'curettage.',\n",
            "  array([0.29759947]),\n",
            "  array([0.20908732, 0.17669767, 0.10785709, 0.13020548, 0.29759947,\n",
            "       0.07855296]),\n",
            "  'pants-fire'),\n",
            " ('Fidel Castro endorses Obama.',\n",
            "  array([0.29788084]),\n",
            "  array([0.29580727, 0.11494005, 0.22610661, 0.01980294, 0.29788084,\n",
            "       0.04546228]),\n",
            "  'pants-fire')]\n",
            "true\n",
            "High probability for correct class\n",
            "[('Congress can tell [the Supreme Court] which cases they ought to hear. We '\n",
            "  'have that authority.',\n",
            "  array([0.26770092]),\n",
            "  array([0.17463601, 0.22584092, 0.09842068, 0.14208797, 0.09131349,\n",
            "       0.26770092]),\n",
            "  'true'),\n",
            " ('Three thousand felons voted in Rhode Island in 2008.',\n",
            "  array([0.26960827]),\n",
            "  array([0.07270988, 0.16105363, 0.20998919, 0.23891191, 0.04772712,\n",
            "       0.26960827]),\n",
            "  'true'),\n",
            " ('During my eight years as county executive, we cut the number of county '\n",
            "  'workers by 20 percent.',\n",
            "  array([0.273499]),\n",
            "  array([0.14661864, 0.11405191, 0.1853236 , 0.25701494, 0.0234919 ,\n",
            "       0.273499  ]),\n",
            "  'true'),\n",
            " ('Biden is \"one of the least wealthy members of the U.S. Senate.\"',\n",
            "  array([0.27757074]),\n",
            "  array([0.12704336, 0.1531252 , 0.13569734, 0.2366981 , 0.06986527,\n",
            "       0.27757074]),\n",
            "  'true'),\n",
            " ('The Walton family, which owns Wal-Mart, controls a fortune equal to the '\n",
            "  'wealth of the bottom 42 percent of Americans combined.',\n",
            "  array([0.30777271]),\n",
            "  array([0.07842781, 0.12054268, 0.23066859, 0.22664774, 0.03594046,\n",
            "       0.30777271]),\n",
            "  'true'),\n",
            " ('Theres actually 600 abortions done after the 20th week of pregnancy every '\n",
            "  'year in Ohio.',\n",
            "  array([0.31191543]),\n",
            "  array([0.11087483, 0.14965583, 0.13955559, 0.2639012 , 0.02409711,\n",
            "       0.31191543]),\n",
            "  'true'),\n",
            " ('As a former federal prosecutor, I prosecuted over 4,000 cases.',\n",
            "  array([0.3187528]),\n",
            "  array([0.14036933, 0.15162856, 0.19085548, 0.17379387, 0.02459997,\n",
            "       0.3187528 ]),\n",
            "  'true'),\n",
            " ('Only 20 colleges and universities have athletic departments with revenue '\n",
            "  'exceeding expenses.',\n",
            "  array([0.33658953]),\n",
            "  array([0.10627888, 0.04654743, 0.19562332, 0.29992628, 0.01503454,\n",
            "       0.33658953]),\n",
            "  'true'),\n",
            " ('The United states is borrowing more than 40 cents of every dollar we spend.',\n",
            "  array([0.3373035]),\n",
            "  array([0.09501435, 0.10498139, 0.17647582, 0.27212799, 0.01409695,\n",
            "       0.3373035 ]),\n",
            "  'true'),\n",
            " ('I am now the No. 2 member of this House in terms of length of service.',\n",
            "  array([0.40515284]),\n",
            "  array([0.06852931, 0.18417744, 0.09424595, 0.18774096, 0.06015351,\n",
            "       0.40515284]),\n",
            "  'true')]\n",
            "Low probability for correct class\n",
            "[('We have a retiree that is collecting a $17,000 paycheck a month . . . tax '\n",
            "  'free.',\n",
            "  array([0.20310778]),\n",
            "  array([0.18443528, 0.18358079, 0.17992923, 0.15920583, 0.08974109,\n",
            "       0.20310778]),\n",
            "  'true'),\n",
            " ('Says Ron Johnson likes to say there are too many lawyers in the Senate 57. '\n",
            "  'Hed be the 70th millionaire.',\n",
            "  array([0.20791651]),\n",
            "  array([0.18260573, 0.16857341, 0.1796177 , 0.20729318, 0.05399348,\n",
            "       0.20791651]),\n",
            "  'true'),\n",
            " ('Proposed fees for Rhode Island beaches will still be less than some of the '\n",
            "  'town beaches.',\n",
            "  array([0.21303415]),\n",
            "  array([0.14965563, 0.17737407, 0.21291379, 0.18689621, 0.06012616,\n",
            "       0.21303415]),\n",
            "  'true'),\n",
            " ('MikeHuckabee.com gets \"more hits than virtually any other presidential '\n",
            "  'candidate.\"',\n",
            "  array([0.21586483]),\n",
            "  array([0.13536042, 0.18381187, 0.21266222, 0.16786224, 0.08443843,\n",
            "       0.21586483]),\n",
            "  'true'),\n",
            " ('More black babies are aborted in NYC than born.',\n",
            "  array([0.21611153]),\n",
            "  array([0.13576021, 0.16634227, 0.20907349, 0.18265409, 0.09005841,\n",
            "       0.21611153]),\n",
            "  'true'),\n",
            " ('As governor, Ted Strickland left only 89 cents in Ohios rainy day fund.',\n",
            "  array([0.21680017]),\n",
            "  array([0.14976046, 0.2143752 , 0.14344883, 0.1956239 , 0.07999144,\n",
            "       0.21680017]),\n",
            "  'true'),\n",
            " (\"Since 1981, reconciliation has been used 21 times. Most of it's been used \"\n",
            "  'by Republicans.',\n",
            "  array([0.23323278]),\n",
            "  array([0.12061918, 0.19184948, 0.16289618, 0.22489509, 0.0665073 ,\n",
            "       0.23323278]),\n",
            "  'true'),\n",
            " ('I have filed every disclosure that has ever been required.',\n",
            "  array([0.24188352]),\n",
            "  array([0.1258002 , 0.2043023 , 0.12497675, 0.22503856, 0.07799867,\n",
            "       0.24188352]),\n",
            "  'true'),\n",
            " ('Pinellas County voters elected me as their chief financial officer (and) '\n",
            "  'elected me as (their) governor four years ago.',\n",
            "  array([0.24222466]),\n",
            "  array([0.16375552, 0.23473126, 0.14646007, 0.14342418, 0.0694043 ,\n",
            "       0.24222466]),\n",
            "  'true'),\n",
            " ('There have been three people tried and convicted by the last administration '\n",
            "  'in military courts. Two are walking the street right now.',\n",
            "  array([0.25299182]),\n",
            "  array([0.15488807, 0.16000265, 0.18664945, 0.19221768, 0.05325032,\n",
            "       0.25299182]),\n",
            "  'true')]\n"
          ]
        }
      ],
      "source": [
        "# do the same for correct predictions\n",
        "for cls in lr_bpemb.classes_:\n",
        "  print(cls)\n",
        "  print('High probability for correct class')\n",
        "  pprint.pprint(sorted(correct_preds[cls], key=lambda x: x[1])[-10:])\n",
        "  print('Low probability for correct class')\n",
        "  pprint.pprint(sorted(correct_preds[cls], key=lambda x: x[1])[:10])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eWxLaWJprp7y"
      },
      "source": [
        "# Error Analysis with a Transformer Model\n",
        "\n",
        "## Learning Curves\n",
        "Error analysis can start as early as fine-tuning the model. Observing different **learning curves** of the loss function of both the training and the development set can tell whether we need more data; we need a bigged dev set; we need a more complex model; whether the model is learning anything/overfitting, etc.\n",
        "\n",
        "http://mlwiki.org/index.php/Learning_Curves\n",
        "\n",
        "https://medium.com/uwaterloo-voice/error-analysis-in-deep-learning-6df3b3d335af\n",
        "\n",
        "\n",
        "Model Debugging can be made easy with external tools, too:\n",
        "\n",
        "https://wandb.ai/latentspace/published-work/The-Science-of-Debugging-with-W&B-Reports--Vmlldzo4OTI3Ng\n",
        "\n",
        "\"Those who don't track training are doomed to repeat it.\""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 68,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Q0uLrNaJvP8S",
        "outputId": "ce305427-c1e5-41f9-8c96-787c364691fa"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: transformers in /usr/local/lib/python3.10/dist-packages (4.33.2)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from transformers) (3.12.2)\n",
            "Requirement already satisfied: huggingface-hub<1.0,>=0.15.1 in /usr/local/lib/python3.10/dist-packages (from transformers) (0.17.2)\n",
            "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.10/dist-packages (from transformers) (1.23.5)\n",
            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from transformers) (23.1)\n",
            "Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.10/dist-packages (from transformers) (6.0.1)\n",
            "Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.10/dist-packages (from transformers) (2023.6.3)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from transformers) (2.31.0)\n",
            "Requirement already satisfied: tokenizers!=0.11.3,<0.14,>=0.11.1 in /usr/local/lib/python3.10/dist-packages (from transformers) (0.13.3)\n",
            "Requirement already satisfied: safetensors>=0.3.1 in /usr/local/lib/python3.10/dist-packages (from transformers) (0.3.3)\n",
            "Requirement already satisfied: tqdm>=4.27 in /usr/local/lib/python3.10/dist-packages (from transformers) (4.66.1)\n",
            "Requirement already satisfied: fsspec in /usr/local/lib/python3.10/dist-packages (from huggingface-hub<1.0,>=0.15.1->transformers) (2023.6.0)\n",
            "Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub<1.0,>=0.15.1->transformers) (4.5.0)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (3.2.0)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (3.4)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (2.0.4)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (2023.7.22)\n"
          ]
        }
      ],
      "source": [
        "!pip3 install transformers"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 69,
      "metadata": {
        "id": "svB69-y6vjoI"
      },
      "outputs": [],
      "source": [
        "import torch\n",
        "import random\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "\n",
        "from functools import partial\n",
        "from torch import nn\n",
        "from torch.utils.data import Dataset, DataLoader\n",
        "from torch.optim import Adam\n",
        "from typing import List, Tuple\n",
        "from tqdm import tqdm_notebook as tqdm"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 70,
      "metadata": {
        "id": "PeULFCpqkKsb"
      },
      "outputs": [],
      "source": [
        "from transformers import PreTrainedTokenizer\n",
        "from transformers import RobertaTokenizer\n",
        "from transformers import RobertaConfig\n",
        "from transformers import RobertaForSequenceClassification\n",
        "from transformers import AdamW\n",
        "from transformers import get_linear_schedule_with_warmup\n",
        "from typing import List, Tuple"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 71,
      "metadata": {
        "id": "GT36DVo260Hg"
      },
      "outputs": [],
      "source": [
        "def accuracy(logits, labels):\n",
        "  logits = np.asarray(logits).reshape(-1, len(logits[0]))\n",
        "  labels = np.asarray(labels).reshape(-1)\n",
        "  return np.sum(np.argmax(logits, axis=-1) == labels).astype(np.float32) / float(labels.shape[0])\n",
        "\n",
        "def evaluate(model: nn.Module, valid_dl: DataLoader):\n",
        "  \"\"\"\n",
        "  Evaluates the model on the given dataset\n",
        "  :param model: The model under evaluation\n",
        "  :param valid_dl: A `DataLoader` reading validation data\n",
        "  :return: The accuracy of the model on the dataset\n",
        "  \"\"\"\n",
        "  # VERY IMPORTANT: Put your model in \"eval\" mode -- this disables things like\n",
        "  # layer normalization and dropout\n",
        "  model.eval()\n",
        "  labels_all = []\n",
        "  logits_all = []\n",
        "  losses_all = []\n",
        "  # ALSO IMPORTANT: Don't accumulate gradients during this process\n",
        "  with torch.no_grad():\n",
        "    for batch in tqdm(valid_dl, desc='Evaluation'):\n",
        "      batch = tuple(t.to(device) for t in batch)\n",
        "      input_ids = batch[0]\n",
        "      attention_mask = batch[1]\n",
        "      labels = batch[2]\n",
        "\n",
        "      loss, logits = model(input_ids, attention_mask, labels=labels, return_dict=False)\n",
        "      labels_all.extend(list(labels.detach().cpu().numpy()))\n",
        "      logits_all.extend(list(logits.detach().cpu().numpy()))\n",
        "      losses_all.append(loss.detach().cpu().numpy())\n",
        "    acc = accuracy(logits_all, labels_all)\n",
        "\n",
        "    return acc, np.mean(losses_all)\n",
        "\n",
        "def train(\n",
        "    model: nn.Module,\n",
        "    train_dl: DataLoader,\n",
        "    valid_dl: DataLoader,\n",
        "    optimizer: torch.optim.Optimizer,\n",
        "    n_epochs: int,\n",
        "    device: torch.device,\n",
        "    scheduler = None\n",
        "):\n",
        "  \"\"\"\n",
        "  The main training loop which will optimize a given model on a given dataset\n",
        "  :param model: The model being optimized\n",
        "  :param train_dl: The training dataset\n",
        "  :param valid_dl: A validation dataset\n",
        "  :param optimizer: The optimizer used to update the model parameters\n",
        "  :param n_epochs: Number of epochs to train for\n",
        "  :param device: The device to train on\n",
        "  :return: (model, losses) The best model and the losses per iteration\n",
        "  \"\"\"\n",
        "\n",
        "  # Keep track of the loss and best accuracy\n",
        "  losses = []\n",
        "  best_acc = 0.0\n",
        "\n",
        "  # Iterate through epochs\n",
        "  for ep in range(n_epochs):\n",
        "\n",
        "    loss_epoch = []\n",
        "\n",
        "    #Iterate through each batch in the dataloader\n",
        "    for i, batch in tqdm(enumerate(train_dl)):\n",
        "      # VERY IMPORTANT: Make sure the model is in training mode, which turns on\n",
        "      # things like dropout and layer normalization\n",
        "      model.train()\n",
        "\n",
        "      # VERY IMPORTANT: zero out all of the gradients on each iteration -- PyTorch\n",
        "      # keeps track of these dynamically in its computation graph so you need to explicitly\n",
        "      # zero them out\n",
        "      optimizer.zero_grad()\n",
        "\n",
        "      # Place each tensor on the GPU\n",
        "      batch = tuple(t.to(device) for t in batch)\n",
        "      input_ids = batch[0]\n",
        "      attention_mask = batch[1]\n",
        "      labels = batch[2]\n",
        "\n",
        "      # Pass the inputs through the model, get the current loss and logits\n",
        "      loss, logits = model(input_ids, attention_mask, labels=labels, return_dict=False)\n",
        "      wandb.log({'loss': loss.item()})\n",
        "      losses.append(loss.item())\n",
        "      loss_epoch.append(loss.item())\n",
        "\n",
        "      # Calculate all of the gradients and weight updates for the model\n",
        "      loss.backward()\n",
        "\n",
        "      # Optional: clip gradients\n",
        "      #torch.nn.utils.clip_grad_norm_(model.parameters(), 1.0)\n",
        "\n",
        "      # Finally, update the weights of the model\n",
        "      optimizer.step()\n",
        "      if scheduler is not None:\n",
        "        scheduler.step()\n",
        "      # CHANGED CODE -- as the Transformer model trains for a few epoch,\n",
        "      # we might want to look at the learning curves each other step\n",
        "      if i % 100 == 0:\n",
        "        acc, val_loss = evaluate(model, valid_dl)\n",
        "        wandb.log({'acc': acc, 'train_loss': np.mean(loss_epoch), 'val_loss': val_loss})\n",
        "\n",
        "    # Perform inline evaluation at the end of the epoch\n",
        "    acc, val_loss = evaluate(model, valid_dl)\n",
        "    wandb.log({'acc': acc, 'train_loss': np.mean(loss_epoch), 'val_loss': val_loss})\n",
        "    print(f'Validation accuracy: {acc}, train loss: {np.mean(loss_epoch)}')\n",
        "\n",
        "    # Keep track of the best model based on the accuracy\n",
        "    best_model = model.state_dict()\n",
        "    if acc > best_acc:\n",
        "      torch.save(model.state_dict(), 'best_model')\n",
        "      best_acc = acc\n",
        "        #gc.collect()\n",
        "\n",
        "  model.load_state_dict(best_model)\n",
        "  return model, losses"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 72,
      "metadata": {
        "id": "70AZ1M12uoXM"
      },
      "outputs": [],
      "source": [
        "def text_to_batch_transformer(text: List, tokenizer: PreTrainedTokenizer) -> Tuple[List, List]:\n",
        "    \"\"\"Turn a piece of text into a batch for transformer model\n",
        "\n",
        "    :param text: The text to tokenize and encode\n",
        "    :param tokenizer: The tokenizer to use\n",
        "    :return: A list of IDs and a mask\n",
        "    \"\"\"\n",
        "    input_ids = [tokenizer.encode(t, add_special_tokens=True, truncation=True) for t in text]\n",
        "\n",
        "    masks = [[1] * len(i) for i in input_ids]\n",
        "\n",
        "    return input_ids, masks\n",
        "\n",
        "class ClassificationDatasetReader(Dataset):\n",
        "  def __init__(self, df, tokenizer):\n",
        "    self.df = df\n",
        "    self.tokenizer = tokenizer\n",
        "\n",
        "  def __len__(self):\n",
        "    return len(self.df)\n",
        "\n",
        "  def __getitem__(self, idx):\n",
        "    row = self.df.values[idx]\n",
        "    # Calls the text_to_batch function\n",
        "    input_ids, masks = text_to_batch_transformer([row[2]], self.tokenizer)\n",
        "    label = label_map[row[1]]\n",
        "    return input_ids, masks, label"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 73,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fWmOYrWp7GXP",
        "outputId": "51c6b7c0-890e-426d-ea6a-972bac3c719c"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'half-true': 0, 'barely-true': 1, 'pants-fire': 2, 'mostly-true': 3, 'false': 4, 'true': 5}\n"
          ]
        }
      ],
      "source": [
        "label_map = {l:i for i,l in enumerate((set(train_data.values[:,1]) | set(valid_data.values[:,1]) | set(test_data.values[:,1])))}\n",
        "num_labels = len(label_map)\n",
        "\n",
        "print(label_map)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 74,
      "metadata": {
        "id": "-B-lCTIe6rgU"
      },
      "outputs": [],
      "source": [
        "def collate_batch_transformer(pad_id, input_data: Tuple) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:\n",
        "    input_ids = [i[0][0] for i in input_data]\n",
        "    masks = [i[1][0] for i in input_data]\n",
        "    labels = [i[2] for i in input_data]\n",
        "\n",
        "    max_length = max([len(i) for i in input_ids])\n",
        "\n",
        "    input_ids = [(i + [pad_id] * (max_length - len(i))) for i in input_ids]\n",
        "    masks = [(m + [pad_id] * (max_length - len(m))) for m in masks]\n",
        "\n",
        "    assert (all(len(i) == max_length for i in input_ids))\n",
        "    assert (all(len(m) == max_length for m in masks))\n",
        "    return torch.tensor(input_ids), torch.tensor(masks), torch.tensor(labels)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "hGL0COfOuZ-o"
      },
      "outputs": [],
      "source": [
        "# a few steps needed to initialize the project in WANDB\n",
        "!pip install wandb -qqq\n",
        "import wandb\n",
        "\n",
        "wandb.login(key='') # you can insert your key here (remember not to share it publicly, wandb also warns use about this)\n",
        "# other solutions: read it in from a txt file, set environment variable or use the command line"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 88,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 164
        },
        "id": "H6Y8YWnbueUM",
        "outputId": "16237587-0dcd-4911-dfd9-e42bf7ef3d58"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\u001b[34m\u001b[1mwandb\u001b[0m: Currently logged in as: \u001b[33memmaleschly\u001b[0m. Use \u001b[1m`wandb login --relogin`\u001b[0m to force relogin\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: Tracking run with wandb version 0.15.10\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: Run data is saved locally in \u001b[35m\u001b[1m/content/wandb/run-20230920_164801-24bl4wf2\u001b[0m\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: Run \u001b[1m`wandb offline`\u001b[0m to turn off syncing.\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: Syncing run \u001b[33mazure-glitter-1\u001b[0m\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: ⭐️ View project at \u001b[34m\u001b[4mhttps://wandb.ai/emmaleschly/lab-5-roberta\u001b[0m\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: 🚀 View run at \u001b[34m\u001b[4mhttps://wandb.ai/emmaleschly/lab-5-roberta/runs/24bl4wf2\u001b[0m\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<button onClick=\"this.nextSibling.style.display='block';this.style.display='none';\">Display W&B run</button><iframe src='https://wandb.ai/emmaleschly/lab-5-roberta/runs/24bl4wf2?jupyter=true' style='border:none;width:100%;height:420px;display:none;'></iframe>"
            ],
            "text/plain": [
              "<wandb.sdk.wandb_run.Run at 0x7d47b77d0970>"
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          },
          "metadata": {},
          "execution_count": 88
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      ],
      "source": [
        "wandb.init(project=\"lab-5-roberta\",\n",
        "           config={\n",
        "               \"batch_size\": 8,\n",
        "               \"learning_rate\": 5e-5,\n",
        "               \"dataset\": \"LIAR\",\n",
        "           })"
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      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)olve/main/vocab.json:   0%|          | 0.00/899k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "00d04d7ca11048a0866cb094cbb6acd2"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)olve/main/merges.txt:   0%|          | 0.00/456k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "16c9736e0bbb4181b865f4190f3b66fd"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)lve/main/config.json:   0%|          | 0.00/481 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "a2e19e9ba78e484aba101b4f184c69f9"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py:560: UserWarning: This DataLoader will create 8 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.\n",
            "  warnings.warn(_create_warning_msg(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading model.safetensors:   0%|          | 0.00/499M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "46c6f3a0f2d54c52a90da2275bb35ecd"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Some weights of RobertaForSequenceClassification were not initialized from the model checkpoint at roberta-base and are newly initialized: ['classifier.out_proj.weight', 'classifier.out_proj.bias', 'classifier.dense.weight', 'classifier.dense.bias']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n",
            "/usr/local/lib/python3.10/dist-packages/transformers/optimization.py:411: FutureWarning: This implementation of AdamW is deprecated and will be removed in a future version. Use the PyTorch implementation torch.optim.AdamW instead, or set `no_deprecation_warning=True` to disable this warning\n",
            "  warnings.warn(\n"
          ]
        }
      ],
      "source": [
        "weight_decay = 0.01\n",
        "n_epochs = 2\n",
        "lr = 3e-5\n",
        "\n",
        "# Get the device\n",
        "device = torch.device(\"cpu\")\n",
        "if torch.cuda.is_available():\n",
        "  device = torch.device(\"cuda\")\n",
        "\n",
        "# Create the tokenizer\n",
        "tokenizer = RobertaTokenizer.from_pretrained('roberta-base')\n",
        "vocabulary = tokenizer.get_vocab()\n",
        "\n",
        "# Create the dataset readers\n",
        "train_dataset = ClassificationDatasetReader(train_data, tokenizer)\n",
        "# dataset loaded lazily with N workers in parallel\n",
        "collate_fn = partial(collate_batch_transformer, 0)\n",
        "train_dl = DataLoader(train_dataset, batch_size=6, shuffle=True, collate_fn=collate_fn, num_workers=8)\n",
        "\n",
        "valid_dataset = ClassificationDatasetReader(valid_data, tokenizer)\n",
        "valid_dl = DataLoader(valid_dataset, batch_size=6, collate_fn=collate_fn, num_workers=8)\n",
        "\n",
        "config = RobertaConfig.from_pretrained('roberta-base', num_labels=6)\n",
        "model = RobertaForSequenceClassification.from_pretrained('roberta-base', config=config).to(device)\n",
        "\n",
        "# Create the optimizer\n",
        "no_decay = ['bias', 'LayerNorm.bias', 'LayerNorm.weight']\n",
        "optimizer_grouped_parameters = [\n",
        "    {'params': [p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay)],\n",
        "      'weight_decay': weight_decay},\n",
        "    {'params': [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay)], 'weight_decay': 0.0}\n",
        "]\n",
        "\n",
        "optimizer = AdamW(optimizer_grouped_parameters, lr=lr)\n",
        "scheduler = get_linear_schedule_with_warmup(\n",
        "    optimizer,\n",
        "    200,\n",
        "    n_epochs * len(train_dl)\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 90,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000,
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            "  for batch in tqdm(valid_dl, desc='Evaluation'):\n"
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          "text": [
            "Validation accuracy: 0.19314641744548286, train loss: 1.7616072356107295\n"
          ]
        }
      ],
      "source": [
        "# magic to have plots of the learning curves\n",
        "%%wandb\n",
        "\n",
        "model, losses = train(model, train_dl, valid_dl, optimizer, n_epochs, device, scheduler)"
      ]
    }
  ],
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