RSG_MuSeRC.ipynb

{
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  "metadata": {
    "colab": {
      "name": "RSG_MuSeRC",
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyMWf8RNRhFDbzj7YEOla/Iv",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/tatiana-iazykova/2020_HACK_RUSSIANSUPERGLUE/blob/main/RSG_MuSeRC.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "d4JcE9Kd_sSa"
      },
      "source": [
        "# Load Data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RsL57-5kTLgJ"
      },
      "source": [
        "%%capture\r\n",
        "%%bash\r\n",
        "# change url if you want to work with a different RSG dataset\r\n",
        "wget -q --show-progress \"https://russiansuperglue.com/tasks/download/MuSeRC\" -O temp.zip\r\n",
        "unzip temp.zip -d data\r\n",
        "\r\n",
        "# remove unnecessary directories and files\r\n",
        "rm temp.zip\r\n",
        "rm -r data/__MACOSX\r\n",
        "rm -r sample_data/"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1JGekp4Jo0Vf"
      },
      "source": [
        "# Load necessary code files and models from https://github.com/RussianNLP/RussianSuperGLUE \r\n",
        "# to recreate TfIdf baseline\r\n",
        "\r\n",
        "%%capture\r\n",
        "%%bash\r\n",
        "# load tfidf pickle created by RSG team\r\n",
        "wget -q --show-progress \"https://russiansuperglue.com/tasks/tf_idf\" -O temp.zip\r\n",
        "unzip temp.zip -d data\r\n",
        "rm temp.zip\r\n",
        "\r\n",
        "# Make sure you donwload with the raw file link\r\n",
        "# Keep the link relevant to your dataset\r\n",
        "wget -q --show-progress \"https://github.com/RussianNLP/RussianSuperGLUE/raw/master/tfidf_baseline/MuSeRC.py\" -O MuSeRC.py\r\n",
        "\r\n",
        "# Load modules to solve this dataset tasks\r\n",
        "wget -q --show-progress \"https://github.com/tatiana-iazykova/2020_HACK_RUSSIANSUPERGLUE/raw/main/base.py\" -O base.py\r\n",
        "wget -q --show-progress \"https://github.com/tatiana-iazykova/2020_HACK_RUSSIANSUPERGLUE/raw/main/utils.py\" -O utils.py\r\n",
        "wget -q --show-progress \"https://github.com/tatiana-iazykova/2020_HACK_RUSSIANSUPERGLUE/raw/main/Solvers/MuSeRCSolver.py\" -O MuSeRCSolver.py"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XAlP91w3_nBf"
      },
      "source": [
        "%%capture\r\n",
        "%%bash\r\n",
        "# add tree package for fancy directory display\r\n",
        "apt-get install tree"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "avzmbfyO_q5G",
        "outputId": "6ce9a6cd-9667-431a-df8d-00b894fbf01c"
      },
      "source": [
        "# display the content of the folder\r\n",
        "!tree"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            ".\n",
            "├── base.py\n",
            "├── data\n",
            "│   ├── MuSeRC\n",
            "│   │   ├── test.jsonl\n",
            "│   │   ├── train.jsonl\n",
            "│   │   └── val.jsonl\n",
            "│   └── tfidf.pkl\n",
            "├── MuSeRC.py\n",
            "├── MuSeRCSolver.py\n",
            "└── utils.py\n",
            "\n",
            "2 directories, 8 files\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ip4SkaRSsA7b"
      },
      "source": [
        "# Solve MuSeRC with Heuristics"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "scRZqIs26lks"
      },
      "source": [
        "%%capture\r\n",
        "# to lemmantize, install these two dependancies\r\n",
        "!pip3 install pyMorphy2[fast]\r\n",
        "!pip3 install razdel\r\n",
        "!pip3 install jsonlines"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JRTQG45wrk71"
      },
      "source": [
        "import numpy as np\r\n",
        "from MuSeRCSolver import MuSeRCSolver\r\n",
        "from MuSeRC import MuSeRC_metrics\r\n",
        "\r\n",
        "solver = MuSeRCSolver(path='data/MuSeRC/train.jsonl') # pass a dataset to get stats\r\n",
        "solver.preprocess_data('data/MuSeRC/val.jsonl') # pass a dataset to solve\r\n",
        "solver.get_stats_MuSeRC() # calculate train statistics to use for majority and random balanced \r\n",
        "\r\n",
        "SEEDS = [42, 23, 1234567]"
      ],
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XW1-L8yxISQn",
        "outputId": "1d78e805-6724-4179-856e-d29c30d00909"
      },
      "source": [
        "val_preds, _, _ = solver.heuristics()\r\n",
        "val_preds[1] # this output is used to upload to RSG leaderboard"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Heuristics appears for 1722 samples, 1180 of them correct\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'idx': 1,\n",
              " 'passage': {'questions': [{'answers': [{'idx': 10, 'label': 0},\n",
              "     {'idx': 11, 'label': 1},\n",
              "     {'idx': 12, 'label': 1}],\n",
              "    'idx': 2},\n",
              "   {'answers': [{'idx': 13, 'label': 1},\n",
              "     {'idx': 14, 'label': 1},\n",
              "     {'idx': 15, 'label': 1},\n",
              "     {'idx': 16, 'label': 1}],\n",
              "    'idx': 3}]}}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rRii7DuCDMs0"
      },
      "source": [
        "## All Heuristics at once"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HO2MLLpw6b8o",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "65bd2c4a-57dd-4cc7-9e0c-b961fdc235e7"
      },
      "source": [
        "print('All heuristics + Majority Class')\r\n",
        "_, y_true, y_pred = solver.heuristics()\r\n",
        "em, f1a = MuSeRC_metrics(y_pred, y_true)\r\n",
        "\r\n",
        "print(f\"EM score is: {em}\")\r\n",
        "print(f\"F1-a score is: {f1a}\")"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "All heuristics + Majority Class\n",
            "Heuristics appears for 1722 samples, 1180 of them correct\n",
            "EM score is: 0.20226843100189035\n",
            "F1-a score is: 0.6642032332563511\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZPkjHCLRQ1sY",
        "outputId": "6b8e74d9-3385-43d0-faa0-334902715757"
      },
      "source": [
        "em_metrics = []\r\n",
        "f1a_metrics = []\r\n",
        "for seed in SEEDS:\r\n",
        "    _, y_true, y_pred = solver.heuristics('RANDOM')\r\n",
        "    em, f1a = MuSeRC_metrics(y_pred, y_true)\r\n",
        "    em_metrics.append(em)\r\n",
        "    f1a_metrics.append(f1a)\r\n",
        "\r\n",
        "print(f'All heuristics + Random choice')\r\n",
        "print(f\"Average EM score over 3 experiments: {np.array(em_metrics).mean()}\")\r\n",
        "print(f\"Average F1-a score over 3 experiments: {np.array(f1a_metrics).mean()}\")"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Heuristics appears for 1722 samples, 1180 of them correct\n",
            "Heuristics appears for 1722 samples, 1180 of them correct\n",
            "Heuristics appears for 1722 samples, 1180 of them correct\n",
            "All heuristics + Random choice\n",
            "Average EM score over 3 experiments: 0.18273471959672338\n",
            "Average F1-a score over 3 experiments: 0.6718926743227439\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "M0IxlldfSPvw",
        "outputId": "6ac8758e-8923-44e8-9445-83c102c5642a"
      },
      "source": [
        "em_metrics = []\r\n",
        "f1a_metrics = []\r\n",
        "for seed in SEEDS:\r\n",
        "    _, y_true, y_pred = solver.heuristics('RB')\r\n",
        "    em, f1a = MuSeRC_metrics(y_pred, y_true)\r\n",
        "    em_metrics.append(em)\r\n",
        "    f1a_metrics.append(f1a)\r\n",
        "\r\n",
        "print(f'All heuristics + Random balanced choice')\r\n",
        "print(f\"Average EM score over 3 experiments: {np.array(em_metrics).mean()}\")\r\n",
        "print(f\"Average F1-a score over 3 experiments: {np.array(f1a_metrics).mean()}\")"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Heuristics appears for 1722 samples, 1180 of them correct\n",
            "Heuristics appears for 1722 samples, 1180 of them correct\n",
            "Heuristics appears for 1722 samples, 1180 of them correct\n",
            "All heuristics + Random balanced choice\n",
            "Average EM score over 3 experiments: 0.18525519848771266\n",
            "Average F1-a score over 3 experiments: 0.6724137602283813\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2wJCyUAYDSyr"
      },
      "source": [
        "## Answer length in tokens is < 4 words leads to False"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lpK1lWMBDXdM",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "da758e95-d20d-405e-c986-1a9d38c0a21d"
      },
      "source": [
        "print('Short answer + Majority Class')\r\n",
        "_, y_true, y_pred = solver.heuristics(heuristic={'0': 'short answer'})\r\n",
        "print(MuSeRC_metrics(y_pred, y_true))"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Short answer + Majority Class\n",
            "Heuristics appears for 1037 samples, 673 of them correct\n",
            "(0.0, 0.0)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "447UEL54TKHh",
        "outputId": "3ac0a702-0b64-4bbf-9893-571f4d0b1a70"
      },
      "source": [
        "em_metrics = []\r\n",
        "f1a_metrics = []\r\n",
        "for seed in SEEDS:\r\n",
        "    _, y_true, y_pred = solver.heuristics('RANDOM',\r\n",
        "                                       heuristic={'0': 'short answer'})\r\n",
        "    em, f1a = MuSeRC_metrics(y_pred, y_true)\r\n",
        "    em_metrics.append(em)\r\n",
        "    f1a_metrics.append(f1a)\r\n",
        "\r\n",
        "print(f'Short + Random choice')\r\n",
        "print(f\"Average EM score over 3 experiments: {np.array(em_metrics).mean()}\")\r\n",
        "print(f\"Average F1-a score over 3 experiments: {np.array(f1a_metrics).mean()}\")"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Heuristics appears for 1037 samples, 673 of them correct\n",
            "Heuristics appears for 1037 samples, 673 of them correct\n",
            "Heuristics appears for 1037 samples, 673 of them correct\n",
            "Short + Random choice\n",
            "Average EM score over 3 experiments: 0.0686830497794581\n",
            "Average F1-a score over 3 experiments: 0.3881267925720064\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "J7HytPJyTRg3",
        "outputId": "9c5fb27f-87bc-4940-b5fc-064239d72fdb"
      },
      "source": [
        "em_metrics = []\r\n",
        "f1a_metrics = []\r\n",
        "for seed in SEEDS:\r\n",
        "    _, y_true, y_pred = solver.heuristics('RB',\r\n",
        "                                       heuristic={'0': 'short answer'})\r\n",
        "    em, f1a = MuSeRC_metrics(y_pred, y_true)\r\n",
        "    em_metrics.append(em)\r\n",
        "    f1a_metrics.append(f1a)\r\n",
        "\r\n",
        "print(f'Short + Random balanced choice')\r\n",
        "print(f\"Average EM score over 3 experiments: {np.array(em_metrics).mean()}\")\r\n",
        "print(f\"Average F1-a score over 3 experiments: {np.array(f1a_metrics).mean()}\")"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Heuristics appears for 1037 samples, 673 of them correct\n",
            "Heuristics appears for 1037 samples, 673 of them correct\n",
            "Heuristics appears for 1037 samples, 673 of them correct\n",
            "Short + Random balanced choice\n",
            "Average EM score over 3 experiments: 0.05986137366099559\n",
            "Average F1-a score over 3 experiments: 0.36737092928655746\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "izlJKpW1Dt6s"
      },
      "source": [
        "## Long Answer (>11 tokens) leads to True"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TzBrhviZD-mI",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e9d80154-9f8a-4e28-b3a7-de81109c77a6"
      },
      "source": [
        "print('Long answer + Majority class')\r\n",
        "_, y_true, y_pred = solver.heuristics(heuristic={'1': 'long answer'})\r\n",
        "print(MuSeRC_metrics(y_pred, y_true))"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Long answer + Majority class\n",
            "Heuristics appears for 231 samples, 167 of them correct\n",
            "(0.07561436672967864, 0.272875816993464)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "k2-irjwiTZ2u",
        "outputId": "73da4351-ae29-4b8f-9128-45392c0d5a00"
      },
      "source": [
        "em_metrics = []\r\n",
        "f1a_metrics = []\r\n",
        "for seed in SEEDS:\r\n",
        "    _, y_true, y_pred = solver.heuristics('RANDOM',\r\n",
        "                                       heuristic={'1': 'long answer'})\r\n",
        "    em, f1a = MuSeRC_metrics(y_pred, y_true)\r\n",
        "    em_metrics.append(em)\r\n",
        "    f1a_metrics.append(f1a)\r\n",
        "\r\n",
        "print(f'Long answer + Random choice')\r\n",
        "print(f\"Average EM score over 3 experiments: {np.array(em_metrics).mean()}\")\r\n",
        "print(f\"Average F1-a score over 3 experiments: {np.array(f1a_metrics).mean()}\")"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Heuristics appears for 231 samples, 167 of them correct\n",
            "Heuristics appears for 231 samples, 167 of them correct\n",
            "Heuristics appears for 231 samples, 167 of them correct\n",
            "Long answer + Random choice\n",
            "Average EM score over 3 experiments: 0.08128544423440454\n",
            "Average F1-a score over 3 experiments: 0.5214952041332822\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yaDSjlWDTk4P",
        "outputId": "b2be2122-4391-4246-9664-9ab691cbcc40"
      },
      "source": [
        "em_metrics = []\r\n",
        "f1a_metrics = []\r\n",
        "for seed in SEEDS:\r\n",
        "    _, y_true, y_pred = solver.heuristics('RB',\r\n",
        "                                       heuristic={'1': 'long answer'})\r\n",
        "    em, f1a = MuSeRC_metrics(y_pred, y_true)\r\n",
        "    em_metrics.append(em)\r\n",
        "    f1a_metrics.append(f1a)\r\n",
        "\r\n",
        "print(f'Short + Random balanced choice')\r\n",
        "print(f\"Average EM score over 3 experiments: {np.array(em_metrics).mean()}\")\r\n",
        "print(f\"Average F1-a score over 3 experiments: {np.array(f1a_metrics).mean()}\")"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Heuristics appears for 231 samples, 167 of them correct\n",
            "Heuristics appears for 231 samples, 167 of them correct\n",
            "Heuristics appears for 231 samples, 167 of them correct\n",
            "Short + Random balanced choice\n",
            "Average EM score over 3 experiments: 0.09010712035286704\n",
            "Average F1-a score over 3 experiments: 0.5096203101541908\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QOTdY6eVPxAu"
      },
      "source": [
        "## Lemmas do not overlap in answer and passage -> False"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2D4ahV5nPkO_",
        "outputId": "06c6dc78-e531-489a-a2a7-5503fa2f6f48"
      },
      "source": [
        "print('No overlap + Majority Class')\r\n",
        "_, y_true, y_pred = solver.heuristics(heuristic={'0': 'no overlap'})\r\n",
        "print(MuSeRC_metrics(y_pred, y_true))"
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "No overlap + Majority Class\n",
            "Heuristics appears for 221 samples, 197 of them correct\n",
            "(0.0, 0.0)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KWTGJprYTwzE",
        "outputId": "0719e55b-638b-4cc5-b238-9e66cd2ffdc7"
      },
      "source": [
        "em_metrics = []\r\n",
        "f1a_metrics = []\r\n",
        "for seed in SEEDS:\r\n",
        "    _, y_true, y_pred = solver.heuristics('RANDOM',\r\n",
        "                                       heuristic={'0': 'no overlap'})\r\n",
        "    em, f1a = MuSeRC_metrics(y_pred, y_true)\r\n",
        "    em_metrics.append(em)\r\n",
        "    f1a_metrics.append(f1a)\r\n",
        "\r\n",
        "print(f'No overlap + Random')\r\n",
        "print(f\"Average EM score over 3 experiments: {np.array(em_metrics).mean()}\")\r\n",
        "print(f\"Average F1-a score over 3 experiments: {np.array(f1a_metrics).mean()}\")"
      ],
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Heuristics appears for 221 samples, 197 of them correct\n",
            "Heuristics appears for 221 samples, 197 of them correct\n",
            "Heuristics appears for 221 samples, 197 of them correct\n",
            "No overlap + Random\n",
            "Average EM score over 3 experiments: 0.0888468809073724\n",
            "Average F1-a score over 3 experiments: 0.48245125204724926\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bjmtf119UMqq",
        "outputId": "868b254d-ea69-43f5-9bb1-a41cbd3e0159"
      },
      "source": [
        "em_metrics = []\r\n",
        "f1a_metrics = []\r\n",
        "for seed in SEEDS:\r\n",
        "    _, y_true, y_pred = solver.heuristics('RB',\r\n",
        "                                       heuristic={'0': 'no overlap'})\r\n",
        "    em, f1a = MuSeRC_metrics(y_pred, y_true)\r\n",
        "    em_metrics.append(em)\r\n",
        "    f1a_metrics.append(f1a)\r\n",
        "\r\n",
        "print(f'No overlap + Random Balanced')\r\n",
        "print(f\"Average EM score over 3 experiments: {np.array(em_metrics).mean()}\")\r\n",
        "print(f\"Average F1-a score over 3 experiments: {np.array(f1a_metrics).mean()}\")"
      ],
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Heuristics appears for 221 samples, 197 of them correct\n",
            "Heuristics appears for 221 samples, 197 of them correct\n",
            "Heuristics appears for 221 samples, 197 of them correct\n",
            "No overlap + Random Balanced\n",
            "Average EM score over 3 experiments: 0.08254568367989917\n",
            "Average F1-a score over 3 experiments: 0.468098507229916\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "M0Zz6oRIot70"
      },
      "source": [
        "## N of overlaping lemmas in an answer and a passage is > a mean size of the answer -> True"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IIjYMnizPvBm",
        "outputId": "b4dedf18-1bf5-4e44-9e28-756def699a85"
      },
      "source": [
        "print('much overlap + Majority class')\r\n",
        "_, y_true, y_pred = solver.heuristics(heuristic={'1': 'much overlap'})\r\n",
        "print(MuSeRC_metrics(y_pred, y_true))"
      ],
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "much overlap + Majority class\n",
            "Heuristics appears for 422 samples, 312 of them correct\n",
            "(0.11720226843100189, 0.44098939929328623)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dErZA41iUboz",
        "outputId": "63366b43-0f2f-4c0b-d4aa-fe03a027d29c"
      },
      "source": [
        "em_metrics = []\r\n",
        "f1a_metrics = []\r\n",
        "for seed in SEEDS:\r\n",
        "    _, y_true, y_pred = solver.heuristics('RANDOM',\r\n",
        "                                       heuristic={'1': 'much overlap'})\r\n",
        "    em, f1a = MuSeRC_metrics(y_pred, y_true)\r\n",
        "    em_metrics.append(em)\r\n",
        "    f1a_metrics.append(f1a)\r\n",
        "\r\n",
        "print(f'much overlap + Random choise')\r\n",
        "print(f\"Average EM score over 3 experiments: {np.array(em_metrics).mean()}\")\r\n",
        "print(f\"Average F1-a score over 3 experiments: {np.array(f1a_metrics).mean()}\")"
      ],
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Heuristics appears for 422 samples, 312 of them correct\n",
            "Heuristics appears for 422 samples, 312 of them correct\n",
            "Heuristics appears for 422 samples, 312 of them correct\n",
            "much overlap + Random choise\n",
            "Average EM score over 3 experiments: 0.0945179584120983\n",
            "Average F1-a score over 3 experiments: 0.5620833889270376\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vxAkUQx8UZxn",
        "outputId": "80ba97bf-11e8-4884-f4ce-9003ac106ca1"
      },
      "source": [
        "em_metrics = []\r\n",
        "f1a_metrics = []\r\n",
        "for seed in SEEDS:\r\n",
        "    _, y_true, y_pred = solver.heuristics('RB',\r\n",
        "                                       heuristic={'1': 'much overlap'})\r\n",
        "    em, f1a = MuSeRC_metrics(y_pred, y_true)\r\n",
        "    em_metrics.append(em)\r\n",
        "    f1a_metrics.append(f1a)\r\n",
        "\r\n",
        "print(f'much overlap + Random Balanced choise')\r\n",
        "print(f\"Average EM score over 3 experiments: {np.array(em_metrics).mean()}\")\r\n",
        "print(f\"Average F1-a score over 3 experiments: {np.array(f1a_metrics).mean()}\")"
      ],
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Heuristics appears for 422 samples, 312 of them correct\n",
            "Heuristics appears for 422 samples, 312 of them correct\n",
            "Heuristics appears for 422 samples, 312 of them correct\n",
            "much overlap + Random Balanced choise\n",
            "Average EM score over 3 experiments: 0.09325771896660366\n",
            "Average F1-a score over 3 experiments: 0.5619936619041939\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "l6B28iDWpKi3"
      },
      "source": [
        "## All lemmas in an answer overlap a passage -> True"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VsCUEItERIUv",
        "outputId": "2d1ef5e4-2931-4d36-d890-5badea9f85b6"
      },
      "source": [
        "print('total overlap + Majority class')\r\n",
        "_, y_true, y_pred = solver.heuristics(heuristic={'1': 'total overlap'})\r\n",
        "print(MuSeRC_metrics(y_pred, y_true))"
      ],
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "total overlap + Majority class\n",
            "Heuristics appears for 877 samples, 516 of them correct\n",
            "(0.1285444234404537, 0.5518716577540107)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Z-vCH51nU-OV",
        "outputId": "bb4f0b29-091e-4f14-f8c0-50176003d08a"
      },
      "source": [
        "em_metrics = []\r\n",
        "f1a_metrics = []\r\n",
        "for seed in SEEDS:\r\n",
        "    _, y_true, y_pred = solver.heuristics('RANDOM',\r\n",
        "                                       heuristic={'1': 'total overlap'})\r\n",
        "    em, f1a = MuSeRC_metrics(y_pred, y_true)\r\n",
        "    em_metrics.append(em)\r\n",
        "    f1a_metrics.append(f1a)\r\n",
        "\r\n",
        "print(f'total overlap + Random choise')\r\n",
        "print(f\"Average EM score over 3 experiments: {np.array(em_metrics).mean()}\")\r\n",
        "print(f\"Average F1-a score over 3 experiments: {np.array(f1a_metrics).mean()}\")"
      ],
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Heuristics appears for 877 samples, 516 of them correct\n",
            "Heuristics appears for 877 samples, 516 of them correct\n",
            "Heuristics appears for 877 samples, 516 of them correct\n",
            "total overlap + Random choise\n",
            "Average EM score over 3 experiments: 0.06742281033396345\n",
            "Average F1-a score over 3 experiments: 0.5874541844408429\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Kclqc9lOVJel",
        "outputId": "3f621eb8-aed1-47d4-ccfd-4b74d62586b2"
      },
      "source": [
        "em_metrics = []\r\n",
        "f1a_metrics = []\r\n",
        "for seed in SEEDS:\r\n",
        "    _, y_true, y_pred = solver.heuristics('RB',\r\n",
        "                                       heuristic={'1': 'total overlap'})\r\n",
        "    em, f1a = MuSeRC_metrics(y_pred, y_true)\r\n",
        "    em_metrics.append(em)\r\n",
        "    f1a_metrics.append(f1a)\r\n",
        "\r\n",
        "print(f'total overlap + Random balanced choise')\r\n",
        "print(f\"Average EM score over 3 experiments: {np.array(em_metrics).mean()}\")\r\n",
        "print(f\"Average F1-a score over 3 experiments: {np.array(f1a_metrics).mean()}\")"
      ],
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Heuristics appears for 877 samples, 516 of them correct\n",
            "Heuristics appears for 877 samples, 516 of them correct\n",
            "Heuristics appears for 877 samples, 516 of them correct\n",
            "total overlap + Random balanced choise\n",
            "Average EM score over 3 experiments: 0.08380592312539382\n",
            "Average F1-a score over 3 experiments: 0.5874210765799811\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Dhl5xSRkpRYQ"
      },
      "source": [
        "## N of overlaping lemmas in an answer and a passage is 1 -> True"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qwB5gE9BX455",
        "outputId": "f6cd008c-1ac5-438f-e021-acb16e5f5d78"
      },
      "source": [
        "print('little overlap + Majority class')\r\n",
        "_, y_true, y_pred = solver.heuristics(heuristic={'0': 'little overlap'})\r\n",
        "print(MuSeRC_metrics(y_pred, y_true))"
      ],
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "little overlap + Majority class\n",
            "Heuristics appears for 415 samples, 288 of them correct\n",
            "(0.0, 0.0)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "V_xPHJh0VUVK",
        "outputId": "a96e45c4-fd0e-40bf-d601-02d77c4f11b6"
      },
      "source": [
        "em_metrics = []\r\n",
        "f1a_metrics = []\r\n",
        "for seed in SEEDS:\r\n",
        "    _, y_true, y_pred = solver.heuristics('RANDOM',\r\n",
        "                                       heuristic={'0': 'little overlap'})\r\n",
        "    em, f1a = MuSeRC_metrics(y_pred, y_true)\r\n",
        "    em_metrics.append(em)\r\n",
        "    f1a_metrics.append(f1a)\r\n",
        "\r\n",
        "print(f'little overlap + Random choise')\r\n",
        "print(f\"Average EM score over 3 experiments: {np.array(em_metrics).mean()}\")\r\n",
        "print(f\"Average F1-a score over 3 experiments: {np.array(f1a_metrics).mean()}\")"
      ],
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Heuristics appears for 415 samples, 288 of them correct\n",
            "Heuristics appears for 415 samples, 288 of them correct\n",
            "Heuristics appears for 415 samples, 288 of them correct\n",
            "little overlap + Random choise\n",
            "Average EM score over 3 experiments: 0.0806553245116572\n",
            "Average F1-a score over 3 experiments: 0.4531349478177571\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qDU0epqSVN4v",
        "outputId": "ef3ee4df-00f5-42b7-81e1-a261672472d5"
      },
      "source": [
        "em_metrics = []\r\n",
        "f1a_metrics = []\r\n",
        "for seed in SEEDS:\r\n",
        "    _, y_true, y_pred = solver.heuristics('RB',\r\n",
        "                                       heuristic={'0': 'little overlap'})\r\n",
        "    em, f1a = MuSeRC_metrics(y_pred, y_true)\r\n",
        "    em_metrics.append(em)\r\n",
        "    f1a_metrics.append(f1a)\r\n",
        "\r\n",
        "print(f'little overlap + Random balanced choise')\r\n",
        "print(f\"Average EM score over 3 experiments: {np.array(em_metrics).mean()}\")\r\n",
        "print(f\"Average F1-a score over 3 experiments: {np.array(f1a_metrics).mean()}\")"
      ],
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Heuristics appears for 415 samples, 288 of them correct\n",
            "Heuristics appears for 415 samples, 288 of them correct\n",
            "Heuristics appears for 415 samples, 288 of them correct\n",
            "little overlap + Random balanced choise\n",
            "Average EM score over 3 experiments: 0.07561436672967864\n",
            "Average F1-a score over 3 experiments: 0.4293658325830246\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GZnOdRN_p4Uj"
      },
      "source": [
        "# Reproduce TfIdf baseline"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CbiVQejPqR2D"
      },
      "source": [
        "1.   build text of passage and queries\r\n",
        "2.   get tfidf of passage and queries\r\n",
        "1.   calculate cosins between passage and queries\r\n",
        "2.   select best 2 by cosin metric"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GorSKv_7_xGB",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9111d74e-ee7b-4f02-c52b-35259b735957"
      },
      "source": [
        "!pip install jsonlines # necessary to handle this dataset\r\n",
        "import pickle\r\n",
        "import codecs\r\n",
        "import joblib\r\n",
        "import MuSeRC\r\n",
        "\r\n",
        "vect = joblib.load(\"data/tfidf.pkl\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: jsonlines in /usr/local/lib/python3.6/dist-packages (2.0.0)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/base.py:318: UserWarning: Trying to unpickle estimator TfidfTransformer from version 0.21.3 when using version 0.22.2.post1. This might lead to breaking code or invalid results. Use at your own risk.\n",
            "  UserWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/base.py:318: UserWarning: Trying to unpickle estimator TfidfVectorizer from version 0.21.3 when using version 0.22.2.post1. This might lead to breaking code or invalid results. Use at your own risk.\n",
            "  UserWarning)\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Fq-I3421qPOw",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e52820e3-c16e-4241-8f9c-516745ff2699"
      },
      "source": [
        "train_path = \"data/MuSeRC/train.jsonl\"\r\n",
        "val_path = \"data/MuSeRC/val.jsonl\"\r\n",
        "test_path = \"data/MuSeRC/test.jsonl\"\r\n",
        "\r\n",
        "_, MuSeRC_scores = MuSeRC.eval_MuSeRC(train_path, val_path, test_path, vect)\r\n",
        "MuSeRC_scores[\"train\"], MuSeRC_scores[\"val\"]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((0.2564722126337591, 0.5966356478167503),\n",
              " (0.2495274102079395, 0.5841053144807411))"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Uu1KNLSrFOiD"
      },
      "source": [
        "fine tuning"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cBkF84d3pyAt"
      },
      "source": [
        "import pandas as pd\r\n",
        "import json\r\n",
        "pd.set_option('display.max_colwidth', None)\r\n",
        "pd.set_option('display.max_rows', 80)\r\n",
        "\r\n",
        "class JSONL_handler():\r\n",
        "    \"\"\" opens a jsonl file and turns it into a necessary data structure \"\"\"\r\n",
        "    \r\n",
        "    def __init__(self, path):\r\n",
        "        self.path = path # path to jsonl file\r\n",
        "\r\n",
        "    def to_pandas(self):\r\n",
        "        \"\"\" get jsonl file content as a pandas DataFrame\"\"\"\r\n",
        "\r\n",
        "        passages_df = []\r\n",
        "        questions_df = pd.DataFrame(columns=['question', 'text', \r\n",
        "                                             'label', 'passage_id'])\r\n",
        "\r\n",
        "        lines = self.yield_lines()\r\n",
        "\r\n",
        "        for passage_id, line in enumerate(lines):\r\n",
        "            passage, questions = self.split_texts_and_questions(line)\r\n",
        "            passages_df.append(passage)\r\n",
        "            questions = pd.json_normalize(\r\n",
        "                questions, 'answers', 'question'\r\n",
        "                )[['question', 'text', 'label']]\r\n",
        "\r\n",
        "            questions['passage_id'] = passage_id\r\n",
        "            \r\n",
        "            questions_df = pd.concat([questions_df, questions])\r\n",
        "\r\n",
        "        passages_df = pd.DataFrame({'passage': passages_df})\r\n",
        "        questions_df = questions_df.rename(columns={'text': 'answer'})\r\n",
        "\r\n",
        "        return passages_df, questions_df\r\n",
        "\r\n",
        "    \r\n",
        "    def yield_lines(self):\r\n",
        "        \"\"\" yields json lines one by one \"\"\"\r\n",
        "        with open(self.path) as f:\r\n",
        "            for line in f:\r\n",
        "                yield json.loads(line)\r\n",
        "\r\n",
        "\r\n",
        "    def split_texts_and_questions(self, line):\r\n",
        "        \"\"\" transforms a complex json object into a single row dataframe\"\"\"\r\n",
        "        text = line['passage']['text']\r\n",
        "        questions = line['passage']['questions']\r\n",
        "\r\n",
        "        return text, questions"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YzxJ2uJK_7Rn"
      },
      "source": [
        "handler = JSONL_handler('data/MuSeRC/train.jsonl')\r\n",
        "passages_df, questions_df = handler.to_pandas()\r\n",
        "\r\n",
        "val_handler = JSONL_handler('data/MuSeRC/val.jsonl')\r\n",
        "val_passages_df, val_questions_df = val_handler.to_pandas()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "njQTNd3fpMjj"
      },
      "source": [
        "from sklearn.feature_extraction.text import TfidfVectorizer"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hNkXKPD2mRtW",
        "outputId": "9887f6dd-7740-4ab2-fb07-de89d06a50d0"
      },
      "source": [
        "vec1 = TfidfVectorizer(ngram_range=(1, 2), analyzer='char_wb', max_df = 0.7, max_features=3000)\r\n",
        "vec2 = TfidfVectorizer(ngram_range=(1, 2), analyzer='word', max_df = 0.7, max_features=3000)\r\n",
        "vec3 = TfidfVectorizer(ngram_range=(1, 3), analyzer='char_wb', max_df = 0.8, max_features=5000)\r\n",
        "vec4 = TfidfVectorizer(ngram_range=(1, 3), analyzer='word', max_df = 0.8, max_features=5000)\r\n",
        "\r\n",
        "vec1.fit(passages_df.passage)\r\n",
        "vec2.fit(passages_df.passage)\r\n",
        "vec3.fit(passages_df.passage)\r\n",
        "vec4.fit(passages_df.passage)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "TfidfVectorizer(analyzer='word', binary=False, decode_error='strict',\n",
              "                dtype=<class 'numpy.float64'>, encoding='utf-8',\n",
              "                input='content', lowercase=True, max_df=0.8, max_features=5000,\n",
              "                min_df=1, ngram_range=(1, 3), norm='l2', preprocessor=None,\n",
              "                smooth_idf=True, stop_words=None, strip_accents=None,\n",
              "                sublinear_tf=False, token_pattern='(?u)\\\\b\\\\w\\\\w+\\\\b',\n",
              "                tokenizer=None, use_idf=True, vocabulary=None)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "h02nLfVFFc2R",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9875e78c-26a0-4403-f158-693875d496e2"
      },
      "source": [
        "_, MuSeRC_scores = MuSeRC.eval_MuSeRC(train_path, val_path, test_path, vec1)\r\n",
        "MuSeRC_scores[\"train\"], MuSeRC_scores[\"val\"]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((0.3279254401104591, 0.6478167501789548),\n",
              " (0.3591682419659735, 0.6699171136031205))"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bxw5A699omqG",
        "outputId": "1dd3c8ad-39e5-4f11-d30d-41673a37ed99"
      },
      "source": [
        "_, MuSeRC_scores = MuSeRC.eval_MuSeRC(train_path, val_path, test_path, vec2)\r\n",
        "MuSeRC_scores[\"train\"], MuSeRC_scores[\"val\"]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((0.27304107697618224, 0.6086256263421618),\n",
              " (0.2589792060491493, 0.5987323256947831))"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5CUM6E75osni",
        "outputId": "cf7a4a08-a8e1-491b-e6a8-56bb5247bf86"
      },
      "source": [
        "_, MuSeRC_scores = MuSeRC.eval_MuSeRC(train_path, val_path, test_path, vec3)\r\n",
        "MuSeRC_scores[\"train\"], MuSeRC_scores[\"val\"]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((0.3648602002071108, 0.6775232641374375),\n",
              " (0.3950850661625709, 0.6952705997074599))"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JoNFxqJPotnZ",
        "outputId": "0add3241-4ea8-4993-e6d3-35e9bd15c0b3"
      },
      "source": [
        "_, MuSeRC_scores = MuSeRC.eval_MuSeRC(train_path, val_path, test_path, vec4)\r\n",
        "MuSeRC_scores[\"train\"], MuSeRC_scores[\"val\"]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((0.2823610631687953, 0.6161417322834646),\n",
              " (0.2684310018903592, 0.606533398342272))"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "o2-doB00_24h"
      },
      "source": [
        "# Explore Train and Val Data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 278
        },
        "id": "h-cquA8dS39A",
        "outputId": "74b6499d-f8a1-4e63-f27a-88c5b8db3ee7"
      },
      "source": [
        "passages_df.iloc[[1]]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>passage</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>(1)Образы природы сопровождают людей с самой глубокой древности. (2)Сцены охоты древние люди оставили нам в наследство на стенах пещер, на камнях в виде скупых царапин, изображающих лосей, кабанов, туров. (3)Нынешняя техника даёт возможность увидеть зверей и птиц в естественных красках и обстановке. (4)Мы видим эпизоды их жизни, и в душе появляется тёплое чувство. (5)Они где-то есть, эти звери, они ещё бегают, рычат, прячутся или проявляют, как и мы, люди, любопытство… (6)Желая испытать это тёплое чувство, на городских этажах мы держим кошек, собак, попугаев, белок, рыбок в аквариуме. (7)Врачи говорят, что поглаживание кошки или кормление синицы с ладони целительны для человеческой психики. (8)Заяц, встреченный в парке, исправил моё настроение. (9)А сколько случаев, когда человека с жизнью связывает только ниточка общения с собакой, с обыкновенными воробьями или воронами! (10)Бывшая работница нашей редакции время от времени по телефону докладывает мне о жизни ворон во дворе, о том, что они любят есть, как безошибочно узнают её в массе людей. (11)А старушка у нас во дворе каждое утро носит воробьям крошки. (12)И я вижу радость на лице женщины, наблюдающей, как суетливые птички подбирают рассыпанный корм. (13)Иногда мне кажется: не прилетят воробьи к её выходу из подъезда, и старушка умрёт от обрыва последней ниточки, соединяющей её с жизнью. (14)Как-то мы разговорились с ней, и она призналась, что ни разу не видела в жизни, как пролетают и кричат журавли. (15)Я рассказал ей, как журавли весною танцуют, разбившись на пары, и кричат так, что голоса их сливаются в один торжествующий звук любви. (16)Старушка слушала внимательно и, прощаясь, сказала три слова: «Какой вы счастливый…» (17)Яснополянский мудрец говорил то же самое в дневниках: «Счастье – это быть с природой, видеть её, говорить с ней». (По В.М. Пескову)</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                             passage\n",
              "1  (1)Образы природы сопровождают людей с самой глубокой древности. (2)Сцены охоты древние люди оставили нам в наследство на стенах пещер, на камнях в виде скупых царапин, изображающих лосей, кабанов, туров. (3)Нынешняя техника даёт возможность увидеть зверей и птиц в естественных красках и обстановке. (4)Мы видим эпизоды их жизни, и в душе появляется тёплое чувство. (5)Они где-то есть, эти звери, они ещё бегают, рычат, прячутся или проявляют, как и мы, люди, любопытство… (6)Желая испытать это тёплое чувство, на городских этажах мы держим кошек, собак, попугаев, белок, рыбок в аквариуме. (7)Врачи говорят, что поглаживание кошки или кормление синицы с ладони целительны для человеческой психики. (8)Заяц, встреченный в парке, исправил моё настроение. (9)А сколько случаев, когда человека с жизнью связывает только ниточка общения с собакой, с обыкновенными воробьями или воронами! (10)Бывшая работница нашей редакции время от времени по телефону докладывает мне о жизни ворон во дворе, о том, что они любят есть, как безошибочно узнают её в массе людей. (11)А старушка у нас во дворе каждое утро носит воробьям крошки. (12)И я вижу радость на лице женщины, наблюдающей, как суетливые птички подбирают рассыпанный корм. (13)Иногда мне кажется: не прилетят воробьи к её выходу из подъезда, и старушка умрёт от обрыва последней ниточки, соединяющей её с жизнью. (14)Как-то мы разговорились с ней, и она призналась, что ни разу не видела в жизни, как пролетают и кричат журавли. (15)Я рассказал ей, как журавли весною танцуют, разбившись на пары, и кричат так, что голоса их сливаются в один торжествующий звук любви. (16)Старушка слушала внимательно и, прощаясь, сказала три слова: «Какой вы счастливый…» (17)Яснополянский мудрец говорил то же самое в дневниках: «Счастье – это быть с природой, видеть её, говорить с ней». (По В.М. Пескову)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 817
        },
        "id": "z_f6dFoXudf1",
        "outputId": "8af5a7a1-4df2-4f65-c268-b6494307f243"
      },
      "source": [
        "questions_df[questions_df.passage_id == 1]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>question</th>\n",
              "      <th>answer</th>\n",
              "      <th>label</th>\n",
              "      <th>passage_id</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Что ни разу в жизни не видела старушка?</td>\n",
              "      <td>Как кричат журавли.</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Что ни разу в жизни не видела старушка?</td>\n",
              "      <td>Счастье.</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Что ни разу в жизни не видела старушка?</td>\n",
              "      <td>Воробьев.</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Что ни разу в жизни не видела старушка?</td>\n",
              "      <td>Как журавли пролетают.</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Что видит автор каждое утро?</td>\n",
              "      <td>Журавлей.</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Что видит автор каждое утро?</td>\n",
              "      <td>Радость на лице женщины.</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Что видит автор каждое утро?</td>\n",
              "      <td>Старушка носит воробьям крошки.</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>Что видит автор каждое утро?</td>\n",
              "      <td>Зайца.</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>Какие образы древние люди оставили нам в наследство на стенах пещер?</td>\n",
              "      <td>Сцены охоты, образы лосей, кабанов, туров.</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>Какие образы древние люди оставили нам в наследство на стенах пещер?</td>\n",
              "      <td>Нынешней техники.</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>Какие образы древние люди оставили нам в наследство на стенах пещер?</td>\n",
              "      <td>Образы животных, сцены охоты.</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>Какие образы древние люди оставили нам в наследство на стенах пещер?</td>\n",
              "      <td>Как пролетают и кричат журавли.</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>О каких птицах рассказывает автор?</td>\n",
              "      <td>О зайцах и рыбках.</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>О каких птицах рассказывает автор?</td>\n",
              "      <td>О воронах, воробьях, журавлях.</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>О каких птицах рассказывает автор?</td>\n",
              "      <td>О воронах, кошках и собаках.</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>О каких птицах рассказывает автор?</td>\n",
              "      <td>О воронах, журавлях, воробьях.</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>Эпизоды чьей жизни мы можем сейчас увидеть благодаря технике?</td>\n",
              "      <td>Древних людей.</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>Эпизоды чьей жизни мы можем сейчас увидеть благодаря технике?</td>\n",
              "      <td>Людей.</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>Эпизоды чьей жизни мы можем сейчас увидеть благодаря технике?</td>\n",
              "      <td>Животных.</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>Эпизоды чьей жизни мы можем сейчас увидеть благодаря технике?</td>\n",
              "      <td>Зверей и птиц.</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>Эпизоды чьей жизни мы можем сейчас увидеть благодаря технике?</td>\n",
              "      <td>Охотников.</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>Кому рассказали о том, как весной танцуют и кричат журавли?</td>\n",
              "      <td>Женщине.</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>Кому рассказали о том, как весной танцуют и кричат журавли?</td>\n",
              "      <td>Воробьям.</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>Кому рассказали о том, как весной танцуют и кричат журавли?</td>\n",
              "      <td>Бывшей работнице нашей редакции.</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>Кому рассказали о том, как весной танцуют и кричат журавли?</td>\n",
              "      <td>Старушке.</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>Кому рассказали о том, как весной танцуют и кричат журавли?</td>\n",
              "      <td>Мудрецу.</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                                                question  ... passage_id\n",
              "0                                Что ни разу в жизни не видела старушка?  ...          1\n",
              "1                                Что ни разу в жизни не видела старушка?  ...          1\n",
              "2                                Что ни разу в жизни не видела старушка?  ...          1\n",
              "3                                Что ни разу в жизни не видела старушка?  ...          1\n",
              "4                                           Что видит автор каждое утро?  ...          1\n",
              "5                                           Что видит автор каждое утро?  ...          1\n",
              "6                                           Что видит автор каждое утро?  ...          1\n",
              "7                                           Что видит автор каждое утро?  ...          1\n",
              "8   Какие образы древние люди оставили нам в наследство на стенах пещер?  ...          1\n",
              "9   Какие образы древние люди оставили нам в наследство на стенах пещер?  ...          1\n",
              "10  Какие образы древние люди оставили нам в наследство на стенах пещер?  ...          1\n",
              "11  Какие образы древние люди оставили нам в наследство на стенах пещер?  ...          1\n",
              "12                                    О каких птицах рассказывает автор?  ...          1\n",
              "13                                    О каких птицах рассказывает автор?  ...          1\n",
              "14                                    О каких птицах рассказывает автор?  ...          1\n",
              "15                                    О каких птицах рассказывает автор?  ...          1\n",
              "16         Эпизоды чьей жизни мы можем сейчас увидеть благодаря технике?  ...          1\n",
              "17         Эпизоды чьей жизни мы можем сейчас увидеть благодаря технике?  ...          1\n",
              "18         Эпизоды чьей жизни мы можем сейчас увидеть благодаря технике?  ...          1\n",
              "19         Эпизоды чьей жизни мы можем сейчас увидеть благодаря технике?  ...          1\n",
              "20         Эпизоды чьей жизни мы можем сейчас увидеть благодаря технике?  ...          1\n",
              "21           Кому рассказали о том, как весной танцуют и кричат журавли?  ...          1\n",
              "22           Кому рассказали о том, как весной танцуют и кричат журавли?  ...          1\n",
              "23           Кому рассказали о том, как весной танцуют и кричат журавли?  ...          1\n",
              "24           Кому рассказали о том, как весной танцуют и кричат журавли?  ...          1\n",
              "25           Кому рассказали о том, как весной танцуют и кричат журавли?  ...          1\n",
              "\n",
              "[26 rows x 4 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TkVQK2o-rMFz"
      },
      "source": [
        "## Number of questions per passage"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TlNc8eA1xDuz"
      },
      "source": [
        "import matplotlib.pyplot as plt\r\n",
        "from matplotlib import gridspec\r\n",
        "import numpy as np"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TgFGGcOI2_EW",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 280
        },
        "outputId": "0b0a68ef-547d-4521-fc6c-4790bdba7aa9"
      },
      "source": [
        "plt.subplot(1,2,1)\r\n",
        "colours = {'#9ddca6', '#f0afb6', '#eeb87c'}\r\n",
        "\r\n",
        "plt.title('Label distribution\\n in Train\\n', fontsize=14)\r\n",
        "plt.pie(questions_df['label'].value_counts(), autopct=\"%.1f%%\", explode=[0.05]*2, labels=questions_df['label'].value_counts().keys(), pctdistance=0.5, textprops = dict(fontsize = 12), colors=colours)\r\n",
        "\r\n",
        "plt.subplot(1,2,2)\r\n",
        "\r\n",
        "plt.title('Label distribution\\n in Val\\n', fontsize=14)\r\n",
        "plt.pie(val_questions_df['label'].value_counts(), autopct=\"%.1f%%\", explode=[0.05]*2, labels=val_questions_df['label'].value_counts().keys(), pctdistance=0.5, textprops = dict(fontsize = 12), colors=colours)\r\n",
        "\r\n",
        "plt.tight_layout()\r\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "D7HWE31GwMo3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 404
        },
        "outputId": "41bdea07-a02d-42b2-fd94-f5a22c026e39"
      },
      "source": [
        "datasets = ['Train', 'Validate']\r\n",
        "fig, ax = plt.subplots(figsize=(5, 5))\r\n",
        "ax.boxplot([\r\n",
        "            questions_df['passage_id'].value_counts(sort=False),\r\n",
        "            val_questions_df['passage_id'].value_counts(sort=False)\r\n",
        "            ],\r\n",
        "           labels=datasets,\r\n",
        "           patch_artist=True)\r\n",
        "ax.yaxis.grid(True)\r\n",
        "ax.set_title('Number of questions per text')\r\n",
        "ax.set_xlabel('Dataset')\r\n",
        "ax.set_ylabel('Quantity')\r\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/numpy/core/_asarray.py:83: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray\n",
            "  return array(a, dtype, copy=False, order=order)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "74leAtbsrUQj"
      },
      "source": [
        "## Number of correct answers per passage"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 317
        },
        "id": "B-hi0nuC5T1I",
        "outputId": "c8462280-708b-4a15-9cc4-d4d3b8e20150"
      },
      "source": [
        "label_distribution = []\r\n",
        "label_distribution.append(questions_df[['passage_id', 'label']].value_counts(sort=False).to_frame())\r\n",
        "label_distribution.append(val_questions_df[['passage_id', 'label']].value_counts(sort=False).to_frame())\r\n",
        "\r\n",
        "correct = []\r\n",
        "incorrect = []\r\n",
        "proportion = []\r\n",
        "\r\n",
        "for i, series in enumerate(label_distribution):\r\n",
        "    label_distribution[i] = label_distribution[i].rename(columns={0:'counter'})\r\n",
        "    label_distribution[i].reset_index(inplace=True)\r\n",
        "    correct.append(label_distribution[i][label_distribution[i].label==1].counter.to_numpy())\r\n",
        "    incorrect.append(label_distribution[i][label_distribution[i].label==0].counter.to_numpy())\r\n",
        "    proportion.append((correct[i] / (incorrect[i]+correct[i])) * 100)\r\n",
        "\r\n",
        "from matplotlib import ticker\r\n",
        "\r\n",
        "fig = plt.figure(constrained_layout=True, figsize=(20,5))\r\n",
        "gs = fig.add_gridspec(1, 5)\r\n",
        "f_ax1 = fig.add_subplot(gs[0, :3])\r\n",
        "f_ax1.set_title('Proportion of correct answers by passage in TRAIN', fontsize=14)\r\n",
        "# f_ax1.yaxis.set_major_locator(ticker.MaxNLocator(4))\r\n",
        "f_ax1.plot(proportion[0])\r\n",
        "f_ax2 = fig.add_subplot(gs[0, 3:])\r\n",
        "f_ax2.set_title('Proportion in VALIDATE', fontsize=14)\r\n",
        "f_ax2.plot(proportion[1])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7face3f383c8>]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 29
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1440x360 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PXYeIhizsI1C"
      },
      "source": [
        "import re\r\n",
        "pd.set_option('display.max_rows', 100)\r\n",
        "\r\n",
        "questions_df = questions_df.assign(answer_tokens=lambda x: x.answer.str.split())\r\n",
        "questions_df['answer_len'] = questions_df['answer_tokens'].apply(len)\r\n",
        "\r\n",
        "val_questions_df = val_questions_df.assign(answer_tokens=lambda x: x.answer.str.split())\r\n",
        "val_questions_df['answer_len'] = val_questions_df['answer_tokens'].apply(len)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3N2vt66RxhZm"
      },
      "source": [
        "number of tokens is larger than 9"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KUR6jzrNy-6V",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "255d2f91-8276-493d-a6df-fe95ec01c9cb"
      },
      "source": [
        "questions_df[['label','answer_len', 'answer_tokens']][questions_df.answer_len > 9].label.value_counts()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1    1284\n",
              "0     433\n",
              "Name: label, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "E5ifnKpc2T4O",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6d4f462a-576d-4889-a082-4e4b523b7cb7"
      },
      "source": [
        "val_questions_df[['label','answer_len', 'answer_tokens']][val_questions_df.answer_len > 9].label.value_counts()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1    240\n",
              "0    105\n",
              "Name: label, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CAhlXGX5x0j1"
      },
      "source": [
        "number of tokens is fewer than 3"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kAoMaahXzcKr",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1078697e-c9f5-4dba-e9aa-2fd4500939e0"
      },
      "source": [
        "questions_df[['label','answer_len']][questions_df.answer_len < 3].label.value_counts()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    2797\n",
              "1    1539\n",
              "Name: label, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "H43EKnBXzGlF",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3ca185c9-08e5-4670-d3da-6881dca3d05b"
      },
      "source": [
        "val_questions_df[['label','answer_len']][val_questions_df.answer_len < 3].label.value_counts()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    484\n",
              "1    256\n",
              "Name: label, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZurpYv-bx4U9",
        "outputId": "073f3153-f11b-4dcf-d618-5c77cd094be7"
      },
      "source": [
        "questions_df[['label','answer_len', 'answer_tokens']][questions_df.answer_len == 2].label.value_counts()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    1541\n",
              "1     884\n",
              "Name: label, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cxVhdsOB8ga1"
      },
      "source": [
        "## Check intersection of lemmas between answers and passages\r\n",
        "\r\n",
        "it seems that a complete intersection of an answer can be a trigger of its correctness"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rVFzGfa8yJVF"
      },
      "source": [
        "from utils import RSG_MorphAnalyzer\r\n",
        "morph = RSG_MorphAnalyzer()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qneoxbGs8uq3"
      },
      "source": [
        "**Train**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xUhANB9A8cGc"
      },
      "source": [
        "passages_df['lemmas'] = morph.lemmantize_sentences(passages_df.passage.to_list())\r\n",
        "questions_df['lemmas'] = morph.lemmantize_sentences(questions_df.answer.to_list())"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jQ5lGzZc1ywJ"
      },
      "source": [
        "def compare_lemmas(row):\r\n",
        "    passage_id = row['passage_id']\r\n",
        "    answer_lemmas = row['lemmas']\r\n",
        "\r\n",
        "    intersection = set(\r\n",
        "        answer_lemmas\r\n",
        "        ).intersection(\r\n",
        "            set(passages_df.iloc[passage_id].lemmas))\r\n",
        "        \r\n",
        "    length = len(intersection)\r\n",
        "\r\n",
        "    return (length == len(answer_lemmas),\r\n",
        "            length == 0,\r\n",
        "            length == 1,\r\n",
        "            length > 6 # larger than mean length of answers\r\n",
        "            )\r\n",
        "\r\n",
        "questions_df[[\r\n",
        "              'overlap',\r\n",
        "              'no_intersect',\r\n",
        "              'little overlap',\r\n",
        "              'much overlap'\r\n",
        "              ]] = questions_df[\r\n",
        "                                ['lemmas', 'passage_id']\r\n",
        "                                ].apply(\r\n",
        "                                    compare_lemmas,\r\n",
        "                                    axis=1,\r\n",
        "                                    result_type=\"expand\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GHgqMN4w9hYT"
      },
      "source": [
        "total overlap in train"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "421Xe4DN_oaz",
        "outputId": "a9823823-c6a8-4d43-f9f8-8c3af932d2a5"
      },
      "source": [
        "questions_df[['label','overlap']][questions_df.overlap == True].label.value_counts()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1    3003\n",
              "0    2366\n",
              "Name: label, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nNiwuKgP_x0O"
      },
      "source": [
        "no intersecting lemmas in train"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tfF-UJoo-Hb0",
        "outputId": "b683d88d-deca-4400-fd70-ecaa0f922ab2"
      },
      "source": [
        "questions_df[['label','no_intersect']][questions_df.no_intersect == True].label.value_counts()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    966\n",
              "1    159\n",
              "Name: label, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "78HGmIFtBK_c"
      },
      "source": [
        "little overlap in train"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Q1mSQeMMBN6z",
        "outputId": "30ff30c7-4471-4574-fd13-7168e0da856d"
      },
      "source": [
        "questions_df[['label','little overlap']][questions_df['little overlap'] == True].label.value_counts()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    1534\n",
              "1     731\n",
              "Name: label, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ykXCniqkDt2z"
      },
      "source": [
        "much overlap in train"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DqKSf4RXDtD5",
        "outputId": "ed5d209a-ecfc-40da-e226-826b0453abc5"
      },
      "source": [
        "questions_df[['label','much overlap']][questions_df['much overlap'] == True].label.value_counts()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1    1672\n",
              "0     594\n",
              "Name: label, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wyasi7Bx89BP"
      },
      "source": [
        "**Validate**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "irbqNHNz9LXU"
      },
      "source": [
        "val_passages_df['lemmas'] = morph.lemmantize_sentences(val_passages_df.passage.to_list())\r\n",
        "val_questions_df['lemmas'] = morph.lemmantize_sentences(val_questions_df.answer.to_list())"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GPMMY7zf9nqU"
      },
      "source": [
        "TOTAL OVERLAP"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MlpVb9QB8USc"
      },
      "source": [
        "def compare_lemmas(row):\r\n",
        "    passage_id = row['passage_id']\r\n",
        "    answer_lemmas = row['lemmas']\r\n",
        "\r\n",
        "    intersection = set(\r\n",
        "        answer_lemmas\r\n",
        "        ).intersection(\r\n",
        "            set(val_passages_df.iloc[passage_id].lemmas))\r\n",
        "\r\n",
        "    length = len(intersection)\r\n",
        "\r\n",
        "    return (length == len(answer_lemmas),\r\n",
        "            length == 0,\r\n",
        "            length == 1,\r\n",
        "            length > 6 # larger than mean length of answers\r\n",
        "            )\r\n",
        "\r\n",
        "val_questions_df[[\r\n",
        "              'overlap',\r\n",
        "              'no_intersect',\r\n",
        "              'little overlap',\r\n",
        "              'much overlap'\r\n",
        "              ]] = val_questions_df[\r\n",
        "                                ['lemmas', 'passage_id']\r\n",
        "                                ].apply(\r\n",
        "                                    compare_lemmas,\r\n",
        "                                    axis=1,\r\n",
        "                                    result_type=\"expand\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-Kca6k6j8PT5",
        "outputId": "9e54f529-eab9-45be-879a-db65dddf2728"
      },
      "source": [
        "val_questions_df[['label','overlap']][val_questions_df.overlap == True].label.value_counts()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1    516\n",
              "0    361\n",
              "Name: label, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6UH7Xmix1KSn",
        "outputId": "9bd54760-1d69-4bb0-a7b0-cecac7a1c160"
      },
      "source": [
        "val_questions_df[['label','no_intersect']][val_questions_df.no_intersect == True].label.value_counts()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    197\n",
              "1     24\n",
              "Name: label, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JLfkxeUGC2ST",
        "outputId": "060e6aa4-cd94-458b-9d0d-a416277873b0"
      },
      "source": [
        "val_questions_df[['label','little overlap']][val_questions_df['little overlap'] == True].label.value_counts()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    288\n",
              "1    127\n",
              "Name: label, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HPNDVDuMC56n",
        "outputId": "15a51e87-16ee-459a-f7cd-5c747f5fda8e"
      },
      "source": [
        "val_questions_df[['label','much overlap']][val_questions_df['much overlap'] == True].label.value_counts()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1    312\n",
              "0    110\n",
              "Name: label, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 48
        }
      ]
    }
  ]
}