Classifier_news.ipynb

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    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/andreytsimbalov/News_Classification_and_Vectorization/blob/main/Classifier_news.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "37aYdtJS4yvM"
      },
      "source": [
        "# Introduction"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gjwgPKhx51Fd",
        "outputId": "c910e10e-1b70-4389-eb69-87f519bebdd0"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')\n",
        "\n",
        "md = '/content/drive/My Drive/Colab Notebooks/Diplom/'"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mounted at /content/drive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UWSL2Wrg4yvR"
      },
      "source": [
        "import sklearn.cluster as sc\n",
        "import gensim\n",
        "import random\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import time\n",
        "import datetime\n",
        "import pickle\n",
        "from tqdm.notebook import tqdm\n",
        "\n",
        "%matplotlib inline\n",
        "import matplotlib.pyplot as plt\n"
      ],
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "l9wJ43s-O5fo"
      },
      "source": [
        "main_tags_list = ['economy',\n",
        " 'entertainment',\n",
        " 'traditions',\n",
        " 'science',\n",
        " 'society',\n",
        " 'sports',\n",
        " 'technology']\n",
        "\n",
        "main_tags_dict = {0: 'economy',\n",
        " 1: 'entertainment',\n",
        " 2: 'traditions',\n",
        " 3: 'science',\n",
        " 4: 'society',\n",
        " 5: 'sports',\n",
        " 6: 'technology'}"
      ],
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Gohe4mScj3Ki"
      },
      "source": [
        "# Извлечение данных для обучения\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "P2PkkxiVkBom",
        "outputId": "370670df-9141-4745-dd24-830a8a98af65"
      },
      "source": [
        "def load_data_to_list_of_dict(path):\n",
        "  # path to lemm/stem data\n",
        "\n",
        "  df  = pd.read_pickle(path)\n",
        "  label = df.label.to_numpy()\n",
        "  try:\n",
        "    text = df.text.to_numpy()\n",
        "  except:\n",
        "    text = df.main_text.to_numpy()\n",
        "    print('text = main_text')\n",
        "\n",
        "  return [{\"id\": i,\"label\": l,\"text\": t, } for i, l, t in zip(range(len(label)),label,text) if l!=-1]\n",
        "\n",
        "path = md + 'data/data_lemm'\n",
        "# path = md + 'data/news_main_2020'\n",
        "# path = md + 'data/data_stem'\n",
        "data_text = load_data_to_list_of_dict(path)\n",
        "len(data_text), data_text[4]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(69955,\n",
              " {'id': 6,\n",
              "  'label': 1,\n",
              "  'text': 'лоза отреагировать критика концерт пугачёв композитор юрий лоза интервью риа новость крым прокомментировать критика концерт алла пугачёв заявить телезритель должный право выбор ранее музыкальный критик сергей соседов признаться рад возвращение сцена ряд исполнитель привести пример алла пугачёв софия ротару николай носков назвать выступление печальный зрелище подчеркнуть примадонна еле дышать стоить сцена лоза встать защита российский артист отметить мир должный цветной человек хотеть видеть этот артист должный видеть тот хотеть мочь смотреть должный разный телепередача каждый житель страна мочь выбрать желаемый рассказать композитор композитор добавить возраст исполнитель иметь значение мнение важно форма находиться артист разница сколько мирей матья граница чувствовать свой ветеран который собирать стадион считать певец апрель прошедшее год государственный кремлёвский дворец состояться первый десять год сольный концерт алла пугачёв p.s. который приурочить число_возраст артистка '})"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Jm_3nx9wkBoo"
      },
      "source": [
        "def data_tr_ts_shaffle(data, koeff = 0.2, random_seed = 42):\n",
        "  # разбиение всего датасета data на \n",
        "  #   тренировочную и тестовую выборки с\n",
        "  #   коеффициентом koeff\n",
        "\n",
        "  random.seed(random_seed)\n",
        "\n",
        "  data = [d for d in data if d['label']!=-1]\n",
        "  random.shuffle(data)\n",
        "\n",
        "  data_tr_idx=[]\n",
        "  data_ts_idx=[]\n",
        "\n",
        "  data_tr=[]\n",
        "  data_ts=[]\n",
        "\n",
        "  tags = [d[\"label\"] for d in data]\n",
        "  mtv = np.zeros(len(main_tags_list))\n",
        "\n",
        "  for d in data:\n",
        "    mtv[d['label']]+=1\n",
        "  mtv_ts = np.zeros(len(mtv))\n",
        "\n",
        "  for i, d in enumerate(data):\n",
        "    now_tag = d['label']\n",
        "    if mtv_ts[now_tag] < koeff * mtv[now_tag]:\n",
        "      data_ts_idx.extend([i])\n",
        "    else:\n",
        "      data_tr_idx.extend([i])\n",
        "\n",
        "    mtv_ts[now_tag]+=1\n",
        "\n",
        "  random.shuffle(data_tr_idx)\n",
        "  random.shuffle(data_ts_idx)\n",
        "  data_tr = [data[i] for i in data_tr_idx]\n",
        "  data_ts = [data[i] for i in data_ts_idx]\n",
        "  data_tr_idx = [data[i]['id'] for i in data_tr_idx]\n",
        "  data_ts_idx = [data[i]['id'] for i in data_ts_idx]\n",
        "\n",
        "  return data_tr, data_tr_idx, data_ts, data_ts_idx\n",
        "\n",
        "# data_train, data_test = data_tr_ts_shaffle(data_text_bal_tfidf, 0.2) \n",
        "# len(data_train), len(data_test) "
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vHjjPn3akBon"
      },
      "source": [
        "def balancing_classes_data(X, more_less = True, koeff_len = 0.5, return_indexes = False):\n",
        "  # more_less = True -> баленсировка вверх\n",
        "  # more_less = False -> баленсировка вниз\n",
        "\n",
        "  X = [d for d in X if d['label']!=-1]\n",
        "\n",
        "  labels = [news[\"label\"] for news in X]\n",
        "  un = np.unique(labels)\n",
        "  un_tags_dict = dict(zip(un, np.zeros(len(un))))\n",
        "  for i in X:\n",
        "    un_tags_dict[i[\"label\"]]+=1\n",
        "\n",
        "  if more_less:\n",
        "    normal_un_len = 0\n",
        "    for u in un:\n",
        "      if un_tags_dict[u]>normal_un_len:\n",
        "        normal_un_len = un_tags_dict[u]\n",
        "  else:\n",
        "    normal_un_len =  un_tags_dict[un[0]]\n",
        "    for u in un:\n",
        "      if un_tags_dict[u]<normal_un_len:\n",
        "        normal_un_len = un_tags_dict[u]\n",
        "\n",
        "  new_dtr=[]\n",
        "  new_dtr_indexes=[]\n",
        "\n",
        "  if more_less:\n",
        "    normal_un_len = normal_un_len * koeff_len\n",
        "    for x_i in X:\n",
        "      new_dtr_indexes+=[x_i['id']]\n",
        "      if un_tags_dict[x_i[\"label\"]]<normal_un_len:\n",
        "        for _ in range(int(normal_un_len//un_tags_dict[x_i[\"label\"]])):\n",
        "          new_dtr_indexes+=[x_i['id']]\n",
        "\n",
        "  else:\n",
        "    normal_un_len = normal_un_len / koeff_len\n",
        "    un_tags_count_less = dict(zip(un, np.zeros(len(un))))\n",
        "    for i, x_i in enumerate(X):\n",
        "      if un_tags_count_less[x_i[\"label\"]]<normal_un_len:\n",
        "        un_tags_count_less[x_i[\"label\"]]+=1\n",
        "        new_dtr_indexes+=[i]\n",
        "  print(f'normal_un_len: {normal_un_len}')\n",
        "  \n",
        "  random.shuffle(new_dtr_indexes)\n",
        "\n",
        "  new_dtr = [X[i] for i in new_dtr_indexes]\n",
        "  new_dtr_indexes = [X[i]['id'] for i in new_dtr_indexes]\n",
        "\n",
        "  return new_dtr, new_dtr_indexes\n",
        "\n",
        "# data_text_bal_tfidf = balancing_classes_data(data_text_tfidf, False, 0.7)\n",
        "# len(data_text_bal_tfidf)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DZEWcWCkkBoo",
        "outputId": "110d9d2a-6e71-42a4-b872-3e8e087b39f5"
      },
      "source": [
        "data_train, data_train_idx, data_test, data_test_idx = data_tr_ts_shaffle(data_text, 0.2) \n",
        "\n",
        "more_less = False # True -> Up; False -> Down\n",
        "koeff_balance = 1\n",
        "data_train, data_train_idx = balancing_classes_data(data_train, more_less, koeff_balance)\n",
        "data_test, data_test_idx = balancing_classes_data(data_test, more_less, koeff_balance)\n",
        "print(len(data_train), len(data_test))\n",
        "\n",
        "X_train_text = [d['text'] for d in data_train]\n",
        "X_test_text = [d['text'] for d in data_test]\n",
        "\n",
        "y_train = [d['label'] for d in data_train]\n",
        "y_test = [d['label'] for d in data_test]\n",
        "data_train[:5]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "normal_un_len: 3876.0\n",
            "normal_un_len: 969.0\n",
            "27132 6783\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[{'id': 94022,\n",
              "  'label': 1,\n",
              "  'text': 'ефремов предложить сдать анализ запах алкоголь суд бывший адвокат михаил ефремов эльман пашаев предложить организовать сдача анализ актёр подсудимый обвинить запах алкоголь заседание пятница число август сообщать тасс бессовестный человек говорить ефрем употреблять заявить пашаев ответ слово артист пахнуть спиртное ранее ефрем заявить суд отказываться услуга пашаев партнёр елизавета шаргородской затем адвокат попасть видео запечатлеть удивление решение артист вечером число июнь михаил ефрем состояние алкогольный опьянение устроить смертельный авария центр москва автомобиль актёр вылететь встречный полоса столкнуться фургон водитель который сергей захаров скончаться больница ефрем заявить помнить произойти '},\n",
              " {'id': 14143,\n",
              "  'label': 4,\n",
              "  'text': 'вор закон пухлый пузырь завести дело лидерство преступный мир число_возраст вор закон владимир жураковский известный криминальный круг пухлый ровесник хизира акбулатов кличка пузырь завести уголовный дело лидерство преступный мир пятница число февраль сообщать агентство прайма крайма данные настоящий момент оба криминальный авторитет находиться территория турция число февраль сообщаться число_возраст вор закон дмитрий чантурия мирон мочь взять государственный защита заявить вымогательство взятка сторона сотрудник правоохранительный орган воровской мир стать беспрецедентный факт сотрудничество криминальный авторитет высокий уровень изобличение коррупционер мирон официально признать потерпевший действие сотрудник полиция который вымогать крупный сумма деньга предстоять стать суд свидетель обвинение '},\n",
              " {'id': 74418,\n",
              "  'label': 2,\n",
              "  'text': 'гостья свадьба чересчур вызывающий наряд вызвать спор сеть девушка прийти свадебный церемония чересчур вызывающий наряд вызвать спор сеть соответствовать дискуссия портал reddit обратить внимание журналист daily star разместить кадр неизвестный гостья позировать рядом молодой человек полупрозрачный платье красный цвет корсет кружевной вставка образ дополнить босоножка каблук многие пользователь платформа раскритиковать образ девушка казаться приходить чей-то свадьба красный табу казаться кто-то хотеть привлечь внимание хотя такой тело одеваться регулярно считать приходить свадьба наряд который затмевать невеста невежливый очень нравиться платье оно свадьба писать менее некоторый юзер выступить защита гостья такой произойти мой свадьба задеть отлично выглядеть обеспокоить кто-то получить внимание прийтись приглашать исключительно некрасивый друг выглядеть прекрасно хотеть мой гость выглядеть выразиться юзер ранее июль невеста прогнать число_возраст сестра свадьба большой грудь слово число_возраст девушка должный занять место подружка невеста торжество сестра выгнать свадьба откровенный декольте виновница торжество аргументировать внешний вид подросток отвлекать жених гость '},\n",
              " {'id': 125936,\n",
              "  'label': 3,\n",
              "  'text': 'назвать новый катастрофический последствие изменение климат учёный эксетерский университет великобритания прислать вывод глобальный потепление градус цельсий привести катастрофический высвобождение число миллиард тонна углекислый газ почва новый последствие изменение климат называться статья опубликовать журнал nature communications почва весь мир содержать число раз углерод атмосфера высокий температура ускорять разложение сокращать время который углерод проводить почва выпустить случай потепление градус цельсий объём четыре раз превышать общий объём выброс китай вдвое выброс сша последний число год оценка включать потеря углерод вечный мерзлота эффект возникать результат положительный обратный связь последствие какой-либо процесс усиливать процесс сей пора прогностический модель изменение климат учитывать влияние почвенный углерод вносить неопределённость предсказание решение проблема учёный скомбинировать модель климат углеродный цикл новый данные наблюдение позволить определить чувствительность почва различный место планета парижский соглашение число_год_2015 год предусматривать человечество должный допустить рост средний температура планета число градус глобальный потепление градус вызвать непредсказуемый изменение система атмосферный циркуляция происходить интенсивный таяние ледник привести очередь повышение уровень море '},\n",
              " {'id': 139280,\n",
              "  'label': 3,\n",
              "  'text': 'назвать способ предотвратить перегрев ноутбук предотвратить перегрев ноутбук нужно следить вентиляционный отверстие также перегружать процессор шесть совет уход гаджет дать журнал business insider вентиляционный отверстие компьютер ничто закрывать иначе туда поступать нужный количество воздух ноутбук ставить твёрдый плоский поверхность кроме использовать специальный охлаждать подставка использовать ресурсоёмкий программа который быть заставлять процессор компонент работать быстрый перегреваться также рекомендоваться изменить настройка производительность ноутбук снизить нагрузка компонент следовать обращать внимание загрязнённость отверстие ноутбук накопиться пыль следовать очистить дать девайс остыть некоторый время выключить ранее руководитель hi-tech mail.ru дмитрий рябинин раскрыть способ продлить срок жизнь ноутбук слово нужно следить состояние аккумулятор гаджет приложение который работать фоновый режим '}]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aHNQDiunJHIl"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WyAJIUbiJRDE"
      },
      "source": [
        "# График длин документов"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 84,
          "referenced_widgets": [
            "36732768007c476e972bee3c5ffe3d29",
            "4f0234e08d454a36878e13692417031f",
            "179f9122511f45909a46ba3fe0e5151d",
            "c0279ec0c9a54026a3fa55b9045c9f2e",
            "0e8fafe91f064bf2a33bec99774eb55b",
            "ebfcaf24140542c1b2ae8b77b0c81a04",
            "5ea3e8084d9046ac9893afb5def610b2",
            "a9aa2170216745a8a255b354b97719a3"
          ]
        },
        "id": "F6hVzRB5j96Z",
        "outputId": "0a54d41e-33b7-42a4-eca1-99568808b757"
      },
      "source": [
        "max_sent_len = 0\n",
        "for text_i in tqdm([d['text'] for d in data_text]):\n",
        "  now_len = len(text_i.split())\n",
        "  max_sent_len = max(max_sent_len, now_len)\n",
        "max_sent_len"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "36732768007c476e972bee3c5ffe3d29",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, max=69955.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "3705"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8lWVku0fgIJI",
        "outputId": "192ad92a-b807-4412-955d-706cd4fcda0d"
      },
      "source": [
        "lens = [len(text_i.split()) for text_i in [d['text'] for d in data_text]]\n",
        "lens.sort()\n",
        "lens = np.array(lens)\n",
        "len(lens[lens>500])/len(lens)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "KrixYKTlgVqN",
        "outputId": "6d9762d5-3c40-47b2-f5cd-99c2f875eb95"
      },
      "source": [
        "plt.plot(lens)\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 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "J_yyjQkUgsct",
        "outputId": "9b8d51a7-3926-45e7-a6c5-c258c8ffc303"
      },
      "source": [
        "len(lens[lens>512])/len(lens)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.013337145307697806"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_dIqP-1fK0XB"
      },
      "source": [
        "# Векторизаторы"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ptscUkgH4yvX"
      },
      "source": [
        "## w2v классификация модели"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ife8e78b_Wma"
      },
      "source": [
        "X_vectors = pickle.load(open(md+\"models/w2v_tfidf_vector_data.pickle\", \"rb\"))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_QDj1rCF_Wph",
        "outputId": "126c7a05-8ef9-4426-87ff-265f97660333"
      },
      "source": [
        "X_train = [X_vectors[i] for i in data_train_idx]\n",
        "X_test = [X_vectors[i] for i in data_test_idx]\n",
        "del X_vectors\n",
        "len(X_train), len(X_test)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(27132, 6783)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TyLt8mDj4yvZ"
      },
      "source": [
        "## d2v классификация модели"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zu1gFR8o4yvZ"
      },
      "source": [
        "d2v = gensim.models.Doc2Vec.load(md+\"models/d2v_300.model\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 116,
          "referenced_widgets": [
            "6d1f0efd42d94c5584d4b85609396eaf",
            "614540a176cc45ebaca15633d61ac384",
            "00998bf1ada64125bb9db3c068513cc7",
            "c1674a9ef5834b269ce9bfd5e24ba913",
            "cb06dc855b894119b250464309b5e034",
            "ef2dd054f24a45188509cc381c1d9515",
            "533788ad9a8b4157a32c2da357ff4d13",
            "7f18e0a9186c4aa9afe681d6e1bb6868",
            "65f995d118e046b0a63908da5a9fd2d1",
            "eb67da9a1330434682dd562b619415dc",
            "4cadb23bde454adeb3c00c63f21f77a0",
            "52c9b931e86044afaf1b40c7174f9c0f",
            "c89dc205058d4f6796b8e76b85e42df4",
            "3a69a0460f084b7f8c34ed3f3f09b2de",
            "dd4c229e1dac4fdd877489f94b56399e",
            "f3b3f35a064a42878aa717cd9734f86a"
          ]
        },
        "id": "hRC8kDwHcmQz",
        "outputId": "32350bdb-44a7-42bf-9bb7-04d7fd7d3d41"
      },
      "source": [
        "X_train = [d2v.infer_vector(text) for text in tqdm(X_train_text)]\n",
        "X_test = [d2v.infer_vector(text) for text in tqdm(X_test_text)]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "6d1f0efd42d94c5584d4b85609396eaf",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, max=27132.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "65f995d118e046b0a63908da5a9fd2d1",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, max=6783.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xejg8DkcMeea"
      },
      "source": [
        "## ft классификация модели"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UloWl9VDMhjX"
      },
      "source": [
        "ft = gensim.models.FastText.load(md+\"models/ft_lemm_300.model\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 116,
          "referenced_widgets": [
            "f9664c732221444b87c2785554103486",
            "1f93cb65be1b4c82a4c1966f9c5079c9",
            "79b88faf393e439cacfad11ff2bd5f58",
            "dfe2cc8d752745a4a07c49702d051bcb",
            "94ea84d5e7d64806a62c4420b46439bd",
            "fac40d03dead4f0a9c0dfbe54c8a9cfb",
            "e5183e07acb243d1976785e1d8165b90",
            "41f59dc32d854f14acc042eab6bb34fd",
            "6ee32fd51604433cbcfbbe487e5cd9d1",
            "401bd72c281f4fcfb3e60a026e44c50c",
            "b86cf444710b4b57b1639299c750e9fc",
            "79e6ef4f4ebc43308f145ff57949763b",
            "38aa7eefd30846db944ffc0d1d283054",
            "c834c46c26334ccf9e58762328518e56",
            "05ab4c0108354c0aac9c826c8ed95570",
            "a55af05fc17b45cba216a529510a1feb"
          ]
        },
        "id": "UrWd3QYwoeK8",
        "outputId": "f7a6b7a8-971e-4bb6-e2a5-9589a01f7e9f"
      },
      "source": [
        "X_train = [ft.wv[text] for text in tqdm(X_train_text)]\n",
        "X_test = [ft.wv[text] for text in tqdm(X_test_text)]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "f9664c732221444b87c2785554103486",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, max=27132.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "6ee32fd51604433cbcfbbe487e5cd9d1",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, max=6783.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xW9JwF38hWzy"
      },
      "source": [
        "## tfidf классификация модели"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AwKeDeLfhcGD"
      },
      "source": [
        "# tfidf_path = 'tfidf_lemm'\n",
        "# tfidf_path = 'tfidf_lemm_100k'\n",
        "tfidf_path = 'tfidf_lemm_500k'\n",
        "tfidf = pickle.load(open(md+\"models/\"+tfidf_path+\".pickle\", \"rb\"))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JFDa1wrkhcJn",
        "outputId": "02c2187b-a5fd-428e-f41c-fc47fadd6faa"
      },
      "source": [
        "X_train = tfidf.transform(X_train_text)\n",
        "X_test = tfidf.transform(X_test_text)\n",
        "X_test.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(6783, 500000)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 57
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Jkh6Sl3_u2Hp"
      },
      "source": [
        "## glove классификация модели"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Xzo2Tg5stN6c"
      },
      "source": [
        "X_vectors = pickle.load(open(md+\"models/glove_tfidf_vector_data.pickle\", \"rb\"))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fkCDE41vtN6c",
        "outputId": "64fe500c-2955-4cf3-ebfa-5c8feff19473"
      },
      "source": [
        "X_train = [X_vectors[i] for i in data_train_idx]\n",
        "X_test = [X_vectors[i] for i in data_test_idx]\n",
        "del X_vectors\n",
        "len(X_train), len(X_test)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(27132, 6783)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 64
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Eb3b51yutN6d",
        "outputId": "692f5b6c-cd92-4f15-eedd-9a0533ddcf08"
      },
      "source": [
        "len(data_train_idx) == len(X_train)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 180
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ScRYAMCCL1Oc"
      },
      "source": [
        "## Sent Bert кдассификация"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xNXw-bSRL9r7"
      },
      "source": [
        "X_vectors = pickle.load(open(md+\"models/bert_vector_data.pickle\", \"rb\"))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QV7-46idL9r7",
        "outputId": "3c3dbdea-9672-4423-f4e4-22043fba126a"
      },
      "source": [
        "X_train = [X_vectors[i] for i in data_train_idx]\n",
        "X_test = [X_vectors[i] for i in data_test_idx]\n",
        "del X_vectors\n",
        "len(X_train), len(X_test)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(27132, 6783)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ezZKDJXUBDhR"
      },
      "source": [
        "## USE классификация"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HzIxilVLBCjZ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 167
        },
        "outputId": "93a981b0-5330-4da0-c3b3-135057dccdc7"
      },
      "source": [
        "X_vectors = pickle.load(open(md+\"models/use_vector_data.pickle\", \"rb\"))"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "error",
          "ename": "NameError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-5-2bad82341f96>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mX_vectors\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpickle\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmd\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0;34m\"models/use_vector_data.pickle\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"rb\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m: name 'pickle' is not defined"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nB7LXwj4BCxd",
        "outputId": "b6b72773-cb05-4755-d255-d1f8171f2709"
      },
      "source": [
        "X_train = [X_vectors[i] for i in data_train_idx]\n",
        "X_test = [X_vectors[i] for i in data_test_idx]\n",
        "del X_vectors\n",
        "len(X_train), len(X_test)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(27132, 6783)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 71
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "W6g3KSlyoZkO"
      },
      "source": [
        "# LogisticRegression & LinearSVC"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Foy8jOafol9C"
      },
      "source": [
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.svm import LinearSVC, SVC\n",
        "from sklearn.model_selection import cross_val_score as cvs\n",
        "from sklearn import metrics"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-661tvPVKYpg"
      },
      "source": [
        "LinearSVC"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "M1xUpK8Wol9C",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "70b9bab3-1fac-4c00-def3-0c73c90cb6ea"
      },
      "source": [
        "clf = LinearSVC(C = 0.6, random_state = 42)\n",
        "%time clf.fit(X_train, y_train) # [:1000]\n",
        "print(clf.score(X_test, y_test))\n",
        "print(clf.score(X_train, y_train))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "CPU times: user 4.85 s, sys: 45.1 ms, total: 4.9 s\n",
            "Wall time: 4.88 s\n",
            "0.8752764263600177\n",
            "0.8875497567448032\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ej22rGlGXH1p",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b4929328-1138-47a7-dcd4-bf069d61cfb3"
      },
      "source": [
        "predicted_lr = clf.predict(X_test)\n",
        "print(metrics.classification_report(y_test, predicted_lr, target_names = main_tags_list, digits = 6))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "               precision    recall  f1-score   support\n",
            "\n",
            "      economy   0.827381  0.860681  0.843703       969\n",
            "entertainment   0.824876  0.855521  0.839919       969\n",
            "   traditions   0.816703  0.777090  0.796404       969\n",
            "      science   0.845545  0.881321  0.863062       969\n",
            "      society   0.803445  0.818369  0.810838       969\n",
            "       sports   0.946930  0.939112  0.943005       969\n",
            "   technology   0.771910  0.708978  0.739107       969\n",
            "\n",
            "     accuracy                       0.834439      6783\n",
            "    macro avg   0.833827  0.834439  0.833720      6783\n",
            " weighted avg   0.833827  0.834439  0.833720      6783\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pMMPigs9aBPc"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "V0wxibiqKbXg"
      },
      "source": [
        "LogisticRegression"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Znipilhpol9D",
        "outputId": "39ab4ef3-90fd-419f-da84-9e6f96e1fd94"
      },
      "source": [
        "clf = LogisticRegression(n_jobs=-1, random_state=42)#,class_weight='balanced'\n",
        "%time clf.fit(X_train, y_train)\n",
        "print(clf.score(X_test, y_test))\n",
        "print(clf.score(X_train, y_train))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "CPU times: user 186 ms, sys: 85.2 ms, total: 272 ms\n",
            "Wall time: 13.9 s\n",
            "0.8674627745835176\n",
            "0.8778564057201829\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "q9UIszkYaqyc",
        "outputId": "1636ad71-3d25-4a69-d58b-9c9f20df70d0"
      },
      "source": [
        "predicted_lr = clf.predict(X_test)\n",
        "# predicted_lr_pr = clf.predict_proba(X_test_idf)\n",
        "# maxes = np.max(predicted_lr_pr, axis=1)\n",
        "# predicted_lr = np.where(maxes>0.45, predicted_lr, np.ones(len(predicted_lr))*10)\n",
        "print(metrics.classification_report(y_test, predicted_lr, target_names = main_tags_list, digits = 6))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "               precision    recall  f1-score   support\n",
            "\n",
            "      economy   0.769833  0.811146  0.789950       969\n",
            "entertainment   0.773227  0.798762  0.785787       969\n",
            "   traditions   0.742077  0.700722  0.720807       969\n",
            "      science   0.830052  0.826625  0.828335       969\n",
            "      society   0.741483  0.763674  0.752415       969\n",
            "       sports   0.931109  0.892673  0.911486       969\n",
            "   technology   0.664570  0.654283  0.659386       969\n",
            "\n",
            "     accuracy                       0.778269      6783\n",
            "    macro avg   0.778907  0.778269  0.778309      6783\n",
            " weighted avg   0.778907  0.778269  0.778309      6783\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oigBkYvrarWH"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4MQgOpgcKt_R"
      },
      "source": [
        "confusion_matrix"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 278
        },
        "id": "gvruZbrjol9E",
        "outputId": "37723339-ffb9-4b69-ab2f-2ece1a6a5593"
      },
      "source": [
        "print(str(clf)[:20])\n",
        "a = np.array(main_tags_list)\n",
        "b = metrics.confusion_matrix(y_test,clf.predict(X_test))\n",
        "pd.DataFrame(data=b,index = a,columns=a)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "LinearSVC(C=0.6, cla\n"
          ],
          "name": "stdout"
        },
        {
          "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>economy</th>\n",
              "      <th>entertainment</th>\n",
              "      <th>traditions</th>\n",
              "      <th>science</th>\n",
              "      <th>society</th>\n",
              "      <th>sports</th>\n",
              "      <th>technology</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>economy</th>\n",
              "      <td>878</td>\n",
              "      <td>3</td>\n",
              "      <td>30</td>\n",
              "      <td>10</td>\n",
              "      <td>33</td>\n",
              "      <td>3</td>\n",
              "      <td>12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>entertainment</th>\n",
              "      <td>3</td>\n",
              "      <td>859</td>\n",
              "      <td>30</td>\n",
              "      <td>6</td>\n",
              "      <td>21</td>\n",
              "      <td>3</td>\n",
              "      <td>47</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>traditions</th>\n",
              "      <td>22</td>\n",
              "      <td>20</td>\n",
              "      <td>848</td>\n",
              "      <td>25</td>\n",
              "      <td>25</td>\n",
              "      <td>1</td>\n",
              "      <td>28</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>science</th>\n",
              "      <td>12</td>\n",
              "      <td>2</td>\n",
              "      <td>9</td>\n",
              "      <td>901</td>\n",
              "      <td>17</td>\n",
              "      <td>0</td>\n",
              "      <td>28</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>society</th>\n",
              "      <td>38</td>\n",
              "      <td>25</td>\n",
              "      <td>20</td>\n",
              "      <td>27</td>\n",
              "      <td>823</td>\n",
              "      <td>4</td>\n",
              "      <td>32</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>sports</th>\n",
              "      <td>0</td>\n",
              "      <td>6</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "      <td>954</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>technology</th>\n",
              "      <td>26</td>\n",
              "      <td>65</td>\n",
              "      <td>27</td>\n",
              "      <td>53</td>\n",
              "      <td>42</td>\n",
              "      <td>7</td>\n",
              "      <td>749</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "               economy  entertainment  traditions  ...  society  sports  technology\n",
              "economy            878              3          30  ...       33       3          12\n",
              "entertainment        3            859          30  ...       21       3          47\n",
              "traditions          22             20         848  ...       25       1          28\n",
              "science             12              2           9  ...       17       0          28\n",
              "society             38             25          20  ...      823       4          32\n",
              "sports               0              6           0  ...        5     954           4\n",
              "technology          26             65          27  ...       42       7         749\n",
              "\n",
              "[7 rows x 7 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Y2vsoMiNt3G4"
      },
      "source": [
        "# Single-layer perceptron"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wUGigAxI6Fi8"
      },
      "source": [
        "https://realpython.com/python-keras-text-classification/"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AnFOdts14eGe"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "plt.style.use('ggplot')\n",
        "\n",
        "def plot_history(history):\n",
        "    acc = history.history['accuracy']\n",
        "    val_acc = history.history['val_accuracy']\n",
        "    loss = history.history['loss']\n",
        "    val_loss = history.history['val_loss']\n",
        "    x = range(1, len(acc) + 1)\n",
        "\n",
        "    plt.figure(figsize=(12, 5))\n",
        "    plt.subplot(1, 2, 1)\n",
        "    plt.plot(x, acc, 'b', label='Training acc')\n",
        "    plt.plot(x, val_acc, 'r', label='Validation acc')\n",
        "    plt.title('Training and validation accuracy')\n",
        "    plt.legend()\n",
        "    plt.subplot(1, 2, 2)\n",
        "    plt.plot(x, loss, 'b', label='Training loss')\n",
        "    plt.plot(x, val_loss, 'r', label='Validation loss')\n",
        "    plt.title('Training and validation loss')\n",
        "    plt.legend()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MkbVdnsnNXZm",
        "outputId": "69b9065b-ebd3-4b97-cb54-84c42cc79fb5"
      },
      "source": [
        "from keras.utils import np_utils\n",
        "from sklearn.metrics import roc_auc_score\n",
        "from sklearn.preprocessing import LabelEncoder\n",
        "\n",
        "encoder = LabelEncoder()\n",
        "encoder.fit(y_train)\n",
        "encoded_Y = encoder.transform(y_train)\n",
        "y_tr = np_utils.to_categorical(encoded_Y)\n",
        "encoded_Y_ts = encoder.fit_transform(y_test)\n",
        "y_ts = np_utils.to_categorical(encoded_Y_ts)\n",
        "print(y_tr.shape)\n",
        "y_tr[-1], y_ts[-1]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(27132, 7)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(array([0., 0., 0., 1., 0., 0., 0.], dtype=float32),\n",
              " array([1., 0., 0., 0., 0., 0., 0.], dtype=float32))"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Pgy9BoacENF6"
      },
      "source": [
        "# for w2v+tfidf; glove+tfidf; d2v; ft; use; bert\n",
        "x_tr = np.array(X_train).astype('float16')\n",
        "x_ts = np.array(X_test).astype('float16')\n",
        "input_dim = len(x_tr[0])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0mIyV7MqwdF-"
      },
      "source": [
        "# for thidf\n",
        "x_tr = X_train.astype('float16')\n",
        "x_ts = X_test.astype('float16')\n",
        "input_dim = x_tr.shape[1]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vMSPS3_hoxph"
      },
      "source": [
        "from keras.models import Sequential\n",
        "from keras import layers\n",
        "\n",
        "model = Sequential()\n",
        "model.add(layers.Dense(32, input_dim=input_dim, activation='relu'))\n",
        "model.add(layers.Dropout(0.2))\n",
        "model.add(layers.Dense(len(main_tags_list), activation='softmax'))\n",
        "\n",
        "model.compile(loss='categorical_crossentropy', \n",
        "              optimizer='adam', \n",
        "              metrics=['accuracy'])\n",
        "\n",
        "# model.summary()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FMtEtGmYu2Xb",
        "outputId": "656b8a67-53c7-4ea5-ddcc-372080464fb7"
      },
      "source": [
        "%time history = model.fit(x_tr, y_tr, epochs=15,validation_data=(x_ts,y_ts))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/15\n",
            "848/848 [==============================] - 3s 3ms/step - loss: 1.2778 - accuracy: 0.6166 - val_loss: 0.5210 - val_accuracy: 0.8367\n",
            "Epoch 2/15\n",
            "848/848 [==============================] - 2s 2ms/step - loss: 0.5498 - accuracy: 0.8224 - val_loss: 0.4484 - val_accuracy: 0.8530\n",
            "Epoch 3/15\n",
            "848/848 [==============================] - 2s 2ms/step - loss: 0.4779 - accuracy: 0.8452 - val_loss: 0.4202 - val_accuracy: 0.8589\n",
            "Epoch 4/15\n",
            "848/848 [==============================] - 2s 2ms/step - loss: 0.4423 - accuracy: 0.8521 - val_loss: 0.4081 - val_accuracy: 0.8650\n",
            "Epoch 5/15\n",
            "848/848 [==============================] - 2s 2ms/step - loss: 0.4197 - accuracy: 0.8600 - val_loss: 0.3937 - val_accuracy: 0.8683\n",
            "Epoch 6/15\n",
            "848/848 [==============================] - 2s 2ms/step - loss: 0.4028 - accuracy: 0.8639 - val_loss: 0.3873 - val_accuracy: 0.8688\n",
            "Epoch 7/15\n",
            "848/848 [==============================] - 2s 2ms/step - loss: 0.3921 - accuracy: 0.8711 - val_loss: 0.3827 - val_accuracy: 0.8719\n",
            "Epoch 8/15\n",
            "848/848 [==============================] - 2s 2ms/step - loss: 0.3737 - accuracy: 0.8720 - val_loss: 0.3765 - val_accuracy: 0.8744\n",
            "Epoch 9/15\n",
            "848/848 [==============================] - 2s 2ms/step - loss: 0.3688 - accuracy: 0.8739 - val_loss: 0.3725 - val_accuracy: 0.8739\n",
            "Epoch 10/15\n",
            "848/848 [==============================] - 2s 2ms/step - loss: 0.3590 - accuracy: 0.8791 - val_loss: 0.3691 - val_accuracy: 0.8734\n",
            "Epoch 11/15\n",
            "848/848 [==============================] - 2s 2ms/step - loss: 0.3510 - accuracy: 0.8824 - val_loss: 0.3672 - val_accuracy: 0.8751\n",
            "Epoch 12/15\n",
            "848/848 [==============================] - 2s 2ms/step - loss: 0.3364 - accuracy: 0.8871 - val_loss: 0.3645 - val_accuracy: 0.8762\n",
            "Epoch 13/15\n",
            "848/848 [==============================] - 2s 2ms/step - loss: 0.3363 - accuracy: 0.8849 - val_loss: 0.3594 - val_accuracy: 0.8784\n",
            "Epoch 14/15\n",
            "848/848 [==============================] - 2s 3ms/step - loss: 0.3252 - accuracy: 0.8879 - val_loss: 0.3598 - val_accuracy: 0.8778\n",
            "Epoch 15/15\n",
            "848/848 [==============================] - 2s 2ms/step - loss: 0.3229 - accuracy: 0.8897 - val_loss: 0.3605 - val_accuracy: 0.8773\n",
            "CPU times: user 34.8 s, sys: 4.75 s, total: 39.6 s\n",
            "Wall time: 31.4 s\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "g7Oo9t8W4d8w",
        "outputId": "5656b71f-b47f-4a28-ca41-1b4e6317813e"
      },
      "source": [
        "loss, accuracy = model.evaluate(x_ts, y_ts, verbose=False)\n",
        "print(\"Testing Accuracy:  {:.6f}\".format(accuracy))\n",
        "loss, accuracy = model.evaluate(x_tr, y_tr, verbose=False)\n",
        "print(\"Training Accuracy: {:.6f}\".format(accuracy))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Testing Accuracy:  0.793012\n",
            "Training Accuracy: 0.798467\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wHGyVde-zNCw",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e31639ee-8589-40b6-eda0-5c1b0835be36"
      },
      "source": [
        "from sklearn import metrics\n",
        "\n",
        "y_pred = model.predict(x_ts)\n",
        "predicted_lr = np.argmax(y_pred, axis = 1)\n",
        "\n",
        "print(metrics.classification_report(y_test, predicted_lr, target_names = main_tags_list, digits = 6))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "               precision    recall  f1-score   support\n",
            "\n",
            "      economy   0.774006  0.823529  0.798000       969\n",
            "entertainment   0.764652  0.861713  0.810286       969\n",
            "   traditions   0.842681  0.635707  0.724706       969\n",
            "      science   0.791474  0.881321  0.833984       969\n",
            "      society   0.716592  0.824561  0.766795       969\n",
            "       sports   0.932782  0.930857  0.931818       969\n",
            "   technology   0.748698  0.593395  0.662061       969\n",
            "\n",
            "     accuracy                       0.793012      6783\n",
            "    macro avg   0.795841  0.793012  0.789664      6783\n",
            " weighted avg   0.795841  0.793012  0.789664      6783\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 337
        },
        "id": "_i84l6Vz4ePW",
        "outputId": "5a51b521-bb3a-49cf-b32f-26187189b40a"
      },
      "source": [
        "plot_history(history)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x360 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LPX-JO5umeCc"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0nUXjoWv_ihh"
      },
      "source": [
        "# Подготовка данных Bert"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 84,
          "referenced_widgets": [
            "14a4ae79eda34190aa085ed3ff50da1f",
            "2f7e5be00a0a466e95e4fdb39e6fc92e",
            "fcb043e9f37144acbccda47b61e0aa93",
            "3533fdc55b554e2aa6dc746fa3eca224",
            "5228753d9e6a4242afc5de2330f79702",
            "edabb6d6efba4ea18d8053c2cfd52917",
            "61c2f9563bc34ecea3abd1e8364ba2c1",
            "b2b0121b0fd4405295cdeb2b13cc018e"
          ]
        },
        "id": "-tK-RRZYXQRs",
        "outputId": "be517384-0c6d-4006-ec54-db6dd2c7be92"
      },
      "source": [
        "import gc\n",
        "df = pd.read_pickle(md+'data/news_main_prepr_2020')\n",
        "data_lists = []\n",
        "\n",
        "# for i,t,l in tqdm(zip(df.index, df['lemm_text'], df['label'])):\n",
        "#   data_lists.append({'id' : , 'text' : t, 'label' : l})\n",
        "for i,t,l in tqdm(zip(df.index, df['lemm_text'], df['label'])):\n",
        "  data_lists.append({'id' : i, 'text' : t, 'label' : l})\n",
        "\n",
        "# print(getsizeof(df))\n",
        "del df\n",
        "gc.collect()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "14a4ae79eda34190aa085ed3ff50da1f",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=1.0, bar_style='info', max=1.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "11"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "G7oVX4Z7boUr",
        "outputId": "f139059c-819c-44ac-a30c-fc0978267088"
      },
      "source": [
        "data_train, data_train_idx, data_test, data_test_idx = data_tr_ts_shaffle(data_lists, 0.2) \n",
        "\n",
        "more_less = False # True -> Up; False -> Down\n",
        "data_train, data_train_idx = balancing_classes_data(data_train, more_less, 1)\n",
        "data_test, data_test_idx = balancing_classes_data(data_test, more_less, 1)\n",
        "print(len(data_train), len(data_test))\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "3876.0\n",
            "969.0\n",
            "27132 6783\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1ZbP6uHLXR_k"
      },
      "source": [
        "def sent_labeling(data):\n",
        "  res = []\n",
        "  for d in tqdm(data):\n",
        "    for t_i in d['text']:\n",
        "      text = ''\n",
        "      for tl_i in t_i:\n",
        "        text+=' '+tl_i\n",
        "      res.append({'label': d['label'], 'text': text})\n",
        "  return res"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 134,
          "referenced_widgets": [
            "dec086ddc1514d87ad0fd784786aaa65",
            "aea7ff2bcc18422f9f0c499f3d1a8cf3",
            "ce0fd42a7bd34557b9e23c7014db6a73",
            "3ca8591d53f647d582ff5fab6234ffce",
            "2eb31e6b2bca41c5b57c7e3349f8f764",
            "25d11b1a2dd04d02a4b0b9e6ed8bb5bc",
            "2f03602320994cf7b20808580eefdeb6",
            "198f20f5bc2c456db81a44ba6419f16a",
            "5694c9611e264f129b8646ffab97dafe",
            "d95262e7e30e4f46bf939dc30b279a42",
            "98dfe849cfe641f4b62c9d73b1b2d4f3",
            "ef7a112eabfd42d9b80db9aba3a357ea",
            "8b232e273cb243939feef215b1bb5575",
            "850f259017e442578f77159b4b50cc12",
            "6a414d44fba5465184ddc711f1a0627c",
            "3e2a81517be248f89459b821b7e0fe73"
          ]
        },
        "id": "y3FFEBrfcM_f",
        "outputId": "02d3b908-8488-4782-e7dc-c1e52d7682d1"
      },
      "source": [
        "data_train = sent_labeling(data_train)\n",
        "data_test = sent_labeling(data_test)\n",
        "len(data_train), len(data_test)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "dec086ddc1514d87ad0fd784786aaa65",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, max=27132.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "5694c9611e264f129b8646ffab97dafe",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, max=6783.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(379940, 96167)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xcXYStgqdzqM"
      },
      "source": [
        "X_train_text = [d['text'] for d in data_train]\n",
        "X_test_text = [d['text'] for d in data_test]\n",
        "\n",
        "y_train = [d['label'] for d in data_train]\n",
        "y_test = [d['label'] for d in data_test]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dteyYmR9exSk",
        "outputId": "b8b4738f-1581-446b-c739-6b7588f7b9b6"
      },
      "source": [
        "del data_train\n",
        "del data_test\n",
        "del data_lists\n",
        "del data_train_idx\n",
        "del data_test_idx\n",
        "gc.collect()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "544220"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xQG00ILyXh_o",
        "outputId": "41b2e51a-5c7f-4b36-87e2-c2cd739e0758"
      },
      "source": [
        "gc.collect()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "239"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nE2DbWgNBuhs"
      },
      "source": [
        "# Bert tests"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DjvdtIYrB_N8"
      },
      "source": [
        "# without tokenizer\n",
        "\n",
        "!pip install transformers\n",
        "\n",
        "from transformers import TFBertForSequenceClassification\n",
        "import tensorflow as tf\n",
        "\n",
        "num_classes = len(main_tags_list)\n",
        "bert_model = TFBertForSequenceClassification.from_pretrained('bert-base-multilingual-uncased',num_labels=num_classes)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4wxkcsyLPAat"
      },
      "source": [
        "# with tokenizer\n",
        "\n",
        "!pip install transformers\n",
        "\n",
        "from transformers import *\n",
        "from transformers import BertTokenizer, TFBertForSequenceClassification, BertConfig\n",
        "import tensorflow as tf\n",
        "\n",
        "num_classes = len(main_tags_list)\n",
        "bert_tokenizer = BertTokenizer.from_pretrained(\"bert-base-multilingual-uncased\", do_lower_case = True)\n",
        "bert_model = TFBertForSequenceClassification.from_pretrained('bert-base-multilingual-uncased',num_labels=num_classes)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VxXUP-_-Z8ri",
        "outputId": "724bedcb-52e9-4d39-971f-8912ef182b07"
      },
      "source": [
        "import tensorflow as tf\n",
        "\n",
        "device_name = tf.test.gpu_device_name()\n",
        "\n",
        "if device_name == '/device:GPU:0':\n",
        "    print('Found GPU at: {}'.format(device_name))\n",
        "else:\n",
        "    raise SystemError('GPU device not found')\n",
        "\n",
        "# Found GPU at: /device:GPU:0"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Found GPU at: /device:GPU:0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5VOzu_Ct5KVz"
      },
      "source": [
        "## tokenizer"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "B22AVfsiUaW5"
      },
      "source": [
        "def prepare_text_for_bert(data_text, max_len = 512):\n",
        "  max_len = min(max_len, 512)\n",
        "  input_ids=[]\n",
        "  attention_masks=[]\n",
        "  for dt in tqdm(data_text):\n",
        "      sent = dt\n",
        "      bert_inp=bert_tokenizer.encode_plus(sent, \\\n",
        "                                          add_special_tokens = True,\\\n",
        "                                          max_length =max_len,\\\n",
        "                                          pad_to_max_length = True,\n",
        "                                          return_attention_mask = True)\n",
        "      input_ids.append(bert_inp['input_ids'])\n",
        "      attention_masks.append(bert_inp['attention_mask'])\n",
        "\n",
        "  input_ids=np.asarray(input_ids)\n",
        "  attention_masks=np.asarray(attention_masks)\n",
        "  return input_ids, attention_masks\n",
        "\n",
        "max_len = 512"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 158,
          "referenced_widgets": [
            "583f18546d6b48b89c9f1399f0310d11",
            "01b12eaae610419393dd972b952da785",
            "b3ab2e1bf8884d41bcaa52fe45d44a69",
            "b6b7766aace04563a19150ba85b734ff",
            "f813fb1c65b544a48ff1a3b0b1c5f7e5",
            "cd49675a56884f11ad50df3b26253207",
            "285655ed2e5d47328e1b59c1f522f761",
            "1b10037e6b7142578cd111a7e365c5b7"
          ]
        },
        "id": "X7MrEIYFU8os",
        "outputId": "df2c9fa6-60d3-4a51-f09b-6aba0e97d1db"
      },
      "source": [
        "test_ids, test_mask = prepare_text_for_bert(X_test_text, max_len)\n",
        "del X_test_text\n",
        "len(test_ids[0]), len(test_mask[0]), len(test_mask)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "583f18546d6b48b89c9f1399f0310d11",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, max=6783.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Truncation was not explicitly activated but `max_length` is provided a specific value, please use `truncation=True` to explicitly truncate examples to max length. Defaulting to 'longest_first' truncation strategy. If you encode pairs of sequences (GLUE-style) with the tokenizer you can select this strategy more precisely by providing a specific strategy to `truncation`.\n",
            "/usr/local/lib/python3.7/dist-packages/transformers/tokenization_utils_base.py:2079: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n",
            "  FutureWarning,\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(512, 512, 6783)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 140,
          "referenced_widgets": [
            "5f914dc2b5fb4f9b94a48396b903bb2d",
            "7068b6c73c0d4bf1bcd43c96d84f416d",
            "eaea1d63797148b783451522e0ae8e48",
            "0dbf5d4efc1841b1aef8165209fc104f",
            "38690ca1157a462dba84ecc3bdc56c5d",
            "f78352ae13c94431a8a83119fffc0227",
            "a06ede82fd44405f9cd2aded375f06bb",
            "adf0b9486bbf4d81a584961976d58a4a"
          ]
        },
        "id": "5i-OWGCpUv0s",
        "outputId": "16ed1520-a4c0-4869-811a-05bbc647cc62"
      },
      "source": [
        "train_ids, train_mask = prepare_text_for_bert(X_train_text,max_len)\n",
        "del X_train_text\n",
        "len(train_ids[0]), len(train_mask[0]), len(train_mask)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "5f914dc2b5fb4f9b94a48396b903bb2d",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, max=27132.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/transformers/tokenization_utils_base.py:2079: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n",
            "  FutureWarning,\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(512, 512, 27132)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "teCMCZUbfS4Y",
        "outputId": "9b3d260d-01c2-4271-8c27-8bcc3d3987bc"
      },
      "source": [
        "gc.collect()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "W9YZnVGgmaFI"
      },
      "source": [
        "#_without_prepr\n",
        "pickle.dump(test_ids, open(md+\"data/bert_tokens/test_ids_down_1_joint_512_without_prepr.pickle\", \"wb\"))\n",
        "pickle.dump(test_mask, open(md+\"data/bert_tokens/test_mask_down_1_joint_512_without_prepr.pickle\", \"wb\"))\n",
        "pickle.dump(train_ids, open(md+\"data/bert_tokens/train_ids_down_1_joint_512_without_prepr.pickle\", \"wb\"))\n",
        "pickle.dump(train_mask, open(md+\"data/bert_tokens/train_mask_down_1_joint_512_without_prepr.pickle\", \"wb\"))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vKtY7DSjmaFI"
      },
      "source": [
        "pickle.dump(y_test, open(md+\"data/bert_tokens/y_test_down_1_joint_512_without_prepr.pickle\", \"wb\"))\n",
        "pickle.dump(y_train, open(md+\"data/bert_tokens/y_train_down_1_joint_512_without_prepr.pickle\", \"wb\"))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OQ59oFJ1mKhv"
      },
      "source": [
        "## train_prepare"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mUlo66yP7hlV"
      },
      "source": [
        "def map_example_to_dict(input_ids, attention_masks, token_type_ids, label):\n",
        "  return {\n",
        "      \"input_ids\": input_ids,\n",
        "      \"token_type_ids\": token_type_ids,\n",
        "      \"attention_mask\": attention_masks,\n",
        "  }, label"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bmeGgg187hoT"
      },
      "source": [
        "def tensor_encoder(input_ids_list, attention_mask_list, label_list, top_len = -1):\n",
        "  token_type_ids_list = [[0]*len(input_ids_list[0])]*len(input_ids_list)\n",
        "  if top_len==-1:\n",
        "    return tf.data.Dataset.from_tensor_slices((input_ids_list, attention_mask_list, token_type_ids_list, label_list)).map(map_example_to_dict)\n",
        "  else:\n",
        "    return tf.data.Dataset.from_tensor_slices((input_ids_list[:top_len], attention_mask_list[:top_len], token_type_ids_list[:top_len], label_list[:top_len])).map(map_example_to_dict)\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "b270jl82mlfF"
      },
      "source": [
        "#_without_prepr\n",
        "test_ids = pickle.load(open(md+\"data/bert_tokens/test_ids_down_1_joint_512_without_prepr.pickle\", \"rb\"))\n",
        "test_mask = pickle.load(open(md+\"data/bert_tokens/test_mask_down_1_joint_512_without_prepr.pickle\", \"rb\"))\n",
        "train_ids = pickle.load(open(md+\"data/bert_tokens/train_ids_down_1_joint_512_without_prepr.pickle\", \"rb\"))\n",
        "train_mask = pickle.load(open(md+\"data/bert_tokens/train_mask_down_1_joint_512_without_prepr.pickle\", \"rb\"))"
      ],
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lj02nF84mlfG"
      },
      "source": [
        "y_test = pickle.load(open(md+\"data/bert_tokens/y_test_down_1_joint_512_without_prepr.pickle\", \"rb\"))\n",
        "y_train = pickle.load(open(md+\"data/bert_tokens/y_train_down_1_joint_512_without_prepr.pickle\", \"rb\"))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9mlilV8JI5BG",
        "outputId": "cd17e0f5-b835-4816-ec2f-6deef7413abe"
      },
      "source": [
        "len(y_train)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "27132"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CrstQYJcTDwk",
        "outputId": "7587b2fa-8a2c-406c-c4ab-afc8be5f9a43"
      },
      "source": [
        "counter = 0\n",
        "\n",
        "for tm in train_mask:\n",
        "  if tm[-1]==0:\n",
        "    counter+=1\n",
        "\n",
        "counter/len(train_mask)"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8008993070912576"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xJSpN6248cIe"
      },
      "source": [
        "# use for SparseCategoricalCrossentropy\n",
        "\n",
        "y_test = [[l] for l in y_test]\n",
        "y_train = [[l] for l in y_train]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PtVB-TvVqWZQ",
        "outputId": "814799d4-4009-4145-f220-cd5a317d4897"
      },
      "source": [
        "# use for CategoricalCrossentropy\n",
        "\n",
        "from keras.utils import np_utils\n",
        "from sklearn.metrics import roc_auc_score\n",
        "from sklearn.preprocessing import LabelEncoder\n",
        "\n",
        "encoder = LabelEncoder()\n",
        "encoder.fit(y_train)\n",
        "encoded_Y = encoder.transform(y_train)\n",
        "y_tr = np_utils.to_categorical(encoded_Y)\n",
        "encoded_Y_ts = encoder.fit_transform(y_test)\n",
        "y_ts = np_utils.to_categorical(encoded_Y_ts)\n",
        "y_tr.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(27132, 7)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HVbSOBB1GYAR"
      },
      "source": [
        "y_test = y_ts\n",
        "y_train = y_tr"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AmS_zZZO875f",
        "outputId": "9ae26b64-aa57-4b84-fcdc-d082597cbebd"
      },
      "source": [
        "y_train[0], y_tr[0]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(array([0., 1., 0., 0., 0., 0., 0.], dtype=float32),\n",
              " array([0., 1., 0., 0., 0., 0., 0.], dtype=float32))"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cb8mb5RDLERk"
      },
      "source": [
        "## train\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uCJBhMDgC3gd"
      },
      "source": [
        "# SparseCategoricalCrossentropy\n",
        "model = bert_model\n",
        "\n",
        "loss = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True) \n",
        "# metric = tf.keras.metrics.SparseCategoricalCrossentropy('accuracy')# categorical_crossentropy accuracy\n",
        "metric = tf.keras.metrics.SparseCategoricalAccuracy('accuracy')\n",
        "optimizer = tf.keras.optimizers.Adam(learning_rate=2e-5,epsilon=1e-08)\n",
        "\n",
        "model.compile(loss=loss,optimizer=optimizer,metrics=[metric])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JV5tv6yjo8Lm"
      },
      "source": [
        "# CategoricalCrossentropy\n",
        "model = bert_model\n",
        "\n",
        "loss = tf.keras.losses.CategoricalCrossentropy(from_logits=True) \n",
        "# metric = tf.keras.metrics.SparseCategoricalCrossentropy('accuracy')# categorical_crossentropy accuracy\n",
        "metric = tf.keras.metrics.CategoricalAccuracy('accuracy')\n",
        "optimizer = tf.keras.optimizers.Adam(learning_rate=2e-5,epsilon=1e-08)\n",
        "\n",
        "model.compile(loss=loss,optimizer=optimizer,metrics=[metric])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ULPUVKwqC3ge"
      },
      "source": [
        "from keras.backend import clear_session\n",
        "clear_session()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UTrEgwZuIWdA",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6ecf3cf9-1cbe-4e84-d891-4b517d1ef62b"
      },
      "source": [
        "batch_size = 8\n",
        "top_numder = -1\n",
        "top_test = top_numder\n",
        "top_train = top_numder\n",
        "ds_test_encoded = tensor_encoder(test_ids, test_mask, y_test, top_test).batch(batch_size)\n",
        "ds_train_encoded = tensor_encoder(train_ids, train_mask, y_train, top_train).batch(batch_size)\n",
        "\n",
        "history=model.fit(\n",
        "    ds_train_encoded,\n",
        "    epochs=5,\n",
        "    validation_data=ds_test_encoded)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/5\n",
            "3392/3392 [==============================] - 3671s 1s/step - loss: 0.3727 - accuracy: 0.8834 - val_loss: 0.2569 - val_accuracy: 0.9163\n",
            "Epoch 2/5\n",
            "3392/3392 [==============================] - 3672s 1s/step - loss: 0.1981 - accuracy: 0.9365 - val_loss: 0.2951 - val_accuracy: 0.9170\n",
            "Epoch 3/5\n",
            "3392/3392 [==============================] - 3672s 1s/step - loss: 0.1364 - accuracy: 0.9564 - val_loss: 0.3137 - val_accuracy: 0.9204\n",
            "Epoch 4/5\n",
            "3392/3392 [==============================] - 3669s 1s/step - loss: 0.0989 - accuracy: 0.9693 - val_loss: 0.2936 - val_accuracy: 0.9254\n",
            "Epoch 5/5\n",
            "3392/3392 [==============================] - 3671s 1s/step - loss: 0.0803 - accuracy: 0.9751 - val_loss: 0.3114 - val_accuracy: 0.9264\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AqApEOPTwWwI",
        "outputId": "301603bf-d1ad-436b-fe5f-3121f4b54cd3"
      },
      "source": [
        "from sklearn import metrics\n",
        "\n",
        "y_pred = model.predict(ds_test_encoded)\n",
        "predicted_lr = np.argmax(y_pred[0], axis = 1)\n",
        "\n",
        "print(metrics.classification_report(y_test, predicted_lr, target_names = main_tags_list, digits = 6))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "               precision    recall  f1-score   support\n",
            "\n",
            "      economy   0.966396  0.860681  0.910480       969\n",
            "entertainment   0.966704  0.898865  0.931551       969\n",
            "   traditions   0.853480  0.961816  0.904415       969\n",
            "      science   0.904215  0.974200  0.937904       969\n",
            "      society   0.937500  0.882353  0.909091       969\n",
            "       sports   0.972700  0.992776  0.982635       969\n",
            "   technology   0.902240  0.914345  0.908252       969\n",
            "\n",
            "     accuracy                       0.926434      6783\n",
            "    macro avg   0.929033  0.926434  0.926333      6783\n",
            " weighted avg   0.929033  0.926434  0.926333      6783\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RKhhtoG1whGM",
        "outputId": "5bea88fc-a9ad-4b4e-c665-29f3a2497522"
      },
      "source": [
        "model.save(md+\"models/bert_classifier\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:The parameters `output_attentions`, `output_hidden_states` and `use_cache` cannot be updated when calling a model.They have to be set to True/False in the config object (i.e.: `config=XConfig.from_pretrained('name', output_attentions=True)`).\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:The parameters `output_attentions`, `output_hidden_states` and `use_cache` cannot be updated when calling a model.They have to be set to True/False in the config object (i.e.: `config=XConfig.from_pretrained('name', output_attentions=True)`).\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:The parameter `return_dict` cannot be set in graph mode and will always be set to `True`.\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:The parameter `return_dict` cannot be set in graph mode and will always be set to `True`.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:The parameters `output_attentions`, `output_hidden_states` and `use_cache` cannot be updated when calling a model.They have to be set to True/False in the config object (i.e.: `config=XConfig.from_pretrained('name', output_attentions=True)`).\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:The parameters `output_attentions`, `output_hidden_states` and `use_cache` cannot be updated when calling a model.They have to be set to True/False in the config object (i.e.: `config=XConfig.from_pretrained('name', output_attentions=True)`).\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:The parameter `return_dict` cannot be set in graph mode and will always be set to `True`.\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:The parameter `return_dict` cannot be set in graph mode and will always be set to `True`.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:The parameters `output_attentions`, `output_hidden_states` and `use_cache` cannot be updated when calling a model.They have to be set to True/False in the config object (i.e.: `config=XConfig.from_pretrained('name', output_attentions=True)`).\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:The parameters `output_attentions`, `output_hidden_states` and `use_cache` cannot be updated when calling a model.They have to be set to True/False in the config object (i.e.: `config=XConfig.from_pretrained('name', output_attentions=True)`).\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:The parameter `return_dict` cannot be set in graph mode and will always be set to `True`.\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:The parameter `return_dict` cannot be set in graph mode and will always be set to `True`.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:The parameters `output_attentions`, `output_hidden_states` and `use_cache` cannot be updated when calling a model.They have to be set to True/False in the config object (i.e.: `config=XConfig.from_pretrained('name', output_attentions=True)`).\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:The parameters `output_attentions`, `output_hidden_states` and `use_cache` cannot be updated when calling a model.They have to be set to True/False in the config object (i.e.: `config=XConfig.from_pretrained('name', output_attentions=True)`).\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:The parameter `return_dict` cannot be set in graph mode and will always be set to `True`.\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:The parameter `return_dict` cannot be set in graph mode and will always be set to `True`.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:The parameters `output_attentions`, `output_hidden_states` and `use_cache` cannot be updated when calling a model.They have to be set to True/False in the config object (i.e.: `config=XConfig.from_pretrained('name', output_attentions=True)`).\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:The parameters `output_attentions`, `output_hidden_states` and `use_cache` cannot be updated when calling a model.They have to be set to True/False in the config object (i.e.: `config=XConfig.from_pretrained('name', output_attentions=True)`).\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:The parameter `return_dict` cannot be set in graph mode and will always be set to `True`.\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:The parameter `return_dict` cannot be set in graph mode and will always be set to `True`.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:The parameters `output_attentions`, `output_hidden_states` and `use_cache` cannot be updated when calling a model.They have to be set to True/False in the config object (i.e.: `config=XConfig.from_pretrained('name', output_attentions=True)`).\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:The parameters `output_attentions`, `output_hidden_states` and `use_cache` cannot be updated when calling a model.They have to be set to True/False in the config object (i.e.: `config=XConfig.from_pretrained('name', output_attentions=True)`).\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:The parameter `return_dict` cannot be set in graph mode and will always be set to `True`.\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:The parameter `return_dict` cannot be set in graph mode and will always be set to `True`.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:The parameters `output_attentions`, `output_hidden_states` and `use_cache` cannot be updated when calling a model.They have to be set to True/False in the config object (i.e.: `config=XConfig.from_pretrained('name', output_attentions=True)`).\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:The parameters `output_attentions`, `output_hidden_states` and `use_cache` cannot be updated when calling a model.They have to be set to True/False in the config object (i.e.: `config=XConfig.from_pretrained('name', output_attentions=True)`).\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:The parameter `return_dict` cannot be set in graph mode and will always be set to `True`.\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:The parameter `return_dict` cannot be set in graph mode and will always be set to `True`.\n",
            "WARNING:absl:Found untraced functions such as embeddings_layer_call_fn, embeddings_layer_call_and_return_conditional_losses, encoder_layer_call_fn, encoder_layer_call_and_return_conditional_losses, pooler_layer_call_fn while saving (showing 5 of 1055). These functions will not be directly callable after loading.\n",
            "WARNING:absl:Found untraced functions such as embeddings_layer_call_fn, embeddings_layer_call_and_return_conditional_losses, encoder_layer_call_fn, encoder_layer_call_and_return_conditional_losses, pooler_layer_call_fn while saving (showing 5 of 1055). These functions will not be directly callable after loading.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:tensorflow:Assets written to: /content/drive/My Drive/Colab Notebooks/Diplom/models/bert_classifier/assets\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:tensorflow:Assets written to: /content/drive/My Drive/Colab Notebooks/Diplom/models/bert_classifier/assets\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LIwDY9_7T1xE"
      },
      "source": [
        "# GPT\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NBx7s5TyT2QN"
      },
      "source": [
        "!pip install transformers"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HGdE5jjflKAc"
      },
      "source": [
        "from transformers import (\n",
        "    GPT2Config,\n",
        "    GPT2Tokenizer, \n",
        "    TFGPT2ForSequenceClassification, \n",
        "    GPT2ForSequenceClassification)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MJhPMFmZuUfe",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 325,
          "referenced_widgets": [
            "58258acb2254493590b44deb662f516a",
            "dca2e54632dc44bc84acc5d937c6305a",
            "35b47dbea3f94b4c85a403feb381e70e",
            "6eaaaa70de9d4ecbab6b9556a79cb468",
            "74766b22888a4a0985b0a2dfdca83d4d",
            "6b5f9b63762e47659839cdbdf6f8fe3c",
            "4b7cfeeb08dd4cd48eb4a7dae826b200",
            "d89beb4399cf40f0ad58c67ea38fb038",
            "1a5618fe9b324e6d8e595baae92e12ef",
            "5e2db0facf4848bfb96baed370dab2cb",
            "3c0973748b054574b22e1c9ab8b4eb7c",
            "3ee78c805d76440fb53361205c6590ec",
            "1a06af7f3815462581d99b972611a3ac",
            "d49989f729fc47cdbc04abf0b9e73b24",
            "ec6a5003297445889b41c914ed1bd9a5",
            "32e6b609761d4a929305f893782531e9",
            "61dffac98b544c95b125c98047ffc451",
            "fd042fcd991346b598db25f9a1864a0b",
            "da0399d08f834c4cb4b11907cb9b1040",
            "d92245d3fc5c49f8afa0466c77fc600a",
            "e744495adea34c16ae758f8c756d2d4b",
            "e11f8f5cbeb049f5bfc52c14f46ac2d4",
            "908c8c3647104e5abdedf9f1b18b1d47",
            "1dcee69e70f24964a91899894cf4a73a",
            "37607772868a4ce78dcb4d57aa0524af",
            "2ff62eadebbf4a5fa0db70b3a156b8ae",
            "1f8f86a418094219ab6d1d61f4a060f7",
            "e9dc19e67de642bf917f2507b81403bc",
            "7b83e741acbc49e7b9b2fced6ab4578b",
            "52de75ca97fd4acfbbde799dbc3e68fa",
            "6c936f529572424bbc42a3f9be1b4f24",
            "25d655b537934784adc9d0160b62a652"
          ]
        },
        "outputId": "7d61accd-6a5d-46b8-c269-5b50d28a04a6"
      },
      "source": [
        "model_path = 'sberbank-ai/rugpt3medium_based_on_gpt2'\n",
        "\n",
        "model_config = GPT2Config.from_pretrained(pretrained_model_name_or_path=model_path, num_labels=7)\n",
        "tokenizer = GPT2Tokenizer.from_pretrained(model_path)\n",
        "gpt_model = GPT2ForSequenceClassification.from_pretrained(model_path, config=model_config)\n",
        "model = gpt_model"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "58258acb2254493590b44deb662f516a",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=674.0, style=ProgressStyle(description_…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "1a5618fe9b324e6d8e595baae92e12ef",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=1612610.0, style=ProgressStyle(descript…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "61dffac98b544c95b125c98047ffc451",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=1270963.0, style=ProgressStyle(descript…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "37607772868a4ce78dcb4d57aa0524af",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=1730074771.0, style=ProgressStyle(descr…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Some weights of the model checkpoint at sberbank-ai/rugpt3medium_based_on_gpt2 were not used when initializing GPT2ForSequenceClassification: ['lm_head.weight']\n",
            "- This IS expected if you are initializing GPT2ForSequenceClassification 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 GPT2ForSequenceClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
            "Some weights of GPT2ForSequenceClassification were not initialized from the model checkpoint at sberbank-ai/rugpt3medium_based_on_gpt2 and are newly initialized: ['score.weight']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "i8EV2q3Cca4H"
      },
      "source": [
        "## classes creator"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nk3gfcJrekZz",
        "outputId": "5fe3ed79-d5cd-4049-cee8-1d247d2ebf2c"
      },
      "source": [
        "!pip install -q git+https://github.com/gmihaila/ml_things.git"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "  Building wheel for ml-things (setup.py) ... \u001b[?25l\u001b[?25hdone\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_ZIlTy7IhPzM"
      },
      "source": [
        "import io\n",
        "import os\n",
        "os.environ['CUDA_LAUNCH_BLOCKING'] = \"1\""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hdH_Zop7Yvz7"
      },
      "source": [
        "import torch\n",
        "from tqdm.notebook import tqdm\n",
        "from torch.utils.data import Dataset, DataLoader\n",
        "from ml_things import plot_dict, plot_confusion_matrix, fix_text\n",
        "from sklearn.metrics import classification_report, accuracy_score\n",
        "from transformers import (set_seed,\n",
        "                          TrainingArguments,\n",
        "                          Trainer,\n",
        "                          GPT2Config,\n",
        "                          GPT2Tokenizer,\n",
        "                          AdamW, \n",
        "                          get_linear_schedule_with_warmup,\n",
        "                          GPT2ForSequenceClassification)\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Uq-SlqdCYv2X",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "735faf2e-b599-4481-b212-6f5fdc0840d4"
      },
      "source": [
        "set_seed(123)\n",
        "epochs = 4\n",
        "batch_size = 8\n",
        "max_length = 128\n",
        "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
        "model_name_or_path = 'gpt2'\n",
        "labels_ids = dict(zip(list(main_tags_dict.keys()), list(main_tags_dict.keys())))\n",
        "n_labels = len(labels_ids)\n",
        "n_labels = len(main_tags_list)\n",
        "n_labels"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "7"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BCda7SfCctar",
        "outputId": "807c54ac-eea4-4ac0-be01-b71b3d44ddb6"
      },
      "source": [
        "tokenizer.padding_side = \"left\"\n",
        "tokenizer.add_special_tokens({'pad_token': '[PAD]'})\n",
        "\n",
        "\n",
        "model.resize_token_embeddings(len(tokenizer))\n",
        "model.config.pad_token_id = model.config.eos_token_id\n",
        "model.to(device)\n",
        "print('Model loaded to `%s`'%device)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model loaded to `cuda`\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DFZmQAuDT2jJ"
      },
      "source": [
        "class MovieReviewsDataset(Dataset):\n",
        "  \n",
        "\n",
        "  def __init__(self, data_text, data_label, use_tokenizer):\n",
        "\n",
        "    self.texts = data_text\n",
        "    self.labels = data_label\n",
        "    self.n_examples = len(self.labels)\n",
        "    return\n",
        "\n",
        "  def __len__(self):\n",
        "    return self.n_examples\n",
        "\n",
        "  def __getitem__(self, item):\n",
        "    return {'text':self.texts[item],\n",
        "            'label':self.labels[item]}\n",
        "\n",
        "\n",
        "\n",
        "class Gpt2ClassificationCollator(object):\n",
        "    \n",
        "\n",
        "    def __init__(self, use_tokenizer, labels_encoder, max_sequence_len=None):\n",
        "\n",
        "        self.use_tokenizer = use_tokenizer\n",
        "        self.max_sequence_len = use_tokenizer.model_max_length if max_sequence_len is None else max_sequence_len\n",
        "        self.labels_encoder = labels_encoder\n",
        "\n",
        "        return\n",
        "\n",
        "    def __call__(self, sequences):\n",
        "\n",
        "        texts = [sequence['text'] for sequence in sequences]\n",
        "        labels = [sequence['label'] for sequence in sequences]\n",
        "        labels = [self.labels_encoder[label] for label in labels]\n",
        "        inputs = self.use_tokenizer(text=texts, return_tensors=\"pt\", padding=True, truncation=True,  max_length=self.max_sequence_len)\n",
        "        inputs.update({'labels':torch.tensor(labels)})\n",
        "\n",
        "        return inputs\n",
        "\n",
        "\n",
        "def train(dataloader, optimizer_, scheduler_, device_):\n",
        "  \n",
        "  global model\n",
        "\n",
        "  predictions_labels = []\n",
        "  true_labels = []\n",
        "  total_loss = 0\n",
        "  model.train()\n",
        "\n",
        "  for batch in tqdm(dataloader, total=len(dataloader)):\n",
        "    true_labels += batch['labels'].numpy().flatten().tolist()\n",
        "    batch = {k:v.type(torch.long).to(device_) for k,v in batch.items()}\n",
        "    model.zero_grad()\n",
        "    outputs = model(**batch)\n",
        "    loss, logits = outputs[:2]\n",
        "    total_loss += loss.item()\n",
        "    loss.backward()\n",
        "    torch.nn.utils.clip_grad_norm_(model.parameters(), 1.0)\n",
        "    optimizer.step()\n",
        "    scheduler.step()\n",
        "    logits = logits.detach().cpu().numpy()\n",
        "    predictions_labels += logits.argmax(axis=-1).flatten().tolist()\n",
        "\n",
        "  avg_epoch_loss = total_loss / len(dataloader)\n",
        "  \n",
        "  return true_labels, predictions_labels, avg_epoch_loss\n",
        "\n",
        "\n",
        "\n",
        "def validation(dataloader, device_):\n",
        "  global model\n",
        "  predictions_labels = []\n",
        "  true_labels = []\n",
        "  total_loss = 0\n",
        "  model.eval()\n",
        "\n",
        "  for batch in tqdm(dataloader, total=len(dataloader)):\n",
        "    true_labels += batch['labels'].numpy().flatten().tolist()\n",
        "    batch = {k:v.type(torch.long).to(device_) for k,v in batch.items()}\n",
        "    with torch.no_grad():        \n",
        "        outputs = model(**batch)\n",
        "        loss, logits = outputs[:2]\n",
        "        logits = logits.detach().cpu().numpy()\n",
        "        total_loss += loss.item()\n",
        "        predict_content = logits.argmax(axis=-1).flatten().tolist()\n",
        "        predictions_labels += predict_content\n",
        "\n",
        "  avg_epoch_loss = total_loss / len(dataloader)\n",
        "\n",
        "  return true_labels, predictions_labels, avg_epoch_loss"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sOnnD8qVdAKh"
      },
      "source": [
        "gpt2_classificaiton_collator = Gpt2ClassificationCollator(use_tokenizer=tokenizer, \n",
        "                                                          labels_encoder=labels_ids, \n",
        "                                                          max_sequence_len=max_length)\n",
        "\n",
        "train_dataset = MovieReviewsDataset(\n",
        "    data_text=X_train_text,\n",
        "    data_label=y_train, \n",
        "    use_tokenizer=tokenizer)\n",
        "\n",
        "train_dataloader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True, collate_fn=gpt2_classificaiton_collator)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CFl_vZB1NCzd"
      },
      "source": [
        "valid_dataset =  MovieReviewsDataset(\n",
        "    data_text=X_test_text,\n",
        "    data_label=y_test, \n",
        "    use_tokenizer=tokenizer)\n",
        "\n",
        "valid_dataloader = DataLoader(valid_dataset, batch_size=batch_size, shuffle=False, collate_fn=gpt2_classificaiton_collator)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "k7DuqwA-mtVY"
      },
      "source": [
        "## train"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4gIu1AN-NaR2"
      },
      "source": [
        "optimizer = AdamW(model.parameters(),\n",
        "                  lr = 2e-5, \n",
        "                  eps = 1e-8 \n",
        "                  )\n",
        "\n",
        "total_steps = len(train_dataloader) * epochs\n",
        "\n",
        "scheduler = get_linear_schedule_with_warmup(optimizer, \n",
        "                                            num_warmup_steps = 0,\n",
        "                                            num_training_steps = total_steps)\n",
        "\n",
        "all_loss = {'train_loss':[], 'val_loss':[]}\n",
        "all_acc = {'train_acc':[], 'val_acc':[]}\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 838,
          "referenced_widgets": [
            "7c2393037d0641cf83323c7700491ffa",
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            "9a8ce906a00d4f6db0112931d70b9a69",
            "0634a0eb3b094dbfa03ca84bcf04c7fc",
            "a09119f42acf4910a01bd633a55efe5c",
            "428a0c3008624cbda496ef27d67a39f9",
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            "5c6c2c01ca734556bc53ad01a64ef973",
            "1e5223225d4d4fda8425fc3cb617935f",
            "cd698a123b57497da2652fc47e4efd1f",
            "cbc65dd7c3ef42418b0a1842a294c272",
            "b133d240ffb14d29805d7ba1e415c4d3",
            "544c13cc9a674b80a3f049fbb07aaf17",
            "6be954f525c24531a13856a6a7198722",
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            "40a159ab6d9e474db883b895f018eb8f",
            "46819199c9314f07a35f5e06698b131d",
            "e1f2691980f647f99411a65bc3971e97",
            "3323ed3a0e0e4224a398d2249ee23713",
            "bd56f0f9fe7c4d53bb375553f56c2689",
            "6081f127fc8e47098ed641b64f4eb645",
            "079ac6073444424f8dc9eb8dc8964731",
            "0b00f5005d94440faefa2a9e31d16bc5",
            "995d25a181d04d9382d71ef23323de35",
            "1696f487910b4243a22b2889bfd5641a",
            "0df54387855547868ad3b2c4b57002f2",
            "34eef362fb4447cc9dd14c9373336f94",
            "ba8e02ce97c54995a395c16eedc6ee67",
            "107c42b8543546c0bf578e01591701ab",
            "bbc8ba651f194d89a6652a062c93eb26",
            "4617d7adbe3b45618d8590b2e20b73e2",
            "d5fb7883d8264e24a533175b6534cd46",
            "de7356c959b74aa99cf6a4e0cbdf3297",
            "a42886068aca4861911bd2e5fab34fff",
            "54baa06e7ff6466e9d1342197e3ca264",
            "40d56d18cd764a0bb972208c7d8a0c73",
            "f47b2ecd78b6471786b0017ae1294f3b",
            "68b84e9e5abe476bb387a2343b9a3371",
            "c4b1f7249e2a4c028ff68e6e7488e3e3",
            "593b110f3a264b7f9b55f0666f91ad5a",
            "49ec8dd4564c4e9ab5d7b198d6090599",
            "6653e47e5b7c448299f3050115a821f0",
            "04ebfe99327f44519830b3622ba84bd7",
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            "1e1dff051793471ca8c8a0d8b1060399",
            "23d8e44e46934e0e80eb448ba83ed10c",
            "bca36b9baa604fa386e2918e478d51d4",
            "06170098c2df4a92a27089ba4d201424",
            "131064e9e89e489da1dcf7cfc4ae029e",
            "419e3014a51445e38efaefbb4010fcdb",
            "14c1a5f4c48d409cb09e8d0071c11e1c",
            "2b63e88e22764d3982f745d14d84ca08",
            "bf64d8ce8e834d32b9de12e3a9ffbb64",
            "6f74f3782c9741e98134b5e08f1e440d",
            "d33c919d9f5f42d4845072b5a5c015b8",
            "678ec26d6df4409e9a093370205b3d86",
            "a6e6f334638c4693a08583d3f0bf8bbc",
            "ca1bff1e72da42c4af58c3a322e8639a",
            "aaf5300787d947cfa6365a7ea436affb",
            "ea246a013ba2424e9869d2df75fb4a13",
            "651b1c46048943dcb5c886680f09d28c",
            "b7925ad3ec484d2da984ac15a9c1efd4",
            "a56f2cc56d654e709063bdb2b0d15b7c",
            "3067be65aac947f8a17da4b768d5a373"
          ]
        },
        "id": "Gu7DUkN-Nlqn",
        "outputId": "12b69f87-173c-49c9-e59e-481430642e0b"
      },
      "source": [
        "print('Epoch')\n",
        "for epoch in tqdm(range(epochs)):\n",
        "  print()\n",
        "  print('Training on batches...')\n",
        "  # Perform one full pass over the training set.\n",
        "  train_labels, train_predict, train_loss = train(train_dataloader, optimizer, scheduler, device)\n",
        "  train_acc = accuracy_score(train_labels, train_predict)\n",
        "\n",
        "  # Get prediction form model on validation data. \n",
        "  print('Validation on batches...')\n",
        "  valid_labels, valid_predict, val_loss = validation(valid_dataloader, device)\n",
        "  val_acc = accuracy_score(valid_labels, valid_predict)\n",
        "\n",
        "  # Print loss and accuracy values to see how training evolves.\n",
        "  print(\"  train_loss: %.5f - val_loss: %.5f - train_acc: %.5f - valid_acc: %.5f\"%(train_loss, val_loss, train_acc, val_acc))\n",
        "  print()\n",
        "\n",
        "  # Store the loss value for plotting the learning curve.\n",
        "  all_loss['train_loss'].append(train_loss)\n",
        "  all_loss['val_loss'].append(val_loss)\n",
        "  all_acc['train_acc'].append(train_acc)\n",
        "  all_acc['val_acc'].append(val_acc)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "7c2393037d0641cf83323c7700491ffa",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, max=4.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Training on batches...\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "9a8ce906a00d4f6db0112931d70b9a69",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, max=3392.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Validation on batches...\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "cbc65dd7c3ef42418b0a1842a294c272",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, max=848.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "  train_loss: 0.44364 - val_loss: 0.34772 - train_acc: 0.86474 - valid_acc: 0.90373\n",
            "\n",
            "\n",
            "Training on batches...\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "3323ed3a0e0e4224a398d2249ee23713",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, max=3392.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Validation on batches...\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "34eef362fb4447cc9dd14c9373336f94",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, max=848.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "  train_loss: 0.23186 - val_loss: 0.34805 - train_acc: 0.93521 - valid_acc: 0.91670\n",
            "\n",
            "\n",
            "Training on batches...\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "54baa06e7ff6466e9d1342197e3ca264",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, max=3392.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Validation on batches...\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "04ebfe99327f44519830b3622ba84bd7",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, max=848.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "  train_loss: 0.11993 - val_loss: 0.42524 - train_acc: 0.96974 - valid_acc: 0.91877\n",
            "\n",
            "\n",
            "Training on batches...\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "131064e9e89e489da1dcf7cfc4ae029e",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, max=3392.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Validation on batches...\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "a6e6f334638c4693a08583d3f0bf8bbc",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, max=848.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "  train_loss: 0.04841 - val_loss: 0.49267 - train_acc: 0.98728 - valid_acc: 0.92378\n",
            "\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 315,
          "referenced_widgets": [
            "aff31a73f93a4278a95431d6acf85434",
            "ad4f89670b1343c78d48ec4651dbc08f",
            "78fb871e8462480d802ce72d7036c75a",
            "e2958da602394013ba10576e422fa8e3",
            "2655d9cd8a0c403f95cf0323b8c8394e",
            "afa36680799d4b86897b87ba3d26c408",
            "c177ea5c27f54c57852268a19b18a731",
            "10d1795d6b834c2bbd19fbfc3db8f101"
          ]
        },
        "id": "KYY86zF-pTUZ",
        "outputId": "24f7646e-749b-45fe-e7fd-346d9c4c858c"
      },
      "source": [
        "from sklearn import metrics\n",
        "\n",
        "valid_labels, valid_predict, val_loss = validation(valid_dataloader, device)\n",
        "predicted_lr = valid_predict\n",
        "\n",
        "print(metrics.classification_report(y_test, predicted_lr, target_names = main_tags_list, digits = 6))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "aff31a73f93a4278a95431d6acf85434",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, max=848.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "               precision    recall  f1-score   support\n",
            "\n",
            "      economy   0.934021  0.934985  0.934502       969\n",
            "entertainment   0.925654  0.912281  0.918919       969\n",
            "   traditions   0.920207  0.916409  0.918304       969\n",
            "      science   0.926188  0.945304  0.935649       969\n",
            "      society   0.905920  0.884417  0.895039       969\n",
            "       sports   0.974593  0.989680  0.982079       969\n",
            "   technology   0.878850  0.883385  0.881112       969\n",
            "\n",
            "     accuracy                       0.923780      6783\n",
            "    macro avg   0.923633  0.923780  0.923658      6783\n",
            " weighted avg   0.923633  0.923780  0.923658      6783\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8cSEMuo8pi-i"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 904
        },
        "id": "S4dBkVAONoCF",
        "outputId": "755fff09-c571-44c6-e556-d8537701ba9d"
      },
      "source": [
        "plot_dict(all_loss, use_xlabel='Epochs', use_ylabel='Value', use_linestyles=['-', '--'])\n",
        "\n",
        "plot_dict(all_acc, use_xlabel='Epochs', use_ylabel='Value', use_linestyles=['-', '--'])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ml_things/plot_functions.py:410: DeprecationWarning: `magnify` needs to have value in [0,1]! `1.2` will be converted to `0.1` as default.\n",
            "  DeprecationWarning)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1944x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ml_things/plot_functions.py:410: DeprecationWarning: `magnify` needs to have value in [0,1]! `1.2` will be converted to `0.1` as default.\n",
            "  DeprecationWarning)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1944x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CTuMSdOkNVFJ"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6lVHsJhMNVIC"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fj-o6uxCNVLt"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ugHg-RyiNVPf"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}