Ignatiev_BigARTM.ipynb

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    "colab": {
      "name": "Ignatiev_BigARTM.ipynb",
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      "name": "python3"
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DGYYCeQh0YFl"
      },
      "source": [
        "## Домашнее задание"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-Gg8h2Ek0YFl"
      },
      "source": [
        "Основаная задача - **построить хорошую тематическую модель с интерпретируемыми топиками с помощью BigARTM**.\n",
        "\n",
        "1) сделайте нормализацию (если pymorphy2 работает долго используйте mystem или попробуйте установить быструю версию - `pip install pymorphy2[fast]`, можно использовать какой-то другой токенизатор) \n",
        "\n",
        "2) добавьте нграммы (в тетрадке есть закомменченая ячейка с Phrases, можно также попробовать другие способы построить нграммы); \n",
        "\n",
        "3) сохраните тексты .vw формате;\n",
        "\n",
        "4) сделайте хороший словарь (отфильтруйте слишком частотные и редкие слова, попробуйте удалить стоп-слова, сохраните словарь и посмотрите на него, вдруг что-то плохое сразу будет заметно - из словаря можно просто вручную или правилом удалять строки, при загрузке ничего не сломается); \n",
        "\n",
        "5) постройте несколько ARTM моделей (переберите количество тем, поменяйте значения tau у регуляризаторов), если получаются плохие темы, поработайте дополнительно над предобработкой и словарем; \n",
        "\n",
        "6) для самой хорошей модели в отдельной ячейке напечатайте 3 хороших (на ваш вкус) темы\n",
        "\n",
        "7) в другой ячейке нарисуйте график обучения этой модели \n",
        "\n",
        "8) в третьей ячейки опишите какие параметры (количество тем, регуляризаторы, их tau) вы использовали и как обучали (например, после скольки проходов добавили регуляризатор разрежнивания тем (Phi), добавляли ли разреженность документам (Theta) и когда, как повышали значения, сколько итерации модель продожала улучшаться (снижалась перплексия, росли другие метрики);\n",
        "\n",
        "Сохраните тетрадку с экспериментами и положите её на гитхаб, ссылку на неё укажите в форме.\n",
        "\n",
        "**Оцениваться будут главным образом пункты 6, 7 и 8. (3, 1, 4 баллов соответственно). Чтобы заработать остальные 2 балла, нужно хотя бы немного изменить мой код на промежуточных этапах (добавить что-то, указать другие параметры и т.д). **"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nZE9rYop14he"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fAA_Q5jy14he"
      },
      "source": [
        "### Установка BigARTM\n",
        "Все точно как в инструкции на их сайте, только без установки питона и пипа."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "onayCA5_0bOP"
      },
      "source": [
        "!apt-get --yes install git make cmake build-essential libboost-all-dev"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RUiaLQ520oZH"
      },
      "source": [
        "!pip install protobuf tqdm wheel"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XUB9Dk0-05fi"
      },
      "source": [
        "!git clone --branch=stable https://github.com/bigartm/bigartm.git"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "C7ARp6n4081Q",
        "outputId": "4b9aae40-3ae3-4ed5-ae1b-a9c298dec059"
      },
      "source": [
        "cd bigartm"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/content/bigartm\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Gy3B--b40_X0"
      },
      "source": [
        "mkdir build"
      ],
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FNqpGY4_1C4n",
        "outputId": "43f805f8-5a77-4d38-f464-8664db9f792d"
      },
      "source": [
        "cd build"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[Errno 2] No such file or directory: 'build'\n",
            "/content/bigartm/build\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ol4bRDUD1EDN"
      },
      "source": [
        "!cmake .."
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AcxbRUrQ1IKk"
      },
      "source": [
        "!make"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lMaKS0DQ4zSE"
      },
      "source": [
        "!pip install python/bigartm*.whl -U"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "f2PVN5q014hg"
      },
      "source": [
        "Вернемся в изначальную папку."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rO_Tezz714hg",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6847a40e-d30e-4e6f-a9d7-82aab379f98e"
      },
      "source": [
        "cd ../.. "
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/content\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "P6Ay2Ayx3v3W"
      },
      "source": [
        "!pip install pymorphy2[fast] nltk gensim"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "syGkglti0YFf"
      },
      "source": [
        "import artm\n",
        "%matplotlib inline\n",
        "import glob\n",
        "import os, re\n",
        "import matplotlib.pyplot as plt\n",
        "from collections import defaultdict, Counter\n",
        "from nltk.corpus import stopwords\n",
        "import string\n",
        "import numpy as np\n",
        "from pymorphy2 import MorphAnalyzer\n",
        "import gensim, nltk\n",
        "morph = MorphAnalyzer()\n",
        "from nltk.tokenize import word_tokenize\n",
        "nltk.download('punkt')\n",
        "nltk.download('stopwords')\n",
        "stops = set(stopwords.words('russian'))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "a2N1FJT6TDDx"
      },
      "source": [
        "## Предобработка"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1hLw-SVu0YFm"
      },
      "source": [
        "def opt_normalize(texts, top=None):\n",
        "    uniq = Counter()\n",
        "    for text in texts:\n",
        "        uniq.update(text)\n",
        "    \n",
        "    norm_uniq = {word:morph.parse(word.replace(':', ''))[0].normal_form for word, _ in uniq.most_common(top)}\n",
        "    \n",
        "    norm_texts = []\n",
        "    for text in texts:\n",
        "        \n",
        "        norm_words = [norm_uniq.get(word) for word in text]\n",
        "        norm_words = [word for word in norm_words if word and word not in stops]\n",
        "        norm_texts.append(norm_words)\n",
        "        \n",
        "    return norm_texts\n",
        "\n",
        "def tokenize(text):\n",
        "    words = re.findall(r\"[а-яА-ЯёЁ]+-*[а-яА-ЯёЁ]*\", text)\n",
        "    return words"
      ],
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mp_zp-zR5PJq"
      },
      "source": [
        "!wget https://github.com/mannefedov/compling_nlp_hse_course/raw/master/data/wiki_data.txt.zip"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "COMPndhTAX1M",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a981a2db-1c8a-4547-b351-f995acb9a221"
      },
      "source": [
        "!unzip wiki_data.txt.zip"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Archive:  wiki_data.txt.zip\n",
            "  inflating: wiki_data.txt           \n",
            "  inflating: __MACOSX/._wiki_data.txt  \n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NDHkAaPb0YFp"
      },
      "source": [
        "texts = open('wiki_data.txt').read().splitlines()\n",
        "texts = opt_normalize([tokenize(text.lower()) for text in texts], 25000)"
      ],
      "execution_count": 31,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "B8BK8YGHS4zt"
      },
      "source": [
        "#### Порог PMI подобран экспериментально, составляет 0.4"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "o_yHUX490YFr"
      },
      "source": [
        "ph = gensim.models.Phrases(texts, scoring='npmi', threshold=0.4, \\\n",
        "                           common_terms=set(stopwords.words('russian')))\n",
        "p = gensim.models.phrases.Phraser(ph)\n",
        "ngrammed_texts = p[texts]"
      ],
      "execution_count": 49,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SZD1bmhR0YFw"
      },
      "source": [
        "f = open('wiki_texts.vw', 'w')\n",
        "\n",
        "for i, text in enumerate(ngrammed_texts):\n",
        "    c = Counter(text)\n",
        "    doc = 'doc_'+ str(i) + ' '\n",
        "    vw_text = ' '.join([x+':'+str(c[x]) for x in c])\n",
        "    \n",
        "    f.write(doc + vw_text  + '\\n')\n",
        "f.close()\n",
        "\n"
      ],
      "execution_count": 51,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xchX4sBs0YFz"
      },
      "source": [
        "def print_measures(model_artm):\n",
        "    print('Sparsity Phi: {0:.3f} ARTM)'.format(\n",
        "        model_artm.score_tracker['SparsityPhiScore'].last_value)\n",
        ")\n",
        "    print('Sparsity Theta: {0:.3f} (ARTM)'.format(\n",
        "        model_artm.score_tracker['SparsityThetaScore'].last_value))\n",
        "\n",
        "    print('Kernel contrast: {0:.3f} (ARTM)'.format(\n",
        "        model_artm.score_tracker['TopicKernelScore'].last_average_contrast))\n",
        "\n",
        "    print('Kernel purity: {0:.3f} (ARTM)'.format(\n",
        "        model_artm.score_tracker['TopicKernelScore'].last_average_purity))\n",
        "\n",
        "    print('Perplexity: {0:.3f} (ARTM)'.format(\n",
        "        model_artm.score_tracker['PerplexityScore'].last_value)\n",
        ")\n",
        "    plt.plot(range(model_artm.num_phi_updates), model_artm.score_tracker['PerplexityScore'].value, 'r--', linewidth=2)\n",
        "    plt.xlabel('Iterations count')\n",
        "    plt.ylabel(' ARTM perp. (red)')\n",
        "    plt.grid(True)\n",
        "    plt.show()\n",
        "    "
      ],
      "execution_count": 53,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NhIADC8O0YF2"
      },
      "source": [
        "batch_vectorizer = artm.BatchVectorizer(data_path='wiki_texts.vw',  # название файла\n",
        "                                        data_format='vowpal_wabbit', # формат файла, у нас vw\n",
        "                                        target_folder='batches', # название папки в которую положаться батчи\n",
        "                                       batch_size=1000) # размер батча, подбирайте под свою память"
      ],
      "execution_count": 54,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AG7xR0gu0YF6"
      },
      "source": [
        "batch_vectorizer = artm.BatchVectorizer(data_path='batches', # название папки с батчами\n",
        "                                        data_format='batches') # указываем формат - батчи"
      ],
      "execution_count": 55,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LgOCfWjDTcCz"
      },
      "source": [
        "## Генерация словаря"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Qe0-ERcn0YF8"
      },
      "source": [
        "# инициализируем словарь, чтобы сделать модель\n",
        "dictionary = artm.Dictionary()"
      ],
      "execution_count": 56,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Fumz2K_7Mqkv"
      },
      "source": [
        "#### Снизим минимальный и максимальный пороги частотностей, рассмотрим результаты"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "S6nXvdOq0YGA",
        "outputId": "4ba321c1-d7f1-4623-c21a-879ed90576da"
      },
      "source": [
        "dictionary.gather(data_path='batches')\n",
        "dictionary.filter(class_id='@default_class',\n",
        "                  min_df=5, max_df=1800)"
      ],
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "artm.Dictionary(name=ad459260-a033-4b0b-87d8-fde2424b957d, num_entries=16822)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 58
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "I273a14r0YGC"
      },
      "source": [
        "dictionary.save_text('dict.txt')\n",
        "# dictionary = artm.Dictionary()\n",
        "# dictionary.load_text('dict.txt')"
      ],
      "execution_count": 59,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "y0NzGEIgMTgE",
        "outputId": "1a434f70-7576-4ded-9947-4fcee35252b5"
      },
      "source": [
        "!head -n 15 dict.txt"
      ],
      "execution_count": 63,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "name: ad459260-a033-4b0b-87d8-fde2424b957d num_items: 20002\n",
            "token, class_id, token_value, token_tf, token_df\n",
            "лихтенштейн_зимний, @default_class, 5.9329399846319575e-06, 14.0, 14.0\n",
            "сезон_отчаянный, @default_class, 2.5426886622881284e-06, 6.0, 6.0\n",
            "кувейт_принимать, @default_class, 4.237814209773205e-06, 10.0, 10.0\n",
            "тобаго_принимать, @default_class, 7.204284429462859e-06, 17.0, 17.0\n",
            "бускеруда_норвегия, @default_class, 8.051847544265911e-06, 19.0, 19.0\n",
            "провинция_тукумать, @default_class, 1.0170754649152514e-05, 24.0, 12.0\n",
            "боковский, @default_class, 2.9664699923159787e-06, 7.0, 7.0\n",
            "игра_лихтенштейн, @default_class, 4.6615955398010556e-06, 11.0, 11.0\n",
            "область_чудновский, @default_class, 1.567991239426192e-05, 37.0, 37.0\n",
            "черняховский_р-н, @default_class, 2.9664699923159787e-06, 7.0, 7.0\n",
            "находиться_черняховский, @default_class, 2.1189071048866026e-05, 50.0, 50.0\n",
            "ружинский_р-н, @default_class, 2.1189071048866026e-06, 5.0, 5.0\n",
            "ружинский_район, @default_class, 4.6615955398010556e-06, 11.0, 11.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JanKa-CwM0oQ",
        "outputId": "e689c86b-6915-4f76-8e11-060fd5399070"
      },
      "source": [
        "!tail -n 15 dict.txt"
      ],
      "execution_count": 64,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "отделка, @default_class, 9.746972864377312e-06, 23.0, 20.0\n",
            "самоходный, @default_class, 2.9664699923159787e-06, 7.0, 5.0\n",
            "сдать_эксплуатация, @default_class, 3.390251322343829e-06, 8.0, 7.0\n",
            "страна_баск, @default_class, 5.085377324576257e-06, 12.0, 7.0\n",
            "тайный_голосование, @default_class, 4.237814209773205e-06, 10.0, 6.0\n",
            "набор_инструмент, @default_class, 3.390251322343829e-06, 8.0, 8.0\n",
            "перевод_библия, @default_class, 8.47562841954641e-06, 20.0, 12.0\n",
            "телефонный_код, @default_class, 0.0005780378705821931, 1364.0, 1364.0\n",
            "продолжить_обучение, @default_class, 1.3984787074150518e-05, 33.0, 33.0\n",
            "ржд, @default_class, 8.051847544265911e-06, 19.0, 12.0\n",
            "достигнуть_совершеннолетие, @default_class, 3.390251322343829e-06, 8.0, 8.0\n",
            "послевоенный, @default_class, 3.559764081728645e-05, 84.0, 77.0\n",
            "томас_томас, @default_class, 4.237814209773205e-06, 10.0, 10.0\n",
            "произойти_разрыв, @default_class, 2.5426886622881284e-06, 6.0, 6.0\n",
            "амадей_савойский, @default_class, 3.390251322343829e-06, 8.0, 5.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "inIL7_eENyiR"
      },
      "source": [
        "### Рассмотрим примеры слов с низкой df, имеют ли они тематическую значимость"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8PbIbSY_OaEl"
      },
      "source": [
        "### Вывод: При объединении в биграммы многие тематически значимые слова попали в редкие сочетания, надо снизить минимальный порог"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ScDk5VblOtkg"
      },
      "source": [
        "dictionary = artm.Dictionary()\r\n",
        "dictionary.gather(data_path='batches')\r\n",
        "dictionary.filter(class_id='@default_class',\r\n",
        "                  min_df=10, max_df=1600)\r\n",
        "!rm dict.txt\r\n",
        "dictionary.save_text('dict.txt')"
      ],
      "execution_count": 66,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_KqSlvB3Nks6",
        "outputId": "d5a747f5-29b6-43cd-c873-4afe91cdb329"
      },
      "source": [
        "!grep -m 30 ', 10' dict.txt"
      ],
      "execution_count": 67,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "кувейт_принимать, @default_class, 4.237814209773205e-06, 10.0, 10.0\n",
            "бирма_принимать, @default_class, 4.237814209773205e-06, 10.0, 10.0\n",
            "бирма_летний, @default_class, 4.237814209773205e-06, 10.0, 10.0\n",
            "нигер_принимать, @default_class, 4.237814209773205e-06, 10.0, 10.0\n",
            "нигер_летний, @default_class, 4.237814209773205e-06, 10.0, 10.0\n",
            "увеличиться_ниже, @default_class, 4.237814209773205e-06, 10.0, 10.0\n",
            "рудить, @default_class, 2.330797906324733e-05, 55.0, 10.0\n",
            "причислить_лик, @default_class, 5.509158654604107e-06, 13.0, 10.0\n",
            "глава_особенный, @default_class, 4.237814209773205e-06, 10.0, 10.0\n",
            "составной_часть, @default_class, 4.237814209773205e-06, 10.0, 10.0\n",
            "полосатый, @default_class, 5.9329399846319575e-06, 14.0, 10.0\n",
            "перу_принадлежать, @default_class, 4.237814209773205e-06, 10.0, 10.0\n",
            "прибрежный_вода, @default_class, 4.6615955398010556e-06, 11.0, 10.0\n",
            "тибет, @default_class, 1.0170754649152514e-05, 24.0, 10.0\n",
            "муважинский_сельсовет, @default_class, 6.356721314659808e-06, 15.0, 10.0\n",
            "алнашский_район, @default_class, 4.619217725121416e-05, 109.0, 73.0\n",
            "отличительный_черта, @default_class, 4.237814209773205e-06, 10.0, 10.0\n",
            "формулы-_разработать, @default_class, 4.237814209773205e-06, 10.0, 10.0\n",
            "виза, @default_class, 2.7122010578750633e-05, 64.0, 10.0\n",
            "провинция_манитоба, @default_class, 4.6615955398010556e-06, 11.0, 10.0\n",
            "ввести_понятие, @default_class, 4.237814209773205e-06, 10.0, 10.0\n",
            "западный_окраина, @default_class, 5.085377324576257e-06, 12.0, 10.0\n",
            "отнести_смоленский, @default_class, 4.237814209773205e-06, 10.0, 10.0\n",
            "соревнование_фигурный, @default_class, 4.6615955398010556e-06, 11.0, 10.0\n",
            "удмурт, @default_class, 6.356721314659808e-06, 15.0, 10.0\n",
            "зд_административно-территориальный, @default_class, 4.237814209773205e-06, 10.0, 10.0\n",
            "отставка_пост, @default_class, 5.085377324576257e-06, 12.0, 10.0\n",
            "фрам, @default_class, 8.221359894378111e-05, 194.0, 10.0\n",
            "олевский_р-н, @default_class, 4.237814209773205e-06, 10.0, 10.0\n",
            "центральный_тело, @default_class, 4.4920830987393856e-05, 106.0, 106.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8MFmnGhnUlnb"
      },
      "source": [
        "#### Удаляем ненужные сочетания страна + принимать"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "M__tnOm2Tt3a"
      },
      "source": [
        "!grep -v '_принимать' dict.txt > new_dict.txt"
      ],
      "execution_count": 75,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TTUr1NvzVfJJ"
      },
      "source": [
        "#### Удаляем названия областей"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dtScZA3xVdh0"
      },
      "source": [
        "!grep -v 'область_' new_dict.txt > dict.txt"
      ],
      "execution_count": 76,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XR9tCvpWWHhn"
      },
      "source": [
        "#### Удаляем неправильные биграммы с \"зимний\" и \"летний\""
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VIzwzIfdWGw3"
      },
      "source": [
        "!grep -v '_зимний\\|_летний' dict.txt > new_dict.txt"
      ],
      "execution_count": 80,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OePNp7-qR_fl",
        "outputId": "868a16ff-3b5f-4731-8b77-a17c84a662ca"
      },
      "source": [
        "!head -n 20 new_dict.txt"
      ],
      "execution_count": 87,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "name: 99183022-c721-4f69-8f11-f26727905a77 num_items: 20002\n",
            "token, class_id, token_value, token_tf, token_df\n",
            "бускеруда_норвегия, @default_class, 8.051847544265911e-06, 19.0, 19.0\n",
            "провинция_тукумать, @default_class, 1.0170754649152514e-05, 24.0, 12.0\n",
            "игра_лихтенштейн, @default_class, 4.6615955398010556e-06, 11.0, 11.0\n",
            "находиться_черняховский, @default_class, 2.1189071048866026e-05, 50.0, 50.0\n",
            "ружинский_район, @default_class, 4.6615955398010556e-06, 11.0, 11.0\n",
            "находиться_романовский, @default_class, 2.330797906324733e-05, 55.0, 55.0\n",
            "радомышльский_р-н, @default_class, 8.899410204321612e-06, 21.0, 21.0\n",
            "находиться_радомышльский, @default_class, 3.135982478852384e-05, 74.0, 74.0\n",
            "находиться_народичский, @default_class, 2.5003104383358732e-05, 59.0, 59.0\n",
            "игра_багамский, @default_class, 5.9329399846319575e-06, 14.0, 14.0\n",
            "деревня_кизнерский, @default_class, 7.204284429462859e-06, 17.0, 16.0\n",
            "юго-запад_можга, @default_class, 5.509158654604107e-06, 13.0, 13.0\n",
            "американский_виргинский, @default_class, 1.1018317309208214e-05, 26.0, 13.0\n",
            "ширина_полотнище, @default_class, 1.1442098184488714e-05, 27.0, 15.0\n",
            "прямоугольный_полотнище, @default_class, 5.085377324576257e-06, 12.0, 12.0\n",
            "суражский_клинцовский, @default_class, 5.085377324576257e-06, 12.0, 11.0\n",
            "республика_гаити, @default_class, 5.085377324576257e-06, 12.0, 12.0\n",
            "увеличиться_ниже, @default_class, 4.237814209773205e-06, 10.0, 10.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2wAoTcGIWwVZ",
        "outputId": "76df23f7-dffd-4244-9854-e733daf042e9"
      },
      "source": [
        "!tail -n 20 new_dict.txt"
      ],
      "execution_count": 88,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "повелеть, @default_class, 9.746972864377312e-06, 23.0, 21.0\n",
            "гдр, @default_class, 4.0683018596610054e-05, 96.0, 65.0\n",
            "сет, @default_class, 3.347873280290514e-05, 79.0, 33.0\n",
            "гуманитарный_наука, @default_class, 9.746972864377312e-06, 23.0, 20.0\n",
            "сун, @default_class, 3.051226303796284e-05, 72.0, 24.0\n",
            "лирика, @default_class, 5.509158654604107e-06, 13.0, 11.0\n",
            "решительный, @default_class, 1.0170754649152514e-05, 24.0, 23.0\n",
            "толчок_развитие, @default_class, 4.237814209773205e-06, 10.0, 10.0\n",
            "океан, @default_class, 8.178981806850061e-05, 193.0, 134.0\n",
            "герцогиня, @default_class, 1.9070164853474125e-05, 45.0, 25.0\n",
            "поклонение, @default_class, 1.652747596381232e-05, 39.0, 23.0\n",
            "ха, @default_class, 1.7798820408643223e-05, 42.0, 35.0\n",
            "концентрационный_лагерь, @default_class, 1.9917726604035124e-05, 47.0, 35.0\n",
            "отделка, @default_class, 9.746972864377312e-06, 23.0, 20.0\n",
            "перевод_библия, @default_class, 8.47562841954641e-06, 20.0, 12.0\n",
            "телефонный_код, @default_class, 0.0005780378705821931, 1364.0, 1364.0\n",
            "продолжить_обучение, @default_class, 1.3984787074150518e-05, 33.0, 33.0\n",
            "ржд, @default_class, 8.051847544265911e-06, 19.0, 12.0\n",
            "послевоенный, @default_class, 3.559764081728645e-05, 84.0, 77.0\n",
            "томас_томас, @default_class, 4.237814209773205e-06, 10.0, 10.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LzCKl9e3Xiw6",
        "outputId": "70705c59-acf8-4aa5-a5f7-fd3cd1dfebc8"
      },
      "source": [
        "!shuf -n 20 new_dict.txt"
      ],
      "execution_count": 89,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "избрать_депутат, @default_class, 2.6698229703470133e-05, 63.0, 52.0\n",
            "богослов, @default_class, 6.780502644687658e-06, 16.0, 16.0\n",
            "большой_урон, @default_class, 4.6615955398010556e-06, 11.0, 10.0\n",
            "оборонительный, @default_class, 2.1189071048866026e-05, 50.0, 39.0\n",
            "мечтать, @default_class, 2.2884196368977427e-05, 54.0, 52.0\n",
            "показатель, @default_class, 0.00011145451571792364, 263.0, 195.0\n",
            "исходный, @default_class, 3.2631171052344143e-05, 77.0, 69.0\n",
            "проект, @default_class, 0.0009446088224649429, 2229.0, 971.0\n",
            "терек, @default_class, 1.567991239426192e-05, 37.0, 23.0\n",
            "немецкий_войско, @default_class, 6.314343045232818e-05, 149.0, 98.0\n",
            "случайно, @default_class, 6.060074520064518e-05, 143.0, 126.0\n",
            "перо, @default_class, 3.941167233278975e-05, 93.0, 65.0\n",
            "мкм, @default_class, 1.1018317309208214e-05, 26.0, 19.0\n",
            "мм_орудие, @default_class, 1.8222601283923723e-05, 43.0, 28.0\n",
            "граф_шалон, @default_class, 8.47562841954641e-06, 20.0, 11.0\n",
            "официальный_символ, @default_class, 9.323191079602111e-06, 22.0, 20.0\n",
            "джон, @default_class, 0.0003411440411582589, 805.0, 453.0\n",
            "переслать_наступление, @default_class, 9.746972864377312e-06, 23.0, 19.0\n",
            "массачусетс, @default_class, 7.204284429462859e-06, 17.0, 15.0\n",
            "обыграть, @default_class, 8.47562841954641e-05, 200.0, 84.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TBv2Ye9PSNXx"
      },
      "source": [
        "### Вывод случайных строк показывает приемлемый состав словаря"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vLX9Vy7ncKhq"
      },
      "source": [
        "dictionary = artm.Dictionary()\r\n",
        "dictionary.load_text('new_dict.txt')"
      ],
      "execution_count": 90,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bdMCSZiB0YGE"
      },
      "source": [
        "Создаем модель и сразу включаем два регуляризатора (Декореляции и Сглаживания)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LXN_YR1m0YGF"
      },
      "source": [
        "`artm.DecorrelatorPhiRegularizer` - регуляризатор декорреляции тем, делает темы менее похожими друг на друга (рекомендуется включать его сразу на всех темах с положительным значением tau и не менять его в процессе обучения)  \n",
        "\n",
        "`artm.SmoothSparsePhiRegularizer` - регуляризатор сглаживания/разреженивания Phi (тем) - сглаживает или расреживает распределение слов в темах (отрицательный tau - разреживает, положительный - сглаживает), рекомендуется сразу включать сглаживание на всех темах и не менять его в процессе обучения;\n",
        "\n",
        "Количество тем нужно подбирать, но 200 часто работает хорошо. Снижение количества тем и уменьшения словаря ведут к уменьшению времени обучения модели (а увеличение, наоборот). "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7gTUMUh90YGG"
      },
      "source": [
        "first_model_artm = artm.ARTM(topic_names=[f'topic_{i}' for i in range(100)],\n",
        "                       \n",
        "                       regularizers=[\n",
        "                           artm.DecorrelatorPhiRegularizer(\n",
        "                                            name='Decorr_1', tau=0.15, \n",
        "                                            class_ids=['@default_class'],\n",
        "                                            topic_names=[f'topic_{i}' for i in range(0, 100)]),\n",
        "                           \n",
        "                           artm.SmoothSparsePhiRegularizer(\n",
        "                                            name='SmoothPhi_1', \n",
        "                                            class_ids=['@default_class'],\n",
        "                                            tau=0.10, \n",
        "                                            topic_names=[f'topic_{i}' for i in range(0, 100)]),\n",
        "                            artm.SmoothSparseThetaRegularizer(\n",
        "                                            name=\"Decorr_2\",\n",
        "                                            tau=0.10,\n",
        "                                            topic_names=[f'topic_{i}' for i in range(0, 100)])\n",
        "                                    ]\n",
        "                        )\n",
        "\n",
        "# не забывайте менять количество топиков в регуляризаторах, они применяются только на тех темах, что заданы"
      ],
      "execution_count": 95,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "y_HUXXACdqBf"
      },
      "source": [
        "second_model_artm = artm.ARTM(topic_names=[f'topic_{i}' for i in range(0, 200)],\r\n",
        "                              \r\n",
        "                              regularizers=[\r\n",
        "                                            artm.DecorrelatorPhiRegularizer(\r\n",
        "                                                name=\"Decorr_1\",\r\n",
        "                                                tau=0.3,\r\n",
        "                                                class_ids=['@default_class'],\r\n",
        "                                                topic_names=[f'topic_{i}' for i in range(0,200)]),\r\n",
        "                                            artm.SmoothSparsePhiRegularizer(\r\n",
        "                                                name=\"SmoothPhi_1\",\r\n",
        "                                                tau=0.1,\r\n",
        "                                                class_ids=['@default_class'],\r\n",
        "                                                topic_names=[f'topic_{i}' for i in range(0,200)]),\r\n",
        "                                            artm.DecorrelatorPhiRegularizer(\r\n",
        "                                                name=\"Decorr_2\",\r\n",
        "                                                tau=0.3,\r\n",
        "                                                class_ids=['@default_class'],\r\n",
        "                                                topic_names=[f'topic_{i}' for i in range(0,200)]),\r\n",
        "                                            artm.SmoothSparsePhiRegularizer(\r\n",
        "                                                name=\"SmoothPhi_2\",\r\n",
        "                                                tau=0.1,\r\n",
        "                                                class_ids=['@default_class'],\r\n",
        "                                                topic_names=[f'topic_{i}' for i in range(0,200)])\r\n",
        "                                            ])"
      ],
      "execution_count": 92,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eKNJo6uBdt2f"
      },
      "source": [
        "third_model_artm = artm.ARTM(topic_names=[f'topic_{i}' for i in range(100)],\r\n",
        "                       \r\n",
        "                       regularizers=[\r\n",
        "                           artm.DecorrelatorPhiRegularizer(\r\n",
        "                                            name='Decorr_1', tau=0.4, \r\n",
        "                                            class_ids=['@default_class'],\r\n",
        "                                            topic_names=[f'topic_{i}' for i in range(0, 100)]),\r\n",
        "                            artm.SmoothSparseThetaRegularizer(\r\n",
        "                                            name=\"Decorr_2\",\r\n",
        "                                            tau=0.15,\r\n",
        "                                            topic_names=[f'topic_{i}' for i in range(0, 100)])\r\n",
        "                                    ]\r\n",
        "                        )"
      ],
      "execution_count": 97,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZXwur8c80YGI"
      },
      "source": [
        "# инициализурем модель словарем\n",
        "first_model_artm.initialize(dictionary=dictionary, )\n",
        "second_model_artm.initialize(dictionary=dictionary, )\n",
        "third_model_artm.initialize(dictionary=dictionary, )"
      ],
      "execution_count": 98,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "M7ofKFaN0YGK"
      },
      "source": [
        "# добавляем метрики\n",
        "for model in [first_model_artm, second_model_artm, third_model_artm]:\n",
        "    model.scores.add(artm.PerplexityScore(name='PerplexityScore',\n",
        "                                                        dictionary=dictionary))\n",
        "    model.scores.add(artm.SparsityPhiScore(name='SparsityPhiScore')) # разреженность слов в темах\n",
        "    model.scores.add(artm.SparsityThetaScore(name='SparsityThetaScore')) # разреженность тем в доках\n",
        "    model.scores.add(artm.TopicKernelScore(name='TopicKernelScore', probability_mass_threshold=0.1)) # когерентность по семантичесим ядрам\n",
        "    model.scores.add(artm.TopTokensScore(class_id='@default_class', name='TopTokensScore_1', num_tokens=10)) # топ-n-слов для каждой темы\n"
      ],
      "execution_count": 99,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "A7ih3cp10YGM"
      },
      "source": [
        "# Обучение моделей"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vkujg5tp0YGN"
      },
      "source": [
        "Пройдемся несколько раз по коллекции, чтобы модель немного сошлась (если доков сильно больше то может быть достаточно и 1 прохода). Для сильно больших коллекций есть fit_online, который обновляется в процессе прохода по коллекции, про него можно почитать в документации."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "d3UPM4bd0YGO"
      },
      "source": [
        "for model in [first_model_artm, second_model_artm, third_model_artm]:\n",
        "    model.num_document_passes = 3\n",
        "    model.fit_offline(batch_vectorizer=batch_vectorizer, num_collection_passes=3)"
      ],
      "execution_count": 101,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 366
        },
        "id": "sJNAHoO30YGR",
        "outputId": "8a5dd904-b883-4c3f-ec85-df65923894e1"
      },
      "source": [
        "# посмотрим, что стало с моделью\n",
        "# если график сильно падал на последних итерациях, то можно прогнать обучение ещё несколько раз \n",
        "# (не добавляя другие регуляризаторы)\n",
        "print_measures(first_model_artm)"
      ],
      "execution_count": 102,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Sparsity Phi: 0.000 ARTM)\n",
            "Sparsity Theta: 0.000 (ARTM)\n",
            "Kernel contrast: 0.014 (ARTM)\n",
            "Kernel purity: 0.001 (ARTM)\n",
            "Perplexity: 643.726 (ARTM)\n"
          ],
          "name": "stdout"
        },
        {
          "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/",
          "height": 366
        },
        "id": "6reHPo7Ikdsz",
        "outputId": "477abd50-e93e-4123-ad71-575667a15f2b"
      },
      "source": [
        "print_measures(second_model_artm)"
      ],
      "execution_count": 103,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Sparsity Phi: 0.000 ARTM)\n",
            "Sparsity Theta: 0.000 (ARTM)\n",
            "Kernel contrast: 0.021 (ARTM)\n",
            "Kernel purity: 0.007 (ARTM)\n",
            "Perplexity: 649.882 (ARTM)\n"
          ],
          "name": "stdout"
        },
        {
          "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/",
          "height": 366
        },
        "id": "Pqra6Oxoki0C",
        "outputId": "3dfecb40-59c9-4b18-8340-be8c1ef04be7"
      },
      "source": [
        "print_measures(third_model_artm)"
      ],
      "execution_count": 104,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Sparsity Phi: 0.000 ARTM)\n",
            "Sparsity Theta: 0.000 (ARTM)\n",
            "Kernel contrast: 0.040 (ARTM)\n",
            "Kernel purity: 0.001 (ARTM)\n",
            "Perplexity: 641.116 (ARTM)\n"
          ],
          "name": "stdout"
        },
        {
          "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": "markdown",
      "metadata": {
        "id": "ESVQUJm90YGU"
      },
      "source": [
        "Добавим регуляризаторы разреженности тем и документов.\n",
        "\n",
        "Их рекомендуется добавлять после того, как модель уже как-то сошлась. После добавления их рекомендуется постепенно уменьшать, чтобы усиливать разреживание. Их можно применять не ко всем темам, чтобы в каких-то темах собрались все стоп-слова.\n",
        "\n",
        "Можно добавлять их вместе или по отдельности."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QfK4kb36nkbm"
      },
      "source": [
        "models = [first_model_artm, second_model_artm, third_model_artm]\r\n",
        "topics = [100, 200, 100]\r\n",
        "for index in range(len(models)):\r\n",
        "    models[index].regularizers.add(artm.SmoothSparsePhiRegularizer(\r\n",
        "        name='SparsePhi',\r\n",
        "        tau=-0.20,\r\n",
        "        topic_names=[f'topic_{i}' for i in range(1, topics[index])]))\r\n",
        "    models[index].regularizers.add(artm.SmoothSparseThetaRegularizer(\r\n",
        "        name='SparseTheta',\r\n",
        "        tau=-0.20,\r\n",
        "        topic_names=['topic_{}'.format(i) for i in range(1, topics[index])]))"
      ],
      "execution_count": 106,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "d-XyXPrF0YGX"
      },
      "source": [
        "for model in models:\r\n",
        "    model.fit_offline(batch_vectorizer=batch_vectorizer,num_collection_passes=1)"
      ],
      "execution_count": 107,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CWrgduzK0YGY"
      },
      "source": [
        "# будем постепенно увеличивать значения этих регуляризаторов\n",
        "for model in models:\n",
        "    phi_tau = model.regularizers['SparsePhi'].tau\n",
        "    theta_tau = model.regularizers['SparseTheta'].tau\n",
        "\n",
        "    for i in range(10):\n",
        "        model.regularizers['SparsePhi'].tau = (phi_tau + (phi_tau*0.6))\n",
        "        model.regularizers['SparseTheta'].tau = (theta_tau + (theta_tau*0.15))\n",
        "        \n",
        "        model.fit_offline(batch_vectorizer=batch_vectorizer,num_collection_passes=1)\n",
        "    "
      ],
      "execution_count": 108,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 366
        },
        "id": "Nz2ZycN80YGb",
        "outputId": "eee86f38-8220-4ea7-a047-c54d6ab2355a"
      },
      "source": [
        "# посмотрим на качество\n",
        "print_measures(first_model_artm)"
      ],
      "execution_count": 109,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Sparsity Phi: 0.905 ARTM)\n",
            "Sparsity Theta: 0.765 (ARTM)\n",
            "Kernel contrast: 0.416 (ARTM)\n",
            "Kernel purity: 0.645 (ARTM)\n",
            "Perplexity: 225.495 (ARTM)\n"
          ],
          "name": "stdout"
        },
        {
          "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/",
          "height": 366
        },
        "id": "OEYg20qop36V",
        "outputId": "1005c3ba-9e41-4a65-e090-6ed6d8ddbe2b"
      },
      "source": [
        "print_measures(second_model_artm)"
      ],
      "execution_count": 110,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Sparsity Phi: 0.924 ARTM)\n",
            "Sparsity Theta: 0.879 (ARTM)\n",
            "Kernel contrast: 0.386 (ARTM)\n",
            "Kernel purity: 0.563 (ARTM)\n",
            "Perplexity: 182.892 (ARTM)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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6Aa8CuwCtgWeAHwELE7YpAyqi+QqgV8K6j4DuqY7Rv39/b4ypU6c26vtfGzLEHdxfeaVp9pdGk+WOgbLHQ9nj0VyzV/+9TzZl8lDeFjNrR3gADzPbB9hc/5r1teOAT9z9y2h/TwHfBbqaWSt33wb0ApZE2y+Jisfi6LJVF+CrRhw/d/78Z+jWDXr2jDuJiEijZNIb6kbgJaDMzB4FptC4Zy8+AwZF90IMOBZ4H5gKnB5tcx7wbDQ/MVomWv9qVAGbvwMOgF69QldaEZE8lvbMwt0nm9ksYBBgwBVe635Bfbj7dDN7EpgFbANmA+OAvwETzOzmqG189JXxwF/MbCGwktBzSkREciiTy1AARwNHEC5FtQaebsxB3f1GvjnW1MfA4Um23QT8W2OOF6uLLoK33oI334TOneNOIyLSIJl0nb0buBh4l3Cz+SIzuyvbwQrGrFlhjKg5c+JOIiLSYJncszgGOMHdH3D3B4AT0UizmdPzFiJSADIpFguBPRKWy6I2yYSKhYgUgEzuWXQC5pvZPwj3LA4HZpjZRAB3PymL+fKfioWIFIBMisV/Zj1FITvgACguhvnzYcMGaN8+7kQiIvWWSdfZ13IRpGC1bQv77Qfz5oVp0KC4E4mI1FumXWelMcaMgRUrYJdd4k4iItIgKha5cNVVcScQEWmUTHpDiYhIC1fnmYWZzatrFeDufmB2IhWo6dNhxgz493+HNm3iTiMiUi+pLkNtJ3SV/T/gOcK7J6ShxoyBDz6AwYNrutOKiOSJOi9DeXi50WigI6Fg/AbYD1ji7p/mJl4B0fMWIpLHUt6zcPcP3P1Gdz+UcHbxMKC7tQ2hYiEieSxlbygz60kYEvwUYBWhUDRqxNkW65BDwqeKhYjkoTrPLMzsNcLZRGvgfMILiP4GlJjZTrmJV0Cqi8XMmTB8OFRV1aybNQsWLoRNm+LJJiKSRqoziz0JN7gvAsYmtFvUvncWcxWebt3gyCPhjTdg7twwBEi100+HTz4J87vuCnvsUTONHAnHHhvWVVWFt+4VqceziORWqmJxtG5kN7EpU+Cf/4RVq2ra3GGffcLn4sWwfHmYZswI60tLa4rFc8/BWWdBWdmOBSVaNhUREcmSVMXiaUB9PJtS69ZhnKhEZjB5cpivqoKlS2HRIvjsszANHVqz7dKlsHlzuGS1MMko8S+/XDPvrnd/i0iTSVUs9Jcm14qLoWfPMCUbcPDii+Gcc3YsJtXT2rV4SUnYbvt2OOwwGDIEfvIT6Ncvtz+HiBScVMWip5ndWddKd/9pFvJIOh06QN++YaqtvDx8TpsWbprPmgV/+lM4O7nkEjj55HB2IyJST6mKxUZgZq6CSBMaMiTcRL/nHvjLX0IRKS+HHj3gxz+Ga68NRUdEJEOpisVX7v5QzpJI0zrwwFAsfv/7UDDuvhvefx/Gj4df/jLudCKSZ1J1n9mSrNHMiszs7CzlkabWuTNceilUVISzizvugFbRvxFWrICDDw5tq1fHGlNEmrdUxeIEM7vBzP5kZsMtuBz4GDijMQc1s65m9qSZfWBm881ssJntZGaTzWxB9Nkt2tbM7E4zW2hm88xMPbQawgyOPhpOO62m7dFHw+WqK6+E3XcPl6j0hLmIJJGqWDwM7Au8C/w7MBU4HTjZ3Uc18rh3AC+5e1/gIGA+cD0wxd37AFOiZYDvAX2iaSxwTyOPLdUuvRSeegqOOw42boT77oP+/UNPrL/8Je50ItKMpCoWe7v7GHe/lzD6bD/gBHef05gDmlkX4ChgPIC7b3H31cAooPoeyUPAydH8KOBhD6YBXc2sR2MySKRVKzjllPCcxwcfhDOMLl3CuzceeSTudCLSjJi7J19hNisabTbpcoMPaHYwMA54n3BWMRO4gjD0eddoGwNWuXtXM3seuMXd/x6tmwJc5+4zau13LNGwJKWlpf0nTJjQ4IyVlZV07Nixwd+PS1PkLtq0iV1ffZWNu+/OmoMPBqBzRQV7PvIIn559NmsPOKApon5Dvv7OQdnjouxNb9iwYTPdfUDSle6edAKqgLXRtA7YljC/tq7vpZuAAdG+BkbLdwA3Aatrbbcq+nweOCKhfQowINUx+vfv740xderURn0/LlnLfc457uDeurX7xIlZOUS+/s7dlT0uyt70gBlex9/VVC8/Knb3ztHUyd1bJcx3bkTxWgwsdvfp0fKThGFFllVfXoo+l0frlwBlCd/vFbVJrtx6a3gSfOvWcIP8uefiTiQiOVavkefMrIOZ/cjM/tbQA7r7UmCRme0bNR1LuCQ1kTAMOtHns9H8RODcqFfUIGCNu3/R0ONLA3TvDnfdFe5pVBeM55+PO5WI5FDKlx8BmFkJ8H3gh8AJwF+BPzfyuJcDj0b7/pjwvowi4AkzuxD4lJruuS8AJwILgQ3RtpJrZuEMA+D22+HUU0NPqpEj480lIjlRZ7Ews+GEXlDDCd1mHwYOc/dG/7H20KMq2U2UY5Ns68CljT2mNIHqguEOd94JX34ZdyIRyZFUZxYvAW8Qbi5/AmBmd+QklTRfZnDbbTB6NAwcGHcaEcmRVPcsDgXeBl6Jnqi+EChOsb20FGY7Foq5c+HFF+PLIyJZl6o31Bx3v97d9wFuBA4GWpvZi9EzDSLh3RrHHBOGP3/hhbjTiEiWZNQbyt3fcvfLCd1WbwOSvJlHWqReveDss2HLlvA0uM4wRApSvbrOuvt2d5/k7hdkK5DkGbMwau3ll9cUjJdeijuViDSxehULkaSqC8Zll4V3hJ98sgqGSIFRsZCmYRa60156aSgYp54KS5fGnUpEmkjah/JEMmYG//M/UFQUhjrfbbe4E4lIE1GxkKZVfYaRaNMmaNs2njwi0iR0GUqya/Zs2GcfmDQp7iQi0ggqFpJdEybA55/DqFHhJUsikpdULCS7fvc7uPjicCnqpJNUMETylIqFZFdRURje/KKLagrGK6/EnUpE6knFQrKvqAjuvhvGjg0F4wc/UMEQyTPqDSW5UVQE99wThje/7z49gyGSZ3RmIblTVAR//jO8+Sb86EdxpxGRetCZheRWUREMHlyzPGsWrF4d2kWk2dL/oRKfjz+G446DkSPpOmtW3GlEJAUVC4lP795hDKmNGzng5z+Hp5+OO5GI1EHFQuJTVATjxsGPf0xx9eCDN98cboKLSLOiYiHxKiqCe+/lo7Fjw7hSv/wlnHUWbNgQdzIRSaBiIfEzY9Ho0TBxInTqBE89Fd7rLSLNhnpDSfMxciRMmwZz5uzYY0pEYhfbmYWZFZvZbDN7Plrey8ymm9lCM3vczEqi9jbR8sJofe+4MksO9OsHP/xhzfKLL8KDD8YWR0SCOC9DXQHMT1j+PXCbu38LWAVcGLVfCKyK2m+LtpOWYNmycP/i/PPhmmtg27a4E4m0WLEUCzPrBXwfuC9aNuAY4Mlok4eAk6P5UdEy0fpjo+2l0JWWwh//CK1awa23hstUq1fHnUqkRTKPoZuimT0J/A7oBPwMGANMi84eMLMy4EV339/MKoAR7r44WvcRMNDdV9Ta51hgLEBpaWn/CRMmNDhfZWUlHTt2bPD345KvuSF19i5z57LfjTdSsmYNG8rKePfmm9m4xx45Tli3Qv29N3fK3vSGDRs2090HJF3p7jmdgJHA3dH8UOB5oDuwMGGbMqAimq8AeiWs+wjonuoY/fv398aYOnVqo74fl3zN7Z5B9k8+cT/wQHdw79LFfdKkXMTKSEH/3psxZW96wAyv4+9qHJehvgucZGb/AiYQLj/dAXQ1s+reWb2AJdH8EkLxIFrfBfgql4GlGejdOwxAeMopsHEjdOgQdyKRFiXnxcLdb3D3Xu7eGzgLeNXdzwamAqdHm50HPBvNT4yWida/GlVAaWk6doQnn4S334YhQ2rat2+PL5NIC9GcHsq7DrjazBYCOwPjo/bxwM5R+9XA9THlk+agqAgOPbRm+emn4aij9H4MkSyL9aE8dy8HyqP5j4HDk2yzCfi3nAaT/FBVBb/4BcyfD4cdBs8+u2MhEZEm05zOLETqp7gYpk4Nl6QWL4YjjoAnnog7lUhBUrGQ/FZaCq++Gh7c27gRzjwT/vM/dR9DpImpWEj+a9MGxo+H224L9zRuugkuuyzuVCIFRcVCCoMZXHllGEuqRw+44IK4E4kUFI06K4Vl+PDwuta2bWvaPv0U9twzvkwiBUBnFlJ4EgvF449Dnz7w61/DV3qWU6ShVCyksM2bB1u3wq9+BWVlcMklsGBB3KlE8o6KhRS23/wmdK898cTQW+qee2DffeHkk+Gdd+JOJ5I3VCyk8A0dCn/7G7z3Hlx4IbRuHR7gmzUr7mQieUPFQlqOfv3gvvvCDe9f/xrOPbdm3a23wp13QmVlfPlEmjEVC2l5dtstPLjXrl1YXrcuFI8rrgj3NW64AT7/PN6MIs2MioVI+/bhPd9DhoQ38d1ySxgSfcwYePfdmMOJNA8qFiLFxeE9GW++CW+9BaedFgYpfOghOPBAqKiIO6FI7FQsRBINHhzembFgAVx+ORx3HOy/f836SZNgy5b48onERMVCJJm99w43vCdNqmmrqIATToC99gqXqlatii+fSI6pWIikYlYzv3p1OMv4/PNwE7ysDK64gnaLFoFe3igFTsVCJFNHHBGeCH/pJTj+eFi/Hu68k4Hnnrvja14h9LASKSAqFiL1YRYuRU2aBHPmwHnnsbVzZ9hnn5ptli2DLl1gv/3C6Lf33hu23bYtvtwijaRRZ0Ua6qCD4MEHefO88xh62GE17R98EJ4Sf//9MD3wQGhv3x769w/v3ujTJ57MIg2kYiHSWGbQsWPN8tFHw9q14Wxi+vSa6aOP4I03YJddarb9yU9g6VIYODBMAwZAp065/xlE0lCxEMmGNm1qCkC1L78MD/l17VrT9txzsGQJPPNMWC4qCsOSDBoUXhF73HG5zS1SBxULkVzZZRc45piaZXeYMmXHs4+5c0MX3YqKcB+kulg8/zzceCPssUfyabfdduy5JdLEVCxE4mIWhkvfd9+aQQ03boTZs0PhOP74mm0//DCMkptspNyiIti0KdwngfAO8q1bv1lQ2rfP/s8kBSvnxcLMyoCHgVLAgXHufoeZ7QQ8DvQG/gWc4e6rzMyAO4ATgQ3AGHfX2NJSmNq1C91wa3fFveACOPLIMGLuZ5/tOFVV1RQKCL2vliz55r67d4frroOf/SwsL1nCrpMnhwK10041U9euYQgUkQRxnFlsA65x91lm1gmYaWaTgTHAFHe/xcyuB64HrgO+B/SJpoHAPdGnSMvRrRscfniY0rnpJvjXv2qKyaefwqJFsGLFjkXgzTfp99vfJt9H167hXebduoXl3/0u7COxqHTrFj732EPvOG8Bcl4s3P0L4Itofp2ZzQd6AqOAodFmDwHlhGIxCnjY3R2YZmZdzaxHtB8Rqe3887/Ztn07LF8ebrxX696dZcccQ2mrVrByZRi+ZOXK8KT66tXQuXPNts88A//4R/LjnXMOPPxwmP/ww3CfpfoMpWPHMHXoED6vvjqM6Avw9tuhqFWvS/zs3DnsQ5oN8xiHKTCz3sDrwP7AZ+7eNWo3YJW7dzWz54Fb3P3v0bopwHXuPqPWvsYCYwFKS0v7T5gwocG5Kisr6ZjYFTJP5GtuUPa4JM1eVUWr9evZllAsdn7rLdouW0ardetotW4drdeu/frzq8GD+ezsswHo/O67HPrTn9Z5vBnjxlEZPWOy7x/+QI8XXki63bpvf5uZ994bFrZvZ/CZZ1LVpg1V7dqxvW1bqtq2ZUurVhS1a8fnJ53E6oMPBqDT+++z8/TpbC8pSTqtOPLIr4/R7rPPMHe2t26943atW2f9Mlxz/W9m2LBhM919QLJ1sd3gNrOOwF+BK919rSX05HB3N7N6VTF3HweMAxgwYIAPHTq0wdnKy8tpzPfjkq+5QdnjknH2FNt0AfauXhgyBEaNqjlTWb8+vH1w/XpYv54Bp54KO+8ctv3ww/BMSa1tqKykU9++NbnWrw+X0Oqw65gxNfnmzq05y6mtpAQ2b65Z3n//8KrdZK64Am6/Pcy/8054Z3tJSZhat95x/uGHw+CSAH/6UxjmvvY2JSWhd9tFFwHw2pQpHD1nDl8HD50AAAiSSURBVLRqtePUunX4HDSo5tLep5/CwoU16xK3a90a+vatyb1iRfj9ZqFnXCzFwsxaEwrFo+7+VNS8rPrykpn1AJZH7UuAsoSv94raRKS5KSmp6X2VzkUXff3HM6V27cIN+1oFpeKdd9j/W98Kf1irDRwYuhhv2lQzbd4cPmv/Ad1773B5LnHb6inxzGL9+tRvTkwcsv6tt+Cxx5Jvd9RRX/+8tmULXHVV3ft85JGaYvHXv8I11yTfrn37kK/aEUeE3nTVb4FsQnH0hjJgPDDf3W9NWDUROA+4Jfp8NqH9MjObQLixvUb3K0RakKIi2H33bzSvaNv2m2c8gwbtWDxSmTgxebt7KCLVBg8ON/e3bAldkrds2XG+LOHfspdfDiNHfnObLVugZ8+a7YqLw9nLtm1h2rq1Zn7bth07DPTqBcOG7bi++jtt2+6YPYs92eI4s/gucA7wrpnNidp+TigST5jZhcCnwBnRuhcI3WYXErrOJrl7JyLSRMx2/IPbpk34g52JwYPDlMb2kpKay1zpnHFGmDIxbVpm2zVAHL2h/g7UdUHt2CTbO3BpVkOJiEhKGqJcRETSUrEQEZG0VCxERCQtFQsREUlLxUJERNJSsRARkbRULEREJK1YBxLMFjP7kvBgX0N1B+oejKb5ytfcoOxxUfZ4NNfse7r7LslWFGSxaCwzm1HXyIvNWb7mBmWPi7LHIx+z6zKUiIikpWIhIiJpqVgkNy7uAA2Ur7lB2eOi7PHIu+y6ZyEiImnpzEJERNJSsRARkbRULBKY2Qgz+9DMFprZ9XHnyZSZlZnZVDN738zeM7Mr4s5UX2ZWbGazzez5uLPUh5l1NbMnzewDM5tvZunffNMMmNlV0X8rFWb2mJm1Tf+t+JjZ/Wa23MwqEtp2MrPJZrYg+uwWZ8Zk6sj9h+i/l3lm9rSZdY0zY6ZULCJmVgzcBXwP6AeMNrN+8abK2DbgGnfvBwwCLs2j7NWuAObHHaIB7gBecve+wEHkwc9gZj2BnwID3H1/oBg4K95UaT0IjKjVdj0wxd37AFOi5ebmQb6ZezKwv7sfCPwTuCHXoRpCxaLG4cBCd//Y3bcAE4BRMWfKiLt/4e6zovl1hD9YPVN/q/kws17A94H74s5SH2bWBTiK8E553H2Lu6+ON1XGWgHtzKwV0B74POY8Kbn768DKWs2jgIei+YeAk3MaKgPJcrv7JHffFi1OAzJ8Z2u8VCxq9AQWJSwvJo/+4FYzs97AIcD0eJPUy+3AtcD2uIPU017Al8AD0SW0+8ysQ9yh0nH3JcAfgc+AL4A17j4p3lQNUuruX0TzS4HSOMM00AXAi3GHyISKRQExs47AX4Er3X1t3HkyYWYjgeXuPjPuLA3QCjgUuMfdDwHW0zwvhewgurY/ilDsdgc6mNmP4k3VOB6eAcir5wDM7BeES8iPxp0lEyoWNZYAZQnLvaK2vGBmrQmF4lF3fyruPPXwXeAkM/sX4dLfMWb2SLyRMrYYWOzu1WdxTxKKR3N3HPCJu3/p7luBp4AhMWdqiGVm1gMg+lwec56MmdkYYCRwtufJw24qFjXeAfqY2V5mVkK44Tcx5kwZMTMjXDef7+63xp2nPtz9Bnfv5e69Cb/zV909L/6V6+5LgUVmtm/UdCzwfoyRMvUZMMjM2kf/7RxLHtyYT2IicF40fx7wbIxZMmZmIwiXXU9y9w1x58mUikUkuuF0GfAy4X+cJ9z9vXhTZey7wDmEf5XPiaYT4w7VQlwOPGpm84CDgd/GnCet6EzoSWAW8C7h70CzHn7CzB4D3gb2NbPFZnYhcAtwvJktIJwt3RJnxmTqyP0noBMwOfp/9c+xhsyQhvsQEZG0dGYhIiJpqViIiEhaKhYiIpKWioWIiKSlYiEiImmpWEhBMrPK6LO3mf2wiff981rLbzXl/nMpG78fKUwqFlLoegP1+mMYDa6Xyg7Fwt3z8ennar2p5+9HWiYVCyl0twBHRg8/XRW9N+MPZvZO9D6BiwDMbKiZvWFmE4mewjazZ8xsZvTeh7FR2y2E0VrnmNmjUVv1WYxF+64ws3fN7MyEfZcnvPfi0ejJaczslug9JPPM7I+1w5tZRzN7INrfPDM7LWofHbVVmNnvE7avTJg/3cwejOYfNLM7zewtM/vYzE5P9vtpyl+8FJZ0/4ISyXfXAz9z95EA0R/9Ne5+mJm1Ad40s+oRVw8lvGfgk2j5AndfaWbtgHfM7K/ufr2ZXebuByc51qmEp7gPArpH33k9WncIsB9hKPA3ge+a2XzgFKCvu3sdL8H5ZZT3gCh/NzPbHfg90B9YBUwys5Pd/Zk0v4sewBFAX8JQGU/W/v2I1EVnFtLSDAfONbM5hGHcdwb6ROv+kVAoAH5qZnMJ7xwoS9iuLkcAj7l7lbsvA14DDkvY92J33w7MIVz+WQNsAsab2alAsnGCjiO8lAsAd18V7bM8GgiwetTSozL42Z9x9+3u/j75OZy3xEjFQloaAy5394Ojaa+Edzms/3ojs6GEP9SD3f0gYDbQmFePbk6YrwJaRX/oDyf8C38k8FIj9l8tcfye2nkTM1gTHEtaEBULKXTrCIO2VXsZ+Ek0pDtm9u06XljUBVjl7hvMrC/hdbXVtlZ/v5Y3gDOj+yK7EP61/4+6gkXvH+ni7i8AVxEuX9U2Gbg04Tvdon0ebWbdLbwOeDThLAbCsN3fMbMiwiWudGr/fkSSUrGQQjcPqDKzudEN3PsIN7BnmVkFcC/J7929BLSK7ivcQrgUVW0cMK/6BneCp6PjzQVeBa6NhjGvSyfg+WjE2r8DVyfZ5magW3Qjey4wLHo73PXA1OhYM929enju64HngbcIb8FLp/bvRyQpjTorIiJp6cxCRETSUrEQEZG0VCxERCQtFQsREUlLxUJERNJSsRARkbRULEREJK3/B+h7vWwCO6u3AAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 366
        },
        "id": "qtuQu_b-p6OH",
        "outputId": "97adbd38-fcb3-41b5-ed53-40bba36d8aa3"
      },
      "source": [
        "print_measures(third_model_artm)"
      ],
      "execution_count": 111,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Sparsity Phi: 0.924 ARTM)\n",
            "Sparsity Theta: 0.729 (ARTM)\n",
            "Kernel contrast: 0.465 (ARTM)\n",
            "Kernel purity: 0.678 (ARTM)\n",
            "Perplexity: 232.471 (ARTM)\n"
          ],
          "name": "stdout"
        },
        {
          "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": "markdown",
      "metadata": {
        "id": "NJzduI0gqIzW"
      },
      "source": [
        "#### По результатам обучения наилучшие значения показывает вторая модель. Проделаем ещё несколько итераций."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8hoP-IK_qYsx"
      },
      "source": [
        "phi_tau = second_model_artm.regularizers['SparsePhi'].tau\r\n",
        "theta_tau = second_model_artm.regularizers['SparseTheta'].tau\r\n",
        "for i in range(6):\r\n",
        "    second_model_artm.regularizers['SparsePhi'].tau = phi_tau * 1.4\r\n",
        "    second_model_artm.regularizers['SparseTheta'].tau = theta_tau * 0.25\r\n",
        "    second_model_artm.fit_offline(batch_vectorizer=batch_vectorizer, num_collection_passes=2)"
      ],
      "execution_count": 112,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dkRy-6I8wiM-"
      },
      "source": [
        "# Итоги"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 366
        },
        "id": "MpTXeIZbrviq",
        "outputId": "ad021b13-7a09-42bb-f8f8-882f016e6933"
      },
      "source": [
        "print_measures(second_model_artm)"
      ],
      "execution_count": 113,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Sparsity Phi: 0.953 ARTM)\n",
            "Sparsity Theta: 0.822 (ARTM)\n",
            "Kernel contrast: 0.447 (ARTM)\n",
            "Kernel purity: 0.725 (ARTM)\n",
            "Perplexity: 173.714 (ARTM)\n"
          ],
          "name": "stdout"
        },
        {
          "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": {
        "id": "tDeYG2kt0YGg"
      },
      "source": [
        "for topic_name in second_model_artm.topic_names[:50]:\n",
        "    print('BEGIN TOPIC')\n",
        "    print(topic_name + ': ')\n",
        "    try:\n",
        "        for x in second_model_artm.score_tracker['TopTokensScore_1'].last_tokens[topic_name]:\n",
        "            print(x)\n",
        "        print('END TOPIC')\n",
        "    \n",
        "    except KeyError:\n",
        "        continue"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Oa3JNMffsR5s"
      },
      "source": [
        "\"\"\"\r\n",
        "BEGIN TOPIC\r\n",
        "topic_20: \r\n",
        "компания\r\n",
        "банк\r\n",
        "рынок\r\n",
        "фирма\r\n",
        "продажа\r\n",
        "крупный\r\n",
        "производитель\r\n",
        "компьютер\r\n",
        "процессор\r\n",
        "корпорация\r\n",
        "END TOPIC\r\n",
        "BEGIN TOPIC\r\n",
        "topic_30: \r\n",
        "право\r\n",
        "закон\r\n",
        "лицо\r\n",
        "наказание\r\n",
        "принять\r\n",
        "законодательство\r\n",
        "положение\r\n",
        "ответственность\r\n",
        "отношение\r\n",
        "кодекс\r\n",
        "END TOPIC\r\n",
        "BEGIN TOPIC\r\n",
        "topic_42: \r\n",
        "клуб\r\n",
        "лига\r\n",
        "выступать\r\n",
        "команда\r\n",
        "чемпионат\r\n",
        "футбол\r\n",
        "кубок\r\n",
        "дивизион\r\n",
        "футбольный_клуб\r\n",
        "футбольный\r\n",
        "END TOPIC\r\n",
        "\"\"\""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xVXJA90KtFfm"
      },
      "source": [
        "Вторая модель: выбранное число топиков - 200. При инициализации попеременно добавлялись два декоррелятора с тау 0.3 и два фи-регуляризатора с тау 0.1. \r\n",
        "\r\n",
        "После двух итераций были добавлены фи- и тета-регуляризатор с параметрами тета -0.2.\r\n",
        "Модель достигла перплексии 200 за 10 итераций, при которых параметры добавочных регуляризаторов менялись на 0.6 и 0.15 соответственно. \r\n",
        "\r\n",
        "После ещё шести итераций с двойным проходом по документам, на которых параметры добавочных регуляризаторов менялись на 1.4 для фи-регуляризатора и на 0.25 для тета-регуляризатора, перплексия достигла значения 172.\r\n",
        "\r\n",
        "\r\n",
        "На последних итерациях значительно выросли параметры Kernel contrast и Kernel Purity.\r\n"
      ]
    }
  ]
}