6/topic_modelling.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FHRONyc1ZcGj"
      },
      "source": [
        "### Тематическое моделирование\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Важно: Во второй половине ноутбука мы будем работать с библиотекой BigARTM, запускать которую локально может быьб удобнее с помощью Docker-образа. Если вы планируете работать с использованием Docker, сперва запустите его и работайте с ноутбуком внутри окружения, чтобы не делать предобработку данных дважды. Инструкция ниже.\n",
        "\n",
        "Есть несколько сценариев работы с BigARTM, среди которых:\n",
        "- установка библиотеки для Питона: инструкция [здесь]( https://bigartm.readthedocs.io/en/stable/installation/index.html)\n",
        "- запуск Docker образа с библиотекой\n",
        "\n",
        "Если вы предпочтете второй сценарий, вам нужно:\n",
        "1. Скачать и установить Docker для своей ОС. Запустить Docker.\n",
        "2. Скачать образ (Dockerfile), в котором устанавливается большинство библиотек для анализа данных, Jupyter Notebook и сама BigARTM: https://bigartm.readthedocs.io/en/stable/installation/docker.html?highlight=docker\n",
        "3. Собрать образ: это можно сделать через командную строку (находясь в папке, где лежит скачанный в п.2 Dockerfile):"
      ],
      "metadata": {
        "id": "GEss_8Z5AQIM"
      }
    },
    {
      "cell_type": "raw",
      "metadata": {
        "id": "ULBeRwyhZcGl"
      },
      "source": [
        "\n",
        "```\n",
        "$ docker build -t artm .\n",
        "$ docker run -p 8888:8888 artm\n",
        "```\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "R-LT_RvsZcGl"
      },
      "source": [
        "После выполнения этих двух команд запустится Jupyter Notebook, в командной строке будет написан путь к нему в таком формате:"
      ]
    },
    {
      "cell_type": "raw",
      "metadata": {
        "id": "xxS1De0WZcGm"
      },
      "source": [
        "\n",
        "```\n",
        "- http://de0ad378d760:8888/?token=5897bc15b494ca17f0d996763a8d05a71f1170af3455e96d\n",
        "- http://127.0.0.1:8888/?token=5897bc15b494ca17f0d996763a8d05a71f1170af3455e96d\n",
        "```\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "N3QWGZDaZcGm"
      },
      "source": [
        "Откройте любой из URL, там поднят Jupyter Notebook с уже установленными библиотеками.\n",
        "\n",
        "При работе не забывайте, что все ноутбуки, которые вы создаете внутри этого Docker образа, исчезнут, как только вы остановите Docker, поэтому перед окончанием работы скачайте ноутбук с результатом себе на компьютер."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "ubQHoJajZcGm"
      },
      "outputs": [],
      "source": [
        "!pip install -q nltk"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install -q pymystem3"
      ],
      "metadata": {
        "id": "QKHK1s3PZ5-g"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install -q gensim"
      ],
      "metadata": {
        "id": "WkgRWIJzZ9U5"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install -q pyLDAvis==2.1.2 ## последние версии сложнее запустить в colab"
      ],
      "metadata": {
        "id": "iawszDEAc09L",
        "outputId": "44b67e61-38af-40b0-92be-fe6731a2f65d",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.6/1.6 MB\u001b[0m \u001b[31m7.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Building wheel for pyLDAvis (setup.py) ... \u001b[?25l\u001b[?25hdone\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install -q bigartm"
      ],
      "metadata": {
        "id": "uRCHIdPiaZb_",
        "outputId": "da188a48-a83a-497f-d947-d37c628770d3",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2.1/2.1 MB\u001b[0m \u001b[31m9.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install -q bertopic"
      ],
      "metadata": {
        "id": "3Tkowfnx_asW",
        "outputId": "df6b27f9-d495-4ffe-b1d8-d1cf3b10e87f",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
            "  and should_run_async(code)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m154.1/154.1 kB\u001b[0m \u001b[31m2.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m5.2/5.2 MB\u001b[0m \u001b[31m44.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
            "  Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
            "  Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m90.9/90.9 kB\u001b[0m \u001b[31m10.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m86.0/86.0 kB\u001b[0m \u001b[31m9.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.3/1.3 MB\u001b[0m \u001b[31m54.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m55.8/55.8 kB\u001b[0m \u001b[31m4.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Building wheel for hdbscan (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
            "  Building wheel for sentence-transformers (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Building wheel for umap-learn (setup.py) ... \u001b[?25l\u001b[?25hdone\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FNCgZAgCZcGn"
      },
      "source": [
        "Построим тематические модели для корпуса новостей Lenta.ru, с которым мы уже работали ранее, добавим поля __date__ и __topic__."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "scrolled": true,
        "id": "PQfQcjdQZcGn",
        "outputId": "86afc4e3-1782-4ff1-9d9e-cbdd2fdbe22e",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--2023-12-06 11:13:29--  https://docs.google.com/uc?export=download&confirm=t&id=1IMtWrTrs54SVzHlTgtBWqcQA990CCrQh\n",
            "Resolving docs.google.com (docs.google.com)... 142.250.148.138, 142.250.148.100, 142.250.148.101, ...\n",
            "Connecting to docs.google.com (docs.google.com)|142.250.148.138|:443... connected.\n",
            "HTTP request sent, awaiting response... 303 See Other\n",
            "Location: https://doc-0s-ac-docs.googleusercontent.com/docs/securesc/ha0ro937gcuc7l7deffksulhg5h7mbp1/e4rf7o0ri0kanc7qafuqbjvigd6pi9dn/1701861150000/14013181690233399305/*/1IMtWrTrs54SVzHlTgtBWqcQA990CCrQh?e=download&uuid=37b3a570-267c-43ee-b3f5-0aded12ea1fa [following]\n",
            "Warning: wildcards not supported in HTTP.\n",
            "--2023-12-06 11:13:29--  https://doc-0s-ac-docs.googleusercontent.com/docs/securesc/ha0ro937gcuc7l7deffksulhg5h7mbp1/e4rf7o0ri0kanc7qafuqbjvigd6pi9dn/1701861150000/14013181690233399305/*/1IMtWrTrs54SVzHlTgtBWqcQA990CCrQh?e=download&uuid=37b3a570-267c-43ee-b3f5-0aded12ea1fa\n",
            "Resolving doc-0s-ac-docs.googleusercontent.com (doc-0s-ac-docs.googleusercontent.com)... 142.250.1.132, 2607:f8b0:4001:c24::84\n",
            "Connecting to doc-0s-ac-docs.googleusercontent.com (doc-0s-ac-docs.googleusercontent.com)|142.250.1.132|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 2084746431 (1.9G) [text/csv]\n",
            "Saving to: ‘lenta-ru-news-full.csv’\n",
            "\n",
            "lenta-ru-news-full. 100%[===================>]   1.94G  87.5MB/s    in 35s     \n",
            "\n",
            "2023-12-06 11:14:05 (56.1 MB/s) - ‘lenta-ru-news-full.csv’ saved [2084746431/2084746431]\n",
            "\n"
          ]
        }
      ],
      "source": [
        "!wget --load-cookies /tmp/cookies.txt \"https://docs.google.com/uc?export=download&confirm=$(wget --quiet --save-cookies /tmp/cookies.txt --keep-session-cookies --no-check-certificate 'https://docs.google.com/uc?export=download&id=1IMtWrTrs54SVzHlTgtBWqcQA990CCrQh' -O- | sed -rn 's/.*confirm=([0-9A-Za-z_]+).*/\\1\\n/p')&id=1IMtWrTrs54SVzHlTgtBWqcQA990CCrQh\" -O lenta-ru-news-full.csv && rm -rf /tmp/cookies.txt"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import warnings\n",
        "warnings.filterwarnings('ignore')"
      ],
      "metadata": {
        "id": "Z_adLLJFeH2F"
      },
      "execution_count": 36,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "id": "6DPLyKbEZcGo",
        "outputId": "82d338ec-2643-4a3a-b68b-1aaf7fe7589e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 354
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                            text   topic        date\n",
              "9992                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                            Белорусская теннисистка Наталья Зверева оштрафована на $1000 после матча, в котором она играла в паре с Анной Курниковой против сестер Уильямс. Потерпев в этой игре поражение, Зверева продемонстрировала публике непристойный жест, сообщает Associated Press. По сообщению агентства, упомянутый жест представлял собой \"оттопыренные средние пальцы на обеих руках\". Арбитр Алан Миллз принял решение наказать белорусскую теннисистку за неспортивное поведение. Сумма штрафа будет вычтена из денег, заработанных Зверевой в ходе турнира.   Спорт  2000/07/10\n",
              "542   На состоявшемся в минувший вторник заседании Мосгорсуд отменил определение районного суда, которое выдвигало особое условие, препятствующее выплате денег пострадавшим от деятельности российского филиала японской секты \"Аум синрике\". Дело направлено на новое рассмотрение. Как сообщает ИТАР-ТАСС, ранее Генпрокуратура признала потерпевшими 26 жителей России и стран СНГ, которым был нанесен вред здоровью, а также материальный и моральный ущерб. Среди них - бывшие члены секты, которым требуется психологическая реабилитация; семьи, лишившиеся кормильца; лица, пожертвовавшие секте все свое имущество; родители, чьи дети покончили жизнь самоубийством. Тяжба о выплате материальной компенсации жертвам секты тянется четыре года. Общественная организация \"Комитет по спасению молодежи от деструктивных культов\" добивается выполнения решения Останкинского межмуниципального суда, принятого 18 апреля 1995 года, по перечислению на ее счет денежной суммы для выплаты потерпевшим. Тогда суд постановил взыскать с российского филиала секты для возмещения вреда пострадавшим 20 миллиардов рублей (за четыре года эта сумма превратилась в 2,435 миллиона рублей). Адвокат \"Комитета по спасению молодежи от деструктивных культов\" выразил надежду, что, возможно, после решения Мосгорсуда юридическая волокита закончится.  Россия  1999/09/29\n",
              "4983                                                                                                                                                                                                                                                                                                                                                                                                                                               Во вторник утром на шахте №5/6 АО \"Прокопьевскуголь\" в Кузбассе произошел взрыв метана. Как сообщили РИА \"Новости\" в пресс-службе МЧС РФ, в результате один человек погиб и шестеро госпитализированы с различными ранениями. Еще четверо шахтеров, работавших в момент взрыва в шахте, не пострадали. По данным РБК, под землей в это время находились 72 шахтера, из них 11 - в непосредственной близости к эпицентру взрыва. В областную травматологическую больницу госпитализированы семь человек, сообщает агентство. К настоящему моменту все люди из шахты эвакуированы, а работы на ней приостановлены до выяснения причин взрыва. Около 30 горноспасателей сейчас занимаются исследованием горных выработок. Пожара в шахте нет. Накануне, 6 марта, на нерабочем участке этой же шахты - ввентиляционном  стволе - горноспасатели обнаружили тела четырех подростков, погибших от отравления газом.  Россия  2000/03/07"
            ],
            "text/html": [
              "\n",
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              "    <div>\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>text</th>\n",
              "      <th>topic</th>\n",
              "      <th>date</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>9992</th>\n",
              "      <td>Белорусская теннисистка Наталья Зверева оштрафована на $1000 после матча, в котором она играла в паре с Анной Курниковой против сестер Уильямс. Потерпев в этой игре поражение, Зверева продемонстрировала публике непристойный жест, сообщает Associated Press. По сообщению агентства, упомянутый жест представлял собой \"оттопыренные средние пальцы на обеих руках\". Арбитр Алан Миллз принял решение наказать белорусскую теннисистку за неспортивное поведение. Сумма штрафа будет вычтена из денег, заработанных Зверевой в ходе турнира.</td>\n",
              "      <td>Спорт</td>\n",
              "      <td>2000/07/10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>542</th>\n",
              "      <td>На состоявшемся в минувший вторник заседании Мосгорсуд отменил определение районного суда, которое выдвигало особое условие, препятствующее выплате денег пострадавшим от деятельности российского филиала японской секты \"Аум синрике\". Дело направлено на новое рассмотрение. Как сообщает ИТАР-ТАСС, ранее Генпрокуратура признала потерпевшими 26 жителей России и стран СНГ, которым был нанесен вред здоровью, а также материальный и моральный ущерб. Среди них - бывшие члены секты, которым требуется психологическая реабилитация; семьи, лишившиеся кормильца; лица, пожертвовавшие секте все свое имущество; родители, чьи дети покончили жизнь самоубийством. Тяжба о выплате материальной компенсации жертвам секты тянется четыре года. Общественная организация \"Комитет по спасению молодежи от деструктивных культов\" добивается выполнения решения Останкинского межмуниципального суда, принятого 18 апреля 1995 года, по перечислению на ее счет денежной суммы для выплаты потерпевшим. Тогда суд постановил взыскать с российского филиала секты для возмещения вреда пострадавшим 20 миллиардов рублей (за четыре года эта сумма превратилась в 2,435 миллиона рублей). Адвокат \"Комитета по спасению молодежи от деструктивных культов\" выразил надежду, что, возможно, после решения Мосгорсуда юридическая волокита закончится.</td>\n",
              "      <td>Россия</td>\n",
              "      <td>1999/09/29</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4983</th>\n",
              "      <td>Во вторник утром на шахте №5/6 АО \"Прокопьевскуголь\" в Кузбассе произошел взрыв метана. Как сообщили РИА \"Новости\" в пресс-службе МЧС РФ, в результате один человек погиб и шестеро госпитализированы с различными ранениями. Еще четверо шахтеров, работавших в момент взрыва в шахте, не пострадали. По данным РБК, под землей в это время находились 72 шахтера, из них 11 - в непосредственной близости к эпицентру взрыва. В областную травматологическую больницу госпитализированы семь человек, сообщает агентство. К настоящему моменту все люди из шахты эвакуированы, а работы на ней приостановлены до выяснения причин взрыва. Около 30 горноспасателей сейчас занимаются исследованием горных выработок. Пожара в шахте нет. Накануне, 6 марта, на нерабочем участке этой же шахты - ввентиляционном  стволе - горноспасатели обнаружили тела четырех подростков, погибших от отравления газом.</td>\n",
              "      <td>Россия</td>\n",
              "      <td>2000/03/07</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-09350fc6-21b1-461c-90b2-63cbb561758e')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-09350fc6-21b1-461c-90b2-63cbb561758e button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-09350fc6-21b1-461c-90b2-63cbb561758e');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-2d1132b6-02c8-497f-a4ae-5068a57e7f9c\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-2d1132b6-02c8-497f-a4ae-5068a57e7f9c')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-2d1132b6-02c8-497f-a4ae-5068a57e7f9c button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ],
      "source": [
        "import pandas as pd\n",
        "pd.set_option('display.max_columns', None)\n",
        "pd.set_option('display.expand_frame_repr', False)\n",
        "pd.set_option('max_colwidth', None)\n",
        "\n",
        "data = pd.read_csv('lenta-ru-news-full.csv', usecols=['text', 'topic',  'date'], nrows = 10000) # ограничим размер 10,000 строк\n",
        "data.sample(3)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "id": "ang5AwPbZcGo",
        "outputId": "979b5963-95a6-4669-c0ff-e72fa8a02d52",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "10000"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ],
      "source": [
        "len(data)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "id": "mpIVMzU4ZcGo",
        "outputId": "d1d1eee2-c3c6-435f-d507-e34c00bf789d",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Россия            5135\n",
              "Мир               2018\n",
              "Экономика         1545\n",
              "Интернет и СМИ     937\n",
              "Спорт              360\n",
              "Библиотека           5\n",
              "Name: topic, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ],
      "source": [
        "data.topic.value_counts().head(15)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ed_1EuQWZcGp"
      },
      "source": [
        "Для ускорения вычислений оставим тексты из 5 тем:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "id": "Ejwum1-4ZcGp",
        "outputId": "f3243e61-1bc0-411c-ac6a-a1bce4a392c1",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "9995"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ],
      "source": [
        "topics = ['Россия', 'Мир', 'Спорт', 'Экономика', 'Интернет и СМИ']\n",
        "\n",
        "data = data[data.topic.isin(topics)]\n",
        "data.dropna(inplace=True)\n",
        "len(data)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RP8vBdE6ZcGp"
      },
      "source": [
        "### Предобработка\n",
        "\n",
        "\n",
        "Все модели, с которыми мы будем работать далее, работают с предобработанными текстами, поэтому сперва проведем стандартную предобработку текста:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "id": "eWIyeHlZZcGq"
      },
      "outputs": [],
      "source": [
        "import re\n",
        "import numpy as np\n",
        "from nltk.corpus import stopwords\n",
        "from tqdm.notebook import tqdm\n",
        "from multiprocessing import Pool\n",
        "from pymystem3 import Mystem"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import nltk\n",
        "nltk.download('stopwords')"
      ],
      "metadata": {
        "id": "w-875_2FbLse",
        "outputId": "da7076bd-c1b4-4c31-9af3-3d42c20fa505",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[nltk_data] Downloading package stopwords to /root/nltk_data...\n",
            "[nltk_data]   Unzipping corpora/stopwords.zip.\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "id": "fUxqsJ9iZcGq",
        "outputId": "b7f5601d-335d-4aff-cb44-0289d861f521",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Installing mystem to /root/.local/bin/mystem from http://download.cdn.yandex.net/mystem/mystem-3.1-linux-64bit.tar.gz\n"
          ]
        }
      ],
      "source": [
        "words_regex = re.compile('\\w+')\n",
        "\n",
        "def find_words(text, regex = words_regex):\n",
        "    tokens =  regex.findall(text.lower())\n",
        "    return [w for w in tokens if w.isalpha() and len(w) >= 3]\n",
        "\n",
        "\n",
        "mystem = Mystem()\n",
        "stopwords_list = stopwords.words('russian')\n",
        "\n",
        "def lemmatize(words, lemmer = mystem, stopwords = stopwords_list):\n",
        "    lemmas = lemmer.lemmatize(' '.join(words))\n",
        "    return [w for w in lemmas if not w in stopwords\n",
        "            and w.isalpha()]\n",
        "\n",
        "def preprocess(text):\n",
        "    return (lemmatize(find_words(text)))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "id": "X98WjoBgZcGr",
        "outputId": "3819935f-e12a-4342-a85f-0391286ca132",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 116
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'В зале игровых автоматов в третьем ярусе подземного комплекса \"Охотный ряд\" на Манежной площади в центре Москвы около 20.00 прогремел мощный взрыв. По данным ФСБ, погибли не менее четырех человек, 20 человек ранены. Однако уточненная оценка числа пострадавших в результате этого взрыва может достигнуть ста человек. Агентство ИТАР-ТАСС, в сообщении от 21.15 со ссылкой на источники в ГУВД Москвы, говорит только о 30 раненых, в том числе о двух пострадавших в тяжелом состоянии. Однако число пострадавших в результате этого взрыва может составить до ста человек. По данным РИА \"Новости\", Боткинская больница, Институт им. Склифосовского, 1-ая Градская, 36-ая и 64-ая горбольница работают только на прием пострадавших. С места происшествия запросили 20 машин скорой помощи. Из торгового центра эвакуированы все посетитетели. Среди причин происшедшего называют террористический акт, связанный с последними событиями в Дагестане, однако, по сообщению ОРТ, не исключается и версия об аварии (взорвался игровой автомат). Более точных данных о причнах взрыва и количестве жертв представители УФСБ называть не торопятся.'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 17
        }
      ],
      "source": [
        "data.text.iloc[1]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "scrolled": true,
        "id": "ZMvvezFcZcGr",
        "outputId": "603e4bd7-d268-4989-9d49-9cb4fae2045b",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "зал, игровой, автомат, третий, ярус, подземный, комплекс, охотный, ряд, манежный, площадь, центр, москва, около, прогреметь, мощный, взрыв, данные, фсб, погибать, мало, четыре, человек, человек, ранить, однако, уточнять, оценка, число, пострадать, результат, взрыв, мочь, достигать, сто, человек, агентство, итар, тасс, сообщение, ссылка, источник, гувд, москва, говорить, раненый, число, пострадавший, тяжелый, состояние, однако, число, пострадать, результат, взрыв, мочь, составлять, сто, человек, данные, риа, новость, боткинский, больница, институт, склифосовский, градский, горбольница, работать, прием, пострадавший, место, происшествие, запрашивать, машина, скорый, помощь, торговый, центр, эвакуировать, посетитетель, среди, причина, происходить, называть, террористический, акт, связывать, последний, событие, дагестан, однако, сообщение, орт, исключаться, версия, авария, взрываться, игровой, автомат, точный, данный, прична, взрыв, количество, жертва, представитель, уфсб, называть, торопиться\n"
          ]
        }
      ],
      "source": [
        "print(*preprocess(data.text.iloc[1]), sep=', ')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "colab": {
          "referenced_widgets": [
            "1e699562ac93458a965e10ad12ab6ae2",
            "d338cd47de554160b0e5c35dbdf7add0",
            "c0e1632be5874e1eab5517b3665f6b4b",
            "3c7e897f66ea4869b5526d40d6e0b209",
            "c18338ecfb02483584a2121018244624",
            "ff905efd7e5948149e6829a47493a324",
            "4aa3bc99a71f4e39b4dcc77e74d33be8",
            "533d0db129b34df78cbc5d80cd2d4e0b",
            "d25072a9c2e24bfbab72ef491f76894c",
            "64acb04fe13340d3a3fd7a2ff88f4db1",
            "8351e8f34cab4e4a9211c5002f597de9"
          ],
          "base_uri": "https://localhost:8080/",
          "height": 49
        },
        "id": "EfMuIyrMZcGs",
        "outputId": "8d9a9593-c4a7-4f84-c810-ab5709344be2"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/9995 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "1e699562ac93458a965e10ad12ab6ae2"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "preprocessed_text = list(tqdm(map(preprocess, data['text']), total=len(data)))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "id": "pw9h-xQHZcGs",
        "outputId": "fea132ac-c2e8-41cb-844d-8a70a905bd19",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 321
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                          text   topic        date\n",
              "2869  [декабрь, президиум, московский, городской, суд, удовлетворять, протест, генеральный, прокуратура, отменять, решение, хамовнический, суд, также, судебный, коллегия, уголовный, дело, московский, городской, суд, признание, незаконный, продление, срок, следствие, дело, юрий, скуратов, шесть, месяц, это, сообщать, агентство, риа, новость, решение, президиум, мосгорсуд, вопрос, законность, продолжение, расследование, повторно, рассматриваться, хамовнический, межмуниципальный, суд, москва, близкий, время, генеральный, прокуратура, возобновлять, следственный, действие, дело, юрий, скуратов, заявлять, журналист, заместитель, генеральный, прокурор, александр, розанов, очень, важный, принятие, решение, становиться, счесть, мосгорсуд, хамовнический, суд, иметь, право, принимать, производство, жалоба, юрий, скуратов, розанов, пояснять, соответствие, уголовный, процессуальный, кодекс, действие, прокурор, следователь, мочь, обжаловать, лишь, подозреваемый, обвиняемый, гражданский, истец, гражданский, ответчик, ...]  Россия  1999/12/23\n",
              "9528                                                                                                                                                                                                                                                                                                 [среда, космодром, плесецк, стартовать, ракета, носитель, легкий, класс, космос, который, вывести, спутник, солнечно, синхронный, орбита, максимальный, удаление, земля, километр, сообщать, риа, новость, пресс, служба, ракетный, войско, стратегический, назначение, ранее, подобный, запуск, осуществляться, космодром, байконур, космос, доставлять, российский, спутник, надежда, предназначать, работа, международный, космический, система, поиск, спасение, аварийный, суд, самолет, коспас, sarsat, иностранный, космический, аппарат, китайский, цинхуа, английский, snap, который, использоваться, проведение, научный, исследование, обучение, студент, слово, специалист, рвсн, разделение, спутник, происходить, штатный, режим, примерно]  Россия  2000/06/28\n",
              "6686                                                                                                                                                                                                                                                                                                                              [балтийский, море, вторник, начинаться, крупный, российский, военный, морской, учение, участвовать, сухопутный, береговой, войско, надводный, корабль, авиация, учение, впервые, принимать, участие, подводный, лодка, новый, боевой, судно, лишь, несколько, месяц, назад, входить, состав, балтийский, флот, передавать, нтв, это, ракетный, катер, корабль, воздушный, подушка, самум, предполагаться, проведение, ракетный, стрельба, торпедный, стрельба, наблюдение, ход, учение, калининградский, область, прибывать, делегация, генеральный, штаб, глава, начальник, управление, боевой, подготовка, вмс, вица, адмирал, николай, михеев, случай, зона, учение, курсировать, иностранный, разведывательный, корабль]  Россия  2000/04/18"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-3836971b-8b01-4f4d-9814-d42927197ff8\" class=\"colab-df-container\">\n",
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              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>text</th>\n",
              "      <th>topic</th>\n",
              "      <th>date</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2869</th>\n",
              "      <td>[декабрь, президиум, московский, городской, суд, удовлетворять, протест, генеральный, прокуратура, отменять, решение, хамовнический, суд, также, судебный, коллегия, уголовный, дело, московский, городской, суд, признание, незаконный, продление, срок, следствие, дело, юрий, скуратов, шесть, месяц, это, сообщать, агентство, риа, новость, решение, президиум, мосгорсуд, вопрос, законность, продолжение, расследование, повторно, рассматриваться, хамовнический, межмуниципальный, суд, москва, близкий, время, генеральный, прокуратура, возобновлять, следственный, действие, дело, юрий, скуратов, заявлять, журналист, заместитель, генеральный, прокурор, александр, розанов, очень, важный, принятие, решение, становиться, счесть, мосгорсуд, хамовнический, суд, иметь, право, принимать, производство, жалоба, юрий, скуратов, розанов, пояснять, соответствие, уголовный, процессуальный, кодекс, действие, прокурор, следователь, мочь, обжаловать, лишь, подозреваемый, обвиняемый, гражданский, истец, гражданский, ответчик, ...]</td>\n",
              "      <td>Россия</td>\n",
              "      <td>1999/12/23</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9528</th>\n",
              "      <td>[среда, космодром, плесецк, стартовать, ракета, носитель, легкий, класс, космос, который, вывести, спутник, солнечно, синхронный, орбита, максимальный, удаление, земля, километр, сообщать, риа, новость, пресс, служба, ракетный, войско, стратегический, назначение, ранее, подобный, запуск, осуществляться, космодром, байконур, космос, доставлять, российский, спутник, надежда, предназначать, работа, международный, космический, система, поиск, спасение, аварийный, суд, самолет, коспас, sarsat, иностранный, космический, аппарат, китайский, цинхуа, английский, snap, который, использоваться, проведение, научный, исследование, обучение, студент, слово, специалист, рвсн, разделение, спутник, происходить, штатный, режим, примерно]</td>\n",
              "      <td>Россия</td>\n",
              "      <td>2000/06/28</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6686</th>\n",
              "      <td>[балтийский, море, вторник, начинаться, крупный, российский, военный, морской, учение, участвовать, сухопутный, береговой, войско, надводный, корабль, авиация, учение, впервые, принимать, участие, подводный, лодка, новый, боевой, судно, лишь, несколько, месяц, назад, входить, состав, балтийский, флот, передавать, нтв, это, ракетный, катер, корабль, воздушный, подушка, самум, предполагаться, проведение, ракетный, стрельба, торпедный, стрельба, наблюдение, ход, учение, калининградский, область, прибывать, делегация, генеральный, штаб, глава, начальник, управление, боевой, подготовка, вмс, вица, адмирал, николай, михеев, случай, зона, учение, курсировать, иностранный, разведывательный, корабль]</td>\n",
              "      <td>Россия</td>\n",
              "      <td>2000/04/18</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-3836971b-8b01-4f4d-9814-d42927197ff8')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-3836971b-8b01-4f4d-9814-d42927197ff8 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-3836971b-8b01-4f4d-9814-d42927197ff8');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-d83dd1f8-a05b-4f0f-a831-b1fbccabcd88\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-d83dd1f8-a05b-4f0f-a831-b1fbccabcd88')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-d83dd1f8-a05b-4f0f-a831-b1fbccabcd88 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ],
      "source": [
        "data['text'] = preprocessed_text\n",
        "data.sample(3)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6hS5mui3ZcGs"
      },
      "source": [
        "## Модель LDA\n",
        "\n",
        "\n",
        "Первая модель, которую мы рассмотрим, LDA - латентное размещение Дирихле. Воспользуемся реализацией из библиотеки gensim."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "id": "k9dAgiVHZcGt"
      },
      "outputs": [],
      "source": [
        "from gensim.models import *\n",
        "from gensim import corpora"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xzzGa7OhZcGt"
      },
      "source": [
        "Модель использует векторное представление документов, например, мешок слов (bow), поэтому сперва создадим словарь:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "id": "DAHjdyXoZcGt"
      },
      "outputs": [],
      "source": [
        "dictionary = corpora.Dictionary(data['text'])\n",
        "\n",
        "dictionary.filter_extremes(no_below = 10, no_above = 0.9, keep_n=None) # игнорируем слова, которые встречаются реже 10 раз или в более 90% документов\n",
        "dictionary.save('lenta.dict')"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#help(dictionary.filter_extremes)"
      ],
      "metadata": {
        "id": "JUJnNnHGb2b0"
      },
      "execution_count": 31,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pCUoyyu9ZcGu"
      },
      "source": [
        "Векторизуем документы:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "id": "QjyQ5TmRZcGu"
      },
      "outputs": [],
      "source": [
        "corpus = [dictionary.doc2bow(text) for text in data['text']]\n",
        "corpora.MmCorpus.serialize('lenta.model', corpus)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8Sj3Ms5LZcGu"
      },
      "source": [
        "Теперь можем обучать модель:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "id": "SZEmlYrYZcGu",
        "outputId": "4395f193-6608-4423-b77d-c03522d1eeac",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "CPU times: user 19.8 s, sys: 56.2 ms, total: 19.9 s\n",
            "Wall time: 20.6 s\n"
          ]
        }
      ],
      "source": [
        "%time lda = ldamodel.LdaModel(corpus, id2word=dictionary, num_topics=20, chunksize=50, update_every=1, passes=2)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#help(ldamodel.LdaModel)"
      ],
      "metadata": {
        "id": "djsno3sHcEzF"
      },
      "execution_count": 34,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "v4FbKu5cZcGu"
      },
      "source": [
        "Посмотрим на получившиеся темы:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "id": "UiufTv9vZcGu",
        "outputId": "e9f8fc17-cc74-4a1a-f97b-853d74404380",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[(19,\n",
              "  '0.029*\"россия\" + 0.019*\"российский\" + 0.014*\"который\" + 0.014*\"это\" + 0.013*\"министр\" + 0.013*\"президент\" + 0.012*\"также\" + 0.012*\"новость\" + 0.012*\"сообщать\" + 0.012*\"правительство\"'),\n",
              " (2,\n",
              "  '0.027*\"человек\" + 0.019*\"который\" + 0.018*\"происходить\" + 0.017*\"сообщать\" + 0.016*\"место\" + 0.015*\"результат\" + 0.015*\"находиться\" + 0.015*\"время\" + 0.014*\"погибать\" + 0.011*\"несколько\"'),\n",
              " (14,\n",
              "  '0.091*\"акция\" + 0.058*\"украина\" + 0.043*\"нью\" + 0.041*\"договор\" + 0.034*\"поражение\" + 0.032*\"протест\" + 0.030*\"правление\" + 0.027*\"прокурор\" + 0.026*\"участник\" + 0.025*\"ответственность\"'),\n",
              " (13,\n",
              "  '0.040*\"президент\" + 0.033*\"депутат\" + 0.029*\"госдума\" + 0.021*\"совет\" + 0.020*\"глава\" + 0.020*\"администрация\" + 0.019*\"председатель\" + 0.019*\"владимир\" + 0.019*\"федерация\" + 0.014*\"лидер\"'),\n",
              " (0,\n",
              "  '0.100*\"компания\" + 0.062*\"интернет\" + 0.044*\"который\" + 0.042*\"система\" + 0.027*\"новый\" + 0.022*\"пользователь\" + 0.020*\"милиционер\" + 0.020*\"электронный\" + 0.019*\"программа\" + 0.017*\"рынок\"'),\n",
              " (5,\n",
              "  '0.117*\"сша\" + 0.059*\"американский\" + 0.043*\"европа\" + 0.036*\"страна\" + 0.033*\"адвокат\" + 0.030*\"мир\" + 0.025*\"свой\" + 0.022*\"год\" + 0.020*\"штат\" + 0.017*\"посольство\"'),\n",
              " (3,\n",
              "  '0.065*\"военный\" + 0.040*\"область\" + 0.040*\"губернатор\" + 0.031*\"второй\" + 0.027*\"победа\" + 0.024*\"игорь\" + 0.019*\"база\" + 0.018*\"первый\" + 0.017*\"округ\" + 0.017*\"голосование\"'),\n",
              " (17,\n",
              "  '0.038*\"свой\" + 0.029*\"это\" + 0.022*\"заявлять\" + 0.022*\"который\" + 0.019*\"слово\" + 0.018*\"первый\" + 0.016*\"однако\" + 0.015*\"мочь\" + 0.015*\"директор\" + 0.014*\"становиться\"'),\n",
              " (1,\n",
              "  '0.081*\"чечня\" + 0.046*\"боевик\" + 0.036*\"район\" + 0.033*\"чеченский\" + 0.030*\"федеральный\" + 0.029*\"сила\" + 0.024*\"республика\" + 0.024*\"сообщать\" + 0.023*\"генерал\" + 0.021*\"войско\"'),\n",
              " (18,\n",
              "  '0.043*\"сообщать\" + 0.039*\"новость\" + 0.036*\"риа\" + 0.032*\"сотрудник\" + 0.030*\"москва\" + 0.024*\"милиция\" + 0.023*\"служба\" + 0.022*\"обнаруживать\" + 0.019*\"управление\" + 0.018*\"город\"')]"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ],
      "source": [
        "lda.show_topics(num_topics=10, num_words=10, formatted=True)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ne86NcI-ZcGv"
      },
      "source": [
        "На полученные темы можно посмотреть, изобразив их на плоскости с помощью библиотеки __pyLDAvis__. Чтобы спроецировать темы на плоскость, внутри применяется PCA-разложение."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {
        "id": "iQ0StIYQZcGv",
        "outputId": "bb63dca8-701a-42f5-b856-f89b2fea753e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 892
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "CPU times: user 3.53 s, sys: 145 ms, total: 3.68 s\n",
            "Wall time: 7.23 s\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "<link rel=\"stylesheet\" type=\"text/css\" href=\"https://cdn.rawgit.com/bmabey/pyLDAvis/files/ldavis.v1.0.0.css\">\n",
              "\n",
              "\n",
              "<div id=\"ldavis_el1811349857581765608374380498\"></div>\n",
              "<script type=\"text/javascript\">\n",
              "\n",
              "var ldavis_el1811349857581765608374380498_data = {\"mdsDat\": {\"x\": [0.22811727689123795, 0.14776141866590542, 0.17347527473643068, 0.14708440971049522, 0.14738288111605394, 0.10820898800328148, 0.07527560779660296, 0.12706788465516788, 0.09531420340949526, -0.07011370725486334, 0.051901026710492446, 0.020464604029627846, -0.0667457295345301, -0.08507188055472362, -0.11353700303574053, -0.13638158139664058, -0.16960383906830104, -0.1894588166073078, -0.23034514586587712, -0.2607958724068075], \"y\": [-0.059429757094076656, 0.1588626090106373, -0.12865825918167748, -0.06577928794122065, -0.06539597417153424, 0.2798234160316237, 0.18917184982471225, 0.01470942319029505, -0.12299788910962493, 0.06873340152697713, -0.07397181284778842, -0.17853645537220966, -0.00671668891928922, 0.09903722090222174, -0.05588670604997289, -0.11455119599098015, 0.1564359625409184, -0.024467903183449373, -0.03570427630058108, -0.03467767686498118], \"topics\": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 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\"\\u044e\\u0441\\u0442\\u0438\\u0446\\u0438\\u044f\", \"\\u044e\\u0441\\u0442\\u0438\\u0446\\u0438\\u044f\", \"\\u044f\\u0432\\u043b\\u044f\\u0442\\u044c\\u0441\\u044f\", \"\\u044f\\u0432\\u043b\\u044f\\u0442\\u044c\\u0441\\u044f\", \"\\u044f\\u0432\\u043b\\u044f\\u0442\\u044c\\u0441\\u044f\", \"\\u044f\\u0432\\u043b\\u044f\\u0442\\u044c\\u0441\\u044f\", \"\\u044f\\u0432\\u043b\\u044f\\u0442\\u044c\\u0441\\u044f\", \"\\u044f\\u0432\\u043b\\u044f\\u0442\\u044c\\u0441\\u044f\", \"\\u044f\\u0432\\u043b\\u044f\\u0442\\u044c\\u0441\\u044f\", \"\\u044f\\u0432\\u043b\\u044f\\u0442\\u044c\\u0441\\u044f\", \"\\u044f\\u0432\\u043b\\u044f\\u0442\\u044c\\u0441\\u044f\", \"\\u044f\\u0432\\u043b\\u044f\\u0442\\u044c\\u0441\\u044f\", \"\\u044f\\u0432\\u043b\\u044f\\u0442\\u044c\\u0441\\u044f\", \"\\u044f\\u043f\\u043e\\u043d\\u0438\\u044f\", \"\\u044f\\u043f\\u043e\\u043d\\u0441\\u043a\\u0438\\u0439\", \"\\u044f\\u0449\\u0438\\u043a\"]}, \"R\": 30, \"lambda.step\": 0.01, \"plot.opts\": {\"xlab\": \"PC1\", \"ylab\": \"PC2\"}, \"topic.order\": [20, 3, 18, 14, 10, 19, 2, 13, 1, 16, 5, 6, 4, 9, 11, 7, 17, 15, 8, 12]};\n",
              "\n",
              "function LDAvis_load_lib(url, callback){\n",
              "  var s = document.createElement('script');\n",
              "  s.src = url;\n",
              "  s.async = true;\n",
              "  s.onreadystatechange = s.onload = callback;\n",
              "  s.onerror = function(){console.warn(\"failed to load library \" + url);};\n",
              "  document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
              "}\n",
              "\n",
              "if(typeof(LDAvis) !== \"undefined\"){\n",
              "   // already loaded: just create the visualization\n",
              "   !function(LDAvis){\n",
              "       new LDAvis(\"#\" + \"ldavis_el1811349857581765608374380498\", ldavis_el1811349857581765608374380498_data);\n",
              "   }(LDAvis);\n",
              "}else if(typeof define === \"function\" && define.amd){\n",
              "   // require.js is available: use it to load d3/LDAvis\n",
              "   require.config({paths: {d3: \"https://cdnjs.cloudflare.com/ajax/libs/d3/3.5.5/d3.min\"}});\n",
              "   require([\"d3\"], function(d3){\n",
              "      window.d3 = d3;\n",
              "      LDAvis_load_lib(\"https://cdn.rawgit.com/bmabey/pyLDAvis/files/ldavis.v1.0.0.js\", function(){\n",
              "        new LDAvis(\"#\" + \"ldavis_el1811349857581765608374380498\", ldavis_el1811349857581765608374380498_data);\n",
              "      });\n",
              "    });\n",
              "}else{\n",
              "    // require.js not available: dynamically load d3 & LDAvis\n",
              "    LDAvis_load_lib(\"https://cdnjs.cloudflare.com/ajax/libs/d3/3.5.5/d3.min.js\", function(){\n",
              "         LDAvis_load_lib(\"https://cdn.rawgit.com/bmabey/pyLDAvis/files/ldavis.v1.0.0.js\", function(){\n",
              "                 new LDAvis(\"#\" + \"ldavis_el1811349857581765608374380498\", ldavis_el1811349857581765608374380498_data);\n",
              "            })\n",
              "         });\n",
              "}\n",
              "</script>"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ],
      "source": [
        "import pyLDAvis\n",
        "import pyLDAvis.gensim as gensimvis\n",
        "\n",
        "%time vis_data = gensimvis.prepare(lda, corpus, dictionary)\n",
        "pyLDAvis.display(vis_data)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "InzEoGcVZcGv"
      },
      "source": [
        "Посмотрим на метрики качества получившейся модели: перплексию и среднюю когерентность:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 37,
      "metadata": {
        "id": "E2aypA3UZcGv",
        "outputId": "1a606d23-1d64-4683-cc40-592d0122178a",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Log_perplexity: -13.8955850568\n",
            "Персплексия: 0.0000656164\n"
          ]
        }
      ],
      "source": [
        "#ppl = lda.log_perplexity(corpus)\n",
        "\n",
        "print('Log_perplexity:', '{:.10f}'.format(ppl))\n",
        "print('Персплексия:', '{:.10f}'.format(2**ppl))"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Перплексия модели (второе значение) лежит в пределах от 0 до |V| (размера корпуса в словах), идеальная перпслекия = 0."
      ],
      "metadata": {
        "id": "J1Ko-g7q4m1N"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#help(lda.log_perplexity)"
      ],
      "metadata": {
        "id": "tPl2W17h2-bi"
      },
      "execution_count": 39,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 40,
      "metadata": {
        "id": "_JILB_-jZcGv",
        "outputId": "cffdbdc1-0f5f-47c4-9a8f-5a9795d2280e",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Средняя когерентность:  0.39115\n"
          ]
        }
      ],
      "source": [
        "coherence_model_lda = CoherenceModel(model=lda, texts=data['text'], dictionary=dictionary, coherence='c_v')\n",
        "coherence_lda = coherence_model_lda.get_coherence()\n",
        "print('Средняя когерентность: ', '{:.5f}'.format(coherence_lda))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-91HelnvZcGw"
      },
      "source": [
        "Чем больше значение когерентности, тем более интерпретируемые темы получились.\n",
        "\n",
        "Теперь можно подобрать оптимальное количество тем, опираясь на значение метрик:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 41,
      "metadata": {
        "id": "8435iw9wZcGw"
      },
      "outputs": [],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 42,
      "metadata": {
        "colab": {
          "referenced_widgets": [
            "012c0cb0e99c418fa99cb6931fded127",
            "99aa2bd0e0554cdb96646c8c0e5d24f2",
            "34eca91193a84cc7a7659d4adabfd0bb",
            "4caf468ce1794834b8bb71c4cf88b380",
            "54827cd0d096449782e0054d9297c7e7",
            "5148aca8bf7e4253967c5b3f8eda1b67",
            "4df57b9c92094c0086e64dee6e789ec1",
            "162c6a822f7c4640b82e048673c86842",
            "94aeccaf61554bcfb4d5974682d25816",
            "ea135ebc6cd142ccbbe79c28490f979e",
            "921875fec8884ac9a0019faf4dc65cb5"
          ],
          "base_uri": "https://localhost:8080/",
          "height": 481
        },
        "id": "AIH1QO32ZcGw",
        "outputId": "d5dd8b10-0290-4468-985c-de521081ce89"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/5 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "012c0cb0e99c418fa99cb6931fded127"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "topics_list = [5, 10, 15, 20, 25]\n",
        "coherences = []\n",
        "\n",
        "for num in tqdm(topics_list):\n",
        "    lda = ldamodel.LdaModel(corpus, id2word=dictionary, num_topics=num, chunksize=50, update_every=1, passes=2)\n",
        "    coherences.append(CoherenceModel(model=lda, texts=data['text'], dictionary=dictionary, coherence='c_v').get_coherence())\n",
        "\n",
        "plt.plot(topics_list, coherences)\n",
        "plt.xlabel(\"Число тем\")\n",
        "plt.ylabel(\"Средняя когерентность\")\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 43,
      "metadata": {
        "id": "66N5NDhdZcGw",
        "outputId": "88ad0580-5ce5-42ad-8195-6a2577bcdf18",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 860
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "<link rel=\"stylesheet\" type=\"text/css\" href=\"https://cdn.rawgit.com/bmabey/pyLDAvis/files/ldavis.v1.0.0.css\">\n",
              "\n",
              "\n",
              "<div id=\"ldavis_el181134985754012000353687457\"></div>\n",
              "<script type=\"text/javascript\">\n",
              "\n",
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\"\\u0447\\u0442\\u0435\\u043d\\u0438\\u0435\", \"\\u0448\\u0432\\u0435\\u0439\\u0446\\u0430\\u0440\\u0441\\u043a\\u0438\\u0439\", \"\\u044d\\u043a\\u043e\\u043d\\u043e\\u043c\\u0438\\u043a\\u0430\", \"\\u044d\\u043a\\u043e\\u043d\\u043e\\u043c\\u0438\\u0447\\u0435\\u0441\\u043a\\u0438\\u0439\", \"\\u044d\\u043a\\u043e\\u043d\\u043e\\u043c\\u0438\\u0447\\u0435\\u0441\\u043a\\u0438\\u0439\", \"\\u044d\\u0442\\u043e\", \"\\u044d\\u0442\\u043e\", \"\\u044d\\u0442\\u043e\", \"\\u044d\\u0442\\u043e\", \"\\u044d\\u0442\\u043e\", \"\\u044d\\u0442\\u043e\", \"\\u044d\\u0442\\u043e\", \"\\u044d\\u0442\\u043e\", \"\\u044d\\u0442\\u043e\", \"\\u044d\\u0442\\u043e\", \"\\u044d\\u0445\\u043e\", \"\\u044d\\u0445\\u043e\", \"\\u044e\\u0433\\u043e\\u0441\\u043b\\u0430\\u0432\\u0438\\u044f\", \"\\u044e\\u0440\\u0438\\u0439\", \"\\u044f\\u0432\\u043b\\u044f\\u0442\\u044c\\u0441\\u044f\", \"\\u044f\\u0432\\u043b\\u044f\\u0442\\u044c\\u0441\\u044f\", \"\\u044f\\u0432\\u043b\\u044f\\u0442\\u044c\\u0441\\u044f\", \"\\u044f\\u0432\\u043b\\u044f\\u0442\\u044c\\u0441\\u044f\", \"\\u044f\\u0432\\u043b\\u044f\\u0442\\u044c\\u0441\\u044f\", \"\\u044f\\u0432\\u043b\\u044f\\u0442\\u044c\\u0441\\u044f\", \"\\u044f\\u0432\\u043b\\u044f\\u0442\\u044c\\u0441\\u044f\", \"\\u044f\\u0432\\u043b\\u044f\\u0442\\u044c\\u0441\\u044f\", \"\\u044f\\u0432\\u043b\\u044f\\u0442\\u044c\\u0441\\u044f\", \"\\u044f\\u0432\\u043b\\u044f\\u0442\\u044c\\u0441\\u044f\", \"\\u044f\\u043f\\u043e\\u043d\\u0438\\u044f\", \"\\u044f\\u0441\\u0442\\u0440\\u0436\\u0435\\u043c\\u0431\\u0441\\u043a\\u0438\\u0439\"]}, \"R\": 30, \"lambda.step\": 0.01, \"plot.opts\": {\"xlab\": \"PC1\", \"ylab\": \"PC2\"}, \"topic.order\": [10, 4, 6, 3, 9, 2, 1, 7, 5, 8]};\n",
              "\n",
              "function LDAvis_load_lib(url, callback){\n",
              "  var s = document.createElement('script');\n",
              "  s.src = url;\n",
              "  s.async = true;\n",
              "  s.onreadystatechange = s.onload = callback;\n",
              "  s.onerror = function(){console.warn(\"failed to load library \" + url);};\n",
              "  document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
              "}\n",
              "\n",
              "if(typeof(LDAvis) !== \"undefined\"){\n",
              "   // already loaded: just create the visualization\n",
              "   !function(LDAvis){\n",
              "       new LDAvis(\"#\" + \"ldavis_el181134985754012000353687457\", ldavis_el181134985754012000353687457_data);\n",
              "   }(LDAvis);\n",
              "}else if(typeof define === \"function\" && define.amd){\n",
              "   // require.js is available: use it to load d3/LDAvis\n",
              "   require.config({paths: {d3: \"https://cdnjs.cloudflare.com/ajax/libs/d3/3.5.5/d3.min\"}});\n",
              "   require([\"d3\"], function(d3){\n",
              "      window.d3 = d3;\n",
              "      LDAvis_load_lib(\"https://cdn.rawgit.com/bmabey/pyLDAvis/files/ldavis.v1.0.0.js\", function(){\n",
              "        new LDAvis(\"#\" + \"ldavis_el181134985754012000353687457\", ldavis_el181134985754012000353687457_data);\n",
              "      });\n",
              "    });\n",
              "}else{\n",
              "    // require.js not available: dynamically load d3 & LDAvis\n",
              "    LDAvis_load_lib(\"https://cdnjs.cloudflare.com/ajax/libs/d3/3.5.5/d3.min.js\", function(){\n",
              "         LDAvis_load_lib(\"https://cdn.rawgit.com/bmabey/pyLDAvis/files/ldavis.v1.0.0.js\", function(){\n",
              "                 new LDAvis(\"#\" + \"ldavis_el181134985754012000353687457\", ldavis_el181134985754012000353687457_data);\n",
              "            })\n",
              "         });\n",
              "}\n",
              "</script>"
            ]
          },
          "metadata": {},
          "execution_count": 43
        }
      ],
      "source": [
        "lda_10 = ldamodel.LdaModel(corpus, id2word=dictionary, num_topics=10, chunksize=50, update_every=1, passes=2)\n",
        "vis_data = gensimvis.prepare(lda_10, corpus, dictionary)\n",
        "pyLDAvis.display(vis_data)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_aUw46omZcGw"
      },
      "source": [
        "### Библиотека BigARTM\n",
        "\n",
        "Теперь попробуем построить модели с помощью библиотеки BigARTM. [Тут](https://github.com/bigartm/bigartm-book/blob/master/ARTM_example_RU.ipynb) и [тут](https://github.com/bigartm/bigartm-book/blob/master/ARTM_tutorial_RU.ipynb) можно посмотреть на примеры использования библиотеки."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 44,
      "metadata": {
        "id": "gtnd6FnBZcGw"
      },
      "outputs": [],
      "source": [
        "import artm"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fcVd8MmwZcGw"
      },
      "source": [
        "Чтобы работать с данными в библиотеке ARTM нужно сперва сделать препроцессинг (мы уже сделали его ранее) и записать данные в [одном из форматов](https://bigartm.readthedocs.io/en/v0.10.1/tutorials/datasets.html), с которыми умеет работать библиотека. Мы конвертируем данные в формат vowpal wabbit:"
      ]
    },
    {
      "cell_type": "raw",
      "metadata": {
        "id": "9iJTrp_yZcGx"
      },
      "source": [
        "doc1 this text will be processed not as bag of words |author Some_Author"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 45,
      "metadata": {
        "id": "pk0vNkuKZcGx",
        "outputId": "f4fce51c-a823-4d94-af5e-3197a41b42bc",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "text     [становиться, известно, агентство, ассошиэйтед, пресс, экипаж, последний, экспедиция, станция, мир, считать, способный, выходить, контроль, командир, виктор, афанасьев, сказать, чувствовать, грустить, оставлять, станция, летать, беспилотный, режим, gazeta, подробно, писать, финансирование, программа, мир, закрывать, конструктор, спешить, топить, тихий, океан, надеяться, изыскание, внебюджетный, средство, надеяться, правительство, оплачивать, финальный, экспедиция, мир, февраль, март, год, новый, экспедиция, должный, постепенно, перевести, станция, низкий, орбита, тонный, мир, основной, сгорать, атмосфера, незначительный, часть, упасть, тихий, океан, никакой, гарантия, переживать, столько, отказ, оборудование, конструкция, поводить, адекватно, команда, афанасьев, перечислять, потенциально, опасный, элемент, мир, пресс, конференция, состояться, понедельник, звездный, городок, мочь, упасть, давление, кабина, сказать, это, становиться, серьезный, препятствие, работа, ...]\n",
              "topic                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                    Россия\n",
              "date                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                 1999/08/31\n",
              "Name: 5, dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 45
        }
      ],
      "source": [
        "i = 0\n",
        "d = data.iloc[i]\n",
        "d"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 46,
      "metadata": {
        "id": "JLQV8q4eZcGx",
        "outputId": "bfaf30b9-1702-468b-86b3-1f8a8e90b1c4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 116
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'doc0 становиться известно агентство ассошиэйтед пресс экипаж последний экспедиция станция мир считать способный выходить контроль командир виктор афанасьев сказать чувствовать грустить оставлять станция летать беспилотный режим gazeta подробно писать финансирование программа мир закрывать конструктор спешить топить тихий океан надеяться изыскание внебюджетный средство надеяться правительство оплачивать финальный экспедиция мир февраль март год новый экспедиция должный постепенно перевести станция низкий орбита тонный мир основной сгорать атмосфера незначительный часть упасть тихий океан никакой гарантия переживать столько отказ оборудование конструкция поводить адекватно команда афанасьев перечислять потенциально опасный элемент мир пресс конференция состояться понедельник звездный городок мочь упасть давление кабина сказать это становиться серьезный препятствие работа следующий экспедиция слишком большой роль человеческий фактор элементарный ошибка мочь приводить потеря топливо потеря ориентация американский космический агентство аэронавтика космический пространство предлагать россия затапливать мир ресурс переводить международный космический станция россия хотеть расставаться легендарный символ |topic Россия'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 46
        }
      ],
      "source": [
        "f'doc{i} {\" \".join(d.text)} |topic {d.topic}'"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 47,
      "metadata": {
        "id": "S4pKkL9_ZcGx"
      },
      "outputs": [],
      "source": [
        "def convert_to_vw_format(index, row):\n",
        "    return f'doc{index} {\" \".join(row.text)} |topic {row.topic}'"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 69,
      "metadata": {
        "id": "6YP5RSrZZcGx"
      },
      "outputs": [],
      "source": [
        "with open('lenta_wv.wv', 'w') as f:\n",
        "    for index, row in data.iterrows():\n",
        "        vw_string = convert_to_vw_format(index, row)\n",
        "        f.write(vw_string)\n",
        "        f.write('\\n')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "p8Wb35peZcGx"
      },
      "source": [
        "Далее нужно создать объект BatchVectorizer для данных, которые мы записали в формате vowpal wabbit:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 70,
      "metadata": {
        "id": "0T-hgiL9ZcGx"
      },
      "outputs": [],
      "source": [
        "batch_vectorizer = artm.BatchVectorizer(data_path='lenta_wv.wv',\n",
        "                                        data_format='vowpal_wabbit',\n",
        "                                        target_folder='lenta_batches')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tSn2IdUOZcGx"
      },
      "source": [
        "Попробуем обучить модель LDA для 10 тем:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 71,
      "metadata": {
        "id": "-PsQznO3ZcGy",
        "outputId": "0392a362-2f5c-4d61-c449-1d8aad62bd4c",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "CPU times: user 29.4 s, sys: 313 ms, total: 29.7 s\n",
            "Wall time: 19.6 s\n"
          ]
        }
      ],
      "source": [
        "%%time\n",
        "lda = artm.LDA(num_topics=10, alpha=0.1, beta=0.001,\n",
        "               cache_theta=True,\n",
        "               num_document_passes=5,\n",
        "               dictionary=batch_vectorizer.dictionary)\n",
        "\n",
        "lda.fit_offline(batch_vectorizer=batch_vectorizer,\n",
        "                num_collection_passes=10)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aHJfQ3x8ZcGy"
      },
      "source": [
        "- __fit_offline__ (офлайн алгоритм): много проходов по коллекции, один проход по документу, одно обновление матрицы $\\Phi$ в конце прохода по коллекции. __Лучше использовать при работе с небольшой коллекцией__.\n",
        "\n",
        "\n",
        "- __fit_online__ (онлайн алгоритм) : один проход по коллекции, несколько проходов по документу, несколько обновлений матрицы $\\Phi$ в конце прохода по коллекции. __Лучше использовать при работе с большой коллекцией, или коллекцией с часто меняющимеся темами.__\n",
        "\n",
        "\n",
        "\n",
        "Можем посмотреть на все значения перплексии (для каждой итерации по коллекции из num_collection_passes=10):"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 72,
      "metadata": {
        "id": "d7PKhU1oZcGy",
        "outputId": "f0e545f8-3874-4ca5-c982-9b92ca8169ca",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[49334.46875,\n",
              " 4345.82666015625,\n",
              " 3892.929931640625,\n",
              " 3221.296630859375,\n",
              " 2751.091796875,\n",
              " 2520.399658203125,\n",
              " 2405.1904296875,\n",
              " 2337.60986328125,\n",
              " 2294.09716796875,\n",
              " 2264.379638671875]"
            ]
          },
          "metadata": {},
          "execution_count": 72
        }
      ],
      "source": [
        "lda.perplexity_value"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 73,
      "metadata": {
        "id": "KxfCtjDGZcGy"
      },
      "outputs": [],
      "source": [
        "import numpy as np"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 74,
      "metadata": {
        "id": "0RDJ0mO1ZcGy",
        "outputId": "04451740-fff3-4be8-afb6-79bd95e5ba07",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "7.725056110191268"
            ]
          },
          "metadata": {},
          "execution_count": 74
        }
      ],
      "source": [
        "np.log(lda.perplexity_last_value)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "R_ICrSngZcGy"
      },
      "source": [
        "Посмотрим на топ-10 токенов для каждой из тем:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 75,
      "metadata": {
        "id": "doE1W76qZcGy",
        "outputId": "74ce159f-6377-44f4-f16b-02aa3e15e9cb",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Topic #0: ['компания', 'процент', 'акция', 'год', 'сша', 'доллар', 'который', 'индекс', 'рынок', 'свой']\n",
            "Topic #1: ['год', 'доллар', 'миллион', 'свой', 'который', 'тысяча', 'сообщать', 'мир', 'клуб', 'это']\n",
            "Topic #2: ['который', 'свой', 'весь', 'человек', 'это', 'время', 'сообщать', 'сказать', 'олимпийский', 'мочь']\n",
            "Topic #3: ['матч', 'первый', 'команда', 'второй', 'сборная', 'чемпионат', 'который', 'турнир', 'свой', 'место']\n",
            "Topic #4: ['год', 'доллар', 'миллион', 'россия', 'банк', 'миллиард', 'российский', 'суд', 'долг', 'который']\n",
            "Topic #5: ['компания', 'россия', 'который', 'сообщать', 'директор', 'президент', 'российский', 'это', 'совет', 'свой']\n",
            "Topic #6: ['который', 'год', 'свой', 'это', 'весь', 'команда', 'заявлять', 'новый', 'сказать', 'концерт']\n",
            "Topic #7: ['год', 'процент', 'рубль', 'доллар', 'нефть', 'россия', 'цена', 'сообщать', 'составлять', 'страна']\n",
            "Topic #8: ['фильм', 'год', 'режиссер', 'роль', 'который', 'картина', 'фестиваль', 'главный', 'свой', 'хороший']\n",
            "Topic #9: ['год', 'россия', 'президент', 'сообщать', 'который', 'проект', 'правительство', 'также', 'путин', 'газ']\n"
          ]
        }
      ],
      "source": [
        "top_tokens = lda.get_top_tokens(num_tokens=10)\n",
        "for i, token_list in enumerate(top_tokens):\n",
        "     print ('Topic #{0}: {1}'.format(i, token_list))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QMf527TiZcGz"
      },
      "source": [
        "[Более подробный ноутбук про модель LDA](https://github.com/bigartm/bigartm-book/blob/master/LDA_tutorial_RU.ipynb)\n",
        "\n",
        "Теперь построим базовую модель pLSA:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 76,
      "metadata": {
        "id": "dxEWK0RGZcGz"
      },
      "outputs": [],
      "source": [
        "plsa = artm.ARTM(num_topics=10,\n",
        "                 dictionary=batch_vectorizer.dictionary)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "l-_1h-qmZcGz"
      },
      "source": [
        "Добавим расчет перплексии:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 77,
      "metadata": {
        "id": "qVEh08z1ZcGz"
      },
      "outputs": [],
      "source": [
        "plsa.scores.add(artm.PerplexityScore(name='lenta_perplexity_score',\n",
        "                 dictionary=batch_vectorizer.dictionary))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pg3lESJYZcGz"
      },
      "source": [
        "И TopTokensScore, чтобы взглянуть на самые вероятные слова в каждой теме:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 78,
      "metadata": {
        "id": "nXEpRXJsZcGz"
      },
      "outputs": [],
      "source": [
        "plsa.scores.add(artm.TopTokensScore(name='TopTokensScore', num_tokens=6))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 79,
      "metadata": {
        "id": "-sRwDteOZcGz"
      },
      "outputs": [],
      "source": [
        "plsa.fit_offline(batch_vectorizer=batch_vectorizer,\n",
        "                 num_collection_passes=20)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 80,
      "metadata": {
        "id": "kkMCBuFhZcGz",
        "outputId": "b8114884-2dca-4221-c346-0f6fd352c932",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
            "  and should_run_async(code)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[48730.21875,\n",
              " 4177.1318359375,\n",
              " 3475.912353515625,\n",
              " 2818.471435546875,\n",
              " 2519.5185546875,\n",
              " 2388.373291015625,\n",
              " 2316.196044921875,\n",
              " 2271.542724609375,\n",
              " 2241.67138671875,\n",
              " 2220.335693359375,\n",
              " 2204.4912109375,\n",
              " 2192.273193359375,\n",
              " 2182.108642578125,\n",
              " 2173.490966796875,\n",
              " 2166.072021484375,\n",
              " 2159.654541015625,\n",
              " 2153.789794921875,\n",
              " 2148.32861328125,\n",
              " 2143.55419921875,\n",
              " 2139.44140625]"
            ]
          },
          "metadata": {},
          "execution_count": 80
        }
      ],
      "source": [
        "plsa.score_tracker['lenta_perplexity_score'].value"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 81,
      "metadata": {
        "id": "kXeAOPoHZcG0",
        "outputId": "a39783a9-c661-4610-9d48-098be9a380c2",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "topic_0:  ['компания', 'процент', 'доллар', 'год', 'сша', 'акция']\n",
            "topic_1:  ['год', 'который', 'музей', 'тысяча', 'свой', 'это']\n",
            "topic_2:  ['который', 'свой', 'это', 'человек', 'весь', 'время']\n",
            "topic_3:  ['матч', 'первый', 'команда', 'второй', 'сборная', 'чемпионат']\n",
            "topic_4:  ['доллар', 'год', 'россия', 'миллион', 'банк', 'миллиард']\n",
            "topic_5:  ['компания', 'директор', 'который', 'россия', 'сообщать', 'президент']\n",
            "topic_6:  ['который', 'свой', 'год', 'команда', 'весь', 'это']\n",
            "topic_7:  ['год', 'процент', 'рубль', 'нефть', 'цена', 'составлять']\n",
            "topic_8:  ['фильм', 'режиссер', 'год', 'роль', 'который', 'главный']\n",
            "topic_9:  ['россия', 'правительство', 'проект', 'сообщать', 'год', 'который']\n"
          ]
        }
      ],
      "source": [
        "for topic_name in plsa.topic_names:\n",
        "    print (topic_name + ': ', plsa.score_tracker['TopTokensScore'].last_tokens[topic_name])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IdV63eLqZcG0"
      },
      "source": [
        "Обратите внимание на слова с заглавной буквы: это поле topic из оригинальных данных.\n",
        "\n",
        "Попробуем удалить из словаря слишком редкие и слишком частые токены:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 82,
      "metadata": {
        "id": "hwGa9MYRZcG0",
        "outputId": "cfa627f9-ebb4-4e5f-efed-d77f5a9b646b",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "artm.Dictionary(name=59930fb7-71fd-4be8-bff8-8a8ec1965431, num_entries=9944)"
            ]
          },
          "metadata": {},
          "execution_count": 82
        }
      ],
      "source": [
        "batch_vectorizer.dictionary.filter(min_tf=10, max_tf=5000)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 84,
      "metadata": {
        "id": "E6qEC6B9ZcG0"
      },
      "outputs": [],
      "source": [
        "plsa = artm.ARTM(num_topics=10,\n",
        "                 dictionary=batch_vectorizer.dictionary)\n",
        "plsa.scores.add(artm.PerplexityScore(name='lenta_perplexity_score',\n",
        "                dictionary=batch_vectorizer.dictionary))\n",
        "\n",
        "plsa.scores.add(artm.TopTokensScore(name='TopTokensScore',\n",
        "                                    num_tokens=6))\n",
        "plsa.fit_offline(batch_vectorizer=batch_vectorizer,\n",
        "                 num_collection_passes=20)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 85,
      "metadata": {
        "id": "-UUgavOpZcG0",
        "outputId": "0d757366-a281-4bc1-e024-53991e050720",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[8789.455078125,\n",
              " 2779.507080078125,\n",
              " 2353.55517578125,\n",
              " 1912.46435546875,\n",
              " 1691.0028076171875,\n",
              " 1584.080078125,\n",
              " 1527.3642578125,\n",
              " 1494.6923828125,\n",
              " 1474.0860595703125,\n",
              " 1460.140380859375,\n",
              " 1449.95263671875,\n",
              " 1442.2021484375,\n",
              " 1436.402099609375,\n",
              " 1431.9168701171875,\n",
              " 1428.3013916015625,\n",
              " 1425.227783203125,\n",
              " 1422.4620361328125,\n",
              " 1419.9444580078125,\n",
              " 1417.6612548828125,\n",
              " 1415.54296875]"
            ]
          },
          "metadata": {},
          "execution_count": 85
        }
      ],
      "source": [
        "plsa.score_tracker['lenta_perplexity_score'].value"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "R2NSZlRPZcG0"
      },
      "source": [
        "Стало заметно лучше :)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 86,
      "metadata": {
        "id": "gBBxTBWKZcG1",
        "outputId": "20a366e1-dee9-447a-c3a1-1b8264b89ffb",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "topic_0:  ['процент', 'компания', 'цена', 'составлять', 'сша', 'рынок']\n",
            "topic_1:  ['матч', 'команда', 'клуб', 'сборная', 'игра', 'минута']\n",
            "topic_2:  ['роль', 'режиссер', 'актер', 'главный', 'the', 'снимать']\n",
            "topic_3:  ['первый', 'турнир', 'второй', 'место', 'российский', 'выигрывать']\n",
            "topic_4:  ['миллион', 'российский', 'миллиард', 'банк', 'долг', 'страна']\n",
            "topic_5:  ['компания', 'директор', 'предприятие', 'президент', 'акция', 'рао']\n",
            "topic_6:  ['нефть', 'заявлять', 'решение', 'баррель', 'страна', 'суд']\n",
            "topic_7:  ['рубль', 'правительство', 'процент', 'бюджет', 'налог', 'закон']\n",
            "topic_8:  ['фестиваль', 'театр', 'премия', 'москва', 'известный', 'российский']\n",
            "topic_9:  ['город', 'тысяча', 'музей', 'передавать', 'время', 'весь']\n"
          ]
        }
      ],
      "source": [
        "for topic_name in plsa.topic_names:\n",
        "    print (topic_name + ': ', plsa.score_tracker['TopTokensScore'].last_tokens[topic_name])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5kvFR5X4ZcG1"
      },
      "source": [
        "__Мультимодальные модели__\n",
        "\n",
        "В библиотеке ARTM можно обучать мультимодальные модели, например, давайте попробуем обучить модель, которая будет учитывать поле topic из наших данных (мы специально записали его отдельно через | )"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 87,
      "metadata": {
        "id": "PS2_Fl9aZcG1"
      },
      "outputs": [],
      "source": [
        "# описываем модель и задаем веса классов\n",
        "martm = artm.ARTM(num_topics=20,\n",
        "                  class_ids={'@default_class': 1.0, '@topic': 5.0},\n",
        "                  dictionary=batch_vectorizer.dictionary)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GLYkii-9ZcG1"
      },
      "source": [
        "Добавим расчет метрик и регуляризаторы:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 88,
      "metadata": {
        "id": "EVGKX050ZcG1"
      },
      "outputs": [],
      "source": [
        "martm.scores.add(artm.SparsityPhiScore(name='sparsity_phi_score',\n",
        "                                       class_id='@topic'))\n",
        "\n",
        "martm.scores.add(artm.TopTokensScore(name='TopTokensScore',\n",
        "                                     num_tokens=6))\n",
        "\n",
        "\n",
        "martm.scores.add(artm.PerplexityScore(name='lenta_perplexity_score',\n",
        "                dictionary=batch_vectorizer.dictionary))\n",
        "\n",
        "martm.regularizers.add(artm.DecorrelatorPhiRegularizer(name='decorrelator_phi_def',\n",
        "                    class_ids=['@default_class']))\n",
        "\n",
        "martm.regularizers.add(artm.DecorrelatorPhiRegularizer(name='decorrelator_phi_lab',\n",
        "                        class_ids=['@topic']))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 89,
      "metadata": {
        "id": "facj5vwoZcG1"
      },
      "outputs": [],
      "source": [
        "martm.fit_offline(batch_vectorizer=batch_vectorizer,\n",
        "                  num_collection_passes=10)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 91,
      "metadata": {
        "id": "5tvZjQtnZcG2",
        "outputId": "fe7505fd-ec71-4243-f79f-66d6939ac9b6",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "topic_0:  ['компания', 'акция', 'процент', 'рынок', 'курс', 'индекс']\n",
            "topic_1:  ['команда', 'клуб', 'матч', 'игрок', 'сборная', 'тренер']\n",
            "topic_2:  ['человек', 'группа', 'весь', 'песня', 'летний', 'концерт']\n",
            "topic_3:  ['мир', 'принимать', 'участие', 'япония', 'весь', 'чемпион']\n",
            "topic_4:  ['миллиард', 'банк', 'миллион', 'долг', 'сумма', 'рубль']\n",
            "topic_5:  ['москва', 'фестиваль', 'премия', 'московский', 'александр', 'российский']\n",
            "topic_6:  ['заявлять', 'дело', 'президент', 'новый', 'слово', 'глава']\n",
            "topic_7:  ['правительство', 'закон', 'бюджет', 'налог', 'федеральный', 'рубль']\n",
            "topic_8:  ['картина', 'театр', 'известный', 'художник', 'выставка', 'писатель']\n",
            "topic_9:  ['музей', 'передавать', 'век', 'город', 'центр', 'президент']\n",
            "topic_10:  ['компания', 'предприятие', 'рао', 'акция', 'директор', 'еэс']\n",
            "topic_11:  ['сша', 'страна', 'евро', 'американский', 'экономика', 'экономический']\n",
            "topic_12:  ['роль', 'сыграть', 'режиссер', 'съемка', 'главный', 'сценарий']\n",
            "topic_13:  ['режиссер', 'хороший', 'кино', 'роль', 'актер', 'получать']\n",
            "topic_14:  ['суд', 'российский', 'газ', 'решение', 'украина', 'соглашение']\n",
            "topic_15:  ['матч', 'первый', 'второй', 'счет', 'турнир', 'победа']\n",
            "topic_16:  ['нефть', 'баррель', 'олимпийский', 'российский', 'опек', 'медаль']\n",
            "topic_17:  ['нью', 'машина', 'йорк', 'команда', 'мир', 'этап']\n",
            "topic_18:  ['компания', 'москва', 'тысяча', 'российский', 'самолет', 'фирма']\n",
            "topic_19:  ['процент', 'рубль', 'составлять', 'цена', 'рост', 'уровень']\n"
          ]
        }
      ],
      "source": [
        "for topic_name in martm.topic_names:\n",
        "    print (topic_name + ': ', martm.score_tracker['TopTokensScore'].last_tokens[topic_name])"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## [BERTopic](https://huggingface.co/blog/bertopic)\n",
        "\n"
      ],
      "metadata": {
        "id": "bXdJwLsi78nZ"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 51,
      "metadata": {
        "id": "KxTrohNOZcG3"
      },
      "outputs": [],
      "source": [
        "from bertopic import BERTopic"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Заново загрузим датасет:"
      ],
      "metadata": {
        "id": "3_Dm-04FAxtQ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data = pd.read_csv('lenta-ru-news-full.csv', usecols=['text', 'topic',  'date'])\n",
        "topics = ['Россия', 'Мир', 'Спорт', 'Экономика', 'Интернет и СМИ']\n",
        "\n",
        "data = data[data.topic.isin(topics)].sample(5000)\n",
        "\n",
        "data.sample()"
      ],
      "metadata": {
        "id": "7doX7yB-_7k_",
        "outputId": "ad91c3b0-2ea6-4fb4-8974-883a497cda95",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 263
        }
      },
      "execution_count": 100,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/ipykernel/ipkernel.py:283: DeprecationWarning:\n",
            "\n",
            "`should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
            "\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                      text      topic        date\n",
              "547569  За первые четыре месяца 2015 года россияне потратили на покупку новых автомобилей 575,7 миллиарда рублей, что на 25 процентов меньше, чем за аналогичный период прошлого года (764,7 миллиарда рублей). Об этом «Ленте.ру» сообщили в аналитическом агентстве «Автостат». В целом продажи снизились на 38 процентов до 487,6 тысячи машин. При этом средняя цена одного купленного автомобиля выросла на 21 процент, до 1,18 миллиона рублей. Иномарки подорожали на 24,5 процента, до 1,35 миллиона, отечественные машины — на 14,9 процента, до 482 тысяч рублей. Больше всего было продано автомобилей Lada — 89 411 штук (выручка составила 41,3 миллиарда рублей). На втором месте Hyundai с 53 326 проданными машинами (выручка — 46,5 миллиарда рублей), на третьем — Kia (49 932 штуки, 44,1 миллиарда рублей). По данным Ассоциации европейского бизнеса (АЕБ), в апреле продажи легковых автомобилей в России упали на 40 процентов по сравнению с аналогичным месяцем 2014 года. Всего удалось реализовать 132 тысячи машин — худший результат за последние десять лет. Кроме того, в первом квартале 2015 года импорт автомобилей в РФ снизился почти в два раза, до 87,3 тысячи штук.  Экономика  2015/05/25"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-b7661353-5baf-4474-87c3-030d1d84e44c\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
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              "\n",
              "    .dataframe thead th {\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>text</th>\n",
              "      <th>topic</th>\n",
              "      <th>date</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>547569</th>\n",
              "      <td>За первые четыре месяца 2015 года россияне потратили на покупку новых автомобилей 575,7 миллиарда рублей, что на 25 процентов меньше, чем за аналогичный период прошлого года (764,7 миллиарда рублей). Об этом «Ленте.ру» сообщили в аналитическом агентстве «Автостат». В целом продажи снизились на 38 процентов до 487,6 тысячи машин. При этом средняя цена одного купленного автомобиля выросла на 21 процент, до 1,18 миллиона рублей. Иномарки подорожали на 24,5 процента, до 1,35 миллиона, отечественные машины — на 14,9 процента, до 482 тысяч рублей. Больше всего было продано автомобилей Lada — 89 411 штук (выручка составила 41,3 миллиарда рублей). На втором месте Hyundai с 53 326 проданными машинами (выручка — 46,5 миллиарда рублей), на третьем — Kia (49 932 штуки, 44,1 миллиарда рублей). По данным Ассоциации европейского бизнеса (АЕБ), в апреле продажи легковых автомобилей в России упали на 40 процентов по сравнению с аналогичным месяцем 2014 года. Всего удалось реализовать 132 тысячи машин — худший результат за последние десять лет. Кроме того, в первом квартале 2015 года импорт автомобилей в РФ снизился почти в два раза, до 87,3 тысячи штук.</td>\n",
              "      <td>Экономика</td>\n",
              "      <td>2015/05/25</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "\n",
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            ]
          },
          "metadata": {},
          "execution_count": 100
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "docs = data['text'].values"
      ],
      "metadata": {
        "id": "6J-OJCK-A3HM"
      },
      "execution_count": 117,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Загрузим модель для русского языка:"
      ],
      "metadata": {
        "id": "MdJ8cj-_GULZ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "topic_model = BERTopic(language=\"russian\", verbose=True)"
      ],
      "metadata": {
        "id": "w8jpOjWQ_Xy5"
      },
      "execution_count": 104,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#help(BERTopic)"
      ],
      "metadata": {
        "id": "t0OmxpxTGWpL"
      },
      "execution_count": 76,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#help(topic_model.fit_transform)"
      ],
      "metadata": {
        "id": "pHrjeoSWCSlr"
      },
      "execution_count": 62,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "%time topics, probs = topic_model.fit_transform(docs)"
      ],
      "metadata": {
        "id": "7V5y5nI4A735",
        "outputId": "f19f6e3c-1f49-4053-ac44-8d53dea693e8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 214,
          "referenced_widgets": [
            "227dbddcd4fb4984809ed27a757c00cb",
            "43ecf0a86035458a8e633a8d615975d6",
            "f2575a360f6f4c9699bd4caffd5828b4",
            "28a9318ee43349928e2944e317315c07",
            "3f2f1ddb05e846ce8244672b6b237c41",
            "a08ed0a3d9e648f095fcefd9898898be",
            "e5f39cc4caaf460e89a88331044192de",
            "dfddccef93d342e6bdc1076a7c0b9c5c",
            "00c729b752354415afd4c584aa3ff2cd",
            "09fa0897060b41f1a97e6cd7efd9a477",
            "f009edb647f6478985c58468d48e3082"
          ]
        }
      },
      "execution_count": 105,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "2023-12-06 13:05:01,971 - BERTopic - Embedding - Transforming documents to embeddings.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Batches:   0%|          | 0/32 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "227dbddcd4fb4984809ed27a757c00cb"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "2023-12-06 13:07:37,881 - BERTopic - Embedding - Completed ✓\n",
            "2023-12-06 13:07:37,884 - BERTopic - Dimensionality - Fitting the dimensionality reduction algorithm\n",
            "2023-12-06 13:07:44,002 - BERTopic - Dimensionality - Completed ✓\n",
            "2023-12-06 13:07:44,007 - BERTopic - Cluster - Start clustering the reduced embeddings\n",
            "2023-12-06 13:07:44,056 - BERTopic - Cluster - Completed ✓\n",
            "2023-12-06 13:07:44,063 - BERTopic - Representation - Extracting topics from clusters using representation models.\n",
            "2023-12-06 13:07:44,682 - BERTopic - Representation - Completed ✓\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "CPU times: user 2min 32s, sys: 4.57 s, total: 2min 36s\n",
            "Wall time: 2min 43s\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "inf = topic_model.get_topic_info()\n",
        "inf[['Topic', 'Count', 'Name', 'Representation']]"
      ],
      "metadata": {
        "id": "3vjcwV88BBIH",
        "outputId": "799ed668-b1f6-4cfa-caaa-ee433ef22c15",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 718
        }
      },
      "execution_count": 106,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    Topic  Count                                     Name                                                                       Representation\n",
              "0      -1    255                          -1_на_что_по_не                                     [на, что, по, не, как, из, его, за, сша, россии]\n",
              "1       0    103                        0_на_по_чечни_что                              [на, по, чечни, что, не, боевиков, он, из, россии, как]\n",
              "2       1    102                   1_интернет_на_что_2000                               [интернет, на, что, 2000, по, сша, для, года, не, как]\n",
              "3       2     89                          2_на_по_для_что                             [на, по, для, что, за, рублей, не, как, граждан, москвы]\n",
              "4       3     50              3_кандидатов_цик_блока_лдпр                     [кандидатов, цик, блока, лдпр, что, по, на, выборах, не, списка]\n",
              "5       4     41                      4_на_за_нефть_нефти                        [на, за, нефть, нефти, цен, цены, по, тонну, доллара, рублей]\n",
              "6       5     31                         5_на_улице_из_по                 [на, улице, из, по, дома, не, сотрудники, устройство, что, взрывное]\n",
              "7       6     31                          6_суд_по_на_что                               [суд, по, на, что, не, его, решение, суда, делу, дело]\n",
              "8       7     31                     7_взрыва_на_по_взрыв                 [взрыва, на, по, взрыв, произошел, пожар, не, как, результате, дома]\n",
              "9       8     28        8_президента_рао_что_тюменьэнерго                 [президента, рао, что, тюменьэнерго, россии, главы, еэс, он, на, по]\n",
              "10      9     28                   9_человек_из_на_ураган         [человек, из, на, ураган, по, землетрясения, более, флойд, погибли, урагана]\n",
              "11     10     27                      10_bank_york_new_of                              [bank, york, new, of, денег, банка, что, счета, по, на]\n",
              "12     11     26                     11_по_из_колокола_на                    [по, из, колокола, на, заложников, данным, они, фсб, грузии, как]\n",
              "13     12     25        12_самолет_самолета_на_пассажиров                 [самолет, самолета, на, пассажиров, из, борту, по, при, су, экипажа]\n",
              "14     13     25                     13_мвф_россии_по_что                         [мвф, россии, по, что, на, банка, не, касьянов, ссср, долга]\n",
              "15     14     19                14_nasa_на_орбиту_станции                [nasa, на, орбиту, станции, что, mars, запуск, космодрома, по, space]\n",
              "16     15     18  15_южно_области_заболевания_сахалинской  [южно, области, заболевания, сахалинской, на, болезнь, ученые, холеры, от, больных]\n",
              "17     16     18              16_на_протеста_карачаево_не       [на, протеста, карачаево, не, перемены, черкесии, митинг, республики, по, что]\n",
              "18     17     15   17_горбачевой_культуры_раиса_горбачева         [горбачевой, культуры, раиса, горбачева, на, фонда, раисы, ее, его, лихачев]\n",
              "19     18     14                  18_убит_был_на_убийства                 [убит, был, на, убийства, его, как, района, убийство, по, пистолета]\n",
              "20     19     12                  19_сша_что_новой_россии                    [сша, что, новой, россии, также, про, по, ракет, обороны, россия]\n",
              "21     20     12                20_автобус_автобуса_из_по        [автобус, автобуса, из, по, результате, на, беженцами, что, погибли, человек]"
            ],
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Topic</th>\n",
              "      <th>Count</th>\n",
              "      <th>Name</th>\n",
              "      <th>Representation</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>-1</td>\n",
              "      <td>255</td>\n",
              "      <td>-1_на_что_по_не</td>\n",
              "      <td>[на, что, по, не, как, из, его, за, сша, россии]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0</td>\n",
              "      <td>103</td>\n",
              "      <td>0_на_по_чечни_что</td>\n",
              "      <td>[на, по, чечни, что, не, боевиков, он, из, россии, как]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>102</td>\n",
              "      <td>1_интернет_на_что_2000</td>\n",
              "      <td>[интернет, на, что, 2000, по, сша, для, года, не, как]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2</td>\n",
              "      <td>89</td>\n",
              "      <td>2_на_по_для_что</td>\n",
              "      <td>[на, по, для, что, за, рублей, не, как, граждан, москвы]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>3</td>\n",
              "      <td>50</td>\n",
              "      <td>3_кандидатов_цик_блока_лдпр</td>\n",
              "      <td>[кандидатов, цик, блока, лдпр, что, по, на, выборах, не, списка]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>4</td>\n",
              "      <td>41</td>\n",
              "      <td>4_на_за_нефть_нефти</td>\n",
              "      <td>[на, за, нефть, нефти, цен, цены, по, тонну, доллара, рублей]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>5</td>\n",
              "      <td>31</td>\n",
              "      <td>5_на_улице_из_по</td>\n",
              "      <td>[на, улице, из, по, дома, не, сотрудники, устройство, что, взрывное]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>6</td>\n",
              "      <td>31</td>\n",
              "      <td>6_суд_по_на_что</td>\n",
              "      <td>[суд, по, на, что, не, его, решение, суда, делу, дело]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>7</td>\n",
              "      <td>31</td>\n",
              "      <td>7_взрыва_на_по_взрыв</td>\n",
              "      <td>[взрыва, на, по, взрыв, произошел, пожар, не, как, результате, дома]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>8</td>\n",
              "      <td>28</td>\n",
              "      <td>8_президента_рао_что_тюменьэнерго</td>\n",
              "      <td>[президента, рао, что, тюменьэнерго, россии, главы, еэс, он, на, по]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>9</td>\n",
              "      <td>28</td>\n",
              "      <td>9_человек_из_на_ураган</td>\n",
              "      <td>[человек, из, на, ураган, по, землетрясения, более, флойд, погибли, урагана]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>10</td>\n",
              "      <td>27</td>\n",
              "      <td>10_bank_york_new_of</td>\n",
              "      <td>[bank, york, new, of, денег, банка, что, счета, по, на]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>11</td>\n",
              "      <td>26</td>\n",
              "      <td>11_по_из_колокола_на</td>\n",
              "      <td>[по, из, колокола, на, заложников, данным, они, фсб, грузии, как]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>12</td>\n",
              "      <td>25</td>\n",
              "      <td>12_самолет_самолета_на_пассажиров</td>\n",
              "      <td>[самолет, самолета, на, пассажиров, из, борту, по, при, су, экипажа]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>13</td>\n",
              "      <td>25</td>\n",
              "      <td>13_мвф_россии_по_что</td>\n",
              "      <td>[мвф, россии, по, что, на, банка, не, касьянов, ссср, долга]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>14</td>\n",
              "      <td>19</td>\n",
              "      <td>14_nasa_на_орбиту_станции</td>\n",
              "      <td>[nasa, на, орбиту, станции, что, mars, запуск, космодрома, по, space]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>15</td>\n",
              "      <td>18</td>\n",
              "      <td>15_южно_области_заболевания_сахалинской</td>\n",
              "      <td>[южно, области, заболевания, сахалинской, на, болезнь, ученые, холеры, от, больных]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>16</td>\n",
              "      <td>18</td>\n",
              "      <td>16_на_протеста_карачаево_не</td>\n",
              "      <td>[на, протеста, карачаево, не, перемены, черкесии, митинг, республики, по, что]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>17</td>\n",
              "      <td>15</td>\n",
              "      <td>17_горбачевой_культуры_раиса_горбачева</td>\n",
              "      <td>[горбачевой, культуры, раиса, горбачева, на, фонда, раисы, ее, его, лихачев]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>18</td>\n",
              "      <td>14</td>\n",
              "      <td>18_убит_был_на_убийства</td>\n",
              "      <td>[убит, был, на, убийства, его, как, района, убийство, по, пистолета]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>19</td>\n",
              "      <td>12</td>\n",
              "      <td>19_сша_что_новой_россии</td>\n",
              "      <td>[сша, что, новой, россии, также, про, по, ракет, обороны, россия]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>20</td>\n",
              "      <td>12</td>\n",
              "      <td>20_автобус_автобуса_из_по</td>\n",
              "      <td>[автобус, автобуса, из, по, результате, на, беженцами, что, погибли, человек]</td>\n",
              "    </tr>\n",
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              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "        document.querySelector('#df-79599a54-0d71-47da-8c9c-62e16ac49c6c button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-79599a54-0d71-47da-8c9c-62e16ac49c6c');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
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              "        if (!dataTable) return;\n",
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              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
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              "\n",
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              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-3633e4e2-cdc5-45a0-98d3-c2c230e1b5c1')\"\n",
              "            title=\"Suggest charts\"\n",
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              "  </button>\n",
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              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
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              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
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              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
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              "  .colab-df-quickchart-complete:disabled,\n",
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              "    background-color: var(--disabled-bg-color);\n",
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              "    border-bottom-color: var(--fill-color);\n",
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              "  @keyframes spin {\n",
              "    0% {\n",
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              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-3633e4e2-cdc5-45a0-98d3-c2c230e1b5c1 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 106
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "topic_model.get_topic(14)"
      ],
      "metadata": {
        "id": "qOILh2HtSY7X",
        "outputId": "22c80618-468e-4278-fdc1-8b66e2458d96",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 123,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[('nasa', 0.035264090151416974),\n",
              " ('на', 0.03176761636083491),\n",
              " ('орбиту', 0.029520609235509865),\n",
              " ('станции', 0.02248463599457192),\n",
              " ('что', 0.0210403495505839),\n",
              " ('mars', 0.02058706101873802),\n",
              " ('запуск', 0.020346356240163302),\n",
              " ('космодрома', 0.020346356240163302),\n",
              " ('по', 0.019098219537429607),\n",
              " ('space', 0.019003440940373553)]"
            ]
          },
          "metadata": {},
          "execution_count": 123
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "topic_model.visualize_topics()"
      ],
      "metadata": {
        "id": "VQ0zvS6fEsRu",
        "outputId": "927e3b4c-8498-4c6c-86d1-5450e1cf851a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 667
        }
      },
      "execution_count": 107,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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              "</body>\n",
              "</html>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Можно использовать другой алгоритм кластеризации в пайплайне:"
      ],
      "metadata": {
        "id": "0912jIKxM0Vi"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.cluster import KMeans\n",
        "\n",
        "cluster_model = KMeans(n_clusters=10)\n",
        "topic_model_kmeans = BERTopic(hdbscan_model=cluster_model, language='russian', verbose=True)\n",
        "\n",
        "topics_kmeans, probs_kmeans = topic_model_kmeans.fit_transform(docs)"
      ],
      "metadata": {
        "id": "UuKriL8xFTs5",
        "outputId": "12060b0a-b5a8-44eb-a10c-49362d8a6396",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 181,
          "referenced_widgets": [
            "be8ab31e91ba4ebbbfe0e671f5e41011",
            "db086c129a864793a97f9ebe32beb644",
            "57d5e3b2be4446f894c1b44a96b1b83a",
            "2703e0ae3d974b4ea34b8df434ce8ac7",
            "a8699a619fe84f05a37c977dde16fa28",
            "2f976db074ac4155b0b04e5794e6a12a",
            "2f0c2eee69f54673ac83db0601edc07c",
            "4b2ce007fa404482b8b408ba35c92ad0",
            "b588dfdb16bd403db88900845a3e1d9d",
            "e74b8815097e47d88bb20731a8f3f2b5",
            "17b7bef5ea7c495287d7a112945032c8"
          ]
        }
      },
      "execution_count": 124,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "2023-12-06 13:20:47,101 - BERTopic - Embedding - Transforming documents to embeddings.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Batches:   0%|          | 0/157 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "be8ab31e91ba4ebbbfe0e671f5e41011"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "2023-12-06 13:32:20,120 - BERTopic - Embedding - Completed ✓\n",
            "2023-12-06 13:32:20,123 - BERTopic - Dimensionality - Fitting the dimensionality reduction algorithm\n",
            "2023-12-06 13:33:03,190 - BERTopic - Dimensionality - Completed ✓\n",
            "2023-12-06 13:33:03,192 - BERTopic - Cluster - Start clustering the reduced embeddings\n",
            "2023-12-06 13:33:03,865 - BERTopic - Cluster - Completed ✓\n",
            "2023-12-06 13:33:03,874 - BERTopic - Representation - Extracting topics from clusters using representation models.\n",
            "2023-12-06 13:33:06,284 - BERTopic - Representation - Completed ✓\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "inf = topic_model_kmeans.get_topic_info()\n",
        "inf[['Topic', 'Count', 'Name', 'Representation']]"
      ],
      "metadata": {
        "id": "gYl6gPbyH9ta",
        "outputId": "b9d4acd7-e13a-4160-d86f-80f5ccde056a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 438
        }
      },
      "execution_count": 125,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/ipykernel/ipkernel.py:283: DeprecationWarning:\n",
            "\n",
            "`should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
            "\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Topic  Count                  Name                                                      Representation\n",
              "0      0    809      0_на_по_что_года      [на, по, что, года, процента, за, до, долларов, не, процентов]\n",
              "1      1    735        1_на_что_по_не                    [на, что, по, не, россии, как, он, года, за, из]\n",
              "2      2    626  2_на_по_сборной_мира           [на, по, сборной, мира, что, не, за, команды, россии, из]\n",
              "3      3    614        3_на_по_что_из                  [на, по, что, из, не, его, был, как, он, сообщает]\n",
              "4      4    457        4_на_что_по_не                    [на, что, по, не, как, для, из, за, года, также]\n",
              "5      5    447        5_по_что_на_не                     [по, что, на, не, суд, его, года, он, был, как]\n",
              "6      6    385    6_на_по_из_человек   [на, по, из, человек, результате, не, сообщает, что, данным, как]\n",
              "7      7    370       7_сша_что_на_по                    [сша, что, на, по, не, как, из, заявил, за, для]\n",
              "8      8    345       8_на_аль_что_по                  [на, аль, что, по, из, не, как, сша, сообщает, за]\n",
              "9      9    212        9_на_что_по_из  [на, что, по, из, самолет, не, самолета, борту, как, авиакомпании]"
            ],
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              "      <td>[по, что, на, не, суд, его, года, он, был, как]</td>\n",
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              "      <th>6</th>\n",
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              "      <td>[на, по, из, человек, результате, не, сообщает, что, данным, как]</td>\n",
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          "metadata": {},
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    },
    {
      "cell_type": "code",
      "source": [
        "topic_model_kmeans.visualize_topics()"
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        "colab": {
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          "height": 752
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      },
      "execution_count": 126,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/ipykernel/ipkernel.py:283: DeprecationWarning:\n",
            "\n",
            "`should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
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        "У BERTopic есть множество модификаций, в частности, можно построить модель с учетом времени:"
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        "timestamps = data['date'].values\n",
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        "outputId": "f87f15e0-46cf-4689-9877-7ea69606f3b8",
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          "text": [
            "/usr/local/lib/python3.10/dist-packages/ipykernel/ipkernel.py:283: DeprecationWarning:\n",
            "\n",
            "`should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
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        "topics_over_time = topic_model_kmeans.topics_over_time(docs, timestamps, nr_bins=10, datetime_format=\"%Y/%m/%d\")"
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      "execution_count": 131,
      "outputs": [
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          "text": [
            "10it [00:11,  1.18s/it]\n"
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        "#help(topic_model.topics_over_time)"
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      "execution_count": 132,
      "outputs": [
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