code/lenta-artm_timestamps.ipynb

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  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "gfnNuFU3A6F4",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "66768508-02ae-4778-8ca2-b47af3c18377"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ]
        }
      ],
      "source": [
        "# Ячейка обязательная, если запускать в коллабе, чтобы файле сохранились на диске\n",
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "%cd /content/drive/MyDrive"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OhdoUEl8Ikub",
        "outputId": "cfc7d9a2-a1c2-483d-c553-78d4e313bd5a"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/drive/MyDrive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install bigartm10"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "12IcK__kIpqG",
        "outputId": "1dcd0ff3-23f9-4f45-d1ea-7800cd4b8c12"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: bigartm10 in /usr/local/lib/python3.10/dist-packages (0.10.1)\n",
            "Requirement already satisfied: pandas in /usr/local/lib/python3.10/dist-packages (from bigartm10) (2.0.3)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (from bigartm10) (1.25.2)\n",
            "Requirement already satisfied: packaging in /usr/local/lib/python3.10/dist-packages (from bigartm10) (24.0)\n",
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.10/dist-packages (from bigartm10) (4.66.2)\n",
            "Requirement already satisfied: protobuf>=3.0 in /usr/local/lib/python3.10/dist-packages (from bigartm10) (3.20.3)\n",
            "Requirement already satisfied: python-dateutil>=2.8.2 in /usr/local/lib/python3.10/dist-packages (from pandas->bigartm10) (2.8.2)\n",
            "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas->bigartm10) (2023.4)\n",
            "Requirement already satisfied: tzdata>=2022.1 in /usr/local/lib/python3.10/dist-packages (from pandas->bigartm10) (2024.1)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.8.2->pandas->bigartm10) (1.16.0)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "%matplotlib inline\n",
        "import glob\n",
        "import os\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "import artm\n",
        "\n",
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "\n",
        "import numpy as np"
      ],
      "metadata": {
        "id": "kJvR8oXCJ3p7"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 1. BatchVectorizer\n",
        "\n",
        "Before starting modeling we need to convert you data in the library format. At first you need to read about supporting formats for source data in Input Data Formats and Datasets. It’s your task to prepare your data in one of these formats. As you had transformed your data into one of source formats, you can convert them in the BigARTM internal format (batches of documents) using BatchVectorizer class object.\n",
        "\n",
        "The built-in library parser converted your data into batches and covered them with the BatchVectorizer class object, that is a general input data type for all methods of Python API. The batches were places in the directory, you specified in the target_folder parameter."
      ],
      "metadata": {
        "id": "Kf5rSUrEcKFY"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Подключаем ту папку lenta_ts, к файлам которой мы предварительно добавили метки времени\n",
        "batch_vectorizer = None\n",
        "if len(glob.glob(os.path.join('batches_ts', '*.batch'))) < 1:\n",
        "  batch_vectorizer = artm.BatchVectorizer(data_path='lenta_ts', data_format='bow_uci',\n",
        "                                                collection_name='lenta_ts', target_folder='batches_ts')\n",
        "else:\n",
        "  batch_vectorizer = artm.BatchVectorizer(data_path='batches_ts', data_format='batches')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0DUMYPJ3J-5I",
        "outputId": "9a53da2c-7b9e-41d9-d17e-55e6a9781f84"
      },
      "execution_count": 5,
      "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"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 2. Dictionary\n",
        "\n",
        "The next step is to create Dictionary. This is a data structure containing the information about all unique tokens in the collection. The dictionary is generating outside the model, and this operation can be done in different ways (load, create, gather). The most basic case is to gather dictionary using batches directory. You need to do this operation only once when starting working with new collection.\n",
        "\n",
        "Dictionary is the object of BigARTM, containing the information about the collection (vocabulary, different counters and values, linked to tokens). Provided dictionary will be used for \\Phi matrix initialization. It means:\n"
      ],
      "metadata": {
        "id": "jyJ7T7XhcN0T"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "if not os.path.isfile('batches_ts/my_dictionary.dict'):\n",
        "  dictionary = artm.Dictionary()\n",
        "  dictionary.gather(data_path='batches_ts')\n",
        "  dictionary.save(dictionary_path='batches_ts/my_dictionary')\n",
        "  dictionary.load(dictionary_path='batches_ts/my_dictionary.dict')\n",
        "else:\n",
        "  dictionary = artm.Dictionary()\n",
        "  dictionary.load(dictionary_path='batches_ts/my_dictionary.dict')"
      ],
      "metadata": {
        "id": "2lRnCAM7KOIt"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 3. Parameters"
      ],
      "metadata": {
        "id": "_EiUN8BRcSOR"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Нужно перебирать параметры и проверять оценки метрик\n",
        "parameters = {\n",
        "    'num_topics': 200,\n",
        "    'num_back_topics': 20,\n",
        "    'num_collection_passes': 20,\n",
        "    'num_document_passes': 1,\n",
        "    'SparsePhiRegularizer': -0.15,\n",
        "    'SmoothPhiRegularizer': 0.15,\n",
        "    'SparseThetaRegularizer': -0.1,\n",
        "    'SmoothThetaRegularizer': 0.1,\n",
        "    'DecorrelatorPhiRegularizer': 0.25,\n",
        "    'TopicSelectionThetaRegularizer': 0.0,\n",
        "    'LabelSparsePsiRegularizer': -0.05,\n",
        "    'LabelSmoothPsiRegularizer': 0.0,\n",
        "    'TimestampsClassWeight': 5\n",
        "}"
      ],
      "metadata": {
        "id": "wwGyCwupKeVs"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "num_topics = parameters['num_topics']\n",
        "num_back_topics = parameters['num_back_topics']\n",
        "num_collection_passes = parameters['num_collection_passes']\n",
        "num_document_passes = parameters['num_document_passes']\n",
        "timestamps_class_weight = parameters['TimestampsClassWeight']\n",
        "\n",
        "topic_names = ['@topic_'+str(i) for i in range(num_topics)]\n",
        "\n",
        "subj_topics = topic_names[:-num_back_topics]\n",
        "back_topics = topic_names[-num_back_topics:]"
      ],
      "metadata": {
        "id": "7lmpR5noKgyn"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model_artm = artm.ARTM(topic_names=topic_names, cache_theta=True)\n",
        "\n",
        "model_artm.initialize(dictionary=dictionary)\n",
        "\n",
        "tokens_class = \"@default_class\"\n",
        "timestamps_class = \"@timestamps_class\"\n",
        "model_artm.class_ids = {tokens_class: 1.0, timestamps_class: timestamps_class_weight}"
      ],
      "metadata": {
        "id": "y7m7yqsWKi6z"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 4. Regularizers\n",
        "- **Smooth/Sparse Phi:** sparse subject topics to increase their quality and smooth background topics to gather there all background tokens, create two regularizers, specify topic_names parameters with corresponding list in both ones and set tau in sparsing regularizer to some negative value, and in smoothing to some positive one;\n",
        "- **Smooth/Sparse Theta:** divide all topics into two groups (subject and background), sparse subject topics to increase their quality and smooth  background topics to gather there all background tokens: create two;\n",
        "- **Decorrelator Phi:** deal only with the tokens of given modality: set class_ids parameter the list with names of modalities to use in this regularizer."
      ],
      "metadata": {
        "id": "0vlZ_L4qcVKF"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Smooth/Sparse Phi\n",
        "model_artm.regularizers.add(artm.SmoothSparsePhiRegularizer(name='SparsePhiRegularizer',\n",
        "                                                           tau=parameters['SparsePhiRegularizer'],\n",
        "                                                           topic_names=subj_topics))\n",
        "\n",
        "model_artm.regularizers.add(artm.SmoothSparsePhiRegularizer(name='SmoothPhiRegularizer',\n",
        "                                                           tau=parameters['SmoothPhiRegularizer'],\n",
        "                                                           topic_names=back_topics))\n",
        "# Decorrelator Phi\n",
        "model_artm.regularizers.add(artm.DecorrelatorPhiRegularizer(name='DecorrelatorPhiRegularizer',\n",
        "                                                           tau=parameters['DecorrelatorPhiRegularizer'],\n",
        "                                                           topic_names=subj_topics,\n",
        "                                                           class_ids=[tokens_class]))\n",
        "# TopicSelectionThetaRegularizer\n",
        "model_artm.regularizers.add(artm.TopicSelectionThetaRegularizer(name='TopicSelectionThetaRegularizer',\n",
        "                                                               tau=parameters['TopicSelectionThetaRegularizer'],\n",
        "                                                               topic_names=subj_topics))\n",
        "\n",
        "# Smooth/Sparse Theta\n",
        "model_artm.regularizers.add(artm.SmoothSparseThetaRegularizer(name='SparseThetaRegularizer',\n",
        "                                                             tau=parameters['SparseThetaRegularizer'],\n",
        "                                                             topic_names=subj_topics))\n",
        "model_artm.regularizers.add(artm.SmoothSparseThetaRegularizer(name='SmoothThetaRegularizer',\n",
        "                                                             tau=parameters['SmoothThetaRegularizer'],\n",
        "                                                             topic_names=back_topics))\n",
        "\n",
        "# LabelSparsePsiRegularizer\n",
        "model_artm.regularizers.add(artm.LabelRegularizationPhiRegularizer(name='LabelSparsePsiRegularizer',\n",
        "                                                                  tau=parameters['LabelSparsePsiRegularizer'],\n",
        "                                                                  topic_names=subj_topics,\n",
        "                                                                  class_ids=[timestamps_class]))\n",
        "model_artm.regularizers.add(artm.LabelRegularizationPhiRegularizer(name='LabelSmoothPsiRegularizer',\n",
        "                                                                  tau=parameters['LabelSmoothPsiRegularizer'],\n",
        "                                                                  topic_names=back_topics,\n",
        "                                                                  class_ids=[timestamps_class]))\n",
        "\n"
      ],
      "metadata": {
        "id": "ZncSsDHZKlpo"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 5. Add scores"
      ],
      "metadata": {
        "id": "ik31WHlBcYmX"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model_artm.scores.add(artm.PerplexityScore(name='PerplexityScore',\n",
        "                                               dictionary=dictionary))\n",
        "model_artm.scores.add(artm.SparsityPhiScore(name='SparsityPhiScore'))\n",
        "model_artm.scores.add(artm.SparsityThetaScore(name='SparsityThetaScore'))\n",
        "model_artm.scores.add(artm.TopicKernelScore(name='TopicKernelScore',\n",
        "                                            probability_mass_threshold=0.3))\n",
        "model_artm.scores.add(artm.TopTokensScore(name='TopTokensScore', num_tokens=15))"
      ],
      "metadata": {
        "id": "uwsm6lFIKoTU"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model_artm.num_document_passes = num_document_passes\n",
        "\n",
        "model_artm.fit_offline(batch_vectorizer=batch_vectorizer, num_collection_passes=20)"
      ],
      "metadata": {
        "id": "iyqzKLQcKqQJ"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def print_measures(model):\n",
        "    print('Sparsity Phi: {0:.3f} (ARTM)'.format(\n",
        "        model.score_tracker['SparsityPhiScore'].last_value))\n",
        "\n",
        "    print('Sparsity Theta: {0:.3f} (ARTM)'.format(\n",
        "        model.score_tracker['SparsityThetaScore'].last_value))\n",
        "\n",
        "    print('Kernel contrast: {0:.3f} (ARTM)'.format(\n",
        "        model.score_tracker['TopicKernelScore'].last_average_contrast))\n",
        "\n",
        "    print('Kernel purity: {0:.3f} (ARTM)'.format(\n",
        "        model.score_tracker['TopicKernelScore'].last_average_purity))\n",
        "\n",
        "    print('Perplexity: {0:.3f} (ARTM)'.format(\n",
        "        model.score_tracker['PerplexityScore'].last_value))\n",
        "\n",
        "\n",
        "def print_scores(scores, save=None):\n",
        "    print_measures(model_artm)\n",
        "    plt.figure()\n",
        "    phi, theta = None, None\n",
        "    phi, = plt.plot(scores['SparsityPhiScore'].value, 'r--', linewidth=2)\n",
        "    theta, = plt.plot(scores['SparsityThetaScore'].value, 'b--', linewidth=2)\n",
        "    plt.xlabel('Iterations count')\n",
        "    plt.ylabel('Sparsity')\n",
        "    plt.legend([phi, theta], ['Phi sparsity', 'Theta sparsity'])\n",
        "    plt.grid(True)\n",
        "    if save:\n",
        "        plt.savefig(save+\"_sparsity.eps\", format='pdf', dpi=1000,\n",
        "                    bbox_inches='tight', pad_inches=0)\n",
        "    plt.show()\n",
        "\n",
        "    plt.figure()\n",
        "    contrast, purity = None, None\n",
        "    contrast, = plt.plot(scores['TopicKernelScore'].average_contrast, 'r', linewidth=2)\n",
        "    purity, = plt.plot(scores['TopicKernelScore'].average_purity, 'b', linewidth=2)\n",
        "    plt.xlabel('Iterations count')\n",
        "    plt.ylabel('Contrast/Purity')\n",
        "    plt.grid(True)\n",
        "    plt.legend([contrast, purity], ['Average contrast', 'Average purity'])\n",
        "    if save:\n",
        "        plt.savefig(save+\"_contrast_purity.eps\", format='pdf', dpi=1000,\n",
        "                    bbox_inches='tight', pad_inches=0)\n",
        "    plt.show()\n",
        "\n",
        "    plt.figure()\n",
        "    perp = None\n",
        "    perp, = plt.plot(scores['PerplexityScore'].value, 'b--', linewidth=2)\n",
        "    plt.xlabel('Iterations count')\n",
        "    plt.ylabel('Perplexity')\n",
        "    plt.xlim(1)\n",
        "    plt.legend([perp], ['Model perplexity'])\n",
        "    plt.grid(True)\n",
        "    if save:\n",
        "        plt.savefig(save+\"_perplexity.eps\", format='pdf', dpi=1000,\n",
        "                    bbox_inches='tight', pad_inches=0)\n",
        "    plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YBNBavrnLHTf",
        "outputId": "7d08294e-f738-41d0-f85c-5121caa1258e"
      },
      "execution_count": 13,
      "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"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Перебрав параметры, у нас меньшилась перплексия (чем меньше перплексия, тем лучше)\n",
        "# Увеличилась чистота и контрастность, что хорошо\n",
        "print_scores(model_artm.score_tracker, save=None)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "fQ8zbkq3LP7w",
        "outputId": "b1e2cb62-8163-4bcb-e25c-95186d1b3311"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Sparsity Phi: 0.893 (ARTM)\n",
            "Sparsity Theta: 0.199 (ARTM)\n",
            "Kernel contrast: 0.782 (ARTM)\n",
            "Kernel purity: 0.414 (ARTM)\n",
            "Perplexity: 1881.285 (ARTM)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def plot_matrix(matrix, figsize=(20,15), xlabel='', ylabel='', title='', save=None):\n",
        "    plt.figure(figsize=figsize)\n",
        "    plt.title(title)\n",
        "    plt.imshow(matrix, interpolation='nearest', cmap=plt.cm.binary)\n",
        "    plt.xlabel(xlabel)\n",
        "    plt.ylabel(ylabel)\n",
        "    plt.colorbar()\n",
        "    if save:\n",
        "        plt.savefig(save, format='eps', dpi=1000, bbox_inches='tight', pad_inches=0)\n",
        "    print(np.any(np.isnan(matrix)))"
      ],
      "metadata": {
        "id": "5N3ieMvZO1VK"
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "theta = model_artm.get_theta().values\n",
        "phi = model_artm.get_phi().values"
      ],
      "metadata": {
        "id": "vlseGkflOsbj"
      },
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plot_matrix(theta[:,:50], figsize=(10,10), xlabel='document', ylabel='topic',\n",
        "            title='$\\Theta$ matrix', save=\"Temporal_Theta.eps\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 889
        },
        "id": "lAMNDrXNPP2l",
        "outputId": "47528050-a5cb-4881-8283-7e344e5641f7"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "False\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x1000 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plot_matrix(phi[:200,:], figsize=(10,10), xlabel='topic', ylabel='word',\n",
        "            title='$\\Phi$ matrix', save='Temporal_Phi.eps')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 832
        },
        "id": "RfPmQ4HDWvKJ",
        "outputId": "3213e407-14e5-42e6-f75e-0057ca724072"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "False\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x1000 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 6. Eventness"
      ],
      "metadata": {
        "id": "wuQAfshPd6Qa"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "phi_m = model_artm.get_phi(class_ids=[timestamps_class]).values"
      ],
      "metadata": {
        "id": "StEwlZ9Ig1y5"
      },
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def get_pyt(phi, t):\n",
        "    return np.nan_to_num(phi[:, t])\n",
        "\n",
        "\n",
        "def delta_roc(pyt):\n",
        "    points = []\n",
        "    for delta in range(1, pyt.size):\n",
        "        max_dens = 0\n",
        "        for pos in range(pyt.size-delta):\n",
        "            density = np.sum(pyt[pos:pos+delta])\n",
        "            if density > max_dens:\n",
        "                max_dens = density\n",
        "        points.append((delta, 1.0-max_dens))\n",
        "    return np.array(points)\n",
        "\n",
        "def auc_delta_roc(delta_roc):\n",
        "    s = 0\n",
        "    return np.sum(list(map(lambda x: x[1], delta_roc)))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "o9MTZDsIjWxf",
        "outputId": "8d634988-3807-43e3-85df-9e175c38c0e8"
      },
      "execution_count": 32,
      "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"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "aucs = []\n",
        "for t in range(parameters['num_topics']):\n",
        "    aucs.append((auc_delta_roc(delta_roc(get_pyt(phi_m, t)))/73.0, t))"
      ],
      "metadata": {
        "id": "3VsbRi9ZlcUr"
      },
      "execution_count": 33,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "aucs_values = []\n",
        "for auc in aucs:\n",
        "  aucs_values.append(auc[0])"
      ],
      "metadata": {
        "id": "9AGHpxjzNwY0"
      },
      "execution_count": 134,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "top_tokens = []\n",
        "for topic_name in model_artm.topic_names:\n",
        "    #print(topic_name + ': ')\n",
        "    top_tokens.append(', '.join(model_artm.score_tracker['TopTokensScore'].last_tokens[topic_name]))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "p0uN5R5qPNzE",
        "outputId": "80ed9ae3-c9cd-4cf7-d97d-a66f13fb2232"
      },
      "execution_count": 138,
      "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"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tokens_aucs = dict(zip(top_tokens, aucs_values))"
      ],
      "metadata": {
        "id": "zx4rWtttPed0"
      },
      "execution_count": 140,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Для событийных тем Delta-AUC меньше\n",
        "sorted_tokens_aucs = sorted(tokens_aucs.items(), key=lambda kv: kv[1], reverse=False)\n",
        "for pair in sorted_tokens_aucs[:10]:\n",
        "  print('Delta-AUC ' + str(pair[1]) + ': ')\n",
        "  print(pair[0])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ICa5sqEUSNS3",
        "outputId": "cc187e39-c9bf-4f23-e191-36b571d6ae40"
      },
      "execution_count": 157,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Delta-AUC 0.0: \n",
            "лидер, президент, корея, путин, владимир, кндр, ким, глава, заявлять, ноябрь, чен, южный, пхеньян, секретарь, корейский\n",
            "Delta-AUC 0.0: \n",
            "российский, сотрудничество, швеция, финляндия, глава, альянс, апрель, принимать, граница, диалог, укрепление, усилие, заявлять, риа, представитель\n",
            "Delta-AUC 0.0: \n",
            "турция, турецкий, огонь, позиция, эрдоган, анкара, заявлять, территория, гуманитарный, граница, реджеп, тайип, зона, стамбул, действие\n",
            "Delta-AUC 0.0: \n",
            "египет, власть, подавать, решение, брат, организация, назначать, июль, паспорт, сообщать, представитель, египетский, законный, отстранять, мурси\n",
            "Delta-AUC 0.0: \n",
            "больница, тело, скончаться, травма, состояние, скорый, здоровье, водитель, девочка, медицинский, рассказывать, жительница, тяжелый, причина, останки\n",
            "Delta-AUC 0.0: \n",
            "мэр, свобода, комитет, виновный, административный, активист, рф, ответственность, кодекс, статья, городской, незаконный, сообщать, вынести, районный\n",
            "Delta-AUC 0.0: \n",
            "декабрь, алексей, заявлять, миллиардер, российский, глава, премьер, отмечать, павел, профсоюз, участие, становиться, являться, январь, проходить\n",
            "Delta-AUC 0.0: \n",
            "выходить, молодой, рассказывать, история, написать, аккаунт, герой, волос, секс, майкл, подарок, становиться, опубликовывать, показывать, любовь\n",
            "Delta-AUC 0.0: \n",
            "сообщество, сми, действие, призыв, новость, называть, российский, глава, сообщать, ситуация, журналист, министр, ответ, совещание, ненависть\n",
            "Delta-AUC 0.0: \n",
            "округ, билет, автономный, регион, купить, потратить, общий, резерв, получать, сообщать, тысяча, область, сорвать, красноярский, составлять\n"
          ]
        },
        {
          "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"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "topics = sorted(aucs)"
      ],
      "metadata": {
        "id": "K_YQkToMj6dj"
      },
      "execution_count": 135,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "eventness_topics = list(map(lambda x: '@topic_' + str(x[1]), topics))"
      ],
      "metadata": {
        "id": "vDpuBqQAnLhm"
      },
      "execution_count": 63,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "eventness_topicnums = list(map(lambda x: x[1], topics))"
      ],
      "metadata": {
        "id": "uS2wxajznS5F"
      },
      "execution_count": 64,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plot_matrix(phi_m[:, eventness_topicnums],\n",
        "            xlabel='Topics sorted by Delta-AUC', ylabel='Month', title='$\\Xi$ matrix sorted by Delta-AUC', save=\"Temporal_sorted_by_auc_Xi.eps\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 994
        },
        "id": "mNU5j4ofnYWQ",
        "outputId": "24a8f4eb-7cb4-46fb-fcdd-d42035852cc3"
      },
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<>:2: DeprecationWarning: invalid escape sequence '\\X'\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "True\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2000x1500 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "eventness_topicnums[:10]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TxpaT4t_r20M",
        "outputId": "e815e1f9-96e0-4ba2-e9bc-ffd6ea96d1a3"
      },
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[1, 3, 14, 28, 50, 53, 62, 74, 75, 89]"
            ]
          },
          "metadata": {},
          "execution_count": 52
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def print_topics(topic_names=None, weights=False):\n",
        "    if topic_names is None:\n",
        "        topic_names = model_artm.topic_names\n",
        "    for topic_name in topic_names:\n",
        "        print(topic_name + ': ')\n",
        "        tokens = model_artm.score_tracker['TopTokensScore'].last_tokens[topic_name]\n",
        "        if weights:\n",
        "            weights = model_artm.score_tracker['TopTokensScore'].last_weights[topic_name]\n",
        "            for token, weight in zip(tokens, weights):\n",
        "                print(\"({0}, {1:.3f}), \".format(token, weight))\n",
        "            print()\n",
        "        else:\n",
        "            print(', '.join(tokens))\n",
        "        print()"
      ],
      "metadata": {
        "id": "rQeozFGe4gor"
      },
      "execution_count": 75,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print_topics(eventness_topics)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dk5bFTbN5g9q",
        "outputId": "4907e398-452d-4be0-a9e7-8eab3301254b"
      },
      "execution_count": 76,
      "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": [
            "@topic_1: \n",
            "лидер, президент, корея, путин, владимир, кндр, ким, глава, заявлять, ноябрь, чен, южный, пхеньян, секретарь, корейский\n",
            "\n",
            "@topic_3: \n",
            "российский, сотрудничество, швеция, финляндия, глава, альянс, апрель, принимать, граница, диалог, укрепление, усилие, заявлять, риа, представитель\n",
            "\n",
            "@topic_14: \n",
            "турция, турецкий, огонь, позиция, эрдоган, анкара, заявлять, территория, гуманитарный, граница, реджеп, тайип, зона, стамбул, действие\n",
            "\n",
            "@topic_28: \n",
            "египет, власть, подавать, решение, брат, организация, назначать, июль, паспорт, сообщать, представитель, египетский, законный, отстранять, мурси\n",
            "\n",
            "@topic_50: \n",
            "больница, тело, скончаться, травма, состояние, скорый, здоровье, водитель, девочка, медицинский, рассказывать, жительница, тяжелый, причина, останки\n",
            "\n",
            "@topic_53: \n",
            "мэр, свобода, комитет, виновный, административный, активист, рф, ответственность, кодекс, статья, городской, незаконный, сообщать, вынести, районный\n",
            "\n",
            "@topic_62: \n",
            "декабрь, алексей, заявлять, миллиардер, российский, глава, премьер, отмечать, павел, профсоюз, участие, становиться, являться, январь, проходить\n",
            "\n",
            "@topic_74: \n",
            "выходить, молодой, рассказывать, история, написать, аккаунт, герой, волос, секс, майкл, подарок, становиться, опубликовывать, показывать, любовь\n",
            "\n",
            "@topic_75: \n",
            "сообщество, сми, действие, призыв, новость, называть, российский, глава, сообщать, ситуация, журналист, министр, ответ, совещание, ненависть\n",
            "\n",
            "@topic_89: \n",
            "округ, билет, автономный, регион, купить, потратить, общий, резерв, получать, сообщать, тысяча, область, сорвать, красноярский, составлять\n",
            "\n",
            "@topic_106: \n",
            "счет, италия, итальянский, россиянин, подниматься, период, балл, швейцария, соответственно, март, общий, составлять, кипр, швейцарский, становиться\n",
            "\n",
            "@topic_107: \n",
            "остров, южный, япония, японский, сердце, плохо, фрагмент, обломок, человеческий, рассказывать, дикий, индонезия, лишний, исчезать, жаловаться\n",
            "\n",
            "@topic_110: \n",
            "сообщать, май, ранее, отмечать, рассказывать, становиться, получать, тасс, информация, июнь, оставаться, афганский, смочь, риа, известно\n",
            "\n",
            "@topic_120: \n",
            "российский, январь, представитель, проект, спг, регион, обеспечение, сообщать, поручать, пресс, срок, обеспечивать, план, направлять, возможность\n",
            "\n",
            "@topic_126: \n",
            "отмечать, высокий, ожидать, уровень, российский, предлагать, проблема, владимир, подчеркивать, снг, добавлять, август, оценивать, мнение, регион\n",
            "\n",
            "@topic_138: \n",
            "январь, александр, становиться, ранее, проходить, дмитрий, трудовой, российский, австрия, беспорядок, массовый, принимать, участие, фон, выступать\n",
            "\n",
            "@topic_144: \n",
            "госдума, законопроект, закон, вносить, поправка, предлагать, принимать, чтение, принятие, совет, решение, июль, наследие, приглашение, идея\n",
            "\n",
            "@topic_145: \n",
            "дочь, становиться, сообщать, би, латвия, чехия, подробность, издание, рассказывать, деталь, юлия, си, известно, руслан, ранее\n",
            "\n",
            "@topic_155: \n",
            "житель, область, край, регион, региональный, центр, местный, оборот, подмосковье, автомат, детский, калашников, свердловский, устанавливать, ставропольский\n",
            "\n",
            "@topic_156: \n",
            "германия, немецкий, безопасность, корпорация, канцлер, фрг, меркель, берлин, поддержка, заявлять, ангел, мсп, гарантия, субъект, ранее\n",
            "\n",
            "@topic_158: \n",
            "комиссия, голосование, выборы, председатель, избирательный, обязанность, губернатор, назначать, общественный, единый, палата, проходить, окончательный, муниципальный, принимать\n",
            "\n",
            "@topic_159: \n",
            "евгений, спортивный, россиянка, мария, спорт, елена, программа, спортсменка, корреспондент, екатерина, тренировка, российская, чемпионка, анастасия, май\n",
            "\n",
            "@topic_170: \n",
            "заявлять, организация, ноябрь, отмечать, коллективный, добавлять, мнение, число, призывать, общественный, безопасность, применять, становиться, власть, данные\n",
            "\n",
            "@topic_177: \n",
            "казахстан, требование, право, юридический, обязывать, имущество, компенсация, регистрация, принадлежать, зарегистрировать, собственность, ржд, срок, нарушение, сообщать\n",
            "\n",
            "@topic_173: \n",
            "счет, набирать, поле, тур, английский, отличаться, бразилия, актив, ру, лента, гость, сообщать, фунт, лондонский, лос\n",
            "\n",
            "@topic_134: \n",
            "расследование, версия, совершать, сообщать, источник, лагерь, сообщаться, данные, устанавливать, действие, причастность, ранее, оперативно, ход, хранение\n",
            "\n",
            "@topic_85: \n",
            "украинский, украина, порошенко, народный, петр, луганский, верховный, лнр, надежда, территория, сбу, рад, донецкий, савченко, ополченец\n",
            "\n",
            "@topic_68: \n",
            "система, использовать, использоваться, становиться, эксперт, представлять, ран, ранее, аппарат, настоящий, рассказывать, автоматический, слой, отмечать, академик\n",
            "\n",
            "@topic_119: \n",
            "объяснять, называть, ранее, михаил, число, причина, опыт, заявлять, пояснять, становиться, добавлять, обработка, вызывать, рассказывать, мнение\n",
            "\n",
            "@topic_102: \n",
            "израиль, сектор, израильский, ранее, распространяться, приводить, штамм, вариант, климат, вероятность, отмечать, повторный, вызывать, возникновение, еврейский\n",
            "\n",
            "@topic_123: \n",
            "россиянин, высокий, число, список, оценка, половина, категория, отпуск, чистый, среди, становиться, входить, поступление, профессия, средство\n",
            "\n",
            "@topic_71: \n",
            "газ, европа, поставка, польша, газпром, украина, газопровод, кубометр, европейский, природный, гривна, транзит, газовый, топливо, польский\n",
            "\n",
            "@topic_87: \n",
            "номер, портал, ресторан, блогер, онлайн, предмет, комментарий, рассказывать, смочь, получать, сеть, знак, сайт, собирать, становиться\n",
            "\n",
            "@topic_104: \n",
            "происходить, местный, подросток, мальчик, сообщать, упасть, находить, сообщаться, спасать, вылетать, экстренный, мск, июль, инцидент, родитель\n",
            "\n",
            "@topic_93: \n",
            "размещать, ремонт, вконтакте, объявление, соцсеть, стена, работодатель, бассейн, особняк, издание, страница, сообщать, ограниченный, сообщение, сообщество\n",
            "\n",
            "@topic_121: \n",
            "отмечать, молдавия, принимать, язык, проблема, заявлять, румыния, решение, передавать, министр, подчеркивать, вица, молдавский, государственный, буш\n",
            "\n",
            "@topic_95: \n",
            "здание, район, сильный, сообщать, помещение, спасатель, повреждать, центр, происходить, северо, чрезвычайный, разрушать, эвакуировать, мощный, сад\n",
            "\n",
            "@topic_117: \n",
            "операция, атака, посольство, войско, армия, афганистан, военный, наступление, отряд, венесуэла, январь, авиация, химический, ан, эль\n",
            "\n",
            "@topic_47: \n",
            "мероприятие, участник, акция, сообщать, служба, область, марш, проходить, ветеран, колонна, шествие, проведение, полк, организовывать, май\n",
            "\n",
            "@topic_36: \n",
            "принимать, решение, предлагать, учитель, заявлять, отказ, чувство, называть, наталья, запрещать, отношение, невеста, сообщать, отказываться, вступать\n",
            "\n",
            "@topic_82: \n",
            "художник, май, представитель, повестка, вячеслав, мобилизация, василий, новость, частичный, глава, уличный, апрель, александр, организованный, пятница\n",
            "\n",
            "@topic_66: \n",
            "нефть, поставка, добыча, экспорт, баррель, прогноз, объем, цена, нефтяной, сокращение, топливо, запас, достигать, импорт, европейский\n",
            "\n",
            "@topic_122: \n",
            "ранее, река, индия, сообщать, отмечать, завод, начинать, отмечаться, течение, июль, восточный, результат, оцениваться, данный, получать\n",
            "\n",
            "@topic_22: \n",
            "февраль, безопасность, парад, проведение, российский, заявлять, подготовка, представитель, принимать, нато, цель, территория, секретный, решение, генсек\n",
            "\n",
            "@topic_116: \n",
            "помощь, обращаться, пытаться, вызывать, рассказывать, сосед, решать, помогать, постоянно, ранее, отравление, спать, замечать, домой, почувствовать\n",
            "\n",
            "@topic_172: \n",
            "цена, рынок, стоимость, рост, вырастать, снижение, показатель, аналитик, эксперт, спрос, покупатель, снижаться, расти, среднее, средний\n",
            "\n",
            "@topic_152: \n",
            "чечня, кадыров, родственник, случаться, брак, муж, рассказывать, находиться, родитель, пропадать, чеченский, гора, приезжать, родной, супруг\n",
            "\n",
            "@topic_151: \n",
            "октябрь, канал, представитель, сообщать, пропаганда, гей, роман, светлана, сентябрь, лгбт, отношение, поздно, ранее, понедельник, собираться\n",
            "\n",
            "@topic_77: \n",
            "украина, киев, донбасс, украинский, республика, самопровозглашенный, минский, киевский, обсе, милиция, конфликт, территория, рад, ополченец, донецкий\n",
            "\n",
            "@topic_65: \n",
            "турист, поездка, отдых, популярный, путешественник, среди, туристический, путешествие, курорт, отдыхать, гостиница, отправляться, перелет, оказываться, направление\n",
            "\n",
            "@topic_5: \n",
            "техника, вертолет, тип, сообщать, флот, база, шойгу, ми, индийский, адмирал, сопровождать, передавать, строй, источник, судно\n",
            "\n",
            "@topic_139: \n",
            "президент, глава, байден, советник, конфликт, путин, государство, владимир, джо, ситуация, украина, ранее, фбр, аксенов, называть\n",
            "\n",
            "@topic_164: \n",
            "пострадать, взрыв, происходить, уголовный, возбуждать, происшествие, ранение, факт, ук, скр, статья, следственный, поселок, преступление, результат\n",
            "\n",
            "@topic_135: \n",
            "процент, клиент, доля, опрос, составлять, опрашивать, сравнение, вклад, сокращаться, респондент, исследование, россиянин, объем, соответственно, средний\n",
            "\n",
            "@topic_109: \n",
            "летний, судья, становиться, получать, устраивать, кафе, благотворительный, сентябрь, знакомый, спустя, известный, отдавать, решать, август, познакомиться\n",
            "\n",
            "@topic_161: \n",
            "северный, поток, ситуация, партнер, отношение, угроза, сообщать, завершение, российский, продовольственный, строительство, останавливать, азиатский, возможность, становиться\n",
            "\n",
            "@topic_48: \n",
            "военный, сша, американский, иран, ракетный, истребитель, генерал, ввс, танк, сбивать, вооружение, стратегический, боевой, пентагон, беспилотник\n",
            "\n",
            "@topic_146: \n",
            "продукция, производство, производитель, предприятие, хозяйство, завод, средство, изделие, система, создавать, комплекс, установка, сон, разрабатывать, недостаток\n",
            "\n",
            "@topic_162: \n",
            "победа, боец, россиянин, встречаться, защищать, легкий, одерживать, побеждать, соперник, абсолютный, стиль, смешанный, макгрегор, чемпион, турнир\n",
            "\n",
            "@topic_11: \n",
            "кремль, разговор, песок, отвечать, высказывание, президент, владимир, дмитрий, выражать, мнение, эфир, критика, добавлять, заявлять, орден\n",
            "\n",
            "@topic_46: \n",
            "вооруженный, оружие, всу, военный, обстрел, уничтожать, район, солдат, войско, боеприпасы, военнослужащий, снаряд, спецоперация, обстреливать, конфликт\n",
            "\n",
            "@topic_33: \n",
            "церковь, общество, храм, православный, кирилл, религиозный, святой, ноябрь, исторический, отдел, подпись, заявлять, собор, разрывать, патриарх\n",
            "\n",
            "@topic_91: \n",
            "являться, эксперимент, позволять, существование, лаборатория, международный, робот, арбитражный, продемонстрировать, участие, процесс, федерация, взаимодействие, применение, создавать\n",
            "\n",
            "@topic_80: \n",
            "президент, закон, референдум, власть, статус, гражданство, проголосовать, конституция, государство, право, совет, документ, конституционный, государственный, ссср\n",
            "\n",
            "@topic_90: \n",
            "линия, состав, сообщать, поезд, железнодорожный, перевозка, железный, март, становиться, вагон, сообщение, пассажирский, причина, папа, ссылка\n",
            "\n",
            "@topic_147: \n",
            "китай, китайский, греция, кнр, пекин, жесткий, ранее, литр, ван, начинать, китаец, проблема, мнение, афины, частность\n",
            "\n",
            "@topic_49: \n",
            "космический, запускать, запуск, создание, программа, союз, носитель, роскосмос, ракета, успешный, предназначать, выводить, орбита, стартовать, космос\n",
            "\n",
            "@topic_2: \n",
            "продукт, питание, посоветовать, нидерланды, напиток, следовать, еда, мясо, правильный, способ, полезный, горячий, предупреждать, раскрывать, диетолог\n",
            "\n",
            "@topic_35: \n",
            "помощь, медицинский, отмечать, искусственный, сообщаться, ранее, добавлять, март, оказывать, известно, получать, чувствовать, информация, называть, поздно\n",
            "\n",
            "@topic_97: \n",
            "нью, российский, являться, ноябрь, сообщать, отмечаться, го, прошлый, становиться, май, ру, момент, потолок, возвращать, апрель\n",
            "\n",
            "@topic_154: \n",
            "игра, проходить, сочи, пройти, становиться, этап, золото, август, российский, канадский, зимний, соревнование, катание, выступать, мировой\n",
            "\n",
            "@topic_44: \n",
            "ноябрь, франция, париж, зал, становиться, отмечать, бельгия, заявлять, получать, осетия, концертный, сентябрь, официальный, гражданин, начало\n",
            "\n",
            "@topic_99: \n",
            "военный, оборона, минобороны, боевой, республика, днр, комплекс, донецкий, военнослужащий, народный, армия, учение, подразделение, бригада, донецк\n",
            "\n",
            "@topic_129: \n",
            "июнь, заявка, лента, джон, возможность, ру, ранее, сентябрь, заявлять, тысяча, потенциальный, отмечать, ежегодный, принимать, включать\n",
            "\n",
            "@topic_30: \n",
            "семья, сын, супруга, принц, королевский, гарри, дворец, рождаться, королева, король, британский, член, меган, становиться, елизавета\n",
            "\n",
            "@topic_153: \n",
            "врач, умирать, заболевание, распространение, заражение, инфекция, медик, перенести, боль, доктор, больной, симптом, смерть, неделя, коронавирусный\n",
            "\n",
            "@topic_76: \n",
            "животное, автор, опубликовывать, изображение, огромный, длина, австралия, рыба, берег, считаться, лед, австралийский, кот, природа, пляж\n",
            "\n",
            "@topic_60: \n",
            "суд, адвокат, приговаривать, заключение, наказание, арест, обвинение, признавать, штраф, срок, лишение, иск, тюрьма, колония, следствие\n",
            "\n",
            "@topic_118: \n",
            "группа, конкурс, артист, песня, музыкант, певица, концерт, фестиваль, певец, музыкальный, исполнитель, музыка, записывать, коллектив, мероприятие\n",
            "\n",
            "@topic_17: \n",
            "боевик, террористический, исламский, запрещать, теракт, террорист, аль, иго, провинция, группировка, ирак, государство, погибать, захватить, нападение\n",
            "\n",
            "@topic_52: \n",
            "тысяча, метр, жилье, квадратный, объект, недвижимость, площадь, составлять, жилой, построить, располагать, комплекс, дорогой, строительный, центр\n",
            "\n",
            "@topic_131: \n",
            "заявлять, подавать, заявление, отмечать, подчеркивать, журналистка, коррупция, секретарь, моральный, собчак, министр, новость, передавать, офис, отношение\n",
            "\n",
            "@topic_130: \n",
            "звезда, примерно, ученый, объект, планета, наблюдение, волна, энергия, тонна, расстояние, поверхность, наблюдать, солнечный, образовываться, позволять\n",
            "\n",
            "@topic_165: \n",
            "декабрь, становиться, январь, новогодний, товар, отмечать, закрытие, называть, основной, индекс, продавец, бизнес, предпочитать, богатый, напротив\n",
            "\n",
            "@topic_42: \n",
            "путин, встреча, переговоры, премьер, российский, контракт, поставка, президент, договариваться, владимир, подписывать, обсуждать, заинтересовывать, соглашение, государство\n",
            "\n",
            "@topic_78: \n",
            "доступный, например, платформа, технология, функция, современный, способ, позволять, эксперт, сербия, инструмент, вариант, создавать, использовать, гаджет\n",
            "\n",
            "@topic_141: \n",
            "московский, нижний, великий, андрей, екатеринбург, площадка, иванов, дон, ру, радиостанция, сообщать, новгород, честь, пресс, подмосковье\n",
            "\n",
            "@topic_112: \n",
            "глава, президент, государство, правительство, реформа, одобрять, политика, вица, телефонный, заявлять, кандидатура, мнение, пост, внешний, цру\n",
            "\n",
            "@topic_16: \n",
            "великобритания, британский, северный, лондон, проект, выход, лодка, подводный, атомный, джонсон, министр, дно, подлодка, арктика, премьер\n",
            "\n",
            "@topic_175: \n",
            "информация, запись, правый, письмо, данные, электронный, почта, хакер, адрес, украсть, получать, сдаваться, отправлять, жертва, документ\n",
            "\n",
            "@topic_24: \n",
            "убийство, следственный, станция, управление, следствие, подозреваемый, столичный, метро, убивать, уголовный, обвиняемый, версия, интерфакс, правоохранительный, сообщать\n",
            "\n",
            "@topic_168: \n",
            "крым, виктор, отставка, митинг, протест, президент, акция, власть, оппозиция, янукович, писатель, политический, выступление, полуостров, верховный\n",
            "\n",
            "@topic_43: \n",
            "санкция, вводить, мера, ограничение, евросоюз, введение, указ, сша, затрагивать, фон, ограничительный, санкционный, заявлять, опек, ситуация\n",
            "\n",
            "@topic_37: \n",
            "площадь, акция, максим, массовый, столица, резиденция, данные, парковка, платный, центр, константин, мэрия, владелец, материал, участник\n",
            "\n",
            "@topic_84: \n",
            "сообщать, данные, наркотик, предварительный, состояние, вещество, изымать, погибать, находиться, область, подозрение, дагестан, сотрудник, сообщаться, ссылка\n",
            "\n",
            "@topic_6: \n",
            "пост, министр, глава, должность, политик, бывший, заявлять, президент, возглавлять, кампания, политика, кандидат, полномочие, выборы, государство\n",
            "\n",
            "@topic_163: \n",
            "международный, де, флаг, доклад, комитет, поддерживать, независимый, токио, август, называть, гонконг, тайвань, болгария, блинкен, заявлять\n",
            "\n",
            "@topic_176: \n",
            "здание, начинаться, губернатор, собираться, священник, насилие, общение, оскорбление, сентябрь, охватывать, организовывать, демонстрация, агентство, центр, активист\n",
            "\n",
            "@topic_103: \n",
            "белоруссия, лукашенко, александр, белорусский, заявлять, минск, государство, власть, президент, протест, глава, республика, риа, пресс, новость\n",
            "\n",
            "@topic_12: \n",
            "сообщать, служба, сотрудник, киргизия, информация, валерий, таджикистан, ведомство, источник, геннадий, данные, оштрафовать, рф, агентство, пресс\n",
            "\n",
            "@topic_61: \n",
            "организация, фсб, деятельность, режим, заблокировать, частный, запрещать, нарушение, ведомство, блокировка, сообщать, адрес, решение, рф, экстремистский\n",
            "\n",
            "@topic_83: \n",
            "участок, строительство, транспорт, транспортный, федеральный, эксплуатация, инфраструктура, средство, объект, полоса, область, аварийный, автомобильный, реконструкция, заказ\n",
            "\n",
            "@topic_96: \n",
            "экономика, правительство, труд, рост, текущий, перспектива, рабочий, условие, средний, сокращать, увеличение, сфера, объем, закупка, долгосрочный\n",
            "\n",
            "@topic_59: \n",
            "ресурс, информационный, разрешение, сайт, роскомнадзор, реестр, ведомство, отмечать, зона, нарушение, волонтер, число, согласно, регион, рассказывать\n",
            "\n",
            "@topic_7: \n",
            "пункт, граница, территория, населенный, сообщать, ранее, пограничный, власть, новость, риа, уточнять, пограничник, министр, контроль, представительство\n",
            "\n",
            "@topic_111: \n",
            "ес, нато, евросоюз, иностранный, соглашение, европейский, вступление, въезд, министр, виза, заявлять, мид, шаг, член, присоединение\n",
            "\n",
            "@topic_34: \n",
            "квартира, возраст, приобретать, студия, аренда, выставлять, дома, сдавать, судебный, продавать, становиться, медведь, переезжать, принадлежать, портал\n",
            "\n",
            "@topic_54: \n",
            "премия, памятник, музей, организатор, праздник, получать, номинация, произведение, вход, становиться, сообщаться, посвящать, исполнение, январь, открываться\n",
            "\n",
            "@topic_19: \n",
            "коронавирус, сутки, выявлять, пандемия, зафиксировать, препарат, пациент, здравоохранение, вирус, вакцина, маска, число, вакцинация, минздрав, роспотребнадзор\n",
            "\n",
            "@topic_88: \n",
            "российский, становиться, заявлять, ранее, приводить, россиянин, слух, называть, подчеркивать, михаил, зерно, июль, ислам, условие, сообщать\n",
            "\n",
            "@topic_26: \n",
            "обвинять, грузия, сексуальный, бывший, сообщать, родина, президент, обвинение, представитель, сентябрь, мечта, отношение, домогательство, тбилиси, коллега\n",
            "\n",
            "@topic_18: \n",
            "самолет, аэропорт, пассажир, воздушный, борт, авиакомпания, рейс, полет, экипаж, пилот, лайнер, маршрут, посадка, находиться, крушение\n",
            "\n",
            "@topic_105: \n",
            "видео, девушка, фотография, кадр, снимок, появляться, актриса, ролик, фото, поделиться, видно, шоу, признаваться, публикация, выкладывать\n",
            "\n",
            "@topic_20: \n",
            "компания, развитие, предприятие, проект, бизнес, форум, экономический, роснефть, отрасль, промышленность, мощность, стратегия, российский, энергетика, глобальный\n",
            "\n",
            "@topic_0: \n",
            "исследование, ученый, университет, организм, клетка, кровь, мозг, анализ, исследователь, болезнь, лечение, употребление, вещество, журнал, рак\n",
            "\n",
            "@topic_32: \n",
            "крым, заявлять, российский, крымский, действие, украина, полуостров, заявление, пакет, западный, документ, глава, представитель, министерство, рф\n",
            "\n",
            "@topic_9: \n",
            "штат, автомобиль, сообщать, угрожать, местный, попытаться, происходить, открывать, флорида, деревня, жертва, находиться, получать, начинать, стрелять\n",
            "\n",
            "@topic_23: \n",
            "матч, завершаться, встреча, финал, очки, чемпионат, сыграть, счет, стадион, лига, выигрывать, забивать, суметь, мяч, польза\n",
            "\n",
            "@topic_45: \n",
            "банк, компания, продажа, кредит, карта, финансовый, кредитный, продавать, банковский, средство, сбербанк, цб, регулятор, актив, размер\n",
            "\n",
            "@topic_72: \n",
            "мужчина, инцидент, происходить, доставлять, двое, нападение, драка, полиция, видео, избивать, нападывать, погибать, нож, пистолет, ударять\n",
            "\n",
            "@topic_143: \n",
            "заявлять, владимир, российский, зеленский, запад, украина, министр, добавлять, ранее, польский, агрессия, готовность, путин, секретарь, подчеркивать\n",
            "\n",
            "@topic_182: \n",
            "дель, поэт, биатлонистка, пабло, джейми, композитор, мэттью, джек, гильермо, аарон, рико, пуэрто, суарес, вулверхэмптон, моди\n",
            "\n",
            "@topic_199: \n",
            "муравей, шашлык, блюдо, шарик, тибетский, якубович, монах, далай, карлсон, атеист, батарейка, форс, брейвик, мугаб, аэроэкспресс\n",
            "\n",
            "@topic_115: \n",
            "франция, сша, формат, песок, ранее, африканский, заявлять, макрон, ответ, подчеркивать, ввоз, приводить, представитель, керри, частность\n",
            "\n",
            "@topic_194: \n",
            "кинематографист, дженнифер, франшиза, комикс, киностудия, лоуренс, мститель, приключение, трилогия, полнометражный, тор, анимационный, кинолента, мистер, боб\n",
            "\n",
            "@topic_150: \n",
            "крупный, акция, компания, инвестор, бумага, падение, ценный, акционер, производство, втб, являться, фас, азия, состояние, госкомпания\n",
            "\n",
            "@topic_57: \n",
            "представлять, красный, цвет, название, черный, кольцо, состоять, появляться, вечеринка, опубликовывать, век, аксессуар, длинный, явление, внешний\n",
            "\n",
            "@topic_55: \n",
            "передавать, миссия, агентство, узбекистан, отмечать, существовать, находиться, сообщать, источник, приводить, результат, ссылка, сигнал, ситуация, данный\n",
            "\n",
            "@topic_58: \n",
            "модель, бренд, одежда, вещь, образ, коллекция, марка, представлять, женский, основывать, выпускать, опубликовывать, дизайнер, знаменитость, костюм\n",
            "\n",
            "@topic_29: \n",
            "российский, заявлять, глава, президент, решение, литва, представитель, отношение, ситуация, путин, владимир, совет, право, сообщать, новость\n",
            "\n",
            "@topic_108: \n",
            "специалист, спутник, быстро, рассказывать, ранее, рекомендовать, опасность, реакция, подчеркивать, способность, эффект, добавлять, нормальный, бояться, последствие\n",
            "\n",
            "@topic_92: \n",
            "сирия, удар, оон, наносить, сирийский, группировка, коалиция, государство, правительственный, борьба, асад, гражданский, резолюция, база, арабский\n",
            "\n",
            "@topic_157: \n",
            "контроль, прекращение, армения, азербайджан, республика, действие, зона, обвинять, нагорный, конфликт, начинаться, карабах, независимость, операция, перемирие\n",
            "\n",
            "@topic_10: \n",
            "ожидаться, воздух, норма, научный, градус, температура, роман, регион, краснодарский, дождь, ветер, зима, погода, атмосфера, предупреждение\n",
            "\n",
            "@topic_127: \n",
            "служба, пресс, охрана, сообщать, стрельба, интерфакс, начальник, агентство, оружие, август, ход, находиться, безопасность, росгвардия, передавать\n",
            "\n",
            "@topic_31: \n",
            "сотрудник, находиться, погибать, госпитализировать, нападать, четверо, девочка, гу, управление, предположительно, сообщать, забирать, пятеро, личность, район\n",
            "\n",
            "@topic_100: \n",
            "бюджет, фонд, налог, финансовый, выплата, средство, правительство, миллиард, пенсия, пенсионный, доход, финансирование, выделять, триллион, налоговый\n",
            "\n",
            "@topic_94: \n",
            "рейтинг, занимать, медведев, открытый, турнир, успех, канада, трансляция, круг, продлиться, российский, становиться, список, американка, уильямс\n",
            "\n",
            "@topic_174: \n",
            "евро, сумма, испания, фонд, испанский, выплачивать, переходить, барселона, переход, заплатить, миллион, страховой, получать, платить, обновление\n",
            "\n",
            "@topic_167: \n",
            "обнаруживать, находить, смерть, сообщать, рождение, тело, мертвый, надпись, гибель, причина, удаваться, некоторые, департамент, оставлять, плакат\n",
            "\n",
            "@topic_69: \n",
            "заявлять, представитель, отмечать, телеканал, частность, отношение, заявление, совет, являться, документ, соответствующий, подчеркивать, повод, речь, называть\n",
            "\n",
            "@topic_125: \n",
            "март, сайт, февраль, российский, принимать, сообщать, сергей, илья, официальный, участие, лента, ру, сообщаться, получать, никита\n",
            "\n",
            "@topic_136: \n",
            "проводить, заявлять, решение, право, правило, признавать, готовый, высказываться, отмечать, совет, добавлять, подписывать, согласный, отмена, главное\n",
            "\n",
            "@topic_188: \n",
            "шахматы, шахматный, индиец, современник, самойлов, фид, публицист, карлсен, шахматист, пацан, мирзиеев, гончаров, кыргызстан, гроссмейстер, псков\n",
            "\n",
            "@topic_70: \n",
            "авария, автобус, район, дтп, результат, дорожный, область, погибать, грузовик, происходить, автомобиль, водитель, городской, столкновение, дерево\n",
            "\n",
            "@topic_149: \n",
            "полиция, задерживать, мвд, орган, уголовный, статья, сотрудник, правоохранительный, задержание, полицейский, арестовывать, возбуждать, задержанный, управление, отпускать\n",
            "\n",
            "@topic_67: \n",
            "команда, сборная, матч, чемпионат, клуб, тренер, футболист, футбольный, футбол, зенит, турнир, болельщик, спартак, игрок, обыгрывать\n",
            "\n",
            "@topic_132: \n",
            "июнь, август, бизнесмен, борис, совет, предприниматель, решение, ру, бывший, пакет, игорь, немцов, руководство, петербургский, владислав\n",
            "\n",
            "@topic_41: \n",
            "депутат, партия, выборы, парламент, кандидат, парламентарий, спикер, оппозиционный, президент, фракция, лидер, владимир, единый, парламентский, палата\n",
            "\n",
            "@topic_27: \n",
            "мужчина, полицейский, задерживать, преступление, скрываться, преступник, убивать, злоумышленник, подозреваемый, кража, похищать, совершение, подозревать, страж, очевидец\n",
            "\n",
            "@topic_8: \n",
            "директор, петербург, санкт, культура, театр, руководитель, образование, искусство, академия, наука, выставка, спектакль, постановка, институт, музей\n",
            "\n",
            "@topic_113: \n",
            "предложение, решение, заявлять, иностранный, мнение, постоянный, интерес, международный, замораживать, отмечать, план, право, являться, условие, договор\n",
            "\n",
            "@topic_187: \n",
            "кличка, детеныш, крокодил, вольер, роджер, горилла, гитарист, камин, родиться, рожать, животный, баста, браконьер, трюк, улыбаться\n",
            "\n",
            "@topic_142: \n",
            "ранее, территория, область, граница, глава, заявлять, республика, сообщать, губернатор, ситуация, риа, крым, режим, российский, государственный\n",
            "\n",
            "@topic_81: \n",
            "находиться, сталкиваться, данные, сообщать, водитель, результат, автомобиль, трасса, ссылка, состояние, выезжать, алкогольный, служба, салон, подписка\n",
            "\n",
            "@topic_40: \n",
            "сообщать, данный, отмечать, говориться, утечка, связь, ссылка, представитель, сайт, получать, пресс, материал, печать, служба, настоящий\n",
            "\n",
            "@topic_86: \n",
            "пользователь, сеть, интернет, сервис, устройство, телефон, приложение, мобильный, смартфон, доступ, реклама, разработчик, яндекс, контент, соцсеть\n",
            "\n",
            "@topic_191: \n",
            "статуя, хэмилтон, пчела, комета, карат, медь, феномен, болид, марино, парагвай, африканец, таймыр, предок, архиепископ, гало\n",
            "\n",
            "@topic_13: \n",
            "ведущий, становиться, ассоциация, получать, сообщать, прошлое, смочь, отмечать, история, собственный, декабрь, марк, выходить, лист, участие\n",
            "\n",
            "@topic_51: \n",
            "оператор, программа, мигрант, беженец, кризис, экономический, население, отмена, норвегия, гражданин, действовать, отмечать, революция, связь, миграционный\n",
            "\n",
            "@topic_169: \n",
            "апрель, заявлять, спецслужба, гражданин, эстония, грозить, ранее, март, сведение, обвинять, сообщать, представитель, отношение, передавать, защита\n",
            "\n",
            "@topic_25: \n",
            "спортсмен, олимпийский, церемония, золотой, участие, спорт, награда, выступать, медаль, олимпиада, федерация, карьера, соревнование, российский, выигрывать\n",
            "\n",
            "@topic_124: \n",
            "выпускать, релиз, альбом, выходить, опубликовывать, входить, камера, становиться, лента, сообщать, редакция, получать, издание, слава, версия\n",
            "\n",
            "@topic_171: \n",
            "миллион, тысяча, доллар, зарабатывать, сделка, составлять, стоимость, долларов, покупка, сумма, общий, месяц, прошлый, данные, криптовалюта\n",
            "\n",
            "@topic_101: \n",
            "ракета, система, ядерный, испытание, су, технический, договор, двигатель, полет, катастрофа, поколение, аппарат, авиационный, дальность, оснащать\n",
            "\n",
            "@topic_195: \n",
            "зуб, губа, оттенок, розовый, прическа, татуировка, рот, восхищаться, бикини, косметический, манекенщица, гардероб, ягодица, секрет, динозавр\n",
            "\n",
            "@topic_178: \n",
            "доллар, миллиард, курс, ставка, составлять, валюта, квартал, торги, рубль, биржа, вырастать, рост, инфляция, валютный, ключевой\n",
            "\n",
            "@topic_98: \n",
            "французский, йорк, франция, нью, получать, помощник, отношение, национальный, май, расследование, сообщать, апрель, марина, ход, ле\n",
            "\n",
            "@topic_186: \n",
            "цунами, блюдо, гватемала, стержень, теща, птичий, павленский, фийон, паломник, уборщица, гбр, шпиль, фукусима, племя, залегать\n",
            "\n",
            "@topic_183: \n",
            "клан, царь, археолог, век, сайрус, онколог, древний, артефакт, эльбрус, багажный, капот, биг, ку, пещера, майль\n",
            "\n",
            "@topic_39: \n",
            "генеральный, гендиректор, коммерсант, заместитель, аэрофлот, получение, сообщать, схема, юрий, директор, претензия, список, частность, сентябрь, летать\n",
            "\n",
            "@topic_64: \n",
            "километр, море, корабль, морской, судно, черный, порт, скорость, побережье, груз, вмс, берег, океан, пролив, флот\n",
            "\n",
            "@topic_133: \n",
            "бой, американец, поражение, вес, поединок, всемирный, килограмм, профессиональный, титул, чемпион, украинец, боксер, карьера, версия, пояс\n",
            "\n",
            "@topic_4: \n",
            "увеличивать, развитие, повышать, уровень, производство, механизм, сокращать, рбк, введение, дефицит, значительно, повышенный, снижать, условие, сказываться\n",
            "\n",
            "@topic_140: \n",
            "рубль, составлять, тысяча, зарплата, доход, минимальный, размер, тариф, рублей, уровень, месяц, стоимость, увеличиваться, сумма, повышение\n",
            "\n",
            "@topic_79: \n",
            "сезон, клуб, лига, игрок, секунда, кубок, форвард, состав, играть, цска, нападающий, становиться, выступать, национальный, хоккейный\n",
            "\n",
            "@topic_148: \n",
            "журналист, телеканал, американский, президент, заявлять, вмешательство, разведка, эфир, лидер, спецслужба, сотрудничать, кампания, агент, сша, бывший\n",
            "\n",
            "@topic_181: \n",
            "зверь, приют, ветеринар, животный, щенок, раскопка, корм, археолог, исмаилов, съедать, мумия, собачий, математический, зоозащитник, гробница\n",
            "\n",
            "@topic_56: \n",
            "пожар, мчс, торговый, магазин, поступать, этаж, возгорание, пожарный, поисковый, верхний, располагать, конструкция, загораться, лесной, шахта\n",
            "\n",
            "@topic_38: \n",
            "мост, сообщать, информация, становиться, увольнение, связь, увольнять, администрация, помощь, новость, угроза, передавать, сми, продолжать, сообщение\n",
            "\n",
            "@topic_137: \n",
            "бывший, канал, сообщать, леонид, телеканал, оппозиционер, май, саакашвили, михаил, левый, экс, георгий, сентябрь, лебедев, беспорядок\n",
            "\n",
            "@topic_198: \n",
            "картофель, йогурт, найт, лосось, росконтроль, пельмень, лапша, салат, склероз, бад, хиросима, сталкивать, барвиха, снуп, горох\n",
            "\n",
            "@topic_160: \n",
            "сша, президент, американский, трамп, вашингтон, дональд, лидер, обама, саммит, дипломат, сенатор, посол, барак, конгресс, республиканец\n",
            "\n",
            "@topic_190: \n",
            "кукла, театральный, мюзикл, чехов, балерина, бриллиант, танцевать, дирижер, филармония, композитор, украшение, универмаг, гусман, худрук, симфонический\n",
            "\n",
            "@topic_128: \n",
            "компания, память, менеджер, отчет, компьютер, концерн, доступ, топ, входить, экран, компьютерный, становиться, конкурент, представлять, владелец\n",
            "\n",
            "@topic_185: \n",
            "слон, уиллис, брюс, купаться, роулинг, писательница, джоан, эверест, скутер, муратов, чудинов, барельеф, хирургия, маз, вишневский\n",
            "\n",
            "@topic_114: \n",
            "власть, сторонник, саудовский, аравия, протест, майдан, казнь, прокурор, королевство, отношение, требовать, смертный, дипломатический, правозащитник, васильев\n",
            "\n",
            "@topic_196: \n",
            "даллас, передозировка, бобби, ред, луи, сирена, эдмонтон, бэй, анахайм, блюз, промысел, раздевалка, мартинес, блю, нэшвилл\n",
            "\n",
            "@topic_166: \n",
            "услуга, размер, оплата, получать, мошенник, коммунальный, обеспечение, банка, социальный, работник, долг, жкх, месяц, условие, возможность\n",
            "\n",
            "@topic_184: \n",
            "галерея, кара, полотно, секта, порноактриса, арашуков, аквариум, чехол, детектор, правозащитница, высоцкий, джереми, бэнксить, кчр, гребенщиков\n",
            "\n",
            "@topic_63: \n",
            "фильм, актер, роль, картина, режиссер, снимать, сериал, отель, съемка, выходить, известный, сценарий, лента, сниматься, премьера\n",
            "\n",
            "@topic_193: \n",
            "чаплин, всеволод, бобров, тетя, кардиолог, стрип, армен, грибаускать, обморок, хрюнов, павлович, хор, зелимхан, исаакиевский, мафия\n",
            "\n",
            "@topic_21: \n",
            "антон, этап, гонка, занимать, ольга, союз, становиться, рубеж, индивидуальный, алкоголь, россиянин, преследование, бутылка, старт, волк\n",
            "\n",
            "@topic_189: \n",
            "змея, крокодил, укусить, ядовитый, укус, гигантский, синтез, плазма, прах, кобра, змей, квинсленд, туркменистан, зуб, килограммовый\n",
            "\n",
            "@topic_179: \n",
            "парк, язык, аппарат, становиться, написать, птица, чужой, маск, лев, появляться, использование, страница, издание, тигр, илона\n",
            "\n",
            "@topic_180: \n",
            "панк, алехин, имение, павлюченков, молебен, левченко, мордовский, толоконников, гуцериев, третьяков, аббатство, сотников, босфор, бо, подтоплять\n",
            "\n",
            "@topic_192: \n",
            "юмористический, любовница, освенцим, мигель, костер, ингушский, ланнистер, ус, евкуры, грачевский, красногорск, склифосовский, шутить, амиран, сноу\n",
            "\n",
            "@topic_197: \n",
            "насекомое, аромат, кишечный, блюдо, крыса, кристалл, флакон, препринт, линза, грызун, ремешок, гамма, желудочный, ультрафиолетовый, фотон\n",
            "\n",
            "@topic_73: \n",
            "проект, школа, класс, планироваться, студент, учебный, школьник, обучение, образование, вуз, туризм, научно, образовательный, среда, реализация\n",
            "\n",
            "@topic_15: \n",
            "оказываться, собака, становиться, рекорд, сообщаться, результат, рассказывать, хозяин, благодаря, приходить, попадать, получать, канал, сообщать, домашний\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "eventness_topicnums[-10:]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Y3wF_ntb6HYD",
        "outputId": "7422ccea-9395-4012-e1ea-e14bc1ecf134"
      },
      "execution_count": 78,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[63, 193, 21, 189, 179, 180, 192, 197, 73, 15]"
            ]
          },
          "metadata": {},
          "execution_count": 78
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def plot_pyt(phi, t, logscale=False, save=None, title=None):\n",
        "    if not title:\n",
        "        title = 'Month distribution given @topic{0}'.format(t)\n",
        "    fig = plt.figure()\n",
        "    ax = fig.add_subplot(1,1,1)\n",
        "    pyt = get_pyt(phi, t)\n",
        "    ax.plot(pyt)\n",
        "    if logscale:\n",
        "        ax.set_yscale('log')\n",
        "    plt.title(title)\n",
        "    plt.xlabel('y')\n",
        "    plt.ylabel('p(y $|$ @topic{0})'.format(t))\n",
        "    plt.ylim(0, 1.0)\n",
        "    plt.xlim(0, 147)\n",
        "    plt.grid(True)\n",
        "    if save:\n",
        "        plt.savefig(save, format='eps', dpi=1000, bbox_inches='tight', pad_inches=0)"
      ],
      "metadata": {
        "id": "4aTMpuiA54yx"
      },
      "execution_count": 77,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plot_pyt(phi_m, 73, title=None, save=None)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "t9cmtN5_56Ae",
        "outputId": "af45bf71-5184-4ce2-885b-af1a49583c25"
      },
      "execution_count": 79,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def get_phi_timestamps(model):\n",
        "    phi_m = model.get_phi(class_ids=[timestamps_class])\n",
        "    phi_m.index = phi_m.index.map(lambda x: int(x[1]))\n",
        "    phi_m = phi_m.sort_index()\n",
        "    return phi_m.values\n",
        "\n",
        "phi_m = get_phi_timestamps(model_artm)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AZH0ryRK6o1Y",
        "outputId": "075cc46a-baa1-4232-8d22-b164c732d176"
      },
      "execution_count": 100,
      "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"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def get_corr_matrix(phi):\n",
        "    df = pd.DataFrame(phi)\n",
        "    return df.corr()\n",
        "\n",
        "phi = model_artm.get_phi(class_ids=[tokens_class]).values\n",
        "corr_matrix = get_corr_matrix(phi).values"
      ],
      "metadata": {
        "id": "gHeI8V1S9sYd"
      },
      "execution_count": 104,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def plot_corr_matrix(matrix, save=None):\n",
        "    sns.set(style=\"white\")\n",
        "    sns.set (font_scale = 2)\n",
        "    f, ax = plt.subplots(figsize=(50, 50))\n",
        "    cmap = sns.diverging_palette(220, 10, as_cmap=True)\n",
        "    sns.heatmap(matrix, cmap='Greens', vmin=0.0,\n",
        "            square=True, xticklabels=2, yticklabels=2,\n",
        "            linewidths=.5, cbar_kws={\"shrink\": .5}, ax=ax)\n",
        "    ax.set_title('Topic-topic correlation matrix')\n",
        "    if save:\n",
        "        plt.savefig(save, format='eps', dpi=1000, bbox_inches='tight', pad_inches=0)"
      ],
      "metadata": {
        "id": "VrI5no7-98P-"
      },
      "execution_count": 116,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plot_corr_matrix(corr_matrix, save=\"ARTM_corr.eps\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "HuAsR2w2955_",
        "outputId": "c107b571-33a4-4a0b-dfa9-3203cbf580ae"
      },
      "execution_count": 117,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 5000x5000 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}