Train.ipynb

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    "colab": {
      "name": "Toxic_Train.ipynb",
      "provenance": [],
      "collapsed_sections": [
        "PHPBoL0mZszY",
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  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dEGjA5vXf7xk",
        "outputId": "b16cc5c7-2253-47b8-a114-a2056a4b7e3d"
      },
      "source": [
        "!pip install catboost"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting catboost\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/47/80/8e9c57ec32dfed6ba2922bc5c96462cbf8596ce1a6f5de532ad1e43e53fe/catboost-0.25.1-cp37-none-manylinux1_x86_64.whl (67.3MB)\n",
            "\u001b[K     |████████████████████████████████| 67.3MB 54kB/s \n",
            "\u001b[?25hRequirement already satisfied: graphviz in /usr/local/lib/python3.7/dist-packages (from catboost) (0.10.1)\n",
            "Requirement already satisfied: scipy in /usr/local/lib/python3.7/dist-packages (from catboost) (1.4.1)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.7/dist-packages (from catboost) (1.15.0)\n",
            "Requirement already satisfied: plotly in /usr/local/lib/python3.7/dist-packages (from catboost) (4.4.1)\n",
            "Requirement already satisfied: matplotlib in /usr/local/lib/python3.7/dist-packages (from catboost) (3.2.2)\n",
            "Requirement already satisfied: pandas>=0.24.0 in /usr/local/lib/python3.7/dist-packages (from catboost) (1.1.5)\n",
            "Requirement already satisfied: numpy>=1.16.0 in /usr/local/lib/python3.7/dist-packages (from catboost) (1.19.5)\n",
            "Requirement already satisfied: retrying>=1.3.3 in /usr/local/lib/python3.7/dist-packages (from plotly->catboost) (1.3.3)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib->catboost) (1.3.1)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.7/dist-packages (from matplotlib->catboost) (0.10.0)\n",
            "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib->catboost) (2.4.7)\n",
            "Requirement already satisfied: python-dateutil>=2.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib->catboost) (2.8.1)\n",
            "Requirement already satisfied: pytz>=2017.2 in /usr/local/lib/python3.7/dist-packages (from pandas>=0.24.0->catboost) (2018.9)\n",
            "Installing collected packages: catboost\n",
            "Successfully installed catboost-0.25.1\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "m8hjGX_2gTp_",
        "outputId": "f0651641-e2f9-4c42-97b2-312f5168ea27"
      },
      "source": [
        "!pip install nltk"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: nltk in /usr/local/lib/python3.7/dist-packages (3.2.5)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.7/dist-packages (from nltk) (1.15.0)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LxlG-h_Df1GE",
        "outputId": "3644db24-ab73-4b69-c11f-1b9770578201"
      },
      "source": [
        "import pandas as pd\n",
        "from sklearn.model_selection import train_test_split\n",
        "import nltk\n",
        "import string\n",
        "from nltk.corpus import stopwords\n",
        "from nltk.tokenize import word_tokenize\n",
        "from nltk.stem import SnowballStemmer\n",
        "nltk.download('punkt')\n",
        "from sklearn.metrics import precision_score, recall_score, precision_recall_curve\n",
        "from sklearn.metrics import roc_auc_score\n",
        "import numpy as np\n",
        "\n",
        "from catboost import CatBoostClassifier\n",
        "\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.feature_extraction.text import TfidfVectorizer"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[nltk_data] Downloading package punkt to /root/nltk_data...\n",
            "[nltk_data]   Unzipping tokenizers/punkt.zip.\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/externals/six.py:31: FutureWarning: The module is deprecated in version 0.21 and will be removed in version 0.23 since we've dropped support for Python 2.7. Please rely on the official version of six (https://pypi.org/project/six/).\n",
            "  \"(https://pypi.org/project/six/).\", FutureWarning)\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PHPBoL0mZszY"
      },
      "source": [
        "# Datasets preprocessing"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QCD78S3wt64e"
      },
      "source": [
        "with open(\"dataset_ok.txt\", \"r+\") as file:\n",
        "  file_readen = file.read().split(\"\\n\")"
      ],
      "execution_count": 147,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rBf-6mtKuZHZ"
      },
      "source": [
        "labels = list(map(lambda x: list(map(lambda y: y[9 :], x.split(\" \")[0].split(\",\"))), file_readen))[: -1]\n",
        "text = list(map(lambda x: \" \".join(x.split(\" \")[1 :]), file_readen))[: -1]"
      ],
      "execution_count": 148,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "E_3hvN-HvK9b",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "806764a6-90df-47ff-ade1-b305aa1780d7"
      },
      "source": [
        "df = pd.DataFrame(np.array([labels, text]).T, columns = [\"label\", \"text\"])"
      ],
      "execution_count": 149,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:1: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray\n",
            "  \"\"\"Entry point for launching an IPython kernel.\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "L_N1wmE7-CH6"
      },
      "source": [
        "def onehot(series, unique):\n",
        "  series_new = pd.DataFrame(data = np.array([[0 for j in range(len(series))] for i in range(len(unique))]).T ,columns = unique)\n",
        "  for elIndex in range(len(series)):\n",
        "      for el_class in series.iloc[elIndex]: series_new[el_class].iloc[elIndex] = 1\n",
        "  return series_new"
      ],
      "execution_count": 150,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fCx0qJ74877B"
      },
      "source": [
        "df_onehoted = onehot(df.label, ['NORMAL', 'INSULT', 'THREAT', 'OBSCENITY'])"
      ],
      "execution_count": 151,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "glhUQXMMC65S",
        "outputId": "4ef01c16-e4d4-4d05-ea31-65702fa8a2c9"
      },
      "source": [
        "df_onehoted.INSULT.value_counts()"
      ],
      "execution_count": 152,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    211464\n",
              "1     36826\n",
              "Name: INSULT, dtype: int64"
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          "metadata": {
            "tags": []
          },
          "execution_count": 152
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RQEXkgYwDI_U",
        "outputId": "1d19b9b9-1deb-4b7e-f7c5-48f283ffd370"
      },
      "source": [
        "df_onehoted.THREAT.value_counts()"
      ],
      "execution_count": 153,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    236263\n",
              "1     12027\n",
              "Name: THREAT, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 153
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Hzyezw2FDNKx",
        "outputId": "77430fe3-8314-42bb-cf6b-26a69267a1d2"
      },
      "source": [
        "df_onehoted.OBSCENITY.value_counts()"
      ],
      "execution_count": 154,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    244029\n",
              "1      4261\n",
              "Name: OBSCENITY, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 154
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 402
        },
        "id": "_3v1yXn6DmeJ",
        "outputId": "786c6ebc-3a1c-4d7f-b595-ecddc011b4cb"
      },
      "source": [
        "df_onehoted"
      ],
      "execution_count": 155,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  <thead>\n",
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              "      <th></th>\n",
              "      <th>NORMAL</th>\n",
              "      <th>INSULT</th>\n",
              "      <th>THREAT</th>\n",
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            "text/plain": [
              "        NORMAL  INSULT  THREAT  OBSCENITY\n",
              "0            0       1       0          0\n",
              "1            1       0       0          0\n",
              "2            1       0       0          0\n",
              "3            1       0       0          0\n",
              "4            1       0       0          0\n",
              "...        ...     ...     ...        ...\n",
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              "248287       1       0       0          0\n",
              "248288       1       0       0          0\n",
              "248289       1       0       0          0\n",
              "\n",
              "[248290 rows x 4 columns]"
            ]
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          "metadata": {
            "tags": []
          },
          "execution_count": 155
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UzXn9pVIf1GH"
      },
      "source": [
        "df_2ch = pd.read_csv(\"labeled.csv\")\n",
        "df_2ch['toxic'] = df_2ch['toxic'].astype(int)"
      ],
      "execution_count": 156,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HwF470tp-pHH",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 402
        },
        "outputId": "23a234fb-4a7d-4e9a-c92e-244de1ad539a"
      },
      "source": [
        "df_2ch"
      ],
      "execution_count": 157,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <th></th>\n",
              "      <th>comment</th>\n",
              "      <th>toxic</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Верблюдов-то за что? Дебилы, бл...\\n</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Хохлы, это отдушина затюканого россиянина, мол...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Собаке - собачья смерть\\n</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Страницу обнови, дебил. Это тоже не оскорблени...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>тебя не убедил 6-страничный пдф в том, что Скр...</td>\n",
              "      <td>1</td>\n",
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              "    <tr>\n",
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              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "    <tr>\n",
              "      <th>14407</th>\n",
              "      <td>Вонючий совковый скот прибежал и ноет. А вот и...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14408</th>\n",
              "      <td>А кого любить? Гоблина тупорылого что-ли? Или ...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14409</th>\n",
              "      <td>Посмотрел Утомленных солнцем 2. И оказалось, ч...</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14410</th>\n",
              "      <td>КРЫМОТРЕД НАРУШАЕТ ПРАВИЛА РАЗДЕЛА Т.К В НЕМ Н...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14411</th>\n",
              "      <td>До сих пор пересматриваю его видео. Орамбо кст...</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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            "text/plain": [
              "                                                 comment  toxic\n",
              "0                   Верблюдов-то за что? Дебилы, бл...\\n      1\n",
              "1      Хохлы, это отдушина затюканого россиянина, мол...      1\n",
              "2                              Собаке - собачья смерть\\n      1\n",
              "3      Страницу обнови, дебил. Это тоже не оскорблени...      1\n",
              "4      тебя не убедил 6-страничный пдф в том, что Скр...      1\n",
              "...                                                  ...    ...\n",
              "14407  Вонючий совковый скот прибежал и ноет. А вот и...      1\n",
              "14408  А кого любить? Гоблина тупорылого что-ли? Или ...      1\n",
              "14409  Посмотрел Утомленных солнцем 2. И оказалось, ч...      0\n",
              "14410  КРЫМОТРЕД НАРУШАЕТ ПРАВИЛА РАЗДЕЛА Т.К В НЕМ Н...      1\n",
              "14411  До сих пор пересматриваю его видео. Орамбо кст...      0\n",
              "\n",
              "[14412 rows x 2 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 157
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zAmiWPXzw4Q8"
      },
      "source": [
        "# snowball = SnowballStemmer(language=\"russian\")\n",
        "# nltk.download('stopwords')\n",
        "# russian_stop_words = stopwords.words(\"russian\")\n",
        "\n",
        "# def tokenize_sentence(sentence: str, remove_stop_words: bool = True) -> list:\n",
        "#     tokens = word_tokenize(sentence, language=\"russian\")\n",
        "#     tokens = [i for i in tokens if i not in string.punctuation]\n",
        "#     if remove_stop_words:\n",
        "#         tokens = [i for i in tokens if i not in russian_stop_words]\n",
        "#     tokens = [snowball.stem(i) for i in tokens]\n",
        "#     return tokens"
      ],
      "execution_count": 158,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "V2y2Dcxc0m5T"
      },
      "source": [
        "X_train_ins, X_test_ins, y_train_ins, y_test_ins = train_test_split(df.text, df_onehoted.INSULT, test_size=0.33, random_state=42)\n",
        "X_train_thr, X_test_thr, y_train_thr, y_test_thr = train_test_split(df.text, df_onehoted.THREAT, test_size=0.33, random_state=42)\n",
        "X_train_obs, X_test_obs, y_train_obs, y_test_obs = train_test_split(df.text, df_onehoted.OBSCENITY, test_size=0.33, random_state=42)"
      ],
      "execution_count": 159,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jqW8MkU4ZyP6"
      },
      "source": [
        "# Sentences vectorizing"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TMjKx8tHfeOU",
        "outputId": "4f0eac15-f839-456c-e2b9-e07cd7577010"
      },
      "source": [
        "!pip install wget"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting wget\n",
            "  Downloading https://files.pythonhosted.org/packages/47/6a/62e288da7bcda82b935ff0c6cfe542970f04e29c756b0e147251b2fb251f/wget-3.2.zip\n",
            "Building wheels for collected packages: wget\n",
            "  Building wheel for wget (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for wget: filename=wget-3.2-cp37-none-any.whl size=9681 sha256=c8aff3ba4657f660315d4cb97c5070f596ce07fdd1ece2b2894e572dca077efc\n",
            "  Stored in directory: /root/.cache/pip/wheels/40/15/30/7d8f7cea2902b4db79e3fea550d7d7b85ecb27ef992b618f3f\n",
            "Successfully built wget\n",
            "Installing collected packages: wget\n",
            "Successfully installed wget-3.2\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AC-BcUqHf6KA",
        "outputId": "0a4ab352-89e6-41c9-8839-b6381b4e2419"
      },
      "source": [
        "!wget http://download.cdn.yandex.net/mystem/mystem-3.0-linux3.1-64bit.tar.gz\n",
        "!tar -xvf mystem-3.0-linux3.1-64bit.tar.gz\n",
        "!cp mystem /root/.local/bin/mystem"
      ],
      "execution_count": 79,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "--2021-06-02 12:13:01--  http://download.cdn.yandex.net/mystem/mystem-3.0-linux3.1-64bit.tar.gz\n",
            "Resolving download.cdn.yandex.net (download.cdn.yandex.net)... 5.45.205.243, 5.45.205.244, 5.45.205.241, ...\n",
            "Connecting to download.cdn.yandex.net (download.cdn.yandex.net)|5.45.205.243|:80... connected.\n",
            "HTTP request sent, awaiting response... 302 Found\n",
            "Location: http://cache-mskm905.cdn.yandex.net/download.cdn.yandex.net/mystem/mystem-3.0-linux3.1-64bit.tar.gz [following]\n",
            "--2021-06-02 12:13:01--  http://cache-mskm905.cdn.yandex.net/download.cdn.yandex.net/mystem/mystem-3.0-linux3.1-64bit.tar.gz\n",
            "Resolving cache-mskm905.cdn.yandex.net (cache-mskm905.cdn.yandex.net)... 5.45.220.15, 2a02:6b8:0:2002::16\n",
            "Connecting to cache-mskm905.cdn.yandex.net (cache-mskm905.cdn.yandex.net)|5.45.220.15|:80... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 16457938 (16M) [application/octet-stream]\n",
            "Saving to: ‘mystem-3.0-linux3.1-64bit.tar.gz.1’\n",
            "\n",
            "mystem-3.0-linux3.1 100%[===================>]  15.70M  8.76MB/s    in 1.8s    \n",
            "\n",
            "2021-06-02 12:13:04 (8.76 MB/s) - ‘mystem-3.0-linux3.1-64bit.tar.gz.1’ saved [16457938/16457938]\n",
            "\n",
            "mystem\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-3Dmwnf3wDbH"
      },
      "source": [
        "import wget, zipfile, gensim\n",
        "\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "\n",
        "from functools import lru_cache\n",
        "from pymystem3 import Mystem\n",
        "\n",
        "class Vectorizer:\n",
        "  def __init__(self, model_file: str = \"\"):\n",
        "    self.mapping = {'A': 'ADJ', 'ADV': 'ADV', 'ADVPRO': 'ADV', 'ANUM': 'ADJ', 'APRO': 'DET', 'COM': 'ADJ', 'CONJ': 'SCONJ', 'INTJ': 'INTJ', 'NONLEX': 'X', 'NUM': 'NUM', 'PART': 'PART', 'PR': 'ADP', 'S': 'NOUN', 'SPRO': 'PRON', 'UNKN': 'X', 'V': 'VERB'}\n",
        "\n",
        "    self.mystem = Mystem()\n",
        "\n",
        "    self.model_vv = None\n",
        "\n",
        "    if (len(model_file) == 0): self.download_model()\n",
        "    else: self.load_model(model_file)\n",
        "  \n",
        "  def download_model(self):\n",
        "    # udpipe url: https://rusvectores.org/static/models/udpipe_syntagrus.model\n",
        "    model_url = 'http://vectors.nlpl.eu/repository/20/204.zip'\n",
        "\n",
        "    m_ = wget.download(model_url)\n",
        "\n",
        "    model_file = model_url.split('/')[-1]\n",
        "\n",
        "    print(f\"Model downloaded from {model_url}\\n Path: {model_file}\")\n",
        "\n",
        "    self.load_model(model_file)\n",
        "\n",
        "  def load_model(self, model_file: str):\n",
        "    with zipfile.ZipFile(model_file, 'r') as archive:\n",
        "      stream = archive.open('model.bin')\n",
        "      self.model_vv = gensim.models.KeyedVectors.load_word2vec_format(stream, binary=True)\n",
        "\n",
        "    print(\"Model loaded.\")\n",
        "\n",
        "  @lru_cache(maxsize=None)\n",
        "  def tag_mystem(self, text: str) -> list:\n",
        "    try:\n",
        "      processed = self.mystem.analyze(text)\n",
        "    except:\n",
        "      return []\n",
        "    tagged = []\n",
        "\n",
        "    for w in processed:\n",
        "      try:\n",
        "        lemma = w[\"analysis\"][0][\"lex\"].lower().strip()\n",
        "        pos = w[\"analysis\"][0][\"gr\"].split(',')[0]\n",
        "        pos = pos.split('=')[0].strip()\n",
        "        if pos in self.mapping:\n",
        "            tagged.append(lemma + '_' + self.mapping[pos])\n",
        "        else:\n",
        "            tagged.append(lemma + '_X')\n",
        "      except KeyError:\n",
        "        continue\n",
        "      except IndexError:\n",
        "        continue\n",
        "    return tagged\n",
        "  \n",
        "  def tokenize_word2vec(self, sentence: str) -> list:\n",
        "    tokens = self.tag_mystem(sentence)\n",
        "    vectors = []\n",
        "\n",
        "    for token in tokens:\n",
        "      try:\n",
        "        vectors.append(self.model_vv[token])\n",
        "      except:\n",
        "        continue\n",
        "\n",
        "    if (len(vectors) == 0): return self.model_vv[\"слэнг_NOUN\"]\n",
        "\n",
        "    return np.mean(vectors, axis = 0)\n",
        "  \n",
        "  def Vectorize_one(self, text: str) -> np.array:\n",
        "    self.mystem = Mystem()\n",
        "    return self.tokenize_word2vec(text)\n",
        "\n",
        "  def Vectorize_corpus(self, dataset: pd.DataFrame) -> pd.DataFrame:\n",
        "    self.mystem = Mystem()\n",
        "    \n",
        "    vectorized = []\n",
        "\n",
        "    for object_index in range(len(dataset)):\n",
        "      if (object_index % 10000 == 0): print(f\"Current progress: {object_index / len(dataset) * 100}%\")\n",
        "      \n",
        "      vector = self.tokenize_word2vec(dataset.iloc[object_index])\n",
        "\n",
        "      vectorized.append(vector)\n",
        "    \n",
        "    return pd.DataFrame(list(vectorized))"
      ],
      "execution_count": 194,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uu7jLRDpfQms",
        "outputId": "08b330be-e185-4ddd-bfc8-809127b3d021"
      },
      "source": [
        "vectorizer = Vectorizer(model_file=\"204.zip\")"
      ],
      "execution_count": 195,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model loaded.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9H3z_ot1gEvv",
        "outputId": "9a55fdb0-55a8-4340-c371-3ba45d5a3215"
      },
      "source": [
        "X_train_vec = vectorizer.Vectorize_corpus(X_train_ins)"
      ],
      "execution_count": 160,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Current progress: 0.0%\n",
            "Current progress: 6.011277155944552%\n",
            "Current progress: 12.022554311889104%\n",
            "Current progress: 18.033831467833654%\n",
            "Current progress: 24.04510862377821%\n",
            "Current progress: 30.05638577972276%\n",
            "Current progress: 36.06766293566731%\n",
            "Current progress: 42.07894009161187%\n",
            "Current progress: 48.09021724755642%\n",
            "Current progress: 54.10149440350097%\n",
            "Current progress: 60.11277155944552%\n",
            "Current progress: 66.12404871539007%\n",
            "Current progress: 72.13532587133462%\n",
            "Current progress: 78.14660302727917%\n",
            "Current progress: 84.15788018322374%\n",
            "Current progress: 90.16915733916828%\n",
            "Current progress: 96.18043449511283%\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "y8tTkvljjRDm",
        "outputId": "c9c7fc15-628f-4435-c1c0-9140cc77dfcd"
      },
      "source": [
        "X_test_vec = vectorizer.Vectorize_corpus(X_test_ins)"
      ],
      "execution_count": 161,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Current progress: 0.0%\n",
            "Current progress: 12.20464752977934%\n",
            "Current progress: 24.40929505955868%\n",
            "Current progress: 36.61394258933802%\n",
            "Current progress: 48.81859011911736%\n",
            "Current progress: 61.023237648896696%\n",
            "Current progress: 73.22788517867605%\n",
            "Current progress: 85.43253270845538%\n",
            "Current progress: 97.63718023823472%\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qDEwhGaiZ3pK"
      },
      "source": [
        "# Train"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "b4z1-4YRr2_y"
      },
      "source": [
        "## Logistic Regression"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dPv9SrA1kUWU"
      },
      "source": [
        "model_lr = LogisticRegression(random_state=42)"
      ],
      "execution_count": 88,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "V-qih-DOkZci",
        "outputId": "891e1668-e2f6-4345-e63b-53a8468e8311"
      },
      "source": [
        "model_lr.fit(X_train_vec, y_train_ins)"
      ],
      "execution_count": 99,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "Please also refer to the documentation for alternative solver options:\n",
            "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
            "  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
              "                   intercept_scaling=1, l1_ratio=None, max_iter=100,\n",
              "                   multi_class='auto', n_jobs=None, penalty='l2',\n",
              "                   random_state=42, solver='lbfgs', tol=0.0001, verbose=0,\n",
              "                   warm_start=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 99
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "I5k9jq_Nkj67",
        "outputId": "c090ef9d-3e0b-4053-fc5a-043bfb9246dc"
      },
      "source": [
        "precision_score(y_true=y_test_ins, y_pred=model_lr.predict(X_test_ins_vec))"
      ],
      "execution_count": 100,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8058740037121956"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 100
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sBFQDEkwrgxw",
        "outputId": "3297c3c1-1ce9-42de-aa7f-2203f608b0be"
      },
      "source": [
        "recall_score(y_true=y_test_ins, y_pred=model_lr.predict(X_test_ins_vec))"
      ],
      "execution_count": 101,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.6029736132668899"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 101
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MewGo8dTrTuU",
        "outputId": "f803c0d8-e204-4b71-d689-33836c6ab284"
      },
      "source": [
        "roc_auc_score(y_test_ins, model_lr.predict_proba(X_test_ins_vec).T[1])"
      ],
      "execution_count": 102,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9478907815331472"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 102
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VOROjV3er6Ej"
      },
      "source": [
        "## CatBoost"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BjZ4mWB0r7zH"
      },
      "source": [
        "model_ins_cbc = CatBoostClassifier(random_state=42)\n",
        "model_thr_cbc = CatBoostClassifier(random_state=42)\n",
        "model_obs_cbc = CatBoostClassifier(random_state=42)"
      ],
      "execution_count": 162,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7VBWuv0PsAB6",
        "outputId": "4bbc43ec-64e5-49c6-a4a7-3a5f64dc76b4"
      },
      "source": [
        "model_ins_cbc.fit(X_train_vec, y_train_ins)"
      ],
      "execution_count": 178,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Learning rate set to 0.091478\n",
            "0:\tlearn: 0.5934829\ttotal: 354ms\tremaining: 5m 53s\n",
            "1:\tlearn: 0.5210373\ttotal: 727ms\tremaining: 6m 2s\n",
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          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<catboost.core.CatBoostClassifier at 0x7f4481c26a90>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 178
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LFm3Qe5J1y-3",
        "outputId": "c6d6ef0c-1f89-4023-e992-04b9404daede"
      },
      "source": [
        "model_thr_cbc.fit(X_train_vec, y_train_thr)"
      ],
      "execution_count": 165,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Learning rate set to 0.091478\n",
            "0:\tlearn: 0.5508853\ttotal: 442ms\tremaining: 7m 21s\n",
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            "999:\tlearn: 0.0273613\ttotal: 5m 44s\tremaining: 0us\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<catboost.core.CatBoostClassifier at 0x7f4481c26450>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 165
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "X-D_3HTr1zZ7",
        "outputId": "a7b1f631-eb07-4d59-8f61-379bb78ec0d2"
      },
      "source": [
        "model_obs_cbc.fit(X_train_vec, y_train_obs)"
      ],
      "execution_count": 167,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Learning rate set to 0.091478\n",
            "0:\tlearn: 0.5247640\ttotal: 309ms\tremaining: 5m 8s\n",
            "1:\tlearn: 0.3950339\ttotal: 616ms\tremaining: 5m 7s\n",
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            "997:\tlearn: 0.0056691\ttotal: 5m 35s\tremaining: 672ms\n",
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            "999:\tlearn: 0.0056479\ttotal: 5m 35s\tremaining: 0us\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<catboost.core.CatBoostClassifier at 0x7f4481c26090>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 167
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XdFMj4wbr8kk"
      },
      "source": [
        "## Models with old vectorization"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FK5bkQ7yiAy5"
      },
      "source": [
        "models_pipeline = {\n",
        "    \"INS\": Pipeline([(\"vectorizer\", TfidfVectorizer(tokenizer=tokenize_sentence)), (\"model\", LogisticRegression(random_state=42, C=10))]),\n",
        "    \"THR\": Pipeline([(\"vectorizer\", TfidfVectorizer(tokenizer=tokenize_sentence)), (\"model\", LogisticRegression(random_state=42, C=10))]), \n",
        "    \"OBS\": Pipeline([(\"vectorizer\", TfidfVectorizer(tokenizer=tokenize_sentence)), (\"model\", LogisticRegression(random_state=42, C=10))])\n",
        "    }"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Em5Pb33E3OUy"
      },
      "source": [
        "models_pipeline['INS'].fit(X_train_ins, y_train_ins)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vJnAtJM_Fvwn",
        "outputId": "62e198b3-3ee8-447a-89ac-90589a82da64"
      },
      "source": [
        "models_pipeline['THR'].fit(X_train_thr, y_train_thr)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "Please also refer to the documentation for alternative solver options:\n",
            "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
            "  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Pipeline(memory=None,\n",
              "         steps=[('vectorizer',\n",
              "                 TfidfVectorizer(analyzer='word', binary=False,\n",
              "                                 decode_error='strict',\n",
              "                                 dtype=<class 'numpy.float64'>,\n",
              "                                 encoding='utf-8', input='content',\n",
              "                                 lowercase=True, max_df=1.0, max_features=None,\n",
              "                                 min_df=1, ngram_range=(1, 1), norm='l2',\n",
              "                                 preprocessor=None, smooth_idf=True,\n",
              "                                 stop_words=None, strip_accents=None,\n",
              "                                 sublinear_tf=False,\n",
              "                                 token_patt...w+\\\\b',\n",
              "                                 tokenizer=<function tokenize_sentence at 0x7fa6bdfcac20>,\n",
              "                                 use_idf=True, vocabulary=None)),\n",
              "                ('model',\n",
              "                 LogisticRegression(C=10, class_weight=None, dual=False,\n",
              "                                    fit_intercept=True, intercept_scaling=1,\n",
              "                                    l1_ratio=None, max_iter=100,\n",
              "                                    multi_class='auto', n_jobs=None,\n",
              "                                    penalty='l2', random_state=42,\n",
              "                                    solver='lbfgs', tol=0.0001, verbose=0,\n",
              "                                    warm_start=False))],\n",
              "         verbose=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OOuG_dusFwVp",
        "outputId": "084af0ac-170a-40dc-ac6e-ec70014af6e8"
      },
      "source": [
        "models_pipeline['OBS'].fit(X_train_obs, y_train_obs)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "Please also refer to the documentation for alternative solver options:\n",
            "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
            "  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Pipeline(memory=None,\n",
              "         steps=[('vectorizer',\n",
              "                 TfidfVectorizer(analyzer='word', binary=False,\n",
              "                                 decode_error='strict',\n",
              "                                 dtype=<class 'numpy.float64'>,\n",
              "                                 encoding='utf-8', input='content',\n",
              "                                 lowercase=True, max_df=1.0, max_features=None,\n",
              "                                 min_df=1, ngram_range=(1, 1), norm='l2',\n",
              "                                 preprocessor=None, smooth_idf=True,\n",
              "                                 stop_words=None, strip_accents=None,\n",
              "                                 sublinear_tf=False,\n",
              "                                 token_patt...w+\\\\b',\n",
              "                                 tokenizer=<function tokenize_sentence at 0x7fa6bdfcac20>,\n",
              "                                 use_idf=True, vocabulary=None)),\n",
              "                ('model',\n",
              "                 LogisticRegression(C=10, class_weight=None, dual=False,\n",
              "                                    fit_intercept=True, intercept_scaling=1,\n",
              "                                    l1_ratio=None, max_iter=100,\n",
              "                                    multi_class='auto', n_jobs=None,\n",
              "                                    penalty='l2', random_state=42,\n",
              "                                    solver='lbfgs', tol=0.0001, verbose=0,\n",
              "                                    warm_start=False))],\n",
              "         verbose=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JIAprBNPkJmK"
      },
      "source": [
        "# Score&Results"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2q91KWVRuUxv"
      },
      "source": [
        "## Old models"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0rFhOmto3w7H",
        "outputId": "594536e5-7c61-45d8-ce71-86ca4ad8c623"
      },
      "source": [
        "print(\"Precision: {}; Recall: {}\".format(precision_score(y_true=y_test_ins, y_pred=models_pipeline['INS'].predict(X_test_ins), average = 'weighted'), recall_score(y_true=y_test_ins, y_pred=models_pipeline['INS'].predict(X_test_ins), average = 'weighted')))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Precision: 0.9599445125554553; Recall: 0.960676625659051\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GPNnRYieGGAk",
        "outputId": "47fb1bc4-a993-492d-f1a5-39349c1d1b41"
      },
      "source": [
        "print(\"Precision: {}; Recall: {}\".format(precision_score(y_true=y_test_thr, y_pred=models_pipeline['THR'].predict(X_test_thr), average = 'weighted'), recall_score(y_true=y_test_thr, y_pred=models_pipeline['THR'].predict(X_test_thr), average = 'weighted')))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Precision: 0.9774182679964719; Recall: 0.9786784807654755\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VQ_VXly8F2fd",
        "outputId": "a4cbb95f-043e-4eef-c0ad-8f90d47b1621"
      },
      "source": [
        "print(\"Precision: {}; Recall: {}\".format(precision_score(y_true=y_test_obs, y_pred=models_pipeline['OBS'].predict(X_test_obs), average = 'weighted'), recall_score(y_true=y_test_obs, y_pred=models_pipeline['OBS'].predict(X_test_obs), average = 'weighted')))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Precision: 0.9913580079452491; Recall: 0.9919693419254052\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Rc4uwWIm-yJ1",
        "outputId": "9bf55e55-8163-4b4e-f337-e0e12b4400fc"
      },
      "source": [
        "roc_auc_score(models_pipeline[\"INS\"].predict(df_2ch.comment), df_2ch.toxic)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8399818766217839"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 402
        },
        "id": "m-dcz7ktBYRR",
        "outputId": "be87ef0b-0148-45a7-dafd-32e262f4dcde"
      },
      "source": [
        "df_2ch.iloc[np.where(models_pipeline[\"THR\"].predict(df_2ch.comment) == 1)]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>comment</th>\n",
              "      <th>toxic</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Собаке - собачья смерть\\n</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>97</th>\n",
              "      <td>Что б он сам здох мразь\\n</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>117</th>\n",
              "      <td>Таких как ты психопатов на хуй отстреливать в ...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>142</th>\n",
              "      <td>Вы привели достаточно аргументов чтобы их не п...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>348</th>\n",
              "      <td>стрелять в орущее быдло\\n</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14003</th>\n",
              "      <td>Пиар параши. Сделайте этому хуесосу уже гребеш...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14107</th>\n",
              "      <td>Это моча так неугодных гнобит. Даёт бан педрюх...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14146</th>\n",
              "      <td>И кто же тебя обоссал на этот раз, россиянец?\\n</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14388</th>\n",
              "      <td>ПРОКЛЯТАЯ ДЕТОЕДСКАЯ МРАЗЬ. ГОРИ В АДУ ВЕЧНО.\\n</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14398</th>\n",
              "      <td>Моча в анимаче решила окончательно убить реакш...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>98 rows × 2 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                                                 comment  toxic\n",
              "2                              Собаке - собачья смерть\\n      1\n",
              "97                             Что б он сам здох мразь\\n      1\n",
              "117    Таких как ты психопатов на хуй отстреливать в ...      1\n",
              "142    Вы привели достаточно аргументов чтобы их не п...      1\n",
              "348                            стрелять в орущее быдло\\n      1\n",
              "...                                                  ...    ...\n",
              "14003  Пиар параши. Сделайте этому хуесосу уже гребеш...      1\n",
              "14107  Это моча так неугодных гнобит. Даёт бан педрюх...      1\n",
              "14146    И кто же тебя обоссал на этот раз, россиянец?\\n      1\n",
              "14388    ПРОКЛЯТАЯ ДЕТОЕДСКАЯ МРАЗЬ. ГОРИ В АДУ ВЕЧНО.\\n      1\n",
              "14398  Моча в анимаче решила окончательно убить реакш...      1\n",
              "\n",
              "[98 rows x 2 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 402
        },
        "id": "kD0vKi0ECMJL",
        "outputId": "8e4beac4-16e3-49de-ebcf-ba4cdb9962ca"
      },
      "source": [
        "df_2ch.iloc[np.where(models_pipeline[\"OBS\"].predict(df_2ch.comment) == 1)]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>comment</th>\n",
              "      <th>toxic</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>163</th>\n",
              "      <td>о жопах Да нет, это твой свидомый мозг дорисов...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>274</th>\n",
              "      <td>мне поебать на его финансы и кто он. Не пизди,...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>373</th>\n",
              "      <td>Я атеист и армянин. Просто рот твой ебал Лучше...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>374</th>\n",
              "      <td>Конечно, вы же собак ебете, блядь.\\n</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>777</th>\n",
              "      <td>Я ШЛЮХА -- ТЕТЯ СРАКА, ВАША ЗАРПЛАТА ПОНИЖЕНА....</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14195</th>\n",
              "      <td>Тебе уже предоставляется такая возможность, бе...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14231</th>\n",
              "      <td>не захотел отсасывать мракобесию И с заглотом ...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14232</th>\n",
              "      <td>Соси хуй подсос единоросский\\n</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14258</th>\n",
              "      <td>Хуй соси, анимеблядь. Мать твою ебал Аноним ID...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14346</th>\n",
              "      <td>Я поглотил знание. Вы будете сосать мой хуй. С...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>106 rows × 2 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                                                 comment  toxic\n",
              "163    о жопах Да нет, это твой свидомый мозг дорисов...      1\n",
              "274    мне поебать на его финансы и кто он. Не пизди,...      1\n",
              "373    Я атеист и армянин. Просто рот твой ебал Лучше...      1\n",
              "374                 Конечно, вы же собак ебете, блядь.\\n      1\n",
              "777    Я ШЛЮХА -- ТЕТЯ СРАКА, ВАША ЗАРПЛАТА ПОНИЖЕНА....      1\n",
              "...                                                  ...    ...\n",
              "14195  Тебе уже предоставляется такая возможность, бе...      1\n",
              "14231  не захотел отсасывать мракобесию И с заглотом ...      1\n",
              "14232                     Соси хуй подсос единоросский\\n      1\n",
              "14258  Хуй соси, анимеблядь. Мать твою ебал Аноним ID...      1\n",
              "14346  Я поглотил знание. Вы будете сосать мой хуй. С...      1\n",
              "\n",
              "[106 rows x 2 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "boGtqRbDf1GL",
        "outputId": "1c354aea-9325-43dc-9100-5efb4d94302d"
      },
      "source": [
        "model_pipeline[0].predict([\"всем привет\"])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['NORMAL'], dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "T_ABCKzTuZbL"
      },
      "source": [
        "## New models"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gkKCbYCRiM2A",
        "outputId": "391eab91-2ce8-4d62-9c78-55ca34593d8f"
      },
      "source": [
        "precision_score(y_true=y_test_ins, y_pred=model_ins_cbc.predict(X_test_vec)), precision_score(y_true=y_test_thr, y_pred=model_thr_cbc.predict(X_test_vec)), precision_score(y_true=y_test_obs, y_pred=model_obs_cbc.predict(X_test_vec))"
      ],
      "execution_count": 197,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(0.8709055876685935, 0.8530405405405406, 0.8199566160520607)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 197
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5CMe95kdiOOz",
        "outputId": "9bd2a12a-7195-4f99-e5af-0e2ba3b2cd40"
      },
      "source": [
        "recall_score(y_true=y_test_ins, y_pred=model_ins_cbc.predict(X_test_vec)), recall_score(y_true=y_test_thr, y_pred=model_thr_cbc.predict(X_test_vec)), recall_score(y_true=y_test_obs, y_pred=model_obs_cbc.predict(X_test_vec))"
      ],
      "execution_count": 198,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(0.7385017563924516, 0.625619425173439, 0.5470332850940666)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 198
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5h0TAQNniPYr",
        "outputId": "f4ed848f-2796-41ef-a6da-48fd4d8440a9"
      },
      "source": [
        "roc_auc_score(y_test_ins, model_ins_cbc.predict_proba(X_test_vec).T[1]), roc_auc_score(y_test_thr, model_thr_cbc.predict_proba(X_test_vec).T[1]), roc_auc_score(y_test_obs, model_obs_cbc.predict_proba(X_test_vec).T[1])"
      ],
      "execution_count": 199,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(0.9670210532958152, 0.9796235421641681, 0.976706747165172)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 199
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "g8_ih_ijux6i",
        "outputId": "c421cf1a-0cae-469c-fb7a-a46dd4069d43"
      },
      "source": [
        "df_2ch_vec = vectorizer.Vectorize(dataset=df_2ch.comment)"
      ],
      "execution_count": 118,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Current progress: 0.0%\n",
            "Current progress: 69.38662225922843%\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ercQmJFH9WbV"
      },
      "source": [
        "### Insults"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KPohablbuakT"
      },
      "source": [
        "df_2ch_toxic_predicted = df_2ch.iloc[np.where(model_ins_cbc.predict(df_2ch_vec) == 1)]\n",
        "df_2ch_untoxic_predicted = df_2ch.iloc[np.where(model_ins_cbc.predict(df_2ch_vec) == 0)]"
      ],
      "execution_count": 183,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "islSvEI7wNNV"
      },
      "source": [
        "False Positive predictions"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 402
        },
        "id": "gYpxhShpvlir",
        "outputId": "abb07a69-9016-40c0-91d4-d3aac7e94e5b"
      },
      "source": [
        "df_2ch_toxic_predicted[df_2ch_toxic_predicted.toxic == 0]"
      ],
      "execution_count": 184,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>comment</th>\n",
              "      <th>toxic</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>109</th>\n",
              "      <td>Да нахуй ты мне это скинул? Если я теку от неё...</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>131</th>\n",
              "      <td>Камера заднего вида и книжная лига? Вы тролль\\n</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>172</th>\n",
              "      <td>Конечно, это резонансное событие требует отдел...</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>178</th>\n",
              "      <td>Хм... По-моему, ты не слышал, как кричит такой...</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>233</th>\n",
              "      <td>Качок дуболом это я так понимаю голубочкин, бл...</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14013</th>\n",
              "      <td>А вот говорить что сниматься в порно хорошо, н...</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14028</th>\n",
              "      <td>Это хуйло хоть 1 полноценный мульт нарисовало?...</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14254</th>\n",
              "      <td>Крупные производители уже давно напрочь охуели...</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14281</th>\n",
              "      <td>Ну давайте тогда всё рассуём по тематике: Танц...</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14406</th>\n",
              "      <td>Потому что запад прошел эту хуйню еще пару сот...</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>111 rows × 2 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                                                 comment  toxic\n",
              "109    Да нахуй ты мне это скинул? Если я теку от неё...      0\n",
              "131      Камера заднего вида и книжная лига? Вы тролль\\n      0\n",
              "172    Конечно, это резонансное событие требует отдел...      0\n",
              "178    Хм... По-моему, ты не слышал, как кричит такой...      0\n",
              "233    Качок дуболом это я так понимаю голубочкин, бл...      0\n",
              "...                                                  ...    ...\n",
              "14013  А вот говорить что сниматься в порно хорошо, н...      0\n",
              "14028  Это хуйло хоть 1 полноценный мульт нарисовало?...      0\n",
              "14254  Крупные производители уже давно напрочь охуели...      0\n",
              "14281  Ну давайте тогда всё рассуём по тематике: Танц...      0\n",
              "14406  Потому что запад прошел эту хуйню еще пару сот...      0\n",
              "\n",
              "[111 rows x 2 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 184
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "F5KeQ9I4wSRS"
      },
      "source": [
        "True positive predictions"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 402
        },
        "id": "IDyFZZaQwJsD",
        "outputId": "7e2040f7-36aa-4aa4-a63d-e9560e83109f"
      },
      "source": [
        "df_2ch_toxic_predicted[df_2ch_toxic_predicted.toxic == 1]"
      ],
      "execution_count": 185,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
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              "\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>comment</th>\n",
              "      <th>toxic</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Верблюдов-то за что? Дебилы, бл...\\n</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Хохлы, это отдушина затюканого россиянина, мол...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Страницу обнови, дебил. Это тоже не оскорблени...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Для каких стан является эталоном современная с...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>Ебать тебя разносит, шизик.\\n</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14400</th>\n",
              "      <td>Не зря, вас, хохлов, свиньями кличут. Вы и ест...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14402</th>\n",
              "      <td>ЭЙ МИХАЛЫЧ, ТАМ ПОЖАРКА ПИЩИТ ДА ХУЙ С НЕЙ ПУЩ...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14403</th>\n",
              "      <td>Пусть лучше само евровидение стримит. Долбоеб ...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14407</th>\n",
              "      <td>Вонючий совковый скот прибежал и ноет. А вот и...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14408</th>\n",
              "      <td>А кого любить? Гоблина тупорылого что-ли? Или ...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1879 rows × 2 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                                                 comment  toxic\n",
              "0                   Верблюдов-то за что? Дебилы, бл...\\n      1\n",
              "1      Хохлы, это отдушина затюканого россиянина, мол...      1\n",
              "3      Страницу обнови, дебил. Это тоже не оскорблени...      1\n",
              "5      Для каких стан является эталоном современная с...      1\n",
              "8                          Ебать тебя разносит, шизик.\\n      1\n",
              "...                                                  ...    ...\n",
              "14400  Не зря, вас, хохлов, свиньями кличут. Вы и ест...      1\n",
              "14402  ЭЙ МИХАЛЫЧ, ТАМ ПОЖАРКА ПИЩИТ ДА ХУЙ С НЕЙ ПУЩ...      1\n",
              "14403  Пусть лучше само евровидение стримит. Долбоеб ...      1\n",
              "14407  Вонючий совковый скот прибежал и ноет. А вот и...      1\n",
              "14408  А кого любить? Гоблина тупорылого что-ли? Или ...      1\n",
              "\n",
              "[1879 rows x 2 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 185
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ajy5TnCwwk1B"
      },
      "source": [
        "False Negative predictions"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 402
        },
        "id": "l_ghjDg1v5ut",
        "outputId": "5b328ac3-b624-4e6a-c554-e25ed8e6a866"
      },
      "source": [
        "df_2ch_untoxic_predicted[df_2ch_untoxic_predicted.toxic == 1]"
      ],
      "execution_count": 186,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>comment</th>\n",
              "      <th>toxic</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Собаке - собачья смерть\\n</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>тебя не убедил 6-страничный пдф в том, что Скр...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>УПАД Т! ТАМ НЕЛЬЗЯ СТРОИТЬ! ТЕХНОЛОГИЙ НЕТ! РА...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>Бактерия, тебе этого не понять Конечно, я же н...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>хорош врать, ты террорист-торчёк-шизофреник пр...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14398</th>\n",
              "      <td>Моча в анимаче решила окончательно убить реакш...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14401</th>\n",
              "      <td>Ну не прямо сразу же ватан бросился к своему к...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14404</th>\n",
              "      <td>ЖЕНЩИНА это ВЕНЕЦ ТВОРЕНИЯ, помните это ваньки...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14405</th>\n",
              "      <td>Сейчас все авиакомпании мира вместе со специал...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14410</th>\n",
              "      <td>КРЫМОТРЕД НАРУШАЕТ ПРАВИЛА РАЗДЕЛА Т.К В НЕМ Н...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>2947 rows × 2 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                                                 comment  toxic\n",
              "2                              Собаке - собачья смерть\\n      1\n",
              "4      тебя не убедил 6-страничный пдф в том, что Скр...      1\n",
              "7      УПАД Т! ТАМ НЕЛЬЗЯ СТРОИТЬ! ТЕХНОЛОГИЙ НЕТ! РА...      1\n",
              "11     Бактерия, тебе этого не понять Конечно, я же н...      1\n",
              "15     хорош врать, ты террорист-торчёк-шизофреник пр...      1\n",
              "...                                                  ...    ...\n",
              "14398  Моча в анимаче решила окончательно убить реакш...      1\n",
              "14401  Ну не прямо сразу же ватан бросился к своему к...      1\n",
              "14404  ЖЕНЩИНА это ВЕНЕЦ ТВОРЕНИЯ, помните это ваньки...      1\n",
              "14405  Сейчас все авиакомпании мира вместе со специал...      1\n",
              "14410  КРЫМОТРЕД НАРУШАЕТ ПРАВИЛА РАЗДЕЛА Т.К В НЕМ Н...      1\n",
              "\n",
              "[2947 rows x 2 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 186
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ukWGSO5swusI"
      },
      "source": [
        "True Negative predictions"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 402
        },
        "id": "5mFpZPiUwstc",
        "outputId": "7484c05c-00d3-410a-99ee-1d49b3d08865"
      },
      "source": [
        "df_2ch_untoxic_predicted[df_2ch_untoxic_predicted.toxic == 0]"
      ],
      "execution_count": 187,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>comment</th>\n",
              "      <th>toxic</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>В шапке были ссылки на инфу по текущему фильму...</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>Почитайте посты у этого автора,может найдете ч...</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>Про графику было обидно) я так то проходил все...</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>https: pp.userapi.com c848520 v848520411 11627...</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>35</th>\n",
              "      <td>Возьмём как пример Россию, западноевропейские ...</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14390</th>\n",
              "      <td>Принимал участие в этом опросе сказал что не д...</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14392</th>\n",
              "      <td>ТАСС, 21 марта. Премьер-министр Новой Зеландии...</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14399</th>\n",
              "      <td>65 А чего так мало? Вот у гражданина был рейти...</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14409</th>\n",
              "      <td>Посмотрел Утомленных солнцем 2. И оказалось, ч...</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14411</th>\n",
              "      <td>До сих пор пересматриваю его видео. Орамбо кст...</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>9475 rows × 2 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                                                 comment  toxic\n",
              "6      В шапке были ссылки на инфу по текущему фильму...      0\n",
              "12     Почитайте посты у этого автора,может найдете ч...      0\n",
              "17     Про графику было обидно) я так то проходил все...      0\n",
              "28     https: pp.userapi.com c848520 v848520411 11627...      0\n",
              "35     Возьмём как пример Россию, западноевропейские ...      0\n",
              "...                                                  ...    ...\n",
              "14390  Принимал участие в этом опросе сказал что не д...      0\n",
              "14392  ТАСС, 21 марта. Премьер-министр Новой Зеландии...      0\n",
              "14399  65 А чего так мало? Вот у гражданина был рейти...      0\n",
              "14409  Посмотрел Утомленных солнцем 2. И оказалось, ч...      0\n",
              "14411  До сих пор пересматриваю его видео. Орамбо кст...      0\n",
              "\n",
              "[9475 rows x 2 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 187
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "H1_YnVQx9j0T"
      },
      "source": [
        "### Threads"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "C2iU6FEz9snw"
      },
      "source": [
        "df_2ch_toxic_predicted = df_2ch.iloc[np.where(model_thr_cbc.predict(df_2ch_vec) == 1)]\n",
        "df_2ch_untoxic_predicted = df_2ch.iloc[np.where(model_thr_cbc.predict(df_2ch_vec) == 0)]"
      ],
      "execution_count": 188,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 166
        },
        "id": "WEIWuNNq90t2",
        "outputId": "4cfc540e-ac1d-4650-e3f7-1f4f433745d6"
      },
      "source": [
        "df_2ch_toxic_predicted[df_2ch_toxic_predicted.toxic == 0]"
      ],
      "execution_count": 189,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>comment</th>\n",
              "      <th>toxic</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>468</th>\n",
              "      <td>Потому что он гомогеев убивал.\\n</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>634</th>\n",
              "      <td>Сначала надо отрезать\\n</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1288</th>\n",
              "      <td>Ору!! Каммент огонь)))</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8031</th>\n",
              "      <td>Но поверх все равно изолентой намертво надо )\\n</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                              comment  toxic\n",
              "468                  Потому что он гомогеев убивал.\\n      0\n",
              "634                           Сначала надо отрезать\\n      0\n",
              "1288                           Ору!! Каммент огонь)))      0\n",
              "8031  Но поверх все равно изолентой намертво надо )\\n      0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 189
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 402
        },
        "id": "nqEcUhef96dR",
        "outputId": "fa815030-c66c-4efd-a664-ed266d3aa813"
      },
      "source": [
        "df_2ch_toxic_predicted[df_2ch_toxic_predicted.toxic == 1]"
      ],
      "execution_count": 190,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>comment</th>\n",
              "      <th>toxic</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Собаке - собачья смерть\\n</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>97</th>\n",
              "      <td>Что б он сам здох мразь\\n</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>227</th>\n",
              "      <td>Правильно разбомбили сербских уродов. Жаль что...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>348</th>\n",
              "      <td>стрелять в орущее быдло\\n</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>373</th>\n",
              "      <td>Я атеист и армянин. Просто рот твой ебал Лучше...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13712</th>\n",
              "      <td>БРОСАЙ БЛЯТЬ Бросил. Пистолет выстрелил. Убил ...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13878</th>\n",
              "      <td>ВОЖДЬ Я УБЬЮ ТЕБЯ СУКА\\n</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13920</th>\n",
              "      <td>Дебил блядь, стреляться в висок надо, полудуро...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14050</th>\n",
              "      <td>Сталинисты - наглухо отбитые животные.\\n</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14388</th>\n",
              "      <td>ПРОКЛЯТАЯ ДЕТОЕДСКАЯ МРАЗЬ. ГОРИ В АДУ ВЕЧНО.\\n</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>77 rows × 2 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                                                 comment  toxic\n",
              "2                              Собаке - собачья смерть\\n      1\n",
              "97                             Что б он сам здох мразь\\n      1\n",
              "227    Правильно разбомбили сербских уродов. Жаль что...      1\n",
              "348                            стрелять в орущее быдло\\n      1\n",
              "373    Я атеист и армянин. Просто рот твой ебал Лучше...      1\n",
              "...                                                  ...    ...\n",
              "13712  БРОСАЙ БЛЯТЬ Бросил. Пистолет выстрелил. Убил ...      1\n",
              "13878                           ВОЖДЬ Я УБЬЮ ТЕБЯ СУКА\\n      1\n",
              "13920  Дебил блядь, стреляться в висок надо, полудуро...      1\n",
              "14050           Сталинисты - наглухо отбитые животные.\\n      1\n",
              "14388    ПРОКЛЯТАЯ ДЕТОЕДСКАЯ МРАЗЬ. ГОРИ В АДУ ВЕЧНО.\\n      1\n",
              "\n",
              "[77 rows x 2 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 190
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Iv0l4-nYkPQC"
      },
      "source": [
        "# Saving models"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ilVzfePTxCbR"
      },
      "source": [
        "from joblib import dump, load"
      ],
      "execution_count": 130,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NIiOZmyy52F0",
        "outputId": "7cdae3ac-c5cd-425f-b60c-84c07e638f9d"
      },
      "source": [
        "dump(models_pipeline[\"INS\"], 'model_ins_logistic_2.joblib')\n",
        "dump(models_pipeline[\"THR\"], 'model_thr_logistic_2.joblib')\n",
        "dump(models_pipeline[\"OBS\"], 'model_obs_logistic_2.joblib')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['model_obs_logistic_2.joblib']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 188
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mAAMewPW9KcV",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c1fdf08f-1a79-4dff-b7ff-85897b11c307"
      },
      "source": [
        "dump(model_ins_cbc, \"model_ins_w2v_CBC.joblib\")\n",
        "dump(model_thr_cbc, \"model_thr_w2v_CBC.joblib\")\n",
        "dump(model_obs_cbc, \"model_obs_w2v_CBC.joblib\")"
      ],
      "execution_count": 191,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['model_obs_w2v_CBC.joblib']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 191
        }
      ]
    }
  ]
}