ipynb/text.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "toc": true
   },
   "source": [
    "<h1>Содержание<span class=\"tocSkip\"></span></h1>\n",
    "<div class=\"toc\"><ul class=\"toc-item\"><li><span><a href=\"#Подготовка\" data-toc-modified-id=\"Подготовка-1\"><span class=\"toc-item-num\">1&nbsp;&nbsp;</span>Подготовка</a></span><ul class=\"toc-item\"><li><span><a href=\"#Лемматизация\" data-toc-modified-id=\"Лемматизация-1.1\"><span class=\"toc-item-num\">1.1&nbsp;&nbsp;</span>Лемматизация</a></span></li></ul></li><li><span><a href=\"#Обучение\" data-toc-modified-id=\"Обучение-2\"><span class=\"toc-item-num\">2&nbsp;&nbsp;</span>Обучение</a></span></li><li><span><a href=\"#Выводы\" data-toc-modified-id=\"Выводы-3\"><span class=\"toc-item-num\">3&nbsp;&nbsp;</span>Выводы</a></span></li><li><span><a href=\"#Чек-лист-проверки\" data-toc-modified-id=\"Чек-лист-проверки-4\"><span class=\"toc-item-num\">4&nbsp;&nbsp;</span>Чек-лист проверки</a></span></li></ul></div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Проект для «Викишоп»"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Интернет-магазин «Викишоп» запускает новый сервис. Теперь пользователи могут редактировать и дополнять описания товаров, как в вики-сообществах. То есть клиенты предлагают свои правки и комментируют изменения других. Магазину нужен инструмент, который будет искать токсичные комментарии и отправлять их на модерацию. \n",
    "\n",
    "Обучите модель классифицировать комментарии на позитивные и негативные. В вашем распоряжении набор данных с разметкой о токсичности правок.\n",
    "\n",
    "Постройте модель со значением метрики качества *F1* не меньше 0.75. \n",
    "\n",
    "**Инструкция по выполнению проекта**\n",
    "\n",
    "1. Загрузите и подготовьте данные.\n",
    "2. Обучите разные модели. \n",
    "3. Сделайте выводы.\n",
    "\n",
    "Для выполнения проекта применять *BERT* необязательно, но вы можете попробовать.\n",
    "\n",
    "**Описание данных**\n",
    "\n",
    "Данные находятся в файле `toxic_comments.csv`. Столбец *text* в нём содержит текст комментария, а *toxic* — целевой признак."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Подготовка"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "from sklearn.model_selection import (\n",
    "    train_test_split,\n",
    "    GridSearchCV,\n",
    "    RandomizedSearchCV,\n",
    ")\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import f1_score\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.compose import ColumnTransformer\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.svm import SVC\n",
    "import re\n",
    "import spacy\n",
    "\n",
    "RANDOM_STATE = 43\n",
    "TEST_SIZE = 0.25"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\n",
    "    'https://code.s3.yandex.net/datasets/toxic_comments.csv', index_col=0\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['text'].duplicated().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0.898388\n",
       "1    0.101612\n",
       "Name: toxic, dtype: float64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['toxic'].value_counts() / len(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Присутствует сильный дисбаланс классов. Токсичных комментариев всего 10%"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Лемматизация"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def lemmatize_it(text, nlp_object):\n",
    "    '''\n",
    "    Лематизирует текст, удаляет стоп-символы и лишние пробелы\n",
    "    text - текст, который надо лемматизировать\n",
    "    nlp_object - объект, проводящий лемматизацию\n",
    "    '''\n",
    "    doc = nlp(text)\n",
    "    lemm = \" \".join([token.lemma_ for token in doc])\n",
    "    clear = re.sub(r'[^a-zA-Z ]', ' ', lemm.lower())\n",
    "    return ' '.join(clear.split())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "nlp = spacy.load('en_core_web_sm', disable=['parser', 'ner'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['lemm_text'] = df['text'].apply(lambda x: lemmatize_it(x, nlp))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "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>text</th>\n",
       "      <th>toxic</th>\n",
       "      <th>lemm_text</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Explanation\\nWhy the edits made under my usern...</td>\n",
       "      <td>0</td>\n",
       "      <td>explanation why the edit make under my usernam...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>D'aww! He matches this background colour I'm s...</td>\n",
       "      <td>0</td>\n",
       "      <td>d aww he match this background colour i be see...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Hey man, I'm really not trying to edit war. It...</td>\n",
       "      <td>0</td>\n",
       "      <td>hey man i be really not try to edit war it be ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>\"\\nMore\\nI can't make any real suggestions on ...</td>\n",
       "      <td>0</td>\n",
       "      <td>more i can not make any real suggestion on imp...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>You, sir, are my hero. Any chance you remember...</td>\n",
       "      <td>0</td>\n",
       "      <td>you sir be my hero any chance you remember wha...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                text  toxic  \\\n",
       "0  Explanation\\nWhy the edits made under my usern...      0   \n",
       "1  D'aww! He matches this background colour I'm s...      0   \n",
       "2  Hey man, I'm really not trying to edit war. It...      0   \n",
       "3  \"\\nMore\\nI can't make any real suggestions on ...      0   \n",
       "4  You, sir, are my hero. Any chance you remember...      0   \n",
       "\n",
       "                                           lemm_text  \n",
       "0  explanation why the edit make under my usernam...  \n",
       "1  d aww he match this background colour i be see...  \n",
       "2  hey man i be really not try to edit war it be ...  \n",
       "3  more i can not make any real suggestion on imp...  \n",
       "4  you sir be my hero any chance you remember wha...  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1327"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['lemm_text'].duplicated().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "После лемматизации обнаружились дубликаты. Уберем их и проверим, изменился ли баланс классов."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0.898433\n",
       "1    0.101567\n",
       "Name: toxic, dtype: float64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df.drop_duplicates(subset=['lemm_text'])\n",
    "df['toxic'].value_counts() / len(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Баланс классов не изменился"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Обучение"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df['lemm_text']\n",
    "y = df['toxic']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(\n",
    "    X, y, stratify=y, test_size=TEST_SIZE, random_state=RANDOM_STATE\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "count_tf_idf = TfidfVectorizer(stop_words='english')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-1 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-1 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-1 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-1 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-1 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;vect&#x27;, TfidfVectorizer()),\n",
       "                (&#x27;model&#x27;, LogisticRegression(random_state=43))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" ><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label  sk-toggleable__label-arrow \">&nbsp;&nbsp;Pipeline<a class=\"sk-estimator-doc-link \" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.pipeline.Pipeline.html\">?<span>Documentation for Pipeline</span></a><span class=\"sk-estimator-doc-link \">i<span>Not fitted</span></span></label><div class=\"sk-toggleable__content \"><pre>Pipeline(steps=[(&#x27;vect&#x27;, TfidfVectorizer()),\n",
       "                (&#x27;model&#x27;, LogisticRegression(random_state=43))])</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label  sk-toggleable__label-arrow \">&nbsp;TfidfVectorizer<a class=\"sk-estimator-doc-link \" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.feature_extraction.text.TfidfVectorizer.html\">?<span>Documentation for TfidfVectorizer</span></a></label><div class=\"sk-toggleable__content \"><pre>TfidfVectorizer()</pre></div> </div></div><div class=\"sk-item\"><div class=\"sk-estimator  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label  sk-toggleable__label-arrow \">&nbsp;LogisticRegression<a class=\"sk-estimator-doc-link \" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.linear_model.LogisticRegression.html\">?<span>Documentation for LogisticRegression</span></a></label><div class=\"sk-toggleable__content \"><pre>LogisticRegression(random_state=43)</pre></div> </div></div></div></div></div></div>"
      ],
      "text/plain": [
       "Pipeline(steps=[('vect', TfidfVectorizer()),\n",
       "                ('model', LogisticRegression(random_state=43))])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pipeline = Pipeline(\n",
    "    [\n",
    "        (\"vect\", TfidfVectorizer()),\n",
    "        (\"model\", LogisticRegression(random_state=RANDOM_STATE)),\n",
    "    ]\n",
    ")\n",
    "pipeline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# LogReg\n",
    "logreg_params = {}\n",
    "\n",
    "logreg_params['vect__max_df'] = [0.2, 0.4, 0.6, 0.8, 1.0]\n",
    "logreg_params['vect__min_df'] = [1, 3, 5, 10]\n",
    "logreg_params['vect__norm'] = ['l1', 'l2']\n",
    "\n",
    "logreg_params['model__l1_ratio'] = [0.6, 0.7, 0.8]\n",
    "logreg_params['model__penalty'] = ['elasticnet']\n",
    "logreg_params['model__solver'] = ['saga']\n",
    "logreg_params['model'] = [LogisticRegression(random_state=RANDOM_STATE)]\n",
    "\n",
    "# Forest\n",
    "forest_params = {}\n",
    "\n",
    "forest_params['vect__max_df'] = [0.2, 0.4, 0.6, 0.8, 1.0]\n",
    "forest_params['vect__min_df'] = [1, 3, 5, 10]\n",
    "forest_params['vect__norm'] = ['l1', 'l2']\n",
    "\n",
    "forest_params['model__max_depth'] = [2, 5, 10]\n",
    "forest_params['model__min_samples_split'] = [10, 50, 100, 250, 500, 1000]\n",
    "forest_params['model'] = [RandomForestClassifier(random_state=RANDOM_STATE)]\n",
    "\n",
    "# SVC\n",
    "svc_params = {}\n",
    "\n",
    "svc_params['vect__max_df'] = [0.2, 0.4, 0.6, 0.8, 1.0]\n",
    "svc_params['vect__min_df'] = [1, 3, 5, 10]\n",
    "svc_params['vect__norm'] = ['l1', 'l2']\n",
    "\n",
    "svc_params['model__C'] = [10**-2, 10**-1, 10**0, 10**1, 10**2]\n",
    "svc_params['model'] = [SVC()]\n",
    "\n",
    "params = [logreg_params, forest_params, svc_params]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-2 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-2 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-2 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-2 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-2 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-2 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-2 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-2 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-2 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomizedSearchCV(cv=5, error_score=&#x27;raise&#x27;,\n",
       "                   estimator=Pipeline(steps=[(&#x27;vect&#x27;, TfidfVectorizer()),\n",
       "                                             (&#x27;model&#x27;,\n",
       "                                              LogisticRegression(random_state=43))]),\n",
       "                   n_jobs=-1,\n",
       "                   param_distributions=[{&#x27;model&#x27;: [LogisticRegression(random_state=43)],\n",
       "                                         &#x27;model__l1_ratio&#x27;: [0.6, 0.7, 0.8],\n",
       "                                         &#x27;model__penalty&#x27;: [&#x27;elasticnet&#x27;],\n",
       "                                         &#x27;model__solver&#x27;: [&#x27;saga&#x27;],\n",
       "                                         &#x27;vect__max_df&#x27;: [0.2, 0.4, 0.6, 0.8,\n",
       "                                                          1.0],\n",
       "                                         &#x27;...\n",
       "                                        {&#x27;model&#x27;: [RandomForestClassifier(random_state=43)],\n",
       "                                         &#x27;model__max_depth&#x27;: [2, 5, 10],\n",
       "                                         &#x27;model__min_samples_split&#x27;: [10, 50,\n",
       "                                                                      100, 250,\n",
       "                                                                      500,\n",
       "                                                                      1000],\n",
       "                                         &#x27;vect__max_df&#x27;: [0.2, 0.4, 0.6, 0.8,\n",
       "                                                          1.0],\n",
       "                                         &#x27;vect__min_df&#x27;: [1, 3, 5, 10],\n",
       "                                         &#x27;vect__norm&#x27;: [&#x27;l1&#x27;, &#x27;l2&#x27;]},\n",
       "                                        {&#x27;model&#x27;: [SVC()],\n",
       "                                         &#x27;model__C&#x27;: [0.01, 0.1, 1, 10, 100],\n",
       "                                         &#x27;vect__max_df&#x27;: [0.2, 0.4, 0.6, 0.8,\n",
       "                                                          1.0],\n",
       "                                         &#x27;vect__min_df&#x27;: [1, 3, 5, 10],\n",
       "                                         &#x27;vect__norm&#x27;: [&#x27;l1&#x27;, &#x27;l2&#x27;]}],\n",
       "                   scoring=&#x27;f1&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" ><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;RandomizedSearchCV<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.model_selection.RandomizedSearchCV.html\">?<span>Documentation for RandomizedSearchCV</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>RandomizedSearchCV(cv=5, error_score=&#x27;raise&#x27;,\n",
       "                   estimator=Pipeline(steps=[(&#x27;vect&#x27;, TfidfVectorizer()),\n",
       "                                             (&#x27;model&#x27;,\n",
       "                                              LogisticRegression(random_state=43))]),\n",
       "                   n_jobs=-1,\n",
       "                   param_distributions=[{&#x27;model&#x27;: [LogisticRegression(random_state=43)],\n",
       "                                         &#x27;model__l1_ratio&#x27;: [0.6, 0.7, 0.8],\n",
       "                                         &#x27;model__penalty&#x27;: [&#x27;elasticnet&#x27;],\n",
       "                                         &#x27;model__solver&#x27;: [&#x27;saga&#x27;],\n",
       "                                         &#x27;vect__max_df&#x27;: [0.2, 0.4, 0.6, 0.8,\n",
       "                                                          1.0],\n",
       "                                         &#x27;...\n",
       "                                        {&#x27;model&#x27;: [RandomForestClassifier(random_state=43)],\n",
       "                                         &#x27;model__max_depth&#x27;: [2, 5, 10],\n",
       "                                         &#x27;model__min_samples_split&#x27;: [10, 50,\n",
       "                                                                      100, 250,\n",
       "                                                                      500,\n",
       "                                                                      1000],\n",
       "                                         &#x27;vect__max_df&#x27;: [0.2, 0.4, 0.6, 0.8,\n",
       "                                                          1.0],\n",
       "                                         &#x27;vect__min_df&#x27;: [1, 3, 5, 10],\n",
       "                                         &#x27;vect__norm&#x27;: [&#x27;l1&#x27;, &#x27;l2&#x27;]},\n",
       "                                        {&#x27;model&#x27;: [SVC()],\n",
       "                                         &#x27;model__C&#x27;: [0.01, 0.1, 1, 10, 100],\n",
       "                                         &#x27;vect__max_df&#x27;: [0.2, 0.4, 0.6, 0.8,\n",
       "                                                          1.0],\n",
       "                                         &#x27;vect__min_df&#x27;: [1, 3, 5, 10],\n",
       "                                         &#x27;vect__norm&#x27;: [&#x27;l1&#x27;, &#x27;l2&#x27;]}],\n",
       "                   scoring=&#x27;f1&#x27;)</pre></div> </div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" ><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">estimator: Pipeline</label><div class=\"sk-toggleable__content fitted\"><pre>Pipeline(steps=[(&#x27;vect&#x27;, TfidfVectorizer()),\n",
       "                (&#x27;model&#x27;, LogisticRegression(random_state=43))])</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-6\" type=\"checkbox\" ><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;TfidfVectorizer<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.feature_extraction.text.TfidfVectorizer.html\">?<span>Documentation for TfidfVectorizer</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>TfidfVectorizer()</pre></div> </div></div><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-7\" type=\"checkbox\" ><label for=\"sk-estimator-id-7\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;LogisticRegression<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.linear_model.LogisticRegression.html\">?<span>Documentation for LogisticRegression</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>LogisticRegression(random_state=43)</pre></div> </div></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "RandomizedSearchCV(cv=5, error_score='raise',\n",
       "                   estimator=Pipeline(steps=[('vect', TfidfVectorizer()),\n",
       "                                             ('model',\n",
       "                                              LogisticRegression(random_state=43))]),\n",
       "                   n_jobs=-1,\n",
       "                   param_distributions=[{'model': [LogisticRegression(random_state=43)],\n",
       "                                         'model__l1_ratio': [0.6, 0.7, 0.8],\n",
       "                                         'model__penalty': ['elasticnet'],\n",
       "                                         'model__solver': ['saga'],\n",
       "                                         'vect__max_df': [0.2, 0.4, 0.6, 0.8,\n",
       "                                                          1.0],\n",
       "                                         '...\n",
       "                                        {'model': [RandomForestClassifier(random_state=43)],\n",
       "                                         'model__max_depth': [2, 5, 10],\n",
       "                                         'model__min_samples_split': [10, 50,\n",
       "                                                                      100, 250,\n",
       "                                                                      500,\n",
       "                                                                      1000],\n",
       "                                         'vect__max_df': [0.2, 0.4, 0.6, 0.8,\n",
       "                                                          1.0],\n",
       "                                         'vect__min_df': [1, 3, 5, 10],\n",
       "                                         'vect__norm': ['l1', 'l2']},\n",
       "                                        {'model': [SVC()],\n",
       "                                         'model__C': [0.01, 0.1, 1, 10, 100],\n",
       "                                         'vect__max_df': [0.2, 0.4, 0.6, 0.8,\n",
       "                                                          1.0],\n",
       "                                         'vect__min_df': [1, 3, 5, 10],\n",
       "                                         'vect__norm': ['l1', 'l2']}],\n",
       "                   scoring='f1')"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rnd = RandomizedSearchCV(\n",
    "    estimator=pipeline,\n",
    "    param_distributions=params,\n",
    "    n_iter=10,\n",
    "    cv=5,\n",
    "    n_jobs=-1,\n",
    "    scoring='f1',\n",
    "    error_score='raise',\n",
    ")\n",
    "\n",
    "rnd.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "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>params</th>\n",
       "      <th>rank_test_score</th>\n",
       "      <th>mean_test_score</th>\n",
       "      <th>std_test_score</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>kernel</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>l2_1_0.6_saga_elasticnet_0.8_LogisticRegression(random_state=43)</th>\n",
       "      <td>{'vect__norm': 'l2', 'vect__min_df': 1, 'vect_...</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.766361</td>\n",
       "      <td>0.002817</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>l2_5_0.8_saga_elasticnet_0.7_LogisticRegression(random_state=43)</th>\n",
       "      <td>{'vect__norm': 'l2', 'vect__min_df': 5, 'vect_...</td>\n",
       "      <td>2</td>\n",
       "      <td>-0.764411</td>\n",
       "      <td>0.003854</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>l1_10_0.4_10_SVC()</th>\n",
       "      <td>{'vect__norm': 'l1', 'vect__min_df': 10, 'vect...</td>\n",
       "      <td>3</td>\n",
       "      <td>-0.737216</td>\n",
       "      <td>0.004292</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>l1_5_1.0_saga_elasticnet_0.7_LogisticRegression(random_state=43)</th>\n",
       "      <td>{'vect__norm': 'l1', 'vect__min_df': 5, 'vect_...</td>\n",
       "      <td>4</td>\n",
       "      <td>-0.576133</td>\n",
       "      <td>0.010505</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>l2_10_1.0_500_10_RandomForestClassifier(random_state=43)</th>\n",
       "      <td>{'vect__norm': 'l2', 'vect__min_df': 10, 'vect...</td>\n",
       "      <td>5</td>\n",
       "      <td>-0.003645</td>\n",
       "      <td>0.003204</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>l1_1_0.6_500_5_RandomForestClassifier(random_state=43)</th>\n",
       "      <td>{'vect__norm': 'l1', 'vect__min_df': 1, 'vect_...</td>\n",
       "      <td>6</td>\n",
       "      <td>-0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>l1_10_0.6_1000_5_RandomForestClassifier(random_state=43)</th>\n",
       "      <td>{'vect__norm': 'l1', 'vect__min_df': 10, 'vect...</td>\n",
       "      <td>6</td>\n",
       "      <td>-0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>l1_5_1.0_100_2_RandomForestClassifier(random_state=43)</th>\n",
       "      <td>{'vect__norm': 'l1', 'vect__min_df': 5, 'vect_...</td>\n",
       "      <td>6</td>\n",
       "      <td>-0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>l1_3_0.6_50_5_RandomForestClassifier(random_state=43)</th>\n",
       "      <td>{'vect__norm': 'l1', 'vect__min_df': 3, 'vect_...</td>\n",
       "      <td>6</td>\n",
       "      <td>-0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>l2_3_0.2_500_5_RandomForestClassifier(random_state=43)</th>\n",
       "      <td>{'vect__norm': 'l2', 'vect__min_df': 3, 'vect_...</td>\n",
       "      <td>6</td>\n",
       "      <td>-0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                                                               params  \\\n",
       "kernel                                                                                                  \n",
       "l2_1_0.6_saga_elasticnet_0.8_LogisticRegression...  {'vect__norm': 'l2', 'vect__min_df': 1, 'vect_...   \n",
       "l2_5_0.8_saga_elasticnet_0.7_LogisticRegression...  {'vect__norm': 'l2', 'vect__min_df': 5, 'vect_...   \n",
       "l1_10_0.4_10_SVC()                                  {'vect__norm': 'l1', 'vect__min_df': 10, 'vect...   \n",
       "l1_5_1.0_saga_elasticnet_0.7_LogisticRegression...  {'vect__norm': 'l1', 'vect__min_df': 5, 'vect_...   \n",
       "l2_10_1.0_500_10_RandomForestClassifier(random_...  {'vect__norm': 'l2', 'vect__min_df': 10, 'vect...   \n",
       "l1_1_0.6_500_5_RandomForestClassifier(random_st...  {'vect__norm': 'l1', 'vect__min_df': 1, 'vect_...   \n",
       "l1_10_0.6_1000_5_RandomForestClassifier(random_...  {'vect__norm': 'l1', 'vect__min_df': 10, 'vect...   \n",
       "l1_5_1.0_100_2_RandomForestClassifier(random_st...  {'vect__norm': 'l1', 'vect__min_df': 5, 'vect_...   \n",
       "l1_3_0.6_50_5_RandomForestClassifier(random_sta...  {'vect__norm': 'l1', 'vect__min_df': 3, 'vect_...   \n",
       "l2_3_0.2_500_5_RandomForestClassifier(random_st...  {'vect__norm': 'l2', 'vect__min_df': 3, 'vect_...   \n",
       "\n",
       "                                                    rank_test_score  \\\n",
       "kernel                                                                \n",
       "l2_1_0.6_saga_elasticnet_0.8_LogisticRegression...                1   \n",
       "l2_5_0.8_saga_elasticnet_0.7_LogisticRegression...                2   \n",
       "l1_10_0.4_10_SVC()                                                3   \n",
       "l1_5_1.0_saga_elasticnet_0.7_LogisticRegression...                4   \n",
       "l2_10_1.0_500_10_RandomForestClassifier(random_...                5   \n",
       "l1_1_0.6_500_5_RandomForestClassifier(random_st...                6   \n",
       "l1_10_0.6_1000_5_RandomForestClassifier(random_...                6   \n",
       "l1_5_1.0_100_2_RandomForestClassifier(random_st...                6   \n",
       "l1_3_0.6_50_5_RandomForestClassifier(random_sta...                6   \n",
       "l2_3_0.2_500_5_RandomForestClassifier(random_st...                6   \n",
       "\n",
       "                                                    mean_test_score  \\\n",
       "kernel                                                                \n",
       "l2_1_0.6_saga_elasticnet_0.8_LogisticRegression...        -0.766361   \n",
       "l2_5_0.8_saga_elasticnet_0.7_LogisticRegression...        -0.764411   \n",
       "l1_10_0.4_10_SVC()                                        -0.737216   \n",
       "l1_5_1.0_saga_elasticnet_0.7_LogisticRegression...        -0.576133   \n",
       "l2_10_1.0_500_10_RandomForestClassifier(random_...        -0.003645   \n",
       "l1_1_0.6_500_5_RandomForestClassifier(random_st...        -0.000000   \n",
       "l1_10_0.6_1000_5_RandomForestClassifier(random_...        -0.000000   \n",
       "l1_5_1.0_100_2_RandomForestClassifier(random_st...        -0.000000   \n",
       "l1_3_0.6_50_5_RandomForestClassifier(random_sta...        -0.000000   \n",
       "l2_3_0.2_500_5_RandomForestClassifier(random_st...        -0.000000   \n",
       "\n",
       "                                                    std_test_score  \n",
       "kernel                                                              \n",
       "l2_1_0.6_saga_elasticnet_0.8_LogisticRegression...        0.002817  \n",
       "l2_5_0.8_saga_elasticnet_0.7_LogisticRegression...        0.003854  \n",
       "l1_10_0.4_10_SVC()                                        0.004292  \n",
       "l1_5_1.0_saga_elasticnet_0.7_LogisticRegression...        0.010505  \n",
       "l2_10_1.0_500_10_RandomForestClassifier(random_...        0.003204  \n",
       "l1_1_0.6_500_5_RandomForestClassifier(random_st...        0.000000  \n",
       "l1_10_0.6_1000_5_RandomForestClassifier(random_...        0.000000  \n",
       "l1_5_1.0_100_2_RandomForestClassifier(random_st...        0.000000  \n",
       "l1_3_0.6_50_5_RandomForestClassifier(random_sta...        0.000000  \n",
       "l2_3_0.2_500_5_RandomForestClassifier(random_st...        0.000000  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results_df = pd.DataFrame(rnd.cv_results_)\n",
    "results_df = results_df.sort_values(by=[\"rank_test_score\"])\n",
    "results_df = results_df.set_index(\n",
    "    results_df[\"params\"].apply(\n",
    "        lambda x: \"_\".join(str(val) for val in x.values())\n",
    "    )\n",
    ").rename_axis(\"kernel\")\n",
    "results_df['mean_test_score'] *= -1\n",
    "results_df[[\"params\", \"rank_test_score\", \"mean_test_score\", \"std_test_score\"]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Даже с помощью randomized search удалось найти удовлетворяющую нас модель всего лишь за 10 итераций."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "best_estimator = rnd.best_estimator_\n",
    "\n",
    "vect = best_estimator[0]\n",
    "model = best_estimator[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "tf_idf_test = vect.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7733524355300859"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred = model.predict(tf_idf_test)\n",
    "f1_score(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Выводы"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Перед нами стояла задача:**\n",
    "\n",
    "- Разработать модель, которая бы маркировала токсичные комментарии в интернет-магазине;\n",
    "\n",
    "**Что мы сделали:**\n",
    "\n",
    "- Загрузили и обработали данные: провели лемматизацию, избавились от дублей, возникших после неё;\n",
    "- Вычислили TF IDF для лемматизированных строк;\n",
    "- Проверили несколько моделей логистической регрессии, опорных векторов и случайного леса. Подобрали необходимые гиперпараметры для модели и для векторизатора;\n",
    "\n",
    "**Результат:**\n",
    "- Логистическая регрессия справилась с заданием, обеспечив F1 = 0.77, что нам и требовалось;\n",
    "- На результате положительно сказалась регуляризация elasticnet с долей L1 = 0.8"
   ]
  }
 ],
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