Task_Timofeev.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import re\n",
    "from nltk.stem.snowball import SnowballStemmer\n",
    "from nltk.tokenize import word_tokenize\n",
    "from nltk.corpus import stopwords\n",
    "\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn import model_selection, preprocessing, naive_bayes, metrics, svm\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer, CountVectorizer, TfidfTransformer\n",
    "from sklearn.utils import resample\n",
    "\n",
    "from sklearn.feature_selection import SelectKBest\n",
    "from sklearn.feature_selection import chi2\n",
    "\n",
    "from sklearn.utils import resample\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " Функция для обработки текста. Нижний регистр => удаление стоп-слов => Портера стеммер => удаляем токены короче 2 символов. В конце склеиваем обратно в строку, чтобы не проделывать обработку каждый раз, когда строим матрицу признаков."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": true,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "stemmer = SnowballStemmer(\"russian\")\n",
    "stop_words = stopwords.words(\"russian\")\n",
    "def preprocess(text):\n",
    "    min_length = 3\n",
    "    words = [word.lower() for word in word_tokenize(text) if word.lower() not in stop_words]\n",
    "    stems = [stemmer.stem(token) for token in words]\n",
    "    tokens = [token for token in stems if len(token)>=min_length]\n",
    "    return \" \".join(tokens)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "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>tags</th>\n",
       "      <th>text</th>\n",
       "      <th>title</th>\n",
       "      <th>topic</th>\n",
       "      <th>url</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Деловой климат</td>\n",
       "      <td>Заместитель председателя правительства Аркадий...</td>\n",
       "      <td>Правительство прокомментировало идею о запрете...</td>\n",
       "      <td>Бизнес</td>\n",
       "      <td>https://lenta.ru/news/2017/04/01/24hours/</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>События</td>\n",
       "      <td>Монреальская конвенция об унификации правил во...</td>\n",
       "      <td>Эксперт отвел год на окончательную ратификацию...</td>\n",
       "      <td>Путешествия</td>\n",
       "      <td>https://lenta.ru/news/2017/03/31/motrealwork/</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Общество</td>\n",
       "      <td>Сотни байкеров в столице Аргентины Буэнос-Айре...</td>\n",
       "      <td>Аргентинские байкеры устроили акцию протеста п...</td>\n",
       "      <td>Мир</td>\n",
       "      <td>https://lenta.ru/news/2017/03/30/bikers/</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Политика</td>\n",
       "      <td>Прямая линия с президентом России Владимиром П...</td>\n",
       "      <td>Прямая линия с Путиным пройдет позже обычного</td>\n",
       "      <td>Россия</td>\n",
       "      <td>https://lenta.ru/news/2017/03/30/straightahead/</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Футбол</td>\n",
       "      <td>Нападающий английского «Сандерленда» Данкан Уо...</td>\n",
       "      <td>Английский футболист спас трех человек на отды...</td>\n",
       "      <td>Спорт</td>\n",
       "      <td>https://lenta.ru/news/2017/03/30/duncanwatmore/</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             tags                                               text  \\\n",
       "0  Деловой климат  Заместитель председателя правительства Аркадий...   \n",
       "1         События  Монреальская конвенция об унификации правил во...   \n",
       "2        Общество  Сотни байкеров в столице Аргентины Буэнос-Айре...   \n",
       "3        Политика  Прямая линия с президентом России Владимиром П...   \n",
       "4          Футбол  Нападающий английского «Сандерленда» Данкан Уо...   \n",
       "\n",
       "                                               title        topic  \\\n",
       "0  Правительство прокомментировало идею о запрете...       Бизнес   \n",
       "1  Эксперт отвел год на окончательную ратификацию...  Путешествия   \n",
       "2  Аргентинские байкеры устроили акцию протеста п...          Мир   \n",
       "3      Прямая линия с Путиным пройдет позже обычного       Россия   \n",
       "4  Английский футболист спас трех человек на отды...        Спорт   \n",
       "\n",
       "                                               url  \n",
       "0        https://lenta.ru/news/2017/04/01/24hours/  \n",
       "1    https://lenta.ru/news/2017/03/31/motrealwork/  \n",
       "2         https://lenta.ru/news/2017/03/30/bikers/  \n",
       "3  https://lenta.ru/news/2017/03/30/straightahead/  \n",
       "4  https://lenta.ru/news/2017/03/30/duncanwatmore/  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"lenta_data.csv\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    635617.000000\n",
       "mean        181.105496\n",
       "std          71.437225\n",
       "min           1.000000\n",
       "25%         133.000000\n",
       "50%         172.000000\n",
       "75%         219.000000\n",
       "max        9016.000000\n",
       "Name: text, dtype: float64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['text'].map(lambda x: len(x.split())).describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Тексты в среднем 180 слов."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    635617.000000\n",
       "mean          7.451184\n",
       "std           1.751855\n",
       "min           1.000000\n",
       "25%           6.000000\n",
       "50%           7.000000\n",
       "75%           9.000000\n",
       "max          18.000000\n",
       "Name: title, dtype: float64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['title'].map(lambda x: len(x.split())).describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Россия               147863\n",
       "Мир                  124749\n",
       "Финансы               72736\n",
       "Спорт                 55395\n",
       "Культура              48264\n",
       "Наука и техника       48129\n",
       "Бывший СССР           47722\n",
       "Интернет и СМИ        38729\n",
       "Из жизни              23267\n",
       "Силовые структуры     13959\n",
       "Бизнес                 6026\n",
       "Путешествия            4006\n",
       "Ценности               3816\n",
       "69-я параллель          567\n",
       "Крым                    116\n",
       "Легпром                 114\n",
       "Культпросвет             85\n",
       "Библиотека               65\n",
       "Недвижимость              3\n",
       "Оружие                    2\n",
       "ЧМ-2014                   2\n",
       "Сочи                      1\n",
       "МедНовости                1\n",
       "Name: topic, dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "topic_counts = df['topic'].value_counts()\n",
    "topic_counts"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Удаляем слабопредставленные темы."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['69-я параллель', 'Крым', 'Легпром', 'Культпросвет ', 'Библиотека',\n",
       "       'Недвижимость', 'Оружие', 'ЧМ-2014', 'Сочи', 'МедНовости'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rare_topics = topic_counts[topic_counts < 1000].keys()\n",
    "rare_topics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df.drop(df[df['topic'].isin(rare_topics)].index, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Россия               147863\n",
       "Мир                  124749\n",
       "Финансы               72736\n",
       "Спорт                 55395\n",
       "Культура              48264\n",
       "Наука и техника       48129\n",
       "Бывший СССР           47722\n",
       "Интернет и СМИ        38729\n",
       "Из жизни              23267\n",
       "Силовые структуры     13959\n",
       "Бизнес                 6026\n",
       "Путешествия            4006\n",
       "Ценности               3816\n",
       "Name: topic, dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['topic'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wall time: 11min 36s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "df['title_processed'] = df['title'].map(preprocess)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wall time: 4h 11min 55s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "df['text_processed'] = df['text'].map(preprocess)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df.to_csv(\"preprocessed_news.csv\", sep='\\t', encoding='utf-8', index=False, columns=['tags', 'text_processed', 'title_processed', 'topic'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 634661 entries, 0 to 634660\n",
      "Data columns (total 4 columns):\n",
      "tags               634661 non-null object\n",
      "text_processed     634658 non-null object\n",
      "title_processed    634661 non-null object\n",
      "topic              634661 non-null object\n",
      "dtypes: object(4)\n",
      "memory usage: 19.4+ MB\n"
     ]
    }
   ],
   "source": [
    "df = pd.read_csv(\"preprocessed_news.csv\", sep='\\t', encoding='utf-8')\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tags</th>\n",
       "      <th>text_processed</th>\n",
       "      <th>title_processed</th>\n",
       "      <th>topic</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Деловой климат</td>\n",
       "      <td>заместител председател правительств аркад двор...</td>\n",
       "      <td>правительств прокомментирова запрет круглосуто...</td>\n",
       "      <td>Бизнес</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>События</td>\n",
       "      <td>монреальск конвенц унификац прав воздушн перев...</td>\n",
       "      <td>эксперт отвел год окончательн ратификац монреа...</td>\n",
       "      <td>Путешествия</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Общество</td>\n",
       "      <td>сотн байкер столиц аргентин буэнос-айрес приня...</td>\n",
       "      <td>аргентинск байкер устро акц протест прот шлем ...</td>\n",
       "      <td>Мир</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Политика</td>\n",
       "      <td>прям лин президент росс владимир путин 2017 го...</td>\n",
       "      <td>прям лин путин пройдет позж обычн</td>\n",
       "      <td>Россия</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Футбол</td>\n",
       "      <td>напада английск сандерленд данка уотмор спас т...</td>\n",
       "      <td>английск футболист спас трех человек отдых бар...</td>\n",
       "      <td>Спорт</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             tags                                     text_processed  \\\n",
       "0  Деловой климат  заместител председател правительств аркад двор...   \n",
       "1         События  монреальск конвенц унификац прав воздушн перев...   \n",
       "2        Общество  сотн байкер столиц аргентин буэнос-айрес приня...   \n",
       "3        Политика  прям лин президент росс владимир путин 2017 го...   \n",
       "4          Футбол  напада английск сандерленд данка уотмор спас т...   \n",
       "\n",
       "                                     title_processed        topic  \n",
       "0  правительств прокомментирова запрет круглосуто...       Бизнес  \n",
       "1  эксперт отвел год окончательн ратификац монреа...  Путешествия  \n",
       "2  аргентинск байкер устро акц протест прот шлем ...          Мир  \n",
       "3                  прям лин путин пройдет позж обычн       Россия  \n",
       "4  английск футболист спас трех человек отдых бар...        Спорт  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Есть null значения, но мало, удалим их."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df.drop(df[df['text_processed'].isnull()].index, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 634658 entries, 0 to 634660\n",
      "Data columns (total 4 columns):\n",
      "tags               634658 non-null object\n",
      "text_processed     634658 non-null object\n",
      "title_processed    634658 non-null object\n",
      "topic              634658 non-null object\n",
      "dtypes: object(4)\n",
      "memory usage: 24.2+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "### Обучаемся на текстах новостей.\n",
    "\n",
    "#### Разбиваем выборку на обучающую и тестовую, стандартные параметры: перемешивание и разбиение 3 к 1."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "train_x, valid_x, train_y, valid_y = model_selection.train_test_split(df['text_processed'], df['topic'], random_state=17)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x187aef85320>"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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axBn03VVnN1z3nlqpdPYcbvqDOha/ejTSZIvnxspp3otUgUXEfiXKeQrxTgNO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\nRK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x187a2611ba8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.xticks(rotation='vertical')\n",
    "train_y.hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x187ae962630>"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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HZf9FwMPAV3PRAcDUiHhz4Th/IzXg3s3al+NFuhrmqsqLgKeROggcWTqR5jgP\nAncC32kuj4gTCse5Hfgz8EVg9Rd2wTaHbn3uNwJHRMSP8v2XAl+Iwl0/JU0C/hIRT+b7E4BNIuLR\nwnG6Wk02nq4cHsn93QNA0h5AiSqMRyXNiIjbmgslzWBNg2sd6sjqZ5Ia1H4MfA24Ouo9e3hOROzc\ndP8KSb+sIc4/kRoLlwCfruGLe2NSI+E5pPr5kLRlDXGeBXyc1EZzYnNVVmF/D7wLeAfwf0hT6P+t\n4P679bk/3EgMABFxdW5/Ku1yUrfolfn+ROAHwIsLx3lOTngNtY5zGE9XDi8kffntBPwK2Bp4U0R0\n1F9Y0j55vyez5rKyn/RP/KFIU5EXk0eXBvByUt99AKLQ6FJJIiWIA4DdSH/kX4yIO0rsvyXWfFJ1\nwjX5/u7AwRHx3tKx8v7fQKryuRg4vdSZXbcaWJviPR04HngmcGxEXFdTnImk6st9gc9HxDcL7Xc+\nXfjcJZ1OGkfzNdLn8y+knkTfgnLViu1qIErUSrSJcxPw6tbyiPhdyTir442X5AAgaUPS2tMCbo2I\nvxba706kL51G28OvSP9MS0vsvyVW29HXEXFl4ThbkBrYTgL+NSLOKbn/HOMW0ufx+1z0DOAW4G8U\nPCNqqavfEHgbsE1EPK3E/rul6cQA0t/w35HOwkv3ImseiStgCjCtVJwufu5XrOPhktWKPwbe30g2\nknYF/isiXlRi/01xutv7arwkB0ltxzRExJc73O+mwOYR8YeW8m2AP9fQyEajGkvSdNLZ41Ulqn9y\n3em+pDOsrUmjfL8eEXd1uu8B4j1zXY+XOiOSdPwA+y9SV5+vSNrtv+go6S6eGLT9XAp+Hl353Lsl\ndzFewJp177cF/qV0w7ekl0bE1SX3uc544yg5nJlvvoU1g6Gi0x4Yks4Gvt/6RSDpQOClEfGeTvbf\nJt45pLmVFpPOHDcjzePT8UAYpbmVbiNdhi+jpV2jhi+7z5Pqs28uud9uk/RX0pnv9azd4F10LqKW\nmNNIjbi/qmHfewA3Nbp+Kk0Js2NEXFto/1353CVNIVW/NcYbXElqq6ljDMpGrKmV+HWpWomWGJ8i\nzd32YL4/ldT54d9Kx4JxlBwaSl+aSbo5InYc4LHivZVyveMLgPtJvWP+Rpp+4rkF9j2fgRu6i3/Z\nSXoHaR6nDUndjL9W8h9X0sJ1PV6wneY5pOo3SG0At5bYb5s4nwMOJnWTfiupV9kPI+LDheP8Anhh\n42pU0gak6UaKdKOs+3NvivMtUhVvo5fV24GdI6LtlV4HcWqplWgTp/LdVWf31vHUW6mhdDZc17D/\nOhZTeizSpIFfizXTjxepuorTB7DgAAAYB0lEQVSIQ0rsZz3inQucm79cDwVuzPW350TEuuqLh+pF\nwF2kK6FrWfdnNWw5Gbwld3o4TdI9wCcj4u7CoV5Pate6lVR18VfqmYBNzdWUEfG33F5XRBc+94a/\ni4g3Nt0/QdINBfff0JjZea1aCaBocgAmSNokIh6H1R0GNikcY7VxkxxytVIA2zUP8um0Wgm4X9Ju\nEbHWbKK5HvIPAzynE42eFu/JcaYARf7gc8PtQ9EyalzS+4EJEVF8FtDcJ/y5+eePpHECH5H0ruh8\nhPHTSN1YDyCdaV9COku9qcP9rqXpbwvSRG+vIFXPbVYyDqkN635JdzadGDxeOAbA7ZI+QBrnAGnA\n2u0lA9T8uTc81lxPL+kl1NC9PCLen/f/0sbtmnwVuFxpYF+Quk8XGXvSzripVpJ0cLvy6HBgj6Td\nSGcL81m7K+tBwP6l6mm7QdKvSNUJT7SUbwJcV7o/taTTgNcBPwTOa06wkm6NiOcUjLUJKUl8jlTv\nfOYgT1mffdfyt9UmzqOktqBn598CnhURRSdezJ0pziC1bQWpH/+HIuL+QvvvyucuaRfSl+cU0nu1\nAjgkIuoYU9GVEcyS5rBmqvnLIuLS2mKNl+QAIGnjXCUzGXhqwd4X25BmL210Zb2J1JWtyD9TS6za\npjaQtDQiZq7vYx3EOwxYEG3GG0iaUqIeOieF15ASw3RgIakxtHSVT+3q7kXULd343Fv2+RSAqGlt\nkqYrx8bcSuR4RaYbaYnVRxp/FKSOKMW/YxrGU7XSScAH8lnLa4FtJJ0bEScN8tTB9rs1sFVEHN9S\n/jxJ0drFtYDX0mZqg1Ik9UXEfa1lpeNkb4iIgVYdK5EYLiAl7O8BJ9TRsyfHaZ4tE1g9crX0ILhP\nRMTcwvusyO/bB1t6xZxasENCrZ97g1omqZTUiFF63qvGPEql52xai6S3kK58F5P+xs6U9LEoNDix\nEm+8XDlIupk0nH05sD1pOuLrOr2ElbSANIL4ypbyvUmjPt/ayf7bxNuQNLXBIRSe2iD3uvgAcCRp\nXQKAXYF/B86qoZqk1stwpTmVVk/N3fwQBafqVpqWRaRqktmN8ii0DGlTnG4te9muV0yxXn5dfB3L\ngdNby6OG5W67QWmKkX9qXC3kE9P/ibWnIilm3Fw5AI9GxIOSfhhpMRsklWicmtmaGAAi4lKl5SKL\niohVpMVX5pGmNviJpCJTG0TElyX9gTSj6E6kL9SbgOMj4nud7r+NZ7Xrblqqi2lE1NFbrF2cPwFI\nWlU6IbRYqzNFU/zS1RcbSJra9H+yJWW/K2r93Jv8oRuJQGketU8DO7L2Wuilrxw3aKlG+hP19IgE\nxldy+Ams+QPMvXxK1NdtNMzHhkXtpzb4OlBkaoOcBOpIBO38gerKdqOO1kx1PkFNaxZH+Yn3HqPm\nqovsVNJJR+OE483AKQX3363PvVvVIueTBtudTrpyPJR6uk1/X9KlpK7ZkGYyKDp3W7NxU61UF0mX\nkKpcFrWU7wN8ICL2KRxvTDRKQvkBib2iMbiymdJiWI25h34YBUczd+t1KE1xflVreekrFElLImLX\n5k4bkn4UES8rGSfv942kJUJFmjbnO4M8ZdjGzZWD6lso5cOkNYTfwtpdWV9EajwubWVr1YXSVB2j\nLjmQLsVHvYioY93rdmrr097GRuS2GcpfAdf6uUt6NtBHmies2StIa3qU9helUeS3SXpfjrFNDXGI\niG+RxzrVrSt1siPEcaSxB5u3/HQkIn4DzCTN2zI9/1wJPD8/Vtr388hSJD1X0uVA8TOUboiIiwAk\nbSXpZEn/Lmn7Xh/X+pJ0Uh7U1bj/lDxQqbTf5erQRpwtJO1XOoikDwIXAluRvuS+mgdClrJWdZuk\nrXPHjlL+g7SWw5XNP6QqmOLvF6ntbzNSZ45dSdN0tB370glJD0v6s6S/5t8PS6qley6Mo2qlXC/8\ncWB36l0opVaS/p70j/tz4PmkLoc/W/ez1jtGV5ajbIr3/4ClpAa2N0ThhdnrJunTwKtIdc1PI63v\ncWZE/FfhOO3WDahjRcAbSeurP5LvTwJ+WmoQpKSrSVWxay2rGhFFEoSkX0VEu6V7axmv023dqpYb\nN9VKuXHwY8oLpUj6KDUulFKXiPhN7ib7beArpRNDdhxtlqOs0TMiYj9YXac6qkTEx/MV3LWkxWRe\nHhHLagjV7kq/jv9hkbp6NzxJ2QbWOcBFkj5GmjbjpYUb7zddx2MTC8YBQGndiMpZdhRaL6KNrpzR\nj5vkoOpCKc8ArqFQL59uUVrmMEjH/TJJn6Vgn/2sK8tRKk1UBzBR0gtIn0vRqSC6QdLLSTOlnkiq\nYvwvSYdFxD3rfuZ6uz4P4jyL9DfwfurpvXQ+cK2kxsnBfsB569h+fdW9rOp1kt4ZLQtUSTqcet6v\nj5L+dr8KHFjD/oEB/1+KrWhXiTeOqpW6slBKNyjNr/914IqI+FyNcWpdjlIDrNQVEbPblY9Ukn5G\nmrPn5nz/DcCnosA06i1xJgHHsmZunR8ApzSqfwrHeiHwUtb0ivlFwX3X2rtLaUT/d4AnWLuTyMbA\n6yPif0vEaRO31uqeAf5foq4rlHGTHJpJemoNo1ebxx8A9Sz+nRtsLwLuIE3bfETJboY5RleWoxwr\nJE2IiCdbyor/jTXte3JErBx8y/Xe76bAu0kT+y0lTYq3qnScbpE0m6b5ziLihzXH68rI724Z88lB\n0kkRcWy+vTvwTVLXvAmks71LOtz/BRFxsKSvAs8j1devnmO/9PgDST8H3hkRSyT1k6ozfhIRHysY\no1vLUdY2iWA35TPVT5HWWZ6Txwi8KFqmPi8Q58XAucDkiHiGpJ2Bd0XEewvt/+ukNSJ+BOwD3BkR\nHyqx75Y415Gqrv5v5PmbRrOmqt7NgEcpPD1LU5zj2pVH+bmiUrxxkBxWZ/PcaPiJiLhG0nNJ6yN3\nNC+JpOsi4h/z7Z2Ak4GHgeMi4o4OD79dvO0iYnnTfQHvjogvruNpI5Kk35GS6Vqi8BxOdZP0PdKX\n3SciYmel+a9+UbpXjKRrgTcBCxvVF+vqmTOM/TcP4tqQNOtn8TPhPA7hUNII3+tJ790PYqx/GXVI\n0pH55odI3XWB+uaKGg8N0s31mltGxDUAEfFrpYnZOtWoZ96StMD4YaQRjN+QdE0UWNu5WXNiyPeD\nNYuyjDZ/Gm2JYABbRcRFkj4Oaf4rSU8O9qThiIi7pLU6DpWMs3rd4/waCu56jdyT6xOSjiUNFJ0H\n/E1pvrD/LNxzqXZNDcVrKd1Q3EgCkt5WV0JoNh6SQwxwu9399d95RGOwyxLWrqcHeHWn+x/jnqu0\nbONfSIn1x6T+78WnIq/ZI0ozszbWXN4DKL4mMnBXrloKSRuTBl3dUnD/OzcNqhKpV8yfqaGaRNLz\nSVcPryaN+L2Q1AD+Q2CXdTx1JLqetPLf3az53w/WTD9SWleusMZDtdKTpGmbRerj3FhgRMCmEVF8\ncrzRTtLciDi7C3GeSWr7mQg8nTTB22YR8ba6Y5ckaVfSymk7kRa03xp4U0QUXd9Z0lakNqa9SH+/\nPyANgqxzJtjiJC0BHiR1j/1W5DWR82Pfjog39OzghkHSK0nVoz8DPl3XlU9TR5GX0zRnVJSfzTbF\nG+vJoVty98WKiPh2DbFmRMRtkqaTupleVbK+tpe9LiR9NiKO7kXsTuQ6+ueQvrRvjYi/DvKUcUvS\nsyKi6JrUI0H+DvgYcDFwerRZ6a7D/Xe1O76TQyGS/kq6xL+epkvLKLd6ViPOOaTL1cWkLqabkRoO\ni7VtSLqdNLBnLaUTnaTPA+dHxE0l99tt3Uqmkj5H+5G4R9Udu6RcBXc8qRopgKtJgy1H1RVQg9ae\nbmZD4G3ANhHxtBpiPROYERH/I2kzYEJEPFw6DoyPNodu2Qk4CZhMGjB2a01xXgz8A2ktiqcBf6Op\n62whU0gNhWsNUiJN2VHSr4Gz81n3+cDXovD6wV1ST8tt1a/blB2Vf0aTBaRqkcZUKQeSBnXuNeAz\nRrbNWTtp1zJrqqR3AnOBLUknhtOAL5FmMigfz1cOZeWeCyeRGlg/GYUXspd0fUT0S/piRLwnl1Um\nZOswRlfXWVCaZfZQ4ABSo/Q5EdF29PRIJOlRoHkupVoGQA4Qu5Z1A+qkvP5BS9n1EdHfq2MarjzG\n5b9IV/OPA98Ajq6jU0XuvLEbcG1TV+baJhL0lUMhks5kzdnD7aS5428jVfuU9C2ApsQwBbihcIyu\nVfMoTXX93PzzR9JEbB+R9K6I2L9bx9GhO4DX1R1EaY3vVlvXHbcGV0janzTSH9LYjY4Go/bQhaQq\n3neQOlZ8hnRyWGxQapPHI+KJRhfjfMVd29m9rxwKkdR2/vbR2o+/pW5zIrBh6bpNpUnkXkfqvnhe\nNM0wK+nWiHhOyXh16daVVj4BafX6iNiu7tgl5RHFk1gzRmMCqUch1DCyuC6StgUuaW5vylOQ3Bzl\n149G0r+TenkdRJp08b051idKxwInh1FjoKHzDSWH0DfXbUbE3yktoP6liChatynpMGBBu14dkqaM\nlvYHSS+NiKt7FHvUVSuNFZJ+S6r/v6vlob8HbqthhPwGwOGktUMEXBotM88WjefkUEaeL6YyyC4i\ndi+0/0dIC7PPY81ZVnOgYiMm667blPSaaDOnldJss5+KiJKrjo0ZA4zEPbdX3Y6HS9LFEVHHErpd\nJWlP0jTtzdN0i9Ru9pKIuLMLx/BG0lXY7yNiccl9u82hnNb6cQFfK7j/HUjdSw8lTb52ZkTUtURg\n3XWb/ynpadE0MZ2ktwKnkJKftdfuBGBUXF21eHqvD6CQK0gzI28WEbcAKC3bemPJxNDSntnqEOAY\naljEyMmhkIj4bWuZpMcK7v9+4CilJSk/BPxSaSbY02sYkXmlpH8lTZ/wT6S6zf8uuP+XAZdImkbq\n1vgF0tz7e7V7Hy2JUbbOxTo8S9LC1sK6RvrWJSJC0vuAyyVdQ/qCfi7lp825fh2PvTUivlA4HuBq\npWKUFpRvXc9hTkRsW1O8zYEjgHeSpiAo1te9Xd0mqfqi5CjszUnjJl5Gmjq95ALzXdWt0fFaMzX4\n0yNiH9U0NXjdJN1G6t2zlrpG+tZN0tbAHqSurD+JGtbaWEfs30fEM2rZt5NDGaqufSzghIh4XqH9\nN+aMb42xIbBxjMKFeHJ11TxgC+Ato3DCPaCro+O7MjV43bo9jmYsqzM5uFqpkIiojIqUVGzRmojY\nvNS+BqM1yziuLqLsMo7Nia6xbvSKPEniqOnK2KRbo+O7NjV4zT7d6wOwwTk5FNIyv0rDtK4fSBn9\npC/tHwLF67m7mei6ISeDt+TeRKdJqmV0PN2bGrxuW0vaIvIqcJKmAgfUVXc+2g1QawBrZpquJ66r\nlcqQdHy78og4odvHUkovZ2cdTVp6k4g0Ov7ZEVF0dLy6NDV43dpN9+KqppHHVw6FjOYk0EppVTuA\nCfmsTgA19IoaK1p7k6yrd8mwRVo3/BWM/qnBN5CkRgeHPIXKxj0+JmvhK4dCJP0LabGaLwJnkUZO\nfiQivtrTAxuGpjaHtWZlrWNKABu6sXIll6cen06aUTSAdwN3RcSR63qedZeTQyGSfgP8K2mAWj+w\nEri8VG8ls7FS9ZK7Ss9l7RXtzo2I0di4PmY5ORTS+MeV9MuI2DmXjcozvTx52HtZsxjLj0hzK43K\nrqZjRS+nBrfxx20O5UyTdAawbf4tRm9vpS8DDwONWUAPAL5Cqjaz3unK1OBm4ORQUmP+9iVNZbU0\nTHbBcxpXP9kVkn7Zs6MZ4fII2aOBHYFNG+URsWfhUE9ExO8K79OsLSeHQkbrug0D+IWkPSLiGgBJ\nu5NmmrT2LiQtc/kaUuPqwaQZdEsrtk74SCBpUkRUZhi2kcFtDoXUPaq4myTdQuou+ftc9AzS9BB/\nw3XcFY1lLyXd2HhvJF0ZEa8oHOcO4JvA+RFxc8l9d5OkF5M6bkyOiGdI2hl4V0S8t8eHZk185VBO\n8/q3m5FWtyq6cloXzen1AYwyjbEG90p6DWn98DpWZ3s+aWr4c3OPn3mkxZLqmrq9LqcDewMLASLi\nl5Je3ttDslYb9PoAxoqI+FNE/InUYPhz4CektRdGnVyvvT2wZ779CLBBRPzOdd5tnZzX8j6StObG\nucCHSweJiIcj4pyIeDFwFHA8KSFdIOnZpePVKSJaV09zN9YRxlcO5b2fNKf7SlKC+HxvD2f95alA\n+klVS+eTRq9+FXhJL49rpIqIi/PNh8hzUeXuwEXlkcSvIZ10TCct/nMhadrzRaTlKUeDu3LVUkja\nGPgAqdrSRhBfOZSnfBXxOG2W8xwlXg/8M/n4I+IeYExNlldS6/rekvYCrqsh1G3AvsDnIuIFEXFa\nRNwXEd8Evl9DvLq8m7QWyTRgObBLvm8jiK8cCpH036QG6cYqVyJ1bRyNnsirXDXmvpnU6wMa4Z4m\n6UvAv5HO5rclfYmX9vyBFpKJiGLTw9ctIv7I2usu2wjk5FBOo/qo3Tq/o81Fkv4PsIWkdwKHAef0\n+JhGrIh4b756uAv4cER8qeT+86DKxu128UdNYoC2qyYCUHpxJOuMu7LWQNJTc+P0qJXXjl69TGhE\nXNbjQxqxmpYJPRSYCpwG5ZYJlfQ74LiBHh9tY2xaVk1cPcFjuwWzrHecHDok6aSIODbf3p3UD30j\nUlfWQyLikl4e33BI+mREfLLXxzFa5DPhVsWWCR0rE+61yv8vp5H+Xz7hE5CRxcmhQ82T60m6nPRH\nfo2k5wJfb5mGYlQYrRMG9oqkLetc62Ksfh6SfgR8ElgBnBMR/et+hnWT2xw611wJvGVjyomI+LWk\nv/XomDq1TbtlTyPitF4czChwraQbSN1+vxc+4xqqSRFxOayecdZGECeHzsUAt9vdHy0mAJNZO/HZ\nwP6etDbBYcCZkr4OzI+I3xTa/86S2o2CbkzR8pRCcbqi6cSjcRIymmcwHrNcrdQhSU+SxgM0Fvtu\nnAEJ2DQiNurVsQ3XWK3j7gZJs0kDBicBvwSOiYif9vaoRpaxuN76WOQrhw5FxIReH0MN3DC4HiQ9\nFXgb8HbgPtIo+YWkwV3fAHbo3dGNPE4Co4NHSFs7/yXpGa0/vT6oEeynwFOA/SLiNRHx7YhYFRHX\nk9ZJtiaS/kXSNyW9UtKvJd0v6W29Pi5bm6uVrELS0nzzWcBv8XKU6yRJboQeOq+3Pjo4OdiA3PYw\nNHkluKOA51HvSnBjwlhab30sc5uDrYvPHIamsRLca6l3JbixYiyttz5mOTlYRdN0EFs03S42HcQY\n9NSIOE/SByPiSuBKSVf2+qBGsLG03vqY5eRg7bwu/76y6XYATg7tdWsluDEhIi7I6zg01p+4NSL+\nuq7nWPe5zcGsQ5JeC/yItHremaSeSydExMKeHtgIJWkWcAFwJ6lKaXvg4Ii4qoeHZS2cHGy11kVr\nWkXEid06Fhu7JC0B3hoRt+b7fw98LSJ27e2RWTNXK1mzo0kNqfMYvavYdY2kM1lHo/1oW2ehizZq\nJAaAiPiNpFE3k8BY5+RgzXYAPkpal+Bc4MyIaDenjyXNjagnAG2nhbCK6yWdB3wl3z+QtRunbQRw\ntZJVSJoKfAg4iDRP0Ol1Tkk9FnhMyNBJ2gR4H/ASUpvDVcAX8rrrNkI4OdiAJG1OWvj9ncC3IuKo\nHh/SiOVBXDbWODnYapIeplqHLlL148ZjdJLBIpwchq7N39monHp8rHNyMBumli+5zVh7unZ/2Q3A\nVXCjgxukzYYpIjbv9TGMUptK2hl4HLg3Ih7q9QFZla8czKyrJF1BWm1wIvB00hrSh+Ypzm2EcHIw\ns56S9FLgPyKiv9fHYms4OZhZz0nq95XDyOI2BzPrKkkfGeAhJ4cRxMnBzLrtONKke9/p8XHYOrha\nycy6StKWwMeB3YETI+J/enxI1oaTg5n1hKSnk+ajeiZwbERc1+NDsiZODmbWVZL+mzWDBwX8HfAc\nj8AfWdzmYGbd9vleH4ANzsnBzLpC0rOBvrzOdnP5y4G7e3NUNpANen0AZjZu/AfwcJvyR/NjNoI4\nOZhZt0yPiBtbC/Pgt+ndPxxbFycHM+uWTdfx2MSuHYUNiZODmXXLdZLe2Voo6XC8TOiI466sZtYV\nkvpIo6KfYE0y6Ac2Bl4fEf/bq2OzKicHM+sqSbOBnfLdmyLih708HmvPycHMzCrc5mBmZhVODmZm\nVuHkYGZmFU4OZmZW4eRgZmYV/x8mWAH6DMOPPgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x187d4626a90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.xticks(rotation='vertical')\n",
    "valid_y.hist()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Т.е. разбиение пропорционально классам."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8411      первомайск суд мурманск оправда капита траулер...\n",
       "249213    административн комисс фсин направ суд материал...\n",
       "479898    руководител управлен госохра украин валер геле...\n",
       "610394    физик австр канад австрал наблюда пар одиночн ...\n",
       "590781    напада барселон лионел месс приня участ японск...\n",
       "Name: text_processed, dtype: object"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_x.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "encoder = preprocessing.LabelEncoder()\n",
    "train_y_labels = encoder.fit_transform(train_y)\n",
    "valid_y_labels = encoder.fit_transform(valid_y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Наивный байесовский классификатор"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "pipeline_nb = Pipeline([\n",
    "    ('vect', CountVectorizer()),\n",
    "    ('tfidf', TfidfTransformer()),\n",
    "    ('dim_red', SelectKBest(chi2)),\n",
    "    ('clf', naive_bayes.MultinomialNB())\n",
    "])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Попробуем подобрать следующие параметры:\n",
    "* число признаков *k* в отборе \n",
    "* сглаживание *alpha* в классификаторе"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "k = [2000, 10000, 50000, 100000]\n",
    "scores = ['f1_macro', 'f1_micro']\n",
    "param_grid = [\n",
    "    {\n",
    "        'dim_red__k': k,\n",
    "    }\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "nb_grid = model_selection.GridSearchCV(estimator=pipeline_nb, cv = 3, \n",
    "                                       param_grid=param_grid, n_jobs=2, scoring=scores, refit='f1_micro', verbose=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 3 folds for each of 4 candidates, totalling 12 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=2)]: Done   1 tasks      | elapsed:  4.8min\n",
      "[Parallel(n_jobs=2)]: Done   4 tasks      | elapsed: 10.2min\n",
      "[Parallel(n_jobs=2)]: Done   9 tasks      | elapsed: 23.7min\n",
      "[Parallel(n_jobs=2)]: Done  12 out of  12 | elapsed: 28.9min finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wall time: 30min 21s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "nb_grid_res = nb_grid.fit(train_x, train_y_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\anaconda\\lib\\site-packages\\sklearn\\utils\\deprecation.py:122: FutureWarning: You are accessing a training score ('mean_train_f1_macro'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n",
      "C:\\anaconda\\lib\\site-packages\\sklearn\\utils\\deprecation.py:122: FutureWarning: You are accessing a training score ('mean_train_f1_micro'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n",
      "C:\\anaconda\\lib\\site-packages\\sklearn\\utils\\deprecation.py:122: FutureWarning: You are accessing a training score ('split0_train_f1_macro'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n",
      "C:\\anaconda\\lib\\site-packages\\sklearn\\utils\\deprecation.py:122: FutureWarning: You are accessing a training score ('split0_train_f1_micro'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n",
      "C:\\anaconda\\lib\\site-packages\\sklearn\\utils\\deprecation.py:122: FutureWarning: You are accessing a training score ('split1_train_f1_macro'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n",
      "C:\\anaconda\\lib\\site-packages\\sklearn\\utils\\deprecation.py:122: FutureWarning: You are accessing a training score ('split1_train_f1_micro'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n",
      "C:\\anaconda\\lib\\site-packages\\sklearn\\utils\\deprecation.py:122: FutureWarning: You are accessing a training score ('split2_train_f1_macro'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n",
      "C:\\anaconda\\lib\\site-packages\\sklearn\\utils\\deprecation.py:122: FutureWarning: You are accessing a training score ('split2_train_f1_micro'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n",
      "C:\\anaconda\\lib\\site-packages\\sklearn\\utils\\deprecation.py:122: FutureWarning: You are accessing a training score ('std_train_f1_macro'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n",
      "C:\\anaconda\\lib\\site-packages\\sklearn\\utils\\deprecation.py:122: FutureWarning: You are accessing a training score ('std_train_f1_micro'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'mean_fit_time': array([ 80.84286785,  87.56164638,  83.18368228,  73.02465034]),\n",
       " 'mean_score_time': array([ 63.27508171,  61.82872979,  58.10117237,  61.71723557]),\n",
       " 'mean_test_f1_macro': array([ 0.60844064,  0.68045806,  0.61845062,  0.56290315]),\n",
       " 'mean_test_f1_micro': array([ 0.74337438,  0.78786453,  0.788291  ,  0.77350717]),\n",
       " 'mean_train_f1_macro': array([ 0.61112217,  0.69095031,  0.63836222,  0.58355266]),\n",
       " 'mean_train_f1_micro': array([ 0.74381557,  0.79178896,  0.79683735,  0.78508823]),\n",
       " 'param_dim_red__k': masked_array(data = [2000 10000 50000 100000],\n",
       "              mask = [False False False False],\n",
       "        fill_value = ?),\n",
       " 'params': [{'dim_red__k': 2000},\n",
       "  {'dim_red__k': 10000},\n",
       "  {'dim_red__k': 50000},\n",
       "  {'dim_red__k': 100000}],\n",
       " 'rank_test_f1_macro': array([3, 1, 2, 4]),\n",
       " 'rank_test_f1_micro': array([4, 2, 1, 3]),\n",
       " 'split0_test_f1_macro': array([ 0.60823544,  0.68011867,  0.61993326,  0.56218258]),\n",
       " 'split0_test_f1_micro': array([ 0.74246225,  0.7865165 ,  0.78718456,  0.77210276]),\n",
       " 'split0_train_f1_macro': array([ 0.61127217,  0.69115921,  0.63801381,  0.58385144]),\n",
       " 'split0_train_f1_micro': array([ 0.7447601 ,  0.79244308,  0.7973119 ,  0.78573072]),\n",
       " 'split1_test_f1_macro': array([ 0.61021696,  0.68125666,  0.61707772,  0.56339854]),\n",
       " 'split1_test_f1_micro': array([ 0.74367378,  0.78825198,  0.78845996,  0.77398922]),\n",
       " 'split1_train_f1_macro': array([ 0.6097806 ,  0.69048067,  0.63837013,  0.58339813]),\n",
       " 'split1_train_f1_micro': array([ 0.74365325,  0.79157843,  0.79653545,  0.78491025]),\n",
       " 'split2_test_f1_macro': array([ 0.60686948,  0.67999885,  0.61834086,  0.56312835]),\n",
       " 'split2_test_f1_micro': array([ 0.74398714,  0.78882516,  0.78922854,  0.7744296 ]),\n",
       " 'split2_train_f1_macro': array([ 0.61231375,  0.69121106,  0.6387027 ,  0.58340842]),\n",
       " 'split2_train_f1_micro': array([ 0.74303334,  0.79134537,  0.7966647 ,  0.78462372]),\n",
       " 'std_fit_time': array([ 10.08497317,   8.60299431,  11.78468636,   4.87277605]),\n",
       " 'std_score_time': array([ 1.20911217,  2.5811243 ,  1.96840041,  2.66850429]),\n",
       " 'std_test_f1_macro': array([ 0.00137428,  0.00056681,  0.00116836,  0.00052133]),\n",
       " 'std_test_f1_micro': array([ 0.00065755,  0.00098152,  0.00084296,  0.00100923]),\n",
       " 'std_train_f1_macro': array([ 0.00103958,  0.00033276,  0.00028129,  0.00021131]),\n",
       " 'std_train_f1_micro': array([ 0.00071423,  0.00047222,  0.00033968,  0.00046912])}"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nb_grid_res.cv_results_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Смотрим на средние значения оценок на тестовых сплитах, из данных значений лучшим является при *k = 10 000*."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "pipeline_nb = Pipeline([\n",
    "    ('vect', CountVectorizer()),\n",
    "    ('tfidf', TfidfTransformer()),\n",
    "    ('dim_red', SelectKBest(chi2, k=10000)),\n",
    "    ('clf', naive_bayes.MultinomialNB())\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "alpha = [1e-2, 1e-1, 1]\n",
    "scores = ['f1_macro', 'f1_micro']\n",
    "param_grid = [\n",
    "    {\n",
    "        'clf__alpha': alpha,\n",
    "    }\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "nb_grid = model_selection.GridSearchCV(estimator=pipeline_nb, cv = 3, param_grid=param_grid,\n",
    "                                       n_jobs=2, scoring=scores, refit='f1_micro', verbose=10, return_train_score=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 3 folds for each of 3 candidates, totalling 9 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=2)]: Done   1 tasks      | elapsed:  4.6min\n",
      "[Parallel(n_jobs=2)]: Done   4 tasks      | elapsed:  9.7min\n",
      "[Parallel(n_jobs=2)]: Done   7 out of   9 | elapsed: 18.7min remaining:  5.3min\n",
      "[Parallel(n_jobs=2)]: Done   9 out of   9 | elapsed: 22.3min remaining:    0.0s\n",
      "[Parallel(n_jobs=2)]: Done   9 out of   9 | elapsed: 22.3min finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wall time: 23min 57s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "nb_grid_res = nb_grid.fit(train_x, train_y_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'mean_fit_time': array([ 78.60774763,  90.85415943,  72.94453788]),\n",
       " 'mean_score_time': array([ 59.75944161,  60.54053609,  56.49108624]),\n",
       " 'mean_test_f1_macro': array([ 0.7187947 ,  0.71560987,  0.68045806]),\n",
       " 'mean_test_f1_micro': array([ 0.79500959,  0.79416714,  0.78786453]),\n",
       " 'mean_train_f1_macro': array([ 0.73578444,  0.72982295,  0.69095031]),\n",
       " 'mean_train_f1_micro': array([ 0.80174079,  0.79977542,  0.79178896]),\n",
       " 'param_clf__alpha': masked_array(data = [0.01 0.1 1],\n",
       "              mask = [False False False],\n",
       "        fill_value = ?),\n",
       " 'params': [{'clf__alpha': 0.01}, {'clf__alpha': 0.1}, {'clf__alpha': 1}],\n",
       " 'rank_test_f1_macro': array([1, 2, 3]),\n",
       " 'rank_test_f1_micro': array([1, 2, 3]),\n",
       " 'split0_test_f1_macro': array([ 0.71804834,  0.71518377,  0.68011867]),\n",
       " 'split0_test_f1_micro': array([ 0.79385257,  0.79298283,  0.7865165 ]),\n",
       " 'split0_train_f1_macro': array([ 0.73706573,  0.73079621,  0.69115921]),\n",
       " 'split0_train_f1_micro': array([ 0.80250217,  0.80052627,  0.79244308]),\n",
       " 'split1_test_f1_macro': array([ 0.72035389,  0.7168577 ,  0.68125666]),\n",
       " 'split1_test_f1_micro': array([ 0.79535499,  0.79453566,  0.78825198]),\n",
       " 'split1_train_f1_macro': array([ 0.73461805,  0.72861857,  0.69048067]),\n",
       " 'split1_train_f1_micro': array([ 0.80149246,  0.79944096,  0.79157843]),\n",
       " 'split2_test_f1_macro': array([ 0.71798186,  0.71478813,  0.67999885]),\n",
       " 'split2_test_f1_micro': array([ 0.79582125,  0.79498298,  0.78882516]),\n",
       " 'split2_train_f1_macro': array([ 0.73566953,  0.73005406,  0.69121106]),\n",
       " 'split2_train_f1_micro': array([ 0.80122773,  0.79935903,  0.79134537]),\n",
       " 'std_fit_time': array([  8.56747298,  10.16998516,  11.69550594]),\n",
       " 'std_score_time': array([ 0.41819707,  2.18253578,  3.35614552]),\n",
       " 'std_test_f1_macro': array([ 0.00110285,  0.00089701,  0.00056681]),\n",
       " 'std_test_f1_micro': array([ 0.00084   ,  0.00085713,  0.00098152]),\n",
       " 'std_train_f1_macro': array([ 0.00100256,  0.00090392,  0.00033276]),\n",
       " 'std_train_f1_micro': array([ 0.00054912,  0.00053199,  0.00047222])}"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nb_grid_res.cv_results_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "*alpha = 0.01*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 271,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "pipeline_nb = Pipeline([\n",
    "    ('vect', CountVectorizer()),\n",
    "    ('tfidf', TfidfTransformer()),\n",
    "    ('dim_red', SelectKBest(chi2, k=10000)),\n",
    "    ('clf', naive_bayes.MultinomialNB(alpha=1e-2))\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 273,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "nb_unbalanced = pipeline_nb.fit(train_x, train_y_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 274,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wall time: 23.4 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "nb_prediction_unbalanced = nb_unbalanced.predict(valid_x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 296,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.7224784145319606, 0.7956638199981092, 0.79129132428041637)"
      ]
     },
     "execution_count": 296,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metrics.f1_score(valid_y_labels, nb_prediction_unbalanced, average='macro'), \\\n",
    "metrics.f1_score(valid_y_labels, nb_prediction_unbalanced, average='micro'), \\\n",
    "metrics.f1_score(valid_y_labels, nb_prediction_unbalanced, average='weighted')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 276,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   precision    recall  f1-score   support\n",
      "\n",
      "           Бизнес       0.62      0.28      0.39      1465\n",
      "      Бывший СССР       0.78      0.74      0.76     11917\n",
      "         Из жизни       0.64      0.50      0.56      5909\n",
      "   Интернет и СМИ       0.77      0.65      0.70      9773\n",
      "         Культура       0.85      0.89      0.87     12244\n",
      "              Мир       0.78      0.82      0.80     31219\n",
      "  Наука и техника       0.82      0.81      0.82     11851\n",
      "      Путешествия       0.69      0.61      0.65      1000\n",
      "           Россия       0.75      0.81      0.78     36942\n",
      "Силовые структуры       0.53      0.31      0.39      3400\n",
      "            Спорт       0.97      0.95      0.96     13854\n",
      "          Финансы       0.83      0.87      0.85     18136\n",
      "         Ценности       0.85      0.87      0.86       955\n",
      "\n",
      "      avg / total       0.79      0.80      0.79    158665\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(metrics.classification_report(y_pred=nb_prediction_unbalanced, y_true=valid_y_labels, target_names=encoder.classes_))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 277,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x20679aedb70>"
      ]
     },
     "execution_count": 277,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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H2Lfvb8qUKQlAo0Z1iYjYSKEiFSlVpialytRk374DVKvRnEOHHO0EEK8+fT6l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\nFIYREREREREt9/9CPlrayUyPEwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x206799d8f98>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cm = metrics.confusion_matrix(y_pred=nb_prediction_unbalanced, y_true=valid_y_labels)\n",
    "plt.figure(figsize = (15,10))\n",
    "sns.heatmap(cm, annot=True, fmt='.5g', xticklabels=encoder.classes_, yticklabels=encoder.classes_)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " Можно заметить, что страдают некоторые малопредставленные классы: *Бизнес*, *Из жизни*, *Силовые структуры* имеют низкое значение полноты, т.е. часто ложно относятся к классам, которые сильнее представлены в коллекции."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Машина опорных векторов\n",
    "\n",
    "C линейным ядром"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "pipeline_svm = Pipeline([\n",
    "    ('vect', CountVectorizer()),\n",
    "    ('tfidf', TfidfTransformer()),\n",
    "    ('dim_red', SelectKBest(chi2)),\n",
    "    ('clf', svm.LinearSVC(C = 1))\n",
    "])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Аналогично, подберем размер словаря с использованием кросс-валидации."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "k = [2000, 10000, 50000, 100000]\n",
    "scores = ['f1_micro', 'f1_macro']\n",
    "param_grid = [\n",
    "    {\n",
    "        'dim_red__k': k,\n",
    "    }\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "svm_grid = model_selection.GridSearchCV(estimator=pipeline_svm, cv = 3, \n",
    "                                        param_grid=param_grid, n_jobs=2, scoring=scores, refit='f1_micro')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wall time: 38min 58s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "svm_grid_res = svm_grid.fit(train_x, train_y_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\anaconda\\lib\\site-packages\\sklearn\\utils\\deprecation.py:122: FutureWarning: You are accessing a training score ('mean_train_f1_macro'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n",
      "C:\\anaconda\\lib\\site-packages\\sklearn\\utils\\deprecation.py:122: FutureWarning: You are accessing a training score ('mean_train_f1_micro'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n",
      "C:\\anaconda\\lib\\site-packages\\sklearn\\utils\\deprecation.py:122: FutureWarning: You are accessing a training score ('split0_train_f1_macro'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n",
      "C:\\anaconda\\lib\\site-packages\\sklearn\\utils\\deprecation.py:122: FutureWarning: You are accessing a training score ('split0_train_f1_micro'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n",
      "C:\\anaconda\\lib\\site-packages\\sklearn\\utils\\deprecation.py:122: FutureWarning: You are accessing a training score ('split1_train_f1_macro'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n",
      "C:\\anaconda\\lib\\site-packages\\sklearn\\utils\\deprecation.py:122: FutureWarning: You are accessing a training score ('split1_train_f1_micro'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n",
      "C:\\anaconda\\lib\\site-packages\\sklearn\\utils\\deprecation.py:122: FutureWarning: You are accessing a training score ('split2_train_f1_macro'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n",
      "C:\\anaconda\\lib\\site-packages\\sklearn\\utils\\deprecation.py:122: FutureWarning: You are accessing a training score ('split2_train_f1_micro'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n",
      "C:\\anaconda\\lib\\site-packages\\sklearn\\utils\\deprecation.py:122: FutureWarning: You are accessing a training score ('std_train_f1_macro'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n",
      "C:\\anaconda\\lib\\site-packages\\sklearn\\utils\\deprecation.py:122: FutureWarning: You are accessing a training score ('std_train_f1_micro'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'mean_fit_time': array([ 138.15480344,  143.84265749,  149.96434824,  121.8545924 ]),\n",
       " 'mean_score_time': array([ 58.96638028,  58.74128723,  73.15260108,  58.80556575]),\n",
       " 'mean_test_f1_macro': array([ 0.74697215,  0.80293963,  0.81626335,  0.81710256]),\n",
       " 'mean_test_f1_micro': array([ 0.81270523,  0.8451868 ,  0.85348524,  0.85486551]),\n",
       " 'mean_train_f1_macro': array([ 0.76019369,  0.85027988,  0.91905207,  0.9391753 ]),\n",
       " 'mean_train_f1_micro': array([ 0.81929882,  0.87089097,  0.91762379,  0.93541607]),\n",
       " 'param_dim_red__k': masked_array(data = [2000 10000 50000 100000],\n",
       "              mask = [False False False False],\n",
       "        fill_value = ?),\n",
       " 'params': [{'dim_red__k': 2000},\n",
       "  {'dim_red__k': 10000},\n",
       "  {'dim_red__k': 50000},\n",
       "  {'dim_red__k': 100000}],\n",
       " 'rank_test_f1_macro': array([4, 3, 2, 1]),\n",
       " 'rank_test_f1_micro': array([4, 3, 2, 1]),\n",
       " 'split0_test_f1_macro': array([ 0.7450598 ,  0.80150033,  0.81552489,  0.8158046 ]),\n",
       " 'split0_test_f1_micro': array([ 0.81050369,  0.84325762,  0.85218822,  0.85344871]),\n",
       " 'split0_train_f1_macro': array([ 0.76105844,  0.85085303,  0.91925673,  0.93944053]),\n",
       " 'split0_train_f1_micro': array([ 0.82038289,  0.87166155,  0.91785394,  0.93575356]),\n",
       " 'split1_test_f1_macro': array([ 0.74799353,  0.80301584,  0.81491489,  0.81515281]),\n",
       " 'split1_test_f1_micro': array([ 0.81239719,  0.8453156 ,  0.85311821,  0.8543157 ]),\n",
       " 'split1_train_f1_macro': array([ 0.76053215,  0.85096629,  0.91944767,  0.93901602]),\n",
       " 'split1_train_f1_micro': array([ 0.81920915,  0.87095686,  0.91821396,  0.93579829]),\n",
       " 'split2_test_f1_macro': array([ 0.74786317,  0.80430277,  0.81835034,  0.8203504 ]),\n",
       " 'split2_test_f1_micro': array([ 0.81521492,  0.84698727,  0.85514938,  0.85683222]),\n",
       " 'split2_train_f1_macro': array([ 0.75899047,  0.84902031,  0.91845181,  0.93906936]),\n",
       " 'split2_train_f1_micro': array([ 0.81830443,  0.87005449,  0.91680348,  0.93469636]),\n",
       " 'std_fit_time': array([ 13.82114533,  13.68619325,  10.02678113,   1.01930085]),\n",
       " 'std_score_time': array([  0.37310866,   1.30525805,  24.1890238 ,   6.18508389]),\n",
       " 'std_test_f1_macro': array([ 0.0013533 ,  0.00114536,  0.00149656,  0.00231189]),\n",
       " 'std_test_f1_micro': array([ 0.00193564,  0.00152534,  0.00123643,  0.00143498]),\n",
       " 'std_train_f1_macro': array([ 0.00087752,  0.00089185,  0.00043155,  0.0001888 ]),\n",
       " 'std_train_f1_micro': array([ 0.00085089,  0.00065773,  0.00059838,  0.00050924])}"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svm_grid_res.cv_results_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Переход от *k = 50 000* к *k = 100 000* не дает существенного улучшения оценки, поэтому остановимся на *k = 50 000*."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "pipeline_svm = Pipeline([\n",
    "    ('vect', CountVectorizer()),\n",
    "    ('tfidf', TfidfTransformer()),\n",
    "    ('dim_red', SelectKBest(chi2, k=50000)),\n",
    "    ('clf', svm.LinearSVC())\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "C = [0.01, 0.1, 1, 10]\n",
    "scores = ['f1_micro', 'f1_macro']\n",
    "param_grid = [\n",
    "    {\n",
    "        'clf__C': C,\n",
    "    }\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "svm_grid = model_selection.GridSearchCV(estimator=pipeline_svm, cv = 3, \n",
    "                                        param_grid=param_grid, n_jobs=2, scoring=scores, refit='f1_micro')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wall time: 44min 6s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "svm_grid_res = svm_grid.fit(train_x, train_y_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\anaconda\\lib\\site-packages\\sklearn\\utils\\deprecation.py:122: FutureWarning: You are accessing a training score ('mean_train_f1_macro'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n",
      "C:\\anaconda\\lib\\site-packages\\sklearn\\utils\\deprecation.py:122: FutureWarning: You are accessing a training score ('mean_train_f1_micro'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n",
      "C:\\anaconda\\lib\\site-packages\\sklearn\\utils\\deprecation.py:122: FutureWarning: You are accessing a training score ('split0_train_f1_macro'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n",
      "C:\\anaconda\\lib\\site-packages\\sklearn\\utils\\deprecation.py:122: FutureWarning: You are accessing a training score ('split0_train_f1_micro'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n",
      "C:\\anaconda\\lib\\site-packages\\sklearn\\utils\\deprecation.py:122: FutureWarning: You are accessing a training score ('split1_train_f1_macro'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n",
      "C:\\anaconda\\lib\\site-packages\\sklearn\\utils\\deprecation.py:122: FutureWarning: You are accessing a training score ('split1_train_f1_micro'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n",
      "C:\\anaconda\\lib\\site-packages\\sklearn\\utils\\deprecation.py:122: FutureWarning: You are accessing a training score ('split2_train_f1_macro'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n",
      "C:\\anaconda\\lib\\site-packages\\sklearn\\utils\\deprecation.py:122: FutureWarning: You are accessing a training score ('split2_train_f1_micro'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n",
      "C:\\anaconda\\lib\\site-packages\\sklearn\\utils\\deprecation.py:122: FutureWarning: You are accessing a training score ('std_train_f1_macro'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n",
      "C:\\anaconda\\lib\\site-packages\\sklearn\\utils\\deprecation.py:122: FutureWarning: You are accessing a training score ('std_train_f1_micro'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'mean_fit_time': array([ 194.58471282,  150.62817105,  140.80820282,  268.93825372]),\n",
       " 'mean_score_time': array([ 59.50206772,  62.98153178,  58.47793754,  61.28575333]),\n",
       " 'mean_test_f1_macro': array([ 0.69012949,  0.78875834,  0.81626223,  0.79934313]),\n",
       " 'mean_test_f1_micro': array([ 0.81388802,  0.84723305,  0.85348314,  0.83831275]),\n",
       " 'mean_train_f1_macro': array([ 0.70077024,  0.82995719,  0.91905189,  0.96647465]),\n",
       " 'mean_train_f1_micro': array([ 0.82017804,  0.87132374,  0.91762379,  0.95329869]),\n",
       " 'param_clf__C': masked_array(data = [0.01 0.1 1 10],\n",
       "              mask = [False False False False],\n",
       "        fill_value = ?),\n",
       " 'params': [{'clf__C': 0.01}, {'clf__C': 0.1}, {'clf__C': 1}, {'clf__C': 10}],\n",
       " 'rank_test_f1_macro': array([4, 3, 1, 2]),\n",
       " 'rank_test_f1_micro': array([4, 2, 1, 3]),\n",
       " 'split0_test_f1_macro': array([ 0.68789574,  0.78695618,  0.81552489,  0.79822664]),\n",
       " 'split0_test_f1_micro': array([ 0.81158772,  0.84529332,  0.85218822,  0.8370686 ]),\n",
       " 'split0_train_f1_macro': array([ 0.70362167,  0.8297014 ,  0.91925899,  0.96640046]),\n",
       " 'split0_train_f1_micro': array([ 0.82136611,  0.87168676,  0.91785709,  0.95344836]),\n",
       " 'split1_test_f1_macro': array([ 0.69162913,  0.79036764,  0.81491489,  0.79849057]),\n",
       " 'split1_test_f1_micro': array([ 0.81430687,  0.84777361,  0.85311821,  0.83761384]),\n",
       " 'split1_train_f1_macro': array([ 0.69802384,  0.8312628 ,  0.91944767,  0.96660815]),\n",
       " 'split1_train_f1_micro': array([ 0.81974487,  0.87153986,  0.91821396,  0.95376393]),\n",
       " 'split2_test_f1_macro': array([ 0.69086365,  0.78895124,  0.81834699,  0.80131228]),\n",
       " 'split2_test_f1_micro': array([ 0.81576957,  0.8486323 ,  0.85514307,  0.84025589]),\n",
       " 'split2_train_f1_macro': array([ 0.70066522,  0.82890738,  0.918449  ,  0.96641533]),\n",
       " 'split2_train_f1_micro': array([ 0.81942313,  0.87074461,  0.91680033,  0.95268377]),\n",
       " 'std_fit_time': array([  3.77384063,  13.2128572 ,  11.02625808,   5.44274318]),\n",
       " 'std_score_time': array([  1.8078674 ,   1.49490189,   3.55185735,  12.53059927]),\n",
       " 'std_test_f1_macro': array([ 0.00161014,  0.00139939,  0.001495  ,  0.00139653]),\n",
       " 'std_test_f1_micro': array([ 0.00173273,  0.00141571,  0.00123361,  0.0013919 ]),\n",
       " 'std_train_f1_macro': array([  2.28650951e-03,   9.78457246e-04,   4.33208387e-04,\n",
       "          9.45961187e-05]),\n",
       " 'std_train_f1_micro': array([ 0.0008503 ,  0.00041387,  0.00060023,  0.00045349])}"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svm_grid_res.cv_results_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 286,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "pipeline_svm = Pipeline([\n",
    "    ('vect', CountVectorizer()),\n",
    "    ('tfidf', TfidfTransformer()),\n",
    "    ('dim_red', SelectKBest(chi2, k=50000)),\n",
    "    ('clf', svm.LinearSVC(C=1))\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 287,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "svm_unbalanced = pipeline_svm.fit(train_x, train_y_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 288,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wall time: 23 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "svm_prediction_unbalanced = svm_unbalanced.predict(valid_x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 295,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.82307656408834184, 0.85858254813600987, 0.85693926909289664)"
      ]
     },
     "execution_count": 295,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metrics.f1_score(valid_y_labels, svm_prediction_unbalanced, average='macro'), \\\n",
    "metrics.f1_score(valid_y_labels, svm_prediction_unbalanced, average='micro'), \\\n",
    "metrics.f1_score(valid_y_labels, svm_prediction_unbalanced, average='weighted')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 291,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   precision    recall  f1-score   support\n",
      "\n",
      "           Бизнес       0.75      0.53      0.62      1465\n",
      "      Бывший СССР       0.86      0.89      0.87     11917\n",
      "         Из жизни       0.73      0.62      0.67      5909\n",
      "   Интернет и СМИ       0.82      0.77      0.79      9773\n",
      "         Культура       0.90      0.92      0.91     12244\n",
      "              Мир       0.83      0.86      0.85     31219\n",
      "  Наука и техника       0.87      0.88      0.87     11851\n",
      "      Путешествия       0.86      0.76      0.81      1000\n",
      "           Россия       0.84      0.86      0.85     36942\n",
      "Силовые структуры       0.79      0.58      0.67      3400\n",
      "            Спорт       0.97      0.98      0.97     13854\n",
      "          Финансы       0.89      0.90      0.89     18136\n",
      "         Ценности       0.94      0.90      0.92       955\n",
      "\n",
      "      avg / total       0.86      0.86      0.86    158665\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(metrics.classification_report(y_pred=svm_prediction_unbalanced, y_true=valid_y_labels, target_names=encoder.classes_))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 292,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "cm = metrics.confusion_matrix(y_pred=svm_prediction_unbalanced, y_true=valid_y_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 293,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x20679a76320>"
      ]
     },
     "execution_count": 293,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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hV8We1MmTp1i5ci2tWjd1lzkcDmbMmOfOEsbH/0m3+5+gXv02vPXWRwCcOvWX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\n4f4HUuQIuB0+YRgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x20679a76d68>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (15,10))\n",
    "sns.heatmap(cm, annot=True, fmt='.5g', xticklabels=encoder.classes_, yticklabels=encoder.classes_)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Получившаяся SVM модель гораздо лучше подошла для работы с данной несбалансированной коллекцией, в сравнении с байесовским классификатором. Каждый класс имеет большую и точность, и полноту.\n",
    "\n",
    "Ниже представлены таблицы сравнения оценок для двух методов: по классам и усредненные."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 294,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>precision</th>\n",
       "      <th>recall</th>\n",
       "      <th>f1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>topic</th>\n",
       "      <th>support</th>\n",
       "      <th>method</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Бизнес</th>\n",
       "      <th rowspan=\"2\" valign=\"top\">1465</th>\n",
       "      <th>NB</th>\n",
       "      <td>0.62</td>\n",
       "      <td>0.28</td>\n",
       "      <td>0.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SVM</th>\n",
       "      <td>0.75</td>\n",
       "      <td>0.53</td>\n",
       "      <td>0.62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Бывший СССР</th>\n",
       "      <th rowspan=\"2\" valign=\"top\">11917</th>\n",
       "      <th>NB</th>\n",
       "      <td>0.78</td>\n",
       "      <td>0.74</td>\n",
       "      <td>0.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SVM</th>\n",
       "      <td>0.86</td>\n",
       "      <td>0.89</td>\n",
       "      <td>0.87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Из жизни</th>\n",
       "      <th rowspan=\"2\" valign=\"top\">5909</th>\n",
       "      <th>NB</th>\n",
       "      <td>0.64</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SVM</th>\n",
       "      <td>0.73</td>\n",
       "      <td>0.62</td>\n",
       "      <td>0.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Интернет и СМИ</th>\n",
       "      <th rowspan=\"2\" valign=\"top\">9773</th>\n",
       "      <th>NB</th>\n",
       "      <td>0.77</td>\n",
       "      <td>0.65</td>\n",
       "      <td>0.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SVM</th>\n",
       "      <td>0.82</td>\n",
       "      <td>0.77</td>\n",
       "      <td>0.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Культура</th>\n",
       "      <th rowspan=\"2\" valign=\"top\">12244</th>\n",
       "      <th>NB</th>\n",
       "      <td>0.85</td>\n",
       "      <td>0.89</td>\n",
       "      <td>0.87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SVM</th>\n",
       "      <td>0.90</td>\n",
       "      <td>0.92</td>\n",
       "      <td>0.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Мир</th>\n",
       "      <th rowspan=\"2\" valign=\"top\">31219</th>\n",
       "      <th>NB</th>\n",
       "      <td>0.78</td>\n",
       "      <td>0.82</td>\n",
       "      <td>0.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SVM</th>\n",
       "      <td>0.83</td>\n",
       "      <td>0.86</td>\n",
       "      <td>0.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Наука и техника</th>\n",
       "      <th rowspan=\"2\" valign=\"top\">11851</th>\n",
       "      <th>NB</th>\n",
       "      <td>0.82</td>\n",
       "      <td>0.81</td>\n",
       "      <td>0.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SVM</th>\n",
       "      <td>0.87</td>\n",
       "      <td>0.88</td>\n",
       "      <td>0.87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Путешествия</th>\n",
       "      <th rowspan=\"2\" valign=\"top\">1000</th>\n",
       "      <th>NB</th>\n",
       "      <td>0.69</td>\n",
       "      <td>0.61</td>\n",
       "      <td>0.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SVM</th>\n",
       "      <td>0.86</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0.81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Россия</th>\n",
       "      <th rowspan=\"2\" valign=\"top\">36942</th>\n",
       "      <th>NB</th>\n",
       "      <td>0.75</td>\n",
       "      <td>0.81</td>\n",
       "      <td>0.78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SVM</th>\n",
       "      <td>0.84</td>\n",
       "      <td>0.86</td>\n",
       "      <td>0.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Силовые структуры</th>\n",
       "      <th rowspan=\"2\" valign=\"top\">3400</th>\n",
       "      <th>NB</th>\n",
       "      <td>0.53</td>\n",
       "      <td>0.31</td>\n",
       "      <td>0.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SVM</th>\n",
       "      <td>0.79</td>\n",
       "      <td>0.58</td>\n",
       "      <td>0.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Спорт</th>\n",
       "      <th rowspan=\"2\" valign=\"top\">13854</th>\n",
       "      <th>NB</th>\n",
       "      <td>0.97</td>\n",
       "      <td>0.95</td>\n",
       "      <td>0.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SVM</th>\n",
       "      <td>0.97</td>\n",
       "      <td>0.98</td>\n",
       "      <td>0.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Финансы</th>\n",
       "      <th rowspan=\"2\" valign=\"top\">18136</th>\n",
       "      <th>NB</th>\n",
       "      <td>0.83</td>\n",
       "      <td>0.87</td>\n",
       "      <td>0.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SVM</th>\n",
       "      <td>0.89</td>\n",
       "      <td>0.90</td>\n",
       "      <td>0.89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Ценности</th>\n",
       "      <th rowspan=\"2\" valign=\"top\">955</th>\n",
       "      <th>NB</th>\n",
       "      <td>0.85</td>\n",
       "      <td>0.87</td>\n",
       "      <td>0.86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SVM</th>\n",
       "      <td>0.94</td>\n",
       "      <td>0.90</td>\n",
       "      <td>0.92</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                  precision  recall    f1\n",
       "topic             support method                         \n",
       "Бизнес            1465    NB           0.62    0.28  0.39\n",
       "                          SVM          0.75    0.53  0.62\n",
       "Бывший СССР       11917   NB           0.78    0.74  0.76\n",
       "                          SVM          0.86    0.89  0.87\n",
       "Из жизни          5909    NB           0.64    0.50  0.56\n",
       "                          SVM          0.73    0.62  0.67\n",
       "Интернет и СМИ    9773    NB           0.77    0.65  0.70\n",
       "                          SVM          0.82    0.77  0.79\n",
       "Культура          12244   NB           0.85    0.89  0.87\n",
       "                          SVM          0.90    0.92  0.91\n",
       "Мир               31219   NB           0.78    0.82  0.80\n",
       "                          SVM          0.83    0.86  0.85\n",
       "Наука и техника   11851   NB           0.82    0.81  0.82\n",
       "                          SVM          0.87    0.88  0.87\n",
       "Путешествия       1000    NB           0.69    0.61  0.65\n",
       "                          SVM          0.86    0.76  0.81\n",
       "Россия            36942   NB           0.75    0.81  0.78\n",
       "                          SVM          0.84    0.86  0.85\n",
       "Силовые структуры 3400    NB           0.53    0.31  0.39\n",
       "                          SVM          0.79    0.58  0.67\n",
       "Спорт             13854   NB           0.97    0.95  0.96\n",
       "                          SVM          0.97    0.98  0.97\n",
       "Финансы           18136   NB           0.83    0.87  0.85\n",
       "                          SVM          0.89    0.90  0.89\n",
       "Ценности          955     NB           0.85    0.87  0.86\n",
       "                          SVM          0.94    0.90  0.92"
      ]
     },
     "execution_count": 294,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metrics1 = metrics.precision_recall_fscore_support(y_pred=svm_prediction_unbalanced, y_true=valid_y_labels, average=None)\n",
    "metrics2 = metrics.precision_recall_fscore_support(y_pred=nb_prediction_unbalanced, y_true=valid_y_labels, average=None)\n",
    "\n",
    "resdf = pd.DataFrame(np.array(metrics1).transpose(), columns=['precision','recall','f1','support'])\n",
    "resdf['method'] = 'SVM'\n",
    "resdf2 = pd.DataFrame(np.array(metrics2).transpose(), columns=['precision','recall','f1','support'])\n",
    "resdf2['method'] = 'NB'\n",
    "\n",
    "ddf = pd.concat([resdf, resdf2], axis=0)\n",
    "ddf.index = ddf.index.map(dict(zip(resdf.index, encoder.classes_)).get)\n",
    "ddf['support'] =ddf['support'].astype(int)\n",
    "ddf = ddf.reset_index() \\\n",
    "    .rename(columns ={'index': 'topic'}) \\\n",
    "    .set_index(['topic', 'support', 'method']) \\\n",
    "    .sort_index() \\\n",
    "    .round(2)\n",
    "    \n",
    "ddf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 308,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>prec-micro</th>\n",
       "      <th>prec-macro</th>\n",
       "      <th>rec-micro</th>\n",
       "      <th>rec-macro</th>\n",
       "      <th>F1-micro</th>\n",
       "      <th>F1-macro</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>method</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>NB</th>\n",
       "      <td>0.80</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SVM</th>\n",
       "      <td>0.86</td>\n",
       "      <td>0.85</td>\n",
       "      <td>0.86</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.86</td>\n",
       "      <td>0.82</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        prec-micro  prec-macro  rec-micro  rec-macro  F1-micro  F1-macro\n",
       "method                                                                  \n",
       "NB            0.80        0.76       0.80        0.7      0.80      0.72\n",
       "SVM           0.86        0.85       0.86        0.8      0.86      0.82"
      ]
     },
     "execution_count": 308,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metrics3 = np.array([\n",
    "    [\n",
    "        'NB',\n",
    "        metrics.precision_score(y_pred=nb_prediction_unbalanced, y_true=valid_y_labels, average='micro'),\n",
    "        metrics.precision_score(y_pred=nb_prediction_unbalanced, y_true=valid_y_labels, average='macro'),\n",
    "        metrics.recall_score(y_pred=nb_prediction_unbalanced, y_true=valid_y_labels, average='micro'),\n",
    "        metrics.recall_score(y_pred=nb_prediction_unbalanced, y_true=valid_y_labels, average='macro'),\n",
    "        metrics.f1_score(y_pred=nb_prediction_unbalanced, y_true=valid_y_labels, average='micro'),\n",
    "        metrics.f1_score(y_pred=nb_prediction_unbalanced, y_true=valid_y_labels, average='macro'),\n",
    "    ],\n",
    "    [\n",
    "        'SVM',\n",
    "        metrics.precision_score(y_pred=svm_prediction_unbalanced, y_true=valid_y_labels, average='micro'),\n",
    "        metrics.precision_score(y_pred=svm_prediction_unbalanced, y_true=valid_y_labels, average='macro'),\n",
    "        metrics.recall_score(y_pred=svm_prediction_unbalanced, y_true=valid_y_labels, average='micro'),\n",
    "        metrics.recall_score(y_pred=svm_prediction_unbalanced, y_true=valid_y_labels, average='macro'),\n",
    "        metrics.f1_score(y_pred=svm_prediction_unbalanced, y_true=valid_y_labels, average='micro'),\n",
    "        metrics.f1_score(y_pred=svm_prediction_unbalanced, y_true=valid_y_labels, average='macro'),\n",
    "    ]\n",
    "])\n",
    "columns = ['method', 'prec-micro', 'prec-macro', 'rec-micro', 'rec-macro', 'F1-micro', 'F1-macro']\n",
    "pd.DataFrame(metrics3, columns=columns).set_index('method').astype(float).round(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Вывод: опираясь на выбранные метрики качества, из данных моделей предпочтение отдаем модели SVM."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# UPD\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Утверждалось, что SVM работает лучше NB поскольку классы в задаче несбалансированные. Если привести принудительную балансировку классов, сохранится ли этот вывод?\n",
    "\n",
    "Ожидается, что да. SVM часто в задаче классификации текстов показывает результаты лучше, чем NB. Исключения обычно - небольшие коллекции коротких текстов.\n",
    "\n",
    "Но можно смоделировать балансировку и проверить."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "\n",
    "Данная функция определяет стратегию разбиения для кросс-валидации.\n",
    "* class_size : желаемое число объектов каждого класса\n",
    "* class_labels : список уникальных меток классов\n",
    "* n_splits : число фолдов для разбиения\n",
    "\n",
    "В начале разбиваем X, y на обуч.\\тест. подвыборки, далее ресемплируем в обучащей выборке каждый класс, до желаемого количества объектов."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 268,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "class UpsampleStratifiedKFold:\n",
    "    def __init__(self, class_size, class_labels, n_splits=3):\n",
    "        self.n_splits = n_splits\n",
    "        self.class_size = class_size\n",
    "        self.class_labels = class_labels\n",
    "\n",
    "    def split(self, X, y):\n",
    "        for train, test in model_selection.StratifiedKFold(n_splits=self.n_splits).split(X,y):\n",
    "            idx = np.array([], dtype=int)\n",
    "            for label in self.class_labels:\n",
    "                train_labels = np.where(y[train]==label)[0]\n",
    "                need_replace = True if train_labels.size < self.class_size else False\n",
    "                sample = np.random.choice(train_labels, self.class_size, replace=need_replace)\n",
    "                idx = np.append(idx, sample)\n",
    "            resampled_train = np.random.permutation(train[idx])\n",
    "            yield resampled_train, test\n",
    "\n",
    "    def get_n_splits(self, X, y):\n",
    "        return self.n_splits"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Как и ранее, посмотрим на влияние параметров на оценки качества. Количество признаков оставим тем же для обоих методов."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "pipeline_nb = Pipeline([\n",
    "    ('vect', CountVectorizer()),\n",
    "    ('tfidf', TfidfTransformer()),\n",
    "    ('dim_red', SelectKBest(chi2, k=10000)),\n",
    "    ('clf', naive_bayes.MultinomialNB(alpha=1e-2))\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  4561,  35805,  17358,  28955,  36020,  93529,  36277,   3006,\n",
       "       110921,  10559,  41541,  54600,   2861], dtype=int64)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "unique_classes, counts = np.unique(train_y_labels, return_counts=True)\n",
    "counts"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ниже два черновых запуска кросс-валидации для оценки времени ее работы в зависимости от выбранного размера класса (13 классов вызывали опасения, что выборка очень быстро растет)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=2)]: Done   3 out of   3 | elapsed: 17.6min remaining:    0.0s\n",
      "[Parallel(n_jobs=2)]: Done   3 out of   3 | elapsed: 17.6min finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wall time: 17min 38s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "stratifier = UpsampleStratifiedKFold(50000, unique_classes, 3)\n",
    "scoring = ['f1_macro', 'recall_macro', 'precision_macro']\n",
    "cvres = model_selection.cross_validate(pipeline_nb, train_x, train_y_labels, cv=stratifier,\n",
    "                                       scoring=scoring, n_jobs=2, verbose=10, return_train_score=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'fit_time': array([ 148.4681983 ,  138.48241329,   99.52821541]),\n",
       " 'score_time': array([ 82.38274288,  81.40164518,  64.97961473]),\n",
       " 'test_f1_macro': array([ 0.6958857 ,  0.69561662,  0.69739523]),\n",
       " 'test_precision_macro': array([ 0.66609024,  0.66597424,  0.66738168]),\n",
       " 'test_recall_macro': array([ 0.77895874,  0.7769163 ,  0.7776849 ]),\n",
       " 'train_f1_macro': array([ 0.80548852,  0.80321791,  0.80449246]),\n",
       " 'train_precision_macro': array([ 0.80857294,  0.8066123 ,  0.80785543]),\n",
       " 'train_recall_macro': array([ 0.80466154,  0.80231692,  0.80343692])}"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cvres"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=2)]: Done   3 out of   3 | elapsed: 31.2min remaining:    0.0s\n",
      "[Parallel(n_jobs=2)]: Done   3 out of   3 | elapsed: 31.2min finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wall time: 31min 13s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "stratifier = UpsampleStratifiedKFold(90000, unique_classes, 3)\n",
    "scoring = ['f1_macro', 'recall_macro', 'precision_macro']\n",
    "cvres2 = model_selection.cross_validate(pipeline_nb, train_x, train_y_labels, cv=stratifier,\n",
    "                                        scoring=scoring, n_jobs=2, verbose=10, return_train_score=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'fit_time': array([ 273.69279265,  259.98858738,  187.19719052]),\n",
       " 'score_time': array([ 84.35333681,  84.26976323,  67.08048606]),\n",
       " 'test_f1_macro': array([ 0.69544756,  0.69527993,  0.69805879]),\n",
       " 'test_precision_macro': array([ 0.66612009,  0.66571898,  0.66746882]),\n",
       " 'test_recall_macro': array([ 0.7774021 ,  0.77667772,  0.77818641]),\n",
       " 'train_f1_macro': array([ 0.80472031,  0.80305691,  0.80356471]),\n",
       " 'train_precision_macro': array([ 0.80794618,  0.80629896,  0.8066604 ]),\n",
       " 'train_recall_macro': array([ 0.8038735 ,  0.80215641,  0.80261111])}"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cvres2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Оставим размер класса 50 000. Подберем параметр сглаживания для наивного байеса."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "alpha = [1e-2, 1e-1, 1]\n",
    "scores = ['f1_macro', 'f1_micro']\n",
    "param_grid = [\n",
    "    {\n",
    "        'clf__alpha': alpha,\n",
    "    }\n",
    "]\n",
    "stratifier = UpsampleStratifiedKFold(50000, unique_classes, 3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "nb_grid = model_selection.GridSearchCV(estimator=pipeline_nb, cv = stratifier, param_grid=param_grid,\n",
    "                                       n_jobs=2, scoring=scores, refit='f1_macro', verbose=10, return_train_score=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 3 folds for each of 3 candidates, totalling 9 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=2)]: Done   1 tasks      | elapsed:  7.8min\n",
      "[Parallel(n_jobs=2)]: Done   4 tasks      | elapsed: 15.7min\n",
      "[Parallel(n_jobs=2)]: Done   7 out of   9 | elapsed: 29.5min remaining:  8.4min\n",
      "[Parallel(n_jobs=2)]: Done   9 out of   9 | elapsed: 35.0min remaining:    0.0s\n",
      "[Parallel(n_jobs=2)]: Done   9 out of   9 | elapsed: 35.0min finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wall time: 36min 35s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "nb_grid_res = nb_grid.fit(train_x, train_y_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'mean_fit_time': array([ 146.2735277 ,  146.54253324,  129.85334953]),\n",
       " 'mean_score_time': array([ 54.61569357,  54.92283487,  49.32605664]),\n",
       " 'mean_test_f1_macro': array([ 0.69608674,  0.69370378,  0.69017465]),\n",
       " 'mean_test_f1_micro': array([ 0.76316879,  0.761507  ,  0.75858469]),\n",
       " 'mean_train_f1_macro': array([ 0.80380555,  0.8009464 ,  0.79634013]),\n",
       " 'mean_train_f1_micro': array([ 0.80291744,  0.80008205,  0.79550821]),\n",
       " 'param_clf__alpha': masked_array(data = [0.01 0.1 1],\n",
       "              mask = [False False False],\n",
       "        fill_value = ?),\n",
       " 'params': [{'clf__alpha': 0.01}, {'clf__alpha': 0.1}, {'clf__alpha': 1}],\n",
       " 'rank_test_f1_macro': array([1, 2, 3]),\n",
       " 'rank_test_f1_micro': array([1, 2, 3]),\n",
       " 'split0_test_f1_macro': array([ 0.69527083,  0.69253662,  0.68892212]),\n",
       " 'split0_test_f1_micro': array([ 0.76187385,  0.7599579 ,  0.75712179]),\n",
       " 'split0_train_f1_macro': array([ 0.80466182,  0.80182597,  0.79738199]),\n",
       " 'split0_train_f1_micro': array([ 0.80391385,  0.80109846,  0.79669231]),\n",
       " 'split1_test_f1_macro': array([ 0.69547557,  0.69320894,  0.68913014]),\n",
       " 'split1_test_f1_micro': array([ 0.76277062,  0.76124539,  0.75806889]),\n",
       " 'split1_train_f1_macro': array([ 0.80368399,  0.80076668,  0.79611378]),\n",
       " 'split1_train_f1_micro': array([ 0.80275077,  0.79985385,  0.79524154]),\n",
       " 'split2_test_f1_macro': array([ 0.69751386,  0.69536587,  0.69247177]),\n",
       " 'split2_test_f1_micro': array([ 0.76486197,  0.76331779,  0.76056347]),\n",
       " 'split2_train_f1_macro': array([ 0.80307084,  0.80024655,  0.79552462]),\n",
       " 'split2_train_f1_micro': array([ 0.80208769,  0.79929385,  0.79459077]),\n",
       " 'std_fit_time': array([  4.10353371,  15.58351585,  22.83955219]),\n",
       " 'std_score_time': array([ 1.90807323,  1.5136683 ,  3.75085867]),\n",
       " 'std_test_f1_macro': array([ 0.00101256,  0.00120687,  0.0016265 ]),\n",
       " 'std_test_f1_micro': array([ 0.00125196,  0.00138408,  0.00145162]),\n",
       " 'std_train_f1_macro': array([ 0.00065518,  0.0006572 ,  0.00077497]),\n",
       " 'std_train_f1_micro': array([ 0.00075478,  0.0007542 ,  0.00087843])}"
      ]
     },
     "execution_count": 142,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nb_grid.cv_results_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Разницы практически нет, оставим alpha = 0.01 как и для несбалансированного случая, и посмотрим на оценку на тестовой подвыборке. Для этого нужно вначале ресемплировать обучающую подвыборку, по той же стратегии, что использовалась выше."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tags</th>\n",
       "      <th>text_processed</th>\n",
       "      <th>title_processed</th>\n",
       "      <th>topic</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>8411</th>\n",
       "      <td>Все</td>\n",
       "      <td>первомайск суд мурманск оправда капита траулер...</td>\n",
       "      <td>суд оправда капита траулер электрон</td>\n",
       "      <td>Россия</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>249213</th>\n",
       "      <td>Следствие и суд</td>\n",
       "      <td>административн комисс фсин направ суд материал...</td>\n",
       "      <td>фсин направ суд материал васильев</td>\n",
       "      <td>Силовые структуры</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>479898</th>\n",
       "      <td>Все</td>\n",
       "      <td>руководител управлен госохра украин валер геле...</td>\n",
       "      <td>руководител управлен госохра украин открест ин...</td>\n",
       "      <td>Бывший СССР</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>610394</th>\n",
       "      <td>Наука</td>\n",
       "      <td>физик австр канад австрал наблюда пар одиночн ...</td>\n",
       "      <td>физик запута фотон наибольш квантов числ</td>\n",
       "      <td>Наука и техника</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>590781</th>\n",
       "      <td>Футбол</td>\n",
       "      <td>напада барселон лионел месс приня участ японск...</td>\n",
       "      <td>месс обыгра гигантск вратаря-робот</td>\n",
       "      <td>Спорт</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   tags                                     text_processed  \\\n",
       "8411                Все  первомайск суд мурманск оправда капита траулер...   \n",
       "249213  Следствие и суд  административн комисс фсин направ суд материал...   \n",
       "479898              Все  руководител управлен госохра украин валер геле...   \n",
       "610394            Наука  физик австр канад австрал наблюда пар одиночн ...   \n",
       "590781           Футбол  напада барселон лионел месс приня участ японск...   \n",
       "\n",
       "                                          title_processed              topic  \n",
       "8411                  суд оправда капита траулер электрон             Россия  \n",
       "249213                  фсин направ суд материал васильев  Силовые структуры  \n",
       "479898  руководител управлен госохра украин открест ин...        Бывший СССР  \n",
       "610394           физик запута фотон наибольш квантов числ    Наука и техника  \n",
       "590781                 месс обыгра гигантск вратаря-робот              Спорт  "
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df, valid_df = model_selection.train_test_split(df, random_state=17)\n",
    "train_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\anaconda\\lib\\site-packages\\ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    }
   ],
   "source": [
    "train_df.drop(['tags', 'title_processed'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def group_resample(group, size):\n",
    "    need_replace = True if group.shape[0] < size else False\n",
    "    return resample(group, replace=need_replace, n_samples=size, random_state=123)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "train_balanced = train_df.groupby('topic').apply(lambda x: group_resample(x, 50000)) \\\n",
    "                            .reset_index(drop=True).sample(frac=1, random_state=123)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>text_processed</th>\n",
       "      <th>topic</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>165732</th>\n",
       "      <td>сам известн радиоведущ сша говард стерн увол ф...</td>\n",
       "      <td>Интернет и СМИ</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>505897</th>\n",
       "      <td>канадск фигурист патрик чан котор явля одн гла...</td>\n",
       "      <td>Спорт</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>130111</th>\n",
       "      <td>херсонск област украин задержа дво местн жител...</td>\n",
       "      <td>Из жизни</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>382591</th>\n",
       "      <td>туристическ сервис рамблер.путешеств запуст пр...</td>\n",
       "      <td>Путешествия</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>562940</th>\n",
       "      <td>совокупн обь списан проч убытк понесен финансо...</td>\n",
       "      <td>Финансы</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                           text_processed           topic\n",
       "165732  сам известн радиоведущ сша говард стерн увол ф...  Интернет и СМИ\n",
       "505897  канадск фигурист патрик чан котор явля одн гла...           Спорт\n",
       "130111  херсонск област украин задержа дво местн жител...        Из жизни\n",
       "382591  туристическ сервис рамблер.путешеств запуст пр...     Путешествия\n",
       "562940  совокупн обь списан проч убытк понесен финансо...         Финансы"
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_balanced.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "train_x_balanced = train_balanced['text_processed']\n",
    "train_y_balanced = train_balanced['topic']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "train_y_balanced_labels = encoder.transform(train_y_balanced)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Обучаем на полученной выборке NB."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "nb_balanced = pipeline_nb.fit(train_x_balanced, train_y_balanced_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "nb_prediction = nb_balanced.predict(valid_x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.6952108070393479, 0.76152900765764342, 0.77308903759768999)"
      ]
     },
     "execution_count": 153,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metrics.f1_score(valid_y_labels, nb_prediction, average='macro'), \\\n",
    "metrics.f1_score(valid_y_labels, nb_prediction, average='micro'), \\\n",
    "metrics.f1_score(valid_y_labels, nb_prediction, average='weighted'),"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   precision    recall  f1-score   support\n",
      "\n",
      "           Бизнес       0.24      0.74      0.36      1465\n",
      "      Бывший СССР       0.71      0.83      0.76     11917\n",
      "         Из жизни       0.44      0.76      0.55      5909\n",
      "   Интернет и СМИ       0.69      0.71      0.70      9773\n",
      "         Культура       0.86      0.88      0.87     12244\n",
      "              Мир       0.83      0.72      0.77     31219\n",
      "  Наука и техника       0.84      0.75      0.79     11851\n",
      "      Путешествия       0.31      0.83      0.46      1000\n",
      "           Россия       0.84      0.69      0.76     36942\n",
      "Силовые структуры       0.35      0.62      0.44      3400\n",
      "            Спорт       0.97      0.95      0.96     13854\n",
      "          Финансы       0.86      0.77      0.81     18136\n",
      "         Ценности       0.69      0.92      0.79       955\n",
      "\n",
      "      avg / total       0.80      0.76      0.77    158665\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(metrics.classification_report(y_pred=nb_prediction, y_true=valid_y_labels, target_names=encoder.classes_))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x205b75fbb00>"
      ]
     },
     "execution_count": 154,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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PWjvbGFMHWAFUt9auMcYMAM5ZawcbY5YA+YAAYKK1doDrRjNB1tqurvV+DYRba8caYyoC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\nwyj8aHlKlgrg2LETdP+hq9Ohx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6fO5oXn/+121AxJqzzB+npzg+bgvwxHgpkiskxEHvZt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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x205c6b24e48>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cm = metrics.confusion_matrix(y_pred=nb_prediction, y_true=valid_y_labels)\n",
    "plt.figure(figsize = (15,10))\n",
    "sns.heatmap(cm, annot=True, fmt='.5g', xticklabels=encoder.classes_, yticklabels=encoder.classes_)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Аналогичный порядок действий для SVC."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "pipeline_svm = Pipeline([\n",
    "    ('vect', CountVectorizer()),\n",
    "    ('tfidf', TfidfTransformer()),\n",
    "    ('dim_red', SelectKBest(chi2, k=50000)),\n",
    "    ('clf', svm.LinearSVC(C=1))\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "C = [0.01, 0.1, 1, 10]\n",
    "scores = ['f1_micro', 'f1_macro']\n",
    "param_grid = [\n",
    "    {\n",
    "        'clf__C': C,\n",
    "    }\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "svm_grid = model_selection.GridSearchCV(estimator=pipeline_svm, cv = stratifier, param_grid=param_grid,\n",
    "                                        n_jobs=2, scoring=scores, refit='f1_macro', return_train_score=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wall time: 1h 21min 5s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "svm_grid_res = svm_grid.fit(train_x, train_y_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'mean_fit_time': array([ 462.68030167,  280.32467675,  281.83186372,  710.53798103]),\n",
       " 'mean_score_time': array([ 52.28894075,  55.7300547 ,  75.80369703,  51.76951925]),\n",
       " 'mean_test_f1_macro': array([ 0.74313846,  0.79279332,  0.8036271 ,  0.78207276]),\n",
       " 'mean_test_f1_micro': array([ 0.79878906,  0.83012355,  0.83489043,  0.81883347]),\n",
       " 'mean_train_f1_macro': array([ 0.87252042,  0.92783851,  0.96452181,  0.98437043]),\n",
       " 'mean_train_f1_micro': array([ 0.87399282,  0.92872256,  0.96499385,  0.98452513]),\n",
       " 'param_clf__C': masked_array(data = [0.01 0.1 1 10],\n",
       "              mask = [False False False False],\n",
       "        fill_value = ?),\n",
       " 'params': [{'clf__C': 0.01}, {'clf__C': 0.1}, {'clf__C': 1}, {'clf__C': 10}],\n",
       " 'rank_test_f1_macro': array([4, 2, 1, 3]),\n",
       " 'rank_test_f1_micro': array([4, 2, 1, 3]),\n",
       " 'split0_test_f1_macro': array([ 0.74275089,  0.7907507 ,  0.8029669 ,  0.78165723]),\n",
       " 'split0_test_f1_micro': array([ 0.79777901,  0.82798674,  0.83352661,  0.81789649]),\n",
       " 'split0_train_f1_macro': array([ 0.87275832,  0.92822198,  0.96459513,  0.98414639]),\n",
       " 'split0_train_f1_micro': array([ 0.87424154,  0.92909538,  0.96505846,  0.98430308]),\n",
       " 'split1_test_f1_macro': array([ 0.74192966,  0.79161832,  0.80250247,  0.78114874]),\n",
       " 'split1_test_f1_micro': array([ 0.79872057,  0.82998141,  0.83475247,  0.81771657]),\n",
       " 'split1_train_f1_macro': array([ 0.87263174,  0.92818617,  0.96504034,  0.98482534]),\n",
       " 'split1_train_f1_micro': array([ 0.87409692,  0.92906308,  0.96551231,  0.98497077]),\n",
       " 'split2_test_f1_macro': array([ 0.7447349 ,  0.79601107,  0.805412  ,  0.78341235]),\n",
       " 'split2_test_f1_micro': array([ 0.79986764,  0.83240262,  0.83639229,  0.82088743]),\n",
       " 'split2_train_f1_macro': array([ 0.87217122,  0.92710738,  0.96392996,  0.98413956]),\n",
       " 'split2_train_f1_micro': array([ 0.87364   ,  0.92800923,  0.96441077,  0.98430154]),\n",
       " 'std_fit_time': array([  5.94993892,   6.82189822,   3.51777488,  13.35580965]),\n",
       " 'std_score_time': array([  0.76437032,   0.90730775,  31.90059799,   3.75973055]),\n",
       " 'std_test_f1_macro': array([ 0.00117756,  0.00230265,  0.00127625,  0.0009697 ]),\n",
       " 'std_test_f1_micro': array([ 0.00085405,  0.00180557,  0.00117397,  0.0014542 ]),\n",
       " 'std_train_f1_macro': array([ 0.00025228,  0.0005172 ,  0.00045626,  0.00032168]),\n",
       " 'std_train_f1_micro': array([ 0.00025637,  0.00050458,  0.00045202,  0.00031512])}"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svm_grid_res.cv_results_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "С = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "svm_balanced = pipeline_svm.fit(train_x_balanced, train_y_balanced_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "svm_prediction = svm_balanced.predict(valid_x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.80692726674296955, 0.83635332303910759, 0.83830500841415501)"
      ]
     },
     "execution_count": 157,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metrics.f1_score(valid_y_labels, svm_prediction, average='macro'), \\\n",
    "metrics.f1_score(valid_y_labels, svm_prediction, average='micro'), \\\n",
    "metrics.f1_score(valid_y_labels, svm_prediction, average='weighted')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   precision    recall  f1-score   support\n",
      "\n",
      "           Бизнес       0.55      0.76      0.64      1465\n",
      "      Бывший СССР       0.81      0.91      0.86     11917\n",
      "         Из жизни       0.57      0.74      0.65      5909\n",
      "   Интернет и СМИ       0.76      0.81      0.78      9773\n",
      "         Культура       0.89      0.92      0.90     12244\n",
      "              Мир       0.85      0.80      0.83     31219\n",
      "  Наука и техника       0.84      0.87      0.86     11851\n",
      "      Путешествия       0.69      0.85      0.76      1000\n",
      "           Россия       0.89      0.75      0.82     36942\n",
      "Силовые структуры       0.53      0.81      0.64      3400\n",
      "            Спорт       0.97      0.97      0.97     13854\n",
      "          Финансы       0.87      0.89      0.88     18136\n",
      "         Ценности       0.90      0.91      0.91       955\n",
      "\n",
      "      avg / total       0.85      0.84      0.84    158665\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(metrics.classification_report(y_pred=svm_prediction, y_true=valid_y_labels, target_names=encoder.classes_))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x205b4b5d6d8>"
      ]
     },
     "execution_count": 159,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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p0iXjP9OqZWN27Nid5t99sw4uXk/ZjvUBKNuxPgcXORKzZ6KOU7ReBQCy5c9F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h1rvkLAVGvtA7HKSgFfA9lwNY77WWsHGWMWAtlxNcDucf9/BMgDTLbWtjfG5MLVZfQ+97KLrLWv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\nRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x205acedf400>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cm = metrics.confusion_matrix(y_pred=svm_prediction, y_true=valid_y_labels)\n",
    "plt.figure(figsize = (15,10))\n",
    "sns.heatmap(cm, annot=True, fmt='.5g', xticklabels=encoder.classes_, yticklabels=encoder.classes_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 269,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>precision</th>\n",
       "      <th>recall</th>\n",
       "      <th>f1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>topic</th>\n",
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       "  <tbody>\n",
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       "      <th rowspan=\"2\" valign=\"top\">Бизнес</th>\n",
       "      <th rowspan=\"2\" valign=\"top\">1465</th>\n",
       "      <th>NB</th>\n",
       "      <td>0.24</td>\n",
       "      <td>0.74</td>\n",
       "      <td>0.36</td>\n",
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       "    <tr>\n",
       "      <th>SVM</th>\n",
       "      <td>0.55</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0.64</td>\n",
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       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Бывший СССР</th>\n",
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       "      <th>NB</th>\n",
       "      <td>0.71</td>\n",
       "      <td>0.83</td>\n",
       "      <td>0.76</td>\n",
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       "    <tr>\n",
       "      <th>SVM</th>\n",
       "      <td>0.81</td>\n",
       "      <td>0.91</td>\n",
       "      <td>0.86</td>\n",
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       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Из жизни</th>\n",
       "      <th rowspan=\"2\" valign=\"top\">5909</th>\n",
       "      <th>NB</th>\n",
       "      <td>0.44</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0.55</td>\n",
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       "    <tr>\n",
       "      <th>SVM</th>\n",
       "      <td>0.57</td>\n",
       "      <td>0.74</td>\n",
       "      <td>0.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Интернет и СМИ</th>\n",
       "      <th rowspan=\"2\" valign=\"top\">9773</th>\n",
       "      <th>NB</th>\n",
       "      <td>0.69</td>\n",
       "      <td>0.71</td>\n",
       "      <td>0.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SVM</th>\n",
       "      <td>0.76</td>\n",
       "      <td>0.81</td>\n",
       "      <td>0.78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Культура</th>\n",
       "      <th rowspan=\"2\" valign=\"top\">12244</th>\n",
       "      <th>NB</th>\n",
       "      <td>0.86</td>\n",
       "      <td>0.88</td>\n",
       "      <td>0.87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SVM</th>\n",
       "      <td>0.89</td>\n",
       "      <td>0.92</td>\n",
       "      <td>0.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Мир</th>\n",
       "      <th rowspan=\"2\" valign=\"top\">31219</th>\n",
       "      <th>NB</th>\n",
       "      <td>0.83</td>\n",
       "      <td>0.72</td>\n",
       "      <td>0.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SVM</th>\n",
       "      <td>0.85</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Наука и техника</th>\n",
       "      <th rowspan=\"2\" valign=\"top\">11851</th>\n",
       "      <th>NB</th>\n",
       "      <td>0.84</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SVM</th>\n",
       "      <td>0.84</td>\n",
       "      <td>0.87</td>\n",
       "      <td>0.86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Путешествия</th>\n",
       "      <th rowspan=\"2\" valign=\"top\">1000</th>\n",
       "      <th>NB</th>\n",
       "      <td>0.31</td>\n",
       "      <td>0.84</td>\n",
       "      <td>0.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SVM</th>\n",
       "      <td>0.69</td>\n",
       "      <td>0.85</td>\n",
       "      <td>0.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Россия</th>\n",
       "      <th rowspan=\"2\" valign=\"top\">36942</th>\n",
       "      <th>NB</th>\n",
       "      <td>0.84</td>\n",
       "      <td>0.69</td>\n",
       "      <td>0.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SVM</th>\n",
       "      <td>0.89</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Силовые структуры</th>\n",
       "      <th rowspan=\"2\" valign=\"top\">3400</th>\n",
       "      <th>NB</th>\n",
       "      <td>0.35</td>\n",
       "      <td>0.62</td>\n",
       "      <td>0.44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SVM</th>\n",
       "      <td>0.53</td>\n",
       "      <td>0.81</td>\n",
       "      <td>0.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Спорт</th>\n",
       "      <th rowspan=\"2\" valign=\"top\">13854</th>\n",
       "      <th>NB</th>\n",
       "      <td>0.97</td>\n",
       "      <td>0.95</td>\n",
       "      <td>0.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SVM</th>\n",
       "      <td>0.97</td>\n",
       "      <td>0.97</td>\n",
       "      <td>0.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Финансы</th>\n",
       "      <th rowspan=\"2\" valign=\"top\">18136</th>\n",
       "      <th>NB</th>\n",
       "      <td>0.86</td>\n",
       "      <td>0.77</td>\n",
       "      <td>0.81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SVM</th>\n",
       "      <td>0.87</td>\n",
       "      <td>0.89</td>\n",
       "      <td>0.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Ценности</th>\n",
       "      <th rowspan=\"2\" valign=\"top\">955</th>\n",
       "      <th>NB</th>\n",
       "      <td>0.69</td>\n",
       "      <td>0.92</td>\n",
       "      <td>0.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SVM</th>\n",
       "      <td>0.90</td>\n",
       "      <td>0.91</td>\n",
       "      <td>0.91</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                  precision  recall    f1\n",
       "topic             support method                         \n",
       "Бизнес            1465    NB           0.24    0.74  0.36\n",
       "                          SVM          0.55    0.76  0.64\n",
       "Бывший СССР       11917   NB           0.71    0.83  0.76\n",
       "                          SVM          0.81    0.91  0.86\n",
       "Из жизни          5909    NB           0.44    0.76  0.55\n",
       "                          SVM          0.57    0.74  0.65\n",
       "Интернет и СМИ    9773    NB           0.69    0.71  0.70\n",
       "                          SVM          0.76    0.81  0.78\n",
       "Культура          12244   NB           0.86    0.88  0.87\n",
       "                          SVM          0.89    0.92  0.90\n",
       "Мир               31219   NB           0.83    0.72  0.77\n",
       "                          SVM          0.85    0.80  0.83\n",
       "Наука и техника   11851   NB           0.84    0.75  0.79\n",
       "                          SVM          0.84    0.87  0.86\n",
       "Путешествия       1000    NB           0.31    0.84  0.46\n",
       "                          SVM          0.69    0.85  0.76\n",
       "Россия            36942   NB           0.84    0.69  0.76\n",
       "                          SVM          0.89    0.75  0.82\n",
       "Силовые структуры 3400    NB           0.35    0.62  0.44\n",
       "                          SVM          0.53    0.81  0.64\n",
       "Спорт             13854   NB           0.97    0.95  0.96\n",
       "                          SVM          0.97    0.97  0.97\n",
       "Финансы           18136   NB           0.86    0.77  0.81\n",
       "                          SVM          0.87    0.89  0.88\n",
       "Ценности          955     NB           0.69    0.92  0.79\n",
       "                          SVM          0.90    0.91  0.91"
      ]
     },
     "execution_count": 269,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metrics1 = metrics.precision_recall_fscore_support(y_pred=svm_prediction, y_true=valid_y_labels, average=None)\n",
    "metrics2 = metrics.precision_recall_fscore_support(y_pred=nb_prediction, y_true=valid_y_labels, average=None)\n",
    "\n",
    "resdf = pd.DataFrame(np.array(metrics1).transpose(), columns=['precision','recall','f1','support'])\n",
    "resdf['method'] = 'SVM'\n",
    "resdf2 = pd.DataFrame(np.array(metrics2).transpose(), columns=['precision','recall','f1','support'])\n",
    "resdf2['method'] = 'NB'\n",
    "\n",
    "ddf = pd.concat([resdf, resdf2], axis=0)\n",
    "ddf.index = ddf.index.map(dict(zip(resdf.index, encoder.classes_)).get)\n",
    "ddf['support'] =ddf['support'].astype(int)\n",
    "ddf = ddf.reset_index() \\\n",
    "    .rename(columns ={'index': 'topic'}) \\\n",
    "    .set_index(['topic', 'support', 'method']) \\\n",
    "    .sort_index() \\\n",
    "    .round(2)\n",
    "    \n",
    "ddf"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Снова лучше себя показывает SVM. По каждому классу его точно как минимум не уступает показателю NB, а в большинстве случаев значительно превосходит. Полнота получилась у NB лучше лишь для класса *Ценности*."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 313,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>prec-micro</th>\n",
       "      <th>prec-macro</th>\n",
       "      <th>rec-micro</th>\n",
       "      <th>rec-macro</th>\n",
       "      <th>F1-micro</th>\n",
       "      <th>F1-macro</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>method</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>NB</th>\n",
       "      <td>0.80</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SVM</th>\n",
       "      <td>0.86</td>\n",
       "      <td>0.85</td>\n",
       "      <td>0.86</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.86</td>\n",
       "      <td>0.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NB-balanced</th>\n",
       "      <td>0.76</td>\n",
       "      <td>0.66</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0.78</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SVM-balanced</th>\n",
       "      <td>0.84</td>\n",
       "      <td>0.78</td>\n",
       "      <td>0.84</td>\n",
       "      <td>0.85</td>\n",
       "      <td>0.84</td>\n",
       "      <td>0.81</td>\n",
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              prec-micro  prec-macro  rec-micro  rec-macro  F1-micro  F1-macro\n",
       "method                                                                        \n",
       "NB                  0.80        0.76       0.80       0.70      0.80      0.72\n",
       "SVM                 0.86        0.85       0.86       0.80      0.86      0.82\n",
       "NB-balanced         0.76        0.66       0.76       0.78      0.76      0.70\n",
       "SVM-balanced        0.84        0.78       0.84       0.85      0.84      0.81"
      ]
     },
     "execution_count": 313,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metrics4 = np.array([\n",
    "    [\n",
    "        'NB-balanced',\n",
    "        metrics.precision_score(y_pred=nb_prediction, y_true=valid_y_labels, average='micro'),\n",
    "        metrics.precision_score(y_pred=nb_prediction, y_true=valid_y_labels, average='macro'),\n",
    "        metrics.recall_score(y_pred=nb_prediction, y_true=valid_y_labels, average='micro'),\n",
    "        metrics.recall_score(y_pred=nb_prediction, y_true=valid_y_labels, average='macro'),\n",
    "        metrics.f1_score(y_pred=nb_prediction, y_true=valid_y_labels, average='micro'),\n",
    "        metrics.f1_score(y_pred=nb_prediction, y_true=valid_y_labels, average='macro'),\n",
    "    ],\n",
    "    [\n",
    "        'SVM-balanced',\n",
    "        metrics.precision_score(y_pred=svm_prediction, y_true=valid_y_labels, average='micro'),\n",
    "        metrics.precision_score(y_pred=svm_prediction, y_true=valid_y_labels, average='macro'),\n",
    "        metrics.recall_score(y_pred=svm_prediction, y_true=valid_y_labels, average='micro'),\n",
    "        metrics.recall_score(y_pred=svm_prediction, y_true=valid_y_labels, average='macro'),\n",
    "        metrics.f1_score(y_pred=svm_prediction, y_true=valid_y_labels, average='micro'),\n",
    "        metrics.f1_score(y_pred=svm_prediction, y_true=valid_y_labels, average='macro'),\n",
    "    ]\n",
    "])\n",
    "columns = ['method', 'prec-micro', 'prec-macro', 'rec-micro', 'rec-macro', 'F1-micro', 'F1-macro']\n",
    "pd.DataFrame(np.vstack((metrics3, metrics4)), columns=columns).set_index('method').astype(float).round(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "По всем метрикам качества следует отдать предпочтение SVM.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "В результате балансировки обе модели стали полнее определять малопредставленные классы (выросло macro recall). остальные показатели снизились. В задаче классификации новостных статей, по моему мнению, важным показателем является именно Precision. И в общем SVM все равно показал себя лучше, чем NB. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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