1_Content_base/content_base.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "df4e4dc5",
   "metadata": {},
   "source": [
    "### Описание задания\n",
    "\n",
    "1) Использовать датасет [MovieLens](https://grouplens.org/datasets/movielens/latest/).\n",
    "\n",
    "2) Построить рекомендации (регрессия, предсказываем оценку) на фичах:\n",
    "\n",
    "    **a)** TF-IDF на тегах и жанрах;\n",
    "    \n",
    "    **b)** средние оценки (+ median, variance и т. д.) пользователя и фильма.\n",
    "\n",
    "3) Оценить RMSE на тестовой выборке."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "b9f4a1ac",
   "metadata": {},
   "outputs": [],
   "source": [
    "import wget\n",
    "import zipfile\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from tqdm.notebook import tqdm\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "from sklearn.linear_model import Ridge\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "521a26f5",
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset = 'ml-latest-small'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "57b379e9",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100% [............................................................................] 978202 / 978202"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'MovieLens (2).zip'"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "url = f'https://files.grouplens.org/datasets/movielens/{dataset}.zip'\n",
    "wget.download(url, 'MovieLens.zip')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "4ddd43ab",
   "metadata": {},
   "outputs": [],
   "source": [
    "with zipfile.ZipFile(\"MovieLens.zip\",\"r\") as zip_ref:\n",
    "    zip_ref.extractall()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "7178e8f7",
   "metadata": {},
   "outputs": [],
   "source": [
    "links = pd.read_csv(f'./{dataset}/links.csv')\n",
    "links.name = 'links'\n",
    "movies = pd.read_csv(f'./{dataset}/movies.csv')\n",
    "movies.name = 'movies'\n",
    "ratings = pd.read_csv(f'./{dataset}/ratings.csv')\n",
    "ratings.name = 'ratings'\n",
    "tags = pd.read_csv(f'./{dataset}/tags.csv')\n",
    "tags.name = 'tags'"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "12194ac5",
   "metadata": {},
   "source": [
    "### Первичный анализ данных"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "37fd3431",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_analises(dataset) -> None:\n",
    "    print(dataset.name)\n",
    "    dataset.info()\n",
    "    print(f'Дублирующих записей: {dataset.duplicated().sum()}')\n",
    "    print('------------')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "d8e9f5d8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "links\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 9742 entries, 0 to 9741\n",
      "Data columns (total 3 columns):\n",
      " #   Column   Non-Null Count  Dtype  \n",
      "---  ------   --------------  -----  \n",
      " 0   movieId  9742 non-null   int64  \n",
      " 1   imdbId   9742 non-null   int64  \n",
      " 2   tmdbId   9734 non-null   float64\n",
      "dtypes: float64(1), int64(2)\n",
      "memory usage: 228.5 KB\n",
      "Дублирующих записей: 0\n",
      "------------\n",
      "movies\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 9742 entries, 0 to 9741\n",
      "Data columns (total 3 columns):\n",
      " #   Column   Non-Null Count  Dtype \n",
      "---  ------   --------------  ----- \n",
      " 0   movieId  9742 non-null   int64 \n",
      " 1   title    9742 non-null   object\n",
      " 2   genres   9742 non-null   object\n",
      "dtypes: int64(1), object(2)\n",
      "memory usage: 228.5+ KB\n",
      "Дублирующих записей: 0\n",
      "------------\n",
      "ratings\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 100836 entries, 0 to 100835\n",
      "Data columns (total 4 columns):\n",
      " #   Column     Non-Null Count   Dtype  \n",
      "---  ------     --------------   -----  \n",
      " 0   userId     100836 non-null  int64  \n",
      " 1   movieId    100836 non-null  int64  \n",
      " 2   rating     100836 non-null  float64\n",
      " 3   timestamp  100836 non-null  int64  \n",
      "dtypes: float64(1), int64(3)\n",
      "memory usage: 3.1 MB\n",
      "Дублирующих записей: 0\n",
      "------------\n",
      "tags\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 3683 entries, 0 to 3682\n",
      "Data columns (total 4 columns):\n",
      " #   Column     Non-Null Count  Dtype \n",
      "---  ------     --------------  ----- \n",
      " 0   userId     3683 non-null   int64 \n",
      " 1   movieId    3683 non-null   int64 \n",
      " 2   tag        3683 non-null   object\n",
      " 3   timestamp  3683 non-null   int64 \n",
      "dtypes: int64(3), object(1)\n",
      "memory usage: 115.2+ KB\n",
      "Дублирующих записей: 0\n",
      "------------\n"
     ]
    }
   ],
   "source": [
    "get_analises(links)\n",
    "get_analises(movies)\n",
    "get_analises(ratings)\n",
    "get_analises(tags)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aa759936",
   "metadata": {},
   "source": [
    "**Пользователи**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "ebc2c9e2",
   "metadata": {},
   "outputs": [],
   "source": [
    "grouped_users = tags['userId'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "ee54398d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Уникальных пользователей в датасете: 58\n"
     ]
    }
   ],
   "source": [
    "print(f'Уникальных пользователей в датасете: {len(grouped_users)}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "9dddf931",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=[8,6])\n",
    "grouped_users.hist(bins=20)\n",
    "plt.title('Распределение по количеству проставленных тегов среди пользователей')\n",
    "plt.xlabel('Число пользователей')\n",
    "plt.xlabel('Количество проставленных тегов')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0725f69c",
   "metadata": {},
   "source": [
    "**Фильмы**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "a85efb90",
   "metadata": {},
   "outputs": [],
   "source": [
    "grouped_movies = tags['movieId'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "036460b0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Уникальных фильмов в датасете: 1572\n"
     ]
    }
   ],
   "source": [
    "print(f'Уникальных фильмов в датасете: {len(grouped_movies)}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "725b82a0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=[8,6])\n",
    "grouped_movies.hist(bins=20)\n",
    "plt.title('Распределение по количеству проставленных тегов в фильмах')\n",
    "plt.xlabel('Число тегов')\n",
    "plt.xlabel('Количество фильмов')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3149fb6f",
   "metadata": {},
   "source": [
    "### Произведём преобразование данных в вектора (TF-IDF) для жанров и тегов"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "c4086323",
   "metadata": {},
   "outputs": [],
   "source": [
    "def change_string(s):\n",
    "    return ' '.join(s.replace(' ', '').replace('-', '').split('|'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "e424aef1",
   "metadata": {},
   "outputs": [],
   "source": [
    "movie_genres = [change_string(value) for value in movies.genres.values]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "fc92d2b8",
   "metadata": {},
   "outputs": [],
   "source": [
    "tfidf = TfidfVectorizer()\n",
    "movie_train_tfidf = tfidf.fit_transform(movie_genres)\n",
    "movie_train_tfidf = pd.DataFrame(movie_train_tfidf.toarray(), columns=tfidf.get_feature_names_out())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "a65092e6",
   "metadata": {},
   "outputs": [],
   "source": [
    "movies = movies.drop(columns=['genres', 'title'])\n",
    "movies = pd.concat([movies, movie_train_tfidf], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "3412776c",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>movieId</th>\n",
       "      <th>action</th>\n",
       "      <th>adventure</th>\n",
       "      <th>animation</th>\n",
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       "      <th>comedy</th>\n",
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       "      <th>drama</th>\n",
       "      <th>fantasy</th>\n",
       "      <th>...</th>\n",
       "      <th>horror</th>\n",
       "      <th>imax</th>\n",
       "      <th>musical</th>\n",
       "      <th>mystery</th>\n",
       "      <th>nogenreslisted</th>\n",
       "      <th>romance</th>\n",
       "      <th>scifi</th>\n",
       "      <th>thriller</th>\n",
       "      <th>war</th>\n",
       "      <th>western</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "<p>5 rows × 21 columns</p>\n",
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      "text/plain": [
       "   movieId  action  adventure  animation  children    comedy  crime  \\\n",
       "0        1     0.0   0.416846   0.516225  0.504845  0.267586    0.0   \n",
       "1        2     0.0   0.512361   0.000000  0.620525  0.000000    0.0   \n",
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       "4        5     0.0   0.000000   0.000000  0.000000  1.000000    0.0   \n",
       "\n",
       "   documentary     drama   fantasy  ...  horror  imax  musical  mystery  \\\n",
       "0          0.0  0.000000  0.482990  ...     0.0   0.0      0.0      0.0   \n",
       "1          0.0  0.000000  0.593662  ...     0.0   0.0      0.0      0.0   \n",
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       "4          0.0  0.000000  0.000000  ...     0.0   0.0      0.0      0.0   \n",
       "\n",
       "   nogenreslisted   romance  scifi  thriller  war  western  \n",
       "0             0.0  0.000000    0.0       0.0  0.0      0.0  \n",
       "1             0.0  0.000000    0.0       0.0  0.0      0.0  \n",
       "2             0.0  0.821009    0.0       0.0  0.0      0.0  \n",
       "3             0.0  0.726241    0.0       0.0  0.0      0.0  \n",
       "4             0.0  0.000000    0.0       0.0  0.0      0.0  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movies.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "7e2ed27a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "9b3d66483915402089708c725fda93da",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/1572 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tag_strings = []\n",
    "new_movies = []\n",
    "\n",
    "for movie, group in tqdm(tags.groupby('movieId')):\n",
    "    tag_strings.append(' '.join(set([change_string(str(s)) for s in group.tag.values])))\n",
    "    new_movies.append(movie)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "38f54261",
   "metadata": {},
   "source": [
    "Добавим set, чтобы у нас остались только уникальные теги"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "86e469e5",
   "metadata": {},
   "outputs": [],
   "source": [
    "tfidf_tag = TfidfVectorizer()\n",
    "tag_train_tfidf = tfidf_tag.fit_transform(tag_strings)\n",
    "tag_train_tfidf = pd.DataFrame(tag_train_tfidf.toarray(), columns=tfidf_tag.get_feature_names_out())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "e3eff877",
   "metadata": {},
   "outputs": [
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       "   movieId  06oscarnominatedbestmovieanimation  1900s  1920s  1950s  1960s  \\\n",
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     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "new_tags = pd.concat([pd.DataFrame(new_movies, columns=['movieId']), tag_train_tfidf], axis=1)\n",
    "new_tags.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "8be3352a",
   "metadata": {},
   "outputs": [
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       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 1493 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   movieId  action_x  adventure_x  animation_x  children_x  comedy_x  crime_x  \\\n",
       "0        1       0.0     0.416846     0.516225    0.504845  0.267586      0.0   \n",
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       "4        7       0.0     0.000000     0.000000    0.000000  0.570915      0.0   \n",
       "\n",
       "   documentary_x  drama_x  fantasy_x  ...  worldwari  worldwarii  writing  \\\n",
       "0            0.0      0.0   0.482990  ...        0.0         0.0      0.0   \n",
       "1            0.0      0.0   0.593662  ...        0.0         0.0      0.0   \n",
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       "4            0.0      0.0   0.000000  ...        0.0         0.0      0.0   \n",
       "\n",
       "   wrongfulimprisonment  wry  youngermen  zither  zoekazan  zombies  \\\n",
       "0                   0.0  0.0         0.0     0.0       0.0      0.0   \n",
       "1                   0.0  0.0         0.0     0.0       0.0      0.0   \n",
       "2                   0.0  0.0         0.0     0.0       0.0      0.0   \n",
       "3                   0.0  0.0         0.0     0.0       0.0      0.0   \n",
       "4                   0.0  0.0         0.0     0.0       0.0      0.0   \n",
       "\n",
       "   zooeydeschanel  \n",
       "0             0.0  \n",
       "1             0.0  \n",
       "2             0.0  \n",
       "3             0.0  \n",
       "4             0.0  \n",
       "\n",
       "[5 rows x 1493 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movies_with_tags = movies.merge(new_tags, on='movieId')\n",
    "movies_with_tags.dropna(inplace=True)\n",
    "movies_with_tags.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5fae42b7",
   "metadata": {},
   "source": [
    "### Добавим в датасет новые признаки:\n",
    "- среднюю оценку фильма\n",
    "- среднюю оценку пользователя"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "6195a5fa",
   "metadata": {},
   "outputs": [
    {
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       "   movieId  action_x  adventure_x  animation_x  children_x  comedy_x  crime_x  \\\n",
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       "\n",
       "[5 rows x 1495 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movies_with_ratings = movies_with_tags.merge(ratings, on='movieId').drop(columns=['timestamp'])\n",
    "movies_with_ratings.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "3fcc1dea",
   "metadata": {},
   "outputs": [],
   "source": [
    "mean_movie_rating = movies_with_ratings.groupby('movieId')['rating'].mean()\n",
    "mean_user_rating = movies_with_ratings.groupby('userId')['rating'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "2e1add41",
   "metadata": {},
   "outputs": [],
   "source": [
    "movies_with_ratings['mean_movie_rating'] = movies_with_ratings['movieId'].apply(lambda x: mean_movie_rating[x])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "749e3c65",
   "metadata": {},
   "outputs": [],
   "source": [
    "movies_with_ratings['mean_user_rating'] = movies_with_ratings['userId'].apply(lambda x: mean_user_rating[x])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "74b25d81",
   "metadata": {},
   "outputs": [
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       "   movieId  action_x  adventure_x  animation_x  children_x  comedy_x  crime_x  \\\n",
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       "\n",
       "   documentary_x  drama_x  fantasy_x  ...  wry  youngermen  zither  zoekazan  \\\n",
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       "4            0.0      0.0    0.48299  ...  0.0         0.0     0.0       0.0   \n",
       "\n",
       "   zombies  zooeydeschanel  userId  rating  mean_movie_rating  \\\n",
       "0      0.0             0.0       1     4.0            3.92093   \n",
       "1      0.0             0.0       5     4.0            3.92093   \n",
       "2      0.0             0.0       7     4.5            3.92093   \n",
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       "4      0.0             0.0      17     4.5            3.92093   \n",
       "\n",
       "   mean_user_rating  \n",
       "0          4.403509  \n",
       "1          3.743590  \n",
       "2          3.543956  \n",
       "3          3.562500  \n",
       "4          4.279070  \n",
       "\n",
       "[5 rows x 1497 columns]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movies_with_ratings.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b80e4b1b",
   "metadata": {},
   "source": [
    "### Обучение модели с TF-IDF"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "37aa8a9e",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = movies_with_ratings.drop(columns=['rating'])\n",
    "y = pd.DataFrame(movies_with_ratings['rating'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "9d2fd2cf",
   "metadata": {},
   "outputs": [],
   "source": [
    "sc_x = StandardScaler()\n",
    "sc_y = StandardScaler()\n",
    "X_std = sc_x.fit_transform(X)\n",
    "y_std = sc_y.fit_transform(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "9a9ca770",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X_std, y_std, test_size=0.2, random_state=21)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "f46b52e9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-3 {color: black;background-color: white;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-3 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-3 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Ridge(alpha=0.1)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Ridge</label><div class=\"sk-toggleable__content\"><pre>Ridge(alpha=0.1)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "Ridge(alpha=0.1)"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = Ridge(alpha=0.1)\n",
    "model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "629ba8cf",
   "metadata": {},
   "outputs": [],
   "source": [
    "pred = model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "5e45c938",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.796173241695998"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sqrt(mean_squared_error(y_test, pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "22d881cc",
   "metadata": {},
   "source": [
    "### Обучение модели без TF-IDF"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "c1ece494",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = movies_with_ratings[['movieId', 'userId', 'mean_movie_rating', 'mean_user_rating']]\n",
    "y = movies_with_ratings['rating']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "e2523be0",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=21)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "1bb1df2c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-4 {color: black;background-color: white;}#sk-container-id-4 pre{padding: 0;}#sk-container-id-4 div.sk-toggleable {background-color: white;}#sk-container-id-4 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-4 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-4 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-4 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-4 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-4 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-4 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-4 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-4 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-4 div.sk-item {position: relative;z-index: 1;}#sk-container-id-4 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-4 div.sk-item::before, #sk-container-id-4 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-4 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-4 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-4 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-4 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-4 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-4 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-4 div.sk-label-container {text-align: center;}#sk-container-id-4 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-4 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" checked><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearRegression</label><div class=\"sk-toggleable__content\"><pre>LinearRegression()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = LinearRegression()\n",
    "model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "29283c93",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7961733250369376"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred = model.predict(X_test)\n",
    "np.sqrt(mean_squared_error(y_test, pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ee614c0c",
   "metadata": {},
   "source": [
    "## Выводы:\n",
    "\n",
    "Были произведены преобразования данных в вектора (TF-IDF) для признаков genre и tag. Также были добавлены новые признаки средняя оценка для фильма и средняя оценка пользователя. На основе них были обучены линейные модели как с использованием преобразованных данных TF-IDF, так и только на основании средних оценок. в качестве метрики для оценки качества модели использовался RMSE. \n",
    "\n",
    "Были получены почти идентичные значения:\n",
    "\n",
    "- модель с TF-IDF: RMSE = 0.796173241695998\n",
    "- модель без TF-IDF: RMSE = 0.7961733250369376\n",
    "\n",
    "Можно сказать, что мы получили достаточно неплохое качество модели для предсказания рейтинга, выставленное пользователем фильму."
   ]
  }
 ],
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