hybrid.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Загружаем данные"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-09-09T16:35:46.937036Z",
     "start_time": "2019-09-09T16:35:46.380599Z"
    }
   },
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-09-09T17:00:47.649875Z",
     "start_time": "2019-09-09T17:00:47.646861Z"
    }
   },
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-09-09T16:36:10.044052Z",
     "start_time": "2019-09-09T16:36:09.950303Z"
    }
   },
   "outputs": [],
   "source": [
    "df_movies = pd.read_csv(\"./netology-recsys-master/lecture-1/movies.csv\")\n",
    "df_ratings = pd.read_csv(\"./netology-recsys-master/lecture-1/ratings.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-09-09T16:36:15.660239Z",
     "start_time": "2019-09-09T16:36:15.634327Z"
    }
   },
   "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>movieId</th>\n",
       "      <th>title</th>\n",
       "      <th>genres</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Toy Story (1995)</td>\n",
       "      <td>Adventure|Animation|Children|Comedy|Fantasy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Jumanji (1995)</td>\n",
       "      <td>Adventure|Children|Fantasy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>Grumpier Old Men (1995)</td>\n",
       "      <td>Comedy|Romance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>Waiting to Exhale (1995)</td>\n",
       "      <td>Comedy|Drama|Romance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>Father of the Bride Part II (1995)</td>\n",
       "      <td>Comedy</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   movieId                               title  \\\n",
       "0        1                    Toy Story (1995)   \n",
       "1        2                      Jumanji (1995)   \n",
       "2        3             Grumpier Old Men (1995)   \n",
       "3        4            Waiting to Exhale (1995)   \n",
       "4        5  Father of the Bride Part II (1995)   \n",
       "\n",
       "                                        genres  \n",
       "0  Adventure|Animation|Children|Comedy|Fantasy  \n",
       "1                   Adventure|Children|Fantasy  \n",
       "2                               Comedy|Romance  \n",
       "3                         Comedy|Drama|Romance  \n",
       "4                                       Comedy  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_movies.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-09-09T16:36:21.883419Z",
     "start_time": "2019-09-09T16:36:21.874961Z"
    }
   },
   "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>userId</th>\n",
       "      <th>movieId</th>\n",
       "      <th>rating</th>\n",
       "      <th>timestamp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4.0</td>\n",
       "      <td>964982703</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>4.0</td>\n",
       "      <td>964981247</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>4.0</td>\n",
       "      <td>964982224</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>47</td>\n",
       "      <td>5.0</td>\n",
       "      <td>964983815</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>50</td>\n",
       "      <td>5.0</td>\n",
       "      <td>964982931</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   userId  movieId  rating  timestamp\n",
       "0       1        1     4.0  964982703\n",
       "1       1        3     4.0  964981247\n",
       "2       1        6     4.0  964982224\n",
       "3       1       47     5.0  964983815\n",
       "4       1       50     5.0  964982931"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_ratings.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-09-09T17:05:21.491622Z",
     "start_time": "2019-09-09T17:05:21.466549Z"
    }
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Строим рекомендацию на основе KNN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-09-09T17:02:44.644936Z",
     "start_time": "2019-09-09T17:02:44.642469Z"
    }
   },
   "outputs": [],
   "source": [
    "from tqdm import tqdm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-09-09T17:02:45.267399Z",
     "start_time": "2019-09-09T17:02:45.264919Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/siak/.local/lib/python3.7/site-packages/tqdm/_tqdm.py:634: FutureWarning: The Panel class is removed from pandas. Accessing it from the top-level namespace will also be removed in the next version\n",
      "  from pandas import Panel\n"
     ]
    }
   ],
   "source": [
    "tqdm.pandas()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-09-09T17:02:45.657257Z",
     "start_time": "2019-09-09T17:02:45.654888Z"
    }
   },
   "outputs": [],
   "source": [
    "df = df_movies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-09-09T17:02:46.088656Z",
     "start_time": "2019-09-09T17:02:46.080637Z"
    }
   },
   "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>movieId</th>\n",
       "      <th>title</th>\n",
       "      <th>genres</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Toy Story (1995)</td>\n",
       "      <td>Adventure|Animation|Children|Comedy|Fantasy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Jumanji (1995)</td>\n",
       "      <td>Adventure|Children|Fantasy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>Grumpier Old Men (1995)</td>\n",
       "      <td>Comedy|Romance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>Waiting to Exhale (1995)</td>\n",
       "      <td>Comedy|Drama|Romance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>Father of the Bride Part II (1995)</td>\n",
       "      <td>Comedy</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   movieId                               title  \\\n",
       "0        1                    Toy Story (1995)   \n",
       "1        2                      Jumanji (1995)   \n",
       "2        3             Grumpier Old Men (1995)   \n",
       "3        4            Waiting to Exhale (1995)   \n",
       "4        5  Father of the Bride Part II (1995)   \n",
       "\n",
       "                                        genres  \n",
       "0  Adventure|Animation|Children|Comedy|Fantasy  \n",
       "1                   Adventure|Children|Fantasy  \n",
       "2                               Comedy|Romance  \n",
       "3                         Comedy|Drama|Romance  \n",
       "4                                       Comedy  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-09-09T17:02:51.520901Z",
     "start_time": "2019-09-09T17:02:51.509013Z"
    }
   },
   "outputs": [],
   "source": [
    "df['genres_splitted'] = df.genres.str.split('|')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-09-09T17:02:52.850119Z",
     "start_time": "2019-09-09T17:02:52.679836Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 9742/9742 [00:00<00:00, 59220.51it/s]\n"
     ]
    }
   ],
   "source": [
    "df['genres_spaced'] = df.progress_apply(lambda r: ' '.join(r['genres_splitted']), axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-09-09T17:02:53.863908Z",
     "start_time": "2019-09-09T17:02:53.853174Z"
    }
   },
   "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>movieId</th>\n",
       "      <th>title</th>\n",
       "      <th>genres</th>\n",
       "      <th>genres_splitted</th>\n",
       "      <th>genres_spaced</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Toy Story (1995)</td>\n",
       "      <td>Adventure|Animation|Children|Comedy|Fantasy</td>\n",
       "      <td>[Adventure, Animation, Children, Comedy, Fantasy]</td>\n",
       "      <td>Adventure Animation Children Comedy Fantasy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Jumanji (1995)</td>\n",
       "      <td>Adventure|Children|Fantasy</td>\n",
       "      <td>[Adventure, Children, Fantasy]</td>\n",
       "      <td>Adventure Children Fantasy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>Grumpier Old Men (1995)</td>\n",
       "      <td>Comedy|Romance</td>\n",
       "      <td>[Comedy, Romance]</td>\n",
       "      <td>Comedy Romance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>Waiting to Exhale (1995)</td>\n",
       "      <td>Comedy|Drama|Romance</td>\n",
       "      <td>[Comedy, Drama, Romance]</td>\n",
       "      <td>Comedy Drama Romance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>Father of the Bride Part II (1995)</td>\n",
       "      <td>Comedy</td>\n",
       "      <td>[Comedy]</td>\n",
       "      <td>Comedy</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   movieId                               title  \\\n",
       "0        1                    Toy Story (1995)   \n",
       "1        2                      Jumanji (1995)   \n",
       "2        3             Grumpier Old Men (1995)   \n",
       "3        4            Waiting to Exhale (1995)   \n",
       "4        5  Father of the Bride Part II (1995)   \n",
       "\n",
       "                                        genres  \\\n",
       "0  Adventure|Animation|Children|Comedy|Fantasy   \n",
       "1                   Adventure|Children|Fantasy   \n",
       "2                               Comedy|Romance   \n",
       "3                         Comedy|Drama|Romance   \n",
       "4                                       Comedy   \n",
       "\n",
       "                                     genres_splitted  \\\n",
       "0  [Adventure, Animation, Children, Comedy, Fantasy]   \n",
       "1                     [Adventure, Children, Fantasy]   \n",
       "2                                  [Comedy, Romance]   \n",
       "3                           [Comedy, Drama, Romance]   \n",
       "4                                           [Comedy]   \n",
       "\n",
       "                                 genres_spaced  \n",
       "0  Adventure Animation Children Comedy Fantasy  \n",
       "1                   Adventure Children Fantasy  \n",
       "2                               Comedy Romance  \n",
       "3                         Comedy Drama Romance  \n",
       "4                                       Comedy  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-09-09T17:02:54.706551Z",
     "start_time": "2019-09-09T17:02:54.703520Z"
    }
   },
   "outputs": [],
   "source": [
    "from sklearn.feature_extraction.text import TfidfVectorizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-09-09T17:02:55.428035Z",
     "start_time": "2019-09-09T17:02:55.425244Z"
    }
   },
   "outputs": [],
   "source": [
    "tfidf = TfidfVectorizer()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "features = tfidf.fit_transform(df['genres_spaced'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-09-09T17:03:09.780766Z",
     "start_time": "2019-09-09T17:03:09.777867Z"
    }
   },
   "outputs": [],
   "source": [
    "columns = [(k, tfidf.vocabulary_[k]) for k in tfidf.vocabulary_]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-09-09T17:03:11.052399Z",
     "start_time": "2019-09-09T17:03:11.049288Z"
    }
   },
   "outputs": [],
   "source": [
    "columns = sorted(columns, key=lambda c: c[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-09-09T17:03:11.731535Z",
     "start_time": "2019-09-09T17:03:11.728774Z"
    }
   },
   "outputs": [],
   "source": [
    "columns = [c[0] for c in columns]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-09-09T17:03:14.985436Z",
     "start_time": "2019-09-09T17:03:14.982401Z"
    }
   },
   "outputs": [],
   "source": [
    "features = features.todense()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-09-09T17:03:16.273823Z",
     "start_time": "2019-09-09T17:03:16.269990Z"
    }
   },
   "outputs": [],
   "source": [
    "df_features = pd.DataFrame(features, columns=columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-09-09T17:03:17.394321Z",
     "start_time": "2019-09-09T17:03:17.386397Z"
    }
   },
   "outputs": [],
   "source": [
    "df_result = pd.concat((df, df_features), axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-09-09T17:03:20.541816Z",
     "start_time": "2019-09-09T17:03:20.537185Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['movieId', 'title', 'genres', 'genres_splitted', 'genres_spaced',\n",
       "       'action', 'adventure', 'animation', 'children', 'comedy', 'crime',\n",
       "       'documentary', 'drama', 'fantasy', 'fi', 'film', 'genres', 'horror',\n",
       "       'imax', 'listed', 'musical', 'mystery', 'no', 'noir', 'romance', 'sci',\n",
       "       'thriller', 'war', 'western'],\n",
       "      dtype='object')"
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       "      movieId                                      title  \\\n",
       "0           1                           Toy Story (1995)   \n",
       "1           2                             Jumanji (1995)   \n",
       "2           3                    Grumpier Old Men (1995)   \n",
       "3           4                   Waiting to Exhale (1995)   \n",
       "4           5         Father of the Bride Part II (1995)   \n",
       "...       ...                                        ...   \n",
       "9737   193581  Black Butler: Book of the Atlantic (2017)   \n",
       "9738   193583               No Game No Life: Zero (2017)   \n",
       "9739   193585                               Flint (2017)   \n",
       "9740   193587        Bungo Stray Dogs: Dead Apple (2018)   \n",
       "9741   193609        Andrew Dice Clay: Dice Rules (1991)   \n",
       "\n",
       "                                           genres  \\\n",
       "0     Adventure|Animation|Children|Comedy|Fantasy   \n",
       "1                      Adventure|Children|Fantasy   \n",
       "2                                  Comedy|Romance   \n",
       "3                            Comedy|Drama|Romance   \n",
       "4                                          Comedy   \n",
       "...                                           ...   \n",
       "9737              Action|Animation|Comedy|Fantasy   \n",
       "9738                     Animation|Comedy|Fantasy   \n",
       "9739                                        Drama   \n",
       "9740                             Action|Animation   \n",
       "9741                                       Comedy   \n",
       "\n",
       "                                        genres_splitted  \\\n",
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       "1                        [Adventure, Children, Fantasy]   \n",
       "2                                     [Comedy, Romance]   \n",
       "3                              [Comedy, Drama, Romance]   \n",
       "4                                              [Comedy]   \n",
       "...                                                 ...   \n",
       "9737               [Action, Animation, Comedy, Fantasy]   \n",
       "9738                       [Animation, Comedy, Fantasy]   \n",
       "9739                                            [Drama]   \n",
       "9740                                [Action, Animation]   \n",
       "9741                                           [Comedy]   \n",
       "\n",
       "                                    genres_spaced    action  adventure  \\\n",
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       "1                      Adventure Children Fantasy  0.000000   0.512361   \n",
       "2                                  Comedy Romance  0.000000   0.000000   \n",
       "3                            Comedy Drama Romance  0.000000   0.000000   \n",
       "4                                          Comedy  0.000000   0.000000   \n",
       "...                                           ...       ...        ...   \n",
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       "9738                     Animation Comedy Fantasy  0.000000   0.000000   \n",
       "9739                                        Drama  0.000000   0.000000   \n",
       "9740                             Action Animation  0.578606   0.000000   \n",
       "9741                                       Comedy  0.000000   0.000000   \n",
       "\n",
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       "9741   0.000000  0.000000  1.000000  ...     0.0      0.0      0.0  0.0   0.0   \n",
       "\n",
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       "\n",
       "[9742 rows x 29 columns]"
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     "metadata": {},
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  {
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   "metadata": {
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     "end_time": "2019-09-09T17:04:00.210702Z",
     "start_time": "2019-09-09T17:04:00.204264Z"
    }
   },
   "outputs": [],
   "source": [
    "df_result = df_result[[\n",
    "       'movieId', 'title', 'action', 'adventure', 'animation',\n",
    "       'children', 'comedy', 'crime', 'documentary', 'drama', 'fantasy', 'fi',\n",
    "       'film', 'genres', 'horror', 'imax', 'listed', 'musical', 'mystery',\n",
    "       'no', 'noir', 'romance', 'sci', 'thriller', 'war', 'western'   \n",
    "]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-09-09T17:04:01.507770Z",
     "start_time": "2019-09-09T17:04:01.484400Z"
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Jumanji (1995)</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.512361</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.620525</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>Grumpier Old Men (1995)</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.570915</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.821009</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>Waiting to Exhale (1995)</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.505015</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.466405</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.726241</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>Father of the Bride Part II (1995)</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 27 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   movieId                               title  action  adventure  animation  \\\n",
       "0        1                    Toy Story (1995)     0.0   0.416846   0.516225   \n",
       "1        2                      Jumanji (1995)     0.0   0.512361   0.000000   \n",
       "2        3             Grumpier Old Men (1995)     0.0   0.000000   0.000000   \n",
       "3        4            Waiting to Exhale (1995)     0.0   0.000000   0.000000   \n",
       "4        5  Father of the Bride Part II (1995)     0.0   0.000000   0.000000   \n",
       "\n",
       "   children    comedy  crime  documentary     drama  ...  listed  musical  \\\n",
       "0  0.504845  0.267586    0.0          0.0  0.000000  ...     0.0      0.0   \n",
       "1  0.620525  0.000000    0.0          0.0  0.000000  ...     0.0      0.0   \n",
       "2  0.000000  0.570915    0.0          0.0  0.000000  ...     0.0      0.0   \n",
       "3  0.000000  0.505015    0.0          0.0  0.466405  ...     0.0      0.0   \n",
       "4  0.000000  1.000000    0.0          0.0  0.000000  ...     0.0      0.0   \n",
       "\n",
       "   mystery   no  noir   romance  sci  thriller  war  western  \n",
       "0      0.0  0.0   0.0  0.000000  0.0       0.0  0.0      0.0  \n",
       "1      0.0  0.0   0.0  0.000000  0.0       0.0  0.0      0.0  \n",
       "2      0.0  0.0   0.0  0.821009  0.0       0.0  0.0      0.0  \n",
       "3      0.0  0.0   0.0  0.726241  0.0       0.0  0.0      0.0  \n",
       "4      0.0  0.0   0.0  0.000000  0.0       0.0  0.0      0.0  \n",
       "\n",
       "[5 rows x 27 columns]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_result.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-09-09T17:04:02.672663Z",
     "start_time": "2019-09-09T17:04:02.668421Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9742"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df_result)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-09-09T17:04:04.613139Z",
     "start_time": "2019-09-09T17:04:04.610552Z"
    }
   },
   "outputs": [],
   "source": [
    "from sklearn.neighbors import NearestNeighbors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-09-09T17:04:05.073076Z",
     "start_time": "2019-09-09T17:04:05.070136Z"
    }
   },
   "outputs": [],
   "source": [
    "nn = NearestNeighbors(n_neighbors=50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-09-09T17:04:05.885971Z",
     "start_time": "2019-09-09T17:04:05.761703Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "NearestNeighbors(algorithm='auto', leaf_size=30, metric='minkowski',\n",
       "                 metric_params=None, n_jobs=None, n_neighbors=50, p=2,\n",
       "                 radius=1.0)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nn.fit(df_result[['action', 'adventure', 'animation',\n",
    "       'children', 'comedy', 'crime', 'documentary', 'drama', 'fantasy', 'fi',\n",
    "       'film', 'horror', 'imax', 'listed', 'musical', 'mystery',\n",
    "       'no', 'noir', 'romance', 'sci', 'thriller', 'war', 'western']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-09-09T17:09:28.468468Z",
     "start_time": "2019-09-09T17:09:28.415019Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
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       "    .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>movieId</th>\n",
       "      <th>title</th>\n",
       "      <th>action</th>\n",
       "      <th>adventure</th>\n",
       "      <th>animation</th>\n",
       "      <th>children</th>\n",
       "      <th>comedy</th>\n",
       "      <th>crime</th>\n",
       "      <th>documentary</th>\n",
       "      <th>drama</th>\n",
       "      <th>...</th>\n",
       "      <th>listed</th>\n",
       "      <th>musical</th>\n",
       "      <th>mystery</th>\n",
       "      <th>no</th>\n",
       "      <th>noir</th>\n",
       "      <th>romance</th>\n",
       "      <th>sci</th>\n",
       "      <th>thriller</th>\n",
       "      <th>war</th>\n",
       "      <th>western</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Jumanji (1995)</td>\n",
       "      <td>0.000000</td>\n",
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       "    <tr>\n",
       "      <td>2</td>\n",
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       "      <td>Grumpier Old Men (1995)</td>\n",
       "      <td>0.000000</td>\n",
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       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>Waiting to Exhale (1995)</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.505015</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>Father of the Bride Part II (1995)</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>...</td>\n",
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       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9737</td>\n",
       "      <td>193581</td>\n",
       "      <td>Black Butler: Book of the Atlantic (2017)</td>\n",
       "      <td>0.436010</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.614603</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.318581</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
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       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9738</td>\n",
       "      <td>193583</td>\n",
       "      <td>No Game No Life: Zero (2017)</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.682937</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.354002</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9739</td>\n",
       "      <td>193585</td>\n",
       "      <td>Flint (2017)</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <td>9740</td>\n",
       "      <td>193587</td>\n",
       "      <td>Bungo Stray Dogs: Dead Apple (2018)</td>\n",
       "      <td>0.578606</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.815607</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <td>9741</td>\n",
       "      <td>193609</td>\n",
       "      <td>Andrew Dice Clay: Dice Rules (1991)</td>\n",
       "      <td>0.000000</td>\n",
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       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>9742 rows × 27 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      movieId                                      title    action  adventure  \\\n",
       "0           1                           Toy Story (1995)  0.000000   0.416846   \n",
       "1           2                             Jumanji (1995)  0.000000   0.512361   \n",
       "2           3                    Grumpier Old Men (1995)  0.000000   0.000000   \n",
       "3           4                   Waiting to Exhale (1995)  0.000000   0.000000   \n",
       "4           5         Father of the Bride Part II (1995)  0.000000   0.000000   \n",
       "...       ...                                        ...       ...        ...   \n",
       "9737   193581  Black Butler: Book of the Atlantic (2017)  0.436010   0.000000   \n",
       "9738   193583               No Game No Life: Zero (2017)  0.000000   0.000000   \n",
       "9739   193585                               Flint (2017)  0.000000   0.000000   \n",
       "9740   193587        Bungo Stray Dogs: Dead Apple (2018)  0.578606   0.000000   \n",
       "9741   193609        Andrew Dice Clay: Dice Rules (1991)  0.000000   0.000000   \n",
       "\n",
       "      animation  children    comedy  crime  documentary     drama  ...  \\\n",
       "0      0.516225  0.504845  0.267586    0.0          0.0  0.000000  ...   \n",
       "1      0.000000  0.620525  0.000000    0.0          0.0  0.000000  ...   \n",
       "2      0.000000  0.000000  0.570915    0.0          0.0  0.000000  ...   \n",
       "3      0.000000  0.000000  0.505015    0.0          0.0  0.466405  ...   \n",
       "4      0.000000  0.000000  1.000000    0.0          0.0  0.000000  ...   \n",
       "...         ...       ...       ...    ...          ...       ...  ...   \n",
       "9737   0.614603  0.000000  0.318581    0.0          0.0  0.000000  ...   \n",
       "9738   0.682937  0.000000  0.354002    0.0          0.0  0.000000  ...   \n",
       "9739   0.000000  0.000000  0.000000    0.0          0.0  1.000000  ...   \n",
       "9740   0.815607  0.000000  0.000000    0.0          0.0  0.000000  ...   \n",
       "9741   0.000000  0.000000  1.000000    0.0          0.0  0.000000  ...   \n",
       "\n",
       "      listed  musical  mystery   no  noir   romance  sci  thriller  war  \\\n",
       "0        0.0      0.0      0.0  0.0   0.0  0.000000  0.0       0.0  0.0   \n",
       "1        0.0      0.0      0.0  0.0   0.0  0.000000  0.0       0.0  0.0   \n",
       "2        0.0      0.0      0.0  0.0   0.0  0.821009  0.0       0.0  0.0   \n",
       "3        0.0      0.0      0.0  0.0   0.0  0.726241  0.0       0.0  0.0   \n",
       "4        0.0      0.0      0.0  0.0   0.0  0.000000  0.0       0.0  0.0   \n",
       "...      ...      ...      ...  ...   ...       ...  ...       ...  ...   \n",
       "9737     0.0      0.0      0.0  0.0   0.0  0.000000  0.0       0.0  0.0   \n",
       "9738     0.0      0.0      0.0  0.0   0.0  0.000000  0.0       0.0  0.0   \n",
       "9739     0.0      0.0      0.0  0.0   0.0  0.000000  0.0       0.0  0.0   \n",
       "9740     0.0      0.0      0.0  0.0   0.0  0.000000  0.0       0.0  0.0   \n",
       "9741     0.0      0.0      0.0  0.0   0.0  0.000000  0.0       0.0  0.0   \n",
       "\n",
       "      western  \n",
       "0         0.0  \n",
       "1         0.0  \n",
       "2         0.0  \n",
       "3         0.0  \n",
       "4         0.0  \n",
       "...       ...  \n",
       "9737      0.0  \n",
       "9738      0.0  \n",
       "9739      0.0  \n",
       "9740      0.0  \n",
       "9741      0.0  \n",
       "\n",
       "[9742 rows x 27 columns]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-09-09T17:06:44.687821Z",
     "start_time": "2019-09-09T17:06:44.623613Z"
    }
   },
   "outputs": [],
   "source": [
    "df_joined = df_ratings.merge(df_result, on='movieId')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(100836, 30)"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_joined.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(100836, 4)"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_ratings.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-09-09T17:04:09.057691Z",
     "start_time": "2019-09-09T17:04:09.055291Z"
    }
   },
   "outputs": [],
   "source": [
    "user_id = 320"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'mean_vector' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-42-66bfbb01dd90>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mnn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkneighbors\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmean_vector\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreshape\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m: name 'mean_vector' is not defined"
     ]
    }
   ],
   "source": [
    "nn.kneighbors(mean_vector.reshape(1, -1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-09-09T17:07:01.688283Z",
     "start_time": "2019-09-09T17:07:01.682962Z"
    }
   },
   "outputs": [],
   "source": [
    "def get_last_seven_films(user_id):\n",
    "    user_films = df_joined[df_joined['userId'] == user_id]\n",
    "    user_films = user_films.sort_values('timestamp', ascending=False)\n",
    "    last_7 = user_films.head(7)\n",
    "    \n",
    "    film_names = last_7['title'].values\n",
    "    film_vectors = last_7[['action', 'adventure', 'animation',\n",
    "       'children', 'comedy', 'crime', 'documentary', 'drama', 'fantasy', 'fi',\n",
    "       'film', 'horror', 'imax', 'listed', 'musical', 'mystery',\n",
    "       'no', 'noir', 'romance', 'sci', 'thriller', 'war', 'western']].values\n",
    "    \n",
    "    return film_names, film_vectors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-09-09T17:19:31.641783Z",
     "start_time": "2019-09-09T17:19:31.638383Z"
    }
   },
   "outputs": [],
   "source": [
    "def get_user_recomendation_by_knn(intereseted_films):\n",
    "    _, films = nn.kneighbors(mean_vector.reshape(1, -1))\n",
    "    return df_movies.iloc[films[0]]['movieId'].values\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-09-09T17:19:32.016020Z",
     "start_time": "2019-09-09T17:19:32.009308Z"
    }
   },
   "outputs": [],
   "source": [
    "names, vectors = get_last_seven_films(user_id)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-09-09T17:19:32.187325Z",
     "start_time": "2019-09-09T17:19:32.181726Z"
    }
   },
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'mean_vector' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-46-05826d90b5d2>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mget_user_recomendation_by_knn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmean\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvectors\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m<ipython-input-44-348c0f79af4f>\u001b[0m in \u001b[0;36mget_user_recomendation_by_knn\u001b[0;34m(intereseted_films)\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mget_user_recomendation_by_knn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mintereseted_films\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m     \u001b[0m_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfilms\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkneighbors\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmean_vector\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreshape\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mdf_movies\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0miloc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mfilms\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'movieId'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'mean_vector' is not defined"
     ]
    }
   ],
   "source": [
    "get_user_recomendation_by_knn(np.mean(vectors, axis=0))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Делаем SVD"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-09-09T17:20:38.563530Z",
     "start_time": "2019-09-09T17:20:38.496605Z"
    }
   },
   "outputs": [],
   "source": [
    "import surprise as s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-09-09T17:20:57.804992Z",
     "start_time": "2019-09-09T17:20:57.800502Z"
    }
   },
   "outputs": [],
   "source": [
    "df_for_surprise = df_ratings[['userId', 'movieId', 'rating']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-09-09T17:21:02.897685Z",
     "start_time": "2019-09-09T17:21:02.889740Z"
    }
   },
   "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>userId</th>\n",
       "      <th>movieId</th>\n",
       "      <th>rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>47</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>50</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   userId  movieId  rating\n",
       "0       1        1     4.0\n",
       "1       1        3     4.0\n",
       "2       1        6     4.0\n",
       "3       1       47     5.0\n",
       "4       1       50     5.0"
      ]
     },
     "execution_count": 147,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_for_surprise.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-09-09T17:21:17.173424Z",
     "start_time": "2019-09-09T17:21:17.170619Z"
    }
   },
   "outputs": [],
   "source": [
    "reader = s.reader.Reader(rating_scale=(0.5, 5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-09-09T17:22:03.332207Z",
     "start_time": "2019-09-09T17:22:03.239917Z"
    }
   },
   "outputs": [],
   "source": [
    "dataset = s.dataset.Dataset.load_from_df(df_for_surprise, reader)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-09-09T17:23:18.793781Z",
     "start_time": "2019-09-09T17:23:18.615410Z"
    }
   },
   "outputs": [],
   "source": [
    "dataset, _ = s.model_selection.train_test_split(dataset, test_size=0.01)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-09-09T17:23:19.882865Z",
     "start_time": "2019-09-09T17:23:19.880341Z"
    }
   },
   "outputs": [],
   "source": [
    "algorithm = s.SVD()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-09-09T17:23:25.462272Z",
     "start_time": "2019-09-09T17:23:20.615229Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<surprise.prediction_algorithms.matrix_factorization.SVD at 0x1a346ff588>"
      ]
     },
     "execution_count": 155,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "algorithm.fit(dataset)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Итоговая рекомендация"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-09-09T17:28:00.496549Z",
     "start_time": "2019-09-09T17:28:00.490873Z"
    }
   },
   "outputs": [],
   "source": [
    "def recomend(user_id):\n",
    "    names, vectors = get_last_seven_films(user_id)\n",
    "    knn_recomendations = get_user_recomendation_by_knn(np.mean(vectors, axis=0))\n",
    "    knn_recomendations = pd.DataFrame(knn_recomendations, columns=['movieId'])\n",
    "    knn_recomendations['Score'] = knn_recomendations.apply(lambda r: algorithm.predict(user_id, r['movieId']).est, axis=1)\n",
    "    knn_recomendations = knn_recomendations.sort_values('Score', ascending=False)\n",
    "    knn_recomendations = knn_recomendations.merge(df_movies, on='movieId')[['movieId', 'title', 'Score']]\n",
    "    \n",
    "    print(\"Last 7 films was: \")\n",
    "    print(names)\n",
    "    \n",
    "    print(\"Recomendations\")\n",
    "    print(knn_recomendations)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-09-09T17:28:01.025862Z",
     "start_time": "2019-09-09T17:28:00.998371Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Last 7 films was: \n",
      "['Avatar (2009)' 'Gladiator (2000)' 'Star Trek (2009)' 'Iron Man (2008)'\n",
      " 'Cowboy Bebop: The Movie (Cowboy Bebop: Tengoku no Tobira) (2001)'\n",
      " 'Appleseed (Appurushîdo) (2004)' 'District 13 (Banlieue 13) (2004)']\n",
      "Recomendations\n",
      "    movieId                                              title     Score\n",
      "0       260          Star Wars: Episode IV - A New Hope (1977)  4.057997\n",
      "1    122918                   Guardians of the Galaxy 2 (2017)  4.013018\n",
      "2      1196  Star Wars: Episode V - The Empire Strikes Back...  3.902829\n",
      "3     59315                                    Iron Man (2008)  3.891793\n",
      "4     34405                                    Serenity (2005)  3.885210\n",
      "5      1210  Star Wars: Episode VI - Return of the Jedi (1983)  3.873675\n",
      "6    122912             Avengers: Infinity War - Part I (2018)  3.823201\n",
      "7    122916                              Thor: Ragnarok (2017)  3.819670\n",
      "8    111362                  X-Men: Days of Future Past (2014)  3.815303\n",
      "9    122906                               Black Panther (2017)  3.726925\n",
      "10   122900                                     Ant-Man (2015)  3.629503\n",
      "11    65982                                   Outlander (2008)  3.628596\n",
      "12   122892                     Avengers: Age of Ultron (2015)  3.608681\n",
      "13    62383                20,000 Leagues Under the Sea (1916)  3.604935\n",
      "14   141544                                   Turbo Kid (2015)  3.562503\n",
      "15   142509                              Hardcore Henry (2015)  3.556032\n",
      "16    63433              Farscape: The Peacekeeper Wars (2004)  3.542237\n",
      "17   135569                            Star Trek Beyond (2016)  3.535057\n",
      "18     3702                                     Mad Max (1979)  3.520111\n",
      "19     3959                           Time Machine, The (1960)  3.516463\n",
      "20   169982                               Power Rangers (2017)  3.514044\n",
      "21     2105                                        Tron (1982)  3.486371\n",
      "22     6882                         Returner (Ritaanaa) (2002)  3.485028\n",
      "23     3793                                       X-Men (2000)  3.469573\n",
      "24     2275                          Six-String Samurai (1998)  3.467963\n",
      "25    33493  Star Wars: Episode III - Revenge of the Sith (...  3.447784\n",
      "26     3704                  Mad Max Beyond Thunderdome (1985)  3.431800\n",
      "27   186587                                     Rampage (2018)  3.430604\n",
      "28    61210                           Mutant Chronicles (2008)  3.425385\n",
      "29   136800                             Robot Overlords (2014)  3.375507\n",
      "30    75416                                Generation X (1996)  3.362743\n",
      "31   130842                               Power/Rangers (2015)  3.349750\n",
      "32   167370                            Assassin's Creed (2016)  3.305653\n",
      "33     4941                                Flash Gordon (1980)  3.291271\n",
      "34   122898                              Justice League (2017)  3.255980\n",
      "35    60514          Journey to the Center of the Earth (2008)  3.241069\n",
      "36     4987  Spacehunter: Adventures in the Forbidden Zone ...  3.215753\n",
      "37      442                              Demolition Man (1993)  3.205632\n",
      "38     2094                              Rocketeer, The (1991)  3.175445\n",
      "39    31553                               Double Dragon (1994)  3.166024\n",
      "40     6959                                    Timeline (2003)  3.064822\n",
      "41   135567                Independence Day: Resurgence (2016)  3.058420\n",
      "42     8865       Sky Captain and the World of Tomorrow (2004)  3.033457\n",
      "43      208                                  Waterworld (1995)  2.964409\n",
      "44     6294                            Bulletproof Monk (2003)  2.919803\n",
      "45     2628   Star Wars: Episode I - The Phantom Menace (1999)  2.905390\n",
      "46   173291  Valerian and the City of a Thousand Planets (2...  2.886914\n",
      "47    53464   Fantastic Four: Rise of the Silver Surfer (2007)  2.789479\n",
      "48     2642                                Superman III (1983)  2.654769\n",
      "49     1831                               Lost in Space (1998)  2.601618\n"
     ]
    }
   ],
   "source": [
    "recomend(user_id)"
   ]
  },
  {
   "cell_type": "code",
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   "metadata": {},
   "outputs": [],
   "source": []
  }
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