{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Майнор по Анализу Данных, Группа ИАД-2\n",
    "## 24/05/2017  Рекомендательные системы"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "%matplotlib inline\n",
    "\n",
    "plt.style.use('ggplot')\n",
    "plt.rcParams['figure.figsize'] = (12,5)\n",
    "\n",
    "# Для кириллицы на графиках\n",
    "font = {'family': 'Verdana',\n",
    "        'weight': 'normal'}\n",
    "plt.rc('font', **font)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Коллаборативная фильтрация"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Метод коллаборативной фильтрации работает по принципу похожести пользователей (**User-based collaborative filtering**) или похожести товаров (**Item-based collaborative filtering**). "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Введем обозначения:\n",
    "* $R_{ui}$ - оценка, которую дал пользователь $u$ товару $i$\n",
    "* $\\hat{R}_{ui}$ - прогноз оценки\n",
    "* $s_{uv}$ - похожесть между пользователями $u$ и $v$\n",
    "* $s_{ij}$ - похожесть между товарами $i$ и $j$\n",
    "* $N(u)$ - Множество пользователей, наиболее похожих на $u$\n",
    "* $N(i)$ - Множество товаров, наиболее похожих на $i$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Прогноз **user-based collaborative filtering** вычисляется по следующей формуле\n",
    "$$ \\hat{R}_{ui} = \\frac{\\sum_{v \\in N(u)} s_{uv} R_{vi}}{\\sum_{v \\in N(u)} s_{uv}} $$\n",
    "\n",
    "Походесть между пользователями можно вычислить разными способами, например с помощью корреляции или косинусной меры\n",
    "$$ s_{uv} = \\frac{R_u^T R_v}{\\lVert R_u \\rVert_2 \\lVert R_v \\rVert_2}$$\n",
    "Стоит отметить, что при подсчете похожести должны учитываться только те товары, для которых и у $u$ и у $v$ есть какая-то оценка"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<center><img src='http://comsysto.files.wordpress.com/2013/02/userbased.png' width=700></center>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Похожим образом делается оценка с помощью **item-based collaborative filtering**:\n",
    "\n",
    "$$\\hat{R}_{u i} = \\frac{\\sum_{j \\in N(i)} s_{ij} R_{u j}}{\\sum_{j \\in N(i)} s_{ij}}$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<center><img src='http://dataconomy.com/wp-content/uploads/2015/03/Beginners-Guide-Recommender-Systems-Content-Based-Filtering.png' width=350></center>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Рейтинги фильмов"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Загрузите данные о рейтингах и фильмах\n",
    "* Разбейте данные на обучение и контроль в пропорции 80/20\n",
    "* Реализуйте функцию расчета попарных схожестей между пользователями\n",
    "* Реализуйте метод расчета рекомендаций на основе $K$ наиболее похожих пользователей. Постройте график зафисимости ошибки MAE от $K$ (1-10)\n",
    "* Выполните нормализацию рейтингов с помощью вычитания средней оценки $\\bar{R}_u$ каждого из пользователей и повторите предыдущий 2 шага. В этом случае предсказание выполняется следующим образом\n",
    "$$ \\hat{R}_{ui} = \\bar{R}_u + \\frac{\\sum_{v \\in N(u)} s_{uv} (R_{vi} - \\bar{R}_v)}{\\sum_{v \\in N(u)} s_{uv}} $$\n",
    "* Перейдите к Item-Based подходу и повторите предыдущие шаги"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "filepath = './data/user_ratedmovies.dat'\n",
    "df_rates = pd.read_csv(filepath, sep='\\t')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "filepath = './data/movies.dat'\n",
    "df_movies = pd.read_csv(filepath, sep='\\t', encoding='iso-8859-1')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "movie_counts = df_rates.movieID.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "movies_one_watch = movie_counts[movie_counts == 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "idx = df_rates.movieID.isin(movies_one_watch.index)\n",
    "df_rates = df_rates.loc[~idx, :]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\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>date_day</th>\n",
       "      <th>date_month</th>\n",
       "      <th>date_year</th>\n",
       "      <th>date_hour</th>\n",
       "      <th>date_minute</th>\n",
       "      <th>date_second</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <td>2006</td>\n",
       "      <td>23</td>\n",
       "      <td>17</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>75</td>\n",
       "      <td>32</td>\n",
       "      <td>4.5</td>\n",
       "      <td>29</td>\n",
       "      <td>10</td>\n",
       "      <td>2006</td>\n",
       "      <td>23</td>\n",
       "      <td>23</td>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>75</td>\n",
       "      <td>110</td>\n",
       "      <td>4.0</td>\n",
       "      <td>29</td>\n",
       "      <td>10</td>\n",
       "      <td>2006</td>\n",
       "      <td>23</td>\n",
       "      <td>30</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>75</td>\n",
       "      <td>160</td>\n",
       "      <td>2.0</td>\n",
       "      <td>29</td>\n",
       "      <td>10</td>\n",
       "      <td>2006</td>\n",
       "      <td>23</td>\n",
       "      <td>16</td>\n",
       "      <td>52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>75</td>\n",
       "      <td>163</td>\n",
       "      <td>4.0</td>\n",
       "      <td>29</td>\n",
       "      <td>10</td>\n",
       "      <td>2006</td>\n",
       "      <td>23</td>\n",
       "      <td>29</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   userID  movieID  rating  date_day  date_month  date_year  date_hour  \\\n",
       "0      75        3     1.0        29          10       2006         23   \n",
       "1      75       32     4.5        29          10       2006         23   \n",
       "2      75      110     4.0        29          10       2006         23   \n",
       "3      75      160     2.0        29          10       2006         23   \n",
       "4      75      163     4.0        29          10       2006         23   \n",
       "\n",
       "   date_minute  date_second  \n",
       "0           17           16  \n",
       "1           23           44  \n",
       "2           30            8  \n",
       "3           16           52  \n",
       "4           29           30  "
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_rates.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Перекодируем ID фильмов"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import LabelEncoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "mov_enc = LabelEncoder()\n",
    "mov_enc.fit(df_rates.movieID.values)\n",
    "n_movies = df_rates.movieID.nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "user_enc = LabelEncoder()\n",
    "user_enc.fit(df_rates.userID.values)\n",
    "n_users = df_rates.userID.nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df_rates.loc[:, 'movieID'] = mov_enc.transform(df_rates.movieID.values)\n",
    "df_rates.loc[:, 'userID'] = user_enc.transform(df_rates.userID.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\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>date_day</th>\n",
       "      <th>date_month</th>\n",
       "      <th>date_year</th>\n",
       "      <th>date_hour</th>\n",
       "      <th>date_minute</th>\n",
       "      <th>date_second</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
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       "      <td>2006</td>\n",
       "      <td>23</td>\n",
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       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
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       "      <td>4.5</td>\n",
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       "      <td>10</td>\n",
       "      <td>2006</td>\n",
       "      <td>23</td>\n",
       "      <td>23</td>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>104</td>\n",
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       "      <td>29</td>\n",
       "      <td>10</td>\n",
       "      <td>2006</td>\n",
       "      <td>23</td>\n",
       "      <td>30</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>147</td>\n",
       "      <td>2.0</td>\n",
       "      <td>29</td>\n",
       "      <td>10</td>\n",
       "      <td>2006</td>\n",
       "      <td>23</td>\n",
       "      <td>16</td>\n",
       "      <td>52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>150</td>\n",
       "      <td>4.0</td>\n",
       "      <td>29</td>\n",
       "      <td>10</td>\n",
       "      <td>2006</td>\n",
       "      <td>23</td>\n",
       "      <td>29</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   userID  movieID  rating  date_day  date_month  date_year  date_hour  \\\n",
       "0       0        2     1.0        29          10       2006         23   \n",
       "1       0       31     4.5        29          10       2006         23   \n",
       "2       0      104     4.0        29          10       2006         23   \n",
       "3       0      147     2.0        29          10       2006         23   \n",
       "4       0      150     4.0        29          10       2006         23   \n",
       "\n",
       "   date_minute  date_second  \n",
       "0           17           16  \n",
       "1           23           44  \n",
       "2           30            8  \n",
       "3           16           52  \n",
       "4           29           30  "
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_rates.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Разбиваем на обучение и контроль"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df_rates_train, df_rates_test = train_test_split(df_rates, \n",
    "                                                 test_size=0.2, \n",
    "                                                 random_state=123)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(171000, 9)"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_rates_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\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>date_day</th>\n",
       "      <th>date_month</th>\n",
       "      <th>date_year</th>\n",
       "      <th>date_hour</th>\n",
       "      <th>date_minute</th>\n",
       "      <th>date_second</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>431521</th>\n",
       "      <td>1019</td>\n",
       "      <td>1000</td>\n",
       "      <td>3.0</td>\n",
       "      <td>27</td>\n",
       "      <td>10</td>\n",
       "      <td>2003</td>\n",
       "      <td>19</td>\n",
       "      <td>43</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>769718</th>\n",
       "      <td>1891</td>\n",
       "      <td>3540</td>\n",
       "      <td>4.5</td>\n",
       "      <td>24</td>\n",
       "      <td>7</td>\n",
       "      <td>2007</td>\n",
       "      <td>3</td>\n",
       "      <td>25</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10766</th>\n",
       "      <td>42</td>\n",
       "      <td>357</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>2007</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>572429</th>\n",
       "      <td>1362</td>\n",
       "      <td>1346</td>\n",
       "      <td>2.0</td>\n",
       "      <td>17</td>\n",
       "      <td>9</td>\n",
       "      <td>2000</td>\n",
       "      <td>5</td>\n",
       "      <td>47</td>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>520032</th>\n",
       "      <td>1238</td>\n",
       "      <td>2239</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4</td>\n",
       "      <td>10</td>\n",
       "      <td>1999</td>\n",
       "      <td>15</td>\n",
       "      <td>20</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        userID  movieID  rating  date_day  date_month  date_year  date_hour  \\\n",
       "431521    1019     1000     3.0        27          10       2003         19   \n",
       "769718    1891     3540     4.5        24           7       2007          3   \n",
       "10766       42      357     2.0         2           9       2007         13   \n",
       "572429    1362     1346     2.0        17           9       2000          5   \n",
       "520032    1238     2239     3.0         4          10       1999         15   \n",
       "\n",
       "        date_minute  date_second  \n",
       "431521           43           35  \n",
       "769718           25           35  \n",
       "10766             0           20  \n",
       "572429           47           44  \n",
       "520032           20           22  "
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_rates_test.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Делаем матрицу с рейтингами"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_rates_test.userID.isin(df_rates_train.userID).all()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2242    1333\n",
       "336     1291\n",
       "4481    1258\n",
       "278     1242\n",
       "5338    1202\n",
       "299     1170\n",
       "2507    1170\n",
       "451     1162\n",
       "6423    1158\n",
       "2600    1152\n",
       "556     1142\n",
       "3832    1127\n",
       "1093    1098\n",
       "695     1097\n",
       "2414    1094\n",
       "1348    1091\n",
       "244     1069\n",
       "3757    1062\n",
       "1024    1062\n",
       "3152    1052\n",
       "1022    1036\n",
       "5854    1036\n",
       "6162    1021\n",
       "1470    1020\n",
       "49      1011\n",
       "553     1009\n",
       "0       1006\n",
       "1035     997\n",
       "4896     997\n",
       "104      995\n",
       "        ... \n",
       "5781       1\n",
       "7715       1\n",
       "7836       1\n",
       "5510       1\n",
       "7017       1\n",
       "7231       1\n",
       "721        1\n",
       "5480       1\n",
       "3828       1\n",
       "7253       1\n",
       "7360       1\n",
       "9239       1\n",
       "1781       1\n",
       "2556       1\n",
       "6585       1\n",
       "8698       1\n",
       "3144       1\n",
       "6901       1\n",
       "7117       1\n",
       "3899       1\n",
       "3971       1\n",
       "8390       1\n",
       "8407       1\n",
       "4992       1\n",
       "5230       1\n",
       "6131       1\n",
       "7674       1\n",
       "9424       1\n",
       "8471       1\n",
       "9326       1\n",
       "Name: movieID, dtype: int64"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_rates_train.movieID.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from scipy.sparse import coo_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [],
   "source": [
    "n_users_train = df_rates.userID.nunique()\n",
    "R_train = coo_matrix((df_rates_train.rating, \n",
    "                     (df_rates_train.userID.values, df_rates_train.movieID.values)),\n",
    "                     shape=(n_users, n_movies))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2113, 9507)"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "R_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<2113x9507 sparse matrix of type '<type 'numpy.float64'>'\n",
       "\twith 683996 stored elements in COOrdinate format>"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "R_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "R_train = R_train.toarray()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0. ,  0. ,  1. , ...,  0. ,  0. ,  0. ],\n",
       "       [ 0. ,  0. ,  0. , ...,  0. ,  0. ,  0. ],\n",
       "       [ 0. ,  0. ,  0. , ...,  0. ,  0. ,  0. ],\n",
       "       ..., \n",
       "       [ 0. ,  0. ,  0. , ...,  0. ,  0. ,  0. ],\n",
       "       [ 4.5,  0. ,  2. , ...,  0. ,  0. ,  0. ],\n",
       "       [ 0. ,  0. ,  0. , ...,  0. ,  0. ,  0. ]])"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "R_train"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Считаем попарные рейтинги"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from scipy.spatial.distance import cosine\n",
    "from scipy.spatial.distance import pdist\n",
    "from scipy.spatial.distance import squareform\n",
    "from scipy.spatial.distance import correlation\n",
    "from sklearn.metrics import pairwise_distances"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0. ,  0. ,  1. , ...,  0. ,  0. ,  0. ],\n",
       "       [ 0. ,  0. ,  0. , ...,  0. ,  0. ,  0. ],\n",
       "       [ 0. ,  0. ,  0. , ...,  0. ,  0. ,  0. ],\n",
       "       ..., \n",
       "       [ 0. ,  0. ,  0. , ...,  0. ,  0. ,  0. ],\n",
       "       [ 4.5,  0. ,  2. , ...,  0. ,  0. ,  0. ],\n",
       "       [ 0. ,  0. ,  0. , ...,  0. ,  0. ,  0. ]])"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "R_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def similarity(u, v):\n",
    "    idx = (u != 0) & (v != 0)\n",
    "    if any(idx):\n",
    "        return -cosine(u[idx], v[idx])+2\n",
    "    else:\n",
    "        return 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 4min 9s, sys: 2.75 s, total: 4min 12s\n",
      "Wall time: 4min 20s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "D = pdist(R_train, metric=similarity)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "D = squareform(D)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x114d9d5d0>"
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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QG3C2njBKh2k7iHNkM9SUxrIVpmymNY3oAfJ8r+oxihXbwbPh5TBsB89WKOUdwecDuhlq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\ncChDQOFK+JTwTDtsB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8bNNA8V7tt/Cyo4hdZseIII/crBI+gz/3TdgyEbBBW6Z+lG3LN0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6YxAUgMZca3LMNJmITEWVQA3Xn0vAhCz/paM5awjuZJykarMyJiav4vcWnluNG2M/lB2IdI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\nFkIVFZVlj1oIVVRUlj3/B0/RMlKk8LwLAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x117482910>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(D)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Делаем рекомендации"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\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>date_day</th>\n",
       "      <th>date_month</th>\n",
       "      <th>date_year</th>\n",
       "      <th>date_hour</th>\n",
       "      <th>date_minute</th>\n",
       "      <th>date_second</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>431521</th>\n",
       "      <td>1019</td>\n",
       "      <td>1000</td>\n",
       "      <td>3.0</td>\n",
       "      <td>27</td>\n",
       "      <td>10</td>\n",
       "      <td>2003</td>\n",
       "      <td>19</td>\n",
       "      <td>43</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>769718</th>\n",
       "      <td>1891</td>\n",
       "      <td>3540</td>\n",
       "      <td>4.5</td>\n",
       "      <td>24</td>\n",
       "      <td>7</td>\n",
       "      <td>2007</td>\n",
       "      <td>3</td>\n",
       "      <td>25</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10766</th>\n",
       "      <td>42</td>\n",
       "      <td>357</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>2007</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>572429</th>\n",
       "      <td>1362</td>\n",
       "      <td>1346</td>\n",
       "      <td>2.0</td>\n",
       "      <td>17</td>\n",
       "      <td>9</td>\n",
       "      <td>2000</td>\n",
       "      <td>5</td>\n",
       "      <td>47</td>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>520032</th>\n",
       "      <td>1238</td>\n",
       "      <td>2239</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4</td>\n",
       "      <td>10</td>\n",
       "      <td>1999</td>\n",
       "      <td>15</td>\n",
       "      <td>20</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        userID  movieID  rating  date_day  date_month  date_year  date_hour  \\\n",
       "431521    1019     1000     3.0        27          10       2003         19   \n",
       "769718    1891     3540     4.5        24           7       2007          3   \n",
       "10766       42      357     2.0         2           9       2007         13   \n",
       "572429    1362     1346     2.0        17           9       2000          5   \n",
       "520032    1238     2239     3.0         4          10       1999         15   \n",
       "\n",
       "        date_minute  date_second  \n",
       "431521           43           35  \n",
       "769718           25           35  \n",
       "10766             0           20  \n",
       "572429           47           44  \n",
       "520032           20           22  "
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_rates_test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from tqdm import tqdm_notebook"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "metadata": {},
   "outputs": [],
   "source": [
    "watched_users = R_train[:, int(r['movieID'])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "0d5c5381b231420c8bf0a1e57b006c47"
      }
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/andrey.shestakov/anaconda2/lib/python2.7/site-packages/ipykernel/__main__.py:14: RuntimeWarning: invalid value encountered in double_scalars\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "predicted_ratings = []\n",
    "\n",
    "for i, r in tqdm_notebook(df_rates_test.iterrows()):\n",
    "    watched_users = np.where(R_train[:, int(r['movieID'])])[0]\n",
    "    sim = D[int(r['userID']), watched_users]\n",
    "    sorted_idx = np.argsort(sim)\n",
    "    for k in range(1, 11):\n",
    "        \n",
    "        ratings = R_train[watched_users[sorted_idx[-k:]], int(r['movieID'])]\n",
    "        \n",
    "        sim_k = sim[sorted_idx[-k:]]\n",
    "        \n",
    "        prediction = ratings.dot(sim_k)/(sim_k.sum()) \n",
    "\n",
    "        predicted_ratings.append({'userID': r['userID'],\n",
    "                                  'movieID': r['movieID'],\n",
    "                                  'prediction': prediction,\n",
    "                                  'k': k})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df_pred = pd.DataFrame.from_dict(predicted_ratings)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_pred = df_pred.set_index(['movieID', 'userID', 'k'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>prediction</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>movieID</th>\n",
       "      <th>userID</th>\n",
       "      <th>k</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">1000.0</th>\n",
       "      <th rowspan=\"5\" valign=\"top\">1019.0</th>\n",
       "      <th>1</th>\n",
       "      <td>2.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3.499850</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3.833018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4.124560</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>4.099670</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  prediction\n",
       "movieID userID k            \n",
       "1000.0  1019.0 1    2.500000\n",
       "               2    3.499850\n",
       "               3    3.833018\n",
       "               4    4.124560\n",
       "               5    4.099670"
      ]
     },
     "execution_count": 201,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "df_pred.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_rates_test = df_rates_test.set_index(['movieID', 'userID'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>rating</th>\n",
       "      <th>date_day</th>\n",
       "      <th>date_month</th>\n",
       "      <th>date_year</th>\n",
       "      <th>date_hour</th>\n",
       "      <th>date_minute</th>\n",
       "      <th>date_second</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>movieID</th>\n",
       "      <th>userID</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1000</th>\n",
       "      <th>1019</th>\n",
       "      <td>3.0</td>\n",
       "      <td>27</td>\n",
       "      <td>10</td>\n",
       "      <td>2003</td>\n",
       "      <td>19</td>\n",
       "      <td>43</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3540</th>\n",
       "      <th>1891</th>\n",
       "      <td>4.5</td>\n",
       "      <td>24</td>\n",
       "      <td>7</td>\n",
       "      <td>2007</td>\n",
       "      <td>3</td>\n",
       "      <td>25</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>357</th>\n",
       "      <th>42</th>\n",
       "      <td>2.0</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>2007</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1346</th>\n",
       "      <th>1362</th>\n",
       "      <td>2.0</td>\n",
       "      <td>17</td>\n",
       "      <td>9</td>\n",
       "      <td>2000</td>\n",
       "      <td>5</td>\n",
       "      <td>47</td>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2239</th>\n",
       "      <th>1238</th>\n",
       "      <td>3.0</td>\n",
       "      <td>4</td>\n",
       "      <td>10</td>\n",
       "      <td>1999</td>\n",
       "      <td>15</td>\n",
       "      <td>20</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                rating  date_day  date_month  date_year  date_hour  \\\n",
       "movieID userID                                                       \n",
       "1000    1019       3.0        27          10       2003         19   \n",
       "3540    1891       4.5        24           7       2007          3   \n",
       "357     42         2.0         2           9       2007         13   \n",
       "1346    1362       2.0        17           9       2000          5   \n",
       "2239    1238       3.0         4          10       1999         15   \n",
       "\n",
       "                date_minute  date_second  \n",
       "movieID userID                            \n",
       "1000    1019             43           35  \n",
       "3540    1891             25           35  \n",
       "357     42                0           20  \n",
       "1346    1362             47           44  \n",
       "2239    1238             20           22  "
      ]
     },
     "execution_count": 207,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_rates_test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 225,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_pred_unstack = df_pred.unstack()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 228,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "rating            2.0\n",
       "date_day          2.0\n",
       "date_month        9.0\n",
       "date_year      2007.0\n",
       "date_hour        13.0\n",
       "date_minute       0.0\n",
       "date_second      20.0\n",
       "Name: (357, 42), dtype: float64"
      ]
     },
     "execution_count": 228,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_rates_test.loc[(357, 42)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 229,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "            k \n",
       "prediction  1     4.500000\n",
       "            2     4.500000\n",
       "            3     4.333383\n",
       "            4     3.875386\n",
       "            5     3.700542\n",
       "            6     3.584049\n",
       "            7     3.429518\n",
       "            8     3.500681\n",
       "            9     3.500606\n",
       "            10    3.550435\n",
       "Name: (357.0, 42.0), dtype: float64"
      ]
     },
     "execution_count": 229,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_pred_unstack.loc[(357, 42), :]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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