{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Building train and test sets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# if you don't have some library installed try using pip (or pip3) to install it - you can do it from the notebook\n",
    "# example: !pip install tqdm\n",
    "# also on labs it's better to use python3 kernel - ipython3 notebook\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import scipy.sparse as sparse\n",
    "import time\n",
    "import random\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "import os\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "import helpers\n",
    "\n",
    "os.makedirs('./Datasets/', exist_ok = True)\n",
    "\n",
    "helpers.download_movielens_100k_dataset()\n",
    "\n",
    "df=pd.read_csv('./Datasets/ml-100k/u.data',delimiter='\\t', header=None)\n",
    "df.columns=['user', 'item', 'rating', 'timestamp']\n",
    "\n",
    "train, test = train_test_split(df, test_size=0.2, random_state=30)\n",
    "\n",
    "train.to_csv('./Datasets/ml-100k/train.csv', sep='\\t', header=None, index=False)\n",
    "test.to_csv('./Datasets/ml-100k/test.csv', sep='\\t', header=None, index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Interactions properties"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### How data looks like?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user</th>\n",
       "      <th>item</th>\n",
       "      <th>rating</th>\n",
       "      <th>timestamp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>196</td>\n",
       "      <td>242</td>\n",
       "      <td>3</td>\n",
       "      <td>881250949</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>186</td>\n",
       "      <td>302</td>\n",
       "      <td>3</td>\n",
       "      <td>891717742</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>22</td>\n",
       "      <td>377</td>\n",
       "      <td>1</td>\n",
       "      <td>878887116</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>244</td>\n",
       "      <td>51</td>\n",
       "      <td>2</td>\n",
       "      <td>880606923</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>166</td>\n",
       "      <td>346</td>\n",
       "      <td>1</td>\n",
       "      <td>886397596</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   user  item  rating  timestamp\n",
       "0   196   242       3  881250949\n",
       "1   186   302       3  891717742\n",
       "2    22   377       1  878887116\n",
       "3   244    51       2  880606923\n",
       "4   166   346       1  886397596"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Sample properties"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "We have 943 users, 1682 items and 100000 ratings.\n",
      "\n",
      "Average number of ratings per user is 106.0445. \n",
      "\n",
      "Average number of ratings per item is 59.453.\n",
      "\n",
      "Data sparsity (% of missing entries) is 93.6953%.\n"
     ]
    }
   ],
   "source": [
    "users, items, ratings=df['user'].nunique(), df['item'].nunique(), len(df)\n",
    "\n",
    "print(f'We have {users} users, {items} items and {ratings} ratings.\\n')\n",
    "\n",
    "print(f'Average number of ratings per user is {round(ratings/users,4)}. \\n')\n",
    "print(f'Average number of ratings per item is {round(ratings/items,4)}.\\n')\n",
    "print(f'Data sparsity (% of missing entries) is {round(100*(1-ratings/(users*items)),4)}%.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "items_per_user=df.groupby(['user']).count()['rating']\n",
    "\n",
    "plt.figure(figsize=(16,8))\n",
    "plt.hist(items_per_user, bins=100)\n",
    "\n",
    "# Let's add median\n",
    "t=items_per_user.median()\n",
    "plt.axvline(t, color='k', linestyle='dashed', linewidth=1)\n",
    "plt.text(t*1.1, plt.ylim()[1]*0.9, 'Median: {:.0f}'.format(t))\n",
    "\n",
    "# Let's add also some percentiles\n",
    "t=items_per_user.quantile(0.25)\n",
    "plt.axvline(t, color='k', linestyle='dashed', linewidth=1)\n",
    "plt.text(t*1.1, plt.ylim()[1]*0.95, '25% quantile: {:.0f}'.format(t))\n",
    "\n",
    "t=items_per_user.quantile(0.75)\n",
    "plt.axvline(t, color='k', linestyle='dashed', linewidth=1)\n",
    "plt.text(t*1.05, plt.ylim()[1]*0.95, '75% quantile: {:.0f}'.format(t))\n",
    "\n",
    "plt.title('Number of ratings per user', fontsize=30)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "users_per_item=df.groupby(['item']).count()['rating']\n",
    "\n",
    "plt.figure(figsize=(16,8))\n",
    "plt.hist(users_per_item, bins=100)\n",
    "\n",
    "# Let's add median\n",
    "t=users_per_item.median()\n",
    "plt.axvline(t, color='k', linestyle='dashed', linewidth=1)\n",
    "plt.text(t*1.1, plt.ylim()[1]*0.9, 'Median: {:.0f}'.format(t))\n",
    "\n",
    "# Let's add also some percentiles\n",
    "t=users_per_item.quantile(0.25)\n",
    "plt.axvline(t, color='k', linestyle='dashed', linewidth=1)\n",
    "plt.text(t*1.1, plt.ylim()[1]*0.95, '25% quantile: {:.0f}'.format(t))\n",
    "\n",
    "t=users_per_item.quantile(0.75)\n",
    "plt.axvline(t, color='k', linestyle='dashed', linewidth=1)\n",
    "plt.text(t*1.05, plt.ylim()[1]*0.95, '75% quantile: {:.0f}'.format(t))\n",
    "\n",
    "plt.title('Number of ratings per item', fontsize=30)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "rating\n",
       "1    0.06110\n",
       "2    0.11370\n",
       "3    0.27145\n",
       "4    0.34174\n",
       "5    0.21201\n",
       "Name: user, dtype: float64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(['rating']).count()['user']/len(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Item attributes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "genres = pd.read_csv('./Datasets/ml-100k/u.genre', sep='|', header=None,\n",
    "                     encoding='latin-1')\n",
    "genres=dict(zip(genres[1], genres[0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{0: 'unknown',\n",
       " 1: 'Action',\n",
       " 2: 'Adventure',\n",
       " 3: 'Animation',\n",
       " 4: \"Children's\",\n",
       " 5: 'Comedy',\n",
       " 6: 'Crime',\n",
       " 7: 'Documentary',\n",
       " 8: 'Drama',\n",
       " 9: 'Fantasy',\n",
       " 10: 'Film-Noir',\n",
       " 11: 'Horror',\n",
       " 12: 'Musical',\n",
       " 13: 'Mystery',\n",
       " 14: 'Romance',\n",
       " 15: 'Sci-Fi',\n",
       " 16: 'Thriller',\n",
       " 17: 'War',\n",
       " 18: 'Western'}"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "genres"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "movies = pd.read_csv('./Datasets/ml-100k/u.item', sep='|', encoding='latin-1', header=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>...</th>\n",
       "      <th>14</th>\n",
       "      <th>15</th>\n",
       "      <th>16</th>\n",
       "      <th>17</th>\n",
       "      <th>18</th>\n",
       "      <th>19</th>\n",
       "      <th>20</th>\n",
       "      <th>21</th>\n",
       "      <th>22</th>\n",
       "      <th>23</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Toy Story (1995)</td>\n",
       "      <td>01-Jan-1995</td>\n",
       "      <td>NaN</td>\n",
       "      <td>http://us.imdb.com/M/title-exact?Toy%20Story%2...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>GoldenEye (1995)</td>\n",
       "      <td>01-Jan-1995</td>\n",
       "      <td>NaN</td>\n",
       "      <td>http://us.imdb.com/M/title-exact?GoldenEye%20(...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Four Rooms (1995)</td>\n",
       "      <td>01-Jan-1995</td>\n",
       "      <td>NaN</td>\n",
       "      <td>http://us.imdb.com/M/title-exact?Four%20Rooms%...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   0                  1            2   3   \\\n",
       "0   1   Toy Story (1995)  01-Jan-1995 NaN   \n",
       "1   2   GoldenEye (1995)  01-Jan-1995 NaN   \n",
       "2   3  Four Rooms (1995)  01-Jan-1995 NaN   \n",
       "\n",
       "                                                  4   5   6   7   8   9   ...  \\\n",
       "0  http://us.imdb.com/M/title-exact?Toy%20Story%2...   0   0   0   1   1  ...   \n",
       "1  http://us.imdb.com/M/title-exact?GoldenEye%20(...   0   1   1   0   0  ...   \n",
       "2  http://us.imdb.com/M/title-exact?Four%20Rooms%...   0   0   0   0   0  ...   \n",
       "\n",
       "   14  15  16  17  18  19  20  21  22  23  \n",
       "0   0   0   0   0   0   0   0   0   0   0  \n",
       "1   0   0   0   0   0   0   0   1   0   0  \n",
       "2   0   0   0   0   0   0   0   1   0   0  \n",
       "\n",
       "[3 rows x 24 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movies[:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in range(19):\n",
    "    movies[i+5]=movies[i+5].apply(lambda x: genres[i] if x==1 else '')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "movies['genre']=movies.iloc[:, 5:].apply(lambda x: ', '.join(x[x!='']), axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "movies=movies[[0,1,'genre']]\n",
    "movies.columns=['id', 'title', 'genres']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>title</th>\n",
       "      <th>genres</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Toy Story (1995)</td>\n",
       "      <td>Animation, Children's, Comedy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>GoldenEye (1995)</td>\n",
       "      <td>Action, Adventure, Thriller</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Four Rooms (1995)</td>\n",
       "      <td>Thriller</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Get Shorty (1995)</td>\n",
       "      <td>Action, Comedy, Drama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Copycat (1995)</td>\n",
       "      <td>Crime, Drama, Thriller</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   id              title                         genres\n",
       "0   1   Toy Story (1995)  Animation, Children's, Comedy\n",
       "1   2   GoldenEye (1995)    Action, Adventure, Thriller\n",
       "2   3  Four Rooms (1995)                       Thriller\n",
       "3   4  Get Shorty (1995)          Action, Comedy, Drama\n",
       "4   5     Copycat (1995)         Crime, Drama, Thriller"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movies.to_csv('./Datasets/ml-100k/movies.csv', index=False)\n",
    "movies[:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Toy example"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "os.makedirs('./Datasets/toy-example/', exist_ok = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "toy_train=pd.DataFrame([[0,0,3,0], [0,10,4,0], [0,40,5,0], [0,70,4,0],\n",
    "              [10,10,1,0], [10,20,2,0], [10,30,3,0],\n",
    "              [20,30,5,0], [20,50,3,0], [20,60,4,0]])\n",
    "toy_test=pd.DataFrame([[0,60,3,0],\n",
    "              [10,40,5,0],\n",
    "              [20,0,5,0], [20,20,4,0], [20,70,2,0]])\n",
    "\n",
    "toy_train.to_csv('./Datasets/toy-example/train.csv', sep='\\t', header=None, index=False)\n",
    "toy_test.to_csv('./Datasets/toy-example/test.csv', sep='\\t', header=None, index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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