{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Building train and test sets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": false
   },
   "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 evaluation_measures as ev\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# df = pd.DataFrame(np.loadtxt( './Datasets/ml-1m.dat',delimiter='::'))\n",
    "df=pd.read_csv('./Datasets/ml-100k/u.data',delimiter='\\t', header=None)\n",
    "df.columns=['user', 'item', 'rating', 'timestamp']\n",
    "\n",
    "from sklearn.model_selection import train_test_split\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": 5,
   "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": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Sample properties"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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 106.04. \n",
      "\n",
      "Average number of ratings per item is 59.453.\n",
      "\n",
      "Data sparsity (% of missing entries) is 6.3047%.\n"
     ]
    }
   ],
   "source": [
    "users, items, ratings=len(set(df['user'])), len(set(df['item'])), len(df)\n",
    "\n",
    "print('We have {} users, {} items and {} ratings.\\n'.format(users, items, ratings))\n",
    "\n",
    "print('Average number of ratings per user is {}. \\n'.format(round(ratings/users,2)))\n",
    "print('Average number of ratings per item is {}.\\n'.format(round(ratings/items,4)))\n",
    "print('Data sparsity (% of missing entries) is {}%.'.format(round(100*ratings/(users*items),4)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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": 8,
   "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(['item']).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 item', fontsize=30)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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": 9,
     "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": 10,
   "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": 11,
   "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": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "genres"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "movies = pd.read_csv('./Datasets/ml-100k/u.item', sep='|', encoding='latin-1', header=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movies[:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "movies['genre']=movies.iloc[:, 5:].apply(lambda x: ', '.join(x[x!='']), axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "movies=movies[[0,1,'genre']]\n",
    "movies.columns=['id', 'title', 'genres']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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": 17,
     "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": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "if not os.path.exists('./Datasets/toy-example/'):\n",
    "    os.mkdir('./Datasets/toy-example/')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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": []
  }
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