Recommender System.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Recommender System for movies"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this notebook we will create a Recommender System for movies using a Restricted Boltzman Machine. \n",
    "The Restricted Boltzman Machine is a Generative Model, with undirectional edges, composed of visible and hidden nodes.\n",
    "We can see the structure of the network in this picture:\n",
    "\n",
    "<img src=\"restrictedBoltzman.png\">\n",
    "\n",
    "In particular, in this model we will use the following datasets:\n",
    "\n",
    "https://grouplens.org/datasets/movielens/1m/\n",
    "\n",
    "https://grouplens.org/datasets/movielens/100k/\n",
    "\n",
    "MovieLens data sets were collected by the GroupLens Research Project\n",
    "at the University of Minnesota.\n",
    " \n",
    "This data set consists of:\n",
    " * 100,000 ratings (1-5) from 943 users on 1682 movies. \n",
    " * Each user has rated at least 20 movies. \n",
    " * Simple demographic info for the users (age, gender, occupation, zip)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Import Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.nn.parallel\n",
    "import torch.optim as optim\n",
    "import torch.utils.data\n",
    "from torch.autograd import Variable"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<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>Genre</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>Jumanji (1995)</td>\n",
       "      <td>Adventure|Children's|Fantasy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Grumpier Old Men (1995)</td>\n",
       "      <td>Comedy|Romance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Waiting to Exhale (1995)</td>\n",
       "      <td>Comedy|Drama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\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                         Genre\n",
       "0        1                    Toy Story (1995)   Animation|Children's|Comedy\n",
       "1        2                      Jumanji (1995)  Adventure|Children's|Fantasy\n",
       "2        3             Grumpier Old Men (1995)                Comedy|Romance\n",
       "3        4            Waiting to Exhale (1995)                  Comedy|Drama\n",
       "4        5  Father of the Bride Part II (1995)                        Comedy"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Movies Data\n",
    "movies = pd.read_csv('ml-1m/ml-1m/movies.dat', sep = '::', header = None, engine = 'python', encoding = 'latin-1', names = ['MovieID', 'Title', 'Genre' ])\n",
    "movies.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>UserID</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>Job</th>\n",
       "      <th>ZipCode</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "      <th>0</th>\n",
       "      <td>1</td>\n",
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       "      <td>48067</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>M</td>\n",
       "      <td>56</td>\n",
       "      <td>16</td>\n",
       "      <td>70072</td>\n",
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       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>M</td>\n",
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       "      <td>M</td>\n",
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       "      <td>7</td>\n",
       "      <td>02460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>M</td>\n",
       "      <td>25</td>\n",
       "      <td>20</td>\n",
       "      <td>55455</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      "text/plain": [
       "   UserID Gender  Age  Job ZipCode\n",
       "0       1      F    1   10   48067\n",
       "1       2      M   56   16   70072\n",
       "2       3      M   25   15   55117\n",
       "3       4      M   45    7   02460\n",
       "4       5      M   25   20   55455"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Users Data\n",
    "users = pd.read_csv('ml-1m/ml-1m/users.dat', sep = '::', header = None, engine = 'python', encoding = 'latin-1', names = ['UserID', 'Gender', 'Age', 'Job', 'ZipCode' ])\n",
    "users.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
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       "<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",
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       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1193</td>\n",
       "      <td>5</td>\n",
       "      <td>978300760</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
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       "      <td>978302109</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>914</td>\n",
       "      <td>3</td>\n",
       "      <td>978301968</td>\n",
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       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>2355</td>\n",
       "      <td>5</td>\n",
       "      <td>978824291</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "   UserID  MovieID  Rating  Timestamp\n",
       "0       1     1193       5  978300760\n",
       "1       1      661       3  978302109\n",
       "2       1      914       3  978301968\n",
       "3       1     3408       4  978300275\n",
       "4       1     2355       5  978824291"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Ratings Data\n",
    "ratings = pd.read_csv('ml-1m/ml-1m/ratings.dat', sep = '::', header = None, engine = 'python', encoding = 'latin-1', names = ['UserID', 'MovieID', 'Rating', 'Timestamp' ])\n",
    "ratings.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Prepare Training Set and Test set"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will use an holdout train test split. In particular we will use 80% of the data as training set and the remaining 20 % as test set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
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     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Train set\n",
    "training_set = pd.read_csv('ml-100k/ml-100k/u1.base', sep = '\\t')\n",
    "training_set.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 79999 entries, 0 to 79998\n",
      "Data columns (total 4 columns):\n",
      "1            79999 non-null int64\n",
      "1.1          79999 non-null int64\n",
      "5            79999 non-null int64\n",
      "874965758    79999 non-null int64\n",
      "dtypes: int64(4)\n",
      "memory usage: 2.4 MB\n"
     ]
    }
   ],
   "source": [
    "training_set.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0, 0, 0, ..., 0, 0, 0],\n",
       "       [0, 0, 0, ..., 0, 0, 0],\n",
       "       [0, 1, 0, ..., 0, 0, 0],\n",
       "       ...,\n",
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     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "# Convert the df into an array\n",
    "training_set = np.array(training_set, dtype = 'int')\n",
    "training_set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
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       "      <th>0</th>\n",
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       "  </tbody>\n",
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       "4  1  20  4  887431883"
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     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Test set\n",
    "test_set = pd.read_csv('ml-100k/ml-100k/u1.test', sep = '\\t')\n",
    "test_set.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 19999 entries, 0 to 19998\n",
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      "887431973    19999 non-null int64\n",
      "dtypes: int64(4)\n",
      "memory usage: 625.0 KB\n"
     ]
    }
   ],
   "source": [
    "test_set.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[        1,        10,         3, 875693118],\n",
       "       [        1,        12,         5, 878542960],\n",
       "       [        1,        14,         5, 874965706],\n",
       "       ...,\n",
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     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "test_set = np.array(test_set, dtype = 'int')\n",
    "test_set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Getting the number of users and movies\n",
    "n_users = int(max(max(training_set[:,0]), max(test_set[:,0])))\n",
    "n_movies = int(max(max(training_set[:,1]), max(test_set[:,1])))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we will convert the data into an array whith users in line and movies in columns. For this reason we extracted the number of users and movies. The values will be the ratings. In particular we will create a list of list, in particular a list of movie ratings for each user."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "def convert(data):\n",
    "    new_data = []\n",
    "    for id_users in range(1, n_users + 1):\n",
    "        id_movies = data[:, 1][data[:,0] == id_users] # Select all the movies ID that corresponds to the user id\n",
    "        id_ratings = data[:, 2][data[:,0] == id_users] # Select all the ratings ID that corresponds to the user id\n",
    "        ratings = np.zeros(n_movies)\n",
    "        ratings[id_movies - 1] = id_ratings# id_movies start at 0\n",
    "        new_data.append(list(ratings))\n",
    "    return new_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "training_set = convert(training_set)\n",
    "test_set = convert(test_set)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we convert the data into Torch Tensor "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "training_set = torch.FloatTensor(training_set)\n",
    "test_set = torch.FloatTensor(test_set)"
   ]
  }
 ],
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