{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importing the libraries\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import torch\n",
    "import torch.nn.parallel\n",
    "import torch.utils.data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importing the dataset\n",
    "movies = pd.read_csv('ml-1m/movies.dat', sep = '::', header = None, engine = 'python', encoding = 'latin-1')\n",
    "users = pd.read_csv('ml-1m/users.dat', sep = '::', header = None, engine = 'python', encoding = 'latin-1')\n",
    "ratings = pd.read_csv('ml-1m/ratings.dat', sep = '::', header = None, engine = 'python', encoding = 'latin-1')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Preparing the training set and the test set\n",
    "training_set = pd.read_csv('ml-100k/u1.base', delimiter = '\\t')\n",
    "training_set = np.array(training_set, dtype = 'int')\n",
    "test_set = pd.read_csv('ml-100k/u1.test', delimiter = '\\t')\n",
    "test_set = np.array(test_set, dtype = 'int')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Getting the number of users and movies\n",
    "nb_users = int(max(max(training_set[:,0]), max(test_set[:,0])))\n",
    "nb_movies = int(max(max(training_set[:,1]), max(test_set[:,1])))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Converting the data into an array with users in lines and movies in columns\n",
    "def convert(data):\n",
    "    new_data = []\n",
    "    for id_users in range(1, nb_users + 1):\n",
    "        id_movies = data[:,1][data[:,0] == id_users]\n",
    "        id_ratings = data[:,2][data[:,0] == id_users]\n",
    "        ratings = np.zeros(nb_movies)\n",
    "        ratings[id_movies - 1] = id_ratings\n",
    "        new_data.append(list(ratings))\n",
    "    return new_data\n",
    "training_set = convert(training_set)\n",
    "test_set = convert(test_set)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Converting the data into Torch tensors\n",
    "training_set = torch.FloatTensor(training_set)\n",
    "test_set = torch.FloatTensor(test_set)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Converting the ratings into binary ratings 1 (Liked) or 0 (Not Liked)\n",
    "training_set[training_set == 0] = -1\n",
    "training_set[training_set == 1] = 0\n",
    "training_set[training_set == 2] = 0\n",
    "training_set[training_set >= 3] = 1\n",
    "test_set[test_set == 0] = -1\n",
    "test_set[test_set == 1] = 0\n",
    "test_set[test_set == 2] = 0\n",
    "test_set[test_set >= 3] = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creating the architecture of the Neural Network\n",
    "class RBM():\n",
    "    def __init__(self, nv, nh):\n",
    "        self.W = torch.randn(nh, nv)\n",
    "        self.a = torch.randn(1, nh)\n",
    "        self.b = torch.randn(1, nv)\n",
    "    def sample_h(self, x):\n",
    "        wx = torch.mm(x, self.W.t())\n",
    "        activation = wx + self.a.expand_as(wx)\n",
    "        p_h_given_v = torch.sigmoid(activation)\n",
    "        return p_h_given_v, torch.bernoulli(p_h_given_v)\n",
    "    def sample_v(self, y):\n",
    "        wy = torch.mm(y, self.W)\n",
    "        activation = wy + self.b.expand_as(wy)\n",
    "        p_v_given_h = torch.sigmoid(activation)\n",
    "        return p_v_given_h, torch.bernoulli(p_v_given_h)\n",
    "    def train(self, v0, vk, ph0, phk):\n",
    "        # self.W += torch.mm(v0.t(), ph0) - torch.mm(vk.t(), phk)\n",
    "        self.W += torch.mm(ph0, v0) - torch.mm(phk, vk)\n",
    "        self.b += torch.sum((v0 - vk), 0)\n",
    "        self.a += torch.sum((ph0 - phk), 0)\n",
    "nv = len(training_set[0])\n",
    "nh = 100\n",
    "batch_size = 100\n",
    "rbm = RBM(nv, nh)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Training the RBM\n",
    "nb_epoch = 10\n",
    "for epoch in range(1, nb_epoch + 1):\n",
    "    train_loss = 0\n",
    "    s = 0.\n",
    "    for id_user in range(0, nb_users - batch_size, batch_size):\n",
    "        vk = training_set[id_user:id_user+batch_size]\n",
    "        v0 = training_set[id_user:id_user+batch_size]\n",
    "        ph0,_ = rbm.sample_h(v0)\n",
    "        for k in range(10):\n",
    "            _,hk = rbm.sample_h(vk)\n",
    "            _,vk = rbm.sample_v(hk)\n",
    "            vk[v0<0] = v0[v0<0]\n",
    "        phk,_ = rbm.sample_h(vk)\n",
    "        rbm.train(v0, vk, ph0, phk)\n",
    "        train_loss += torch.mean(torch.abs(v0[v0>=0] - vk[v0>=0]))\n",
    "        s += 1.\n",
    "    print('epoch: '+str(epoch)+' loss: '+str(train_loss/s))\n",
    "    \n",
    "rbm.W += torch.mm(ph0, v0) - torch.mm(phk, vk)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Testing the RBM\n",
    "test_loss = 0\n",
    "s = 0.\n",
    "for id_user in range(nb_users):\n",
    "    v = training_set[id_user:id_user+1]\n",
    "    vt = test_set[id_user:id_user+1]\n",
    "    if len(vt[vt>=0]) > 0:\n",
    "        _,h = rbm.sample_h(v)\n",
    "        _,v = rbm.sample_v(h)\n",
    "        test_loss += torch.mean(torch.abs(vt[vt>=0] - v[vt>=0]))\n",
    "        s += 1.\n",
    "print('test loss: '+str(test_loss/s))"
   ]
  }
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