Day 45.ipynb

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   "source": [
    "## Recommendation System for Movies in Pytorch using the Boltzmann Machine"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Part 1: Data Preprocessing"
   ]
  },
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   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import the Libraries\n",
    "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": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import the dataset\n",
    "movies = pd.read_csv('dataset\\ml-1m/movies.dat', sep='::', header = None, engine='python', encoding='latin-1')\n",
    "users = pd.read_csv('dataset\\ml-1m/users.dat', sep='::', header = None, engine='python', encoding='latin-1')\n",
    "ratings = pd.read_csv('dataset\\ml-1m/ratings.dat', sep='::', header = None, engine='python', encoding='latin-1')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# preparing the training set and test set\n",
    "training_set = pd.read_csv('dataset\\ml-100k/u1.base', delimiter = '\\t')\n",
    "training_set = np.array(training_set, dtype = 'int')\n",
    "test_set = pd.read_csv('dataset\\ml-100k/u1.test', delimiter = '\\t')\n",
    "test_set = np.array(test_set, dtype = 'int')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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])))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# converting the data into array with users in lines and movies in column\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": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# converting the data into torch tensor\n",
    "training_set = torch.FloatTensor(training_set)\n",
    "test_set = torch.FloatTensor(test_set)"
   ]
  }
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