06.00-boltmann-machines.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# importando as bibliotecas necessárias\n",
    "\n",
    "import numpy as np                       # biblioteca para cálculos matemáticos\n",
    "import pandas as pd                      # biblioteca para manipulação de dados\n",
    "import torch                             # biblioteca para cálculos com GPUs\n",
    "import torch.nn as nn                    # biblioteca para criação de redes neurais\n",
    "import torch.nn.parallel                 # biblioteca para criação de redes neurais paralelas\n",
    "import torch.optim as optim              # biblioteca para otimização de redes neurais\n",
    "from torch.autograd import Variable      # biblioteca para criação de variáveis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# carregando os dados\n",
    "\n",
    "# filmes\n",
    "movies = pd.read_csv('./data/movies.dat', sep='::', header=None, engine='python', encoding='latin-1')\n",
    "\n",
    "# usuários\n",
    "users = pd.read_csv('./data/users.dat', sep='::', header=None, engine='python', encoding='latin-1')\n",
    "\n",
    "# avaliações dos usuários\n",
    "ratings = pd.read_csv('./data/ratings.dat', sep='::', header=None, engine='python', encoding='latin-1')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((3883, 3), (6040, 5), (1000209, 4))"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# tamanho dos dados\n",
    "\n",
    "movies.shape, users.shape, ratings.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# carregando os dados de treino e teste\n",
    "\n",
    "train_set = pd.read_csv('./data/u1.base', delimiter='\\t')\n",
    "test_set = pd.read_csv('./data/u1.test', delimiter='\\t')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((79999, 4), (19999, 4))"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# tamanho dos dados\n",
    "\n",
    "train_set.shape, test_set.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# transformando os dados em um array para uso com o torch\n",
    "\n",
    "train_set = np.array(train_set, dtype='int')\n",
    "test_set = np.array(test_set, dtype='int')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# pegando o identificador máximo dos filmes e usuários\n",
    "\n",
    "nb_users = int(max(max(train_set[:, 0]), max(test_set[:, 0])))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# pegando o identificador máximo das avaliações\n",
    "\n",
    "nb_movies = int(max(max(train_set[:, 1]), max(test_set[:, 1])))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# função para conversão dos dados para o formato do torch\n",
    "# será necessário lista de listas \n",
    "\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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# conversão dos dados\n",
    "\n",
    "train_set = convert(train_set)\n",
    "test_set = convert(test_set)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
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       " ...]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# visualizando dos dados após a conversão\n",
    "\n",
    "train_set[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# conversão dos dados para o formato do torch tensor .float()\n",
    "\n",
    "train_set = torch.FloatTensor(train_set)\n",
    "test_set = torch.FloatTensor(test_set)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# conversão das avaliações para um formato binario\n",
    "# de 1-5 em -1, 0, 1\n",
    "\n",
    "train_set[train_set == 0] = -1\n",
    "train_set[train_set == 1] =  0\n",
    "train_set[train_set == 2] =  0\n",
    "train_set[train_set >= 3] =  1\n",
    "\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": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([-1.,  1.,  1.,  ..., -1., -1., -1.])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# visualizando os dados após a conversão\n",
    "\n",
    "train_set[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# criando uma classe para a rede neural\n",
    "\n",
    "class RBM():\n",
    "\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",
    "    \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",
    "    \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",
    "    \n",
    "    def train(self, v0, vk, ph0, phk):\n",
    "        self.W += (torch.mm(v0.t(), ph0) - torch.mm(vk.t(), phk)).t()\n",
    "        self.b += torch.sum((v0 - vk), 0)\n",
    "        self.a += torch.sum((ph0 - phk), 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# parametros de treinamento\n",
    "\n",
    "nv = len(train_set[0])\n",
    "nh = 100\n",
    "batch_size = 100\n",
    "nb_ephochs = 10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# criação da classe RBM\n",
    "\n",
    "rbm = RBM(nv, nh)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch: 1 loss: tensor(0.3483)\n",
      "epoch: 2 loss: tensor(0.2399)\n",
      "epoch: 3 loss: tensor(0.2325)\n",
      "epoch: 4 loss: tensor(0.2493)\n",
      "epoch: 5 loss: tensor(0.2473)\n",
      "epoch: 6 loss: tensor(0.2491)\n",
      "epoch: 7 loss: tensor(0.2435)\n",
      "epoch: 8 loss: tensor(0.2454)\n",
      "epoch: 9 loss: tensor(0.2471)\n",
      "epoch: 10 loss: tensor(0.2503)\n"
     ]
    }
   ],
   "source": [
    "# treinamento da rede\n",
    "\n",
    "for epoch in range(1, nb_ephochs + 1):\n",
    "    \n",
    "    train_loss = 0\n",
    "    s = 0.\n",
    "    \n",
    "    for id_user in range(0, nb_users - batch_size, batch_size):\n",
    "        vk = train_set[id_user:id_user+batch_size]\n",
    "        v0 = train_set[id_user:id_user+batch_size]\n",
    "        ph0,_ = rbm.sample_h(v0)\n",
    "    \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",
    "        \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",
    "    \n",
    "    print('epoch: '+str(epoch)+' loss: '+str(train_loss/s))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "test loss: tensor(0.2657)\n"
     ]
    }
   ],
   "source": [
    "# teste do modelo\n",
    "\n",
    "test_loss = 0\n",
    "s = 0.\n",
    "\n",
    "for id_user in range(nb_users):\n",
    "    \n",
    "    v = train_set[id_user:id_user+1]\n",
    "    vt = test_set[id_user:id_user+1]\n",
    "    \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",
    "    \n",
    "print('test loss: '+str(test_loss/s))"
   ]
  }
 ],
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