RecSys-Projeto.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# **Nanodegree Engenheiro de Machine Learning** \n",
    "-------------------------------------------\n",
    "## **Projeto Final | Modelo de um sistema de recomendação de filmes**\n",
    "-------------------------------------------\n",
    "Ricardo Luiz Araujo Junior <br>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## **Observando os dados**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# importando as bibliotecas necessárias\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from surprise import accuracy, Reader, Dataset, SVD, model_selection, KNNBaseline, dataset\n",
    "from surprise.model_selection import train_test_split, GridSearchCV\n",
    "from collections import defaultdict\n",
    "from wordcloud import WordCloud, STOPWORDS\n",
    "from IPython.display import Latex\n",
    "import time\n",
    "from IPython.display import Latex, Image, display\n",
    "from IPython.core.display import HTML\n",
    "import requests as req\n",
    "import json\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### **Carregando o dataset de classificações de usuários**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Vamos começar observando a estrutura do dataset ratings:<br>\n",
    "**userId**: ID do usuário.<br>\n",
    "**movieId**: ID do filme classificado pelo usuário.<br>\n",
    "**rating**: Nota que o usuário deu para o filme. Em escala de 1 a 5, com incrementos de\n",
    "meio (0.5).<br>\n",
    "**timestamp**: Horário da classificação em formato timestamp."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<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",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>31</td>\n",
       "      <td>2.5</td>\n",
       "      <td>1260759144</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1029</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1260759179</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>1061</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1260759182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1129</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1260759185</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>1172</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1260759205</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   userId  movieId  rating   timestamp\n",
       "0       1       31     2.5  1260759144\n",
       "1       1     1029     3.0  1260759179\n",
       "2       1     1061     3.0  1260759182\n",
       "3       1     1129     2.0  1260759185\n",
       "4       1     1172     4.0  1260759205"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_ratings = pd.read_csv('ratings.csv')\n",
    "df_ratings.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Convertendo a coluna timestamp para que possamos visualizar o ano"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<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",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>31</td>\n",
       "      <td>2.5</td>\n",
       "      <td>2009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1029</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>1061</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1129</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>1172</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2009</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   userId  movieId  rating timestamp\n",
       "0       1       31     2.5      2009\n",
       "1       1     1029     3.0      2009\n",
       "2       1     1061     3.0      2009\n",
       "3       1     1129     2.0      2009\n",
       "4       1     1172     4.0      2009"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_ratings['timestamp'] = df_ratings['timestamp'].apply(lambda x: time.strftime('%Y', time.localtime(x)))\n",
    "df_ratings.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "O dataset ratings contém 100004 entradas e 4 colunas.\n"
     ]
    }
   ],
   "source": [
    "print('O dataset ratings contém ' + str(df_ratings.shape[0]) + ' entradas e ' + str(df_ratings.shape[1]) + ' colunas.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
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       "\n",
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       "    }\n",
       "\n",
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       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>100004.000000</td>\n",
       "      <td>100004.000000</td>\n",
       "      <td>100004.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>347.011310</td>\n",
       "      <td>12548.664363</td>\n",
       "      <td>3.543608</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>195.163838</td>\n",
       "      <td>26369.198969</td>\n",
       "      <td>1.058064</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>182.000000</td>\n",
       "      <td>1028.000000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>367.000000</td>\n",
       "      <td>2406.500000</td>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>520.000000</td>\n",
       "      <td>5418.000000</td>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>671.000000</td>\n",
       "      <td>163949.000000</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              userId        movieId         rating\n",
       "count  100004.000000  100004.000000  100004.000000\n",
       "mean      347.011310   12548.664363       3.543608\n",
       "std       195.163838   26369.198969       1.058064\n",
       "min         1.000000       1.000000       0.500000\n",
       "25%       182.000000    1028.000000       3.000000\n",
       "50%       367.000000    2406.500000       4.000000\n",
       "75%       520.000000    5418.000000       4.000000\n",
       "max       671.000000  163949.000000       5.000000"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_ratings.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Vemos que grande parte das notas de usuários se concentra entre 3 e 5."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Carregando o Dataset de filmes**\n",
    "\n",
    "Cada linha deste dataset representa um filme.<br>\n",
    "**movieId** : ID do respectivo filme.<br>\n",
    "**title** : Nome do filme.<br>\n",
    "**genres** : Gênero do filme"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "        text-align: right;\n",
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       "    }\n",
       "\n",
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       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<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>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>Adventure|Animation|Children|Comedy|Fantasy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Jumanji (1995)</td>\n",
       "      <td>Adventure|Children|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|Romance</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  \\\n",
       "0        1                    Toy Story (1995)   \n",
       "1        2                      Jumanji (1995)   \n",
       "2        3             Grumpier Old Men (1995)   \n",
       "3        4            Waiting to Exhale (1995)   \n",
       "4        5  Father of the Bride Part II (1995)   \n",
       "\n",
       "                                        genres  \n",
       "0  Adventure|Animation|Children|Comedy|Fantasy  \n",
       "1                   Adventure|Children|Fantasy  \n",
       "2                               Comedy|Romance  \n",
       "3                         Comedy|Drama|Romance  \n",
       "4                                       Comedy  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_filmes = pd.read_csv('movies.csv')\n",
    "df_filmes.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Isolando o ano em outra coluna **"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "        text-align: right;\n",
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       "</style>\n",
       "<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>genres</th>\n",
       "      <th>year</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>Adventure|Animation|Children|Comedy|Fantasy</td>\n",
       "      <td>1995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Jumanji (1995)</td>\n",
       "      <td>Adventure|Children|Fantasy</td>\n",
       "      <td>1995</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",
       "      <td>1995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Waiting to Exhale (1995)</td>\n",
       "      <td>Comedy|Drama|Romance</td>\n",
       "      <td>1995</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",
       "      <td>1995</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   movieId                               title  \\\n",
       "0        1                    Toy Story (1995)   \n",
       "1        2                      Jumanji (1995)   \n",
       "2        3             Grumpier Old Men (1995)   \n",
       "3        4            Waiting to Exhale (1995)   \n",
       "4        5  Father of the Bride Part II (1995)   \n",
       "\n",
       "                                        genres  year  \n",
       "0  Adventure|Animation|Children|Comedy|Fantasy  1995  \n",
       "1                   Adventure|Children|Fantasy  1995  \n",
       "2                               Comedy|Romance  1995  \n",
       "3                         Comedy|Drama|Romance  1995  \n",
       "4                                       Comedy  1995  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_filmes[\"year\"] = df_filmes['title'].str.extract('.*\\((.*)\\).*', expand=False)\n",
    "df_filmes.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "O dataset movies contém 9125 entradas e 4 colunas.\n"
     ]
    }
   ],
   "source": [
    "print('O dataset movies contém ' + str(df_filmes.shape[0]) + ' entradas e ' + str(df_filmes.shape[1]) + ' colunas.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>movieId</th>\n",
       "      <th>imdbId</th>\n",
       "      <th>tmdbId</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>114709</td>\n",
       "      <td>862.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>113497</td>\n",
       "      <td>8844.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>113228</td>\n",
       "      <td>15602.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>114885</td>\n",
       "      <td>31357.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>113041</td>\n",
       "      <td>11862.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   movieId  imdbId   tmdbId\n",
       "0        1  114709    862.0\n",
       "1        2  113497   8844.0\n",
       "2        3  113228  15602.0\n",
       "3        4  114885  31357.0\n",
       "4        5  113041  11862.0"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_links = pd.read_csv('links.csv')\n",
    "df_links.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "O dataset links contém 9125 entradas e 3 colunas.\n"
     ]
    }
   ],
   "source": [
    "print('O dataset links contém ' + str(df_links.shape[0]) + ' entradas e ' + str(df_links.shape[1]) + ' colunas.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "O conjunto de dados possui 671 usuários e 9125 filmes\n",
      "O links.csv possui 9125 entradas de filmes\n"
     ]
    }
   ],
   "source": [
    "# Quantidade de usuários\n",
    "users_count = df_ratings['userId'].nunique()\n",
    "\n",
    "# Quantidade de filmes\n",
    "movies_count = df_filmes['movieId'].nunique()\n",
    "\n",
    "# Verificando se a quantidade de filmes equivale ao do movies.csv\n",
    "links_count = df_links['movieId'].nunique()\n",
    "\n",
    "print('O conjunto de dados possui {0} usuários e {1} filmes'.format(users_count,movies_count))\n",
    "print('O links.csv possui {0} entradas de filmes'.format(links_count))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## **Data Cleaning**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Verificando se os dataframes possuem valores nulos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "userId       False\n",
       "movieId      False\n",
       "rating       False\n",
       "timestamp    False\n",
       "dtype: bool"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_ratings.isnull().any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "movieId    False\n",
       "title      False\n",
       "genres     False\n",
       "year        True\n",
       "dtype: bool"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_filmes.isnull().any()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "O dataframe df_filmes possui valores nulos na coluna \"year\". Não vai atrapalhar no treinamento do modelo, mas pode atrapalhar na visualização exploratória, a depender da quantidade de valores nulos."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "year    4\n",
       "dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "null_columns=df_filmes.columns[df_filmes.isnull().any()]\n",
    "df_filmes[null_columns].isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Existem apenas 4 valores nulos na coluna \"year\", não irá fazer diferença na visualização. Vou manter a linha pois não poderá faltar filmes neste dataframe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "movieId    False\n",
       "imdbId     False\n",
       "tmdbId      True\n",
       "dtype: bool"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_links.isnull().any()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Temos links do TMDB faltando, vamos verificar quantos."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tmdbId    13\n",
       "dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "null_columns=df_links.columns[df_links.isnull().any()]\n",
    "df_links[null_columns].isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "13 links faltando. Agora vamos verificar quais."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "607        720\n",
       "608        721\n",
       "640        769\n",
       "910       1133\n",
       "2282      2851\n",
       "3246      4051\n",
       "5826     26587\n",
       "7408     72781\n",
       "7438     73759\n",
       "7588     79299\n",
       "8045     94466\n",
       "8505    108548\n",
       "8507    108583\n",
       "Name: movieId, dtype: int64"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "null_links = df_links[df_links['tmdbId'].isnull()]['movieId']\n",
    "null_links"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Caso algum desses \"movieId\" seja selecionado para buscar dados da API do TMDb, estaremos cientes do problema. Pode prejudicar somente a visualização da capa do filme, não é algo que vai prejuticar o modelo em si."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## **Visualização Exploratória**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Vamos observar a distribuição dos dados de classificações feitas por usuários:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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cmd8AXh3Q1FGHXqiWYpwCTAT2DugzWPvAtn2D9JUkSZLa2kg2DB4e8Lob2EMVhrsbtDfq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\nJEmFDM+SJElSIcOzJEmSVOj/AT+fwyQ4W4hgAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x222ad818240>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,5))\n",
    "ax = sns.distplot(df_ratings['rating'], hist= True, kde=False, axlabel='Classificações',\n",
    "                 hist_kws={\"rwidth\": 5.0, \"linewidth\":1, 'edgecolor':'black', 'alpha': 1.0})\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Aqui confirmamos a análise onde verificamos que os usuários tendem a classificar filmes que acharam medianos a muito bons."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(9125, 4)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_plot = df_filmes\n",
    "df_plot.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<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>genres</th>\n",
       "      <th>year</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>Adventure</td>\n",
       "      <td>1995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Toy Story (1995)</td>\n",
       "      <td>Animation</td>\n",
       "      <td>1995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Toy Story (1995)</td>\n",
       "      <td>Children</td>\n",
       "      <td>1995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Toy Story (1995)</td>\n",
       "      <td>Comedy</td>\n",
       "      <td>1995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Toy Story (1995)</td>\n",
       "      <td>Fantasy</td>\n",
       "      <td>1995</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   movieId             title     genres  year\n",
       "0        1  Toy Story (1995)  Adventure  1995\n",
       "0        1  Toy Story (1995)  Animation  1995\n",
       "0        1  Toy Story (1995)   Children  1995\n",
       "0        1  Toy Story (1995)     Comedy  1995\n",
       "0        1  Toy Story (1995)    Fantasy  1995"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# A função tidy_split() passa pelos valores de uma coluna de um dataframe\n",
    "# e separa em linhas direfentes as strings de cada célula, utilizando como separador o '|'.\n",
    "# Solução inspirada em um comentário do Stack Overflow: \n",
    "# https://stackoverflow.com/questions/12680754/split-explode-pandas-dataframe-string-entry-to-separate-rows\n",
    "# Necessário para fazer a contagem de lançamentos anuais de cada gênero\n",
    "\n",
    "def tidy_split(df, column, sep='|', keep=False):\n",
    "    indexes = list()\n",
    "    new_values = list()\n",
    "    df = df.dropna(subset=[column])\n",
    "    for i, presplit in enumerate(df[column].astype(str)):\n",
    "        values = presplit.split(sep)\n",
    "        if keep and len(values) > 1:\n",
    "            indexes.append(i)\n",
    "            new_values.append(presplit)\n",
    "        for value in values:\n",
    "            indexes.append(i)\n",
    "            new_values.append(value)\n",
    "    new_df = df.iloc[indexes, :].copy()\n",
    "    new_df[column] = new_values\n",
    "    return new_df\n",
    "\n",
    "df_plot = tidy_split(df_plot, \"genres\", sep=\"|\")\n",
    "\n",
    "# convertendo a df_plot['year'] para numérico\n",
    "df_plot[\"year\"] = pd.to_numeric(df_plot[\"year\"] ,errors='coerce')\n",
    "# excluindo colunas com valores não definidos\n",
    "df_plot = df_plot.dropna()\n",
    "# convertendo df_plot['year'] para inteiro\n",
    "df_plot.year = df_plot.year.astype(int)\n",
    "df_plot.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Quantidade de produções cinematográfica ao longo dos anos**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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WITg6t/aWhaYkWkRERCSFHMfhO49aWjuHuHHHCq7cWDPpeXOtRO/ef4aegRA7L6unuMA3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\nSUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x222ae4969e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "byYear = df_plot.groupby(['year'])['year'].count()\n",
    "byYear.plot(kind='line', figsize=(12,7))\n",
    "plt.xlabel('Ano')\n",
    "plt.ylabel('Quantidade')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Anos que houveram mais lançamentos:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "year\n",
       "2008    612\n",
       "2002    618\n",
       "2007    626\n",
       "2006    647\n",
       "2009    673\n",
       "Name: year, dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "byYear.sort_values().tail(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Quantidade de ocorrências de gêneros ao longo dos anos**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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JJ56Ix+PhzTffpLKykqysLEaNGsWTTz7JpZdeis/nY+nSpQwdOrTT1h8u9P6E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\nKw0LNgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x222ae4fe048>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Agrupando em uma hierarquia de ano -> gênero e fazendo a contagem por gênero.\n",
    "df_plot = df_plot.groupby(['year', 'genres'])['genres'].count()\n",
    "# Pivotando os gêneros em colunas para realizar a plotagem\n",
    "df_plot2 = df_plot.unstack()\n",
    "\n",
    "plt.style.use('fivethirtyeight')\n",
    "fig, ax = plt.subplots(figsize=(12,7))\n",
    "ax.plot(df_plot2.index, df_plot2.Drama, lw=2, label='Drama')\n",
    "ax.plot(df_plot2.index, df_plot2.Comedy, lw=2, label='Comédia')\n",
    "ax.plot(df_plot2.index, df_plot2.Thriller, lw=2, label='Suspense')\n",
    "ax.plot(df_plot2.index, df_plot2.Action, lw=2, label='Ação')\n",
    "ax.plot(df_plot2.index, df_plot2.Romance, lw=2, label='Romance')\n",
    "ax.plot(df_plot2.index, df_plot2.Adventure, lw=2, label='Aventura')\n",
    "ax.legend(prop={'size': 12})\n",
    "plt.xlabel('Ano')\n",
    "plt.ylabel('Quantidade')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Quantidade de ocorrências de gêneros ao longo dos anos**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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YbPVVcosXL6Z79+4Rzz9w4ADPP/88W7duZcqUKTz66KPcfvvtLFy4kBUrVnDP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6lOpYi0yZDQ3aYto0PgvBXuEe91MrsUJEF+pM3xdWKI05W/5/v6DLRD/xmSs2fPYts2k5OT/Owr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\nREREDKNCIiIiIiIihlEhERERERERw/wHbm1bbgtLgvsAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x222ae840c18>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.style.use('fivethirtyeight')\n",
    "fig, ax = plt.subplots(figsize=(12,7))\n",
    "ax.plot(df_plot2.index, df_plot2.Animation, lw=2, label='Animação')\n",
    "ax.plot(df_plot2.index, df_plot2.War, lw=2, label='Guerra')\n",
    "ax.plot(df_plot2.index, df_plot2['Sci-Fi'], lw=2, label='Ficção científica')\n",
    "ax.plot(df_plot2.index, df_plot2.IMAX, lw=2, label='IMAX')\n",
    "ax.plot(df_plot2.index, df_plot2.Children, lw=2, label='Infantil')\n",
    "ax.plot(df_plot2.index, df_plot2['Film-Noir'], lw=2, label='Film-Noir')\n",
    "\n",
    "ax.legend(prop={'size': 12})\n",
    "plt.xlabel('Ano')\n",
    "plt.ylabel('Quantidade')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### **Observando a popupalidade dos gêneros**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Definindo uma função para fazer o trabalho de contagem de quais os gêneros mais populares fazer uma visualização."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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hqt6QvPqGw+49Fm991UWjOXUipdVS5Nmtw59HRoojf5pr6rH12DKByjDuIQ0z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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x184825ba390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "[['Drama', 4365],\n",
       " ['Comedy', 3315],\n",
       " ['Thriller', 1729],\n",
       " ['Romance', 1545],\n",
       " ['Action', 1545],\n",
       " ['Adventure', 1117],\n",
       " ['Crime', 1100],\n",
       " ['Horror', 877],\n",
       " ['Sci-Fi', 792],\n",
       " ['Fantasy', 654]]"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# conta o número de ocorrências de cada gênero e plota\n",
    "# em uma imagem. Quanto maior o número de ocorrências, maior a letra. \n",
    "\n",
    "def contador(df, ref_col, liste):\n",
    "    keyword_count = dict()\n",
    "    for s in liste: keyword_count[s] = 0\n",
    "    for liste_keywords in df[ref_col].str.split('|'):        \n",
    "        if type(liste_keywords) == float and pd.isnull(liste_keywords): continue        \n",
    "        for s in [s for s in liste_keywords if s in liste]: \n",
    "            if pd.notnull(s): keyword_count[s] += 1\n",
    "    keyword_occurences = []\n",
    "    for k,v in keyword_count.items():\n",
    "        keyword_occurences.append([k,v])\n",
    "    keyword_occurences.sort(key = lambda x:x[1], reverse = True)\n",
    "    return keyword_occurences, keyword_count           \n",
    "\n",
    "generos = set()\n",
    "for s in df_filmes['genres'].str.split('|').values:\n",
    "    generos = generos.union(set(s))\n",
    "    \n",
    "ocorrencias_genero, dum = contador(df_filmes, 'genres', generos) \n",
    "\n",
    "words = dict()\n",
    "trunc_occurences = ocorrencias_genero[0:50]\n",
    "\n",
    "for s in ocorrencias_genero:\n",
    "    words[s[0]] = s[1]\n",
    "\n",
    "f, ax = plt.subplots(figsize=(10, 7))\n",
    "wordcloud = WordCloud(max_font_size=40).generate_from_frequencies(words)\n",
    "\n",
    "plt.imshow(wordcloud, cmap=plt.cm.tab20, interpolation=\"bilinear\")\n",
    "plt.axis('off')\n",
    "plt.show()\n",
    "ocorrencias_genero[:10] "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Calibrando o algoritmo"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Nesta seção vamos utilizar a Busca de Grade para encontrar a melhor combinação de parâmetros para utilizar no treinamento do algoritmo SVD. A busca de grade fornecida pela biblioteca Surprise foi inspirada na que encontramos no scikit-learn.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.892543206267\n",
      "0.688931430331\n",
      "{'n_epochs': 30, 'lr_all': 0.005, 'reg_all': 0.1, 'biased': True}\n",
      "{'n_epochs': 30, 'lr_all': 0.005, 'reg_all': 0.1, 'biased': True}\n"
     ]
    }
   ],
   "source": [
    "reader = Reader(rating_scale=(1, 5))\n",
    "data = Dataset.load_from_df(df_ratings[['userId', 'movieId','rating']], reader)\n",
    "\n",
    "param_grid = {'n_epochs': [5, 10, 20, 30], 'lr_all': [0.002, 0.005],\n",
    "              'reg_all': [0.1, 0.4, 0.8], 'biased':[True]}\n",
    "gs = GridSearchCV(SVD, param_grid, measures=['rmse', 'mae'], cv=3)\n",
    "gs.fit(data)\n",
    "\n",
    "# melhor pontuação para RMSE\n",
    "print(gs.best_score['rmse'])\n",
    "\n",
    "# melhor pontuação para MAE\n",
    "print(gs.best_score['mae'])\n",
    "\n",
    "# melhores parâmetros de acordo com o RMSE\n",
    "print(gs.best_params['rmse'])\n",
    "\n",
    "# melhores parâmetros de acordo com o MAE\n",
    "print(gs.best_params['mae'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "OBS: <br>\n",
    "n_epochs = número de repetições de gradiente descendente estocástico<br>\n",
    "lr_all = learning rate<br>\n",
    "reg_all = parâmetro de regularização<br>\n",
    "biased = ajuste de tendências do usuário"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Estimating biases using als...\n",
      "Computing the pearson_baseline similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Estimating biases using als...\n",
      "Computing the pearson_baseline similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Estimating biases using als...\n",
      "Computing the pearson_baseline similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Estimating biases using als...\n",
      "Computing the pearson_baseline similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Estimating biases using als...\n",
      "Computing the pearson_baseline similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Estimating biases using als...\n",
      "Computing the pearson_baseline similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Estimating biases using als...\n",
      "Computing the cosine similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Estimating biases using als...\n",
      "Computing the cosine similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Estimating biases using als...\n",
      "Computing the cosine similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Estimating biases using als...\n",
      "Computing the cosine similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Estimating biases using als...\n",
      "Computing the cosine similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Estimating biases using als...\n",
      "Computing the cosine similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Estimating biases using als...\n",
      "Computing the pearson_baseline similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Estimating biases using als...\n",
      "Computing the pearson_baseline similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Estimating biases using als...\n",
      "Computing the pearson_baseline similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Estimating biases using als...\n",
      "Computing the pearson_baseline similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Estimating biases using als...\n",
      "Computing the pearson_baseline similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Estimating biases using als...\n",
      "Computing the pearson_baseline similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Estimating biases using als...\n",
      "Computing the cosine similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Estimating biases using als...\n",
      "Computing the cosine similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Estimating biases using als...\n",
      "Computing the cosine similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Estimating biases using als...\n",
      "Computing the cosine similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Estimating biases using als...\n",
      "Computing the cosine similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Estimating biases using als...\n",
      "Computing the cosine similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Estimating biases using als...\n",
      "Computing the pearson_baseline similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Estimating biases using als...\n",
      "Computing the pearson_baseline similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Estimating biases using als...\n",
      "Computing the pearson_baseline similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Estimating biases using als...\n",
      "Computing the pearson_baseline similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Estimating biases using als...\n",
      "Computing the pearson_baseline similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Estimating biases using als...\n",
      "Computing the pearson_baseline similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Estimating biases using als...\n",
      "Computing the cosine similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Estimating biases using als...\n",
      "Computing the cosine similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Estimating biases using als...\n",
      "Computing the cosine similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Estimating biases using als...\n",
      "Computing the cosine similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Estimating biases using als...\n",
      "Computing the cosine similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Estimating biases using als...\n",
      "Computing the cosine similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Estimating biases using als...\n",
      "Computing the pearson_baseline similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Estimating biases using als...\n",
      "Computing the pearson_baseline similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Estimating biases using als...\n",
      "Computing the pearson_baseline similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Estimating biases using als...\n",
      "Computing the pearson_baseline similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Estimating biases using als...\n",
      "Computing the pearson_baseline similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Estimating biases using als...\n",
      "Computing the pearson_baseline similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Estimating biases using als...\n",
      "Computing the cosine similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Estimating biases using als...\n",
      "Computing the cosine similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Estimating biases using als...\n",
      "Computing the cosine similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Estimating biases using als...\n",
      "Computing the cosine similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Estimating biases using als...\n",
      "Computing the cosine similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Estimating biases using als...\n",
      "Computing the cosine similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Estimating biases using sgd...\n",
      "Computing the pearson_baseline similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Estimating biases using sgd...\n",
      "Computing the pearson_baseline similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Estimating biases using sgd...\n",
      "Computing the pearson_baseline similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Estimating biases using sgd...\n",
      "Computing the pearson_baseline similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Estimating biases using sgd...\n",
      "Computing the pearson_baseline similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Estimating biases using sgd...\n",
      "Computing the pearson_baseline similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Estimating biases using sgd...\n",
      "Computing the cosine similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Estimating biases using sgd...\n",
      "Computing the cosine similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Estimating biases using sgd...\n",
      "Computing the cosine similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Estimating biases using sgd...\n",
      "Computing the cosine similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Estimating biases using sgd...\n",
      "Computing the cosine similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Estimating biases using sgd...\n",
      "Computing the cosine similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Estimating biases using sgd...\n",
      "Computing the pearson_baseline similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Estimating biases using sgd...\n",
      "Computing the pearson_baseline similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Estimating biases using sgd...\n",
      "Computing the pearson_baseline similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Estimating biases using sgd...\n",
      "Computing the pearson_baseline similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Estimating biases using sgd...\n",
      "Computing the pearson_baseline similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Estimating biases using sgd...\n",
      "Computing the pearson_baseline similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Estimating biases using sgd...\n",
      "Computing the cosine similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Estimating biases using sgd...\n",
      "Computing the cosine similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Estimating biases using sgd...\n",
      "Computing the cosine similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Estimating biases using sgd...\n",
      "Computing the cosine similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Estimating biases using sgd...\n",
      "Computing the cosine similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Estimating biases using sgd...\n",
      "Computing the cosine similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Estimating biases using sgd...\n",
      "Computing the pearson_baseline similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Estimating biases using sgd...\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Computing the pearson_baseline similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Estimating biases using sgd...\n",
      "Computing the pearson_baseline similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Estimating biases using sgd...\n",
      "Computing the pearson_baseline similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Estimating biases using sgd...\n",
      "Computing the pearson_baseline similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Estimating biases using sgd...\n",
      "Computing the pearson_baseline similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Estimating biases using sgd...\n",
      "Computing the cosine similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Estimating biases using sgd...\n",
      "Computing the cosine similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Estimating biases using sgd...\n",
      "Computing the cosine similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Estimating biases using sgd...\n",
      "Computing the cosine similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Estimating biases using sgd...\n",
      "Computing the cosine similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Estimating biases using sgd...\n",
      "Computing the cosine similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Estimating biases using sgd...\n",
      "Computing the pearson_baseline similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Estimating biases using sgd...\n",
      "Computing the pearson_baseline similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Estimating biases using sgd...\n",
      "Computing the pearson_baseline similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Estimating biases using sgd...\n",
      "Computing the pearson_baseline similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Estimating biases using sgd...\n",
      "Computing the pearson_baseline similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Estimating biases using sgd...\n",
      "Computing the pearson_baseline similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Estimating biases using sgd...\n",
      "Computing the cosine similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Estimating biases using sgd...\n",
      "Computing the cosine similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Estimating biases using sgd...\n",
      "Computing the cosine similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Estimating biases using sgd...\n",
      "Computing the cosine similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Estimating biases using sgd...\n",
      "Computing the cosine similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Estimating biases using sgd...\n",
      "Computing the cosine similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "0.951820691987\n",
      "{'bsl_options': {'method': 'als', 'reg': 1}, 'k': 3, 'sim_options': {'name': 'pearson_baseline', 'min_support': 5, 'user_based': False}}\n"
     ]
    }
   ],
   "source": [
    "reader = Reader(rating_scale=(1, 5))\n",
    "data = Dataset.load_from_df(df_ratings[['userId', 'movieId','rating']], reader)\n",
    "\n",
    "param_grid = {'bsl_options': {'method': ['als', 'sgd'],\n",
    "                              'reg': [1, 2]},\n",
    "              'k': [2, 3],\n",
    "              'sim_options': {'name': ['pearson_baseline', 'cosine'],\n",
    "                              'min_support': [1, 5],\n",
    "                              'user_based': [False]}\n",
    "              }\n",
    "\n",
    "knn_gs = GridSearchCV(KNNBaseline, param_grid, measures=['rmse', 'mae'], cv=3)\n",
    "knn_gs.fit(data)\n",
    "\n",
    "# melhor pontuação para RMSE\n",
    "print(knn_gs.best_score['rmse'])\n",
    "\n",
    "# melhores parâmetros para o modelo\n",
    "print(knn_gs.best_params['rmse'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.72329153285\n",
      "{'bsl_options': {'method': 'als', 'reg': 1}, 'k': 3, 'sim_options': {'name': 'pearson_baseline', 'min_support': 5, 'user_based': False}}\n"
     ]
    }
   ],
   "source": [
    "# melhor pontuação para MAE\n",
    "print(knn_gs.best_score['mae'])\n",
    "# melhores parâmetros de acordo com o MAE\n",
    "print(knn_gs.best_params['mae'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Implementação: K-Nearest Neighbour "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**K-Nearest Neighbour com coeficiente de correlação de Pearson**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Estimating biases using als...\n",
      "Computing the pearson_baseline similarity matrix...\n",
      "Done computing similarity matrix.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<surprise.prediction_algorithms.knns.KNNBaseline at 0x184f2731f60>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Carregando os dados de df_ratings na biblioteca Surprise\n",
    "reader = Reader(rating_scale=(1, 5))\n",
    "data = Dataset.load_from_df(df_ratings[['userId', 'movieId','rating']], reader)\n",
    "\n",
    "# ajustando os parâmetros e definindo no algoritmo KNN\n",
    "sim_options = {'name': 'pearson_baseline','min_support': 5,'user_based': False}\n",
    "bsl_options = {'method': 'als',\n",
    "               'n_epochs': 5,\n",
    "               'reg': 1,\n",
    "               }\n",
    "algo = KNNBaseline(k=3, sim_options=sim_options, bsl_options=bsl_options)\n",
    "\n",
    "# treinando o conjunto de dados\n",
    "trainset = data.build_full_trainset()\n",
    "algo.fit(trainset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Busca o raw id (movieId) através nome do título no dataset df_filmes\n",
    "# depois converte o raw id para inner id\n",
    "# OBS: cada raw id é mapeado para um inteiro chamado inner id,\n",
    "# que é mais conveniente para o Surprise manipular.\n",
    "def title_to_rid(title):\n",
    "    raw_iid = df_filmes[df_filmes['title'] == title]['movieId'].item()\n",
    "    inner_iid = algo.trainset.to_inner_iid(raw_iid)\n",
    "    return inner_iid, raw_iid\n",
    "\n",
    "# Define o título e quantos K-neighbour deseja retornar.\n",
    "# Converte raw id do Surprise para inner id usando title_to_rid.\n",
    "# Busca os K neighbors usando a inner id e \n",
    "# Retorna as inner ids dos similares.\n",
    "# Converte inner ids dos similares para raw ids  \n",
    "\n",
    "# Define o título e quantos K-neighbour deseja retornar.\n",
    "def visualizar_sim(title, k):\n",
    "    # converte o title para inner id e raw id\n",
    "    inner_raw_iid = title_to_rid(title)\n",
    "    iid_similares = []\n",
    "    # recebe o inner id dos K Neighbours mais similares\n",
    "    iid_similares = algo.get_neighbors(inner_raw_iid[0], k)\n",
    "    #converte inner id para raw id (movieId)\n",
    "    rid = (algo.trainset.to_raw_iid(iid)\n",
    "          for iid in iid_similares)\n",
    "    # define os raw ids em uma lista\n",
    "    lst = list(rid)\n",
    "    filmes = []\n",
    "    image_urls = []\n",
    "    nomes_filmes = []\n",
    "    # Percorre a lista para buscar quais filmes pertencem ao raw id e\n",
    "    # busca na API do TMDb a capa do filme, para visualização, e o gênero.\n",
    "    for mid in lst:\n",
    "        nome_filme = df_filmes[df_filmes['movieId'] == mid]['title'].item()\n",
    "        nomes_filmes.append(nome_filme)\n",
    "        tmdbid = int(df_links[df_links['movieId'] == mid]['tmdbId'].item())\n",
    "        url = 'https://api.themoviedb.org/3/movie/' + str(tmdbid) + '?api_key=835087eb6e4fb4b6991c31b490316f2a'\n",
    "        resp = req.get(url)\n",
    "        parsed = json.loads(resp.text)\n",
    "        image = parsed['poster_path']        \n",
    "        image_url = 'https://image.tmdb.org/t/p/w200' +  str(image)\n",
    "        image_urls.append(image_url)\n",
    "        # exibe o nome do filme\n",
    "        display(nome_filme)      \n",
    "        for i in range(len(parsed['genres'])):\n",
    "            # exibe os gêneros do último filme para fazer comparações\n",
    "            display(parsed['genres'][i]['name']) #height='200' width='100'\n",
    "    html_str = '<table>\\\n",
    "                <tr> \\\n",
    "                <td><table><tr><td>'+ str(nomes_filmes[0]) +'</td></tr><tr><td><img src=' + str(image_urls[0]) + '></td></tr></table></td>\\\n",
    "                <td><table><tr><td>'+ str(nomes_filmes[1]) +'</td></tr><tr><td><img src=' + str(image_urls[1]) + '></td></tr></table></td>\\\n",
    "                <td><table><tr><td>'+ str(nomes_filmes[2]) +'</td></tr><tr><td><img src=' + str(image_urls[2]) + '></td></tr></table></td>\\\n",
    "                <td><table><tr><td>'+ str(nomes_filmes[3]) +'</td></tr><tr><td><img src=' + str(image_urls[3]) + '></td></tr></table></td>\\\n",
    "                <td><table><tr><td>'+ str(nomes_filmes[4]) +'</td></tr><tr><td><img src=' + str(image_urls[4]) + '></td></tr></table></td>\\\n",
    "                </tr> \\\n",
    "                <tr> \\\n",
    "                <td><table><tr><td>'+ str(nomes_filmes[5]) +'</td></tr><tr><td><img src=' + str(image_urls[5]) + ' ></td></tr></table></td>\\\n",
    "                <td><table><tr><td>'+ str(nomes_filmes[6]) +'</td></tr><tr><td><img src=' + str(image_urls[6]) + ' ></td></tr></table></td>\\\n",
    "                <td><table><tr><td>'+ str(nomes_filmes[7]) +'</td></tr><tr><td><img src=' + str(image_urls[7]) + ' ></td></tr></table></td>\\\n",
    "                <td><table><tr><td>'+ str(nomes_filmes[8]) +'</td></tr><tr><td><img src=' + str(image_urls[8]) + ' ></td></tr></table></td>\\\n",
    "                <td><table><tr><td>'+ str(nomes_filmes[9]) +'</td></tr><tr><td><img src=' + str(image_urls[9]) + ' ></td></tr></table></td>\\\n",
    "                </tr>\\\n",
    "                </table>'\n",
    "    \n",
    "    display(HTML(html_str))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "KNN com Pearson ---> TOP 10 filmes mais similares a Toy Story:\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'Toy Story 2 (1999)'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Animation'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Comedy'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Family'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "\"Bug's Life, A (1998)\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Adventure'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Animation'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Comedy'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Family'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Toy Story 3 (2010)'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Animation'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Family'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Comedy'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Finding Nemo (2003)'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Animation'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Family'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Incredibles, The (2004)'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Action'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Adventure'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Animation'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Family'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Shrek (2001)'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Adventure'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Animation'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Comedy'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Family'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Fantasy'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Willy Wonka & the Chocolate Factory (1971)'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Music'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Family'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Fantasy'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Comedy'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Dark Knight, The (2008)'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Drama'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Action'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Crime'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Thriller'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Monsters, Inc. (2001)'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Animation'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Comedy'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Family'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Sin City (2005)'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Action'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Thriller'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Crime'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Science Fiction'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<table>                <tr>                 <td><table><tr><td>Toy Story 2 (1999)</td></tr><tr><td><img src=https://image.tmdb.org/t/p/w200/3CmK3XurcLeUyMifCR28ibzupbB.jpg></td></tr></table></td>                <td><table><tr><td>Bug's Life, A (1998)</td></tr><tr><td><img src=https://image.tmdb.org/t/p/w200/u9qGMRwcPwP0WETxulS5hKUsEum.jpg></td></tr></table></td>                <td><table><tr><td>Toy Story 3 (2010)</td></tr><tr><td><img src=https://image.tmdb.org/t/p/w200/mMltbSxwEdNE4Cv8QYLpzkHWTDo.jpg></td></tr></table></td>                <td><table><tr><td>Finding Nemo (2003)</td></tr><tr><td><img src=https://image.tmdb.org/t/p/w200/syPWyeeqzTQIxjIUaIFI7d0TyEY.jpg></td></tr></table></td>                <td><table><tr><td>Incredibles, The (2004)</td></tr><tr><td><img src=https://image.tmdb.org/t/p/w200/2LqaLgk4Z226KkgPJuiOQ58wvrm.jpg></td></tr></table></td>                </tr>                 <tr>                 <td><table><tr><td>Shrek (2001)</td></tr><tr><td><img src=https://image.tmdb.org/t/p/w200/140ewbWv8qHStD3mlBDvvGd0Zvu.jpg ></td></tr></table></td>                <td><table><tr><td>Willy Wonka & the Chocolate Factory (1971)</td></tr><tr><td><img src=https://image.tmdb.org/t/p/w200/b94qXd1FcIwgzv0NYMUe2bjrzJR.jpg ></td></tr></table></td>                <td><table><tr><td>Dark Knight, The (2008)</td></tr><tr><td><img src=https://image.tmdb.org/t/p/w200/1hRoyzDtpgMU7Dz4JF22RANzQO7.jpg ></td></tr></table></td>                <td><table><tr><td>Monsters, Inc. (2001)</td></tr><tr><td><img src=https://image.tmdb.org/t/p/w200/93Y9BGx8blzmZOPSoivkFfaifqU.jpg ></td></tr></table></td>                <td><table><tr><td>Sin City (2005)</td></tr><tr><td><img src=https://image.tmdb.org/t/p/w200/vKJVGOKPyWqp9d2EX5NH1liqRqR.jpg ></td></tr></table></td>                </tr>                </table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print('KNN com Pearson ---> TOP 10 filmes mais similares a Toy Story:')\n",
    "visualizar_sim('Toy Story (1995)', 10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**K-Nearest Neighbour com Similaridade Cosseno**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Estimating biases using als...\n",
      "Computing the cosine similarity matrix...\n",
      "Done computing similarity matrix.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<surprise.prediction_algorithms.knns.KNNBaseline at 0x239cd892dd8>"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reader = Reader(rating_scale=(1, 5))\n",
    "data = Dataset.load_from_df(df_ratings[['userId', 'movieId','rating']], reader)\n",
    "\n",
    "sim_options = {'name': 'cosine','min_support': 5,'user_based': False}\n",
    "bsl_options = {'method': 'als',\n",
    "               'n_epochs': 5,\n",
    "               'reg': 1,\n",
    "               }\n",
    "algo = KNNBaseline(k=3, sim_options=sim_options, bsl_options=bsl_options)\n",
    "trainset = data.build_full_trainset()\n",
    "algo.fit(trainset)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Similaridade Cosseno ---> TOP 10 filmes similares a Toy Story:\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'Born into Brothels (2004)'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Documentary'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Days of Wine and Roses (1962)'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Drama'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Romance'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "\"It's Kind of a Funny Story (2010)\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Comedy'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Drama'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "\"World's Fastest Indian, The (2005)\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Drama'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Shop Around the Corner, The (1940)'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Comedy'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Romance'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Lady Vanishes, The (1938)'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Mystery'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Thriller'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Lean on Me (1989)'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Drama'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Muppets From Space (1999)'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Fantasy'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Comedy'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Science Fiction'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Family'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Green Hornet, The (2011)'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Action'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Crime'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Comedy'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'I Love You, Man (2009)'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Comedy'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<table>                <tr>                 <td><table><tr><td>Born into Brothels (2004)</td></tr><tr><td><img src=https://image.tmdb.org/t/p/w200/gteL79uWvdec9Me0mCNHxI1kZdn.jpg></td></tr></table></td>                <td><table><tr><td>Days of Wine and Roses (1962)</td></tr><tr><td><img src=https://image.tmdb.org/t/p/w200/zVbL3eVcN90J7FzQfdTNW2UZfp0.jpg></td></tr></table></td>                <td><table><tr><td>It's Kind of a Funny Story (2010)</td></tr><tr><td><img src=https://image.tmdb.org/t/p/w200/wADblWdHMk2SIaZRBazTGOTBf4w.jpg></td></tr></table></td>                <td><table><tr><td>World's Fastest Indian, The (2005)</td></tr><tr><td><img src=https://image.tmdb.org/t/p/w200/h3xDXEEjppiyuRtMv6Cy5bcMTrG.jpg></td></tr></table></td>                <td><table><tr><td>Shop Around the Corner, The (1940)</td></tr><tr><td><img src=https://image.tmdb.org/t/p/w200/avHj8x3T21dYIGrnx55I57PxdEo.jpg></td></tr></table></td>                </tr>                 <tr>                 <td><table><tr><td>Lady Vanishes, The (1938)</td></tr><tr><td><img src=https://image.tmdb.org/t/p/w200/edL8YmyR1BjICz3mp1fhUqSOPnF.jpg ></td></tr></table></td>                <td><table><tr><td>Lean on Me (1989)</td></tr><tr><td><img src=https://image.tmdb.org/t/p/w200/oFGnvc58Ashxfbp3HDFhhlSGT0W.jpg ></td></tr></table></td>                <td><table><tr><td>Muppets From Space (1999)</td></tr><tr><td><img src=https://image.tmdb.org/t/p/w200/zhiHQIgLSfII3ynPg2LRc1ZBrAE.jpg ></td></tr></table></td>                <td><table><tr><td>Green Hornet, The (2011)</td></tr><tr><td><img src=https://image.tmdb.org/t/p/w200/hZvTFnyqiTiQDO1FRsQvwhFNg3G.jpg ></td></tr></table></td>                <td><table><tr><td>I Love You, Man (2009)</td></tr><tr><td><img src=https://image.tmdb.org/t/p/w200/gFL5cazCOhgsoO2mQuYPMdFekfE.jpg ></td></tr></table></td>                </tr>                </table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print('Similaridade Cosseno ---> TOP 10 filmes similares a Toy Story:')\n",
    "visualizar_sim('Toy Story (1995)', 10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Estimating biases using sgd...\n",
      "Computing the pearson_baseline similarity matrix...\n",
      "Done computing similarity matrix.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<surprise.prediction_algorithms.knns.KNNBaseline at 0x23981224e10>"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Carregando os dados de df_ratings na biblioteca Surprise\n",
    "reader = Reader(rating_scale=(1, 5))\n",
    "data = Dataset.load_from_df(df_ratings[['userId', 'movieId','rating']], reader)\n",
    "\n",
    "# ajustando os parâmetros e definindo no algoritmo KNN\n",
    "# desta vez utilizando SGD\n",
    "sim_options = {'name': 'pearson_baseline','min_support': 5,'user_based': False}\n",
    "bsl_options = {'method': 'sgd',\n",
    "               'n_epochs': 5,\n",
    "               'reg': 1,\n",
    "               }\n",
    "algo = KNNBaseline(k=3, sim_options=sim_options, bsl_options=bsl_options)\n",
    "\n",
    "# treinando o conjunto de dados\n",
    "trainset = data.build_full_trainset()\n",
    "algo.fit(trainset)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Utilizando KNN com Descida de Gradiente Estocástico e Coeficiente de Correlação de Pearson**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "KNN com Pearson ---> TOP 10 filmes mais similares a Toy Story:\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'Toy Story 2 (1999)'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Animation'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Comedy'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Family'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "\"Bug's Life, A (1998)\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Adventure'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Animation'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Comedy'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Family'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Monsters, Inc. (2001)'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Animation'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Comedy'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Family'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Lion King, The (1994)'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Family'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Animation'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Drama'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Finding Nemo (2003)'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Animation'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Family'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Shrek (2001)'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Adventure'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Animation'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Comedy'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Family'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Fantasy'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Incredibles, The (2004)'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Action'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Adventure'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Animation'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Family'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Toy Story 3 (2010)'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Animation'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Family'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Comedy'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Aladdin (1992)'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Animation'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Family'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Comedy'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Adventure'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Fantasy'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Romance'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Willy Wonka & the Chocolate Factory (1971)'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Music'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Family'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Fantasy'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Comedy'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<table>                <tr>                 <td><table><tr><td>Toy Story 2 (1999)</td></tr><tr><td><img src=https://image.tmdb.org/t/p/w200/3CmK3XurcLeUyMifCR28ibzupbB.jpg></td></tr></table></td>                <td><table><tr><td>Bug's Life, A (1998)</td></tr><tr><td><img src=https://image.tmdb.org/t/p/w200/u9qGMRwcPwP0WETxulS5hKUsEum.jpg></td></tr></table></td>                <td><table><tr><td>Monsters, Inc. (2001)</td></tr><tr><td><img src=https://image.tmdb.org/t/p/w200/93Y9BGx8blzmZOPSoivkFfaifqU.jpg></td></tr></table></td>                <td><table><tr><td>Lion King, The (1994)</td></tr><tr><td><img src=https://image.tmdb.org/t/p/w200/bKPtXn9n4M4s8vvZrbw40mYsefB.jpg></td></tr></table></td>                <td><table><tr><td>Finding Nemo (2003)</td></tr><tr><td><img src=https://image.tmdb.org/t/p/w200/syPWyeeqzTQIxjIUaIFI7d0TyEY.jpg></td></tr></table></td>                </tr>                 <tr>                 <td><table><tr><td>Shrek (2001)</td></tr><tr><td><img src=https://image.tmdb.org/t/p/w200/140ewbWv8qHStD3mlBDvvGd0Zvu.jpg ></td></tr></table></td>                <td><table><tr><td>Incredibles, The (2004)</td></tr><tr><td><img src=https://image.tmdb.org/t/p/w200/2LqaLgk4Z226KkgPJuiOQ58wvrm.jpg ></td></tr></table></td>                <td><table><tr><td>Toy Story 3 (2010)</td></tr><tr><td><img src=https://image.tmdb.org/t/p/w200/mMltbSxwEdNE4Cv8QYLpzkHWTDo.jpg ></td></tr></table></td>                <td><table><tr><td>Aladdin (1992)</td></tr><tr><td><img src=https://image.tmdb.org/t/p/w200/7f53XAE4nPiGe9XprpGAeWHuKPw.jpg ></td></tr></table></td>                <td><table><tr><td>Willy Wonka & the Chocolate Factory (1971)</td></tr><tr><td><img src=https://image.tmdb.org/t/p/w200/b94qXd1FcIwgzv0NYMUe2bjrzJR.jpg ></td></tr></table></td>                </tr>                </table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print('KNN com Pearson ---> TOP 10 filmes mais similares a Toy Story:')\n",
    "visualizar_sim('Toy Story (1995)', 10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Validando a recomendação usando outro filme com os parâmetros SGD e Coeficiente de Correlação de Pearson**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "KNN com Pearson ---> TOP 10 filmes mais similares a Lord of the Rings: The Fellowship of the Ring:\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'Lord of the Rings: The Two Towers, The (2002)'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Adventure'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Fantasy'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Action'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Lord of the Rings: The Return of the King, The (2003)'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Adventure'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Fantasy'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Action'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Star Wars: Episode VI - Return of the Jedi (1983)'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Adventure'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Action'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Science Fiction'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Star Wars: Episode IV - A New Hope (1977)'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Adventure'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Action'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Science Fiction'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Matrix, The (1999)'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Action'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Science Fiction'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Raiders of the Lost Ark (Indiana Jones and the Raiders of the Lost Ark) (1981)'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Adventure'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Action'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Star Wars: Episode V - The Empire Strikes Back (1980)'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Adventure'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Action'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Science Fiction'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Gladiator (2000)'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Action'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Drama'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Adventure'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Fight Club (1999)'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Drama'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Die Hard (1988)'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Action'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Thriller'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<table>                <tr>                 <td><table><tr><td>Lord of the Rings: The Two Towers, The (2002)</td></tr><tr><td><img src=https://image.tmdb.org/t/p/w200/5VTN0pR8gcqV3EPUHHfMGnJYN9L.jpg></td></tr></table></td>                <td><table><tr><td>Lord of the Rings: The Return of the King, The (2003)</td></tr><tr><td><img src=https://image.tmdb.org/t/p/w200/rCzpDGLbOoPwLjy3OAm5NUPOTrC.jpg></td></tr></table></td>                <td><table><tr><td>Star Wars: Episode VI - Return of the Jedi (1983)</td></tr><tr><td><img src=https://image.tmdb.org/t/p/w200/lrJWyjOVjPhghl4KyAMtOepAxs.jpg></td></tr></table></td>                <td><table><tr><td>Star Wars: Episode IV - A New Hope (1977)</td></tr><tr><td><img src=https://image.tmdb.org/t/p/w200/btTdmkgIvOi0FFip1sPuZI2oQG6.jpg></td></tr></table></td>                <td><table><tr><td>Matrix, The (1999)</td></tr><tr><td><img src=https://image.tmdb.org/t/p/w200/hEpWvX6Bp79eLxY1kX5ZZJcme5U.jpg></td></tr></table></td>                </tr>                 <tr>                 <td><table><tr><td>Raiders of the Lost Ark (Indiana Jones and the Raiders of the Lost Ark) (1981)</td></tr><tr><td><img src=https://image.tmdb.org/t/p/w200/44sKJOGP3fTm4QXBcIuqu0RkdP7.jpg ></td></tr></table></td>                <td><table><tr><td>Star Wars: Episode V - The Empire Strikes Back (1980)</td></tr><tr><td><img src=https://image.tmdb.org/t/p/w200/9SKDSFbaM6LuGqG1aPWN3wYGEyD.jpg ></td></tr></table></td>                <td><table><tr><td>Gladiator (2000)</td></tr><tr><td><img src=https://image.tmdb.org/t/p/w200/6WBIzCgmDCYrqh64yDREGeDk9d3.jpg ></td></tr></table></td>                <td><table><tr><td>Fight Club (1999)</td></tr><tr><td><img src=https://image.tmdb.org/t/p/w200/adw6Lq9FiC9zjYEpOqfq03ituwp.jpg ></td></tr></table></td>                <td><table><tr><td>Die Hard (1988)</td></tr><tr><td><img src=https://image.tmdb.org/t/p/w200/mc7MubOLcIw3MDvnuQFrO9psfCa.jpg ></td></tr></table></td>                </tr>                </table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print('KNN com Pearson ---> TOP 10 filmes mais similares a Lord of the Rings: The Fellowship of the Ring:')\n",
    "visualizar_sim('Lord of the Rings: The Fellowship of the Ring, The (2001)', 10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Implementação: Singular Value Decomposition (SVD) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# carregando o dataframe do pandas no Surprise\n",
    "reader = Reader(rating_scale=(1, 5))\n",
    "data = Dataset.load_from_df(df_ratings[['userId', 'movieId','rating']], reader)\n",
    "\n",
    "# Definindo os parâmetros e treinando o algoritmo\n",
    "#trainset = data.build_full_trainset()\n",
    "trainset, testset = train_test_split(data, test_size=0.25)\n",
    "algo = SVD(n_epochs= 30, lr_all= 0.005, reg_all= 0.1, biased=True)\n",
    "algo.fit(trainset)\n",
    "\n",
    "# predictions retorna uma tupla que contém todas as notas estimadas que\n",
    "# os usuários deram aos filmes\n",
    "svd_predictions = algo.test(testset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Top 10 de filmes mais recomendados para o usuário 4\n",
      "Hustler White (1996)\n",
      "Romance\n",
      "Drop Dead Fred (1991)\n",
      "Comedy|Fantasy\n",
      "Frequency (2000)\n",
      "Drama|Thriller\n",
      "Anaconda (1997)\n",
      "Action|Adventure|Thriller\n",
      "Nowhere (1997)\n",
      "Comedy|Drama\n",
      "Daytrippers, The (1996)\n",
      "Comedy|Drama|Mystery|Romance\n",
      "Puppet Master II (1991)\n",
      "Horror|Sci-Fi|Thriller\n",
      "Kull the Conqueror (1997)\n",
      "Action|Adventure\n",
      "Loaded (1994)\n",
      "Drama|Thriller\n",
      "Soul Food (1997)\n",
      "Drama\n"
     ]
    }
   ],
   "source": [
    "# Esta função retira os valores uid e iid de predictions quando \n",
    "# fazemos as predições utilizando o testset. \n",
    "# uid corresponde ao userId e iid corresponde ao movieId.\n",
    "# OBS: Função encontrada no repositório do Github da biblioteca Surprise\n",
    "# https://github.com/NicolasHug/Surprise/blob/master/examples/top_n_recommendations.py\n",
    "\n",
    "def get_top_n(predictions, n=10):\n",
    "    top_n = defaultdict(list)\n",
    "    for uid, iid, true_r, est, _ in predictions:\n",
    "        top_n[uid].append((iid, est))\n",
    "        \n",
    "    for uid, user_ratings in top_n.items():\n",
    "        user_ratings.sort(key=lambda x: x[1], reverse=True)\n",
    "        top_n[uid] = user_ratings[:n]\n",
    "    return top_n\n",
    "\n",
    "top_n = get_top_n(svd_predictions, n=10)\n",
    "# ----------------------------------------------------------------------\n",
    "\n",
    "# Esta função tem como entrada o userId \n",
    "# e printa os nomes dos 10 filmes mais recomendados para este usuário\n",
    "# tem como retorno também a contagem dos gêneros dos filmes\n",
    "\n",
    "def best_recommended(userId):\n",
    "    print('Top 10 de filmes mais recomendados para o usuário {}'.format(userId))\n",
    "    from collections import Counter\n",
    "    count_genre = []\n",
    "    for n in range(10):\n",
    "        best = top_n[userId][n][0]\n",
    "        print (df_filmes.loc[best]['title'])\n",
    "        print (df_filmes.loc[best]['genres'])\n",
    "        best_genres = df_filmes.loc[best]['genres']\n",
    "        best_genre_split = best_genres.split('|')\n",
    "        count_genre += best_genre_split\n",
    "    return Counter(count_genre)\n",
    "\n",
    "count_genre = best_recommended(4) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Contando a quantidade de ocorrência de determinado gênero de filme entre os filmes que o usuário 4 gostou**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Usuário gostou anteriormente com nota 5:\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Counter({'Action': 33,\n",
       "         'Adventure': 32,\n",
       "         'Animation': 19,\n",
       "         'Children': 30,\n",
       "         'Comedy': 50,\n",
       "         'Crime': 21,\n",
       "         'Documentary': 1,\n",
       "         'Drama': 35,\n",
       "         'Fantasy': 26,\n",
       "         'Film-Noir': 1,\n",
       "         'Horror': 8,\n",
       "         'IMAX': 1,\n",
       "         'Musical': 21,\n",
       "         'Mystery': 7,\n",
       "         'Romance': 16,\n",
       "         'Sci-Fi': 23,\n",
       "         'Thriller': 18,\n",
       "         'War': 3})"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Obtendo os filmes com nota 4.0 ou superior do usuário 4\n",
    "df_user_4 = df_ratings[(df_ratings['userId'] == 4) & (df_ratings['rating'] == 5.0)]['movieId']\n",
    "df_user_4 = list(df_user_4)\n",
    "genre_lst = []\n",
    "\n",
    "# concatenando os gêneros destes filmes em uma lista\n",
    "for index in range(len(df_user_4)):\n",
    "    get_movieid = df_user_4[index]\n",
    "    get_genres = df_filmes[df_filmes['movieId'] == get_movieid]['genres'].item()\n",
    "    genre_split = get_genres.split('|')\n",
    "    genre_lst += genre_split \n",
    "\n",
    "# importando a função Counter para contar a ocorrência de cada gênero presente na lista    \n",
    "from collections import Counter\n",
    "print(\"Usuário gostou anteriormente com nota 5:\")\n",
    "Counter(genre_lst)    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Verificar se a recomendação para o usuário 4 segue um padrão de gêneros:\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Counter({'Action': 2,\n",
       "         'Adventure': 2,\n",
       "         'Comedy': 3,\n",
       "         'Drama': 5,\n",
       "         'Fantasy': 1,\n",
       "         'Horror': 1,\n",
       "         'Mystery': 1,\n",
       "         'Romance': 2,\n",
       "         'Sci-Fi': 1,\n",
       "         'Thriller': 4})"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print('Verificar se a recomendação para o usuário 4 segue um padrão de gêneros:')\n",
    "count_genre"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Reimplementação Após Avaliação**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# carregando o dataframe do pandas no Surprise\n",
    "reader = Reader(rating_scale=(1, 5))\n",
    "data = Dataset.load_from_df(df_ratings[['userId', 'movieId','rating']], reader)\n",
    "\n",
    "# Definindo os parâmetros e treinando o algoritmo\n",
    "#trainset = data.build_full_trainset()\n",
    "trainset, testset = train_test_split(data, test_size=0.25)\n",
    "#Este conjunto de parâmetros obteve RMSE: 0.97 MAE: 0.77\n",
    "algo = SVD(n_epochs= 1, lr_all= 0.005, reg_all= 0.8, biased=True)\n",
    "algo.fit(trainset)\n",
    "\n",
    "# predictions retorna uma tupla que contém todas as notas estimadas que\n",
    "# os usuários deram aos filmes\n",
    "svd_predictions = algo.test(testset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Top 10 de filmes mais recomendados para o usuário 4\n",
      "Van, The (1996)\n",
      "Comedy|Drama\n",
      "Mirage (1995)\n",
      "Action|Thriller\n",
      "Crash (1996)\n",
      "Drama|Thriller\n",
      "Twin Town (1997)\n",
      "Comedy|Crime\n",
      "Nowhere (1997)\n",
      "Comedy|Drama\n",
      "Keys to Tulsa (1997)\n",
      "Crime\n",
      "Amityville Curse, The (1990)\n",
      "Horror\n",
      "Soul Food (1997)\n",
      "Drama\n",
      "Fly, The (1958)\n",
      "Horror|Mystery|Sci-Fi\n",
      "Frequency (2000)\n",
      "Drama|Thriller\n"
     ]
    }
   ],
   "source": [
    "top_n = get_top_n(svd_predictions, n=10)\n",
    "count_genre = best_recommended(4) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Verificar se a recomendação para o usuário 4 segue um padrão de gêneros:\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Counter({'Action': 1,\n",
       "         'Comedy': 3,\n",
       "         'Crime': 2,\n",
       "         'Drama': 5,\n",
       "         'Horror': 2,\n",
       "         'Mystery': 1,\n",
       "         'Sci-Fi': 1,\n",
       "         'Thriller': 3})"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print('Verificar se a recomendação para o usuário 4 segue um padrão de gêneros:')\n",
    "count_genre"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## Resultados"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Validação: SVD**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Testes realizados com diferentes parâmetros utilizando os dados de teste. O comentário em cada célula são os parâmetros que utilizei no algoritmo SVD. Logo abaixo se encontra o resumo em uma tabela."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def svd_accu(predictions):\n",
    "    svd_rmse = accuracy.rmse(predictions, verbose=True)\n",
    "    svd_mae = accuracy.mae(predictions, verbose=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE: 0.9681\n",
      "MAE:  0.7645\n"
     ]
    }
   ],
   "source": [
    "# n_epochs= 1, lr_all= 0.005, reg_all= 0.65\n",
    "svd_accu(svd_predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE: 0.9049\n",
      "MAE:  0.6971\n"
     ]
    }
   ],
   "source": [
    "# n_epochs= 20, lr_all= 0.005, reg_all= 0.02\n",
    "svd_accu(svd_predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE: 0.9757\n",
      "MAE:  0.7705\n"
     ]
    }
   ],
   "source": [
    "# n_epochs= 1, lr_all= 0.005, reg_all= 0.8\n",
    "svd_accu(svd_predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE: 0.9596\n",
      "MAE:  0.7540\n"
     ]
    }
   ],
   "source": [
    "# n_epochs= 5, lr_all= 0.005, reg_all= 0.8\n",
    "svd_accu(svd_predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE: 0.8891\n",
      "MAE:  0.6840\n"
     ]
    }
   ],
   "source": [
    "# n_epochs': 30, 'lr_all': 0.005, 'reg_all': 0.1\n",
    "svd_accu(svd_predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE: 0.9352\n",
      "MAE:  0.7299\n"
     ]
    }
   ],
   "source": [
    "# n_epochs= 5, lr_all= 0.005, reg_all= 0.65\n",
    "svd_accu(svd_predictions)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Singular Value Decomposition**\n",
    "\n",
    "| Repetições SGD | Learning Rate | Regularização | RMSE | MAE |\n",
    "| :--------------------------------: | :------------------: | :-------------------------: | :------------------------: | :------------------: |\n",
    "| 1    |   0.005   |   0.8 |   0.9757  |    0.7705      |\n",
    "| 1    |   0.005   |   0.65 |   0.9681  |    0.7645      |\n",
    "| 5    |   0.005   |   0.8 |   0.9596  |   0.7540      |\n",
    "| 5    |   0.005   |   0.65 |   0.9352  |    0.7299      |\n",
    "| 20   |  0.005    |  0.02  |   0.9049  |    0.6971     |\n",
    "| 30   |  0.005    |  0.02  |   0.8891  |    0.6840     |"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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