Protótipo II.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "4ccfa187",
   "metadata": {},
   "source": [
    "## Protótipo II - Sistema de recomendação baseado em modelos de <i>Machine Learning</i>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e84d2b4e",
   "metadata": {},
   "source": [
    "Nesse notebook, continuamos o desenvolvimento do TCC acerca do tema acima, aqui iremos pegar os arquivos .pkl criados e trabalharmos um pouco mais no processamento e organização dos dados ali presentes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "3c66e93b",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-07-12T18:08:08.588341Z",
     "start_time": "2021-07-12T18:08:08.573337Z"
    }
   },
   "outputs": [],
   "source": [
    "# CONSTANTES COLLABORATIVE FILTERING\n",
    "\n",
    "# CAMINHOS\n",
    "\n",
    "# \n",
    "PATH_TO_FULL_CF_FILE = \"preprocessed-data/CF/data_cf.pkl\"\n",
    "\n",
    "PATH_TO_MOVIES_CF_FILE = \"preprocessed-data/CF/movies_cf.pkl\"\n",
    "\n",
    "PATH_TO_RATINGS_CF_FILE = \"preprocessed-data/CF/ratings_cf.pkl\"\n",
    "\n",
    "# DataFrames Names\n",
    "\n",
    "# data_cf = arquivo completo \n",
    "# movies_cf = arquivos de filmes\n",
    "# ratings_cf = arquivos de ratings\n",
    "\n",
    "# KNN\n",
    "N_NEIGHBORS = 10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "2ab0c069",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-07-12T18:08:08.791349Z",
     "start_time": "2021-07-12T18:08:08.771346Z"
    }
   },
   "outputs": [],
   "source": [
    "# Importando bibliotecas necessárias\n",
    "import pandas as pd\n",
    "pd.set_option(\"display.max_rows\", 25)\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "import sys\n",
    "\n",
    "# Importando garbage collector\n",
    "import gc\n",
    "\n",
    "# Importando bibliotecas para o sistema de recomendação\n",
    "import scipy.sparse as sparse # Matriz esparsa (csr_matrix)\n",
    "\n",
    "# Importando sklearn\n",
    "import sklearn\n",
    "from sklearn.neighbors import NearestNeighbors\n",
    "\n",
    "# Importando Regular Expression operations\n",
    "import re"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b2cf7a5a",
   "metadata": {},
   "source": [
    "# 1 - Preparação dos dados para Collaborative Filtering"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3d6fde2f",
   "metadata": {},
   "source": [
    "#### Trabalharemos aqui com a criação da matriz filme x usuário da melhor maneira possível."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "c1023049",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-07-12T18:08:09.524778Z",
     "start_time": "2021-07-12T18:08:09.499772Z"
    }
   },
   "outputs": [],
   "source": [
    "# Definindo Função que carrega os arquivos\n",
    "def load_cf_files(full_file=True, movie_file=False, ratings_file=False):\n",
    "    if(full_file):\n",
    "        # Carregando o arquivo pré processado completo\n",
    "        data_cf = pd.read_pickle(PATH_TO_FULL_CF_FILE)\n",
    "        data_cf = data_cf[[\"movieId\", \"title\", \"userId\", \"rating\"]] # Reajustando ordem das colunas\n",
    "        \n",
    "        print(\"Arquivo completo: Carregado com sucesso!\")\n",
    "    else:\n",
    "        print(\"Arquivo completo: Não foi carregado, verifique os parâmetros para ver se essa era a intenção!\")\n",
    "        \n",
    "    if(movie_file):\n",
    "        # Carregando arquivo de filmes\n",
    "        movies_cf = pd.read_pickle(PATH_TO_MOVIES_CF_FILE)\n",
    "        print(\"Arquivo de filmes: Carregado com sucesso!\")\n",
    "    else:\n",
    "        print(\"Arquivo de filmes: Não foi carregado, verifique os parâmetros para ver se essa era a intenção!\")\n",
    "        \n",
    "    if(ratings_file):\n",
    "        ratings_cf = pd.read_pickle(PATH_TO_RATINGS_CF_FILE)\n",
    "        print(\"Arquivo de ratings: Carregado com sucesso!\")\n",
    "    else:\n",
    "        print(\"Arquivo de ratings: Não foi carregado, verifique os parâmetros para ver se essa era a intenção!\")\n",
    "        \n",
    "    #definindo retornos\n",
    "    if(\"data_cf\" in locals()):\n",
    "        if(\"movies_cf\" in locals()):\n",
    "            if(\"ratings_cf\" in locals()):\n",
    "                return data_cf, movies_cf, ratings_cf\n",
    "            else:\n",
    "                return data_cf, movies_cf\n",
    "        else:\n",
    "            return data_cf\n",
    "    elif(\"movies_cf\" in locals()):\n",
    "        if(\"ratings_cf\" in locals()):\n",
    "            return movies_cf, ratings_cf\n",
    "        else:\n",
    "            return movies_cf\n",
    "    elif(\"ratings_cf\" in locals()):\n",
    "        return ratings_cf\n",
    "    \n",
    "    else:\n",
    "        return None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "01d1fd72",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-07-12T18:08:13.160525Z",
     "start_time": "2021-07-12T18:08:11.043850Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Arquivo completo: Carregado com sucesso!\n",
      "Arquivo de filmes: Carregado com sucesso!\n",
      "Arquivo de ratings: Não foi carregado, verifique os parâmetros para ver se essa era a intenção!\n"
     ]
    }
   ],
   "source": [
    "# Chamando função de carregar os arquivos\n",
    "data_cf, movies_cf = load_cf_files(full_file=True, movie_file=True, ratings_file=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "8d353ad2",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-07-12T18:08:13.346566Z",
     "start_time": "2021-07-12T18:08:13.183529Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\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>userId</th>\n",
       "      <th>rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>25000090</th>\n",
       "      <td>209157</td>\n",
       "      <td>We</td>\n",
       "      <td>119571</td>\n",
       "      <td>1.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25000091</th>\n",
       "      <td>209159</td>\n",
       "      <td>Window of the Soul</td>\n",
       "      <td>115835</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25000092</th>\n",
       "      <td>209163</td>\n",
       "      <td>Bad Poems</td>\n",
       "      <td>6964</td>\n",
       "      <td>4.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25000093</th>\n",
       "      <td>209169</td>\n",
       "      <td>A Girl Thing</td>\n",
       "      <td>119571</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25000094</th>\n",
       "      <td>209171</td>\n",
       "      <td>Women of Devil's Island</td>\n",
       "      <td>119571</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          movieId                    title  userId  rating\n",
       "25000090   209157                       We  119571     1.5\n",
       "25000091   209159       Window of the Soul  115835     3.0\n",
       "25000092   209163                Bad Poems    6964     4.5\n",
       "25000093   209169             A Girl Thing  119571     3.0\n",
       "25000094   209171  Women of Devil's Island  119571     3.0"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_cf.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1f700e66",
   "metadata": {},
   "source": [
    "#### Analisando um pouco o arquivo de filmes e o completo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "9e1a651a",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-07-12T18:08:22.919463Z",
     "start_time": "2021-07-12T18:08:19.363774Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "            movieId        userId        rating\n",
      "count  2.500010e+07  2.500010e+07  2.500010e+07\n",
      "mean   2.138798e+04  8.118928e+04  3.533854e+00\n",
      "std    3.919886e+04  4.679172e+04  1.060744e+00\n",
      "min    1.000000e+00  1.000000e+00  5.000000e-01\n",
      "25%    1.196000e+03  4.051000e+04  3.000000e+00\n",
      "50%    2.947000e+03  8.091400e+04  3.500000e+00\n",
      "75%    8.623000e+03  1.215570e+05  4.000000e+00\n",
      "max    2.091710e+05  1.625410e+05  5.000000e+00\n",
      "\n",
      "\n",
      "\n",
      "Forrest Gump                          81491\n",
      "Shawshank Redemption, The             81482\n",
      "Pulp Fiction                          79672\n",
      "Silence of the Lambs, The             74127\n",
      "Matrix, The                           72674\n",
      "                                      ...  \n",
      "Young Billy Young                         1\n",
      "I Promessi sposi - Secondo il Trio        1\n",
      "Woman Basketball Player No. 5             1\n",
      "First Round Down                          1\n",
      "H3 - Halloween Horror Hostel              1\n",
      "Name: title, Length: 55463, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# Informações gerais sobre o arquivo completo\n",
    "print(data_cf.describe())\n",
    "print(\"\\n\\n\")\n",
    "print(data_cf['title'].value_counts())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cb004c8c",
   "metadata": {},
   "source": [
    "#### Podemos notar acima que, alguns filmes tem apenas 1 avaliação, o que pode complicar na comparação, devemos removê-los?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "14226c2f",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-07-12T18:08:24.874753Z",
     "start_time": "2021-07-12T18:08:22.921465Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>title</th>\n",
       "      <th>movieId</th>\n",
       "      <th>rating count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Toy Story</td>\n",
       "      <td>1</td>\n",
       "      <td>57309</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Jumanji</td>\n",
       "      <td>2</td>\n",
       "      <td>24228</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Grumpier Old Men</td>\n",
       "      <td>3</td>\n",
       "      <td>11804</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Waiting to Exhale</td>\n",
       "      <td>4</td>\n",
       "      <td>2523</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Father of the Bride Part II</td>\n",
       "      <td>5</td>\n",
       "      <td>11714</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         title  movieId  rating count\n",
       "0                    Toy Story        1         57309\n",
       "1                      Jumanji        2         24228\n",
       "2             Grumpier Old Men        3         11804\n",
       "3            Waiting to Exhale        4          2523\n",
       "4  Father of the Bride Part II        5         11714"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Mostrando filmes com menos de X votos\n",
    "cpy = movies_cf.copy()\n",
    "\n",
    "rating_count = pd.DataFrame(data_cf.groupby(\"movieId\").count()[\"rating\"])\n",
    "rating_count.reset_index(inplace=True)\n",
    "rating_count.rename(columns={\"rating\": \"rating count\"},inplace=True)\n",
    "\n",
    "res = cpy.merge(rating_count)\n",
    "\n",
    "res.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "e36cddd1",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-07-12T18:08:24.905761Z",
     "start_time": "2021-07-12T18:08:24.875754Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6.0\n"
     ]
    },
    {
     "data": {
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>title</th>\n",
       "      <th>movieId</th>\n",
       "      <th>rating count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1759</th>\n",
       "      <td>Ratchet</td>\n",
       "      <td>1847</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3462</th>\n",
       "      <td>Stacy's Knights</td>\n",
       "      <td>3561</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5793</th>\n",
       "      <td>Soap Girl</td>\n",
       "      <td>5905</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8461</th>\n",
       "      <td>B.F.'s Daughter</td>\n",
       "      <td>25935</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8522</th>\n",
       "      <td>All at Sea</td>\n",
       "      <td>26016</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59042</th>\n",
       "      <td>We</td>\n",
       "      <td>209157</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59043</th>\n",
       "      <td>Window of the Soul</td>\n",
       "      <td>209159</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59044</th>\n",
       "      <td>Bad Poems</td>\n",
       "      <td>209163</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59045</th>\n",
       "      <td>A Girl Thing</td>\n",
       "      <td>209169</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59046</th>\n",
       "      <td>Women of Devil's Island</td>\n",
       "      <td>209171</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10298 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                         title  movieId  rating count\n",
       "1759                   Ratchet     1847             1\n",
       "3462           Stacy's Knights     3561             1\n",
       "5793                 Soap Girl     5905             1\n",
       "8461           B.F.'s Daughter    25935             1\n",
       "8522                All at Sea    26016             1\n",
       "...                        ...      ...           ...\n",
       "59042                       We   209157             1\n",
       "59043       Window of the Soul   209159             1\n",
       "59044                Bad Poems   209163             1\n",
       "59045             A Girl Thing   209169             1\n",
       "59046  Women of Devil's Island   209171             1\n",
       "\n",
       "[10298 rows x 3 columns]"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Exibindo\n",
    "X = rating_count[\"rating count\"].quantile(0.50) # 90% dos filmes da lista tem pelo menos essa quantidade de votos\n",
    "\n",
    "print(X)\n",
    "\n",
    "res[res[\"rating count\"] == 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "88df3483",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-07-12T18:08:26.970221Z",
     "start_time": "2021-07-12T18:08:24.906761Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "title\n",
       "Forrest Gump                                        81491\n",
       "Shawshank Redemption, The                           81482\n",
       "Pulp Fiction                                        79672\n",
       "Silence of the Lambs, The                           74127\n",
       "Matrix, The                                         72674\n",
       "                                                    ...  \n",
       "L'uccello migratore                                     1\n",
       "L'opéra de quat'sous                                    1\n",
       "L'onorata famiglia                                      1\n",
       "L'oeuvre au noir                                        1\n",
       "\"BLOW THE NIGHT!\" Let's Spend the Night Together        1\n",
       "Length: 55463, dtype: int64"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_cf.value_counts(\"title\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "43711964",
   "metadata": {},
   "source": [
    "#### Vamos verificar agora ocorrências de filmes com títulos em linguas diferentes de inglês e removê-los."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "b3f030b2",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-07-12T18:08:31.082139Z",
     "start_time": "2021-07-12T18:08:26.971222Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Amelie (Fabuleux destin d'Amélie Poulain, Le)              34320\n",
       "Léon: The Professional (a.k.a. The Professional) (Léon)    33680\n",
       "WALL·E                                                     27374\n",
       "Life Is Beautiful (La Vita è bella)                        23976\n",
       "Alien³ (a.k.a. Alien 3)                                    13783\n",
       "                                                           ...  \n",
       "Komisarz Blond i Oko sprawiedliwości                           1\n",
       "Мышонок Пик                                                    1\n",
       "Dzień dobry, kocham cię!                                       1\n",
       "Krakonoš a lyžníci                                             1\n",
       "Attack of La Niña                                              1\n",
       "Name: title, Length: 2322, dtype: int64"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Mostrando filmes que contem caracteres não ascii no titulo\n",
    "data_cf[data_cf[\"title\"].map(lambda x: not x.isascii())][\"title\"].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d98ef35c",
   "metadata": {},
   "source": [
    "### 1.1 - Problemas do Collaborative Filtering:\n",
    "<ul>\n",
    "    <li>Esparsidade</li>\n",
    "    <li>Cold Start</li>\n",
    "</ul>\n",
    "\n",
    "#### Técnicas possíveis:\n",
    "<ul>\n",
    "    <li><b>Algoritmos não probabilisticos:</b></li>\n",
    "    <li>User-based nearest neighbor</li>\n",
    "    <li>Item-based nearest neighbor</li>\n",
    "    <li>Reducing dimensionality</li>\n",
    "</ul>\n",
    "\n",
    "<ul>\n",
    "    <li><b>Algoritmos probabilisticos:</b></li>\n",
    "    <li>Bayesian-network model</li>\n",
    "    <li>Expectation-minimization</li>\n",
    "</ul>\n",
    "\n",
    "\n",
    "Ver: https://pub.towardsai.net/recommendation-system-in-depth-tutorial-with-python-for-netflix-using-collaborative-filtering-533ff8a0e444"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "39c53cb1",
   "metadata": {},
   "source": [
    "### 1.2 - Criando uma estrutura de matriz esparsa com o dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "a1c2c7ee",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-07-12T18:09:00.801666Z",
     "start_time": "2021-07-12T18:09:00.148415Z"
    }
   },
   "outputs": [],
   "source": [
    "# Usando o dataframe para criar uma matriz esparsa que contenha todos os filmes\n",
    "def create_sparse_matrix(df):\n",
    "    sparse_matrix = sparse.csr_matrix((df[\"rating\"], (df[\"userId\"], df[\"movieId\"])))\n",
    "    \n",
    "    return sparse_matrix\n",
    "\n",
    "# a matriz gerada é uma User x Movie, queremos Movie x User, então fazemos a transposta\n",
    "user_movie_matrix = create_sparse_matrix(data_cf).transpose()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5e34ffac",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-06-14T20:39:46.153413Z",
     "start_time": "2021-06-14T20:39:46.145410Z"
    }
   },
   "source": [
    "### 1.3 - Criando um modelo simples utilizando o KNN -  CF - Item Based"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "fc384c5c",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-07-12T18:09:01.693783Z",
     "start_time": "2021-07-12T18:09:01.149659Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "NearestNeighbors(metric='cosine', n_neighbors=10)"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Criando o modelo knn\n",
    "knn_cf = NearestNeighbors(n_neighbors=N_NEIGHBORS, algorithm='auto', metric='cosine') # temos que mexer nos parâmetros posteriormente\n",
    "\n",
    "knn_cf.fit(user_movie_matrix)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "845a4730",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-07-12T18:11:05.849480Z",
     "start_time": "2021-07-12T18:11:05.843478Z"
    }
   },
   "outputs": [],
   "source": [
    "# criando função que gera recomendações basedo em um filme - utilizando um modelo KNN\n",
    "def get_recommendations_cf(movie_name, model): #nome do filme, modelo\n",
    "    # Pegando o Id do filme que tenha o nome passado\n",
    "    movieId = data_cf.loc[data_cf[\"title\"] == movie_name][\"movieId\"].values[0]\n",
    "    \n",
    "    distances, suggestions = model.kneighbors(user_movie_matrix.getrow(movieId).todense().tolist(), n_neighbors=N_NEIGHBORS)\n",
    "    \n",
    "    for i in range(0, len(distances.flatten())):\n",
    "        if(i == 0):\n",
    "            print('Recomendações para {0}: \\n'.format(movie_name))\n",
    "        else:\n",
    "            print('{0}: {1}, com distância de {2}:'.format(i, data_cf.loc[data_cf[\"movieId\"] == suggestions.flatten()[i]][\"title\"].values[0], distances.flatten()[i]))\n",
    "    \n",
    "    return distances, suggestions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "081ce274",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-07-12T18:11:06.209915Z",
     "start_time": "2021-07-12T18:11:06.194911Z"
    }
   },
   "outputs": [],
   "source": [
    "# Função para pesquisar o nome correto do filme\n",
    "def search_movies_cf(search_word):\n",
    "    return movies_cf[movies_cf.title.str.contains(search_word, flags=re.IGNORECASE)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "8be5c9d3",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-07-12T18:11:06.525093Z",
     "start_time": "2021-07-12T18:11:06.475080Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>title</th>\n",
       "      <th>movieId</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5241</th>\n",
       "      <td>Spider-Man</td>\n",
       "      <td>5349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7923</th>\n",
       "      <td>Spider-Man 2</td>\n",
       "      <td>8636</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11561</th>\n",
       "      <td>Spider-Man 3</td>\n",
       "      <td>52722</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14527</th>\n",
       "      <td>Spider-Man: The Ultimate Villain Showdown</td>\n",
       "      <td>76709</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18241</th>\n",
       "      <td>Amazing Spider-Man, The</td>\n",
       "      <td>95510</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21454</th>\n",
       "      <td>The Amazing Spider-Man 2</td>\n",
       "      <td>110553</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25074</th>\n",
       "      <td>Untitled Spider-Man Reboot</td>\n",
       "      <td>122926</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56890</th>\n",
       "      <td>Spider-Man: Into the Spider-Verse</td>\n",
       "      <td>195159</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59844</th>\n",
       "      <td>Spider-Man: Far from Home</td>\n",
       "      <td>201773</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                           title  movieId\n",
       "5241                                  Spider-Man     5349\n",
       "7923                                Spider-Man 2     8636\n",
       "11561                               Spider-Man 3    52722\n",
       "14527  Spider-Man: The Ultimate Villain Showdown    76709\n",
       "18241                    Amazing Spider-Man, The    95510\n",
       "21454                   The Amazing Spider-Man 2   110553\n",
       "25074                 Untitled Spider-Man Reboot   122926\n",
       "56890          Spider-Man: Into the Spider-Verse   195159\n",
       "59844                  Spider-Man: Far from Home   201773"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Setando um tamanho de coluna, para ver o nome completo dos filmes\n",
    "pd.set_option('display.max_colwidth', 500)\n",
    "# Pesquisando filmes\n",
    "search_movies_cf(\"Spider-Man\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "b0a1e0ff",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-07-12T18:11:06.919477Z",
     "start_time": "2021-07-12T18:11:06.915476Z"
    }
   },
   "outputs": [],
   "source": [
    "#Voltando a coluna ao normal \n",
    "pd.set_option('display.max_colwidth', 50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "e6341012",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-07-12T18:11:09.054056Z",
     "start_time": "2021-07-12T18:11:07.387387Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Recomendações para Spider-Man 2: \n",
      "\n",
      "1: Spider-Man, com distância de 0.30051949781903664:\n",
      "2: X2: X-Men United, com distância de 0.38035866481375724:\n",
      "3: Pirates of the Caribbean: The Curse of the Black Pearl, com distância de 0.44583045604832083:\n",
      "4: X-Men, com distância de 0.45347815941142156:\n",
      "5: Incredibles, The, com distância de 0.45585894066206556:\n",
      "6: Shrek 2, com distância de 0.4564660168298432:\n",
      "7: Batman Begins, com distância de 0.45717348204712305:\n",
      "8: Star Wars: Episode III - Revenge of the Sith, com distância de 0.46928468125362977:\n",
      "9: Finding Nemo, com distância de 0.4844064554284505:\n"
     ]
    }
   ],
   "source": [
    "# Pegando recomendações\n",
    "a, b = get_recommendations_cf(\"Spider-Man 2\", knn_cf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a6f80c79",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "16940e34",
   "metadata": {},
   "source": [
    "## Ideia: Se o filme tiver menos que X votos, utilizar exclusivamente Content-Based, caso contrario, fazemos a abordagem entrelaçada."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "20489787",
   "metadata": {},
   "source": [
    "# 2 - Preparação dos dados para Content-Based"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "27d5abe6",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-07-12T16:26:24.109483Z",
     "start_time": "2021-07-12T16:26:24.096480Z"
    }
   },
   "outputs": [],
   "source": [
    "# CONSTANTES CONTENT BASED\n",
    "\n",
    "# CAMINHOS\n",
    "\n",
    "PATH_TO_FULL_CB_FILE = \"preprocessed-data/CB/data_cb.pkl\"\n",
    "\n",
    "PATH_TO_MOVIES_CB_FILE = \"preprocessed-data/CB/movies_cb.pkl\"\n",
    "\n",
    "PATH_TO_RATINGS_CB_FILE = \"preprocessed-data/CB/ratings_cb.pkl\"\n",
    "\n",
    "PATH_TO_RATINGS_INFOS_CB_FILE = \"preprocessed-data/CB/ratings_info_cb.pkl\"\n",
    "\n",
    "PATH_TO_TAG_RELEVANCE_GROUPED_CB_FILE = \"preprocessed-data/CB/tag_relevance_grouped_cb.pkl\"\n",
    "\n",
    "PATH_TO_TAG_RELEVANCE_CB_FILE = \"preprocessed-data/CB/tag_relevance_cb.pkl\"\n",
    "\n",
    "PATH_TO_TAGS_PROCESSED_CB_FILE = \"preprocessed-data/CB/tags_processed_cb.pkl\"\n",
    "\n",
    "# DataFrames Names\n",
    "\n",
    "# data_cb = arquivo completo \n",
    "# movies_cb = arquivos de filmes\n",
    "# ratings_cb = arquivos de ratings\n",
    "# ratings_infos_cb = arquivos de informações sobre os ratings\n",
    "# tag_relevance_grouped_cb = relevancia de tags após o agrupamento\n",
    "# tag_relevance_cb = relevancia de tags original\n",
    "# tags_processed_cb = tags todas juntas em uma coluna e processadas pelo nltk"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "58553e97",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-07-12T16:26:25.048387Z",
     "start_time": "2021-07-12T16:26:25.034385Z"
    }
   },
   "outputs": [],
   "source": [
    "def load_cb_files(full=True, movies=False, ratings=False, ratings_infos=False ,relevance_grouped=False, relevance=False, tags_processed=False):\n",
    "    data_cb = None\n",
    "    movies_cb = None \n",
    "    ratings_cb = None\n",
    "    ratings_infos_cb = None\n",
    "    tag_relevance_grouped_cb = None\n",
    "    tag_relevance_cb = None\n",
    "    tags_processed_cb = None\n",
    "    \n",
    "    # Caso se queira carregar o completo\n",
    "    if(full):\n",
    "        data_cb = pd.read_pickle(PATH_TO_FULL_CB_FILE)\n",
    "        print(\"Arquivo completo: Carregado com sucesso!\")\n",
    "    else:\n",
    "        print(\"Arquivo completo: Não foi carregado, verifique se era o que desejava.\")\n",
    "    \n",
    "    # Caso queira-se carregar o arquivo de filmes\n",
    "    if(movies):\n",
    "        movies_cb = pd.read_pickle(PATH_TO_MOVIES_CB_FILE)\n",
    "        print(\"Arquivo movies: Carregado com sucesso!\")\n",
    "    else:\n",
    "        print(\"Arquivo movies: Não foi carregado, verifique se era o que desejava.\")\n",
    "        \n",
    "    if(ratings):\n",
    "        ratings_cb = pd.read_pickle(PATH_TO_RATINGS_CB_FILE)\n",
    "        print(\"Arquivo ratings: Carregado com sucesso!\")\n",
    "    else:\n",
    "        print(\"Arquivo ratings: Não foi carregado, verifique se era o que desejava.\")\n",
    "    \n",
    "    if(ratings_infos):\n",
    "        ratings_infos_cb = pd.read_pickle(PATH_TO_RATINGS_INFOS_CB_FILE)\n",
    "        print(\"Arquivo ratings infos: Carregado com sucesso!\")\n",
    "    else:\n",
    "        print(\"Arquivo ratings infos: Não foi carregado, verifique se era o que desejava.\")\n",
    "        \n",
    "    if(relevance_grouped):\n",
    "        tag_relevance_grouped_cb = pd.read_pickle(PATH_TO_TAG_RELEVANCE_GROUPED_CB_FILE)\n",
    "        print(\"Arquivo relevance grouped: Carregado com sucesso!\")\n",
    "    else:\n",
    "        print(\"Arquivo relevance grouped: Não foi carregado, verifique se era o que desejava.\")\n",
    "    \n",
    "    if(relevance):\n",
    "        tag_relevance_cb = pd.read_pickle(PATH_TO_TAG_RELEVANCE_CB_FILE)\n",
    "        print(\"Arquivo relevance: Carregado com sucesso!\")\n",
    "    else:\n",
    "        print(\"Arquivo relevance: Não foi carregado, verifique se era o que desejava.\")\n",
    "        \n",
    "    if(tags_processed):\n",
    "        tags_processed_cb = pd.read_pickle(PATH_TO_TAGS_PROCESSED_CB_FILE)\n",
    "        print(\"Arquivo tags processed: Carregado com sucesso!\")\n",
    "    else:\n",
    "        print(\"Arquivo tags processed: Não foi carregado, verifique se era o que desejava.\")\n",
    "        \n",
    "        \n",
    "    return data_cb, movies_cb, ratings_cb, ratings_infos_cb, tag_relevance_grouped_cb, tag_relevance_cb, tags_processed_cb\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "7256cef4",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-07-12T16:26:29.314497Z",
     "start_time": "2021-07-12T16:26:26.113232Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Arquivo completo: Carregado com sucesso!\n",
      "Arquivo movies: Carregado com sucesso!\n",
      "Arquivo ratings: Não foi carregado, verifique se era o que desejava.\n",
      "Arquivo ratings infos: Carregado com sucesso!\n",
      "Arquivo relevance grouped: Não foi carregado, verifique se era o que desejava.\n",
      "Arquivo relevance: Não foi carregado, verifique se era o que desejava.\n",
      "Arquivo tags processed: Carregado com sucesso!\n"
     ]
    }
   ],
   "source": [
    "data_cb, movies_cb, ratings_cb, ratings_infos_cb, tag_relevance_grouped_cb, tag_relevance_cb, tags_processed_cb = load_cb_files(full=True, movies=True, ratings_infos=True, tags_processed=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "f124adb5",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-07-12T16:26:29.329504Z",
     "start_time": "2021-07-12T16:26:29.315497Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\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>62418</th>\n",
       "      <td>209157</td>\n",
       "      <td>We (2018)</td>\n",
       "      <td>[Drama]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62419</th>\n",
       "      <td>209159</td>\n",
       "      <td>Window of the Soul (2001)</td>\n",
       "      <td>[Documentary]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62420</th>\n",
       "      <td>209163</td>\n",
       "      <td>Bad Poems (2018)</td>\n",
       "      <td>[Comedy, Drama]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62421</th>\n",
       "      <td>209169</td>\n",
       "      <td>A Girl Thing (2001)</td>\n",
       "      <td>[(no genres listed)]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62422</th>\n",
       "      <td>209171</td>\n",
       "      <td>Women of Devil's Island (1962)</td>\n",
       "      <td>[Action, Adventure, Drama]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       movieId                           title                      genres\n",
       "62418   209157                       We (2018)                     [Drama]\n",
       "62419   209159       Window of the Soul (2001)               [Documentary]\n",
       "62420   209163                Bad Poems (2018)             [Comedy, Drama]\n",
       "62421   209169             A Girl Thing (2001)        [(no genres listed)]\n",
       "62422   209171  Women of Devil's Island (1962)  [Action, Adventure, Drama]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movies_cb.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "5e91c2e0",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-07-12T16:26:29.345505Z",
     "start_time": "2021-07-12T16:26:29.331501Z"
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       "       movieId  average rating  rating count  weighted rating\n",
       "59042   209157             1.5             1         3.067578\n",
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     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ratings_infos_cb.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a89fd019",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-06-22T17:34:12.948887Z",
     "start_time": "2021-06-22T17:34:12.933885Z"
    }
   },
   "source": [
    "Olhando aqui o fim da lista de filmes e de informações de ratings, podemos notar que varios filmes não tem nenhum rating, e isso é importante de se lembrar mais para frente."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6545ac3e",
   "metadata": {},
   "source": [
    "## 2.1 - FOCANDO NOS GÊNEROS: Separando gêneros em colunas diferentes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "9dba2855",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-07-12T16:26:34.352294Z",
     "start_time": "2021-07-12T16:26:29.369510Z"
    }
   },
   "outputs": [],
   "source": [
    "movies_genres_separated_cb = movies_cb.copy()\n",
    "\n",
    "genres_list = []\n",
    "#Para cada linha no dataframe, iteramos pela lista de generos e colocamos 1 na coluna correspondente\n",
    "for index, row in movies_genres_separated_cb.iterrows():\n",
    "    for genre in row['genres']:\n",
    "        movies_genres_separated_cb.at[index, genre] = 1\n",
    "        if(not genre in genres_list):\n",
    "            genres_list.append(genre)\n",
    "            \n",
    "#Todos os valores não preenchidos se tornam 0\n",
    "movies_genres_separated_cb = movies_genres_separated_cb.fillna(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "5b82e982",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-07-12T16:26:34.384301Z",
     "start_time": "2021-07-12T16:26:34.353294Z"
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   },
   "outputs": [
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       "      <th>Fantasy</th>\n",
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       "      <th>IMAX</th>\n",
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       "      <th>Musical</th>\n",
       "      <th>Western</th>\n",
       "      <th>Film-Noir</th>\n",
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       "       movieId                    title                      genres  year  \\\n",
       "62418   209157                       We                     [Drama]  2018   \n",
       "62419   209159       Window of the Soul               [Documentary]  2001   \n",
       "62420   209163                Bad Poems             [Comedy, Drama]  2018   \n",
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       "62422   209171  Women of Devil's Island  [Action, Adventure, Drama]  1962   \n",
       "\n",
       "       Adventure  Animation  Children  Comedy  Fantasy  Romance  ...  Horror  \\\n",
       "62418        0.0        0.0       0.0     0.0      0.0      0.0  ...     0.0   \n",
       "62419        0.0        0.0       0.0     0.0      0.0      0.0  ...     0.0   \n",
       "62420        0.0        0.0       0.0     1.0      0.0      0.0  ...     0.0   \n",
       "62421        0.0        0.0       0.0     0.0      0.0      0.0  ...     0.0   \n",
       "62422        1.0        0.0       0.0     0.0      0.0      0.0  ...     0.0   \n",
       "\n",
       "       Mystery  Sci-Fi  IMAX  Documentary  War  Musical  Western  Film-Noir  \\\n",
       "62418      0.0     0.0   0.0          0.0  0.0      0.0      0.0        0.0   \n",
       "62419      0.0     0.0   0.0          1.0  0.0      0.0      0.0        0.0   \n",
       "62420      0.0     0.0   0.0          0.0  0.0      0.0      0.0        0.0   \n",
       "62421      0.0     0.0   0.0          0.0  0.0      0.0      0.0        0.0   \n",
       "62422      0.0     0.0   0.0          0.0  0.0      0.0      0.0        0.0   \n",
       "\n",
       "       (no genres listed)  \n",
       "62418                 0.0  \n",
       "62419                 0.0  \n",
       "62420                 0.0  \n",
       "62421                 1.0  \n",
       "62422                 0.0  \n",
       "\n",
       "[5 rows x 24 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movies_genres_separated_cb.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "735ff342",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-06-22T17:38:44.001063Z",
     "start_time": "2021-06-22T17:38:43.994061Z"
    }
   },
   "source": [
    "### 2.1.1 - Renomeando a coluna \"(no genres listed)\" para \"None\", para ficar mais legivel e mais facil de acessar."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "c5ad3c0f",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-07-12T16:27:08.909136Z",
     "start_time": "2021-07-12T16:27:08.885130Z"
    }
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       "       movieId                    title                      genres  year  \\\n",
       "62418   209157                       We                     [Drama]  2018   \n",
       "62419   209159       Window of the Soul               [Documentary]  2001   \n",
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       "62422   209171  Women of Devil's Island  [Action, Adventure, Drama]  1962   \n",
       "\n",
       "       Adventure  Animation  Children  Comedy  Fantasy  Romance  ...  Horror  \\\n",
       "62418        0.0        0.0       0.0     0.0      0.0      0.0  ...     0.0   \n",
       "62419        0.0        0.0       0.0     0.0      0.0      0.0  ...     0.0   \n",
       "62420        0.0        0.0       0.0     1.0      0.0      0.0  ...     0.0   \n",
       "62421        0.0        0.0       0.0     0.0      0.0      0.0  ...     0.0   \n",
       "62422        1.0        0.0       0.0     0.0      0.0      0.0  ...     0.0   \n",
       "\n",
       "       Mystery  Sci-Fi  IMAX  Documentary  War  Musical  Western  Film-Noir  \\\n",
       "62418      0.0     0.0   0.0          0.0  0.0      0.0      0.0        0.0   \n",
       "62419      0.0     0.0   0.0          1.0  0.0      0.0      0.0        0.0   \n",
       "62420      0.0     0.0   0.0          0.0  0.0      0.0      0.0        0.0   \n",
       "62421      0.0     0.0   0.0          0.0  0.0      0.0      0.0        0.0   \n",
       "62422      0.0     0.0   0.0          0.0  0.0      0.0      0.0        0.0   \n",
       "\n",
       "       None  \n",
       "62418   0.0  \n",
       "62419   0.0  \n",
       "62420   0.0  \n",
       "62421   1.0  \n",
       "62422   0.0  \n",
       "\n",
       "[5 rows x 24 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movies_genres_separated_cb.rename({\"(no genres listed)\":\"None\"}, axis=1, inplace=True)\n",
    "\n",
    "movies_genres_separated_cb.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "ba3e2fab",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-07-12T16:27:13.158988Z",
     "start_time": "2021-07-12T16:27:13.139982Z"
    }
   },
   "outputs": [],
   "source": [
    "# Backup do dataset com a lista de generos ainda incluida\n",
    "movies_genres_separated = movies_genres_separated_cb.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "90c57f89",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-07-12T16:27:15.919268Z",
     "start_time": "2021-07-12T16:27:15.899265Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        [Adventure, Animation, Children, Comedy, Fantasy]\n",
       "1                           [Adventure, Children, Fantasy]\n",
       "2                                        [Comedy, Romance]\n",
       "3                                 [Comedy, Drama, Romance]\n",
       "4                                                 [Comedy]\n",
       "                               ...                        \n",
       "62418                                              [Drama]\n",
       "62419                                        [Documentary]\n",
       "62420                                      [Comedy, Drama]\n",
       "62421                                 [(no genres listed)]\n",
       "62422                           [Action, Adventure, Drama]\n",
       "Name: genres, Length: 62423, dtype: object"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# removendo coluna de generos\n",
    "movies_genres_separated_cb.pop(\"genres\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "3021a0e4",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-07-12T16:27:19.353382Z",
     "start_time": "2021-07-12T16:27:19.324372Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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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>year</th>\n",
       "      <th>Adventure</th>\n",
       "      <th>Animation</th>\n",
       "      <th>Children</th>\n",
       "      <th>Comedy</th>\n",
       "      <th>Fantasy</th>\n",
       "      <th>Romance</th>\n",
       "      <th>Drama</th>\n",
       "      <th>...</th>\n",
       "      <th>Horror</th>\n",
       "      <th>Mystery</th>\n",
       "      <th>Sci-Fi</th>\n",
       "      <th>IMAX</th>\n",
       "      <th>Documentary</th>\n",
       "      <th>War</th>\n",
       "      <th>Musical</th>\n",
       "      <th>Western</th>\n",
       "      <th>Film-Noir</th>\n",
       "      <th>None</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Toy Story</td>\n",
       "      <td>1995</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Jumanji</td>\n",
       "      <td>1995</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Grumpier Old Men</td>\n",
       "      <td>1995</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Waiting to Exhale</td>\n",
       "      <td>1995</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Father of the Bride Part II</td>\n",
       "      <td>1995</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   movieId                        title  year  Adventure  Animation  Children  \\\n",
       "0        1                    Toy Story  1995        1.0        1.0       1.0   \n",
       "1        2                      Jumanji  1995        1.0        0.0       1.0   \n",
       "2        3             Grumpier Old Men  1995        0.0        0.0       0.0   \n",
       "3        4            Waiting to Exhale  1995        0.0        0.0       0.0   \n",
       "4        5  Father of the Bride Part II  1995        0.0        0.0       0.0   \n",
       "\n",
       "   Comedy  Fantasy  Romance  Drama  ...  Horror  Mystery  Sci-Fi  IMAX  \\\n",
       "0     1.0      1.0      0.0    0.0  ...     0.0      0.0     0.0   0.0   \n",
       "1     0.0      1.0      0.0    0.0  ...     0.0      0.0     0.0   0.0   \n",
       "2     1.0      0.0      1.0    0.0  ...     0.0      0.0     0.0   0.0   \n",
       "3     1.0      0.0      1.0    1.0  ...     0.0      0.0     0.0   0.0   \n",
       "4     1.0      0.0      0.0    0.0  ...     0.0      0.0     0.0   0.0   \n",
       "\n",
       "   Documentary  War  Musical  Western  Film-Noir  None  \n",
       "0          0.0  0.0      0.0      0.0        0.0   0.0  \n",
       "1          0.0  0.0      0.0      0.0        0.0   0.0  \n",
       "2          0.0  0.0      0.0      0.0        0.0   0.0  \n",
       "3          0.0  0.0      0.0      0.0        0.0   0.0  \n",
       "4          0.0  0.0      0.0      0.0        0.0   0.0  \n",
       "\n",
       "[5 rows x 23 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movies_genres_separated_cb.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b02b2eb7",
   "metadata": {},
   "source": [
    "## 2.2 Criando o sistema de recomendação Content-Based - KNN"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0c367ca0",
   "metadata": {},
   "source": [
    "### 2.2.1 Utilizando generos e ano"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "c815e1fd",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-07-12T16:27:24.934631Z",
     "start_time": "2021-07-12T16:27:24.900623Z"
    }
   },
   "outputs": [
    {
     "data": {
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       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1995</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>1.0</td>\n",
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       "      <td>...</td>\n",
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       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>1995</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "</div>"
      ],
      "text/plain": [
       "   movieId  year  Adventure  Animation  Children  Comedy  Fantasy  Romance  \\\n",
       "0        1  1995        1.0        1.0       1.0     1.0      1.0      0.0   \n",
       "1        2  1995        1.0        0.0       1.0     0.0      1.0      0.0   \n",
       "2        3  1995        0.0        0.0       0.0     1.0      0.0      1.0   \n",
       "3        4  1995        0.0        0.0       0.0     1.0      0.0      1.0   \n",
       "4        5  1995        0.0        0.0       0.0     1.0      0.0      0.0   \n",
       "\n",
       "   Drama  Action  ...  Horror  Mystery  Sci-Fi  IMAX  Documentary  War  \\\n",
       "0    0.0     0.0  ...     0.0      0.0     0.0   0.0          0.0  0.0   \n",
       "1    0.0     0.0  ...     0.0      0.0     0.0   0.0          0.0  0.0   \n",
       "2    0.0     0.0  ...     0.0      0.0     0.0   0.0          0.0  0.0   \n",
       "3    1.0     0.0  ...     0.0      0.0     0.0   0.0          0.0  0.0   \n",
       "4    0.0     0.0  ...     0.0      0.0     0.0   0.0          0.0  0.0   \n",
       "\n",
       "   Musical  Western  Film-Noir  None  \n",
       "0      0.0      0.0        0.0   0.0  \n",
       "1      0.0      0.0        0.0   0.0  \n",
       "2      0.0      0.0        0.0   0.0  \n",
       "3      0.0      0.0        0.0   0.0  \n",
       "4      0.0      0.0        0.0   0.0  \n",
       "\n",
       "[5 rows x 22 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Removendo colunas \"inuteis\" para a criação da matriz\n",
    "movies_genres_separated_cb_cpy = movies_genres_separated_cb.copy()\n",
    "\n",
    "movies_genres_separated_cb_cpy.pop(\"title\")\n",
    "#movies_genres_separated_cb_cpy.pop(\"year\")\n",
    "\n",
    "movies_genres_separated_cb_cpy.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "6cea9653",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-07-12T16:27:28.639449Z",
     "start_time": "2021-07-12T16:27:28.611442Z"
    }
   },
   "outputs": [],
   "source": [
    "# Convertendo coluna Year para float\n",
    "movies_genres_separated_cb_cpy['year'] = movies_genres_separated_cb_cpy['year'].astype(float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "1a8ae2a0",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-07-12T16:27:28.999364Z",
     "start_time": "2021-07-12T16:27:28.931347Z"
    }
   },
   "outputs": [],
   "source": [
    "# Criando matriz com o dataFrame acima\n",
    "movie_genres_matrix = pd.pivot_table(movies_genres_separated_cb_cpy, index = [\"movieId\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "e2b3ce6c",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-07-12T16:27:30.448338Z",
     "start_time": "2021-07-12T16:27:30.415331Z"
    }
   },
   "outputs": [
    {
     "data": {
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       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Action</th>\n",
       "      <th>Adventure</th>\n",
       "      <th>Animation</th>\n",
       "      <th>Children</th>\n",
       "      <th>Comedy</th>\n",
       "      <th>Crime</th>\n",
       "      <th>Documentary</th>\n",
       "      <th>Drama</th>\n",
       "      <th>Fantasy</th>\n",
       "      <th>Film-Noir</th>\n",
       "      <th>...</th>\n",
       "      <th>IMAX</th>\n",
       "      <th>Musical</th>\n",
       "      <th>Mystery</th>\n",
       "      <th>None</th>\n",
       "      <th>Romance</th>\n",
       "      <th>Sci-Fi</th>\n",
       "      <th>Thriller</th>\n",
       "      <th>War</th>\n",
       "      <th>Western</th>\n",
       "      <th>year</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>movieId</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>1995.0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1995.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
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       "      <td>1995.0</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2018.0</td>\n",
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       "    <tr>\n",
       "      <th>209159</th>\n",
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       "    <tr>\n",
       "      <th>209163</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>1.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2018.0</td>\n",
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       "    <tr>\n",
       "      <th>209169</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2001.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>209171</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1962.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>62423 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         Action  Adventure  Animation  Children  Comedy  Crime  Documentary  \\\n",
       "movieId                                                                       \n",
       "1           0.0        1.0        1.0       1.0     1.0    0.0          0.0   \n",
       "2           0.0        1.0        0.0       1.0     0.0    0.0          0.0   \n",
       "3           0.0        0.0        0.0       0.0     1.0    0.0          0.0   \n",
       "4           0.0        0.0        0.0       0.0     1.0    0.0          0.0   \n",
       "5           0.0        0.0        0.0       0.0     1.0    0.0          0.0   \n",
       "...         ...        ...        ...       ...     ...    ...          ...   \n",
       "209157      0.0        0.0        0.0       0.0     0.0    0.0          0.0   \n",
       "209159      0.0        0.0        0.0       0.0     0.0    0.0          1.0   \n",
       "209163      0.0        0.0        0.0       0.0     1.0    0.0          0.0   \n",
       "209169      0.0        0.0        0.0       0.0     0.0    0.0          0.0   \n",
       "209171      1.0        1.0        0.0       0.0     0.0    0.0          0.0   \n",
       "\n",
       "         Drama  Fantasy  Film-Noir  ...  IMAX  Musical  Mystery  None  \\\n",
       "movieId                             ...                                 \n",
       "1          0.0      1.0        0.0  ...   0.0      0.0      0.0   0.0   \n",
       "2          0.0      1.0        0.0  ...   0.0      0.0      0.0   0.0   \n",
       "3          0.0      0.0        0.0  ...   0.0      0.0      0.0   0.0   \n",
       "4          1.0      0.0        0.0  ...   0.0      0.0      0.0   0.0   \n",
       "5          0.0      0.0        0.0  ...   0.0      0.0      0.0   0.0   \n",
       "...        ...      ...        ...  ...   ...      ...      ...   ...   \n",
       "209157     1.0      0.0        0.0  ...   0.0      0.0      0.0   0.0   \n",
       "209159     0.0      0.0        0.0  ...   0.0      0.0      0.0   0.0   \n",
       "209163     1.0      0.0        0.0  ...   0.0      0.0      0.0   0.0   \n",
       "209169     0.0      0.0        0.0  ...   0.0      0.0      0.0   1.0   \n",
       "209171     1.0      0.0        0.0  ...   0.0      0.0      0.0   0.0   \n",
       "\n",
       "         Romance  Sci-Fi  Thriller  War  Western    year  \n",
       "movieId                                                   \n",
       "1            0.0     0.0       0.0  0.0      0.0  1995.0  \n",
       "2            0.0     0.0       0.0  0.0      0.0  1995.0  \n",
       "3            1.0     0.0       0.0  0.0      0.0  1995.0  \n",
       "4            1.0     0.0       0.0  0.0      0.0  1995.0  \n",
       "5            0.0     0.0       0.0  0.0      0.0  1995.0  \n",
       "...          ...     ...       ...  ...      ...     ...  \n",
       "209157       0.0     0.0       0.0  0.0      0.0  2018.0  \n",
       "209159       0.0     0.0       0.0  0.0      0.0  2001.0  \n",
       "209163       0.0     0.0       0.0  0.0      0.0  2018.0  \n",
       "209169       0.0     0.0       0.0  0.0      0.0  2001.0  \n",
       "209171       0.0     0.0       0.0  0.0      0.0  1962.0  \n",
       "\n",
       "[62423 rows x 21 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Mostrando matriz\n",
    "movie_genres_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "b1fba2cc",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-07-12T16:27:34.183228Z",
     "start_time": "2021-07-12T16:27:34.148219Z"
    }
   },
   "outputs": [],
   "source": [
    "# Gerando matriz esparsa com a matriz acima\n",
    "sparse_matrix_cb = sparse.csr_matrix(movie_genres_matrix)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "48d699bc",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-07-12T16:27:35.352368Z",
     "start_time": "2021-07-12T16:27:35.348368Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "NearestNeighbors(metric='cosine', n_neighbors=10)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Criando o modelo knn\n",
    "knn_genres_cb = NearestNeighbors(n_neighbors=N_NEIGHBORS, algorithm='auto', metric='cosine') # temos que mexer nos parâmetros posteriormente\n",
    "\n",
    "knn_genres_cb.fit(sparse_matrix_cb)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "4bc69c24",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-07-12T16:27:38.849589Z",
     "start_time": "2021-07-12T16:27:38.843588Z"
    }
   },
   "outputs": [],
   "source": [
    "# procura filmes baseando-se nas bases de dados que contém os generos\n",
    "def search_movies_genres_cb(search_word):\n",
    "    return movies_cb[movies_cb.title.str.contains(search_word, flags=re.IGNORECASE)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "7fa32279",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-07-12T16:27:39.415401Z",
     "start_time": "2021-07-12T16:27:39.404400Z"
    }
   },
   "outputs": [],
   "source": [
    "# criando função que gera recomendações basedo em um filme - utilizando um modelo KNN - baseando-se nos generos\n",
    "def get_recommendations_genres_cb(movie_name, model): #nome do filme, modelo\n",
    "    # Pegando o Id do filme que tenha o nome passado\n",
    "    movieId = movies_cb.loc[movies_cb[\"title\"] == movie_name][\"movieId\"].values[0]\n",
    "    \n",
    "    index = movies_cb.loc[movies_cb[\"movieId\"] == movieId].index.values[0]\n",
    "    \n",
    "    distances, suggestions = model.kneighbors(sparse_matrix_cb.getrow(index).todense().tolist(), n_neighbors=N_NEIGHBORS)\n",
    "\n",
    "    for i in range(0, len(distances.flatten())):\n",
    "        if(i == 0):\n",
    "            print('Recomendações para {0}: \\n'.format(movie_name))\n",
    "        else:\n",
    "            print('{0}: {1}, com distância de {2}:'.format(i, movies_cb.loc[movies_cb.index == suggestions.flatten()[i]][\"title\"].values[0], distances.flatten()[i]))\n",
    "    \n",
    "    return distances, suggestions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "5a5d32cb",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-07-12T16:27:41.559410Z",
     "start_time": "2021-07-12T16:27:41.508399Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\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>5241</th>\n",
       "      <td>5349</td>\n",
       "      <td>Spider-Man</td>\n",
       "      <td>[Action, Adventure, Sci-Fi, Thriller]</td>\n",
       "      <td>2002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7923</th>\n",
       "      <td>8636</td>\n",
       "      <td>Spider-Man 2</td>\n",
       "      <td>[Action, Adventure, Sci-Fi, IMAX]</td>\n",
       "      <td>2004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11561</th>\n",
       "      <td>52722</td>\n",
       "      <td>Spider-Man 3</td>\n",
       "      <td>[Action, Adventure, Sci-Fi, Thriller, IMAX]</td>\n",
       "      <td>2007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14527</th>\n",
       "      <td>76709</td>\n",
       "      <td>Spider-Man: The Ultimate Villain Showdown</td>\n",
       "      <td>[Animation]</td>\n",
       "      <td>2002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18241</th>\n",
       "      <td>95510</td>\n",
       "      <td>Amazing Spider-Man, The</td>\n",
       "      <td>[Action, Adventure, Sci-Fi, IMAX]</td>\n",
       "      <td>2012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21454</th>\n",
       "      <td>110553</td>\n",
       "      <td>The Amazing Spider-Man 2</td>\n",
       "      <td>[Action, Sci-Fi, IMAX]</td>\n",
       "      <td>2014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25074</th>\n",
       "      <td>122926</td>\n",
       "      <td>Untitled Spider-Man Reboot</td>\n",
       "      <td>[Action, Adventure, Fantasy]</td>\n",
       "      <td>2017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56890</th>\n",
       "      <td>195159</td>\n",
       "      <td>Spider-Man: Into the Spider-Verse</td>\n",
       "      <td>[Action, Adventure, Animation, Sci-Fi]</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59844</th>\n",
       "      <td>201773</td>\n",
       "      <td>Spider-Man: Far from Home</td>\n",
       "      <td>[Action, Adventure, Sci-Fi]</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       movieId                                      title  \\\n",
       "5241      5349                                 Spider-Man   \n",
       "7923      8636                               Spider-Man 2   \n",
       "11561    52722                               Spider-Man 3   \n",
       "14527    76709  Spider-Man: The Ultimate Villain Showdown   \n",
       "18241    95510                    Amazing Spider-Man, The   \n",
       "21454   110553                   The Amazing Spider-Man 2   \n",
       "25074   122926                 Untitled Spider-Man Reboot   \n",
       "56890   195159          Spider-Man: Into the Spider-Verse   \n",
       "59844   201773                  Spider-Man: Far from Home   \n",
       "\n",
       "                                            genres  year  \n",
       "5241         [Action, Adventure, Sci-Fi, Thriller]  2002  \n",
       "7923             [Action, Adventure, Sci-Fi, IMAX]  2004  \n",
       "11561  [Action, Adventure, Sci-Fi, Thriller, IMAX]  2007  \n",
       "14527                                  [Animation]  2002  \n",
       "18241            [Action, Adventure, Sci-Fi, IMAX]  2012  \n",
       "21454                       [Action, Sci-Fi, IMAX]  2014  \n",
       "25074                 [Action, Adventure, Fantasy]  2017  \n",
       "56890       [Action, Adventure, Animation, Sci-Fi]  2018  \n",
       "59844                  [Action, Adventure, Sci-Fi]  2019  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Setando um tamanho de coluna, para ver o nome completo dos filmes\n",
    "pd.set_option('display.max_colwidth', 500)\n",
    "\n",
    "search_movies_genres_cb(\"Spider-Man\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "2a1a0287",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-07-12T16:27:43.299389Z",
     "start_time": "2021-07-12T16:27:43.292388Z"
    }
   },
   "outputs": [],
   "source": [
    "# Setando o tamanho de coluna de volta ao normal\n",
    "pd.set_option('display.max_colwidth', 50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "b1feffb8",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-07-12T16:27:43.730921Z",
     "start_time": "2021-07-12T16:27:43.707915Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Recomendações para Spider-Man 2: \n",
      "\n",
      "1: Superman Returns, com distância de 4.950484466803573e-13:\n",
      "2: Star Wars: Episode II - Attack of the Clones, com distância de 4.970468481246826e-13:\n",
      "3: Star Trek, com distância de 3.0845326293160724e-12:\n",
      "4: Transformers: Revenge of the Fallen, com distância de 3.0845326293160724e-12:\n",
      "5: Avatar, com distância de 3.0845326293160724e-12:\n",
      "6: Tron: Legacy, com distância de 4.437561429426751e-12:\n",
      "7: John Carter, com distância de 7.87325760143176e-12:\n",
      "8: Avengers, The, com distância de 7.87325760143176e-12:\n",
      "9: Amazing Spider-Man, The, com distância de 7.87325760143176e-12:\n"
     ]
    }
   ],
   "source": [
    "a, b = get_recommendations_genres_cb(\"Spider-Man 2\", knn_genres_cb)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7dddf9aa",
   "metadata": {},
   "source": [
    "### 2.2.2 Utilizando as tags para fazer um sistema de recomendação baseado na matriz TFidf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "7100af65",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-07-12T16:27:56.494458Z",
     "start_time": "2021-07-12T16:27:56.485456Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\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>tags</th>\n",
       "      <th>tags_processed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>owned imdb top 250 pixar pixar time travel chi...</td>\n",
       "      <td>imdb top pixar pixar time travel child comedy ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>robin williams time travel fantasy based on ch...</td>\n",
       "      <td>robin williams time travel fantasy base child ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>funny best friend duringcreditsstinger fishing...</td>\n",
       "      <td>funny best friend duringcreditsstinger fish ol...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>based on novel or book chick flick divorce int...</td>\n",
       "      <td>base novel book chick flick divorce interracia...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>aging baby confidence contraception daughter g...</td>\n",
       "      <td>age baby confidence contraception daughter gyn...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   movieId                                               tags  \\\n",
       "0        1  owned imdb top 250 pixar pixar time travel chi...   \n",
       "1        2  robin williams time travel fantasy based on ch...   \n",
       "2        3  funny best friend duringcreditsstinger fishing...   \n",
       "3        4  based on novel or book chick flick divorce int...   \n",
       "4        5  aging baby confidence contraception daughter g...   \n",
       "\n",
       "                                      tags_processed  \n",
       "0  imdb top pixar pixar time travel child comedy ...  \n",
       "1  robin williams time travel fantasy base child ...  \n",
       "2  funny best friend duringcreditsstinger fish ol...  \n",
       "3  base novel book chick flick divorce interracia...  \n",
       "4  age baby confidence contraception daughter gyn...  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Carregando o arquivo que contém \n",
    "tags_processed_cb.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7601432e",
   "metadata": {},
   "source": [
    "#### Criação de uma matriz palavras_chave x filmes"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4e9a3696",
   "metadata": {},
   "source": [
    "### PODE-SE FAZER APENAS COM A CONTAGEM DE OCORRENCIA DAS PALAVRAS."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "1d1c5fd8",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-07-12T16:30:39.885156Z",
     "start_time": "2021-07-12T16:29:52.500038Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(45251, 32277)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "tfidf = TfidfVectorizer(stop_words='english')\n",
    "tfidf_matrix = tfidf.fit_transform(tags_processed_cb[\"tags_processed\"].values.astype('U'))\n",
    "#tfidf_df = pd.DataFrame(tfidf_matrix.toarray(), index=tags_processed_cb.index.tolist())\n",
    "print(tfidf_matrix.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "d016784c",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-07-12T16:30:39.933167Z",
     "start_time": "2021-07-12T16:30:39.892158Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['choreographed',\n",
       " 'choreographer',\n",
       " 'choreographic',\n",
       " 'choreography',\n",
       " 'chores',\n",
       " 'chorine',\n",
       " 'chorus',\n",
       " 'chosen',\n",
       " 'chou',\n",
       " 'chow']"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tfidf.get_feature_names()[5000:5010]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "f58ba240",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-07-12T16:30:39.981178Z",
     "start_time": "2021-07-12T16:30:39.935168Z"
    }
   },
   "outputs": [],
   "source": [
    "# Verificando graficamente\n",
    "\n",
    "# Compress with SVD\n",
    "# from sklearn.decomposition import TruncatedSVD\n",
    "# svd = TruncatedSVD(n_components=700)\n",
    "# latent_matrix = svd.fit_transform(tfidf_matrix_cpy)\n",
    " \n",
    "# # plot var expalined to see what latent dimensions to use\n",
    "# explained = svd.explained_variance_ratio_.cumsum()\n",
    "# plt.plot(explained, '.-', ms = 16, color='red')\n",
    "# plt.xlabel('Singular value components', fontsize= 12)\n",
    "# plt.ylabel('Cumulative percent of variance', fontsize=12)        \n",
    "# plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "06206c6a",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-07-12T16:30:39.997182Z",
     "start_time": "2021-07-12T16:30:39.982178Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "NearestNeighbors(metric='cosine', n_neighbors=10)"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Criando o modelo knn utilizando as tags\n",
    "knn_tags_cb = NearestNeighbors(n_neighbors=N_NEIGHBORS, algorithm='auto', metric='cosine') # temos que mexer nos parâmetros posteriormente\n",
    "\n",
    "knn_tags_cb.fit(tfidf_matrix)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "13d018c9",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-07-12T16:30:40.013185Z",
     "start_time": "2021-07-12T16:30:39.998183Z"
    }
   },
   "outputs": [],
   "source": [
    "# criando função que gera recomendações basedo em um filme - utilizando um modelo KNN - baseando-se nas tags\n",
    "def get_recommendations_tags_cb(movie_name, model): #nome do filme, modelo\n",
    "    # Pegando o Id do filme que tenha o nome passado\n",
    "    movieId = movies_cb.loc[movies_cb[\"title\"] == movie_name][\"movieId\"].values[0]\n",
    "    \n",
    "    index = movies_cb.loc[movies_cb[\"movieId\"] == movieId].index.values[0]\n",
    "    \n",
    "    distances, suggestions = model.kneighbors(tfidf_matrix.getrow(index).todense().tolist(), n_neighbors=N_NEIGHBORS)\n",
    "\n",
    "    for i in range(0, len(distances.flatten())):\n",
    "        if(i == 0):\n",
    "            print('Recomendações para {0}: \\n'.format(movie_name))\n",
    "        else:\n",
    "            print('{0}: {1}, com distância de {2}:'.format(i, movies_cb.loc[movies_cb.index == suggestions.flatten()[i]][\"title\"].values[0], distances.flatten()[i]))\n",
    "    \n",
    "    return distances, suggestions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "a656e1d6",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-07-12T16:30:40.125211Z",
     "start_time": "2021-07-12T16:30:40.015185Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Recomendações para Spider-Man 2: \n",
      "\n",
      "1: Stateside, com distância de 0.43413231440510947:\n",
      "2: 47 Ronin, The (Genroku Chûshingura), com distância de 0.4668735525384128:\n",
      "3: Symphony No. 42, com distância de 0.4923495317314386:\n",
      "4: Going Home, com distância de 0.5466584022331984:\n",
      "5: Secret Garden, The, com distância de 0.5468300241019454:\n",
      "6: Heat, The, com distância de 0.5530226005100427:\n",
      "7: Submarine X-1, com distância de 0.5600991413092565:\n",
      "8: Nothing Left Unsaid: Gloria Vanderbilt & Anderson Cooper, com distância de 0.5627528096766188:\n",
      "9: Chosen, com distância de 0.5737336287443238:\n",
      "\n",
      " [[ 7923  8281  8409 41548 33215  8479 19925 18740 40517 41918]]\n"
     ]
    }
   ],
   "source": [
    "a, b = get_recommendations_tags_cb(\"Spider-Man 2\", knn_tags_cb)\n",
    "\n",
    "print(\"\\n\", b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "de3eb704",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-07-12T16:30:40.157216Z",
     "start_time": "2021-07-12T16:30:40.126210Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    .dataframe thead th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>movieId</th>\n",
       "      <th>average rating</th>\n",
       "      <th>rating count</th>\n",
       "      <th>weighted rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>3.251527</td>\n",
       "      <td>24228</td>\n",
       "      <td>3.248508</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>3.142028</td>\n",
       "      <td>11804</td>\n",
       "      <td>3.139640</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>2.853547</td>\n",
       "      <td>2523</td>\n",
       "      <td>2.884188</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>3.058434</td>\n",
       "      <td>11714</td>\n",
       "      <td>3.058875</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   movieId  average rating  rating count  weighted rating\n",
       "0        1        3.893708         57309         3.887824\n",
       "1        2        3.251527         24228         3.248508\n",
       "2        3        3.142028         11804         3.139640\n",
       "3        4        2.853547          2523         2.884188\n",
       "4        5        3.058434         11714         3.058875"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ratings_infos_cb.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4b20fff1",
   "metadata": {},
   "source": [
    "### 2.2.2 Utilizando as tags para fazer um sistema de recomendação baseado na ocorrência e ausencia de tags"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "8d1db5ed",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-07-12T16:30:40.411274Z",
     "start_time": "2021-07-12T16:30:40.160217Z"
    }
   },
   "outputs": [
    {
     "data": {
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
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       "    .dataframe thead th {\n",
       "        text-align: right;\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>tags_processed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>[imdb, top, pixar, pixar, time, travel, child,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>[robin, williams, time, travel, fantasy, base,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>[funny, best, friend, duringcreditsstinger, fi...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>[base, novel, book, chick, flick, divorce, int...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>[age, baby, confidence, contraception, daughte...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   movieId                                     tags_processed\n",
       "0        1  [imdb, top, pixar, pixar, time, travel, child,...\n",
       "1        2  [robin, williams, time, travel, fantasy, base,...\n",
       "2        3  [funny, best, friend, duringcreditsstinger, fi...\n",
       "3        4  [base, novel, book, chick, flick, divorce, int...\n",
       "4        5  [age, baby, confidence, contraception, daughte..."
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# colocando as tags processadas em listas\n",
    "tags_list_df = tags_processed_cb.copy()\n",
    "\n",
    "tags_list_df['tags_processed'] = tags_list_df['tags_processed'].str.split(' ')\n",
    "\n",
    "tags_list_df.drop(\"tags\", inplace=True, axis=1)\n",
    "\n",
    "tags_list_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "372bb5a8",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-07-12T16:30:40.459285Z",
     "start_time": "2021-07-12T16:30:40.412273Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>45251.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>106384.672206</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>62915.776571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>55009.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>121797.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>159553.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>209063.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             movieId\n",
       "count   45251.000000\n",
       "mean   106384.672206\n",
       "std     62915.776571\n",
       "min         1.000000\n",
       "25%     55009.500000\n",
       "50%    121797.000000\n",
       "75%    159553.000000\n",
       "max    209063.000000"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tags_list_df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2c11cede",
   "metadata": {},
   "source": [
    "#### AVISO: Não executar as duas células abaixo, apenas carregar o arquivo na posterior"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "855b6223",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-07-09T15:54:45.804003Z",
     "start_time": "2021-07-09T15:53:37.487433Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "32443"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# fazendo uma lista de tags, para cada tag na lista de tags daquela linha, caso não esteja na lista, adicionamos.\n",
    "tags_list = []\n",
    "\n",
    "for index, row in tags_list_df.iterrows():\n",
    "    for tag in row['tags_processed']:\n",
    "        #tags_list_df.at[index, tag] = 1\n",
    "        if(not tag in tags_list):\n",
    "            tags_list.append(tag)\n",
    "\n",
    "len(tags_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "6202125c",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-07-09T15:55:29.727216Z",
     "start_time": "2021-07-09T15:55:29.665190Z"
    }
   },
   "outputs": [],
   "source": [
    "# salvando a lista de tags em um arquivo pickle\n",
    "import pickle\n",
    "with open('preprocessed-data/CB/movies_tags_matrix/processed_tags_list.pkl', 'w+b') as f:\n",
    "     pickle.dump(tags_list, f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "ca78296b",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-07-12T16:32:08.861002Z",
     "start_time": "2021-07-12T16:32:08.653740Z"
    }
   },
   "outputs": [],
   "source": [
    "tags_list = pd.read_pickle('preprocessed-data/CB/movies_tags_matrix/processed_tags_list.pkl');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "0c5315e4",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-07-12T16:32:10.483724Z",
     "start_time": "2021-07-12T16:32:10.344733Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['imdb', 'top', 'pixar', 'time', 'travel', 'child', 'comedy', 'funny', 'witty', 'animation', 'computer', 'good', 'cartoon', 'chindren', 'friendship', 'bright', 'dare', 'rescue', 'fanciful', 'heroic', 'mission', 'humorous', 'light', 'rouse', 'toy', 'come', 'life', 'unlikely', 'warm', 'disney', 'boy', 'next', 'door', 'bully', 'friend', 'jealousy', 'martial', 'art', 'neighborhood', 'new', 'resourcefulness', 'rivalry', 'walkie', 'talkie', 'clever', 'tom', 'hank', 'fun', 'dolls', 'national', 'film', 'registry', 'adventure', 'animate', 'cgi', 'family', 'fantasy', 'classic', 'watch', 'action', 'figure', 'buzz', 'lightyear', 'cg', 'woody', 'nostalgic', 'tim', 'allen', 'childish', 'buddy', 'movie', 'ya', 'cute', 'story', 'voice', 'act', 'played', 'cowboy', 'dinosaur', 'animated', 'best', 'rotten', 'tomato', 'john', 'lasseter', 'soothe', 'kid', 'feature', 'leoni', 'star', 'excite', 'plot', 'line', 'touch', 'reissue', 'stereoscopic', 'every', 'see', 'entertainment', 'weekly', 'clv', 'rainy', 'day', 'watchlist', 'villian', 'hurt', 'itaege', 'animmation', 'avi', 'buy', 'hearted', 'whimsica', 'great', 'favorite', 'tumey', 'vhs', 'want', 'innovative', 'engage', 'first', 'joss', 'whedon', 'unny', 'usa', 'lot', 'heart', 'american', 'robin', 'williams', 'base', 'book', 'board', 'game', 'disappearance', 'giant', 'insect', 'home', 'recluse', 'bad', 'flick', 'scary', 'animal', 'magic', 'fiction', 'adapt', 'chris', 'van', 'allsburg', 'kirsten', 'dunst', 'jungle', 'monkey', 'childhood', 'recaptured', 'herd', 'joe', 'johnston', 'recapture', 'also', 'zathura', 'bc', 'lebbat', 'adaptation', 'dynamic', 'horrifying', 'horror', 'genre', 'duringcreditsstinger', 'fish', 'old', 'man', 'sequel', 'fever', 'moldy', 'jack', 'lemmon', 'walter', 'matthau', 'grun', 'run', 'comedinha', 'de', 'velhinhos', 'people', 'actually', 'howard', 'deutch', 'ann', 'margaret', 'burgess', 'meredith', 'daryl', 'hannah', 'soundtrack', 'sophia', 'loren', 'novel', 'chick', 'divorce', 'interracial', 'relationship', 'single', 'mother', 'revenge', 'character', 'age', 'baby', 'confidence', 'contraception', 'daughter', 'gynecologist', 'midlife', 'crisis', 'parent', 'pregnancy', 'steve', 'martin', 'wed', 'diane', 'keaton', 'wedding', 'realistic', 'suspense', 'al', 'pacino', 'atmospheric', 'bank', 'robbery', 'crime', 'robert', 'niro', 'tense', 'val', 'kilmer', 'job', 'dialogue', 'heist', 'long', 'los', 'angeles', 'michael', 'mann', 'visceral', 'bleak', 'slick', 'somber', 'betrayal', 'cat', 'mouse', 'chase', 'epic', 'criminal', 'mastermind', 'cult', 'detective', 'gang', 'honor', 'loner', 'murder', 'obsession', 'one', 'last', 'thief', 'recommendz', 'pick', 'gunfight', 'overrate', 'philosophy', 'natalie', 'portman', 'electronic', 'slow', 'pace', 'mine', 'cliched', 'dumbed', 'hollywoodization', 'lame', 'e', 'un', 'poc', 'lo', 'sempre', 'overrated', 'ashley', 'judd', 'bechdel', 'test', 'fail', 'ensemble', 'cast', 'police', 'acting', 'individualism', 'complex', 'setting', 'la', 'care', 'dvds', 'jon', 'voight', 'third', 'problem', 'v', 'authentic', 'nucleus', 'btaege', 'hd', 'mkv', 'ca', 'remember', 'synthesizer', 'understated', 'ever', 'netflix', 'queue', 'adultery', 'career', 'gun', 'professional', 'real', 'suicide', 'violent', 'need', 'mary', 'kircher', 'viviane', 'vives', 'bibliothek', 'dvd', 'vontatott', 'amazing', 'captivate', 'authenticity', 'amaze', 'teenage', 'girl', 'tragic', 'end', 'r', 'language', 'violence', 'tough', 'guy', 'remake', 'chauffeur', 'fusion', 'island', 'millionaire', 'paris', 'sibling', 'play', 'harrison', 'ford', 'romance', 'ugly', 'duckling', 'love', 'drama', 'find', 'greg', 'kinnear', 'julia', 'ormond', 'remadeas', 'sabrina', 'remakeof', 'maybe', 'better', 'original', 'breakout', 'nancy', 'marchand', 'mark', 'twain', 'peter', 'hewitt', 'author', 'library', 'system', 'explosive', 'hostage', 'terrorist', 'vice', 'president', 'damme', 'hyams', 'bond', 'gadget', 'secret', 'service', 'spy', 'virus', 'cossack', 'cuba', 'electromagnetic', 'pulse', 'falsely', 'accuse', 'kgb', 'red', 'army', 'satellite', 'identity', 'intelligence', 'special', 'car', 'petersburg', 'russia', 'bomb', 'james', 'pierce', 'brosnan', 'pseudo', 'rat', 'win', 'sean', 'bean', 'brosnon', 'tank', 'scene', 'caribbean', 'espionage', 'franchise', 'jam', 'latin', 'america', 'puerto', 'rico', 'dark', 'memorable', 'sexuality', 'famke', 'janssen', 'thriller', 'series', 'assassin', 'killer', 'protagonist', 'die', 'bungee', 'jump', 'stunt', 'hacker', 'emp', 'flash', 'freeze', 'death', 'stealth', 'helicopter', 'bob', 'ola', 'mtskaf', 'always', 'white', 'house', 'widower', 'wildlife', 'conservation', 'aaron', 'sorkin', 'politics', 'u', 'kung', 'fu', 'decent', 'annette', 'bening', 'ethic', 'performance', 'douglas', 'moral', 'rob', 'reiner', 'girlie', 'collection', 'screenwriter', 'dracula', 'spoof', 'mel', 'brook', 'gothic', 'leslie', 'nielsen', 'parody', 'farce', 'vampire', 'ei', 'muista', 'alaska', 'bear', 'attack', 'dog', 'sled', 'race', 'frozen', 'lake', 'goose', 'pet', 'wolf', 'horner', 'dogsled', 'sort', 'bore', 'talk', 'simon', 'well', 'biography', 'government', 'historical', 'presidential', 'election', 'watergate', 'scandal', 'corruption', 'true', 'oliver', 'stone', 'biographical', 'biopic', 'person', 'richard', 'nixon', 'cinematographer', 'richardson', 'political', 'opinion', 'anthony', 'hopkins', 'exotic', 'map', 'pirate', 'scalp', 'ship', 'treasure', 'big', 'budget', 'frank', 'langella', 'geena', 'davis', 'humor', 'sword', 'fight', 'hunt', 'mafia', 'scorsese', 'organize', 'deniro', 'gangster', 'pesci', 'vega', 'casino', 'drug', 'abuse', 'illegal', 'prostitution', 'overdose', 'poker', 'hysterical', 'wife', 'vegas', 'mob', 'sharon', 'rewatch', 'narrate', 'memasa', 'narrated', 'nudity', 'rear', 'indulgent', 'muddle', 'motivation', 'stylish', 'documentary', 'like', 'gamble', 'cannibalism', 'scorcese', 'development', 'excellent', 'masterpiece', 'pink', 'las', 'etaege', 'na', 'dysku', 'topless', 'brief', 'goodfellas', 'ii', 'british', 'jane', 'austen', 'century', 'hugh', 'grant', 'bowling', 'country', 'decorum', 'devon', 'horse', 'carriage', 'inheritance', 'military', 'officer', 'period', 'rainstorm', 'rural', 'set', 'servant', 'finland', 'alan', 'rickman', 'emma', 'thompson', 'emotional', 'kate', 'winslet', 'romantic', 'oscar', 'nominee', 'picture', 'shakespeare', 'costume', 'literature', 'fast', 'boring', 'nothing', 'happen', 'ang', 'lee', 'britain', 'courtship', 'heartbreak', 'laurie', 'imogen', 'stubbs', 'london', 'patriarchy', 'upper', 'class', 'english', 'regency', 'england', 'hulu', 'screenplay', 'heartwarming', 'piece', 'bgab', 'lrc', 'sven', 'list', 'trial', 'tribulation', 'ago', 'anthology', 'paced', 'gross', 'hotel', 'multiple', 'storyline', 'year', 'night', 'quentin', 'tarantino', 'rodriguez', 'role', 'roth', 'vomit', 'witchcraft', 'antonio', 'banderas', 'madonna', 'bet', 'hoodlum', 'room', 'eve', 'sperm', 'witch', 'woman', 'director', 'bruce', 'willis', 'jennifer', 'beals', 'short', 'alexandre', 'rockwell', 'allison', 'anders', 'awesome', 'combustible', 'edison', 'music', 'blasphemous', 'foul', 'salma', 'hayek', 'jim', 'carrey', 'silly', 'fav', 'callow', 'oedekerk', 'africa', 'bat', 'human', 'indigenous', 'goofy', 'stupid', 'hell', 'crazy', 'rhino', 'private', 'hilarious', 'simple', 'worst', 'spit', 'hehehe', 'dumb', 'kind', 'york', 'city', 'subway', 'train', 'joseph', 'ruben', 'worthwhile', 'hollywood', 'business', 'con', 'debt', 'miami', 'mobster', 'money', 'producer', 'foqam', 'elmore', 'leonard', 'travolta', 'satire', 'get', 'still', 'barry', 'sonnenfeld', 'insider', 'contrive', 'forgetable', 'gene', 'hackman', 'danny', 'devito', 'airport', 'escobar', 'plane', 'crash', 'stuntman', 'cowardliness', 'brutality', 'operation', 'protection', 'psychologist', 'psychology', 'serial', 'suspenseful', 'claustrophobic', 'holly', 'hunter', 'freeman', 'sigourney', 'weaver', 'agoraphobia', 'female', 'trauma', 'mystery', 'assassination', 'cia', 'competition', 'detroit', 'double', 'cross', 'hidden', 'camera', 'hitman', 'mexican', 'standoff', 'seattle', 'shootout', 'silencer', 'sniper', 'rifle', 'donner', 'sylvester', 'stallone', 'chess', 'contract', 'escape', 'explosion', 'festival', 'julianne', 'moore', 'twist', 'spain', 'strong', 'earnest', 'elegiac', 'poignant', 'reflective', 'tearjerking', 'albino', 'christ', 'allegory', 'dead', 'extrasensory', 'perception', 'faint', 'flicker', 'heal', 'power', 'hold', 'gunpoint', 'jacob', 'ladder', 'pale', 'psionic', 'shot', 'teacher', 'student', 'post', 'apocalyptic', 'victor', 'salva', 'controversial', 'inane', 'pretentious', 'outcast', 'teach', 'cheesy', 'lesson', 'empathy', 'nicolas', 'cage', 'alcoholism', 'intimate', 'melancholy', 'prostitute', 'lover', 'understate', 'alcohol', 'individual', 'sight', 'lovesickness', 'rage', 'hate', 'unsociability', 'depress', 'elisabeth', 'shue', 'existential', 'melancholic', 'self', 'destruction', 'addiction', 'favorites', 'annemari', 'drink', 'loneliness', 'drinking', 'actor', 'mike', 'figgis', 'haunt', 'nihilism', 'emily', 'procter', 'julian', 'sand', 'kim', 'adam', 'mariska', 'hargitay', 'valeria', 'golino', 'elizabeth', 'implausible', 'sad', 'enough', 'overact', 'moor', 'parker', 'playwright', 'shakespearean', 'photocopier', 'truth', 'pas', 'brendan', 'fraser', 'christina', 'ricci', 'demi', 'gaby', 'hoffman', 'janeane', 'garofalo', 'melanie', 'griffith', 'rita', 'wilson', 'rosie', 'sisterhood', 'song', 'thora', 'birch', 'retro', 'prospect', 'preferred', 'ciarán', 'hind', 'admiral', 'captain', 'napoleonic', 'war', 'flame', 'fiona', 'shaw', 'amanda', 'root', 'hinds', 'bland', 'fabulous', 'review', 'pam', 'roger', 'michell', 'nick', 'dear', 'dystopia', 'surreal', 'kidnap', 'steampunk', 'weird', 'fairytale', 'dreamlike', 'hallucinatory', 'stylize', 'whimsical', 'clone', 'dream', 'eye', 'strange', 'visual', 'clowny', 'jeunet', 'visually', 'appeal', 'stun', 'beautiful', 'santa', 'claus', 'dominique', 'pinon', 'marc', 'caro', 'ron', 'perlman', 'kidnapping', 'legenda', 'circus', 'deer', 'shit', 'orphan', 'nightmare', 'creepy', 'tweens', 'dwarf', 'freak', 'french', 'twin', 'awkward', 'skinut', 'cabaret', 'diva', 'mistress', 'shanghai', 'umbrella', 'uncle', 'zhang', 'yimou', 'inspirational', 'amusement', 'park', 'high', 'school', 'karate', 'naval', 'rap', 'education', 'michelle', 'pfeiffer', 'social', 'commentary', 'douchebags', 'emilio', 'kinda', 'nigga', 'poetry', 'professor', 'smith', 'cinema', 'change', 'highschool', 'asylum', 'biological', 'warfare', 'brad', 'pitt', 'complicate', 'future', 'mental', 'illness', 'loop', 'mindfuck', 'terry', 'gilliam', 'epidemic', 'practical', 'effect', 'cassandra', 'syndrom', 'lethal', 'philadelphia', 'schizophrenia', 'stockholm', 'syndrome', 'subterranean', 'paradox', 'agonize', 'inmate', 'institution', 'genetics', 'dystopic', 'world', 'imagination', 'biology', 'brilliant', 'hard', 'follow', 'marker', 'david', 'webb', 'annoy', 'reality', 'dystopian', 'science', 'jeffrey', 'scenario', 'complicated', 'memory', 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'quevedo', 'treacher', 'comingofage', 'peerpressure', 'syrupy', 'jdrama', 'bungalow', 'thich', 'nhat', 'devotional', 'juventud', 'frustada', 'gondolier', 'disembodied', 'luchador', 'quinceanera', 'swept', 'somnambulant', 'falangism', 'dramaticized', 'dullish', 'jwatch', 'jour', 'bigby', 'georgianna', 'burner', 'gombe', 'geo', 'interclass', 'auntie', 'mame', 'traumatize', 'bügeleisen', 'lichtspruch', 'romeon', 'brodsky', 'baiser', 'neues', 'agrarian', 'showman', 'magnificently', 'psa', 'moazzam', 'begg', 'unmature', 'santpere', 'playback', 'strawberry', 'shortcake', 'dacoits', 'honnold', 'caldwell', 'lusitania', 'charleston', 'exaggeration', 'cuñadismo', 'supermoon', 'staithes', 'upsidedown', 'krieps', 'lavey', 'mansfield', 'justicier', 'masqué', 'bashful', 'inebriation', 'kilpisjärvi', 'fapbate', 'iwan', 'rheon', 'telepathic', 'corupt', 'lechery', 'bootle', 'dinah', 'dacshund', 'korhonen', 'tauno', 'horace', 'horsecollar', 'cleo', 'prokofiev', 'brahms', 'appel', 'buzzer', 'hikki', 'sensationalism', 'camiño', 'compaña', 'ponoc', 'cathryn', 'michon', 'shalem', 'malabar', 'kalari', 'qiurky', 'nitrate', 'multietinic', 'statement', 'binary', 'boum', 'trenet', 'gavira', 'mortadelo', 'filemón', 'rompetechos', 'strabism', 'krazy', 'fauna', 'jeez', 'novotny', 'vandermeer', 'dhavernas', 'libanese', 'universalism', 'heartlessness', 'habibie', 'donetsk', 'battalion', 'chennai', 'pandyas', 'cholan', 'thugs', 'russe', 'desintegration', 'genderqueer', 'debby', 'ori', 'sctipt', 'matemáticas', 'jihadist', 'scotsman', 'submersible', 'counterspy', 'phobic', 'adana', 'engin', 'günaydın', 'gülse', 'birsel', 'rohm', 'unrealistix', 'jelous', 'wickd', 'abductor', 'grieving', 'afganistan', 'animarion', 'circumstantial', 'ambiuous', 'seizures', 'demonology', 'aster', 'dures', 'keane', 'spuff', 'infomercial', 'chafarinas', 'melilla', 'nourteen', 'dewulf', 'hennig', 'agfacolor', 'closas', 'sevilla', 'maslona', 'realisim', 'brialy', 'melancholically', 'giannis', 'blowout', 'kriti', 'kharbanda', 'beatrix', 'daisey', 'carlist', 'interdimension', 'bruhl', 'shoehorn', 'junked', 'unfold', 'yachtsman', 'waterhole', 'wiretapping', 'embarrassment', 'loveable', 'vouyerism', 'realisticish', 'overprocessed', 'redubbed', 'bloemfontein', 'argetinian', 'quebecois', 'riebeeck', 'bbq', 'chemo', 'drammi', 'romantici', 'disruption', 'revealing', 'dileesh', 'pothan', 'suraj', 'venjaramoodu', 'ponnani', 'owlish', 'kadhar', 'countryfolk', 'werecat', 'gnomeo', 'nonbeliever', 'brude', 'camila', 'morrone', 'athelte', 'slowmoving', 'frien', 'ilusion', 'shumer', 'embarassment', 'whalen', 'beacon', 'paltz', 'pawling', 'normie', 'unforgiving', 'sequelesque', 'smothery', 'kinship', 'geyser', 'sawmill', 'bildungsoman', 'mosanto', 'gallaudet', 'лёд', 'wresting', 'zandvliet', 'frugal', 'cabbage', 'teenaged', 'analog', 'abbi', 'adderall', 'productivity', 'ritalin', 'betacam', 'ike', 'barinholtz', 'sexpact', 'supervivencia', 'camen', 'manolo', 'mercyme', 'income', 'brainiac', 'relatinship', 'masseur', 'kirkland', 'grierson', 'nfb', 'dimitris', 'docuseries', 'yucatan', 'recycling', 'truthordare', 'abysmal', 'bff', 'database', 'wargames', 'psychotropic', 'czechploitation', 'cascanueces', 'interactive', 'nokia', 'cccp', 'karavaychuk', 'intertaining', 'environtmental', 'bauchredner', 'mayakovsky', 'remount', 'grenze', 'ostzone', 'salad', 'wienerwald', 'sighting', 'hapa', 'elon', 'musk', 'agi', 'reprogram', 'crispr', 'gorillas', 'diverges', 'christians', 'isef', 'demonetisation', 'dosa', 'ias', 'uncompleated', 'dybbuk', 'junction', 'ventilator', 'worldbuilding', 'naxalites', 'bertold', 'fei', 'chauvinisim', 'hearfelt', 'seperated', 'triplets', 'emocional', 'mazinga', 'hehir', 'maniacal', 'rockies', 'favar', 'mounty', 'traffik', 'avrich', 'hirst', 'surburb', 'mummified', 'succint', 'babadook', 'creampie', 'brute', 'expositional', 'egor', 'letov', 'yegor', 'misbehave', 'douro', 'screenslaver', 'vizma', 'belševica', 'hundephobie', 'nonconforming', 'strengh', 'nowaday', 'hyperbolic', 'oparnica', 'sebal', 'zazie', 'beetz', 'notmyhan', 'undpredictable', 'augmented', 'stuckmann', 'layperson', 'austrailian', 'humble', 'salutary', 'cambodian', 'soyuzmultfilm', 'zinoviy', 'gerdt', 'kalyagin', 'vyacheslav', 'nevinnyy', 'liebknecht', 'syonage', 'armen', 'dzhigarkhanyan', 'theseus', 'kononov', 'сlaymation', 'tinderbox', 'infamous', 'telemarketer', 'idiocracy', 'zver', 'socha', 'devore', 'lapp', 'blindeness', 'grahics', 'feblowitz', 'fortson', 'dastmalchian', 'miniaturize', 'alco', 'joa', 'lil', 'rel', 'howery', 'ketogenic', 'lankan', 'isolationism', 'laurentides', 'québec', 'gst', 'merzlikin', 'irina', 'kupchenko', 'ferrandis', 'winton', 'vagueness', 'glastonbury', 'sisterly', 'heartful', 'sequeal', 'schwarzer', 'greif', 'oshima', 'nuberu', 'bagu', 'issei', 'sagawa', 'vache', 'illegals', 'redone', 'underbelly', 'popculture', 'furyo', 'yankii', 'prequell', 'tig', 'notaro', 'jagveld', 'sollima', 'adolfo', 'marsillach', 'retaliation', 'unnatural', 'autonomous', 'cybernetic', 'yaoi', 'membership', 'oldboy', 'redempton', 'café', 'inclán', 'rubén', 'darío', 'cnc', 'mauvais', 'goût', 'ivanna', 'sakhno', 'ticking', 'copter', 'allegiance', 'labyrinthine', 'awkwafina', 'mindy', 'kaling', 'jewellery', 'abducted', 'mumbelcore', 'homofobic', 'seventh', 'zeitreise', 'mabe', 'hysterectomy', 'bulliyng', 'comupter', 'observance', 'modernistic', 'belafonte', 'prefictable', 'acre', 'portagonist', 'domee', 'shi', 'jehovah', 'möller', 'cedergren', 'theatri', 'everlasting', 'pyrokinetic', 'telepath', 'débilifiant', 'requins', 'zèbres', 'ont', 'aileron', 'ladyboy', 'pyongyang', 'daveed', 'jurnalism', 'constructed', 'desktop', 'aneesh', 'chaganty', 'sponsor', 'yyz', 'aek', 'ovnis', 'indepedent', 'spaceopera', 'gazda', 'ivica', 'todorić', 'privatisation', 'sorround', 'smutty', 'golding', 'anaotomy', 'catfish', 'kristine', 'froseth', 'purser', 'bureaucratic', 'eurepean', 'kronental', 'slimeo', 'letigation', 'relationsip', 'nafta', 'involved', 'unofficial', 'antitraditional', 'fermat', 'happines', 'iliza', 'progressively', 'gentelman', 'perugorría', 'gracia', 'overnight', 'wireless', 'neightbour', 'imbroglio', 'mandrill', 'skiffle', 'brume', 'франция', 'metafictional', 'yarn', 'chavela', 'visibility', 'wayuu', 'survives', 'ooh', 'poach', 'boda', 'jessen', 'gemini', 'ultrarealistic', 'timothee', 'gaga', 'tradegic', 'uninvolving', 'alcoh', 'uncanonical', 'topical', 'standout', 'moselle', 'dislexic', 'vitamin', 'kindess', 'bellairs', 'hallowen', 'lesploitation', 'sully', 'shangela', 'energizing', 'gwilym', 'sanitize', 'colorfull', 'sacristán', 'seychelle', 'méxico', 'yalitza', 'aparicio', 'latent', 'sofie', 'gråbøl', 'amaia', 'catchphrase', 'dolera', 'mouglalis', 'kazhakstan', 'venicefilmfestival', 'breitbart', 'rohingya', 'keery', 'prositution', 'suljic', 'scottland', 'frindship', 'gintama', 'remaster', 'discimination', 'velázquez', 'anticonception', 'coll', 'landismo', 'angolan', 'confusao', 'portunhol', 'portuñol', 'kapuściński', 'telex', 'knifefight', 'ultraviolent', 'isabela', 'moner', 'orator', 'zasania', 'jacek', 'braciak', 'kulig', 'więckiewicz', 'shabby', 'homsexuality', 'punta', 'este', 'arles', 'gaugin', 'telikinetic', 'danila', 'kozlovsky', 'ivf', 'memorabilia', 'ensamble', 'turning', 'evander', 'holyfield', 'mahershala', 'individuation', 'mahersala', 'banish', 'rebeck', 'digitally', 'anytime', 'vat', 'ilk', 'televise', 'skopje', 'gengis', 'fabrizio', 'andrè', 'koko', 'latinoamérica', 'camgirl', 'rosa', 'nubian', 'anavitória', 'majorem', 'amdg', 'castile', 'formation', 'ignatius', 'loyola', 'renounce', 'gwen', 'ears', 'siquences', 'libfem', 'hajdi', 'astou', 'vedel', 'brana', 'naccache', 'epaud', 'idir', 'chender', 'mougeolle', 'erhardy', 'smet', 'youn', 'winograd', 'catalifo', 'sciortino', 'jacinda', 'tope', 'ollie', 'katerine', 'thrilless', 'castaways', 'chimeras', 'yayoi', 'mitterrand', 'sanctimonious', 'cheney', 'fanfic', 'quants', 'laila', 'wilkas', 'antariksa', 'aritificial', 'trisha', 'krishnan', 'sethupathi', 'rashmika', 'mandanna', 'vennela', 'kishore', 'devarakonda', 'jouranlism', 'hera', 'hilmar', 'friandship', 'illogocal', 'athiest', 'missesota', 'pretense', 'crossdresser', 'mutable', 'deseo', 'natxo', 'oreka', 'redpilled', 'weta', 'walz', 'romcon', 'acrobatic', 'westmore', 'woll', 'survivour', 'absurdly', 'bizarrchitecture', 'darwinist', 'fill', 'snoozefest', 'salgueiro', 'kartheiser', 'malaemde', 'analytica', 'zawe', 'limited', 'lumpen', 'supervillians', 'ecologic', 'womanchild', 'sensationalised', 'fnny', 'sprouse', 'vlog', 'rotation', 'spoonerism', 'telugu', 'jemima', 'wwwii', 'corbero', 'coxless', 'rede', 'incarnation', 'ingravity', 'periodo', 'directionc', 'scavenge', 'convulsion', 'asphixiantig', 'aaryan', 'sanon', 'tripathi', 'admirable', 'impermanence', 'tajikistan', 'mauthausen', 'xmen', 'phlebotomy', 'theranos', 'impantatiion', 'interpretive', 'acedmic', 'beanie', 'feldstein', 'humility', 'obejrzenia', 'vod', 'pimenova', 'magdalena', 'cielecka', 'wałbrzych', 'hilbillies', 'krav', 'maga', 'sleeping', 'lan', 'antichristianity', 'wick', 'actionized', 'bioluminescence', 'destryoed', 'zomvies', 'arbitrarily', 'disme', 'rewind', 'hunstville', 'huntsville', 'volition', 'orthochromatic', 'stuyvesant', 'bnb', 'brawn', 'cris', 'denman', 'waist', 'rakshasa', 'buggin', 'unuusal', 'psychodelic', 'pl', 'mj', 'technolgy', 'guilting', 'waldheim', 'honk', 'rugaard', 'biomass', 'chiquito', 'calzada', 'florentino', 'josema', 'yuste', 'masami', 'nagasawa', 'janeausten', 'directed', 'gerardi', 'corrado', 'invernizzi', 'dajana', 'roncione', 'mariani', 'camerini', 'edoardo', 'coniglio', 'turrini', 'gioia', 'spaziani', 'mascino', 'maurizio', 'lastrico', 'torresi', 'serena', 'coproration', 'sardá', 'honky', 'tonk', 'inxs', 'hutchence', 'firstnations', 'photorealistic', 'liveaction', 'marem', 'hassler', 'mcclain', 'brandoni', 'nobunaga', 'nabha', 'natesh', 'spiritualistic', 'analitica', 'seti', 'negga', 'edita', 'brychta', 'squel', 'orsini', 'cataplexy', 'lega', 'nord', 'saltarello', 'concha', 'mazurka', 'steubenville', 'westward', 'logo', 'stinger', 'chickasaw', 'decolonization', 'khurrana', 'gilligan', 'camino', 'moloch', 'proud', 'abraha', 'benrubi', 'acher', 'potala', 'thumbelina', 'vizier', 'hgh', 'yelp', 'keogh', 'comin', 'loathe', 'nagaski', 'mexicano', 'política', 'alcázar', 'unamuno', 'cinetrain', 'weirdness', 'vaporwave', 'recast', 'shinging', 'alemania', 'altenrate', 'enhanced', 'ac', 'puff', 'cowell']\n"
     ]
    }
   ],
   "source": [
    "print(tags_list)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "72eac9f7",
   "metadata": {},
   "source": [
    "#### \"tags_list\" são as nossas colunas e \"moviesId_list\" as linhas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "dad5ef67",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-07-12T16:32:14.693688Z",
     "start_time": "2021-07-12T16:32:14.688689Z"
    }
   },
   "outputs": [],
   "source": [
    "# pegando lista de moviesID\n",
    "moviesId_list = tags_list_df[\"movieId\"].tolist()\n",
    "\n",
    "# recuperando dimensoes da matriz\n",
    "number_of_movies_with_tags = len(moviesId_list) # num linhas\n",
    "number_of_tags = len(tags_list) # num colunas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "81f42ff6",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-07-12T16:32:15.865405Z",
     "start_time": "2021-07-12T16:32:15.640352Z"
    }
   },
   "outputs": [],
   "source": [
    "# criando uma matriz temporaria preenchida nos locais corretos\n",
    "temp = np.zeros((number_of_movies_with_tags, number_of_tags))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "13224492",
   "metadata": {},
   "source": [
    "#### AVISO: Não executar as duas células abaixo, apenas carregar o arquivo na posterior"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "e8b8caad",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-07-12T16:34:09.913205Z",
     "start_time": "2021-07-12T16:32:16.264568Z"
    }
   },
   "outputs": [
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-66-d3220e58b6c6>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mindex\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrow\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mtags_list_df\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0miterrows\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m     \u001b[1;32mfor\u001b[0m \u001b[0mtag\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mrow\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'tags_processed'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 5\u001b[1;33m         \u001b[0mcoordinates_to_fill\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mmoviesId_list\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrow\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'movieId'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtags_list\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtag\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "coordinates_to_fill = []\n",
    "# recuperando os lugares a serem preenchidos na matriz\n",
    "for index, row in tags_list_df.iterrows():\n",
    "    for tag in row['tags_processed']:\n",
    "        coordinates_to_fill.append([moviesId_list.index(row['movieId']), tags_list.index(tag)])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4403ba33",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-07-12T16:34:09.914207Z",
     "start_time": "2021-07-12T16:32:35.775Z"
    }
   },
   "outputs": [],
   "source": [
    "# salvando as coordenadas a serem preenchidas num arquivo pickle\n",
    "import pickle\n",
    "with open('preprocessed-data/CB/movies_tags_matrix/coordinates_to_fill.pkl', 'w+b') as f:\n",
    "     pickle.dump(coordinates_to_fill, f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "a6271af5",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-07-12T16:34:15.673878Z",
     "start_time": "2021-07-12T16:34:13.553513Z"
    }
   },
   "outputs": [],
   "source": [
    "coordinates_to_fill = pd.read_pickle('preprocessed-data/CB/movies_tags_matrix/coordinates_to_fill.pkl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "bf437616",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-07-12T16:34:18.456591Z",
     "start_time": "2021-07-12T16:34:17.565452Z"
    }
   },
   "outputs": [],
   "source": [
    "# preenchendo essa matriz nos locais corretos\n",
    "for coordinate in coordinates_to_fill:\n",
    "    temp[coordinate[0]][coordinate[1]] = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "7dec3892",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-07-12T16:34:33.099563Z",
     "start_time": "2021-07-12T16:34:19.565589Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<45251x32443 sparse matrix of type '<class 'numpy.intc'>'\n",
       "\twith 608415 stored elements in Compressed Sparse Row format>"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# criando a matriz final\n",
    "movies_tags_matrix = sparse.csr_matrix(temp, dtype=int)\n",
    "\n",
    "movies_tags_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "4f047dc0",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-07-12T16:34:41.624567Z",
     "start_time": "2021-07-12T16:34:41.601560Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "NearestNeighbors(metric='cosine', n_neighbors=10)"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Criando o modelo knn utilizando as tags\n",
    "knn_tags_processed_cb = NearestNeighbors(n_neighbors=N_NEIGHBORS, algorithm='auto', metric='cosine') # temos que mexer nos parâmetros posteriormente\n",
    "\n",
    "knn_tags_processed_cb.fit(movies_tags_matrix)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "8c79a2e8",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-07-12T16:40:36.738683Z",
     "start_time": "2021-07-12T16:40:36.722680Z"
    }
   },
   "outputs": [],
   "source": [
    "# criando função que gera recomendações basedo em um filme - utilizando um modelo KNN - baseando-se nas tags processadas\n",
    "def get_recommendations_tags_processed_cb(movie_name, model): #nome do filme, modelo\n",
    "    # Pegando o Id do filme que tenha o nome passado\n",
    "    movieId = movies_cb.loc[movies_cb[\"title\"] == movie_name][\"movieId\"].values[0]\n",
    "    \n",
    "    index = moviesId_list.index(movieId)\n",
    "    \n",
    "    distances, suggestions = model.kneighbors(movies_tags_matrix.getrow(index).todense().tolist(), n_neighbors=N_NEIGHBORS)\n",
    "    \n",
    "    suggestionsIds_list = [moviesId_list[index] for index in suggestions.flatten()]\n",
    "    \n",
    "    for i in range(0, len(distances.flatten())):\n",
    "        if(i == 0):\n",
    "            print('Recomendações para {0}: \\n'.format(movie_name))\n",
    "        else:\n",
    "            print('{0}: {1}, com distância de: {2}'.format(i, movies_cb.loc[movies_cb[\"movieId\"] == suggestionsIds_list[i]][\"title\"].values[0], distances.flatten()[i]))\n",
    "    \n",
    "    return distances, suggestions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "8fdf3415",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-07-12T16:40:36.976757Z",
     "start_time": "2021-07-12T16:40:36.934749Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Recomendações para Spider-Man 2: \n",
      "\n",
      "1: Spider-Man, com distância de: 0.47533317688720234\n",
      "2: Spider-Man 3, com distância de: 0.5851433159120089\n",
      "3: Batman Begins, com distância de: 0.6811919836281075\n",
      "4: Batman Returns, com distância de: 0.7031840172183927\n",
      "5: Superman III, com distância de: 0.734730957399361\n",
      "6: Batman, com distância de: 0.741303137003815\n",
      "7: Superman Returns, com distância de: 0.7444917564055193\n",
      "8: Superman IV: The Quest for Peace, com distância de: 0.7522839326113557\n",
      "9: X-Men: The Last Stand, com distância de: 0.753803711212755\n"
     ]
    }
   ],
   "source": [
    "a, b = get_recommendations_tags_processed_cb(\"Spider-Man 2\", knn_tags_processed_cb)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a402b4df",
   "metadata": {},
   "source": [
    "# AS RECOMENDACOES KNN CONTENT BASED NAO ESTAO TAO BOAS.\n",
    "# TODO - PESQUISAR OUTRAS FORMAS DE RECOMENDAR\n",
    "\n",
    "# TODO2 - VER UMA FORMA DE MEDIR A ACURACIA DO KNN"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "28eef954",
   "metadata": {},
   "source": [
    "# IDEIA: UTILIZAR PARA COMPARAÇÃO DE ACURÁCIA: CALCULAR O AVERAGE RATING DOS FILMES RECOMENDADOS E COMPARAR COM O AVERAGE RATING DO FILME INPUT.  \n",
    "# SOURCE: https://hendra-herviawan.github.io/Movie-Recommendation-based-on-KNN-K-Nearest-Neighbors.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1ba7b050",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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