Recommendation System Algorithm.ipynb

{
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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-info alert-info\"><b><h1>Recommendation system</h1></b>\n",
    "\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "id": "9d20ZFQZRDC5"
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import math\n",
    "\n",
    "from sklearn.neighbors import NearestNeighbors"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-info alert-info\"><b><h3>1. Obtener los datos:</h3></b>\n",
    "\n",
    "El conjunto de datos de MovieLens es una colección de calificaciones de películas recopiladas por el GrupoLens de la Universidad de Minnesota. El conjunto de datos incluye información sobre aproximadamente 100,000 calificaciones de películas realizadas por más de 600 usuarios.\n",
    "\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "id": "B3oN5hLUEy06"
   },
   "outputs": [],
   "source": [
    "ratings = pd.read_csv('dataset/ratings.csv') #usecols=['userId','movieId','rating'])\n",
    "movies_df = pd.read_csv('dataset/movies.csv')"
   ]
  },
  {
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   "execution_count": 3,
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       "        userId  movieId  rating   timestamp\n",
       "0            1        1     4.0   964982703\n",
       "1            1        3     4.0   964981247\n",
       "2            1        6     4.0   964982224\n",
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       "...        ...      ...     ...         ...\n",
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       "100833     610   168250     5.0  1494273047\n",
       "100834     610   168252     5.0  1493846352\n",
       "100835     610   170875     3.0  1493846415\n",
       "\n",
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     "execution_count": 3,
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   "source": [
    "ratings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
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   "outputs": [
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       "      <th>9738</th>\n",
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      "text/plain": [
       "      movieId                                      title  \\\n",
       "0           1                           Toy Story (1995)   \n",
       "1           2                             Jumanji (1995)   \n",
       "2           3                    Grumpier Old Men (1995)   \n",
       "3           4                   Waiting to Exhale (1995)   \n",
       "4           5         Father of the Bride Part II (1995)   \n",
       "...       ...                                        ...   \n",
       "9737   193581  Black Butler: Book of the Atlantic (2017)   \n",
       "9738   193583               No Game No Life: Zero (2017)   \n",
       "9739   193585                               Flint (2017)   \n",
       "9740   193587        Bungo Stray Dogs: Dead Apple (2018)   \n",
       "9741   193609        Andrew Dice Clay: Dice Rules (1991)   \n",
       "\n",
       "                                           genres  \n",
       "0     Adventure|Animation|Children|Comedy|Fantasy  \n",
       "1                      Adventure|Children|Fantasy  \n",
       "2                                  Comedy|Romance  \n",
       "3                            Comedy|Drama|Romance  \n",
       "4                                          Comedy  \n",
       "...                                           ...  \n",
       "9737              Action|Animation|Comedy|Fantasy  \n",
       "9738                     Animation|Comedy|Fantasy  \n",
       "9739                                        Drama  \n",
       "9740                             Action|Animation  \n",
       "9741                                       Comedy  \n",
       "\n",
       "[9742 rows x 3 columns]"
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   "source": [
    "movies_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-info alert-info\"><b><h3>2. Limpiar los datos:</h3></b>\n",
    "\n",
    "El primer paso será asegurarnos de que los datos estén limpios y en un formato adecuado. Para ello lo primero será crear una nueva columna donde añadir el año.\n",
    "\n",
    "**Nota:** el genero de la película no se tendrá en cuenta para el desarrollo del algoritmo.\n",
    "    \n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "movies_df['year'] = movies_df.title.str.extract('(\\(\\d\\d\\d\\d\\))',expand=False) \n",
    "movies_df['year'] = movies_df.year.str.extract('(\\d\\d\\d\\d)',expand=False) \n",
    "movies_df['title'] = movies_df.title.str.replace(' (\\(\\d\\d\\d\\d\\))', '', regex=True) \n",
    "movies_df = movies_df.drop(['genres'], axis=1) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-info alert-info\"><b><h3>3. Crear una matriz de usuario-item:</h3></b>\n",
    "    \n",
    "Crea una matriz en la que las filas representen a los usuarios y las columnas representen a las películas. Los valores en la matriz representan la valoración de un usuario a una película.\n",
    "\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "ratings_df = pd.merge(ratings, movies_df, how='inner', on='movieId')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true
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       "      <td>Blair Witch</td>\n",
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      "text/plain": [
       "        userId  movieId  rating   timestamp                      title  year\n",
       "0            1        1     4.0   964982703                  Toy Story  1995\n",
       "1            5        1     4.0   847434962                  Toy Story  1995\n",
       "2            7        1     4.5  1106635946                  Toy Story  1995\n",
       "3           15        1     2.5  1510577970                  Toy Story  1995\n",
       "4           17        1     4.5  1305696483                  Toy Story  1995\n",
       "...        ...      ...     ...         ...                        ...   ...\n",
       "100831     610   160341     2.5  1479545749                  Bloodmoon  1997\n",
       "100832     610   160527     4.5  1479544998  Sympathy for the Underdog  1971\n",
       "100833     610   160836     3.0  1493844794                     Hazard  2005\n",
       "100834     610   163937     3.5  1493848789                Blair Witch  2016\n",
       "100835     610   163981     3.5  1493850155                         31  2016\n",
       "\n",
       "[100836 rows x 6 columns]"
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     "execution_count": 7,
     "metadata": {},
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   "source": [
    "ratings_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_matrix = ratings_df.pivot_table(index='title',columns='userId',values='rating').fillna(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>'71</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>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>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>'Hellboy': The Seeds of Creation</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>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>'Round Midnight</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>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>'Salem's Lot</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>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>'Til There Was You</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>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>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>eXistenZ</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>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>xXx</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>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>3.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>xXx: State of the Union</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>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>1.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>¡Three Amigos!</th>\n",
       "      <td>4.0</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>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>À nous la liberté (Freedom for Us)</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>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>9446 rows × 610 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "userId                              1    2    3    4    5    6    7    8    \\\n",
       "title                                                                        \n",
       "'71                                 0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0   \n",
       "'Hellboy': The Seeds of Creation    0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0   \n",
       "'Round Midnight                     0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0   \n",
       "'Salem's Lot                        0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0   \n",
       "'Til There Was You                  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0   \n",
       "...                                 ...  ...  ...  ...  ...  ...  ...  ...   \n",
       "eXistenZ                            0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0   \n",
       "xXx                                 0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0   \n",
       "xXx: State of the Union             0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0   \n",
       "¡Three Amigos!                      4.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0   \n",
       "À nous la liberté (Freedom for Us)  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0   \n",
       "\n",
       "userId                              9    10   ...  601  602  603  604  605  \\\n",
       "title                                         ...                            \n",
       "'71                                 0.0  0.0  ...  0.0  0.0  0.0  0.0  0.0   \n",
       "'Hellboy': The Seeds of Creation    0.0  0.0  ...  0.0  0.0  0.0  0.0  0.0   \n",
       "'Round Midnight                     0.0  0.0  ...  0.0  0.0  0.0  0.0  0.0   \n",
       "'Salem's Lot                        0.0  0.0  ...  0.0  0.0  0.0  0.0  0.0   \n",
       "'Til There Was You                  0.0  0.0  ...  0.0  0.0  0.0  0.0  0.0   \n",
       "...                                 ...  ...  ...  ...  ...  ...  ...  ...   \n",
       "eXistenZ                            0.0  0.0  ...  0.0  0.0  5.0  0.0  0.0   \n",
       "xXx                                 1.0  0.0  ...  0.0  0.0  0.0  0.0  0.0   \n",
       "xXx: State of the Union             0.0  0.0  ...  0.0  0.0  0.0  0.0  0.0   \n",
       "¡Three Amigos!                      0.0  0.0  ...  0.0  0.0  0.0  0.0  0.0   \n",
       "À nous la liberté (Freedom for Us)  0.0  0.0  ...  0.0  0.0  0.0  0.0  0.0   \n",
       "\n",
       "userId                              606  607  608  609  610  \n",
       "title                                                        \n",
       "'71                                 0.0  0.0  0.0  0.0  4.0  \n",
       "'Hellboy': The Seeds of Creation    0.0  0.0  0.0  0.0  0.0  \n",
       "'Round Midnight                     0.0  0.0  0.0  0.0  0.0  \n",
       "'Salem's Lot                        0.0  0.0  0.0  0.0  0.0  \n",
       "'Til There Was You                  0.0  0.0  0.0  0.0  0.0  \n",
       "...                                 ...  ...  ...  ...  ...  \n",
       "eXistenZ                            0.0  0.0  4.5  0.0  0.0  \n",
       "xXx                                 0.0  0.0  3.5  0.0  2.0  \n",
       "xXx: State of the Union             0.0  0.0  0.0  0.0  1.5  \n",
       "¡Three Amigos!                      0.0  0.0  0.0  0.0  0.0  \n",
       "À nous la liberté (Freedom for Us)  0.0  0.0  0.0  0.0  0.0  \n",
       "\n",
       "[9446 rows x 610 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0. , 0. , 0. , ..., 0. , 0. , 4. ],\n",
       "       [0. , 0. , 0. , ..., 0. , 0. , 0. ],\n",
       "       [0. , 0. , 0. , ..., 0. , 0. , 0. ],\n",
       "       ...,\n",
       "       [0. , 0. , 0. , ..., 0. , 0. , 1.5],\n",
       "       [4. , 0. , 0. , ..., 0. , 0. , 0. ],\n",
       "       [0. , 0. , 0. , ..., 0. , 0. , 0. ]])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_matrix.values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Usaremos **NearestNeighbors()** para calcular la distancia entre películas usando la **cosine similarity** y encontrar las películas más similares a cada película."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.neighbors import NearestNeighbors\n",
    "\n",
    "num_neighbors = 8\n",
    "\n",
    "knn = NearestNeighbors(metric='cosine', algorithm='brute')\n",
    "knn.fit(df_matrix.values)\n",
    "distances, indices = knn.kneighbors(df_matrix.values, num_neighbors)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "El parámetro para el número de vecinos más cercanos se ha establecido en 8."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[8809, 6281, 1910, ..., 5523, 8808, 9306],\n",
       "       [5602, 7659,    1, ...,   11,    2, 4547],\n",
       "       [   2,  607,  646, ..., 5602,  389,  403],\n",
       "       ...,\n",
       "       [9443, 6658, 4740, ..., 3696, 6718, 2879],\n",
       "       [9444, 5582, 3199, ..., 7665, 2723,  722],\n",
       "       [9445, 5813, 3058, ..., 1867,  708, 7829]], dtype=int64)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "indices"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**indices** muestra las películas más cercanas a cada película. Cada fila corresponde a la fila en df_matrix. El primer elemento de una fila es la película más similar (más cercana). Es la película en sí. El segundo elemento es el segundo más cercano y el tercero es el tercero más cercano, etc.\n",
    "\n",
    "Por ejemplo, la primera fila **[8809,66281, 1910, ...., 5523, 8808, 9306]**, la película más cercana a 'movie_8809' es ella misma, la segunda película más cercana es 'movie_66281', la tercera es 'movie_1910' y así sucesivamente."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.        , 0.        , 0.        , ..., 0.        , 0.        ,\n",
       "        0.        ],\n",
       "       [0.        , 0.        , 0.        , ..., 0.28445825, 0.29289322,\n",
       "        0.45727958],\n",
       "       [0.        , 0.29289322, 0.29289322, ..., 0.29289322, 0.29289322,\n",
       "        0.29289322],\n",
       "       ...,\n",
       "       [0.        , 0.30170275, 0.30239885, ..., 0.30398834, 0.30563493,\n",
       "        0.30563493],\n",
       "       [0.        , 0.46429966, 0.48672079, ..., 0.53204949, 0.53611603,\n",
       "        0.53811618],\n",
       "       [0.        , 0.        , 0.        , ..., 0.24742331, 0.25721865,\n",
       "        0.29289322]])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "distances"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**distances** muestra la distancia entre pelícluas. Cada número en esta matriz corresponde al número en la matriz de **indices**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "indices_df = pd.DataFrame(indices, columns=['col1', 'col2', 'col3', 'col4','col5', 'col6', 'col7', 'col8'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "cols_to_concat = ['col2', 'col3', 'col4', 'col5', 'col6','col7','col8']\n",
    "networkMovies = np.empty((0, 2), int)\n",
    "\n",
    "for col in cols_to_concat:\n",
    "    data = indices_df.loc[:, ['col1', col]].values\n",
    "    networkMovies = np.concatenate((networkMovies, data), axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[8809, 6281],\n",
       "       [5602, 7659],\n",
       "       [   2,  607],\n",
       "       ...,\n",
       "       [9443, 2879],\n",
       "       [9444,  722],\n",
       "       [9445, 7829]], dtype=int64)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "networkMovies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "import csv\n",
    "\n",
    "with open('networkMovies.csv', 'w') as file:\n",
    "    writer = csv.writer(file)\n",
    "    writer.writerows(networkMovies)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-info alert-info\"><b><h3> 4. Calcular similitudes:</h3></b>\n",
    "    \n",
    "El algoritmo consta de tres pasos:\n",
    "\n",
    "* **1.** Calcular la similitud entre películas utilizando similitud de Coseno o de Pearson. Las similitudes se calculan en función de todas las calificaciones realizadas por todos los usuarios.\n",
    "\n",
    "* **2.** Predecir la calificación de cada película que el usuario no ha visto:\n",
    "\n",
    "    * Primero, se buscan las películas más similares a la película que el usuario no ha visto, utilizando la similitud calculada en el Paso 1.\n",
    "    * A continuación, se calcula el promedio ponderado de las calificaciones realizadas por el usuario para las películas más similares. Se utiliza la métrica de distancia inversa como peso en este cálculo.\n",
    "    * El promedio ponderado se utiliza como la calificación predicha para la película no vista por el usuario.\n",
    "\n",
    "* **3.** Se recomiendan las películas que tienen las calificaciones predichas más altas para el usuario.\n",
    "\n",
    "De esta manera, el recomendador sugiere películas basándose en las calificaciones de otras películas similares, lo que permite ofrecer recomendaciones personalizadas y precisas al usuario.\n",
    "\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 4.1. Cosine similarity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df_matrix\n",
    "df1 = df.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "def recommend_movies(user, num_recommended_movies):\n",
    "    \n",
    "    print('Lista de las películas vistas por el usuario {} \\n'.format(user))\n",
    "\n",
    "    for m in df[df[user] > 0][user].index.tolist():\n",
    "        print(m)\n",
    "  \n",
    "    print('\\n')\n",
    "\n",
    "    recommended_movies = []\n",
    "\n",
    "    for m in df[df[user] == 0].index.tolist():\n",
    "        index_df = df.index.tolist().index(m)\n",
    "        predicted_rating = df1.iloc[index_df, df1.columns.tolist().index(user)]\n",
    "        recommended_movies.append((m, predicted_rating))\n",
    "\n",
    "    sorted_rm = sorted(recommended_movies, key=lambda x:x[1], reverse=True)\n",
    "  \n",
    "    print('Lista de recomendación \\n')\n",
    "    rank = 1\n",
    "    for recommended_movie in sorted_rm[:num_recommended_movies]:\n",
    "        \n",
    "        print('{}: {} - valoración:{}'.format(rank, recommended_movie[0], recommended_movie[1]))\n",
    "        rank = rank + 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "id": "I8CE-bhCRT6Q"
   },
   "outputs": [],
   "source": [
    "def movie_recommender_cosinus(user, num_recommendation):\n",
    "    \n",
    "    user_index = df.columns.tolist().index(user)\n",
    "\n",
    "    for m,t in list(enumerate(df.index)):\n",
    "        if df.iloc[m, user_index] == 0:\n",
    "            sim_movies = indices[m].tolist()\n",
    "            movie_distances = distances[m].tolist()\n",
    "            \n",
    "            if m in sim_movies:\n",
    "                id_movie = sim_movies.index(m)\n",
    "                sim_movies.remove(m)\n",
    "                movie_distances.pop(id_movie) \n",
    "\n",
    "            else:\n",
    "                sim_movies = sim_movies[:num_neighbors-1]\n",
    "                movie_distances = movie_distances[:num_neighbors-1]\n",
    "           \n",
    "            movie_similarity = [1-x for x in movie_distances]\n",
    "            movie_similarity_copy = movie_similarity.copy()\n",
    "            nominator = 0\n",
    "            \n",
    "            for s in range(0, len(movie_similarity)):\n",
    "                if df.iloc[sim_movies[s], user_index] == 0:\n",
    "                    if len(movie_similarity_copy) == (num_neighbors - 1):\n",
    "                        movie_similarity_copy.pop(s)\n",
    "          \n",
    "                    else:\n",
    "                        movie_similarity_copy.pop(s-(len(movie_similarity)-len(movie_similarity_copy)))\n",
    "            \n",
    "                else:\n",
    "                    nominator = nominator + movie_similarity[s]*df.iloc[sim_movies[s],user_index]\n",
    "          \n",
    "            if len(movie_similarity_copy) > 0:\n",
    "                if sum(movie_similarity_copy) > 0:\n",
    "                    predicted_r = nominator/sum(movie_similarity_copy)\n",
    "        \n",
    "                else:\n",
    "                    predicted_r = 0\n",
    "\n",
    "            else:\n",
    "                predicted_r = 0\n",
    "        \n",
    "            df1.iloc[m,user_index] = predicted_r\n",
    "    recommend_movies(user,num_recommendation)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 4.2. Pearson Correlation\n",
    "\n",
    "Para calcular el coeficiente de correlación empezaremos por crear un usuario de entrada al que recomendar películas. En este caso se ha procedido a cagar mi propia base de datos de netflix en vez de utilizar a un usuario desconocido de la base de datos de MovieLens."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 4.2.1. Netflix Count Data Ratings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "scrolled": true
   },
   "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>Profile</th>\n",
       "      <th>title</th>\n",
       "      <th>rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Casa</td>\n",
       "      <td>The Wedding Unplanner</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Casa</td>\n",
       "      <td>Freud</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>guillermo</td>\n",
       "      <td>Gladiator</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>guillermo</td>\n",
       "      <td>Bohemian Rhapsody</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>guillermo</td>\n",
       "      <td>Seven Pounds</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>25</td>\n",
       "      <td>Thor: Ragnarok</td>\n",
       "      <td>4.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>25</td>\n",
       "      <td>Three Billboards Outside Ebbing, Missouri</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>25</td>\n",
       "      <td>Up</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>25</td>\n",
       "      <td>WALL·E</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>25</td>\n",
       "      <td>Wonder</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>63 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Profile                                      title  rating\n",
       "0        Casa                      The Wedding Unplanner     1.0\n",
       "1        Casa                                      Freud     2.0\n",
       "2   guillermo                                  Gladiator     5.0\n",
       "3   guillermo                          Bohemian Rhapsody     4.0\n",
       "4   guillermo                               Seven Pounds     2.0\n",
       "..        ...                                        ...     ...\n",
       "58         25                             Thor: Ragnarok     4.5\n",
       "59         25  Three Billboards Outside Ebbing, Missouri     5.0\n",
       "60         25                                         Up     5.0\n",
       "61         25                                     WALL·E     5.0\n",
       "62         25                                     Wonder     5.0\n",
       "\n",
       "[63 rows x 3 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "netflix = pd.read_csv(\"dataset/netflix_ratings.csv\",  usecols=['Profile Name','Title Name','Thumbs Value'])\n",
    "netflix.columns = ['Profile','title','rating']\n",
    "\n",
    "netflix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "netflix = netflix[netflix['Profile'] == '25']\n",
    "netflix = netflix.drop(['Profile'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>title</th>\n",
       "      <th>rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>Avengers: Infinity War - Part I</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>Blade Runner 2049</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>Dark Knight Rises, The</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>Dark Knight, The</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>Deadpool 2</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>Dumb &amp; Dumber (Dumb and Dumber)</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>Gladiator</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>Inception</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>Incredibles 2</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>Inglourious Basterds</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>Iron Man</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>Lord of the Rings: The Fellowship of the Ring,...</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>Lord of the Rings: The Return of the King, The</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>Lord of the Rings: The Two Towers, The</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>Matrix, The</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>Raiders of the Lost Ark (Indiana Jones and the...</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>Saving Private Ryan</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>Schindler's List</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>Shutter Island</td>\n",
       "      <td>4.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>Star Wars: Episode IV - A New Hope</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>The Imitation Game</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>Thor: Ragnarok</td>\n",
       "      <td>4.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>Three Billboards Outside Ebbing, Missouri</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>Up</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>WALL·E</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>Wonder</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                title  rating\n",
       "37                    Avengers: Infinity War - Part I     5.0\n",
       "38                                  Blade Runner 2049     4.0\n",
       "39                             Dark Knight Rises, The     5.0\n",
       "40                                   Dark Knight, The     5.0\n",
       "41                                         Deadpool 2     5.0\n",
       "42                    Dumb & Dumber (Dumb and Dumber)     4.0\n",
       "43                                          Gladiator     5.0\n",
       "44                                          Inception     5.0\n",
       "45                                      Incredibles 2     4.0\n",
       "46                               Inglourious Basterds     5.0\n",
       "47                                           Iron Man     4.0\n",
       "48  Lord of the Rings: The Fellowship of the Ring,...     5.0\n",
       "49     Lord of the Rings: The Return of the King, The     5.0\n",
       "50             Lord of the Rings: The Two Towers, The     5.0\n",
       "51                                        Matrix, The     5.0\n",
       "52  Raiders of the Lost Ark (Indiana Jones and the...     5.0\n",
       "53                                Saving Private Ryan     5.0\n",
       "54                                   Schindler's List     5.0\n",
       "55                                     Shutter Island     4.5\n",
       "56                 Star Wars: Episode IV - A New Hope     5.0\n",
       "57                                 The Imitation Game     5.0\n",
       "58                                     Thor: Ragnarok     4.5\n",
       "59          Three Billboards Outside Ebbing, Missouri     5.0\n",
       "60                                                 Up     5.0\n",
       "61                                             WALL·E     5.0\n",
       "62                                             Wonder     5.0"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "netflix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "inputId = movies_df[movies_df['title'].isin(netflix['title'].tolist())]\n",
    "netflix = pd.merge(inputId, netflix)\n",
    "netflix = netflix.drop(['year'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>userId</th>\n",
       "      <th>movieId</th>\n",
       "      <th>rating</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>title</th>\n",
       "      <th>year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1365</th>\n",
       "      <td>1</td>\n",
       "      <td>231</td>\n",
       "      <td>5.0</td>\n",
       "      <td>964981179</td>\n",
       "      <td>Dumb &amp; Dumber (Dumb and Dumber)</td>\n",
       "      <td>1994</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1366</th>\n",
       "      <td>6</td>\n",
       "      <td>231</td>\n",
       "      <td>3.0</td>\n",
       "      <td>845553174</td>\n",
       "      <td>Dumb &amp; Dumber (Dumb and Dumber)</td>\n",
       "      <td>1994</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1367</th>\n",
       "      <td>8</td>\n",
       "      <td>231</td>\n",
       "      <td>4.0</td>\n",
       "      <td>839463470</td>\n",
       "      <td>Dumb &amp; Dumber (Dumb and Dumber)</td>\n",
       "      <td>1994</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1368</th>\n",
       "      <td>14</td>\n",
       "      <td>231</td>\n",
       "      <td>3.0</td>\n",
       "      <td>835441042</td>\n",
       "      <td>Dumb &amp; Dumber (Dumb and Dumber)</td>\n",
       "      <td>1994</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1369</th>\n",
       "      <td>18</td>\n",
       "      <td>231</td>\n",
       "      <td>2.5</td>\n",
       "      <td>1455748462</td>\n",
       "      <td>Dumb &amp; Dumber (Dumb and Dumber)</td>\n",
       "      <td>1994</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      userId  movieId  rating   timestamp                            title  \\\n",
       "1365       1      231     5.0   964981179  Dumb & Dumber (Dumb and Dumber)   \n",
       "1366       6      231     3.0   845553174  Dumb & Dumber (Dumb and Dumber)   \n",
       "1367       8      231     4.0   839463470  Dumb & Dumber (Dumb and Dumber)   \n",
       "1368      14      231     3.0   835441042  Dumb & Dumber (Dumb and Dumber)   \n",
       "1369      18      231     2.5  1455748462  Dumb & Dumber (Dumb and Dumber)   \n",
       "\n",
       "      year  \n",
       "1365  1994  \n",
       "1366  1994  \n",
       "1367  1994  \n",
       "1368  1994  \n",
       "1369  1994  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "userRatings = ratings_df[ratings_df['movieId'].isin(netflix['movieId'].tolist())]\n",
    "userRatings.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Primero filtramos a los usuarios que han visto nuestras entradas y las almacenamos en una lista "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "userRatingsGroup = userRatings.groupby(['userId'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "userRatingsGroup = sorted(userRatingsGroup, key=lambda x: len(x[1]), reverse=True)\n",
    "userRatingsGroup = userRatingsGroup[0:100]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Seleccionamos a los usuarios que han puntuado a las peliculas en común con nuestra entrada"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "pearsonCorrelationDict = {}\n",
    "\n",
    "for name, group in userRatingsGroup:\n",
    "    \n",
    "     \n",
    "    group = group.sort_values(by='movieId')\n",
    "    netflix = netflix.sort_values(by='movieId')\n",
    "    \n",
    "    \n",
    "    temp_df = netflix[netflix['movieId'].isin(group['movieId'].tolist())]\n",
    "    \n",
    "     \n",
    "    tempRatingList = temp_df['rating'].tolist()\n",
    "    \n",
    "     \n",
    "    tempGroupList = group['rating'].tolist()\n",
    "    data_corr = {'tempGroupList': tempGroupList,\n",
    "            'tempRatingList': tempRatingList}\n",
    "    pd_corr = pd.DataFrame(data_corr)\n",
    "    r = pd_corr.corr(method=\"pearson\")[\"tempRatingList\"][\"tempGroupList\"]\n",
    "    \n",
    "    \n",
    "    if math.isnan(r) == True:\n",
    "        r = 0\n",
    "    pearsonCorrelationDict[name] = r"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>movieId</th>\n",
       "      <th>title</th>\n",
       "      <th>rating</th>\n",
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       "      <td>260</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1198</td>\n",
       "      <td>Raiders of the Lost Ark (Indiana Jones and the...</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2028</td>\n",
       "      <td>Saving Private Ryan</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2571</td>\n",
       "      <td>Matrix, The</td>\n",
       "      <td>5.0</td>\n",
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       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>4993</td>\n",
       "      <td>Lord of the Rings: The Fellowship of the Ring,...</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>5952</td>\n",
       "      <td>Lord of the Rings: The Two Towers, The</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>7153</td>\n",
       "      <td>Lord of the Rings: The Return of the King, The</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>58559</td>\n",
       "      <td>Dark Knight, The</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>68954</td>\n",
       "      <td>Up</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>79132</td>\n",
       "      <td>Inception</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    movieId                                              title  rating\n",
       "1       260                 Star Wars: Episode IV - A New Hope     5.0\n",
       "3      1198  Raiders of the Lost Ark (Indiana Jones and the...     5.0\n",
       "4      2028                                Saving Private Ryan     5.0\n",
       "5      2571                                        Matrix, The     5.0\n",
       "8      4993  Lord of the Rings: The Fellowship of the Ring,...     5.0\n",
       "9      5952             Lord of the Rings: The Two Towers, The     5.0\n",
       "10     7153     Lord of the Rings: The Return of the King, The     5.0\n",
       "11    58559                                   Dark Knight, The     5.0\n",
       "16    68954                                                 Up     5.0\n",
       "18    79132                                          Inception     5.0"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'tempGroupList': [2.0, 4.0, 3.0, 2.5, 2.0, 2.0, 2.0, 4.0, 5.0, 2.0],\n",
       " 'tempRatingList': [5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0]}"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_corr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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       "      <th>userId</th>\n",
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       "      <td>62</td>\n",
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       "      <th>2</th>\n",
       "      <td>0.007564</td>\n",
       "      <td>249</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.648546</td>\n",
       "      <td>305</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.366900</td>\n",
       "      <td>68</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   similarityIndex  userId\n",
       "0         1.000000      25\n",
       "1         0.613722      62\n",
       "2         0.007564     249\n",
       "3         0.648546     305\n",
       "4         0.366900      68"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "userMatrix = pd.DataFrame.from_dict(pearsonCorrelationDict, orient='index')\n",
    "userMatrix.columns = ['similarityIndex']\n",
    "userMatrix['userId'] = userMatrix.index\n",
    "userMatrix.index = range(len(userMatrix))\n",
    "userMatrix.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>-0.061898</td>\n",
       "      <td>378</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>-0.241863</td>\n",
       "      <td>407</td>\n",
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       "      <th>98</th>\n",
       "      <td>0.709299</td>\n",
       "      <td>480</td>\n",
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       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>497</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>100 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    similarityIndex  userId\n",
       "0          1.000000      25\n",
       "1          0.613722      62\n",
       "2          0.007564     249\n",
       "3          0.648546     305\n",
       "4          0.366900      68\n",
       "..              ...     ...\n",
       "95         0.703526     366\n",
       "96        -0.061898     378\n",
       "97        -0.241863     407\n",
       "98         0.709299     480\n",
       "99         0.000000     497\n",
       "\n",
       "[100 rows x 2 columns]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "userMatrix"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Los valores dados por la función varían de r = -1 a r = 1, donde 1 forma una correlación directa entre las dos entidades "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "topUsers=userMatrix.sort_values(by='similarityIndex', ascending=False)[0:50]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "topUsersRating=topUsers.merge(ratings_df, left_on='userId', right_on='userId', how='inner')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>similarityIndex</th>\n",
       "      <th>userId</th>\n",
       "      <th>movieId</th>\n",
       "      <th>rating</th>\n",
       "      <th>timestamp</th>\n",
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       "  <tbody>\n",
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       "      <td>231</td>\n",
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       "      <td>1535470451</td>\n",
       "      <td>Dumb &amp; Dumber (Dumb and Dumber)</td>\n",
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       "      <td>4.0</td>\n",
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       "      <td>260</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1535470429</td>\n",
       "      <td>Star Wars: Episode IV - A New Hope</td>\n",
       "      <td>1977</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.0</td>\n",
       "      <td>25</td>\n",
       "      <td>527</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1535470432</td>\n",
       "      <td>Schindler's List</td>\n",
       "      <td>1993</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>25</td>\n",
       "      <td>1198</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1535470495</td>\n",
       "      <td>Raiders of the Lost Ark (Indiana Jones and the...</td>\n",
       "      <td>1981</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.0</td>\n",
       "      <td>25</td>\n",
       "      <td>2028</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1535470505</td>\n",
       "      <td>Saving Private Ryan</td>\n",
       "      <td>1998</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   similarityIndex  userId  movieId  rating   timestamp  \\\n",
       "0              1.0      25      231     4.0  1535470451   \n",
       "1              1.0      25      260     5.0  1535470429   \n",
       "2              1.0      25      527     5.0  1535470432   \n",
       "3              1.0      25     1198     5.0  1535470495   \n",
       "4              1.0      25     2028     5.0  1535470505   \n",
       "\n",
       "                                               title  year  weightedRating  \n",
       "0                    Dumb & Dumber (Dumb and Dumber)  1994             4.0  \n",
       "1                 Star Wars: Episode IV - A New Hope  1977             5.0  \n",
       "2                                   Schindler's List  1993             5.0  \n",
       "3  Raiders of the Lost Ark (Indiana Jones and the...  1981             5.0  \n",
       "4                                Saving Private Ryan  1998             5.0  "
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "topUsersRating['weightedRating'] = topUsersRating['similarityIndex']*topUsersRating['rating']\n",
    "topUsersRating.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th></th>\n",
       "      <th>sum_similarityIndex</th>\n",
       "      <th>sum_weightedRating</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>movieId</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>16.793302</td>\n",
       "      <td>64.229690</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>9.505035</td>\n",
       "      <td>30.445308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2.699094</td>\n",
       "      <td>7.696141</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>4.197755</td>\n",
       "      <td>10.521883</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>9.178755</td>\n",
       "      <td>37.021836</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         sum_similarityIndex  sum_weightedRating\n",
       "movieId                                         \n",
       "1                  16.793302           64.229690\n",
       "2                   9.505035           30.445308\n",
       "3                   2.699094            7.696141\n",
       "5                   4.197755           10.521883\n",
       "6                   9.178755           37.021836"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tempTopUsersRating = topUsersRating.groupby('movieId').sum()[['similarityIndex','weightedRating']]\n",
    "tempTopUsersRating.columns = ['sum_similarityIndex','sum_weightedRating']\n",
    "tempTopUsersRating.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "recommendation_df = pd.DataFrame()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>weighted average recommendation score</th>\n",
       "      <th>movieId</th>\n",
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       "    <tr>\n",
       "      <th>movieId</th>\n",
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       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3.824721</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3.203072</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2.851379</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2.506550</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>4.033427</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         weighted average recommendation score  movieId\n",
       "movieId                                                \n",
       "1                                     3.824721        1\n",
       "2                                     3.203072        2\n",
       "3                                     2.851379        3\n",
       "5                                     2.506550        5\n",
       "6                                     4.033427        6"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "recommendation_df['weighted average recommendation score'] = tempTopUsersRating['sum_weightedRating']/tempTopUsersRating['sum_similarityIndex']\n",
    "recommendation_df['movieId'] = tempTopUsersRating.index\n",
    "recommendation_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>weighted average recommendation score</th>\n",
       "      <th>movieId</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>movieId</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>92494</th>\n",
       "      <td>5.0</td>\n",
       "      <td>92494</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71033</th>\n",
       "      <td>5.0</td>\n",
       "      <td>71033</td>\n",
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       "    <tr>\n",
       "      <th>232</th>\n",
       "      <td>5.0</td>\n",
       "      <td>232</td>\n",
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       "      <th>8607</th>\n",
       "      <td>5.0</td>\n",
       "      <td>8607</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60333</th>\n",
       "      <td>5.0</td>\n",
       "      <td>60333</td>\n",
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       "    <tr>\n",
       "      <th>27320</th>\n",
       "      <td>5.0</td>\n",
       "      <td>27320</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5279</th>\n",
       "      <td>5.0</td>\n",
       "      <td>5279</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109968</th>\n",
       "      <td>5.0</td>\n",
       "      <td>109968</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94810</th>\n",
       "      <td>5.0</td>\n",
       "      <td>94810</td>\n",
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       "    <tr>\n",
       "      <th>3814</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3814</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         weighted average recommendation score  movieId\n",
       "movieId                                                \n",
       "92494                                      5.0    92494\n",
       "71033                                      5.0    71033\n",
       "232                                        5.0      232\n",
       "8607                                       5.0     8607\n",
       "60333                                      5.0    60333\n",
       "27320                                      5.0    27320\n",
       "5279                                       5.0     5279\n",
       "109968                                     5.0   109968\n",
       "94810                                      5.0    94810\n",
       "3814                                       5.0     3814"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "recommendation_df = recommendation_df.sort_values(by='weighted average recommendation score', ascending=False)\n",
    "recommendation_df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "def movie_recommender_pearson(num_recommended_movies):\n",
    "    \n",
    "    print('Lista de las películas vistas por el usuario. \\n')\n",
    "\n",
    "    for m in netflix['title']:\n",
    "        print(m)\n",
    "  \n",
    "    print('\\n')\n",
    "    \n",
    "    recommended_movies = movies_df.loc[movies_df['movieId'].isin(recommendation_df.head(num_recommended_movies)['movieId'].tolist())]\n",
    "    \n",
    "    print('Playlist Recomendada: \\n')\n",
    "    rank = 1\n",
    "    \n",
    "    for n in recommended_movies['title']:\n",
    "        print('{}: {} - valoración: 5.0'.format(rank,n))\n",
    "        rank = rank + 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-info alert-info\"><b><h3>5. Hacer predicciones:</h3></b>\n",
    "    \n",
    "Usa la matriz de similitud de usuario para hacer predicciones sobre la valoración de un usuario a una película específica."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 5.1. Recomendación de películas para un usuario seleccionado"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Lista de las películas vistas por el usuario 25 \n",
      "\n",
      "Avengers: Infinity War - Part I\n",
      "Blade Runner 2049\n",
      "Dark Knight Rises, The\n",
      "Dark Knight, The\n",
      "Deadpool 2\n",
      "Dumb & Dumber (Dumb and Dumber)\n",
      "Gladiator\n",
      "Inception\n",
      "Incredibles 2\n",
      "Inglourious Basterds\n",
      "Iron Man\n",
      "Lord of the Rings: The Fellowship of the Ring, The\n",
      "Lord of the Rings: The Return of the King, The\n",
      "Lord of the Rings: The Two Towers, The\n",
      "Matrix, The\n",
      "Raiders of the Lost Ark (Indiana Jones and the Raiders of the Lost Ark)\n",
      "Saving Private Ryan\n",
      "Schindler's List\n",
      "Shutter Island\n",
      "Star Wars: Episode IV - A New Hope\n",
      "The Imitation Game\n",
      "Thor: Ragnarok\n",
      "Three Billboards Outside Ebbing, Missouri\n",
      "Up\n",
      "WALL·E\n",
      "Wonder\n",
      "\n",
      "\n",
      "Lista de recomendación \n",
      "\n",
      "1: Green Mile, The - valoración:5.000000000000001\n",
      "2: Intouchables - valoración:5.000000000000001\n",
      "3: Memento - valoración:5.000000000000001\n",
      "4: Patriot, The - valoración:5.000000000000001\n",
      "5: Spirited Away (Sen to Chihiro no kamikakushi) - valoración:5.000000000000001\n"
     ]
    }
   ],
   "source": [
    "movie_recommender_cosinus(25,5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Lista de las películas vistas por el usuario. \n",
      "\n",
      "Dumb & Dumber (Dumb and Dumber)\n",
      "Star Wars: Episode IV - A New Hope\n",
      "Schindler's List\n",
      "Raiders of the Lost Ark (Indiana Jones and the Raiders of the Lost Ark)\n",
      "Saving Private Ryan\n",
      "Matrix, The\n",
      "Gladiator\n",
      "Lord of the Rings: The Fellowship of the Ring, The\n",
      "Lord of the Rings: The Two Towers, The\n",
      "Lord of the Rings: The Return of the King, The\n",
      "Gladiator\n",
      "Dark Knight, The\n",
      "Iron Man\n",
      "WALL·E\n",
      "Inglourious Basterds\n",
      "Up\n",
      "Shutter Island\n",
      "Inception\n",
      "Dark Knight Rises, The\n",
      "The Imitation Game\n",
      "Avengers: Infinity War - Part I\n",
      "Thor: Ragnarok\n",
      "Iron Man\n",
      "Blade Runner 2049\n",
      "Three Billboards Outside Ebbing, Missouri\n",
      "Wonder\n",
      "Incredibles 2\n",
      "Deadpool 2\n",
      "\n",
      "\n",
      "Playlist Recomendada: \n",
      "\n",
      "1: Eat Drink Man Woman (Yin shi nan nu) - valoración: 5.0\n",
      "2: Tokyo Godfathers - valoración: 5.0\n",
      "3: Encounters at the End of the World - valoración: 5.0\n",
      "4: Secret in Their Eyes, The (El secreto de sus ojos) - valoración: 5.0\n",
      "5: Dylan Moran: Monster - valoración: 5.0\n"
     ]
    }
   ],
   "source": [
    "movie_recommender_pearson(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 5.2. Recomendación de películas similares a una película seleccionada"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "def recommend_movie(title):\n",
    "    \n",
    "    index_movie = df_matrix.index.tolist().index(title) # get an index for a movie\n",
    "    similar_movies = indices[index_movie].tolist() # make list for similar movies\n",
    "    movie_distances = distances[index_movie].tolist() # the list for distances of similar movies\n",
    "    id_movie = similar_movies.index(index_movie) # get the position of the movie itself in indices and distances\n",
    "\n",
    "    print('Similares a '+str(df_matrix.index[index_movie])+': \\n')\n",
    "\n",
    "    similar_movies.remove(index_movie) # remove the movie itself in indices\n",
    "    movie_distances.pop(id_movie) # remove the movie itself in distances\n",
    "\n",
    "    j = 1\n",
    "    \n",
    "    for i in similar_movies:\n",
    "        print(str(j)+': '+str(df_matrix.index[i])+' - the distance with '+str(title)+' is: '+str(movie_distances[j-1]))\n",
    "        j = j + 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Similares a Toy Story: \n",
      "\n",
      "1: Toy Story 2 - the distance with Toy Story is: 0.42739873968028474\n",
      "2: Jurassic Park - the distance with Toy Story is: 0.43436319591384365\n",
      "3: Independence Day (a.k.a. ID4) - the distance with Toy Story is: 0.43573830647233414\n",
      "4: Star Wars: Episode IV - A New Hope - the distance with Toy Story is: 0.4426118294200635\n",
      "5: Forrest Gump - the distance with Toy Story is: 0.45290409205982585\n",
      "6: Lion King, The - the distance with Toy Story is: 0.4588546505397664\n",
      "7: Star Wars: Episode VI - Return of the Jedi - the distance with Toy Story is: 0.458910695227416\n"
     ]
    }
   ],
   "source": [
    "recommend_movie('Toy Story')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "pelis = df_matrix.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "pelis[8615]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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