notebook_projet_baptiste.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "import re\n",
    "\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.neighbors import KNeighborsClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [],
   "source": [
    "movies=pd.read_csv(r\"C:/Users/Baptiste/Desktop/projet_3/moviz/ml-latest/movies.csv\")\n",
    "ratings=pd.read_csv(r\"C:/Users/Baptiste/Desktop/projet_3/moviz/ml-latest/ratings.csv\")\n",
    "genome_score=pd.read_csv(r\"C:/Users/Baptiste/Desktop/projet_3/moviz/ml-latest/genome-scores.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [],
   "source": [
    "moviesC=movies.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Les genre sont dans une liste, séparés par des |.\n",
    "# On les splot pour les mettre dans des listes.\n",
    "\n",
    "movies.genres=movies[\"genres\"].apply(lambda x:x.split(\"|\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>movieId</th>\n",
       "      <th>title</th>\n",
       "      <th>genres</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Toy Story (1995)</td>\n",
       "      <td>[Adventure, Animation, Children, Comedy, Fantasy]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Jumanji (1995)</td>\n",
       "      <td>[Adventure, Children, Fantasy]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Grumpier Old Men (1995)</td>\n",
       "      <td>[Comedy, Romance]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Waiting to Exhale (1995)</td>\n",
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       "      <th>4</th>\n",
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       "      <td>Les tribulations d'une caissière (2011)</td>\n",
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       "      <td>Flora (2017)</td>\n",
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       "      <td>Leal (2018)</td>\n",
       "      <td>[Action, Crime, Drama]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>58098 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       movieId                                    title  \\\n",
       "0            1                         Toy Story (1995)   \n",
       "1            2                           Jumanji (1995)   \n",
       "2            3                  Grumpier Old Men (1995)   \n",
       "3            4                 Waiting to Exhale (1995)   \n",
       "4            5       Father of the Bride Part II (1995)   \n",
       "...        ...                                      ...   \n",
       "58093   193876                  The Great Glinka (1946)   \n",
       "58094   193878  Les tribulations d'une caissière (2011)   \n",
       "58095   193880                 Her Name Was Mumu (2016)   \n",
       "58096   193882                             Flora (2017)   \n",
       "58097   193886                              Leal (2018)   \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",
       "58093                               [(no genres listed)]  \n",
       "58094                                           [Comedy]  \n",
       "58095                                            [Drama]  \n",
       "58096                 [Adventure, Drama, Horror, Sci-Fi]  \n",
       "58097                             [Action, Crime, Drama]  \n",
       "\n",
       "[58098 rows x 3 columns]"
      ]
     },
     "execution_count": 108,
     "metadata": {},
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    }
   ],
   "source": [
    "movies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [],
   "source": [
    "# On explode pour avoir une ligne par film et par genre.\n",
    "# Puis on utilise get_dummies pour créer une colonne par genre.\n",
    "# Ensuite on somme par film pour avoir une ligne par film avec un 1 si le film a un genre, et 0 sinon.\n",
    "\n",
    "movies2=movies.explode(\"genres\")\n",
    "dummies_genres=pd.concat([movies2[\"movieId\"],movies2[\"genres\"].str.get_dummies()],axis=1).groupby(by=\"movieId\",as_index=False).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
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      ],
      "text/plain": [
       "       movieId  (no genres listed)  Action  Adventure  Animation  Children  \\\n",
       "0            1                   0       0          1          1         1   \n",
       "1            2                   0       0          1          0         1   \n",
       "2            3                   0       0          0          0         0   \n",
       "3            4                   0       0          0          0         0   \n",
       "4            5                   0       0          0          0         0   \n",
       "...        ...                 ...     ...        ...        ...       ...   \n",
       "58093   193876                   1       0          0          0         0   \n",
       "58094   193878                   0       0          0          0         0   \n",
       "58095   193880                   0       0          0          0         0   \n",
       "58096   193882                   0       0          1          0         0   \n",
       "58097   193886                   0       1          0          0         0   \n",
       "\n",
       "       Comedy  Crime  Documentary  Drama  ...  Film-Noir  Horror  IMAX  \\\n",
       "0           1      0            0      0  ...          0       0     0   \n",
       "1           0      0            0      0  ...          0       0     0   \n",
       "2           1      0            0      0  ...          0       0     0   \n",
       "3           1      0            0      1  ...          0       0     0   \n",
       "4           1      0            0      0  ...          0       0     0   \n",
       "...       ...    ...          ...    ...  ...        ...     ...   ...   \n",
       "58093       0      0            0      0  ...          0       0     0   \n",
       "58094       1      0            0      0  ...          0       0     0   \n",
       "58095       0      0            0      1  ...          0       0     0   \n",
       "58096       0      0            0      1  ...          0       1     0   \n",
       "58097       0      1            0      1  ...          0       0     0   \n",
       "\n",
       "       Musical  Mystery  Romance  Sci-Fi  Thriller  War  Western  \n",
       "0            0        0        0       0         0    0        0  \n",
       "1            0        0        0       0         0    0        0  \n",
       "2            0        0        1       0         0    0        0  \n",
       "3            0        0        1       0         0    0        0  \n",
       "4            0        0        0       0         0    0        0  \n",
       "...        ...      ...      ...     ...       ...  ...      ...  \n",
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       "58094        0        0        0       0         0    0        0  \n",
       "58095        0        0        0       0         0    0        0  \n",
       "58096        0        0        0       1         0    0        0  \n",
       "58097        0        0        0       0         0    0        0  \n",
       "\n",
       "[58098 rows x 21 columns]"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dummies_genres"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [],
   "source": [
    "# On ajoute les titres et les Id a dummies_genre.\n",
    "\n",
    "movies3=pd.concat([dummies_genres.movieId,movies.title,dummies_genres.drop(columns=\"movieId\")],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
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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>movieId</th>\n",
       "      <th>title</th>\n",
       "      <th>(no genres listed)</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>...</th>\n",
       "      <th>Film-Noir</th>\n",
       "      <th>Horror</th>\n",
       "      <th>IMAX</th>\n",
       "      <th>Musical</th>\n",
       "      <th>Mystery</th>\n",
       "      <th>Romance</th>\n",
       "      <th>Sci-Fi</th>\n",
       "      <th>Thriller</th>\n",
       "      <th>War</th>\n",
       "      <th>Western</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Toy Story (1995)</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Jumanji (1995)</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Grumpier Old Men (1995)</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Waiting to Exhale (1995)</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Father of the Bride Part II (1995)</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <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>58093</th>\n",
       "      <td>193876</td>\n",
       "      <td>The Great Glinka (1946)</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58094</th>\n",
       "      <td>193878</td>\n",
       "      <td>Les tribulations d'une caissière (2011)</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>58095</th>\n",
       "      <td>193880</td>\n",
       "      <td>Her Name Was Mumu (2016)</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58096</th>\n",
       "      <td>193882</td>\n",
       "      <td>Flora (2017)</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58097</th>\n",
       "      <td>193886</td>\n",
       "      <td>Leal (2018)</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>58098 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       movieId                                    title  (no genres listed)  \\\n",
       "0            1                         Toy Story (1995)                   0   \n",
       "1            2                           Jumanji (1995)                   0   \n",
       "2            3                  Grumpier Old Men (1995)                   0   \n",
       "3            4                 Waiting to Exhale (1995)                   0   \n",
       "4            5       Father of the Bride Part II (1995)                   0   \n",
       "...        ...                                      ...                 ...   \n",
       "58093   193876                  The Great Glinka (1946)                   1   \n",
       "58094   193878  Les tribulations d'une caissière (2011)                   0   \n",
       "58095   193880                 Her Name Was Mumu (2016)                   0   \n",
       "58096   193882                             Flora (2017)                   0   \n",
       "58097   193886                              Leal (2018)                   0   \n",
       "\n",
       "       Action  Adventure  Animation  Children  Comedy  Crime  Documentary  \\\n",
       "0           0          1          1         1       1      0            0   \n",
       "1           0          1          0         1       0      0            0   \n",
       "2           0          0          0         0       1      0            0   \n",
       "3           0          0          0         0       1      0            0   \n",
       "4           0          0          0         0       1      0            0   \n",
       "...       ...        ...        ...       ...     ...    ...          ...   \n",
       "58093       0          0          0         0       0      0            0   \n",
       "58094       0          0          0         0       1      0            0   \n",
       "58095       0          0          0         0       0      0            0   \n",
       "58096       0          1          0         0       0      0            0   \n",
       "58097       1          0          0         0       0      1            0   \n",
       "\n",
       "       ...  Film-Noir  Horror  IMAX  Musical  Mystery  Romance  Sci-Fi  \\\n",
       "0      ...          0       0     0        0        0        0       0   \n",
       "1      ...          0       0     0        0        0        0       0   \n",
       "2      ...          0       0     0        0        0        1       0   \n",
       "3      ...          0       0     0        0        0        1       0   \n",
       "4      ...          0       0     0        0        0        0       0   \n",
       "...    ...        ...     ...   ...      ...      ...      ...     ...   \n",
       "58093  ...          0       0     0        0        0        0       0   \n",
       "58094  ...          0       0     0        0        0        0       0   \n",
       "58095  ...          0       0     0        0        0        0       0   \n",
       "58096  ...          0       1     0        0        0        0       1   \n",
       "58097  ...          0       0     0        0        0        0       0   \n",
       "\n",
       "       Thriller  War  Western  \n",
       "0             0    0        0  \n",
       "1             0    0        0  \n",
       "2             0    0        0  \n",
       "3             0    0        0  \n",
       "4             0    0        0  \n",
       "...         ...  ...      ...  \n",
       "58093         0    0        0  \n",
       "58094         0    0        0  \n",
       "58095         0    0        0  \n",
       "58096         0    0        0  \n",
       "58097         0    0        0  \n",
       "\n",
       "[58098 rows x 22 columns]"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movies3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Cette fonction renvoit l'année qui est spécifiée en parenthese a la fin du titre.\n",
    "\n",
    "def getyear(string):\n",
    "    result=re.search(r'\\(\\d{4}\\)',string)\n",
    "    if result:\n",
    "        out=result.group().strip(\"(\").strip(\")\")\n",
    "        return out\n",
    "    return 1800"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [],
   "source": [
    "# On crée une nouvelle colonne year avec getyear.\n",
    "\n",
    "movies3[\"year\"]=movies3.title.apply(getyear)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [],
   "source": [
    "# On convertit les années en int.\n",
    "\n",
    "movies3.year=movies3.year.astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>userId</th>\n",
       "      <th>movieId</th>\n",
       "      <th>rating</th>\n",
       "      <th>timestamp</th>\n",
       "    </tr>\n",
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       "      <th>0</th>\n",
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       "      <th>27753441</th>\n",
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       "      <td>1354159718</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>27753444 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          userId  movieId  rating   timestamp\n",
       "0              1      307     3.5  1256677221\n",
       "1              1      481     3.5  1256677456\n",
       "2              1     1091     1.5  1256677471\n",
       "3              1     1257     4.5  1256677460\n",
       "4              1     1449     4.5  1256677264\n",
       "...          ...      ...     ...         ...\n",
       "27753439  283228     8542     4.5  1379882795\n",
       "27753440  283228     8712     4.5  1379882751\n",
       "27753441  283228    34405     4.5  1379882889\n",
       "27753442  283228    44761     4.5  1354159524\n",
       "27753443  283228    54286     4.5  1354159718\n",
       "\n",
       "[27753444 rows x 4 columns]"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ratings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [],
   "source": [
    "# On utilise pas timestamp, on la drop.\n",
    "\n",
    "ratings.drop(columns=\"timestamp\",inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [],
   "source": [
    "# On crée une colonne dans movies avec la note moyenne du film.\n",
    "\n",
    "movies3[\"av_rating\"]=ratings.groupby(by=\"movieId\",as_index=False).mean().rating"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [],
   "source": [
    "# On crée une colonne dans movies avec le nombre de notes.\n",
    "\n",
    "movies3[\"count_ratings\"]=ratings.groupby(by=\"movieId\",as_index=False).count().rating\n",
    "movies3.count_ratings.fillna(0,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "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>movieId</th>\n",
       "      <th>title</th>\n",
       "      <th>(no genres listed)</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>...</th>\n",
       "      <th>Musical</th>\n",
       "      <th>Mystery</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",
       "      <th>av_rating</th>\n",
       "      <th>count_ratings</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Toy Story (1995)</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1995</td>\n",
       "      <td>3.886649</td>\n",
       "      <td>68469.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Jumanji (1995)</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1995</td>\n",
       "      <td>3.246583</td>\n",
       "      <td>27143.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Grumpier Old Men (1995)</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1995</td>\n",
       "      <td>3.173981</td>\n",
       "      <td>15585.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Waiting to Exhale (1995)</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1995</td>\n",
       "      <td>2.874540</td>\n",
       "      <td>2989.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Father of the Bride Part II (1995)</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1995</td>\n",
       "      <td>3.077291</td>\n",
       "      <td>15474.0</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>58093</th>\n",
       "      <td>193876</td>\n",
       "      <td>The Great Glinka (1946)</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1946</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58094</th>\n",
       "      <td>193878</td>\n",
       "      <td>Les tribulations d'une caissière (2011)</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2011</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58095</th>\n",
       "      <td>193880</td>\n",
       "      <td>Her Name Was Mumu (2016)</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2016</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58096</th>\n",
       "      <td>193882</td>\n",
       "      <td>Flora (2017)</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2017</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58097</th>\n",
       "      <td>193886</td>\n",
       "      <td>Leal (2018)</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2018</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>58098 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       movieId                                    title  (no genres listed)  \\\n",
       "0            1                         Toy Story (1995)                   0   \n",
       "1            2                           Jumanji (1995)                   0   \n",
       "2            3                  Grumpier Old Men (1995)                   0   \n",
       "3            4                 Waiting to Exhale (1995)                   0   \n",
       "4            5       Father of the Bride Part II (1995)                   0   \n",
       "...        ...                                      ...                 ...   \n",
       "58093   193876                  The Great Glinka (1946)                   1   \n",
       "58094   193878  Les tribulations d'une caissière (2011)                   0   \n",
       "58095   193880                 Her Name Was Mumu (2016)                   0   \n",
       "58096   193882                             Flora (2017)                   0   \n",
       "58097   193886                              Leal (2018)                   0   \n",
       "\n",
       "       Action  Adventure  Animation  Children  Comedy  Crime  Documentary  \\\n",
       "0           0          1          1         1       1      0            0   \n",
       "1           0          1          0         1       0      0            0   \n",
       "2           0          0          0         0       1      0            0   \n",
       "3           0          0          0         0       1      0            0   \n",
       "4           0          0          0         0       1      0            0   \n",
       "...       ...        ...        ...       ...     ...    ...          ...   \n",
       "58093       0          0          0         0       0      0            0   \n",
       "58094       0          0          0         0       1      0            0   \n",
       "58095       0          0          0         0       0      0            0   \n",
       "58096       0          1          0         0       0      0            0   \n",
       "58097       1          0          0         0       0      1            0   \n",
       "\n",
       "       ...  Musical  Mystery  Romance  Sci-Fi  Thriller  War  Western  year  \\\n",
       "0      ...        0        0        0       0         0    0        0  1995   \n",
       "1      ...        0        0        0       0         0    0        0  1995   \n",
       "2      ...        0        0        1       0         0    0        0  1995   \n",
       "3      ...        0        0        1       0         0    0        0  1995   \n",
       "4      ...        0        0        0       0         0    0        0  1995   \n",
       "...    ...      ...      ...      ...     ...       ...  ...      ...   ...   \n",
       "58093  ...        0        0        0       0         0    0        0  1946   \n",
       "58094  ...        0        0        0       0         0    0        0  2011   \n",
       "58095  ...        0        0        0       0         0    0        0  2016   \n",
       "58096  ...        0        0        0       1         0    0        0  2017   \n",
       "58097  ...        0        0        0       0         0    0        0  2018   \n",
       "\n",
       "       av_rating  count_ratings  \n",
       "0       3.886649        68469.0  \n",
       "1       3.246583        27143.0  \n",
       "2       3.173981        15585.0  \n",
       "3       2.874540         2989.0  \n",
       "4       3.077291        15474.0  \n",
       "...          ...            ...  \n",
       "58093        NaN            0.0  \n",
       "58094        NaN            0.0  \n",
       "58095        NaN            0.0  \n",
       "58096        NaN            0.0  \n",
       "58097        NaN            0.0  \n",
       "\n",
       "[58098 rows x 25 columns]"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movies3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [],
   "source": [
    "# On recrée temporairement une colonne genre avec les valeurs de toutes les autres colonnes.\n",
    "\n",
    "movies3[\"genre\"]=movies3.drop(columns=[\"movieId\",\"title\",\"year\",\"av_rating\",\"count_ratings\"]).values.tolist()\n",
    "\n",
    "movies3.genre=movies3.genre.apply(lambda x:tuple(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Pour les films qui n'ont pas de notes, \n",
    "#on leur donne la note médiane de tous les films qui ont exactement les mêmes genres.\n",
    "\n",
    "movies3.av_rating.fillna(movies3.groupby(by=\"genre\")[\"av_rating\"].transform(\"median\"),inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "# Après ca, il reste 21 films sans notes.\n",
    "# Tant pis, on leur met 0.\n",
    "\n",
    "movies3.fillna(0,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    58098.000000\n",
       "mean       477.700506\n",
       "std       2829.603251\n",
       "min          0.000000\n",
       "10%          1.000000\n",
       "20%          1.000000\n",
       "30%          2.000000\n",
       "40%          3.000000\n",
       "50%          5.000000\n",
       "60%         10.000000\n",
       "70%         23.000000\n",
       "80%         74.000000\n",
       "90%        453.000000\n",
       "95%       1661.150000\n",
       "98%       5928.180000\n",
       "99%      11367.300000\n",
       "max      97999.000000\n",
       "Name: count_ratings, dtype: float64"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# On regarde la distribution des cnombre de votes.\n",
    "\n",
    "movies3.count_ratings.describe(percentiles=[0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 0.95, 0.98, 0.99])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x23788b282b0>"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.kdeplot(movies3.count_ratings)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [],
   "source": [
    "# On crée 7 catégories en fonction du nombre de votes.\n",
    "# C'est une sorte de métrique de popularité.\n",
    "\n",
    "def setRatingGroup(numberOfRatings):\n",
    "    # if (numberOfRatings is None): return 0\n",
    "    if (1 <= numberOfRatings <= 10): return 1\n",
    "    elif (11 <= numberOfRatings <= 30): return 2\n",
    "    elif (31 <= numberOfRatings <= 100): return 3\n",
    "    elif (101 <= numberOfRatings <= 300): return 4\n",
    "    elif (301 <= numberOfRatings <= 1000): return 5\n",
    "    elif (1001 <= numberOfRatings): return 6\n",
    "    else: return 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [],
   "source": [
    "movies3[\"rating_group\"]=movies3.count_ratings.apply(setRatingGroup)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x237823a4760>"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Beaucoup de films avec entre 1 et 10 notes, mais ca a peu de sens de les séparer encore plus.\n",
    "\n",
    "sns.countplot(x=movies3.rating_group)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [],
   "source": [
    "# On enleve les colonnes qui ne servent plus.\n",
    "\n",
    "movies4=movies3.drop(columns=[\"count_ratings\",\"genre\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "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>(no genres listed)</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>...</th>\n",
       "      <th>Musical</th>\n",
       "      <th>Mystery</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",
       "      <th>av_rating</th>\n",
       "      <th>rating_group</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Toy Story (1995)</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1995</td>\n",
       "      <td>3.886649</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Jumanji (1995)</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1995</td>\n",
       "      <td>3.246583</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Grumpier Old Men (1995)</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1995</td>\n",
       "      <td>3.173981</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Waiting to Exhale (1995)</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
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       "      <td>2.874540</td>\n",
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       "      <td>5</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58093</th>\n",
       "      <td>193876</td>\n",
       "      <td>The Great Glinka (1946)</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "      <td>1946</td>\n",
       "      <td>3.015764</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58094</th>\n",
       "      <td>193878</td>\n",
       "      <td>Les tribulations d'une caissière (2011)</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2011</td>\n",
       "      <td>3.078536</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58095</th>\n",
       "      <td>193880</td>\n",
       "      <td>Her Name Was Mumu (2016)</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2016</td>\n",
       "      <td>3.200000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58096</th>\n",
       "      <td>193882</td>\n",
       "      <td>Flora (2017)</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2017</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58097</th>\n",
       "      <td>193886</td>\n",
       "      <td>Leal (2018)</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2018</td>\n",
       "      <td>3.151376</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>58098 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       movieId                                    title  (no genres listed)  \\\n",
       "0            1                         Toy Story (1995)                   0   \n",
       "1            2                           Jumanji (1995)                   0   \n",
       "2            3                  Grumpier Old Men (1995)                   0   \n",
       "3            4                 Waiting to Exhale (1995)                   0   \n",
       "4            5       Father of the Bride Part II (1995)                   0   \n",
       "...        ...                                      ...                 ...   \n",
       "58093   193876                  The Great Glinka (1946)                   1   \n",
       "58094   193878  Les tribulations d'une caissière (2011)                   0   \n",
       "58095   193880                 Her Name Was Mumu (2016)                   0   \n",
       "58096   193882                             Flora (2017)                   0   \n",
       "58097   193886                              Leal (2018)                   0   \n",
       "\n",
       "       Action  Adventure  Animation  Children  Comedy  Crime  Documentary  \\\n",
       "0           0          1          1         1       1      0            0   \n",
       "1           0          1          0         1       0      0            0   \n",
       "2           0          0          0         0       1      0            0   \n",
       "3           0          0          0         0       1      0            0   \n",
       "4           0          0          0         0       1      0            0   \n",
       "...       ...        ...        ...       ...     ...    ...          ...   \n",
       "58093       0          0          0         0       0      0            0   \n",
       "58094       0          0          0         0       1      0            0   \n",
       "58095       0          0          0         0       0      0            0   \n",
       "58096       0          1          0         0       0      0            0   \n",
       "58097       1          0          0         0       0      1            0   \n",
       "\n",
       "       ...  Musical  Mystery  Romance  Sci-Fi  Thriller  War  Western  year  \\\n",
       "0      ...        0        0        0       0         0    0        0  1995   \n",
       "1      ...        0        0        0       0         0    0        0  1995   \n",
       "2      ...        0        0        1       0         0    0        0  1995   \n",
       "3      ...        0        0        1       0         0    0        0  1995   \n",
       "4      ...        0        0        0       0         0    0        0  1995   \n",
       "...    ...      ...      ...      ...     ...       ...  ...      ...   ...   \n",
       "58093  ...        0        0        0       0         0    0        0  1946   \n",
       "58094  ...        0        0        0       0         0    0        0  2011   \n",
       "58095  ...        0        0        0       0         0    0        0  2016   \n",
       "58096  ...        0        0        0       1         0    0        0  2017   \n",
       "58097  ...        0        0        0       0         0    0        0  2018   \n",
       "\n",
       "       av_rating  rating_group  \n",
       "0       3.886649             6  \n",
       "1       3.246583             6  \n",
       "2       3.173981             6  \n",
       "3       2.874540             6  \n",
       "4       3.077291             6  \n",
       "...          ...           ...  \n",
       "58093   3.015764             0  \n",
       "58094   3.078536             0  \n",
       "58095   3.200000             0  \n",
       "58096   3.000000             0  \n",
       "58097   3.151376             0  \n",
       "\n",
       "[58098 rows x 25 columns]"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movies4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [],
   "source": [
    "# On garde les colonnes movieId et title.\n",
    "# On crée tags qui a True si le film est dans les genome_scores, False sinon.\n",
    "# Ca sert pour savoir si on peut utiiser l'algo de Lucas.\n",
    "# On crée une colonne title2 en enlevant l'année et en passant en lowercase.\n",
    "# On utilisera cette colonne pour comparer à l'entree de l'utilisateur.\n",
    "\n",
    "def removeyear(string):\n",
    "    result=re.search(r'(\\d{4})',string)\n",
    "    if result:\n",
    "        return string[:-6].strip()\n",
    "    return string\n",
    "\n",
    "movies5=movies4[[\"movieId\",\"title\"]]\n",
    "movies5[\"tags\"]=np.where(movies5.movieId.isin(genome_score.movieId.unique()),True,False)\n",
    "movies5[\"title2\"]=movies.title.apply(lambda x: removeyear(x.lower()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>movieId</th>\n",
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       "      <th>title2</th>\n",
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       "      <td>Jumanji (1995)</td>\n",
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       "      <td>3</td>\n",
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       "      <td>grumpier old men</td>\n",
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       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Waiting to Exhale (1995)</td>\n",
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       "      <td>5</td>\n",
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       "      <th>58094</th>\n",
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       "      <td>Les tribulations d'une caissière (2011)</td>\n",
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       "      <td>les tribulations d'une caissière</td>\n",
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       "    <tr>\n",
       "      <th>58095</th>\n",
       "      <td>193880</td>\n",
       "      <td>Her Name Was Mumu (2016)</td>\n",
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       "    <tr>\n",
       "      <th>58096</th>\n",
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       "      <td>Flora (2017)</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>58097</th>\n",
       "      <td>193886</td>\n",
       "      <td>Leal (2018)</td>\n",
       "      <td>False</td>\n",
       "      <td>leal</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>58098 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       movieId                                    title   tags  \\\n",
       "0            1                         Toy Story (1995)   True   \n",
       "1            2                           Jumanji (1995)   True   \n",
       "2            3                  Grumpier Old Men (1995)   True   \n",
       "3            4                 Waiting to Exhale (1995)   True   \n",
       "4            5       Father of the Bride Part II (1995)   True   \n",
       "...        ...                                      ...    ...   \n",
       "58093   193876                  The Great Glinka (1946)  False   \n",
       "58094   193878  Les tribulations d'une caissière (2011)  False   \n",
       "58095   193880                 Her Name Was Mumu (2016)  False   \n",
       "58096   193882                             Flora (2017)  False   \n",
       "58097   193886                              Leal (2018)  False   \n",
       "\n",
       "                                 title2  \n",
       "0                             toy story  \n",
       "1                               jumanji  \n",
       "2                      grumpier old men  \n",
       "3                     waiting to exhale  \n",
       "4           father of the bride part ii  \n",
       "...                                 ...  \n",
       "58093                  the great glinka  \n",
       "58094  les tribulations d'une caissière  \n",
       "58095                 her name was mumu  \n",
       "58096                             flora  \n",
       "58097                              leal  \n",
       "\n",
       "[58098 rows x 4 columns]"
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movies5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [],
   "source": [
    "# On sauvegarde ca.\n",
    "\n",
    "movies5.to_csv(\"movies2.csv\",index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "# On scale toutes les colonnes.\n",
    "\n",
    "scaler=StandardScaler()\n",
    "X=movies4.drop(columns=[\"movieId\",\"title\"])\n",
    "y=movies4[\"movieId\"]\n",
    "X_scaled=scaler.fit_transform(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
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       "      <td>-0.179832</td>\n",
       "      <td>-0.155868</td>\n",
       "      <td>0.162976</td>\n",
       "      <td>0.144027</td>\n",
       "      <td>2.456778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.281508</td>\n",
       "      <td>-0.374021</td>\n",
       "      <td>-0.274357</td>\n",
       "      <td>-0.219176</td>\n",
       "      <td>-0.222860</td>\n",
       "      <td>1.625158</td>\n",
       "      <td>-0.310376</td>\n",
       "      <td>-0.310809</td>\n",
       "      <td>1.185880</td>\n",
       "      <td>-0.218053</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.139755</td>\n",
       "      <td>-0.223879</td>\n",
       "      <td>2.615028</td>\n",
       "      <td>-0.251027</td>\n",
       "      <td>-0.405843</td>\n",
       "      <td>-0.179832</td>\n",
       "      <td>-0.155868</td>\n",
       "      <td>0.162976</td>\n",
       "      <td>-0.276319</td>\n",
       "      <td>2.456778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.281508</td>\n",
       "      <td>-0.374021</td>\n",
       "      <td>-0.274357</td>\n",
       "      <td>-0.219176</td>\n",
       "      <td>-0.222860</td>\n",
       "      <td>1.625158</td>\n",
       "      <td>-0.310376</td>\n",
       "      <td>-0.310809</td>\n",
       "      <td>-0.843255</td>\n",
       "      <td>-0.218053</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.139755</td>\n",
       "      <td>-0.223879</td>\n",
       "      <td>-0.382405</td>\n",
       "      <td>-0.251027</td>\n",
       "      <td>-0.405843</td>\n",
       "      <td>-0.179832</td>\n",
       "      <td>-0.155868</td>\n",
       "      <td>0.162976</td>\n",
       "      <td>0.008296</td>\n",
       "      <td>2.456778</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>58093</th>\n",
       "      <td>3.552302</td>\n",
       "      <td>-0.374021</td>\n",
       "      <td>-0.274357</td>\n",
       "      <td>-0.219176</td>\n",
       "      <td>-0.222860</td>\n",
       "      <td>-0.615325</td>\n",
       "      <td>-0.310376</td>\n",
       "      <td>-0.310809</td>\n",
       "      <td>-0.843255</td>\n",
       "      <td>-0.218053</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.139755</td>\n",
       "      <td>-0.223879</td>\n",
       "      <td>-0.382405</td>\n",
       "      <td>-0.251027</td>\n",
       "      <td>-0.405843</td>\n",
       "      <td>-0.179832</td>\n",
       "      <td>-0.155868</td>\n",
       "      <td>-1.544120</td>\n",
       "      <td>-0.078074</td>\n",
       "      <td>-1.191264</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58094</th>\n",
       "      <td>-0.281508</td>\n",
       "      <td>-0.374021</td>\n",
       "      <td>-0.274357</td>\n",
       "      <td>-0.219176</td>\n",
       "      <td>-0.222860</td>\n",
       "      <td>1.625158</td>\n",
       "      <td>-0.310376</td>\n",
       "      <td>-0.310809</td>\n",
       "      <td>-0.843255</td>\n",
       "      <td>-0.218053</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.139755</td>\n",
       "      <td>-0.223879</td>\n",
       "      <td>-0.382405</td>\n",
       "      <td>-0.251027</td>\n",
       "      <td>-0.405843</td>\n",
       "      <td>-0.179832</td>\n",
       "      <td>-0.155868</td>\n",
       "      <td>0.720396</td>\n",
       "      <td>0.010044</td>\n",
       "      <td>-1.191264</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58095</th>\n",
       "      <td>-0.281508</td>\n",
       "      <td>-0.374021</td>\n",
       "      <td>-0.274357</td>\n",
       "      <td>-0.219176</td>\n",
       "      <td>-0.222860</td>\n",
       "      <td>-0.615325</td>\n",
       "      <td>-0.310376</td>\n",
       "      <td>-0.310809</td>\n",
       "      <td>1.185880</td>\n",
       "      <td>-0.218053</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.139755</td>\n",
       "      <td>-0.223879</td>\n",
       "      <td>-0.382405</td>\n",
       "      <td>-0.251027</td>\n",
       "      <td>-0.405843</td>\n",
       "      <td>-0.179832</td>\n",
       "      <td>-0.155868</td>\n",
       "      <td>0.894589</td>\n",
       "      <td>0.180551</td>\n",
       "      <td>-1.191264</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58096</th>\n",
       "      <td>-0.281508</td>\n",
       "      <td>-0.374021</td>\n",
       "      <td>3.644890</td>\n",
       "      <td>-0.219176</td>\n",
       "      <td>-0.222860</td>\n",
       "      <td>-0.615325</td>\n",
       "      <td>-0.310376</td>\n",
       "      <td>-0.310809</td>\n",
       "      <td>1.185880</td>\n",
       "      <td>-0.218053</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.139755</td>\n",
       "      <td>-0.223879</td>\n",
       "      <td>-0.382405</td>\n",
       "      <td>3.983634</td>\n",
       "      <td>-0.405843</td>\n",
       "      <td>-0.179832</td>\n",
       "      <td>-0.155868</td>\n",
       "      <td>0.929428</td>\n",
       "      <td>-0.100203</td>\n",
       "      <td>-1.191264</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58097</th>\n",
       "      <td>-0.281508</td>\n",
       "      <td>2.673647</td>\n",
       "      <td>-0.274357</td>\n",
       "      <td>-0.219176</td>\n",
       "      <td>-0.222860</td>\n",
       "      <td>-0.615325</td>\n",
       "      <td>3.221895</td>\n",
       "      <td>-0.310809</td>\n",
       "      <td>1.185880</td>\n",
       "      <td>-0.218053</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.139755</td>\n",
       "      <td>-0.223879</td>\n",
       "      <td>-0.382405</td>\n",
       "      <td>-0.251027</td>\n",
       "      <td>-0.405843</td>\n",
       "      <td>-0.179832</td>\n",
       "      <td>-0.155868</td>\n",
       "      <td>0.964266</td>\n",
       "      <td>0.112294</td>\n",
       "      <td>-1.191264</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>58098 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       (no genres listed)    Action  Adventure  Animation  Children    Comedy  \\\n",
       "0               -0.281508 -0.374021   3.644890   4.562537  4.487118  1.625158   \n",
       "1               -0.281508 -0.374021   3.644890  -0.219176  4.487118 -0.615325   \n",
       "2               -0.281508 -0.374021  -0.274357  -0.219176 -0.222860  1.625158   \n",
       "3               -0.281508 -0.374021  -0.274357  -0.219176 -0.222860  1.625158   \n",
       "4               -0.281508 -0.374021  -0.274357  -0.219176 -0.222860  1.625158   \n",
       "...                   ...       ...        ...        ...       ...       ...   \n",
       "58093            3.552302 -0.374021  -0.274357  -0.219176 -0.222860 -0.615325   \n",
       "58094           -0.281508 -0.374021  -0.274357  -0.219176 -0.222860  1.625158   \n",
       "58095           -0.281508 -0.374021  -0.274357  -0.219176 -0.222860 -0.615325   \n",
       "58096           -0.281508 -0.374021   3.644890  -0.219176 -0.222860 -0.615325   \n",
       "58097           -0.281508  2.673647  -0.274357  -0.219176 -0.222860 -0.615325   \n",
       "\n",
       "          Crime  Documentary     Drama   Fantasy  ...   Musical   Mystery  \\\n",
       "0     -0.310376    -0.310809 -0.843255  4.586050  ... -0.139755 -0.223879   \n",
       "1     -0.310376    -0.310809 -0.843255  4.586050  ... -0.139755 -0.223879   \n",
       "2     -0.310376    -0.310809 -0.843255 -0.218053  ... -0.139755 -0.223879   \n",
       "3     -0.310376    -0.310809  1.185880 -0.218053  ... -0.139755 -0.223879   \n",
       "4     -0.310376    -0.310809 -0.843255 -0.218053  ... -0.139755 -0.223879   \n",
       "...         ...          ...       ...       ...  ...       ...       ...   \n",
       "58093 -0.310376    -0.310809 -0.843255 -0.218053  ... -0.139755 -0.223879   \n",
       "58094 -0.310376    -0.310809 -0.843255 -0.218053  ... -0.139755 -0.223879   \n",
       "58095 -0.310376    -0.310809  1.185880 -0.218053  ... -0.139755 -0.223879   \n",
       "58096 -0.310376    -0.310809  1.185880 -0.218053  ... -0.139755 -0.223879   \n",
       "58097  3.221895    -0.310809  1.185880 -0.218053  ... -0.139755 -0.223879   \n",
       "\n",
       "        Romance    Sci-Fi  Thriller       War   Western      year  av_rating  \\\n",
       "0     -0.382405 -0.251027 -0.405843 -0.179832 -0.155868  0.162976   1.144448   \n",
       "1     -0.382405 -0.251027 -0.405843 -0.179832 -0.155868  0.162976   0.245943   \n",
       "2      2.615028 -0.251027 -0.405843 -0.179832 -0.155868  0.162976   0.144027   \n",
       "3      2.615028 -0.251027 -0.405843 -0.179832 -0.155868  0.162976  -0.276319   \n",
       "4     -0.382405 -0.251027 -0.405843 -0.179832 -0.155868  0.162976   0.008296   \n",
       "...         ...       ...       ...       ...       ...       ...        ...   \n",
       "58093 -0.382405 -0.251027 -0.405843 -0.179832 -0.155868 -1.544120  -0.078074   \n",
       "58094 -0.382405 -0.251027 -0.405843 -0.179832 -0.155868  0.720396   0.010044   \n",
       "58095 -0.382405 -0.251027 -0.405843 -0.179832 -0.155868  0.894589   0.180551   \n",
       "58096 -0.382405  3.983634 -0.405843 -0.179832 -0.155868  0.929428  -0.100203   \n",
       "58097 -0.382405 -0.251027 -0.405843 -0.179832 -0.155868  0.964266   0.112294   \n",
       "\n",
       "       rating_group  \n",
       "0          2.456778  \n",
       "1          2.456778  \n",
       "2          2.456778  \n",
       "3          2.456778  \n",
       "4          2.456778  \n",
       "...             ...  \n",
       "58093     -1.191264  \n",
       "58094     -1.191264  \n",
       "58095     -1.191264  \n",
       "58096     -1.191264  \n",
       "58097     -1.191264  \n",
       "\n",
       "[58098 rows x 23 columns]"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "XX=pd.DataFrame(X_scaled,index=X.index,columns=X.columns)\n",
    "XX"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsClassifier(n_neighbors=10)"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# On fit un KNN pour avoir acces aux index des 10 voisins les plus proches.\n",
    "\n",
    "modelKNN=KNeighborsClassifier(n_neighbors=10)\n",
    "modelKNN.fit(XX,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[    0,  3028,  4791, ..., 24657, 24734, 12226],\n",
       "       [    1,    59,  2078, ..., 12292, 10965, 12692],\n",
       "       [    2,   249,   234, ...,   546,  3524,   791],\n",
       "       ...,\n",
       "       [54369, 54256, 53889, ..., 54290, 54219, 54503],\n",
       "       [58096, 49832, 28609, ..., 26395, 27507, 34448],\n",
       "       [54147, 58097, 57564, ..., 54482, 55067, 55213]], dtype=int64)"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# La matrice de distances: \n",
    "#Chaque ligne représente un film, et dans chaque ligne il y a ses 10 plu proches voisins(lui compris).\n",
    "\n",
    "sims=modelKNN.kneighbors(XX)[1]\n",
    "sims"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "SIMS=pd.DataFrame(sims,columns=[\"Voisin0\",\"Voisin1\",\"Voisin2\",\"Voisin3\",\"Voisin4\",\"Voisin5\",\"Voisin6\",\"Voisin7\",\"Voisin8\",\"Voisin9\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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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>Voisin0</th>\n",
       "      <th>Voisin1</th>\n",
       "      <th>Voisin2</th>\n",
       "      <th>Voisin3</th>\n",
       "      <th>Voisin4</th>\n",
       "      <th>Voisin5</th>\n",
       "      <th>Voisin6</th>\n",
       "      <th>Voisin7</th>\n",
       "      <th>Voisin8</th>\n",
       "      <th>Voisin9</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>3028</td>\n",
       "      <td>4791</td>\n",
       "      <td>18403</td>\n",
       "      <td>3923</td>\n",
       "      <td>2210</td>\n",
       "      <td>10130</td>\n",
       "      <td>24657</td>\n",
       "      <td>24734</td>\n",
       "      <td>12226</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>59</td>\n",
       "      <td>2078</td>\n",
       "      <td>2010</td>\n",
       "      <td>4801</td>\n",
       "      <td>990</td>\n",
       "      <td>2315</td>\n",
       "      <td>12292</td>\n",
       "      <td>10965</td>\n",
       "      <td>12692</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>249</td>\n",
       "      <td>234</td>\n",
       "      <td>374</td>\n",
       "      <td>2340</td>\n",
       "      <td>1742</td>\n",
       "      <td>1491</td>\n",
       "      <td>546</td>\n",
       "      <td>3524</td>\n",
       "      <td>791</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>538</td>\n",
       "      <td>1805</td>\n",
       "      <td>6140</td>\n",
       "      <td>3734</td>\n",
       "      <td>193</td>\n",
       "      <td>8265</td>\n",
       "      <td>1420</td>\n",
       "      <td>2285</td>\n",
       "      <td>8286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>815</td>\n",
       "      <td>2241</td>\n",
       "      <td>516</td>\n",
       "      <td>275</td>\n",
       "      <td>87</td>\n",
       "      <td>245</td>\n",
       "      <td>2112</td>\n",
       "      <td>1977</td>\n",
       "      <td>201</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58093</th>\n",
       "      <td>58093</td>\n",
       "      <td>55412</td>\n",
       "      <td>55404</td>\n",
       "      <td>56127</td>\n",
       "      <td>55020</td>\n",
       "      <td>55022</td>\n",
       "      <td>55448</td>\n",
       "      <td>55917</td>\n",
       "      <td>55026</td>\n",
       "      <td>54782</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58094</th>\n",
       "      <td>58094</td>\n",
       "      <td>54172</td>\n",
       "      <td>55567</td>\n",
       "      <td>58073</td>\n",
       "      <td>57399</td>\n",
       "      <td>55663</td>\n",
       "      <td>56595</td>\n",
       "      <td>54386</td>\n",
       "      <td>55584</td>\n",
       "      <td>54451</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58095</th>\n",
       "      <td>54369</td>\n",
       "      <td>54256</td>\n",
       "      <td>53889</td>\n",
       "      <td>53973</td>\n",
       "      <td>54581</td>\n",
       "      <td>54244</td>\n",
       "      <td>54179</td>\n",
       "      <td>54290</td>\n",
       "      <td>54219</td>\n",
       "      <td>54503</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58096</th>\n",
       "      <td>58096</td>\n",
       "      <td>49832</td>\n",
       "      <td>28609</td>\n",
       "      <td>19477</td>\n",
       "      <td>55997</td>\n",
       "      <td>44782</td>\n",
       "      <td>30077</td>\n",
       "      <td>26395</td>\n",
       "      <td>27507</td>\n",
       "      <td>34448</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58097</th>\n",
       "      <td>54147</td>\n",
       "      <td>58097</td>\n",
       "      <td>57564</td>\n",
       "      <td>56737</td>\n",
       "      <td>53918</td>\n",
       "      <td>53944</td>\n",
       "      <td>56912</td>\n",
       "      <td>54482</td>\n",
       "      <td>55067</td>\n",
       "      <td>55213</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>58098 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Voisin0  Voisin1  Voisin2  Voisin3  Voisin4  Voisin5  Voisin6  Voisin7  \\\n",
       "0            0     3028     4791    18403     3923     2210    10130    24657   \n",
       "1            1       59     2078     2010     4801      990     2315    12292   \n",
       "2            2      249      234      374     2340     1742     1491      546   \n",
       "3            3      538     1805     6140     3734      193     8265     1420   \n",
       "4            4      815     2241      516      275       87      245     2112   \n",
       "...        ...      ...      ...      ...      ...      ...      ...      ...   \n",
       "58093    58093    55412    55404    56127    55020    55022    55448    55917   \n",
       "58094    58094    54172    55567    58073    57399    55663    56595    54386   \n",
       "58095    54369    54256    53889    53973    54581    54244    54179    54290   \n",
       "58096    58096    49832    28609    19477    55997    44782    30077    26395   \n",
       "58097    54147    58097    57564    56737    53918    53944    56912    54482   \n",
       "\n",
       "       Voisin8  Voisin9  \n",
       "0        24734    12226  \n",
       "1        10965    12692  \n",
       "2         3524      791  \n",
       "3         2285     8286  \n",
       "4         1977      201  \n",
       "...        ...      ...  \n",
       "58093    55026    54782  \n",
       "58094    55584    54451  \n",
       "58095    54219    54503  \n",
       "58096    27507    34448  \n",
       "58097    55067    55213  \n",
       "\n",
       "[58098 rows x 10 columns]"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "SIMS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [],
   "source": [
    "# On sauvegarde ce dataframe. C'est ce qu'on utilise pour faire des recommandations.\n",
    "\n",
    "SIMS.to_csv(\"distances.csv\",index=False)"
   ]
  }
 ],
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