{
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  {
   "metadata": {
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     "end_time": "2024-12-07T01:55:39.637007Z",
     "start_time": "2024-12-07T01:55:39.633398Z"
    }
   },
   "cell_type": "code",
   "source": [
    "import pandas as pd\n",
    "from sklearn.metrics.pairwise import cosine_similarity\n",
    "import numpy as np"
   ],
   "id": "5a823fabad3c186f",
   "outputs": [],
   "execution_count": 2
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-12-07T01:55:39.646149Z",
     "start_time": "2024-12-07T01:55:39.641016Z"
    }
   },
   "cell_type": "code",
   "source": [
    "def split_by_user(data, test_size=0.2, random_state=42):\n",
    "    unique_users = data['userId'].unique()\n",
    "    np.random.seed(random_state)\n",
    "    test_users = np.random.choice(unique_users, size=int(len(unique_users) * test_size), replace=False)\n",
    "    test_data = data[data['userId'].isin(test_users)]\n",
    "    train_data = data[~data['userId'].isin(test_users)]\n",
    "    return train_data, test_data"
   ],
   "id": "d694dbe819b591ac",
   "outputs": [],
   "execution_count": 3
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-12-07T01:55:39.715567Z",
     "start_time": "2024-12-07T01:55:39.660120Z"
    }
   },
   "cell_type": "code",
   "source": [
    "movies = pd.read_csv('ml-latest-small/movies.csv')\n",
    "ratings = pd.read_csv('ml-latest-small/ratings.csv')"
   ],
   "id": "1bc78bafbae06c89",
   "outputs": [],
   "execution_count": 4
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-12-07T01:55:39.729648Z",
     "start_time": "2024-12-07T01:55:39.717570Z"
    }
   },
   "cell_type": "code",
   "source": "movies['genres'] = movies['genres'].str.split('|')",
   "id": "2543aa8216425342",
   "outputs": [],
   "execution_count": 5
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-12-07T01:55:39.758043Z",
     "start_time": "2024-12-07T01:55:39.730651Z"
    }
   },
   "cell_type": "code",
   "source": "data = pd.merge(ratings, movies, on=\"movieId\")",
   "id": "b3fa37255dccb066",
   "outputs": [],
   "execution_count": 6
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-12-07T01:55:39.775205Z",
     "start_time": "2024-12-07T01:55:39.759045Z"
    }
   },
   "cell_type": "code",
   "source": "data",
   "id": "c9542abeaa0c59d7",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "        userId  movieId  rating   timestamp                           title  \\\n",
       "0            1        1     4.0   964982703                Toy Story (1995)   \n",
       "1            1        3     4.0   964981247         Grumpier Old Men (1995)   \n",
       "2            1        6     4.0   964982224                     Heat (1995)   \n",
       "3            1       47     5.0   964983815     Seven (a.k.a. Se7en) (1995)   \n",
       "4            1       50     5.0   964982931      Usual Suspects, The (1995)   \n",
       "...        ...      ...     ...         ...                             ...   \n",
       "100831     610   166534     4.0  1493848402                    Split (2017)   \n",
       "100832     610   168248     5.0  1493850091   John Wick: Chapter Two (2017)   \n",
       "100833     610   168250     5.0  1494273047                  Get Out (2017)   \n",
       "100834     610   168252     5.0  1493846352                    Logan (2017)   \n",
       "100835     610   170875     3.0  1493846415  The Fate of the Furious (2017)   \n",
       "\n",
       "                                                   genres  \n",
       "0       [Adventure, Animation, Children, Comedy, Fantasy]  \n",
       "1                                       [Comedy, Romance]  \n",
       "2                               [Action, Crime, Thriller]  \n",
       "3                                     [Mystery, Thriller]  \n",
       "4                              [Crime, Mystery, Thriller]  \n",
       "...                                                   ...  \n",
       "100831                          [Drama, Horror, Thriller]  \n",
       "100832                          [Action, Crime, Thriller]  \n",
       "100833                                           [Horror]  \n",
       "100834                                   [Action, Sci-Fi]  \n",
       "100835                   [Action, Crime, Drama, Thriller]  \n",
       "\n",
       "[100836 rows x 6 columns]"
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     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
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   "execution_count": 7
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-12-07T01:55:39.789305Z",
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   "cell_type": "code",
   "source": "train_data, test_data = split_by_user(data)",
   "id": "b66149513c0c6b0e",
   "outputs": [],
   "execution_count": 8
  },
  {
   "metadata": {
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   "cell_type": "code",
   "source": "train_data",
   "id": "2cf1266ad52d70b0",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "        userId  movieId  rating   timestamp                           title  \\\n",
       "0            1        1     4.0   964982703                Toy Story (1995)   \n",
       "1            1        3     4.0   964981247         Grumpier Old Men (1995)   \n",
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       "...        ...      ...     ...         ...                             ...   \n",
       "100831     610   166534     4.0  1493848402                    Split (2017)   \n",
       "100832     610   168248     5.0  1493850091   John Wick: Chapter Two (2017)   \n",
       "100833     610   168250     5.0  1494273047                  Get Out (2017)   \n",
       "100834     610   168252     5.0  1493846352                    Logan (2017)   \n",
       "100835     610   170875     3.0  1493846415  The Fate of the Furious (2017)   \n",
       "\n",
       "                                                   genres  \n",
       "0       [Adventure, Animation, Children, Comedy, Fantasy]  \n",
       "1                                       [Comedy, Romance]  \n",
       "2                               [Action, Crime, Thriller]  \n",
       "3                                     [Mystery, Thriller]  \n",
       "4                              [Crime, Mystery, Thriller]  \n",
       "...                                                   ...  \n",
       "100831                          [Drama, Horror, Thriller]  \n",
       "100832                          [Action, Crime, Thriller]  \n",
       "100833                                           [Horror]  \n",
       "100834                                   [Action, Sci-Fi]  \n",
       "100835                   [Action, Crime, Drama, Thriller]  \n",
       "\n",
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    {
     "data": {
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       "261         3       31     0.5  1306463578   \n",
       "262         3      527     0.5  1306464275   \n",
       "263         3      647     0.5  1306463619   \n",
       "264         3      688     0.5  1306464228   \n",
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       "...       ...      ...     ...         ...   \n",
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       "99530     609     1056     3.0   847221080   \n",
       "99531     609     1059     3.0   847221054   \n",
       "99532     609     1150     4.0   847221054   \n",
       "99533     609     1161     4.0   847221080   \n",
       "\n",
       "                                                   title  \\\n",
       "261                               Dangerous Minds (1995)   \n",
       "262                              Schindler's List (1993)   \n",
       "263                            Courage Under Fire (1996)   \n",
       "264                          Operation Dumbo Drop (1995)   \n",
       "265    Wallace & Gromit: The Best of Aardman Animatio...   \n",
       "...                                                  ...   \n",
       "99529                               Twelfth Night (1996)   \n",
       "99530                                        Jude (1996)   \n",
       "99531        William Shakespeare's Romeo + Juliet (1996)   \n",
       "99532  Return of Martin Guerre, The (Retour de Martin...   \n",
       "99533           Tin Drum, The (Blechtrommel, Die) (1979)   \n",
       "\n",
       "                                 genres  \n",
       "261                             [Drama]  \n",
       "262                        [Drama, War]  \n",
       "263         [Action, Crime, Drama, War]  \n",
       "264    [Action, Adventure, Comedy, War]  \n",
       "265      [Adventure, Animation, Comedy]  \n",
       "...                                 ...  \n",
       "99529          [Comedy, Drama, Romance]  \n",
       "99530                           [Drama]  \n",
       "99531                  [Drama, Romance]  \n",
       "99532                           [Drama]  \n",
       "99533                      [Drama, War]  \n",
       "\n",
       "[27659 rows x 6 columns]"
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       "      <td>688</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1306464228</td>\n",
       "      <td>Operation Dumbo Drop (1995)</td>\n",
       "      <td>[Action, Adventure, Comedy, War]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>265</th>\n",
       "      <td>3</td>\n",
       "      <td>720</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1306463595</td>\n",
       "      <td>Wallace &amp; Gromit: The Best of Aardman Animatio...</td>\n",
       "      <td>[Adventure, Animation, Comedy]</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99529</th>\n",
       "      <td>609</td>\n",
       "      <td>892</td>\n",
       "      <td>3.0</td>\n",
       "      <td>847221080</td>\n",
       "      <td>Twelfth Night (1996)</td>\n",
       "      <td>[Comedy, Drama, Romance]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99530</th>\n",
       "      <td>609</td>\n",
       "      <td>1056</td>\n",
       "      <td>3.0</td>\n",
       "      <td>847221080</td>\n",
       "      <td>Jude (1996)</td>\n",
       "      <td>[Drama]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99531</th>\n",
       "      <td>609</td>\n",
       "      <td>1059</td>\n",
       "      <td>3.0</td>\n",
       "      <td>847221054</td>\n",
       "      <td>William Shakespeare's Romeo + Juliet (1996)</td>\n",
       "      <td>[Drama, Romance]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99532</th>\n",
       "      <td>609</td>\n",
       "      <td>1150</td>\n",
       "      <td>4.0</td>\n",
       "      <td>847221054</td>\n",
       "      <td>Return of Martin Guerre, The (Retour de Martin...</td>\n",
       "      <td>[Drama]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99533</th>\n",
       "      <td>609</td>\n",
       "      <td>1161</td>\n",
       "      <td>4.0</td>\n",
       "      <td>847221080</td>\n",
       "      <td>Tin Drum, The (Blechtrommel, Die) (1979)</td>\n",
       "      <td>[Drama, War]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>27659 rows × 6 columns</p>\n",
       "</div>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 10
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-12-07T01:56:24.904397Z",
     "start_time": "2024-12-07T01:55:39.833745Z"
    }
   },
   "cell_type": "code",
   "source": [
    "expanded_train_data = train_data.explode('genres')\n",
    "train_data = train_data.copy()\n",
    "test_data = test_data.copy()\n",
    "\n",
    "train_user_genre_rating = (\n",
    "    expanded_train_data.groupby(['userId', 'genres'])['rating']\n",
    "    .mean()\n",
    "    .reset_index()\n",
    "    .rename(columns={'rating': 'avg_genre_rating'})\n",
    ")\n",
    "\n",
    "train_user_preferences = train_user_genre_rating.pivot(index='userId', columns='genres', values='avg_genre_rating').fillna(0)\n",
    "\n",
    "def train_genre_match_calc(row):\n",
    "    user_id = row['userId']\n",
    "    genres = row['genres']\n",
    "\n",
    "    if isinstance(genres, str):\n",
    "        genres = [genres]\n",
    "\n",
    "    user_pref = train_user_genre_rating[train_user_genre_rating['userId'] == user_id]\n",
    "    genre_scores = user_pref[user_pref['genres'].isin(genres)]['avg_genre_rating']\n",
    "\n",
    "    if not genre_scores.empty:\n",
    "        return genre_scores.mean()\n",
    "    else:\n",
    "        return 0\n",
    "\n",
    "train_data['genreMatch'] = train_data.apply(train_genre_match_calc, axis=1)\n",
    "\n",
    "\n",
    "expanded_test_data = test_data.explode('genres')\n",
    "\n",
    "test_user_genre_rating = (\n",
    "    expanded_test_data.groupby(['userId', 'genres'])['rating']\n",
    "    .mean()\n",
    "    .reset_index()\n",
    "    .rename(columns={'rating': 'avg_genre_rating'})\n",
    ")\n",
    "\n",
    "test_user_preferences = test_user_genre_rating.pivot(index='userId', columns='genres', values='avg_genre_rating').fillna(0)\n",
    "\n",
    "def test_genre_match_calc(row):\n",
    "    user_id = row['userId']\n",
    "    genres = row['genres']\n",
    "\n",
    "    if isinstance(genres, str):\n",
    "        genres = [genres]\n",
    "\n",
    "    user_pref = test_user_genre_rating[test_user_genre_rating['userId'] == user_id]\n",
    "    genre_scores = user_pref[user_pref['genres'].isin(genres)]['avg_genre_rating']\n",
    "\n",
    "    if not genre_scores.empty:\n",
    "        return genre_scores.mean()\n",
    "    else:\n",
    "        return 0\n",
    "\n",
    "test_data['genreMatch'] = test_data.apply(test_genre_match_calc, axis=1)"
   ],
   "id": "88296c8c47cdbf60",
   "outputs": [],
   "execution_count": 11
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-12-07T01:56:54.150918Z",
     "start_time": "2024-12-07T01:56:24.905400Z"
    }
   },
   "cell_type": "code",
   "source": [
    "train_user_movie = train_data.pivot(index='userId', columns='movieId', values='rating')\n",
    "train_user_movie_filled = train_user_movie.fillna(0)\n",
    "train_user_similarity = cosine_similarity(train_user_movie_filled)\n",
    "train_user_similarity_df = pd.DataFrame(train_user_similarity, index=train_user_movie.index, columns=train_user_movie.index)\n",
    "\n",
    "def train_average(user_id, movie_id, top_n=5):\n",
    "    similar_users = train_user_similarity_df[user_id].sort_values(ascending=False).index[1:top_n + 1]\n",
    "    similar_ratings = train_user_movie.loc[similar_users, movie_id]\n",
    "\n",
    "    return similar_ratings.dropna().mean()\n",
    "\n",
    "train_data['similarUsers'] = train_data.apply(\n",
    "    lambda row: train_average(row['userId'], row['movieId']), axis=1\n",
    ")\n",
    "\n",
    "test_user_movie = test_data.pivot(index='userId', columns='movieId', values='rating')\n",
    "test_user_movie_filled = test_user_movie.fillna(0)\n",
    "test_user_similarity = cosine_similarity(test_user_movie_filled)\n",
    "test_user_similarity_df = pd.DataFrame(test_user_similarity, index=test_user_movie.index, columns=test_user_movie.index)\n",
    "\n",
    "def test_average(user_id, movie_id, top_n=5):\n",
    "    similar_users = test_user_similarity_df[user_id].sort_values(ascending=False).index[1:top_n + 1]\n",
    "    similar_ratings = test_user_movie.loc[similar_users, movie_id]\n",
    "\n",
    "    return similar_ratings.dropna().mean()\n",
    "\n",
    "test_data['similarUsers'] = test_data.apply(\n",
    "    lambda row: test_average(row['userId'], row['movieId']), axis=1\n",
    ")\n"
   ],
   "id": "e931f4041a1802fb",
   "outputs": [],
   "execution_count": 12
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-12-07T01:56:54.194857Z",
     "start_time": "2024-12-07T01:56:54.151920Z"
    }
   },
   "cell_type": "code",
   "source": [
    "train_ratings = train_data[['userId', 'movieId', 'similarUsers']]\n",
    "train_data = pd.merge(train_data, train_ratings, on=['userId', 'movieId'], how='left')\n",
    "\n",
    "test_ratings = test_data[['userId', 'movieId', 'similarUsers']]\n",
    "test_data = pd.merge(test_data, test_ratings, on=['userId', 'movieId'], how='left')"
   ],
   "id": "ce65ce417e7f5207",
   "outputs": [],
   "execution_count": 13
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-12-07T01:56:54.224008Z",
     "start_time": "2024-12-07T01:56:54.195860Z"
    }
   },
   "cell_type": "code",
   "source": [
    "train_popularity = train_data.groupby('movieId').size().reset_index(name='popularity')\n",
    "train_data = pd.merge(train_data, train_popularity, on='movieId', how='left')\n",
    "\n",
    "test_popularity = test_data.groupby('movieId').size().reset_index(name='popularity')\n",
    "test_data = pd.merge(test_data, test_popularity, on='movieId', how='left')"
   ],
   "id": "aa798201db531188",
   "outputs": [],
   "execution_count": 14
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-12-07T01:56:54.819973Z",
     "start_time": "2024-12-07T01:56:54.225011Z"
    }
   },
   "cell_type": "code",
   "source": [
    "train_data.to_csv('datasets/train_all.csv', index=False)\n",
    "test_data.to_csv('datasets/test_all.csv', index=False)"
   ],
   "id": "f7b5130c72ad35af",
   "outputs": [],
   "execution_count": 15
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-12-07T01:56:54.846938Z",
     "start_time": "2024-12-07T01:56:54.820977Z"
    }
   },
   "cell_type": "code",
   "source": [
    "def get_top_movies(data):\n",
    "    top = (data.sort_values(by=['userId', 'rating', 'popularity'], ascending=[True, False, False]).groupby('userId').head(5))\n",
    "    return top\n",
    "\n",
    "train_top = get_top_movies(train_data)\n",
    "test_top = get_top_movies(test_data)"
   ],
   "id": "20dba13e7a3d105b",
   "outputs": [],
   "execution_count": 16
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-12-07T01:56:54.873768Z",
     "start_time": "2024-12-07T01:56:54.847941Z"
    }
   },
   "cell_type": "code",
   "source": [
    "train_top.to_csv('datasets/train_top.csv', index=False)\n",
    "test_top.to_csv('datasets/test_top.csv', index=False)"
   ],
   "id": "be9f6106c5e4b04a",
   "outputs": [],
   "execution_count": 17
  }
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