roughwork.ipynb

{
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  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
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    "jupyter": {
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    },
    "pycharm": {
     "name": "#%% Import File\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Import Success\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "from sklearn.metrics.pairwise import linear_kernel\n",
    "from surprise import Reader, Dataset, SVD\n",
    "from surprise.model_selection import KFold\n",
    "from surprise.model_selection.validation import cross_validate\n",
    "\n",
    "print(\"Import Success\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
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     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/Caskroom/miniconda/base/envs/contextualRecommender/lib/python3.8/site-packages/IPython/core/interactiveshell.py:3145: DtypeWarning: Columns (10) have mixed types.Specify dtype option on import or set low_memory=False.\n",
      "  has_raised = await self.run_ast_nodes(code_ast.body, cell_name,\n"
     ]
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>adult</th>\n",
       "      <th>belongs_to_collection</th>\n",
       "      <th>budget</th>\n",
       "      <th>genres</th>\n",
       "      <th>homepage</th>\n",
       "      <th>id</th>\n",
       "      <th>imdb_id</th>\n",
       "      <th>original_language</th>\n",
       "      <th>original_title</th>\n",
       "      <th>overview</th>\n",
       "      <th>...</th>\n",
       "      <th>release_date</th>\n",
       "      <th>revenue</th>\n",
       "      <th>runtime</th>\n",
       "      <th>spoken_languages</th>\n",
       "      <th>status</th>\n",
       "      <th>tagline</th>\n",
       "      <th>title</th>\n",
       "      <th>video</th>\n",
       "      <th>vote_average</th>\n",
       "      <th>vote_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>False</td>\n",
       "      <td>{'id': 10194, 'name': 'Toy Story Collection', ...</td>\n",
       "      <td>30000000</td>\n",
       "      <td>[{'id': 16, 'name': 'Animation'}, {'id': 35, '...</td>\n",
       "      <td>http://toystory.disney.com/toy-story</td>\n",
       "      <td>862</td>\n",
       "      <td>tt0114709</td>\n",
       "      <td>en</td>\n",
       "      <td>Toy Story</td>\n",
       "      <td>Led by Woody, Andy's toys live happily in his ...</td>\n",
       "      <td>...</td>\n",
       "      <td>1995-10-30</td>\n",
       "      <td>373554033.0</td>\n",
       "      <td>81.0</td>\n",
       "      <td>[{'iso_639_1': 'en', 'name': 'English'}]</td>\n",
       "      <td>Released</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Toy Story</td>\n",
       "      <td>False</td>\n",
       "      <td>7.7</td>\n",
       "      <td>5415.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>65000000</td>\n",
       "      <td>[{'id': 12, 'name': 'Adventure'}, {'id': 14, '...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8844</td>\n",
       "      <td>tt0113497</td>\n",
       "      <td>en</td>\n",
       "      <td>Jumanji</td>\n",
       "      <td>When siblings Judy and Peter discover an encha...</td>\n",
       "      <td>...</td>\n",
       "      <td>1995-12-15</td>\n",
       "      <td>262797249.0</td>\n",
       "      <td>104.0</td>\n",
       "      <td>[{'iso_639_1': 'en', 'name': 'English'}, {'iso...</td>\n",
       "      <td>Released</td>\n",
       "      <td>Roll the dice and unleash the excitement!</td>\n",
       "      <td>Jumanji</td>\n",
       "      <td>False</td>\n",
       "      <td>6.9</td>\n",
       "      <td>2413.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>False</td>\n",
       "      <td>{'id': 119050, 'name': 'Grumpy Old Men Collect...</td>\n",
       "      <td>0</td>\n",
       "      <td>[{'id': 10749, 'name': 'Romance'}, {'id': 35, ...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>15602</td>\n",
       "      <td>tt0113228</td>\n",
       "      <td>en</td>\n",
       "      <td>Grumpier Old Men</td>\n",
       "      <td>A family wedding reignites the ancient feud be...</td>\n",
       "      <td>...</td>\n",
       "      <td>1995-12-22</td>\n",
       "      <td>0.0</td>\n",
       "      <td>101.0</td>\n",
       "      <td>[{'iso_639_1': 'en', 'name': 'English'}]</td>\n",
       "      <td>Released</td>\n",
       "      <td>Still Yelling. Still Fighting. Still Ready for...</td>\n",
       "      <td>Grumpier Old Men</td>\n",
       "      <td>False</td>\n",
       "      <td>6.5</td>\n",
       "      <td>92.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>16000000</td>\n",
       "      <td>[{'id': 35, 'name': 'Comedy'}, {'id': 18, 'nam...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>31357</td>\n",
       "      <td>tt0114885</td>\n",
       "      <td>en</td>\n",
       "      <td>Waiting to Exhale</td>\n",
       "      <td>Cheated on, mistreated and stepped on, the wom...</td>\n",
       "      <td>...</td>\n",
       "      <td>1995-12-22</td>\n",
       "      <td>81452156.0</td>\n",
       "      <td>127.0</td>\n",
       "      <td>[{'iso_639_1': 'en', 'name': 'English'}]</td>\n",
       "      <td>Released</td>\n",
       "      <td>Friends are the people who let you be yourself...</td>\n",
       "      <td>Waiting to Exhale</td>\n",
       "      <td>False</td>\n",
       "      <td>6.1</td>\n",
       "      <td>34.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>False</td>\n",
       "      <td>{'id': 96871, 'name': 'Father of the Bride Col...</td>\n",
       "      <td>0</td>\n",
       "      <td>[{'id': 35, 'name': 'Comedy'}]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11862</td>\n",
       "      <td>tt0113041</td>\n",
       "      <td>en</td>\n",
       "      <td>Father of the Bride Part II</td>\n",
       "      <td>Just when George Banks has recovered from his ...</td>\n",
       "      <td>...</td>\n",
       "      <td>1995-02-10</td>\n",
       "      <td>76578911.0</td>\n",
       "      <td>106.0</td>\n",
       "      <td>[{'iso_639_1': 'en', 'name': 'English'}]</td>\n",
       "      <td>Released</td>\n",
       "      <td>Just When His World Is Back To Normal... He's ...</td>\n",
       "      <td>Father of the Bride Part II</td>\n",
       "      <td>False</td>\n",
       "      <td>5.7</td>\n",
       "      <td>173.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   adult                              belongs_to_collection    budget  \\\n",
       "0  False  {'id': 10194, 'name': 'Toy Story Collection', ...  30000000   \n",
       "1  False                                                NaN  65000000   \n",
       "2  False  {'id': 119050, 'name': 'Grumpy Old Men Collect...         0   \n",
       "3  False                                                NaN  16000000   \n",
       "4  False  {'id': 96871, 'name': 'Father of the Bride Col...         0   \n",
       "\n",
       "                                              genres  \\\n",
       "0  [{'id': 16, 'name': 'Animation'}, {'id': 35, '...   \n",
       "1  [{'id': 12, 'name': 'Adventure'}, {'id': 14, '...   \n",
       "2  [{'id': 10749, 'name': 'Romance'}, {'id': 35, ...   \n",
       "3  [{'id': 35, 'name': 'Comedy'}, {'id': 18, 'nam...   \n",
       "4                     [{'id': 35, 'name': 'Comedy'}]   \n",
       "\n",
       "                               homepage     id    imdb_id original_language  \\\n",
       "0  http://toystory.disney.com/toy-story    862  tt0114709                en   \n",
       "1                                   NaN   8844  tt0113497                en   \n",
       "2                                   NaN  15602  tt0113228                en   \n",
       "3                                   NaN  31357  tt0114885                en   \n",
       "4                                   NaN  11862  tt0113041                en   \n",
       "\n",
       "                original_title  \\\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",
       "                                            overview  ... release_date  \\\n",
       "0  Led by Woody, Andy's toys live happily in his ...  ...   1995-10-30   \n",
       "1  When siblings Judy and Peter discover an encha...  ...   1995-12-15   \n",
       "2  A family wedding reignites the ancient feud be...  ...   1995-12-22   \n",
       "3  Cheated on, mistreated and stepped on, the wom...  ...   1995-12-22   \n",
       "4  Just when George Banks has recovered from his ...  ...   1995-02-10   \n",
       "\n",
       "       revenue runtime                                   spoken_languages  \\\n",
       "0  373554033.0    81.0           [{'iso_639_1': 'en', 'name': 'English'}]   \n",
       "1  262797249.0   104.0  [{'iso_639_1': 'en', 'name': 'English'}, {'iso...   \n",
       "2          0.0   101.0           [{'iso_639_1': 'en', 'name': 'English'}]   \n",
       "3   81452156.0   127.0           [{'iso_639_1': 'en', 'name': 'English'}]   \n",
       "4   76578911.0   106.0           [{'iso_639_1': 'en', 'name': 'English'}]   \n",
       "\n",
       "     status                                            tagline  \\\n",
       "0  Released                                                NaN   \n",
       "1  Released          Roll the dice and unleash the excitement!   \n",
       "2  Released  Still Yelling. Still Fighting. Still Ready for...   \n",
       "3  Released  Friends are the people who let you be yourself...   \n",
       "4  Released  Just When His World Is Back To Normal... He's ...   \n",
       "\n",
       "                         title  video vote_average vote_count  \n",
       "0                    Toy Story  False          7.7     5415.0  \n",
       "1                      Jumanji  False          6.9     2413.0  \n",
       "2             Grumpier Old Men  False          6.5       92.0  \n",
       "3            Waiting to Exhale  False          6.1       34.0  \n",
       "4  Father of the Bride Part II  False          5.7      173.0  \n",
       "\n",
       "[5 rows x 24 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# The main Movies Metadata file\n",
    "meta = pd.read_csv('Dataset/movies_metadata.csv')\n",
    "meta.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
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    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
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       "      <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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       "      <td>2.5</td>\n",
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       "      <th>1</th>\n",
       "      <td>1</td>\n",
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       "      <td>3.0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
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       "      <td>1129</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1260759185</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>1172</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1260759205</td>\n",
       "    </tr>\n",
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      "text/plain": [
       "   userId  movieId  rating   timestamp\n",
       "0       1       31     2.5  1260759144\n",
       "1       1     1029     3.0  1260759179\n",
       "2       1     1061     3.0  1260759182\n",
       "3       1     1129     2.0  1260759185\n",
       "4       1     1172     4.0  1260759205"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Rating\n",
    "ratings = pd.read_csv('Dataset/ratings_small.csv')\n",
    "ratings.head() # Movies in this dataset are rated out of 5 instead of 10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
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    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>movieId</th>\n",
       "      <th>imdbId</th>\n",
       "      <th>tmdbId</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>114709</td>\n",
       "      <td>862.0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>113497</td>\n",
       "      <td>8844.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>113228</td>\n",
       "      <td>15602.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>114885</td>\n",
       "      <td>31357.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>113041</td>\n",
       "      <td>11862.0</td>\n",
       "    </tr>\n",
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      ],
      "text/plain": [
       "   movieId  imdbId   tmdbId\n",
       "0        1  114709    862.0\n",
       "1        2  113497   8844.0\n",
       "2        3  113228  15602.0\n",
       "3        4  114885  31357.0\n",
       "4        5  113041  11862.0"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Links of IMDb and TMDb\n",
    "links = pd.read_csv('Dataset/links_small.csv')\n",
    "links.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>keywords</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>862</td>\n",
       "      <td>[{'id': 931, 'name': 'jealousy'}, {'id': 4290,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>8844</td>\n",
       "      <td>[{'id': 10090, 'name': 'board game'}, {'id': 1...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>15602</td>\n",
       "      <td>[{'id': 1495, 'name': 'fishing'}, {'id': 12392...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>31357</td>\n",
       "      <td>[{'id': 818, 'name': 'based on novel'}, {'id':...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>11862</td>\n",
       "      <td>[{'id': 1009, 'name': 'baby'}, {'id': 1599, 'n...</td>\n",
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      "text/plain": [
       "      id                                           keywords\n",
       "0    862  [{'id': 931, 'name': 'jealousy'}, {'id': 4290,...\n",
       "1   8844  [{'id': 10090, 'name': 'board game'}, {'id': 1...\n",
       "2  15602  [{'id': 1495, 'name': 'fishing'}, {'id': 12392...\n",
       "3  31357  [{'id': 818, 'name': 'based on novel'}, {'id':...\n",
       "4  11862  [{'id': 1009, 'name': 'baby'}, {'id': 1599, 'n..."
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "keywords = pd.read_csv('Dataset/keywords.csv')\n",
    "keywords.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
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    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    Led by Woody, Andy's toys live happily in his ...\n",
       "1    When siblings Judy and Peter discover an encha...\n",
       "2    A family wedding reignites the ancient feud be...\n",
       "3    Cheated on, mistreated and stepped on, the wom...\n",
       "4    Just when George Banks has recovered from his ...\n",
       "Name: overview, dtype: object"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# -- Content-focused recommender --\n",
    "\n",
    "meta['overview'] = meta['overview'].fillna('')\n",
    "meta['overview'].head() # Sample descriptions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "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>feature</th>\n",
       "      <th>dtype</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>adult</td>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>belongs_to_collection</td>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>budget</td>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>genres</td>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>homepage</td>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>id</td>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>imdb_id</td>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>original_language</td>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>original_title</td>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>overview</td>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>popularity</td>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>poster_path</td>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>production_companies</td>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>production_countries</td>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>release_date</td>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>revenue</td>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>runtime</td>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>spoken_languages</td>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>status</td>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>tagline</td>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>title</td>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>video</td>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>vote_average</td>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>vote_count</td>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  feature    dtype\n",
       "0                   adult   object\n",
       "1   belongs_to_collection   object\n",
       "2                  budget   object\n",
       "3                  genres   object\n",
       "4                homepage   object\n",
       "5                      id   object\n",
       "6                 imdb_id   object\n",
       "7       original_language   object\n",
       "8          original_title   object\n",
       "9                overview   object\n",
       "10             popularity   object\n",
       "11            poster_path   object\n",
       "12   production_companies   object\n",
       "13   production_countries   object\n",
       "14           release_date   object\n",
       "15                revenue  float64\n",
       "16                runtime  float64\n",
       "17       spoken_languages   object\n",
       "18                 status   object\n",
       "19                tagline   object\n",
       "20                  title   object\n",
       "21                  video   object\n",
       "22           vote_average  float64\n",
       "23             vote_count  float64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check the datatype of \"id\" in movies_metadata.csv\n",
    "pd.DataFrame({'feature':meta.dtypes.index, 'dtype':meta.dtypes.values})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "meta = meta.drop([19730, 29503, 35587]) # Remove these ids to solve ValueError: \"Unable to parse string...\"\n",
    "\n",
    "# Convert object to int64 for compatibility during merging\n",
    "meta['id'] = pd.to_numeric(meta['id'])\n",
    "\n",
    "# Run  the following code for converting more than one value to integer\n",
    "# def convert_int(x):\n",
    "#     try:\n",
    "#         return int(x)\n",
    "#     except:\n",
    "#         return np.nan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "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>feature</th>\n",
       "      <th>dtype</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>movieId</td>\n",
       "      <td>int64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>imdbId</td>\n",
       "      <td>int64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>tmdbId</td>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   feature    dtype\n",
       "0  movieId    int64\n",
       "1   imdbId    int64\n",
       "2   tmdbId  float64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check the datatype of \"tmdbId\" in links_small.csv\n",
    "pd.DataFrame({'feature':links.dtypes.index, 'dtype':links.dtypes.values})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "# Convert float64 to int64\n",
    "col=np.array(links['tmdbId'], np.int64)\n",
    "links['tmdbId']=col"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>adult</th>\n",
       "      <th>belongs_to_collection</th>\n",
       "      <th>budget</th>\n",
       "      <th>genres</th>\n",
       "      <th>homepage</th>\n",
       "      <th>tmdbId</th>\n",
       "      <th>original_language</th>\n",
       "      <th>original_title</th>\n",
       "      <th>overview</th>\n",
       "      <th>popularity</th>\n",
       "      <th>...</th>\n",
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       "      <th>vote_count</th>\n",
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       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>False</td>\n",
       "      <td>{'id': 10194, 'name': 'Toy Story Collection', ...</td>\n",
       "      <td>30000000</td>\n",
       "      <td>[{'id': 16, 'name': 'Animation'}, {'id': 35, '...</td>\n",
       "      <td>http://toystory.disney.com/toy-story</td>\n",
       "      <td>862</td>\n",
       "      <td>en</td>\n",
       "      <td>Toy Story</td>\n",
       "      <td>Led by Woody, Andy's toys live happily in his ...</td>\n",
       "      <td>21.9469</td>\n",
       "      <td>...</td>\n",
       "      <td>81.0</td>\n",
       "      <td>[{'iso_639_1': 'en', 'name': 'English'}]</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>Toy Story</td>\n",
       "      <td>False</td>\n",
       "      <td>7.7</td>\n",
       "      <td>5415.0</td>\n",
       "      <td>1</td>\n",
       "      <td>114709</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>65000000</td>\n",
       "      <td>[{'id': 12, 'name': 'Adventure'}, {'id': 14, '...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8844</td>\n",
       "      <td>en</td>\n",
       "      <td>Jumanji</td>\n",
       "      <td>When siblings Judy and Peter discover an encha...</td>\n",
       "      <td>17.0155</td>\n",
       "      <td>...</td>\n",
       "      <td>104.0</td>\n",
       "      <td>[{'iso_639_1': 'en', 'name': 'English'}, {'iso...</td>\n",
       "      <td>Released</td>\n",
       "      <td>Roll the dice and unleash the excitement!</td>\n",
       "      <td>Jumanji</td>\n",
       "      <td>False</td>\n",
       "      <td>6.9</td>\n",
       "      <td>2413.0</td>\n",
       "      <td>2</td>\n",
       "      <td>113497</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>False</td>\n",
       "      <td>{'id': 119050, 'name': 'Grumpy Old Men Collect...</td>\n",
       "      <td>0</td>\n",
       "      <td>[{'id': 10749, 'name': 'Romance'}, {'id': 35, ...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>15602</td>\n",
       "      <td>en</td>\n",
       "      <td>Grumpier Old Men</td>\n",
       "      <td>A family wedding reignites the ancient feud be...</td>\n",
       "      <td>11.7129</td>\n",
       "      <td>...</td>\n",
       "      <td>101.0</td>\n",
       "      <td>[{'iso_639_1': 'en', 'name': 'English'}]</td>\n",
       "      <td>Released</td>\n",
       "      <td>Still Yelling. Still Fighting. Still Ready for...</td>\n",
       "      <td>Grumpier Old Men</td>\n",
       "      <td>False</td>\n",
       "      <td>6.5</td>\n",
       "      <td>92.0</td>\n",
       "      <td>3</td>\n",
       "      <td>113228</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>16000000</td>\n",
       "      <td>[{'id': 35, 'name': 'Comedy'}, {'id': 18, 'nam...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>31357</td>\n",
       "      <td>en</td>\n",
       "      <td>Waiting to Exhale</td>\n",
       "      <td>Cheated on, mistreated and stepped on, the wom...</td>\n",
       "      <td>3.85949</td>\n",
       "      <td>...</td>\n",
       "      <td>127.0</td>\n",
       "      <td>[{'iso_639_1': 'en', 'name': 'English'}]</td>\n",
       "      <td>Released</td>\n",
       "      <td>Friends are the people who let you be yourself...</td>\n",
       "      <td>Waiting to Exhale</td>\n",
       "      <td>False</td>\n",
       "      <td>6.1</td>\n",
       "      <td>34.0</td>\n",
       "      <td>4</td>\n",
       "      <td>114885</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>False</td>\n",
       "      <td>{'id': 96871, 'name': 'Father of the Bride Col...</td>\n",
       "      <td>0</td>\n",
       "      <td>[{'id': 35, 'name': 'Comedy'}]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11862</td>\n",
       "      <td>en</td>\n",
       "      <td>Father of the Bride Part II</td>\n",
       "      <td>Just when George Banks has recovered from his ...</td>\n",
       "      <td>8.38752</td>\n",
       "      <td>...</td>\n",
       "      <td>106.0</td>\n",
       "      <td>[{'iso_639_1': 'en', 'name': 'English'}]</td>\n",
       "      <td>Released</td>\n",
       "      <td>Just When His World Is Back To Normal... He's ...</td>\n",
       "      <td>Father of the Bride Part II</td>\n",
       "      <td>False</td>\n",
       "      <td>5.7</td>\n",
       "      <td>173.0</td>\n",
       "      <td>5</td>\n",
       "      <td>113041</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   adult                              belongs_to_collection    budget  \\\n",
       "0  False  {'id': 10194, 'name': 'Toy Story Collection', ...  30000000   \n",
       "1  False                                                NaN  65000000   \n",
       "2  False  {'id': 119050, 'name': 'Grumpy Old Men Collect...         0   \n",
       "3  False                                                NaN  16000000   \n",
       "4  False  {'id': 96871, 'name': 'Father of the Bride Col...         0   \n",
       "\n",
       "                                              genres  \\\n",
       "0  [{'id': 16, 'name': 'Animation'}, {'id': 35, '...   \n",
       "1  [{'id': 12, 'name': 'Adventure'}, {'id': 14, '...   \n",
       "2  [{'id': 10749, 'name': 'Romance'}, {'id': 35, ...   \n",
       "3  [{'id': 35, 'name': 'Comedy'}, {'id': 18, 'nam...   \n",
       "4                     [{'id': 35, 'name': 'Comedy'}]   \n",
       "\n",
       "                               homepage  tmdbId original_language  \\\n",
       "0  http://toystory.disney.com/toy-story     862                en   \n",
       "1                                   NaN    8844                en   \n",
       "2                                   NaN   15602                en   \n",
       "3                                   NaN   31357                en   \n",
       "4                                   NaN   11862                en   \n",
       "\n",
       "                original_title  \\\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",
       "                                            overview popularity  ... runtime  \\\n",
       "0  Led by Woody, Andy's toys live happily in his ...    21.9469  ...    81.0   \n",
       "1  When siblings Judy and Peter discover an encha...    17.0155  ...   104.0   \n",
       "2  A family wedding reignites the ancient feud be...    11.7129  ...   101.0   \n",
       "3  Cheated on, mistreated and stepped on, the wom...    3.85949  ...   127.0   \n",
       "4  Just when George Banks has recovered from his ...    8.38752  ...   106.0   \n",
       "\n",
       "                                    spoken_languages    status  \\\n",
       "0           [{'iso_639_1': 'en', 'name': 'English'}]  Released   \n",
       "1  [{'iso_639_1': 'en', 'name': 'English'}, {'iso...  Released   \n",
       "2           [{'iso_639_1': 'en', 'name': 'English'}]  Released   \n",
       "3           [{'iso_639_1': 'en', 'name': 'English'}]  Released   \n",
       "4           [{'iso_639_1': 'en', 'name': 'English'}]  Released   \n",
       "\n",
       "                                             tagline  \\\n",
       "0                                                NaN   \n",
       "1          Roll the dice and unleash the excitement!   \n",
       "2  Still Yelling. Still Fighting. Still Ready for...   \n",
       "3  Friends are the people who let you be yourself...   \n",
       "4  Just When His World Is Back To Normal... He's ...   \n",
       "\n",
       "                         title  video vote_average vote_count movieId  imdbId  \n",
       "0                    Toy Story  False          7.7     5415.0       1  114709  \n",
       "1                      Jumanji  False          6.9     2413.0       2  113497  \n",
       "2             Grumpier Old Men  False          6.5       92.0       3  113228  \n",
       "3            Waiting to Exhale  False          6.1       34.0       4  114885  \n",
       "4  Father of the Bride Part II  False          5.7      173.0       5  113041  \n",
       "\n",
       "[5 rows x 25 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Merge the dataframes on column \"tmdbId\"\n",
    "meta.rename(columns={'id':'tmdbId'}, inplace=True)\n",
    "meta = pd.merge(meta,links,on='tmdbId')\n",
    "meta.drop(['imdb_id'], axis=1, inplace=True)\n",
    "meta.head()\n",
    "\n",
    "# Alternatively, run the following code to reduce the size of movies_metadata.csv to match links_small.csv\n",
    "# meta = meta[meta['tmdbId'].isin(links)]\n",
    "# meta.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(9099, 29727)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Remove stop words and use TF-IDF vectorizer\n",
    "tfidf = TfidfVectorizer(stop_words='english')\n",
    "# Construct TF-IDF matrix\n",
    "tfidf_matrix = tfidf.fit_transform(meta['overview'])\n",
    "tfidf_matrix.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "# Compute cosine similarity\n",
    "cosine_sim = linear_kernel(tfidf_matrix, tfidf_matrix)\n",
    "# Get corresponding indices of the movies\n",
    "indices = pd.Series(meta.index, index=meta['original_title']).drop_duplicates()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "# Recommendation function\n",
    "def recommend(title, cosine_sim=cosine_sim):\n",
    "\n",
    "    # Get the index of the movie that matches the title\n",
    "    idx = indices[title]\n",
    "    # Get the pairwise similarity scores of all movies with the given movie\n",
    "    sim_scores = list(enumerate(cosine_sim[idx]))\n",
    "    # Sort the movies based on the similarity scores\n",
    "    sim_scores = sorted(sim_scores, key=lambda x: x[1], reverse=True)\n",
    "    # Get the scores of the 15 most similar movies\n",
    "    sim_scores = sim_scores[1:16]\n",
    "    # Get the movie indices\n",
    "    movie_indices = [i[0] for i in sim_scores]\n",
    "    # Remove low-rated movies or outliers\n",
    "    for i in movie_indices:\n",
    "        pop = meta.at[i,'vote_average']\n",
    "        if pop<5 or pop>10:\n",
    "            movie_indices.remove(i)\n",
    "\n",
    "    # Return the most similar movies qualifying the 5.0 rating threshold\n",
    "    return meta[['original_title','vote_average']].iloc[movie_indices]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "                  original_title  vote_average\n",
       "7516                  Iron Man 2           6.6\n",
       "8296                  Iron Man 3           6.8\n",
       "5670                    Scarface           7.5\n",
       "8766     Avengers: Age of Ultron           7.3\n",
       "8100                       Brake           5.3\n",
       "4274        Saturday Night Fever           6.5\n",
       "6063                     Hostage           6.2\n",
       "2322               The Dark Half           5.4\n",
       "6152               Batman Begins           7.5\n",
       "1650  Return from Witch Mountain           5.6\n",
       "5057     Dr. Jekyll and Mr. Hyde           6.6\n",
       "1001               Touch of Evil           7.6\n",
       "7183          Echelon Conspiracy           5.4\n",
       "813     Escape to Witch Mountain           6.5\n",
       "1774          Married to the Mob           5.8"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "recommend('Iron Man')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<generator object KFold.split at 0x7fc0af212cf0>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# -- User-focused recommender --\n",
    "\n",
    "reader = Reader() # Used to parse a file containing ratings\n",
    "df = Dataset.load_from_df(ratings[['userId', 'movieId', 'rating']], reader)\n",
    "kf = KFold(n_splits=5)\n",
    "kf.split(df) # Split the data into folds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<surprise.prediction_algorithms.matrix_factorization.SVD at 0x7fc0ae86be80>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Use Single Value Decomposition (SVD) for cross-validation and fitting\n",
    "svd = SVD()\n",
    "cross_validate(svd, df, measures=['RMSE', 'MAE'])\n",
    "\n",
    "trainset = df.build_full_trainset()\n",
    "svd.fit(trainset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
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      ],
      "text/plain": [
       "     userId  movieId  rating  timestamp\n",
       "744      10       50     5.0  942766420\n",
       "745      10      152     4.0  942766793\n",
       "746      10      318     4.0  942766515\n",
       "747      10      344     3.0  942766603\n",
       "748      10      345     4.0  942766603\n",
       "749      10      592     3.0  942767328\n",
       "750      10      735     4.0  942766974\n",
       "751      10     1036     3.0  942767258\n",
       "752      10     1089     3.0  942766420\n",
       "753      10     1101     2.0  942767328\n",
       "754      10     1127     4.0  942767328\n",
       "755      10     1196     4.0  942767258\n",
       "756      10     1197     4.0  942767258\n",
       "757      10     1198     4.0  942767258\n",
       "758      10     1200     4.0  942767258\n",
       "759      10     1210     4.0  942767258\n",
       "760      10     1220     3.0  942767328\n",
       "761      10     1240     4.0  942767258\n",
       "762      10     1291     4.0  942767258\n",
       "763      10     1358     5.0  942766420\n",
       "764      10     1423     4.0  942766420\n",
       "765      10     1459     3.0  942766725\n",
       "766      10     1499     3.0  942766845\n",
       "767      10     1611     5.0  942767029\n",
       "768      10     1690     3.0  942766679\n",
       "769      10     1704     4.0  942766472\n",
       "770      10     1719     5.0  942766472\n",
       "771      10     1887     2.0  942766845\n",
       "772      10     1923     5.0  942766515\n",
       "773      10     2108     3.0  942766472\n",
       "774      10     2344     5.0  942766991\n",
       "775      10     2406     4.0  942767328\n",
       "776      10     2410     2.0  942767328\n",
       "777      10     2539     4.0  942766845\n",
       "778      10     2571     5.0  942766515\n",
       "779      10     2826     5.0  942766109\n",
       "780      10     2827     3.0  942766251\n",
       "781      10     2840     3.0  942766213\n",
       "782      10     2841     4.0  942766029\n",
       "783      10     2881     3.0  942766164\n",
       "784      10     2890     4.0  942765978\n",
       "785      10     2907     2.0  942766213\n",
       "786      10     2926     5.0  942767121\n",
       "787      10     2995     2.0  942766251\n",
       "788      10     3005     3.0  942766059\n",
       "789      10     3019     4.0  942767571"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check a random user's ratings\n",
    "ratings[ratings['userId'] == 10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "# Read the smaller links file again\n",
    "links_df = pd.read_csv('Dataset/links_small.csv')\n",
    "col=np.array(links_df['tmdbId'], np.int64)\n",
    "links_df['tmdbId']=col\n",
    "\n",
    "# Merge movies_metadata.csv and links_small.csv files\n",
    "links_df = links_df.merge(meta[['title', 'tmdbId']], on='tmdbId').set_index('title')\n",
    "links_index = links_df.set_index('tmdbId') # For label indexing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "# Recommendation function\n",
    "def hybrid(userId, title):\n",
    "    idx = indices[title]\n",
    "    tmdbId = links_df.loc[title]['tmdbId'] # Get the corresponding tmdb id\n",
    "    \n",
    "    sim_scores = list(enumerate(cosine_sim[idx]))\n",
    "    sim_scores = sorted(sim_scores, key=lambda x: x[1], reverse=True)\n",
    "    sim_scores = sim_scores[1:31] # Scores of the 30 most similar movies\n",
    "    movie_indices = [i[0] for i in sim_scores]\n",
    "    \n",
    "    movies = meta.iloc[movie_indices][['title', 'vote_average', 'tmdbId']]\n",
    "    movies['est'] = movies['tmdbId'].apply(lambda x: svd.predict(userId, links_index.loc[x]['movieId']).est) # Estimated prediction using svd\n",
    "    movies = movies.sort_values('est', ascending=False) # Rank movies according to the predicted values\n",
    "    print(\"ATTENTION   \")\n",
    "    print(movies)\n",
    "    movies.head()\n",
    "    print(\"ATTENTION OVER\")\n",
    "    movies.columns = ['Title', 'Vote Average', 'TMDb Id', 'Estimated Prediction']\n",
    "    return movies.head(30) # Display top 15 similar movies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ATTENTION   \n",
      "                           title  vote_average  tmdbId       est\n",
      "1001               Touch of Evil           7.6    1480  4.102174\n",
      "6152               Batman Begins           7.5     272  4.042090\n",
      "1503          All the King's Men           6.3   25430  3.975325\n",
      "8766     Avengers: Age of Ultron           7.3   99861  3.828715\n",
      "5670                    Scarface           7.5     877  3.794117\n",
      "2640           To Sir, with Love           7.6   25934  3.748362\n",
      "892        Rebel Without a Cause           7.6     221  3.747054\n",
      "5057     Dr. Jekyll and Mr. Hyde           6.6    3022  3.717376\n",
      "8100                       Brake           5.3   85414  3.683998\n",
      "3410                      Rififi           7.7     934  3.662107\n",
      "6063                     Hostage           6.2    2026  3.658518\n",
      "7183          Echelon Conspiracy           5.4   16325  3.627559\n",
      "3846                       Baran           7.6   43774  3.603444\n",
      "2322               The Dark Half           5.4   10349  3.573212\n",
      "6286                  Wolf Creek           6.1    9885  3.564846\n",
      "7516                  Iron Man 2           6.6   10138  3.550062\n",
      "8025                The Big Bang           5.5   54271  3.548407\n",
      "3011           Missing in Action           5.2   15379  3.533852\n",
      "1774          Married to the Mob           5.8    2321  3.530623\n",
      "673                       Ransom           6.4    3595  3.503378\n",
      "4377                   Zoot Suit           6.9   76397  3.480058\n",
      "1650  Return from Witch Mountain           5.6   14822  3.452936\n",
      "8296                  Iron Man 3           6.8   68721  3.405572\n",
      "4274        Saturday Night Fever           6.5   11009  3.390692\n",
      "5184             The Magic Flute           7.1   44864  3.350019\n",
      "813     Escape to Witch Mountain           6.5   14821  3.332198\n",
      "2198  The Little Shop of Horrors           6.2   24452  3.289483\n",
      "1704   The Clan of the Cave Bear           4.7   13853  3.061864\n",
      "279            The Swan Princess           6.5   22586  3.057682\n",
      "2047               Baby Geniuses           3.3   22345  2.846119\n",
      "ATTENTION OVER\n",
      "data getting passed in contextual\n",
      "                           Title  Vote Average  TMDb Id  Estimated Prediction\n",
      "1001               Touch of Evil           7.6     1480              4.102174\n",
      "6152               Batman Begins           7.5      272              4.042090\n",
      "1503          All the King's Men           6.3    25430              3.975325\n",
      "8766     Avengers: Age of Ultron           7.3    99861              3.828715\n",
      "5670                    Scarface           7.5      877              3.794117\n",
      "2640           To Sir, with Love           7.6    25934              3.748362\n",
      "892        Rebel Without a Cause           7.6      221              3.747054\n",
      "5057     Dr. Jekyll and Mr. Hyde           6.6     3022              3.717376\n",
      "8100                       Brake           5.3    85414              3.683998\n",
      "3410                      Rififi           7.7      934              3.662107\n",
      "6063                     Hostage           6.2     2026              3.658518\n",
      "7183          Echelon Conspiracy           5.4    16325              3.627559\n",
      "3846                       Baran           7.6    43774              3.603444\n",
      "2322               The Dark Half           5.4    10349              3.573212\n",
      "6286                  Wolf Creek           6.1     9885              3.564846\n",
      "7516                  Iron Man 2           6.6    10138              3.550062\n",
      "8025                The Big Bang           5.5    54271              3.548407\n",
      "3011           Missing in Action           5.2    15379              3.533852\n",
      "1774          Married to the Mob           5.8     2321              3.530623\n",
      "673                       Ransom           6.4     3595              3.503378\n",
      "4377                   Zoot Suit           6.9    76397              3.480058\n",
      "1650  Return from Witch Mountain           5.6    14822              3.452936\n",
      "8296                  Iron Man 3           6.8    68721              3.405572\n",
      "4274        Saturday Night Fever           6.5    11009              3.390692\n",
      "5184             The Magic Flute           7.1    44864              3.350019\n",
      "813     Escape to Witch Mountain           6.5    14821              3.332198\n",
      "2198  The Little Shop of Horrors           6.2    24452              3.289483\n",
      "1704   The Clan of the Cave Bear           4.7    13853              3.061864\n",
      "279            The Swan Princess           6.5    22586              3.057682\n",
      "2047               Baby Geniuses           3.3    22345              2.846119\n"
     ]
    }
   ],
   "source": [
    "#hybrid(30,'The Conjuring')\n",
    "\n",
    "result = hybrid(10,'Iron Man')\n",
    "print(\"data getting passed in contextual\")\n",
    "print(result)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "22:38:07.426164\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'night'"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# necessary functions for contextual_update function\n",
    "from datetime import datetime\n",
    "def day_time():\n",
    "    now = datetime.now().time()\n",
    "    print(now)\n",
    "    morning=now.replace(hour=12,minute=0,second=0,microsecond=0)\n",
    "    afternoon=now.replace(hour=16,minute=0,second=0,microsecond=0)\n",
    "    evening=now.replace(hour=19,minute=0,second=0,microsecond=0)\n",
    "\n",
    "    if now< morning :\n",
    "        return \"morning\"\n",
    "    elif now<afternoon :\n",
    "        return \"afternoon\"\n",
    "    elif now<evening :\n",
    "        return \"evening\"\n",
    "    else :\n",
    "        return \"night\"\n",
    "\n",
    "\n",
    "#testing function\n",
    "day_time()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "function reached 2\n",
      "22:39:23.547985\n",
      "                         Title  Vote Average  TMDb Id  Estimated Prediction  \\\n",
      "0                Touch of Evil           7.6     1480              0.352174   \n",
      "1                Batman Begins           7.5      272              0.292090   \n",
      "2           All the King's Men           6.3    25430              0.225325   \n",
      "3      Avengers: Age of Ultron           7.3    99861              0.078715   \n",
      "4                     Scarface           7.5      877              0.044117   \n",
      "5            To Sir, with Love           7.6    25934             -0.001638   \n",
      "6        Rebel Without a Cause           7.6      221             -0.002946   \n",
      "7      Dr. Jekyll and Mr. Hyde           6.6     3022             -0.032624   \n",
      "8                        Brake           5.3    85414             -0.066002   \n",
      "9                       Rififi           7.7      934             -0.087893   \n",
      "10                     Hostage           6.2     2026             -0.091482   \n",
      "11          Echelon Conspiracy           5.4    16325             -0.122441   \n",
      "12                       Baran           7.6    43774             -0.146556   \n",
      "13               The Dark Half           5.4    10349             -0.176788   \n",
      "14                  Wolf Creek           6.1     9885             -0.185154   \n",
      "15                  Iron Man 2           6.6    10138             -0.199938   \n",
      "16                The Big Bang           5.5    54271             -0.201593   \n",
      "17           Missing in Action           5.2    15379             -0.216148   \n",
      "18          Married to the Mob           5.8     2321             -0.219377   \n",
      "19                      Ransom           6.4     3595             -0.246622   \n",
      "20                   Zoot Suit           6.9    76397             -0.269942   \n",
      "21  Return from Witch Mountain           5.6    14822             -0.297064   \n",
      "22                  Iron Man 3           6.8    68721             -0.344428   \n",
      "23        Saturday Night Fever           6.5    11009             -0.359308   \n",
      "24             The Magic Flute           7.1    44864             -0.399981   \n",
      "25    Escape to Witch Mountain           6.5    14821             -0.417802   \n",
      "26  The Little Shop of Horrors           6.2    24452             -0.460517   \n",
      "27   The Clan of the Cave Bear           4.7    13853             -0.688136   \n",
      "28           The Swan Princess           6.5    22586             -0.692318   \n",
      "29               Baby Geniuses           3.3    22345             -0.903881   \n",
      "\n",
      "    movieId   imdbId  tmdbId  \n",
      "0      1248    52311    1480  \n",
      "1     33794   372784     272  \n",
      "2      1942    41113   25430  \n",
      "3    122892  2395427   99861  \n",
      "4     25788    23427     877  \n",
      "5      3296    62376   25934  \n",
      "6      1103    48545     221  \n",
      "7      7208    33553    3022  \n",
      "8     96114  1990181   85414  \n",
      "9      4298    48021     934  \n",
      "10    32029   340163    2026  \n",
      "11    67361  1124039   16325  \n",
      "12     4964   233841   43774  \n",
      "13     2898   106664   10349  \n",
      "14    40723   416315    9885  \n",
      "15    77561  1228705   10138  \n",
      "16    94011  1307873   54271  \n",
      "17     3766    87727   15379  \n",
      "18     2247    95593    2321  \n",
      "19      832   117438    3595  \n",
      "20     5917    83365   76397  \n",
      "21     2091    78158   14822  \n",
      "22   102125  1300854   68721  \n",
      "23     5693    76666   11009  \n",
      "24     7577    73822   44864  \n",
      "25     1009    72951   14821  \n",
      "26     2747    54033   24452  \n",
      "27     2147    90848   13853  \n",
      "28      313   111333   22586  \n",
      "29     2555   118665   22345  \n"
     ]
    }
   ],
   "source": [
    "def contextual_update(list_passed,family=False) :\n",
    "    # categories we have romance,action,comedy,drama ,crime and thriller ,documentary,sci-fi\n",
    "    recommended_list=list_passed\n",
    "    print(\"function reached 2\")\n",
    "    effect_rate = 0.75\n",
    "    recommended_list['Estimated Prediction']=recommended_list['Estimated Prediction']-effect_rate\n",
    "    #adding genre to recommended list\n",
    "    recommended_list2 = pd.merge(recommended_list,links,left_on=['TMDb Id'],right_on=['tmdbId']).dropna()\n",
    "    recommended_list2\n",
    "\n",
    "\n",
    "    # Timing based\n",
    "\n",
    "    day_part = day_time()\n",
    "    #if day_part == \"morning\":x\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "    print(recommended_list2)\n",
    "\n",
    "\n",
    "\n",
    "contextual_update(result)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
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    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
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  },
  {
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