knn_recommender.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import pandas as pd\n",
    "\n",
    "data_path = 'ml-20m/'\n",
    "movies_filename = 'movies.csv'\n",
    "ratings_filename = 'ratings.csv'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# read data\n",
    "df_movies = pd.read_csv(\n",
    "    os.path.join(data_path, movies_filename),\n",
    "\n",
    "    usecols=['movieId', 'title'],\n",
    "    dtype={'movieId': 'int32', 'title': 'str'})\n",
    "\n",
    "df_ratings = pd.read_csv(\n",
    "    os.path.join(data_path, ratings_filename),\n",
    "    usecols=['userId', 'movieId', 'rating'],\n",
    "    dtype={'userId': 'int32', 'movieId': 'int32', 'rating': 'float32'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(27278, 2)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_movies.head()\n",
    "df_movies.shape\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>userId</th>\n",
       "      <th>movieId</th>\n",
       "      <th>rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
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       "      <td>3.5</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>29</td>\n",
       "      <td>3.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>32</td>\n",
       "      <td>3.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>47</td>\n",
       "      <td>3.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>50</td>\n",
       "      <td>3.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   userId  movieId  rating\n",
       "0       1        2     3.5\n",
       "1       1       29     3.5\n",
       "2       1       32     3.5\n",
       "3       1       47     3.5\n",
       "4       1       50     3.5"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_ratings.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# df_ratings.shape\n",
    "df_ratings=df_ratings[:2000000]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2000000, 3)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_ratings.shape\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy.sparse import csr_matrix\n",
    "# pivot ratings into movie features\n",
    "df_movie_features = df_ratings.pivot(\n",
    "    index='movieId',\n",
    "    columns='userId',\n",
    "    values='rating'\n",
    ").fillna(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "mat_movie_features = csr_matrix(df_movie_features.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>userId</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
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       "      <th>4</th>\n",
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       "      <th>13565</th>\n",
       "      <th>13566</th>\n",
       "      <th>13567</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>movieId</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <td>4.0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
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       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
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       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 13567 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "userId   1      2      3      4      5      6      7      8      9      10     \\\n",
       "movieId                                                                         \n",
       "1          0.0    0.0    4.0    0.0    0.0    5.0    0.0    4.0    0.0    4.0   \n",
       "2          3.5    0.0    0.0    0.0    3.0    0.0    0.0    0.0    0.0    0.0   \n",
       "3          0.0    4.0    0.0    0.0    0.0    3.0    3.0    5.0    0.0    0.0   \n",
       "4          0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
       "5          0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
       "\n",
       "userId   ...  13558  13559  13560  13561  13562  13563  13564  13565  13566  \\\n",
       "movieId  ...                                                                  \n",
       "1        ...    3.0    4.0    0.0    2.0    4.0    0.0    5.0    4.5    0.0   \n",
       "2        ...    0.0    0.0    0.0    0.0    0.0    0.0    0.0    3.0    0.0   \n",
       "3        ...    0.0    2.0    0.0    2.0    0.0    0.0    0.0    0.0    0.0   \n",
       "4        ...    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
       "5        ...    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
       "\n",
       "userId   13567  \n",
       "movieId         \n",
       "1          0.0  \n",
       "2          0.0  \n",
       "3          0.0  \n",
       "4          0.0  \n",
       "5          0.0  \n",
       "\n",
       "[5 rows x 13567 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_movie_features.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.neighbors import NearestNeighbors\n",
    "model_knn = NearestNeighbors(metric='cosine', algorithm='brute', n_neighbors=20, n_jobs=-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 13567 unique users and 16715 unique movies in this data set\n"
     ]
    }
   ],
   "source": [
    "num_users = len(df_ratings.userId.unique())\n",
    "num_items = len(df_ratings.movieId.unique())\n",
    "print('There are {} unique users and {} unique movies in this data set'.format(num_users, num_items))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rating</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "      <th>0.5</th>\n",
       "      <td>23427</td>\n",
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       "      <td>68420</td>\n",
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       "      <td>27518</td>\n",
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       "      <td>557816</td>\n",
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       "      <th>4.5</th>\n",
       "      <td>151291</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5.0</th>\n",
       "      <td>285563</td>\n",
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      ],
      "text/plain": [
       "         count\n",
       "rating        \n",
       "0.5      23427\n",
       "1.0      68420\n",
       "1.5      27518\n",
       "2.0     146105\n",
       "2.5      89328\n",
       "3.0     432669\n",
       "3.5     217863\n",
       "4.0     557816\n",
       "4.5     151291\n",
       "5.0     285563"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# get count\n",
    "df_ratings_cnt_tmp = pd.DataFrame(df_ratings.groupby('rating').size(), columns=['count'])\n",
    "df_ratings_cnt_tmp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
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       "      <td>146105</td>\n",
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       "      <th>2.5</th>\n",
       "      <td>89328</td>\n",
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       "      <th>3.0</th>\n",
       "      <td>432669</td>\n",
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       "      <th>3.5</th>\n",
       "      <td>217863</td>\n",
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       "      <th>4.0</th>\n",
       "      <td>557816</td>\n",
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      ],
      "text/plain": [
       "         count\n",
       "0.0  224772405\n",
       "0.5      23427\n",
       "1.0      68420\n",
       "1.5      27518\n",
       "2.0     146105\n",
       "2.5      89328\n",
       "3.0     432669\n",
       "3.5     217863\n",
       "4.0     557816\n",
       "4.5     151291\n",
       "5.0     285563"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# there are a lot more counts in rating of zero\n",
    "total_cnt = num_users * num_items\n",
    "rating_zero_cnt = total_cnt - df_ratings.shape[0]\n",
    "\n",
    "df_ratings_cnt = df_ratings_cnt_tmp.append(\n",
    "    pd.DataFrame({'count': rating_zero_cnt}, index=[0.0]),\n",
    "    verify_integrity=True,\n",
    ").sort_index()\n",
    "df_ratings_cnt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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>count</th>\n",
       "      <th>log_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0.0</th>\n",
       "      <td>224772405</td>\n",
       "      <td>19.230599</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.5</th>\n",
       "      <td>23427</td>\n",
       "      <td>10.061644</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.0</th>\n",
       "      <td>68420</td>\n",
       "      <td>11.133420</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.5</th>\n",
       "      <td>27518</td>\n",
       "      <td>10.222596</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2.0</th>\n",
       "      <td>146105</td>\n",
       "      <td>11.892081</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2.5</th>\n",
       "      <td>89328</td>\n",
       "      <td>11.400070</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3.0</th>\n",
       "      <td>432669</td>\n",
       "      <td>12.977728</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3.5</th>\n",
       "      <td>217863</td>\n",
       "      <td>12.291622</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4.0</th>\n",
       "      <td>557816</td>\n",
       "      <td>13.231784</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4.5</th>\n",
       "      <td>151291</td>\n",
       "      <td>11.926960</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5.0</th>\n",
       "      <td>285563</td>\n",
       "      <td>12.562218</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         count  log_count\n",
       "0.0  224772405  19.230599\n",
       "0.5      23427  10.061644\n",
       "1.0      68420  11.133420\n",
       "1.5      27518  10.222596\n",
       "2.0     146105  11.892081\n",
       "2.5      89328  11.400070\n",
       "3.0     432669  12.977728\n",
       "3.5     217863  12.291622\n",
       "4.0     557816  13.231784\n",
       "4.5     151291  11.926960\n",
       "5.0     285563  12.562218"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#log normalise to make it easier to interpret on a graph\n",
    "import numpy as np\n",
    "df_ratings_cnt['log_count'] = np.log(df_ratings_cnt['count'])\n",
    "df_ratings_cnt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'number of ratings')"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "plt.style.use('ggplot')\n",
    "\n",
    "%matplotlib inline\n",
    "ax = df_ratings_cnt[['count']].reset_index().rename(columns={'index': 'rating score'}).plot(\n",
    "    x='rating score',\n",
    "    y='count',\n",
    "    kind='bar',\n",
    "    figsize=(12, 8),\n",
    "    title='Count for Each Rating Score (in Log Scale)',\n",
    "    logy=True,\n",
    "    fontsize=12,\n",
    ")\n",
    "ax.set_xlabel(\"movie rating score\")\n",
    "ax.set_ylabel(\"number of ratings\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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>count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>movieId</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4883</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2209</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1256</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>272</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1227</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         count\n",
       "movieId       \n",
       "1         4883\n",
       "2         2209\n",
       "3         1256\n",
       "4          272\n",
       "5         1227"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# get rating frequency\n",
    "#number of ratings each movie got.\n",
    "df_movies_cnt = pd.DataFrame(df_ratings.groupby('movieId').size(), columns=['count'])\n",
    "df_movies_cnt.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "shape of original ratings data:  (2000000, 3)\n",
      "shape of ratings data after dropping unpopular movies:  (1876209, 3)\n"
     ]
    }
   ],
   "source": [
    "#now we need to take only movies that have been rated atleast 50 times to get some idea of the reactions of users towards it\n",
    "\n",
    "popularity_thres = 50\n",
    "popular_movies = list(set(df_movies_cnt.query('count >= @popularity_thres').index))\n",
    "df_ratings_drop_movies = df_ratings[df_ratings.movieId.isin(popular_movies)]\n",
    "print('shape of original ratings data: ', df_ratings.shape)\n",
    "print('shape of ratings data after dropping unpopular movies: ', df_ratings_drop_movies.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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>count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>userId</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>166</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>187</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>66</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        count\n",
       "userId       \n",
       "1         166\n",
       "2          58\n",
       "3         187\n",
       "4          28\n",
       "5          66"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# get number of ratings given by every user\n",
    "df_users_cnt = pd.DataFrame(df_ratings_drop_movies.groupby('userId').size(), columns=['count'])\n",
    "df_users_cnt.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "shape of original ratings data:  (2000000, 3)\n",
      "shape of ratings data after dropping both unpopular movies and inactive users:  (1712154, 3)\n"
     ]
    }
   ],
   "source": [
    "# filter data to come to an approximation of user likings.\n",
    "ratings_thres = 50\n",
    "active_users = list(set(df_users_cnt.query('count >= @ratings_thres').index))\n",
    "df_ratings_drop_users = df_ratings_drop_movies[df_ratings_drop_movies.userId.isin(active_users)]\n",
    "print('shape of original ratings data: ', df_ratings.shape)\n",
    "print('shape of ratings data after dropping both unpopular movies and inactive users: ', df_ratings_drop_users.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# pivot and create movie-user matrix\n",
    "movie_user_mat = df_ratings_drop_users.pivot(index='movieId', columns='userId', values='rating').fillna(0)\n",
    "#map movie titles to images\n",
    "movie_to_idx = {\n",
    "    movie: i for i, movie in \n",
    "    enumerate(list(df_movies.set_index('movieId').loc[movie_user_mat.index].title))\n",
    "}\n",
    "# transform matrix to scipy sparse matrix\n",
    "movie_user_mat_sparse = csr_matrix(movie_user_mat.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<4525x8276 sparse matrix of type '<class 'numpy.float32'>'\n",
       "\twith 1712154 stored elements in Compressed Sparse Row format>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movie_user_mat_sparse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "NearestNeighbors(algorithm='brute', leaf_size=30, metric='cosine',\n",
       "         metric_params=None, n_jobs=-1, n_neighbors=20, p=2, radius=1.0)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# define model\n",
    "model_knn = NearestNeighbors(metric='cosine', algorithm='brute', n_neighbors=20, n_jobs=-1)\n",
    "# fit\n",
    "model_knn.fit(movie_user_mat_sparse)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\nikita\\anaconda3\\envs\\tensorflow\\lib\\site-packages\\fuzzywuzzy\\fuzz.py:11: UserWarning: Using slow pure-python SequenceMatcher. Install python-Levenshtein to remove this warning\n",
      "  warnings.warn('Using slow pure-python SequenceMatcher. Install python-Levenshtein to remove this warning')\n"
     ]
    }
   ],
   "source": [
    "from fuzzywuzzy import fuzz"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "def fuzzy_matching(mapper, fav_movie, verbose=True):\n",
    "    \"\"\"\n",
    "    return the closest match via fuzzy ratio. \n",
    "    \n",
    "    Parameters\n",
    "    ----------    \n",
    "    mapper: dict, map movie title name to index of the movie in data\n",
    "\n",
    "    fav_movie: str, name of user input movie\n",
    "    \n",
    "    verbose: bool, print log if True\n",
    "\n",
    "    Return\n",
    "    ------\n",
    "    index of the closest match\n",
    "    \"\"\"\n",
    "    match_tuple = []\n",
    "    # get match\n",
    "    for title, idx in mapper.items():\n",
    "        ratio = fuzz.ratio(title.lower(), fav_movie.lower())\n",
    "        if ratio >= 60:\n",
    "            match_tuple.append((title, idx, ratio))\n",
    "    # sort\n",
    "    match_tuple = sorted(match_tuple, key=lambda x: x[2])[::-1]\n",
    "    if not match_tuple:\n",
    "        print('Oops! No match is found')\n",
    "        return\n",
    "    if verbose:\n",
    "        print('Found possible matches in our database: {0}\\n'.format([x[0] for x in match_tuple]))\n",
    "    return match_tuple[0][1]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "def make_recommendation(model_knn, data, mapper, fav_movie, n_recommendations):\n",
    "    \"\"\"\n",
    "    return top n similar movie recommendations based on user's input movie\n",
    "\n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    model_knn: sklearn model, knn model\n",
    "\n",
    "    data: movie-user matrix\n",
    "\n",
    "    mapper: dict, map movie title name to index of the movie in data\n",
    "\n",
    "    fav_movie: str, name of user input movie\n",
    "\n",
    "    n_recommendations: int, top n recommendations\n",
    "\n",
    "    Return\n",
    "    ------\n",
    "    list of top n similar movie recommendations\n",
    "    \"\"\"\n",
    "    # fit\n",
    "    model_knn.fit(data)\n",
    "    # get input movie index\n",
    "    print('You have input movie:', fav_movie)\n",
    "    idx = fuzzy_matching(mapper, fav_movie, verbose=True)\n",
    "    \n",
    "    print('Recommendation system start to make inference')\n",
    "    print('......\\n')\n",
    "    distances, indices = model_knn.kneighbors(data[idx], n_neighbors=n_recommendations+1)\n",
    "    \n",
    "    raw_recommends = \\\n",
    "        sorted(list(zip(indices.squeeze().tolist(), distances.squeeze().tolist())), key=lambda x: x[1])[:0:-1]\n",
    "    # get reverse mapper\n",
    "    reverse_mapper = {v: k for k, v in mapper.items()}\n",
    "    # print recommendations\n",
    "    print('Recommendations for {}:'.format(fav_movie))\n",
    "    for i, (idx, dist) in enumerate(raw_recommends):\n",
    "        print('{0}: {1}, with distance of {2}'.format(i+1, reverse_mapper[idx], dist))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "You have input movie: Iron Man\n",
      "Found possible matches in our database: ['Iron Man (2008)', 'Iron Man 3 (2013)', 'Iron Man 2 (2010)']\n",
      "\n",
      "Recommendation system start to make inference\n",
      "......\n",
      "\n",
      "Recommendations for Iron Man:\n",
      "1: Sherlock Holmes (2009), with distance of 0.44469374418258667\n",
      "2: V for Vendetta (2006), with distance of 0.43665528297424316\n",
      "3: Iron Man 2 (2010), with distance of 0.431448757648468\n",
      "4: Bourne Ultimatum, The (2007), with distance of 0.4297747015953064\n",
      "5: 300 (2007), with distance of 0.4274405837059021\n",
      "6: Batman Begins (2005), with distance of 0.42290133237838745\n",
      "7: Avatar (2009), with distance of 0.41733992099761963\n",
      "8: WALL·E (2008), with distance of 0.3926910161972046\n",
      "9: Star Trek (2009), with distance of 0.3709280490875244\n",
      "10: Dark Knight, The (2008), with distance of 0.3216145634651184\n"
     ]
    }
   ],
   "source": [
    "my_favorite = 'Iron Man'\n",
    "\n",
    "make_recommendation(\n",
    "    model_knn=model_knn,\n",
    "    data=movie_user_mat_sparse,\n",
    "    fav_movie=my_favorite,\n",
    "    mapper=movie_to_idx,\n",
    "    n_recommendations=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Rosewood (1997)': 999,\n",
       " 'Days of Thunder (1990)': 752,\n",
       " 'Wild Things (1998)': 1165,\n",
       " 'Jersey Girl (2004)': 3438,\n",
       " 'French Connection, The (1971)': 1243,\n",
       " 'Bodyguard, The (1992)': 2092,\n",
       " 'Lord of the Rings: The Fellowship of the Ring, The (2001)': 2857,\n",
       " 'Kung Fu Hustle (Gong fu) (2004)': 3705,\n",
       " 'New World, The (2005)': 3832,\n",
       " 'Gimme Shelter (1970)': 3461,\n",
       " 'After Life (Wandafuru raifu) (1998)': 1718,\n",
       " '8MM (1999)': 1646,\n",
       " 'Cronos (1993)': 448,\n",
       " 'End of Days (1999)': 2022,\n",
       " 'Lawnmower Man, The (1992)': 712,\n",
       " 'My Blue Heaven (1990)': 1460,\n",
       " 'Love Affair (1994)': 212,\n",
       " 'Soul Man (1986)': 1627,\n",
       " 'Live Free or Die Hard (2007)': 4020,\n",
       " 'Adventures of Tintin, The (2011)': 4428,\n",
       " 'Perks of Being a Wallflower, The (2012)': 4459,\n",
       " 'Congo (1995)': 121,\n",
       " 'Omen, The (2006)': 3881,\n",
       " 'Sid and Nancy (1986)': 1523,\n",
       " 'Damien: Omen II (1978)': 1811,\n",
       " 'Harvey (1950)': 2003,\n",
       " 'Shampoo (1975)': 2013,\n",
       " 'Tsotsi (2005)': 3857,\n",
       " 'Willow (1988)': 1438,\n",
       " 'One Crazy Summer (1986)': 1464,\n",
       " 'Spawn (1997)': 1049,\n",
       " 'Priest (1994)': 234,\n",
       " 'Secrets & Lies (1996)': 713,\n",
       " 'Michael Clayton (2007)': 4053,\n",
       " \"Jet Li's Fearless (Huo Yuan Jia) (2006)\": 3894,\n",
       " 'Social Network, The (2010)': 4351,\n",
       " 'Garden State (2004)': 3569,\n",
       " 'Spanking the Monkey (1994)': 450,\n",
       " 'Great Race, The (1965)': 737,\n",
       " 'Metallica: Some Kind of Monster (2004)': 3671,\n",
       " 'Menace II Society (1993)': 391,\n",
       " 'Thing from Another World, The (1951)': 1733,\n",
       " 'American President, The (1995)': 10,\n",
       " 'Under Siege (1992)': 952,\n",
       " 'Short Cuts (1993)': 426,\n",
       " 'Ghost Ship (2002)': 3076,\n",
       " 'I Love Trouble (1994)': 286,\n",
       " 'Religulous (2008)': 4123,\n",
       " 'Passion of the Christ, The (2004)': 3427,\n",
       " '7th Voyage of Sinbad, The (1958)': 2039,\n",
       " 'Purple Rain (1984)': 3517,\n",
       " 'Failure to Launch (2006)': 3851,\n",
       " 'American Pie (1999)': 1760,\n",
       " 'Outrageous Fortune (1987)': 1794,\n",
       " 'Way of the Gun, The (2000)': 2394,\n",
       " \"Son's Room, The (Stanza del figlio, La) (2001)\": 2890,\n",
       " 'Kalifornia (1993)': 379,\n",
       " 'Land of the Dead (2005)': 3738,\n",
       " 'Cider House Rules, The (1999)': 2037,\n",
       " \"Coal Miner's Daughter (1980)\": 2795,\n",
       " \"Before the Devil Knows You're Dead (2007)\": 4064,\n",
       " 'Super 8 (2011)': 4406,\n",
       " 'Three Colors: Blue (Trois couleurs: Bleu) (1993)': 241,\n",
       " \"Devil's Advocate, The (1997)\": 1089,\n",
       " 'Nausicaä of the Valley of the Wind (Kaze no tani no Naushika) (1984)': 3384,\n",
       " 'Head-On (Gegen die Wand) (2004)': 3652,\n",
       " 'Blade II (2002)': 2927,\n",
       " 'Total Recall (1990)': 1889,\n",
       " 'Journey of Natty Gann, The (1985)': 1346,\n",
       " 'Deuce Bigalow: Male Gigolo (1999)': 2035,\n",
       " 'Two Mules for Sister Sara (1970)': 3212,\n",
       " 'Detroit Rock City (1999)': 1804,\n",
       " 'Charlie Bartlett (2007)': 4030,\n",
       " 'Kids Are All Right, The (2010)': 4341,\n",
       " '12 Angry Men (1957)': 806,\n",
       " 'Walking and Talking (1996)': 564,\n",
       " 'Alligator (1980)': 1660,\n",
       " 'Eddie (1996)': 494,\n",
       " 'Starsky & Hutch (2004)': 3430,\n",
       " 'Arrival, The (1996)': 540,\n",
       " 'Stuart Little (1999)': 2042,\n",
       " 'Little Voice (1998)': 1560,\n",
       " 'Swingers (1996)': 725,\n",
       " 'Dark Water (2005)': 3744,\n",
       " 'Elling (2001)': 3099,\n",
       " 'GoldenEye (1995)': 9,\n",
       " 'Hudson Hawk (1991)': 3352,\n",
       " 'Hero (Ying xiong) (2002)': 3382,\n",
       " 'Name of the Rose, The (Name der Rose, Der) (1986)': 1621,\n",
       " 'Kama Sutra: A Tale of Love (1996)': 1003,\n",
       " 'Magnificent Ambersons, The (1942)': 2916,\n",
       " 'Dr. Strangelove or: How I Learned to Stop Worrying and Love the Bomb (1964)': 541,\n",
       " 'Eragon (2006)': 3951,\n",
       " 'You, Me and Dupree (2006)': 3887,\n",
       " 'Escape to Witch Mountain (1975)': 685,\n",
       " 'Jurassic Park III (2001)': 2717,\n",
       " 'Bigger, Stronger, Faster* (2008)': 4134,\n",
       " 'UHF (1989)': 2733,\n",
       " 'Lucas (1986)': 2199,\n",
       " 'Millennium Actress (Sennen joyû) (2001)': 3269,\n",
       " 'Blood Diamond (2006)': 3950,\n",
       " 'Adventures of Pinocchio, The (1996)': 573,\n",
       " 'Good Morning, Vietnam (1987)': 2181,\n",
       " 'Women on the Verge of a Nervous Breakdown (Mujeres al borde de un ataque de nervios) (1988)': 1988,\n",
       " 'Omen, The (1976)': 925,\n",
       " 'Brady Bunch Movie, The (1995)': 455,\n",
       " \"Kid in King Arthur's Court, A (1995)\": 202,\n",
       " 'Sleepless in Seattle (1993)': 430,\n",
       " 'Hot Chick, The (2002)': 3108,\n",
       " 'Walk in the Clouds, A (1995)': 160,\n",
       " \"Breakfast at Tiffany's (1961)\": 611,\n",
       " 'Happy, Texas (1999)': 1875,\n",
       " 'Chaplin (1992)': 3471,\n",
       " 'Gladiator (2000)': 2254,\n",
       " 'Domino (2005)': 3776,\n",
       " 'Shanghai Knights (2003)': 3145,\n",
       " 'Little Shop of Horrors (1986)': 1790,\n",
       " 'Batman & Robin (1997)': 1035,\n",
       " 'Willy Wonka & the Chocolate Factory (1971)': 730,\n",
       " 'Signs (2002)': 3010,\n",
       " 'Out of Africa (1985)': 1249,\n",
       " 'Victor/Victoria (1982)': 735,\n",
       " 'Terms of Endearment (1983)': 1248,\n",
       " 'Kindergarten Cop (1990)': 3354,\n",
       " 'Death Race 2000 (1975)': 3499,\n",
       " 'Election (1999)': 1705,\n",
       " 'Minority Report (2002)': 2986,\n",
       " 'American Dreamz (2006)': 3871,\n",
       " 'Producers, The (2005)': 3827,\n",
       " 'Orphanage, The (Orfanato, El) (2007)': 4080,\n",
       " 'Strange Brew (1983)': 3056,\n",
       " 'Blink (1994)': 328,\n",
       " 'Harriet the Spy (1996)': 562,\n",
       " \"Pan's Labyrinth (Laberinto del fauno, El) (2006)\": 3930,\n",
       " 'Buffalo Soldiers (2001)': 3239,\n",
       " 'Bring It On (2000)': 2390,\n",
       " 'Party Girl (1995)': 146,\n",
       " \"Devil's Rejects, The (2005)\": 3750,\n",
       " 'Truth About Cats & Dogs, The (1996)': 517,\n",
       " 'Sunshine (2007)': 3994,\n",
       " 'Hideous Kinky (1998)': 1700,\n",
       " 'Best of Youth, The (La meglio gioventù) (2003)': 3484,\n",
       " 'Substitute, The (1996)': 511,\n",
       " 'Color of Night (1994)': 341,\n",
       " 'American History X (1998)': 1510,\n",
       " 'Secondhand Lions (2003)': 3274,\n",
       " 'Four Rooms (1995)': 17,\n",
       " 'Monster Squad, The (1987)': 2527,\n",
       " 'Just Cause (1995)': 201,\n",
       " 'Birdman (2014)': 4517,\n",
       " 'Battlestar Galactica (2003)': 3646,\n",
       " 'Gingerbread Man, The (1998)': 1180,\n",
       " 'Sleeping with the Enemy (1991)': 3299,\n",
       " 'Take the Money and Run (1969)': 1253,\n",
       " 'Arachnophobia (1990)': 1756,\n",
       " 'Sideways (2004)': 3602,\n",
       " 'Gorillas in the Mist (1988)': 2677,\n",
       " 'I Love You, Man (2009)': 4224,\n",
       " 'Hard Candy (2005)': 3867,\n",
       " 'Body Snatcher, The (1945)': 915,\n",
       " 'Straight Story, The (1999)': 1924,\n",
       " 'Harley Davidson and the Marlboro Man (1991)': 2548,\n",
       " 'Nick of Time (1995)': 76,\n",
       " 'Police Academy 5: Assignment: Miami Beach (1988)': 1553,\n",
       " \"Something's Gotta Give (2003)\": 3397,\n",
       " 'Space Jam (1996)': 503,\n",
       " 'Murder at 1600 (1997)': 978,\n",
       " '200 Cigarettes (1999)': 1645,\n",
       " 'Amazing Grace (2006)': 3979,\n",
       " 'Interview with the Vampire: The Vampire Chronicles (1994)': 197,\n",
       " 'Friends with Benefits (2011)': 4415,\n",
       " '50 First Dates (2004)': 3424,\n",
       " 'Red Violin, The (Violon rouge, Le) (1998)': 1747,\n",
       " 'My Science Project (1985)': 1713,\n",
       " 'Ghost Writer, The (2010)': 4312,\n",
       " 'Dog Day Afternoon (1975)': 2141,\n",
       " 'Angel Heart (1987)': 2308,\n",
       " 'Born in East L.A. (1987)': 2508,\n",
       " \"One Flew Over the Cuckoo's Nest (1975)\": 797,\n",
       " 'Urban Legends: Final Cut (2000)': 2398,\n",
       " 'Bell, Book and Candle (1958)': 2221,\n",
       " 'To Die For (1995)': 42,\n",
       " 'First Wives Club, The (1996)': 575,\n",
       " 'Cop Land (1997)': 1047,\n",
       " 'Vantage Point (2008)': 4104,\n",
       " 'Batman (1989)': 461,\n",
       " 'Tomcats (2001)': 2567,\n",
       " 'From Russia with Love (1963)': 1914,\n",
       " 'Miracle on 34th Street (1994)': 219,\n",
       " 'Perez Family, The (1995)': 231,\n",
       " 'Girl with the Dragon Tattoo, The (2011)': 4436,\n",
       " 'Wrong Man, The (1956)': 1433,\n",
       " \"White Man's Burden (1995)\": 162,\n",
       " 'Knight and Day (2010)': 4339,\n",
       " \"It's Complicated (2009)\": 4298,\n",
       " 'American Wedding (American Pie 3) (2003)': 3240,\n",
       " 'Living Out Loud (1998)': 1512,\n",
       " 'What Dreams May Come (1998)': 1489,\n",
       " 'Lost & Found (1999)': 1703,\n",
       " 'Serpent and the Rainbow, The (1988)': 2689,\n",
       " 'Road Warrior, The (Mad Max 2) (1981)': 2305,\n",
       " 'Piano Teacher, The (La pianiste) (2001)': 2933,\n",
       " 'Flight of the Phoenix, The (1965)': 3215,\n",
       " 'Kull the Conqueror (1997)': 1064,\n",
       " 'Treasure Planet (2002)': 3098,\n",
       " 'Jewel of the Nile, The (1985)': 1573,\n",
       " 'My Man Godfrey (1957)': 2010,\n",
       " 'Hocus Pocus (1993)': 1326,\n",
       " 'Dreamscape (1984)': 2342,\n",
       " 'My Family (1995)': 221,\n",
       " 'Last Seduction, The (1994)': 3100,\n",
       " 'Mary Reilly (1996)': 77,\n",
       " 'When a Man Loves a Woman (1994)': 306,\n",
       " 'Death Becomes Her (1992)': 2093,\n",
       " 'Ideal Husband, An (1999)': 1750,\n",
       " 'Sound of Music, The (1965)': 710,\n",
       " \"There's Something About Mary (1998)\": 1219,\n",
       " 'Scanner Darkly, A (2006)': 3674,\n",
       " 'Pitch Black (2000)': 2117,\n",
       " 'Crossing Guard, The (1995)': 67,\n",
       " 'Faster Pussycat! Kill! Kill! (1965)': 312,\n",
       " 'Richard Pryor Live on the Sunset Strip (1982)': 3143,\n",
       " 'Halloween (2007)': 4037,\n",
       " 'Dungeons & Dragons (2000)': 2451,\n",
       " 'Kate & Leopold (2001)': 2856,\n",
       " 'Fantasia 2000 (1999)': 2043,\n",
       " 'Strictly Ballroom (1992)': 792,\n",
       " 'Police Academy (1984)': 1549,\n",
       " 'Enemy Mine (1985)': 3459,\n",
       " 'Return from Witch Mountain (1978)': 1358,\n",
       " 'Passenger 57 (1992)': 3360,\n",
       " 'Kite Runner, The (2007)': 4083,\n",
       " 'L.A. Confidential (1997)': 1072,\n",
       " 'He Got Game (1998)': 1179,\n",
       " \"General's Daughter, The (1999)\": 1749,\n",
       " 'Blue Crush (2002)': 3018,\n",
       " 'Son of Rambow (2007)': 4124,\n",
       " \"Rosemary's Baby (1968)\": 1416,\n",
       " 'Bird on a Wire (1990)': 2307,\n",
       " 'Cool Runnings (1993)': 696,\n",
       " 'Funny Farm (1988)': 1818,\n",
       " 'Angels and Insects (1995)': 72,\n",
       " 'Mary Poppins (1964)': 703,\n",
       " 'Judge Dredd (1995)': 133,\n",
       " 'Godzilla (Gojira) (1954)': 1535,\n",
       " 'Julie & Julia (2009)': 4265,\n",
       " 'Bourne Identity, The (1988)': 3635,\n",
       " 'Serendipity (2001)': 2792,\n",
       " 'Flushed Away (2006)': 3941,\n",
       " 'Prairie Home Companion, A (2006)': 3868,\n",
       " 'Hamburger Hill (1987)': 3505,\n",
       " 'King Kong (1976)': 1539,\n",
       " 'Demolition Man (1993)': 346,\n",
       " 'Not Another Teen Movie (2001)': 2848,\n",
       " 'Superman (1978)': 1725,\n",
       " 'Lassie (1994)': 382,\n",
       " 'Wolf (1994)': 307,\n",
       " 'Bats (1999)': 1931,\n",
       " 'Bad Lieutenant (1992)': 2107,\n",
       " 'Bullets Over Broadway (1994)': 275,\n",
       " 'Monster-in-Law (2005)': 3727,\n",
       " 'Johnny Mnemonic (1995)': 132,\n",
       " 'Underworld: Evolution (2006)': 3838,\n",
       " 'Bulworth (1998)': 1195,\n",
       " 'Moulin Rouge (2001)': 2600,\n",
       " 'Why We Fight (2005)': 3774,\n",
       " 'International, The (2009)': 4217,\n",
       " 'Here on Earth (2000)': 2185,\n",
       " '6th Day, The (2000)': 2441,\n",
       " 'House on Haunted Hill (1999)': 1945,\n",
       " 'Final Cut, The (2004)': 3599,\n",
       " 'White Heat (1949)': 3534,\n",
       " 'Journey to the Center of the Earth (2008)': 4146,\n",
       " 'Homegrown (1998)': 1171,\n",
       " 'Office Space (1999)': 1644,\n",
       " 'Monsters vs. Aliens (2009)': 4229,\n",
       " 'Gone with the Wind (1939)': 629,\n",
       " 'Elizabethtown (2005)': 3793,\n",
       " 'Tape (2001)': 2815,\n",
       " 'Fandango (1985)': 1343,\n",
       " 'Blood Simple (1984)': 2291,\n",
       " 'Alfie (1966)': 2546,\n",
       " 'Seven Psychopaths (2012)': 4462,\n",
       " 'Salvador (1986)': 2640,\n",
       " 'Solaris (2002)': 3097,\n",
       " 'Radio (2003)': 3317,\n",
       " 'My Big Fat Greek Wedding (2002)': 2944,\n",
       " 'Me and You and Everyone We Know (2005)': 3740,\n",
       " 'Addiction, The (1995)': 113,\n",
       " 'Prometheus (2012)': 4447,\n",
       " 'Top Secret! (1984)': 3008,\n",
       " 'Hide and Seek (2005)': 3697,\n",
       " 'Hunger (2008)': 4193,\n",
       " 'Unforgettable (1996)': 86,\n",
       " 'Break-Up, The (2006)': 3879,\n",
       " 'Manhunter (1986)': 2551,\n",
       " 'Spartan (2004)': 3434,\n",
       " 'Rush (2013)': 4491,\n",
       " 'Robin Hood (1973)': 1968,\n",
       " \"It's My Party (1996)\": 507,\n",
       " 'In the Company of Men (1997)': 1052,\n",
       " 'Phenomenon (1996)': 563,\n",
       " 'Indiana Jones and the Last Crusade (1989)': 891,\n",
       " 'Suspiria (1977)': 2647,\n",
       " 'Meteor (1979)': 1661,\n",
       " \"Heaven's Prisoners (1996)\": 530,\n",
       " 'Brainstorm (1983)': 2882,\n",
       " 'Gone Baby Gone (2007)': 4056,\n",
       " 'Swan Princess, The (1994)': 245,\n",
       " 'Fright Night (1985)': 1858,\n",
       " 'Lost in America (1985)': 2586,\n",
       " 'Italian Job, The (1969)': 3321,\n",
       " 'Haunting, The (1963)': 1677,\n",
       " 'Program, The (1993)': 405,\n",
       " 'Friday the 13th Part VII: The New Blood (1988)': 1270,\n",
       " 'Double Indemnity (1944)': 2173,\n",
       " 'Harry Potter and the Deathly Hallows: Part 2 (2011)': 4411,\n",
       " 'Crimson Tide (1995)': 122,\n",
       " 'G.I. Jane (1997)': 1044,\n",
       " 'Lars and the Real Girl (2007)': 4054,\n",
       " 'Tourist, The (2010)': 4369,\n",
       " 'Aladdin (1992)': 458,\n",
       " 'Operation Petticoat (1959)': 2783,\n",
       " 'Out of the Past (1947)': 1336,\n",
       " 'Laputa: Castle in the Sky (Tenkû no shiro Rapyuta) (1986)': 3198,\n",
       " 'Dr. Dolittle (1998)': 1209,\n",
       " 'Fear of a Black Hat (1994)': 352,\n",
       " 'Falcon and the Snowman, The (1985)': 2048,\n",
       " 'Eddie and the Cruisers (1983)': 2704,\n",
       " 'Hunt for Red October, The (1990)': 1067,\n",
       " 'Lethal Weapon 3 (1992)': 1289,\n",
       " 'Across the Universe (2007)': 4025,\n",
       " 'Paprika (Papurika) (2006)': 4001,\n",
       " '1984 (Nineteen Eighty-Four) (1984)': 1380,\n",
       " 'Virgin Suicides, The (1999)': 2245,\n",
       " 'Matrix Reloaded, The (2003)': 3200,\n",
       " 'How to Lose Friends & Alienate People (2008)': 4166,\n",
       " 'Lost in Translation (2003)': 3268,\n",
       " 'Village of the Damned (1995)': 262,\n",
       " 'Amazon Women on the Moon (1987)': 2502,\n",
       " 'Bad Boys (1995)': 107,\n",
       " 'Dead Zone, The (1983)': 1381,\n",
       " 'Austin Powers in Goldmember (2002)': 3004,\n",
       " 'Lightning Jack (1994)': 385,\n",
       " 'Underworld: Rise of the Lycans (2009)': 4214,\n",
       " 'Ruby in Paradise (1993)': 417,\n",
       " 'Eye for an Eye (1996)': 53,\n",
       " \"World's End, The (2013)\": 4486,\n",
       " 'Jay and Silent Bob Strike Back (2001)': 2758,\n",
       " 'Source Code (2011)': 4385,\n",
       " 'Heavenly Creatures (1994)': 192,\n",
       " 'Joe Versus the Volcano (1990)': 2945,\n",
       " \"Butcher's Wife, The (1991)\": 1469,\n",
       " 'Good Luck Chuck (2007)': 4047,\n",
       " 'Mysterious Skin (2004)': 3724,\n",
       " 'I Spy (2002)': 3082,\n",
       " 'Best Years of Our Lives, The (1946)': 1229,\n",
       " 'Cabaret (1972)': 2235,\n",
       " 'Notting Hill (1999)': 1740,\n",
       " '101 Dalmatians (One Hundred and One Dalmatians) (1961)': 1353,\n",
       " 'Jury Duty (1995)': 134,\n",
       " 'South Pacific (1958)': 1908,\n",
       " 'Hope and Glory (1987)': 2518,\n",
       " 'Seventh Seal, The (Sjunde inseglet, Det) (1957)': 838,\n",
       " 'Dredd (2012)': 4457,\n",
       " 'From Hell (2001)': 2803,\n",
       " 'Showgirls (1995)': 150,\n",
       " 'Winslow Boy, The (1999)': 1710,\n",
       " 'Ghosts of Mississippi (1996)': 966,\n",
       " 'Parent Trap, The (1998)': 1331,\n",
       " 'Kiss of Death (1995)': 203,\n",
       " 'Witness for the Prosecution (1957)': 2861,\n",
       " 'Doubt (2008)': 4195,\n",
       " 'Governess, The (1998)': 1333,\n",
       " 'Animal, The (2001)': 2613,\n",
       " 'Hot Shots! Part Deux (1993)': 367,\n",
       " 'Tie Me Up! Tie Me Down! (¡Átame!) (1990)': 794,\n",
       " 'Zack and Miri Make a Porno (2008)': 4174,\n",
       " 'Once Were Warriors (1994)': 227,\n",
       " 'Snow Day (2000)': 2112,\n",
       " 'In the Line of Fire (1993)': 374,\n",
       " 'Man from Earth, The (2007)': 4069,\n",
       " 'MirrorMask (2005)': 3786,\n",
       " 'Last Temptation of Christ, The (1988)': 1307,\n",
       " 'Wolverine, The (2013)': 4487,\n",
       " 'Wolf Creek (2005)': 3811,\n",
       " 'Seabiscuit (2003)': 3237,\n",
       " 'Babylon 5: A Call to Arms (1999)': 3486,\n",
       " 'Antwone Fisher (2002)': 3114,\n",
       " \"Gone Fishin' (1997)\": 593,\n",
       " 'X-Men (2000)': 2353,\n",
       " 'Police Academy 2: Their First Assignment (1985)': 1550,\n",
       " 'Beverly Hills Cop III (1994)': 326,\n",
       " 'Species II (1998)': 1187,\n",
       " 'Fast and the Furious, The (2001)': 2627,\n",
       " \"Kiki's Delivery Service (Majo no takkyûbin) (1989)\": 3636,\n",
       " 'Mad Love (1995)': 139,\n",
       " 'Bad Girls (1994)': 322,\n",
       " 'Out of Sight (1998)': 1210,\n",
       " 'Howling, The (1980)': 767,\n",
       " 'Going My Way (1944)': 1227,\n",
       " 'Sahara (2005)': 3715,\n",
       " 'Barbershop (2002)': 3032,\n",
       " 'First Kid (1996)': 597,\n",
       " 'Midnight Express (1978)': 2205,\n",
       " 'Beastmaster, The (1982)': 2827,\n",
       " 'Interpreter, The (2005)': 3664,\n",
       " 'Ghost (1990)': 457,\n",
       " 'Welcome to Sarajevo (1997)': 1103,\n",
       " \"Smilla's Sense of Snow (1997)\": 1006,\n",
       " 'Mulholland Falls (1996)': 516,\n",
       " 'Smoke (1995)': 151,\n",
       " 'On the Waterfront (1954)': 1235,\n",
       " 'Apocalypse Now (1979)': 810,\n",
       " 'Mothman Prophecies, The (2002)': 2886,\n",
       " 'Phone Booth (2002)': 3177,\n",
       " 'Fly, The (1958)': 1612,\n",
       " 'Talk to Her (Hable con Ella) (2002)': 3095,\n",
       " 'Atomic Cafe, The (1982)': 2936,\n",
       " 'For a Few Dollars More (Per qualche dollaro in più) (1965)': 2289,\n",
       " 'Paycheck (2003)': 3408,\n",
       " 'Month by the Lake, A (1995)': 542,\n",
       " 'Night at the Museum: Battle of the Smithsonian (2009)': 4244,\n",
       " 'Finding Forrester (2000)': 2470,\n",
       " 'EuroTrip (2004)': 3426,\n",
       " 'Freshman, The (1990)': 3341,\n",
       " 'Kung Pow: Enter the Fist (2002)': 2878,\n",
       " 'Toy, The (1982)': 2830,\n",
       " 'Edge, The (1997)': 1070,\n",
       " 'Life as a House (2001)': 2810,\n",
       " 'Ghost and Mrs. Muir, The (1947)': 650,\n",
       " 'Miss Congeniality 2: Armed and Fabulous (2005)': 3713,\n",
       " 'Ran (1985)': 819,\n",
       " 'Blown Away (1994)': 329,\n",
       " 'Under the Tuscan Sun (2003)': 3279,\n",
       " \"Nick and Norah's Infinite Playlist (2008)\": 4167,\n",
       " 'Boiler Room (2000)': 2115,\n",
       " 'Jungle Fever (1991)': 2170,\n",
       " 'Limitless (2011)': 4376,\n",
       " 'Anastasia (1997)': 1116,\n",
       " 'Cabin Fever (2002)': 3265,\n",
       " 'Wimbledon (2004)': 3586,\n",
       " 'Wasabi (2001)': 3050,\n",
       " 'Angel Eyes (2001)': 2598,\n",
       " 'Cyrano de Bergerac (1990)': 877,\n",
       " 'Eye, The (Gin gwai) (Jian gui) (2002)': 3323,\n",
       " 'Karate Kid, The (1984)': 1586,\n",
       " 'Enemy at the Gates (2001)': 2560,\n",
       " 'U2: Rattle and Hum (1988)': 2033,\n",
       " 'Barefoot in the Park (1967)': 1860,\n",
       " 'Saturn 3 (1980)': 1848,\n",
       " 'Enough (2002)': 2970,\n",
       " 'Exorcism of Emily Rose, The (2005)': 3779,\n",
       " 'Idle Hands (1999)': 1708,\n",
       " 'Millions (2004)': 3659,\n",
       " 'Little Man Tate (1991)': 2779,\n",
       " 'Arsenic and Old Lace (1944)': 869,\n",
       " 'Green Lantern (2011)': 4407,\n",
       " 'Mallrats (1995)': 140,\n",
       " 'Speechless (1994)': 303,\n",
       " 'Flight of the Phoenix (2004)': 3667,\n",
       " 'Adventures of Baron Munchausen, The (1988)': 2659,\n",
       " 'Strada, La (1954)': 3415,\n",
       " 'Commitments, The (1991)': 1983,\n",
       " 'Meet Joe Black (1998)': 1519,\n",
       " 'Pallbearer, The (1996)': 475,\n",
       " 'Oscar and Lucinda (a.k.a. Oscar & Lucinda) (1997)': 1822,\n",
       " 'Creature Comforts (1989)': 2171,\n",
       " 'Cobb (1994)': 281,\n",
       " 'Quantum of Solace (2008)': 4183,\n",
       " '20,000 Leagues Under the Sea (1954)': 695,\n",
       " 'Love and Death (1975)': 2362,\n",
       " 'Uptown Girls (2003)': 3248,\n",
       " 'Proof (2005)': 3770,\n",
       " 'Patriot, The (2000)': 2334,\n",
       " 'Tampopo (1985)': 2363,\n",
       " 'Shop Around the Corner, The (1940)': 2011,\n",
       " 'Animal Crackers (1930)': 3479,\n",
       " 'King of Masks, The (Bian Lian) (1996)': 1709,\n",
       " 'Home Alone (1990)': 456,\n",
       " 'Harold and Kumar Go to White Castle (2004)': 3573,\n",
       " \"White Men Can't Jump (1992)\": 2098,\n",
       " 'Dead Ringers (1988)': 1678,\n",
       " 'Teaching Mrs. Tingle (1999)': 1827,\n",
       " 'Muppet Treasure Island (1996)': 89,\n",
       " 'Butcher Boy, The (1997)': 1121,\n",
       " 'Avalon (1990)': 2541,\n",
       " 'Last King of Scotland, The (2006)': 3935,\n",
       " 'Elite Squad: The Enemy Within (Tropa de Elite 2 - O Inimigo Agora É Outro) (2010)': 4384,\n",
       " 'Crouching Tiger, Hidden Dragon (Wo hu cang long) (2000)': 2450,\n",
       " 'House of Mirth, The (2000)': 2475,\n",
       " 'Funny People (2009)': 4221,\n",
       " 'Cocoon: The Return (1988)': 1576,\n",
       " 'Envy (2004)': 3454,\n",
       " 'In America (2002)': 3394,\n",
       " 'Silverado (1985)': 3364,\n",
       " 'Goonies, The (1985)': 1292,\n",
       " 'Trainspotting (1996)': 549,\n",
       " 'Strangers on a Train (1951)': 1436,\n",
       " '9 (2009)': 4269,\n",
       " 'Bend It Like Beckham (2002)': 3161,\n",
       " 'Other Sister, The (1999)': 1647,\n",
       " 'Billy Madison (1995)': 167,\n",
       " 'Chicken Little (2005)': 3806,\n",
       " 'Thomas Crown Affair, The (1999)': 1799,\n",
       " 'Black Swan (2010)': 4361,\n",
       " 'Aristocrats, The (2005)': 3754,\n",
       " 'Nosferatu the Vampyre (Nosferatu: Phantom der Nacht) (1979)': 3007,\n",
       " 'Peter Pan (1953)': 1354,\n",
       " 'Warlock (1989)': 2736,\n",
       " 'Madness of King George, The (1994)': 214,\n",
       " 'Circle of Friends (1995)': 171,\n",
       " 'Virus (1999)': 1608,\n",
       " 'Sesame Street Presents Follow That Bird (1985)': 2157,\n",
       " 'Mighty Joe Young (1998)': 1594,\n",
       " 'Sorority Boys (2002)': 2928,\n",
       " 'Life with Mikey (1993)': 384,\n",
       " 'Rat Race (2001)': 2755,\n",
       " 'Executive Decision (1996)': 392,\n",
       " 'Death Race (2008)': 4159,\n",
       " 'Kids in the Hall: Brain Candy (1996)': 497,\n",
       " 'Shaun of the Dead (2004)': 3589,\n",
       " 'Threesome (1994)': 440,\n",
       " '40-Year-Old Virgin, The (2005)': 3763,\n",
       " 'Hustle & Flow (2005)': 3748,\n",
       " 'Monkeybone (2001)': 2535,\n",
       " 'Ghoulies (1985)': 2426,\n",
       " 'Karate Kid, Part III, The (1989)': 1588,\n",
       " 'Bully (2001)': 2654,\n",
       " 'Out to Sea (1997)': 1042,\n",
       " 'Rebecca (1940)': 636,\n",
       " 'Guinevere (1999)': 1871,\n",
       " 'Science of Sleep, The (La science des rêves) (2006)': 3925,\n",
       " 'Abyss, The (1989)': 764,\n",
       " 'Poison Ivy II (1996)': 228,\n",
       " 'eXistenZ (1999)': 1706,\n",
       " 'Hostage (2005)': 3709,\n",
       " 'Earth Girls Are Easy (1988)': 2703,\n",
       " 'Shopgirl (2005)': 3797,\n",
       " 'Oliver & Company (1988)': 518,\n",
       " '3 Ninjas (1992)': 2765,\n",
       " 'Three Men and a Little Lady (1990)': 2947,\n",
       " 'Killer, The (Die xue shuang xiong) (1989)': 820,\n",
       " 'On the Town (1949)': 2262,\n",
       " 'Evita (1996)': 975,\n",
       " 'Nightmare on Elm Street 3: Dream Warriors, A (1987)': 1260,\n",
       " 'M*A*S*H (a.k.a. MASH) (1970)': 2884,\n",
       " 'Cloudy with a Chance of Meatballs (2009)': 4274,\n",
       " 'Batman: Mask of the Phantasm (1993)': 2073,\n",
       " 'American Psycho (2000)': 2231,\n",
       " 'My Man Godfrey (1936)': 654,\n",
       " 'Untouchables, The (1987)': 1439,\n",
       " 'Closet, The (Placard, Le) (2001)': 2632,\n",
       " 'Swordfish (2001)': 2615,\n",
       " 'Blank Check (1994)': 1316,\n",
       " 'Scrooged (1988)': 2002,\n",
       " 'Happy Feet (2006)': 3946,\n",
       " \"Ulee's Gold (1997)\": 1082,\n",
       " 'Safe (1995)': 148,\n",
       " 'Star Wars: Episode IV - A New Hope (1977)': 204,\n",
       " 'Proposal, The (2009)': 4251,\n",
       " 'Giant (1956)': 655,\n",
       " 'Sphere (1998)': 1154,\n",
       " 'Hollywood Homicide (2003)': 3218,\n",
       " 'Valentine (2001)': 2529,\n",
       " 'Daylight (1996)': 559,\n",
       " 'Pagemaster, The (1994)': 446,\n",
       " 'Sea Inside, The (Mar adentro) (2004)': 3661,\n",
       " 'Raisin in the Sun, A (1961)': 2134,\n",
       " 'Mirror Has Two Faces, The (1996)': 927,\n",
       " 'Analyze This (1999)': 1670,\n",
       " '3-Iron (Bin-jip) (2004)': 3679,\n",
       " 'Absence of Malice (1981)': 3071,\n",
       " 'Shine (1996)': 931,\n",
       " 'Species (1995)': 153,\n",
       " 'Star Wars: The Clone Wars (2008)': 4156,\n",
       " 'Escape from Alcatraz (1979)': 3519,\n",
       " 'Indian Summer (a.k.a. Alive & Kicking) (1996)': 1086,\n",
       " 'Medicine Man (1992)': 1836,\n",
       " \"Ocean's Thirteen (2007)\": 4012,\n",
       " 'Flesh and Bone (1993)': 354,\n",
       " 'Kill Bill: Vol. 1 (2003)': 3313,\n",
       " 'Oblivion (2013)': 4475,\n",
       " 'Hope Floats (1998)': 1199,\n",
       " 'Unlawful Entry (1992)': 2619,\n",
       " 'Cell, The (2000)': 2381,\n",
       " 'Sudden Death (1995)': 8,\n",
       " 'Crooklyn (1994)': 279,\n",
       " 'Shutter Island (2010)': 4309,\n",
       " 'Sleeping Beauty (1959)': 1363,\n",
       " \"You've Got Mail (1998)\": 1590,\n",
       " 'Thinner (1996)': 536,\n",
       " 'Comedian (2002)': 3062,\n",
       " 'Armageddon (1998)': 1214,\n",
       " 'NeverEnding Story, The (1984)': 1417,\n",
       " 'Elvira, Mistress of the Dark (1988)': 2674,\n",
       " 'Big Lebowski, The (1998)': 1138,\n",
       " 'I Shot Andy Warhol (1996)': 548,\n",
       " 'Duplex (2003)': 3277,\n",
       " 'John Tucker Must Die (2006)': 3909,\n",
       " 'Frenzy (1972)': 1430,\n",
       " 'Thirty-Two Short Films About Glenn Gould (1993)': 439,\n",
       " \"She's Gotta Have It (1986)\": 2167,\n",
       " 'Red (2010)': 4357,\n",
       " 'Laurel Canyon (2002)': 3159,\n",
       " 'Adventures of Milo and Otis, The (Koneko monogatari) (1986)': 1847,\n",
       " 'Waterboy, The (1998)': 1515,\n",
       " 'American Buffalo (1996)': 567,\n",
       " 'Klute (1971)': 1254,\n",
       " 'Backbeat (1993)': 273,\n",
       " 'License to Drive (1988)': 2681,\n",
       " 'Dr. T and the Women (2000)': 2421,\n",
       " 'Spellbound (1945)': 639,\n",
       " 'Scorpion King, The (2002)': 2950,\n",
       " 'Core, The (2003)': 3173,\n",
       " 'Hunted, The (2003)': 3162,\n",
       " 'Capturing the Friedmans (2003)': 3208,\n",
       " 'All That Jazz (1979)': 1929,\n",
       " 'Body of Lies (2008)': 4171,\n",
       " 'Bubba Ho-tep (2002)': 3276,\n",
       " 'Melinda and Melinda (2004)': 3712,\n",
       " 'Texas Chainsaw Massacre 2, The (1986)': 1618,\n",
       " 'Killers, The (1946)': 3495,\n",
       " 'Mr. Nobody (2009)': 4343,\n",
       " 'C.R.A.Z.Y. (2005)': 3817,\n",
       " 'Boyz N the Hood (1991)': 3294,\n",
       " 'Brotherhood of the Wolf (Pacte des loups, Le) (2001)': 2872,\n",
       " 'Dark Knight Rises, The (2012)': 4432,\n",
       " \"Krippendorf's Tribe (1998)\": 1184,\n",
       " 'Tell No One (Ne le dis à personne) (2006)': 4032,\n",
       " 'Iron Man 2 (2010)': 4322,\n",
       " 'Training Day (2001)': 2775,\n",
       " 'Big Kahuna, The (2000)': 2250,\n",
       " 'Ernest Goes to Camp (1987)': 2515,\n",
       " 'House II: The Second Story (1987)': 1408,\n",
       " 'Incredible Journey, The (1963)': 1329,\n",
       " '21 (2008)': 4116,\n",
       " 'Down to You (2000)': 2076,\n",
       " 'Good Shepherd, The (2006)': 3954,\n",
       " 'Bogus (1996)': 599,\n",
       " 'Touching the Void (2003)': 3420,\n",
       " 'Transporter, The (2002)': 3033,\n",
       " 'Alamo, The (1960)': 2488,\n",
       " 'Associate, The (1996)': 758,\n",
       " 'Possession (2002)': 3019,\n",
       " 'Secret of Roan Inish, The (1994)': 246,\n",
       " 'Smokey and the Bandit II (1980)': 2924,\n",
       " '48 Hrs. (1982)': 2880,\n",
       " 'Bolt (2008)': 4188,\n",
       " 'Aristocats, The (1970)': 478,\n",
       " 'Mumford (1999)': 1870,\n",
       " 'Frantic (1988)': 2676,\n",
       " 'Buddy Holly Story, The (1978)': 1857,\n",
       " 'Invasion, The (2007)': 4036,\n",
       " 'Spy Kids 3-D: Game Over (2003)': 3238,\n",
       " 'Man on Fire (2004)': 3453,\n",
       " 'Sherlock Holmes: A Game of Shadows (2011)': 4434,\n",
       " 'Bad Education (La mala educación) (2004)': 3614,\n",
       " 'Proof of Life (2000)': 2452,\n",
       " 'Beerfest (2006)': 3917,\n",
       " 'Patton (1970)': 872,\n",
       " 'Moon (2009)': 4239,\n",
       " 'Serial Mom (1994)': 423,\n",
       " 'RoboCop (1987)': 1937,\n",
       " 'Rescuers Down Under, The (1990)': 1356,\n",
       " 'Atonement (2007)': 4042,\n",
       " 'Pink Flamingos (1972)': 1534,\n",
       " 'Sabotage (1936)': 1447,\n",
       " 'Nutty Professor, The (1996)': 558,\n",
       " 'Shall We Dance (1937)': 728,\n",
       " 'Cast Away (2000)': 2473,\n",
       " 'Deep End of the Ocean, The (1999)': 1674,\n",
       " 'Conan the Destroyer (1984)': 2876,\n",
       " \"Spanish Apartment, The (L'auberge espagnole) (2002)\": 3202,\n",
       " 'Gran Torino (2008)': 4196,\n",
       " 'Pump Up the Volume (1990)': 868,\n",
       " 'Men in Black II (a.k.a. MIIB) (a.k.a. MIB 2) (2002)': 2993,\n",
       " 'Greystoke: The Legend of Tarzan, Lord of the Apes (1984)': 3349,\n",
       " 'Dogtown and Z-Boyz (2001)': 2955,\n",
       " 'Metro (1997)': 983,\n",
       " 'Transylvania 6-5000 (1985)': 2948,\n",
       " 'Big Trouble in Little China (1986)': 2325,\n",
       " \"Porky's II: The Next Day (1983)\": 2296,\n",
       " 'Love & Human Remains (1993)': 138,\n",
       " 'Hancock (2008)': 4141,\n",
       " 'Sex and Lucia (Lucía y el sexo) (2001)': 3000,\n",
       " 'Sixteen Candles (1984)': 1403,\n",
       " 'Apple Dumpling Gang Rides Again, The (1979)': 1301,\n",
       " 'Strange Days (1995)': 154,\n",
       " 'Broadcast News (1987)': 1456,\n",
       " 'Serious Man, A (2009)': 4278,\n",
       " 'Clockers (1995)': 120,\n",
       " \"Jane Austen's Mafia! (1998)\": 1312,\n",
       " 'Dreamlife of Angels, The (Vie rêvée des anges, La) (1998)': 1693,\n",
       " 'Step Up (2006)': 3911,\n",
       " 'Cujo (1983)': 1384,\n",
       " 'Boys and Girls (2000)': 2328,\n",
       " 'Parent Trap, The (1961)': 689,\n",
       " 'No Escape (1994)': 398,\n",
       " 'Mrs. Brown (a.k.a. Her Majesty, Mrs. Brown) (1997)': 1087,\n",
       " 'Powder (1995)': 23,\n",
       " 'Cradle Will Rock (1999)': 2034,\n",
       " 'Gilda (1946)': 1907,\n",
       " 'Pillow Talk (1959)': 3150,\n",
       " '10,000 BC (2008)': 4109,\n",
       " 'Rock, The (1996)': 531,\n",
       " 'Men with Guns (1997)': 1158,\n",
       " 'Beyond Rangoon (1995)': 116,\n",
       " 'Dead Man Walking (1995)': 34,\n",
       " 'Her (2013)': 4503,\n",
       " 'Family Stone, The (2005)': 3823,\n",
       " 'Harry Potter and the Half-Blood Prince (2009)': 4260,\n",
       " 'Curious Case of Benjamin Button, The (2008)': 4201,\n",
       " 'Mrs. Parker and the Vicious Circle (1994)': 295,\n",
       " 'Inside Job (2010)': 4355,\n",
       " 'Exit Through the Gift Shop (2010)': 4321,\n",
       " 'Simpsons Movie, The (2007)': 4028,\n",
       " 'Ladykillers, The (1955)': 3038,\n",
       " 'Elephant Man, The (1980)': 1498,\n",
       " 'Something to Talk About (1995)': 152,\n",
       " 'One False Move (1992)': 2320,\n",
       " 'Four Feathers, The (2002)': 3043,\n",
       " 'Kickboxer (1989)': 2709,\n",
       " 'High and Low (Tengoku to jigoku) (1963)': 3493,\n",
       " 'Phantoms (1998)': 1096,\n",
       " \"Hang 'Em High (1968)\": 1895,\n",
       " 'Groundhog Day (1993)': 865,\n",
       " 'Lolita (1962)': 1780,\n",
       " 'Farewell My Concubine (Ba wang bie ji) (1993)': 350,\n",
       " \"Lorenzo's Oil (1992)\": 3358,\n",
       " 'Breakfast Club, The (1985)': 1258,\n",
       " \"Hitchhiker's Guide to the Galaxy, The (2005)\": 3717,\n",
       " 'Primal Fear (1996)': 483,\n",
       " 'Paradise Lost: The Child Murders at Robin Hood Hills (1996)': 934,\n",
       " 'Hideaway (1995)': 188,\n",
       " 'MatchMaker, The (1997)': 1080,\n",
       " 'Lords of Dogtown (2005)': 3733,\n",
       " 'Saboteur (1942)': 1443,\n",
       " \"Weekend at Bernie's (1989)\": 744,\n",
       " 'Fargo (1996)': 471,\n",
       " 'Tigerland (2000)': 2419,\n",
       " 'Barry Lyndon (1975)': 1781,\n",
       " 'Splendor in the Grass (1961)': 2129,\n",
       " 'Amityville II: The Possession (1982)': 909,\n",
       " 'Quiet American, The (2002)': 3094,\n",
       " 'Planes, Trains & Automobiles (1987)': 2455,\n",
       " 'Apple Dumpling Gang, The (1975)': 683,\n",
       " 'In the Army Now (1994)': 373,\n",
       " 'Pitch Perfect (2012)': 4455,\n",
       " 'Sunset Blvd. (a.k.a. Sunset Boulevard) (1950)': 631,\n",
       " 'Bambi (1942)': 1303,\n",
       " 'Courage Under Fire (1996)': 490,\n",
       " 'Squid and the Whale, The (2005)': 3791,\n",
       " 'Mosquito Coast, The (1986)': 1784,\n",
       " 'K-9 (1989)': 2708,\n",
       " 'This Is Spinal Tap (1984)': 888,\n",
       " 'Star Wars: Episode III - Revenge of the Sith (2005)': 3726,\n",
       " 'Cry, the Beloved Country (1995)': 37,\n",
       " 'Saving Silverman (Evil Woman) (2001)': 2532,\n",
       " 'Caddyshack (1980)': 2241,\n",
       " \"Twelve O'Clock High (1949)\": 2611,\n",
       " 'Andalusian Dog, An (Chien andalou, Un) (1929)': 3623,\n",
       " 'Blow Out (1981)': 3072,\n",
       " 'Tuxedo, The (2002)': 3048,\n",
       " 'Patriot Games (1992)': 2091,\n",
       " 'Master of Disguise, The (2002)': 3009,\n",
       " 'Pretty Woman (1990)': 466,\n",
       " 'Tropic Thunder (2008)': 4155,\n",
       " 'Sudden Impact (1983)': 2839,\n",
       " 'Craft, The (1996)': 526,\n",
       " 'Manon of the Spring (Manon des sources) (1986)': 769,\n",
       " 'Majestic, The (2001)': 2858,\n",
       " 'Time Machine, The (2002)': 2915,\n",
       " 'Radioland Murders (1994)': 407,\n",
       " 'Accepted (2006)': 3914,\n",
       " 'Final Fantasy VII: Advent Children (2004)': 3785,\n",
       " 'Great Mouse Detective, The (1986)': 1323,\n",
       " 'Before Night Falls (2000)': 2472,\n",
       " 'Total Recall (2012)': 4453,\n",
       " 'Watchmen (2009)': 4147,\n",
       " 'Rain Man (1988)': 1251,\n",
       " 'Great White Hype, The (1996)': 527,\n",
       " '42 Up (1998)': 1996,\n",
       " 'Verdict, The (1982)': 1989,\n",
       " 'Lookout, The (2007)': 3989,\n",
       " \"She's the Man (2006)\": 3854,\n",
       " 'Deep End, The (2001)': 2752,\n",
       " 'Drowning Mona (2000)': 2124,\n",
       " 'Shakespeare in Love (1998)': 1566,\n",
       " 'Mrs. Winterbourne (1996)': 509,\n",
       " 'Ruling Class, The (1972)': 776,\n",
       " 'Crocodile Dundee in Los Angeles (2001)': 2577,\n",
       " 'Blame It on Rio (1984)': 1463,\n",
       " 'Longtime Companion (1990)': 2556,\n",
       " 'Bone Collector, The (1999)': 1950,\n",
       " 'Little Rascals, The (1994)': 451,\n",
       " 'Face in the Crowd, A (1957)': 1972,\n",
       " 'Stalingrad (1993)': 543,\n",
       " 'Nightcrawler (2014)': 4523,\n",
       " 'Entrapment (1999)': 1707,\n",
       " 'Divergent (2014)': 4505,\n",
       " 'Striking Distance (1993)': 435,\n",
       " 'Bridge on the River Kwai, The (1957)': 850,\n",
       " 'Navigator: A Mediaeval Odyssey, The (1988)': 1427,\n",
       " 'Ever After: A Cinderella Story (1998)': 1388,\n",
       " 'Mummy, The (1999)': 1715,\n",
       " 'Kurt & Courtney (1998)': 1185,\n",
       " 'Mad Max Beyond Thunderdome (1985)': 2306,\n",
       " \"She's the One (1996)\": 565,\n",
       " 'Fifth Element, The (1997)': 1024,\n",
       " 'Mummy, The (1932)': 1722,\n",
       " 'Assassins (1995)': 22,\n",
       " 'Night of the Comet (1984)': 1712,\n",
       " 'Formula 51 (2001)': 3064,\n",
       " 'Moneyball (2011)': 4419,\n",
       " 'F/X (1986)': 2338,\n",
       " 'Dawn of the Dead (2004)': 3435,\n",
       " 'Hitch (2005)': 3701,\n",
       " 'Before Sunrise (1995)': 166,\n",
       " 'Ghost in the Shell 2: Innocence (a.k.a. Innocence) (Inosensu) (2004)': 3653,\n",
       " 'My Beautiful Laundrette (1985)': 2963,\n",
       " 'Doors, The (1991)': 746,\n",
       " 'Bye Bye, Love (1995)': 468,\n",
       " 'Fantastic Mr. Fox (2009)': 4288,\n",
       " 'Inherit the Wind (1960)': 2191,\n",
       " 'Bride of Frankenstein, The (Bride of Frankenstein) (1935)': 917,\n",
       " 'Stalker (1979)': 833,\n",
       " 'Kid, The (1921)': 2121,\n",
       " 'Working Girl (1988)': 1457,\n",
       " 'Choke (2008)': 4205,\n",
       " 'Moonrise Kingdom (2012)': 4448,\n",
       " 'Trekkies (1997)': 1753,\n",
       " 'Big Fat Liar (2002)': 2893,\n",
       " 'When We Were Kings (1996)': 772,\n",
       " 'Muppet Christmas Carol, The (1992)': 1351,\n",
       " 'Elephant (2003)': 3320,\n",
       " 'Mud (2012)': 4477,\n",
       " 'Mark of Zorro, The (1940)': 648,\n",
       " 'Good Year, A (2006)': 3943,\n",
       " 'Radio Days (1987)': 1793,\n",
       " 'Blind Date (1987)': 2153,\n",
       " 'Cabin Boy (1994)': 335,\n",
       " \"Guess Who's Coming to Dinner (1967)\": 2183,\n",
       " 'Drive Me Crazy (1999)': 1872,\n",
       " \"America's Sweethearts (2001)\": 2718,\n",
       " 'Ruthless People (1986)': 1620,\n",
       " 'Men in Black III (M.III.B.) (M.I.B.³) (2012)': 4446,\n",
       " 'Night of the Hunter, The (1955)': 3359,\n",
       " 'Red Rock West (1992)': 299,\n",
       " 'Band of Brothers (2001)': 3462,\n",
       " 'Teen Wolf (1985)': 3368,\n",
       " 'Exorcist: The Beginning (2004)': 3577,\n",
       " 'Great Expectations (1998)': 1141,\n",
       " 'August Rush (2007)': 4068,\n",
       " 'Missing, The (2003)': 3331,\n",
       " 'Better Luck Tomorrow (2002)': 3181,\n",
       " 'Now You See Me (2013)': 4479,\n",
       " 'Let the Right One In (Låt den rätte komma in) (2008)': 4158,\n",
       " 'Snatch (2000)': 2463,\n",
       " 'RocketMan (a.k.a. Rocket Man) (1997)': 1090,\n",
       " 'In the Mouth of Madness (1995)': 315,\n",
       " 'Forrest Gump (1994)': 283,\n",
       " 'Happening, The (2008)': 4137,\n",
       " 'My Cousin Vinny (1992)': 1492,\n",
       " 'Children of the Damned (1963)': 1680,\n",
       " 'Epic Movie (2007)': 3966,\n",
       " 'Black Mirror (2011)': 4443,\n",
       " 'Firefox (1982)': 3140,\n",
       " 'American Movie (1999)': 1952,\n",
       " 'Blue Chips (1994)': 330,\n",
       " 'Driven (2001)': 2581,\n",
       " 'Ready to Wear (Pret-A-Porter) (1994)': 239,\n",
       " 'Lethal Weapon (1987)': 1287,\n",
       " 'Red Planet (2000)': 2437,\n",
       " 'How to Steal a Million (1966)': 3502,\n",
       " 'Tristram Shandy: A Cock and Bull Story (2005)': 3841,\n",
       " 'Maverick (1994)': 294,\n",
       " 'Days of Heaven (1978)': 1902,\n",
       " 'Ordinary People (1980)': 1246,\n",
       " 'Doom Generation, The (1995)': 127,\n",
       " 'Brothers Bloom, The (2008)': 4245,\n",
       " \"Grandma's Boy (2006)\": 3836,\n",
       " 'Rudy (1993)': 418,\n",
       " 'Frost/Nixon (2008)': 4197,\n",
       " 'Apollo 13 (1995)': 111,\n",
       " \"Knight's Tale, A (2001)\": 2596,\n",
       " 'Holiday Inn (1942)': 1984,\n",
       " 'Zero Effect (1998)': 1182,\n",
       " 'My Dinner with André (1981)': 3074,\n",
       " 'Master and Commander: The Far Side of the World (2003)': 3330,\n",
       " 'Winter War (Talvisota) (1989)': 3548,\n",
       " 'Hedwig and the Angry Inch (2000)': 2721,\n",
       " 'Invasion of the Body Snatchers (1978)': 3353,\n",
       " 'Restoration (1995)': 40,\n",
       " 'Necessary Roughness (1991)': 2559,\n",
       " 'Kingpin (1996)': 556,\n",
       " 'Love Actually (2003)': 3328,\n",
       " 'Fire Down Below (1997)': 1078,\n",
       " 'Shirley Valentine (1989)': 2727,\n",
       " 'Importance of Being Earnest, The (2002)': 2967,\n",
       " 'Gypsy (1962)': 2261,\n",
       " 'Be Kind Rewind (2008)': 4067,\n",
       " 'Guardian, The (2006)': 3931,\n",
       " 'Cops and Robbersons (1994)': 342,\n",
       " 'Salt (2010)': 4342,\n",
       " 'Jakob the Liar (1999)': 1869,\n",
       " 'Desperate Measures (1998)': 1056,\n",
       " 'Instinct (1999)': 1742,\n",
       " 'Twilight Samurai, The (Tasogare Seibei) (2002)': 3655,\n",
       " 'Eyes Wide Shut (1999)': 1765,\n",
       " 'Ben-Hur: A Tale of the Christ (1925)': 3347,\n",
       " 'Jeepers Creepers 2 (2003)': 3264,\n",
       " 'Jumanji (1995)': 1,\n",
       " 'Practical Magic (1998)': 1501,\n",
       " 'Spice World (1997)': 1149,\n",
       " 'Great Muppet Caper, The (1981)': 2155,\n",
       " 'Brazil (1985)': 802,\n",
       " 'Run Fatboy Run (2007)': 4108,\n",
       " 'Pumpkinhead (1988)': 2374,\n",
       " 'It Takes Two (1995)': 35,\n",
       " 'My Girl (1991)': 3119,\n",
       " 'Jeffrey (1995)': 131,\n",
       " 'Clear and Present Danger (1994)': 276,\n",
       " 'Seven (a.k.a. Se7en) (1995)': 44,\n",
       " 'Seven Brides for Seven Brothers (1954)': 3287,\n",
       " 'Hot Shots! (1991)': 3023,\n",
       " 'Fame (1980)': 2832,\n",
       " 'Frankie and Johnny (1991)': 2590,\n",
       " 'Sweet Hereafter, The (1997)': 1130,\n",
       " 'Z (1969)': 2998,\n",
       " \"Man Who Wasn't There, The (2001)\": 2811,\n",
       " 'Glass House, The (2001)': 2772,\n",
       " 'Mister Roberts (1955)': 1969,\n",
       " 'Dial M for Murder (1954)': 740,\n",
       " 'Spy Game (2001)': 2821,\n",
       " 'Fast, Cheap & Out of Control (1997)': 1093,\n",
       " 'Weird Science (1985)': 1393,\n",
       " 'Triumph of the Will (Triumph des Willens) (1934)': 2588,\n",
       " 'Reds (1981)': 1901,\n",
       " 'And the Band Played On (1993)': 1341,\n",
       " 'They Live (1988)': 2693,\n",
       " 'Carrie (1976)': 921,\n",
       " \"Look Who's Talking Too (1990)\": 3478,\n",
       " 'Moonstruck (1987)': 1992,\n",
       " 'Fantastic Planet, The (Planète sauvage, La) (1973)': 1638,\n",
       " 'Scooby-Doo 2: Monsters Unleashed (2004)': 3440,\n",
       " 'Silver Linings Playbook (2012)': 4465,\n",
       " 'Joe Dirt (2001)': 2574,\n",
       " 'Cherry 2000 (1987)': 2511,\n",
       " 'See No Evil, Hear No Evil (1989)': 2726,\n",
       " 'Dark City (1998)': 1144,\n",
       " 'Inglorious Bastards (Quel maledetto treno blindato) (1978)': 3993,\n",
       " 'Fresh (1994)': 359,\n",
       " 'American Pie 2 (2001)': 2749,\n",
       " 'Lovely Bones, The (2009)': 4294,\n",
       " 'Brother Bear (2003)': 3319,\n",
       " 'Infernal Affairs (Mou gaan dou) (2002)': 3497,\n",
       " 'Cobra (1986)': 3296,\n",
       " 'Houseguest (1994)': 193,\n",
       " 'Goodbye Girl, The (1977)': 2080,\n",
       " 'Silent Running (1972)': 2976,\n",
       " 'Vanya on 42nd Street (1994)': 264,\n",
       " 'Glimmer Man, The (1996)': 680,\n",
       " 'Old School (2003)': 3153,\n",
       " 'Regarding Henry (1991)': 3302,\n",
       " \"Porky's (1982)\": 2295,\n",
       " 'Scanners (1981)': 3091,\n",
       " 'Next Best Thing, The (2000)': 2125,\n",
       " '12 Years a Slave (2013)': 4493,\n",
       " \"Jumpin' Jack Flash (1986)\": 1622,\n",
       " 'Rocky IV (1985)': 1579,\n",
       " 'I Now Pronounce You Chuck and Larry (2007)': 4024,\n",
       " 'Devil in a Blue Dress (1995)': 125,\n",
       " 'Raising Arizona (1987)': 960,\n",
       " 'Dark Water (Honogurai mizu no soko kara) (2002)': 3501,\n",
       " 'Gate, The (1987)': 1610,\n",
       " 'King of New York (1990)': 1157,\n",
       " 'Series 7: The Contenders (2001)': 2538,\n",
       " 'Matilda (1996)': 580,\n",
       " 'Adventureland (2009)': 4231,\n",
       " 'Illusionist, The (2006)': 3915,\n",
       " 'Lincoln Lawyer, The (2011)': 4378,\n",
       " 'Rocky Balboa (2006)': 3952,\n",
       " 'Defiance (2008)': 4208,\n",
       " 'Cat People (1982)': 922,\n",
       " 'Dogfight (1991)': 3133,\n",
       " 'South Park: Bigger, Longer and Uncut (1999)': 1757,\n",
       " 'Crow: City of Angels, The (1996)': 582,\n",
       " 'Spirit, The (2008)': 4163,\n",
       " 'Syriana (2005)': 3808,\n",
       " 'Heaven & Earth (1993)': 366,\n",
       " 'Cowboy Bebop: The Movie (Cowboy Bebop: Tengoku no Tobira) (2001)': 3178,\n",
       " 'Jason X (2002)': 2953,\n",
       " 'Django Unchained (2012)': 4472,\n",
       " 'Batman Begins (2005)': 3736,\n",
       " 'Gods Must Be Crazy II, The (1989)': 1410,\n",
       " 'Moll Flanders (1996)': 492,\n",
       " 'Quiet Man, The (1952)': 828,\n",
       " 'Lifeforce (1985)': 1548,\n",
       " 'Wicker Park (2004)': 3581,\n",
       " ...}"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movie_to_idx"
   ]
  },
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   "cell_type": "code",
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   "metadata": {},
   "outputs": [],
   "source": []
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