KNN Recommendation.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from scipy.sparse import csr_matrix\n",
    "from sklearn.neighbors import NearestNeighbors\n",
    "import re"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "movies_df = pd.read_csv('movies.csv',usecols=['movieId','title'],dtype={'movieId': 'int32', 'title': 'str'})\n",
    "rating_df=pd.read_csv('ratings.csv',usecols=['userId', 'movieId', 'rating'],\n",
    "    dtype={'userId': 'int32', 'movieId': 'int32', 'rating': 'float32'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "   userId  movieId  rating             title\n",
       "0       1        1   4.000  Toy Story (1995)\n",
       "1       5        1   4.000  Toy Story (1995)\n",
       "2       7        1   4.500  Toy Story (1995)\n",
       "3      15        1   2.500  Toy Story (1995)\n",
       "4      17        1   4.500  Toy Story (1995)"
      ],
      "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>userId</th>\n      <th>movieId</th>\n      <th>rating</th>\n      <th>title</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1</td>\n      <td>1</td>\n      <td>4.000</td>\n      <td>Toy Story (1995)</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>5</td>\n      <td>1</td>\n      <td>4.000</td>\n      <td>Toy Story (1995)</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>7</td>\n      <td>1</td>\n      <td>4.500</td>\n      <td>Toy Story (1995)</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>15</td>\n      <td>1</td>\n      <td>2.500</td>\n      <td>Toy Story (1995)</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>17</td>\n      <td>1</td>\n      <td>4.500</td>\n      <td>Toy Story (1995)</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 37
    }
   ],
   "source": [
    "df = pd.merge(rating_df, movies_df, on='movieId')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "                                     title  totalRatingCount\n",
       "0                               '71 (2014)                 1\n",
       "1  'Hellboy': The Seeds of Creation (2004)                 1\n",
       "2                   'Round Midnight (1986)                 2\n",
       "3                      'Salem's Lot (2004)                 1\n",
       "4                'Til There Was You (1997)                 2"
      ],
      "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>title</th>\n      <th>totalRatingCount</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>'71 (2014)</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>'Hellboy': The Seeds of Creation (2004)</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>'Round Midnight (1986)</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>'Salem's Lot (2004)</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>'Til There Was You (1997)</td>\n      <td>2</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 38
    }
   ],
   "source": [
    "combine_movie_rating = df.dropna(axis = 0, subset = ['title'])\n",
    "movie_ratingCount = combine_movie_rating.groupby(['title'])['rating'].count()\n",
    "movie_ratingCount = movie_ratingCount.reset_index()\n",
    "movie_ratingCount = movie_ratingCount.rename(columns = {'rating': 'totalRatingCount'})\n",
    "movie_ratingCount = movie_ratingCount[['title', 'totalRatingCount']]\n",
    "\n",
    "movie_ratingCount.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "   userId  movieId  rating             title  totalRatingCount\n",
       "0       1        1   4.000  Toy Story (1995)               215\n",
       "1       5        1   4.000  Toy Story (1995)               215\n",
       "2       7        1   4.500  Toy Story (1995)               215\n",
       "3      15        1   2.500  Toy Story (1995)               215\n",
       "4      17        1   4.500  Toy Story (1995)               215"
      ],
      "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>userId</th>\n      <th>movieId</th>\n      <th>rating</th>\n      <th>title</th>\n      <th>totalRatingCount</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1</td>\n      <td>1</td>\n      <td>4.000</td>\n      <td>Toy Story (1995)</td>\n      <td>215</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>5</td>\n      <td>1</td>\n      <td>4.000</td>\n      <td>Toy Story (1995)</td>\n      <td>215</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>7</td>\n      <td>1</td>\n      <td>4.500</td>\n      <td>Toy Story (1995)</td>\n      <td>215</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>15</td>\n      <td>1</td>\n      <td>2.500</td>\n      <td>Toy Story (1995)</td>\n      <td>215</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>17</td>\n      <td>1</td>\n      <td>4.500</td>\n      <td>Toy Story (1995)</td>\n      <td>215</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 39
    }
   ],
   "source": [
    "rating_with_totalRatingCount = combine_movie_rating.merge(movie_ratingCount, left_on = 'title', right_on = 'title', how = 'left')\n",
    "rating_with_totalRatingCount.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "count   9719.000\nmean      10.375\nstd       22.406\nmin        1.000\n25%        1.000\n50%        3.000\n75%        9.000\nmax      329.000\nName: totalRatingCount, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "pd.set_option('display.float_format', lambda x: '%.3f' % x)\n",
    "print(movie_ratingCount['totalRatingCount'].describe())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "   userId  movieId  rating             title  totalRatingCount\n",
       "0       1        1   4.000  Toy Story (1995)               215\n",
       "1       5        1   4.000  Toy Story (1995)               215\n",
       "2       7        1   4.500  Toy Story (1995)               215\n",
       "3      15        1   2.500  Toy Story (1995)               215\n",
       "4      17        1   4.500  Toy Story (1995)               215"
      ],
      "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>userId</th>\n      <th>movieId</th>\n      <th>rating</th>\n      <th>title</th>\n      <th>totalRatingCount</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1</td>\n      <td>1</td>\n      <td>4.000</td>\n      <td>Toy Story (1995)</td>\n      <td>215</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>5</td>\n      <td>1</td>\n      <td>4.000</td>\n      <td>Toy Story (1995)</td>\n      <td>215</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>7</td>\n      <td>1</td>\n      <td>4.500</td>\n      <td>Toy Story (1995)</td>\n      <td>215</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>15</td>\n      <td>1</td>\n      <td>2.500</td>\n      <td>Toy Story (1995)</td>\n      <td>215</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>17</td>\n      <td>1</td>\n      <td>4.500</td>\n      <td>Toy Story (1995)</td>\n      <td>215</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 41
    }
   ],
   "source": [
    "popularity_threshold = 50\n",
    "rating_popular_movie = rating_with_totalRatingCount.query('totalRatingCount >= @popularity_threshold')\n",
    "rating_popular_movie.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "(41362, 5)"
      ]
     },
     "metadata": {},
     "execution_count": 42
    }
   ],
   "source": [
    "rating_popular_movie.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "userId                              1     2     3     4     5     6     7    \\\n",
       "title                                                                         \n",
       "10 Things I Hate About You (1999) 0.000 0.000 0.000 0.000 0.000 0.000 0.000   \n",
       "12 Angry Men (1957)               0.000 0.000 0.000 5.000 0.000 0.000 0.000   \n",
       "2001: A Space Odyssey (1968)      0.000 0.000 0.000 0.000 0.000 0.000 4.000   \n",
       "28 Days Later (2002)              0.000 0.000 0.000 0.000 0.000 0.000 0.000   \n",
       "300 (2007)                        0.000 0.000 0.000 0.000 0.000 0.000 0.000   \n",
       "\n",
       "userId                              8     9     10   ...   601   602   603  \\\n",
       "title                                                ...                     \n",
       "10 Things I Hate About You (1999) 0.000 0.000 0.000  ... 0.000 0.000 3.000   \n",
       "12 Angry Men (1957)               0.000 0.000 0.000  ... 5.000 0.000 0.000   \n",
       "2001: A Space Odyssey (1968)      0.000 0.000 0.000  ... 0.000 0.000 5.000   \n",
       "28 Days Later (2002)              0.000 0.000 0.000  ... 0.000 0.000 0.000   \n",
       "300 (2007)                        0.000 0.000 3.000  ... 0.000 0.000 0.000   \n",
       "\n",
       "userId                              604   605   606   607   608   609   610  \n",
       "title                                                                        \n",
       "10 Things I Hate About You (1999) 0.000 5.000 0.000 0.000 0.000 0.000 0.000  \n",
       "12 Angry Men (1957)               0.000 0.000 0.000 0.000 0.000 0.000 0.000  \n",
       "2001: A Space Odyssey (1968)      0.000 0.000 5.000 0.000 3.000 0.000 4.500  \n",
       "28 Days Later (2002)              0.000 0.000 0.000 0.000 3.500 0.000 5.000  \n",
       "300 (2007)                        0.000 3.000 0.000 0.000 5.000 0.000 4.000  \n",
       "\n",
       "[5 rows x 606 columns]"
      ],
      "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>userId</th>\n      <th>1</th>\n      <th>2</th>\n      <th>3</th>\n      <th>4</th>\n      <th>5</th>\n      <th>6</th>\n      <th>7</th>\n      <th>8</th>\n      <th>9</th>\n      <th>10</th>\n      <th>...</th>\n      <th>601</th>\n      <th>602</th>\n      <th>603</th>\n      <th>604</th>\n      <th>605</th>\n      <th>606</th>\n      <th>607</th>\n      <th>608</th>\n      <th>609</th>\n      <th>610</th>\n    </tr>\n    <tr>\n      <th>title</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>10 Things I Hate About You (1999)</th>\n      <td>0.000</td>\n      <td>0.000</td>\n      <td>0.000</td>\n      <td>0.000</td>\n      <td>0.000</td>\n      <td>0.000</td>\n      <td>0.000</td>\n      <td>0.000</td>\n      <td>0.000</td>\n      <td>0.000</td>\n      <td>...</td>\n      <td>0.000</td>\n      <td>0.000</td>\n      <td>3.000</td>\n      <td>0.000</td>\n      <td>5.000</td>\n      <td>0.000</td>\n      <td>0.000</td>\n      <td>0.000</td>\n      <td>0.000</td>\n      <td>0.000</td>\n    </tr>\n    <tr>\n      <th>12 Angry Men (1957)</th>\n      <td>0.000</td>\n      <td>0.000</td>\n      <td>0.000</td>\n      <td>5.000</td>\n      <td>0.000</td>\n      <td>0.000</td>\n      <td>0.000</td>\n      <td>0.000</td>\n      <td>0.000</td>\n      <td>0.000</td>\n      <td>...</td>\n      <td>5.000</td>\n      <td>0.000</td>\n      <td>0.000</td>\n      <td>0.000</td>\n      <td>0.000</td>\n      <td>0.000</td>\n      <td>0.000</td>\n      <td>0.000</td>\n      <td>0.000</td>\n      <td>0.000</td>\n    </tr>\n    <tr>\n      <th>2001: A Space Odyssey (1968)</th>\n      <td>0.000</td>\n      <td>0.000</td>\n      <td>0.000</td>\n      <td>0.000</td>\n      <td>0.000</td>\n      <td>0.000</td>\n      <td>4.000</td>\n      <td>0.000</td>\n      <td>0.000</td>\n      <td>0.000</td>\n      <td>...</td>\n      <td>0.000</td>\n      <td>0.000</td>\n      <td>5.000</td>\n      <td>0.000</td>\n      <td>0.000</td>\n      <td>5.000</td>\n      <td>0.000</td>\n      <td>3.000</td>\n      <td>0.000</td>\n      <td>4.500</td>\n    </tr>\n    <tr>\n      <th>28 Days Later (2002)</th>\n      <td>0.000</td>\n      <td>0.000</td>\n      <td>0.000</td>\n      <td>0.000</td>\n      <td>0.000</td>\n      <td>0.000</td>\n      <td>0.000</td>\n      <td>0.000</td>\n      <td>0.000</td>\n      <td>0.000</td>\n      <td>...</td>\n      <td>0.000</td>\n      <td>0.000</td>\n      <td>0.000</td>\n      <td>0.000</td>\n      <td>0.000</td>\n      <td>0.000</td>\n      <td>0.000</td>\n      <td>3.500</td>\n      <td>0.000</td>\n      <td>5.000</td>\n    </tr>\n    <tr>\n      <th>300 (2007)</th>\n      <td>0.000</td>\n      <td>0.000</td>\n      <td>0.000</td>\n      <td>0.000</td>\n      <td>0.000</td>\n      <td>0.000</td>\n      <td>0.000</td>\n      <td>0.000</td>\n      <td>0.000</td>\n      <td>3.000</td>\n      <td>...</td>\n      <td>0.000</td>\n      <td>0.000</td>\n      <td>0.000</td>\n      <td>0.000</td>\n      <td>3.000</td>\n      <td>0.000</td>\n      <td>0.000</td>\n      <td>5.000</td>\n      <td>0.000</td>\n      <td>4.000</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows × 606 columns</p>\n</div>"
     },
     "metadata": {},
     "execution_count": 43
    }
   ],
   "source": [
    "## First lets create a Pivot matrix\n",
    "\n",
    "movie_features_df=rating_popular_movie.pivot_table(index='title',columns='userId',values='rating').fillna(0)\n",
    "movie_features_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "NearestNeighbors(algorithm='brute', metric='cosine')"
      ]
     },
     "metadata": {},
     "execution_count": 44
    }
   ],
   "source": [
    "\n",
    "\n",
    "movie_features_df_matrix = csr_matrix(movie_features_df.values)\n",
    "\n",
    "model_knn = NearestNeighbors(metric = 'cosine', algorithm = 'brute')\n",
    "model_knn.fit(movie_features_df_matrix)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "(450, 606)"
      ]
     },
     "metadata": {},
     "execution_count": 45
    }
   ],
   "source": [
    "movie_features_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "'10 Things I Hate About You (1999)'"
      ]
     },
     "metadata": {},
     "execution_count": 46
    }
   ],
   "source": [
    "movie_features_df.index.to_series().head().values[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Recommendations for Wolf of Wall Street, The (2013):\n\n1: Django Unchained (2012), with distance of 0.4431077837944031:\n2: Dark Knight Rises, The (2012), with distance of 0.4578045606613159:\n3: Shutter Island (2010), with distance of 0.4606325030326843:\n4: Deadpool (2016), with distance of 0.4759785532951355:\n5: Grand Budapest Hotel, The (2014), with distance of 0.527129054069519:\n"
     ]
    }
   ],
   "source": [
    "query_index = -1\n",
    "titles = movie_features_df.index.to_series().values\n",
    "titles = np.array([re.sub(r'[^\\w]','',x).lower() if isinstance(x, str) else x for x in titles])\n",
    "while query_index == -1 or not query_index:\n",
    "    query_str = input(\"Enter movie title:\")\n",
    "    query_str = re.sub(r'[^\\w]','',query_str).lower()\n",
    "    query_index  = [i for i,s in enumerate(titles) if query_str in s]\n",
    "\n",
    "query_index = query_index[0]\n",
    "distances, indices = model_knn.kneighbors(movie_features_df.iloc[query_index,:].values.reshape(1, -1), n_neighbors = 6)\n",
    "\n",
    "\n",
    "for i in range(0, len(distances.flatten())):\n",
    "    if i == 0:\n",
    "        print('Recommendations for {0}:\\n'.format(movie_features_df.index[query_index]))\n",
    "    else:\n",
    "        print('{0}: {1}, with distance of {2}:'.format(i, movie_features_df.index[indices.flatten()[i]], distances.flatten()[i]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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