Recom_walk_through.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt \n",
    "import sys\n",
    "import pickle"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Read in the data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Load movies data\n",
    "movies = pd.read_csv('ml-20m/movies.csv')\n",
    "genome_scores = pd.read_csv('ml-20m/genome-scores.csv')\n",
    "tags = pd.read_csv('ml-20m/tags.csv')\n",
    "genome_tags = pd.read_csv('ml-20m/genome-tags.csv')\n",
    "#Use ratings data to downsample tags data to only movies with ratings \n",
    "ratings = pd.read_csv('ml-20m/ratings.csv')\n",
    "#ratings = ratings.drop_duplicates('movieId')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "movies.tail()\n",
    "movies['genres'] = movies['genres'].str.replace('|',' ')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "27278"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(movies.movieId.unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "26744"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(ratings.movieId.unique())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Filtering and cleaning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "#limit ratings to user ratings that have rated more that 55 movies -- \n",
    "#Otherwise it becomes impossible to pivot the rating dataframe later for collaborative filtering.\n",
    "\n",
    "ratings_f = ratings.groupby('userId').filter(lambda x: len(x) >= 55)\n",
    "\n",
    "# list the movie titles that survive the filtering\n",
    "movie_list_rating = ratings_f.movieId.unique().tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "97.85908057775497"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# no worries: we have kept 97% of the original movie titles in ratings data frame\n",
    "len(ratings_f.movieId.unique())/len(movies.movieId.unique()) * 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "57.797867040211415"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# but only 57% of the users \n",
    "len(ratings_f.userId.unique())/len(ratings.userId.unique()) * 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "#filter the movies data frame\n",
    "movies = movies[movies.movieId.isin(movie_list_rating)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>movieId</th>\n",
       "      <th>title</th>\n",
       "      <th>genres</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Toy Story (1995)</td>\n",
       "      <td>Adventure Animation Children Comedy Fantasy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Jumanji (1995)</td>\n",
       "      <td>Adventure Children Fantasy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Grumpier Old Men (1995)</td>\n",
       "      <td>Comedy Romance</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   movieId                    title  \\\n",
       "0        1         Toy Story (1995)   \n",
       "1        2           Jumanji (1995)   \n",
       "2        3  Grumpier Old Men (1995)   \n",
       "\n",
       "                                        genres  \n",
       "0  Adventure Animation Children Comedy Fantasy  \n",
       "1                   Adventure Children Fantasy  \n",
       "2                               Comedy Romance  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movies.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# map movie to id:\n",
    "Mapping_file = dict(zip(movies.title.tolist(), movies.movieId.tolist()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "tags.drop(['timestamp'],1, inplace=True)\n",
    "ratings_f.drop(['timestamp'],1, inplace=True)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Merge the movies and the tags data frame and create a metadata tag for each movie:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>movieId</th>\n",
       "      <th>title</th>\n",
       "      <th>genres</th>\n",
       "      <th>userId</th>\n",
       "      <th>tag</th>\n",
       "      <th>timestamp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Toy Story (1995)</td>\n",
       "      <td>Adventure Animation Children Comedy Fantasy</td>\n",
       "      <td>1644.0</td>\n",
       "      <td>Watched</td>\n",
       "      <td>1.417737e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>Toy Story (1995)</td>\n",
       "      <td>Adventure Animation Children Comedy Fantasy</td>\n",
       "      <td>1741.0</td>\n",
       "      <td>computer animation</td>\n",
       "      <td>1.183903e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>Toy Story (1995)</td>\n",
       "      <td>Adventure Animation Children Comedy Fantasy</td>\n",
       "      <td>1741.0</td>\n",
       "      <td>Disney animated feature</td>\n",
       "      <td>1.183933e+09</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   movieId             title                                       genres  \\\n",
       "0        1  Toy Story (1995)  Adventure Animation Children Comedy Fantasy   \n",
       "1        1  Toy Story (1995)  Adventure Animation Children Comedy Fantasy   \n",
       "2        1  Toy Story (1995)  Adventure Animation Children Comedy Fantasy   \n",
       "\n",
       "   userId                      tag     timestamp  \n",
       "0  1644.0                  Watched  1.417737e+09  \n",
       "1  1741.0       computer animation  1.183903e+09  \n",
       "2  1741.0  Disney animated feature  1.183933e+09  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# create a mixed dataframe of movies title, genres \n",
    "# and all user tags given to each movie\n",
    "mixed = pd.merge(movies, tags, on='movieId', how='left')\n",
    "mixed.head(3)"
   ]
  },
  {
   "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>movieId</th>\n",
       "      <th>title</th>\n",
       "      <th>metadata</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Toy Story (1995)</td>\n",
       "      <td>Watched computer animation Disney animated fea...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Jumanji (1995)</td>\n",
       "      <td>time travel adapted from:book board game child...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Grumpier Old Men (1995)</td>\n",
       "      <td>old people that is actually funny sequel fever...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   movieId                    title  \\\n",
       "0        1         Toy Story (1995)   \n",
       "1        2           Jumanji (1995)   \n",
       "2        3  Grumpier Old Men (1995)   \n",
       "\n",
       "                                            metadata  \n",
       "0  Watched computer animation Disney animated fea...  \n",
       "1  time travel adapted from:book board game child...  \n",
       "2  old people that is actually funny sequel fever...  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# create metadata from tags and genres\n",
    "mixed.fillna(\"\", inplace=True)\n",
    "mixed = pd.DataFrame(mixed.groupby('movieId')['tag'].apply(\n",
    "                                          lambda x: \"%s\" % ' '.join(x)))\n",
    "Final = pd.merge(movies, mixed, on='movieId', how='left')\n",
    "Final ['metadata'] = Final[['tag', 'genres']].apply(\n",
    "                                          lambda x: ' '.join(x), axis = 1)\n",
    "Final[['movieId','title','metadata']].head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Creating a content latent matrix from movie metadata:\n",
    "#### tf-idf vectors and truncated SVD :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(26694, 23704)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "tfidf = TfidfVectorizer(stop_words='english')\n",
    "tfidf_matrix = tfidf.fit_transform(Final['metadata'])\n",
    "tfidf_df = pd.DataFrame(tfidf_matrix.toarray(), index=Final.index.tolist())\n",
    "print(tfidf_df.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### The first 200 components explain over 50% of the variance:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Compress with SVD\n",
    "from sklearn.decomposition import TruncatedSVD\n",
    "svd = TruncatedSVD(n_components=200)\n",
    "latent_matrix = svd.fit_transform(tfidf_df)\n",
    "# plot var expalined to see what latent dimensions to use\n",
    "explained = svd.explained_variance_ratio_.cumsum()\n",
    "plt.plot(explained, '.-', ms = 16, color='red')\n",
    "plt.xlabel('Singular value components', fontsize= 12)\n",
    "plt.ylabel('Cumulative percent of variance', fontsize=12)        \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "#number of latent dimensions to keep\n",
    "n = 200 \n",
    "latent_matrix_1_df = pd.DataFrame(latent_matrix[:,0:n], index=Final.title.tolist())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(26694, 200)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# our content latent matrix:\n",
    "latent_matrix.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Creating a collaborative latent matrix from user ratings:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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>userId</th>\n",
       "      <th>movieId</th>\n",
       "      <th>rating</th>\n",
       "      <th>timestamp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1112486027</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>29</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1112484676</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>32</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1112484819</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>47</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1112484727</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>50</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1112484580</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   userId  movieId  rating   timestamp\n",
       "0       1        2     3.5  1112486027\n",
       "1       1       29     3.5  1112484676\n",
       "2       1       32     3.5  1112484819\n",
       "3       1       47     3.5  1112484727\n",
       "4       1       50     3.5  1112484580"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ratings_f.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "ratings_f1 = pd.merge(movies[['movieId']], ratings_f, on=\"movieId\", how=\"right\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "ratings_f2 = ratings_f1.pivot(index = 'movieId', columns ='userId', values = 'rating').fillna(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "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>userId</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>5</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>11</th>\n",
       "      <th>13</th>\n",
       "      <th>14</th>\n",
       "      <th>16</th>\n",
       "      <th>...</th>\n",
       "      <th>138474</th>\n",
       "      <th>138475</th>\n",
       "      <th>138477</th>\n",
       "      <th>138483</th>\n",
       "      <th>138484</th>\n",
       "      <th>138486</th>\n",
       "      <th>138487</th>\n",
       "      <th>138490</th>\n",
       "      <th>138492</th>\n",
       "      <th>138493</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>movieId</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>1</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.5</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</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",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 80046 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "userId   1       2       3       5       7       8       11      13      \\\n",
       "movieId                                                                   \n",
       "1           0.0     0.0     4.0     0.0     0.0     4.0     4.5     4.0   \n",
       "2           3.5     0.0     0.0     3.0     0.0     0.0     0.0     3.0   \n",
       "3           0.0     4.0     0.0     0.0     3.0     5.0     0.0     0.0   \n",
       "\n",
       "userId   14      16       ...    138474  138475  138477  138483  138484  \\\n",
       "movieId                   ...                                             \n",
       "1           4.5     3.0   ...       5.0     0.0     3.0     4.0     0.0   \n",
       "2           0.0     0.0   ...       4.0     0.0     0.0     3.0     3.0   \n",
       "3           0.0     0.0   ...       0.0     0.0     0.0     0.0     4.0   \n",
       "\n",
       "userId   138486  138487  138490  138492  138493  \n",
       "movieId                                          \n",
       "1           5.0     0.0     0.0     0.0     3.5  \n",
       "2           0.0     0.0     0.0     0.0     4.0  \n",
       "3           0.0     0.0     0.0     0.0     0.0  \n",
       "\n",
       "[3 rows x 80046 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ratings_f2.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "26694"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(ratings_f.movieId.unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.decomposition import TruncatedSVD\n",
    "svd = TruncatedSVD(n_components=200)\n",
    "latent_matrix_2 = svd.fit_transform(ratings_f2)\n",
    "latent_matrix_2_df = pd.DataFrame(\n",
    "                             latent_matrix_2,\n",
    "                             index=Final.title.tolist())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot variance expalined to see what latent dimensions to use\n",
    "explained = svd.explained_variance_ratio_.cumsum()\n",
    "plt.plot(explained, '.-', ms = 16, color='red')\n",
    "plt.xlabel('Singular value components', fontsize= 12)\n",
    "plt.ylabel('Cumulative percent of variance', fontsize=12)        \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Running a content / collaborative and hybrid cosine Similarity: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "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>content</th>\n",
       "      <th>collaborative</th>\n",
       "      <th>hybrid</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Burnt by the Sun (Utomlyonnye solntsem) (1994)</th>\n",
       "      <td>0.783687</td>\n",
       "      <td>0.347234</td>\n",
       "      <td>0.565460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Babette's Feast (Babettes gæstebud) (1987)</th>\n",
       "      <td>0.745094</td>\n",
       "      <td>0.610579</td>\n",
       "      <td>0.677836</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Purple Noon (Plein soleil) (1960)</th>\n",
       "      <td>0.730353</td>\n",
       "      <td>0.528732</td>\n",
       "      <td>0.629543</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mon Oncle (My Uncle) (1958)</th>\n",
       "      <td>0.662756</td>\n",
       "      <td>0.946326</td>\n",
       "      <td>0.804541</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sundays and Cybele (Les dimanches de Ville d'Avray) (1962)</th>\n",
       "      <td>0.640997</td>\n",
       "      <td>0.592128</td>\n",
       "      <td>0.616563</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kolya (Kolja) (1996)</th>\n",
       "      <td>0.621453</td>\n",
       "      <td>0.326573</td>\n",
       "      <td>0.474013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Nights of Cabiria (Notti di Cabiria, Le) (1957)</th>\n",
       "      <td>0.612939</td>\n",
       "      <td>0.834067</td>\n",
       "      <td>0.723503</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Moscow Does Not Believe in Tears (Moskva slezam ne verit) (1979)</th>\n",
       "      <td>0.611397</td>\n",
       "      <td>0.695872</td>\n",
       "      <td>0.653635</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Journey of Hope (Reise der Hoffnung) (1990)</th>\n",
       "      <td>0.608204</td>\n",
       "      <td>0.597546</td>\n",
       "      <td>0.602875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hamlet (1948)</th>\n",
       "      <td>0.592787</td>\n",
       "      <td>0.496560</td>\n",
       "      <td>0.544674</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Country Girl, The (1954)</th>\n",
       "      <td>0.590681</td>\n",
       "      <td>0.453769</td>\n",
       "      <td>0.522225</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                     content  collaborative  \\\n",
       "Burnt by the Sun (Utomlyonnye solntsem) (1994)      0.783687       0.347234   \n",
       "Babette's Feast (Babettes gæstebud) (1987)          0.745094       0.610579   \n",
       "Purple Noon (Plein soleil) (1960)                   0.730353       0.528732   \n",
       "Mon Oncle (My Uncle) (1958)                         0.662756       0.946326   \n",
       "Sundays and Cybele (Les dimanches de Ville d'Av...  0.640997       0.592128   \n",
       "Kolya (Kolja) (1996)                                0.621453       0.326573   \n",
       "Nights of Cabiria (Notti di Cabiria, Le) (1957)     0.612939       0.834067   \n",
       "Moscow Does Not Believe in Tears (Moskva slezam...  0.611397       0.695872   \n",
       "Journey of Hope (Reise der Hoffnung) (1990)         0.608204       0.597546   \n",
       "Hamlet (1948)                                       0.592787       0.496560   \n",
       "Country Girl, The (1954)                            0.590681       0.453769   \n",
       "\n",
       "                                                      hybrid  \n",
       "Burnt by the Sun (Utomlyonnye solntsem) (1994)      0.565460  \n",
       "Babette's Feast (Babettes gæstebud) (1987)          0.677836  \n",
       "Purple Noon (Plein soleil) (1960)                   0.629543  \n",
       "Mon Oncle (My Uncle) (1958)                         0.804541  \n",
       "Sundays and Cybele (Les dimanches de Ville d'Av...  0.616563  \n",
       "Kolya (Kolja) (1996)                                0.474013  \n",
       "Nights of Cabiria (Notti di Cabiria, Le) (1957)     0.723503  \n",
       "Moscow Does Not Believe in Tears (Moskva slezam...  0.653635  \n",
       "Journey of Hope (Reise der Hoffnung) (1990)         0.602875  \n",
       "Hamlet (1948)                                       0.544674  \n",
       "Country Girl, The (1954)                            0.522225  "
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics.pairwise import cosine_similarity\n",
    "# take the latent vectors for a selected movie from both content \n",
    "# and collaborative matrixes\n",
    "a_1 = np.array(latent_matrix_1_df.loc['Strada, La (1954)']).reshape(1, -1)\n",
    "a_2 = np.array(latent_matrix_2_df.loc['Strada, La (1954)']).reshape(1, -1)\n",
    "\n",
    "# calculate the similartity of this movie with the others in the list\n",
    "score_1 = cosine_similarity(latent_matrix_1_df, a_1).reshape(-1)\n",
    "score_2 = cosine_similarity(latent_matrix_2_df, a_2).reshape(-1)\n",
    "\n",
    "# an average measure of both content and collaborative \n",
    "hybrid = ((score_1 + score_2)/2.0)\n",
    "\n",
    "# form a data frame of similar movies \n",
    "dictDf = {'content': score_1 , 'collaborative': score_2, 'hybrid': hybrid} \n",
    "similar = pd.DataFrame(dictDf, index = latent_matrix_1_df.index )\n",
    "\n",
    "#sort it on the basis of either: content, collaborative or hybrid, \n",
    "# here : content\n",
    "similar.sort_values('content', ascending=False, inplace=True)\n",
    "\n",
    "similar[1:].head(11)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Use matrix factorization model:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from surprise import Dataset, Reader, SVD, accuracy\n",
    "from surprise.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE: 0.7728\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.7727642384605172"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# instantiate a reader and read in our rating data\n",
    "reader = Reader(rating_scale=(1, 5))\n",
    "data = Dataset.load_from_df(ratings_f[['userId','movieId','rating']], reader)\n",
    "\n",
    "# train SVD on 75% of known rates\n",
    "trainset, testset = train_test_split(data, test_size=.25)\n",
    "algorithm = SVD()\n",
    "algorithm.fit(trainset)\n",
    "predictions = algorithm.test(testset)\n",
    "\n",
    "# check the accuracy using Root Mean Square Error\n",
    "accuracy.rmse(predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "def pred_user_rating(ui):\n",
    "    if ui in ratings_f.userId.unique():\n",
    "        ui_list = ratings_f[ratings_f.userId == ui].movieId.tolist()\n",
    "        d = {k: v for k,v in Mapping_file.items() if not v in ui_list}        \n",
    "        predictedL = []\n",
    "        for i, j in d.items():     \n",
    "            predicted = algorithm.predict(ui, j)\n",
    "            predictedL.append((i, predicted[3])) \n",
    "        pdf = pd.DataFrame(predictedL, columns = ['movies', 'ratings'])\n",
    "        pdf.sort_values('ratings', ascending=False, inplace=True)  \n",
    "        pdf.set_index('movies', inplace=True)    \n",
    "        return pdf.head(10)        \n",
    "    else:\n",
    "        print(\"User Id does not exist in the list!\")\n",
    "        return None"
   ]
  },
  {
   "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",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ratings</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>movies</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Bleak House (2005)</th>\n",
       "      <td>4.850939</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Prime Suspect (1991)</th>\n",
       "      <td>4.795222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bill Hicks: Revelations (1993)</th>\n",
       "      <td>4.789093</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Children of Heaven, The (Bacheha-Ye Aseman) (1997)</th>\n",
       "      <td>4.723910</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Heimat - A Chronicle of Germany (Heimat - Eine deutsche Chronik) (1984)</th>\n",
       "      <td>4.718742</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Song of the Little Road (Pather Panchali) (1955)</th>\n",
       "      <td>4.693297</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Betrayal (1983)</th>\n",
       "      <td>4.684712</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wings of Desire (Himmel über Berlin, Der) (1987)</th>\n",
       "      <td>4.683682</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Witness for the Prosecution (1957)</th>\n",
       "      <td>4.674544</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Connections (1978)</th>\n",
       "      <td>4.674173</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                     ratings\n",
       "movies                                                      \n",
       "Bleak House (2005)                                  4.850939\n",
       "Prime Suspect (1991)                                4.795222\n",
       "Bill Hicks: Revelations (1993)                      4.789093\n",
       "Children of Heaven, The (Bacheha-Ye Aseman) (1997)  4.723910\n",
       "Heimat - A Chronicle of Germany (Heimat - Eine ...  4.718742\n",
       "Song of the Little Road (Pather Panchali) (1955)    4.693297\n",
       "Betrayal (1983)                                     4.684712\n",
       "Wings of Desire (Himmel über Berlin, Der) (1987)    4.683682\n",
       "Witness for the Prosecution (1957)                  4.674544\n",
       "Connections (1978)                                  4.674173"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "user_id = 7020\n",
    "pred_user_rating(user_id)\n"
   ]
  }
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