vik-lesson5-movielens-explore.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "## Movielens"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "%reload_ext autoreload\n",
    "%autoreload 2\n",
    "%matplotlib inline\n",
    "\n",
    "from fastai.learner import *\n",
    "from fastai.column_data import *"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "Data available from http://files.grouplens.org/datasets/movielens/ml-latest-small.zip"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "path='data/movielens/'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "links.csv  models  movies.csv  ratings.csv  README.txt\ttags.csv  tmp\r\n"
     ]
    }
   ],
   "source": [
    "!ls {path}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "We're working with the movielens data, which contains one rating per row, like this:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
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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",
       "      <th>timestamp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "      <th>0</th>\n",
       "      <td>1</td>\n",
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       "      <td>2.5</td>\n",
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       "      <th>3</th>\n",
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       "  </tbody>\n",
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      "text/plain": [
       "   userId  movieId  rating   timestamp\n",
       "0       1       31     2.5  1260759144\n",
       "1       1     1029     3.0  1260759179\n",
       "2       1     1061     3.0  1260759182\n",
       "3       1     1129     2.0  1260759185\n",
       "4       1     1172     4.0  1260759205"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ratings = pd.read_csv(path+'ratings.csv')\n",
    "ratings.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "Just for display purposes, let's read in the movie names too."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
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       "<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",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Waiting to Exhale (1995)</td>\n",
       "      <td>Comedy|Drama|Romance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Father of the Bride Part II (1995)</td>\n",
       "      <td>Comedy</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      "text/plain": [
       "   movieId                               title  \\\n",
       "0        1                    Toy Story (1995)   \n",
       "1        2                      Jumanji (1995)   \n",
       "2        3             Grumpier Old Men (1995)   \n",
       "3        4            Waiting to Exhale (1995)   \n",
       "4        5  Father of the Bride Part II (1995)   \n",
       "\n",
       "                                        genres  \n",
       "0  Adventure|Animation|Children|Comedy|Fantasy  \n",
       "1                   Adventure|Children|Fantasy  \n",
       "2                               Comedy|Romance  \n",
       "3                         Comedy|Drama|Romance  \n",
       "4                                       Comedy  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movies = pd.read_csv(path+'movies.csv')\n",
    "movies.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "## Create subset for Excel"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "We create a crosstab of the most popular movies and most movie-addicted users which we'll copy into Excel for creating a simple example. This isn't necessary for any of the modeling below however."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "hidden": true
   },
   "outputs": [
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       "      <th>movieId</th>\n",
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      "text/plain": [
       "movieId  1     110   260   296   318   356   480   527   589   593   608   \\\n",
       "userId                                                                      \n",
       "15        2.0   3.0   5.0   5.0   2.0   1.0   3.0   4.0   4.0   5.0   5.0   \n",
       "30        4.0   5.0   4.0   5.0   5.0   5.0   4.0   5.0   4.0   4.0   5.0   \n",
       "73        5.0   4.0   4.5   5.0   5.0   5.0   4.0   5.0   3.0   4.5   4.0   \n",
       "212       3.0   5.0   4.0   4.0   4.5   4.0   3.0   5.0   3.0   4.0   NaN   \n",
       "213       3.0   2.5   5.0   NaN   NaN   2.0   5.0   NaN   4.0   2.5   2.0   \n",
       "294       4.0   3.0   4.0   NaN   3.0   4.0   4.0   4.0   3.0   NaN   NaN   \n",
       "311       3.0   3.0   4.0   3.0   4.5   5.0   4.5   5.0   4.5   2.0   4.0   \n",
       "380       4.0   5.0   4.0   5.0   4.0   5.0   4.0   NaN   4.0   5.0   4.0   \n",
       "452       3.5   4.0   4.0   5.0   5.0   4.0   5.0   4.0   4.0   5.0   5.0   \n",
       "468       4.0   3.0   3.5   3.5   3.5   3.0   2.5   NaN   NaN   3.0   4.0   \n",
       "509       3.0   5.0   5.0   5.0   4.0   4.0   3.0   5.0   2.0   4.0   4.5   \n",
       "547       3.5   NaN   NaN   5.0   5.0   2.0   3.0   5.0   NaN   5.0   5.0   \n",
       "564       4.0   1.0   2.0   5.0   NaN   3.0   5.0   4.0   5.0   5.0   5.0   \n",
       "580       4.0   4.5   4.0   4.5   4.0   3.5   3.0   4.0   4.5   4.0   4.5   \n",
       "624       5.0   NaN   5.0   5.0   NaN   3.0   3.0   NaN   3.0   5.0   4.0   \n",
       "\n",
       "movieId  1196  1198  1270  2571  \n",
       "userId                           \n",
       "15        5.0   4.0   5.0   5.0  \n",
       "30        4.0   5.0   5.0   3.0  \n",
       "73        5.0   5.0   5.0   4.5  \n",
       "212       NaN   3.0   3.0   5.0  \n",
       "213       5.0   3.0   3.0   4.0  \n",
       "294       4.0   4.5   4.0   4.5  \n",
       "311       3.0   4.5   4.5   4.0  \n",
       "380       4.0   NaN   3.0   5.0  \n",
       "452       4.0   4.0   4.0   2.0  \n",
       "468       3.0   3.5   3.0   3.0  \n",
       "509       5.0   5.0   3.0   4.5  \n",
       "547       2.5   2.0   3.5   3.5  \n",
       "564       5.0   5.0   3.0   3.0  \n",
       "580       4.0   3.5   3.0   4.5  \n",
       "624       5.0   5.0   5.0   2.0  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "g=ratings.groupby('userId')['rating'].count()\n",
    "topUsers=g.sort_values(ascending=False)[:15]\n",
    "\n",
    "g=ratings.groupby('movieId')['rating'].count()\n",
    "topMovies=g.sort_values(ascending=False)[:15]\n",
    "\n",
    "top_r = ratings.join(topUsers, rsuffix='_r', how='inner', on='userId')\n",
    "top_r = top_r.join(topMovies, rsuffix='_r', how='inner', on='movieId')\n",
    "\n",
    "pd.crosstab(top_r.userId, top_r.movieId, top_r.rating, aggfunc=np.sum)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "## Collaborative filtering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "hidden": true
   },
   "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>31</td>\n",
       "      <td>2.5</td>\n",
       "      <td>1260759144</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1029</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1260759179</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>1061</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1260759182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1129</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1260759185</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>1172</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1260759205</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   userId  movieId  rating   timestamp\n",
       "0       1       31     2.5  1260759144\n",
       "1       1     1029     3.0  1260759179\n",
       "2       1     1061     3.0  1260759182\n",
       "3       1     1129     2.0  1260759185\n",
       "4       1     1172     4.0  1260759205"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ratings.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "val_idxs = get_cv_idxs(len(ratings))\n",
    "wd=2e-4\n",
    "n_factors = 50"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 40 ms, sys: 0 ns, total: 40 ms\n",
      "Wall time: 41.4 ms\n"
     ]
    }
   ],
   "source": [
    "cf = CollabFilterDataset.from_csv(path, 'ratings.csv', 'userId', 'movieId', 'rating')\n",
    "%time learn = cf.get_learner(n_factors, val_idxs, 64, opt_fn=optim.Adam)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<fastai.column_data.CollabFilterDataset at 0x7fcf2c16e2b0>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(100004, 9125, 20000)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(ratings), len(movies), len(val_idxs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "hidden": true
   },
   "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>31</td>\n",
       "      <td>2.5</td>\n",
       "      <td>1260759144</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1029</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1260759179</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>1061</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1260759182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1129</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1260759185</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>1172</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1260759205</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   userId  movieId  rating   timestamp\n",
       "0       1       31     2.5  1260759144\n",
       "1       1     1029     3.0  1260759179\n",
       "2       1     1061     3.0  1260759182\n",
       "3       1     1129     2.0  1260759185\n",
       "4       1     1172     4.0  1260759205"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ratings.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "hidden": true
   },
   "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",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Waiting to Exhale (1995)</td>\n",
       "      <td>Comedy|Drama|Romance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Father of the Bride Part II (1995)</td>\n",
       "      <td>Comedy</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",
       "3        4            Waiting to Exhale (1995)   \n",
       "4        5  Father of the Bride Part II (1995)   \n",
       "\n",
       "                                        genres  \n",
       "0  Adventure|Animation|Children|Comedy|Fantasy  \n",
       "1                   Adventure|Children|Fantasy  \n",
       "2                               Comedy|Romance  \n",
       "3                         Comedy|Drama|Romance  \n",
       "4                                       Comedy  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movies.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "671"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(ratings.groupby('userId'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9125"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(movies.groupby('movieId'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9066"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(ratings.groupby('movieId').count())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "EmbeddingDotBias (\n",
       "  (u): Embedding(671, 50)\n",
       "  (i): Embedding(9066, 50)\n",
       "  (ub): Embedding(671, 1)\n",
       "  (ib): Embedding(9066, 1)\n",
       ")"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "learn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "6338e719df6e495d840c015b8df9a82f",
       "version_major": 2,
       "version_minor": 0
      },
      "text/html": [
       "<p>Failed to display Jupyter Widget of type <code>HBox</code>.</p>\n",
       "<p>\n",
       "  If you're reading this message in the Jupyter Notebook or JupyterLab Notebook, it may mean\n",
       "  that the widgets JavaScript is still loading. If this message persists, it\n",
       "  likely means that the widgets JavaScript library is either not installed or\n",
       "  not enabled. See the <a href=\"https://ipywidgets.readthedocs.io/en/stable/user_install.html\">Jupyter\n",
       "  Widgets Documentation</a> for setup instructions.\n",
       "</p>\n",
       "<p>\n",
       "  If you're reading this message in another frontend (for example, a static\n",
       "  rendering on GitHub or <a href=\"https://nbviewer.jupyter.org/\">NBViewer</a>),\n",
       "  it may mean that your frontend doesn't currently support widgets.\n",
       "</p>\n"
      ],
      "text/plain": [
       "HBox(children=(IntProgress(value=0, description='Epoch', max=1), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 71%|███████   | 884/1251 [00:04<00:02, 178.98it/s, loss=4.77]\n",
      "                                                              \r"
     ]
    }
   ],
   "source": [
    "lrf = learn.lr_find()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fcf29464fd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "learn.sched.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "hidden": true,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "27833dcf446443469b5ca3b8aad0ec0b",
       "version_major": 2,
       "version_minor": 0
      },
      "text/html": [
       "<p>Failed to display Jupyter Widget of type <code>HBox</code>.</p>\n",
       "<p>\n",
       "  If you're reading this message in the Jupyter Notebook or JupyterLab Notebook, it may mean\n",
       "  that the widgets JavaScript is still loading. If this message persists, it\n",
       "  likely means that the widgets JavaScript library is either not installed or\n",
       "  not enabled. See the <a href=\"https://ipywidgets.readthedocs.io/en/stable/user_install.html\">Jupyter\n",
       "  Widgets Documentation</a> for setup instructions.\n",
       "</p>\n",
       "<p>\n",
       "  If you're reading this message in another frontend (for example, a static\n",
       "  rendering on GitHub or <a href=\"https://nbviewer.jupyter.org/\">NBViewer</a>),\n",
       "  it may mean that your frontend doesn't currently support widgets.\n",
       "</p>\n"
      ],
      "text/plain": [
       "HBox(children=(IntProgress(value=0, description='Epoch', max=3), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 0.       0.84526  0.82863]                                    \n",
      "[ 1.       0.66     0.93668]                                    \n",
      "[ 2.       0.30057  0.89448]                                    \n",
      "\n",
      "CPU times: user 22.3 s, sys: 4.46 s, total: 26.7 s\n",
      "Wall time: 21.6 s\n"
     ]
    }
   ],
   "source": [
    "%time learn.fit(1e-2, 2, wds=wd, cycle_len=1, cycle_mult=2, use_wd_sched=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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LkKxU0pI8pCbqn0JEgkcVJUhOGJbV9HhwVioAG3YdZMOug/zro20ATBjYm6zURJYW7eam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\n/X9Z37arRXRCLAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fcf29a874a8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "learn.sched.plot_loss()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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Z9mngLuCkN9lN6YKxeasa1fEaKzYqnL9d3IH1uw7xzBfr3Y5jfMCbIVKuA+YA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\nAT8vJ7u4Ght+AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fcf2ce250f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "learn.sched.plot_lr()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "Let's compare to some benchmarks. Here's [some benchmarks](https://www.librec.net/release/v1.3/example.html) on the same dataset for the popular Librec system for collaborative filtering. They show best results based on [RMSE](http://www.statisticshowto.com/rmse/) of 0.91. We'll need to take the square root of our loss, since we use plain MSE."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9457695279506525"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "math.sqrt(0.89448)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "## This looks different than notebook"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "preds = learn.predict()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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F/3HdzfzRX1yFsZa//OMP8KtvfgMmivj0pz/NVVddxcqVK7n66qt5xSsuIV8s8bt/+lm+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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fcf2cde7ef0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y=learn.data.val_y\n",
    "sns.jointplot(preds, y, kind='hex', stat_func=None);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "## Analyze results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true
   },
   "source": [
    "### Movie bias"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "movie_names = movies.set_index('movieId')['title'].to_dict()\n",
    "g=ratings.groupby('movieId')['rating'].count()\n",
    "topMovies=g.sort_values(ascending=False).index.values[:3000]\n",
    "topMovieIdx = np.array([cf.item2idx[o] for o in topMovies])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9066"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(ratings.groupby('movieId')['rating'].count())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "hidden": true
   },
   "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",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   movieId             title                                       genres\n",
       "0        1  Toy Story (1995)  Adventure|Animation|Children|Comedy|Fantasy\n",
       "1        2    Jumanji (1995)                   Adventure|Children|Fantasy"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movies.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "## id to name mapping\n",
    "#movie_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "movieId name\n",
      "1 Toy Story (1995)\n"
     ]
    }
   ],
   "source": [
    "print('movieId name')\n",
    "for id, name in movie_names.items():\n",
    "    print('{} {}'.format(id, name))\n",
    "    break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "movieId\n",
       "1    247\n",
       "2    107\n",
       "3     59\n",
       "4     13\n",
       "5     56\n",
       "Name: rating, dtype: int64"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "g.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "movieId\n",
       "356    341\n",
       "296    324\n",
       "318    311\n",
       "593    304\n",
       "260    291\n",
       "Name: rating, dtype: int64"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "g.sort_values(ascending=False).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Int64Index([   356,    296,    318,    593,    260,    480,   2571,      1,\n",
       "               527,    589,\n",
       "            ...\n",
       "             26422,  26430,  26435,  26462,  26464,  26467,  26471,  26480,\n",
       "             26485, 163949],\n",
       "           dtype='int64', name='movieId', length=9066)"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "g.sort_values(ascending=False).index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([   356,    296,    318, ...,  26480,  26485, 163949])"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "g.sort_values(ascending=False).index.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  57,   49,   99, ...,  668, 4252, 4213])"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.array([cf.item2idx[o] for o in topMovies])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "EmbeddingDotBias (\n",
       "  (u): Embedding(671, 50)\n",
       "  (i): Embedding(9066, 50)\n",
       "  (ub): Embedding(671, 1)\n",
       "  (ib): Embedding(9066, 1)\n",
       ")"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m=learn.model; m"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "First, we'll look at the movie bias term. Here, our input is the movie id (a single id), and the output is the movie bias (a single float)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "movie_bias = to_np(m.ib(V(topMovieIdx)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 57,  49,  99,  92, 143])"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "topMovieIdx[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Variable containing:\n",
       " 0.7793\n",
       " 0.8230\n",
       " 1.1167\n",
       " 0.8440\n",
       " 0.7551\n",
       "[torch.cuda.FloatTensor of size 5x1 (GPU 0)]"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m.ib(V(topMovieIdx[:5]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "movie_ratings = [(b[0], movie_names[i]) for i,b in zip(topMovies,movie_bias)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(356, array([ 0.77934], dtype=float32)),\n",
       " (296, array([ 0.82304], dtype=float32)),\n",
       " (318, array([ 1.11669], dtype=float32)),\n",
       " (593, array([ 0.84399], dtype=float32)),\n",
       " (260, array([ 0.7551], dtype=float32))]"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[(i, b) for i,b in zip(topMovies[:5],movie_bias[:5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('Forrest Gump (1994)', 0.77933908),\n",
       " ('Pulp Fiction (1994)', 0.82304233),\n",
       " ('Shawshank Redemption, The (1994)', 1.1166852),\n",
       " ('Silence of the Lambs, The (1991)', 0.84398538),\n",
       " ('Star Wars: Episode IV - A New Hope (1977)', 0.75509918)]"
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[(movie_names[i], b[0]) for i,b in zip(topMovies[:5],movie_bias[:5])]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "Now we can look at the top and bottom rated movies. These ratings are corrected for different levels of reviewer sentiment, as well as different types of movies that different reviewers watch."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(-0.80548936, 'Super Mario Bros. (1993)'),\n",
       " (-0.73008245, 'Mighty Morphin Power Rangers: The Movie (1995)'),\n",
       " (-0.68448746, 'Bio-Dome (1996)'),\n",
       " (-0.67634869, 'Congo (1995)'),\n",
       " (-0.6697734, 'Speed 2: Cruise Control (1997)')]"
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sorted(movie_ratings, key=lambda x: x[0])[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(-0.80548936, 'Super Mario Bros. (1993)'),\n",
       " (-0.73008245, 'Mighty Morphin Power Rangers: The Movie (1995)'),\n",
       " (-0.68448746, 'Bio-Dome (1996)'),\n",
       " (-0.67634869, 'Congo (1995)'),\n",
       " (-0.6697734, 'Speed 2: Cruise Control (1997)'),\n",
       " (-0.66653156, 'Wild Wild West (1999)'),\n",
       " (-0.66581583, 'Inspector Gadget (1999)'),\n",
       " (-0.66526103, 'Jingle All the Way (1996)'),\n",
       " (-0.66465974, 'Richie Rich (1994)'),\n",
       " (-0.65527999, 'Battlefield Earth (2000)'),\n",
       " (-0.64745486, 'Batman & Robin (1997)'),\n",
       " (-0.61910999, 'Dungeons & Dragons (2000)'),\n",
       " (-0.61674351, 'Johnny Mnemonic (1995)'),\n",
       " (-0.60429972, \"Joe's Apartment (1996)\"),\n",
       " (-0.60089654, 'Godzilla (1998)')]"
      ]
     },
     "execution_count": 142,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sorted(movie_ratings, key=lambda o: o[0])[:15]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(-0.96070349, 'Battlefield Earth (2000)'),\n",
       " (-0.76858485, 'Speed 2: Cruise Control (1997)'),\n",
       " (-0.73675376, 'Wild Wild West (1999)'),\n",
       " (-0.73655486, 'Anaconda (1997)'),\n",
       " (-0.72457194, 'Super Mario Bros. (1993)'),\n",
       " (-0.69564718, 'Congo (1995)'),\n",
       " (-0.67043746, 'Superman III (1983)'),\n",
       " (-0.64385736, 'Mighty Morphin Power Rangers: The Movie (1995)'),\n",
       " (-0.62750411, \"Joe's Apartment (1996)\"),\n",
       " (-0.60154277, 'Police Academy 4: Citizens on Patrol (1987)'),\n",
       " (-0.59929478, 'Batman & Robin (1997)'),\n",
       " (-0.59667748, 'Jaws 3-D (1983)'),\n",
       " (-0.5921765, 'Dungeons & Dragons (2000)'),\n",
       " (-0.59074384, 'Inspector Gadget (1999)'),\n",
       " (-0.57559621, 'Haunting, The (1999)')]"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sorted(movie_ratings, key=itemgetter(0))[:15]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(1.1166852, 'Shawshank Redemption, The (1994)'),\n",
       " (0.96424037, 'Godfather, The (1972)'),\n",
       " (0.92390549, \"Schindler's List (1993)\"),\n",
       " (0.88776302, 'Fargo (1996)'),\n",
       " (0.85633016, 'Usual Suspects, The (1995)'),\n",
       " (0.85295206, 'African Queen, The (1951)'),\n",
       " (0.84398538, 'Silence of the Lambs, The (1991)'),\n",
       " (0.83642733, '12 Angry Men (1957)'),\n",
       " (0.8326174, 'Godfather: Part II, The (1974)'),\n",
       " (0.82773715, 'Her (2013)'),\n",
       " (0.82755011, 'Lord of the Rings: The Return of the King, The (2003)'),\n",
       " (0.82304233, 'Pulp Fiction (1994)'),\n",
       " (0.79711473, 'To Kill a Mockingbird (1962)'),\n",
       " (0.79591626, 'Fight Club (1999)'),\n",
       " (0.7890864, 'Ideal Husband, An (1999)')]"
      ]
     },
     "execution_count": 144,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sorted(movie_ratings, key=lambda o: o[0], reverse=True)[:15]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true
   },
   "source": [
    "### Embedding interpretation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "We can now do the same thing for the embeddings."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3000, 50)"
      ]
     },
     "execution_count": 145,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movie_emb = to_np(m.i(V(topMovieIdx)))\n",
    "movie_emb.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "Because it's hard to interpret 50 embeddings, we use [PCA](https://plot.ly/ipython-notebooks/principal-component-analysis/) to simplify them down to just 3 vectors. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "from sklearn.decomposition import PCA\n",
    "pca = PCA(n_components=3)\n",
    "movie_pca = pca.fit(movie_emb.T).components_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(50, 3000)"
      ]
     },
     "execution_count": 147,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movie_emb.T.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3, 3000)"
      ]
     },
     "execution_count": 149,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movie_pca.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "x = pca.fit(movie_emb.T)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PCA(copy=True, iterated_power='auto', n_components=3, random_state=None,\n",
       "  svd_solver='auto', tol=0.0, whiten=False)"
      ]
     },
     "execution_count": 152,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "fac0 = movie_pca[0]\n",
    "movie_comp = [(f, movie_names[i]) for f,i in zip(fac0, topMovies)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "Here's the 1st component. It seems to be 'easy watching' vs 'serious'."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(0.06406749, 'Godfather: Part II, The (1974)'),\n",
       " (0.061123442, 'Raging Bull (1980)'),\n",
       " (0.060424272, 'Godfather, The (1972)'),\n",
       " (0.05582723, 'Chinatown (1974)'),\n",
       " (0.052691575, 'Sunset Blvd. (a.k.a. Sunset Boulevard) (1950)'),\n",
       " (0.05216936, 'On the Waterfront (1954)'),\n",
       " (0.050915852, 'Papillon (1973)'),\n",
       " (0.050827861, 'Best Years of Our Lives, The (1946)'),\n",
       " (0.048372194, 'When We Were Kings (1996)'),\n",
       " (0.048335169, 'Steamboat Willie (1928)')]"
      ]
     },
     "execution_count": 154,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sorted(movie_comp, key=itemgetter(0), reverse=True)[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(-0.048598152, 'Police Academy 4: Citizens on Patrol (1987)'),\n",
       " (-0.046513684, 'Home Alone 3 (1997)'),\n",
       " (-0.045534093, 'Eragon (2006)'),\n",
       " (-0.04467405, 'Flintstones in Viva Rock Vegas, The (2000)'),\n",
       " (-0.044447906, 'Jack Frost (1998)'),\n",
       " (-0.044011995, 'Anaconda (1997)'),\n",
       " (-0.042837434, 'Police Academy: Mission to Moscow (1994)'),\n",
       " (-0.042489212, 'Original Kings of Comedy, The (2000)'),\n",
       " (-0.040218655, 'Police Academy 3: Back in Training (1986)'),\n",
       " (-0.040044438, 'Karate Kid, Part III, The (1989)')]"
      ]
     },
     "execution_count": 155,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sorted(movie_comp, key=itemgetter(0))[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "fac1 = movie_pca[1]\n",
    "movie_comp = [(f, movie_names[i]) for f,i in zip(fac1, topMovies)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "Here's the 2nd component. It seems to be 'CGI' vs 'dialog driven'."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(0.062417474, 'Great Dictator, The (1940)'),\n",
       " (0.054983199, 'Shutter Island (2010)'),\n",
       " (0.053738847, 'In Bruges (2008)'),\n",
       " (0.051847111, 'American Werewolf in Paris, An (1997)'),\n",
       " (0.051694069, 'The Amazing Spider-Man 2 (2014)'),\n",
       " (0.051555816, 'Unbearable Lightness of Being, The (1988)'),\n",
       " (0.051021799, 'Talk to Her (Hable con Ella) (2002)'),\n",
       " (0.050935511, 'Jackie Brown (1997)'),\n",
       " (0.049334493, 'Slumdog Millionaire (2008)'),\n",
       " (0.04864065, 'Right Stuff, The (1983)')]"
      ]
     },
     "execution_count": 157,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sorted(movie_comp, key=itemgetter(0), reverse=True)[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(-0.072888352, 'Amateur (1994)'),\n",
       " (-0.065193743, 'One False Move (1992)'),\n",
       " (-0.053718369, 'Clue (1985)'),\n",
       " (-0.053324044, '9 1/2 Weeks (Nine 1/2 Weeks) (1986)'),\n",
       " (-0.052652016,\n",
       "  'Double Life of Veronique, The (Double Vie de Véronique, La) (1991)'),\n",
       " (-0.051180635, 'Happiness (1998)'),\n",
       " (-0.05071203, \"Jumpin' Jack Flash (1986)\"),\n",
       " (-0.048877686, 'Chef (2014)'),\n",
       " (-0.047553718, 'Piano Teacher, The (La pianiste) (2001)'),\n",
       " (-0.047547352, 'Constantine (2005)')]"
      ]
     },
     "execution_count": 158,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sorted(movie_comp, key=itemgetter(0))[:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "We can draw a picture to see how various movies appear on the map of these components. This picture shows the first two components."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {
    "hidden": true,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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N5qGnnx6rvn3bqGXLKEkq8HnPnkMaNuxnZWfnqXr165SU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\n0YvHF61j+vpdxPl5EndysbyGBkQE8emNQ5g4cwnfT7qGm+OjKKquYdQXC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XEAACAASURB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cB0CuUHL6/C26dWoMgImJUZVta5JDrdaZiEplUrTlzNwViop+7SYm1e8uGxkZoijd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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fcf295d67b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "idxs = np.random.choice(len(topMovies), 50, replace=False)\n",
    "X = fac0[idxs]\n",
    "Y = fac1[idxs]\n",
    "plt.figure(figsize=(15,15))\n",
    "plt.scatter(X, Y)\n",
    "for i, x, y in zip(topMovies[idxs], X, Y):\n",
    "    plt.text(x,y,movie_names[i], color=np.random.rand(3)*0.7, fontsize=11)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Collab filtering from scratch"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "### Dot product example"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(\n",
       "  1  2\n",
       "  3  4\n",
       " [torch.FloatTensor of size 2x2], \n",
       "   2   2\n",
       "  10  10\n",
       " [torch.FloatTensor of size 2x2])"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = T([[1.,2],[3,4]])\n",
    "b = T([[2.,2],[10,10]])\n",
    "a,b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "  2   4\n",
       " 30  40\n",
       "[torch.FloatTensor of size 2x2]"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a*b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "  6\n",
       " 70\n",
       "[torch.FloatTensor of size 2]"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(a*b).sum(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {
    "collapsed": true,
    "hidden": true
   },
   "outputs": [],
   "source": [
    "class DotProduct(nn.Module):\n",
    "    def forward(self, u, m): return (u*m).sum(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {
    "collapsed": true,
    "hidden": true
   },
   "outputs": [],
   "source": [
    "model=DotProduct()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "  6\n",
       " 70\n",
       "[torch.FloatTensor of size 2]"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model(a,b)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "### Dot product model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true,
    "hidden": true
   },
   "outputs": [],
   "source": [
    "u_uniq = ratings.userId.unique()\n",
    "user2idx = {o:i for i,o in enumerate(u_uniq)}\n",
    "ratings.userId = ratings.userId.apply(lambda x: user2idx[x])\n",
    "\n",
    "m_uniq = ratings.movieId.unique()\n",
    "movie2idx = {o:i for i,o in enumerate(m_uniq)}\n",
    "ratings.movieId = ratings.movieId.apply(lambda x: movie2idx[x])\n",
    "\n",
    "n_users=int(ratings.userId.nunique())\n",
    "n_movies=int(ratings.movieId.nunique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true,
    "hidden": true
   },
   "outputs": [],
   "source": [
    "class EmbeddingDot(nn.Module):\n",
    "    def __init__(self, n_users, n_movies):\n",
    "        super().__init__()\n",
    "        self.u = nn.Embedding(n_users, n_factors)\n",
    "        self.m = nn.Embedding(n_movies, n_factors)\n",
    "        self.u.weight.data.uniform_(0,0.05)\n",
    "        self.m.weight.data.uniform_(0,0.05)\n",
    "        \n",
    "    def forward(self, cats, conts):\n",
    "        users,movies = cats[:,0],cats[:,1]\n",
    "        u,m = self.u(users),self.m(movies)\n",
    "        return (u*m).sum(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true,
    "hidden": true
   },
   "outputs": [],
   "source": [
    "x = ratings.drop(['rating', 'timestamp'],axis=1)\n",
    "y = ratings['rating'].astype(np.float32)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true,
    "hidden": true
   },
   "outputs": [],
   "source": [
    "data = ColumnarModelData.from_data_frame(path, val_idxs, x, y, ['userId', 'movieId'], 64)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true,
    "hidden": true
   },
   "outputs": [],
   "source": [
    "wd=1e-5\n",
    "model = EmbeddingDot(n_users, n_movies).cuda()\n",
    "opt = optim.SGD(model.parameters(), 1e-1, weight_decay=wd, momentum=0.9)"
   ]
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      "[ 0.      1.6368  1.6415]                                      \n",
      "[ 1.       1.13749  1.29373]                                   \n",
      "[ 2.       0.89736  1.22818]                                    \n",
      "\n"
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   "source": [
    "fit(model, data, 3, opt, F.mse_loss)"
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   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
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   "source": [
    "set_lrs(opt, 0.01)"
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   "cell_type": "code",
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   "metadata": {
    "hidden": true
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       "<p>Failed to display Jupyter Widget of type <code>HBox</code>.</p>\n",
       "<p>\n",
       "  If you're reading this message in the Jupyter Notebook or JupyterLab Notebook, it may mean\n",
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       "HBox(children=(IntProgress(value=0, description='Epoch', max=3), HTML(value='')))"
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      "[ 0.       0.69763  1.14979]                                    \n",
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      "\n"
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   "source": [
    "fit(model, data, 3, opt, F.mse_loss)"
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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Bias"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.5, 5.0)"
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    "min_rating,max_rating = ratings.rating.min(),ratings.rating.max()\n",
    "min_rating,max_rating"
   ]
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   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
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   "source": [
    "def get_emb(ni,nf):\n",
    "    e = nn.Embedding(ni, nf)\n",
    "    e.weight.data.uniform_(-0.01,0.01)\n",
    "    return e\n",
    "\n",
    "class EmbeddingDotBias(nn.Module):\n",
    "    def __init__(self, n_users, n_movies):\n",
    "        super().__init__()\n",
    "        (self.u, self.m, self.ub, self.mb) = [get_emb(*o) for o in [\n",
    "            (n_users, n_factors), (n_movies, n_factors), (n_users,1), (n_movies,1)\n",
    "        ]]\n",
    "        \n",
    "    def forward(self, cats, conts):\n",
    "        users,movies = cats[:,0],cats[:,1]\n",
    "        um = (self.u(users)* self.m(movies)).sum(1)\n",
    "        res = um + self.ub(users).squeeze() + self.mb(movies).squeeze()\n",
    "        res = F.sigmoid(res) * (max_rating-min_rating) + min_rating\n",
    "        return res"
   ]
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   "source": [
    "wd=2e-4\n",
    "model = EmbeddingDotBias(cf.n_users, cf.n_items).cuda()\n",
    "opt = optim.SGD(model.parameters(), 1e-1, weight_decay=wd, momentum=0.9)"
   ]
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   "cell_type": "code",
   "execution_count": 20,
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     "text": [
      "[ 0.       0.88212  0.83626]                                    \n",
      "[ 1.       0.8108   0.81831]                                    \n",
      "[ 2.       0.78864  0.80989]                                    \n",
      "\n"
     ]
    }
   ],
   "source": [
    "fit(model, data, 3, opt, F.mse_loss)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": true
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   "outputs": [],
   "source": [
    "set_lrs(opt, 1e-2)"
   ]
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  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
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     "text": [
      "[ 0.       0.72795  0.80337]                                    \n",
      "[ 1.       0.75064  0.80203]                                    \n",
      "[ 2.       0.75122  0.80124]                                    \n",
      "\n"
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    "fit(model, data, 3, opt, F.mse_loss)"
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  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Mini net"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "code_folding": [],
    "collapsed": true
   },
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   "source": [
    "class EmbeddingNet(nn.Module):\n",
    "    def __init__(self, n_users, n_movies, nh=10, p1=0.05, p2=0.5):\n",
    "        super().__init__()\n",
    "        (self.u, self.m) = [get_emb(*o) for o in [\n",
    "            (n_users, n_factors), (n_movies, n_factors)]]\n",
    "        self.lin1 = nn.Linear(n_factors*2, nh)\n",
    "        self.lin2 = nn.Linear(nh, 1)\n",
    "        self.drop1 = nn.Dropout(p1)\n",
    "        self.drop2 = nn.Dropout(p2)\n",
    "        \n",
    "    def forward(self, cats, conts):\n",
    "        users,movies = cats[:,0],cats[:,1]\n",
    "        x = self.drop1(torch.cat([self.u(users),self.m(movies)], dim=1))\n",
    "        x = self.drop2(F.relu(self.lin1(x)))\n",
    "        return F.sigmoid(self.lin2(x)) * (max_rating-min_rating+1) + min_rating-0.5"
   ]
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   "cell_type": "code",
   "execution_count": 54,
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   "outputs": [],
   "source": [
    "wd=1e-5\n",
    "model = EmbeddingNet(n_users, n_movies).cuda()\n",
    "opt = optim.Adam(model.parameters(), 1e-3, weight_decay=wd)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
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      "[ 0.       0.88043  0.82363]                                    \n",
      "[ 1.       0.8941   0.81264]                                    \n",
      "[ 2.       0.86179  0.80706]                                    \n",
      "\n"
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   "source": [
    "fit(model, data, 3, opt, F.mse_loss)"
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  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
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   "source": [
    "set_lrs(opt, 1e-3)"
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   "execution_count": 53,
   "metadata": {},
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      "[ 0.       0.7669   0.78622]                                    \n",
      "[ 1.       0.74277  0.78152]                                    \n",
      "[ 2.       0.69891  0.78075]                                    \n",
      "\n"
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   "source": [
    "fit(model, data, 3, opt, F.mse_loss)"
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  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
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   "source": []
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