{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Important: This notebook will only work with fastai-0.7.x. Do not try to run any fastai-1.x code from this path in the repository because it will load fastai-0.7.x**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Movielens"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "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": {},
   "source": [
    "Data available from http://files.grouplens.org/datasets/movielens/ml-latest-small.zip"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "path='data/ml-latest-small/'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We're working with the movielens data, which contains one rating per row, like this:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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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>userId</th>\n",
       "      <th>movieId</th>\n",
       "      <th>rating</th>\n",
       "      <th>timestamp</th>\n",
       "    </tr>\n",
       "  </thead>\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": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ratings = pd.read_csv(path+'ratings.csv')\n",
    "ratings.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Just for display purposes, let's read in the movie names too."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<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",
       "    </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": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movies = pd.read_csv(path+'movies.csv')\n",
    "movies.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create subset for Excel"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "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": null,
   "metadata": {},
   "outputs": [
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      ],
      "text/plain": [
       "movieId  1     110   260   296   318   356   480   527   589   593   608   \\\n",
       "userId                                                                      \n",
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       "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": null,
     "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": {},
   "source": [
    "## Collaborative filtering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "val_idxs = get_cv_idxs(len(ratings))\n",
    "wd=2e-4\n",
    "n_factors = 50"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "cf = CollabFilterDataset.from_csv(path, 'ratings.csv', 'userId', 'movieId', 'rating')\n",
    "learn = cf.get_learner(n_factors, val_idxs, 64, opt_fn=optim.Adam)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c86a4a4147274defabddd02909d31a9f",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "A Jupyter Widget"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 0.       0.84581  0.82454]                                    \n",
      "[ 1.       0.75627  0.93568]                                    \n",
      "[ 2.       0.29527  0.89037]                                    \n",
      "\n"
     ]
    }
   ],
   "source": [
    "learn.fit(1e-2, 2, wds=wd, cycle_len=1, cycle_mult=2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "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": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8809086218218096"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "math.sqrt(0.776)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looking good - we've found a solution better than any of those benchmarks! Let's take a look at how the predictions compare to actuals for this model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "preds = learn.predict()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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J4/2f+y9+dMf9bDhpLV/+0B+xtL+bHTu287Y/+EM2btzI7/7um/m3L32JdDrNrbffwTv/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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9da9229320>"
      ]
     },
     "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": {},
   "source": [
    "## Analyze results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Movie bias"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "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": null,
   "metadata": {},
   "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": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m=learn.model; m.cuda()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "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": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "movie_bias = to_np(m.ib(V(topMovieIdx)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.80916],\n",
       "       [ 0.85248],\n",
       "       [ 1.13049],\n",
       "       ..., \n",
       "       [ 0.52353],\n",
       "       [-0.0782 ],\n",
       "       [ 0.33268]], dtype=float32)"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movie_bias"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "movie_ratings = [(b[0], movie_names[i]) for i,b in zip(topMovies,movie_bias)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "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": null,
   "metadata": {},
   "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": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sorted(movie_ratings, key=lambda o: o[0])[:15]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "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": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sorted(movie_ratings, key=itemgetter(0))[:15]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(1.3070084, 'Shawshank Redemption, The (1994)'),\n",
       " (1.1196285, 'Godfather, The (1972)'),\n",
       " (1.0844109, 'Usual Suspects, The (1995)'),\n",
       " (0.96578616, \"Schindler's List (1993)\"),\n",
       " (0.90921378, 'Silence of the Lambs, The (1991)'),\n",
       " (0.89407367, 'Godfather: Part II, The (1974)'),\n",
       " (0.87860429, '12 Angry Men (1957)'),\n",
       " (0.87099487, 'Pulp Fiction (1994)'),\n",
       " (0.85731125, 'Memento (2000)'),\n",
       " (0.85192037, 'Matrix, The (1999)'),\n",
       " (0.84797066, 'Dark Knight, The (2008)'),\n",
       " (0.8479442, 'To Kill a Mockingbird (1962)'),\n",
       " (0.83670187, 'Forrest Gump (1994)'),\n",
       " (0.8192088, 'Star Wars: Episode V - The Empire Strikes Back (1980)'),\n",
       " (0.81887919, 'Rear Window (1954)')]"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sorted(movie_ratings, key=lambda o: o[0], reverse=True)[:15]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Embedding interpretation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can now do the same thing for the embeddings."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3000, 50)"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movie_emb = to_np(m.i(V(topMovieIdx)))\n",
    "movie_emb.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "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": null,
   "metadata": {},
   "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": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3, 3000)"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movie_pca.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fac0 = movie_pca[0]\n",
    "movie_comp = [(f, movie_names[i]) for f,i in zip(fac0, topMovies)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here's the 1st component. It seems to be 'easy watching' vs 'serious'."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(0.06748189, 'Independence Day (a.k.a. ID4) (1996)'),\n",
       " (0.061572548, 'Police Academy 4: Citizens on Patrol (1987)'),\n",
       " (0.061050549, 'Waterworld (1995)'),\n",
       " (0.057877172, 'Rocky V (1990)'),\n",
       " (0.057183612, 'Home Alone 3 (1997)'),\n",
       " (0.056849808, 'Armageddon (1998)'),\n",
       " (0.056735475, 'Miss Congeniality (2000)'),\n",
       " (0.054530937, 'Outbreak (1995)'),\n",
       " (0.053475372, 'Evolution (2001)'),\n",
       " (0.052995622, 'Pay It Forward (2000)')]"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sorted(movie_comp, key=itemgetter(0), reverse=True)[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(-0.078433245, 'Godfather: Part II, The (1974)'),\n",
       " (-0.072180331, 'Fargo (1996)'),\n",
       " (-0.071351372, 'Pulp Fiction (1994)'),\n",
       " (-0.068537779, 'Goodfellas (1990)'),\n",
       " (-0.067418814, 'Chinatown (1974)'),\n",
       " (-0.066787124, 'Taxi Driver (1976)'),\n",
       " (-0.06392362, 'Apocalypse Now (1979)'),\n",
       " (-0.060093477, 'Brokeback Mountain (2005)'),\n",
       " (-0.057078246, 'Godfather, The (1972)'),\n",
       " (-0.055729419, 'Player, The (1992)')]"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sorted(movie_comp, key=itemgetter(0))[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fac1 = movie_pca[1]\n",
    "movie_comp = [(f, movie_names[i]) for f,i in zip(fac1, topMovies)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here's the 2nd component. It seems to be 'CGI' vs 'dialog driven'."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(0.058975246, 'Bonfire of the Vanities (1990)'),\n",
       " (0.055992026, '2001: A Space Odyssey (1968)'),\n",
       " (0.054682467, 'Tank Girl (1995)'),\n",
       " (0.054429606, 'Purple Rose of Cairo, The (1985)'),\n",
       " (0.050998077, 'Mulholland Drive (2001)'),\n",
       " (0.049576689, \"Joe's Apartment (1996)\"),\n",
       " (0.047549088, 'What Ever Happened to Baby Jane? (1962)'),\n",
       " (0.046446536, 'Island of Dr. Moreau, The (1996)'),\n",
       " (0.045140576, 'L.A. Story (1991)'),\n",
       " (0.045048587, 'Mouse Hunt (1997)')]"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sorted(movie_comp, key=itemgetter(0), reverse=True)[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(-0.1064609, 'Lord of the Rings: The Return of the King, The (2003)'),\n",
       " (-0.090635143, 'Aladdin (1992)'),\n",
       " (-0.089208141, 'Star Wars: Episode V - The Empire Strikes Back (1980)'),\n",
       " (-0.088854566, 'Star Wars: Episode IV - A New Hope (1977)'),\n",
       " (-0.085997969, 'Beauty and the Beast (1991)'),\n",
       " (-0.085541978, \"Schindler's List (1993)\"),\n",
       " (-0.080922142, 'Saving Private Ryan (1998)'),\n",
       " (-0.079378694,\n",
       "  'Raiders of the Lost Ark (Indiana Jones and the Raiders of the Lost Ark) (1981)'),\n",
       " (-0.079295151, 'Lord of the Rings: The Two Towers, The (2002)'),\n",
       " (-0.078875825, 'My Big Fat Greek Wedding (2002)')]"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sorted(movie_comp, key=itemgetter(0))[:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "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": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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6AjeGBR2Bd6+9ngH8zzAMwzTN7TfU2Qt4GYbhaZpmSg6MS0RE/mI3Pnngofhk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\nRERERETEicICEREREREREXGisEBEREREREREnCgsEBEREREREREnCgtERER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LjuNixUrj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YByVarzcNce1zyfbRtWO32q75evAH75ChAbdeKaujGnjlPv/hYAeHh6Ubp8RSI2r6dEUNls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\nYgwLJEmSJElSzP8P2jdA4S4Q5ocAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fcc3c05df28>"
      ]
     },
     "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": {},
   "source": [
    "### Dot product example"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "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": null,
     "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": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "  2   4\n",
       " 30  40\n",
       "[torch.FloatTensor of size 2x2]"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a*b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "  6\n",
       " 70\n",
       "[torch.FloatTensor of size 2]"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(a*b).sum(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "class DotProduct(nn.Module):\n",
    "    def forward(self, u, m): return (u*m).sum(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model=DotProduct()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "  6\n",
       " 70\n",
       "[torch.FloatTensor of size 2]"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model(a,b)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Dot product model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "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": null,
   "metadata": {},
   "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).view(-1, 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = ratings.drop(['rating', 'timestamp'],axis=1)\n",
    "y = ratings['rating'].astype(np.float32)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = ColumnarModelData.from_data_frame(path, val_idxs, x, y, ['userId', 'movieId'], 64)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "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)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "e5c39cb24aff416d9057d4e6f9d46d6a",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "A Jupyter Widget"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 0.      1.6368  1.6415]                                      \n",
      "[ 1.       1.13749  1.29373]                                   \n",
      "[ 2.       0.89736  1.22818]                                    \n",
      "\n"
     ]
    }
   ],
   "source": [
    "fit(model, data, 3, opt, F.mse_loss)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "set_lrs(opt, 0.01)"
   ]
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  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
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      "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"
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      "text/plain": [
       "HBox(children=(IntProgress(value=0, description='Epoch', max=3), HTML(value='')))"
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     "metadata": {},
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     "text": [
      "[ 0.       0.69763  1.14979]                                    \n",
      "[ 1.       0.70115  1.13657]                                    \n",
      "[ 2.       0.66739  1.1303 ]                                    \n",
      "\n"
     ]
    }
   ],
   "source": [
    "fit(model, data, 3, opt, F.mse_loss)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Bias"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.5, 5.0)"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "min_rating,max_rating = ratings.rating.min(),ratings.rating.max()\n",
    "min_rating,max_rating"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "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.view(-1, 1)"
   ]
  },
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   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "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)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
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       "A Jupyter Widget"
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     "metadata": {},
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    {
     "name": "stdout",
     "output_type": "stream",
     "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": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "set_lrs(opt, 1e-2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "model_id": "4ab6c0fd5887430b9b5f0cda8f8a1772",
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      "text/plain": [
       "A Jupyter Widget"
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     "metadata": {},
     "output_type": "display_data"
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 0.       0.72795  0.80337]                                    \n",
      "[ 1.       0.75064  0.80203]                                    \n",
      "[ 2.       0.75122  0.80124]                                    \n",
      "\n"
     ]
    }
   ],
   "source": [
    "fit(model, data, 3, opt, F.mse_loss)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Mini net"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "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": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
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       "A Jupyter Widget"
      ]
     },
     "metadata": {},
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 0.       0.88043  0.82363]                                    \n",
      "[ 1.       0.8941   0.81264]                                    \n",
      "[ 2.       0.86179  0.80706]                                    \n",
      "\n"
     ]
    }
   ],
   "source": [
    "fit(model, data, 3, opt, F.mse_loss)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "set_lrs(opt, 1e-3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
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       "A Jupyter Widget"
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     "output_type": "stream",
     "text": [
      "[ 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",
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   "outputs": [],
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