dev_nbs/course/lesson4-collab.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from fastai.collab import *\n",
    "from fastai.tabular.all import *"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Collaborative filtering example"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`collab` models use data in a `DataFrame` of user, items, and ratings."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "user,item,title = 'userId','movieId','title'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PosixPath('/home/sgugger/.fastai/data/movie_lens_sample')"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "path = untar_data(URLs.ML_SAMPLE)\n",
    "path"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>userId</th>\n",
       "      <th>movieId</th>\n",
       "      <th>rating</th>\n",
       "      <th>timestamp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>73</td>\n",
       "      <td>1097</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1255504951</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>561</td>\n",
       "      <td>924</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1172695223</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>157</td>\n",
       "      <td>260</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1291598691</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>358</td>\n",
       "      <td>1210</td>\n",
       "      <td>5.0</td>\n",
       "      <td>957481884</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>130</td>\n",
       "      <td>316</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1138999234</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   userId  movieId  rating   timestamp\n",
       "0      73     1097     4.0  1255504951\n",
       "1     561      924     3.5  1172695223\n",
       "2     157      260     3.5  1291598691\n",
       "3     358     1210     5.0   957481884\n",
       "4     130      316     2.0  1138999234"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ratings = pd.read_csv(path/'ratings.csv')\n",
    "ratings.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "That's all we need to create and train a model:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "dls = CollabDataLoaders.from_df(ratings, bs=64, seed=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_range = [0,5.5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "learn = collab_learner(dls, n_factors=50, y_range=y_range)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: left;\">\n",
       "      <th>epoch</th>\n",
       "      <th>train_loss</th>\n",
       "      <th>valid_loss</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1.539011</td>\n",
       "      <td>0.819069</td>\n",
       "      <td>00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>0.843134</td>\n",
       "      <td>0.698604</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>0.656233</td>\n",
       "      <td>0.688964</td>\n",
       "      <td>00:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "learn.fit_one_cycle(3, 5e-3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Movielens 100k"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's try with the full Movielens 100k data dataset, available from http://files.grouplens.org/datasets/movielens/ml-100k.zip"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "path=Config().data/'ml-100k'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>userId</th>\n",
       "      <th>movieId</th>\n",
       "      <th>rating</th>\n",
       "      <th>timestamp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>196</td>\n",
       "      <td>242</td>\n",
       "      <td>3</td>\n",
       "      <td>881250949</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>186</td>\n",
       "      <td>302</td>\n",
       "      <td>3</td>\n",
       "      <td>891717742</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>22</td>\n",
       "      <td>377</td>\n",
       "      <td>1</td>\n",
       "      <td>878887116</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>244</td>\n",
       "      <td>51</td>\n",
       "      <td>2</td>\n",
       "      <td>880606923</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>166</td>\n",
       "      <td>346</td>\n",
       "      <td>1</td>\n",
       "      <td>886397596</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   userId  movieId  rating  timestamp\n",
       "0     196      242       3  881250949\n",
       "1     186      302       3  891717742\n",
       "2      22      377       1  878887116\n",
       "3     244       51       2  880606923\n",
       "4     166      346       1  886397596"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ratings = pd.read_csv(path/'u.data', delimiter='\\t', header=None,\n",
    "                      names=[user,item,'rating','timestamp'])\n",
    "ratings.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>movieId</th>\n",
       "      <th>title</th>\n",
       "      <th>date</th>\n",
       "      <th>N</th>\n",
       "      <th>url</th>\n",
       "      <th>g0</th>\n",
       "      <th>g1</th>\n",
       "      <th>g2</th>\n",
       "      <th>g3</th>\n",
       "      <th>g4</th>\n",
       "      <th>...</th>\n",
       "      <th>g9</th>\n",
       "      <th>g10</th>\n",
       "      <th>g11</th>\n",
       "      <th>g12</th>\n",
       "      <th>g13</th>\n",
       "      <th>g14</th>\n",
       "      <th>g15</th>\n",
       "      <th>g16</th>\n",
       "      <th>g17</th>\n",
       "      <th>g18</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Toy Story (1995)</td>\n",
       "      <td>01-Jan-1995</td>\n",
       "      <td>NaN</td>\n",
       "      <td>http://us.imdb.com/M/title-exact?Toy%20Story%20(1995)</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>GoldenEye (1995)</td>\n",
       "      <td>01-Jan-1995</td>\n",
       "      <td>NaN</td>\n",
       "      <td>http://us.imdb.com/M/title-exact?GoldenEye%20(1995)</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>Four Rooms (1995)</td>\n",
       "      <td>01-Jan-1995</td>\n",
       "      <td>NaN</td>\n",
       "      <td>http://us.imdb.com/M/title-exact?Four%20Rooms%20(1995)</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>Get Shorty (1995)</td>\n",
       "      <td>01-Jan-1995</td>\n",
       "      <td>NaN</td>\n",
       "      <td>http://us.imdb.com/M/title-exact?Get%20Shorty%20(1995)</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>Copycat (1995)</td>\n",
       "      <td>01-Jan-1995</td>\n",
       "      <td>NaN</td>\n",
       "      <td>http://us.imdb.com/M/title-exact?Copycat%20(1995)</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   movieId              title         date   N  \\\n",
       "0        1   Toy Story (1995)  01-Jan-1995 NaN   \n",
       "1        2   GoldenEye (1995)  01-Jan-1995 NaN   \n",
       "2        3  Four Rooms (1995)  01-Jan-1995 NaN   \n",
       "3        4  Get Shorty (1995)  01-Jan-1995 NaN   \n",
       "4        5     Copycat (1995)  01-Jan-1995 NaN   \n",
       "\n",
       "                                                      url  g0  g1  g2  g3  g4  \\\n",
       "0   http://us.imdb.com/M/title-exact?Toy%20Story%20(1995)   0   0   0   1   1   \n",
       "1     http://us.imdb.com/M/title-exact?GoldenEye%20(1995)   0   1   1   0   0   \n",
       "2  http://us.imdb.com/M/title-exact?Four%20Rooms%20(1995)   0   0   0   0   0   \n",
       "3  http://us.imdb.com/M/title-exact?Get%20Shorty%20(1995)   0   1   0   0   0   \n",
       "4       http://us.imdb.com/M/title-exact?Copycat%20(1995)   0   0   0   0   0   \n",
       "\n",
       "   ...  g9  g10  g11  g12  g13  g14  g15  g16  g17  g18  \n",
       "0  ...   0    0    0    0    0    0    0    0    0    0  \n",
       "1  ...   0    0    0    0    0    0    0    1    0    0  \n",
       "2  ...   0    0    0    0    0    0    0    1    0    0  \n",
       "3  ...   0    0    0    0    0    0    0    0    0    0  \n",
       "4  ...   0    0    0    0    0    0    0    1    0    0  \n",
       "\n",
       "[5 rows x 24 columns]"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movies = pd.read_csv(path/'u.item',  delimiter='|', encoding='latin-1', header=None,\n",
    "                    names=[item, 'title', 'date', 'N', 'url', *[f'g{i}' for i in range(19)]])\n",
    "movies.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "100000"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(ratings)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>userId</th>\n",
       "      <th>movieId</th>\n",
       "      <th>rating</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>title</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>196</td>\n",
       "      <td>242</td>\n",
       "      <td>3</td>\n",
       "      <td>881250949</td>\n",
       "      <td>Kolya (1996)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>63</td>\n",
       "      <td>242</td>\n",
       "      <td>3</td>\n",
       "      <td>875747190</td>\n",
       "      <td>Kolya (1996)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>226</td>\n",
       "      <td>242</td>\n",
       "      <td>5</td>\n",
       "      <td>883888671</td>\n",
       "      <td>Kolya (1996)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>154</td>\n",
       "      <td>242</td>\n",
       "      <td>3</td>\n",
       "      <td>879138235</td>\n",
       "      <td>Kolya (1996)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>306</td>\n",
       "      <td>242</td>\n",
       "      <td>5</td>\n",
       "      <td>876503793</td>\n",
       "      <td>Kolya (1996)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   userId  movieId  rating  timestamp         title\n",
       "0     196      242       3  881250949  Kolya (1996)\n",
       "1      63      242       3  875747190  Kolya (1996)\n",
       "2     226      242       5  883888671  Kolya (1996)\n",
       "3     154      242       3  879138235  Kolya (1996)\n",
       "4     306      242       5  876503793  Kolya (1996)"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rating_movie = ratings.merge(movies[[item, title]])\n",
    "rating_movie.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "dls = CollabDataLoaders.from_df(rating_movie, seed=42, valid_pct=0.1, bs=64, item_name=title, path=path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>userId</th>\n",
       "      <th>title</th>\n",
       "      <th>rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>695</td>\n",
       "      <td>Bean (1997)</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>373</td>\n",
       "      <td>River Wild, The (1994)</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>288</td>\n",
       "      <td>Liar Liar (1997)</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>94</td>\n",
       "      <td>Mars Attacks! (1996)</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>293</td>\n",
       "      <td>My Favorite Year (1982)</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>650</td>\n",
       "      <td>Beauty and the Beast (1991)</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>200</td>\n",
       "      <td>Terminator 2: Judgment Day (1991)</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>113</td>\n",
       "      <td>Conspiracy Theory (1997)</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>160</td>\n",
       "      <td>Down Periscope (1996)</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>59</td>\n",
       "      <td>Quiz Show (1994)</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dls.show_batch()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_range = [0,5.5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "learn = collab_learner(dls, n_factors=40, y_range=y_range)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "learn.lr_find()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: left;\">\n",
       "      <th>epoch</th>\n",
       "      <th>train_loss</th>\n",
       "      <th>valid_loss</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0.931693</td>\n",
       "      <td>0.953088</td>\n",
       "      <td>00:16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>0.840349</td>\n",
       "      <td>0.870622</td>\n",
       "      <td>00:16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>0.757327</td>\n",
       "      <td>0.820326</td>\n",
       "      <td>00:17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>0.655944</td>\n",
       "      <td>0.803768</td>\n",
       "      <td>00:16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>0.562621</td>\n",
       "      <td>0.803080</td>\n",
       "      <td>00:17</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "learn.fit_one_cycle(5, 5e-3,  wd=1e-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "learn.save('dotprod')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "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 of 0.91, which corresponds to an MSE of `0.91**2 = 0.83`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Interpretation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "learn.load('dotprod');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "EmbeddingDotBias(\n",
       "  (u_weight): Embedding(944, 40)\n",
       "  (i_weight): Embedding(1650, 40)\n",
       "  (u_bias): Embedding(944, 1)\n",
       "  (i_bias): Embedding(1650, 1)\n",
       ")"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "learn.model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Star Wars (1977)', 'Contact (1997)', 'Fargo (1996)',\n",
       "       'Return of the Jedi (1983)', 'Liar Liar (1997)',\n",
       "       'English Patient, The (1996)', 'Scream (1996)', 'Toy Story (1995)',\n",
       "       'Air Force One (1997)', 'Independence Day (ID4) (1996)'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "g = rating_movie.groupby('title')['rating'].count()\n",
    "top_movies = g.sort_values(ascending=False).index.values[:1000]\n",
    "top_movies[:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Movie bias"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([1000])"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movie_bias = learn.model.bias(top_movies, is_item=True)\n",
    "movie_bias.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "mean_ratings = rating_movie.groupby('title')['rating'].mean()\n",
    "movie_ratings = [(b, i, mean_ratings.loc[i]) for i,b in zip(top_movies,movie_bias)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "item0 = lambda o:o[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(tensor(-0.3480),\n",
       "  'Children of the Corn: The Gathering (1996)',\n",
       "  1.3157894736842106),\n",
       " (tensor(-0.3199),\n",
       "  'Lawnmower Man 2: Beyond Cyberspace (1996)',\n",
       "  1.7142857142857142),\n",
       " (tensor(-0.2892), 'Crow: City of Angels, The (1996)', 1.9487179487179487),\n",
       " (tensor(-0.2810), \"Joe's Apartment (1996)\", 2.2444444444444445),\n",
       " (tensor(-0.2784), 'Mortal Kombat: Annihilation (1997)', 1.9534883720930232),\n",
       " (tensor(-0.2743), 'Cable Guy, The (1996)', 2.339622641509434),\n",
       " (tensor(-0.2514), 'Home Alone 3 (1997)', 1.894736842105263),\n",
       " (tensor(-0.2509), 'Beautician and the Beast, The (1997)', 2.313953488372093),\n",
       " (tensor(-0.2329), 'Grease 2 (1982)', 2.0),\n",
       " (tensor(-0.2298), 'Bio-Dome (1996)', 1.903225806451613),\n",
       " (tensor(-0.2295), 'Kansas City (1996)', 2.260869565217391),\n",
       " (tensor(-0.2254), 'Island of Dr. Moreau, The (1996)', 2.1578947368421053),\n",
       " (tensor(-0.2178), 'Striptease (1996)', 2.2388059701492535),\n",
       " (tensor(-0.2178), 'Thinner (1996)', 2.4489795918367347),\n",
       " (tensor(-0.2099), 'Flintstones, The (1994)', 2.064516129032258)]"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sorted(movie_ratings, key=item0)[:15]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(tensor(0.5769), 'Titanic (1997)', 4.2457142857142856),\n",
       " (tensor(0.5740), 'Shawshank Redemption, The (1994)', 4.445229681978798),\n",
       " (tensor(0.5597), \"Schindler's List (1993)\", 4.466442953020135),\n",
       " (tensor(0.5412), 'L.A. Confidential (1997)', 4.161616161616162),\n",
       " (tensor(0.5299), 'Silence of the Lambs, The (1991)', 4.28974358974359),\n",
       " (tensor(0.5277), 'Star Wars (1977)', 4.3584905660377355),\n",
       " (tensor(0.5270), 'Rear Window (1954)', 4.3875598086124405),\n",
       " (tensor(0.5267), 'Apt Pupil (1998)', 4.1),\n",
       " (tensor(0.5005), 'As Good As It Gets (1997)', 4.196428571428571),\n",
       " (tensor(0.4942), 'Casablanca (1942)', 4.45679012345679),\n",
       " (tensor(0.4938), 'Close Shave, A (1995)', 4.491071428571429),\n",
       " (tensor(0.4844), 'Good Will Hunting (1997)', 4.262626262626263),\n",
       " (tensor(0.4650), 'Usual Suspects, The (1995)', 4.385767790262173),\n",
       " (tensor(0.4602), 'Godfather, The (1972)', 4.283292978208232),\n",
       " (tensor(0.4561), 'Vertigo (1958)', 4.251396648044692)]"
      ]
     },
     "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": [
    "### Movie weights"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([1000, 40])"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movie_w = learn.model.weight(top_movies, is_item=True)\n",
    "movie_w.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([1000, 3])"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movie_pca = movie_w.pca(3)\n",
    "movie_pca.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fac0,fac1,fac2 = movie_pca.t()\n",
    "movie_comp = [(f, i) for f,i in zip(fac0, top_movies)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(tensor(1.1449), 'Chinatown (1974)'),\n",
       " (tensor(1.0863), 'Close Shave, A (1995)'),\n",
       " (tensor(1.0594),\n",
       "  'Dr. Strangelove or: How I Learned to Stop Worrying and Love the Bomb (1963)'),\n",
       " (tensor(1.0126), 'Casablanca (1942)'),\n",
       " (tensor(0.9971), 'When We Were Kings (1996)'),\n",
       " (tensor(0.9898), 'Some Folks Call It a Sling Blade (1993)'),\n",
       " (tensor(0.9893), '12 Angry Men (1957)'),\n",
       " (tensor(0.9696), 'North by Northwest (1959)'),\n",
       " (tensor(0.9677), 'Lawrence of Arabia (1962)'),\n",
       " (tensor(0.9567), 'Ran (1985)')]"
      ]
     },
     "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": [
       "[(tensor(-1.2181), 'Home Alone 3 (1997)'),\n",
       " (tensor(-1.1975), 'Jungle2Jungle (1997)'),\n",
       " (tensor(-1.1753), 'Congo (1995)'),\n",
       " (tensor(-1.1661), 'Bio-Dome (1996)'),\n",
       " (tensor(-1.1285), 'Children of the Corn: The Gathering (1996)'),\n",
       " (tensor(-1.1258), \"McHale's Navy (1997)\"),\n",
       " (tensor(-1.0802), 'Free Willy 3: The Rescue (1997)'),\n",
       " (tensor(-1.0568), 'Batman & Robin (1997)'),\n",
       " (tensor(-1.0467), 'Leave It to Beaver (1997)'),\n",
       " (tensor(-1.0363), 'Mortal Kombat: Annihilation (1997)')]"
      ]
     },
     "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": [
    "movie_comp = [(f, i) for f,i in zip(fac1, top_movies)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(tensor(0.8935), 'Trainspotting (1996)'),\n",
       " (tensor(0.8681), 'Beavis and Butt-head Do America (1996)'),\n",
       " (tensor(0.8247), 'Exotica (1994)'),\n",
       " (tensor(0.8018), 'Brazil (1985)'),\n",
       " (tensor(0.7887), 'Clockwork Orange, A (1971)'),\n",
       " (tensor(0.7714), 'Jude (1996)'),\n",
       " (tensor(0.7573), 'Lost Highway (1997)'),\n",
       " (tensor(0.7393), 'Keys to Tulsa (1997)'),\n",
       " (tensor(0.7232), 'Cable Guy, The (1996)'),\n",
       " (tensor(0.7216), 'Pallbearer, The (1996)')]"
      ]
     },
     "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": [
       "[(tensor(-1.1362), 'Braveheart (1995)'),\n",
       " (tensor(-1.1113), 'Raiders of the Lost Ark (1981)'),\n",
       " (tensor(-1.0690), 'Titanic (1997)'),\n",
       " (tensor(-0.9674), 'American President, The (1995)'),\n",
       " (tensor(-0.8977), \"It's a Wonderful Life (1946)\"),\n",
       " (tensor(-0.8572), 'Sleepless in Seattle (1993)'),\n",
       " (tensor(-0.8516), 'Pretty Woman (1990)'),\n",
       " (tensor(-0.8425), 'Field of Dreams (1989)'),\n",
       " (tensor(-0.8065), 'Independence Day (ID4) (1996)'),\n",
       " (tensor(-0.7923), 'Back to the Future (1985)')]"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sorted(movie_comp, key=itemgetter(0))[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "idxs = np.random.choice(len(top_movies), 50, replace=False)\n",
    "idxs = list(range(50))\n",
    "X = fac0[idxs]\n",
    "Y = fac2[idxs]\n",
    "plt.figure(figsize=(15,15))\n",
    "plt.scatter(X, Y)\n",
    "for i, x, y in zip(top_movies[idxs], X, Y):\n",
    "    plt.text(x,y,i, color=np.random.rand(3)*0.7, fontsize=11)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "jupytext": {
   "split_at_heading": true
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}