_notebooks/2021-09-01-chapter-8.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Chapter 8 - Collaborative Filtering Deep Dive \n",
    "> Deep Learning For Coders with fastai & Pytorch- Collaborative Filtering Deep Dive - Recommender systems works differently than classic DL classifiers. They are mostly used for known data, no prediction expected based on previously unknown data like bear classifier do. Yes, there is a generalization process but still, all data is known by the model. What is not known is latent factors at the beginning of the training. The model learn these latent factors and the recommender is ready.\n",
    "- toc: true \n",
    "- badges: true\n",
    "- comments: true\n",
    "- categories: [fastbook]\n",
    "- image: images/fastbook_images/chapter-08/my_dougther.jpg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![](images/chapter-08/my_dougther.jpg)\n",
    "This my daughter at the IKEA very close to our home."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "import fastbook\n",
    "fastbook.setup_book()\n",
    "from fastbook import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "%config Completer.use_jedi = False"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Collaborative filtering modules:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exploring the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "from fastai.collab import *\n",
    "from fastai.tabular.all import*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Downloading and extracting data from the URL list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "path = untar_data(URLs.ML_100k)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Giving columns names and readind first five rows."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "    .dataframe tbody tr th {\n",
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       "\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>user</th>\n",
       "      <th>movie</th>\n",
       "      <th>rating</th>\n",
       "      <th>timestamp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>196</td>\n",
       "      <td>242</td>\n",
       "      <td>3</td>\n",
       "      <td>881250949</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>186</td>\n",
       "      <td>302</td>\n",
       "      <td>3</td>\n",
       "      <td>891717742</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>22</td>\n",
       "      <td>377</td>\n",
       "      <td>1</td>\n",
       "      <td>878887116</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>244</td>\n",
       "      <td>51</td>\n",
       "      <td>2</td>\n",
       "      <td>880606923</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\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": [
       "   user  movie  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": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ratings = pd.read_csv(path/'u.data',delimiter = '\\t', header= None, engine='python',names=['user','movie','rating','timestamp'])\n",
    "ratings.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "How to recommend movies.\n",
    "Assume the movie has three properties, scince fiction(ness), action, old(ness).\n",
    "\n",
    "Last skywalker is a sci-fi, and action and not old."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## How model learn about our preferences"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "last_skywalker = np.array([0.98,0.9,-0.9])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And a user  who likes sci-fi and action movies and not so old movies would like this."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "user1= np.array([.9,.8,-.6])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If we multiply these two vectors and sum it. We get:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.1420000000000003"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(user1*last_skywalker).sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "this our matching score, it is a positive value that shows there is a match between the movie and the user1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "casablanka= np.array([-.99,-.33,.8])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-1.635"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(user1*casablanka).sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "this is low at this time. There is no match."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Latent Factors"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can pick arbitrary number of parameters for the array. Above, we use three. That could be much more of them. We call them **Latent Factors**. We start training  with random parameters  and learn from the ratings given by users. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## How to create Dataloaders"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> Note: It is not easy to use data as it was. For this dataset, movie id and movie title are not on the same table. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>movie</th>\n",
       "      <th>title</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Toy Story (1995)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>GoldenEye (1995)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Four Rooms (1995)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Get Shorty (1995)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Copycat (1995)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   movie              title\n",
       "0      1   Toy Story (1995)\n",
       "1      2   GoldenEye (1995)\n",
       "2      3  Four Rooms (1995)\n",
       "3      4  Get Shorty (1995)\n",
       "4      5     Copycat (1995)"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movies = pd.read_csv(path/'u.item', delimiter='|', engine= 'python',header=None,encoding='latin1', usecols=(0,1),names=('movie','title'))\n",
    "movies.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Let's bring ratings and movies together.** (movie id will be the key parameter)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user</th>\n",
       "      <th>movie</th>\n",
       "      <th>rating</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>title</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\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",
       "      <th>1</th>\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",
       "      <th>2</th>\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",
       "      <th>3</th>\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",
       "      <th>4</th>\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": [
       "   user  movie  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": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ratings=ratings.merge(movies)\n",
    "ratings.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For Dataloaders, we use `CollabDataLoaders` this Dataloader use first column for the user and second one for the `item`, in our situation we should change the default one because our `item` will be `title`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user</th>\n",
       "      <th>title</th>\n",
       "      <th>rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>581</td>\n",
       "      <td>Brassed Off (1996)</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>864</td>\n",
       "      <td>Jaws (1975)</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>873</td>\n",
       "      <td>Contact (1997)</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>58</td>\n",
       "      <td>Wings of Desire (1987)</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>497</td>\n",
       "      <td>Hard Target (1993)</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>892</td>\n",
       "      <td>Jungle Book, The (1994)</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>43</td>\n",
       "      <td>Santa Clause, The (1994)</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>751</td>\n",
       "      <td>Strictly Ballroom (1992)</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>894</td>\n",
       "      <td>Mighty Aphrodite (1995)</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>390</td>\n",
       "      <td>Spitfire Grill, The (1996)</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dls=CollabDataLoaders.from_df(ratings, item_name='title',bs=64)\n",
    "dls.show_batch()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(#15) ['#na#',1,2,3,4,5,6,7,8,9...]"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dls.classes['user'][:15]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(#15) ['#na#',\"'Til There Was You (1997)\",'1-900 (1994)','101 Dalmatians (1996)','12 Angry Men (1957)','187 (1997)','2 Days in the Valley (1996)','20,000 Leagues Under the Sea (1954)','2001: A Space Odyssey (1968)','3 Ninjas: High Noon At Mega Mountain (1998)'...]"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dls.classes['title'][:15]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "n_users = len(dls.classes['user'])\n",
    "n_movies = len(dls.classes['title'])\n",
    "n_factors = 5\n",
    "user_factors = torch.randn(n_users,n_factors)\n",
    "movie_factors = torch.randn(n_movies, n_factors)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## More PyTorch Less Python"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> Tip: This is how one_hot works."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([1, 0, 0, 0, 0], dtype=torch.uint8)"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "one_hot(0,5)"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "Let's create one_hot such that third index equal to one and the rest of them 0(lenght of number of users)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([0., 0., 0., 1., 0., 0., 0., 0., 0., 0.])"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "one_hot_3 = one_hot(3,n_users).float()\n",
    "one_hot_3[:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "and multiply by users_factors(matrix multiplication)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([ 0.4286,  0.8374, -0.5413, -1.6935,  0.1618])"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "user_factors.t() @ one_hot_3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This might look a bit daunting but it is not. Basically we want utilize pytorch more and python less. PyTorch very good at matrix multiplication, python is not. With this matrix multiplication we can access every index of the latent factor tensor in one move. Otherwise we would have use regular python loop and index which is very very slow."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is Python version:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([ 0.4286,  0.8374, -0.5413, -1.6935,  0.1618])"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "user_factors[3]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is same. Great."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Collaborative Filtering from Scratch"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "At this point there is a section regarding OOP if you want to learn OOP the check the original book  page 260 (3rd release) or the course [notebook](https://colab.research.google.com/github/fastai/fastbook/blob/master/08_collab.ipynb) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "class DotProduct(Module):\n",
    "    def __init__(self, n_users, n_movies, n_factors):\n",
    "        self.user_factors = Embedding(n_users, n_factors)\n",
    "        self.movie_factors = Embedding(n_movies, n_factors)\n",
    "        \n",
    "    def forward(self, x):\n",
    "        users = self.user_factors(x[:,0])\n",
    "        movies = self.movie_factors(x[:,1])\n",
    "        return (users * movies).sum(dim=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> Important: This forward method is a bit confusing but I guess what happens there is, `x` is merged df (it became part of the dls) from above so first column is user id and the second is movie id. check this part:\n",
    "```python\n",
    "ratings=ratings.merge(movies)\n",
    "ratings.head()\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([64, 2])"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x,y = dls.one_batch()\n",
    "x.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([804, 763])"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "first one is user id and the second is movie."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([5], dtype=torch.int8)"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "must be the rating."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Let's train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = DotProduct(n_users, n_movies, 50)\n",
    "learn = Learner(dls, model, loss_func=MSELossFlat())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "scrolled": true
   },
   "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>1.382019</td>\n",
       "      <td>1.291539</td>\n",
       "      <td>00:06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>1.064109</td>\n",
       "      <td>1.072716</td>\n",
       "      <td>00:06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>0.977546</td>\n",
       "      <td>0.980324</td>\n",
       "      <td>00:06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>0.869058</td>\n",
       "      <td>0.885319</td>\n",
       "      <td>00:06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>0.803102</td>\n",
       "      <td>0.871484</td>\n",
       "      <td>00:07</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)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "not bad but we can force our model to make predictions into range 0-5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "class DotProduct(Module):\n",
    "    def __init__(self, n_users, n_movies, n_factors, y_range=(0,5.5)):\n",
    "        self.user_factors = Embedding(n_users, n_factors)\n",
    "        self.movie_factors = Embedding(n_movies, n_factors)\n",
    "        self.y_range = y_range\n",
    "        \n",
    "    def forward(self, x):\n",
    "        users = self.user_factors(x[:,0])\n",
    "        movies = self.movie_factors(x[:,1])\n",
    "        return sigmoid_range((users * movies).sum(dim=1), *self.y_range)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The dls has values in this range as dependent variables (ratings) and there is a special method in the fastai(I assume) for that."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "doc(sigmoid_range)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "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>0.991997</td>\n",
       "      <td>0.972494</td>\n",
       "      <td>00:08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>0.856079</td>\n",
       "      <td>0.889023</td>\n",
       "      <td>00:08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>0.677160</td>\n",
       "      <td>0.858434</td>\n",
       "      <td>00:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>0.455940</td>\n",
       "      <td>0.864097</td>\n",
       "      <td>00:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>0.371842</td>\n",
       "      <td>0.868755</td>\n",
       "      <td>00:07</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model = DotProduct(n_users, n_movies, 50)\n",
    "learn = Learner(dls, model, loss_func=MSELossFlat())\n",
    "learn.fit_one_cycle(5, 5e-3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A little bit better results."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Bias"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Sometimes a user could give low  (or high) ratings based on his/her subjective preference even the others thinks that is a very good movie. Let's add a net parameter for that is `bias`. Bias effects all other parameters in negative or positive way."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "class DotProductBias(Module):\n",
    "    def __init__(self, n_users, n_movies, n_factors, y_range=(0,5.5)):\n",
    "        self.user_factors = Embedding(n_users, n_factors)\n",
    "        self.user_bias = Embedding(n_users, 1)\n",
    "        self.movie_factors = Embedding(n_movies, n_factors)\n",
    "        self.movie_bias = Embedding(n_movies, 1)\n",
    "        self.y_range = y_range\n",
    "        \n",
    "    def forward(self, x):\n",
    "        users = self.user_factors(x[:,0])\n",
    "        movies = self.movie_factors(x[:,1])\n",
    "        res = (users * movies).sum(dim=1, keepdim=True)\n",
    "        res += self.user_bias(x[:,0]) + self.movie_bias(x[:,1])\n",
    "        return sigmoid_range(res, *self.y_range)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "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>0.950526</td>\n",
       "      <td>0.923924</td>\n",
       "      <td>00:08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>0.811043</td>\n",
       "      <td>0.851933</td>\n",
       "      <td>00:08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>0.609098</td>\n",
       "      <td>0.852216</td>\n",
       "      <td>00:08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>0.400987</td>\n",
       "      <td>0.877794</td>\n",
       "      <td>00:08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>0.289632</td>\n",
       "      <td>0.884916</td>\n",
       "      <td>00:08</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model = DotProductBias(n_users, n_movies, 50)\n",
    "learn = Learner(dls, model, loss_func=MSELossFlat())\n",
    "learn.fit_one_cycle(5, 5e-3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "    And the training loss goes down faster and faster, but valid loss not so."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Weight Decay (or L2 regularization)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "from the book:\n",
    "\n",
    "Weight decay, or *L2 regularization*, consists in adding to your loss function the sum of all the weights squared. Why do that? Because when we compute the gradients, it will add a contribution to them that will encourage the weights to be as small as possible.**\n",
    "\n",
    "Why would it prevent overfitting? The idea is that the larger the coefficients are, the sharper canyons we will have in the loss function. If we take the basic example of a parabola, `y = a * (x**2)`, the larger `a` is, the more *narrow* the parabola is (<<parabolas>>).\n",
    "    \n",
    "So, letting our model learn high parameters might cause it to fit all the data points in the training set with an overcomplex function that has very sharp changes, which will lead to overfitting.\n",
    "\n",
    "Limiting our weights from growing too much is going to hinder the training of the model, but it will yield a state where it generalizes better. Going back to the theory briefly, weight decay (or just `wd`) is a parameter that controls that sum of squares we add to our loss (assuming `parameters` is a tensor of all parameters):\n",
    "\n",
    "``` python\n",
    "loss_with_wd = loss + wd * (parameters**2).sum()\n",
    "```\n",
    "\n",
    "In practice, though, it would be very inefficient (and maybe numerically unstable) to compute that big sum and add it to the loss. If you remember a little bit of high school math, you might recall that the derivative of `p**2` with respect to `p` is `2*p`, so adding that big sum to our loss is exactly the same as doing:\n",
    "\n",
    "``` python\n",
    "parameters.grad += wd * 2 * parameters\n",
    "```\n",
    "\n",
    "In practice, since `wd` is a parameter that we choose, we can just make it twice as big, so we don't even need the `*2` in this equation. To use weight decay in fastai, just pass `wd` in your call to `fit` or `fit_one_cycle`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = np.linspace(-2,2,100)\n",
    "a_s = [1,2,5,10,50] \n",
    "ys = [a * x**2 for a in a_s]\n",
    "_,ax = plt.subplots(figsize=(8,6))\n",
    "for a,y in zip(a_s,ys): ax.plot(x,y, label=f'a={a}')\n",
    "ax.set_ylim([0,5])\n",
    "ax.legend();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "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>0.977155</td>\n",
       "      <td>0.931246</td>\n",
       "      <td>00:08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>0.870503</td>\n",
       "      <td>0.858914</td>\n",
       "      <td>00:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>0.746876</td>\n",
       "      <td>0.823343</td>\n",
       "      <td>00:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>0.573917</td>\n",
       "      <td>0.810015</td>\n",
       "      <td>00:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>0.480767</td>\n",
       "      <td>0.810702</td>\n",
       "      <td>00:07</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model = DotProductBias(n_users, n_movies, 50)\n",
    "learn = Learner(dls, model, loss_func=MSELossFlat())\n",
    "learn.fit_one_cycle(5, 5e-3, wd=0.1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Not so good traing loss but at this time validation loss is far better."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Creating Our Own Embedding Module"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(#0) []"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "class T(Module):\n",
    "    def __init__(self): self.a = torch.ones(3)\n",
    "\n",
    "L(T().parameters())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There is  no pararameters, by its definition parameters must be trainable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Tensor"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(torch.ones(3)[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> Note: this is tensor not parameter.So it is not trainable. No gradiend tracking."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "from the book:\n",
    "\n",
    "To tell `Module` that we want to treat a tensor as a parameter, we have to wrap it in the `nn.Parameter` class. This class doesn't actually add any functionality (other than automatically calling `requires_grad_` for us). It's only used as a \"marker\" to show what to include in `parameters`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(#1) [Parameter containing:\n",
       "tensor([1., 1., 1.], requires_grad=True)]"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "class T(Module):\n",
    "    def __init__(self): self.a = nn.Parameter(torch.ones(3))\n",
    "\n",
    "L(T().parameters())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "and"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(#1) [Parameter containing:\n",
       "tensor([[-0.0643],\n",
       "        [-0.8105],\n",
       "        [ 0.1346]], requires_grad=True)]"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "class T(Module):\n",
    "    def __init__(self): self.a = nn.Linear(1, 3, bias=False)\n",
    "\n",
    "t = T()\n",
    "L(t.parameters())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.nn.parameter.Parameter"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(t.a.weight)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> Note: This is a  parameter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Tensor"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(t.a.weight.data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> Note: This is not."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Custom Embedding without using Pytorch ``Embedding``"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_params(size):\n",
    "    return nn.Parameter(torch.zeros(*size).normal_(0, 0.01))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "doc(create_params)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "class DotProductBias(Module):\n",
    "    def __init__(self, n_users, n_movies, n_factors, y_range=(0,5.5)):\n",
    "        self.user_factors = create_params([n_users, n_factors])\n",
    "        self.user_bias = create_params([n_users])\n",
    "        self.movie_factors = create_params([n_movies, n_factors])\n",
    "        self.movie_bias = create_params([n_movies])\n",
    "        self.y_range = y_range\n",
    "        \n",
    "    def forward(self, x):\n",
    "        users = self.user_factors[x[:,0]]\n",
    "        movies = self.movie_factors[x[:,1]]\n",
    "        res = (users*movies).sum(dim=1)\n",
    "        res += self.user_bias[x[:,0]] + self.movie_bias[x[:,1]]\n",
    "        return sigmoid_range(res, *self.y_range)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "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>0.925678</td>\n",
       "      <td>0.936475</td>\n",
       "      <td>00:08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>0.820004</td>\n",
       "      <td>0.864908</td>\n",
       "      <td>00:08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>0.718156</td>\n",
       "      <td>0.818959</td>\n",
       "      <td>00:08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>0.589835</td>\n",
       "      <td>0.812693</td>\n",
       "      <td>00:08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>0.462965</td>\n",
       "      <td>0.813873</td>\n",
       "      <td>00:08</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model = DotProductBias(n_users, n_movies, 50)\n",
    "learn = Learner(dls, model, loss_func=MSELossFlat())\n",
    "learn.fit_one_cycle(5, 5e-3, wd=0.1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Very similiar results."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Interpreting Embeddings and Biases"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lowest biases in the model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Children of the Corn: The Gathering (1996)',\n",
       " 'Crow: City of Angels, The (1996)',\n",
       " 'Jury Duty (1995)',\n",
       " 'Mortal Kombat: Annihilation (1997)',\n",
       " 'Cable Guy, The (1996)']"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movie_bias=learn.model.movie_bias.squeeze()\n",
    "idxs=movie_bias.argsort()[0:5]\n",
    "[dls.classes['title'][i] for i in idxs]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "from the book:\n",
    "\n",
    "Think about what this means. What it's saying is that for each of these movies, even when a user is very well matched to its latent factors (which, as we will see in a moment, tend to represent things like level of action, age of movie, and so forth), they still generally don't like it. We could have simply sorted the movies directly by their average rating, but looking at the learned bias tells us something much more interesting. It tells us not just whether a movie is of a kind that people tend not to enjoy watching, but that people tend not to like watching it even if it is of a kind that they would otherwise enjoy! By the same token, here are the movies with the highest bias:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Titanic (1997)',\n",
       " \"Schindler's List (1993)\",\n",
       " 'Star Wars (1977)',\n",
       " 'Shawshank Redemption, The (1994)',\n",
       " 'Rear Window (1954)']"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "idxs = movie_bias.argsort(descending=True)[:5]\n",
    "[dls.classes['title'][i] for i in idxs]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "from the book:\n",
    "\n",
    "So, for instance, even if you don't normally enjoy detective movies, you might enjoy *LA Confidential*!\n",
    "\n",
    "It is not quite so easy to directly interpret the embedding matrices. There are just too many factors for a human to look at. But there is a technique that can pull out the most important underlying *directions* in such a matrix, called *principal component analysis* (PCA). We will not be going into this in detail in this book, because it is not particularly important for you to understand to be a deep learning practitioner, but if you are interested then we suggest you check out the fast.ai course [Computational Linear Algebra for Coders](https://github.com/fastai/numerical-linear-algebra). <<img_pca_movie>> shows what our movies look like based on two of the strongest PCA components."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x864 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = ratings.groupby('title')['rating'].count()\n",
    "top_movies = g.sort_values(ascending=False).index.values[:1000]\n",
    "top_idxs = tensor([learn.dls.classes['title'].o2i[m] for m in top_movies])\n",
    "movie_w = learn.model.movie_factors[top_idxs].cpu().detach()\n",
    "movie_pca = movie_w.pca(3)\n",
    "fac0,fac1,fac2 = movie_pca.t()\n",
    "idxs = list(range(50))\n",
    "X = fac0[idxs]\n",
    "Y = fac2[idxs]\n",
    "plt.figure(figsize=(12,12))\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": "markdown",
   "metadata": {},
   "source": [
    "Lets try changing X axis.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x864 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = ratings.groupby('title')['rating'].count()\n",
    "top_movies = g.sort_values(ascending=False).index.values[:1000]\n",
    "top_idxs = tensor([learn.dls.classes['title'].o2i[m] for m in top_movies])\n",
    "movie_w = learn.model.movie_factors[top_idxs].cpu().detach()\n",
    "movie_pca = movie_w.pca(3)\n",
    "fac0,fac1,fac2 = movie_pca.t()\n",
    "idxs = list(range(50))\n",
    "X = fac1[idxs]\n",
    "Y = fac2[idxs]\n",
    "plt.figure(figsize=(12,12))\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": "markdown",
   "metadata": {},
   "source": [
    "Very interesting to study changes."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Using fastai.collab¶"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Same thing with fastai `collab_learner`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "learn = collab_learner(dls, n_factors=50, y_range=(0, 5.5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "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>0.940206</td>\n",
       "      <td>0.939116</td>\n",
       "      <td>00:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>0.886674</td>\n",
       "      <td>0.867349</td>\n",
       "      <td>00:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>0.750853</td>\n",
       "      <td>0.824058</td>\n",
       "      <td>00:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>0.610644</td>\n",
       "      <td>0.811104</td>\n",
       "      <td>00:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>0.497868</td>\n",
       "      <td>0.812237</td>\n",
       "      <td>00:07</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=0.1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "EmbeddingDotBias(\n",
       "  (u_weight): Embedding(944, 50)\n",
       "  (i_weight): Embedding(1665, 50)\n",
       "  (u_bias): Embedding(944, 1)\n",
       "  (i_bias): Embedding(1665, 1)\n",
       ")"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "learn.model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[\"Schindler's List (1993)\",\n",
       " 'Titanic (1997)',\n",
       " 'Shawshank Redemption, The (1994)',\n",
       " 'Rear Window (1954)',\n",
       " 'Star Wars (1977)']"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movie_bias = learn.model.i_bias.weight.squeeze()\n",
    "idxs = movie_bias.argsort(descending=True)[:5]\n",
    "[dls.classes['title'][i] for i in idxs]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "similar results."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Embedding Distance"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Basically it means if two movies has similar latent factors.(embedding vector)\n",
    "This is the movie very similar latent factors with Silence of the lambs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Body Snatcher, The (1945)'"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movie_factors = learn.model.i_weight.weight\n",
    "idx = dls.classes['title'].o2i['Silence of the Lambs, The (1991)']\n",
    "distances = nn.CosineSimilarity(dim=1)(movie_factors, movie_factors[idx][None])\n",
    "idx = distances.argsort(descending=True)[1]\n",
    "dls.classes['title'][idx]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Bootstrapping a Collaborative Filtering Model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "read the all section from the original book at page 270 (3rd release) or the course [notebook](https://colab.research.google.com/github/fastai/fastbook/blob/master/08_collab.ipynb) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Deep Learning for Collaborative Filtering"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First fastai could make a recommendation for right embedding sizes(latent factors)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(944, 74), (1665, 102)]"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "embs = get_emb_sz(dls)\n",
    "embs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "class CollabNN(Module):\n",
    "    def __init__(self, user_sz, item_sz, y_range=(0,5.5), n_act=100):\n",
    "        self.user_factors = Embedding(*user_sz)\n",
    "        self.item_factors = Embedding(*item_sz)\n",
    "        self.layers = nn.Sequential(\n",
    "            nn.Linear(user_sz[1]+item_sz[1], n_act),\n",
    "            nn.ReLU(),\n",
    "            nn.Linear(n_act, 1))\n",
    "        self.y_range = y_range\n",
    "        \n",
    "    def forward(self, x):\n",
    "        embs = self.user_factors(x[:,0]),self.item_factors(x[:,1])\n",
    "        x = self.layers(torch.cat(embs, dim=1))\n",
    "        return sigmoid_range(x, *self.y_range)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = CollabNN(*embs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "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>0.959750</td>\n",
       "      <td>0.937144</td>\n",
       "      <td>00:09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>0.919930</td>\n",
       "      <td>0.894244</td>\n",
       "      <td>00:08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>0.857974</td>\n",
       "      <td>0.870025</td>\n",
       "      <td>00:08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>0.814399</td>\n",
       "      <td>0.854047</td>\n",
       "      <td>00:08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>0.763636</td>\n",
       "      <td>0.860031</td>\n",
       "      <td>00:08</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "learn = Learner(dls, model, loss_func=MSELossFlat())\n",
    "learn.fit_one_cycle(5, 5e-3, wd=0.01)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### with one step."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "above is possibble(again) with collab_learner with one step. just use `use_nn=True`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "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>0.989683</td>\n",
       "      <td>0.959795</td>\n",
       "      <td>00:10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>0.902582</td>\n",
       "      <td>0.904747</td>\n",
       "      <td>00:10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>0.864139</td>\n",
       "      <td>0.879289</td>\n",
       "      <td>00:10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>0.824376</td>\n",
       "      <td>0.847727</td>\n",
       "      <td>00:10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>0.790679</td>\n",
       "      <td>0.850178</td>\n",
       "      <td>00:10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "learn = collab_learner(dls, use_nn=True, y_range=(0, 5.5), layers=[100,50])\n",
    "learn.fit_one_cycle(5, 5e-3, wd=0.1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "from the book:\n",
    "\n",
    "Although the results of EmbeddingNN are a bit worse than the dot product approach (which shows the power of carefully constructing an architecture for a domain), it does allow us to do something very important: we can now directly incorporate other user and movie information, date and time information, or any other information that may be relevant to the recommendation. That's exactly what TabularModel does. In fact, we've now seen that EmbeddingNN is just a TabularModel, with n_cont=0 and out_sz=1. So, we'd better spend some time learning about TabularModel, and how to use it to get great results! We'll do that in the next chapter."
   ]
  }
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