notebooks/case_studies/probabilistic_matrix_factorization.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Probabilistic Matrix Factorization for Making Personalized Recommendations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running on PyMC3 v3.9.0\n"
     ]
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import pymc3 as pm\n",
    "\n",
    "from matplotlib import pyplot as plt\n",
    "\n",
    "plt.style.use(\"seaborn-darkgrid\")\n",
    "print(f\"Running on PyMC3 v{pm.__version__}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Motivation\n",
    "\n",
    "So you are browsing for something to watch on Netflix and just not liking the suggestions. You just know you can do better. All you need to do is collect some ratings data from yourself and friends and build a recommendation algorithm. This notebook will guide you in doing just that!\n",
    "\n",
    "We'll start out by getting some intuition for how our model will work. Then we'll formalize our intuition. Afterwards, we'll examine the dataset we are going to use. Once we have some notion of what our data looks like, we'll define some baseline methods for predicting preferences for movies. Following that, we'll look at Probabilistic Matrix Factorization (PMF), which is a more sophisticated Bayesian method for predicting preferences. Having detailed the PMF model, we'll use PyMC3 for MAP estimation and MCMC inference. Finally, we'll compare the results obtained with PMF to those obtained from our baseline methods and discuss the outcome.\n",
    "\n",
    "## Intuition\n",
    "\n",
    "Normally if we want recommendations for something, we try to find people who are similar to us and ask their opinions. If Bob, Alice, and Monty are all similar to me, and they all like crime dramas, I'll probably like crime dramas. Now this isn't always true. It depends on what we consider to be \"similar\". In order to get the best bang for our buck, we really want to look for people who have the most similar taste. Taste being a complex beast, we'd probably like to break it down into something more understandable. We might try to characterize each movie in terms of various factors. Perhaps films can be moody, light-hearted, cinematic, dialogue-heavy, big-budget, etc. Now imagine we go through IMDB and assign each movie a rating in each of the categories. How moody is it? How much dialogue does it have? What's its budget? Perhaps we use numbers between 0 and 1 for each category. Intuitively, we might call this the film's profile.\n",
    "\n",
    "Now let's suppose we go back to those 5 movies we rated. At this point, we can get a richer picture of our own preferences by looking at the film profiles of each of the movies we liked and didn't like. Perhaps we take the averages across the 5 film profiles and call this our ideal type of film. In other words, we have computed some notion of our inherent _preferences_ for various types of movies. Suppose Bob, Alice, and Monty all do the same. Now we can compare our preferences and determine how similar each of us really are. I might find that Bob is the most similar and the other two are still more similar than other people, but not as much as Bob. So I want recommendations from all three people, but when I make my final decision, I'm going to put more weight on Bob's recommendation than those I get from Alice and Monty.\n",
    "\n",
    "While the above procedure sounds fairly effective as is, it also reveals an unexpected additional source of information. If we rated a particular movie highly, and we know its film profile, we can compare with the profiles of other movies. If we find one with very close numbers, it is probable we'll also enjoy this movie. Both this approach and the one above are commonly known as _neighborhood approaches_. Techniques that leverage both of these approaches simultaneously are often called _collaborative filtering_ [[1]](http://www2.research.att.com/~volinsky/papers/ieeecomputer.pdf). The first approach we talked about uses user-user similarity, while the second uses item-item similarity. Ideally, we'd like to use both sources of information. The idea is we have a lot of items available to us, and we'd like to work together with others to filter the list of items down to those we'll each like best. My list should have the items I'll like best at the top and those I'll like least at the bottom. Everyone else wants the same. If I get together with a bunch of other people, we all watch 5 movies, and we have some efficient computational process to determine similarity, we can very quickly order the movies to our liking.\n",
    "\n",
    "## Formalization\n",
    "\n",
    "Let's take some time to make the intuitive notions we've been discussing more concrete. We have a set of $M$ movies, or _items_ ($M = 100$ in our example above). We also have $N$ people, whom we'll call _users_ of our recommender system. For each item, we'd like to find a $D$ dimensional factor composition (film profile above) to describe the item. Ideally, we'd like to do this without actually going through and manually labeling all of the movies. Manual labeling would be both slow and error-prone, as different people will likely label movies differently. So we model each movie as a $D$ dimensional vector, which is its latent factor composition. Furthermore, we expect each user to have some preferences, but without our manual labeling and averaging procedure, we have to rely on the latent factor compositions to learn $D$ dimensional latent preference vectors for each user. The only thing we get to observe is the $N \\times M$ ratings matrix $R$ provided by the users. Entry $R_{ij}$ is the rating user $i$ gave to item $j$. Many of these entries may be missing, since most users will not have rated all 100 movies. Our goal is to fill in the missing values with predicted ratings based on the latent variables $U$ and $V$. We denote the predicted ratings by $R_{ij}^*$. We also define an indicator matrix $I$, with entry $I_{ij} = 0$ if $R_{ij}$ is missing and $I_{ij} = 1$ otherwise.\n",
    "\n",
    "So we have an $N \\times D$ matrix of user preferences which we'll call $U$ and an $M \\times D$ factor composition matrix we'll call $V$. We also have a $N \\times M$ rating matrix we'll call $R$. We can think of each row $U_i$ as indications of how much each user prefers each of the $D$ latent factors. Each row $V_j$ can be thought of as how much each item can be described by each of the latent factors. In order to make a recommendation, we need a suitable prediction function which maps a user preference vector $U_i$ and an item latent factor vector $V_j$ to a predicted ranking. The choice of this prediction function is an important modeling decision, and a variety of prediction functions have been used. Perhaps the most common is the dot product of the two vectors, $U_i \\cdot V_j$ [[1]](http://www2.research.att.com/~volinsky/papers/ieeecomputer.pdf).\n",
    "\n",
    "To better understand CF techniques, let us explore a particular example. Imagine we are seeking to recommend movies using a model which infers five latent factors, $V_j$, for $j = 1,2,3,4,5$. In reality, the latent factors are often unexplainable in a straightforward manner, and most models make no attempt to understand what information is being captured by each factor.  However, for the purposes of explanation, let us assume the five latent factors might end up capturing the film profile we were discussing above. So our five latent factors are: moody, light-hearted, cinematic, dialogue, and budget. Then for a particular user $i$, imagine we infer a preference vector $U_i = <0.5, 0.1, 1.5, 1.1, 0.3>$. Also, for a particular item $j$, we infer these values for the latent factors: $V_j = <0.5, 1.5, 1.25, 0.8, 0.9>$. Using the dot product as the prediction function, we would calculate 3.425 as the ranking for that item, which is more or less a neutral preference given our 1 to 5 rating scale.\n",
    "\n",
    "$$ 0.5 \\times 0.5 + 0.1 \\times 1.5 + 1.5 \\times 1.25 + 1.1 \\times 0.8 + 0.3 \\times 0.9 = 3.425 $$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data\n",
    "\n",
    "The [MovieLens 100k dataset](https://grouplens.org/datasets/movielens/100k/) was collected by the GroupLens Research Project at the University of Minnesota. This data set consists of 100,000 ratings (1-5) from 943 users on 1682 movies. Each user rated at least 20 movies, and be have basic information on the users (age, gender, occupation, zip). Each movie includes basic information like title, release date, video release date, and genre. We will implement a model that is suitable for collaborative filtering on this data and evaluate it in terms of root mean squared error (RMSE) to validate the results.\n",
    "\n",
    "The data was collected through the MovieLens web site\n",
    "(movielens.umn.edu) during the seven-month period from September 19th,\n",
    "1997 through April 22nd, 1998. This data has been cleaned up - users\n",
    "who had less than 20 ratings or did not have complete demographic\n",
    "information were removed from this data set.\n",
    "\n",
    "\n",
    "Let's begin by exploring our data. We want to get a general feel for what it looks like and a sense for what sort of patterns it might contain. Here are the user rating data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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>itemid</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": [
       "   userid  itemid  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": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv(\n",
    "    pm.get_data(\"ml_100k_u.data\"), sep=\"\\t\", names=[\"userid\", \"itemid\", \"rating\", \"timestamp\"]\n",
    ")\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And here is the movie detail data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>movie title</th>\n",
       "      <th>release date</th>\n",
       "      <th>video release date</th>\n",
       "      <th>IMDb URL</th>\n",
       "      <th>unknown</th>\n",
       "      <th>Action</th>\n",
       "      <th>Adventure</th>\n",
       "      <th>Animation</th>\n",
       "      <th>Children's</th>\n",
       "      <th>Comedy</th>\n",
       "      <th>...</th>\n",
       "      <th>Fantasy</th>\n",
       "      <th>Film-Noir</th>\n",
       "      <th>Horror</th>\n",
       "      <th>Musical</th>\n",
       "      <th>Mystery</th>\n",
       "      <th>Romance</th>\n",
       "      <th>Sci-Fi</th>\n",
       "      <th>Thriller</th>\n",
       "      <th>War</th>\n",
       "      <th>Western</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>movie id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Toy Story (1995)</td>\n",
       "      <td>1995-01-01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>http://us.imdb.com/M/title-exact?Toy%20Story%2...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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",
       "      <th>2</th>\n",
       "      <td>GoldenEye (1995)</td>\n",
       "      <td>1995-01-01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>http://us.imdb.com/M/title-exact?GoldenEye%20(...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Four Rooms (1995)</td>\n",
       "      <td>1995-01-01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>http://us.imdb.com/M/title-exact?Four%20Rooms%...</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>...</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",
       "      <th>4</th>\n",
       "      <td>Get Shorty (1995)</td>\n",
       "      <td>1995-01-01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>http://us.imdb.com/M/title-exact?Get%20Shorty%...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</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",
       "      <th>5</th>\n",
       "      <td>Copycat (1995)</td>\n",
       "      <td>1995-01-01</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>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 × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                movie title release date  video release date  \\\n",
       "movie id                                                       \n",
       "1          Toy Story (1995)   1995-01-01                 NaN   \n",
       "2          GoldenEye (1995)   1995-01-01                 NaN   \n",
       "3         Four Rooms (1995)   1995-01-01                 NaN   \n",
       "4         Get Shorty (1995)   1995-01-01                 NaN   \n",
       "5            Copycat (1995)   1995-01-01                 NaN   \n",
       "\n",
       "                                                   IMDb URL  unknown  Action  \\\n",
       "movie id                                                                       \n",
       "1         http://us.imdb.com/M/title-exact?Toy%20Story%2...        0       0   \n",
       "2         http://us.imdb.com/M/title-exact?GoldenEye%20(...        0       1   \n",
       "3         http://us.imdb.com/M/title-exact?Four%20Rooms%...        0       0   \n",
       "4         http://us.imdb.com/M/title-exact?Get%20Shorty%...        0       1   \n",
       "5         http://us.imdb.com/M/title-exact?Copycat%20(1995)        0       0   \n",
       "\n",
       "          Adventure  Animation  Children's  Comedy  ...  Fantasy  Film-Noir  \\\n",
       "movie id                                            ...                       \n",
       "1                 0          1           1       1  ...        0          0   \n",
       "2                 1          0           0       0  ...        0          0   \n",
       "3                 0          0           0       0  ...        0          0   \n",
       "4                 0          0           0       1  ...        0          0   \n",
       "5                 0          0           0       0  ...        0          0   \n",
       "\n",
       "          Horror  Musical  Mystery  Romance  Sci-Fi  Thriller  War  Western  \n",
       "movie id                                                                     \n",
       "1              0        0        0        0       0         0    0        0  \n",
       "2              0        0        0        0       0         1    0        0  \n",
       "3              0        0        0        0       0         1    0        0  \n",
       "4              0        0        0        0       0         0    0        0  \n",
       "5              0        0        0        0       0         1    0        0  \n",
       "\n",
       "[5 rows x 23 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# fmt: off\n",
    "movie_columns  = ['movie id', 'movie title', 'release date', 'video release date', 'IMDb URL', \n",
    "                  'unknown','Action','Adventure', 'Animation',\"Children's\", 'Comedy', 'Crime',\n",
    "                  'Documentary', 'Drama', 'Fantasy', 'Film-Noir', 'Horror', 'Musical', 'Mystery',\n",
    "                  'Romance', 'Sci-Fi', 'Thriller', 'War', 'Western']\n",
    "# fmt: on\n",
    "movies = pd.read_csv(\n",
    "    pm.get_data(\"ml_100k_u.item\"),\n",
    "    sep=\"|\",\n",
    "    names=movie_columns,\n",
    "    index_col=\"movie id\",\n",
    "    parse_dates=[\"release date\"],\n",
    ")\n",
    "movies.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Extract the ratings from the DataFrame\n",
    "ratings = data.rating\n",
    "\n",
    "# Plot histogram\n",
    "data.groupby(\"rating\").size().plot(kind=\"bar\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    100000.000000\n",
       "mean          3.529860\n",
       "std           1.125674\n",
       "min           1.000000\n",
       "25%           3.000000\n",
       "50%           4.000000\n",
       "75%           4.000000\n",
       "max           5.000000\n",
       "Name: rating, dtype: float64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.rating.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This must be a decent batch of movies. From our exploration above, we know most ratings are in the range 3 to 5, and positive ratings are more likely than negative ratings. Let's look at the means for each movie to see if we have any particularly good (or bad) movie here.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "movie_means = data.join(movies[\"movie title\"], on=\"itemid\").groupby(\"movie title\").rating.mean()\n",
    "movie_means[:50].plot(kind=\"bar\", grid=False, figsize=(16, 6), title=\"Mean ratings for 50 movies\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "While the majority of the movies generally get positive feedback from users, there are definitely a few that stand out as bad. Let's take a look at the worst and best movies, just for fun:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, (ax1, ax2) = plt.subplots(ncols=2, figsize=(16, 4), sharey=True)\n",
    "movie_means.nlargest(30).plot(kind=\"bar\", ax=ax1, title=\"Top 30 movies in data set\")\n",
    "movie_means.nsmallest(30).plot(kind=\"bar\", ax=ax2, title=\"Bottom 30 movies in data set\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Make sense to me. We now know there are definite popularity differences between the movies. Some of them are simply better than others, and some are downright lousy. Looking at the movie means allowed us to discover these general trends. Perhaps there are similar trends across users. It might be the case that some users are simply more easily entertained than others. Let's take a look."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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yWR0dUcVjMfX29u7Vhtvjld3hVKiySosvulKFwVK5PF5VjB6n0Kgq+QIFslptObpC4PBHGAUAAEBOfBgea2p2qaOjQ8lkUt3dSSUSA1+TyaQ6OztUX1+ndDqtVCqljo4OtbdH1NERPeAzmiaTSf6CIhUESzRq0gxNLyiS0+2Vw+mU3eGUv6BI0+YsUGn5qCxfMYCPIowCAADgkOjr61NbW5t6e3v2fN+qDz5Yp0QioWQyoa1bt2jL1s3qTnYrHG5TNNo+ZHsfLvpTHKqQzeGQzWZXUeU4jZ1+lDw+vwKFQUkmuTwe+QuK5HJ7VVQcUmnFKNnsjuxcNIBPjTAKAACAg9bT06PW1hY1Nu7Whg3rtXbtar3//nuKRtvV3Nw05IqyBUXFGj91lgIhlybMmq/iUIWKQ+UqrRgjf0Gh7A6nHE7XwAqzdofMZnMWrwxAthFGAQAA8lR/f7/i8Zja2tq0ceMGJRLxwemw3d3d6uyMqqOjY88rqq3btqq2plqGYQy24QsUasLUmRo/aoLmnVSmYKh8MEg6XW5NnDZbHp9fdoeT5y8B7IUwCgAAMEIZhqFwOKzdu+vV0NCglpZmJRKJwectu7o6FYvF1NXVpa6uTnV1dSkWjynW1aWuri7F47Eh2zebzXJ7ffL6AnJ7/QqNmaT5C89SUUlIhcFSjRo3SaHKMTKZTFm6YgCHE8IoAABADkWj7YrH40qlUkqn00qnU0qlBl6xWEzbtm1VY2OD0ukepdMppdMpNTc3a/fuBu1u3K1Ud/d+2zVbLHJ7vHK5vXINfvWpNFiuqo+87/Z45fb4NGbCFHn9AVltdtnsdtkdTjldHqbKAsgYwigAAMAh1NfXp/b2yODPhmGoo6NDzc3Nam1tVktLsyKRiKLRdm3evElvvPHax7bp9fllsztktdlktdrkLwiqfPwRmnnsyQqGylVcWqFgqFxFxSE5XC7Z7Q5ZbXbuWAIY1gijAAAgrxmGIcMw1N/fr1isS01NTYpG25VIxBWPD7wSicSe10d//vvXeCKheCymRCKh5uZGJRKJj+3X4/UrUFSsL11xw8Bzlja7bPaBFWOte+5MutweFZeWK1BUnIXfBABkF2EUAAAcFvr6+tTZObBX5YdbiSST3Uok4qqpqVYkElZnZ6ei0XZVV+/Szl071dLcpL6+vk/Uj8M5MH3V4XTJ4XLL4XTJ6XLLWVCiggqPZh13kopKKqSP3JT0ePwqCBaroLhUBUXF8vkLZbHyZxiA/MZ/BQEAQM4ZhqF4PK6Ojqii0ag6OzsUjUbV0RFVV1en0uke9fX1qre3V+l0Sh0dHYPHJZNJhcNt2rFzh9Kp1JD92OwOeX1+lZaP1qRZ83VMabmsVqtMZrPMZrMcTreKSkLy+QvkcLkHQqbLLYfLJYdzIHh+3DOUHo9D8fjQdQAACKMAAOAQ6e3tHdwCZODVMRgu//7+h++1K/qRQNnV2aHe3t6D6sdkMsnj88vt9cvrC8judMpdWKbTlx6vYGn5QHB0uPbsWTmwX2WocowChUHZ7I4M/xYAAAeLMAoAAPaRTCbV0FCv7u5u9fSklUgk1NLSrKamJkUiYcXjMXV3d2vTpo2qravd87xkfMg2rVabPP6AvHuCpMcX0OhJo+Tx+QdeXv+ezwe2EfH6A/L4AnJ7fLLZ7TKbLbJYLDJbLCzMAwCHAcIoAACHuXQ6raamRnV0dKizs2Pwa0NDvdraWvdanCcWi6mmplqNjbtlGMZ+2zNbLHK5PHI4nSopH6XZx54kp8sjt9c3GCq9voA8/sBAwPQF5PUHZHc4CZEAgEGEUQAARgDDMNTd3a1YLKZYrEuxWEzx+N+///D9rq4uNTc37wmZce2q3qX6utoDLtLj9QfkdLr3PB85sCDPxFnz9LnF4xSqHCOHwyWL1Sq7w6nCYIkKi0Py+PyESgDAZ0YYBQAghyKRsLZu3aK33npT0WhUyWRS3d1JJZNJtba1avv2bepOJpRMJg/6mcpAYXDg+UiHU5UTpunoL5yp0vLR8voL9jxr6ZPH61dBsEQOpyvDVwgAwP4RRgEA+IwMw1AikVAkElYkElY43KZwOKyOjujgFNh4PKauri51dnaqs7NTXV2dirRHVFO9a7Ad555tQj5cdMfjC2jWgs/L6fLI7nTJ5fbseXnl9Pz9e5fbI+dHvmfLEADASMD/WgEA8BGGYSiVSimRiKuhoUFNTbvV29un3t5e7dq1Q+FwWL29PUomk9qwYb2amhoViUSUSnUfsE2TySSnyy231ye3xyeXxyu3x6fKCWU6/vQvacz4yZo6e568/kAWrxQAgNwijAIADmuGYSidTisejykej+95DXzf0FCvF198Xh0dHUomEwpHwqqtqT7g85WS5HC6ZLPZZLXZVTl2oqbOPU6+QKH8hUXyBYrkCxTIXxBUoLBIHl9ADqdbdoeDZywBAPg/CKMAgBGlv79f0Wi7Vqx4U62tLWpvjygabVckElF7e7va2yPq6OgYCJyJuBLx+JDPWhaHKgb2pnQ6FaqapCOPP00uj1cOl0sFRSUqKa+U1WqT2WxWsLRCvkBBFq8WAIDDF2EUADCspNNpJRJxJZNJtbW1qqurSz09PYpG2/Xoo7/X62+8pp50eq9zXB6vfP4Cef0F8gYKVDx6gka7vXK63XK6PHK63IPfu1xuOd0D73l8AY0eP1lmszlHVwsAQP4ijAIAMioWi2njxk1qbm7Shg3rlUwm1NfXq97ePvX09Kizs0M1NdXqinVpd0ODWltbDthWUXGpTjvnEhUESzRtzgKFKkfL4wvIZrNn8YoAAMChQBgFAAzJMAyFw2F1dkaVSCTV05NWT0+v2vZsO5JIxBSPJ7RjxzbV1NQo3ZNWT7pHPT1ppdIpdXZ07NOm2WyWxWqTxWKR0+1RqGKM3N6AZsyfoLJRVXI43XI4nfIXBOXx+WWxWmWz2VU1capsdkcOfgsAAOBQI4wCACRJnZ0damtrVTrdo507d2jdurV6//339P6699U2xN1Ks9ksh9OlYGmZRo2bLLvDIavVLqvNJovVqlB5hQpLKhQoKlbVhKny+PxMiwUAAIRRADicGIahWKxL7767Uu3tETU2NioSCWvt2tUHXMTHMAw1NzeppqZ6r/fNZrNGjZukGfM+p7GTpslfUCi7Y2D/S6vNJpfHq9FjJ8np9gy5UqzH41A8njqUlwkAAA4DhFEAGEF6e3vV3t6uSCSsRGJgm5KWlma1tDTrxRef16pV7yqZTOx1jtls1rgpM+Ryew/QqkkVE6bp+DPOV7C0TBarTaXlo1Q1caocTlfmLwoAAOQlwigAZIlhGIrHY2ptbVU43Ka2tjaFw22KxbqUTvconU4NPGeZSqunJ61EIqGamhpFowNblUQ7ovt9/vJDZZVjdNJZF6gwWKpxU6YPbFlSUia70yWLxZLFKwUAAPh4hFEAOETa2yN65ZWX1NLSoo6OqLq6OtXZ2amOjo7BxX6i0fYh2zCZTLLZ7bLZHLI5HCotHyV/QYnGVo6Xx+uXr6BIvkCh/AVFcjhdcjhdKiwulX/P+0NNlwUAABhOCKMAsIdhGGptbVV398CKsel0j5LJhNrbI4pEIvt8Hfg+rEhkYNpsd3dyr/bcXp+8Xr9cXp98gULN+8IZClWOVkFhifxFQQUKg/IXFMnt8clqs8lqHVjwBwAAIB/wVw+Aw1YymVQkEt4THMN7fR8Ot6m2tkZdXV1Kp9Pq7k6qpqZGiUR8yDZNJpN8/gJ5A4XyBQrkDZRoStUUef0F8hcUafpRx6iyaoJcbi8rxgIAAAyBMApgRPtwJdh33vmbHnvsf9XU3KRIOKz29vZ9FvL5KI/Xr5LySnn9hbLaPfJ4C/WFmfNVPmqsXG7v4LYkNrtDvj3B0xcoYlsSAACAQ4QwCmBY6e/vVyqVUirVrVQqpe7ubiWTScViXYpG29Xa2qqNG9dr166dqqurU3X1rsHQWVo+SqPGTdbk0ZP2BMhC+QoK5Q8UDYTJgiL5A4Xy+guYDgsAAJBj/DUGIGO6u7vV0dGh2tpqRaPtMgxDvb19qqur0c6dO5RIJNTc3KT6hga1tDQpmUyqJ53+2HYdTqcqRo9TUWm5Fs6cr7LRY1VZNUHT5ywgZAIAAIwQ/NUG4KAYhqFIJKL6+lrV1taqpqZa27ZtUSKRGHzmsqOjY+DV2aHOjg6l06kDtuf1+eV0eRQIlihYMVYTZ82X0+2Rze6Q3e6Qbc/L4XDK7nDI6fHK4/UrUBhUcVmFrFZbFq8eAAAAhxphFIAkKRptV3X1Lu3atVNtba1KJru1YsXr2r59m+LxuLpiXfvctSwqLpXL45PNZpfVbpfb61NpVanG+fzyeP1ye/3y+PwqLi1XQbBEMplkNpsVLC1XoDCYoysFAADAcEAYBfJEd3e3otF2dXZ2qrFxt5qbm5RIJPTBB+v05z8/vd/9L0vLKzV19jy53F453R4VBktVWj5KxWWVKi0fJa8/kIMrAQAAwOGAMAocJgzD0OrV72rLls3q7+9XbW2NWltbtHPXTrW1tmrnzu3q7+/f5zybza5jTz5TYydNU1lllUKVY1RYXCqb3SGnyy2TyZSDqwEAAMDhjjAKDGOGYai7u1uJRELt7RG1tbWqtbVV4XCb2tsje/bRjGjrti3aumWzIpHw4LkWq1WBgiKVlI9ScNR4zT7hVAVLK+T2eFVYXKqikpCcbq/cHp8cTmcOrxIAAAD5iDAKZFFfX59isZi6ujrV0dGhzs5OdXZGFY1GtXz5K9q4cb3iiYRisZhiXV1D7pMpSWazWR6fXxVjJmjO8Sdr8oy5mjnveJnNZvkLgrI7HFm6MgAAAOCTIYwCh0Bvb68aGuq1du1q1dXVqaMjqvb2drW2NmvVqnfV1dWlnp70fqfJfsjl8eqI2fMVKPfIvecZTafLLbvDKbvTKV+gUIHC4J5XsbyBAtls9ixeJQAAAHDoEEaBTygej6uhoV719XVqaKhXe3tEDz10v2pqqgePsVpt8voD8voLNH3e5xQoDMpmt8vldkkmi9yegVVmPV7fwIqzXr+CoXI5Xe7cXRgAAACQRYRRYA/DMFRbW6NIJKz6+jpt375NO3ZsV01Ntbq6OhVPJNTZ0aH29sg+51ZWjde1t/6zxk2ersqxEw+48I/H41A8fuC9NwEAAIB8QRhFXuvp6dF//de/67E/PKKG+nqlUt17fR4sLVOoYozcwXIVjfLI7fGqOFSh4lC5issqVVJWKa8vIIfLLbPZnKOrAAAAAEYewijyTjqd1u7dDXrxxef0//3uN9q5Y7tmzT9Bpx+zUGWjqlQQLFFxqFJlo6rkcntyXS4AAABwWCKM4rDS09Oj7du3acOGD7Rx4wbt2LFd6XRKvb29isfjqq+vU3NzkwzDkCRNnn6kvv2v92r+iafluHIAAAAgvxBGMeKl02lt2PCB7vjB7Vqz+l319PRIGlhEqHz0WDmcLpktFtkdTk2de5w+V1ap4rJKTZg6Q2MnTctx9QAAAEB+IoxiRGpvj2jz5k26+0c/1OpVK2UYhgqKinXmhV/T2IlHqGriVFVUjZfVast1qQAAAAD2gzCKYSsej6urq1PhcFhNTbvV2tqqFSve0JYtm7Vx0walUyl5/QGd95XrVVoxWkefcLIChcFclw0AAADgIBBGkTPxeFxNTbvV2NiopqZGNTY2qrl54OuOHdu1ceP6fc7x+vwaf8QsnfzFizRr3vGaOO1IFQZLclA9AAAAgM+CMIqMMQxDkUhETz75mN56a4X6+nrV2dWl5qYmtbQ2K9bVtc85bo9XRcUhBcsqdOFV31SgqFhen1/BUIW8/oDKKqtksTJsAQAAgJGOv+rxmcTjcUWj7Xrnnb9p06aNqqnZpba2Nr33/lol4nH19/dLkipGj5PD5ZLT5VZo7GRNOep4FRWHVFQSUlFJ2eD3Lo83x1cEAAAAIBsIo/jEDMPQU089rt/9v99q9aqVg4HTYrWqtHyUXG6vjj/1bHn9AXn9BZoy6yhNnj4nx1UDAAAAGE4IozgohmHoD394RK+++rI2b96kjRvXa9TYiTrvK9erIFiiidNmq2riVNls9lyXCgAAAGAEIIxiUF9fn8LhsFpamgdf0Wi7tm/frrr5ZQwAAB3HSURBVPfXrdX7761VcWm5ykaP1TXf/medcvZFMpvNuS4bAAAAwAiUsTCaSqV0ySWXKJ1Oq6+vT4sWLdKNN96Yqe4wBMMwlEwmlUgklEjEFY/HVVNTrR07tqu9PaKurk7t2rVTK1a8oZ6enn3Od3m8Gjd5ui69/lZ98eJrCKAAAAAAPrOMhVG73a4HHnhAHo9HPT09uvjii3XiiSfqyCOPzFSX+IiWlhbdc8+/6q9/fUZNTbsPeJzFapXb41VBUYlOO/dSlY2qUmFxqQqKSlQQLJEvUCin2yOLxZLF6gEAAAAc7jIWRk0mkzwejySpt7dXvb29MplMmeou7zQ1NSqRiO/z/gcfrNMzz/xJL774grpT3Vrw+UU64Yylcrjccjpdg1+LyytVWTVBbo+Pfy4AAAAAsi6jz4z29fXpvPPOU21trS6++GLNnj07k93lhXA4rJtvvkHPPvuXAx4TLC3T3BNO1vlfu1EVY8ZnsToAAAAAODgmwzCMTHfS2dmp66+/Xt///vc1efLkwff7+/vV15fx7kesVCqluro61dXVauXKlXrjjTe0atUqxWIxfemK61Q5dsI+5wQKinTkMZ9jWu0wZTGb1NfPmEd+Ydwj3zDmkW8Y89gfm8WkEyeVyGY7cC7Jymq6fr9fCxYs0BtvvLFXGO3rMxSNJrJRwrDS29urjo4ORSJh1dXVqKamRk1NuxWPDywu1NLSrK3btqqutmZwD09JGjNhiqbPO0GLL7hCE6cd+C5zd3evpN4sXAk+KY/HoXg8lesygKxi3CPfMOaRbxjz2B+r2aRoNKGSEt+Bj8lU55FIRFarVX6/X93d3Xrrrbd09dVXZ6q7YSuRSOj115dr7dpV2rhxg7Zs2aLa2uq9QqYkmc1mOd0euVxu+QKFqpwwTfMXnqXSitEKlpSpauJUBYqKc3QVAAAAAHBoZSyMtrS06Dvf+Y76+vpkGIZOP/10nXTSSZnqbtjZuXOHnnvur/rP/7xHkUhYZotFlWPGa9T4qTrqC2fIXxiUz1+g4rJKhSrGqLC4lC1TAAAAAOSNjIXRqVOn6umnn85U88Pajh3bdNqik9TV2akpM+fqH37w75o6e57sDkeuSwMAAACAYSErz4zmg9raGj311OPavn2bXnjhOZnMVv3b75/XqHGT2DoFAAAAAP4Pwuhn9Mwzf9avf/1fevfdd9Tf36/i0nKNn3akLvuH2zR63KRclwcAAAAAwxJh9CD19/dr7drVamlp0ebNG/X000+qsbFR0Wi7Kqsm6JzLvq7Tzr1ExaGKXJcKAAAAAMMeYfRjGIahl156XnfeeYe2bt08+P70OQu0YOFcVVSN12nnXiKbzZ7DKgEAAABgZCGMDiGdTuvss8/Q6tXvqmLMeN1wxy80evxk+QuKuAMKAAAAAJ8BYXQIzz33jFavfleXXn+rFl9whWx2VsMFAAAAgEOBMHoA7e0R3Xvvf6ukrFJnfflqWSyWXJcEAAAAAIcNwugeDQ31euWVl7R162a99bcV2rD+A/X39+uqb91FEAUAAACAQ4wwKikW69KSJaepoaFedodDk2fM1dIrbtCCLyzS2IlH5Lo8AAAAADjs5H0YNQxD3/72zdq9u0Hf+/cHNH3uMayMCwAAAAAZltdh1DAM/fCH39MTTzymC6/6po5ccGKuSwIAAACAvJCXYXTNmlX64Q+/p9bWVu3YsU1nfOlyfelrN+S6LAAAAADIG3kZRv/lX36kDzas18yjj9P8U87WuZd/QyaTKddlAQAAAEDeOKgw+sILL+zzns/n0+TJkxUMBg95UZn01ltvavnyV/Tla/9JS7/6D7kuBwAAAADy0kGF0SeeeELvvfeeFixYIElauXKlpk+frvr6el133XU655xzMlrkZ5VOp/XHPz6hl19+UU8//aSCpWU69ZyLc10WAAAAAOStgwqjfX19+utf/6ri4mJJUltbm2699Vb94Q9/0KWXXjosw6hhGHr++WdVV1ejJ598XGvWrJLD4dR5X7lO51z2dbk9vlyXCAAAAAB566DCaGNj42AQlaRgMKjGxkYVFBTIah1+j522trbqrru+r8ce+19JUlFJSP/4w3/XcacskcViyXF1AAAAAICDSpLz58/Xtddeq9NPP12S9Pzzz2v+/PlKJBLy+YbXHcZly57SzTffoFgspqVfvV5nnP8VBQqLWaAIAAAAAIYRk2EYxscdNDDl9XmtWbNGkjR37lwtWrToMwe8np4+RaOJz9TGh1KplB577H91yy03afKMObruuz9V5dgJh6Rt4FDxeByKx1O5LgPIKsY98g1jHvmGMY/9sZpNOm5ckUpKDnzz8qDujJpMJp1++umDd0aHE8Mw1Ni4WxdeeJ62bNmkaUfO0+3/9qAcTmeuSwMAAAAAHMBBb+3y85//XOFwWIZhyDAMmUymwTuluZJIJHT55Rfp9deXy2Z36Jt3/1ILvrBIVqstp3UBAAAAAIZ2UGH0Zz/7mX79619rwoThNe31u9+9RW+88ZrOvfw6Lfj8aZo4bXauSwIAAAAAHISDCqPBYHDYBdGdO7fr0Ud/r8UXfk2XfOOWXJcDAAAAAPgEDiqMzpgxQzfddJNOOeUU2e32wfdPO+20jBW2P4ZhaOXKd/T000/ooYcfkM1m19mXXpvVGgAAAAAAn91BhdF4PC6Xy6UVK1bs9X62w+iPf3yX/uM/fiGTyaSFZ12g0869RIXBkqzWAAAAAAD47A5qa5dM+SRbu1RX79LxJ8zT0Secoku+8W2VjarKcHXAocfS58hHjHvkG8Y88g1jHvvzmbd2+c1vfqOrr75ad9999373FP3e97732as8SHfffYfMZou++o/fV7C0LGv9AgAAAAAOvSHD6IeLFs2YMSMrxRzImjWr9Oc/L9OFV32TIAoAAAAAh4Ehw+jChQslSU6nU2ecccZenz377LOZq+r/eOih++Vye7Tky1dmrU8AAAAAQOaYD+ag++6776Dey4R4PK5ly57SMSedKZfbk5U+AQAAAACZNeSd0ddee02vv/66mpub9aMf/Wjw/VgsJovFkvHiJOm++/5bsVhMC886Pyv9AQAAAAAyb8gwGgqFNGPGDL3yyiuaPn364Psej0e33XZbxourr6/TL+75qY456QwdMXtexvsDAAAAAGTHkGF06tSpmjp1qpYsWSKbzZatmgY9+OD/U29Pj75y4+1Z7xsAAAAAkDlDhtEPNTQ06J577tH27duVSv19D6GXX345Y4Wl02n9/vcPau7xC1VSVpmxfgAAAAAA2XdQCxjddttt+vKXvyyLxaIHH3xQ55xzjr74xS9mtLC33npTra0tWriEZ0UBAAAA4HBzUGE0lUrp2GOPlSRVVlbqhhtu0GuvvZbRwpYvf0VWq00zjz4+o/0AAAAAALLvoKbp2u129ff3q6qqSg8//LBCoZDi8XhGC3vl1Zc0dfbRbOcCAAAAAIehg7oz+t3vflfJZFLf+973tGHDBi1btkw//elPM1ZUU1OjNm/aqNkLTsxYHwAAAACA3DmoO6OzZs2SNLCly09+8hP19fXpmWee0ezZszNS1PLlr0iSjiSMAgAAAMBhacg7o7FYTPfee6/uuusuvfnmmzIMQw8//LBOPfVUPfvssxkravnyl1VQVKKqiVMz1gcAAAAAIHeGvDN6yy23KBAI6Mgjj9Tjjz+ue++9V4Zh6Fe/+pWOOOKIjBTU39+v5ctf1az5n5PZfFCziAEAAAAAI8yQYbS+vl7/8z//I0k6//zzdcIJJ2j58uVyOBwZK6ixcbcikbCmzDwqY30AAAAAAHJryFuPVuvfs6rFYlFZWVlGg6gk1dXVSpJCFaMz2g8AAAAAIHeGvDO6efNmzZ07V5JkGIZSqZTmzp0rwzBkMpm0Zs2aQ15QbW2NJKmkfNQhbxsAAAAAMDwMGUY3bdqUrToGfXhntDhUmfW+AQAAAADZMexWCKqvr1NhsEQOpzPXpQAAAAAAMmTYhdHa2hqm6AIAAADAYW74hdG6WsIoAAAAABzmhlUY/dnPfqLammpVjB6X61IAAAAAABk0bMJoOp3WL37xr5o9/3Na8uUrc10OAAAAACCDhk0Yra+vVX9/v44/9Sx5vP5clwMAAAAAyKBhE0arq3dJkkKVY3JcCQAAAAAg04ZRGK2WJJVVVuW2EAAAAABAxg2jMLpLdodDhcWluS4FAAAAAJBhwyqMhirGyGQy5boUAAAAAECGDYsw2tzcpPXr1/G8KAAAAADkiYyF0cbGRl122WU688wztXjxYj3wwAMHPPaSSy5QU1OTPrfonEyVAwAAAAAYRqyZathiseg73/mOpk+frlgspqVLl+r444/XxIkT9zquvT2ideve05ev/Scdf8qSTJUDAAAAABhGMnZntLS0VNOnT5ckeb1ejR8/Xs3Nzfsct3r1u5KkKTOPylQpAAAAAIBhJivPjNbX12vTpk2aPXv2Pp+tWrVSZotFE46YlY1SAAAAAADDQMam6X4oHo/rxhtv1He/+115vd69PrNYTHr//bUaP/kIFZcUZboUIOcsZpM8HkeuywCyinGPfMOYR75hzGN/bBaTCgrcQx6T0TDa09OjG2+8UWeddZZOO+20fT7v6zO0Y+dOVUyYpng8lclSgGHB43Ew1pF3GPfIN4x55BvGPPbHajYpGk2opMR3wGMyNk3XMAzdfvvtGj9+vK644ooDHtfa0qLCYGmmygAAAAAADEMZC6OrV6/WsmXL9Pbbb+vss8/W2Wefrddee22vY2KxmOLxmAqLSzJVBgAAAABgGMrYNN2jjz5aW7ZsGfKYxsZGSVJBEWEUAAAAAPJJVlbTPZDm5iZJUmEx03QBAAAAIJ/kNIw2Nu4JozwzCgAAAAB5JadhtKlpzzTdINN0AQAAACCf5DiMNslqtckXKMxlGQAAAACALMt5GC0MlshkMuWyDAAAAABAluU0jLa0NCvAFF0AAAAAyDs5DaOdnZ1yub25LAEAAAAAkAM5DaOxeFwOlyuXJQAAAAAAciCnYTQRj8vpdOeyBAAAAABADuQ2jCaScjiduSwBAAAAAJADOQ2j8URcDhd3RgEAAAAg3+R8mq6DaboAAAAAkHdyGkZ7e3vlZAEjAAAAAMg7OQ2jkrgzCgAAAAB5KPdhlDujAAAAAJB3ch9GnYRRAAAAAMg3OQ+j7DMKAAAAAPkn52GUaboAAAAAkH9yH0a5MwoAAAAAeSfnYZStXQAAAAAg/+Q8jHJnFAAAAADyT87DqNNFGAUAAACAfJPzMGp3OnNdAgAAAAAgy3IeRtnaBQAAAADyT07DqM1ml8VqzWUJAAAAAIAcyGkYdbq5KwoAAAAA+Si3YZTFiwAAAAAgLxFGAQAAAABZl9MwuvTya3LZPQAAAAAgR3IaRpdccFkuuwcAAAAA5EjOt3YBAAAAAOQfwigAAAAAIOsIowAAAACArCOMAgAAAACyjjAKAAAAAMg6wigAAAAAIOsIowAAAACArCOMAgAAAACyjjAKAAAAAMg6wigAAAAAIOsIowAAAACArCOMAgAAAACyjjAKAAAAAMg6wigAAAAAIOsIowAAAACArCOMAgAAAACyjjAKAAAAAMg6wigAAAAAIOsIowAAAACArCOMAgAAAACyjjAKAAAAAMg6wigAAAAAIOsIowAAAACArCOMAgAAAACyjjAKAAAAAMg6wigAAAAAIOsyFkZvu+02HXvssVqyZEmmugAAAAAAjFAZC6PnnXeefvvb32aqeQAAAADACJaxMDpv3jwFAoFMNQ8AAAAAGMF4ZhQAAAAAkHXWXHZuNpvk8ThyWQKQVRbGPPIQ4x75hjGPfMOYx/7YLCYVFLiHPCanYbS/31A8nsplCUBWeTwOxjzyDuMe+YYxj3zDmMf+WM0mRaMJlZT4DngM03QBAAAAAFmXsTB6880366KLLtKuXbt04okn6vHHH89UVwAAAACAESZj03TvueeeTDUNAAAAABjhmKYLAAAAAMg6wigAAAAAIOsIowAAAACArCOMAgAAAACyjjAKAAAAAMg6wigAAAAAIOsIowAAAACArCOMAgAAAACyjjAKAAAAAMg6wigAAAAAIOsIowAAAACArCOMAgAAAACyjjAKAAAAAMg6wigAAAAAIOsIowAAAACArCOMAgAAAACyjjAKAAAAAMg6wigAAAAAIOsIowAAAACArCOMAgAAAACyjjAKAAAAAMg6wigAAAAAIOsIowAAAACArCOMAgAAAACyjjAKAAAAAMg6wigAAAAAIOsIowAAAACArCOMAgAAAACyjjAKAAAAAMg6wigAAAAAIOsIowAAAACArCOMAgAAAACyjjAKAAAAAMg6wigAAAAAIOsIowAAAACArCOMAgAAAACyjjAKAAAAAMg6wigAAAAAIOsIowAAAACArCOMAgAAAACyjjAKAAAAAMg6wigAAAAAIOsIowAAAACArCOMAgAAAACyjjAKAAAAAMg6wigAAAAAIOsIowAAAACArCOMAgAAAACyjjAKAAAAAMg6wigAAAAAIOsIowAAAACArCOMAgAAAACyjjAKAAAAAMg6wigAAAAAIOsIowAAAACArCOMAgAAAACyLqNh9PXXX9eiRYt06qmn6r777stkVwAAAACAESRjYbSvr0933XWXfvvb3+qZZ57RX/7yF23fvj1T3QEAAAAARpCMhdF169apqqpKo0ePlt1u1+LFi/Xyyy9nqjsAAAAAwAiSsTDa3NyssrKywZ9DoZCam5sz1R0AAAAAYASx5rLzk6eVffxBAAAAAIDDTsbujIZCITU1NQ3+3NzcrFAolKnuAAAAAAAjSMbC6MyZM1VdXa26ujql02k988wzWrhwYaa6AwAAAACMIBmbpmu1WnXHHXfoqquuUl9fn5YuXapJkyZlqjsAAAAAwAhiMgzDyHURAAAAAID8krFpugAAAAAAHAhhFAAAAACQdYRRAAAAAEDWEUYBAIfUAw88oCVLlmjx4sW6//779/n8d7/7naZMmaJIJCJJeumll3TWWWfp7LPP1nnnnadVq1ZlueLcuv/++5VMJgd/vvrqq9XZ2ZnDigAAyI6MraYLAMg/W7du1eOPP67HH39cNptNV111lU466SRVVVVJkhobG7VixQpVVFQMnnPsscfq5JNPlslk0ubNm3XTTTfpueeey0q9fX19slgsGe3DMAwZhiGzef///++DDz6oL37xi3K5XJKk3/zmNxmt57Po7e2V1ZrZPx2y0QcAYHjgv/YAgENmx44dmjVr1mCwmjdvnl544QVdffXVkqSf/OQnuuWWW3TdddcNnuPxeAa/TyaTMplM+237uuuuU1NTk1KplC6//HJdeOGFeuSRR1RbW6tbb71VkvTUU09p/fr1uuOOO7Rs2TI99NBD6unp0ezZs/WDH/xAFotFc+bM0YUXXqi33npLd9xxh95++229+uqrSqVSmjNnju666y6ZTCatW7dOt99+u8xms4477ji98cYb+stf/qK+vj79/Oc/18qVK5VOp3XJJZfooosu2qvW+vp6XXnllZo9e7Y2bNig++67T/fdd58++OADpVIpLVq0SDfeeKMefPBBtbS06Ctf+YoKCgr00EMPaeHChXriiSeUSCR09dVX66ijjtLatWsVCoX03//933I6nQesbdu2bbrtttvU09Oj/v5+/fKXv9TYsWP3qm3OnDk6//zztWLFChUXF+vf/u3fVFRUpNraWt15551qb2+X0+nU3XffrQkTJug73/mO7Ha7Nm3apLlz5+q2224bbOujv29Juvbaa/W1r31NRx99tG6//XatX79eJpNJS5cu1Ve/+tVP1QcA4DBmAABwiGzfvt047bTTjEgkYiQSCeOCCy4w7rrrLsMwDOPFF1807r77bsMwDOOkk04ywuHw4HkvvPCCsWjRImPevHnGmjVr9tt2e3u7YRiGkUwmjcWLFxuRSMQIh8PGKaecMnjMlVdeabz77rvG9u3bjWuvvdZIp9OGYRjGD37wA+OPf/yjYRiGMXnyZOOZZ57Zp13DMIxvfetbxssvv2wYhmEsXrx4sJaf/exnxuLFiw3DMIxHH33U+NWvfmUYhmGkUinj3HPPNWpra/eqta6uzpgyZYqxdu3affrp7e01Lr30UmPTpk37/V18+HNdXZ1xxBFHGBs3bjQMwzBuvPFG4+mnnx6ytrvuustYtmzZYG3JZHKf3+PkyZMHj/nlL39p3HnnnYZhGMbll19u7Nq1yzAMw3jvvfeMyy67zDAMw7j11luNa665xujt7d2nrSeffHLwfMMwjGuuucZ4++23//927i0kqi2O4/g3xy6jNsOYljkOURZNSTVmPgRWDzGZkyJJ01sRJfVSJPVSNFQSQRB0IciM8EUiu4IUDRaJRRekB80uKGQxTVNiJZZMqdnMeRA3dOzoOR2P55z4fZ723muvtf57vf3nv2ZFHz9+HN2wYYPx/OPHjz89h4iI/LpUGRURkRGTnp5OcXExmzZtwmw243Q6iYmJ4cuXL5SXl1NRUfHDfm63G7fbzcOHDzl+/PgP/2taWVnJzZs3gf7tvoFAAJfLhcPhoLGxkWnTpvHixQuysrI4e/YsT548Yc2aNQB0d3czadIkAEwmE7m5uca49fX1nDlzhu7ubjo7O5k1axaLFi0iHA6TmZkJQH5+PnV1dQDcu3ePlpYWampqAOjq6iIQCOBwOL6LNzU1FZfLZdz7/X4uXLhAX18f7969o7W1FafTOeR6pqWlMWfOHAAyMjIIhUJ8+vTpD2NzuVycOnWKtrY2VqxYMagqChATE4PH4wGgsLCQrVu3Eg6HaWhoYPv27cZ7vb29xvXKlSv/0nZmh8NBMBjkwIEDLFu2jJycnBGfQ0RE/v+UjIqIyIjyer14vV4Ajhw5wpQpU3j16hWvX7+msLAQgLa2NoqKirh48SLJyclG3+zsbILBIB0dHSQmJhrP6+vruX//PufPn8dsNrNu3Tp6enoA8Hg8+P1+ZsyYgdvtZsyYMUSjUVavXs3OnTsHxTd+/Hgj6enp6aG0tJTLly8zdepUTpw4YYz7R6LRKD6fjyVLlgz5XlxcnHEdDAapqKjg0qVLWK1Wdu3aNew8AOPGjTOuTSbTsH0KCgpYsGABdXV1bN68mdLSUhYvXjxkn4H1slgsVFdX//CdgW3Xv2cymYhEIsb9QHxWq5Xq6mru3r1LVVUVfr+fPXv2/NQcIiLy69JpuiIiMqI+fPgAwJs3b7hx4wYFBQXMnj2bBw8eUFtbS21tLSkpKVy5coXk5GQCgQDRaBSAp0+f0tvbi81m+27Mrq4urFYrZrOZ1tZWGhsbjTa3282tW7e4du0aq1atAvoPRaqpqTFi6ezsJBQKDYp1IHmy2WyEw2Gj2mmxWIiPj+fRo0cAXL9+3eiTk5PDuXPn+Pr1KwAvX77k8+fPQ65JOBzGbDYzceJE3r9/z507d4y2+Ph4wuHwcMtqGCq2YDCIw+Fg/fr1LF++nJaWlkH9I5GI8Z1Xr14lKyuLhIQE0tLS8Pv9QH/C3dzcPGwsdrud5uZmIpEIb9++pampCYCOjg6i0Si5ubmUlJTw7Nmzn55DRER+XaqMiojIiNq2bRudnZ3Exsayb98+LBbLkO/X1NRQXV1NbGwsEyZM4OjRo4MOMVq6dClVVVXk5eUxffr077a/Wq1W0tPTef78OfPnzwdg5syZlJSUsHHjRiKRCGPHjmXv3r3Y7fbvxrVYLHi9XvLz80lKSmLevHlG28GDB/H5fMTExJCdnU1CQgLQX/kNhUIUFRURjUax2WycPHlyyG90Op3MnTuXvLw8UlJSWLhwodG2du1aiouLmTx5MpWVlUOOM1xsfr/fWMukpCS2bNkyqG9cXBxNTU2UlZWRmJjIsWPHADh8+DD79++nrKyMvr4+PB7PsNuIs7KysNvteDwe0tPTycjIAKC9vZ3du3cbVdMdO3b89BwiIvLrGhMd+DlaREREDOFw2Djp9/Tp07S3t+Pz+f7lqPr9ndgyMzNpaGj4J8MTERH5U1QZFRER+YHbt29TXl7Ot2/fSE1N5dChQ/92SIb/cmwiIiJ/liqjIiIiIiIiMup0gJGIiIiIiIiMOiWjIiIiIiIiMuqUjIqIiIiIiMioUzIqIiIiIiIio07JqIiIiIiIiIw6JaMiIiIiIiIy6n4D8DDHCu+3RCMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1152x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "user_means = data.groupby(\"userid\").rating.mean().sort_values()\n",
    "_, ax = plt.subplots(figsize=(16, 6))\n",
    "ax.plot(np.arange(len(user_means)), user_means.values, \"k-\")\n",
    "\n",
    "ax.fill_between(np.arange(len(user_means)), user_means.values, alpha=0.3)\n",
    "ax.set_xticklabels(\"\")\n",
    "# 1000 labels is nonsensical\n",
    "ax.set_ylabel(\"Rating\")\n",
    "ax.set_xlabel(f\"{len(user_means)} average ratings per user\")\n",
    "ax.set_ylim(0, 5)\n",
    "ax.set_xlim(0, len(user_means));"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We see even more significant trends here. Some users rate nearly everything highly, and some (though not as many) rate nearly everything negatively. These observations will come in handy when considering models to use for predicting user preferences on unseen movies."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Methods\n",
    "\n",
    "Having explored the data, we're now ready to dig in and start addressing the problem. We want to predict how much each user is going to like all of the movies he or she has not yet read.\n",
    "\n",
    "\n",
    "### Baselines\n",
    "\n",
    "Every good analysis needs some kind of baseline methods to compare against. It's difficult to claim we've produced good results if we have no reference point for what defines \"good\". We'll define three very simple baseline methods and find the RMSE using these methods. Our goal will be to obtain lower RMSE scores with whatever model we produce.\n",
    "\n",
    "#### Uniform Random Baseline\n",
    "\n",
    "Our first baseline is about as dead stupid as you can get. Every place we see a missing value in $R$, we'll simply fill it with a number drawn uniformly at random in the range [1, 5]. We expect this method to do the worst by far.\n",
    "\n",
    "$$R_{ij}^* \\sim Uniform$$\n",
    "\n",
    "#### Global Mean Baseline\n",
    "\n",
    "This method is only slightly better than the last. Wherever we have a missing value, we'll fill it in with the mean of all observed ratings.\n",
    "\n",
    "$$\\text{global_mean} = \\frac{1}{N \\times M} \\sum_{i=1}^N \\sum_{j=1}^M I_{ij}(R_{ij})$$\n",
    "\n",
    "$$R_{ij}^* = \\text{global_mean}$$\n",
    "\n",
    "#### Mean of Means Baseline\n",
    "\n",
    "Now we're going to start getting a bit smarter. We imagine some users might be easily amused, and inclined to rate all movies more highly. Other users might be the opposite. Additionally, some movies might simply be more witty than others, so all users might rate some movies more highly than others in general. We can clearly see this in our graph of the movie means above. We'll attempt to capture these general trends through per-user and per-movie rating means. We'll also incorporate the global mean to smooth things out a bit. So if we see a missing value in cell $R_{ij}$, we'll average the global mean with the mean of $U_i$ and the mean of $V_j$ and use that value to fill it in.\n",
    "\n",
    "$$\\text{user_means} = \\frac{1}{M} \\sum_{j=1}^M I_{ij}(R_{ij})$$\n",
    "\n",
    "$$\\text{movie_means} = \\frac{1}{N} \\sum_{i=1}^N I_{ij}(R_{ij})$$\n",
    "\n",
    "$$R_{ij}^* = \\frac{1}{3} \\left(\\text{user_means}_i + \\text{ movie_means}_j + \\text{ global_mean} \\right)$$\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a base class with scaffolding for our 3 baselines.\n",
    "\n",
    "\n",
    "def split_title(title):\n",
    "    \"\"\"Change \"BaselineMethod\" to \"Baseline Method\".\"\"\"\n",
    "    words = []\n",
    "    tmp = [title[0]]\n",
    "    for c in title[1:]:\n",
    "        if c.isupper():\n",
    "            words.append(\"\".join(tmp))\n",
    "            tmp = [c]\n",
    "        else:\n",
    "            tmp.append(c)\n",
    "    words.append(\"\".join(tmp))\n",
    "    return \" \".join(words)\n",
    "\n",
    "\n",
    "class Baseline:\n",
    "    \"\"\"Calculate baseline predictions.\"\"\"\n",
    "\n",
    "    def __init__(self, train_data):\n",
    "        \"\"\"Simple heuristic-based transductive learning to fill in missing\n",
    "        values in data matrix.\"\"\"\n",
    "        self.predict(train_data.copy())\n",
    "\n",
    "    def predict(self, train_data):\n",
    "        raise NotImplementedError(\"baseline prediction not implemented for base class\")\n",
    "\n",
    "    def rmse(self, test_data):\n",
    "        \"\"\"Calculate root mean squared error for predictions on test data.\"\"\"\n",
    "        return rmse(test_data, self.predicted)\n",
    "\n",
    "    def __str__(self):\n",
    "        return split_title(self.__class__.__name__)\n",
    "\n",
    "\n",
    "# Implement the 3 baselines.\n",
    "\n",
    "\n",
    "class UniformRandomBaseline(Baseline):\n",
    "    \"\"\"Fill missing values with uniform random values.\"\"\"\n",
    "\n",
    "    def predict(self, train_data):\n",
    "        nan_mask = np.isnan(train_data)\n",
    "        masked_train = np.ma.masked_array(train_data, nan_mask)\n",
    "        pmin, pmax = masked_train.min(), masked_train.max()\n",
    "        N = nan_mask.sum()\n",
    "        train_data[nan_mask] = np.random.uniform(pmin, pmax, N)\n",
    "        self.predicted = train_data\n",
    "\n",
    "\n",
    "class GlobalMeanBaseline(Baseline):\n",
    "    \"\"\"Fill in missing values using the global mean.\"\"\"\n",
    "\n",
    "    def predict(self, train_data):\n",
    "        nan_mask = np.isnan(train_data)\n",
    "        train_data[nan_mask] = train_data[~nan_mask].mean()\n",
    "        self.predicted = train_data\n",
    "\n",
    "\n",
    "class MeanOfMeansBaseline(Baseline):\n",
    "    \"\"\"Fill in missing values using mean of user/item/global means.\"\"\"\n",
    "\n",
    "    def predict(self, train_data):\n",
    "        nan_mask = np.isnan(train_data)\n",
    "        masked_train = np.ma.masked_array(train_data, nan_mask)\n",
    "        global_mean = masked_train.mean()\n",
    "        user_means = masked_train.mean(axis=1)\n",
    "        item_means = masked_train.mean(axis=0)\n",
    "        self.predicted = train_data.copy()\n",
    "        n, m = train_data.shape\n",
    "        for i in range(n):\n",
    "            for j in range(m):\n",
    "                if np.ma.isMA(item_means[j]):\n",
    "                    self.predicted[i, j] = np.mean((global_mean, user_means[i]))\n",
    "                else:\n",
    "                    self.predicted[i, j] = np.mean((global_mean, user_means[i], item_means[j]))\n",
    "\n",
    "\n",
    "baseline_methods = {}\n",
    "baseline_methods[\"ur\"] = UniformRandomBaseline\n",
    "baseline_methods[\"gm\"] = GlobalMeanBaseline\n",
    "baseline_methods[\"mom\"] = MeanOfMeansBaseline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Users: 943\n",
      "Movies: 1682\n",
      "Sparsity: 0.9369533063577546\n"
     ]
    }
   ],
   "source": [
    "num_users = data.userid.unique().shape[0]\n",
    "num_items = data.itemid.unique().shape[0]\n",
    "sparsity = 1 - len(data) / (num_users * num_items)\n",
    "print(f\"Users: {num_users}\\nMovies: {num_items}\\nSparsity: {sparsity}\")\n",
    "\n",
    "dense_data = data.pivot(index=\"userid\", columns=\"itemid\", values=\"rating\").values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Probabilistic Matrix Factorization\n",
    "\n",
    "[Probabilistic Matrix Factorization (PMF)](http://papers.nips.cc/paper/3208-probabilistic-matrix-factorization.pdf) [3] is a probabilistic approach to the collaborative filtering problem that takes a Bayesian perspective. The ratings $R$ are modeled as draws from a Gaussian distribution.  The mean for $R_{ij}$ is $U_i V_j^T$. The precision $\\alpha$ is a fixed parameter that reflects the uncertainty of the estimations; the normal distribution is commonly reparameterized in terms of precision, which is the inverse of the variance. Complexity is controlled by placing zero-mean spherical Gaussian priors on $U$ and $V$. In other words, each row of $U$ is drawn from a multivariate Gaussian with mean $\\mu = 0$ and precision which is some multiple of the identity matrix $I$. Those multiples are $\\alpha_U$ for $U$ and $\\alpha_V$ for $V$. So our model is defined by:\n",
    "\n",
    "$\\newcommand\\given[1][]{\\:#1\\vert\\:}$\n",
    "\n",
    "$$\n",
    "P(R \\given U, V, \\alpha^2) = \n",
    "    \\prod_{i=1}^N \\prod_{j=1}^M\n",
    "        \\left[ \\mathcal{N}(R_{ij} \\given U_i V_j^T, \\alpha^{-1}) \\right]^{I_{ij}}\n",
    "$$\n",
    "\n",
    "$$\n",
    "P(U \\given \\alpha_U^2) =\n",
    "    \\prod_{i=1}^N \\mathcal{N}(U_i \\given 0, \\alpha_U^{-1} \\boldsymbol{I})\n",
    "$$\n",
    "\n",
    "$$\n",
    "P(V \\given \\alpha_U^2) =\n",
    "    \\prod_{j=1}^M \\mathcal{N}(V_j \\given 0, \\alpha_V^{-1} \\boldsymbol{I})\n",
    "$$\n",
    "\n",
    "Given small precision parameters, the priors on $U$ and $V$ ensure our latent variables do not grow too far from 0. This prevents overly strong user preferences and item factor compositions from being learned. This is commonly known as complexity control, where the complexity of the model here is measured by the magnitude of the latent variables. Controlling complexity like this helps prevent overfitting, which allows the model to generalize better for unseen data. We must also choose an appropriate $\\alpha$ value for the normal distribution for $R$. So the challenge becomes choosing appropriate values for $\\alpha_U$, $\\alpha_V$, and $\\alpha$. This challenge can be tackled with the soft weight-sharing methods discussed by [Nowland and Hinton, 1992](http://www.cs.toronto.edu/~fritz/absps/sunspots.pdf) [4]. However, for the purposes of this analysis, we will stick to using point estimates obtained from our data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "import logging\n",
    "import time\n",
    "\n",
    "import scipy as sp\n",
    "import theano\n",
    "\n",
    "# Enable on-the-fly graph computations, but ignore\n",
    "# absence of intermediate test values.\n",
    "theano.config.compute_test_value = \"ignore\"\n",
    "\n",
    "# Set up logging.\n",
    "logger = logging.getLogger()\n",
    "logger.setLevel(logging.INFO)\n",
    "\n",
    "\n",
    "class PMF:\n",
    "    \"\"\"Probabilistic Matrix Factorization model using pymc3.\"\"\"\n",
    "\n",
    "    def __init__(self, train, dim, alpha=2, std=0.01, bounds=(1, 5)):\n",
    "        \"\"\"Build the Probabilistic Matrix Factorization model using pymc3.\n",
    "\n",
    "        :param np.ndarray train: The training data to use for learning the model.\n",
    "        :param int dim: Dimensionality of the model; number of latent factors.\n",
    "        :param int alpha: Fixed precision for the likelihood function.\n",
    "        :param float std: Amount of noise to use for model initialization.\n",
    "        :param (tuple of int) bounds: (lower, upper) bound of ratings.\n",
    "            These bounds will simply be used to cap the estimates produced for R.\n",
    "\n",
    "        \"\"\"\n",
    "        self.dim = dim\n",
    "        self.alpha = alpha\n",
    "        self.std = np.sqrt(1.0 / alpha)\n",
    "        self.bounds = bounds\n",
    "        self.data = train.copy()\n",
    "        n, m = self.data.shape\n",
    "\n",
    "        # Perform mean value imputation\n",
    "        nan_mask = np.isnan(self.data)\n",
    "        self.data[nan_mask] = self.data[~nan_mask].mean()\n",
    "\n",
    "        # Low precision reflects uncertainty; prevents overfitting.\n",
    "        # Set to the mean variance across users and items.\n",
    "        self.alpha_u = 1 / self.data.var(axis=1).mean()\n",
    "        self.alpha_v = 1 / self.data.var(axis=0).mean()\n",
    "\n",
    "        # Specify the model.\n",
    "        logging.info(\"building the PMF model\")\n",
    "        with pm.Model() as pmf:\n",
    "            U = pm.MvNormal(\n",
    "                \"U\",\n",
    "                mu=0,\n",
    "                tau=self.alpha_u * np.eye(dim),\n",
    "                shape=(n, dim),\n",
    "                testval=np.random.randn(n, dim) * std,\n",
    "            )\n",
    "            V = pm.MvNormal(\n",
    "                \"V\",\n",
    "                mu=0,\n",
    "                tau=self.alpha_v * np.eye(dim),\n",
    "                shape=(m, dim),\n",
    "                testval=np.random.randn(m, dim) * std,\n",
    "            )\n",
    "            R = pm.Normal(\n",
    "                \"R\", mu=(U @ V.T)[~nan_mask], tau=self.alpha, observed=self.data[~nan_mask]\n",
    "            )\n",
    "\n",
    "        logging.info(\"done building the PMF model\")\n",
    "        self.model = pmf\n",
    "\n",
    "    def __str__(self):\n",
    "        return self.name"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We'll also need functions for calculating the MAP and performing sampling on our PMF model. When the observation noise variance $\\alpha$ and the prior variances $\\alpha_U$ and $\\alpha_V$ are all kept fixed, maximizing the log posterior is equivalent to minimizing the sum-of-squared-errors objective function with quadratic regularization terms.\n",
    "\n",
    "$$ E = \\frac{1}{2} \\sum_{i=1}^N \\sum_{j=1}^M I_{ij} (R_{ij} - U_i V_j^T)^2 + \\frac{\\lambda_U}{2} \\sum_{i=1}^N \\|U\\|_{Fro}^2 + \\frac{\\lambda_V}{2} \\sum_{j=1}^M \\|V\\|_{Fro}^2, $$\n",
    "\n",
    "where $\\lambda_U = \\alpha_U / \\alpha$, $\\lambda_V = \\alpha_V / \\alpha$, and $\\|\\cdot\\|_{Fro}^2$ denotes the Frobenius norm [3]. Minimizing this objective function gives a local minimum, which is essentially a maximum a posteriori (MAP) estimate. While it is possible to use a fast Stochastic Gradient Descent procedure to find this MAP, we'll be finding it using the utilities built into `pymc3`. In particular, we'll use `find_MAP` with Powell optimization (`scipy.optimize.fmin_powell`). Having found this MAP estimate, we can use it as our starting point for MCMC sampling.\n",
    "\n",
    "Since it is a reasonably complex model, we expect the MAP estimation to take some time. So let's save it after we've found it. Note that we define a function for finding the MAP below, assuming it will receive a namespace with some variables in it. Then we attach that function to the PMF class, where it will have such a namespace after initialization. The PMF class is defined in pieces this way so I can say a few things between each piece to make it clearer."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "def _find_map(self):\n",
    "    \"\"\"Find mode of posterior using L-BFGS-B optimization.\"\"\"\n",
    "    tstart = time.time()\n",
    "    with self.model:\n",
    "        logging.info(\"finding PMF MAP using L-BFGS-B optimization...\")\n",
    "        self._map = pm.find_MAP(method=\"L-BFGS-B\")\n",
    "\n",
    "    elapsed = int(time.time() - tstart)\n",
    "    logging.info(\"found PMF MAP in %d seconds\" % elapsed)\n",
    "    return self._map\n",
    "\n",
    "\n",
    "def _map(self):\n",
    "    try:\n",
    "        return self._map\n",
    "    except:\n",
    "        return self.find_map()\n",
    "\n",
    "\n",
    "# Update our class with the new MAP infrastructure.\n",
    "PMF.find_map = _find_map\n",
    "PMF.map = property(_map)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So now our PMF class has a `map` `property` which will either be found using Powell optimization or loaded from a previous optimization. Once we have the MAP, we can use it as a starting point for our MCMC sampler. We'll need a sampling function in order to draw MCMC samples to approximate the posterior distribution of the PMF model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Draw MCMC samples.\n",
    "def _draw_samples(self, **kwargs):\n",
    "    kwargs.setdefault(\"chains\", 1)\n",
    "    with self.model:\n",
    "        self.trace = pm.sample(**kwargs)\n",
    "\n",
    "\n",
    "# Update our class with the sampling infrastructure.\n",
    "PMF.draw_samples = _draw_samples"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We could define some kind of default trace property like we did for the MAP, but that would mean using possibly nonsensical values for `nsamples` and `cores`. Better to leave it as a non-optional call to `draw_samples`. Finally, we'll need a function to make predictions using our inferred values for $U$ and $V$. For user $i$ and movie $j$, a prediction is generated by drawing from $\\mathcal{N}(U_i V_j^T, \\alpha)$. To generate predictions from the sampler, we generate an $R$ matrix for each $U$ and $V$ sampled, then we combine these by averaging over the $K$ samples.\n",
    "\n",
    "$$\n",
    "P(R_{ij}^* \\given R, \\alpha, \\alpha_U, \\alpha_V) \\approx\n",
    "    \\frac{1}{K} \\sum_{k=1}^K \\mathcal{N}(U_i V_j^T, \\alpha)\n",
    "$$\n",
    "\n",
    "We'll want to inspect the individual $R$ matrices before averaging them for diagnostic purposes. So we'll write code for the averaging piece during evaluation. The function below simply draws an $R$ matrix given a $U$ and $V$ and the fixed $\\alpha$ stored in the PMF object."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "def _predict(self, U, V):\n",
    "    \"\"\"Estimate R from the given values of U and V.\"\"\"\n",
    "    R = np.dot(U, V.T)\n",
    "    n, m = R.shape\n",
    "    sample_R = np.random.normal(R, self.std)\n",
    "    # bound ratings\n",
    "    low, high = self.bounds\n",
    "    sample_R[sample_R < low] = low\n",
    "    sample_R[sample_R > high] = high\n",
    "    return sample_R\n",
    "\n",
    "\n",
    "PMF.predict = _predict"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "One final thing to note: the dot products in this model are often constrained using a logistic function $g(x) = 1/(1 + exp(-x))$, that bounds the predictions to the range [0, 1]. To facilitate this bounding, the ratings are also mapped to the range [0, 1] using $t(x) = (x + min) / range$. The authors of PMF also introduced a constrained version which performs better on users with less ratings [3]. Both models are generally improvements upon the basic model presented here. However, in the interest of time and space, these will not be implemented here."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Evaluation\n",
    "\n",
    "### Metrics\n",
    "\n",
    "In order to understand how effective our models are, we'll need to be able to evaluate them. We'll be evaluating in terms of root mean squared error (RMSE), which looks like this:\n",
    "\n",
    "$$\n",
    "RMSE = \\sqrt{ \\frac{ \\sum_{i=1}^N \\sum_{j=1}^M I_{ij} (R_{ij} - R_{ij}^*)^2 }\n",
    "                   { \\sum_{i=1}^N \\sum_{j=1}^M I_{ij} } }\n",
    "$$\n",
    "\n",
    "In this case, the RMSE can be thought of as the standard deviation of our predictions from the actual user preferences."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define our evaluation function.\n",
    "def rmse(test_data, predicted):\n",
    "    \"\"\"Calculate root mean squared error.\n",
    "    Ignoring missing values in the test data.\n",
    "    \"\"\"\n",
    "    I = ~np.isnan(test_data)  # indicator for missing values\n",
    "    N = I.sum()  # number of non-missing values\n",
    "    sqerror = abs(test_data - predicted) ** 2  # squared error array\n",
    "    mse = sqerror[I].sum() / N  # mean squared error\n",
    "    return np.sqrt(mse)  # RMSE"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Training Data vs. Test Data\n",
    "\n",
    "The next thing we need to do is split our data into a training set and a test set. Matrix factorization techniques use [transductive learning](http://en.wikipedia.org/wiki/Transduction_%28machine_learning%29) rather than inductive learning. So we produce a test set by taking a random sample of the cells in the full $N \\times M$ data matrix. The values selected as test samples are replaced with `nan` values in a copy of the original data matrix to produce the training set. Since we'll be producing random splits, let's also write out the train/test sets generated. This will allow us to replicate our results. We'd like to be able to idenfity which split is which, so we'll take a hash of the indices selected for testing and use that to save the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define a function for splitting train/test data.\n",
    "def split_train_test(data, percent_test=0.1):\n",
    "    \"\"\"Split the data into train/test sets.\n",
    "    :param int percent_test: Percentage of data to use for testing. Default 10.\n",
    "    \"\"\"\n",
    "    n, m = data.shape  # # users, # movies\n",
    "    N = n * m  # # cells in matrix\n",
    "\n",
    "    # Prepare train/test ndarrays.\n",
    "    train = data.copy()\n",
    "    test = np.ones(data.shape) * np.nan\n",
    "\n",
    "    # Draw random sample of training data to use for testing.\n",
    "    tosample = np.where(~np.isnan(train))  # ignore nan values in data\n",
    "    idx_pairs = list(zip(tosample[0], tosample[1]))  # tuples of row/col index pairs\n",
    "\n",
    "    test_size = int(len(idx_pairs) * percent_test)  # use 10% of data as test set\n",
    "    train_size = len(idx_pairs) - test_size  # and remainder for training\n",
    "\n",
    "    indices = np.arange(len(idx_pairs))  # indices of index pairs\n",
    "    sample = np.random.choice(indices, replace=False, size=test_size)\n",
    "\n",
    "    # Transfer random sample from train set to test set.\n",
    "    for idx in sample:\n",
    "        idx_pair = idx_pairs[idx]\n",
    "        test[idx_pair] = train[idx_pair]  # transfer to test set\n",
    "        train[idx_pair] = np.nan  # remove from train set\n",
    "\n",
    "    # Verify everything worked properly\n",
    "    assert train_size == N - np.isnan(train).sum()\n",
    "    assert test_size == N - np.isnan(test).sum()\n",
    "\n",
    "    # Return train set and test set\n",
    "    return train, test\n",
    "\n",
    "\n",
    "train, test = split_train_test(dense_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Uniform Random Baseline RMSE:\t1.69769\n",
      "Global Mean Baseline RMSE:\t1.12359\n",
      "Mean Of Means Baseline RMSE:\t1.01168\n"
     ]
    }
   ],
   "source": [
    "# Let's see the results:\n",
    "baselines = {}\n",
    "for name in baseline_methods:\n",
    "    Method = baseline_methods[name]\n",
    "    method = Method(train)\n",
    "    baselines[name] = method.rmse(test)\n",
    "    print(\"{} RMSE:\\t{:.5f}\".format(method, baselines[name]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As expected: the uniform random baseline is the worst by far, the global mean baseline is next best, and the mean of means method is our best baseline. Now let's see how PMF stacks up."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:root:building the PMF model\n",
      "/env/miniconda3/lib/python3.7/site-packages/theano/tensor/subtensor.py:2197: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  rval = inputs[0].__getitem__(inputs[1:])\n",
      "INFO:root:done building the PMF model\n"
     ]
    }
   ],
   "source": [
    "# We use a fixed precision for the likelihood.\n",
    "# This reflects uncertainty in the dot product.\n",
    "# We choose 2 in the footsteps Salakhutdinov\n",
    "# Mnihof.\n",
    "ALPHA = 2\n",
    "\n",
    "# The dimensionality D; the number of latent factors.\n",
    "# We can adjust this higher to try to capture more subtle\n",
    "# characteristics of each movie. However, the higher it is,\n",
    "# the more expensive our inference procedures will be.\n",
    "# Specifically, we have D(N + M) latent variables. For our\n",
    "# Movielens dataset, this means we have D(2625), so for 5\n",
    "# dimensions, we are sampling 13125 latent variables.\n",
    "DIM = 10\n",
    "\n",
    "\n",
    "pmf = PMF(train, DIM, ALPHA, std=0.05)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Predictions Using MAP"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:root:finding PMF MAP using L-BFGS-B optimization...\n"
     ]
    },
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       "      <progress value='1264' class='' max='1264' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
       "      100.00% [1264/1264 01:40<00:00 logp = -1.537e+05, ||grad|| = 0.8563]\n",
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     "text": [
      "/env/miniconda3/lib/python3.7/site-packages/theano/tensor/subtensor.py:2197: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  rval = inputs[0].__getitem__(inputs[1:])\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:root:found PMF MAP in 111 seconds\n"
     ]
    }
   ],
   "source": [
    "# Find MAP for PMF.\n",
    "pmf.find_map();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Excellent. The first thing we want to do is make sure the MAP estimate we obtained is reasonable. We can do this by computing RMSE on the predicted ratings obtained from the MAP values of $U$ and $V$. First we define a function for generating the predicted ratings $R$ from $U$ and $V$. We ensure the actual rating bounds are enforced by setting all values below 1 to 1 and all values above 5 to 5. Finally, we compute RMSE for both the training set and the test set. We expect the test RMSE to be higher. The difference between the two gives some idea of how much we have overfit. Some difference is always expected, but a very low RMSE on the training set with a high RMSE on the test set is a definite sign of overfitting."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "def eval_map(pmf_model, train, test):\n",
    "    U = pmf_model.map[\"U\"]\n",
    "    V = pmf_model.map[\"V\"]\n",
    "\n",
    "    # Make predictions and calculate RMSE on train & test sets.\n",
    "    predictions = pmf_model.predict(U, V)\n",
    "    train_rmse = rmse(train, predictions)\n",
    "    test_rmse = rmse(test, predictions)\n",
    "    overfit = test_rmse - train_rmse\n",
    "\n",
    "    # Print report.\n",
    "    print(\"PMF MAP training RMSE: %.5f\" % train_rmse)\n",
    "    print(\"PMF MAP testing RMSE:  %.5f\" % test_rmse)\n",
    "    print(\"Train/test difference: %.5f\" % overfit)\n",
    "\n",
    "    return test_rmse\n",
    "\n",
    "\n",
    "# Add eval function to PMF class.\n",
    "PMF.eval_map = eval_map"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PMF MAP training RMSE: 1.01333\n",
      "PMF MAP testing RMSE:  1.13761\n",
      "Train/test difference: 0.12427\n",
      "PMF MAP Improvement:   -0.12593\n"
     ]
    }
   ],
   "source": [
    "# Evaluate PMF MAP estimates.\n",
    "pmf_map_rmse = pmf.eval_map(train, test)\n",
    "pmf_improvement = baselines[\"mom\"] - pmf_map_rmse\n",
    "print(\"PMF MAP Improvement:   %.5f\" % pmf_improvement)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We actually see a decrease in performance between the MAP estimate and the mean of means performance. We also have a fairly large difference in the RMSE values between the train and the test sets. This indicates that the point estimates for $\\alpha_U$ and $\\alpha_V$ that we calculated from our data are not doing a great job of controlling model complexity. \n",
    "\n",
    "Let's see if we can improve our estimates by approximating our posterior distribution with MCMC sampling. We'll draw 500 samples, with 500 tuning samples."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Predictions using MCMC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Auto-assigning NUTS sampler...\n",
      "INFO:pymc3:Auto-assigning NUTS sampler...\n",
      "Initializing NUTS using jitter+adapt_diag...\n",
      "INFO:pymc3:Initializing NUTS using jitter+adapt_diag...\n",
      "/env/miniconda3/lib/python3.7/site-packages/theano/tensor/subtensor.py:2197: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  rval = inputs[0].__getitem__(inputs[1:])\n",
      "Sequential sampling (1 chains in 1 job)\n",
      "INFO:pymc3:Sequential sampling (1 chains in 1 job)\n",
      "NUTS: [V, U]\n",
      "INFO:pymc3:NUTS: [V, U]\n"
     ]
    },
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       "      100.00% [1000/1000 46:06<00:00 Sampling chain 0, 0 divergences]\n",
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     "output_type": "stream",
     "text": [
      "Sampling 1 chain for 500 tune and 500 draw iterations (500 + 500 draws total) took 2766 seconds.\n",
      "INFO:pymc3:Sampling 1 chain for 500 tune and 500 draw iterations (500 + 500 draws total) took 2766 seconds.\n",
      "/env/miniconda3/lib/python3.7/site-packages/theano/tensor/subtensor.py:2197: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  rval = inputs[0].__getitem__(inputs[1:])\n",
      "Only one chain was sampled, this makes it impossible to run some convergence checks\n",
      "INFO:pymc3:Only one chain was sampled, this makes it impossible to run some convergence checks\n"
     ]
    }
   ],
   "source": [
    "# Draw MCMC samples.\n",
    "pmf.draw_samples(\n",
    "    draws=500,\n",
    "    tune=500,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Diagnostics and Posterior Predictive Check\n",
    "\n",
    "The next step is to check how many samples we should discard as burn-in. Normally, we'd do this using a traceplot to get some idea of where the sampled variables start to converge. In this case, we have high-dimensional samples, so we need to find a way to approximate them. One way was proposed by [Salakhutdinov and Mnih, p.886](https://www.cs.toronto.edu/~amnih/papers/bpmf.pdf). We can calculate the Frobenius norms of $U$ and $V$ at each step and monitor those for convergence. This essentially gives us some idea when the average magnitude of the latent variables is stabilizing. The equations for the Frobenius norms of $U$ and $V$ are shown below. We will use `numpy`'s `linalg` package to calculate these.\n",
    "\n",
    "$$ \\|U\\|_{Fro}^2 = \\sqrt{\\sum_{i=1}^N \\sum_{d=1}^D |U_{id}|^2}, \\hspace{40pt} \\|V\\|_{Fro}^2 = \\sqrt{\\sum_{j=1}^M \\sum_{d=1}^D |V_{jd}|^2} $$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "def _norms(pmf_model, monitor=(\"U\", \"V\"), ord=\"fro\"):\n",
    "    \"\"\"Return norms of latent variables at each step in the\n",
    "    sample trace. These can be used to monitor convergence\n",
    "    of the sampler.\n",
    "    \"\"\"\n",
    "    monitor = (\"U\", \"V\")\n",
    "    norms = {var: [] for var in monitor}\n",
    "    for sample in pmf_model.trace:\n",
    "        for var in monitor:\n",
    "            norms[var].append(np.linalg.norm(sample[var], ord))\n",
    "    return norms\n",
    "\n",
    "\n",
    "def _traceplot(pmf_model):\n",
    "    \"\"\"Plot Frobenius norms of U and V as a function of sample #.\"\"\"\n",
    "    trace_norms = pmf_model.norms()\n",
    "    u_series = pd.Series(trace_norms[\"U\"])\n",
    "    v_series = pd.Series(trace_norms[\"V\"])\n",
    "    fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(15, 7))\n",
    "    u_series.plot(kind=\"line\", ax=ax1, grid=False, title=r\"$\\|U\\|_{Fro}^2$ at Each Sample\")\n",
    "    v_series.plot(kind=\"line\", ax=ax2, grid=False, title=r\"$\\|V\\|_{Fro}^2$ at Each Sample\")\n",
    "    ax1.set_xlabel(\"Sample Number\")\n",
    "    ax2.set_xlabel(\"Sample Number\")\n",
    "\n",
    "\n",
    "PMF.norms = _norms\n",
    "PMF.traceplot = _traceplot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
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\n",
      "text/plain": [
       "<Figure size 1080x504 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pmf.traceplot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It appears we get convergence of $U$ and $V$ after about the default tuning. When testing for convergence, we also want to see convergence of the particular statistics we are looking for, since different characteristics of the posterior may converge at different rates. Let's also do a traceplot of the RSME. We'll compute RMSE for both the train and the test set, even though the convergence is indicated by RMSE on the training set alone. In addition, let's compute a running RMSE on the train/test sets to see how aggregate performance improves or decreases as we continue to sample.\n",
    "\n",
    "Notice here that we are sampling from 1 chain only, which makes the convergence statisitcs like $\\hat{r}$ impossible (we can still compute the split-rhat but the purpose is different). The reason of not sampling multiple chain is that PMF might not have unique solution. Thus without constraints, the solutions are at best symmetrical, at worse identical under any rotation, in any case subject to label switching. In fact if we sample from multiple chains we will see large $\\hat{r}$ indicating the sampler is exploring different solutions in different part of parameter space."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "def _running_rmse(pmf_model, test_data, train_data, burn_in=0, plot=True):\n",
    "    \"\"\"Calculate RMSE for each step of the trace to monitor convergence.\"\"\"\n",
    "    burn_in = burn_in if len(pmf_model.trace) >= burn_in else 0\n",
    "    results = {\"per-step-train\": [], \"running-train\": [], \"per-step-test\": [], \"running-test\": []}\n",
    "    R = np.zeros(test_data.shape)\n",
    "    for cnt, sample in enumerate(pmf_model.trace[burn_in:]):\n",
    "        sample_R = pmf_model.predict(sample[\"U\"], sample[\"V\"])\n",
    "        R += sample_R\n",
    "        running_R = R / (cnt + 1)\n",
    "        results[\"per-step-train\"].append(rmse(train_data, sample_R))\n",
    "        results[\"running-train\"].append(rmse(train_data, running_R))\n",
    "        results[\"per-step-test\"].append(rmse(test_data, sample_R))\n",
    "        results[\"running-test\"].append(rmse(test_data, running_R))\n",
    "\n",
    "    results = pd.DataFrame(results)\n",
    "\n",
    "    if plot:\n",
    "        results.plot(\n",
    "            kind=\"line\",\n",
    "            grid=False,\n",
    "            figsize=(15, 7),\n",
    "            title=\"Per-step and Running RMSE From Posterior Predictive\",\n",
    "        )\n",
    "\n",
    "    # Return the final predictions, and the RMSE calculations\n",
    "    return running_R, results\n",
    "\n",
    "\n",
    "PMF.running_rmse = _running_rmse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "predicted, results = pmf.running_rmse(test, train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Posterior predictive train RMSE: 0.78030\n",
      "Posterior predictive test RMSE:  0.91377\n",
      "Train/test difference:           0.13347\n",
      "Improvement from MAP:            0.22384\n",
      "Improvement from Mean of Means:  0.09791\n"
     ]
    }
   ],
   "source": [
    "# And our final RMSE?\n",
    "final_test_rmse = results[\"running-test\"].values[-1]\n",
    "final_train_rmse = results[\"running-train\"].values[-1]\n",
    "print(\"Posterior predictive train RMSE: %.5f\" % final_train_rmse)\n",
    "print(\"Posterior predictive test RMSE:  %.5f\" % final_test_rmse)\n",
    "print(\"Train/test difference:           %.5f\" % (final_test_rmse - final_train_rmse))\n",
    "print(\"Improvement from MAP:            %.5f\" % (pmf_map_rmse - final_test_rmse))\n",
    "print(\"Improvement from Mean of Means:  %.5f\" % (baselines[\"mom\"] - final_test_rmse))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We have some interesting results here. As expected, our MCMC sampler provides lower error on the training set. However, it seems it does so at the cost of overfitting the data. This results in a decrease in test RMSE as compared to the MAP, even though it is still much better than our best baseline. So why might this be the case? Recall that we used point estimates for our precision paremeters $\\alpha_U$ and $\\alpha_V$ and we chose a fixed precision $\\alpha$. It is quite likely that by doing this, we constrained our posterior in a way that biased it towards the training data. In reality, the variance in the user ratings and the movie ratings is unlikely to be equal to the means of sample variances we used. Also, the most reasonable observation precision $\\alpha$ is likely different as well."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We have some interesting results here. As expected, our MCMC sampler provides lower error on the training set. However, it seems it does so at the cost of overfitting the data. This results in a decrease in test RMSE as compared to the MAP, even though it is still much better than our best baseline. So why might this be the case? Recall that we used point estimates for our precision paremeters $\\alpha_U$ and $\\alpha_V$ and we chose a fixed precision $\\alpha$. It is quite likely that by doing this, we constrained our posterior in a way that biased it towards the training data. In reality, the variance in the user ratings and the movie ratings is unlikely to be equal to the means of sample variances we used. Also, the most reasonable observation precision $\\alpha$ is likely different as well."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We have some interesting results here. As expected, our MCMC sampler provides lower error on the training set. However, it seems it does so at the cost of overfitting the data. This results in a decrease in test RMSE as compared to the MAP, even though it is still much better than our best baseline. So why might this be the case? Recall that we used point estimates for our precision paremeters $\\alpha_U$ and $\\alpha_V$ and we chose a fixed precision $\\alpha$. It is quite likely that by doing this, we constrained our posterior in a way that biased it towards the training data. In reality, the variance in the user ratings and the movie ratings is unlikely to be equal to the means of sample variances we used. Also, the most reasonable observation precision $\\alpha$ is likely different as well."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Summary of Results\n",
    "\n",
    "Let's summarize our results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "size = 100  # RMSE doesn't really change after 100th sample anyway.\n",
    "all_results = pd.DataFrame(\n",
    "    {\n",
    "        \"uniform random\": np.repeat(baselines[\"ur\"], size),\n",
    "        \"global means\": np.repeat(baselines[\"gm\"], size),\n",
    "        \"mean of means\": np.repeat(baselines[\"mom\"], size),\n",
    "        \"PMF MAP\": np.repeat(pmf_map_rmse, size),\n",
    "        \"PMF MCMC\": results[\"running-test\"][:size],\n",
    "    }\n",
    ")\n",
    "fig, ax = plt.subplots(figsize=(10, 5))\n",
    "all_results.plot(kind=\"line\", grid=False, ax=ax, title=\"RMSE for all methods\")\n",
    "ax.set_xlabel(\"Number of Samples\")\n",
    "ax.set_ylabel(\"RMSE\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Summary\n",
    "\n",
    "We set out to predict user preferences for unseen movies. First we discussed the intuitive notion behind the user-user and item-item neighborhood approaches to collaborative filtering. Then we formalized our intuitions. With a firm understanding of our problem context, we moved on to exploring our subset of the Movielens data. After discovering some general patterns, we defined three baseline methods: uniform random, global mean, and mean of means. With the goal of besting our baseline methods, we implemented the basic version of Probabilistic Matrix Factorization (PMF) using `pymc3`.\n",
    "\n",
    "Our results demonstrate that the mean of means method is our best baseline on our prediction task. As expected, we are able to obtain a significant decrease in RMSE using the PMF MAP estimate obtained via Powell optimization. We illustrated one way to monitor convergence of an MCMC sampler with a high-dimensionality sampling space using the Frobenius norms of the sampled variables. The traceplots using this method seem to indicate that our sampler converged to the posterior. Results using this posterior showed that attempting to improve the MAP estimation using MCMC sampling actually overfit the training data and increased test RMSE. This was likely caused by the constraining of the posterior via fixed precision parameters $\\alpha$, $\\alpha_U$, and $\\alpha_V$.\n",
    "\n",
    "As a followup to this analysis, it would be interesting to also implement the logistic and constrained versions of PMF. We expect both models to outperform the basic PMF model. We could also implement the [fully Bayesian version of PMF](https://www.cs.toronto.edu/~amnih/papers/bpmf.pdf) (BPMF), which places hyperpriors on the model parameters to automatically learn ideal mean and precision parameters for $U$ and $V$. This would likely resolve the issue we faced in this analysis. We would expect BPMF to improve upon the MAP estimation produced here by learning more suitable hyperparameters and parameters. For a basic (but working!) implementation of BPMF in `pymc3`, see [this gist](https://gist.github.com/macks22/00a17b1d374dfc267a9a).\n",
    "\n",
    "If you made it this far, then congratulations! You now have some idea of how to build a basic recommender system. These same ideas and methods can be used on many different recommendation tasks. Items can be movies, products, advertisements, courses, or even other people. Any time you can build yourself a user-item matrix with user preferences in the cells, you can use these types of collaborative filtering algorithms to predict the missing values. If you want to learn more about recommender systems, the first reference is a good place to start."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## References\n",
    "\n",
    "1.  Y. Koren, R. Bell, and C. Volinsky, “Matrix Factorization Techniques for Recommender Systems,” Computer, vol. 42, no. 8, pp. 30–37, Aug. 2009.\n",
    "2.  K. Goldberg, T. Roeder, D. Gupta, and C. Perkins, “Eigentaste: A constant time collaborative filtering algorithm,” Information Retrieval, vol. 4, no. 2, pp. 133–151, 2001.\n",
    "3.  A. Mnih and R. Salakhutdinov, “Probabilistic matrix factorization,” in Advances in neural information processing systems, 2007, pp. 1257–1264.\n",
    "4.  S. J. Nowlan and G. E. Hinton, “Simplifying Neural Networks by Soft Weight-sharing,” Neural Comput., vol. 4, no. 4, pp. 473–493, Jul. 1992.\n",
    "5.  R. Salakhutdinov and A. Mnih, “Bayesian Probabilistic Matrix Factorization Using Markov Chain Monte Carlo,” in Proceedings of the 25th International Conference on Machine Learning, New York, NY, USA, 2008, pp. 880–887.\n",
    "\n",
    "\n",
    "The model discussed in this analysis was developed by Ruslan Salakhutdinov and Andriy Mnih. Code and supporting text are the original work of [Mack Sweeney](https://www.linkedin.com/in/macksweeney) with changes made to adapt the code and text for the Movielens dataset by Colin Carroll and Rob Zinkov.\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "theano  1.0.4\n",
      "numpy   1.18.5\n",
      "pymc3   3.9.0\n",
      "logging 0.5.1.2\n",
      "scipy   1.4.1\n",
      "pandas  1.0.4\n",
      "last updated: Mon Jun 15 2020 \n",
      "\n",
      "CPython 3.7.7\n",
      "IPython 7.15.0\n",
      "watermark 2.0.2\n"
     ]
    }
   ],
   "source": [
    "%load_ext watermark\n",
    "%watermark -n -u -v -iv -w"
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}