examples/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 v3.11.0\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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       "\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": "iVBORw0KGgoAAAANSUhEUgAAAXwAAAD9CAYAAAC/fMwDAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8vihELAAAACXBIWXMAAAsTAAALEwEAmpwYAAAbfElEQVR4nO3df2zU9R3H8ef17lrK3TUdwpxTyw/1YoQU+2PsD45uLGIdmRMJNF5NXfjl6AZItaxYipVUqJ0pM/yoOHRZVtfWOpxjwWRRRBq0IUu3gtR0yxomP3Ss4Ij3Pewdbb/7Y1vPSqWALXfj83okJtyHz/l9f95pX3783PfuHLZt24iIyDUvKd4FiIjI1aHAFxExhAJfRMQQCnwREUMo8EVEDOGKdwEX090dincJeL0pWFYk3mUkBPUiRr2IUS9iEqUXEyb4hhzXDn8YLpcz3iUkDPUiRr2IUS9iEr0XCnwREUMMe6TT19dHRUUFR48exel0Ul1dTSgUYvny5UyaNAmAYDDI3LlzaW5upqmpCZfLRXFxMbNnz6anp4c1a9Zw5swZPB4PNTU1jBs3jvb2djZu3IjT6SQQCLBixYrRXquIiNGGDfx9+/YB0NTUxMGDB6muruY73/kOixYtYvHixQPzuru7qa+vZ9euXUQiEQoLC5k5cyaNjY34/X5WrlzJnj17qKuro6KigsrKSrZu3crNN9/Mww8/TEdHB1OnTh29lYqIGG7YI5277rqLqqoqAD788EPGjx/PkSNHePvtt3nwwQcpLy/HsiwOHz5MVlYWycnJ+Hw+MjIy6OzspK2tjVmzZgGQl5dHa2srlmURjUbJyMjA4XAQCARobW0d3ZWKiBjuku7ScblclJWV8cYbb7BlyxZOnTrFwoULmTZtGs899xzbt2/n9ttvx+eLvTLs8XiwLAvLsgbGPR4PoVAIy7Lwer2D5h4/fvyC63q9KXF/EcTpTCI9fWxca0gU6kWMehGjXsQkei8u+bbMmpoaSktLKSgooKmpieuvvx6AOXPmUFVVRW5uLuFweGB+OBzG5/Ph9XoHxsPhMGlpaYPGPjv+eYlwe1N6+ljOnj0X7zISgnoRo17EqBcxidKLK74t87XXXuP5558HIDU1FYfDwYoVKzh8+DAAra2tTJ06lczMTNra2ohEIoRCIbq6uvD7/WRnZ7N//34AWlpayMnJwev14na7OXbsGLZtc+DAAXJzc0dqrSIiMgTHcB+PfO7cOR5//HFOnz5Nb28vy5Yt44YbbqCqqgq328348eOpqqrC6/XS3NzMyy+/jG3b/PCHPyQ/P59PP/2UsrIyuru7cbvd1NbWMmHCBNrb29m0aRN9fX0EAgFKSkouuHYivPEqUf6LnQjUixj1Ika9iEmUXnzRDn/YwI8nBX5iUS9i1IsY9SImUXrxRYGf0B+tICKj6xu1LfEuAYA/PpYX7xKMoHfaiogYQoEvImIIBb6IiCEU+CIihlDgi4gYQoEvImIIBb6IiCEU+CIihlDgi4gYQoEvImIIBb6IiCEU+CIihlDgi4gYQoEvImIIBb6IiCEU+CIihlDgi4gYQoEvImIIBb6IiCEU+CIihlDgi4gYwjXchL6+PioqKjh69ChOp5Pq6mps22bt2rU4HA5uu+02KisrSUpKorm5maamJlwuF8XFxcyePZuenh7WrFnDmTNn8Hg81NTUMG7cONrb29m4cSNOp5NAIMCKFSuuxnpFRIw17A5/3759ADQ1NbFq1Sqqq6uprq5m9erVNDQ0YNs2e/fupbu7m/r6epqamnjxxRfZvHkz0WiUxsZG/H4/DQ0NzJs3j7q6OgAqKyupra2lsbGRQ4cO0dHRMborFREx3LCBf9ddd1FVVQXAhx9+yPjx4+no6GDGjBkA5OXl8e6773L48GGysrJITk7G5/ORkZFBZ2cnbW1tzJo1a2Bua2srlmURjUbJyMjA4XAQCARobW0dxWWKiMiwRzoALpeLsrIy3njjDbZs2cK+fftwOBwAeDweQqEQlmXh8/kGnuPxeLAsa9D4Z+d6vd5Bc48fP37Bdb3eFFwu55da4JfldCaRnj42rjUkCvUiRr0YWddKLxP95+KSAh+gpqaG0tJSCgoKiEQiA+PhcJi0tDS8Xi/hcHjQuM/nGzR+sblpaWkXXNOyIheMXW3p6WM5e/ZcvMtICOpFjHoxsq6VXibKz8WECb4hx4c90nnttdd4/vnnAUhNTcXhcDBt2jQOHjwIQEtLC7m5uWRmZtLW1kYkEiEUCtHV1YXf7yc7O5v9+/cPzM3JycHr9eJ2uzl27Bi2bXPgwAFyc3NHaq0iIjKEYXf4d999N48//jgPPvggvb29lJeXc8stt7B+/Xo2b97MlClTyM/Px+l0UlRURGFhIbZtU1JSQkpKCsFgkLKyMoLBIG63m9raWgA2bNhAaWkpfX19BAIBpk+fPuqLFRExmcO2bTveRXyR7u5QvEtImP9FSwTqRcy10otv1LbEuwQA/vhYXrxLGBGJ8nNxxUc6IiJybVDgi4gYQoEvImIIBb6IiCEU+CIihlDgi4gYQoEvImIIBb6IiCEU+CIihlDgi4gYQoEvImIIBb6IiCEu+fPwRa4V+sAwMZV2+CIihlDgi4gYQoEvImIIBb6IiCEU+CIihlDgi4gYQoEvImIIBb6IiCEU+CIihlDgi4gY4qIfrXD+/HnKy8s5efIk0WiU4uJivva1r7F8+XImTZoEQDAYZO7cuTQ3N9PU1ITL5aK4uJjZs2fT09PDmjVrOHPmDB6Ph5qaGsaNG0d7ezsbN27E6XQSCARYsWLF1ViriIjRLhr4u3fvJj09nWeeeYZ//etf3H///fz4xz9m0aJFLF68eGBed3c39fX17Nq1i0gkQmFhITNnzqSxsRG/38/KlSvZs2cPdXV1VFRUUFlZydatW7n55pt5+OGH6ejoYOrUqaO+WBERk1008O+55x7y8/MHHjudTo4cOcLRo0fZu3cvEydOpLy8nMOHD5OVlUVycjLJyclkZGTQ2dlJW1sbS5cuBSAvL4+6ujosyyIajZKRkQFAIBCgtbV1yMD3elNwuZwjud7L5nQmkZ4+Nq41JAr1YmSplzHXSi8S/XfkooHv8XgAsCyLVatWsXr1aqLRKAsXLmTatGk899xzbN++ndtvvx2fzzfoeZZlYVnWwLjH4yEUCmFZFl6vd9Dc48ePD3l9y4p86QV+WenpYzl79ly8y0gI6sXIUi9jrpVeJMrvyIQJviHHh33R9qOPPuKhhx7ivvvu495772XOnDlMmzYNgDlz5vD+++/j9XoJh8MDzwmHw/h8vkHj4XCYtLS0IeempaV9qcWJiMjwLhr4p0+fZvHixaxZs4YFCxYAsGTJEg4fPgwwcBSTmZlJW1sbkUiEUChEV1cXfr+f7Oxs9u/fD0BLSws5OTl4vV7cbjfHjh3Dtm0OHDhAbm7uKC9TREQueqSzY8cOPvnkE+rq6qirqwNg7dq1bNq0Cbfbzfjx46mqqsLr9VJUVERhYSG2bVNSUkJKSgrBYJCysjKCwSBut5va2loANmzYQGlpKX19fQQCAaZPnz76KxURMZzDtm073kV8ke7uULxLSJgzuURwrfRC33gVo16MrET5HbniM3wREbk2KPBFRAyhwBcRMYQCX0TEEAp8ERFDKPBFRAyhwBcRMYQCX0TEEAp8ERFDKPBFRAyhwBcRMYQCX0TEEAp8ERFDKPBFRAyhwBcRMYQCX0TEEAp8ERFDXPQrDkVETGHCt39phy8iYggFvoiIIRT4IiKGUOCLiBjioi/anj9/nvLyck6ePEk0GqW4uJhbb72VtWvX4nA4uO2226isrCQpKYnm5maamppwuVwUFxcze/Zsenp6WLNmDWfOnMHj8VBTU8O4ceNob29n48aNOJ1OAoEAK1asuFrrFREx1kV3+Lt37yY9PZ2GhgZ27txJVVUV1dXVrF69moaGBmzbZu/evXR3d1NfX09TUxMvvvgimzdvJhqN0tjYiN/vp6GhgXnz5lFXVwdAZWUltbW1NDY2cujQITo6Oq7KYkVETHbRHf4999xDfn7+wGOn00lHRwczZswAIC8vj3feeYekpCSysrJITk4mOTmZjIwMOjs7aWtrY+nSpQNz6+rqsCyLaDRKRkYGAIFAgNbWVqZOnXrB9b3eFFwu54gt9ko4nUmkp4+Naw2JQr0YWepljHoRM5q9uGjgezweACzLYtWqVaxevZqamhocDsfA34dCISzLwufzDXqeZVmDxj871+v1Dpp7/PjxIa9vWZEvt7oRkJ4+lrNnz8W7jISgXows9TJGvYgZiV5MmOAbcnzYF20/+ugjHnroIe677z7uvfdekpJiTwmHw6SlpeH1egmHw4PGfT7foPGLzU1LS7vihYmIyKW5aOCfPn2axYsXs2bNGhYsWADAHXfcwcGDBwFoaWkhNzeXzMxM2traiEQihEIhurq68Pv9ZGdns3///oG5OTk5eL1e3G43x44dw7ZtDhw4QG5u7igvU0RELnqks2PHDj755BPq6uoGXnBdt24dTz31FJs3b2bKlCnk5+fjdDopKiqisLAQ27YpKSkhJSWFYDBIWVkZwWAQt9tNbW0tABs2bKC0tJS+vj4CgQDTp08f/ZWKiBjOYdu2He8ivkh3dyjeJejc+jOulV6Y8Jkpl0q9iLmWenHFZ/giInJtUOCLiBhCgS8iYggFvoiIIRT4IiKGUOCLiBhCgS8iYggFvoiIIRT4IiKGUOCLiBhCgS8iYggFvoiIIRT4IiKGUOCLiBhCgS8iYggFvoiIIRT4IiKGUOCLiBhCgS8iYggFvoiIIRT4IiKGUOCLiBjikgL/0KFDFBUVAdDR0cGsWbMoKiqiqKiI119/HYDm5mbmz59PQUEB+/btA6Cnp4eVK1dSWFjIsmXL+PjjjwFob29n4cKFPPDAA2zbtm001iUiIp/jGm7Czp072b17N6mpqQC8//77LFq0iMWLFw/M6e7upr6+nl27dhGJRCgsLGTmzJk0Njbi9/tZuXIle/bsoa6ujoqKCiorK9m6dSs333wzDz/8MB0dHUydOnX0VikiIsPv8DMyMti6devA4yNHjvD222/z4IMPUl5ejmVZHD58mKysLJKTk/H5fGRkZNDZ2UlbWxuzZs0CIC8vj9bWVizLIhqNkpGRgcPhIBAI0NraOnorFBER4BJ2+Pn5+Zw4cWLgcWZmJgsXLmTatGk899xzbN++ndtvvx2fzzcwx+PxYFkWlmUNjHs8HkKhEJZl4fV6B809fvz4kNf2elNwuZxXvLiR4HQmkZ4+Nq41JAr1YmSplzHqRcxo9mLYwP+8OXPmkJaWNvDnqqoqcnNzCYfDA3PC4TA+nw+v1zswHg6HSUtLGzT22fGhWFbkcssbcenpYzl79ly8y0gI6sXIUi9j1IuYkejFhAm+Iccv+y6dJUuWcPjwYQBaW1uZOnUqmZmZtLW1EYlECIVCdHV14ff7yc7OZv/+/QC0tLSQk5OD1+vF7XZz7NgxbNvmwIED5ObmfomliYjIpbjsHf6TTz5JVVUVbreb8ePHU1VVhdfrpaioiMLCQmzbpqSkhJSUFILBIGVlZQSDQdxuN7W1tQBs2LCB0tJS+vr6CAQCTJ8+fcQXJiIigzls27bjXcQX6e4OxbsEHWN8xrXSi2/UtsS7BAD++FhevEtQLz7jWurFiB3piIjI/ycFvoiIIRT4IiKGUOCLiBhCgS8iYggFvoiIIRT4IiKGUOCLiBhCgS8iYggFvoiIIRT4IiKGUOCLiBhCgS8iYggFvoiIIRT4IiKGUOCLiBhCgS8iYojL/opD+f+VCN/okwjfbCRiKu3wRUQMocAXETGEAl9ExBAKfBERQyjwRUQMcUmBf+jQIYqKigD44IMPCAaDFBYWUllZSX9/PwDNzc3Mnz+fgoIC9u3bB0BPTw8rV66ksLCQZcuW8fHHHwPQ3t7OwoULeeCBB9i2bdtorEtERD5n2MDfuXMnFRUVRCIRAKqrq1m9ejUNDQ3Yts3evXvp7u6mvr6epqYmXnzxRTZv3kw0GqWxsRG/309DQwPz5s2jrq4OgMrKSmpra2lsbOTQoUN0dHSM7ipFRGT4+/AzMjLYunUrP/nJTwDo6OhgxowZAOTl5fHOO++QlJREVlYWycnJJCcnk5GRQWdnJ21tbSxdunRgbl1dHZZlEY1GycjIACAQCNDa2srUqVMvuLbXm4LL5RyxxV4JpzOJ9PSxca3hWqJexqgXMepFzGj2YtjAz8/P58SJEwOPbdvG4XAA4PF4CIVCWJaFz+cbmOPxeLAsa9D4Z+d6vd5Bc48fPz7ktS0rcmWrGkHp6WM5e/ZcvMu4ZqiXMepFjHoRMxK9mDDBN+T4Zb9om5QUe0o4HCYtLQ2v10s4HB407vP5Bo1fbG5aWtrlliEiIpfpsgP/jjvu4ODBgwC0tLSQm5tLZmYmbW1tRCIRQqEQXV1d+P1+srOz2b9//8DcnJwcvF4vbrebY8eOYds2Bw4cIDc3d2RXJSIiF7jsz9IpKytj/fr1bN68mSlTppCfn4/T6aSoqIjCwkJs26akpISUlBSCwSBlZWUEg0Hcbje1tbUAbNiwgdLSUvr6+ggEAkyfPn3EFyYiIoNdUuDfdNNNNDc3AzB58mReeumlC+YUFBRQUFAwaCw1NZUtW7ZcMPfOO+8c+PeJiMjVoTdeiYgYQoEvImIIBb6IiCEU+CIihlDgi4gYQoEvImIIBb6IiCEU+CIihlDgi4gYQoEvImIIBb6IiCEU+CIihlDgi4gYQoEvImIIBb6IiCEU+CIihlDgi4gYQoEvImKIy/5O2/8336htiXcJ/PGxvHiXICKiHb6IiCkU+CIihlDgi4gY4orP8OfNm4fP5wPgpptuYvny5axduxaHw8Ftt91GZWUlSUlJNDc309TUhMvlori4mNmzZ9PT08OaNWs4c+YMHo+Hmpoaxo0bN2KLEhGRC11R4EciEQDq6+sHxpYvX87q1av55je/yRNPPMHevXu58847qa+vZ9euXUQiEQoLC5k5cyaNjY34/X5WrlzJnj17qKuro6KiYmRWJCIiQ7qiwO/s7OTTTz9l8eLF9Pb28uijj9LR0cGMGTMAyMvL45133iEpKYmsrCySk5NJTk4mIyODzs5O2traWLp06cDcurq6Ia/j9abgcjmvcGmJIz19bLxLSBjqRYx6EaNexIxmL64o8MeMGcOSJUtYuHAhf//731m2bBm2beNwOADweDyEQiEsyxo49vnfuGVZg8b/N3colhW5kvISztmz5+JdQsJQL2LUixj1ImYkejFhgm/I8SsK/MmTJzNx4kQcDgeTJ08mPT2djo6Ogb8Ph8OkpaXh9XoJh8ODxn0+36Dx/80VEZHRdUV36fzmN7/h6aefBuDUqVNYlsXMmTM5ePAgAC0tLeTm5pKZmUlbWxuRSIRQKERXVxd+v5/s7Gz2798/MDcnJ2eEliMiIl/kinb4CxYs4PHHHycYDOJwONi0aRNf+cpXWL9+PZs3b2bKlCnk5+fjdDopKiqisLAQ27YpKSkhJSWFYDBIWVkZwWAQt9tNbW3tSK9LREQ+54oCPzk5eciQfumlly4YKygooKCgYNBYamoqW7ZsuZJLi4jIFdIbr0REDKHAFxExhAJfRMQQCnwREUMo8EVEDKHAFxExhAJfRMQQCnwREUMo8EVEDKHAFxExhAJfRMQQCnwREUMo8EVEDKHAFxExhAJfRMQQCnwREUMo8EVEDKHAFxExhAJfRMQQCnwREUMo8EVEDKHAFxExhCteF+7v7+fJJ5/kL3/5C8nJyTz11FNMnDgxXuWIiFzz4rbDf/PNN4lGo7z88ss89thjPP300/EqRUTECHEL/La2NmbNmgXAnXfeyZEjR+JVioiIERy2bdvxuPC6deu4++67+da3vgXAt7/9bd58801crridMomIXNPitsP3er2Ew+GBx/39/Qp7EZFRFLfAz87OpqWlBYD29nb8fn+8ShERMULcjnT+d5fOX//6V2zbZtOmTdxyyy3xKEVExAhxC3wREbm69MYruSzRaDTeJcRdT0+P+vBfZ86ciXcJCaG/v59Tp07R398f71IuSoEvQ3rrrbeYPXs2c+bM4fXXXx8YX7p0aRyrio/jx4/zox/9iCeeeIJ3332XuXPnMnfuXPbt2xfv0q66o0ePDvqnuLh44M+mKS8vB+DQoUPk5+ezYsUKvve979He3h7fwi5Ct8XIkHbs2MFvf/tbbNvmkUceIRKJcP/992PiCWB5eTkrV67k5MmTrFq1ij/84Q+kpKSwdOlSZs+eHe/yrqpFixYxZswYvvrVr2LbNkePHuWJJ57A4XDwq1/9Kt7lXVUnTpwA4Gc/+xk7d+5k0qRJnDp1iscee4yXXnopztUNTYH/OUVFRZw/f37QmG3bOBwOmpqa4lTV1ed2u0lPTwegrq6OH/zgB9xwww04HI74FhYHvb29zJgxA4CDBw9y3XXXARh5G/GuXbuorKwkGAwyc+ZMioqKqK+vj3dZceV0Opk0aRIA119/fUIf65j3EzuM0tJSKioq2L59O06nM97lxM2NN95IdXU1jzzyCF6vl23btrFkyRI++eSTeJd21U2ePJl169ZRVVU18BEgP//5zxk/fnycK7v6rrvuOp599llqamp477334l1OXIVCIebPn8+5c+d45ZVX+P73v8/TTz/N17/+9XiX9oV0l84QXnjhBSZOnMicOXPiXUrc9Pb2snv3br773e+SmpoKwOnTp3n++edZt25dnKu7uvr7+3nrrbe46667BsZ+97vfcffddw/0xkSvvvoqr776asIeX1wN0WiUzs5OxowZw6RJk9i1axcLFizA7XbHu7QhKfBFRAyhu3RERAyhwBcRMYQCX+QSRCIRXnnlFeA/Z9d79+6Nc0Uil09n+CKX4MSJEzz66KM0NzfHuxSRK6bbMkX4z659165d9Pf3c88997B37156e3vx+Xxs3bqVHTt28Le//Y1t27Zh2zbjx49nypQp7Ny5E7fbzYkTJ5g7dy7FxcV88MEHrF27FpfLxY033sjJkyeNv1ddEoOOdET+Ky0tjV//+teEQiF++ctf0tDQQG9vL++99x7Lly/n1ltvZcWKFYOe8+GHH7J161ZefvllXnjhBQB++tOfsnz5curr68nOzo7HUkSGpB2+yH9NnjyZpKQk3G43jz76KGPHjuUf//gHvb29X/gcv9+Py+XC5XIxZswYALq6usjKygIgJyeH3//+91elfpHhaIcv8l9JSUl0dnby5ptv8uyzz7J+/Xr6+/uxbZukpKQh3zI/1EdN+P1+/vznPwP/+WAtkUShHb7IZ0ycOJHU1FTmz59PcnIyEyZM4J///CdZWVmcP3+eZ555ZmAn/0VKS0spLy/nF7/4BT6fz8jP3JHEpLt0REbY7t27mT59OhMnTuSVV17hT3/6E9XV1fEuS0Q7fJGRdsMNN1BSUkJqaipJSUls2rQp3iWJANrhi4gYQy/aiogYQoEvImIIBb6IiCEU+CIihlDgi4gY4t+xqR4fDIY0FwAAAABJRU5ErkJggg==\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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\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:\t1.67456\n",
      "Global Mean Baseline RMSE:\t1.13491\n",
      "Mean Of Means Baseline RMSE:\t1.02368\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",
      "INFO:filelock:Lock 140478053917456 acquired on /Users/CloudChaoszero/.theano/compiledir_macOS-10.16-x86_64-i386-64bit-i386-3.8.6-64/.lock\n",
      "INFO:filelock:Lock 140478053917456 released on /Users/CloudChaoszero/.theano/compiledir_macOS-10.16-x86_64-i386-64bit-i386-3.8.6-64/.lock\n",
      "INFO:filelock:Lock 140478055446032 acquired on /Users/CloudChaoszero/.theano/compiledir_macOS-10.16-x86_64-i386-64bit-i386-3.8.6-64/.lock\n",
      "INFO:filelock:Lock 140478055446032 released on /Users/CloudChaoszero/.theano/compiledir_macOS-10.16-x86_64-i386-64bit-i386-3.8.6-64/.lock\n",
      "INFO:filelock:Lock 140478053980768 acquired on /Users/CloudChaoszero/.theano/compiledir_macOS-10.16-x86_64-i386-64bit-i386-3.8.6-64/.lock\n",
      "INFO:filelock:Lock 140478053980768 released on /Users/CloudChaoszero/.theano/compiledir_macOS-10.16-x86_64-i386-64bit-i386-3.8.6-64/.lock\n",
      "INFO:filelock:Lock 140478056199504 acquired on /Users/CloudChaoszero/.theano/compiledir_macOS-10.16-x86_64-i386-64bit-i386-3.8.6-64/.lock\n",
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      "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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     "data": {
      "text/html": [
       "\n",
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       "                /* gets rid of default border in Firefox and Opera. */\n",
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       "                /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
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       "      100.00% [535/535 01:03<00:00 logp = -1.5381e+05, ||grad|| = 1.2025]\n",
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       "<IPython.core.display.HTML object>"
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     "metadata": {},
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    {
     "name": "stdout",
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      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:filelock:Lock 140478070087440 acquired on /Users/CloudChaoszero/.theano/compiledir_macOS-10.16-x86_64-i386-64bit-i386-3.8.6-64/.lock\n",
      "INFO:filelock:Lock 140478070087440 released on /Users/CloudChaoszero/.theano/compiledir_macOS-10.16-x86_64-i386-64bit-i386-3.8.6-64/.lock\n",
      "INFO:root:found PMF MAP in 86 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: 1.01099\n",
      "PMF MAP testing RMSE:  1.13324\n",
      "Train/test difference: 0.12225\n",
      "PMF MAP Improvement:   -0.10956\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": [
      "/Users/CloudChaoszero/Documents/Projects-Dev/pymc3/pymc3/sampling.py:465: FutureWarning: In an upcoming release, pm.sample will return an `arviz.InferenceData` object instead of a `MultiTrace` by default. You can pass return_inferencedata=True or return_inferencedata=False to be safe and silence this warning.\n",
      "  warnings.warn(\n",
      "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",
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      "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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       "            }\n",
       "        </style>\n",
       "      <progress value='1000' class='' max='1000' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
       "      100.00% [1000/1000 58:30<00:00 Sampling chain 0, 0 divergences]\n",
       "    </div>\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Sampling 1 chain for 500 tune and 500 draw iterations (500 + 500 draws total) took 3511 seconds.\n",
      "INFO:pymc3:Sampling 1 chain for 500 tune and 500 draw iterations (500 + 500 draws total) took 3511 seconds.\n",
      "INFO:filelock:Lock 140478096936144 acquired on /Users/CloudChaoszero/.theano/compiledir_macOS-10.16-x86_64-i386-64bit-i386-3.8.6-64/.lock\n",
      "INFO:filelock:Lock 140478096936144 released on /Users/CloudChaoszero/.theano/compiledir_macOS-10.16-x86_64-i386-64bit-i386-3.8.6-64/.lock\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": [
    {
     "data": {
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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: 0.78051\n",
      "Posterior predictive test RMSE:  0.91320\n",
      "Train/test difference:           0.13269\n",
      "Improvement from MAP:            0.22004\n",
      "Improvement from Mean of Means:  0.11048\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": [
      "Last updated: Sun Feb 07 2021\n",
      "\n",
      "Python implementation: CPython\n",
      "Python version       : 3.8.6\n",
      "IPython version      : 7.20.0\n",
      "\n",
      "scipy     : 1.6.0\n",
      "numpy     : 1.20.0\n",
      "pandas    : 1.2.1\n",
      "logging   : 0.5.1.2\n",
      "pymc3     : 3.11.0\n",
      "theano    : 1.1.2\n",
      "matplotlib: None\n",
      "\n",
      "Watermark: 2.1.0\n",
      "\n"
     ]
    }
   ],
   "source": [
    "%load_ext watermark\n",
    "%watermark -n -u -v -iv -w"
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python PyMC3 (Dev)",
   "language": "python",
   "name": "pymc3-dev-py38"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}