{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "needed-aerospace",
   "metadata": {},
   "source": [
    "# Stochastic Variational Inference\n",
    "\n",
    "Implementation of Stochastic Variational Inference (SVI) using [PyTorch](https://pytorch.org/), for the purpose of uncertainty quantification.\n",
    "\n",
    "We'll consider the [OLS Regression Challenge](https://data.world/nrippner/ols-regression-challenge), which aims at predicting cancer mortality rates for US counties.\n",
    "\n",
    "## Data Dictionary\n",
    "\n",
    "* **TARGET_deathRate**: Dependent variable. Mean per capita (100,000) cancer mortalities (a)\n",
    "* **avgAnnCount**: Mean number of reported cases of cancer diagnosed annually (a)\n",
    "* **avgDeathsPerYear**: Mean number of reported mortalities due to cancer (a)\n",
    "* **incidenceRate**: Mean per capita (100,000) cancer diagoses (a)\n",
    "* **medianIncome**: Median income per county (b)\n",
    "* **popEst2015**: Population of county (b)\n",
    "* **povertyPercent**: Percent of populace in poverty (b)\n",
    "* **studyPerCap**: Per capita number of cancer-related clinical trials per county (a)\n",
    "* **binnedInc**: Median income per capita binned by decile (b)\n",
    "* **MedianAge**: Median age of county residents (b)\n",
    "* **MedianAgeMale**: Median age of male county residents (b)\n",
    "* **MedianAgeFemale**: Median age of female county residents (b)\n",
    "* **Geography**: County name (b)\n",
    "* **AvgHouseholdSize**: Mean household size of county (b)\n",
    "* **PercentMarried**: Percent of county residents who are married (b)\n",
    "* **PctNoHS18_24**: Percent of county residents ages 18-24 highest education attained: less than high school (b)\n",
    "* **PctHS18_24**: Percent of county residents ages 18-24 highest education attained: high school diploma (b)\n",
    "* **PctSomeCol18_24**: Percent of county residents ages 18-24 highest education attained: some college (b)\n",
    "* **PctBachDeg18_24**: Percent of county residents ages 18-24 highest education attained: bachelor's degree (b)\n",
    "* **PctHS25_Over**: Percent of county residents ages 25 and over highest education attained: high school diploma (b)\n",
    "* **PctBachDeg25_Over**: Percent of county residents ages 25 and over highest education attained: bachelor's degree (b)\n",
    "* **PctEmployed16_Over**: Percent of county residents ages 16 and over employed (b)\n",
    "* **PctUnemployed16_Over**: Percent of county residents ages 16 and over unemployed (b)\n",
    "* **PctPrivateCoverage**: Percent of county residents with private health coverage (b)\n",
    "* **PctPrivateCoverageAlone**: Percent of county residents with private health coverage alone (no public assistance) (b)\n",
    "* **PctEmpPrivCoverage**: Percent of county residents with employee-provided private health coverage (b)\n",
    "* **PctPublicCoverage**: Percent of county residents with government-provided health coverage (b)\n",
    "* **PctPubliceCoverageAlone**: Percent of county residents with government-provided health coverage alone (b)\n",
    "* **PctWhite**: Percent of county residents who identify as White (b)\n",
    "* **PctBlack**: Percent of county residents who identify as Black (b)\n",
    "* **PctAsian**: Percent of county residents who identify as Asian (b)\n",
    "* **PctOtherRace**: Percent of county residents who identify in a category which is not White, Black, or Asian (b)\n",
    "* **PctMarriedHouseholds**: Percent of married households (b)\n",
    "* **BirthRate**: Number of live births relative to number of women in county (b)\n",
    "\n",
    "Notes:\n",
    "* (a): years 2010-2016\n",
    "* (b): 2013 Census Estimates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "alien-charger",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "from os.path import join\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import torch\n",
    "import torch.nn.functional as F\n",
    "from sklearn.metrics import r2_score\n",
    "from sklearn.model_selection import train_test_split\n",
    "from scipy.stats import binned_statistic\n",
    "\n",
    "cwd = os.getcwd()\n",
    "if cwd.endswith('notebook'):\n",
    "    os.chdir('..')\n",
    "    cwd = os.getcwd()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "physical-memphis",
   "metadata": {},
   "outputs": [],
   "source": [
    "sns.set(palette='colorblind', font_scale=1.3)\n",
    "palette = sns.color_palette()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "consolidated-person",
   "metadata": {},
   "outputs": [],
   "source": [
    "seed = 444\n",
    "np.random.seed(seed);\n",
    "torch.manual_seed(seed);\n",
    "torch.set_default_dtype(torch.float64)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "unexpected-enterprise",
   "metadata": {},
   "source": [
    "## Dataset\n",
    "\n",
    "### Load"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "elect-camera",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>avgAnnCount</th>\n",
       "      <th>avgDeathsPerYear</th>\n",
       "      <th>TARGET_deathRate</th>\n",
       "      <th>incidenceRate</th>\n",
       "      <th>medIncome</th>\n",
       "      <th>popEst2015</th>\n",
       "      <th>povertyPercent</th>\n",
       "      <th>studyPerCap</th>\n",
       "      <th>binnedInc</th>\n",
       "      <th>MedianAge</th>\n",
       "      <th>...</th>\n",
       "      <th>PctPrivateCoverageAlone</th>\n",
       "      <th>PctEmpPrivCoverage</th>\n",
       "      <th>PctPublicCoverage</th>\n",
       "      <th>PctPublicCoverageAlone</th>\n",
       "      <th>PctWhite</th>\n",
       "      <th>PctBlack</th>\n",
       "      <th>PctAsian</th>\n",
       "      <th>PctOtherRace</th>\n",
       "      <th>PctMarriedHouseholds</th>\n",
       "      <th>BirthRate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1397.0</td>\n",
       "      <td>469</td>\n",
       "      <td>164.9</td>\n",
       "      <td>489.8</td>\n",
       "      <td>61898</td>\n",
       "      <td>260131</td>\n",
       "      <td>11.2</td>\n",
       "      <td>499.748204</td>\n",
       "      <td>(61494.5, 125635]</td>\n",
       "      <td>39.3</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>41.6</td>\n",
       "      <td>32.9</td>\n",
       "      <td>14.0</td>\n",
       "      <td>81.780529</td>\n",
       "      <td>2.594728</td>\n",
       "      <td>4.821857</td>\n",
       "      <td>1.843479</td>\n",
       "      <td>52.856076</td>\n",
       "      <td>6.118831</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>173.0</td>\n",
       "      <td>70</td>\n",
       "      <td>161.3</td>\n",
       "      <td>411.6</td>\n",
       "      <td>48127</td>\n",
       "      <td>43269</td>\n",
       "      <td>18.6</td>\n",
       "      <td>23.111234</td>\n",
       "      <td>(48021.6, 51046.4]</td>\n",
       "      <td>33.0</td>\n",
       "      <td>...</td>\n",
       "      <td>53.8</td>\n",
       "      <td>43.6</td>\n",
       "      <td>31.1</td>\n",
       "      <td>15.3</td>\n",
       "      <td>89.228509</td>\n",
       "      <td>0.969102</td>\n",
       "      <td>2.246233</td>\n",
       "      <td>3.741352</td>\n",
       "      <td>45.372500</td>\n",
       "      <td>4.333096</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>102.0</td>\n",
       "      <td>50</td>\n",
       "      <td>174.7</td>\n",
       "      <td>349.7</td>\n",
       "      <td>49348</td>\n",
       "      <td>21026</td>\n",
       "      <td>14.6</td>\n",
       "      <td>47.560164</td>\n",
       "      <td>(48021.6, 51046.4]</td>\n",
       "      <td>45.0</td>\n",
       "      <td>...</td>\n",
       "      <td>43.5</td>\n",
       "      <td>34.9</td>\n",
       "      <td>42.1</td>\n",
       "      <td>21.1</td>\n",
       "      <td>90.922190</td>\n",
       "      <td>0.739673</td>\n",
       "      <td>0.465898</td>\n",
       "      <td>2.747358</td>\n",
       "      <td>54.444868</td>\n",
       "      <td>3.729488</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>427.0</td>\n",
       "      <td>202</td>\n",
       "      <td>194.8</td>\n",
       "      <td>430.4</td>\n",
       "      <td>44243</td>\n",
       "      <td>75882</td>\n",
       "      <td>17.1</td>\n",
       "      <td>342.637253</td>\n",
       "      <td>(42724.4, 45201]</td>\n",
       "      <td>42.8</td>\n",
       "      <td>...</td>\n",
       "      <td>40.3</td>\n",
       "      <td>35.0</td>\n",
       "      <td>45.3</td>\n",
       "      <td>25.0</td>\n",
       "      <td>91.744686</td>\n",
       "      <td>0.782626</td>\n",
       "      <td>1.161359</td>\n",
       "      <td>1.362643</td>\n",
       "      <td>51.021514</td>\n",
       "      <td>4.603841</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>57.0</td>\n",
       "      <td>26</td>\n",
       "      <td>144.4</td>\n",
       "      <td>350.1</td>\n",
       "      <td>49955</td>\n",
       "      <td>10321</td>\n",
       "      <td>12.5</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>(48021.6, 51046.4]</td>\n",
       "      <td>48.3</td>\n",
       "      <td>...</td>\n",
       "      <td>43.9</td>\n",
       "      <td>35.1</td>\n",
       "      <td>44.0</td>\n",
       "      <td>22.7</td>\n",
       "      <td>94.104024</td>\n",
       "      <td>0.270192</td>\n",
       "      <td>0.665830</td>\n",
       "      <td>0.492135</td>\n",
       "      <td>54.027460</td>\n",
       "      <td>6.796657</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 34 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   avgAnnCount  avgDeathsPerYear  TARGET_deathRate  incidenceRate  medIncome  \\\n",
       "0       1397.0               469             164.9          489.8      61898   \n",
       "1        173.0                70             161.3          411.6      48127   \n",
       "2        102.0                50             174.7          349.7      49348   \n",
       "3        427.0               202             194.8          430.4      44243   \n",
       "4         57.0                26             144.4          350.1      49955   \n",
       "\n",
       "   popEst2015  povertyPercent  studyPerCap           binnedInc  MedianAge  \\\n",
       "0      260131            11.2   499.748204   (61494.5, 125635]       39.3   \n",
       "1       43269            18.6    23.111234  (48021.6, 51046.4]       33.0   \n",
       "2       21026            14.6    47.560164  (48021.6, 51046.4]       45.0   \n",
       "3       75882            17.1   342.637253    (42724.4, 45201]       42.8   \n",
       "4       10321            12.5     0.000000  (48021.6, 51046.4]       48.3   \n",
       "\n",
       "   ...  PctPrivateCoverageAlone  PctEmpPrivCoverage PctPublicCoverage  \\\n",
       "0  ...                      NaN                41.6              32.9   \n",
       "1  ...                     53.8                43.6              31.1   \n",
       "2  ...                     43.5                34.9              42.1   \n",
       "3  ...                     40.3                35.0              45.3   \n",
       "4  ...                     43.9                35.1              44.0   \n",
       "\n",
       "   PctPublicCoverageAlone   PctWhite  PctBlack  PctAsian  PctOtherRace  \\\n",
       "0                    14.0  81.780529  2.594728  4.821857      1.843479   \n",
       "1                    15.3  89.228509  0.969102  2.246233      3.741352   \n",
       "2                    21.1  90.922190  0.739673  0.465898      2.747358   \n",
       "3                    25.0  91.744686  0.782626  1.161359      1.362643   \n",
       "4                    22.7  94.104024  0.270192  0.665830      0.492135   \n",
       "\n",
       "   PctMarriedHouseholds  BirthRate  \n",
       "0             52.856076   6.118831  \n",
       "1             45.372500   4.333096  \n",
       "2             54.444868   3.729488  \n",
       "3             51.021514   4.603841  \n",
       "4             54.027460   6.796657  \n",
       "\n",
       "[5 rows x 34 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(join(cwd, 'data/cancer_reg.csv'))\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "difficult-fighter",
   "metadata": {},
   "source": [
    "### Describe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "changed-princess",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>avgAnnCount</th>\n",
       "      <th>avgDeathsPerYear</th>\n",
       "      <th>TARGET_deathRate</th>\n",
       "      <th>incidenceRate</th>\n",
       "      <th>medIncome</th>\n",
       "      <th>popEst2015</th>\n",
       "      <th>povertyPercent</th>\n",
       "      <th>studyPerCap</th>\n",
       "      <th>MedianAge</th>\n",
       "      <th>MedianAgeMale</th>\n",
       "      <th>...</th>\n",
       "      <th>PctPrivateCoverageAlone</th>\n",
       "      <th>PctEmpPrivCoverage</th>\n",
       "      <th>PctPublicCoverage</th>\n",
       "      <th>PctPublicCoverageAlone</th>\n",
       "      <th>PctWhite</th>\n",
       "      <th>PctBlack</th>\n",
       "      <th>PctAsian</th>\n",
       "      <th>PctOtherRace</th>\n",
       "      <th>PctMarriedHouseholds</th>\n",
       "      <th>BirthRate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>3047.000000</td>\n",
       "      <td>3047.000000</td>\n",
       "      <td>3047.000000</td>\n",
       "      <td>3047.000000</td>\n",
       "      <td>3047.000000</td>\n",
       "      <td>3.047000e+03</td>\n",
       "      <td>3047.000000</td>\n",
       "      <td>3047.000000</td>\n",
       "      <td>3047.000000</td>\n",
       "      <td>3047.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>2438.000000</td>\n",
       "      <td>3047.000000</td>\n",
       "      <td>3047.000000</td>\n",
       "      <td>3047.000000</td>\n",
       "      <td>3047.000000</td>\n",
       "      <td>3047.000000</td>\n",
       "      <td>3047.000000</td>\n",
       "      <td>3047.000000</td>\n",
       "      <td>3047.000000</td>\n",
       "      <td>3047.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>606.338544</td>\n",
       "      <td>185.965868</td>\n",
       "      <td>178.664063</td>\n",
       "      <td>448.268586</td>\n",
       "      <td>47063.281917</td>\n",
       "      <td>1.026374e+05</td>\n",
       "      <td>16.878175</td>\n",
       "      <td>155.399415</td>\n",
       "      <td>45.272333</td>\n",
       "      <td>39.570725</td>\n",
       "      <td>...</td>\n",
       "      <td>48.453774</td>\n",
       "      <td>41.196324</td>\n",
       "      <td>36.252642</td>\n",
       "      <td>19.240072</td>\n",
       "      <td>83.645286</td>\n",
       "      <td>9.107978</td>\n",
       "      <td>1.253965</td>\n",
       "      <td>1.983523</td>\n",
       "      <td>51.243872</td>\n",
       "      <td>5.640306</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1416.356223</td>\n",
       "      <td>504.134286</td>\n",
       "      <td>27.751511</td>\n",
       "      <td>54.560733</td>\n",
       "      <td>12040.090836</td>\n",
       "      <td>3.290592e+05</td>\n",
       "      <td>6.409087</td>\n",
       "      <td>529.628366</td>\n",
       "      <td>45.304480</td>\n",
       "      <td>5.226017</td>\n",
       "      <td>...</td>\n",
       "      <td>10.083006</td>\n",
       "      <td>9.447687</td>\n",
       "      <td>7.841741</td>\n",
       "      <td>6.113041</td>\n",
       "      <td>16.380025</td>\n",
       "      <td>14.534538</td>\n",
       "      <td>2.610276</td>\n",
       "      <td>3.517710</td>\n",
       "      <td>6.572814</td>\n",
       "      <td>1.985816</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>6.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>59.700000</td>\n",
       "      <td>201.300000</td>\n",
       "      <td>22640.000000</td>\n",
       "      <td>8.270000e+02</td>\n",
       "      <td>3.200000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>22.300000</td>\n",
       "      <td>22.400000</td>\n",
       "      <td>...</td>\n",
       "      <td>15.700000</td>\n",
       "      <td>13.500000</td>\n",
       "      <td>11.200000</td>\n",
       "      <td>2.600000</td>\n",
       "      <td>10.199155</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>22.992490</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>76.000000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>161.200000</td>\n",
       "      <td>420.300000</td>\n",
       "      <td>38882.500000</td>\n",
       "      <td>1.168400e+04</td>\n",
       "      <td>12.150000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>37.700000</td>\n",
       "      <td>36.350000</td>\n",
       "      <td>...</td>\n",
       "      <td>41.000000</td>\n",
       "      <td>34.500000</td>\n",
       "      <td>30.900000</td>\n",
       "      <td>14.850000</td>\n",
       "      <td>77.296180</td>\n",
       "      <td>0.620675</td>\n",
       "      <td>0.254199</td>\n",
       "      <td>0.295172</td>\n",
       "      <td>47.763063</td>\n",
       "      <td>4.521419</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>171.000000</td>\n",
       "      <td>61.000000</td>\n",
       "      <td>178.100000</td>\n",
       "      <td>453.549422</td>\n",
       "      <td>45207.000000</td>\n",
       "      <td>2.664300e+04</td>\n",
       "      <td>15.900000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>41.000000</td>\n",
       "      <td>39.600000</td>\n",
       "      <td>...</td>\n",
       "      <td>48.700000</td>\n",
       "      <td>41.100000</td>\n",
       "      <td>36.300000</td>\n",
       "      <td>18.800000</td>\n",
       "      <td>90.059774</td>\n",
       "      <td>2.247576</td>\n",
       "      <td>0.549812</td>\n",
       "      <td>0.826185</td>\n",
       "      <td>51.669941</td>\n",
       "      <td>5.381478</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>518.000000</td>\n",
       "      <td>149.000000</td>\n",
       "      <td>195.200000</td>\n",
       "      <td>480.850000</td>\n",
       "      <td>52492.000000</td>\n",
       "      <td>6.867100e+04</td>\n",
       "      <td>20.400000</td>\n",
       "      <td>83.650776</td>\n",
       "      <td>44.000000</td>\n",
       "      <td>42.500000</td>\n",
       "      <td>...</td>\n",
       "      <td>55.600000</td>\n",
       "      <td>47.700000</td>\n",
       "      <td>41.550000</td>\n",
       "      <td>23.100000</td>\n",
       "      <td>95.451693</td>\n",
       "      <td>10.509732</td>\n",
       "      <td>1.221037</td>\n",
       "      <td>2.177960</td>\n",
       "      <td>55.395132</td>\n",
       "      <td>6.493677</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>38150.000000</td>\n",
       "      <td>14010.000000</td>\n",
       "      <td>362.800000</td>\n",
       "      <td>1206.900000</td>\n",
       "      <td>125635.000000</td>\n",
       "      <td>1.017029e+07</td>\n",
       "      <td>47.400000</td>\n",
       "      <td>9762.308998</td>\n",
       "      <td>624.000000</td>\n",
       "      <td>64.700000</td>\n",
       "      <td>...</td>\n",
       "      <td>78.900000</td>\n",
       "      <td>70.700000</td>\n",
       "      <td>65.100000</td>\n",
       "      <td>46.600000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>85.947799</td>\n",
       "      <td>42.619425</td>\n",
       "      <td>41.930251</td>\n",
       "      <td>78.075397</td>\n",
       "      <td>21.326165</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 32 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        avgAnnCount  avgDeathsPerYear  TARGET_deathRate  incidenceRate  \\\n",
       "count   3047.000000       3047.000000       3047.000000    3047.000000   \n",
       "mean     606.338544        185.965868        178.664063     448.268586   \n",
       "std     1416.356223        504.134286         27.751511      54.560733   \n",
       "min        6.000000          3.000000         59.700000     201.300000   \n",
       "25%       76.000000         28.000000        161.200000     420.300000   \n",
       "50%      171.000000         61.000000        178.100000     453.549422   \n",
       "75%      518.000000        149.000000        195.200000     480.850000   \n",
       "max    38150.000000      14010.000000        362.800000    1206.900000   \n",
       "\n",
       "           medIncome    popEst2015  povertyPercent  studyPerCap    MedianAge  \\\n",
       "count    3047.000000  3.047000e+03     3047.000000  3047.000000  3047.000000   \n",
       "mean    47063.281917  1.026374e+05       16.878175   155.399415    45.272333   \n",
       "std     12040.090836  3.290592e+05        6.409087   529.628366    45.304480   \n",
       "min     22640.000000  8.270000e+02        3.200000     0.000000    22.300000   \n",
       "25%     38882.500000  1.168400e+04       12.150000     0.000000    37.700000   \n",
       "50%     45207.000000  2.664300e+04       15.900000     0.000000    41.000000   \n",
       "75%     52492.000000  6.867100e+04       20.400000    83.650776    44.000000   \n",
       "max    125635.000000  1.017029e+07       47.400000  9762.308998   624.000000   \n",
       "\n",
       "       MedianAgeMale  ...  PctPrivateCoverageAlone  PctEmpPrivCoverage  \\\n",
       "count    3047.000000  ...              2438.000000         3047.000000   \n",
       "mean       39.570725  ...                48.453774           41.196324   \n",
       "std         5.226017  ...                10.083006            9.447687   \n",
       "min        22.400000  ...                15.700000           13.500000   \n",
       "25%        36.350000  ...                41.000000           34.500000   \n",
       "50%        39.600000  ...                48.700000           41.100000   \n",
       "75%        42.500000  ...                55.600000           47.700000   \n",
       "max        64.700000  ...                78.900000           70.700000   \n",
       "\n",
       "       PctPublicCoverage  PctPublicCoverageAlone     PctWhite     PctBlack  \\\n",
       "count        3047.000000             3047.000000  3047.000000  3047.000000   \n",
       "mean           36.252642               19.240072    83.645286     9.107978   \n",
       "std             7.841741                6.113041    16.380025    14.534538   \n",
       "min            11.200000                2.600000    10.199155     0.000000   \n",
       "25%            30.900000               14.850000    77.296180     0.620675   \n",
       "50%            36.300000               18.800000    90.059774     2.247576   \n",
       "75%            41.550000               23.100000    95.451693    10.509732   \n",
       "max            65.100000               46.600000   100.000000    85.947799   \n",
       "\n",
       "          PctAsian  PctOtherRace  PctMarriedHouseholds    BirthRate  \n",
       "count  3047.000000   3047.000000           3047.000000  3047.000000  \n",
       "mean      1.253965      1.983523             51.243872     5.640306  \n",
       "std       2.610276      3.517710              6.572814     1.985816  \n",
       "min       0.000000      0.000000             22.992490     0.000000  \n",
       "25%       0.254199      0.295172             47.763063     4.521419  \n",
       "50%       0.549812      0.826185             51.669941     5.381478  \n",
       "75%       1.221037      2.177960             55.395132     6.493677  \n",
       "max      42.619425     41.930251             78.075397    21.326165  \n",
       "\n",
       "[8 rows x 32 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "adjustable-premises",
   "metadata": {},
   "source": [
    "### Plot distribution of target variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "academic-lincoln",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "_, ax = plt.subplots(1, 1, figsize=(10, 5))\n",
    "df['TARGET_deathRate'].hist(bins=50, ax=ax);\n",
    "ax.set_title('Distribution of cancer death rate per 100,000 people');\n",
    "ax.set_xlabel('Cancer death rate in county (per 100,000 people)');\n",
    "ax.set_ylabel('Count');"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "clear-printing",
   "metadata": {},
   "source": [
    "### Define feature & target variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "certain-clearing",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "28 features\n"
     ]
    }
   ],
   "source": [
    "target = 'TARGET_deathRate'\n",
    "features = [\n",
    "    col for col in df.columns\n",
    "    if col not in [\n",
    "        target, \n",
    "        'Geography',                # Label describing the county - each row has a different one\n",
    "        'binnedInc',                # Redundant with median income?\n",
    "        'PctSomeCol18_24',          # contains null values - ignoring for now\n",
    "        'PctEmployed16_Over',       # contains null values - ignoring for now\n",
    "        'PctPrivateCoverageAlone',  # contains null values - ignoring for now\n",
    "    ]\n",
    "]\n",
    "print(len(features), 'features')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "normal-newsletter",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(3047, 28) (3047, 1)\n"
     ]
    }
   ],
   "source": [
    "x = df[features].values\n",
    "y = df[[target]].values\n",
    "print(x.shape, y.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "parliamentary-festival",
   "metadata": {},
   "source": [
    "## Define model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "aboriginal-worker",
   "metadata": {},
   "outputs": [],
   "source": [
    "class VariationalModel(torch.nn.Module):\n",
    "    def __init__(\n",
    "        self, \n",
    "        n_inputs,\n",
    "        n_hidden,\n",
    "        priors,\n",
    "        x_scaler, \n",
    "        y_scaler,\n",
    "        n_shared_layers=1,\n",
    "    ):\n",
    "        super().__init__()\n",
    "        \n",
    "        self.priors = priors\n",
    "        self.x_scaler = x_scaler\n",
    "        self.y_scaler = y_scaler\n",
    "        self.jitter = 1e-6\n",
    "        \n",
    "        # Shared layers\n",
    "        self.shared_layers = [\n",
    "            torch.nn.Linear(n_inputs, n_hidden)\n",
    "        ]\n",
    "        if n_shared_layers > 1:\n",
    "            for _ in range(n_shared_layers - 1):\n",
    "                self.shared_layers.append(\n",
    "                    torch.nn.Linear(n_hidden, n_hidden)\n",
    "                )\n",
    "        \n",
    "        # Parameters for the mean \n",
    "        self.mean_loc_hidden = torch.nn.Linear(n_hidden, n_hidden)\n",
    "        self.mean_loc = torch.nn.Linear(n_hidden, 1)\n",
    "        self.mean_scale_hidden = torch.nn.Linear(n_hidden, n_hidden)\n",
    "        self.mean_scale = torch.nn.Linear(n_hidden, 1)\n",
    "        \n",
    "        # Parameters for the standard deviation\n",
    "        self.std_loc_hidden = torch.nn.Linear(n_hidden, n_hidden)\n",
    "        self.std_loc = torch.nn.Linear(n_hidden, 1)\n",
    "        self.std_scale_hidden = torch.nn.Linear(n_hidden, n_hidden)\n",
    "        self.std_scale = torch.nn.Linear(n_hidden, 1)\n",
    "        \n",
    "    def encoder(self, x):\n",
    "        shared = self.x_scaler(x)\n",
    "        \n",
    "        for layer in self.shared_layers:\n",
    "            shared = layer(shared)\n",
    "            shared = F.relu(shared)\n",
    "            #shared = self.dropout(shared)\n",
    "        \n",
    "        # Parametrization of the mean (normal)\n",
    "        mean_loc = self.mean_loc_hidden(shared)\n",
    "        mean_loc = F.relu(mean_loc)\n",
    "        mean_loc = self.mean_loc(mean_loc)\n",
    "        \n",
    "        mean_scale = self.mean_scale_hidden(shared)\n",
    "        mean_scale = F.relu(mean_scale)\n",
    "        mean_scale = F.softplus(self.mean_scale(mean_scale)) + self.jitter\n",
    "        \n",
    "        # Parametrization of the standard deviation (log normal)\n",
    "        std_loc = self.std_loc_hidden(shared)\n",
    "        std_loc = F.relu(std_loc)\n",
    "        std_loc = self.std_loc(std_loc)\n",
    "        \n",
    "        std_scale = self.std_scale_hidden(shared)\n",
    "        std_scale = F.relu(std_scale)\n",
    "        std_scale = F.softplus(self.std_scale(std_scale)) + self.jitter    \n",
    "        \n",
    "        return (\n",
    "            torch.distributions.Normal(mean_loc, mean_scale),\n",
    "            torch.distributions.LogNormal(std_loc, std_scale)\n",
    "        )\n",
    "    \n",
    "    def decoder(self, mean, std):\n",
    "        return torch.distributions.Normal(mean, std)\n",
    "    \n",
    "    def forward(self, x, sample_shape=torch.Size([])):\n",
    "        mean_dist, std_dist = self.encoder(x)\n",
    "        \n",
    "        mean = mean_dist.rsample(sample_shape)\n",
    "        std = std_dist.rsample(sample_shape) + self.jitter\n",
    "        \n",
    "        y_hat = self.decoder(mean, std)\n",
    "        \n",
    "        kl_divergence = None\n",
    "        if self.priors is not None:\n",
    "            kl_divergence1 = torch.distributions.kl.kl_divergence(mean_dist, self.priors[0])\n",
    "            kl_divergence2 = torch.distributions.kl.kl_divergence(std_dist, self.priors[1])\n",
    "            kl_divergence = kl_divergence1 + kl_divergence2\n",
    "            \n",
    "        return y_hat, kl_divergence\n",
    "\n",
    "\n",
    "def compute_loss(model, x, y, kl_reg=0.1):\n",
    "    \"\"\"\n",
    "    ELBO loss - https://en.wikipedia.org/wiki/Evidence_lower_bound\n",
    "    \"\"\"\n",
    "    y_scaled = model.y_scaler(y)\n",
    "    \n",
    "    y_hat, kl_divergence = model(x)\n",
    "    \n",
    "    neg_log_likelihood = -y_hat.log_prob(y_scaled)\n",
    "    \n",
    "    if kl_divergence is not None:\n",
    "        neg_log_likelihood += kl_reg * kl_divergence\n",
    "\n",
    "    return torch.mean(neg_log_likelihood)\n",
    "\n",
    "\n",
    "def compute_rmse(model, x_test, y_test):\n",
    "    model.eval()\n",
    "    y_hat, _ = model(x_test)\n",
    "    pred = model.y_scaler.inverse_transform(y_hat.mean)\n",
    "    return torch.sqrt(torch.mean((pred - y_test)**2))\n",
    "\n",
    "\n",
    "def train_one_step(model, optimizer, x_batch, y_batch):\n",
    "    model.train()\n",
    "    optimizer.zero_grad()\n",
    "    loss = compute_loss(model, x_batch, y_batch)\n",
    "    loss.backward()\n",
    "    optimizer.step()\n",
    "    return loss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "tribal-trainer",
   "metadata": {},
   "outputs": [],
   "source": [
    "def train(model, optimizer, x_train, x_val, y_train, y_val, n_epochs, batch_size, scheduler=None, print_every=10):\n",
    "    train_losses, val_losses = [], []\n",
    "    for epoch in range(n_epochs):\n",
    "        batch_indices = sample_batch_indices(x_train, y_train, batch_size)\n",
    "        \n",
    "        batch_losses_t, batch_losses_v, batch_rmse_v = [], [], []\n",
    "        for batch_ix in batch_indices:\n",
    "            b_train_loss = train_one_step(model, optimizer, x_train[batch_ix], y_train[batch_ix])\n",
    "\n",
    "            model.eval()\n",
    "            b_val_loss = compute_loss(model, x_val, y_val)\n",
    "            b_val_rmse = compute_rmse(model, x_val, y_val)\n",
    "\n",
    "            batch_losses_t.append(b_train_loss.detach().numpy())\n",
    "            batch_losses_v.append(b_val_loss.detach().numpy())\n",
    "            batch_rmse_v.append(b_val_rmse.detach().numpy())\n",
    "            \n",
    "        if scheduler is not None:\n",
    "            scheduler.step()\n",
    "            \n",
    "        train_loss = np.mean(batch_losses_t)\n",
    "        val_loss = np.mean(batch_losses_v)\n",
    "        val_rmse = np.mean(batch_rmse_v)\n",
    "        \n",
    "        train_losses.append(train_loss)\n",
    "        val_losses.append(val_loss)\n",
    "\n",
    "        if epoch == 0 or (epoch + 1) % print_every == 0:\n",
    "            print(f'Epoch {epoch+1} | Validation loss = {val_loss:.4f} | Validation RMSE = {val_rmse:.4f}')\n",
    "        \n",
    "    _, ax = plt.subplots(1, 1, figsize=(12, 6))\n",
    "    ax.plot(range(1, n_epochs + 1), train_losses, label='Train loss')\n",
    "    ax.plot(range(1, n_epochs + 1), val_losses, label='Validation loss')\n",
    "    ax.set_xlabel('Epoch')\n",
    "    ax.set_ylabel('Loss')\n",
    "    ax.set_title('Training Overview')\n",
    "    ax.legend()\n",
    "    \n",
    "    return train_losses, val_losses\n",
    "\n",
    "\n",
    "def sample_batch_indices(x, y, batch_size, rs=None):\n",
    "    if rs is None:\n",
    "        rs = np.random.RandomState()\n",
    "    \n",
    "    train_ix = np.arange(len(x))\n",
    "    rs.shuffle(train_ix)\n",
    "    \n",
    "    n_batches = int(np.ceil(len(x) / batch_size))\n",
    "    \n",
    "    batch_indices = []\n",
    "    for i in range(n_batches):\n",
    "        start = i * batch_size\n",
    "        end = start + batch_size\n",
    "        batch_indices.append(\n",
    "            train_ix[start:end].tolist()\n",
    "        )\n",
    "\n",
    "    return batch_indices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "right-county",
   "metadata": {},
   "outputs": [],
   "source": [
    "class StandardScaler(object):\n",
    "    \"\"\"\n",
    "    Standardize data by removing the mean and scaling to unit variance.\n",
    "    \"\"\"\n",
    "    def __init__(self):\n",
    "        self.mean = None\n",
    "        self.scale = None\n",
    "\n",
    "    def fit(self, sample):\n",
    "        self.mean = sample.mean(0, keepdim=True)\n",
    "        self.scale = sample.std(0, unbiased=False, keepdim=True)\n",
    "        return self\n",
    "\n",
    "    def __call__(self, sample):\n",
    "        return self.transform(sample)\n",
    "    \n",
    "    def transform(self, sample):\n",
    "        return (sample - self.mean) / self.scale\n",
    "\n",
    "    def inverse_transform(self, sample):\n",
    "        return sample * self.scale + self.mean"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "soviet-senegal",
   "metadata": {},
   "source": [
    "## Split between training and validation sets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "fresh-gazette",
   "metadata": {},
   "outputs": [],
   "source": [
    "def compute_train_test_split(x, y, test_size):\n",
    "    x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=test_size)\n",
    "    return (\n",
    "        torch.from_numpy(x_train),\n",
    "        torch.from_numpy(x_test),\n",
    "        torch.from_numpy(y_train),\n",
    "        torch.from_numpy(y_test),\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "defensive-affairs",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([2437, 28]) torch.Size([2437, 1])\n",
      "torch.Size([610, 28]) torch.Size([610, 1])\n"
     ]
    }
   ],
   "source": [
    "x_train, x_val, y_train, y_val = compute_train_test_split(x, y, test_size=0.2)\n",
    "\n",
    "print(x_train.shape, y_train.shape)\n",
    "print(x_val.shape, y_val.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "second-clause",
   "metadata": {},
   "source": [
    "## Train model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "square-municipality",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_scaler = StandardScaler().fit(torch.from_numpy(x))\n",
    "y_scaler = StandardScaler().fit(torch.from_numpy(y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "fitted-participant",
   "metadata": {},
   "outputs": [],
   "source": [
    "def lr_steep_schedule(epoch):\n",
    "    threshold = 5\n",
    "    if epoch < threshold:\n",
    "        return 1.0\n",
    "    \n",
    "    multiplier = 2 / 10\n",
    "    step_change = (epoch - threshold) // 300\n",
    "    \n",
    "    return multiplier * 0.5 ** (step_change)\n",
    "\n",
    "\n",
    "def lr_simple_schedule(epoch):\n",
    "    return 0.5 ** (epoch // 200)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "changing-campaign",
   "metadata": {},
   "outputs": [],
   "source": [
    "def make_priors(y_train, y_scaler):\n",
    "    std = torch.Tensor((0.5,))\n",
    "    scale = torch.Tensor((0.2,))\n",
    "    \n",
    "    std_loc = torch.log(std**2 / torch.sqrt(std**2 + scale**2))\n",
    "    std_scale = torch.sqrt(torch.log(1 + scale**2 / std**2))\n",
    "    \n",
    "    return [\n",
    "        torch.distributions.Normal(0, 1),\n",
    "        torch.distributions.LogNormal(std_loc, std_scale),\n",
    "    ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "brutal-script",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "40,804 trainable parameters\n",
      "\n",
      "Epoch 1 | Validation loss = 2.1849 | Validation RMSE = 31.4644\n",
      "Epoch 50 | Validation loss = 1.3104 | Validation RMSE = 20.1894\n",
      "Epoch 100 | Validation loss = 1.2640 | Validation RMSE = 19.5906\n",
      "Epoch 150 | Validation loss = 1.2476 | Validation RMSE = 19.1869\n",
      "Epoch 200 | Validation loss = 1.2440 | Validation RMSE = 19.0512\n",
      "Epoch 250 | Validation loss = 1.2401 | Validation RMSE = 18.9143\n",
      "Epoch 300 | Validation loss = 1.2316 | Validation RMSE = 18.9218\n",
      "Epoch 350 | Validation loss = 1.2327 | Validation RMSE = 18.8604\n",
      "Epoch 400 | Validation loss = 1.2312 | Validation RMSE = 18.8614\n",
      "CPU times: user 2min 15s, sys: 25.4 s, total: 2min 41s\n",
      "Wall time: 2min 30s\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "learning_rate = 1e-3\n",
    "momentum = 0.9\n",
    "weight_decay = 1e-4\n",
    "\n",
    "n_epochs = 400\n",
    "batch_size = 64\n",
    "print_every = 50\n",
    "\n",
    "n_hidden = 100\n",
    "n_shared_layers = 1\n",
    "\n",
    "priors = make_priors(y_train, y_scaler)\n",
    "\n",
    "model = VariationalModel(\n",
    "    n_inputs=x.shape[1],\n",
    "    n_hidden=n_hidden,\n",
    "    priors=priors,\n",
    "    n_shared_layers=n_shared_layers,\n",
    "    x_scaler=x_scaler, \n",
    "    y_scaler=y_scaler,\n",
    ")\n",
    "\n",
    "pytorch_total_params = sum(p.numel() for p in model.parameters() if p.requires_grad)\n",
    "print(f'{pytorch_total_params:,} trainable parameters')\n",
    "print()\n",
    "\n",
    "optimizer = torch.optim.SGD(\n",
    "    model.parameters(), \n",
    "    lr=learning_rate, \n",
    "    momentum=momentum, \n",
    "    nesterov=True,\n",
    "    weight_decay=weight_decay,\n",
    ")\n",
    "scheduler = torch.optim.lr_scheduler.LambdaLR(\n",
    "    optimizer, \n",
    "    lr_lambda=lr_steep_schedule,\n",
    ")\n",
    "\n",
    "train_losses, val_losses = train(\n",
    "    model, \n",
    "    optimizer, \n",
    "    x_train, \n",
    "    x_val, \n",
    "    y_train, \n",
    "    y_val, \n",
    "    n_epochs=n_epochs, \n",
    "    batch_size=batch_size, \n",
    "    scheduler=scheduler, \n",
    "    print_every=print_every,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "involved-korean",
   "metadata": {},
   "source": [
    "### Validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "legitimate-microphone",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_dist, _ = model(x_val)\n",
    "y_hat = model.y_scaler.inverse_transform(y_dist.mean)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "monthly-andrew",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Validation RMSE = 18.89\n"
     ]
    }
   ],
   "source": [
    "val_rmse = float(compute_rmse(model, x_val, y_val).detach().numpy())\n",
    "print(f'Validation RMSE = {val_rmse:.2f}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "religious-feeding",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Validation $R^2$ = 0.54\n"
     ]
    }
   ],
   "source": [
    "val_r2 = r2_score(\n",
    "    y_val.detach().numpy().flatten(),\n",
    "    y_hat.detach().numpy().flatten(),\n",
    ")\n",
    "print(f'Validation $R^2$ = {val_r2:.2f}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "starting-daily",
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_results(y_true, y_pred):\n",
    "    _, ax = plt.subplots(1, 1, figsize=(7, 7))\n",
    "    palette = sns.color_palette()\n",
    "    \n",
    "    min_value = min(np.amin(y_true), np.amin(y_pred))\n",
    "    max_value = max(np.amax(y_true), np.amax(y_pred))\n",
    "    y_mid = np.linspace(min_value, max_value)\n",
    "    \n",
    "    ax.plot(y_mid, y_mid, '--', color=palette[1])\n",
    "    ax.scatter(y_true, y_pred, color=palette[0], alpha=0.5);\n",
    "    \n",
    "    return ax"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "experimental-exhibit",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = plot_results(\n",
    "    y_val.detach().numpy().flatten(),\n",
    "    y_hat.detach().numpy().flatten(),\n",
    ");\n",
    "\n",
    "ax.text(270, 120, f'$R^2 = {val_r2:.2f}$')\n",
    "ax.text(270, 100, f'$RMSE = {val_rmse:.2f}$')\n",
    "    \n",
    "ax.set_xlabel('Actuals');\n",
    "ax.set_ylabel('Predictions');\n",
    "ax.set_title('Regression results on validation set');"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "returning-concept",
   "metadata": {},
   "source": [
    "## Uncertainty"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "accomplished-monday",
   "metadata": {},
   "outputs": [],
   "source": [
    "def make_predictions(model, x):\n",
    "    mean_dist, std_dist = model.encoder(x)\n",
    "    \n",
    "    inv_tr = model.y_scaler.inverse_transform\n",
    "    \n",
    "    y_hat = inv_tr(mean_dist.mean)\n",
    "    \n",
    "    # Recover standard deviation's original scale\n",
    "    std_from_mean_dist = inv_tr(mean_dist.mean + mean_dist.stddev) - y_hat\n",
    "    std_from_std_dist = inv_tr(mean_dist.mean + std_dist.mean + std_dist.stddev) - y_hat\n",
    "    \n",
    "    return y_hat, std_from_mean_dist, std_from_std_dist"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "harmful-referral",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_hat, std_from_mean_dist , std_from_std_dist = make_predictions(model, x_val)\n",
    "std = std_from_mean_dist + std_from_std_dist"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "difficult-finnish",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(y_hat.detach().numpy().flatten(), bins=50);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "incomplete-adobe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(std_from_mean_dist.detach().numpy().flatten(), bins=50);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "successful-cartridge",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(std_from_std_dist.detach().numpy().flatten(), bins=50);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "sublime-child",
   "metadata": {},
   "source": [
    "### Plot absolute error against uncertainty"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "pursuant-farming",
   "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>error</th>\n",
       "      <th>uncertainty</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>error</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.313831</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>uncertainty</th>\n",
       "      <td>0.313831</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                error  uncertainty\n",
       "error        1.000000     0.313831\n",
       "uncertainty  0.313831     1.000000"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "absolute_errors = torch.abs(y_hat - y_val).detach().numpy().flatten()\n",
    "stds = std.detach().numpy().flatten()\n",
    "\n",
    "df = pd.DataFrame.from_dict({\n",
    "    'error': absolute_errors,\n",
    "    'uncertainty': stds\n",
    "})\n",
    "df.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "satisfied-kruger",
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_absolute_error_vs_uncertainty(y_val, y_hat, std, binned):\n",
    "    absolute_errors = torch.abs(y_hat - y_val).detach().numpy().flatten()\n",
    "    stds = std.detach().numpy().flatten()\n",
    "    \n",
    "    if binned:\n",
    "        ret = binned_statistic(absolute_errors, stds, bins=100)\n",
    "        a = ret.bin_edges[1:]\n",
    "        b = ret.statistic\n",
    "        alpha = 1.0\n",
    "    else:\n",
    "        a = absolute_errors\n",
    "        b = stds\n",
    "        alpha = 0.3\n",
    "    \n",
    "    _, ax = plt.subplots(1, 1, figsize=(8, 8))\n",
    "    ax.scatter(a, b, alpha=alpha)\n",
    "    \n",
    "    max_val = max(ax.get_xlim()[-1], ax.get_ylim()[-1])\n",
    "    x = np.linspace(0, max_val)\n",
    "    ax.plot(x, x, '--', color='#ccc', alpha=0.8)\n",
    "    \n",
    "    return ax"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "altered-buffalo",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = plot_absolute_error_vs_uncertainty(y_val, y_hat, std, binned=True)\n",
    "\n",
    "ax.set_title('Absolute error vs uncertainty (binned & averaged)');\n",
    "ax.set_xlabel('Absolute error');\n",
    "ax.set_ylabel('Standard deviations');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "incorporated-advocate",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = plot_absolute_error_vs_uncertainty(y_val, y_hat, std, binned=False)\n",
    "\n",
    "ax.set_title('Absolute error vs uncertainty');\n",
    "ax.set_xlabel('Absolute error');\n",
    "ax.set_ylabel('Standard deviations');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "unknown-chorus",
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_absolute_error_vs_uncertainty_normalized(y_val, y_hat, std, binned):\n",
    "    absolute_errors = (torch.abs(y_hat - y_val) / y_val).detach().numpy().flatten()\n",
    "    stds = (std / y_val).detach().numpy().flatten()\n",
    "    \n",
    "    if binned:\n",
    "        ret = binned_statistic(absolute_errors, stds, bins=100)\n",
    "        a = ret.bin_edges[1:]\n",
    "        b = ret.statistic\n",
    "        alpha = 1.0\n",
    "    else:\n",
    "        a = absolute_errors\n",
    "        b = stds\n",
    "        alpha = 0.3\n",
    "    \n",
    "    _, ax = plt.subplots(1, 1, figsize=(8, 8))\n",
    "    ax.scatter(a, b, alpha=alpha)\n",
    "    \n",
    "    max_val = max(ax.get_xlim()[-1], ax.get_ylim()[-1])\n",
    "    x = np.linspace(0, max_val)\n",
    "    ax.plot(x, x, '--', color='#ccc', alpha=0.8)\n",
    "    \n",
    "    return ax"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "animated-pendant",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = plot_absolute_error_vs_uncertainty_normalized(y_val, y_hat, std, binned=True)\n",
    "\n",
    "ax.set_title('Absolute error vs uncertainty (normalized)');\n",
    "ax.set_xlabel('Absolute error / actual');\n",
    "ax.set_ylabel('Standard deviations / actual');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "fifty-browser",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = plot_absolute_error_vs_uncertainty_normalized(y_val, y_hat, std, binned=False)\n",
    "\n",
    "ax.set_title('Absolute error vs uncertainty');\n",
    "ax.set_xlabel('Absolute error / actual');\n",
    "ax.set_ylabel('Standard deviations / actual');"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "marine-diving",
   "metadata": {},
   "source": [
    "### Uncertainty on an input not yet seen & very different from the rest of the dataset\n",
    "\n",
    "We'll consider an input composed of the maximum of all features."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "bibliographic-sullivan",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([1, 28])"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "random_input_np = np.zeros((1, x_val.shape[1]))\n",
    "for i in range(x_val.shape[1]):\n",
    "    fn = torch.amax\n",
    "    random_input_np[0, i] = fn(x_val[:, i]) * 2\n",
    "    \n",
    "random_input = torch.from_numpy(random_input_np)\n",
    "random_input.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "standard-discovery",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Uncertainty on made up input: 711.99\n"
     ]
    }
   ],
   "source": [
    "_, std_epistemic, std_aleatoric = make_predictions(model, random_input)\n",
    "uncertainty = float((std_epistemic + std_aleatoric).detach().numpy())\n",
    "print(f'Uncertainty on made up input: {uncertainty:.2f}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "several-evening",
   "metadata": {},
   "source": [
    "Uncertainty is high, which is what we want!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "worst-kazakhstan",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Aleatoric uncertainty: 711.99\n",
      "Epistemic uncertainty: 0.00\n"
     ]
    }
   ],
   "source": [
    "_, std_epistemic, std_aleatoric = make_predictions(model, random_input)\n",
    "print(f'Aleatoric uncertainty: {float(std_aleatoric.detach().numpy()):.2f}')\n",
    "print(f'Epistemic uncertainty: {float(std_epistemic.detach().numpy()):.2f}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "compliant-bankruptcy",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "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": "3.7.9"
  }
 },
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}