{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "hidden-vitamin",
   "metadata": {},
   "source": [
    "# Parametrising a distribution with a neural network\n",
    "\n",
    "Parametrization of a [PyTorch](https://pytorch.org/) distribution with a neural network, 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": "static-green",
   "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": "beautiful-worker",
   "metadata": {},
   "outputs": [],
   "source": [
    "sns.set(palette='colorblind', font_scale=1.3)\n",
    "palette = sns.color_palette()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "medical-chick",
   "metadata": {},
   "outputs": [],
   "source": [
    "seed = 456\n",
    "np.random.seed(seed);\n",
    "torch.manual_seed(seed);\n",
    "torch.set_default_dtype(torch.float64)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "classified-factory",
   "metadata": {},
   "source": [
    "## Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "pacific-directory",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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": "code",
   "execution_count": 5,
   "id": "underlying-jenny",
   "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": "dried-speaking",
   "metadata": {},
   "source": [
    "### Plot distribution of target variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "classical-large",
   "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": "ceramic-credits",
   "metadata": {},
   "source": [
    "### Define feature & target variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "terminal-front",
   "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": "fantastic-transfer",
   "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": "stone-addiction",
   "metadata": {},
   "source": [
    "## Define model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "elementary-parts",
   "metadata": {},
   "outputs": [],
   "source": [
    "class DeepNormalModel(torch.nn.Module):\n",
    "    def __init__(\n",
    "        self, \n",
    "        n_inputs,\n",
    "        n_hidden,\n",
    "        x_scaler, \n",
    "        y_scaler,\n",
    "    ):\n",
    "        super().__init__()\n",
    "        \n",
    "        self.x_scaler = x_scaler\n",
    "        self.y_scaler = y_scaler\n",
    "        self.jitter = 1e-6\n",
    "        \n",
    "        self.shared = torch.nn.Linear(n_inputs, n_hidden)\n",
    "        \n",
    "        self.mean_hidden = torch.nn.Linear(n_hidden, n_hidden)\n",
    "        self.mean_linear = torch.nn.Linear(n_hidden, 1)\n",
    "        \n",
    "        self.std_hidden = torch.nn.Linear(n_hidden, n_hidden)\n",
    "        self.std_linear = torch.nn.Linear(n_hidden, 1)\n",
    "        \n",
    "        self.dropout = torch.nn.Dropout()\n",
    "        \n",
    "    def forward(self, x):\n",
    "        # Normalization\n",
    "        shared = self.x_scaler(x)\n",
    "        \n",
    "        # Shared layer\n",
    "        shared = self.shared(shared)\n",
    "        shared = F.relu(shared)\n",
    "        shared = self.dropout(shared)\n",
    "        \n",
    "        # Parametrization of the mean\n",
    "        mean_hidden = self.mean_hidden(shared)\n",
    "        mean_hidden = F.relu(mean_hidden)\n",
    "        mean_hidden = self.dropout(mean_hidden)\n",
    "        mean = self.mean_linear(mean_hidden)\n",
    "        \n",
    "        # Parametrization fo the standard deviation\n",
    "        std_hidden = self.std_hidden(shared)\n",
    "        std_hidden = F.relu(std_hidden)\n",
    "        std_hidden = self.dropout(std_hidden)\n",
    "        std = F.softplus(self.std_linear(std_hidden)) + self.jitter\n",
    "        \n",
    "        return torch.distributions.Normal(mean, std)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "generous-lightweight",
   "metadata": {},
   "outputs": [],
   "source": [
    "def compute_loss(model, x, y, kl_reg=0.1):\n",
    "    y_scaled = model.y_scaler(y)\n",
    "    y_hat = model(x)\n",
    "    neg_log_likelihood = -y_hat.log_prob(y_scaled)\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": 11,
   "id": "binary-dylan",
   "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": 12,
   "id": "collectible-limitation",
   "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": "structural-truck",
   "metadata": {},
   "source": [
    "## Split between training and validation sets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "naughty-neighborhood",
   "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": 14,
   "id": "welsh-fraud",
   "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": "floppy-drove",
   "metadata": {},
   "source": [
    "## Train model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "powerful-horror",
   "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": 16,
   "id": "herbal-insider",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "23,302 trainable parameters\n",
      "\n",
      "Epoch 1 | Validation loss = 1.3502 | Validation RMSE = 25.9476\n",
      "Epoch 50 | Validation loss = 0.9483 | Validation RMSE = 18.4543\n",
      "Epoch 100 | Validation loss = 0.9232 | Validation RMSE = 18.0808\n",
      "Epoch 150 | Validation loss = 0.9703 | Validation RMSE = 18.5300\n",
      "Epoch 200 | Validation loss = 0.8858 | Validation RMSE = 17.6373\n",
      "Epoch 250 | Validation loss = 0.9364 | Validation RMSE = 18.0332\n",
      "Epoch 300 | Validation loss = 0.9045 | Validation RMSE = 17.4502\n",
      "CPU times: user 57.7 s, sys: 6.7 s, total: 1min 4s\n",
      "Wall time: 1min\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-5\n",
    "\n",
    "n_epochs = 300\n",
    "batch_size = 64\n",
    "print_every = 50\n",
    "\n",
    "n_hidden = 100\n",
    "\n",
    "model = DeepNormalModel(\n",
    "    n_inputs=x.shape[1],\n",
    "    n_hidden=n_hidden,\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 = None\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": "laden-headquarters",
   "metadata": {},
   "source": [
    "### Validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "intermediate-identifier",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_dist = model(x_val)\n",
    "y_hat = model.y_scaler.inverse_transform(y_dist.mean)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "ahead-parliament",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Validation RMSE = 17.46\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": 19,
   "id": "accomplished-donor",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Validation $R^2$ = 0.56\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": 34,
   "id": "substantial-airline",
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_results(y_true, y_pred):\n",
    "    f, 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 f, ax"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "related-concentrate",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, ax = plot_results(\n",
    "    y_val.detach().numpy().flatten(),\n",
    "    y_hat.detach().numpy().flatten(),\n",
    ");\n",
    "\n",
    "ax.text(225, 95, f'$R^2 = {val_r2:.2f}$')\n",
    "ax.text(225, 80, 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": "caroline-nurse",
   "metadata": {},
   "source": [
    "## Uncertainty"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "proof-cigarette",
   "metadata": {},
   "outputs": [],
   "source": [
    "def make_predictions(model, x):\n",
    "    dist = model(x)\n",
    "    \n",
    "    inv_tr = model.y_scaler.inverse_transform\n",
    "    y_hat = inv_tr(dist.mean)\n",
    "    \n",
    "    # Recover standard deviation's original scale\n",
    "    std = inv_tr(dist.mean + dist.stddev) - y_hat\n",
    "    \n",
    "    return y_hat, std"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "guided-corrections",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_hat, std = make_predictions(model, x_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "pressed-simple",
   "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": 52,
   "id": "humanitarian-quantum",
   "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.detach().numpy().flatten(), bins=50);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "developmental-consent",
   "metadata": {},
   "source": [
    "### Plot absolute error against uncertainty"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "opponent-canvas",
   "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>0.332653</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>uncertainty</th>\n",
       "      <td>0.332653</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                error  uncertainty\n",
       "error        1.000000     0.332653\n",
       "uncertainty  0.332653     1.000000"
      ]
     },
     "execution_count": 53,
     "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': 2 * stds\n",
    "})\n",
    "df.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "underlying-contrast",
   "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",
    "    \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",
    "    f, ax = plt.subplots(1, 1, figsize=(7, 7))\n",
    "    ax.scatter(a, b, alpha=alpha)\n",
    "    \n",
    "    return f, ax"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "decreased-cheese",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, 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 (binned)');\n",
    "ax.set_ylabel('Uncertainty (average within bin)');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "finished-prison",
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_results_with_uncertainty(y_true, y_pred, y_pred_std, subset):\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",
    "    \n",
    "    ax.scatter(y_true, y_pred, color=palette[0], alpha=0.1);\n",
    "    \n",
    "    ax.errorbar(y_true[subset], y_pred[subset], yerr=y_pred_std[subset], color=palette[0], fmt='o')\n",
    "    \n",
    "    return ax"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "cognitive-offset",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ix = [i for i, v in enumerate(stds) if v > stds.mean() + 3 * stds.std()]\n",
    "\n",
    "plot_results_with_uncertainty(\n",
    "    y_val.detach().numpy().flatten(),\n",
    "    y_hat.detach().numpy().flatten(),\n",
    "    std.detach().numpy().flatten(),\n",
    "    subset=ix,\n",
    ");"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "destroyed-humor",
   "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": 31,
   "id": "exclusive-location",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([1, 28])"
      ]
     },
     "execution_count": 31,
     "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": 32,
   "id": "billion-harmony",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Uncertainty on made up input: 19.59\n"
     ]
    }
   ],
   "source": [
    "_, std = make_predictions(model, random_input)\n",
    "uncertainty = float(std.detach().numpy())\n",
    "print(f'Uncertainty on made up input: {uncertainty:.2f}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "descending-falls",
   "metadata": {},
   "source": [
    "Uncertainty is high, which is what we want!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "incorporate-rehabilitation",
   "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"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}