{
 "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": 94,
   "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.linear_model import LinearRegression\n",
    "from sklearn.model_selection import train_test_split\n",
    "from scipy.stats import binned_statistic\n",
    "import statsmodels.api as sm\n",
    "\n",
    "cwd = os.getcwd()\n",
    "if cwd.endswith('notebook'):\n",
    "    os.chdir('..')\n",
    "    cwd = os.getcwd()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 267,
   "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",
       "    .dataframe tbody tr th {\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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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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_ix, test_ix = train_test_split(\n",
    "        x, \n",
    "        y, \n",
    "        list(range(len(x))),\n",
    "        test_size=test_size,\n",
    "    )\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",
    "        train_ix,\n",
    "        test_ix,\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]) 2437\n",
      "torch.Size([610, 28]) torch.Size([610, 1]) 610\n"
     ]
    }
   ],
   "source": [
    "x_train, x_val, y_train, y_val, train_ix, test_ix = compute_train_test_split(x, y, test_size=0.2)\n",
    "\n",
    "print(x_train.shape, y_train.shape, len(train_ix))\n",
    "print(x_val.shape, y_val.shape, len(test_ix))"
   ]
  },
  {
   "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 = 1.3449 | Validation RMSE = 25.9272\n",
      "Epoch 50 | Validation loss = 0.9299 | Validation RMSE = 18.2366\n",
      "Epoch 100 | Validation loss = 0.9070 | Validation RMSE = 17.9943\n",
      "Epoch 150 | Validation loss = 0.8992 | Validation RMSE = 17.8391\n",
      "Epoch 200 | Validation loss = 0.8917 | Validation RMSE = 17.6591\n",
      "Epoch 250 | Validation loss = 0.8986 | Validation RMSE = 17.6407\n",
      "Epoch 300 | Validation loss = 0.8858 | Validation RMSE = 17.4868\n",
      "CPU times: user 54.6 s, sys: 6.19 s, total: 1min\n",
      "Wall time: 57.1 s\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 = 17.54\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 $R^2$ = 0.55\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": 20,
   "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": 21,
   "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": 22,
   "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": 23,
   "id": "guided-corrections",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_hat, std = make_predictions(model, x_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 337,
   "id": "wanted-symphony",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x432 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, axes = plt.subplots(1, 2, figsize=(18, 6))\n",
    "ax1, ax2 = axes\n",
    "\n",
    "ax1.hist(y_hat.detach().numpy().flatten(), bins=50);\n",
    "ax2.hist(std.detach().numpy().flatten(), bins=50, color=palette[1]);\n",
    "\n",
    "ax1.set_xlabel('Cancer mortality rate (mean)');\n",
    "ax1.set_ylabel('Instance count');\n",
    "ax1.set_title('Distribution of mean $\\mu$ on validation set');\n",
    "\n",
    "ax2.set_xlabel('Cancer mortality rate (stddev)');\n",
    "ax2.set_ylabel('Instance count');\n",
    "ax2.set_title('Distribution of stddev $\\sigma$ on validation set');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 240,
   "id": "humanitarian-boxing",
   "metadata": {},
   "outputs": [],
   "source": [
    "low_ix = [i for i, v in enumerate(y_hat.detach().numpy().flatten()) if float(v) < 140][0]\n",
    "average_ix = [i for i, v in enumerate(y_hat.detach().numpy().flatten()) if 170 < float(v) < 190][0]\n",
    "high_ix = [i for i, v in enumerate(y_hat.detach().numpy().flatten()) if float(v) > 220][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 265,
   "id": "rocky-playing",
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_normal_distributions(y_hat, std, indices):\n",
    "    f, ax = plt.subplots(1, 1, figsize=(12, 6))\n",
    "    \n",
    "    normal_fn = lambda z, m, s: (1/(s * np.sqrt(2*np.pi))) * np.exp(-0.5*((z - m) / s)**2)\n",
    "    \n",
    "    x = np.linspace(80, 300, 300)\n",
    "    \n",
    "    for ix in indices:\n",
    "        mu = y_hat.detach().numpy().flatten()[ix]\n",
    "        sigma = std.detach().numpy().flatten()[ix]\n",
    "        \n",
    "        ax.plot(x, normal_fn(x, mu, sigma), label=rf'$\\mu = {mu:.0f}$, $\\sigma = {sigma:.1f}$')\n",
    "    \n",
    "    ax.set_xlabel('Cancer mortality rate ($y$)');\n",
    "    ax.set_ylabel(r'$P(y \\mid \\mu, \\sigma)$');\n",
    "    ax.set_title('PDF of three instances of the validation set');\n",
    "    ax.legend(loc='upper left');\n",
    "    \n",
    "    return f, ax"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 338,
   "id": "prime-timber",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, ax = plot_normal_distributions(y_hat, std, [low_ix, average_ix, high_ix])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "developmental-consent",
   "metadata": {},
   "source": [
    "### Plot absolute error against uncertainty"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 291,
   "id": "underlying-contrast",
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_absolute_error_vs_uncertainty(y_val, y_hat, std):\n",
    "    absolute_errors = 100 * (torch.abs(y_hat - y_val) / y_val).detach().numpy().flatten()\n",
    "    stds = std.detach().numpy().flatten()\n",
    "    \n",
    "    a = absolute_errors\n",
    "    b = stds\n",
    "    alpha = 0.5\n",
    "\n",
    "    f, ax = plt.subplots(1, 1, figsize=(7, 7))\n",
    "    ax.scatter(a, b, alpha=alpha)\n",
    "    \n",
    "    X = a.reshape((-1, 1))\n",
    "    y = b\n",
    "    linreg = LinearRegression().fit(X, y)\n",
    "    r2 = linreg.score(X, y)\n",
    "    corr = np.corrcoef(a, b)[0, 1]\n",
    "    print(f'R2: {r2:.2f}')\n",
    "    print(f'Correlation: {corr:.2f}')\n",
    "    \n",
    "    y_fit = linreg.coef_[0] * a + linreg.intercept_\n",
    "    \n",
    "    ax.plot(a, y_fit, '-', color=palette[1], alpha=0.5)\n",
    "\n",
    "    ax.text(56, 7.5, f'Corr  =  {corr:.2f}')\n",
    "    \n",
    "    return f, ax"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 292,
   "id": "decreased-cheese",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R2: 0.08\n",
      "Correlation: 0.28\n"
     ]
    },
    {
     "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)\n",
    "\n",
    "ax.set_title('Error vs uncertainty');\n",
    "ax.set_xlabel('Absolute error / actual (%)');\n",
    "ax.set_ylabel('Standard deviation');"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "corresponding-genetics",
   "metadata": {},
   "source": [
    "### Uncertainty on an input not yet seen & very different from the rest of the dataset\n",
    "\n",
    "We'll consider an input made of the maximum of all features."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "working-obligation",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([1, 28])"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "max_input_np = np.zeros((1, x_val.shape[1]))\n",
    "for i in range(x_val.shape[1]):\n",
    "    max_input_np[0, i] = torch.amax(x_val[:, i]) * 2\n",
    "\n",
    "max_input = torch.from_numpy(max_input_np)\n",
    "max_input.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "id": "closed-hollow",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "First order uncertainty: 2.59\n"
     ]
    }
   ],
   "source": [
    "r_y_hat, r_first_order_std = make_predictions(model, max_input)\n",
    "\n",
    "print(f'First order uncertainty: {float((r_first_order_std / r_y_hat).detach().numpy()):.2f}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "expected-therapist",
   "metadata": {},
   "source": [
    "## Unexpectedness\n",
    "\n",
    "An instance's expectedness can be measured by its average feature quantile minus the median 0.5.\n",
    "\n",
    "We can then estimate the relation between uncertainty and expectedness."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "random-novel",
   "metadata": {},
   "outputs": [],
   "source": [
    "def set_feature_quantile(df, features):\n",
    "    series = []\n",
    "    quantiles = np.arange(0., 1., 0.01)\n",
    "    for q in quantiles:\n",
    "        series.append(df[features].quantile(q))\n",
    "        \n",
    "    def apply_quantile(row):\n",
    "        v = np.zeros((len(features)))\n",
    "        \n",
    "        for i, f in enumerate(features):\n",
    "            value = row[f]\n",
    "            for j, q in enumerate(quantiles):\n",
    "                if value <= series[j][f]:\n",
    "                    v[i] = q\n",
    "                    break\n",
    "        \n",
    "        return v.mean()\n",
    "    \n",
    "    df['quantile'] = df.apply(apply_quantile, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "fossil-tulsa",
   "metadata": {},
   "outputs": [],
   "source": [
    "set_feature_quantile(df, features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "sufficient-trash",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['quantile'].hist(bins=20);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 331,
   "id": "armed-marker",
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_expectedness_vs_uncertainty(df, test_ix, y_val, std):\n",
    "    quantiles = df['quantile'].iloc[test_ix].values\n",
    "    expectedness = 100 * np.abs(quantiles - 0.5)\n",
    "    stds = std.detach().numpy().flatten()\n",
    "    \n",
    "    f, ax = plt.subplots(1, 1, figsize=(7, 7))\n",
    "    ax.scatter(expectedness, stds, alpha=0.5)\n",
    "    \n",
    "    X = sm.add_constant(expectedness.reshape((-1, 1)))\n",
    "    y = stds.reshape((-1, 1))\n",
    "    \n",
    "    linreg = sm.OLS(y, X).fit()\n",
    "    y_fit = linreg.predict(X).flatten()\n",
    "    \n",
    "    corr = np.corrcoef(expectedness, stds)[0, 1]\n",
    "    print(f'Correlation: {corr:.2f}')\n",
    "    \n",
    "    print(linreg.summary())\n",
    "    \n",
    "    ax.plot(expectedness, y_fit, '-', color=palette[1], alpha=0.5)\n",
    "    \n",
    "    ax.text(27, 7.5, f'Corr  =  {corr:.2f}')\n",
    "    \n",
    "    return f, ax"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 332,
   "id": "incoming-rogers",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Correlation: 0.33\n",
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                      y   R-squared:                       0.108\n",
      "Model:                            OLS   Adj. R-squared:                  0.107\n",
      "Method:                 Least Squares   F-statistic:                     73.98\n",
      "Date:                Fri, 07 Jan 2022   Prob (F-statistic):           6.72e-17\n",
      "Time:                        11:36:17   Log-Likelihood:                -1754.4\n",
      "No. Observations:                 610   AIC:                             3513.\n",
      "Df Residuals:                     608   BIC:                             3522.\n",
      "Df Model:                           1                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "const         12.7535      0.281     45.323      0.000      12.201      13.306\n",
      "x1             0.3532      0.041      8.601      0.000       0.273       0.434\n",
      "==============================================================================\n",
      "Omnibus:                      145.715   Durbin-Watson:                   2.140\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):              468.950\n",
      "Skew:                           1.117   Prob(JB):                    1.48e-102\n",
      "Kurtosis:                       6.669   Cond. No.                         11.2\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    },
    {
     "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_expectedness_vs_uncertainty(df, test_ix, y_val, std)\n",
    "\n",
    "ax.set_xlabel('Unexpectedness (%)')\n",
    "ax.set_ylabel('Standard deviation')\n",
    "ax.set_title('Unexpectedness vs uncertainty');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "covered-chess",
   "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
}