adult income.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "0fa6b102",
   "metadata": {},
   "source": [
    "Copyright 2021 Twitter, Inc.\n",
    "\n",
    "SPDX-License-Identifier: Apache-2.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "6308233d-153d-46b7-b477-186f0053fa69",
   "metadata": {
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "── \u001b[1mAttaching packages\u001b[22m ──────────────────────────────────────────────────────────────────────────────────────────────────── tidyverse 1.3.1 ──\n",
      "\n",
      "\u001b[32m✔\u001b[39m \u001b[34mggplot2\u001b[39m 3.3.5     \u001b[32m✔\u001b[39m \u001b[34mpurrr  \u001b[39m 0.3.4\n",
      "\u001b[32m✔\u001b[39m \u001b[34mtibble \u001b[39m 3.1.5     \u001b[32m✔\u001b[39m \u001b[34mdplyr  \u001b[39m 1.0.7\n",
      "\u001b[32m✔\u001b[39m \u001b[34mtidyr  \u001b[39m 1.1.4     \u001b[32m✔\u001b[39m \u001b[34mstringr\u001b[39m 1.4.0\n",
      "\u001b[32m✔\u001b[39m \u001b[34mreadr  \u001b[39m 2.0.2     \u001b[32m✔\u001b[39m \u001b[34mforcats\u001b[39m 0.5.1\n",
      "\n",
      "── \u001b[1mConflicts\u001b[22m ─────────────────────────────────────────────────────────────────────────────────────────────────────── tidyverse_conflicts() ──\n",
      "\u001b[31m✖\u001b[39m \u001b[34mdplyr\u001b[39m::\u001b[32mfilter()\u001b[39m masks \u001b[34mstats\u001b[39m::filter()\n",
      "\u001b[31m✖\u001b[39m \u001b[34mdplyr\u001b[39m::\u001b[32mlag()\u001b[39m    masks \u001b[34mstats\u001b[39m::lag()\n",
      "\n",
      "\n",
      "Attaching package: ‘data.table’\n",
      "\n",
      "\n",
      "The following objects are masked from ‘package:dplyr’:\n",
      "\n",
      "    between, first, last\n",
      "\n",
      "\n",
      "The following object is masked from ‘package:purrr’:\n",
      "\n",
      "    transpose\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "library(tidyverse)\n",
    "library(data.table)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "b9ec56b4-ca26-41e6-8141-5d3a88164783",
   "metadata": {
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [],
   "source": [
    "apply_corrections <- function(dat, threshold = FALSE){\n",
    "  #applies three candidate corrections to the simulated binomial data\n",
    "  uncorrected_var <- apply(dat$mu_hats, 2, var)\n",
    "  sigma2_hats <- dat$mu_hats*(1-dat$mu_hats)/dat$nks\n",
    "  corrected_var <- uncorrected_var - apply(sigma2_hats, 2, mean)\n",
    "  double_corrected_var <-uncorrected_var - 2*apply(sigma2_hats, 2, mean) + apply(sigma2_hats/dat$nks, 2, mean)\n",
    "  \n",
    "  \n",
    "  \n",
    "  if(threshold){\n",
    "    corrected_var <- pmax(0, corrected_var)\n",
    "    double_corrected_var <- pmax(0, double_corrected_var)\n",
    "  }\n",
    "  \n",
    "  out <- data.frame(uncorrected_var, corrected_var, double_corrected_var)\n",
    "  \n",
    "}\n",
    "\n",
    "double_correction <- function(df, nsample=500) {\n",
    "    d <- rowwise(df) %>% mutate(sample_rate = list(rbinom(nsample, n, rate)/n))\n",
    "    results <- apply_corrections(list(mu_hats=do.call(rbind, d$sample_rate), nks=d$n), TRUE)\n",
    "    bounds <- results %>% summarise_all(~list(as_tibble(as.list(quantile(., probs=c(.025, .975)))))) %>% unnest(cols = 1:3, names_sep='_')\n",
    "    append(lapply(results, median), bounds)\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "ea04b374-06ef-4d73-a1f6-376cb46b9f98",
   "metadata": {
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[1m\u001b[1mRows: \u001b[1m\u001b[22m\u001b[34m\u001b[34m14653\u001b[34m\u001b[39m \u001b[1m\u001b[1mColumns: \u001b[1m\u001b[22m\u001b[34m\u001b[34m15\u001b[34m\u001b[39m\n",
      "\n",
      "\u001b[36m──\u001b[39m \u001b[1m\u001b[1mColumn specification\u001b[1m\u001b[22m \u001b[36m─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\u001b[39m\n",
      "\u001b[1mDelimiter:\u001b[22m \",\"\n",
      "\u001b[31mchr\u001b[39m (8): workclass, education, marital-status, occupation, relationship, rac...\n",
      "\u001b[32mdbl\u001b[39m (7): age, education-num, capital-gain, capital-loss, hours-per-week, sco...\n",
      "\n",
      "\n",
      "\u001b[36mℹ\u001b[39m Use \u001b[30m\u001b[47m\u001b[30m\u001b[47m`spec()`\u001b[47m\u001b[30m\u001b[49m\u001b[39m to retrieve the full column specification for this data.\n",
      "\u001b[36mℹ\u001b[39m Specify the column types or set \u001b[30m\u001b[47m\u001b[30m\u001b[47m`show_col_types = FALSE`\u001b[47m\u001b[30m\u001b[49m\u001b[39m to quiet this message.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "income <- read_csv('adult_income_with_predictions.csv') %>% mutate(age_group = cut_width(age, width=10, center=50))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "b6fb7d6a-7003-40d9-ba9a-88339eb1af4b",
   "metadata": {
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "14653"
      ],
      "text/latex": [
       "14653"
      ],
      "text/markdown": [
       "14653"
      ],
      "text/plain": [
       "[1] 14653"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "nrow(income)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "12450e43-11d4-4a1e-915f-d61b4e1cde2f",
   "metadata": {
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "0.872790554835187"
      ],
      "text/latex": [
       "0.872790554835187"
      ],
      "text/markdown": [
       "0.872790554835187"
      ],
      "text/plain": [
       "[1] 0.8727906"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# mean accuracy\n",
    "mean(income$income_gt_50K == (income$score >= 0.5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "518eae59-a6f3-434d-a389-a32960668157",
   "metadata": {
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. \n",
       "  17.00   28.00   37.00   38.68   48.00   90.00 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "summary(income$age)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "f357239d-2db9-458b-b4dd-a7e95708f392",
   "metadata": {
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A tibble: 8 × 3</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>age_group</th><th scope=col>n</th><th scope=col>prop</th></tr>\n",
       "\t<tr><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>[15,25]</td><td>2845</td><td>0.194158193</td></tr>\n",
       "\t<tr><td>(25,35]</td><td>3898</td><td>0.266020610</td></tr>\n",
       "\t<tr><td>(35,45]</td><td>3515</td><td>0.239882618</td></tr>\n",
       "\t<tr><td>(45,55]</td><td>2504</td><td>0.170886508</td></tr>\n",
       "\t<tr><td>(55,65]</td><td>1333</td><td>0.090971132</td></tr>\n",
       "\t<tr><td>(65,75]</td><td> 438</td><td>0.029891490</td></tr>\n",
       "\t<tr><td>(75,85]</td><td>  99</td><td>0.006756296</td></tr>\n",
       "\t<tr><td>(85,95]</td><td>  21</td><td>0.001433154</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A tibble: 8 × 3\n",
       "\\begin{tabular}{lll}\n",
       " age\\_group & n & prop\\\\\n",
       " <fct> & <int> & <dbl>\\\\\n",
       "\\hline\n",
       "\t {[}15,25{]} & 2845 & 0.194158193\\\\\n",
       "\t (25,35{]} & 3898 & 0.266020610\\\\\n",
       "\t (35,45{]} & 3515 & 0.239882618\\\\\n",
       "\t (45,55{]} & 2504 & 0.170886508\\\\\n",
       "\t (55,65{]} & 1333 & 0.090971132\\\\\n",
       "\t (65,75{]} &  438 & 0.029891490\\\\\n",
       "\t (75,85{]} &   99 & 0.006756296\\\\\n",
       "\t (85,95{]} &   21 & 0.001433154\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A tibble: 8 × 3\n",
       "\n",
       "| age_group &lt;fct&gt; | n &lt;int&gt; | prop &lt;dbl&gt; |\n",
       "|---|---|---|\n",
       "| [15,25] | 2845 | 0.194158193 |\n",
       "| (25,35] | 3898 | 0.266020610 |\n",
       "| (35,45] | 3515 | 0.239882618 |\n",
       "| (45,55] | 2504 | 0.170886508 |\n",
       "| (55,65] | 1333 | 0.090971132 |\n",
       "| (65,75] |  438 | 0.029891490 |\n",
       "| (75,85] |   99 | 0.006756296 |\n",
       "| (85,95] |   21 | 0.001433154 |\n",
       "\n"
      ],
      "text/plain": [
       "  age_group n    prop       \n",
       "1 [15,25]   2845 0.194158193\n",
       "2 (25,35]   3898 0.266020610\n",
       "3 (35,45]   3515 0.239882618\n",
       "4 (45,55]   2504 0.170886508\n",
       "5 (55,65]   1333 0.090971132\n",
       "6 (65,75]    438 0.029891490\n",
       "7 (75,85]     99 0.006756296\n",
       "8 (85,95]     21 0.001433154"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "income %>% group_by(age_group) %>% summarise(n=n()) %>% ungroup() %>% mutate(prop=n/sum(n))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "5c15c986-c0d5-48a2-9740-02dd2c9b2dbc",
   "metadata": {
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A tibble: 5 × 3</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>race</th><th scope=col>count</th><th scope=col>prop</th></tr>\n",
       "\t<tr><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>Amer-Indian-Eskimo</td><td>  125</td><td>0.008530676</td></tr>\n",
       "\t<tr><td>Asian-Pac-Islander</td><td>  401</td><td>0.027366410</td></tr>\n",
       "\t<tr><td>Black             </td><td> 1347</td><td>0.091926568</td></tr>\n",
       "\t<tr><td>Other             </td><td>  115</td><td>0.007848222</td></tr>\n",
       "\t<tr><td>White             </td><td>12665</td><td>0.864328124</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A tibble: 5 × 3\n",
       "\\begin{tabular}{lll}\n",
       " race & count & prop\\\\\n",
       " <chr> & <int> & <dbl>\\\\\n",
       "\\hline\n",
       "\t Amer-Indian-Eskimo &   125 & 0.008530676\\\\\n",
       "\t Asian-Pac-Islander &   401 & 0.027366410\\\\\n",
       "\t Black              &  1347 & 0.091926568\\\\\n",
       "\t Other              &   115 & 0.007848222\\\\\n",
       "\t White              & 12665 & 0.864328124\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A tibble: 5 × 3\n",
       "\n",
       "| race &lt;chr&gt; | count &lt;int&gt; | prop &lt;dbl&gt; |\n",
       "|---|---|---|\n",
       "| Amer-Indian-Eskimo |   125 | 0.008530676 |\n",
       "| Asian-Pac-Islander |   401 | 0.027366410 |\n",
       "| Black              |  1347 | 0.091926568 |\n",
       "| Other              |   115 | 0.007848222 |\n",
       "| White              | 12665 | 0.864328124 |\n",
       "\n"
      ],
      "text/plain": [
       "  race               count prop       \n",
       "1 Amer-Indian-Eskimo   125 0.008530676\n",
       "2 Asian-Pac-Islander   401 0.027366410\n",
       "3 Black               1347 0.091926568\n",
       "4 Other                115 0.007848222\n",
       "5 White              12665 0.864328124"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "income %>% group_by(race) %>% summarise(count=n()) %>% ungroup() %>% mutate(prop=count/sum(count))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "a0674384-1275-4ede-8448-38c34c9ec6f3",
   "metadata": {
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [],
   "source": [
    "df <- list()\n",
    "for (feature in c('race', 'age_group')) {\n",
    "    tpr <- income %>% filter(income_gt_50K == 1) %>% group_by(!!as.name(feature)) %>% summarise(rate=mean(score >= 0.5), n=n())\n",
    "    fpr <- income %>% filter(income_gt_50K == 0) %>% group_by(!!as.name(feature)) %>% summarise(rate=mean(score >= 0.5), n=n())\n",
    "    sr <- income %>% group_by(!!as.name(feature)) %>% summarise(rate=mean(score >= 0.5), n=n())\n",
    "     \n",
    "    df <- append(df, list(c(metric='TPR', feature=feature, double_correction(tpr))))\n",
    "    df <- append(df, list(c(metric='FPR', feature=feature, double_correction(fpr))))\n",
    "    df <- append(df, list(c(metric='SR', feature=feature, double_correction(sr))))\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "ae250e76-6f67-4a69-ab60-9f980061bc14",
   "metadata": {
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [],
   "source": [
    "df <- rbindlist(df, fill=TRUE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "341b185e-7944-48ce-b60a-ac97a6dd1031",
   "metadata": {
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A data.table: 6 × 11</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>metric</th><th scope=col>feature</th><th scope=col>uncorrected_var</th><th scope=col>corrected_var</th><th scope=col>double_corrected_var</th><th scope=col>uncorrected_var_2.5%</th><th scope=col>uncorrected_var_97.5%</th><th scope=col>corrected_var_2.5%</th><th scope=col>corrected_var_97.5%</th><th scope=col>double_corrected_var_2.5%</th><th scope=col>double_corrected_var_97.5%</th></tr>\n",
       "\t<tr><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>FPR</td><td>age_group</td><td>0.0028</td><td>0.0021</td><td>0.0013</td><td>0.0017</td><td>0.0116</td><td>0.0011</td><td>0.0097</td><td>0.0003</td><td>0.0079</td></tr>\n",
       "\t<tr><td>FPR</td><td>race     </td><td>0.0014</td><td>0.0012</td><td>0.0011</td><td>0.0007</td><td>0.0025</td><td>0.0006</td><td>0.0023</td><td>0.0005</td><td>0.0022</td></tr>\n",
       "\t<tr><td>SR </td><td>age_group</td><td>0.0157</td><td>0.0142</td><td>0.0128</td><td>0.0122</td><td>0.0287</td><td>0.0110</td><td>0.0270</td><td>0.0098</td><td>0.0255</td></tr>\n",
       "\t<tr><td>SR </td><td>race     </td><td>0.0072</td><td>0.0068</td><td>0.0065</td><td>0.0045</td><td>0.0108</td><td>0.0041</td><td>0.0105</td><td>0.0037</td><td>0.0103</td></tr>\n",
       "\t<tr><td>TPR</td><td>age_group</td><td>0.0286</td><td>0.0227</td><td>0.0176</td><td>0.0118</td><td>0.0477</td><td>0.0043</td><td>0.0455</td><td>0.0000</td><td>0.0431</td></tr>\n",
       "\t<tr><td>TPR</td><td>race     </td><td>0.0155</td><td>0.0084</td><td>0.0018</td><td>0.0028</td><td>0.0520</td><td>0.0000</td><td>0.0463</td><td>0.0000</td><td>0.0407</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.table: 6 × 11\n",
       "\\begin{tabular}{lllllllllll}\n",
       " metric & feature & uncorrected\\_var & corrected\\_var & double\\_corrected\\_var & uncorrected\\_var\\_2.5\\% & uncorrected\\_var\\_97.5\\% & corrected\\_var\\_2.5\\% & corrected\\_var\\_97.5\\% & double\\_corrected\\_var\\_2.5\\% & double\\_corrected\\_var\\_97.5\\%\\\\\n",
       " <chr> & <chr> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl>\\\\\n",
       "\\hline\n",
       "\t FPR & age\\_group & 0.0028 & 0.0021 & 0.0013 & 0.0017 & 0.0116 & 0.0011 & 0.0097 & 0.0003 & 0.0079\\\\\n",
       "\t FPR & race      & 0.0014 & 0.0012 & 0.0011 & 0.0007 & 0.0025 & 0.0006 & 0.0023 & 0.0005 & 0.0022\\\\\n",
       "\t SR  & age\\_group & 0.0157 & 0.0142 & 0.0128 & 0.0122 & 0.0287 & 0.0110 & 0.0270 & 0.0098 & 0.0255\\\\\n",
       "\t SR  & race      & 0.0072 & 0.0068 & 0.0065 & 0.0045 & 0.0108 & 0.0041 & 0.0105 & 0.0037 & 0.0103\\\\\n",
       "\t TPR & age\\_group & 0.0286 & 0.0227 & 0.0176 & 0.0118 & 0.0477 & 0.0043 & 0.0455 & 0.0000 & 0.0431\\\\\n",
       "\t TPR & race      & 0.0155 & 0.0084 & 0.0018 & 0.0028 & 0.0520 & 0.0000 & 0.0463 & 0.0000 & 0.0407\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.table: 6 × 11\n",
       "\n",
       "| metric &lt;chr&gt; | feature &lt;chr&gt; | uncorrected_var &lt;dbl&gt; | corrected_var &lt;dbl&gt; | double_corrected_var &lt;dbl&gt; | uncorrected_var_2.5% &lt;dbl&gt; | uncorrected_var_97.5% &lt;dbl&gt; | corrected_var_2.5% &lt;dbl&gt; | corrected_var_97.5% &lt;dbl&gt; | double_corrected_var_2.5% &lt;dbl&gt; | double_corrected_var_97.5% &lt;dbl&gt; |\n",
       "|---|---|---|---|---|---|---|---|---|---|---|\n",
       "| FPR | age_group | 0.0028 | 0.0021 | 0.0013 | 0.0017 | 0.0116 | 0.0011 | 0.0097 | 0.0003 | 0.0079 |\n",
       "| FPR | race      | 0.0014 | 0.0012 | 0.0011 | 0.0007 | 0.0025 | 0.0006 | 0.0023 | 0.0005 | 0.0022 |\n",
       "| SR  | age_group | 0.0157 | 0.0142 | 0.0128 | 0.0122 | 0.0287 | 0.0110 | 0.0270 | 0.0098 | 0.0255 |\n",
       "| SR  | race      | 0.0072 | 0.0068 | 0.0065 | 0.0045 | 0.0108 | 0.0041 | 0.0105 | 0.0037 | 0.0103 |\n",
       "| TPR | age_group | 0.0286 | 0.0227 | 0.0176 | 0.0118 | 0.0477 | 0.0043 | 0.0455 | 0.0000 | 0.0431 |\n",
       "| TPR | race      | 0.0155 | 0.0084 | 0.0018 | 0.0028 | 0.0520 | 0.0000 | 0.0463 | 0.0000 | 0.0407 |\n",
       "\n"
      ],
      "text/plain": [
       "  metric feature   uncorrected_var corrected_var double_corrected_var\n",
       "1 FPR    age_group 0.0028          0.0021        0.0013              \n",
       "2 FPR    race      0.0014          0.0012        0.0011              \n",
       "3 SR     age_group 0.0157          0.0142        0.0128              \n",
       "4 SR     race      0.0072          0.0068        0.0065              \n",
       "5 TPR    age_group 0.0286          0.0227        0.0176              \n",
       "6 TPR    race      0.0155          0.0084        0.0018              \n",
       "  uncorrected_var_2.5% uncorrected_var_97.5% corrected_var_2.5%\n",
       "1 0.0017               0.0116                0.0011            \n",
       "2 0.0007               0.0025                0.0006            \n",
       "3 0.0122               0.0287                0.0110            \n",
       "4 0.0045               0.0108                0.0041            \n",
       "5 0.0118               0.0477                0.0043            \n",
       "6 0.0028               0.0520                0.0000            \n",
       "  corrected_var_97.5% double_corrected_var_2.5% double_corrected_var_97.5%\n",
       "1 0.0097              0.0003                    0.0079                    \n",
       "2 0.0023              0.0005                    0.0022                    \n",
       "3 0.0270              0.0098                    0.0255                    \n",
       "4 0.0105              0.0037                    0.0103                    \n",
       "5 0.0455              0.0000                    0.0431                    \n",
       "6 0.0463              0.0000                    0.0407                    "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df %>% arrange(metric, feature) %>%\n",
    " mutate_if(is.numeric, round, digits=4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "c9f6e107-fa12-4410-8a42-cfb7219dd064",
   "metadata": {
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [],
   "source": [
    "pd <- df %>% select(-(3:5)) %>% pivot_longer(cols=3:8, names_sep='_var_', names_to=c(\"method\", \"bound\")) %>% filter(method != 'corrected') %>% pivot_wider(names_from=bound)\n",
    "pd <- rename(pd, c('lower'='2.5%', 'upper'='97.5%'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "05764fca-53f0-4a90-89fa-4dc36e94c76c",
   "metadata": {
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A tibble: 12 × 5</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>metric</th><th scope=col>feature</th><th scope=col>method</th><th scope=col>lower</th><th scope=col>upper</th></tr>\n",
       "\t<tr><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>TPR</td><td>race     </td><td>uncorrected     </td><td>0.0028097355</td><td>0.052041650</td></tr>\n",
       "\t<tr><td>TPR</td><td>race     </td><td>double_corrected</td><td>0.0000000000</td><td>0.040719331</td></tr>\n",
       "\t<tr><td>FPR</td><td>race     </td><td>uncorrected     </td><td>0.0007183938</td><td>0.002466362</td></tr>\n",
       "\t<tr><td>FPR</td><td>race     </td><td>double_corrected</td><td>0.0005151764</td><td>0.002218034</td></tr>\n",
       "\t<tr><td>SR </td><td>race     </td><td>uncorrected     </td><td>0.0044530790</td><td>0.010778322</td></tr>\n",
       "\t<tr><td>SR </td><td>race     </td><td>double_corrected</td><td>0.0036981694</td><td>0.010252778</td></tr>\n",
       "\t<tr><td>TPR</td><td>age_group</td><td>uncorrected     </td><td>0.0118473758</td><td>0.047725370</td></tr>\n",
       "\t<tr><td>TPR</td><td>age_group</td><td>double_corrected</td><td>0.0000000000</td><td>0.043064355</td></tr>\n",
       "\t<tr><td>FPR</td><td>age_group</td><td>uncorrected     </td><td>0.0017429443</td><td>0.011628342</td></tr>\n",
       "\t<tr><td>FPR</td><td>age_group</td><td>double_corrected</td><td>0.0003075220</td><td>0.007931422</td></tr>\n",
       "\t<tr><td>SR </td><td>age_group</td><td>uncorrected     </td><td>0.0122233591</td><td>0.028682498</td></tr>\n",
       "\t<tr><td>SR </td><td>age_group</td><td>double_corrected</td><td>0.0097690090</td><td>0.025450620</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A tibble: 12 × 5\n",
       "\\begin{tabular}{lllll}\n",
       " metric & feature & method & lower & upper\\\\\n",
       " <chr> & <chr> & <chr> & <dbl> & <dbl>\\\\\n",
       "\\hline\n",
       "\t TPR & race      & uncorrected      & 0.0028097355 & 0.052041650\\\\\n",
       "\t TPR & race      & double\\_corrected & 0.0000000000 & 0.040719331\\\\\n",
       "\t FPR & race      & uncorrected      & 0.0007183938 & 0.002466362\\\\\n",
       "\t FPR & race      & double\\_corrected & 0.0005151764 & 0.002218034\\\\\n",
       "\t SR  & race      & uncorrected      & 0.0044530790 & 0.010778322\\\\\n",
       "\t SR  & race      & double\\_corrected & 0.0036981694 & 0.010252778\\\\\n",
       "\t TPR & age\\_group & uncorrected      & 0.0118473758 & 0.047725370\\\\\n",
       "\t TPR & age\\_group & double\\_corrected & 0.0000000000 & 0.043064355\\\\\n",
       "\t FPR & age\\_group & uncorrected      & 0.0017429443 & 0.011628342\\\\\n",
       "\t FPR & age\\_group & double\\_corrected & 0.0003075220 & 0.007931422\\\\\n",
       "\t SR  & age\\_group & uncorrected      & 0.0122233591 & 0.028682498\\\\\n",
       "\t SR  & age\\_group & double\\_corrected & 0.0097690090 & 0.025450620\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A tibble: 12 × 5\n",
       "\n",
       "| metric &lt;chr&gt; | feature &lt;chr&gt; | method &lt;chr&gt; | lower &lt;dbl&gt; | upper &lt;dbl&gt; |\n",
       "|---|---|---|---|---|\n",
       "| TPR | race      | uncorrected      | 0.0028097355 | 0.052041650 |\n",
       "| TPR | race      | double_corrected | 0.0000000000 | 0.040719331 |\n",
       "| FPR | race      | uncorrected      | 0.0007183938 | 0.002466362 |\n",
       "| FPR | race      | double_corrected | 0.0005151764 | 0.002218034 |\n",
       "| SR  | race      | uncorrected      | 0.0044530790 | 0.010778322 |\n",
       "| SR  | race      | double_corrected | 0.0036981694 | 0.010252778 |\n",
       "| TPR | age_group | uncorrected      | 0.0118473758 | 0.047725370 |\n",
       "| TPR | age_group | double_corrected | 0.0000000000 | 0.043064355 |\n",
       "| FPR | age_group | uncorrected      | 0.0017429443 | 0.011628342 |\n",
       "| FPR | age_group | double_corrected | 0.0003075220 | 0.007931422 |\n",
       "| SR  | age_group | uncorrected      | 0.0122233591 | 0.028682498 |\n",
       "| SR  | age_group | double_corrected | 0.0097690090 | 0.025450620 |\n",
       "\n"
      ],
      "text/plain": [
       "   metric feature   method           lower        upper      \n",
       "1  TPR    race      uncorrected      0.0028097355 0.052041650\n",
       "2  TPR    race      double_corrected 0.0000000000 0.040719331\n",
       "3  FPR    race      uncorrected      0.0007183938 0.002466362\n",
       "4  FPR    race      double_corrected 0.0005151764 0.002218034\n",
       "5  SR     race      uncorrected      0.0044530790 0.010778322\n",
       "6  SR     race      double_corrected 0.0036981694 0.010252778\n",
       "7  TPR    age_group uncorrected      0.0118473758 0.047725370\n",
       "8  TPR    age_group double_corrected 0.0000000000 0.043064355\n",
       "9  FPR    age_group uncorrected      0.0017429443 0.011628342\n",
       "10 FPR    age_group double_corrected 0.0003075220 0.007931422\n",
       "11 SR     age_group uncorrected      0.0122233591 0.028682498\n",
       "12 SR     age_group double_corrected 0.0097690090 0.025450620"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "10b97825-3276-469f-aa2a-89e4d26dc932",
   "metadata": {
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 375,
       "width": 750
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "options(repr.plot.width=5, repr.plot.height=2.5, repr.plot.res=300)\n",
    "ggplot(pd, aes(y=method, color=method, xmax=upper, xmin=lower)) + \n",
    "    geom_errorbarh(height=0.25) + \n",
    "    geom_vline(xintercept=0, size=0.25) +\n",
    "    scale_y_discrete(labels=function(x) gsub('_', '\\n', x)) +\n",
    "    facet_grid(feature~metric) + \n",
    "    theme_bw(base_size=10) + theme(legend.position='none', axis.text.x=element_text(angle=45, hjust=1))\n",
    "                     \n",
    "ggsave(\"adult_intervals.pdf\", width=5, height=2.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "734e5b68-e332-4d92-89df-e15d955a8aed",
   "metadata": {
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3517d41c-1f4b-453f-b6aa-3b2f0da89276",
   "metadata": {
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "R",
   "language": "R",
   "name": "ir"
  },
  "language_info": {
   "codemirror_mode": "r",
   "file_extension": ".r",
   "mimetype": "text/x-r-source",
   "name": "R",
   "pygments_lexer": "r",
   "version": "4.1.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}