From 6727639d18239b53e086d33d5e8a0e269df2cebf Mon Sep 17 00:00:00 2001
From: Riku-Laine <28960190+Riku-Laineusers.noreply.github.com>
Date: Mon, 13 May 2019 16:23:37 +0300
Subject: [PATCH] Analyses, simple data.
---
Kandi.tex | 2 +-
.../Analysis_07MAY2019_new.ipynb | 1320 ++++++-----------
2 files changed, 462 insertions(+), 860 deletions(-)
diff --git a/Kandi.tex b/Kandi.tex
index c95be60..4950e91 100644
--- a/Kandi.tex
+++ b/Kandi.tex
@@ -75,7 +75,7 @@
\addtolength{\voffset}{0.45cm}
\addtolength{\textheight}{-0.9cm}
-\title{Kandidaatintutkielma\\ {\Large Kausaalipäättely ja valikoitumisharha}} % Parempi otsikko
+\title{Kandidaatintutkielma\\ {\Large Kausaalipäättely valikoitumisharha korjaamisessa}} % Parempi otsikko
\author{Riku Laine\\ Valtiotieteellinen tiedekunta \\ Helsingin yliopisto}
\date{\today}
diff --git a/analysis_and_scripts/Analysis_07MAY2019_new.ipynb b/analysis_and_scripts/Analysis_07MAY2019_new.ipynb
index f09da07..7b41b37 100644
--- a/analysis_and_scripts/Analysis_07MAY2019_new.ipynb
+++ b/analysis_and_scripts/Analysis_07MAY2019_new.ipynb
@@ -7,7 +7,7 @@
},
"source": [
"<h1>Table of Contents<span class=\"tocSkip\"></span></h1>\n",
- "<div class=\"toc\"><ul class=\"toc-item\"><li><span><a href=\"#Causal-model\" data-toc-modified-id=\"Causal-model-1\"><span class=\"toc-item-num\">1 </span>Causal model</a></span><ul class=\"toc-item\"><li><span><a href=\"#Notes\" data-toc-modified-id=\"Notes-1.1\"><span class=\"toc-item-num\">1.1 </span>Notes</a></span></li></ul></li><li><span><a href=\"#Synthetic-data\" data-toc-modified-id=\"Synthetic-data-2\"><span class=\"toc-item-num\">2 </span>Synthetic data</a></span></li><li><span><a href=\"#Algorithms\" data-toc-modified-id=\"Algorithms-3\"><span class=\"toc-item-num\">3 </span>Algorithms</a></span><ul class=\"toc-item\"><li><span><a href=\"#Contraction-algorithm\" data-toc-modified-id=\"Contraction-algorithm-3.1\"><span class=\"toc-item-num\">3.1 </span>Contraction algorithm</a></span></li><li><span><a href=\"#Causal-algorithm\" data-toc-modified-id=\"Causal-algorithm-3.2\"><span class=\"toc-item-num\">3.2 </span>Causal algorithm</a></span></li></ul></li><li><span><a href=\"#Performance-comparison\" data-toc-modified-id=\"Performance-comparison-4\"><span class=\"toc-item-num\">4 </span>Performance comparison</a></span><ul class=\"toc-item\"><li><span><a href=\"#Predictive-models\" data-toc-modified-id=\"Predictive-models-4.1\"><span class=\"toc-item-num\">4.1 </span>Predictive models</a></span></li><li><span><a href=\"#Visual-comparison\" data-toc-modified-id=\"Visual-comparison-4.2\"><span class=\"toc-item-num\">4.2 </span>Visual comparison</a></span></li></ul></li></ul></div>"
+ "<div class=\"toc\"><ul class=\"toc-item\"><li><span><a href=\"#Causal-model\" data-toc-modified-id=\"Causal-model-1\"><span class=\"toc-item-num\">1 </span>Causal model</a></span><ul class=\"toc-item\"><li><span><a href=\"#Notes\" data-toc-modified-id=\"Notes-1.1\"><span class=\"toc-item-num\">1.1 </span>Notes</a></span></li></ul></li><li><span><a href=\"#Data-sets\" data-toc-modified-id=\"Data-sets-2\"><span class=\"toc-item-num\">2 </span>Data sets</a></span><ul class=\"toc-item\"><li><span><a href=\"#Synthetic-data-with-unobservables\" data-toc-modified-id=\"Synthetic-data-with-unobservables-2.1\"><span class=\"toc-item-num\">2.1 </span>Synthetic data with unobservables</a></span></li><li><span><a href=\"#Data-without-unobservables\" data-toc-modified-id=\"Data-without-unobservables-2.2\"><span class=\"toc-item-num\">2.2 </span>Data without unobservables</a></span></li></ul></li><li><span><a href=\"#Algorithms\" data-toc-modified-id=\"Algorithms-3\"><span class=\"toc-item-num\">3 </span>Algorithms</a></span><ul class=\"toc-item\"><li><span><a href=\"#Contraction-algorithm\" data-toc-modified-id=\"Contraction-algorithm-3.1\"><span class=\"toc-item-num\">3.1 </span>Contraction algorithm</a></span></li><li><span><a href=\"#Causal-algorithm\" data-toc-modified-id=\"Causal-algorithm-3.2\"><span class=\"toc-item-num\">3.2 </span>Causal algorithm</a></span></li></ul></li><li><span><a href=\"#Performance-comparison\" data-toc-modified-id=\"Performance-comparison-4\"><span class=\"toc-item-num\">4 </span>Performance comparison</a></span><ul class=\"toc-item\"><li><span><a href=\"#With-unobservables-in-the-data\" data-toc-modified-id=\"With-unobservables-in-the-data-4.1\"><span class=\"toc-item-num\">4.1 </span>With unobservables in the data</a></span><ul class=\"toc-item\"><li><span><a href=\"#Predictive-models\" data-toc-modified-id=\"Predictive-models-4.1.1\"><span class=\"toc-item-num\">4.1.1 </span>Predictive models</a></span></li><li><span><a href=\"#Visual-comparison\" data-toc-modified-id=\"Visual-comparison-4.1.2\"><span class=\"toc-item-num\">4.1.2 </span>Visual comparison</a></span></li></ul></li><li><span><a href=\"#Without-unobservables\" data-toc-modified-id=\"Without-unobservables-4.2\"><span class=\"toc-item-num\">4.2 </span>Without unobservables</a></span><ul class=\"toc-item\"><li><span><a href=\"#Predictive-model\" data-toc-modified-id=\"Predictive-model-4.2.1\"><span class=\"toc-item-num\">4.2.1 </span>Predictive model</a></span></li></ul></li></ul></li></ul></div>"
]
},
{
@@ -54,7 +54,7 @@
},
{
"cell_type": "code",
- "execution_count": 188,
+ "execution_count": 192,
"metadata": {},
"outputs": [],
"source": [
@@ -95,7 +95,9 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "## Synthetic data\n",
+ "## Data sets\n",
+ "\n",
+ "### Synthetic data with unobservables\n",
"\n",
"In the chunk below, we generate the synthetic data as described by Lakkaraju et al. The default values and definitions of $Y$ and $T$ values follow their description.\n",
"\n",
@@ -118,7 +120,7 @@
},
{
"cell_type": "code",
- "execution_count": 189,
+ "execution_count": 193,
"metadata": {},
"outputs": [
{
@@ -141,831 +143,57 @@
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
- " <th></th>\n",
- " <th>judgeID_J</th>\n",
- " <th>acceptanceRate_R</th>\n",
- " <th>X</th>\n",
- " <th>Z</th>\n",
- " <th>W</th>\n",
" <th>result_Y</th>\n",
- " <th>probabilities_T</th>\n",
+ " <th>0.0</th>\n",
+ " <th>1.0</th>\n",
+ " <th>All</th>\n",
+ " </tr>\n",
+ " <tr>\n",
" <th>decision_T</th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
- " <td>99.0</td>\n",
- " <td>0.186313</td>\n",
- " <td>0.826274</td>\n",
- " <td>2.105072</td>\n",
- " <td>1.476504</td>\n",
- " <td>0.0</td>\n",
- " <td>1.692504</td>\n",
- " <td>0</td>\n",
+ " <td>16884</td>\n",
+ " <td>6985</td>\n",
+ " <td>23869</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
- " <td>99.0</td>\n",
- " <td>0.186313</td>\n",
- " <td>-0.028464</td>\n",
- " <td>0.297879</td>\n",
- " <td>-0.975877</td>\n",
- " <td>0.0</td>\n",
- " <td>1.681097</td>\n",
- " <td>0</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>2</th>\n",
- " <td>99.0</td>\n",
- " <td>0.186313</td>\n",
- " <td>0.521224</td>\n",
- " <td>1.288442</td>\n",
- " <td>-0.120573</td>\n",
- " <td>0.0</td>\n",
- " <td>1.633221</td>\n",
- " <td>0</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>3</th>\n",
- " <td>99.0</td>\n",
- " <td>0.186313</td>\n",
- " <td>-0.020646</td>\n",
- " <td>1.636132</td>\n",
- " <td>-0.460398</td>\n",
- " <td>0.0</td>\n",
- " <td>1.596941</td>\n",
- " <td>0</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>4</th>\n",
- " <td>99.0</td>\n",
- " <td>0.186313</td>\n",
- " <td>0.473597</td>\n",
- " <td>0.570864</td>\n",
- " <td>-1.763391</td>\n",
- " <td>0.0</td>\n",
- " <td>1.549796</td>\n",
- " <td>0</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>5</th>\n",
- " <td>99.0</td>\n",
- " <td>0.186313</td>\n",
- " <td>0.968785</td>\n",
- " <td>2.441796</td>\n",
- " <td>-2.163222</td>\n",
- " <td>0.0</td>\n",
- " <td>1.496034</td>\n",
- " <td>0</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>6</th>\n",
- " <td>99.0</td>\n",
- " <td>0.186313</td>\n",
- " <td>1.546339</td>\n",
- " <td>1.173717</td>\n",
- " <td>0.775945</td>\n",
- " <td>0.0</td>\n",
- " <td>1.423059</td>\n",
- " <td>0</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>7</th>\n",
- " <td>99.0</td>\n",
- " <td>0.186313</td>\n",
- " <td>-0.005012</td>\n",
- " <td>0.854531</td>\n",
- " <td>0.772508</td>\n",
- " <td>0.0</td>\n",
- " <td>1.406603</td>\n",
- " <td>0</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>8</th>\n",
- " <td>99.0</td>\n",
- " <td>0.186313</td>\n",
- " <td>1.124008</td>\n",
- " <td>0.194219</td>\n",
- " <td>-0.793756</td>\n",
- " <td>0.0</td>\n",
- " <td>1.363169</td>\n",
- " <td>0</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>9</th>\n",
- " <td>99.0</td>\n",
- " <td>0.186313</td>\n",
- " <td>0.954372</td>\n",
- " <td>2.227896</td>\n",
- " <td>0.471742</td>\n",
- " <td>0.0</td>\n",
- " <td>1.351900</td>\n",
- " <td>0</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>10</th>\n",
- " <td>99.0</td>\n",
- " <td>0.186313</td>\n",
- " <td>0.139221</td>\n",
- " <td>-0.118334</td>\n",
- " <td>-2.218456</td>\n",
- " <td>1.0</td>\n",
- " <td>1.328329</td>\n",
- " <td>0</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>11</th>\n",
- " <td>99.0</td>\n",
- " <td>0.186313</td>\n",
- " <td>-0.894260</td>\n",
- " <td>0.806683</td>\n",
- " <td>-0.174320</td>\n",
- " <td>1.0</td>\n",
- " <td>1.328299</td>\n",
- " <td>0</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>12</th>\n",
- " <td>99.0</td>\n",
- " <td>0.186313</td>\n",
- " <td>0.146317</td>\n",
- " <td>0.090848</td>\n",
- " <td>0.575790</td>\n",
- " <td>0.0</td>\n",
- " <td>1.326697</td>\n",
- " <td>0</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>13</th>\n",
- " <td>99.0</td>\n",
- " <td>0.186313</td>\n",
- " <td>1.061130</td>\n",
- " <td>0.311556</td>\n",
- " <td>0.271090</td>\n",
- " <td>0.0</td>\n",
- " <td>1.304708</td>\n",
- " <td>0</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>14</th>\n",
- " <td>99.0</td>\n",
- " <td>0.186313</td>\n",
- " <td>0.364795</td>\n",
- " <td>1.340871</td>\n",
- " <td>0.741578</td>\n",
- " <td>0.0</td>\n",
- " <td>1.283888</td>\n",
- " <td>0</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>15</th>\n",
- " <td>99.0</td>\n",
- " <td>0.186313</td>\n",
- " <td>1.359628</td>\n",
- " <td>-0.331233</td>\n",
- " <td>1.216847</td>\n",
- " <td>0.0</td>\n",
- " <td>1.276119</td>\n",
- " <td>0</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>16</th>\n",
- " <td>99.0</td>\n",
- " <td>0.186313</td>\n",
- " <td>0.098206</td>\n",
- " <td>1.030595</td>\n",
- " <td>1.369878</td>\n",
- " <td>0.0</td>\n",
- " <td>1.249515</td>\n",
- " <td>0</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>17</th>\n",
- " <td>99.0</td>\n",
- " <td>0.186313</td>\n",
- " <td>2.009392</td>\n",
- " <td>0.348242</td>\n",
- " <td>-0.918357</td>\n",
- " <td>0.0</td>\n",
- " <td>1.242619</td>\n",
- " <td>0</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>18</th>\n",
- " <td>99.0</td>\n",
- " <td>0.186313</td>\n",
- " <td>2.650009</td>\n",
- " <td>0.548038</td>\n",
- " <td>-0.734014</td>\n",
- " <td>0.0</td>\n",
- " <td>1.224764</td>\n",
- " <td>0</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>19</th>\n",
- " <td>99.0</td>\n",
- " <td>0.186313</td>\n",
- " <td>1.104756</td>\n",
- " <td>-1.249380</td>\n",
- " <td>1.073779</td>\n",
- " <td>0.0</td>\n",
- " <td>1.207050</td>\n",
- " <td>0</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>20</th>\n",
- " <td>99.0</td>\n",
- " <td>0.186313</td>\n",
- " <td>1.418292</td>\n",
- " <td>0.664494</td>\n",
- " <td>0.564188</td>\n",
- " <td>0.0</td>\n",
- " <td>1.188848</td>\n",
- " <td>0</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>21</th>\n",
- " <td>99.0</td>\n",
- " <td>0.186313</td>\n",
- " <td>-0.895593</td>\n",
- " <td>-0.054607</td>\n",
- " <td>0.983115</td>\n",
- " <td>1.0</td>\n",
- " <td>1.185423</td>\n",
- " <td>0</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>22</th>\n",
- " <td>99.0</td>\n",
- " <td>0.186313</td>\n",
- " <td>0.685227</td>\n",
- " <td>1.374469</td>\n",
- " <td>-0.796697</td>\n",
- " <td>0.0</td>\n",
- " <td>1.183456</td>\n",
- " <td>0</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>23</th>\n",
- " <td>99.0</td>\n",
- " <td>0.186313</td>\n",
- " <td>-0.422198</td>\n",
- " <td>0.394662</td>\n",
- " <td>-2.208503</td>\n",
- " <td>1.0</td>\n",
- " <td>1.174958</td>\n",
- " <td>0</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>24</th>\n",
- " <td>99.0</td>\n",
- " <td>0.186313</td>\n",
- " <td>0.956056</td>\n",
- " <td>0.839686</td>\n",
- " <td>-1.294539</td>\n",
- " <td>0.0</td>\n",
- " <td>1.170929</td>\n",
- " <td>0</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>25</th>\n",
- " <td>99.0</td>\n",
- " <td>0.186313</td>\n",
- " <td>0.588787</td>\n",
- " <td>1.398952</td>\n",
- " <td>2.395113</td>\n",
- " <td>0.0</td>\n",
- " <td>1.165979</td>\n",
- " <td>0</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>26</th>\n",
- " <td>99.0</td>\n",
- " <td>0.186313</td>\n",
- " <td>-1.344696</td>\n",
- " <td>1.856761</td>\n",
- " <td>0.909326</td>\n",
- " <td>0.0</td>\n",
- " <td>1.159617</td>\n",
- " <td>0</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>27</th>\n",
- " <td>99.0</td>\n",
- " <td>0.186313</td>\n",
- " <td>-0.551863</td>\n",
- " <td>-0.071659</td>\n",
- " <td>1.297963</td>\n",
- " <td>1.0</td>\n",
- " <td>1.154631</td>\n",
- " <td>0</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>28</th>\n",
- " <td>99.0</td>\n",
- " <td>0.186313</td>\n",
- " <td>0.735048</td>\n",
- " <td>1.507983</td>\n",
- " <td>-0.539594</td>\n",
- " <td>0.0</td>\n",
- " <td>1.146622</td>\n",
- " <td>0</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>29</th>\n",
- " <td>99.0</td>\n",
- " <td>0.186313</td>\n",
- " <td>2.294530</td>\n",
- " <td>-1.381245</td>\n",
- " <td>-0.660446</td>\n",
- " <td>0.0</td>\n",
- " <td>1.138679</td>\n",
- " <td>0</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>...</th>\n",
- " <td>...</td>\n",
- " <td>...</td>\n",
- " <td>...</td>\n",
- " <td>...</td>\n",
- " <td>...</td>\n",
- " <td>...</td>\n",
- " <td>...</td>\n",
- " <td>...</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>49970</th>\n",
- " <td>0.0</td>\n",
- " <td>0.399632</td>\n",
- " <td>0.711615</td>\n",
- " <td>-2.915390</td>\n",
- " <td>0.172405</td>\n",
- " <td>1.0</td>\n",
- " <td>-0.110999</td>\n",
- " <td>1</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>49971</th>\n",
- " <td>0.0</td>\n",
- " <td>0.399632</td>\n",
- " <td>-0.827231</td>\n",
- " <td>-1.777676</td>\n",
- " <td>0.876569</td>\n",
- " <td>1.0</td>\n",
- " <td>-0.113757</td>\n",
- " <td>1</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>49972</th>\n",
- " <td>0.0</td>\n",
- " <td>0.399632</td>\n",
- " <td>-1.200296</td>\n",
- " <td>-1.591935</td>\n",
- " <td>-2.098238</td>\n",
- " <td>1.0</td>\n",
- " <td>-0.117259</td>\n",
- " <td>1</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>49973</th>\n",
- " <td>0.0</td>\n",
- " <td>0.399632</td>\n",
- " <td>-0.236819</td>\n",
- " <td>-0.646515</td>\n",
- " <td>-0.073568</td>\n",
- " <td>1.0</td>\n",
- " <td>-0.118655</td>\n",
- " <td>1</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>49974</th>\n",
- " <td>0.0</td>\n",
- " <td>0.399632</td>\n",
- " <td>-1.067620</td>\n",
- " <td>0.232980</td>\n",
- " <td>0.700920</td>\n",
- " <td>1.0</td>\n",
- " <td>-0.123346</td>\n",
- " <td>1</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>49975</th>\n",
- " <td>0.0</td>\n",
- " <td>0.399632</td>\n",
- " <td>-0.767348</td>\n",
- " <td>-1.909278</td>\n",
- " <td>-0.040354</td>\n",
- " <td>1.0</td>\n",
- " <td>-0.128207</td>\n",
- " <td>1</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>49976</th>\n",
- " <td>0.0</td>\n",
- " <td>0.399632</td>\n",
- " <td>-0.937825</td>\n",
- " <td>-1.963602</td>\n",
- " <td>0.233229</td>\n",
- " <td>1.0</td>\n",
- " <td>-0.128611</td>\n",
- " <td>1</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>49977</th>\n",
- " <td>0.0</td>\n",
- " <td>0.399632</td>\n",
- " <td>0.614167</td>\n",
- " <td>-2.312697</td>\n",
- " <td>0.246288</td>\n",
- " <td>1.0</td>\n",
- " <td>-0.139810</td>\n",
- " <td>1</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>49978</th>\n",
- " <td>0.0</td>\n",
- " <td>0.399632</td>\n",
- " <td>-0.245388</td>\n",
- " <td>-0.787053</td>\n",
- " <td>-1.123660</td>\n",
- " <td>1.0</td>\n",
- " <td>-0.145423</td>\n",
- " <td>1</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>49979</th>\n",
- " <td>0.0</td>\n",
- " <td>0.399632</td>\n",
- " <td>-1.514847</td>\n",
- " <td>-1.908273</td>\n",
- " <td>-0.349488</td>\n",
- " <td>1.0</td>\n",
- " <td>-0.154516</td>\n",
- " <td>1</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>49980</th>\n",
- " <td>0.0</td>\n",
- " <td>0.399632</td>\n",
- " <td>-0.269407</td>\n",
- " <td>-0.587080</td>\n",
- " <td>-1.341434</td>\n",
- " <td>1.0</td>\n",
- " <td>-0.167074</td>\n",
- " <td>1</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>49981</th>\n",
- " <td>0.0</td>\n",
- " <td>0.399632</td>\n",
- " <td>-0.089120</td>\n",
- " <td>-0.700974</td>\n",
- " <td>0.623632</td>\n",
- " <td>1.0</td>\n",
- " <td>-0.179464</td>\n",
- " <td>1</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>49982</th>\n",
- " <td>0.0</td>\n",
- " <td>0.399632</td>\n",
- " <td>0.690144</td>\n",
- " <td>-0.734551</td>\n",
- " <td>0.779340</td>\n",
- " <td>0.0</td>\n",
- " <td>-0.183530</td>\n",
- " <td>1</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>49983</th>\n",
- " <td>0.0</td>\n",
- " <td>0.399632</td>\n",
- " <td>-0.875618</td>\n",
- " <td>-0.732720</td>\n",
- " <td>-0.642521</td>\n",
- " <td>1.0</td>\n",
- " <td>-0.190108</td>\n",
- " <td>1</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>49984</th>\n",
- " <td>0.0</td>\n",
- " <td>0.399632</td>\n",
- " <td>-1.401851</td>\n",
- " <td>-0.333382</td>\n",
- " <td>0.338832</td>\n",
- " <td>1.0</td>\n",
- " <td>-0.195607</td>\n",
- " <td>1</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>49985</th>\n",
- " <td>0.0</td>\n",
- " <td>0.399632</td>\n",
- " <td>-0.977555</td>\n",
- " <td>-1.152621</td>\n",
- " <td>-1.279561</td>\n",
- " <td>1.0</td>\n",
- " <td>-0.219627</td>\n",
- " <td>1</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>49986</th>\n",
- " <td>0.0</td>\n",
- " <td>0.399632</td>\n",
- " <td>-2.038125</td>\n",
- " <td>-0.382141</td>\n",
- " <td>-0.997567</td>\n",
- " <td>1.0</td>\n",
- " <td>-0.224630</td>\n",
- " <td>1</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>49987</th>\n",
- " <td>0.0</td>\n",
- " <td>0.399632</td>\n",
- " <td>-0.315269</td>\n",
- " <td>0.453257</td>\n",
- " <td>0.625528</td>\n",
- " <td>0.0</td>\n",
- " <td>-0.230150</td>\n",
- " <td>1</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>49988</th>\n",
- " <td>0.0</td>\n",
- " <td>0.399632</td>\n",
- " <td>-0.506943</td>\n",
- " <td>-0.522032</td>\n",
- " <td>0.850031</td>\n",
- " <td>1.0</td>\n",
- " <td>-0.231718</td>\n",
- " <td>1</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>49989</th>\n",
- " <td>0.0</td>\n",
- " <td>0.399632</td>\n",
- " <td>-1.377669</td>\n",
- " <td>-0.269111</td>\n",
- " <td>-2.687594</td>\n",
- " <td>1.0</td>\n",
- " <td>-0.243685</td>\n",
- " <td>1</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>49990</th>\n",
- " <td>0.0</td>\n",
- " <td>0.399632</td>\n",
- " <td>-0.142379</td>\n",
- " <td>-0.175690</td>\n",
- " <td>0.513888</td>\n",
- " <td>1.0</td>\n",
- " <td>-0.249886</td>\n",
- " <td>1</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>49991</th>\n",
- " <td>0.0</td>\n",
- " <td>0.399632</td>\n",
- " <td>-1.607483</td>\n",
- " <td>1.504558</td>\n",
- " <td>0.239068</td>\n",
- " <td>1.0</td>\n",
- " <td>-0.280709</td>\n",
- " <td>1</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>49992</th>\n",
- " <td>0.0</td>\n",
- " <td>0.399632</td>\n",
- " <td>-0.818221</td>\n",
- " <td>-1.289808</td>\n",
- " <td>-1.831835</td>\n",
- " <td>1.0</td>\n",
- " <td>-0.285505</td>\n",
- " <td>1</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>49993</th>\n",
- " <td>0.0</td>\n",
- " <td>0.399632</td>\n",
- " <td>-0.599375</td>\n",
- " <td>-0.171306</td>\n",
- " <td>-0.084856</td>\n",
- " <td>1.0</td>\n",
- " <td>-0.299202</td>\n",
- " <td>1</td>\n",
+ " <td>8128</td>\n",
+ " <td>18003</td>\n",
+ " <td>26131</td>\n",
" </tr>\n",
" <tr>\n",
- " <th>49994</th>\n",
- " <td>0.0</td>\n",
- " <td>0.399632</td>\n",
- " <td>-0.469176</td>\n",
- " <td>-1.982240</td>\n",
- " <td>-1.780709</td>\n",
- " <td>1.0</td>\n",
- " <td>-0.315370</td>\n",
- " <td>1</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>49995</th>\n",
- " <td>0.0</td>\n",
- " <td>0.399632</td>\n",
- " <td>-1.006017</td>\n",
- " <td>-0.388426</td>\n",
- " <td>-1.401947</td>\n",
- " <td>1.0</td>\n",
- " <td>-0.321114</td>\n",
- " <td>1</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>49996</th>\n",
- " <td>0.0</td>\n",
- " <td>0.399632</td>\n",
- " <td>-0.219101</td>\n",
- " <td>-1.348249</td>\n",
- " <td>0.724337</td>\n",
- " <td>1.0</td>\n",
- " <td>-0.330852</td>\n",
- " <td>1</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>49997</th>\n",
- " <td>0.0</td>\n",
- " <td>0.399632</td>\n",
- " <td>-2.471645</td>\n",
- " <td>-0.267131</td>\n",
- " <td>-0.077586</td>\n",
- " <td>1.0</td>\n",
- " <td>-0.357114</td>\n",
- " <td>1</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>49998</th>\n",
- " <td>0.0</td>\n",
- " <td>0.399632</td>\n",
- " <td>-1.077745</td>\n",
- " <td>-0.237860</td>\n",
- " <td>-1.101762</td>\n",
- " <td>1.0</td>\n",
- " <td>-0.372307</td>\n",
- " <td>1</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>49999</th>\n",
- " <td>0.0</td>\n",
- " <td>0.399632</td>\n",
- " <td>-0.964923</td>\n",
- " <td>-0.476811</td>\n",
- " <td>0.241492</td>\n",
- " <td>1.0</td>\n",
- " <td>-0.434957</td>\n",
- " <td>1</td>\n",
+ " <th>All</th>\n",
+ " <td>25012</td>\n",
+ " <td>24988</td>\n",
+ " <td>50000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
- "<p>50000 rows × 8 columns</p>\n",
"</div>"
],
"text/plain": [
- " judgeID_J acceptanceRate_R X Z W result_Y \\\n",
- "0 99.0 0.186313 0.826274 2.105072 1.476504 0.0 \n",
- "1 99.0 0.186313 -0.028464 0.297879 -0.975877 0.0 \n",
- "2 99.0 0.186313 0.521224 1.288442 -0.120573 0.0 \n",
- "3 99.0 0.186313 -0.020646 1.636132 -0.460398 0.0 \n",
- "4 99.0 0.186313 0.473597 0.570864 -1.763391 0.0 \n",
- "5 99.0 0.186313 0.968785 2.441796 -2.163222 0.0 \n",
- "6 99.0 0.186313 1.546339 1.173717 0.775945 0.0 \n",
- "7 99.0 0.186313 -0.005012 0.854531 0.772508 0.0 \n",
- "8 99.0 0.186313 1.124008 0.194219 -0.793756 0.0 \n",
- "9 99.0 0.186313 0.954372 2.227896 0.471742 0.0 \n",
- "10 99.0 0.186313 0.139221 -0.118334 -2.218456 1.0 \n",
- "11 99.0 0.186313 -0.894260 0.806683 -0.174320 1.0 \n",
- "12 99.0 0.186313 0.146317 0.090848 0.575790 0.0 \n",
- "13 99.0 0.186313 1.061130 0.311556 0.271090 0.0 \n",
- "14 99.0 0.186313 0.364795 1.340871 0.741578 0.0 \n",
- "15 99.0 0.186313 1.359628 -0.331233 1.216847 0.0 \n",
- "16 99.0 0.186313 0.098206 1.030595 1.369878 0.0 \n",
- "17 99.0 0.186313 2.009392 0.348242 -0.918357 0.0 \n",
- "18 99.0 0.186313 2.650009 0.548038 -0.734014 0.0 \n",
- "19 99.0 0.186313 1.104756 -1.249380 1.073779 0.0 \n",
- "20 99.0 0.186313 1.418292 0.664494 0.564188 0.0 \n",
- "21 99.0 0.186313 -0.895593 -0.054607 0.983115 1.0 \n",
- "22 99.0 0.186313 0.685227 1.374469 -0.796697 0.0 \n",
- "23 99.0 0.186313 -0.422198 0.394662 -2.208503 1.0 \n",
- "24 99.0 0.186313 0.956056 0.839686 -1.294539 0.0 \n",
- "25 99.0 0.186313 0.588787 1.398952 2.395113 0.0 \n",
- "26 99.0 0.186313 -1.344696 1.856761 0.909326 0.0 \n",
- "27 99.0 0.186313 -0.551863 -0.071659 1.297963 1.0 \n",
- "28 99.0 0.186313 0.735048 1.507983 -0.539594 0.0 \n",
- "29 99.0 0.186313 2.294530 -1.381245 -0.660446 0.0 \n",
- "... ... ... ... ... ... ... \n",
- "49970 0.0 0.399632 0.711615 -2.915390 0.172405 1.0 \n",
- "49971 0.0 0.399632 -0.827231 -1.777676 0.876569 1.0 \n",
- "49972 0.0 0.399632 -1.200296 -1.591935 -2.098238 1.0 \n",
- "49973 0.0 0.399632 -0.236819 -0.646515 -0.073568 1.0 \n",
- "49974 0.0 0.399632 -1.067620 0.232980 0.700920 1.0 \n",
- "49975 0.0 0.399632 -0.767348 -1.909278 -0.040354 1.0 \n",
- "49976 0.0 0.399632 -0.937825 -1.963602 0.233229 1.0 \n",
- "49977 0.0 0.399632 0.614167 -2.312697 0.246288 1.0 \n",
- "49978 0.0 0.399632 -0.245388 -0.787053 -1.123660 1.0 \n",
- "49979 0.0 0.399632 -1.514847 -1.908273 -0.349488 1.0 \n",
- "49980 0.0 0.399632 -0.269407 -0.587080 -1.341434 1.0 \n",
- "49981 0.0 0.399632 -0.089120 -0.700974 0.623632 1.0 \n",
- "49982 0.0 0.399632 0.690144 -0.734551 0.779340 0.0 \n",
- "49983 0.0 0.399632 -0.875618 -0.732720 -0.642521 1.0 \n",
- "49984 0.0 0.399632 -1.401851 -0.333382 0.338832 1.0 \n",
- "49985 0.0 0.399632 -0.977555 -1.152621 -1.279561 1.0 \n",
- "49986 0.0 0.399632 -2.038125 -0.382141 -0.997567 1.0 \n",
- "49987 0.0 0.399632 -0.315269 0.453257 0.625528 0.0 \n",
- "49988 0.0 0.399632 -0.506943 -0.522032 0.850031 1.0 \n",
- "49989 0.0 0.399632 -1.377669 -0.269111 -2.687594 1.0 \n",
- "49990 0.0 0.399632 -0.142379 -0.175690 0.513888 1.0 \n",
- "49991 0.0 0.399632 -1.607483 1.504558 0.239068 1.0 \n",
- "49992 0.0 0.399632 -0.818221 -1.289808 -1.831835 1.0 \n",
- "49993 0.0 0.399632 -0.599375 -0.171306 -0.084856 1.0 \n",
- "49994 0.0 0.399632 -0.469176 -1.982240 -1.780709 1.0 \n",
- "49995 0.0 0.399632 -1.006017 -0.388426 -1.401947 1.0 \n",
- "49996 0.0 0.399632 -0.219101 -1.348249 0.724337 1.0 \n",
- "49997 0.0 0.399632 -2.471645 -0.267131 -0.077586 1.0 \n",
- "49998 0.0 0.399632 -1.077745 -0.237860 -1.101762 1.0 \n",
- "49999 0.0 0.399632 -0.964923 -0.476811 0.241492 1.0 \n",
- "\n",
- " probabilities_T decision_T \n",
- "0 1.692504 0 \n",
- "1 1.681097 0 \n",
- "2 1.633221 0 \n",
- "3 1.596941 0 \n",
- "4 1.549796 0 \n",
- "5 1.496034 0 \n",
- "6 1.423059 0 \n",
- "7 1.406603 0 \n",
- "8 1.363169 0 \n",
- "9 1.351900 0 \n",
- "10 1.328329 0 \n",
- "11 1.328299 0 \n",
- "12 1.326697 0 \n",
- "13 1.304708 0 \n",
- "14 1.283888 0 \n",
- "15 1.276119 0 \n",
- "16 1.249515 0 \n",
- "17 1.242619 0 \n",
- "18 1.224764 0 \n",
- "19 1.207050 0 \n",
- "20 1.188848 0 \n",
- "21 1.185423 0 \n",
- "22 1.183456 0 \n",
- "23 1.174958 0 \n",
- "24 1.170929 0 \n",
- "25 1.165979 0 \n",
- "26 1.159617 0 \n",
- "27 1.154631 0 \n",
- "28 1.146622 0 \n",
- "29 1.138679 0 \n",
- "... ... ... \n",
- "49970 -0.110999 1 \n",
- "49971 -0.113757 1 \n",
- "49972 -0.117259 1 \n",
- "49973 -0.118655 1 \n",
- "49974 -0.123346 1 \n",
- "49975 -0.128207 1 \n",
- "49976 -0.128611 1 \n",
- "49977 -0.139810 1 \n",
- "49978 -0.145423 1 \n",
- "49979 -0.154516 1 \n",
- "49980 -0.167074 1 \n",
- "49981 -0.179464 1 \n",
- "49982 -0.183530 1 \n",
- "49983 -0.190108 1 \n",
- "49984 -0.195607 1 \n",
- "49985 -0.219627 1 \n",
- "49986 -0.224630 1 \n",
- "49987 -0.230150 1 \n",
- "49988 -0.231718 1 \n",
- "49989 -0.243685 1 \n",
- "49990 -0.249886 1 \n",
- "49991 -0.280709 1 \n",
- "49992 -0.285505 1 \n",
- "49993 -0.299202 1 \n",
- "49994 -0.315370 1 \n",
- "49995 -0.321114 1 \n",
- "49996 -0.330852 1 \n",
- "49997 -0.357114 1 \n",
- "49998 -0.372307 1 \n",
- "49999 -0.434957 1 \n",
- "\n",
- "[50000 rows x 8 columns]"
+ "result_Y 0.0 1.0 All\n",
+ "decision_T \n",
+ "0 16884 6985 23869\n",
+ "1 8128 18003 26131\n",
+ "All 25012 24988 50000"
]
},
+ "execution_count": 193,
"metadata": {},
- "output_type": "display_data"
+ "output_type": "execute_result"
}
],
"source": [
"# Set seed for reproducibility\n",
- "npr.seed(42)\n",
+ "#npr.seed(0)\n",
"\n",
"def sigmoid(x):\n",
" return 1 / (1 + np.exp(-x))\n",
@@ -1031,14 +259,12 @@
"\n",
"df = generateData()\n",
"\n",
- "pd.crosstab(df.decision_T, df.result_Y, margins=True)\n",
- "\n",
- "display(df)"
+ "pd.crosstab(df.decision_T, df.result_Y, margins=True)"
]
},
{
"cell_type": "code",
- "execution_count": 190,
+ "execution_count": 194,
"metadata": {},
"outputs": [
{
@@ -1082,11 +308,11 @@
" <tbody>\n",
" <tr>\n",
" <th>0.0</th>\n",
- " <td>3565</td>\n",
+ " <td>4082</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1.0</th>\n",
- " <td>8163</td>\n",
+ " <td>8923</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
@@ -1095,11 +321,11 @@
"text/plain": [
"decision_T 1\n",
"result_Y \n",
- "0.0 3565\n",
- "1.0 8163"
+ "0.0 4082\n",
+ "1.0 8923"
]
},
- "execution_count": 190,
+ "execution_count": 194,
"metadata": {},
"output_type": "execute_result"
}
@@ -1126,6 +352,312 @@
"tab.unstack()"
]
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Data without unobservables\n",
+ "\n",
+ "In the chunk below, we generate a simplified data. The default values and definitions of $Y$ and $T$ values follow the previous description.\n",
+ "\n",
+ "**Parameters**\n",
+ "\n",
+ "* M = `nJudges_M`, number of judges\n",
+ "* N = `nSubjects_N`, number of subjects assigned to each judge\n",
+ "* betas $\\beta_i$ = `beta_i`, where $i \\in \\{X, Z, W\\}$ are coefficients for the respected variables\n",
+ "\n",
+ "**Columns of the data:**\n",
+ "\n",
+ "* `judgeID_J` = judge IDs as running numbering from 0 to `nJudges_M - 1`\n",
+ "* R = `acceptanceRate_R`, acceptance rates\n",
+ "* X = `X`, invidual's features observable to all (models and judges), now $X \\sim \\mathcal{N}(0, 1)$\n",
+ "* T = `decision_T`, bail-or-jail decisions where $T=0$ represents jail decision and $T=1$ bail decision.\n",
+ "* $p_y$ = `probabilities_Y`, variable where $p_y = P(Y=1)$\n",
+ "* Y = `result_Y`, result variable, if $Y=0$ person will or would recidivate and if $Y=1$ person will or would not commit a crime. Here $Y \\sim \\text{Bernoulli}(1/exp(\\beta_X \\cdot X))$"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 195,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "['judgeID_J' 'acceptanceRate_R' 'X' 'probabilities_Y' 'result_Y'\n",
+ " 'decision_T']\n"
+ ]
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+ "source": [
+ "# Set seed for reproducibility\n",
+ "#npr.seed(0)\n",
+ "\n",
+ "def generateDataNoUnobservables(nJudges_M=100, nSubjects_N=500, beta_X=1.0):\n",
+ "\n",
+ " df = pd.DataFrame()\n",
+ "\n",
+ " # Assign judge IDs as running numbering from 0 to nJudges_M - 1\n",
+ " df = df.assign(judgeID_J=np.repeat(np.arange(0, nJudges_M, dtype=np.int32),\n",
+ " nSubjects_N))\n",
+ "\n",
+ " # Sample acceptance rates uniformly from a closed interval\n",
+ " # from 0.1 to 0.9 and round to tenth decimal place.\n",
+ " acceptance_rates = np.round(npr.uniform(.1, .9, nJudges_M), 10)\n",
+ "\n",
+ " # Replicate the rates so they can be attached to the corresponding judge ID.\n",
+ " df = df.assign(acceptanceRate_R=np.repeat(acceptance_rates, nSubjects_N))\n",
+ "\n",
+ " # Sample the variables from standard Gaussian distributions.\n",
+ " df = df.assign(X=npr.normal(size=nJudges_M * nSubjects_N))\n",
+ "\n",
+ " df = df.assign(probabilities_Y=sigmoid(beta_X * df.X))\n",
+ "\n",
+ " # Y ~ Bernoulli(sigmoid(beta_X * x))\n",
+ " df = df.assign(result_Y=npr.binomial(\n",
+ " n=1, p=df.probabilities_Y, size=nJudges_M * nSubjects_N))\n",
+ "\n",
+ " # Sort by judges then probabilities in decreasing order.\n",
+ " # I.e. most dangerous are last.\n",
+ " df = df.sort_values(by=[\"judgeID_J\", \"probabilities_Y\"], ascending=True)\n",
+ "\n",
+ " # Iterate over the data. Subject is in the top (1-r)*100% if\n",
+ " # his within-judge-index is over acceptance threshold times\n",
+ " # the number of subjects assigned to each judge. If subject\n",
+ " # is over the limit they are assigned a zero, else one.\n",
+ " df.reset_index(drop=True, inplace=True)\n",
+ "\n",
+ " df['decision_T'] = np.where((df.index.values % nSubjects_N) <\n",
+ " ((1 - df['acceptanceRate_R']) * nSubjects_N),\n",
+ " 0, 1)\n",
+ "\n",
+ " return df\n",
+ "\n",
+ "simple_data = generateDataNoUnobservables()\n",
+ "\n",
+ "# Split the data set to test and train\n",
+ "s_train, s_test = train_test_split(simple_data, test_size=0.5, random_state=0)\n",
+ "\n",
+ "s_train_labeled = s_train.copy()\n",
+ "s_test_labeled = s_test.copy()\n",
+ "\n",
+ "# Set results as NA if decision is negative.\n",
+ "s_train_labeled.result_Y = np.where(s_train.decision_T == 0, np.nan, s_train.result_Y)\n",
+ "s_test_labeled.result_Y = np.where(s_test.decision_T == 0, np.nan, s_test.result_Y)\n",
+ "\n",
+ "#display(simple_data.head(20))\n",
+ "\n",
+ "print(simple_data.columns.values)\n",
+ "\n",
+ "display(pd.crosstab(simple_data.decision_T, simple_data.result_Y,\n",
+ " margins=True))\n",
+ "\n",
+ "display(pd.crosstab(s_train.decision_T, s_train.result_Y,\n",
+ " margins=True))\n",
+ "\n",
+ "display(pd.crosstab(s_test.decision_T, s_test.result_Y,\n",
+ " margins=True))"
+ ]
+ },
{
"cell_type": "markdown",
"metadata": {},
@@ -1139,7 +671,7 @@
},
{
"cell_type": "code",
- "execution_count": 191,
+ "execution_count": 196,
"metadata": {},
"outputs": [],
"source": [
@@ -1149,8 +681,7 @@
" resultY_col,\n",
" modelProbS_col,\n",
" accRateR_col,\n",
- " r,\n",
- " binning=False):\n",
+ " r):\n",
" '''\n",
" This is an implementation of the algorithm presented by Lakkaraju\n",
" et al. in their paper \"The Selective Labels Problem: Evaluating \n",
@@ -1168,20 +699,15 @@
" accRateR_col = String, the name of the column containing the judges' \n",
" acceptance rates\n",
" r = Float between 0 and 1, the given acceptance rate.\n",
- " binning = Boolean, should judges with same acceptance rate be binned\n",
" \n",
" Returns:\n",
" u = The estimated failure rate at acceptance rate r.\n",
" '''\n",
- " # First sort by acceptance rate and judge ID.\n",
- " sorted_df = df.sort_values(by=[accRateR_col, judgeIDJ_col],\n",
- " ascending=False)\n",
- "\n",
- " # Get the ID of the most lenient judge\n",
- " most_lenient_ID = sorted_df[judgeIDJ_col].values[0]\n",
+ " # Get ID of the most lenient judge.\n",
+ " most_lenient_ID = df[judgeIDJ_col].loc[df[accRateR_col].idxmax()]\n",
"\n",
" # Subset\n",
- " D_q = sorted_df[sorted_df[judgeIDJ_col] == most_lenient_ID].copy()\n",
+ " D_q = df[df[judgeIDJ_col] == most_lenient_ID].copy()\n",
"\n",
" # All observations of R_q have observed outcome labels\n",
" R_q = D_q[D_q[decisionT_col] == 1]\n",
@@ -1208,7 +734,7 @@
},
{
"cell_type": "code",
- "execution_count": 192,
+ "execution_count": 197,
"metadata": {},
"outputs": [],
"source": [
@@ -1240,14 +766,16 @@
"\n",
"Below we try to replicate the results obtained by Lakkaraju and compare their model's performance to the one of ours.\n",
"\n",
- "### Predictive models\n",
+ "### With unobservables in the data\n",
+ "\n",
+ "#### Predictive models\n",
"\n",
"Lakkaraju says that they used logistic regression. We train the predictive models using only *observed observations*, i.e. observations for which labels are available. We then predict the probability of negative outcome for all observations in the test data and attach it to our data set."
]
},
{
"cell_type": "code",
- "execution_count": 193,
+ "execution_count": 198,
"metadata": {},
"outputs": [],
"source": [
@@ -1275,7 +803,7 @@
},
{
"cell_type": "code",
- "execution_count": 194,
+ "execution_count": 199,
"metadata": {},
"outputs": [],
"source": [
@@ -1291,19 +819,19 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "### Visual comparison\n",
+ "#### Visual comparison\n",
"\n",
"Let's plot the failure rates against the acceptance rates using the difference. For the causal model we plot $P(Y=0|do(R=r))$ against r."
]
},
{
"cell_type": "code",
- "execution_count": 195,
+ "execution_count": 200,
"metadata": {},
"outputs": [
{
"data": {
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\n",
+ "image/png": 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\n",
"text/plain": [
"<Figure size 1008x576 with 1 Axes>"
]
@@ -1321,25 +849,22 @@
" \n",
" #### True evaluation\n",
" # Sort by failure probabilities, subjects with the smallest risk are first. \n",
- " df_sorted = test.sort_values(by='B_prob_0_logreg', inplace=False, \n",
- " ascending=True)\n",
+ " test.sort_values(by='B_prob_0_logreg', inplace=True, ascending=True)\n",
"\n",
- " to_release = int(round(df_sorted.shape[0] * r / 10))\n",
+ " to_release = int(round(test.shape[0] * r / 10))\n",
"\n",
- " # Failure was coded as zero.\n",
- " failure_rates[r - 1, 0] = np.mean(df_sorted.result_Y[0:to_release] == 0)\n",
+ " # Calculate failure rate as the ratio of failures to successes among those \n",
+ " # who were given a positive decision, i.e. those whose probability of negative\n",
+ " # outcome was low enough.\n",
+ " failure_rates[r - 1, 0] = np.mean(test.result_Y[0:to_release] == 0)\n",
" \n",
" #### Labeled outcomes only\n",
" # Sort by failure probabilities, subjects with the smallest risk are first. \n",
- " df_sorted = test_labeled.sort_values(by='B_prob_0_logreg', inplace=False,\n",
- " ascending=True)\n",
- " \n",
- " # Ensure that only labeled outcomes are available\n",
- " df_sorted = df_sorted[df_sorted.decision_T == 1]\n",
- " \n",
- " to_release = int(round(df_sorted.shape[0] * r / 10))\n",
+ " test_labeled.sort_values(by='B_prob_0_logreg', inplace=True, ascending=True)\n",
+ " \n",
+ " to_release = int(round(test_labeled.shape[0] * r / 10))\n",
"\n",
- " failure_rates[r - 1, 1] = np.mean(df_sorted.result_Y[0:to_release] == 0)\n",
+ " failure_rates[r - 1, 1] = np.mean(test_labeled.result_Y[0:to_release] == 0)\n",
" \n",
" #### Human error rate\n",
" # Get judges with correct leniency as list\n",
@@ -1357,9 +882,9 @@
" #### Contraction, logistic regression\n",
" failure_rates[r - 1, 3] = contraction(\n",
" test_labeled, 'judgeID_J', 'decision_T', 'result_Y', 'B_prob_0_logreg',\n",
- " 'acceptanceRate_R', r / 10, False)\n",
+ " 'acceptanceRate_R', r / 10)\n",
"\n",
- " #### Causal effect\n",
+ " #### Causal effect - \"vanilla\" (wrong) model\n",
" # Integral of P(Y=0 | T=1, X=x)*P(T=1 | R=r, X=x)*P(X=x) from negative to\n",
" # positive infinity.\n",
" failure_rates[r - 1, 4] = si.quad(lambda x: f(np.array([x]), logreg, 0) * \n",
@@ -1370,10 +895,10 @@
"\n",
"plt.figure(figsize=(14, 8))\n",
"plt.plot(np.arange(0.1, 0.9, .1), failure_rates[:, 0], label='True Evaluation', c='green')\n",
- "plt.plot(np.arange(0.1, 0.9, .1), failure_rates[:, 1], label='Labeled outcomes', c='magenta')\n",
+ "plt.plot(np.arange(0.1, 0.9, .1), failure_rates[:, 1], label='Labeled outcomes', c='black')\n",
"plt.plot(np.arange(0.1, 0.9, .1), failure_rates[:, 2], label='Human evaluation', c='red')\n",
"plt.plot(np.arange(0.1, 0.9, .1), failure_rates[:, 3], label='Contraction, log.', c='blue')\n",
- "#plt.plot(np.arange(0.1, 0.9, .1), failure_rates[:, 4], label='Causal effect', c='magenta')\n",
+ "plt.plot(np.arange(0.1, 0.9, .1), failure_rates[:, 4], label='Causal effect', c='magenta')\n",
"\n",
"plt.title('Failure rate vs. Acceptance rate')\n",
"plt.xlabel('Acceptance rate')\n",
@@ -1385,15 +910,15 @@
},
{
"cell_type": "code",
- "execution_count": 196,
+ "execution_count": 201,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
- "0.0 (0.017088007566874997, 7.490802276143562e-11)\n",
- "1.0 (0.33637396099663436, 6.582823022108186e-09)\n"
+ "0.0 (0.018287661925724383, 8.00221559955285e-11)\n",
+ "1.0 (0.3419746629567682, 6.491768654045975e-09)\n"
]
}
],
@@ -1405,12 +930,7 @@
"\n",
"r = 1.0\n",
"print(r, si.quad(lambda x: f(np.array([[x, r]]), decision_model, 1) * \\\n",
- " f(np.array([x]), logreg, 0) * scs.norm.pdf(x), -np.inf, np.inf))\n",
- "\n",
- "# Multiple runs -> error bars\n",
- "# result -> coinflipping, in y\n",
- "# email, jure and himabindu\n",
- "# delta(F(x) < r) , kertymääfunktio jotenkin"
+ " f(np.array([x]), logreg, 0) * scs.norm.pdf(x), -np.inf, np.inf))"
]
},
{
@@ -1424,6 +944,81 @@
"P(Y=0 | \\text{do}(R=1)) \\approx 0.340 \\\\\n",
"\\end{equation*}"
]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Without unobservables\n",
+ "\n",
+ "\n",
+ "#### Predictive model\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 203,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# instantiate the model (using the default parameters)\n",
+ "logreg = LogisticRegression(solver='lbfgs')\n",
+ "\n",
+ "# fit, reshape X to be of shape (n_samples, n_features)\n",
+ "logreg = logreg.fit(\n",
+ " s_train.X[s_train.decision_T == 1].values.reshape(-1, 1),\n",
+ " s_train.result_Y[s_train.decision_T == 1])\n",
+ "\n",
+ "## predict probabilities and attach to data\n",
+ "#label_probs_logreg = logreg.predict_proba(s_test.X.values.reshape(-1, 1))\n",
+ "#\n",
+ "#test = test.assign(B_prob_0_logreg=label_probs_logreg[:, 0])\n",
+ "#test_labeled = test_labeled.assign(B_prob_0_logreg=label_probs_logreg[:, 0])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 215,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[0.072 0.14 0.2 0.25 0.3 0.344 0.378 0.412]\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 1008x504 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "f_rates = np.zeros(0)\n",
+ "\n",
+ "for r in range(1, 9):\n",
+ " f_rates = np.append(\n",
+ " f_rates,\n",
+ " contraction(simple_train, 'judgeID_J', 'decision_T', 'result_Y',\n",
+ " 'probabilities_Y', 'acceptanceRate_R', r / 10))\n",
+ "\n",
+ "print(f_rates)\n",
+ "plt.plot(f_rates, label=\"Contraction\")\n",
+ "plt.title('Failure rate vs. Acceptance rate, simple data')\n",
+ "plt.xlabel('Acceptance rate')\n",
+ "plt.ylabel('Failure rate')\n",
+ "plt.legend()\n",
+ "plt.grid()\n",
+ "plt.show()"
+ ]
}
],
"metadata": {
@@ -1442,7 +1037,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.7.0"
+ "version": "3.7.3"
},
"toc": {
"base_numbering": 1,
@@ -1455,7 +1050,7 @@
"toc_cell": true,
"toc_position": {},
"toc_section_display": true,
- "toc_window_display": false
+ "toc_window_display": true
},
"varInspector": {
"cols": {
@@ -1477,6 +1072,13 @@
"varRefreshCmd": "cat(var_dic_list()) "
}
},
+ "position": {
+ "height": "352.85px",
+ "left": "1070px",
+ "right": "20px",
+ "top": "120px",
+ "width": "350px"
+ },
"types_to_exclude": [
"module",
"function",
--
GitLab