From 0a8679e32ed3b6cdc6b1aeb1a946248708901ad9 Mon Sep 17 00:00:00 2001
From: Kai <kairawal@users.noreply.github.com>
Date: Fri, 28 Jun 2024 16:49:56 +0530
Subject: [PATCH] change fair-predictor fit from train to val

---
 examples/quickstart_xgboost.ipynb | 1975 +++++++++++++----------------
 1 file changed, 892 insertions(+), 1083 deletions(-)

diff --git a/examples/quickstart_xgboost.ipynb b/examples/quickstart_xgboost.ipynb
index a4a3418..c89dea1 100644
--- a/examples/quickstart_xgboost.ipynb
+++ b/examples/quickstart_xgboost.ipynb
@@ -37,24 +37,8 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-06-17T19:20:41.442722Z",
-     "iopub.status.busy": "2024-06-17T19:20:41.442599Z",
-     "iopub.status.idle": "2024-06-17T19:20:49.841551Z",
-     "shell.execute_reply": "2024-06-17T19:20:49.840928Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/opt/miniconda3/envs/ag/lib/python3.10/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
-      "  from .autonotebook import tqdm as notebook_tqdm\n"
-     ]
-    }
-   ],
+   "metadata": {},
+   "outputs": [],
    "source": [
     "# Load and train a baseline classifier\n",
     "\n",
@@ -72,14 +56,7 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-06-17T19:20:49.844308Z",
-     "iopub.status.busy": "2024-06-17T19:20:49.844131Z",
-     "iopub.status.idle": "2024-06-17T19:20:49.848616Z",
-     "shell.execute_reply": "2024-06-17T19:20:49.848139Z"
-    }
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -99,14 +76,7 @@
   {
    "cell_type": "code",
    "execution_count": 3,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-06-17T19:20:49.850974Z",
-     "iopub.status.busy": "2024-06-17T19:20:49.850786Z",
-     "iopub.status.idle": "2024-06-17T19:20:49.875719Z",
-     "shell.execute_reply": "2024-06-17T19:20:49.875176Z"
-    }
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stderr",
@@ -118,7 +88,7 @@
    ],
    "source": [
     "# Modify predictor to enforce fairness over the val_data with respect to groups given by the column 'sex'\n",
-    "fpredictor = FairPredictor(predictor,train_data, 'sex')\n",
+    "fpredictor = FairPredictor(predictor,val_data, 'sex')\n",
     "# Maximize accuracy while enforcing that the demographic parity (the difference in positive decision rates between men and women is at most 0.02)\n",
     "fpredictor.fit(gm.accuracy,gm.demographic_parity,0.02)\n"
    ]
@@ -126,19 +96,12 @@
   {
    "cell_type": "code",
    "execution_count": 4,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-06-17T19:20:49.878359Z",
-     "iopub.status.busy": "2024-06-17T19:20:49.878167Z",
-     "iopub.status.idle": "2024-06-17T19:20:49.886881Z",
-     "shell.execute_reply": "2024-06-17T19:20:49.886133Z"
-    }
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([0, 0, 0, ..., 0, 0, 0])"
+       "array([0, 0, 0, ..., 1, 0, 0])"
       ]
      },
      "execution_count": 4,
@@ -154,14 +117,7 @@
   {
    "cell_type": "code",
    "execution_count": 5,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-06-17T19:20:49.889405Z",
-     "iopub.status.busy": "2024-06-17T19:20:49.889209Z",
-     "iopub.status.idle": "2024-06-17T19:20:49.919984Z",
-     "shell.execute_reply": "2024-06-17T19:20:49.919264Z"
-    }
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -191,38 +147,38 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>Accuracy</th>\n",
-       "      <td>0.863156</td>\n",
-       "      <td>0.845222</td>\n",
+       "      <td>0.869871</td>\n",
+       "      <td>0.852756</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Balanced Accuracy</th>\n",
-       "      <td>0.789951</td>\n",
-       "      <td>0.759749</td>\n",
+       "      <td>0.797063</td>\n",
+       "      <td>0.761652</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>F1 score</th>\n",
-       "      <td>0.694348</td>\n",
-       "      <td>0.648176</td>\n",
+       "      <td>0.707420</td>\n",
+       "      <td>0.656083</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>MCC</th>\n",
-       "      <td>0.609134</td>\n",
-       "      <td>0.553450</td>\n",
+       "      <td>0.627367</td>\n",
+       "      <td>0.570455</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Precision</th>\n",
-       "      <td>0.745776</td>\n",
-       "      <td>0.710612</td>\n",
+       "      <td>0.765644</td>\n",
+       "      <td>0.743712</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Recall</th>\n",
-       "      <td>0.649555</td>\n",
-       "      <td>0.595825</td>\n",
+       "      <td>0.657426</td>\n",
+       "      <td>0.586927</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>ROC AUC</th>\n",
-       "      <td>0.920422</td>\n",
-       "      <td>0.813340</td>\n",
+       "      <td>0.924595</td>\n",
+       "      <td>0.821728</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -230,13 +186,13 @@
       ],
       "text/plain": [
        "                   original   updated\n",
-       "Accuracy           0.863156  0.845222\n",
-       "Balanced Accuracy  0.789951  0.759749\n",
-       "F1 score           0.694348  0.648176\n",
-       "MCC                0.609134  0.553450\n",
-       "Precision          0.745776  0.710612\n",
-       "Recall             0.649555  0.595825\n",
-       "ROC AUC            0.920422  0.813340"
+       "Accuracy           0.869871  0.852756\n",
+       "Balanced Accuracy  0.797063  0.761652\n",
+       "F1 score           0.707420  0.656083\n",
+       "MCC                0.627367  0.570455\n",
+       "Precision          0.765644  0.743712\n",
+       "Recall             0.657426  0.586927\n",
+       "ROC AUC            0.924595  0.821728"
       ]
      },
      "execution_count": 5,
@@ -252,14 +208,7 @@
   {
    "cell_type": "code",
    "execution_count": 6,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-06-17T19:20:49.922055Z",
-     "iopub.status.busy": "2024-06-17T19:20:49.921915Z",
-     "iopub.status.idle": "2024-06-17T19:20:49.946433Z",
-     "shell.execute_reply": "2024-06-17T19:20:49.945879Z"
-    }
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -289,43 +238,43 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>Statistical Parity</th>\n",
-       "      <td>0.194998</td>\n",
-       "      <td>0.008199</td>\n",
+       "      <td>0.183936</td>\n",
+       "      <td>0.026034</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Predictive Parity</th>\n",
-       "      <td>0.030115</td>\n",
-       "      <td>0.359417</td>\n",
+       "      <td>0.002504</td>\n",
+       "      <td>0.336268</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Equal Opportunity</th>\n",
-       "      <td>0.114899</td>\n",
-       "      <td>0.281616</td>\n",
+       "      <td>0.094851</td>\n",
+       "      <td>0.249449</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Average Group Difference in False Negative Rate</th>\n",
-       "      <td>0.114899</td>\n",
-       "      <td>0.281616</td>\n",
+       "      <td>0.094851</td>\n",
+       "      <td>0.249449</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Equalized Odds</th>\n",
-       "      <td>0.098056</td>\n",
-       "      <td>0.173844</td>\n",
+       "      <td>0.081254</td>\n",
+       "      <td>0.150011</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Conditional Use Accuracy</th>\n",
-       "      <td>0.058129</td>\n",
-       "      <td>0.253697</td>\n",
+       "      <td>0.044046</td>\n",
+       "      <td>0.240197</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Average Group Difference in Accuracy</th>\n",
-       "      <td>0.104928</td>\n",
-       "      <td>0.048980</td>\n",
+       "      <td>0.096361</td>\n",
+       "      <td>0.061360</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Treatment Equality</th>\n",
-       "      <td>0.332490</td>\n",
-       "      <td>5.507195</td>\n",
+       "      <td>0.202847</td>\n",
+       "      <td>3.607847</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -333,14 +282,14 @@
       ],
       "text/plain": [
        "                                                 original   updated\n",
-       "Statistical Parity                               0.194998  0.008199\n",
-       "Predictive Parity                                0.030115  0.359417\n",
-       "Equal Opportunity                                0.114899  0.281616\n",
-       "Average Group Difference in False Negative Rate  0.114899  0.281616\n",
-       "Equalized Odds                                   0.098056  0.173844\n",
-       "Conditional Use Accuracy                         0.058129  0.253697\n",
-       "Average Group Difference in Accuracy             0.104928  0.048980\n",
-       "Treatment Equality                               0.332490  5.507195"
+       "Statistical Parity                               0.183936  0.026034\n",
+       "Predictive Parity                                0.002504  0.336268\n",
+       "Equal Opportunity                                0.094851  0.249449\n",
+       "Average Group Difference in False Negative Rate  0.094851  0.249449\n",
+       "Equalized Odds                                   0.081254  0.150011\n",
+       "Conditional Use Accuracy                         0.044046  0.240197\n",
+       "Average Group Difference in Accuracy             0.096361  0.061360\n",
+       "Treatment Equality                               0.202847  3.607847"
       ]
      },
      "execution_count": 6,
@@ -356,14 +305,7 @@
   {
    "cell_type": "code",
    "execution_count": 7,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-06-17T19:20:49.948771Z",
-     "iopub.status.busy": "2024-06-17T19:20:49.948616Z",
-     "iopub.status.idle": "2024-06-17T19:20:49.998948Z",
-     "shell.execute_reply": "2024-06-17T19:20:49.998544Z"
-    }
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -419,116 +361,116 @@
        "    <tr>\n",
        "      <th rowspan=\"4\" valign=\"top\">original</th>\n",
        "      <th>Overall</th>\n",
-       "      <td>0.863156</td>\n",
-       "      <td>0.789951</td>\n",
-       "      <td>0.694348</td>\n",
-       "      <td>0.609134</td>\n",
-       "      <td>0.745776</td>\n",
-       "      <td>0.649555</td>\n",
-       "      <td>0.920422</td>\n",
+       "      <td>0.869871</td>\n",
+       "      <td>0.797063</td>\n",
+       "      <td>0.707420</td>\n",
+       "      <td>0.627367</td>\n",
+       "      <td>0.765644</td>\n",
+       "      <td>0.657426</td>\n",
+       "      <td>0.924595</td>\n",
        "      <td>2922.0</td>\n",
        "      <td>9289.0</td>\n",
        "      <td>0.239292</td>\n",
-       "      <td>0.208419</td>\n",
+       "      <td>0.205470</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
-       "      <td>0.933300</td>\n",
-       "      <td>0.766035</td>\n",
-       "      <td>0.643799</td>\n",
-       "      <td>0.618555</td>\n",
-       "      <td>0.772152</td>\n",
-       "      <td>0.552036</td>\n",
-       "      <td>0.938214</td>\n",
+       "      <td>0.934289</td>\n",
+       "      <td>0.777508</td>\n",
+       "      <td>0.657216</td>\n",
+       "      <td>0.629122</td>\n",
+       "      <td>0.763473</td>\n",
+       "      <td>0.576923</td>\n",
+       "      <td>0.940204</td>\n",
        "      <td>442.0</td>\n",
        "      <td>3606.0</td>\n",
        "      <td>0.109190</td>\n",
-       "      <td>0.078063</td>\n",
+       "      <td>0.082510</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
-       "      <td>0.828372</td>\n",
-       "      <td>0.782878</td>\n",
-       "      <td>0.702485</td>\n",
-       "      <td>0.584003</td>\n",
-       "      <td>0.742037</td>\n",
-       "      <td>0.666935</td>\n",
-       "      <td>0.900383</td>\n",
+       "      <td>0.837927</td>\n",
+       "      <td>0.791104</td>\n",
+       "      <td>0.715789</td>\n",
+       "      <td>0.605652</td>\n",
+       "      <td>0.765977</td>\n",
+       "      <td>0.671774</td>\n",
+       "      <td>0.906924</td>\n",
        "      <td>2480.0</td>\n",
        "      <td>5683.0</td>\n",
        "      <td>0.303810</td>\n",
-       "      <td>0.273061</td>\n",
+       "      <td>0.266446</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Maximum difference</th>\n",
-       "      <td>0.104928</td>\n",
-       "      <td>0.016844</td>\n",
-       "      <td>0.058685</td>\n",
-       "      <td>0.034551</td>\n",
-       "      <td>0.030115</td>\n",
-       "      <td>0.114899</td>\n",
-       "      <td>0.037831</td>\n",
+       "      <td>0.096361</td>\n",
+       "      <td>0.013597</td>\n",
+       "      <td>0.058573</td>\n",
+       "      <td>0.023470</td>\n",
+       "      <td>0.002504</td>\n",
+       "      <td>0.094851</td>\n",
+       "      <td>0.033280</td>\n",
        "      <td>2038.0</td>\n",
        "      <td>2077.0</td>\n",
        "      <td>0.194620</td>\n",
-       "      <td>0.194998</td>\n",
+       "      <td>0.183936</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"4\" valign=\"top\">updated</th>\n",
        "      <th>Overall</th>\n",
-       "      <td>0.845222</td>\n",
-       "      <td>0.759749</td>\n",
-       "      <td>0.648176</td>\n",
-       "      <td>0.553450</td>\n",
-       "      <td>0.710612</td>\n",
-       "      <td>0.595825</td>\n",
-       "      <td>0.813340</td>\n",
+       "      <td>0.852756</td>\n",
+       "      <td>0.761652</td>\n",
+       "      <td>0.656083</td>\n",
+       "      <td>0.570455</td>\n",
+       "      <td>0.743712</td>\n",
+       "      <td>0.586927</td>\n",
+       "      <td>0.821728</td>\n",
        "      <td>2922.0</td>\n",
        "      <td>9289.0</td>\n",
        "      <td>0.239292</td>\n",
-       "      <td>0.200639</td>\n",
+       "      <td>0.188846</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
-       "      <td>0.877964</td>\n",
-       "      <td>0.859046</td>\n",
-       "      <td>0.599026</td>\n",
-       "      <td>0.565086</td>\n",
-       "      <td>0.467089</td>\n",
-       "      <td>0.834842</td>\n",
-       "      <td>0.938214</td>\n",
+       "      <td>0.893775</td>\n",
+       "      <td>0.852039</td>\n",
+       "      <td>0.621479</td>\n",
+       "      <td>0.582617</td>\n",
+       "      <td>0.508646</td>\n",
+       "      <td>0.798643</td>\n",
+       "      <td>0.940204</td>\n",
        "      <td>442.0</td>\n",
        "      <td>3606.0</td>\n",
        "      <td>0.109190</td>\n",
-       "      <td>0.195158</td>\n",
+       "      <td>0.171443</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
-       "      <td>0.828984</td>\n",
-       "      <td>0.751274</td>\n",
-       "      <td>0.662802</td>\n",
-       "      <td>0.574224</td>\n",
-       "      <td>0.826506</td>\n",
-       "      <td>0.553226</td>\n",
-       "      <td>0.900383</td>\n",
+       "      <td>0.832415</td>\n",
+       "      <td>0.752601</td>\n",
+       "      <td>0.665689</td>\n",
+       "      <td>0.583639</td>\n",
+       "      <td>0.844913</td>\n",
+       "      <td>0.549194</td>\n",
+       "      <td>0.906924</td>\n",
        "      <td>2480.0</td>\n",
        "      <td>5683.0</td>\n",
        "      <td>0.303810</td>\n",
-       "      <td>0.203357</td>\n",
+       "      <td>0.197476</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Maximum difference</th>\n",
-       "      <td>0.048980</td>\n",
-       "      <td>0.107772</td>\n",
-       "      <td>0.063776</td>\n",
-       "      <td>0.009138</td>\n",
-       "      <td>0.359417</td>\n",
-       "      <td>0.281616</td>\n",
-       "      <td>0.037831</td>\n",
+       "      <td>0.061360</td>\n",
+       "      <td>0.099438</td>\n",
+       "      <td>0.044210</td>\n",
+       "      <td>0.001021</td>\n",
+       "      <td>0.336268</td>\n",
+       "      <td>0.249449</td>\n",
+       "      <td>0.033280</td>\n",
        "      <td>2038.0</td>\n",
        "      <td>2077.0</td>\n",
        "      <td>0.194620</td>\n",
-       "      <td>0.008199</td>\n",
+       "      <td>0.026034</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -537,25 +479,25 @@
       "text/plain": [
        "                             Accuracy  Balanced Accuracy  F1 score       MCC  \\\n",
        "         Groups                                                                \n",
-       "original Overall             0.863156           0.789951  0.694348  0.609134   \n",
-       "         0                   0.933300           0.766035  0.643799  0.618555   \n",
-       "         1                   0.828372           0.782878  0.702485  0.584003   \n",
-       "         Maximum difference  0.104928           0.016844  0.058685  0.034551   \n",
-       "updated  Overall             0.845222           0.759749  0.648176  0.553450   \n",
-       "         0                   0.877964           0.859046  0.599026  0.565086   \n",
-       "         1                   0.828984           0.751274  0.662802  0.574224   \n",
-       "         Maximum difference  0.048980           0.107772  0.063776  0.009138   \n",
+       "original Overall             0.869871           0.797063  0.707420  0.627367   \n",
+       "         0                   0.934289           0.777508  0.657216  0.629122   \n",
+       "         1                   0.837927           0.791104  0.715789  0.605652   \n",
+       "         Maximum difference  0.096361           0.013597  0.058573  0.023470   \n",
+       "updated  Overall             0.852756           0.761652  0.656083  0.570455   \n",
+       "         0                   0.893775           0.852039  0.621479  0.582617   \n",
+       "         1                   0.832415           0.752601  0.665689  0.583639   \n",
+       "         Maximum difference  0.061360           0.099438  0.044210  0.001021   \n",
        "\n",
        "                             Precision    Recall   ROC AUC  Positive Count  \\\n",
        "         Groups                                                              \n",
-       "original Overall              0.745776  0.649555  0.920422          2922.0   \n",
-       "         0                    0.772152  0.552036  0.938214           442.0   \n",
-       "         1                    0.742037  0.666935  0.900383          2480.0   \n",
-       "         Maximum difference   0.030115  0.114899  0.037831          2038.0   \n",
-       "updated  Overall              0.710612  0.595825  0.813340          2922.0   \n",
-       "         0                    0.467089  0.834842  0.938214           442.0   \n",
-       "         1                    0.826506  0.553226  0.900383          2480.0   \n",
-       "         Maximum difference   0.359417  0.281616  0.037831          2038.0   \n",
+       "original Overall              0.765644  0.657426  0.924595          2922.0   \n",
+       "         0                    0.763473  0.576923  0.940204           442.0   \n",
+       "         1                    0.765977  0.671774  0.906924          2480.0   \n",
+       "         Maximum difference   0.002504  0.094851  0.033280          2038.0   \n",
+       "updated  Overall              0.743712  0.586927  0.821728          2922.0   \n",
+       "         0                    0.508646  0.798643  0.940204           442.0   \n",
+       "         1                    0.844913  0.549194  0.906924          2480.0   \n",
+       "         Maximum difference   0.336268  0.249449  0.033280          2038.0   \n",
        "\n",
        "                             Negative Count  Positive Label Rate  \\\n",
        "         Groups                                                    \n",
@@ -570,14 +512,14 @@
        "\n",
        "                             Positive Prediction Rate  \n",
        "         Groups                                        \n",
-       "original Overall                             0.208419  \n",
-       "         0                                   0.078063  \n",
-       "         1                                   0.273061  \n",
-       "         Maximum difference                  0.194998  \n",
-       "updated  Overall                             0.200639  \n",
-       "         0                                   0.195158  \n",
-       "         1                                   0.203357  \n",
-       "         Maximum difference                  0.008199  "
+       "original Overall                             0.205470  \n",
+       "         0                                   0.082510  \n",
+       "         1                                   0.266446  \n",
+       "         Maximum difference                  0.183936  \n",
+       "updated  Overall                             0.188846  \n",
+       "         0                                   0.171443  \n",
+       "         1                                   0.197476  \n",
+       "         Maximum difference                  0.026034  "
       ]
      },
      "execution_count": 7,
@@ -593,14 +535,7 @@
   {
    "cell_type": "code",
    "execution_count": 8,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-06-17T19:20:50.001279Z",
-     "iopub.status.busy": "2024-06-17T19:20:50.001111Z",
-     "iopub.status.idle": "2024-06-17T19:20:54.692206Z",
-     "shell.execute_reply": "2024-06-17T19:20:54.691715Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "train_data, val_data, test_data = dataset_loader.adult('sex')\n",
@@ -610,14 +545,7 @@
   {
    "cell_type": "code",
    "execution_count": 9,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-06-17T19:20:54.694914Z",
-     "iopub.status.busy": "2024-06-17T19:20:54.694743Z",
-     "iopub.status.idle": "2024-06-17T19:21:20.557631Z",
-     "shell.execute_reply": "2024-06-17T19:21:20.556715Z"
-    }
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stderr",
@@ -636,14 +564,7 @@
   {
    "cell_type": "code",
    "execution_count": 10,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-06-17T19:21:20.561396Z",
-     "iopub.status.busy": "2024-06-17T19:21:20.561247Z",
-     "iopub.status.idle": "2024-06-17T19:21:20.593515Z",
-     "shell.execute_reply": "2024-06-17T19:21:20.593088Z"
-    }
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -673,43 +594,43 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>Statistical Parity</th>\n",
-       "      <td>0.125058</td>\n",
-       "      <td>0.073183</td>\n",
+       "      <td>0.101532</td>\n",
+       "      <td>0.013839</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Predictive Parity</th>\n",
-       "      <td>0.013161</td>\n",
-       "      <td>0.134499</td>\n",
+       "      <td>0.081097</td>\n",
+       "      <td>0.120251</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Equal Opportunity</th>\n",
-       "      <td>0.190932</td>\n",
-       "      <td>0.079689</td>\n",
+       "      <td>0.216640</td>\n",
+       "      <td>0.216882</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Average Group Difference in False Negative Rate</th>\n",
-       "      <td>0.190932</td>\n",
-       "      <td>0.079689</td>\n",
+       "      <td>0.216640</td>\n",
+       "      <td>0.216882</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Equalized Odds</th>\n",
-       "      <td>0.114792</td>\n",
-       "      <td>0.047310</td>\n",
+       "      <td>0.132354</td>\n",
+       "      <td>0.116069</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Conditional Use Accuracy</th>\n",
-       "      <td>0.035533</td>\n",
-       "      <td>0.101868</td>\n",
+       "      <td>0.066816</td>\n",
+       "      <td>0.100688</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Average Group Difference in Accuracy</th>\n",
-       "      <td>0.055388</td>\n",
-       "      <td>0.041906</td>\n",
+       "      <td>0.066625</td>\n",
+       "      <td>0.055585</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Treatment Equality</th>\n",
-       "      <td>0.220158</td>\n",
-       "      <td>1.118408</td>\n",
+       "      <td>0.450762</td>\n",
+       "      <td>2.258786</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -717,14 +638,14 @@
       ],
       "text/plain": [
        "                                                 original   updated\n",
-       "Statistical Parity                               0.125058  0.073183\n",
-       "Predictive Parity                                0.013161  0.134499\n",
-       "Equal Opportunity                                0.190932  0.079689\n",
-       "Average Group Difference in False Negative Rate  0.190932  0.079689\n",
-       "Equalized Odds                                   0.114792  0.047310\n",
-       "Conditional Use Accuracy                         0.035533  0.101868\n",
-       "Average Group Difference in Accuracy             0.055388  0.041906\n",
-       "Treatment Equality                               0.220158  1.118408"
+       "Statistical Parity                               0.101532  0.013839\n",
+       "Predictive Parity                                0.081097  0.120251\n",
+       "Equal Opportunity                                0.216640  0.216882\n",
+       "Average Group Difference in False Negative Rate  0.216640  0.216882\n",
+       "Equalized Odds                                   0.132354  0.116069\n",
+       "Conditional Use Accuracy                         0.066816  0.100688\n",
+       "Average Group Difference in Accuracy             0.066625  0.055585\n",
+       "Treatment Equality                               0.450762  2.258786"
       ]
      },
      "execution_count": 10,
@@ -740,14 +661,7 @@
   {
    "cell_type": "code",
    "execution_count": 11,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-06-17T19:21:20.595709Z",
-     "iopub.status.busy": "2024-06-17T19:21:20.595582Z",
-     "iopub.status.idle": "2024-06-17T19:21:20.615596Z",
-     "shell.execute_reply": "2024-06-17T19:21:20.615276Z"
-    }
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -777,43 +691,43 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>Statistical Parity</th>\n",
-       "      <td>0.095165</td>\n",
-       "      <td>0.016903</td>\n",
+       "      <td>0.102841</td>\n",
+       "      <td>0.017995</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Predictive Parity</th>\n",
-       "      <td>0.106777</td>\n",
-       "      <td>0.142254</td>\n",
+       "      <td>0.062764</td>\n",
+       "      <td>0.176582</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Equal Opportunity</th>\n",
-       "      <td>0.191597</td>\n",
-       "      <td>0.177972</td>\n",
+       "      <td>0.198661</td>\n",
+       "      <td>0.159256</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Average Group Difference in False Negative Rate</th>\n",
-       "      <td>0.191597</td>\n",
-       "      <td>0.177972</td>\n",
+       "      <td>0.198661</td>\n",
+       "      <td>0.159256</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Equalized Odds</th>\n",
-       "      <td>0.118433</td>\n",
-       "      <td>0.099354</td>\n",
+       "      <td>0.117458</td>\n",
+       "      <td>0.094345</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Conditional Use Accuracy</th>\n",
-       "      <td>0.070180</td>\n",
-       "      <td>0.100921</td>\n",
+       "      <td>0.055220</td>\n",
+       "      <td>0.126215</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Average Group Difference in Accuracy</th>\n",
-       "      <td>0.053687</td>\n",
-       "      <td>0.048322</td>\n",
+       "      <td>0.055219</td>\n",
+       "      <td>0.039814</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Treatment Equality</th>\n",
-       "      <td>0.394770</td>\n",
-       "      <td>1.736889</td>\n",
+       "      <td>0.352693</td>\n",
+       "      <td>1.856197</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -821,14 +735,14 @@
       ],
       "text/plain": [
        "                                                 original   updated\n",
-       "Statistical Parity                               0.095165  0.016903\n",
-       "Predictive Parity                                0.106777  0.142254\n",
-       "Equal Opportunity                                0.191597  0.177972\n",
-       "Average Group Difference in False Negative Rate  0.191597  0.177972\n",
-       "Equalized Odds                                   0.118433  0.099354\n",
-       "Conditional Use Accuracy                         0.070180  0.100921\n",
-       "Average Group Difference in Accuracy             0.053687  0.048322\n",
-       "Treatment Equality                               0.394770  1.736889"
+       "Statistical Parity                               0.102841  0.017995\n",
+       "Predictive Parity                                0.062764  0.176582\n",
+       "Equal Opportunity                                0.198661  0.159256\n",
+       "Average Group Difference in False Negative Rate  0.198661  0.159256\n",
+       "Equalized Odds                                   0.117458  0.094345\n",
+       "Conditional Use Accuracy                         0.055220  0.126215\n",
+       "Average Group Difference in Accuracy             0.055219  0.039814\n",
+       "Treatment Equality                               0.352693  1.856197"
       ]
      },
      "execution_count": 11,
@@ -843,18 +757,11 @@
   {
    "cell_type": "code",
    "execution_count": 12,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-06-17T19:21:20.617231Z",
-     "iopub.status.busy": "2024-06-17T19:21:20.617123Z",
-     "iopub.status.idle": "2024-06-17T19:21:21.137764Z",
-     "shell.execute_reply": "2024-06-17T19:21:21.137302Z"
-    }
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -871,18 +778,11 @@
   {
    "cell_type": "code",
    "execution_count": 13,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-06-17T19:21:21.139908Z",
-     "iopub.status.busy": "2024-06-17T19:21:21.139680Z",
-     "iopub.status.idle": "2024-06-17T19:21:21.287872Z",
-     "shell.execute_reply": "2024-06-17T19:21:21.287394Z"
-    }
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -899,14 +799,7 @@
   {
    "cell_type": "code",
    "execution_count": 14,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-06-17T19:21:21.292402Z",
-     "iopub.status.busy": "2024-06-17T19:21:21.292266Z",
-     "iopub.status.idle": "2024-06-17T19:21:21.341972Z",
-     "shell.execute_reply": "2024-06-17T19:21:21.341555Z"
-    }
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -962,200 +855,200 @@
        "    <tr>\n",
        "      <th rowspan=\"7\" valign=\"top\">original</th>\n",
        "      <th>Overall</th>\n",
-       "      <td>0.874212</td>\n",
-       "      <td>0.800385</td>\n",
-       "      <td>0.714816</td>\n",
-       "      <td>0.638670</td>\n",
-       "      <td>0.781250</td>\n",
-       "      <td>0.658795</td>\n",
-       "      <td>0.926794</td>\n",
+       "      <td>0.871919</td>\n",
+       "      <td>0.803569</td>\n",
+       "      <td>0.715326</td>\n",
+       "      <td>0.635274</td>\n",
+       "      <td>0.763997</td>\n",
+       "      <td>0.672485</td>\n",
+       "      <td>0.924047</td>\n",
        "      <td>2922.0</td>\n",
        "      <td>9289.0</td>\n",
        "      <td>0.239292</td>\n",
-       "      <td>0.201785</td>\n",
+       "      <td>0.210630</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
-       "      <td>0.863158</td>\n",
-       "      <td>0.618671</td>\n",
-       "      <td>0.380952</td>\n",
-       "      <td>0.397779</td>\n",
-       "      <td>0.800000</td>\n",
-       "      <td>0.250000</td>\n",
-       "      <td>0.879747</td>\n",
-       "      <td>16.0</td>\n",
-       "      <td>79.0</td>\n",
-       "      <td>0.168421</td>\n",
-       "      <td>0.052632</td>\n",
+       "      <td>0.892308</td>\n",
+       "      <td>0.653390</td>\n",
+       "      <td>0.461538</td>\n",
+       "      <td>0.480136</td>\n",
+       "      <td>0.857143</td>\n",
+       "      <td>0.315789</td>\n",
+       "      <td>0.893789</td>\n",
+       "      <td>19.0</td>\n",
+       "      <td>111.0</td>\n",
+       "      <td>0.146154</td>\n",
+       "      <td>0.053846</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
-       "      <td>0.837838</td>\n",
-       "      <td>0.805269</td>\n",
-       "      <td>0.750000</td>\n",
-       "      <td>0.634899</td>\n",
-       "      <td>0.810811</td>\n",
-       "      <td>0.697674</td>\n",
-       "      <td>0.919554</td>\n",
-       "      <td>129.0</td>\n",
-       "      <td>241.0</td>\n",
-       "      <td>0.348649</td>\n",
-       "      <td>0.300000</td>\n",
+       "      <td>0.825193</td>\n",
+       "      <td>0.761461</td>\n",
+       "      <td>0.653061</td>\n",
+       "      <td>0.537282</td>\n",
+       "      <td>0.680851</td>\n",
+       "      <td>0.627451</td>\n",
+       "      <td>0.867869</td>\n",
+       "      <td>102.0</td>\n",
+       "      <td>287.0</td>\n",
+       "      <td>0.262211</td>\n",
+       "      <td>0.241645</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
-       "      <td>0.939138</td>\n",
-       "      <td>0.792021</td>\n",
-       "      <td>0.686957</td>\n",
-       "      <td>0.661844</td>\n",
-       "      <td>0.797980</td>\n",
-       "      <td>0.603053</td>\n",
-       "      <td>0.960479</td>\n",
-       "      <td>131.0</td>\n",
-       "      <td>1052.0</td>\n",
-       "      <td>0.110735</td>\n",
-       "      <td>0.083686</td>\n",
+       "      <td>0.932143</td>\n",
+       "      <td>0.790032</td>\n",
+       "      <td>0.685950</td>\n",
+       "      <td>0.656913</td>\n",
+       "      <td>0.798077</td>\n",
+       "      <td>0.601449</td>\n",
+       "      <td>0.958772</td>\n",
+       "      <td>138.0</td>\n",
+       "      <td>982.0</td>\n",
+       "      <td>0.123214</td>\n",
+       "      <td>0.092857</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
-       "      <td>0.937500</td>\n",
-       "      <td>0.797591</td>\n",
-       "      <td>0.695652</td>\n",
-       "      <td>0.668558</td>\n",
-       "      <td>0.800000</td>\n",
-       "      <td>0.615385</td>\n",
-       "      <td>0.973582</td>\n",
-       "      <td>13.0</td>\n",
-       "      <td>99.0</td>\n",
-       "      <td>0.116071</td>\n",
-       "      <td>0.089286</td>\n",
+       "      <td>0.958763</td>\n",
+       "      <td>0.897463</td>\n",
+       "      <td>0.818182</td>\n",
+       "      <td>0.794926</td>\n",
+       "      <td>0.818182</td>\n",
+       "      <td>0.818182</td>\n",
+       "      <td>0.973573</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>86.0</td>\n",
+       "      <td>0.113402</td>\n",
+       "      <td>0.113402</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
-       "      <td>0.867572</td>\n",
-       "      <td>0.799523</td>\n",
-       "      <td>0.715928</td>\n",
-       "      <td>0.633844</td>\n",
-       "      <td>0.778919</td>\n",
-       "      <td>0.662362</td>\n",
-       "      <td>0.922501</td>\n",
-       "      <td>2633.0</td>\n",
-       "      <td>7818.0</td>\n",
-       "      <td>0.251938</td>\n",
-       "      <td>0.214238</td>\n",
+       "      <td>0.866158</td>\n",
+       "      <td>0.804588</td>\n",
+       "      <td>0.720048</td>\n",
+       "      <td>0.634420</td>\n",
+       "      <td>0.765280</td>\n",
+       "      <td>0.679864</td>\n",
+       "      <td>0.921546</td>\n",
+       "      <td>2652.0</td>\n",
+       "      <td>7823.0</td>\n",
+       "      <td>0.253174</td>\n",
+       "      <td>0.224916</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Maximum difference</th>\n",
-       "      <td>0.101300</td>\n",
-       "      <td>0.186598</td>\n",
-       "      <td>0.369048</td>\n",
-       "      <td>0.270780</td>\n",
-       "      <td>0.031892</td>\n",
-       "      <td>0.447674</td>\n",
-       "      <td>0.093835</td>\n",
-       "      <td>2620.0</td>\n",
-       "      <td>7739.0</td>\n",
-       "      <td>0.237913</td>\n",
-       "      <td>0.247368</td>\n",
+       "      <td>0.133570</td>\n",
+       "      <td>0.244073</td>\n",
+       "      <td>0.356643</td>\n",
+       "      <td>0.314790</td>\n",
+       "      <td>0.176292</td>\n",
+       "      <td>0.502392</td>\n",
+       "      <td>0.105704</td>\n",
+       "      <td>2641.0</td>\n",
+       "      <td>7737.0</td>\n",
+       "      <td>0.148809</td>\n",
+       "      <td>0.187799</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"7\" valign=\"top\">updated</th>\n",
        "      <th>Overall</th>\n",
-       "      <td>0.872001</td>\n",
-       "      <td>0.792246</td>\n",
-       "      <td>0.705039</td>\n",
-       "      <td>0.629830</td>\n",
-       "      <td>0.785865</td>\n",
-       "      <td>0.639288</td>\n",
-       "      <td>0.907573</td>\n",
+       "      <td>0.868479</td>\n",
+       "      <td>0.777851</td>\n",
+       "      <td>0.687305</td>\n",
+       "      <td>0.615379</td>\n",
+       "      <td>0.797200</td>\n",
+       "      <td>0.604038</td>\n",
+       "      <td>0.900418</td>\n",
        "      <td>2922.0</td>\n",
        "      <td>9289.0</td>\n",
        "      <td>0.239292</td>\n",
-       "      <td>0.194661</td>\n",
+       "      <td>0.181312</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
-       "      <td>0.884211</td>\n",
-       "      <td>0.780854</td>\n",
-       "      <td>0.645161</td>\n",
-       "      <td>0.576493</td>\n",
-       "      <td>0.666667</td>\n",
-       "      <td>0.625000</td>\n",
-       "      <td>0.879747</td>\n",
-       "      <td>16.0</td>\n",
-       "      <td>79.0</td>\n",
-       "      <td>0.168421</td>\n",
-       "      <td>0.157895</td>\n",
+       "      <td>0.884615</td>\n",
+       "      <td>0.779753</td>\n",
+       "      <td>0.615385</td>\n",
+       "      <td>0.547813</td>\n",
+       "      <td>0.600000</td>\n",
+       "      <td>0.631579</td>\n",
+       "      <td>0.893789</td>\n",
+       "      <td>19.0</td>\n",
+       "      <td>111.0</td>\n",
+       "      <td>0.146154</td>\n",
+       "      <td>0.153846</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
-       "      <td>0.837838</td>\n",
-       "      <td>0.794461</td>\n",
-       "      <td>0.736842</td>\n",
-       "      <td>0.633956</td>\n",
-       "      <td>0.848485</td>\n",
-       "      <td>0.651163</td>\n",
-       "      <td>0.919554</td>\n",
-       "      <td>129.0</td>\n",
-       "      <td>241.0</td>\n",
-       "      <td>0.348649</td>\n",
-       "      <td>0.267568</td>\n",
+       "      <td>0.822622</td>\n",
+       "      <td>0.702842</td>\n",
+       "      <td>0.571429</td>\n",
+       "      <td>0.497446</td>\n",
+       "      <td>0.779661</td>\n",
+       "      <td>0.450980</td>\n",
+       "      <td>0.867869</td>\n",
+       "      <td>102.0</td>\n",
+       "      <td>287.0</td>\n",
+       "      <td>0.262211</td>\n",
+       "      <td>0.151671</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
-       "      <td>0.913779</td>\n",
-       "      <td>0.864642</td>\n",
-       "      <td>0.673077</td>\n",
-       "      <td>0.635721</td>\n",
-       "      <td>0.580110</td>\n",
-       "      <td>0.801527</td>\n",
-       "      <td>0.960479</td>\n",
-       "      <td>131.0</td>\n",
-       "      <td>1052.0</td>\n",
-       "      <td>0.110735</td>\n",
-       "      <td>0.153001</td>\n",
+       "      <td>0.911607</td>\n",
+       "      <td>0.849944</td>\n",
+       "      <td>0.681672</td>\n",
+       "      <td>0.636540</td>\n",
+       "      <td>0.612717</td>\n",
+       "      <td>0.768116</td>\n",
+       "      <td>0.958772</td>\n",
+       "      <td>138.0</td>\n",
+       "      <td>982.0</td>\n",
+       "      <td>0.123214</td>\n",
+       "      <td>0.154464</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
-       "      <td>0.919643</td>\n",
-       "      <td>0.787490</td>\n",
-       "      <td>0.640000</td>\n",
-       "      <td>0.595459</td>\n",
+       "      <td>0.938144</td>\n",
+       "      <td>0.925476</td>\n",
+       "      <td>0.769231</td>\n",
+       "      <td>0.746273</td>\n",
        "      <td>0.666667</td>\n",
-       "      <td>0.615385</td>\n",
-       "      <td>0.973582</td>\n",
-       "      <td>13.0</td>\n",
-       "      <td>99.0</td>\n",
-       "      <td>0.116071</td>\n",
-       "      <td>0.107143</td>\n",
+       "      <td>0.909091</td>\n",
+       "      <td>0.973573</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>86.0</td>\n",
+       "      <td>0.113402</td>\n",
+       "      <td>0.154639</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
-       "      <td>0.867860</td>\n",
-       "      <td>0.789262</td>\n",
-       "      <td>0.706358</td>\n",
-       "      <td>0.630176</td>\n",
-       "      <td>0.802415</td>\n",
-       "      <td>0.630839</td>\n",
-       "      <td>0.922501</td>\n",
-       "      <td>2633.0</td>\n",
-       "      <td>7818.0</td>\n",
-       "      <td>0.251938</td>\n",
-       "      <td>0.198067</td>\n",
+       "      <td>0.864726</td>\n",
+       "      <td>0.777209</td>\n",
+       "      <td>0.691890</td>\n",
+       "      <td>0.619732</td>\n",
+       "      <td>0.817155</td>\n",
+       "      <td>0.599925</td>\n",
+       "      <td>0.921546</td>\n",
+       "      <td>2652.0</td>\n",
+       "      <td>7823.0</td>\n",
+       "      <td>0.253174</td>\n",
+       "      <td>0.185871</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Maximum difference</th>\n",
-       "      <td>0.081805</td>\n",
-       "      <td>0.083787</td>\n",
-       "      <td>0.096842</td>\n",
-       "      <td>0.059228</td>\n",
-       "      <td>0.268374</td>\n",
-       "      <td>0.186142</td>\n",
-       "      <td>0.093835</td>\n",
-       "      <td>2620.0</td>\n",
-       "      <td>7739.0</td>\n",
-       "      <td>0.237913</td>\n",
-       "      <td>0.160425</td>\n",
+       "      <td>0.115522</td>\n",
+       "      <td>0.222634</td>\n",
+       "      <td>0.197802</td>\n",
+       "      <td>0.248827</td>\n",
+       "      <td>0.217155</td>\n",
+       "      <td>0.458111</td>\n",
+       "      <td>0.105704</td>\n",
+       "      <td>2641.0</td>\n",
+       "      <td>7737.0</td>\n",
+       "      <td>0.148809</td>\n",
+       "      <td>0.034200</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -1164,71 +1057,71 @@
       "text/plain": [
        "                             Accuracy  Balanced Accuracy  F1 score       MCC  \\\n",
        "         Groups                                                                \n",
-       "original Overall             0.874212           0.800385  0.714816  0.638670   \n",
-       "         0                   0.863158           0.618671  0.380952  0.397779   \n",
-       "         1                   0.837838           0.805269  0.750000  0.634899   \n",
-       "         2                   0.939138           0.792021  0.686957  0.661844   \n",
-       "         3                   0.937500           0.797591  0.695652  0.668558   \n",
-       "         4                   0.867572           0.799523  0.715928  0.633844   \n",
-       "         Maximum difference  0.101300           0.186598  0.369048  0.270780   \n",
-       "updated  Overall             0.872001           0.792246  0.705039  0.629830   \n",
-       "         0                   0.884211           0.780854  0.645161  0.576493   \n",
-       "         1                   0.837838           0.794461  0.736842  0.633956   \n",
-       "         2                   0.913779           0.864642  0.673077  0.635721   \n",
-       "         3                   0.919643           0.787490  0.640000  0.595459   \n",
-       "         4                   0.867860           0.789262  0.706358  0.630176   \n",
-       "         Maximum difference  0.081805           0.083787  0.096842  0.059228   \n",
+       "original Overall             0.871919           0.803569  0.715326  0.635274   \n",
+       "         0                   0.892308           0.653390  0.461538  0.480136   \n",
+       "         1                   0.825193           0.761461  0.653061  0.537282   \n",
+       "         2                   0.932143           0.790032  0.685950  0.656913   \n",
+       "         3                   0.958763           0.897463  0.818182  0.794926   \n",
+       "         4                   0.866158           0.804588  0.720048  0.634420   \n",
+       "         Maximum difference  0.133570           0.244073  0.356643  0.314790   \n",
+       "updated  Overall             0.868479           0.777851  0.687305  0.615379   \n",
+       "         0                   0.884615           0.779753  0.615385  0.547813   \n",
+       "         1                   0.822622           0.702842  0.571429  0.497446   \n",
+       "         2                   0.911607           0.849944  0.681672  0.636540   \n",
+       "         3                   0.938144           0.925476  0.769231  0.746273   \n",
+       "         4                   0.864726           0.777209  0.691890  0.619732   \n",
+       "         Maximum difference  0.115522           0.222634  0.197802  0.248827   \n",
        "\n",
        "                             Precision    Recall   ROC AUC  Positive Count  \\\n",
        "         Groups                                                              \n",
-       "original Overall              0.781250  0.658795  0.926794          2922.0   \n",
-       "         0                    0.800000  0.250000  0.879747            16.0   \n",
-       "         1                    0.810811  0.697674  0.919554           129.0   \n",
-       "         2                    0.797980  0.603053  0.960479           131.0   \n",
-       "         3                    0.800000  0.615385  0.973582            13.0   \n",
-       "         4                    0.778919  0.662362  0.922501          2633.0   \n",
-       "         Maximum difference   0.031892  0.447674  0.093835          2620.0   \n",
-       "updated  Overall              0.785865  0.639288  0.907573          2922.0   \n",
-       "         0                    0.666667  0.625000  0.879747            16.0   \n",
-       "         1                    0.848485  0.651163  0.919554           129.0   \n",
-       "         2                    0.580110  0.801527  0.960479           131.0   \n",
-       "         3                    0.666667  0.615385  0.973582            13.0   \n",
-       "         4                    0.802415  0.630839  0.922501          2633.0   \n",
-       "         Maximum difference   0.268374  0.186142  0.093835          2620.0   \n",
+       "original Overall              0.763997  0.672485  0.924047          2922.0   \n",
+       "         0                    0.857143  0.315789  0.893789            19.0   \n",
+       "         1                    0.680851  0.627451  0.867869           102.0   \n",
+       "         2                    0.798077  0.601449  0.958772           138.0   \n",
+       "         3                    0.818182  0.818182  0.973573            11.0   \n",
+       "         4                    0.765280  0.679864  0.921546          2652.0   \n",
+       "         Maximum difference   0.176292  0.502392  0.105704          2641.0   \n",
+       "updated  Overall              0.797200  0.604038  0.900418          2922.0   \n",
+       "         0                    0.600000  0.631579  0.893789            19.0   \n",
+       "         1                    0.779661  0.450980  0.867869           102.0   \n",
+       "         2                    0.612717  0.768116  0.958772           138.0   \n",
+       "         3                    0.666667  0.909091  0.973573            11.0   \n",
+       "         4                    0.817155  0.599925  0.921546          2652.0   \n",
+       "         Maximum difference   0.217155  0.458111  0.105704          2641.0   \n",
        "\n",
        "                             Negative Count  Positive Label Rate  \\\n",
        "         Groups                                                    \n",
        "original Overall                     9289.0             0.239292   \n",
-       "         0                             79.0             0.168421   \n",
-       "         1                            241.0             0.348649   \n",
-       "         2                           1052.0             0.110735   \n",
-       "         3                             99.0             0.116071   \n",
-       "         4                           7818.0             0.251938   \n",
-       "         Maximum difference          7739.0             0.237913   \n",
+       "         0                            111.0             0.146154   \n",
+       "         1                            287.0             0.262211   \n",
+       "         2                            982.0             0.123214   \n",
+       "         3                             86.0             0.113402   \n",
+       "         4                           7823.0             0.253174   \n",
+       "         Maximum difference          7737.0             0.148809   \n",
        "updated  Overall                     9289.0             0.239292   \n",
-       "         0                             79.0             0.168421   \n",
-       "         1                            241.0             0.348649   \n",
-       "         2                           1052.0             0.110735   \n",
-       "         3                             99.0             0.116071   \n",
-       "         4                           7818.0             0.251938   \n",
-       "         Maximum difference          7739.0             0.237913   \n",
+       "         0                            111.0             0.146154   \n",
+       "         1                            287.0             0.262211   \n",
+       "         2                            982.0             0.123214   \n",
+       "         3                             86.0             0.113402   \n",
+       "         4                           7823.0             0.253174   \n",
+       "         Maximum difference          7737.0             0.148809   \n",
        "\n",
        "                             Positive Prediction Rate  \n",
        "         Groups                                        \n",
-       "original Overall                             0.201785  \n",
-       "         0                                   0.052632  \n",
-       "         1                                   0.300000  \n",
-       "         2                                   0.083686  \n",
-       "         3                                   0.089286  \n",
-       "         4                                   0.214238  \n",
-       "         Maximum difference                  0.247368  \n",
-       "updated  Overall                             0.194661  \n",
-       "         0                                   0.157895  \n",
-       "         1                                   0.267568  \n",
-       "         2                                   0.153001  \n",
-       "         3                                   0.107143  \n",
-       "         4                                   0.198067  \n",
-       "         Maximum difference                  0.160425  "
+       "original Overall                             0.210630  \n",
+       "         0                                   0.053846  \n",
+       "         1                                   0.241645  \n",
+       "         2                                   0.092857  \n",
+       "         3                                   0.113402  \n",
+       "         4                                   0.224916  \n",
+       "         Maximum difference                  0.187799  \n",
+       "updated  Overall                             0.181312  \n",
+       "         0                                   0.153846  \n",
+       "         1                                   0.151671  \n",
+       "         2                                   0.154464  \n",
+       "         3                                   0.154639  \n",
+       "         4                                   0.185871  \n",
+       "         Maximum difference                  0.034200  "
       ]
      },
      "execution_count": 14,
@@ -1245,14 +1138,7 @@
   {
    "cell_type": "code",
    "execution_count": 15,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-06-17T19:21:21.346173Z",
-     "iopub.status.busy": "2024-06-17T19:21:21.345994Z",
-     "iopub.status.idle": "2024-06-17T19:21:21.396723Z",
-     "shell.execute_reply": "2024-06-17T19:21:21.396269Z"
-    }
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -1308,200 +1194,200 @@
        "    <tr>\n",
        "      <th rowspan=\"7\" valign=\"top\">original</th>\n",
        "      <th>Overall</th>\n",
-       "      <td>0.873464</td>\n",
-       "      <td>0.800249</td>\n",
-       "      <td>0.713942</td>\n",
-       "      <td>0.636936</td>\n",
-       "      <td>0.777733</td>\n",
-       "      <td>0.659822</td>\n",
-       "      <td>0.928441</td>\n",
+       "      <td>0.867568</td>\n",
+       "      <td>0.795669</td>\n",
+       "      <td>0.703900</td>\n",
+       "      <td>0.621685</td>\n",
+       "      <td>0.756991</td>\n",
+       "      <td>0.657769</td>\n",
+       "      <td>0.923969</td>\n",
        "      <td>2922.0</td>\n",
        "      <td>9288.0</td>\n",
        "      <td>0.239312</td>\n",
-       "      <td>0.203030</td>\n",
+       "      <td>0.207944</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
-       "      <td>0.888889</td>\n",
-       "      <td>0.615909</td>\n",
-       "      <td>0.363636</td>\n",
-       "      <td>0.362441</td>\n",
-       "      <td>0.666667</td>\n",
-       "      <td>0.250000</td>\n",
-       "      <td>0.900000</td>\n",
+       "      <td>0.909836</td>\n",
+       "      <td>0.709316</td>\n",
+       "      <td>0.560000</td>\n",
+       "      <td>0.540611</td>\n",
+       "      <td>0.777778</td>\n",
+       "      <td>0.437500</td>\n",
+       "      <td>0.916863</td>\n",
        "      <td>16.0</td>\n",
-       "      <td>110.0</td>\n",
-       "      <td>0.126984</td>\n",
-       "      <td>0.047619</td>\n",
+       "      <td>106.0</td>\n",
+       "      <td>0.131148</td>\n",
+       "      <td>0.073770</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
-       "      <td>0.831978</td>\n",
-       "      <td>0.759259</td>\n",
-       "      <td>0.621951</td>\n",
-       "      <td>0.514021</td>\n",
-       "      <td>0.614458</td>\n",
-       "      <td>0.629630</td>\n",
-       "      <td>0.879630</td>\n",
-       "      <td>81.0</td>\n",
-       "      <td>288.0</td>\n",
-       "      <td>0.219512</td>\n",
-       "      <td>0.224932</td>\n",
+       "      <td>0.859459</td>\n",
+       "      <td>0.816824</td>\n",
+       "      <td>0.745098</td>\n",
+       "      <td>0.649240</td>\n",
+       "      <td>0.775510</td>\n",
+       "      <td>0.716981</td>\n",
+       "      <td>0.928423</td>\n",
+       "      <td>106.0</td>\n",
+       "      <td>264.0</td>\n",
+       "      <td>0.286486</td>\n",
+       "      <td>0.264865</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
-       "      <td>0.927920</td>\n",
-       "      <td>0.785367</td>\n",
-       "      <td>0.678967</td>\n",
-       "      <td>0.647838</td>\n",
-       "      <td>0.793103</td>\n",
-       "      <td>0.593548</td>\n",
-       "      <td>0.947179</td>\n",
-       "      <td>155.0</td>\n",
-       "      <td>1052.0</td>\n",
-       "      <td>0.128418</td>\n",
-       "      <td>0.096106</td>\n",
+       "      <td>0.924370</td>\n",
+       "      <td>0.766790</td>\n",
+       "      <td>0.628099</td>\n",
+       "      <td>0.591542</td>\n",
+       "      <td>0.710280</td>\n",
+       "      <td>0.562963</td>\n",
+       "      <td>0.949142</td>\n",
+       "      <td>135.0</td>\n",
+       "      <td>1055.0</td>\n",
+       "      <td>0.113445</td>\n",
+       "      <td>0.089916</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
-       "      <td>0.936170</td>\n",
-       "      <td>0.833808</td>\n",
-       "      <td>0.750000</td>\n",
-       "      <td>0.716978</td>\n",
-       "      <td>0.818182</td>\n",
-       "      <td>0.692308</td>\n",
-       "      <td>0.951567</td>\n",
-       "      <td>13.0</td>\n",
-       "      <td>81.0</td>\n",
-       "      <td>0.138298</td>\n",
-       "      <td>0.117021</td>\n",
+       "      <td>0.965217</td>\n",
+       "      <td>0.918670</td>\n",
+       "      <td>0.857143</td>\n",
+       "      <td>0.837341</td>\n",
+       "      <td>0.857143</td>\n",
+       "      <td>0.857143</td>\n",
+       "      <td>0.960396</td>\n",
+       "      <td>14.0</td>\n",
+       "      <td>101.0</td>\n",
+       "      <td>0.121739</td>\n",
+       "      <td>0.121739</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
-       "      <td>0.867870</td>\n",
-       "      <td>0.801810</td>\n",
-       "      <td>0.720325</td>\n",
-       "      <td>0.638056</td>\n",
-       "      <td>0.783031</td>\n",
-       "      <td>0.666918</td>\n",
-       "      <td>0.926850</td>\n",
-       "      <td>2657.0</td>\n",
-       "      <td>7757.0</td>\n",
-       "      <td>0.255137</td>\n",
-       "      <td>0.217304</td>\n",
+       "      <td>0.859791</td>\n",
+       "      <td>0.794180</td>\n",
+       "      <td>0.705764</td>\n",
+       "      <td>0.616791</td>\n",
+       "      <td>0.757681</td>\n",
+       "      <td>0.660505</td>\n",
+       "      <td>0.919260</td>\n",
+       "      <td>2651.0</td>\n",
+       "      <td>7762.0</td>\n",
+       "      <td>0.254586</td>\n",
+       "      <td>0.221934</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Maximum difference</th>\n",
-       "      <td>0.104192</td>\n",
-       "      <td>0.217899</td>\n",
-       "      <td>0.386364</td>\n",
-       "      <td>0.354537</td>\n",
-       "      <td>0.203724</td>\n",
-       "      <td>0.442308</td>\n",
-       "      <td>0.071937</td>\n",
-       "      <td>2644.0</td>\n",
-       "      <td>7676.0</td>\n",
-       "      <td>0.128153</td>\n",
-       "      <td>0.177313</td>\n",
+       "      <td>0.105758</td>\n",
+       "      <td>0.209354</td>\n",
+       "      <td>0.297143</td>\n",
+       "      <td>0.296730</td>\n",
+       "      <td>0.146862</td>\n",
+       "      <td>0.419643</td>\n",
+       "      <td>0.043533</td>\n",
+       "      <td>2637.0</td>\n",
+       "      <td>7661.0</td>\n",
+       "      <td>0.173041</td>\n",
+       "      <td>0.191094</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"7\" valign=\"top\">updated</th>\n",
        "      <th>Overall</th>\n",
-       "      <td>0.872072</td>\n",
-       "      <td>0.791593</td>\n",
-       "      <td>0.704502</td>\n",
-       "      <td>0.629731</td>\n",
-       "      <td>0.787648</td>\n",
-       "      <td>0.637235</td>\n",
-       "      <td>0.908668</td>\n",
+       "      <td>0.866093</td>\n",
+       "      <td>0.772651</td>\n",
+       "      <td>0.679600</td>\n",
+       "      <td>0.607410</td>\n",
+       "      <td>0.795048</td>\n",
+       "      <td>0.593429</td>\n",
+       "      <td>0.899048</td>\n",
        "      <td>2922.0</td>\n",
        "      <td>9288.0</td>\n",
        "      <td>0.239312</td>\n",
-       "      <td>0.193612</td>\n",
+       "      <td>0.178624</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
-       "      <td>0.880952</td>\n",
-       "      <td>0.798295</td>\n",
+       "      <td>0.877049</td>\n",
+       "      <td>0.796580</td>\n",
        "      <td>0.594595</td>\n",
-       "      <td>0.533002</td>\n",
+       "      <td>0.530414</td>\n",
        "      <td>0.523810</td>\n",
        "      <td>0.687500</td>\n",
-       "      <td>0.900000</td>\n",
+       "      <td>0.916863</td>\n",
        "      <td>16.0</td>\n",
-       "      <td>110.0</td>\n",
-       "      <td>0.126984</td>\n",
-       "      <td>0.166667</td>\n",
+       "      <td>106.0</td>\n",
+       "      <td>0.131148</td>\n",
+       "      <td>0.172131</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
-       "      <td>0.845528</td>\n",
-       "      <td>0.736883</td>\n",
-       "      <td>0.606897</td>\n",
-       "      <td>0.517919</td>\n",
-       "      <td>0.687500</td>\n",
-       "      <td>0.543210</td>\n",
-       "      <td>0.879630</td>\n",
-       "      <td>81.0</td>\n",
-       "      <td>288.0</td>\n",
-       "      <td>0.219512</td>\n",
-       "      <td>0.173442</td>\n",
+       "      <td>0.837838</td>\n",
+       "      <td>0.745212</td>\n",
+       "      <td>0.651163</td>\n",
+       "      <td>0.579185</td>\n",
+       "      <td>0.848485</td>\n",
+       "      <td>0.528302</td>\n",
+       "      <td>0.928423</td>\n",
+       "      <td>106.0</td>\n",
+       "      <td>264.0</td>\n",
+       "      <td>0.286486</td>\n",
+       "      <td>0.178378</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
-       "      <td>0.907208</td>\n",
-       "      <td>0.844999</td>\n",
-       "      <td>0.678161</td>\n",
-       "      <td>0.629830</td>\n",
-       "      <td>0.611399</td>\n",
-       "      <td>0.761290</td>\n",
-       "      <td>0.947179</td>\n",
-       "      <td>155.0</td>\n",
-       "      <td>1052.0</td>\n",
-       "      <td>0.128418</td>\n",
-       "      <td>0.159901</td>\n",
+       "      <td>0.910084</td>\n",
+       "      <td>0.829788</td>\n",
+       "      <td>0.646865</td>\n",
+       "      <td>0.600727</td>\n",
+       "      <td>0.583333</td>\n",
+       "      <td>0.725926</td>\n",
+       "      <td>0.949142</td>\n",
+       "      <td>135.0</td>\n",
+       "      <td>1055.0</td>\n",
+       "      <td>0.113445</td>\n",
+       "      <td>0.141176</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
-       "      <td>0.946809</td>\n",
-       "      <td>0.936847</td>\n",
-       "      <td>0.827586</td>\n",
-       "      <td>0.802539</td>\n",
-       "      <td>0.750000</td>\n",
-       "      <td>0.923077</td>\n",
-       "      <td>0.951567</td>\n",
-       "      <td>13.0</td>\n",
-       "      <td>81.0</td>\n",
-       "      <td>0.138298</td>\n",
-       "      <td>0.170213</td>\n",
+       "      <td>0.913043</td>\n",
+       "      <td>0.888967</td>\n",
+       "      <td>0.705882</td>\n",
+       "      <td>0.671106</td>\n",
+       "      <td>0.600000</td>\n",
+       "      <td>0.857143</td>\n",
+       "      <td>0.960396</td>\n",
+       "      <td>14.0</td>\n",
+       "      <td>101.0</td>\n",
+       "      <td>0.121739</td>\n",
+       "      <td>0.173913</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
-       "      <td>0.868158</td>\n",
-       "      <td>0.790250</td>\n",
-       "      <td>0.709541</td>\n",
-       "      <td>0.634121</td>\n",
-       "      <td>0.810145</td>\n",
-       "      <td>0.631163</td>\n",
-       "      <td>0.926850</td>\n",
-       "      <td>2657.0</td>\n",
-       "      <td>7757.0</td>\n",
-       "      <td>0.255137</td>\n",
-       "      <td>0.198771</td>\n",
+       "      <td>0.861423</td>\n",
+       "      <td>0.771181</td>\n",
+       "      <td>0.683344</td>\n",
+       "      <td>0.610987</td>\n",
+       "      <td>0.816894</td>\n",
+       "      <td>0.587326</td>\n",
+       "      <td>0.919260</td>\n",
+       "      <td>2651.0</td>\n",
+       "      <td>7762.0</td>\n",
+       "      <td>0.254586</td>\n",
+       "      <td>0.183040</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Maximum difference</th>\n",
-       "      <td>0.101280</td>\n",
-       "      <td>0.199964</td>\n",
-       "      <td>0.232992</td>\n",
-       "      <td>0.284620</td>\n",
-       "      <td>0.286335</td>\n",
-       "      <td>0.379867</td>\n",
-       "      <td>0.071937</td>\n",
-       "      <td>2644.0</td>\n",
-       "      <td>7676.0</td>\n",
-       "      <td>0.128153</td>\n",
-       "      <td>0.038870</td>\n",
+       "      <td>0.075206</td>\n",
+       "      <td>0.143756</td>\n",
+       "      <td>0.111288</td>\n",
+       "      <td>0.140692</td>\n",
+       "      <td>0.324675</td>\n",
+       "      <td>0.328841</td>\n",
+       "      <td>0.043533</td>\n",
+       "      <td>2637.0</td>\n",
+       "      <td>7661.0</td>\n",
+       "      <td>0.173041</td>\n",
+       "      <td>0.041864</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -1510,71 +1396,71 @@
       "text/plain": [
        "                             Accuracy  Balanced Accuracy  F1 score       MCC  \\\n",
        "         Groups                                                                \n",
-       "original Overall             0.873464           0.800249  0.713942  0.636936   \n",
-       "         0                   0.888889           0.615909  0.363636  0.362441   \n",
-       "         1                   0.831978           0.759259  0.621951  0.514021   \n",
-       "         2                   0.927920           0.785367  0.678967  0.647838   \n",
-       "         3                   0.936170           0.833808  0.750000  0.716978   \n",
-       "         4                   0.867870           0.801810  0.720325  0.638056   \n",
-       "         Maximum difference  0.104192           0.217899  0.386364  0.354537   \n",
-       "updated  Overall             0.872072           0.791593  0.704502  0.629731   \n",
-       "         0                   0.880952           0.798295  0.594595  0.533002   \n",
-       "         1                   0.845528           0.736883  0.606897  0.517919   \n",
-       "         2                   0.907208           0.844999  0.678161  0.629830   \n",
-       "         3                   0.946809           0.936847  0.827586  0.802539   \n",
-       "         4                   0.868158           0.790250  0.709541  0.634121   \n",
-       "         Maximum difference  0.101280           0.199964  0.232992  0.284620   \n",
+       "original Overall             0.867568           0.795669  0.703900  0.621685   \n",
+       "         0                   0.909836           0.709316  0.560000  0.540611   \n",
+       "         1                   0.859459           0.816824  0.745098  0.649240   \n",
+       "         2                   0.924370           0.766790  0.628099  0.591542   \n",
+       "         3                   0.965217           0.918670  0.857143  0.837341   \n",
+       "         4                   0.859791           0.794180  0.705764  0.616791   \n",
+       "         Maximum difference  0.105758           0.209354  0.297143  0.296730   \n",
+       "updated  Overall             0.866093           0.772651  0.679600  0.607410   \n",
+       "         0                   0.877049           0.796580  0.594595  0.530414   \n",
+       "         1                   0.837838           0.745212  0.651163  0.579185   \n",
+       "         2                   0.910084           0.829788  0.646865  0.600727   \n",
+       "         3                   0.913043           0.888967  0.705882  0.671106   \n",
+       "         4                   0.861423           0.771181  0.683344  0.610987   \n",
+       "         Maximum difference  0.075206           0.143756  0.111288  0.140692   \n",
        "\n",
        "                             Precision    Recall   ROC AUC  Positive Count  \\\n",
        "         Groups                                                              \n",
-       "original Overall              0.777733  0.659822  0.928441          2922.0   \n",
-       "         0                    0.666667  0.250000  0.900000            16.0   \n",
-       "         1                    0.614458  0.629630  0.879630            81.0   \n",
-       "         2                    0.793103  0.593548  0.947179           155.0   \n",
-       "         3                    0.818182  0.692308  0.951567            13.0   \n",
-       "         4                    0.783031  0.666918  0.926850          2657.0   \n",
-       "         Maximum difference   0.203724  0.442308  0.071937          2644.0   \n",
-       "updated  Overall              0.787648  0.637235  0.908668          2922.0   \n",
-       "         0                    0.523810  0.687500  0.900000            16.0   \n",
-       "         1                    0.687500  0.543210  0.879630            81.0   \n",
-       "         2                    0.611399  0.761290  0.947179           155.0   \n",
-       "         3                    0.750000  0.923077  0.951567            13.0   \n",
-       "         4                    0.810145  0.631163  0.926850          2657.0   \n",
-       "         Maximum difference   0.286335  0.379867  0.071937          2644.0   \n",
+       "original Overall              0.756991  0.657769  0.923969          2922.0   \n",
+       "         0                    0.777778  0.437500  0.916863            16.0   \n",
+       "         1                    0.775510  0.716981  0.928423           106.0   \n",
+       "         2                    0.710280  0.562963  0.949142           135.0   \n",
+       "         3                    0.857143  0.857143  0.960396            14.0   \n",
+       "         4                    0.757681  0.660505  0.919260          2651.0   \n",
+       "         Maximum difference   0.146862  0.419643  0.043533          2637.0   \n",
+       "updated  Overall              0.795048  0.593429  0.899048          2922.0   \n",
+       "         0                    0.523810  0.687500  0.916863            16.0   \n",
+       "         1                    0.848485  0.528302  0.928423           106.0   \n",
+       "         2                    0.583333  0.725926  0.949142           135.0   \n",
+       "         3                    0.600000  0.857143  0.960396            14.0   \n",
+       "         4                    0.816894  0.587326  0.919260          2651.0   \n",
+       "         Maximum difference   0.324675  0.328841  0.043533          2637.0   \n",
        "\n",
        "                             Negative Count  Positive Label Rate  \\\n",
        "         Groups                                                    \n",
        "original Overall                     9288.0             0.239312   \n",
-       "         0                            110.0             0.126984   \n",
-       "         1                            288.0             0.219512   \n",
-       "         2                           1052.0             0.128418   \n",
-       "         3                             81.0             0.138298   \n",
-       "         4                           7757.0             0.255137   \n",
-       "         Maximum difference          7676.0             0.128153   \n",
+       "         0                            106.0             0.131148   \n",
+       "         1                            264.0             0.286486   \n",
+       "         2                           1055.0             0.113445   \n",
+       "         3                            101.0             0.121739   \n",
+       "         4                           7762.0             0.254586   \n",
+       "         Maximum difference          7661.0             0.173041   \n",
        "updated  Overall                     9288.0             0.239312   \n",
-       "         0                            110.0             0.126984   \n",
-       "         1                            288.0             0.219512   \n",
-       "         2                           1052.0             0.128418   \n",
-       "         3                             81.0             0.138298   \n",
-       "         4                           7757.0             0.255137   \n",
-       "         Maximum difference          7676.0             0.128153   \n",
+       "         0                            106.0             0.131148   \n",
+       "         1                            264.0             0.286486   \n",
+       "         2                           1055.0             0.113445   \n",
+       "         3                            101.0             0.121739   \n",
+       "         4                           7762.0             0.254586   \n",
+       "         Maximum difference          7661.0             0.173041   \n",
        "\n",
        "                             Positive Prediction Rate  \n",
        "         Groups                                        \n",
-       "original Overall                             0.203030  \n",
-       "         0                                   0.047619  \n",
-       "         1                                   0.224932  \n",
-       "         2                                   0.096106  \n",
-       "         3                                   0.117021  \n",
-       "         4                                   0.217304  \n",
-       "         Maximum difference                  0.177313  \n",
-       "updated  Overall                             0.193612  \n",
-       "         0                                   0.166667  \n",
-       "         1                                   0.173442  \n",
-       "         2                                   0.159901  \n",
-       "         3                                   0.170213  \n",
-       "         4                                   0.198771  \n",
-       "         Maximum difference                  0.038870  "
+       "original Overall                             0.207944  \n",
+       "         0                                   0.073770  \n",
+       "         1                                   0.264865  \n",
+       "         2                                   0.089916  \n",
+       "         3                                   0.121739  \n",
+       "         4                                   0.221934  \n",
+       "         Maximum difference                  0.191094  \n",
+       "updated  Overall                             0.178624  \n",
+       "         0                                   0.172131  \n",
+       "         1                                   0.178378  \n",
+       "         2                                   0.141176  \n",
+       "         3                                   0.173913  \n",
+       "         4                                   0.183040  \n",
+       "         Maximum difference                  0.041864  "
       ]
      },
      "execution_count": 15,
@@ -1607,14 +1493,7 @@
   {
    "cell_type": "code",
    "execution_count": 16,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-06-17T19:21:21.398854Z",
-     "iopub.status.busy": "2024-06-17T19:21:21.398702Z",
-     "iopub.status.idle": "2024-06-17T19:21:23.010811Z",
-     "shell.execute_reply": "2024-06-17T19:21:23.010340Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "#Generate two sets of training, validation and test, with race and without.\n",
@@ -1625,14 +1504,7 @@
   {
    "cell_type": "code",
    "execution_count": 17,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-06-17T19:21:23.013427Z",
-     "iopub.status.busy": "2024-06-17T19:21:23.013300Z",
-     "iopub.status.idle": "2024-06-17T19:21:24.403737Z",
-     "shell.execute_reply": "2024-06-17T19:21:24.403246Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "predictor = xgboost.XGBClassifier().fit(X=train_g['data'],y=train_g['target'])\n",
@@ -1643,14 +1515,7 @@
   {
    "cell_type": "code",
    "execution_count": 18,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-06-17T19:21:24.406237Z",
-     "iopub.status.busy": "2024-06-17T19:21:24.406077Z",
-     "iopub.status.idle": "2024-06-17T19:21:25.115068Z",
-     "shell.execute_reply": "2024-06-17T19:21:25.114579Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "fpredictor=FairPredictor(predictor2, train, inferred_groups=protected)\n",
@@ -1660,14 +1525,7 @@
   {
    "cell_type": "code",
    "execution_count": 19,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-06-17T19:21:25.117654Z",
-     "iopub.status.busy": "2024-06-17T19:21:25.117485Z",
-     "iopub.status.idle": "2024-06-17T19:21:25.121478Z",
-     "shell.execute_reply": "2024-06-17T19:21:25.121011Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "#However, instead we will show how a family of fairness measures can be individually optimized. \n",
@@ -1692,14 +1550,7 @@
   {
    "cell_type": "code",
    "execution_count": 20,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-06-17T19:21:25.123458Z",
-     "iopub.status.busy": "2024-06-17T19:21:25.123357Z",
-     "iopub.status.idle": "2024-06-17T19:21:25.125997Z",
-     "shell.execute_reply": "2024-06-17T19:21:25.125602Z"
-    }
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -1721,14 +1572,7 @@
   {
    "cell_type": "code",
    "execution_count": 21,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-06-17T19:21:25.127993Z",
-     "iopub.status.busy": "2024-06-17T19:21:25.127879Z",
-     "iopub.status.idle": "2024-06-17T19:21:25.413549Z",
-     "shell.execute_reply": "2024-06-17T19:21:25.413175Z"
-    }
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stderr",
@@ -1767,73 +1611,73 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>Demographic Parity</th>\n",
-       "      <td>0.170373</td>\n",
-       "      <td>0.063117</td>\n",
-       "      <td>0.671849</td>\n",
-       "      <td>0.663866</td>\n",
+       "      <td>0.149878</td>\n",
+       "      <td>0.034548</td>\n",
+       "      <td>0.653361</td>\n",
+       "      <td>0.636975</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Disparate Impact</th>\n",
-       "      <td>0.651723</td>\n",
-       "      <td>0.892211</td>\n",
-       "      <td>0.671849</td>\n",
-       "      <td>0.663866</td>\n",
+       "      <td>0.660512</td>\n",
+       "      <td>0.933217</td>\n",
+       "      <td>0.653361</td>\n",
+       "      <td>0.636134</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Average Group Difference in Conditional Acceptance Rate</th>\n",
-       "      <td>0.392026</td>\n",
-       "      <td>0.071445</td>\n",
-       "      <td>0.671849</td>\n",
-       "      <td>0.673950</td>\n",
+       "      <td>0.365188</td>\n",
+       "      <td>0.105676</td>\n",
+       "      <td>0.653361</td>\n",
+       "      <td>0.647479</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Average Group Difference in Conditional Rejectance Rate</th>\n",
-       "      <td>0.111950</td>\n",
-       "      <td>0.034648</td>\n",
-       "      <td>0.671849</td>\n",
-       "      <td>0.674790</td>\n",
+       "      <td>0.071960</td>\n",
+       "      <td>0.038268</td>\n",
+       "      <td>0.653361</td>\n",
+       "      <td>0.649160</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Average Group Difference in Accuracy</th>\n",
-       "      <td>0.022351</td>\n",
-       "      <td>0.025632</td>\n",
-       "      <td>0.671849</td>\n",
-       "      <td>0.667647</td>\n",
+       "      <td>0.014843</td>\n",
+       "      <td>0.004748</td>\n",
+       "      <td>0.653361</td>\n",
+       "      <td>0.649580</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Average Group Difference in Recall</th>\n",
-       "      <td>0.177895</td>\n",
-       "      <td>0.069213</td>\n",
-       "      <td>0.671849</td>\n",
-       "      <td>0.671008</td>\n",
+       "      <td>0.161616</td>\n",
+       "      <td>0.068086</td>\n",
+       "      <td>0.653361</td>\n",
+       "      <td>0.643697</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Average Group Difference in Acceptance Rate</th>\n",
-       "      <td>0.040866</td>\n",
-       "      <td>0.055503</td>\n",
-       "      <td>0.671849</td>\n",
-       "      <td>0.670168</td>\n",
+       "      <td>0.077211</td>\n",
+       "      <td>0.100334</td>\n",
+       "      <td>0.653361</td>\n",
+       "      <td>0.655042</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Average Group Difference in Specificity</th>\n",
-       "      <td>0.121835</td>\n",
-       "      <td>0.057111</td>\n",
-       "      <td>0.671849</td>\n",
-       "      <td>0.674370</td>\n",
+       "      <td>0.102890</td>\n",
+       "      <td>0.051929</td>\n",
+       "      <td>0.653361</td>\n",
+       "      <td>0.639916</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Average Group Difference in Rejection Rate</th>\n",
-       "      <td>0.050291</td>\n",
-       "      <td>0.038554</td>\n",
-       "      <td>0.671849</td>\n",
-       "      <td>0.668487</td>\n",
+       "      <td>0.057218</td>\n",
+       "      <td>0.049770</td>\n",
+       "      <td>0.653361</td>\n",
+       "      <td>0.648319</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Treatment Equality</th>\n",
-       "      <td>0.360054</td>\n",
-       "      <td>0.052966</td>\n",
-       "      <td>0.671849</td>\n",
-       "      <td>0.673950</td>\n",
+       "      <td>0.244338</td>\n",
+       "      <td>0.120008</td>\n",
+       "      <td>0.653361</td>\n",
+       "      <td>0.643697</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -1841,52 +1685,52 @@
       ],
       "text/plain": [
        "                                                    Measure (original)  \\\n",
-       "Demographic Parity                                            0.170373   \n",
-       "Disparate Impact                                              0.651723   \n",
-       "Average Group Difference in Conditional Accepta...            0.392026   \n",
-       "Average Group Difference in Conditional Rejecta...            0.111950   \n",
-       "Average Group Difference in Accuracy                          0.022351   \n",
-       "Average Group Difference in Recall                            0.177895   \n",
-       "Average Group Difference in Acceptance Rate                   0.040866   \n",
-       "Average Group Difference in Specificity                       0.121835   \n",
-       "Average Group Difference in Rejection Rate                    0.050291   \n",
-       "Treatment Equality                                            0.360054   \n",
+       "Demographic Parity                                            0.149878   \n",
+       "Disparate Impact                                              0.660512   \n",
+       "Average Group Difference in Conditional Accepta...            0.365188   \n",
+       "Average Group Difference in Conditional Rejecta...            0.071960   \n",
+       "Average Group Difference in Accuracy                          0.014843   \n",
+       "Average Group Difference in Recall                            0.161616   \n",
+       "Average Group Difference in Acceptance Rate                   0.077211   \n",
+       "Average Group Difference in Specificity                       0.102890   \n",
+       "Average Group Difference in Rejection Rate                    0.057218   \n",
+       "Treatment Equality                                            0.244338   \n",
        "\n",
        "                                                    Measure (updated)  \\\n",
-       "Demographic Parity                                           0.063117   \n",
-       "Disparate Impact                                             0.892211   \n",
-       "Average Group Difference in Conditional Accepta...           0.071445   \n",
-       "Average Group Difference in Conditional Rejecta...           0.034648   \n",
-       "Average Group Difference in Accuracy                         0.025632   \n",
-       "Average Group Difference in Recall                           0.069213   \n",
-       "Average Group Difference in Acceptance Rate                  0.055503   \n",
-       "Average Group Difference in Specificity                      0.057111   \n",
-       "Average Group Difference in Rejection Rate                   0.038554   \n",
-       "Treatment Equality                                           0.052966   \n",
+       "Demographic Parity                                           0.034548   \n",
+       "Disparate Impact                                             0.933217   \n",
+       "Average Group Difference in Conditional Accepta...           0.105676   \n",
+       "Average Group Difference in Conditional Rejecta...           0.038268   \n",
+       "Average Group Difference in Accuracy                         0.004748   \n",
+       "Average Group Difference in Recall                           0.068086   \n",
+       "Average Group Difference in Acceptance Rate                  0.100334   \n",
+       "Average Group Difference in Specificity                      0.051929   \n",
+       "Average Group Difference in Rejection Rate                   0.049770   \n",
+       "Treatment Equality                                           0.120008   \n",
        "\n",
        "                                                    Accuracy (original)  \\\n",
-       "Demographic Parity                                             0.671849   \n",
-       "Disparate Impact                                               0.671849   \n",
-       "Average Group Difference in Conditional Accepta...             0.671849   \n",
-       "Average Group Difference in Conditional Rejecta...             0.671849   \n",
-       "Average Group Difference in Accuracy                           0.671849   \n",
-       "Average Group Difference in Recall                             0.671849   \n",
-       "Average Group Difference in Acceptance Rate                    0.671849   \n",
-       "Average Group Difference in Specificity                        0.671849   \n",
-       "Average Group Difference in Rejection Rate                     0.671849   \n",
-       "Treatment Equality                                             0.671849   \n",
+       "Demographic Parity                                             0.653361   \n",
+       "Disparate Impact                                               0.653361   \n",
+       "Average Group Difference in Conditional Accepta...             0.653361   \n",
+       "Average Group Difference in Conditional Rejecta...             0.653361   \n",
+       "Average Group Difference in Accuracy                           0.653361   \n",
+       "Average Group Difference in Recall                             0.653361   \n",
+       "Average Group Difference in Acceptance Rate                    0.653361   \n",
+       "Average Group Difference in Specificity                        0.653361   \n",
+       "Average Group Difference in Rejection Rate                     0.653361   \n",
+       "Treatment Equality                                             0.653361   \n",
        "\n",
        "                                                    Accuracy (updated)  \n",
-       "Demographic Parity                                            0.663866  \n",
-       "Disparate Impact                                              0.663866  \n",
-       "Average Group Difference in Conditional Accepta...            0.673950  \n",
-       "Average Group Difference in Conditional Rejecta...            0.674790  \n",
-       "Average Group Difference in Accuracy                          0.667647  \n",
-       "Average Group Difference in Recall                            0.671008  \n",
-       "Average Group Difference in Acceptance Rate                   0.670168  \n",
-       "Average Group Difference in Specificity                       0.674370  \n",
-       "Average Group Difference in Rejection Rate                    0.668487  \n",
-       "Treatment Equality                                            0.673950  "
+       "Demographic Parity                                            0.636975  \n",
+       "Disparate Impact                                              0.636134  \n",
+       "Average Group Difference in Conditional Accepta...            0.647479  \n",
+       "Average Group Difference in Conditional Rejecta...            0.649160  \n",
+       "Average Group Difference in Accuracy                          0.649580  \n",
+       "Average Group Difference in Recall                            0.643697  \n",
+       "Average Group Difference in Acceptance Rate                   0.655042  \n",
+       "Average Group Difference in Specificity                       0.639916  \n",
+       "Average Group Difference in Rejection Rate                    0.648319  \n",
+       "Treatment Equality                                            0.643697  "
       ]
      },
      "execution_count": 21,
@@ -1909,14 +1753,7 @@
   {
    "cell_type": "code",
    "execution_count": 22,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-06-17T19:21:25.415329Z",
-     "iopub.status.busy": "2024-06-17T19:21:25.415128Z",
-     "iopub.status.idle": "2024-06-17T19:21:29.921083Z",
-     "shell.execute_reply": "2024-06-17T19:21:29.920745Z"
-    }
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -1948,73 +1785,73 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>Demographic Parity</th>\n",
-       "      <td>0.142173</td>\n",
-       "      <td>0.107317</td>\n",
-       "      <td>0.653361</td>\n",
-       "      <td>0.624370</td>\n",
+       "      <td>0.135583</td>\n",
+       "      <td>0.074996</td>\n",
+       "      <td>0.657563</td>\n",
+       "      <td>0.610084</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Disparate Impact</th>\n",
-       "      <td>0.682094</td>\n",
-       "      <td>0.731433</td>\n",
-       "      <td>0.653361</td>\n",
-       "      <td>0.618487</td>\n",
+       "      <td>0.698826</td>\n",
+       "      <td>0.775239</td>\n",
+       "      <td>0.657563</td>\n",
+       "      <td>0.594538</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Average Group Difference in Conditional Acceptance Rate</th>\n",
-       "      <td>0.287004</td>\n",
-       "      <td>0.163108</td>\n",
-       "      <td>0.653361</td>\n",
-       "      <td>0.643697</td>\n",
+       "      <td>0.244732</td>\n",
+       "      <td>0.128222</td>\n",
+       "      <td>0.657563</td>\n",
+       "      <td>0.634454</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Average Group Difference in Conditional Rejectance Rate</th>\n",
-       "      <td>0.068234</td>\n",
-       "      <td>0.063120</td>\n",
-       "      <td>0.653361</td>\n",
-       "      <td>0.649580</td>\n",
+       "      <td>0.067420</td>\n",
+       "      <td>0.064152</td>\n",
+       "      <td>0.657563</td>\n",
+       "      <td>0.648319</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Average Group Difference in Accuracy</th>\n",
-       "      <td>0.028167</td>\n",
-       "      <td>0.026010</td>\n",
-       "      <td>0.653361</td>\n",
+       "      <td>0.055076</td>\n",
+       "      <td>0.054847</td>\n",
+       "      <td>0.657563</td>\n",
        "      <td>0.650000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Average Group Difference in Recall</th>\n",
-       "      <td>0.143907</td>\n",
-       "      <td>0.130195</td>\n",
-       "      <td>0.653361</td>\n",
-       "      <td>0.628992</td>\n",
+       "      <td>0.118756</td>\n",
+       "      <td>0.055106</td>\n",
+       "      <td>0.657563</td>\n",
+       "      <td>0.614706</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Average Group Difference in Acceptance Rate</th>\n",
-       "      <td>0.059639</td>\n",
-       "      <td>0.061339</td>\n",
-       "      <td>0.653361</td>\n",
-       "      <td>0.660924</td>\n",
+       "      <td>0.024263</td>\n",
+       "      <td>0.044214</td>\n",
+       "      <td>0.657563</td>\n",
+       "      <td>0.655882</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Average Group Difference in Specificity</th>\n",
-       "      <td>0.103289</td>\n",
-       "      <td>0.089840</td>\n",
-       "      <td>0.653361</td>\n",
-       "      <td>0.645378</td>\n",
+       "      <td>0.111172</td>\n",
+       "      <td>0.107794</td>\n",
+       "      <td>0.657563</td>\n",
+       "      <td>0.643697</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Average Group Difference in Rejection Rate</th>\n",
-       "      <td>0.073389</td>\n",
-       "      <td>0.062303</td>\n",
-       "      <td>0.653361</td>\n",
-       "      <td>0.655882</td>\n",
+       "      <td>0.097222</td>\n",
+       "      <td>0.099675</td>\n",
+       "      <td>0.657563</td>\n",
+       "      <td>0.648739</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Treatment Equality</th>\n",
-       "      <td>0.237167</td>\n",
-       "      <td>0.160742</td>\n",
-       "      <td>0.653361</td>\n",
-       "      <td>0.646218</td>\n",
+       "      <td>0.231623</td>\n",
+       "      <td>0.157130</td>\n",
+       "      <td>0.657563</td>\n",
+       "      <td>0.638655</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -2022,52 +1859,52 @@
       ],
       "text/plain": [
        "                                                    Measure (original)  \\\n",
-       "Demographic Parity                                            0.142173   \n",
-       "Disparate Impact                                              0.682094   \n",
-       "Average Group Difference in Conditional Accepta...            0.287004   \n",
-       "Average Group Difference in Conditional Rejecta...            0.068234   \n",
-       "Average Group Difference in Accuracy                          0.028167   \n",
-       "Average Group Difference in Recall                            0.143907   \n",
-       "Average Group Difference in Acceptance Rate                   0.059639   \n",
-       "Average Group Difference in Specificity                       0.103289   \n",
-       "Average Group Difference in Rejection Rate                    0.073389   \n",
-       "Treatment Equality                                            0.237167   \n",
+       "Demographic Parity                                            0.135583   \n",
+       "Disparate Impact                                              0.698826   \n",
+       "Average Group Difference in Conditional Accepta...            0.244732   \n",
+       "Average Group Difference in Conditional Rejecta...            0.067420   \n",
+       "Average Group Difference in Accuracy                          0.055076   \n",
+       "Average Group Difference in Recall                            0.118756   \n",
+       "Average Group Difference in Acceptance Rate                   0.024263   \n",
+       "Average Group Difference in Specificity                       0.111172   \n",
+       "Average Group Difference in Rejection Rate                    0.097222   \n",
+       "Treatment Equality                                            0.231623   \n",
        "\n",
        "                                                    Measure (updated)  \\\n",
-       "Demographic Parity                                           0.107317   \n",
-       "Disparate Impact                                             0.731433   \n",
-       "Average Group Difference in Conditional Accepta...           0.163108   \n",
-       "Average Group Difference in Conditional Rejecta...           0.063120   \n",
-       "Average Group Difference in Accuracy                         0.026010   \n",
-       "Average Group Difference in Recall                           0.130195   \n",
-       "Average Group Difference in Acceptance Rate                  0.061339   \n",
-       "Average Group Difference in Specificity                      0.089840   \n",
-       "Average Group Difference in Rejection Rate                   0.062303   \n",
-       "Treatment Equality                                           0.160742   \n",
+       "Demographic Parity                                           0.074996   \n",
+       "Disparate Impact                                             0.775239   \n",
+       "Average Group Difference in Conditional Accepta...           0.128222   \n",
+       "Average Group Difference in Conditional Rejecta...           0.064152   \n",
+       "Average Group Difference in Accuracy                         0.054847   \n",
+       "Average Group Difference in Recall                           0.055106   \n",
+       "Average Group Difference in Acceptance Rate                  0.044214   \n",
+       "Average Group Difference in Specificity                      0.107794   \n",
+       "Average Group Difference in Rejection Rate                   0.099675   \n",
+       "Treatment Equality                                           0.157130   \n",
        "\n",
        "                                                    Accuracy (original)  \\\n",
-       "Demographic Parity                                             0.653361   \n",
-       "Disparate Impact                                               0.653361   \n",
-       "Average Group Difference in Conditional Accepta...             0.653361   \n",
-       "Average Group Difference in Conditional Rejecta...             0.653361   \n",
-       "Average Group Difference in Accuracy                           0.653361   \n",
-       "Average Group Difference in Recall                             0.653361   \n",
-       "Average Group Difference in Acceptance Rate                    0.653361   \n",
-       "Average Group Difference in Specificity                        0.653361   \n",
-       "Average Group Difference in Rejection Rate                     0.653361   \n",
-       "Treatment Equality                                             0.653361   \n",
+       "Demographic Parity                                             0.657563   \n",
+       "Disparate Impact                                               0.657563   \n",
+       "Average Group Difference in Conditional Accepta...             0.657563   \n",
+       "Average Group Difference in Conditional Rejecta...             0.657563   \n",
+       "Average Group Difference in Accuracy                           0.657563   \n",
+       "Average Group Difference in Recall                             0.657563   \n",
+       "Average Group Difference in Acceptance Rate                    0.657563   \n",
+       "Average Group Difference in Specificity                        0.657563   \n",
+       "Average Group Difference in Rejection Rate                     0.657563   \n",
+       "Treatment Equality                                             0.657563   \n",
        "\n",
        "                                                    Accuracy (updated)  \n",
-       "Demographic Parity                                            0.624370  \n",
-       "Disparate Impact                                              0.618487  \n",
-       "Average Group Difference in Conditional Accepta...            0.643697  \n",
-       "Average Group Difference in Conditional Rejecta...            0.649580  \n",
+       "Demographic Parity                                            0.610084  \n",
+       "Disparate Impact                                              0.594538  \n",
+       "Average Group Difference in Conditional Accepta...            0.634454  \n",
+       "Average Group Difference in Conditional Rejecta...            0.648319  \n",
        "Average Group Difference in Accuracy                          0.650000  \n",
-       "Average Group Difference in Recall                            0.628992  \n",
-       "Average Group Difference in Acceptance Rate                   0.660924  \n",
-       "Average Group Difference in Specificity                       0.645378  \n",
-       "Average Group Difference in Rejection Rate                    0.655882  \n",
-       "Treatment Equality                                            0.646218  "
+       "Average Group Difference in Recall                            0.614706  \n",
+       "Average Group Difference in Acceptance Rate                   0.655882  \n",
+       "Average Group Difference in Specificity                       0.643697  \n",
+       "Average Group Difference in Rejection Rate                    0.648739  \n",
+       "Treatment Equality                                            0.638655  "
       ]
      },
      "execution_count": 22,
@@ -2087,14 +1924,7 @@
   {
    "cell_type": "code",
    "execution_count": 23,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-06-17T19:21:29.922746Z",
-     "iopub.status.busy": "2024-06-17T19:21:29.922627Z",
-     "iopub.status.idle": "2024-06-17T19:21:29.967142Z",
-     "shell.execute_reply": "2024-06-17T19:21:29.966853Z"
-    }
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -2150,144 +1980,144 @@
        "    <tr>\n",
        "      <th rowspan=\"5\" valign=\"top\">original</th>\n",
        "      <th>Overall</th>\n",
-       "      <td>0.653361</td>\n",
-       "      <td>0.642428</td>\n",
-       "      <td>0.580153</td>\n",
-       "      <td>0.292805</td>\n",
-       "      <td>0.639013</td>\n",
-       "      <td>0.531221</td>\n",
-       "      <td>0.700889</td>\n",
+       "      <td>0.657563</td>\n",
+       "      <td>0.647421</td>\n",
+       "      <td>0.589007</td>\n",
+       "      <td>0.301889</td>\n",
+       "      <td>0.641758</td>\n",
+       "      <td>0.544268</td>\n",
+       "      <td>0.697111</td>\n",
        "      <td>1073.0</td>\n",
        "      <td>1307.0</td>\n",
        "      <td>0.450840</td>\n",
-       "      <td>0.374790</td>\n",
+       "      <td>0.382353</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>African-American</th>\n",
-       "      <td>0.641509</td>\n",
-       "      <td>0.642416</td>\n",
-       "      <td>0.636741</td>\n",
-       "      <td>0.285145</td>\n",
-       "      <td>0.664931</td>\n",
-       "      <td>0.610845</td>\n",
-       "      <td>0.701756</td>\n",
+       "      <td>0.632486</td>\n",
+       "      <td>0.633314</td>\n",
+       "      <td>0.628524</td>\n",
+       "      <td>0.266852</td>\n",
+       "      <td>0.654577</td>\n",
+       "      <td>0.604466</td>\n",
+       "      <td>0.683172</td>\n",
        "      <td>627.0</td>\n",
        "      <td>592.0</td>\n",
        "      <td>0.514356</td>\n",
-       "      <td>0.472518</td>\n",
+       "      <td>0.474979</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Caucasian</th>\n",
-       "      <td>0.658025</td>\n",
-       "      <td>0.611952</td>\n",
-       "      <td>0.476371</td>\n",
-       "      <td>0.249640</td>\n",
-       "      <td>0.600000</td>\n",
-       "      <td>0.394984</td>\n",
-       "      <td>0.673311</td>\n",
+       "      <td>0.670370</td>\n",
+       "      <td>0.627626</td>\n",
+       "      <td>0.504638</td>\n",
+       "      <td>0.280394</td>\n",
+       "      <td>0.618182</td>\n",
+       "      <td>0.426332</td>\n",
+       "      <td>0.675842</td>\n",
        "      <td>319.0</td>\n",
        "      <td>491.0</td>\n",
        "      <td>0.393827</td>\n",
-       "      <td>0.259259</td>\n",
+       "      <td>0.271605</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Other</th>\n",
-       "      <td>0.683761</td>\n",
-       "      <td>0.639711</td>\n",
-       "      <td>0.523605</td>\n",
-       "      <td>0.292449</td>\n",
-       "      <td>0.575472</td>\n",
-       "      <td>0.480315</td>\n",
-       "      <td>0.688607</td>\n",
+       "      <td>0.715100</td>\n",
+       "      <td>0.677904</td>\n",
+       "      <td>0.579832</td>\n",
+       "      <td>0.367683</td>\n",
+       "      <td>0.621622</td>\n",
+       "      <td>0.543307</td>\n",
+       "      <td>0.729436</td>\n",
        "      <td>127.0</td>\n",
        "      <td>224.0</td>\n",
        "      <td>0.361823</td>\n",
-       "      <td>0.301994</td>\n",
+       "      <td>0.316239</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Maximum difference</th>\n",
-       "      <td>0.042251</td>\n",
-       "      <td>0.030463</td>\n",
-       "      <td>0.160371</td>\n",
-       "      <td>0.042809</td>\n",
-       "      <td>0.089459</td>\n",
-       "      <td>0.215861</td>\n",
-       "      <td>0.028445</td>\n",
+       "      <td>0.082614</td>\n",
+       "      <td>0.050277</td>\n",
+       "      <td>0.123886</td>\n",
+       "      <td>0.100831</td>\n",
+       "      <td>0.036395</td>\n",
+       "      <td>0.178133</td>\n",
+       "      <td>0.053594</td>\n",
        "      <td>500.0</td>\n",
        "      <td>368.0</td>\n",
        "      <td>0.152533</td>\n",
-       "      <td>0.213259</td>\n",
+       "      <td>0.203375</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"5\" valign=\"top\">updated</th>\n",
        "      <th>Overall</th>\n",
-       "      <td>0.646218</td>\n",
-       "      <td>0.632754</td>\n",
-       "      <td>0.558237</td>\n",
-       "      <td>0.276979</td>\n",
        "      <td>0.638655</td>\n",
-       "      <td>0.495806</td>\n",
-       "      <td>0.688616</td>\n",
+       "      <td>0.627870</td>\n",
+       "      <td>0.563895</td>\n",
+       "      <td>0.262470</td>\n",
+       "      <td>0.618465</td>\n",
+       "      <td>0.518173</td>\n",
+       "      <td>0.675213</td>\n",
        "      <td>1073.0</td>\n",
        "      <td>1307.0</td>\n",
        "      <td>0.450840</td>\n",
-       "      <td>0.350000</td>\n",
+       "      <td>0.377731</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>African-American</th>\n",
-       "      <td>0.634947</td>\n",
-       "      <td>0.636885</td>\n",
-       "      <td>0.616048</td>\n",
-       "      <td>0.275896</td>\n",
-       "      <td>0.671053</td>\n",
-       "      <td>0.569378</td>\n",
-       "      <td>0.693082</td>\n",
+       "      <td>0.616899</td>\n",
+       "      <td>0.618162</td>\n",
+       "      <td>0.606571</td>\n",
+       "      <td>0.237010</td>\n",
+       "      <td>0.642857</td>\n",
+       "      <td>0.574163</td>\n",
+       "      <td>0.663147</td>\n",
        "      <td>627.0</td>\n",
        "      <td>592.0</td>\n",
        "      <td>0.514356</td>\n",
-       "      <td>0.436423</td>\n",
+       "      <td>0.459393</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Caucasian</th>\n",
-       "      <td>0.654321</td>\n",
-       "      <td>0.605603</td>\n",
-       "      <td>0.461538</td>\n",
-       "      <td>0.238911</td>\n",
-       "      <td>0.597015</td>\n",
-       "      <td>0.376176</td>\n",
-       "      <td>0.655683</td>\n",
+       "      <td>0.650617</td>\n",
+       "      <td>0.609137</td>\n",
+       "      <td>0.482633</td>\n",
+       "      <td>0.237142</td>\n",
+       "      <td>0.578947</td>\n",
+       "      <td>0.413793</td>\n",
+       "      <td>0.654135</td>\n",
        "      <td>319.0</td>\n",
        "      <td>491.0</td>\n",
        "      <td>0.393827</td>\n",
-       "      <td>0.248148</td>\n",
+       "      <td>0.281481</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Other</th>\n",
-       "      <td>0.666667</td>\n",
-       "      <td>0.616089</td>\n",
-       "      <td>0.484581</td>\n",
-       "      <td>0.247178</td>\n",
-       "      <td>0.550000</td>\n",
-       "      <td>0.433071</td>\n",
-       "      <td>0.673703</td>\n",
+       "      <td>0.686610</td>\n",
+       "      <td>0.647058</td>\n",
+       "      <td>0.537815</td>\n",
+       "      <td>0.303932</td>\n",
+       "      <td>0.576577</td>\n",
+       "      <td>0.503937</td>\n",
+       "      <td>0.707185</td>\n",
        "      <td>127.0</td>\n",
        "      <td>224.0</td>\n",
        "      <td>0.361823</td>\n",
-       "      <td>0.284900</td>\n",
+       "      <td>0.316239</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Maximum difference</th>\n",
-       "      <td>0.031720</td>\n",
-       "      <td>0.031282</td>\n",
-       "      <td>0.154510</td>\n",
-       "      <td>0.036986</td>\n",
-       "      <td>0.121053</td>\n",
-       "      <td>0.193202</td>\n",
-       "      <td>0.037399</td>\n",
+       "      <td>0.069711</td>\n",
+       "      <td>0.037921</td>\n",
+       "      <td>0.123939</td>\n",
+       "      <td>0.066922</td>\n",
+       "      <td>0.066281</td>\n",
+       "      <td>0.160370</td>\n",
+       "      <td>0.053050</td>\n",
        "      <td>500.0</td>\n",
        "      <td>368.0</td>\n",
        "      <td>0.152533</td>\n",
-       "      <td>0.188275</td>\n",
+       "      <td>0.177911</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -2296,29 +2126,29 @@
       "text/plain": [
        "                             Accuracy  Balanced Accuracy  F1 score       MCC  \\\n",
        "         Groups                                                                \n",
-       "original Overall             0.653361           0.642428  0.580153  0.292805   \n",
-       "         African-American    0.641509           0.642416  0.636741  0.285145   \n",
-       "         Caucasian           0.658025           0.611952  0.476371  0.249640   \n",
-       "         Other               0.683761           0.639711  0.523605  0.292449   \n",
-       "         Maximum difference  0.042251           0.030463  0.160371  0.042809   \n",
-       "updated  Overall             0.646218           0.632754  0.558237  0.276979   \n",
-       "         African-American    0.634947           0.636885  0.616048  0.275896   \n",
-       "         Caucasian           0.654321           0.605603  0.461538  0.238911   \n",
-       "         Other               0.666667           0.616089  0.484581  0.247178   \n",
-       "         Maximum difference  0.031720           0.031282  0.154510  0.036986   \n",
+       "original Overall             0.657563           0.647421  0.589007  0.301889   \n",
+       "         African-American    0.632486           0.633314  0.628524  0.266852   \n",
+       "         Caucasian           0.670370           0.627626  0.504638  0.280394   \n",
+       "         Other               0.715100           0.677904  0.579832  0.367683   \n",
+       "         Maximum difference  0.082614           0.050277  0.123886  0.100831   \n",
+       "updated  Overall             0.638655           0.627870  0.563895  0.262470   \n",
+       "         African-American    0.616899           0.618162  0.606571  0.237010   \n",
+       "         Caucasian           0.650617           0.609137  0.482633  0.237142   \n",
+       "         Other               0.686610           0.647058  0.537815  0.303932   \n",
+       "         Maximum difference  0.069711           0.037921  0.123939  0.066922   \n",
        "\n",
        "                             Precision    Recall   ROC AUC  Positive Count  \\\n",
        "         Groups                                                              \n",
-       "original Overall              0.639013  0.531221  0.700889          1073.0   \n",
-       "         African-American     0.664931  0.610845  0.701756           627.0   \n",
-       "         Caucasian            0.600000  0.394984  0.673311           319.0   \n",
-       "         Other                0.575472  0.480315  0.688607           127.0   \n",
-       "         Maximum difference   0.089459  0.215861  0.028445           500.0   \n",
-       "updated  Overall              0.638655  0.495806  0.688616          1073.0   \n",
-       "         African-American     0.671053  0.569378  0.693082           627.0   \n",
-       "         Caucasian            0.597015  0.376176  0.655683           319.0   \n",
-       "         Other                0.550000  0.433071  0.673703           127.0   \n",
-       "         Maximum difference   0.121053  0.193202  0.037399           500.0   \n",
+       "original Overall              0.641758  0.544268  0.697111          1073.0   \n",
+       "         African-American     0.654577  0.604466  0.683172           627.0   \n",
+       "         Caucasian            0.618182  0.426332  0.675842           319.0   \n",
+       "         Other                0.621622  0.543307  0.729436           127.0   \n",
+       "         Maximum difference   0.036395  0.178133  0.053594           500.0   \n",
+       "updated  Overall              0.618465  0.518173  0.675213          1073.0   \n",
+       "         African-American     0.642857  0.574163  0.663147           627.0   \n",
+       "         Caucasian            0.578947  0.413793  0.654135           319.0   \n",
+       "         Other                0.576577  0.503937  0.707185           127.0   \n",
+       "         Maximum difference   0.066281  0.160370  0.053050           500.0   \n",
        "\n",
        "                             Negative Count  Positive Label Rate  \\\n",
        "         Groups                                                    \n",
@@ -2335,16 +2165,16 @@
        "\n",
        "                             Positive Prediction Rate  \n",
        "         Groups                                        \n",
-       "original Overall                             0.374790  \n",
-       "         African-American                    0.472518  \n",
-       "         Caucasian                           0.259259  \n",
-       "         Other                               0.301994  \n",
-       "         Maximum difference                  0.213259  \n",
-       "updated  Overall                             0.350000  \n",
-       "         African-American                    0.436423  \n",
-       "         Caucasian                           0.248148  \n",
-       "         Other                               0.284900  \n",
-       "         Maximum difference                  0.188275  "
+       "original Overall                             0.382353  \n",
+       "         African-American                    0.474979  \n",
+       "         Caucasian                           0.271605  \n",
+       "         Other                               0.316239  \n",
+       "         Maximum difference                  0.203375  \n",
+       "updated  Overall                             0.377731  \n",
+       "         African-American                    0.459393  \n",
+       "         Caucasian                           0.281481  \n",
+       "         Other                               0.316239  \n",
+       "         Maximum difference                  0.177911  "
       ]
      },
      "execution_count": 23,
@@ -2359,14 +2189,7 @@
   {
    "cell_type": "code",
    "execution_count": 24,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-06-17T19:21:29.968836Z",
-     "iopub.status.busy": "2024-06-17T19:21:29.968711Z",
-     "iopub.status.idle": "2024-06-17T19:21:30.728647Z",
-     "shell.execute_reply": "2024-06-17T19:21:30.728237Z"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "from oxonfair import conditional_group_metrics as cgm\n",
@@ -2377,14 +2200,7 @@
   {
    "cell_type": "code",
    "execution_count": 25,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-06-17T19:21:30.730457Z",
-     "iopub.status.busy": "2024-06-17T19:21:30.730328Z",
-     "iopub.status.idle": "2024-06-17T19:21:30.750226Z",
-     "shell.execute_reply": "2024-06-17T19:21:30.749941Z"
-    }
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -2414,13 +2230,13 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>Accuracy</th>\n",
-       "      <td>0.738975</td>\n",
-       "      <td>0.693406</td>\n",
+       "      <td>0.747165</td>\n",
+       "      <td>0.678706</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Average Group Difference in Conditional Positive Prediction Rate</th>\n",
-       "      <td>0.137529</td>\n",
-       "      <td>0.019887</td>\n",
+       "      <td>0.140569</td>\n",
+       "      <td>0.019400</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -2428,8 +2244,8 @@
       ],
       "text/plain": [
        "                                                    original   updated\n",
-       "Accuracy                                            0.738975  0.693406\n",
-       "Average Group Difference in Conditional Positiv...  0.137529  0.019887"
+       "Accuracy                                            0.747165  0.678706\n",
+       "Average Group Difference in Conditional Positiv...  0.140569  0.019400"
       ]
      },
      "execution_count": 25,
@@ -2444,14 +2260,7 @@
   {
    "cell_type": "code",
    "execution_count": 26,
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2024-06-17T19:21:30.751715Z",
-     "iopub.status.busy": "2024-06-17T19:21:30.751600Z",
-     "iopub.status.idle": "2024-06-17T19:21:30.764769Z",
-     "shell.execute_reply": "2024-06-17T19:21:30.764470Z"
-    }
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -2481,13 +2290,13 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>Accuracy</th>\n",
-       "      <td>0.653361</td>\n",
-       "      <td>0.626471</td>\n",
+       "      <td>0.657563</td>\n",
+       "      <td>0.610084</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Average Group Difference in Conditional Positive Prediction Rate</th>\n",
-       "      <td>0.136923</td>\n",
-       "      <td>0.100760</td>\n",
+       "      <td>0.130682</td>\n",
+       "      <td>0.071482</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -2495,8 +2304,8 @@
       ],
       "text/plain": [
        "                                                    original   updated\n",
-       "Accuracy                                            0.653361  0.626471\n",
-       "Average Group Difference in Conditional Positiv...  0.136923  0.100760"
+       "Accuracy                                            0.657563  0.610084\n",
+       "Average Group Difference in Conditional Positiv...  0.130682  0.071482"
       ]
      },
      "execution_count": 26,
@@ -2511,7 +2320,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "base",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -2525,9 +2334,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.13"
+   "version": "3.10.0"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 2
+ "nbformat_minor": 4
 }