add: Sample notebook was added to test

$Subject

examples/ethaiaudithub.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d3c389e5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Load all necessary packages\n",
+    "import sys\n",
+    "sys.path.insert(1, \"../\")  \n",
+    "\n",
+    "import numpy as np\n",
+    "np.random.seed(0)\n",
+    "\n",
+    "import time\n",
+    "\n",
+    "# Add a sleep statement to wait for imports to be fully loaded\n",
+    "time.sleep(20)  # You can adjust the sleep duration based on your needs\n",
+    "\n",
+    "from aif360.datasets import GermanDataset\n",
+    "time.sleep(20)\n",
+    "from aif360.metrics import BinaryLabelDatasetMetric\n",
+    "time.sleep(20)\n",
+    "from aif360.algorithms.preprocessing import Reweighing\n",
+    "time.sleep(20)\n",
+    "\n",
+    "from IPython.display import Markdown, display\n",
+    "time.sleep(20)\n",
+    "\n",
+    "dataset_orig = GermanDataset(\n",
+    "    protected_attribute_names=['age'],           # this dataset also contains protected\n",
+    "                                                 # attribute for \"sex\" which we do not\n",
+    "                                                 # consider in this evaluation\n",
+    "    privileged_classes=[lambda x: x >= 25],      # age >=25 is considered privileged\n",
+    "    features_to_drop=['personal_status', 'sex'] # ignore sex-related attributes\n",
+    ")\n",
+    "\n",
+    "dataset_orig_train, dataset_orig_test = dataset_orig.split([0.7], shuffle=True)\n",
+    "\n",
+    "privileged_groups = [{'age': 1}]\n",
+    "unprivileged_groups = [{'age': 0}]\n",
+    "\n",
+    "metric_orig_train = BinaryLabelDatasetMetric(dataset_orig_train, \n",
+    "                                             unprivileged_groups=unprivileged_groups,\n",
+    "                                             privileged_groups=privileged_groups)\n",
+    "display(Markdown(\"#### Original training dataset\"))\n",
+    "print(\"Difference in mean outcomes between unprivileged and privileged groups = %f\" % metric_orig_train.mean_difference())\n",
+    "\n",
+    "RW = Reweighing(unprivileged_groups=unprivileged_groups,\n",
+    "                privileged_groups=privileged_groups)\n",
+    "dataset_transf_train = RW.fit_transform(dataset_orig_train)\n",
+    "\n",
+    "metric_transf_train = BinaryLabelDatasetMetric(dataset_transf_train, \n",
+    "                                               unprivileged_groups=unprivileged_groups,\n",
+    "                                               privileged_groups=privileged_groups)\n",
+    "display(Markdown(\"#### Transformed training dataset\"))\n",
+    "print(\"Difference in mean outcomes between unprivileged and privileged groups = %f\" % metric_transf_train.mean_difference())\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}