Initial commit.

Author: simon.o'callaghan

README.md

@@ -1,2 +1,14 @@
 # sim_algo_bias
 Simulator code for AHRC's Addressing Algorithmic Bias tech paper.
+
+The notebook `generate_scenario_figures.ipynb`generates the data, model and fairness plots in the technical paper when run.
+
+
+
+## Installation
+
+The `requirements.txt` provides the Python prerequisites for running the notebook. These can be installed in a virtualenv, for example with:
+
+`pip install -r requirements.txt`
+
+Then, run `jupyter notebook` from the root directory of the repository and open the notebook `generate_scenario_figures.ipynb`.

generate_scenerio_figures.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "from sklearn.metrics import confusion_matrix\n",
+    "from src.datagen import scenarios\n",
+    "\n",
+    "\n",
+    "from sklearn.linear_model import LogisticRegression\n",
+    "import src.vis as vis\n",
+    "import ipywidgets as widgets\n",
+    "    \n",
+    "\n",
+    "scale = {\n",
+    "    'training': 1000,\n",
+    "    'deployment': 100, \n",
+    "} # one meeple = this many people\n",
+    "\n",
+    "# import bqplot.pyplot as plt\n",
+    "# import bqplot as bq\n",
+    "import matplotlib.pyplot as plt\n",
+    "backend = \"matplotlib\" #bqplot\n",
+    "%matplotlib inline\n",
+    "\n",
+    "%load_ext autoreload\n",
+    "%autoreload 2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [],
+   "source": [
+    "vis.plot_profitability_distributions(scenarios,backend)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [],
+   "source": [
+    "vis.plot_feature_importances(scenarios,backend)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [],
+   "source": [
+    "vis.plot_fairness_metrics(scenarios,backend)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [],
+   "source": [
+    "vis.make_fairness_maps(scenarios)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}

images/README.md

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+Regenerated report images are saved here.

requirements.txt

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+jupyter>=1.0.0
+numpy>=1.19.4
+scipy>=1.5.4
+pandas>=1.1.4
+matplotlib>=3.3.2
+seaborn>=0.11.0
+scikit-learn>=0.23.2
+bqplot>=0.12.5