generate_scenerio_figures.ipynb

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   "cell_type": "code",
   "execution_count": null,
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   "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",
    "from sklearn.linear_model import LogisticRegression\n",
    "import src.vis as vis\n",
    "import ipywidgets as widgets\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": [
    "# Generate data and plot population breakdowns\n",
    "vis.plot_profitability_distributions(scenarios,backend)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "# Plot the feature imporance for each scenario\n",
    "vis.plot_feature_importances(scenarios,backend)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "# Plot fairness metrics for each scenario\n",
    "vis.plot_fairness_metrics(scenarios,backend)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "# Plot fairness trade-off landscape\n",
    "vis.make_fairness_maps(scenarios)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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