examples/example-with-postprocessing-and-inprocessing.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Example usage of `error-parity` with other fairness-constrained classifiers\n",
    "\n",
    "Contents:\n",
    "1. Train a standard (unconstrained) model;\n",
    "2. Check attainable fairness-accuracy trade-offs via post-processing, with the `error-parity` package;\n",
    "3. Train fairness-constrained model (in-processing fairness intervention), with the `fairlearn` package;\n",
    "5. Map results for post-processing + in-processing interventions;\n",
    "\n",
    "---\n",
    "\n",
    "**NOTE**: This notebook has the following extra requirements: `fairlearn` `lightgbm`.\n",
    "\n",
    "Install them with ```pip install fairlearn lightgbm```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "error-parity==0.3.11\n"
     ]
    }
   ],
   "source": [
    "from error_parity import __version__\n",
    "print(f\"error-parity=={__version__}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "import seaborn as sns\n",
    "sns.set(palette=\"colorblind\", style=\"whitegrid\", rc={\"grid.linestyle\": \"--\", \"figure.dpi\": 200, \"figure.figsize\": (4,3)})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Some useful global constants:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "SEED = 2\n",
    "\n",
    "TEST_SIZE = 0.3\n",
    "VALIDATION_SIZE = None\n",
    "\n",
    "PERF_METRIC = \"accuracy\"\n",
    "DISP_METRIC = \"equalized_odds_diff\"\n",
    "\n",
    "N_JOBS = max(2, os.cpu_count() - 2)\n",
    "\n",
    "np.random.seed(SEED)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Fetch UCI Adult data\n",
    "\n",
    "We'll use the `sex` column as the sensitive attribute.\n",
    "That is, false positive (FP) and false negative (FN) errors should not disproportionately impact individuals based on their sex."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "SENSITIVE_COL = \"sex\"\n",
    "sensitive_col_map = {\"Male\": 0, \"Female\": 1}\n",
    "\n",
    "# NOTE: You can also try to run this using the `race` column as sensitive attribute (as commented below).\n",
    "# SENSITIVE_COL = \"race\"\n",
    "# sensitive_col_map = {\"White\": 0, \"Black\": 1, \"Asian-Pac-Islander\": 1, \"Amer-Indian-Eskimo\": 1, \"Other\": 1}\n",
    "\n",
    "sensitive_col_inverse = {val: key for key, val in sensitive_col_map.items()}\n",
    "\n",
    "POS_LABEL = \">50K\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Download data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "from fairlearn.datasets import fetch_adult\n",
    "\n",
    "X, Y = fetch_adult(\n",
    "    as_frame=True,\n",
    "    return_X_y=True,\n",
    ")\n",
    "\n",
    "# Map labels and sensitive column to numeric data\n",
    "Y = np.array(Y == POS_LABEL, dtype=int)\n",
    "S = np.array([sensitive_col_map[elem] for elem in X[SENSITIVE_COL]], dtype=int)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Split in train/test/validation data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X_train, X_other, y_train, y_other, s_train, s_other = train_test_split(\n",
    "    X, Y, S,\n",
    "    test_size=TEST_SIZE + (VALIDATION_SIZE or 0),\n",
    "    stratify=Y, random_state=SEED,\n",
    ")\n",
    "\n",
    "if VALIDATION_SIZE is not None and VALIDATION_SIZE > 0:\n",
    "    X_val, X_test, y_val, y_test, s_val, s_test = train_test_split(\n",
    "        X_other, y_other, s_other,\n",
    "        test_size=TEST_SIZE / (TEST_SIZE + VALIDATION_SIZE),\n",
    "        stratify=y_other, random_state=SEED,\n",
    "    )\n",
    "else:\n",
    "    X_test, y_test, s_test = X_other, y_other, s_other\n",
    "    X_val, y_val, s_val = X_train, y_train, s_train"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Log the accuracy attainable by a dummy constant classifier."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'train': 0.7607125098715961,\n",
       " 'test': 0.7607315908005187,\n",
       " 'validation': 0.7607125098715961}"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def compute_constant_clf_accuracy(labels: np.ndarray) -> float:\n",
    "    return max((labels == const_pred).mean() for const_pred in np.unique(labels))\n",
    "\n",
    "constant_clf_accuracy = {\n",
    "    \"train\": compute_constant_clf_accuracy(y_train),\n",
    "    \"test\": compute_constant_clf_accuracy(y_test),\n",
    "    \"validation\": compute_constant_clf_accuracy(y_val),\n",
    "}\n",
    "constant_clf_accuracy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Train a standard (unconstrained) classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
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      ],
      "text/plain": [
       "LGBMClassifier(verbosity=-1)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from lightgbm import LGBMClassifier\n",
    "\n",
    "unconstr_clf = LGBMClassifier(verbosity=-1)\n",
    "unconstr_clf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "In-processing model: \n",
      ">       accuracy = 0.87\n",
      "> equalized odds = 0.0673\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from error_parity.evaluation import evaluate_predictions_bootstrap\n",
    "\n",
    "unconstr_test_results = evaluate_predictions_bootstrap(\n",
    "    y_true=y_test,\n",
    "    y_pred_scores=unconstr_clf.predict(X_test, random_state=SEED).astype(float),\n",
    "    sensitive_attribute=s_test,\n",
    ")\n",
    "\n",
    "print(\n",
    "    f\"In-processing model: \\n\"\n",
    "    f\">       accuracy = {unconstr_test_results['accuracy_mean']:.3}\\n\"\n",
    "    f\"> equalized odds = {unconstr_test_results['equalized_odds_diff_mean']:.3}\\n\"\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Map attainable fairness-accuracy trade-offs via (relaxed) post-processing\n",
    "\n",
    "By varying the tolerance (or slack) of the fairness constraint we can map the different trade-offs attainable by the same model (each trade-off corresponds to a different post-processing intervention).\n",
    "\n",
    "**Post-processing** fairness methods intervene on the predictions of an already trained model, using different (possibly randomized) thresholds to binarize predictions of different groups.\n",
    "\n",
    "We'll be using the [`error-parity`](https://github.com/socialfoundations/error-parity) package [[Cruz and Hardt, 2023]](https://arxiv.org/abs/2306.07261)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
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       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/19 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from error_parity.pareto_curve import compute_postprocessing_curve\n",
    "\n",
    "# Data to fit postprocessing adjustment\n",
    "fit_data = (X_train, y_train, s_train)\n",
    "# fit_data = (X_val, y_val, s_val)\n",
    "\n",
    "# Common kwargs for the `compute_postprocessing_curve` call\n",
    "compute_postproc_kwargs = dict(\n",
    "    fit_data=fit_data,\n",
    "    eval_data={\n",
    "        \"validation\": (X_val, y_val, s_val),\n",
    "        \"test\": (X_test, y_test, s_test),\n",
    "    },\n",
    "    fairness_constraint=\"equalized_odds\",\n",
    "    tolerance_ticks=np.hstack((\n",
    "        np.arange(0.0, 0.1, 1e-2),\n",
    "        np.arange(0.1, 1.0, 1e-1),\n",
    "    )),\n",
    "    bootstrap=True,\n",
    "    n_jobs=N_JOBS,\n",
    "    seed=SEED,\n",
    ")\n",
    "\n",
    "postproc_results_df = compute_postprocessing_curve(\n",
    "    model=unconstr_clf,\n",
    "    **compute_postproc_kwargs,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot post-processing adjustment frontier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "SHOW_RESULTS_ON = \"test\"\n",
    "# SHOW_RESULTS_ON = \"validation\"\n",
    "\n",
    "ax_kwargs = dict(\n",
    "    xlim=(constant_clf_accuracy[SHOW_RESULTS_ON] - 5e-3, 0.885),\n",
    "    ylim=(0.0, 0.3),\n",
    "    title=\"Random Hyperparameter Search (val.)\",\n",
    "    xlabel=PERF_METRIC + r\"$\\rightarrow$\",\n",
    "    ylabel=\"equalized odds (diff.) $\\leftarrow$\" if DISP_METRIC == \"equalized_odds_diff\" else DISP_METRIC,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from error_parity.plotting import plot_postprocessing_frontier\n",
    "\n",
    "# Plot unconstrained model results with 95% CIs\n",
    "unconstr_performance = unconstr_test_results[f\"{PERF_METRIC}_mean\"]\n",
    "unconstr_disparity = unconstr_test_results[f\"{DISP_METRIC}_mean\"]\n",
    "\n",
    "sns.scatterplot(\n",
    "    x=[unconstr_performance],\n",
    "    y=[unconstr_disparity],\n",
    "    color=\"black\",\n",
    "    marker=\"*\",\n",
    "    s=100,\n",
    ")\n",
    "\n",
    "plt.plot(\n",
    "    (unconstr_test_results[f\"{PERF_METRIC}_low-percentile\"], unconstr_test_results[f\"{PERF_METRIC}_high-percentile\"]),\n",
    "    (unconstr_disparity, unconstr_disparity),\n",
    "    color=\"black\",\n",
    "    ls=\":\",\n",
    "    marker=\"|\",\n",
    "    lw=1,\n",
    "    ms=3,\n",
    ")\n",
    "\n",
    "plt.plot(\n",
    "    (unconstr_performance, unconstr_performance),\n",
    "    (unconstr_test_results[f\"{DISP_METRIC}_low-percentile\"], unconstr_test_results[f\"{DISP_METRIC}_high-percentile\"]),\n",
    "    color=\"black\",\n",
    "    ls=\":\",\n",
    "    marker=\"_\",\n",
    "    lw=1,\n",
    "    ms=3,\n",
    ")\n",
    "\n",
    "# Plot postprocessing of unconstrained model\n",
    "plot_postprocessing_frontier(\n",
    "    postproc_results_df,\n",
    "    perf_metric=PERF_METRIC,\n",
    "    disp_metric=DISP_METRIC,\n",
    "    show_data_type=SHOW_RESULTS_ON,\n",
    "    constant_clf_perf=constant_clf_accuracy[SHOW_RESULTS_ON],\n",
    "    model_name=r\"$\\bigstar$\",\n",
    ")\n",
    "\n",
    "# Vertical line with minimum \"useful\" accuracy on this data\n",
    "curr_const_clf_acc = constant_clf_accuracy[SHOW_RESULTS_ON]\n",
    "plt.axvline(\n",
    "    x=curr_const_clf_acc,\n",
    "    ls=\"--\",\n",
    "    color=\"grey\",\n",
    ")\n",
    "plt.gca().annotate(\n",
    "    \"constant predictor acc.\",\n",
    "    xy=(curr_const_clf_acc, ax_kwargs[\"ylim\"][1] / 2),\n",
    "    zorder=10,\n",
    "    rotation=90,\n",
    "    horizontalalignment=\"right\",\n",
    "    verticalalignment=\"center\",\n",
    "    fontsize=\"small\",\n",
    "    \n",
    ")\n",
    "\n",
    "# Title and legend\n",
    "ax_kwargs[\"title\"] = f\"Post-processing ({SHOW_RESULTS_ON} results)\"\n",
    "ax_kwargs[\"xlim\"] = (curr_const_clf_acc - 1e-2, 0.885)\n",
    "\n",
    "plt.legend(\n",
    "    loc=\"upper left\",\n",
    "    bbox_to_anchor=(1.03, 1),\n",
    "    borderaxespad=0)\n",
    "\n",
    "plt.gca().set(**ax_kwargs)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Let's train another type of fairness-aware model\n",
    "\n",
    "**In-processing** fairness methods introduce fairness criteria during model training.\n",
    "\n",
    "_Main disadvantage_: state-of-the-art in-processing methods can be considerably slower to run (e.g., increasing training time by 20-100 times).\n",
    "\n",
    "We'll be using the [`fairlearn`](https://github.com/fairlearn/fairlearn) package [[Weerts et al., 2020]](https://arxiv.org/abs/2303.16626)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "from fairlearn.reductions import ExponentiatedGradient, EqualizedOdds\n",
    "\n",
    "inproc_clf = ExponentiatedGradient(\n",
    "    estimator=unconstr_clf,\n",
    "    constraints=EqualizedOdds(),\n",
    "    max_iter=10,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Fit the `ExponentiatedGradient` [[Agarwal et al., 2018]](https://proceedings.mlr.press/v80/agarwal18a.html) in-processing intervention (**note**: may take a few minutes to fit)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 49.1 s, sys: 1min 9s, total: 1min 58s\n",
      "Wall time: 1min 59s\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>ExponentiatedGradient(constraints=&lt;fairlearn.reductions._moments.utility_parity.EqualizedOdds object at 0x16dac2650&gt;,\n",
       "                      estimator=LGBMClassifier(verbosity=-1), max_iter=10,\n",
       "                      nu=0.000851617415307666)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">ExponentiatedGradient</label><div class=\"sk-toggleable__content\"><pre>ExponentiatedGradient(constraints=&lt;fairlearn.reductions._moments.utility_parity.EqualizedOdds object at 0x16dac2650&gt;,\n",
       "                      estimator=LGBMClassifier(verbosity=-1), max_iter=10,\n",
       "                      nu=0.000851617415307666)</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: LGBMClassifier</label><div class=\"sk-toggleable__content\"><pre>LGBMClassifier(verbosity=-1)</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" ><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LGBMClassifier</label><div class=\"sk-toggleable__content\"><pre>LGBMClassifier(verbosity=-1)</pre></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "ExponentiatedGradient(constraints=<fairlearn.reductions._moments.utility_parity.EqualizedOdds object at 0x16dac2650>,\n",
       "                      estimator=LGBMClassifier(verbosity=-1), max_iter=10,\n",
       "                      nu=0.000851617415307666)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "inproc_clf.fit(X_train, y_train, sensitive_features=s_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Evaluate in-processing model on test data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "In-processing model: \n",
      ">       accuracy = 0.867\n",
      "> equalized odds = 0.0498\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from error_parity.evaluation import evaluate_predictions_bootstrap\n",
    "\n",
    "inproc_test_results = evaluate_predictions_bootstrap(\n",
    "    y_true=y_test,\n",
    "    y_pred_scores=inproc_clf.predict(X_test, random_state=SEED).astype(float),\n",
    "    sensitive_attribute=s_test,\n",
    ")\n",
    "\n",
    "print(\n",
    "    f\"In-processing model: \\n\"\n",
    "    f\">       accuracy = {inproc_test_results['accuracy_mean']:.3}\\n\"\n",
    "    f\"> equalized odds = {inproc_test_results['equalized_odds_diff_mean']:.3}\\n\"\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**We can go one step further and post-process this in-processing model :)**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "e9c3a354bef24dbb84b96781fb28b9ad",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/19 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from error_parity.pareto_curve import compute_postprocessing_curve\n",
    "\n",
    "inproc_postproc_results_df = compute_postprocessing_curve(\n",
    "    model=inproc_clf,\n",
    "    predict_method=\"_pmf_predict\",\n",
    "    **compute_postproc_kwargs,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot unconstrained model results with 95% CIs\n",
    "sns.scatterplot(\n",
    "    x=[unconstr_performance],\n",
    "    y=[unconstr_disparity],\n",
    "    color=\"black\",\n",
    "    marker=\"*\",\n",
    "    s=100,\n",
    ")\n",
    "\n",
    "plt.plot(\n",
    "    (unconstr_test_results[f\"{PERF_METRIC}_low-percentile\"], unconstr_test_results[f\"{PERF_METRIC}_high-percentile\"]),\n",
    "    (unconstr_disparity, unconstr_disparity),\n",
    "    color=\"black\",\n",
    "    ls=\":\",\n",
    "    marker=\"|\",\n",
    "    lw=1,\n",
    "    ms=3,\n",
    ")\n",
    "\n",
    "plt.plot(\n",
    "    (unconstr_performance, unconstr_performance),\n",
    "    (unconstr_test_results[f\"{DISP_METRIC}_low-percentile\"], unconstr_test_results[f\"{DISP_METRIC}_high-percentile\"]),\n",
    "    color=\"black\",\n",
    "    ls=\":\",\n",
    "    marker=\"_\",\n",
    "    lw=1,\n",
    "    ms=3,\n",
    ")\n",
    "\n",
    "# Plot postprocessing of unconstrained model\n",
    "plot_postprocessing_frontier(\n",
    "    postproc_results_df,\n",
    "    perf_metric=PERF_METRIC,\n",
    "    disp_metric=DISP_METRIC,\n",
    "    show_data_type=SHOW_RESULTS_ON,\n",
    "    constant_clf_perf=constant_clf_accuracy[SHOW_RESULTS_ON],\n",
    "    model_name=r\"$\\bigstar$\",\n",
    ")\n",
    "\n",
    "# Plot inprocessing intervention results with 95% CIs\n",
    "sns.scatterplot(\n",
    "    x=[inproc_test_results[f\"{PERF_METRIC}_mean\"]],\n",
    "    y=[inproc_test_results[f\"{DISP_METRIC}_mean\"]],\n",
    "    color=\"red\",\n",
    "    marker=\"P\",\n",
    "    s=50,\n",
    ")\n",
    "\n",
    "plt.plot(\n",
    "    (inproc_test_results[f\"{PERF_METRIC}_low-percentile\"], inproc_test_results[f\"{PERF_METRIC}_high-percentile\"]),\n",
    "    (inproc_test_results[f\"{DISP_METRIC}_mean\"], inproc_test_results[f\"{DISP_METRIC}_mean\"]),\n",
    "    color='red',\n",
    "    ls=\":\",\n",
    "    marker=\"|\",\n",
    "    lw=1,\n",
    "    ms=3,\n",
    ")\n",
    "\n",
    "plt.plot(\n",
    "    (inproc_test_results[f\"{PERF_METRIC}_mean\"], inproc_test_results[f\"{PERF_METRIC}_mean\"]),\n",
    "    (inproc_test_results[f\"{DISP_METRIC}_low-percentile\"], inproc_test_results[f\"{DISP_METRIC}_high-percentile\"]),\n",
    "    color='red',\n",
    "    ls=\":\",\n",
    "    marker=\"_\",\n",
    "    lw=1,\n",
    "    ms=3,\n",
    ")\n",
    "\n",
    "# Plot postprocessing of inprocessing model\n",
    "plot_postprocessing_frontier(\n",
    "    inproc_postproc_results_df,\n",
    "    perf_metric=PERF_METRIC,\n",
    "    disp_metric=DISP_METRIC,\n",
    "    show_data_type=SHOW_RESULTS_ON,\n",
    "    constant_clf_perf=constant_clf_accuracy[SHOW_RESULTS_ON],\n",
    "    model_name=r\"$+$\",\n",
    "    color=\"red\",\n",
    ")\n",
    "\n",
    "# Vertical line with minimum \"useful\" accuracy on this data\n",
    "curr_const_clf_acc = constant_clf_accuracy[SHOW_RESULTS_ON]\n",
    "plt.axvline(\n",
    "    x=curr_const_clf_acc,\n",
    "    ls=\"--\",\n",
    "    color=\"grey\",\n",
    ")\n",
    "plt.gca().annotate(\n",
    "    \"constant predictor acc.\",\n",
    "    xy=(curr_const_clf_acc, ax_kwargs[\"ylim\"][1] / 2),\n",
    "    zorder=10,\n",
    "    rotation=90,\n",
    "    horizontalalignment=\"right\",\n",
    "    verticalalignment=\"center\",\n",
    "    fontsize=\"small\",\n",
    "    \n",
    ")\n",
    "\n",
    "# Title and legend\n",
    "ax_kwargs[\"title\"] = f\"Post-processing ({SHOW_RESULTS_ON})\"\n",
    "ax_kwargs[\"xlim\"] = (curr_const_clf_acc - 1e-2, 0.885)\n",
    "\n",
    "plt.legend(\n",
    "    loc=\"upper left\",\n",
    "    bbox_to_anchor=(1.03, 1),\n",
    "    borderaxespad=0)\n",
    "\n",
    "plt.gca().set(**ax_kwargs)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  }
 ],
 "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": "3.10.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}