eq_odds_postprocessing.py

# Original work Copyright (c) 2017 Geoff Pleiss
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# SOFTWARE.
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# Modified work Copyright 2018 IBM Corporation
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# Licensed under the Apache License, Version 2.0 (the "License"); you may not
# use this file except in compliance with the License. You may obtain a copy of
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import numpy as np
from scipy.optimize import linprog

from aif360.algorithms import Transformer
from aif360.metrics import ClassificationMetric, utils


class EqOddsPostprocessing(Transformer):
    """Equalized odds postprocessing is a post-processing technique that solves
    a linear program to find probabilities with which to change output labels to
    optimize equalized odds [8]_ [9]_.

    References:
        .. [8] M. Hardt, E. Price, and N. Srebro, "Equality of Opportunity in
           Supervised Learning," Conference on Neural Information Processing
           Systems, 2016.
        .. [9] G. Pleiss, M. Raghavan, F. Wu, J. Kleinberg, and
           K. Q. Weinberger, "On Fairness and Calibration," Conference on Neural
           Information Processing Systems, 2017.
    """

    def __init__(self, unprivileged_groups, privileged_groups, seed=None):
        """
        Args:
            unprivileged_groups (list(dict)): Representation for unprivileged
                group.
            privileged_groups (list(dict)): Representation for privileged
                group.
            seed (int, optional): Seed to make `predict` repeatable.
        """
        super(EqOddsPostprocessing, self).__init__(
            unprivileged_groups=unprivileged_groups,
            privileged_groups=privileged_groups,
            seed=seed)

        self.seed = seed
        self.model_params = None
        self.unprivileged_groups = unprivileged_groups
        self.privileged_groups = privileged_groups

    def fit(self, dataset_true, dataset_pred):
        """Compute parameters for equalizing odds using true and predicted
        labels.

        Args:
            true_dataset (BinaryLabelDataset): Dataset containing true labels.
            pred_dataset (BinaryLabelDataset): Dataset containing predicted
                labels.

        Returns:
            EqOddsPostprocessing: Returns self.
        """
        metric = ClassificationMetric(dataset_true, dataset_pred,
            unprivileged_groups=self.unprivileged_groups,
            privileged_groups=self.privileged_groups)

        # compute basic statistics
        sbr = metric.base_rate(privileged=True)
        obr = metric.base_rate(privileged=False)

        fpr0 = metric.false_positive_rate(privileged=True)
        fpr1 = metric.false_positive_rate(privileged=False)
        fnr0 = metric.false_negative_rate(privileged=True)
        fnr1 = metric.false_negative_rate(privileged=False)
        tpr0 = metric.true_positive_rate(privileged=True)
        tpr1 = metric.true_positive_rate(privileged=False)
        tnr0 = metric.true_negative_rate(privileged=True)
        tnr1 = metric.true_negative_rate(privileged=False)

        # linear program has 4 decision variables:
        # [Pr[label_tilde = 1 | label_hat = 1, protected_attributes = 0];
        #  Pr[label_tilde = 1 | label_hat = 0, protected_attributes = 0];
        #  Pr[label_tilde = 1 | label_hat = 1, protected_attributes = 1];
        #  Pr[label_tilde = 1 | label_hat = 0, protected_attributes = 1]]
        # Coefficients of the linear objective function to be minimized.
        c = np.array([fpr0 - tpr0, tnr0 - fnr0, fpr1 - tpr1, tnr1 - fnr1])

        # A_ub - 2-D array which, when matrix-multiplied by x, gives the values
        # of the upper-bound inequality constraints at x
        # b_ub - 1-D array of values representing the upper-bound of each
        # inequality constraint (row) in A_ub.
        # Just to keep these between zero and one
        A_ub = np.array([[ 1,  0,  0,  0],
                         [-1,  0,  0,  0],
                         [ 0,  1,  0,  0],
                         [ 0, -1,  0,  0],
                         [ 0,  0,  1,  0],
                         [ 0,  0, -1,  0],
                         [ 0,  0,  0,  1],
                         [ 0,  0,  0, -1]], dtype=np.float64)
        b_ub = np.array([1, 0, 1, 0, 1, 0, 1, 0], dtype=np.float64)

        # Create boolean conditioning vectors for protected groups
        cond_vec_priv = utils.compute_boolean_conditioning_vector(
            dataset_pred.protected_attributes,
            dataset_pred.protected_attribute_names,
            self.privileged_groups)
        cond_vec_unpriv = utils.compute_boolean_conditioning_vector(
            dataset_pred.protected_attributes,
            dataset_pred.protected_attribute_names,
            self.unprivileged_groups)

        sconst = np.ravel(
            dataset_pred.labels[cond_vec_priv] == dataset_pred.favorable_label)
        sflip = np.ravel(
            dataset_pred.labels[cond_vec_priv] == dataset_pred.unfavorable_label)
        oconst = np.ravel(
            dataset_pred.labels[cond_vec_unpriv] == dataset_pred.favorable_label)
        oflip = np.ravel(
            dataset_pred.labels[cond_vec_unpriv] == dataset_pred.unfavorable_label)

        y_true = dataset_true.labels.ravel()

        sm_tn = np.logical_and(sflip,
            y_true[cond_vec_priv] == dataset_true.unfavorable_label,
            ).astype(np.float64)
        sm_fn = np.logical_and(sflip,
            y_true[cond_vec_priv] == dataset_true.favorable_label,
            ).astype(np.float64)
        sm_fp = np.logical_and(sconst,
            y_true[cond_vec_priv] == dataset_true.unfavorable_label,
            ).astype(np.float64)
        sm_tp = np.logical_and(sconst,
            y_true[cond_vec_priv] == dataset_true.favorable_label,
            ).astype(np.float64)

        om_tn = np.logical_and(oflip,
            y_true[cond_vec_unpriv] == dataset_true.unfavorable_label,
            ).astype(np.float64)
        om_fn = np.logical_and(oflip,
            y_true[cond_vec_unpriv] == dataset_true.favorable_label,
            ).astype(np.float64)
        om_fp = np.logical_and(oconst,
            y_true[cond_vec_unpriv] == dataset_true.unfavorable_label,
            ).astype(np.float64)
        om_tp = np.logical_and(oconst,
            y_true[cond_vec_unpriv] == dataset_true.favorable_label,
            ).astype(np.float64)

        # A_eq - 2-D array which, when matrix-multiplied by x,
        # gives the values of the equality constraints at x
        # b_eq - 1-D array of values representing the RHS of each equality
        # constraint (row) in A_eq.
        # Used to impose equality of odds constraint
        A_eq = [[(np.mean(sconst*sm_tp) - np.mean(sflip*sm_tp)) / sbr,
                 (np.mean(sflip*sm_fn) - np.mean(sconst*sm_fn)) / sbr,
                 (np.mean(oflip*om_tp) - np.mean(oconst*om_tp)) / obr,
                 (np.mean(oconst*om_fn) - np.mean(oflip*om_fn)) / obr],
                [(np.mean(sconst*sm_fp) - np.mean(sflip*sm_fp)) / (1-sbr),
                 (np.mean(sflip*sm_tn) - np.mean(sconst*sm_tn)) / (1-sbr),
                 (np.mean(oflip*om_fp) - np.mean(oconst*om_fp)) / (1-obr),
                 (np.mean(oconst*om_tn) - np.mean(oflip*om_tn)) / (1-obr)]]

        b_eq = [(np.mean(oflip*om_tp) + np.mean(oconst*om_fn)) / obr
              - (np.mean(sflip*sm_tp) + np.mean(sconst*sm_fn)) / sbr,
                (np.mean(oflip*om_fp) + np.mean(oconst*om_tn)) / (1-obr)
              - (np.mean(sflip*sm_fp) + np.mean(sconst*sm_tn)) / (1-sbr)]

        # Linear program
        self.model_params = linprog(c, A_ub=A_ub, b_ub=b_ub, A_eq=A_eq, b_eq=b_eq)

        return self

    def predict(self, dataset):
        """Perturb the predicted labels to obtain new labels that satisfy
        equalized odds constraints.

        Args:
            dataset (BinaryLabelDataset): Dataset containing labels that needs
                to be transformed.
            dataset (BinaryLabelDataset): Transformed dataset.
        """
        if self.seed is not None:
            np.random.seed(self.seed)

        # Get the model parameters output from fit
        sp2p, sn2p, op2p, on2p = self.model_params.x

        # Create boolean conditioning vectors for protected groups
        cond_vec_priv = utils.compute_boolean_conditioning_vector(
            dataset.protected_attributes, dataset.protected_attribute_names,
            self.privileged_groups)
        cond_vec_unpriv = utils.compute_boolean_conditioning_vector(
            dataset.protected_attributes, dataset.protected_attribute_names,
            self.unprivileged_groups)

        # Randomly flip labels according to the probabilities in model_params
        self_fair_pred = dataset.labels[cond_vec_priv].copy()
        self_pp_indices, _ = np.nonzero(
            dataset.labels[cond_vec_priv] == dataset.favorable_label)
        self_pn_indices, _ = np.nonzero(
            dataset.labels[cond_vec_priv] == dataset.unfavorable_label)
        np.random.shuffle(self_pp_indices)
        np.random.shuffle(self_pn_indices)

        n2p_indices = self_pn_indices[:int(len(self_pn_indices) * sn2p)]
        self_fair_pred[n2p_indices] = dataset.favorable_label
        p2n_indices = self_pp_indices[:int(len(self_pp_indices) * (1 - sp2p))]
        self_fair_pred[p2n_indices] = dataset.unfavorable_label

        othr_fair_pred = dataset.labels[cond_vec_unpriv].copy()
        othr_pp_indices, _ = np.nonzero(
            dataset.labels[cond_vec_unpriv] == dataset.favorable_label)
        othr_pn_indices, _ = np.nonzero(
            dataset.labels[cond_vec_unpriv] == dataset.unfavorable_label)
        np.random.shuffle(othr_pp_indices)
        np.random.shuffle(othr_pn_indices)

        n2p_indices = othr_pn_indices[:int(len(othr_pn_indices) * on2p)]
        othr_fair_pred[n2p_indices] = dataset.favorable_label
        p2n_indices = othr_pp_indices[:int(len(othr_pp_indices) * (1 - op2p))]
        othr_fair_pred[p2n_indices] = dataset.unfavorable_label

        # Mutated, fairer dataset with new labels
        dataset_new = dataset.copy()

        new_labels = np.zeros_like(dataset.labels, dtype=np.float64)
        new_labels[cond_vec_priv] = self_fair_pred
        new_labels[cond_vec_unpriv] = othr_fair_pred

        dataset_new.labels = new_labels

        return dataset_new

    def fit_predict(self, dataset_true, dataset_pred):
        """fit and predict methods sequentially."""
        return self.fit(dataset_true, dataset_pred).predict(dataset_pred)