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from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals
import numpy as np
from warnings import warn
from aif360.algorithms import Transformer
from aif360.metrics import utils
from aif360.metrics import BinaryLabelDatasetMetric, ClassificationMetric
class RejectOptionClassification(Transformer):
"""Reject option classification is a postprocessing technique that gives
favorable outcomes to unpriviliged groups and unfavorable outcomes to
priviliged groups in a confidence band around the decision boundary with the
highest uncertainty [10]_.
References:
.. [10] F. Kamiran, A. Karim, and X. Zhang, "Decision Theory for
Discrimination-Aware Classification," IEEE International Conference
on Data Mining, 2012.
"""
def __init__(self, unprivileged_groups, privileged_groups,
low_class_thresh=0.01, high_class_thresh=0.99,
num_class_thresh=100, num_ROC_margin=50,
metric_name="Statistical parity difference",
metric_ub=0.05, metric_lb=-0.05):
"""
Args:
unprivileged_groups (dict or list(dict)): Representation for
unprivileged group.
privileged_groups (dict or list(dict)): Representation for
privileged group.
low_class_thresh (float): Smallest classification threshold to use
in the optimization. Should be between 0. and 1.
high_class_thresh (float): Highest classification threshold to use
in the optimization. Should be between 0. and 1.
num_class_thresh (int): Number of classification thresholds between
low_class_thresh and high_class_thresh for the optimization
search. Should be > 0.
num_ROC_margin (int): Number of relevant ROC margins to be used in
the optimization search. Should be > 0.
metric_name (str): Name of the metric to use for the optimization.
Allowed options are "Statistical parity difference",
"Average odds difference", "Equal opportunity difference".
metric_ub (float): Upper bound of constraint on the metric value
metric_lb (float): Lower bound of constraint on the metric value
"""
super(RejectOptionClassification, self).__init__(
unprivileged_groups=unprivileged_groups,
privileged_groups=privileged_groups,
low_class_thresh=low_class_thresh, high_class_thresh=high_class_thresh,
num_class_thresh=num_class_thresh, num_ROC_margin=num_ROC_margin,
metric_name=metric_name)
allowed_metrics = ["Statistical parity difference",
"Average odds difference",
"Equal opportunity difference"]
self.unprivileged_groups = unprivileged_groups
self.privileged_groups = privileged_groups
self.low_class_thresh = low_class_thresh
self.high_class_thresh = high_class_thresh
self.num_class_thresh = num_class_thresh
self.num_ROC_margin = num_ROC_margin
self.metric_name = metric_name
self.metric_ub = metric_ub
self.metric_lb = metric_lb
self.classification_threshold = None
self.ROC_margin = None
if ((self.low_class_thresh < 0.0) or (self.low_class_thresh > 1.0) or\
(self.high_class_thresh < 0.0) or (self.high_class_thresh > 1.0) or\
(self.low_class_thresh >= self.high_class_thresh) or\
(self.num_class_thresh < 1) or (self.num_ROC_margin < 1)):
raise ValueError("Input parameter values out of bounds")
if metric_name not in allowed_metrics:
raise ValueError("metric name not in the list of allowed metrics")
def fit(self, dataset_true, dataset_pred):
"""Estimates the optimal classification threshold and margin for reject
option classification that optimizes the metric provided.
Note:
The `fit` function is a no-op for this algorithm.
Args:
dataset_true (BinaryLabelDataset): Dataset containing the true
`labels`.
dataset_pred (BinaryLabelDataset): Dataset containing the predicted
`scores`.
Returns:
RejectOptionClassification: Returns self.
"""
fair_metric_arr = np.zeros(self.num_class_thresh*self.num_ROC_margin)
balanced_acc_arr = np.zeros_like(fair_metric_arr)
ROC_margin_arr = np.zeros_like(fair_metric_arr)
class_thresh_arr = np.zeros_like(fair_metric_arr)
cnt = 0
# Iterate through class thresholds
for class_thresh in np.linspace(self.low_class_thresh,
self.high_class_thresh,
self.num_class_thresh):
self.classification_threshold = class_thresh
if class_thresh <= 0.5:
low_ROC_margin = 0.0
high_ROC_margin = class_thresh
else:
low_ROC_margin = 0.0
high_ROC_margin = (1.0-class_thresh)
# Iterate through ROC margins
for ROC_margin in np.linspace(
low_ROC_margin,
high_ROC_margin,
self.num_ROC_margin):
self.ROC_margin = ROC_margin
# Predict using the current threshold and margin
dataset_transf_pred = self.predict(dataset_pred)
dataset_transf_metric_pred = BinaryLabelDatasetMetric(
dataset_transf_pred,
unprivileged_groups=self.unprivileged_groups,
privileged_groups=self.privileged_groups)
classified_transf_metric = ClassificationMetric(
dataset_true,
dataset_transf_pred,
unprivileged_groups=self.unprivileged_groups,
privileged_groups=self.privileged_groups)
ROC_margin_arr[cnt] = self.ROC_margin
class_thresh_arr[cnt] = self.classification_threshold
# Balanced accuracy and fairness metric computations
balanced_acc_arr[cnt] = 0.5*(classified_transf_metric.true_positive_rate()\
+classified_transf_metric.true_negative_rate())
if self.metric_name == "Statistical parity difference":
fair_metric_arr[cnt] = dataset_transf_metric_pred.mean_difference()
elif self.metric_name == "Average odds difference":
fair_metric_arr[cnt] = classified_transf_metric.average_odds_difference()
elif self.metric_name == "Equal opportunity difference":
fair_metric_arr[cnt] = classified_transf_metric.equal_opportunity_difference()
cnt += 1
rel_inds = np.logical_and(fair_metric_arr >= self.metric_lb,
fair_metric_arr <= self.metric_ub)
if any(rel_inds):
best_ind = np.where(balanced_acc_arr[rel_inds]
== np.max(balanced_acc_arr[rel_inds]))[0][0]
else:
warn("Unable to satisy fairness constraints")
rel_inds = np.ones(len(fair_metric_arr), dtype=bool)
best_ind = np.where(fair_metric_arr[rel_inds]
== np.min(fair_metric_arr[rel_inds]))[0][0]
self.ROC_margin = ROC_margin_arr[rel_inds][best_ind]
self.classification_threshold = class_thresh_arr[rel_inds][best_ind]
return self
def predict(self, dataset):
"""Obtain fair predictions using the ROC method.
Args:
dataset (BinaryLabelDataset): Dataset containing scores that will
be used to compute predicted labels.
Returns:
dataset_pred (BinaryLabelDataset): Output dataset with potentially
fair predictions obtain using the ROC method.
"""
dataset_new = dataset.copy(deepcopy=False)
fav_pred_inds = (dataset.scores > self.classification_threshold)
unfav_pred_inds = ~fav_pred_inds
y_pred = np.zeros(dataset.scores.shape)
y_pred[fav_pred_inds] = dataset.favorable_label
y_pred[unfav_pred_inds] = dataset.unfavorable_label
# Indices of critical region around the classification boundary
crit_region_inds = np.logical_and(
dataset.scores <= self.classification_threshold+self.ROC_margin,
dataset.scores > self.classification_threshold-self.ROC_margin)
# Indices of privileged and unprivileged groups
cond_priv = utils.compute_boolean_conditioning_vector(
dataset.protected_attributes,
dataset.protected_attribute_names,
self.privileged_groups)
cond_unpriv = utils.compute_boolean_conditioning_vector(
dataset.protected_attributes,
dataset.protected_attribute_names,
self.unprivileged_groups)
# New, fairer labels
dataset_new.labels = y_pred
dataset_new.labels[np.logical_and(crit_region_inds,
cond_priv.reshape(-1,1))] = dataset.unfavorable_label
dataset_new.labels[np.logical_and(crit_region_inds,
cond_unpriv.reshape(-1,1))] = dataset.favorable_label
return dataset_new
def fit_predict(self, dataset):
"""fit and predict methods sequentially."""
return self.fit().predict(dataset)
# Function to obtain the pareto frontier
def _get_pareto_frontier(costs, return_mask = True): # <- Fastest for many points
"""
:param costs: An (n_points, n_costs) array
:param return_mask: True to return a mask, False to return integer indices of efficient points.
:return: An array of indices of pareto-efficient points.
If return_mask is True, this will be an (n_points, ) boolean array
Otherwise it will be a (n_efficient_points, ) integer array of indices.
adapted from: https://stackoverflow.com/questions/32791911/fast-calculation-of-pareto-front-in-python
"""
is_efficient = np.arange(costs.shape[0])
n_points = costs.shape[0]
next_point_index = 0 # Next index in the is_efficient array to search for
while next_point_index<len(costs):
nondominated_point_mask = np.any(costs<=costs[next_point_index], axis=1)
is_efficient = is_efficient[nondominated_point_mask] # Remove dominated points
costs = costs[nondominated_point_mask]
next_point_index = np.sum(nondominated_point_mask[:next_point_index])+1
if return_mask:
is_efficient_mask = np.zeros(n_points, dtype = bool)
is_efficient_mask[is_efficient] = True
return is_efficient_mask
else:
return is_efficient