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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 aif360.algorithms import Transformer
class DisparateImpactRemover(Transformer):
"""Disparate impact remover is a preprocessing technique that edits feature
values increase group fairness while preserving rank-ordering within groups
.. [1] M. Feldman, S. A. Friedler, J. Moeller, C. Scheidegger, and
S. Venkatasubramanian, "Certifying and removing disparate impact."
ACM SIGKDD International Conference on Knowledge Discovery and Data
Mining, 2015.
def __init__(self, repair_level=1.0):
super(DisparateImpactRemover, self).__init__(repair_level=repair_level)
# avoid importing early since this package can throw warnings in some
# jupyter notebooks
from BlackBoxAuditing.repairers.GeneralRepairer import Repairer
self.Repairer = Repairer
if not 0.0 <= repair_level <= 1.0:
raise ValueError("'repair_level' must be between 0.0 and 1.0.")
self.repair_level = repair_level
def fit_transform(self, dataset):
"""Run a repairer on the non-protected features and return the
transformed dataset.
dataset (BinaryLabelDataset): Dataset that needs repair.
dataset (BinaryLabelDataset): Transformed Dataset.
In order to transform test data in the same manner as training data,
the distributions of attributes conditioned on the protected
attribute must be the same.
if len(dataset.protected_attribute_names) > 1:
raise ValueError("'dataset' must have only a single protected "
sensitive_attribute = dataset.protected_attribute_names[0]
features = dataset.features.tolist()
index = dataset.feature_names.index(sensitive_attribute)
repairer = self.Repairer(features, index, self.repair_level, False)
repaired = dataset.copy()
repaired_features =
repaired.features = np.array(repaired_features, dtype=np.float64)
# protected attribute shouldn't change
repaired.features[:, index] = repaired.protected_attributes[:, 0]
return repaired