Spaces:
Runtime error
Runtime error
File size: 3,313 Bytes
d2a8669 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 |
# The code for Meta-Classification-Algorithm is based on, the paper https://arxiv.org/abs/1806.06055
# See: https://github.com/vijaykeswani/FairClassification
import numpy as np
from aif360.algorithms import Transformer
from aif360.algorithms.inprocessing.celisMeta import FalseDiscovery
from aif360.algorithms.inprocessing.celisMeta import StatisticalRate
class MetaFairClassifier(Transformer):
"""The meta algorithm here takes the fairness metric as part of the input
and returns a classifier optimized w.r.t. that fairness metric [11]_.
References:
.. [11] L. E. Celis, L. Huang, V. Keswani, and N. K. Vishnoi.
"Classification with Fairness Constraints: A Meta-Algorithm with
Provable Guarantees," 2018.
"""
def __init__(self, tau=0.8, sensitive_attr="", type="fdr", seed=None):
"""
Args:
tau (double, optional): Fairness penalty parameter.
sensitive_attr (str, optional): Name of protected attribute.
type (str, optional): The type of fairness metric to be used.
Currently "fdr" (false discovery rate ratio) and "sr"
(statistical rate/disparate impact) are supported. To use
another type, the corresponding optimization class has to be
implemented.
seed (int, optional): Random seed.
"""
super(MetaFairClassifier, self).__init__(tau=tau,
sensitive_attr=sensitive_attr, type=type, seed=seed)
self.tau = tau
self.sensitive_attr = sensitive_attr
if type == "fdr":
self.obj = FalseDiscovery()
elif type == "sr":
self.obj = StatisticalRate()
else:
raise NotImplementedError("Only 'fdr' and 'sr' are supported yet.")
self.seed = seed
def fit(self, dataset):
"""Learns the fair classifier.
Args:
dataset (BinaryLabelDataset): Dataset containing true labels.
Returns:
MetaFairClassifier: Returns self.
"""
if not self.sensitive_attr:
self.sensitive_attr = dataset.protected_attribute_names[0]
sens_idx = dataset.protected_attribute_names.index(self.sensitive_attr)
x_train = dataset.features
y_train = np.where(dataset.labels.flatten() == dataset.favorable_label,
1, -1)
x_control_train = np.where(
np.isin(dataset.protected_attributes[:, sens_idx],
dataset.privileged_protected_attributes[sens_idx]),
1, 0)
self.model = self.obj.getModel(self.tau, x_train, y_train,
x_control_train, self.seed)
return self
def predict(self, dataset):
"""Obtain the predictions for the provided dataset using the learned
classifier model.
Args:
dataset (BinaryLabelDataset): Dataset containing labels that needs
to be transformed.
Returns:
BinaryLabelDataset: Transformed dataset.
"""
t = self.model(dataset.features)
pred_dataset = dataset.copy()
pred_dataset.labels = (t > 0).astype(int).reshape((-1, 1))
pred_dataset.scores = ((t + 1) / 2).reshape((-1, 1))
return pred_dataset
|