RuleTree.evaluation.evaluate_fairness

RuleTree.evaluation.evaluate_fairness#

evaluate_fairness(labels, sensitive_attribute, ideal_distribution=None) → dict[source]#

Fairness of the groups of a partition (clusters, leaves or predicted classes) with respect to a sensitive attribute.

Parameters:

  • labels (array-like of shape (n_samples,)) – Group of each instance, e.g. its cluster.

  • sensitive_attribute (array-like of shape (n_samples,)) – Value of the sensitive attribute of each instance.

  • ideal_distribution (dict, default=None) – Value of the sensitive attribute -> ideal fraction in each group. If None, the distribution in the whole data set.

Returns:

scores – balance: the smallest ratio between the fraction of a value of the sensitive attribute in a group and in the whole data set (or its inverse), from 0 to 1, the higher the fairer; max_fairness_cost: the largest mean absolute difference between the distribution of the sensitive attribute in a group and the ideal one, the lower the fairer.

Return type:

dict

See also

RuleTree.stumps.regression.FairTreeStumpRegressor

the stump that builds fair trees.

Examples

>>> import numpy as np
>>> from RuleTree.evaluation import evaluate_fairness
>>> labels = np.array([0, 0, 0, 0, 1, 1, 1, 1])
>>> sex = np.array(["F", "M", "F", "M", "F", "F", "F", "M"])
>>> evaluate_fairness(labels, sex)
{'balance': 0.6666666666666666, 'max_fairness_cost': 0.125}