Fairness Metrics

aif360.metrics

metrics.DatasetMetric(dataset[, …])	Class for computing metrics based on one StructuredDataset.
metrics.BinaryLabelDatasetMetric(dataset[, …])	Class for computing metrics based on a single BinaryLabelDataset.
metrics.ClassificationMetric(dataset, …[, …])	Class for computing metrics based on two BinaryLabelDatasets.
metrics.SampleDistortionMetric(dataset, …)	Class for computing metrics based on two StructuredDatasets.

aif360.metrics.utils

This is the helper script for implementing metrics.

metrics.utils.compute_boolean_conditioning_vector(X, …)	Compute the boolean conditioning vector.
metrics.utils.compute_num_instances(X, w, …)	Compute the number of instances, \(n\), conditioned on the protected attribute(s).
metrics.utils.compute_num_pos_neg(X, y, w, …)	Compute the number of positives, \(P\), or negatives, \(N\), optionally conditioned on protected attributes.
metrics.utils.compute_num_TF_PN(X, y_true, …)	Compute the number of true/false positives/negatives optionally conditioned on protected attributes.
metrics.utils.compute_num_gen_TF_PN(X, …)	Compute the number of generalized true/false positives/negatives optionally conditioned on protected attributes.
metrics.utils.compute_distance(X_orig, …)	Compute the distance element-wise for two sets of vectors.