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. |
metrics.MDSSClassificationMetric (dataset, …) |
Bias subset scanning is proposed as a technique to identify bias in predictive models using subset scanning [#zhang16]_. |
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. |