aif360.metrics.SampleDistortionMetric

class aif360.metrics.SampleDistortionMetric(dataset, distorted_dataset, unprivileged_groups=None, privileged_groups=None)

Class for computing metrics based on two StructuredDatasets.

Parameters:

• dataset (StructuredDataset) – A StructuredDataset.

• distorted_dataset (StructuredDataset) – A StructuredDataset.

• privileged_groups (list(dict)) – Privileged groups. Format is a list of dicts where the keys are protected_attribute_names and the values are values in protected_attributes. Each dict element describes a single group. See examples for more details.

• unprivileged_groups (list(dict)) – Unprivileged groups in the same format as privileged_groups.

Raises:

TypeError – dataset and distorted_dataset must be StructuredDataset types.

Methods

average
average_euclidean_distance
average_mahalanobis_distance
average_manhattan_distance
difference	Compute difference of the metric for unprivileged and privileged groups.
euclidean_distance	Compute the average Euclidean distance between the samples from the two datasets.
mahalanobis_distance	Compute the average Mahalanobis distance between the samples from the two datasets.
manhattan_distance	Compute the average Manhattan distance between the samples from the two datasets.
maximum
maximum_euclidean_distance
maximum_mahalanobis_distance
maximum_manhattan_distance
mean_euclidean_distance_difference	Difference of the averages.
mean_euclidean_distance_ratio	Ratio of the averages.
mean_mahalanobis_distance_difference	Difference of the averages.
mean_mahalanobis_distance_ratio	Ratio of the averages.
mean_manhattan_distance_difference	Difference of the averages.
mean_manhattan_distance_ratio	Ratio of the averages.
num_instances	Compute the number of instances, \(n\), in the dataset conditioned on protected attributes if necessary.
ratio	Compute ratio of the metric for unprivileged and privileged groups.
total
total_euclidean_distance
total_mahalanobis_distance
total_manhattan_distance

__init__(dataset, distorted_dataset, unprivileged_groups=None, privileged_groups=None)

Parameters:

• dataset (StructuredDataset) – A StructuredDataset.

• distorted_dataset (StructuredDataset) – A StructuredDataset.

• privileged_groups (list(dict)) – Privileged groups. Format is a list of dicts where the keys are protected_attribute_names and the values are values in protected_attributes. Each dict element describes a single group. See examples for more details.

• unprivileged_groups (list(dict)) – Unprivileged groups in the same format as privileged_groups.

Raises:

TypeError – dataset and distorted_dataset must be StructuredDataset types.

euclidean_distance(privileged=None, returned=False)

Compute the average Euclidean distance between the samples from the two datasets.

mahalanobis_distance(privileged=None, returned=False)

Compute the average Mahalanobis distance between the samples from the two datasets.

manhattan_distance(privileged=None, returned=False)

Compute the average Manhattan distance between the samples from the two datasets.

mean_euclidean_distance_difference(privileged=None)

Difference of the averages.

mean_euclidean_distance_ratio(privileged=None)

Ratio of the averages.

mean_mahalanobis_distance_difference(privileged=None)

Difference of the averages.

mean_mahalanobis_distance_ratio(privileged=None)

Ratio of the averages.

mean_manhattan_distance_difference(privileged=None)

Difference of the averages.

mean_manhattan_distance_ratio(privileged=None)

Ratio of the averages.