aif360.metrics.SampleDistortionMetric

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

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)[source]
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)[source]

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

mahalanobis_distance(privileged=None, returned=False)[source]

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

manhattan_distance(privileged=None, returned=False)[source]

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

mean_euclidean_distance_difference(privileged=None)[source]

Difference of the averages.

mean_euclidean_distance_ratio(privileged=None)[source]

Ratio of the averages.

mean_mahalanobis_distance_difference(privileged=None)[source]

Difference of the averages.

mean_mahalanobis_distance_ratio(privileged=None)[source]

Ratio of the averages.

mean_manhattan_distance_difference(privileged=None)[source]

Difference of the averages.

mean_manhattan_distance_ratio(privileged=None)[source]

Ratio of the averages.