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 areprotected_attribute_names
and the values are values inprotected_attributes
. Eachdict
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
anddistorted_dataset
must beStructuredDataset
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 areprotected_attribute_names
and the values are values inprotected_attributes
. Eachdict
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
anddistorted_dataset
must beStructuredDataset
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.