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from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals
from functools import partial
import numpy as np
import scipy.spatial.distance as scdist
from aif360.datasets import StructuredDataset
from aif360.metrics import DatasetMetric, utils
class SampleDistortionMetric(DatasetMetric):
"""Class for computing metrics based on two StructuredDatasets."""
def __init__(self, dataset, distorted_dataset, unprivileged_groups=None,
privileged_groups=None):
"""
Args:
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
:obj:`~aif360.datasets.StructuredDataset` types.
"""
# sets self.dataset, self.unprivileged_groups, self.privileged_groups
super(SampleDistortionMetric, self).__init__(dataset,
unprivileged_groups=unprivileged_groups,
privileged_groups=privileged_groups)
if isinstance(distorted_dataset, StructuredDataset):
self.distorted_dataset = distorted_dataset
else:
raise TypeError("'distorted_dataset' should be a StructuredDataset")
with dataset.temporarily_ignore('features', 'labels', 'scores'):
if dataset != distorted_dataset:
raise ValueError("The two datasets may differ in features and "
"labels/scores only.")
def total(self, dist, privileged):
distance, weights = dist(privileged=privileged, returned=True)
return np.sum(distance * weights)
def average(self, dist, privileged):
distance, weights = dist(privileged=privileged, returned=True)
return np.average(distance, weights=weights)
def maximum(self, dist, privileged):
return np.max(dist(privileged=privileged))
def euclidean_distance(self, privileged=None, returned=False):
"""Compute the average Euclidean distance between the samples from the
two datasets.
"""
condition = self._to_condition(privileged)
distance, mask = utils.compute_distance(self.dataset.features,
self.distorted_dataset.features, self.dataset.protected_attributes,
self.dataset.protected_attribute_names, dist_fun=scdist.euclidean,
condition=condition)
if returned:
return distance, self.dataset.instance_weights[mask]
return distance
def manhattan_distance(self, privileged=None, returned=False):
"""Compute the average Manhattan distance between the samples from the
two datasets.
"""
condition = self._to_condition(privileged)
distance, mask = utils.compute_distance(self.dataset.features,
self.distorted_dataset.features, self.dataset.protected_attributes,
self.dataset.protected_attribute_names, dist_fun=scdist.cityblock,
condition=condition)
if returned:
return distance, self.dataset.instance_weights[mask]
return distance
def mahalanobis_distance(self, privileged=None, returned=False):
"""Compute the average Mahalanobis distance between the samples from the
two datasets.
"""
condition = self._to_condition(privileged)
X_orig = self.dataset.features
X_distort = self.distorted_dataset.features
dist_fun = partial(scdist.mahalanobis,
VI=np.linalg.inv(np.cov(np.vstack([X_orig, X_distort]).T)).T)
distance, mask = utils.compute_distance(X_orig, X_distort,
self.dataset.protected_attributes,
self.dataset.protected_attribute_names, dist_fun=dist_fun,
condition=condition)
if returned:
return distance, self.dataset.instance_weights[mask]
return distance
def total_euclidean_distance(self, privileged=None):
return self.total(self.euclidean_distance, privileged=privileged)
def total_manhattan_distance(self, privileged=None):
return self.total(self.manhattan_distance, privileged=privileged)
def total_mahalanobis_distance(self, privileged=None):
return self.total(self.mahalanobis_distance, privileged=privileged)
def average_euclidean_distance(self, privileged=None):
return self.average(self.euclidean_distance, privileged=privileged)
def average_manhattan_distance(self, privileged=None):
return self.average(self.manhattan_distance, privileged=privileged)
def average_mahalanobis_distance(self, privileged=None):
return self.average(self.mahalanobis_distance, privileged=privileged)
def maximum_euclidean_distance(self, privileged=None):
return self.maximum(self.euclidean_distance, privileged=privileged)
def maximum_manhattan_distance(self, privileged=None):
return self.maximum(self.manhattan_distance, privileged=privileged)
def maximum_mahalanobis_distance(self, privileged=None):
return self.maximum(self.mahalanobis_distance, privileged=privileged)
def mean_euclidean_distance_difference(self, privileged=None):
"""Difference of the averages."""
return self.difference(
self.average(self.euclidean_distance, privileged=privileged))
def mean_manhattan_distance_difference(self, privileged=None):
"""Difference of the averages."""
return self.difference(
self.average(self.manhattan_distance, privileged=privileged))
def mean_mahalanobis_distance_difference(self, privileged=None):
"""Difference of the averages."""
return self.difference(
self.average(self.mahalanobis_distance, privileged=privileged))
def mean_euclidean_distance_ratio(self, privileged=None):
"""Ratio of the averages."""
return self.ratio(
self.average(self.euclidean_distance, privileged=privileged))
def mean_manhattan_distance_ratio(self, privileged=None):
"""Ratio of the averages."""
return self.ratio(
self.average(self.manhattan_distance, privileged=privileged))
def mean_mahalanobis_distance_ratio(self, privileged=None):
"""Ratio of the averages."""
return self.ratio(
self.average(self.mahalanobis_distance, privileged=privileged))
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