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| 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)) | |