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