aif360.sklearn.preprocessing.LearnedFairRepresentations

class aif360.sklearn.preprocessing.LearnedFairRepresentations(prot_attr=None, n_prototypes=5, reconstruct_weight=0.01, target_weight=1.0, fairness_weight=50.0, tol=0.0001, max_iter=200, verbose=0, random_state=None)

Learned Fair Representations.

Learned fair representations is a pre-processing technique that finds a latent representation which encodes the data well but obfuscates information about protected attributes [1]. It can also be used as an in- processing method by utilizing the learned target coefficients.

References

[1]	R. Zemel, Y. Wu, K. Swersky, T. Pitassi, and C. Dwork, “Learning Fair Representations.” International Conference on Machine Learning, 2013.

# Based on code from https://github.com/zjelveh/learning-fair-representations

Variables:

• prot_attr (str or list(str)) – Protected attribute(s) used for reweighing.
• groups (array, shape (n_groups,)) – A list of group labels known to the transformer.
• classes (array, shape (n_classes,)) – A list of class labels known to the transformer.
• priv_group (scalar) – The label of the privileged group.
• coef (array, shape (n_prototypes, 1) or (n_prototypes, n_classes)) – Coefficient of the intermediate representation for classification.
• prototypes (array, shape (n_prototypes, n_features)) – The prototype set used to form a probabilistic mapping to the intermediate representation. These act as clusters and are in the same space as the samples.
• n_iter (int) – Actual number of iterations.

Parameters:

• prot_attr (single label or list-like, optional) – Protected attribute(s) to use in the reweighing process. If more than one attribute, all combinations of values (intersections) are considered. Default is None meaning all protected attributes from the dataset are used.
• n_prototypes (int, optional) – Size of the set of “prototypes,” Z.
• reconstruct_weight (float, optional) – Weight coefficient on the L_x loss term, A_x.
• target_weight (float, optional) – Weight coefficient on the L_y loss term, A_y.
• fairness_weight (float, optional) – Weight coefficient on the L_z loss term, A_z.
• tol (float, optional) – Tolerance for stopping criteria.
• max_iter (int, optional) – Maximum number of iterations taken for the solver to converge.
• verbose (int, optional) – Verbosity. 0 = silent, 1 = final loss only, 2 = print loss every 50 iterations.
• random_state (int or numpy.RandomState, optional) – Seed of pseudo- random number generator for shuffling data and seeding weights.

Methods

fit	Compute the transformation parameters that lead to fair representations.
fit_transform	Fit to data, then transform it.
get_params	Get parameters for this estimator.
predict	Transform the targets using the learned model parameters.
predict_proba	Transform the targets using the learned model parameters.
score	Return the mean accuracy on the given test data and labels.
set_params	Set the parameters of this estimator.
transform	Transform the dataset using the learned model parameters.

__init__(prot_attr=None, n_prototypes=5, reconstruct_weight=0.01, target_weight=1.0, fairness_weight=50.0, tol=0.0001, max_iter=200, verbose=0, random_state=None)

Parameters:

• prot_attr (single label or list-like, optional) – Protected attribute(s) to use in the reweighing process. If more than one attribute, all combinations of values (intersections) are considered. Default is None meaning all protected attributes from the dataset are used.
• n_prototypes (int, optional) – Size of the set of “prototypes,” Z.
• reconstruct_weight (float, optional) – Weight coefficient on the L_x loss term, A_x.
• target_weight (float, optional) – Weight coefficient on the L_y loss term, A_y.
• fairness_weight (float, optional) – Weight coefficient on the L_z loss term, A_z.
• tol (float, optional) – Tolerance for stopping criteria.
• max_iter (int, optional) – Maximum number of iterations taken for the solver to converge.
• verbose (int, optional) – Verbosity. 0 = silent, 1 = final loss only, 2 = print loss every 50 iterations.
• random_state (int or numpy.RandomState, optional) – Seed of pseudo- random number generator for shuffling data and seeding weights.

fit(X, y, priv_group=1, sample_weight=None)

Compute the transformation parameters that lead to fair representations.

Parameters:

• X (pandas.DataFrame) – Training samples.
• y (array-like) – Training labels.
• priv_group (scalar, optional) – The label of the privileged group.
• sample_weight (array-like, optional) – Sample weights.

Returns:

self

predict(X)

Transform the targets using the learned model parameters.

Parameters:X (pandas.DataFrame) – Training samples. Returns:numpy.ndarray – Transformed targets.

predict_proba(X)

Transform the targets using the learned model parameters.

Parameters:X (pandas.DataFrame) – Training samples. Returns:numpy.ndarray – Transformed targets. Returns the probability of the sample for each class in the model, where classes are ordered as they are in self.classes_.

transform(X)

Transform the dataset using the learned model parameters.

Parameters:X (pandas.DataFrame) – Training samples. Returns:pandas.DataFrame – Transformed samples.