aif360.sklearn.inprocessing.ExponentiatedGradientReduction

class aif360.sklearn.inprocessing.ExponentiatedGradientReduction(prot_attr, estimator, constraints, eps=0.01, max_iter=50, nu=None, eta0=2.0, run_linprog_step=True, drop_prot_attr=True)

Exponentiated gradient reduction for fair classification.

Exponentiated gradient reduction is an in-processing technique that reduces fair classification to a sequence of cost-sensitive classification problems, returning a randomized classifier with the lowest empirical error subject to fair classification constraints [1].

References

[1]

A. Agarwal, A. Beygelzimer, M. Dudik, J. Langford, and H. Wallach, “A Reductions Approach to Fair Classification,” International Conference on Machine Learning, 2018.

Parameters:

• prot_attr – String or array-like column indices or column names of protected attributes.

• estimator – An estimator implementing methods fit(X, y, sample_weight) and predict(X), where X is the matrix of features, y is the vector of labels, and sample_weight is a vector of weights; labels y and predictions returned by predict(X) are either 0 or 1 – e.g. scikit-learn classifiers.

• constraints (str or fairlearn.reductions.Moment) – If string, keyword denoting the fairlearn.reductions.Moment object defining the disparity constraints – e.g., “DemographicParity” or “EqualizedOdds”. For a full list of possible options see self.model.moments. Otherwise, provide the desired Moment object defining the disparity constraints.

• eps – Allowed fairness constraint violation; the solution is guaranteed to have the error within 2*best_gap of the best error under constraint eps; the constraint violation is at most 2*(eps+best_gap).

• max_iter – Maximum number of iterations.

• nu – Convergence threshold for the duality gap, corresponding to a conservative automatic setting based on the statistical uncertainty in measuring classification error.

• eta0 – Initial setting of the learning rate.

• run_linprog_step – If True each step of exponentiated gradient is followed by the saddle point optimization over the convex hull of classifiers returned so far.

• drop_prot_attr – Boolean flag indicating whether to drop protected attributes from training data.

Methods

fit	Learns randomized model with less bias
get_metadata_routing	Get metadata routing of this object.
get_params	Get parameters for this estimator.
predict	Predict class labels for the given samples.
predict_proba	Probability estimates.
score	Return the mean accuracy on the given test data and labels.
set_params	Set the parameters of this estimator.
set_score_request	Request metadata passed to the score method.

__init__(prot_attr, estimator, constraints, eps=0.01, max_iter=50, nu=None, eta0=2.0, run_linprog_step=True, drop_prot_attr=True)

Parameters:

• prot_attr – String or array-like column indices or column names of protected attributes.

• estimator – An estimator implementing methods fit(X, y, sample_weight) and predict(X), where X is the matrix of features, y is the vector of labels, and sample_weight is a vector of weights; labels y and predictions returned by predict(X) are either 0 or 1 – e.g. scikit-learn classifiers.

• constraints (str or fairlearn.reductions.Moment) – If string, keyword denoting the fairlearn.reductions.Moment object defining the disparity constraints – e.g., “DemographicParity” or “EqualizedOdds”. For a full list of possible options see self.model.moments. Otherwise, provide the desired Moment object defining the disparity constraints.

• eps – Allowed fairness constraint violation; the solution is guaranteed to have the error within 2*best_gap of the best error under constraint eps; the constraint violation is at most 2*(eps+best_gap).

• max_iter – Maximum number of iterations.

• nu – Convergence threshold for the duality gap, corresponding to a conservative automatic setting based on the statistical uncertainty in measuring classification error.

• eta0 – Initial setting of the learning rate.

• run_linprog_step – If True each step of exponentiated gradient is followed by the saddle point optimization over the convex hull of classifiers returned so far.

• drop_prot_attr – Boolean flag indicating whether to drop protected attributes from training data.

fit(X, y)

Learns randomized model with less bias

Parameters:

• X (pandas.DataFrame) – Training samples.

• y (array-like) – Training labels.

Returns:

self

predict(X)

Predict class labels for the given samples. :param X: Test samples. :type X: pandas.DataFrame

Returns:

numpy.ndarray – Predicted class label per sample.

predict_proba(X)

Probability estimates.

The returned estimates for all classes are ordered by the label of classes.

Parameters:

X (pandas.DataFrame) – Test samples.

Returns:

numpy.ndarray – Returns the probability of the sample for each class in the model, where classes are ordered as they are in self.classes_.

set_score_request(*, sample_weight: bool | None | str = '$UNCHANGED$') → ExponentiatedGradientReduction

Request metadata passed to the score method.

Note that this method is only relevant if enable_metadata_routing=True (see sklearn.set_config()). Please see User Guide on how the routing mechanism works.

The options for each parameter are:

• True: metadata is requested, and passed to score if provided. The request is ignored if metadata is not provided.

• False: metadata is not requested and the meta-estimator will not pass it to score.

• None: metadata is not requested, and the meta-estimator will raise an error if the user provides it.

• str: metadata should be passed to the meta-estimator with this given alias instead of the original name.

The default (sklearn.utils.metadata_routing.UNCHANGED) retains the existing request. This allows you to change the request for some parameters and not others.

New in version 1.3.

Note

This method is only relevant if this estimator is used as a sub-estimator of a meta-estimator, e.g. used inside a Pipeline. Otherwise it has no effect.

Parameters:

sample_weight (str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED) – Metadata routing for sample_weight parameter in score.

Returns:

self (object) – The updated object.