aif360.algorithms.inprocessing.ExponentiatedGradientReduction

class aif360.algorithms.inprocessing.ExponentiatedGradientReduction(estimator, constraints, eps=0.01, T=50, nu=None, eta_mul=2.0, drop_prot_attr=True)[source]

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:
  • 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).
  • T – 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.
  • eta_mul – Initial setting of the learning rate.
  • drop_prot_attr – Boolean flag indicating whether to drop protected attributes from training data.

Methods

fit Learns randomized model with less bias
fit_predict Train a model on the input and predict the labels.
fit_transform Train a model on the input and transform the dataset accordingly.
predict Obtain the predictions for the provided dataset using the randomized model learned.
transform Return a new dataset generated by running this Transformer on the input.
__init__(estimator, constraints, eps=0.01, T=50, nu=None, eta_mul=2.0, drop_prot_attr=True)[source]
Parameters:
  • 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).
  • T – 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.
  • eta_mul – Initial setting of the learning rate.
  • drop_prot_attr – Boolean flag indicating whether to drop protected attributes from training data.
fit(dataset)[source]

Learns randomized model with less bias

Parameters:dataset – (Binary label) Dataset containing true labels.
Returns:ExponentiatedGradientReduction – Returns self.
predict(dataset)[source]

Obtain the predictions for the provided dataset using the randomized model learned.

Parameters:dataset – (Binary label) Dataset containing labels that needs to be transformed.
Returns:dataset – Transformed (Binary label) dataset.