aif360.algorithms.postprocessing
.EqOddsPostprocessing¶
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class
aif360.algorithms.postprocessing.
EqOddsPostprocessing
(unprivileged_groups, privileged_groups, seed=None)[source]¶ Equalized odds postprocessing is a post-processing technique that solves a linear program to find probabilities with which to change output labels to optimize equalized odds [8] [9].
References
[8] M. Hardt, E. Price, and N. Srebro, “Equality of Opportunity in Supervised Learning,” Conference on Neural Information Processing Systems, 2016. [9] G. Pleiss, M. Raghavan, F. Wu, J. Kleinberg, and K. Q. Weinberger, “On Fairness and Calibration,” Conference on Neural Information Processing Systems, 2017. Parameters: Methods
fit
Compute parameters for equalizing odds using true and predicted labels. fit_predict
fit and predict methods sequentially. fit_transform
Train a model on the input and transform the dataset accordingly. predict
Perturb the predicted labels to obtain new labels that satisfy equalized odds constraints. transform
Return a new dataset generated by running this Transformer on the input. -
fit
(dataset_true, dataset_pred)[source]¶ Compute parameters for equalizing odds using true and predicted labels.
Parameters: - true_dataset (BinaryLabelDataset) – Dataset containing true labels.
- pred_dataset (BinaryLabelDataset) – Dataset containing predicted labels.
Returns: EqOddsPostprocessing – Returns self.
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predict
(dataset)[source]¶ Perturb the predicted labels to obtain new labels that satisfy equalized odds constraints.
Parameters: - dataset (BinaryLabelDataset) – Dataset containing labels that needs to be transformed.
- dataset – Transformed dataset.
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