aif360.algorithms.postprocessing.CalibratedEqOddsPostprocessing

class aif360.algorithms.postprocessing.CalibratedEqOddsPostprocessing(unprivileged_groups, privileged_groups, cost_constraint='weighted', seed=None)

Calibrated equalized odds postprocessing is a post-processing technique that optimizes over calibrated classifier score outputs to find probabilities with which to change output labels with an equalized odds objective [7].

References

[7]

G. Pleiss, M. Raghavan, F. Wu, J. Kleinberg, and K. Q. Weinberger, “On Fairness and Calibration,” Conference on Neural Information Processing Systems, 2017

Adapted from: https://github.com/gpleiss/equalized_odds_and_calibration/blob/master/calib_eq_odds.py

Parameters:

• unprivileged_groups (dict or list(dict)) – Representation for unprivileged group.

• privileged_groups (dict or list(dict)) – Representation for privileged group.

• cost_contraint – fpr, fnr or weighted

• seed (int, optional) – Seed to make predict repeatable.

Methods

fit	Compute parameters for equalizing generalized odds using true and predicted scores, while preserving calibration.
fit_predict	fit and predict methods sequentially.
fit_transform	Train a model on the input and transform the dataset accordingly.
predict	Perturb the predicted scores to obtain new labels that satisfy equalized odds constraints, while preserving calibration.
transform	Return a new dataset generated by running this Transformer on the input.

__init__(unprivileged_groups, privileged_groups, cost_constraint='weighted', seed=None)

Parameters:

• unprivileged_groups (dict or list(dict)) – Representation for unprivileged group.

• privileged_groups (dict or list(dict)) – Representation for privileged group.

• cost_contraint – fpr, fnr or weighted

• seed (int, optional) – Seed to make predict repeatable.

fit(dataset_true, dataset_pred)

Compute parameters for equalizing generalized odds using true and predicted scores, while preserving calibration.

Parameters:

• dataset_true (BinaryLabelDataset) – Dataset containing true labels.

• dataset_pred (BinaryLabelDataset) – Dataset containing predicted scores.

Returns:

CalibratedEqOddsPostprocessing – Returns self.

fit_predict(dataset_true, dataset_pred, threshold=0.5)

fit and predict methods sequentially.

predict(dataset, threshold=0.5)

Perturb the predicted scores to obtain new labels that satisfy equalized odds constraints, while preserving calibration.

Parameters:

• dataset (BinaryLabelDataset) – Dataset containing scores that needs to be transformed.

• threshold (float) – Threshold for converting scores to labels. Values greater than or equal to this threshold are predicted to be the favorable_label. Default is 0.5.

Returns:

dataset (BinaryLabelDataset) – transformed dataset.