aif360.algorithms.postprocessing
.EqOddsPostprocessing
- 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
- Parameters:
Methods
Compute parameters for equalizing odds using true and predicted labels.
fit and predict methods sequentially.
fit_transform
Train a model on the input and transform the dataset accordingly.
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.
- 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.