aif360.algorithms.postprocessing.RejectOptionClassification

class aif360.algorithms.postprocessing.RejectOptionClassification(unprivileged_groups, privileged_groups, low_class_thresh=0.01, high_class_thresh=0.99, num_class_thresh=100, num_ROC_margin=50, metric_name='Statistical parity difference', metric_ub=0.05, metric_lb=-0.05)

Reject option classification is a postprocessing technique that gives favorable outcomes to unpriviliged groups and unfavorable outcomes to priviliged groups in a confidence band around the decision boundary with the highest uncertainty [10].

References

[10]

F. Kamiran, A. Karim, and X. Zhang, “Decision Theory for Discrimination-Aware Classification,” IEEE International Conference on Data Mining, 2012.

Parameters:

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

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

• low_class_thresh (float) – Smallest classification threshold to use in the optimization. Should be between 0. and 1.

• high_class_thresh (float) – Highest classification threshold to use in the optimization. Should be between 0. and 1.

• num_class_thresh (int) – Number of classification thresholds between low_class_thresh and high_class_thresh for the optimization search. Should be > 0.

• num_ROC_margin (int) – Number of relevant ROC margins to be used in the optimization search. Should be > 0.

• metric_name (str) – Name of the metric to use for the optimization. Allowed options are “Statistical parity difference”, “Average odds difference”, “Equal opportunity difference”.

• metric_ub (float) – Upper bound of constraint on the metric value

• metric_lb (float) – Lower bound of constraint on the metric value

Methods

fit	Estimates the optimal classification threshold and margin for reject option classification that optimizes the metric provided.
fit_predict	fit and predict methods sequentially.
fit_transform	Train a model on the input and transform the dataset accordingly.
predict	Obtain fair predictions using the ROC method.
transform	Return a new dataset generated by running this Transformer on the input.

__init__(unprivileged_groups, privileged_groups, low_class_thresh=0.01, high_class_thresh=0.99, num_class_thresh=100, num_ROC_margin=50, metric_name='Statistical parity difference', metric_ub=0.05, metric_lb=-0.05)

Parameters:

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

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

• low_class_thresh (float) – Smallest classification threshold to use in the optimization. Should be between 0. and 1.

• high_class_thresh (float) – Highest classification threshold to use in the optimization. Should be between 0. and 1.

• num_class_thresh (int) – Number of classification thresholds between low_class_thresh and high_class_thresh for the optimization search. Should be > 0.

• num_ROC_margin (int) – Number of relevant ROC margins to be used in the optimization search. Should be > 0.

• metric_name (str) – Name of the metric to use for the optimization. Allowed options are “Statistical parity difference”, “Average odds difference”, “Equal opportunity difference”.

• metric_ub (float) – Upper bound of constraint on the metric value

• metric_lb (float) – Lower bound of constraint on the metric value

fit(dataset_true, dataset_pred)

Estimates the optimal classification threshold and margin for reject option classification that optimizes the metric provided.

Note

The fit function is a no-op for this algorithm.

Parameters:

• dataset_true (BinaryLabelDataset) – Dataset containing the true labels.

• dataset_pred (BinaryLabelDataset) – Dataset containing the predicted scores.

Returns:

RejectOptionClassification – Returns self.

fit_predict(dataset_true, dataset_pred)

fit and predict methods sequentially.

predict(dataset)

Obtain fair predictions using the ROC method.

Parameters:

dataset (BinaryLabelDataset) – Dataset containing scores that will be used to compute predicted labels.

Returns:

dataset_pred (BinaryLabelDataset) – Output dataset with potentially fair predictions obtain using the ROC method.