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)[source]

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)[source]
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)[source]

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:
Returns:

RejectOptionClassification – Returns self.
fit_predict(dataset_true, dataset_pred)[source]

fit and predict methods sequentially.

predict(dataset)[source]

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.