aif360.algorithms.inprocessing.MetaFairClassifier

class aif360.algorithms.inprocessing.MetaFairClassifier(tau=0.8, sensitive_attr='', type='fdr', seed=None)[source]

The meta algorithm here takes the fairness metric as part of the input and returns a classifier optimized w.r.t. that fairness metric [11].

References

Parameters:

  • tau (double, optional) – Fairness penalty parameter.

  • sensitive_attr (str, optional) – Name of protected attribute.

  • type (str, optional) – The type of fairness metric to be used. Currently “fdr” (false discovery rate ratio) and “sr” (statistical rate/disparate impact) are supported. To use another type, the corresponding optimization class has to be implemented.

  • seed (int, optional) – Random seed.

Methods

fit

Learns the fair classifier.

fit_predict

Train a model on the input and predict the labels.

fit_transform

Train a model on the input and transform the dataset accordingly.

predict

Obtain the predictions for the provided dataset using the learned classifier model.

transform

Return a new dataset generated by running this Transformer on the input.
__init__(tau=0.8, sensitive_attr='', type='fdr', seed=None)[source]
Parameters:

  • tau (double, optional) – Fairness penalty parameter.

  • sensitive_attr (str, optional) – Name of protected attribute.

  • type (str, optional) – The type of fairness metric to be used. Currently “fdr” (false discovery rate ratio) and “sr” (statistical rate/disparate impact) are supported. To use another type, the corresponding optimization class has to be implemented.

  • seed (int, optional) – Random seed.

fit(dataset)[source]

Learns the fair classifier.

Parameters:

dataset (BinaryLabelDataset) – Dataset containing true labels.

Returns:

MetaFairClassifier – Returns self.

predict(dataset)[source]

Obtain the predictions for the provided dataset using the learned classifier model.

Parameters:

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

Returns:

BinaryLabelDataset – Transformed dataset.