holisticai.bias.mitigation.WassersteinBarycenter#

class holisticai.bias.mitigation.WassersteinBarycenter(*args, **kargs)[source]#

Fair Regression with Wasserstein Barycenters learning a real-valued function that satisfies the Demographic Parity constraint [1] . The strategy founds the optimal fair predictor computing the Wasserstein barycenter of the distributions induced by the standard regression function on the sensitive groups.

Examples

>>> from holisticai.bias.mitigation import WassersteinBarycenter
>>> mitigator = WassersteinBarycenter()
>>> mitigator.fit_transform(y_pred, group_a, group_b)
>>> test_data_transformed = mitigator.transform(y_pred, group_a, group_b)

References

fit(y_pred: ndarray, group_a: ndarray, group_b: ndarray)[source]#

Compute parameters for calibrated equalized odds.

Description

Compute parameters for calibrated equalized odds algorithm.

Parameters

y_predarray-like: Predicted vector (num_examples,).
group_aarray-like: Group membership vector (binary)
group_barray-like: Group membership vector (binary)

Returns

Self

fit_transform(y_pred: ndarray, group_a: ndarray, group_b: ndarray)[source]#

Fit and transform

Description

Fit and transform

Parameters

y_predarray-like: Predicted vector (num_examples,).
group_aarray-like: Group membership vector (binary)
group_barray-like: Group membership vector (binary)

Returns

dict: A dictionary with new predictions

transform(y_pred: ndarray, group_a: ndarray, group_b: ndarray)[source]#

Apply transform function to predictions and likelihoods

Description

Use a fitted probability to change the output label and invert the likelihood

Parameters

y_predarray-like: Predicted vector (nb_examples,)
group_aarray-like: Group membership vector (binary)
group_barray-like: Group membership vector (binary)
thresholdfloat: float value to discriminate between 0 and 1

Returns

dict: A dictionary with new predictions