v0.3.0#
Major changes to the API. In particular the
expgrad
function is now implemented by theExponentiatedGradient
class.Added new algorithms
Threshold Optimization
Grid Search
Added grouped metrics
Migrating to v0.3 from v0.2#
Up to version 0.2, Fairlearn contained only the exponentiated gradient method.
The Fairlearn repository now has a more comprehensive scope and aims to
incorporate other methods. The same exponentiated gradient technique is now
the class fairlearn.reductions.ExponentiatedGradient
. While in the past
exponentiated gradient was invoked via
import numpy as np
from fairlearn.classred import expgrad
from fairlearn.moments import DP
estimator = LogisticRegression() # or any other estimator
exponentiated_gradient_result = expgrad(X, sensitive_features, y, estimator, constraints=DP())
positive_probabilities = exponentiated_gradient_result.best_classifier(X)
randomized_predictions = (positive_probabilities >= np.random.rand(len(positive_probabilities))) * 1
the equivalent operation is now
from fairlearn.reductions import ExponentiatedGradient, DemographicParity
estimator = LogisticRegression() # or any other estimator
exponentiated_gradient = ExponentiatedGradient(estimator, constraints=DemographicParity())
exponentiated_gradient.fit(X, y, sensitive_features=sensitive_features)
randomized_predictions = exponentiated_gradient.predict(X)