aif360.sklearn.preprocessing.ReweighingMeta

class aif360.sklearn.preprocessing.ReweighingMeta(estimator, reweigher=Reweighing())

A meta-estimator which wraps a given estimator with a reweighing preprocessing step.

This is necessary for use in a Pipeline, etc.

Variables:

• estimator (sklearn.BaseEstimator) – The fitted underlying estimator.
• reweigher – The fitted underlying reweigher.

Parameters:

• estimator (sklearn.BaseEstimator) – Estimator to be wrapped.
• reweigher – Preprocessor which returns new sample weights from transform().

Methods

fit	Performs self.reweigher_.fit_transform(X, y, sample_weight) and then self.estimator_.fit(X, y, sample_weight) using the reweighed samples.
get_params	Get parameters for this estimator.
predict	Predict class labels for the given samples using self.estimator_.
predict_log_proba	Log of probability estimates from self.estimator_.
predict_proba	Probability estimates from self.estimator_.
score	Returns the output of the estimator’s score function on the given test data and labels.
set_params	Set the parameters of this estimator.

__init__(estimator, reweigher=Reweighing())

Parameters:

• estimator (sklearn.BaseEstimator) – Estimator to be wrapped.
• reweigher – Preprocessor which returns new sample weights from transform().

fit(X, y, sample_weight=None)

Performs self.reweigher_.fit_transform(X, y, sample_weight) and then self.estimator_.fit(X, y, sample_weight) using the reweighed samples.

Parameters:

• X (pandas.DataFrame) – Training samples.
• y (array-like) – Training labels.
• sample_weight (array-like, optional) – Sample weights.

Returns:

self

predict(X)

Predict class labels for the given samples using self.estimator_.

Parameters:X (array-like) – Test samples. Returns:array – Predicted class label per sample.

predict_log_proba(X)

Log of probability estimates from self.estimator_.

The returned estimates for all classes are ordered by the label of classes.

Parameters:X (array-like) – Test samples. Returns:array – Returns the log-probability of the sample for each class in the model, where classes are ordered as they are in self.classes_.

predict_proba(X)

Probability estimates from self.estimator_.

The returned estimates for all classes are ordered by the label of classes.

Parameters:X (array-like) – Test samples. Returns:array – Returns the probability of the sample for each class in the model, where classes are ordered as they are in self.classes_.

score(X, y, sample_weight=None)

Returns the output of the estimator’s score function on the given test data and labels.

Parameters:

• X (array-like) – Test samples.
• y (array-like) – True labels for X.
• sample_weight (array-like, optional) – Sample weights.

Returns:

float – self.estimator.score(X, y, sample_weight)