aif360.sklearn.preprocessing
.ReweighingMeta¶
-
class
aif360.sklearn.preprocessing.
ReweighingMeta
(estimator, reweigher=Reweighing())[source]¶ 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 thenself.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())[source]¶ Parameters: - estimator (sklearn.BaseEstimator) – Estimator to be wrapped.
- reweigher – Preprocessor which returns new sample weights from
transform()
.
-
fit
(X, y, sample_weight=None)[source]¶ Performs
self.reweigher_.fit_transform(X, y, sample_weight)
and thenself.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)[source]¶ 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)[source]¶ 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)[source]¶ 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)[source]¶ 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)