aif360.sklearn.postprocessing.PostProcessingMeta

class aif360.sklearn.postprocessing.PostProcessingMeta(estimator, postprocessor, *, prefit=False, val_size=0.25, **options)

A meta-estimator which wraps a given estimator with a post-processing step.

The post-processor trains on a separate training set from the estimator to prevent leakage.

Note

Because of the dataset splitting, if a Pipeline is necessary it should be used as the input to this meta-estimator not the other way around.

Variables:

• estimator_ – Fitted estimator.

• postprocessor_ – Fitted postprocessor.

• classes_ (array, shape (n_classes,)) – Class labels from estimator_.

Parameters:

• estimator (sklearn.BaseEstimator) – Original estimator.

• postprocessor – Post-processing algorithm.

• prefit (bool) – If True, it is assumed that estimator has been fitted already and all data is used to train postprocessor.

• val_size (int or float) – Size of validation set used to fit the postprocessor. The estimator fits on the remainder of the training set. See train_test_split() for details.

• **options – Keyword options passed through to train_test_split(). Note: ‘train_size’ and ‘test_size’ will be ignored in favor of ‘val_size’.

Methods

fit	Splits the training samples with train_test_split() and uses the resultant 'train' portion to train the estimator.
get_metadata_routing	Get metadata routing of this object.
get_params	Get parameters for this estimator.
predict	Predict class labels for the given samples.
predict_log_proba	Log of probability estimates.
predict_proba	Probability estimates.
score	Returns the output of the post-processor's score function on the given test data and labels.
set_fit_request	Request metadata passed to the fit method.
set_params	Set the parameters of this estimator.
set_score_request	Request metadata passed to the score method.

Attributes

classes_

Class labels from the base estimator.

__init__(estimator, postprocessor, *, prefit=False, val_size=0.25, **options)

Parameters:

• estimator (sklearn.BaseEstimator) – Original estimator.

• postprocessor – Post-processing algorithm.

• prefit (bool) – If True, it is assumed that estimator has been fitted already and all data is used to train postprocessor.

• val_size (int or float) – Size of validation set used to fit the postprocessor. The estimator fits on the remainder of the training set. See train_test_split() for details.

• **options – Keyword options passed through to train_test_split(). Note: ‘train_size’ and ‘test_size’ will be ignored in favor of ‘val_size’.

property classes_

Class labels from the base estimator.

fit(X, y, sample_weight=None, **fit_params)

Splits the training samples with train_test_split() and uses the resultant ‘train’ portion to train the estimator. Then the estimator predicts on the ‘test’ portion of the split data and the post-processor is trained with those prediction-ground-truth target pairs.

Parameters:

• X (array-like) – Training samples.

• y (pandas.Series) – Training labels.

• sample_weight (array-like, optional) – Sample weights.

• **fit_params – Parameters passed to the post-processor fit() method. Note: these do not need to be prefixed with __ notation.

Returns:

self

predict(X)

Predict class labels for the given samples.

First, runs self.estimator_.predict() (or predict_proba() if required) then returns the post-processed output from those predictions.

Parameters:

X (pandas.DataFrame) – Test samples.

Returns:

numpy.ndarray – Predicted class label per sample.

predict_log_proba(X)

Log of probability estimates.

First, runs self.estimator_.predict() (or predict_proba() if required) then returns the post-processed output from those predictions.

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

Parameters:

X (pandas.DataFrame) – 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.

First, runs self.estimator_.predict() (or predict_proba() if required) then returns the post-processed output from those predictions.

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

Parameters:

X (pandas.DataFrame) – Test samples.

Returns:

numpy.ndarray – 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 post-processor’s score function on the given test data and labels.

First, runs self.estimator_.predict() (or predict_proba() if required) then gets the post-processed output from those predictions and scores it.

Parameters:

• X (pandas.DataFrame) – Test samples.

• y (array-like) – True labels for X.

• sample_weight (array-like, optional) – Sample weights.

Returns:

float – Score value.

set_fit_request(*, sample_weight: bool | None | str = '$UNCHANGED$') → PostProcessingMeta

Request metadata passed to the fit method.

Note that this method is only relevant if enable_metadata_routing=True (see sklearn.set_config()). Please see User Guide on how the routing mechanism works.

The options for each parameter are:

• True: metadata is requested, and passed to fit if provided. The request is ignored if metadata is not provided.

• False: metadata is not requested and the meta-estimator will not pass it to fit.

• None: metadata is not requested, and the meta-estimator will raise an error if the user provides it.

• str: metadata should be passed to the meta-estimator with this given alias instead of the original name.

The default (sklearn.utils.metadata_routing.UNCHANGED) retains the existing request. This allows you to change the request for some parameters and not others.

New in version 1.3.

Note

This method is only relevant if this estimator is used as a sub-estimator of a meta-estimator, e.g. used inside a Pipeline. Otherwise it has no effect.

Parameters:

sample_weight (str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED) – Metadata routing for sample_weight parameter in fit.

Returns:

self (object) – The updated object.

set_score_request(*, sample_weight: bool | None | str = '$UNCHANGED$') → PostProcessingMeta

Request metadata passed to the score method.

Note that this method is only relevant if enable_metadata_routing=True (see sklearn.set_config()). Please see User Guide on how the routing mechanism works.

The options for each parameter are:

• True: metadata is requested, and passed to score if provided. The request is ignored if metadata is not provided.

• False: metadata is not requested and the meta-estimator will not pass it to score.

• None: metadata is not requested, and the meta-estimator will raise an error if the user provides it.

• str: metadata should be passed to the meta-estimator with this given alias instead of the original name.

The default (sklearn.utils.metadata_routing.UNCHANGED) retains the existing request. This allows you to change the request for some parameters and not others.

New in version 1.3.

Note

This method is only relevant if this estimator is used as a sub-estimator of a meta-estimator, e.g. used inside a Pipeline. Otherwise it has no effect.

Parameters:

sample_weight (str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED) – Metadata routing for sample_weight parameter in score.

Returns:

self (object) – The updated object.