aif360.sklearn.preprocessing.Reweighing

class aif360.sklearn.preprocessing.Reweighing(prot_attr=None)

Sample reweighing.

Reweighing is a preprocessing technique that weights the examples in each (group, label) combination differently to ensure fairness before classification [1].

Note

This breaks the scikit-learn API by returning new sample weights from fit_transform(). See ReweighingMeta for a workaround.

See also

ReweighingMeta

References

[1]

F. Kamiran and T. Calders, “Data Preprocessing Techniques for Classification without Discrimination,” Knowledge and Information Systems, 2012.

Variables:

• prot_attr_ (str or list(str)) – Protected attribute(s) used for reweighing.

• groups_ (array, shape (n_groups,)) – A list of group labels known to the transformer.

• classes_ (array, shape (n_classes,)) – A list of class labels known to the transformer.

• reweigh_factors_ (array, shape (n_groups, n_labels)) – Reweighing factors for each combination of group and class labels used to debias samples. Existing sample weights are multiplied by the corresponding factor for that sample’s group and class.

Parameters:

prot_attr (single label or list-like, optional) – Protected attribute(s) to use in the reweighing process. If more than one attribute, all combinations of values (intersections) are considered. Default is None meaning all protected attributes from the dataset are used.

Methods

fit	Only fit_transform() is allowed for this algorithm.
fit_transform	Compute the factors for reweighing the dataset and transform the sample weights.
get_metadata_routing	Get metadata routing of this object.
get_params	Get parameters for this estimator.
set_fit_request	Request metadata passed to the fit method.
set_params	Set the parameters of this estimator.

__init__(prot_attr=None)

Parameters:

prot_attr (single label or list-like, optional) – Protected attribute(s) to use in the reweighing process. If more than one attribute, all combinations of values (intersections) are considered. Default is None meaning all protected attributes from the dataset are used.

fit(X, y, sample_weight=None)

Only fit_transform() is allowed for this algorithm.

fit_transform(X, y, sample_weight=None)

Compute the factors for reweighing the dataset and transform the sample weights.

Parameters:

• X (pandas.DataFrame) – Training samples.

• y (array-like) – Training labels.

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

Returns:

tuple – Samples and their weights.

• X – Unchanged samples.

• sample_weight – Transformed sample weights.

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

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.