holisticai.bias.mitigation.FairKCenterClustering#

class holisticai.bias.mitigation.FairKCenterClustering(*args, **kargs)[source]#

Fair K-Center Clustering

Fair K-Center Clustering [1] inprocessing bias mitigation implements an approximation algorithm for the k-centers problem under the fairness contraint with running time linear in the size of the dataset and k (number of cluster).

Parameters

req_nr_per_grouplist

Number of cluster for each group that will be founded.

  • Integer-vector of length m with entries in 0,…,k.

  • Sum of all entries must be equal to k (total number of clusters).

nr_initially_givenint

Number of initial random centers.

strategystr

Strategy used to compute the cluster centers. Available are:

  • ‘Fair K-Center’ (default)

  • ‘Heuristic Greedy by Group’

  • ‘Heuristic Greedy by Constraint’

seedint,

Initial random seed.

Examples

>>> from holisticai.bias.mitigation import FairKCenterClustering
>>> mitigator = FairKCenterClustering(**params)
>>> mitigator.fit(train_data, group_a, group_b)
>>> test_data_transformed = mitigator.predict(test_data, group_a, group_b)

References

fit(X, group_a, group_b)[source]#

Fit model using Fair K-Center Clustering.

Parameters

Xmatrix-like

Input matrix

group_aarray-like

Group membership vector (binary)

group_barray-like

Group membership vector (binary)

Returns

Self

predict(X)[source]#

Predict the closest cluster each sample in X belongs to.

Parameters

Xmatrix-like

Input matrix

Returns

numpy array

A distance matrix between the samples and the clusters.

set_fit_request(*, group_a: bool | None | str = '$UNCHANGED$', group_b: bool | None | str = '$UNCHANGED$') → FairKCenterClustering#

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.

Added 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

group_astr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED

Metadata routing for group_a parameter in fit.

group_bstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED

Metadata routing for group_b parameter in fit.

Returns

selfobject

The updated object.