holisticai.bias.mitigation.VariationalFairClustering#

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

Variational Fair Clustering [1] helps you to find clusters with specified proportions of different demographic groups pertaining to a sensitive attribute of the dataset (group_a and group_b) for any well-known clustering method such as K-means, K-median or Spectral clustering (Normalized cut).

Parameters

n_clustersint: The number of clusters to form as well as the number of centroids to generate.
lipchitz_valuefloat: Lipchitz value in bound update
lmbdafloat: specified lambda parameter
methodstr: cluster option : {‘kmeans’, ‘kmedian’}
normalize_inputstr: Normalize input data X
seedint: Random seed.
verbosebool: If true , print metrics

Examples

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

References

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

Fit the model

Description

Learn a fair cluster.

Parameters

Xnumpy array: input matrix
group_anumpy array: binary mask vector
group_bnumpy array: binary mask vector

Returns

self

fit_predict(X: ndarray, group_a: ndarray, group_b: ndarray)[source]#

Prediction

Description

Fit and Predict the cluster for the given samples.

Parameters

Xpandas.DataFrame or numpy array: Test samples.
group_anumpy array: binary mask vector
group_bnumpy array: binary mask vector

Returns

numpy.ndarray: Predicted cluster per sample.

predict(X: ndarray, group_a: ndarray, group_b: ndarray)[source]#

Prediction

Description

Predict cluster for the given samples.

Parameters

Xpandas.DataFrame or numpy array: Test samples.
group_anumpy array: binary mask vector
group_bnumpy array: binary mask vector

Returns

numpy.ndarray: Predicted output per sample.

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

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.

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

Request metadata passed to the predict 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 predict 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 predict.
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 predict.
group_bstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED: Metadata routing for group_b parameter in predict.

Returns

selfobject: The updated object.