hfs.HNBs

class hfs.HNBs(hierarchy=None)

Select non-redundant features following the algorithm proposed by Wan and Freitas.

__init__(hierarchy=None)

Initializes a HNBs-Selector.

Parameters

hierarchy: np.ndarray

The hierarchy graph as an adjacency matrix.

select_and_predict(predict=True, saveFeatures=False, estimator=BernoulliNB())

Select features lazy for each test instance amd optionally predict target value of test instances. It selects the features, such that redundancy along each path is removed. Parameters ———- predict : bool

true if predictions shall be obtained.

saveFeaturesbool

true if features selected for each test instance shall be saved.

estimatorsklearn-compatible estimator

Estimator to use for predictions.

Returns

predictions for test input samples, if predict = false, returns empty array.