Feature selection

Wrappers for the feature selectors of scikit-matter.

class equisolve.numpy.feature_selection.CUR(recompute_every=1, k=1, tolerance=1e-12, n_to_select=None, score_threshold=None, score_threshold_type='absolute', progress_bar=False, full=False, random_state=0)

Bases: GreedySelector

Transformer that performs Greedy Feature Selection with CUR.

If n_to_select is an int, all blocks will have this many features selected. In this case, n_to_select must be <= than the fewest number of features in any block.

If n_to_select is a dict, it must have keys that are tuples corresponding to the key values of each block. In this case, the values of the n_to_select dict can be int that specify different number of features to select for each block.

If n_to_select is -1, all features for every block will be selected.

Refer to skmatter.feature_selection.CUR for full documentation.

class equisolve.numpy.feature_selection.FPS(initialize=0, n_to_select=None, score_threshold=None, score_threshold_type='absolute', progress_bar=False, full=False, random_state=0)

Bases: GreedySelector

Transformer that performs Greedy Feature Selection using Farthest Point Sampling.

If n_to_select is an int, all blocks will have this many features selected. In this case, n_to_select must be <= than the fewest number of features in any block.

If n_to_select is a dict, it must have keys that are tuples corresponding to the key values of each block. In this case, the values of the n_to_select dict can be int that specify different number of features to select for each block.

If n_to_select is -1, all features for every block will be selected. This is useful, for instance, for plotting Hausdorff distances, which can be accessed through the selector.haussdorf_at_select property after calling the fit() method.

Refer to skmatter.feature_selection.FPS for full documentation.