GreedySelector#

class equisolve.numpy._selection.GreedySelector(selector_class: Type[GreedySelector], selection_type: str, **selector_arguments)[source]#

Wraps skmatter._selection.GreedySelector for a TensorMap.

The class creates a selector for each block. The selection will be done based the values of each TensorBlock. Gradients will not be considered for the selection.

fit(X: TensorMap, warm_start: bool = False) → None[source]#

Learn the features to select.

Parameters:

  • X – Training vectors.

  • warm_start – Whether the fit should continue after having already run, after increasing n_to_select. Assumes it is called with the same X.

fit_transform(X: TensorMap, warm_start: bool = False) → TensorMap[source]#

Fit to data, then transform it.

Parameters:

  • X – Training vectors.

  • warm_start – Whether the fit should continue after having already run, after increasing n_to_select. Assumes it is called with the same X.

property selection_type: str#

Whether to choose a subset of columns (‘feature’) or rows (‘sample’).

property selector_arguments: dict#

Arguments passed to the selector_class.

property selector_class: Type[GreedySelector]#

The class to perform the selection.

property support: TensorMap#

TensorMap containing the support.

transform(X: TensorMap) → TensorMap[source]#

Reduce X to the selected features.

Parameters:

X – The input tensor.

Returns:

The selected subset of the input.