GroupVariableImportanceMixin#

class hidimstat.base_variable_importance.GroupVariableImportanceMixin(features_groups=None)[source]#

Bases: object

Mixin class for adding group functionality to variable importance methods. This class provides functionality for handling grouped features in variable importance calculations, enabling group-wise selection and importance evaluation.

Parameters:

features_groups: dict or None, default=None: Dictionary mapping group names to lists of feature column names/indices. If None, each feature is treated as its own group.

Attributes:

n_features_groups_int: Number of feature groups.
_features_groups_idsarray-like: List of feature indices for each group.

Methods

fit(X, y=None)	Identifies feature groups and validates input data structure.
_check_fit()	Verifies if the instance has been fitted.
_check_compatibility(X)	Validates compatibility between input data and fitted groups.

__init__(features_groups=None)[source]#

fit(X, y=None)[source]#

Base fit method for perturbation-based methods. Identifies the groups.

Parameters:

X: array-like of shape (n_samples, n_features): The input samples.
y: array-like of shape (n_samples,): Not used, only present for consistency with the sklearn API.

Returns: