hidimstat.base_variable_importance.BaseVariableImportance#

class hidimstat.base_variable_importance.BaseVariableImportance[source]#

Bases: BaseEstimator

Base class for variable importance methods.

This class provides a foundation for implementing variable importance methods, including feature selection based on importance scores and p-values.

Attributes:

importances_array-like of shape (n_features,), default=None: The computed importance scores for each feature.
pvalues_array-like of shape (n_features,), default=None: The computed p-values for each feature.
selections_array-like of shape (n_features,), default=None: Binary mask indicating selected features.

Methods

selection(k_best=None, percentile=None, threshold=None, threshold_pvalue=None)	Selects features based on importance scores and/or p-values using various criteria.
_check_importance()	Checks if importance scores and p-values have been computed.

__init__()[source]#

selection(k_best=None, percentile=None, threshold=None, threshold_pvalue=None)[source]#

Selects features based on variable importance. In case several arguments are different from None, the returned selection is the conjunction of all of them.

Parameters:

k_bestint, optional, default=None: Selects the top k features based on importance scores.
percentilefloat, optional, default=None: Selects features based on a specified percentile of importance scores.
thresholdfloat, optional, default=None: Selects features with importance scores above the specified threshold.
threshold_pvaluefloat, optional, default=None: Selects features with p-values below the specified threshold.

Returns:

selectionarray-like of shape (n_features,): Binary array indicating the selected features.

plot_importance(ax=None, ascending=False, **seaborn_barplot_kwargs)[source]#

Plot feature importances as a horizontal bar plot.

Parameters:

axmatplotlib.axes.Axes or None, (default=None): Axes object to draw the plot onto, otherwise uses the current Axes.
ascending: bool, default=False: Whether to sort features by ascending importance.
**seaborn_barplot_kwargsadditional keyword arguments: Additional arguments passed to seaborn.barplot. https://seaborn.pydata.org/generated/seaborn.barplot.html

Returns:

axmatplotlib.axes.Axes: The Axes object with the plot.

get_metadata_routing()[source]#

Get metadata routing of this object.

Please check User Guide on how the routing mechanism works.

Returns:

routingMetadataRequest: A MetadataRequest encapsulating routing information.

get_params(deep=True)[source]#

Get parameters for this estimator.

Parameters:

deepbool, default=True: If True, will return the parameters for this estimator and contained subobjects that are estimators.

Returns:

paramsdict: Parameter names mapped to their values.

set_params(**params)[source]#

Set the parameters of this estimator.

The method works on simple estimators as well as on nested objects (such as Pipeline). The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.

Parameters: