BasePerturbationCV

class hidimstat.base_perturbation.BasePerturbationCV(estimators, cv, statistical_test='nb-ttest', n_jobs: int = 1)

Bases: BaseVariableImportance

Base class for perturbation methods with cross-validation.

This class extends the BasePerturbation class to handle cross-validated. The fit is performed iteratively on each fold, and the importance is computed by computing the mean loss over samples of each fold. The statistical test is performed on the importance scores obtained from each fold.

Parameters:

estimators: list of sklearn estimators or single sklearn estimator

Can be a list of fitted sklearn estimators (one per fold) or a single sklearn estimator that will then be cloned and fitted on each fold.

cv: cross-validation generator

A cross-validation generator object (e.g., KFold, StratifiedKFold).

statistical_testcallable or str, default=”nb-ttest”

Statistical test function for computing p-values from importance scores. Defaults to Nadeau-Bengio test to deal with correlation across folds

n_jobsint, default=1

Number of parallel jobs for computation. Parallelization is done over the folds.

Attributes:

importance_estimators_list of BasePerturbation instances

List of BasePerturbation instances for each fold.

importances_ndarray of shape (n_groups, n_splits)

Importance scores for each fold and each group of covariates.

pvalues_ndarray of shape (n_groups,)

P-values for importance scores computed across folds.

estimators_list of sklearn estimators

List of fitted estimators for each fold.

test_train_frac_float

Fraction of test samples over train samples in each fold. Approximated as 1 / (n_splits - 1).

__init__(estimators, cv, statistical_test='nb-ttest', n_jobs: int = 1)

fit(X, y)

Fit the importance estimators on each fold of the cross-validation.

importance(X, y)

Compute the importance scores using cross-validation.

Parameters:

Xarray-like of shape (n_samples, n_features)

The input samples to compute importance scores for.

yarray-like of shape (n_samples,)

Returns:

importances_ndarray of shape (n_features, n_groups)

The importance scores for each group of features.

fdr_selection(fdr, fdr_control='bhq', reshaping_function=None, two_tailed_test=False)

Performs feature selection based on False Discovery Rate (FDR) control.

Parameters:

fdrfloat

The target false discovery rate level (between 0 and 1)

fdr_control: {‘bhq’, ‘bhy’}, default=’bhq’

The FDR control method to use: - ‘bhq’: Benjamini-Hochberg procedure - ‘bhy’: Benjamini-Hochberg-Yekutieli procedure

reshaping_function: callable or None, default=None

Optional reshaping function for FDR control methods. If None, defaults to sum of reciprocals for ‘bhy’.

two_tailed_test: bool, default=False

If True, performs two-tailed test selection using both p-values for positive effects and one-minus p-values for negative effects. The sign of the effect is determined from the sign of the importance scores.

Returns:

selectedndarray of int

Integer array indicating the selected features. 1 indicates selected features with positive effects, -1 indicates selected features with negative effects, 0 indicates non-selected features.

Raises:

ValueError

If importances_ haven’t been computed yet

AssertionError

If pvalues_ are missing or fdr_control is invalid

fit_importance(X, y)

Fit the model to the data and computes feature importance scores.

fwer_selection(fwer, procedure='bonferroni', n_tests=None, two_tailed_test=False)

Performs feature selection based on Family-Wise Error Rate (FWER) control.

Parameters:

fwerfloat

The target family-wise error rate level (between 0 and 1)

procedure{‘bonferroni’}, default=’bonferroni’

The FWER control method to use: - ‘bonferroni’: Bonferroni correction

n_testsint or None, default=None

Factor for multiple testing correction. If None, uses the number of clusters or the number of features in this order.

two_tailed_testbool, default=False

If True, uses the sign of the importance scores to indicate whether the selected features have positive or negative effects.

Returns:

selectedndarray of int

Integer array indicating the selected features. 1 indicates selected features with positive effects, -1 indicates selected features with negative effects, 0 indicates non-selected features.

get_metadata_routing()

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)

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.

importance_selection(k_best=None, percentile=None, threshold_max=None, threshold_min=None)

Selects features based on variable importance.

Parameters:

k_bestint, default=None

Selects the top k features based on importance scores.

percentilefloat, default=None

Selects features based on a specified percentile of importance scores.

threshold_maxfloat, default=None

Selects features with importance scores below the specified maximum threshold.

threshold_minfloat, default=None

Selects features with importance scores above the specified minimum threshold.

Returns:

selectionarray-like of shape (n_features,)

Binary array indicating the selected features.

plot_importance(ax=None, ascending=False, feature_names=None, **seaborn_barplot_kwargs)

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.

pvalue_selection(k_lowest=None, percentile=None, threshold_max=0.05, threshold_min=None, alternative_hypothesis=False)

Selects features based on p-values.

Parameters:

k_lowestint, default=None

Selects the k features with lowest p-values.

percentilefloat, default=None

Selects features based on a specified percentile of p-values.

threshold_maxfloat, default=0.05

Selects features with p-values below the specified maximum threshold (0 to 1).

threshold_minfloat, default=None

Selects features with p-values above the specified minimum threshold (0 to 1).

alternative_hypothesisbool, default=False

If True, selects based on 1-pvalues instead of p-values.

Returns:

selectionarray-like of shape (n_features,)

Binary array indicating the selected features (True for selected).

set_params(**params)

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:

**paramsdict

Estimator parameters.

Returns:

selfestimator instance

Estimator instance.

Examples using hidimstat.base_perturbation.BasePerturbationCV

Feature Importance on diabetes dataset using cross-validation

Feature Importance on diabetes dataset using cross-validation