PFICV

class hidimstat.PFICV(estimators, cv, statistical_test='nb-ttest', method='predict', loss=<function mean_squared_error>, n_permutations=50, features_groups=None, random_state=None, n_jobs=1)

Bases: BasePerturbationCV

Permutation Feature Importance (PFI) algorithm with Cross-Validation.

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.

methodstr, default=”predict”

The method to use for the prediction. This determines the predictions passed to the loss function. Supported methods are “predict”, “predict_proba” or “decision_function”.

losscallable, default=mean_squared_error

The loss function to use when comparing the perturbed model to the full model.

n_permutationsint, default=50

The number of permutations to perform. For each variable/group of variables, the mean of the losses over the n_permutations is computed.

features_groups: dict or None, default=None

A dictionary where the keys are the group names and the values are the list of column names corresponding to each features group. If None, the features_groups are identified based on the columns of X.

random_stateint or None, default=None

The random state to use for sampling.

n_jobsint, default=1

The number of jobs to run in parallel. Parallelization is done over the folds.

Attributes:

importance_estimators_list of PFI instances

The PFI instances fitted on each fold.

importances_ndarray of shape (n_groups, n_folds)

The calculated importance scores for each feature group and each fold. Higher values indicate greater importance.

pvalues_ndarray of shape (n_groups,)

The p-values for the 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', method='predict', loss=<function mean_squared_error>, n_permutations=50, features_groups=None, random_state=None, n_jobs=1)

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(X, y)

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

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(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.

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.PFICV

Feature Importance on diabetes dataset using cross-validation

Feature Importance on diabetes dataset using cross-validation