ClusterImportance#

class hidimstat.ClusterImportance(vim, clustering)[source]#

Bases: BaseVariableImportance

Clustered inference with any variable importance method.

This algorithm computes a single clustered inference on groups of features using an arbitrary variable importance measure for statistical inference.

Parameters:

vim: hidimstat.BaseVariableImportance: An instance of any variable importance method that derives from hidimstat’s BaseVariableImportance.
clustering: sklearn.cluster.FeatureAgglomeration: An instance of a clustering method that operates on features.

Attributes:

vim_hidimstat.BaseVariableImportance: Fitted variable importance method that will be clustered.
clustering_sklearn.cluster.FeatureAgglomeration: Fitted clustering object.
clustering_samples_ndarray, (n_samples*cluster_frac,): Indices of samples used for clustering.
importances_ndarray, shape (n_clusters,) or (n_clusters, n_tasks): Estimated coefficients at cluster level.
pvalues_ndarray, shape (n_clusters,): P-values for each cluster.
n_features_int: Number of features in the original data.

Notes

Added in version 0.4.0.

__init__(vim, clustering)[source]#

fit(X, y)[source]#

Fit the clustering and variable importance method on the data.

Parameters:

Xndarray, shape (n_samples, n_features): Input data matrix.
yndarray, shape (n_samples,) or (n_samples, n_tasks): Target variable(s).

Returns:

selfCluVI: Fitted estimator.

importance(X, y)[source]#

Compute feature importance from the underlying variable importance method. Then map the importance scores from cluster level back to feature level.

Parameters:

Xndarray, shape (n_samples, n_features): Input data matrix.
yndarray, shape (n_samples,) or (n_samples, n_tasks): Target variable(s).

fit_importance(X, y)[source]#

Fit the model and compute feature importance.

Parameters:

Xndarray, shape (n_samples, n_features): Input data matrix.
yndarray, shape (n_samples,) or (n_samples, n_tasks): Target variable(s).

Returns:

importances_ndarray, shape (n_features,) or (n_features, n_tasks): Estimated importance values at feature level.

fdr_selection(fdr, fdr_control='bhq', reshaping_function=None, two_tailed_test=False)[source]#

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 of shape (n_features,): 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

fwer_selection(fwer, procedure='bonferroni', n_tests=None, two_tailed_test=False)[source]#

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 of shape (n_features,): 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()[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.

importance_selection(k_best=None, percentile=None, threshold_max=None, threshold_min=None)[source]#

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)[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.

pvalue_selection(k_lowest=None, percentile=None, threshold_max=0.05, threshold_min=None, alternative_hypothesis=False)[source]#

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)[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:

**paramsdict: Estimator parameters.

Returns:

selfestimator instance: Estimator instance.

Examples using `hidimstat.ClusterImportance`#

Support Recovery on fMRI Data

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ClusterImportance#

Examples using hidimstat.ClusterImportance#

Examples using `hidimstat.ClusterImportance`#