SAGE

class hidimstat.SAGE(estimator, method='predict', loss=<function mean_squared_error>, imputation='marginal', n_subsets=50, n_permutations=50, features_groups=None, random_state=None, n_jobs=1)

Bases: BaseVariableImportance, GroupVariableImportanceMixin

Shapley Additive Global Importance (SAGE) values for feature importance.

This method is presented in Covert et al.[1]. Read more in the User Guide.

Parameters:

estimatorobject

The fitted model for which to compute the SAGE values.

methodstr, default=”predict”

The method of the estimator to use for predictions.

losscallable, default=mean_squared_error

The loss function to use for computing the SAGE values.

imputationstr, default=”marginal”

The imputation strategy to use for sampling the complement of the subset. Currently, only “marginal” is implemented.

n_subsetsint, default=100

The number of subsets to sample for estimating the SAGE values, for each feature.

n_permutationsint, default=50

The number of samples to draw using the imputation strategy for each subset (the samples are drawn from the complement of the subset).

features_groupsdict, optional

Group of features for which to compute the importance. Default is None, which means that the importance of each individual feature will be computed.

random_stateint or RandomState, optional

Random state for reproducibility. Default is None.

n_jobsint, default=1

The number of parallel jobs to run for computing the SAGE values.

Notes

Added in version 0.4.0.

__init__(estimator, method='predict', loss=<function mean_squared_error>, imputation='marginal', n_subsets=50, n_permutations=50, features_groups=None, random_state=None, n_jobs=1)

fit(X, y=None)

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:

selfobject

Returns the instance itself.

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 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)

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()

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.SAGE

Shapley Additive Global Importance (SAGE) example

Shapley Additive Global Importance (SAGE) example