Struct StabilitySelector 

pub struct StabilitySelector<E, State = Untrained> { /* private fields */ }

Stability selection using randomized feature selection with subsampling

Stability selection combines subsampling with feature selection algorithms to provide reproducible and stable feature selection results. It selects features that are consistently chosen across different bootstrap samples of the data.

Implementations

impl<E> StabilitySelector<E, Untrained>

pub fn new(estimator: E) -> Self

Create a new stability selector

pub fn n_bootstrap(self, n_bootstrap: usize) -> Result<Self, SklearsError>

Set the number of bootstrap iterations

pub fn subsample_fraction(self, fraction: f64) -> Result<Self, SklearsError>

Set the fraction of samples to use in each bootstrap iteration

pub fn selection_threshold(self, threshold: f64) -> Result<Self, SklearsError>

Set the threshold for feature selection stability Features selected in at least this fraction of bootstrap iterations are considered stable

pub fn max_features(self, max_features: Option<usize>) -> Self

Set maximum number of features to select (if None, all stable features are selected)

pub fn random_state(self, seed: u64) -> Self

Set random state for reproducible results

impl<E> StabilitySelector<E, Trained>

pub fn selection_probabilities(&self) -> SklResult<&Array1<Float>>

Get the selection probabilities for all features

pub fn stable_features(&self) -> SklResult<&[usize]>

Get the stable features

pub fn bootstrap_results(&self) -> SklResult<&[Vec<usize>]>

Get all bootstrap selection results for analysis

pub fn stability_score(&self) -> SklResult<f64>

Calculate the stability score as the average selection probability of selected features

pub fn n_features(&self) -> SklResult<usize>

Get the number of features in the original dataset

Trait Implementations

impl<E: Clone, State: Clone> Clone for StabilitySelector<E, State>

fn clone(&self) -> Self

Returns a duplicate of the value.

fn clone_from(&mut self, source: &Self)

Performs copy-assignment from source.

impl<E: Debug, State: Debug> Debug for StabilitySelector<E, State>

fn fmt(&self, f: &mut Formatter<'_>) -> Result

Formats the value using the given formatter.

impl<E> Default for StabilitySelector<E, Untrained>

where E: Default,

fn default() -> Self

Returns the “default value” for a type.

impl<E> Estimator for StabilitySelector<E, Untrained>

where E: Estimator,

type Config = <E as Estimator>::Config

Configuration type for the estimator

type Error = SklearsError

Error type for the estimator

type Float = f64

The numeric type used by this estimator

fn config(&self) -> &Self::Config

Get estimator configuration

fn validate_config(&self) -> Result<(), SklearsError>

Validate estimator configuration with detailed error context

fn check_compatibility( &self, n_samples: usize, n_features: usize, ) -> Result<(), SklearsError>

Check if estimator is compatible with given data dimensions

fn metadata(&self) -> EstimatorMetadata

Get estimator metadata

impl<E> Fit<ArrayBase<OwnedRepr<f64>, Dim<[usize; 2]>, f64>, ArrayBase<OwnedRepr<f64>, Dim<[usize; 1]>, f64>> for StabilitySelector<E, Untrained>

where E: Clone + Fit<ArrayBase<OwnedRepr<f64>, Dim<[usize; 2]>, f64>, ArrayBase<OwnedRepr<f64>, Dim<[usize; 1]>, f64>> + Send + Sync, E::Fitted: FeatureImportance + Send + Sync,

type Fitted = StabilitySelector<E, Trained>

The fitted model type

fn fit( self, x: &ArrayBase<OwnedRepr<f64>, Dim<[usize; 2]>, f64>, y: &ArrayBase<OwnedRepr<f64>, Dim<[usize; 1]>, f64>, ) -> SklResult<Self::Fitted>

Fit the model to the provided data with validation

fn fit_with_validation( self, x: &X, y: &Y, _x_val: Option<&X>, _y_val: Option<&Y>, ) -> Result<(Self::Fitted, FitMetrics), SklearsError>

where Self: Sized,

Fit with custom validation and early stopping

impl<E> Fit<ArrayBase<OwnedRepr<f64>, Dim<[usize; 2]>, f64>, ArrayBase<OwnedRepr<i32>, Dim<[usize; 1]>, i32>> for StabilitySelector<E, Untrained>

where E: Clone + Fit<ArrayBase<OwnedRepr<f64>, Dim<[usize; 2]>, f64>, ArrayBase<OwnedRepr<i32>, Dim<[usize; 1]>, i32>> + Send + Sync, E::Fitted: FeatureImportance + Send + Sync,

type Fitted = StabilitySelector<E, Trained>

The fitted model type

fn fit( self, x: &ArrayBase<OwnedRepr<f64>, Dim<[usize; 2]>, f64>, y: &ArrayBase<OwnedRepr<i32>, Dim<[usize; 1]>, i32>, ) -> SklResult<Self::Fitted>

Fit the model to the provided data with validation

fn fit_with_validation( self, x: &X, y: &Y, _x_val: Option<&X>, _y_val: Option<&Y>, ) -> Result<(Self::Fitted, FitMetrics), SklearsError>

where Self: Sized,

Fit with custom validation and early stopping

impl<E> Fit<ArrayBase<OwnedRepr<f64>, Dim<[usize; 2]>, f64>, ArrayBase<OwnedRepr<usize>, Dim<[usize; 1]>, usize>> for StabilitySelector<E, Untrained>

where E: Clone + Fit<ArrayBase<OwnedRepr<f64>, Dim<[usize; 2]>, f64>, ArrayBase<OwnedRepr<usize>, Dim<[usize; 1]>, usize>> + Send + Sync, E::Fitted: FeatureImportance + Send + Sync,

type Fitted = StabilitySelector<E, Trained>

The fitted model type

fn fit( self, x: &ArrayBase<OwnedRepr<f64>, Dim<[usize; 2]>, f64>, y: &ArrayBase<OwnedRepr<usize>, Dim<[usize; 1]>, usize>, ) -> SklResult<Self::Fitted>

Fit the model to the provided data with validation

fn fit_with_validation( self, x: &X, y: &Y, _x_val: Option<&X>, _y_val: Option<&Y>, ) -> Result<(Self::Fitted, FitMetrics), SklearsError>

where Self: Sized,

Fit with custom validation and early stopping

impl<E> SelectorMixin for StabilitySelector<E, Trained>

fn get_support(&self) -> SklResult<Array1<bool>>

Get support mask

fn transform_features(&self, indices: &[usize]) -> SklResult<Vec<usize>>

Transform by selecting features

impl<E> Transform<ArrayBase<OwnedRepr<f64>, Dim<[usize; 2]>>> for StabilitySelector<E, Trained>

fn transform(&self, x: &Array2<Float>) -> SklResult<Array2<Float>>

Transform the input data