Struct BayesianVariableSelector 

pub struct BayesianVariableSelector<State = Untrained> { /* private fields */ }

Bayesian variable selection with spike-and-slab priors

Implementations

impl BayesianVariableSelector<Untrained>

pub fn new() -> Self

Create a new Bayesian variable selector

pub fn prior(self, prior: PriorType) -> Self

Set the prior type

pub fn inference(self, inference: BayesianInferenceMethod) -> Self

Set the inference method

pub fn n_features_select(self, n_features: usize) -> Self

Set the number of features to select (if None, use threshold)

pub fn inclusion_threshold(self, threshold: Float) -> Self

Set the inclusion probability threshold

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

Set the random state for reproducibility

impl BayesianVariableSelector<Trained>

pub fn inclusion_probabilities(&self) -> &Array1<Float>

Get posterior inclusion probabilities

pub fn coefficients(&self) -> &Array1<Float>

Get feature coefficients

pub fn evidence(&self) -> Float

Get model evidence (log marginal likelihood)

pub fn n_features_out(&self) -> usize

Get the number of selected features

pub fn is_feature_selected(&self, feature_idx: usize) -> bool

Check if a feature was selected

Trait Implementations

impl<State: Clone> Clone for BayesianVariableSelector<State>

fn clone(&self) -> BayesianVariableSelector<State>

Returns a duplicate of the value.

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

Performs copy-assignment from source.

impl<State: Debug> Debug for BayesianVariableSelector<State>

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

Formats the value using the given formatter.

impl Default for BayesianVariableSelector<Untrained>

fn default() -> Self

Returns the “default value” for a type.

impl Estimator for BayesianVariableSelector<Untrained>

type 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 FeatureSelector for BayesianVariableSelector<Trained>

fn selected_features(&self) -> &Vec<usize>

Get selected feature indices

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

type Fitted = BayesianVariableSelector<Trained>

The fitted model type

fn fit( self, features: &Array2<Float>, target: &Array1<Float>, ) -> 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 SelectorMixin for BayesianVariableSelector<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 Transform<ArrayBase<OwnedRepr<f64>, Dim<[usize; 2]>>> for BayesianVariableSelector<Trained>

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

Transform the input data