Struct SequentialFeatureSelector 

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

Sequential Feature Selection (forward, backward, or bidirectional)

Implements greedy, cross-validation-based sequential feature selection. Uses the concrete-Fit macro pattern to avoid ndarray 0.17 HRTB issues that arise with fully-generic Y parameters in where bounds.

Implementations

impl<E: Clone> SequentialFeatureSelector<E, Untrained>

pub fn new(estimator: E) -> Self

Create a new SequentialFeatureSelector

pub fn n_features_to_select(self, n: usize) -> Self

Set the number of features to select

pub fn direction(self, direction: &str) -> Result<Self, SklearsError>

Set the direction (“forward”, “backward”, or “bidirectional”)

pub fn cv(self, cv_folds: usize) -> Self

Set the number of CV folds

impl<E> SequentialFeatureSelector<E, Trained>

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

Get the support mask

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

Get the selected feature indices

Trait Implementations

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

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

Formats the value using the given formatter.

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

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

type Fitted = SequentialFeatureSelector<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 SequentialFeatureSelector<E, Untrained>

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

type Fitted = SequentialFeatureSelector<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 SequentialFeatureSelector<E, Untrained>

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

type Fitted = SequentialFeatureSelector<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> Transform<ArrayBase<OwnedRepr<f64>, Dim<[usize; 2]>>> for SequentialFeatureSelector<E, Trained>

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

Transform the input data