Struct OnlineFeatureSelector 

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

Online feature selection using incremental statistics

This selector maintains running statistics for each feature and updates feature selection based on these statistics as new data arrives.

Implementations

impl OnlineFeatureSelector<Untrained>

pub fn new(k: usize) -> Self

Create a new online feature selector

pub fn window_size(self, window_size: usize) -> Self

Set the window size for concept drift detection

pub fn decay_factor(self, decay_factor: f64) -> Self

Set the decay factor for exponential moving statistics

pub fn min_samples(self, min_samples: usize) -> Self

Set minimum samples before selection begins

impl OnlineFeatureSelector<Trained>

pub fn partial_fit_sample( &mut self, sample: &Array1<Float>, target: Float, ) -> SklResult<()>

Update the selector with a new sample

pub fn detect_concept_drift(&self) -> SklResult<bool>

Detect concept drift using window statistics

pub fn reset(&mut self) -> SklResult<()>

Reset the selector (useful when concept drift is detected)

Trait Implementations

impl<State: Clone> Clone for OnlineFeatureSelector<State>

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

Returns a duplicate of the value.

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

Performs copy-assignment from source.

impl<State: Debug> Debug for OnlineFeatureSelector<State>

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

Formats the value using the given formatter.

impl Default for OnlineFeatureSelector<Untrained>

fn default() -> Self

Returns the “default value” for a type.

impl Estimator for OnlineFeatureSelector<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 OnlineFeatureSelector<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 OnlineFeatureSelector<Untrained>

type Fitted = OnlineFeatureSelector<Trained>

The fitted model type

fn fit(self, x: &Array2<Float>, y: &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 Transform<ArrayBase<OwnedRepr<f64>, Dim<[usize; 2]>>> for OnlineFeatureSelector<Trained>

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

Transform the input data