Struct DynamicEnsembleSelector 

pub struct DynamicEnsembleSelector<S = Untrained> { /* private fields */ }

Dynamic Ensemble Selector

Dynamically selects the best subset of classifiers for each prediction based on local competence estimation.

Examples

use sklears_compose::{DynamicEnsembleSelector, MockPredictor, SelectionStrategy};
use scirs2_core::ndarray::array;

let selector = DynamicEnsembleSelector::builder()
    .estimator("clf1", Box::new(MockPredictor::new()))
    .estimator("clf2", Box::new(MockPredictor::new()))
    .selection_strategy(SelectionStrategy::KBest { k: 2 })
    .build();

Implementations

impl DynamicEnsembleSelector<Untrained>

pub fn new() -> Self

Create a new DynamicEnsembleSelector

pub fn builder() -> DynamicEnsembleSelectorBuilder

Create a dynamic ensemble selector builder

pub fn add_estimator( &mut self, name: String, estimator: Box<dyn PipelinePredictor>, )

Add an estimator

pub fn selection_strategy(self, strategy: SelectionStrategy) -> Self

Set selection strategy

pub fn competence_estimation(self, method: CompetenceEstimation) -> Self

Set competence estimation method

pub fn validation_split(self, split: f64) -> Self

Set validation split ratio

pub fn n_jobs(self, n_jobs: Option<i32>) -> Self

Set number of jobs

impl DynamicEnsembleSelector<DynamicEnsembleSelectorTrained>

pub fn predict(&self, x: &ArrayView2<'_, Float>) -> SklResult<Array1<f64>>

Predict using dynamic ensemble selection

pub fn estimators(&self) -> &[(String, Box<dyn PipelinePredictor>)]

Get fitted estimators

Trait Implementations

impl Default for DynamicEnsembleSelector<Untrained>

fn default() -> Self

Returns the “default value” for a type.

impl Estimator for DynamicEnsembleSelector<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 Fit<ArrayBase<ViewRepr<&f64>, Dim<[usize; 2]>>, Option<&ArrayBase<ViewRepr<&f64>, Dim<[usize; 1]>>>> for DynamicEnsembleSelector<Untrained>

type Fitted = DynamicEnsembleSelector<DynamicEnsembleSelectorTrained>

The fitted model type

fn fit( self, x: &ArrayView2<'_, Float>, y: &Option<&ArrayView1<'_, 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