Struct WarmStartRegressor 

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

Warm Start Multi-Output Regressor

Multi-output regressor with warm start capabilities for iterative optimization. Supports resuming training from previous state and early stopping.

Examples

use sklears_multioutput::performance::{WarmStartRegressor, WarmStartRegressorConfig};
// Use SciRS2-Core for arrays and random number generation (SciRS2 Policy)
use scirs2_core::ndarray::array;
use sklears_core::traits::{Fit, Predict};

let X = array![[1.0, 2.0], [2.0, 3.0], [3.0, 4.0]];
let y = array![[1.0, 2.0], [2.0, 3.0], [3.0, 4.0]];

let mut config = WarmStartRegressorConfig::default();
config.max_iter = 100;

let model = WarmStartRegressor::new().config(config);
let trained = model.fit(&X.view(), &y.view()).unwrap();

// Continue training with warm start
let continued = trained.continue_training(&X.view(), &y.view(), 50).unwrap();

let predictions = continued.predict(&X.view()).unwrap();
assert_eq!(predictions.dim(), (3, 2));

Implementations

impl WarmStartRegressor<Untrained>

pub fn new() -> Self

Create a new warm start regressor

pub fn config(self, config: WarmStartRegressorConfig) -> Self

Set the configuration

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

Set maximum iterations

pub fn learning_rate(self, lr: Float) -> Self

Set learning rate

pub fn early_stopping(self, config: EarlyStoppingConfig) -> Self

Enable early stopping

impl WarmStartRegressor<WarmStartRegressorTrained>

pub fn continue_training( self, X: &ArrayView2<'_, Float>, y: &ArrayView2<'_, Float>, additional_iterations: usize, ) -> SklResult<Self>

Continue training from current state

pub fn loss_history(&self) -> &[Float]

Get training history

pub fn best_loss(&self) -> Float

Get best loss

pub fn converged(&self) -> bool

Check if converged

pub fn coef(&self) -> &Array2<Float>

Get coefficients

pub fn n_iter(&self) -> usize

Get number of iterations performed

Trait Implementations

impl<S: Clone> Clone for WarmStartRegressor<S>

fn clone(&self) -> WarmStartRegressor<S>

Returns a duplicate of the value.

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

Performs copy-assignment from source.

impl<S: Debug> Debug for WarmStartRegressor<S>

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

Formats the value using the given formatter.

impl Default for WarmStartRegressor<Untrained>

fn default() -> Self

Returns the “default value” for a type.

impl Estimator for WarmStartRegressor<Untrained>

type Config = WarmStartRegressorConfig

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 Estimator for WarmStartRegressor<WarmStartRegressorTrained>

type Config = WarmStartRegressorConfig

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]>>, ArrayBase<ViewRepr<&f64>, Dim<[usize; 2]>>> for WarmStartRegressor<Untrained>

type Fitted = WarmStartRegressor<WarmStartRegressorTrained>

The fitted model type

fn fit( self, X: &ArrayView2<'_, Float>, y: &ArrayView2<'_, 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 Predict<ArrayBase<ViewRepr<&f64>, Dim<[usize; 2]>>, ArrayBase<OwnedRepr<f64>, Dim<[usize; 2]>>> for WarmStartRegressor<WarmStartRegressorTrained>

fn predict(&self, X: &ArrayView2<'_, Float>) -> SklResult<Array2<Float>>

Make predictions on the provided data

fn predict_with_uncertainty( &self, x: &X, ) -> Result<(Output, UncertaintyMeasure), SklearsError>

Make predictions with confidence intervals