Struct ScalarizationOptimizer 

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

Scalarization optimizer for converting multi-objective problems to single-objective

Implementations

impl ScalarizationOptimizer<Untrained>

pub fn new(method: ScalarizationMethod) -> Self

Create a new scalarization optimizer

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

Set the configuration

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

Set maximum iterations

pub fn tol(self, tol: Float) -> Self

Set convergence tolerance

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

Set learning rate

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

Set random state

pub fn scalarize_objectives(&self, objectives: &[Float]) -> SklResult<Float>

Compute scalarized objective value

pub fn generate_scalarized_problems( &self, n_problems: usize, n_objectives: usize, ) -> SklResult<Vec<ScalarizationMethod>>

Generate multiple scalarized problems for comprehensive optimization

impl ScalarizationOptimizer<ScalarizationOptimizerTrained>

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

Get the optimized parameters

pub fn scalarized_value(&self) -> Float

Get the scalarized objective value

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

Get the original objective values

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

Get the convergence history

pub fn method(&self) -> &ScalarizationMethod

Get the scalarization method used

pub fn is_feasible(&self) -> bool

Check if the solution is feasible (for constraint-based methods)

Trait Implementations

impl<S: Clone> Clone for ScalarizationOptimizer<S>

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

Returns a duplicate of the value.

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

Performs copy-assignment from source.

impl<S: Debug> Debug for ScalarizationOptimizer<S>

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

Formats the value using the given formatter.

impl Estimator for ScalarizationOptimizer<Untrained>

type Config = ScalarizationConfig

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 ScalarizationOptimizer<ScalarizationOptimizerTrained>

type Config = ScalarizationConfig

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 ScalarizationOptimizer<Untrained>

type Fitted = ScalarizationOptimizer<ScalarizationOptimizerTrained>

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