Struct MetaLearningFeatureSelector 

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

Meta-learning feature selector Meta-learning approach that learns from multiple feature selection tasks

Implementations

impl MetaLearningFeatureSelector<Untrained>

pub fn new() -> Self

pub fn base_methods(self, methods: Vec<String>) -> Self

pub fn meta_learning_rate(self, lr: f64) -> Self

pub fn meta_epochs(self, epochs: usize) -> Self

pub fn k(self, k: Option<usize>) -> Self

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

Trait Implementations

impl<State: Clone> Clone for MetaLearningFeatureSelector<State>

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

Returns a duplicate of the value.

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

Performs copy-assignment from source.

impl<State: Debug> Debug for MetaLearningFeatureSelector<State>

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

Formats the value using the given formatter.

impl Default for MetaLearningFeatureSelector<Untrained>

fn default() -> Self

Returns the “default value” for a type.

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

type Fitted = MetaLearningFeatureSelector<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 SelectorMixin for MetaLearningFeatureSelector<Trained>

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

Get support mask

fn transform_features(&self, indices: &[usize]) -> SklResult<Vec<usize>>

Transform by selecting features

impl Transform<ArrayBase<OwnedRepr<f64>, Dim<[usize; 2]>>> for MetaLearningFeatureSelector<Trained>

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

Transform the input data