Struct MultiTaskFeatureSelector 

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

Multi-task feature selection Selects features that are relevant across multiple related tasks simultaneously

Implementations

impl MultiTaskFeatureSelector<Untrained>

pub fn new() -> Self

Create a new multi-task feature selector

pub fn n_features_to_select(self, n_features: usize) -> Self

Set the number of features to select

pub fn task_weights(self, weights: Option<Vec<f64>>) -> Self

Set task weights (if None, all tasks are weighted equally)

pub fn regularization(self, reg: f64) -> Self

Set regularization strength for task coupling

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

Set maximum iterations for optimization

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

Set convergence tolerance

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

Set random state for reproducibility

impl MultiTaskFeatureSelector<Trained>

pub fn get_selected_features(&self) -> SklResult<&[usize]>

Get selected features

pub fn get_feature_scores(&self) -> SklResult<&Array2<f64>>

Get feature scores for all tasks

pub fn get_task_scores(&self) -> SklResult<&[Array1<f64>]>

Get task-specific feature scores

pub fn get_task_specific_features( &self, task_idx: usize, n_features: usize, ) -> SklResult<Vec<usize>>

Get the most important features for a specific task

pub fn get_consensus_features(&self, threshold: f64) -> SklResult<Vec<usize>>

Get features that are consistently important across all tasks

pub fn get_task_similarity(&self) -> SklResult<Array2<f64>>

Get task similarity matrix based on feature importance patterns

Trait Implementations

impl<State: Clone> Clone for MultiTaskFeatureSelector<State>

fn clone(&self) -> Self

Returns a duplicate of the value.

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

Performs copy-assignment from source.

impl<State: Debug> Debug for MultiTaskFeatureSelector<State>

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

Formats the value using the given formatter.

impl Default for MultiTaskFeatureSelector<Untrained>

fn default() -> Self

Returns the “default value” for a type.

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

type Fitted = MultiTaskFeatureSelector<Trained>

The fitted model type

fn fit( self, x: &Array2<f64>, y_tasks: &Vec<Array1<f64>>, ) -> 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 MultiTaskFeatureSelector<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 MultiTaskFeatureSelector<Trained>

fn transform(&self, x: &Array2<f64>) -> SklResult<Array2<f64>>

Transform the input data