Struct FeatureUnion 

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

FeatureUnion concatenates the results of multiple transformers

Unlike ColumnTransformer which applies different transformers to different columns, FeatureUnion applies all transformers to the same input data and concatenates their outputs column-wise.

This is useful for creating feature combinations, such as applying both PCA and polynomial features to the same input.

Implementations

impl FeatureUnion<Untrained>

pub fn new() -> Self

Create a new FeatureUnion

pub fn add_transformer<T>(self, name: &str, transformer: T) -> Self

where T: TransformerWrapper + 'static,

Add a transformer to the union

pub fn add_weighted_transformer<T>( self, name: &str, transformer: T, weight: Float, ) -> Self

where T: TransformerWrapper + 'static,

Add a weighted transformer to the union

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

Set number of parallel jobs

pub fn validate_input(self, validate: bool) -> Self

Set input validation

pub fn preserve_order(self, preserve: bool) -> Self

Set whether to preserve transformer order

pub fn feature_selection(self, strategy: FeatureSelectionStrategy) -> Self

Set feature selection strategy

pub fn importance_method(self, method: FeatureImportanceMethod) -> Self

Set feature importance calculation method

pub fn enable_feature_selection(self, enable: bool) -> Self

Enable or disable feature selection

impl FeatureUnion<Trained>

pub fn n_features_in(&self) -> usize

Get the number of input features

pub fn n_features_out(&self) -> usize

Get the number of output features

pub fn get_transformers(&self) -> &Vec<FeatureUnionStep>

Get the fitted transformers

pub fn get_weights(&self) -> &Vec<Float>

Get the weights used for each transformer

pub fn get_selected_features(&self) -> Option<&Vec<usize>>

Get the selected feature indices (if feature selection is enabled)

pub fn get_feature_importances(&self) -> Option<&Array1<Float>>

Get the feature importance scores (if feature selection is enabled)

pub fn n_features_selected(&self) -> usize

Get the number of features that were selected

pub fn is_feature_selection_enabled(&self) -> bool

Check if feature selection is enabled

Trait Implementations

impl<State: Debug> Debug for FeatureUnion<State>

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

Formats the value using the given formatter.

impl Default for FeatureUnion<Untrained>

fn default() -> Self

Returns the “default value” for a type.

impl Estimator for FeatureUnion<Untrained>

type Config = FeatureUnionConfig

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 FeatureUnion<Trained>

type Config = FeatureUnionConfig

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]>>, ()> for FeatureUnion<Untrained>

type Fitted = FeatureUnion<Trained>

The fitted model type

fn fit(self, x: &Array2<Float>, _y: &()) -> Result<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 Transform<ArrayBase<OwnedRepr<f64>, Dim<[usize; 2]>>> for FeatureUnion<Trained>

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

Transform the input data