Struct TransformedTargetRegressor 

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

Examples

use sklears_compose::TransformedTargetRegressor;
use scirs2_core::ndarray::array;

let X = array![[1.0], [2.0], [3.0]];
let y = array![1.0, 4.0, 9.0];

Implementations

impl TransformedTargetRegressor<Untrained>

pub fn new(regressor: Box<dyn PipelinePredictor>) -> Self

Create a new TransformedTargetRegressor

pub fn transformer( self, transformer: Box<dyn for<'a> Transform<ArrayView1<'a, Float>, Array1<f64>> + Send + Sync>, ) -> Self

Set the transformer

pub fn func(self, func: fn(&ArrayView1<'_, Float>) -> Array1<f64>) -> Self

Set transformation function

pub fn inverse_func( self, inverse_func: fn(&ArrayView1<'_, Float>) -> Array1<f64>, ) -> Self

Set inverse transformation function

pub fn check_inverse(self, check: bool) -> Self

Set whether to check inverse transform

impl TransformedTargetRegressor<TransformedTargetRegressorTrained>

pub fn predict(&self, x: &ArrayView2<'_, Float>) -> SklResult<Array1<f64>>

Predict using the fitted regressor and inverse transform

pub fn regressor(&self) -> &dyn PipelinePredictor

Get the fitted regressor

Trait Implementations

impl Estimator for TransformedTargetRegressor<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<ViewRepr<&f64>, Dim<[usize; 2]>>, Option<&ArrayBase<ViewRepr<&f64>, Dim<[usize; 1]>>>> for TransformedTargetRegressor<Untrained>

type Fitted = TransformedTargetRegressor<TransformedTargetRegressorTrained>

The fitted model type

fn fit( self, x: &ArrayView2<'_, Float>, y: &Option<&ArrayView1<'_, 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