Struct GaussianProcessImputer 

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

Gaussian Process Regression Imputer

Imputation using Gaussian Process Regression with Bayesian inference. This method provides uncertainty quantification along with predictions and can capture complex non-linear relationships with principled uncertainty.

Parameters

• kernel - Kernel function type (‘rbf’, ‘matern32’, ‘matern52’, ‘linear’)
• length_scale - Length scale parameter for the kernel
• length_scale_bounds - Bounds for length scale optimization
• nu - Smoothness parameter for Matern kernels
• alpha - Noise regularization parameter
• optimizer - Method for hyperparameter optimization
• n_restarts_optimizer - Number of optimizer restarts
• missing_values - The placeholder for missing values

Examples

use sklears_impute::GaussianProcessImputer;
use sklears_core::traits::{Transform, Fit};
use scirs2_core::ndarray::array;

let X = array![[1.0, 2.0, 3.0], [f64::NAN, 3.0, 4.0], [7.0, f64::NAN, 6.0]];

let imputer = GaussianProcessImputer::new()
    .kernel("rbf".to_string())
    .length_scale(1.0)
    .alpha(1e-6);
let fitted = imputer.fit(&X.view(), &()).unwrap();
let X_imputed = fitted.transform(&X.view()).unwrap();

Implementations

impl GaussianProcessImputer<Untrained>

pub fn new() -> Self

Create a new GaussianProcessImputer instance

pub fn kernel(self, kernel: String) -> Self

Set the kernel function

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

Set the length scale parameter

pub fn length_scale_bounds(self, bounds: (f64, f64)) -> Self

Set the bounds for length scale optimization

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

Set the smoothness parameter for Matern kernels

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

Set the noise regularization parameter

pub fn optimizer(self, optimizer: String) -> Self

Set the optimizer method

pub fn n_restarts_optimizer(self, n_restarts: usize) -> Self

Set the number of optimizer restarts

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

Set the missing values placeholder

pub fn normalize_y(self, normalize_y: bool) -> Self

Set whether to normalize target values

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

Set the random state for reproducibility

impl GaussianProcessImputer<GaussianProcessImputerTrained>

pub fn predict_with_uncertainty( &self, X: &ArrayView2<'_, Float>, ) -> SklResult<Vec<Vec<GPPredictionResult>>>

Predict with uncertainty quantification

pub fn log_marginal_likelihood(&self) -> &HashMap<usize, f64>

Get the log marginal likelihood for each feature

pub fn optimized_kernel_params(&self) -> &HashMap<usize, (f64, f64)>

Get the optimized kernel parameters for each feature

Trait Implementations

impl<S: Clone> Clone for GaussianProcessImputer<S>

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

Returns a duplicate of the value.

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

Performs copy-assignment from source.

impl<S: Debug> Debug for GaussianProcessImputer<S>

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

Formats the value using the given formatter.

impl Default for GaussianProcessImputer<Untrained>

fn default() -> Self

Returns the “default value” for a type.

impl Estimator for GaussianProcessImputer<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]>>, ()> for GaussianProcessImputer<Untrained>

type Fitted = GaussianProcessImputer<GaussianProcessImputerTrained>

The fitted model type

fn fit(self, X: &ArrayView2<'_, Float>, _y: &()) -> 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 Transform<ArrayBase<ViewRepr<&f64>, Dim<[usize; 2]>>, ArrayBase<OwnedRepr<f64>, Dim<[usize; 2]>>> for GaussianProcessImputer<GaussianProcessImputerTrained>

fn transform(&self, X: &ArrayView2<'_, Float>) -> SklResult<Array2<Float>>

Transform the input data