Struct TensorPolynomialFeatures 

pub struct TensorPolynomialFeatures<State = Untrained> {
    pub degree: u32,
    pub n_dimensions: usize,
    pub include_bias: bool,
    pub interaction_only: bool,
    pub tensor_ordering: TensorOrdering,
    pub contraction_method: ContractionMethod,
    /* private fields */
}

Tensor Product Polynomial Features

Generates tensor product polynomial features for multi-dimensional data. This captures higher-order interactions between features across multiple dimensions and feature groups.

Parameters

• degree - Maximum degree of polynomial features (default: 2)
• n_dimensions - Number of tensor dimensions (default: 2)
• include_bias - Include bias term (default: true)
• interaction_only - Include only interaction terms (default: false)
• tensor_ordering - Ordering scheme for tensor indices
• contraction_method - Method for tensor contraction

Examples

use sklears_kernel_approximation::tensor_polynomial::TensorPolynomialFeatures;
use sklears_core::traits::{Transform, Fit, Untrained}
use scirs2_core::ndarray::array;

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

let tensor_poly = TensorPolynomialFeatures::new(2, 2);
let fitted_tensor = tensor_poly.fit(&X, &()).unwrap();
let X_transformed = fitted_tensor.transform(&X).unwrap();

TensorPolynomialFeatures

Fields

degree: u32

Maximum degree of polynomial features

n_dimensions: usize

Number of tensor dimensions

include_bias: bool

Include bias term

interaction_only: bool

Include only interaction terms

tensor_ordering: TensorOrdering

Tensor index ordering

contraction_method: ContractionMethod

Tensor contraction method

Implementations

impl TensorPolynomialFeatures<Untrained>

pub fn new(degree: u32, n_dimensions: usize) -> Self

Create a new tensor polynomial features transformer

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

Set include_bias parameter

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

Set interaction_only parameter

pub fn tensor_ordering(self, ordering: TensorOrdering) -> Self

Set tensor ordering

pub fn contraction_method(self, method: ContractionMethod) -> Self

Set contraction method

impl TensorPolynomialFeatures<Trained>

pub fn n_input_features(&self) -> usize

Get the number of input features

pub fn n_output_features(&self) -> usize

Get the number of output features

pub fn tensor_indices(&self) -> &[Vec<Vec<u32>>]

Get the tensor indices

pub fn contraction_map(&self) -> &[Vec<usize>]

Get the contraction map

Trait Implementations

impl<State: Clone> Clone for TensorPolynomialFeatures<State>

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

Returns a duplicate of the value.

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

Performs copy-assignment from source.

impl<State: Debug> Debug for TensorPolynomialFeatures<State>

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

Formats the value using the given formatter.

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

type Fitted = TensorPolynomialFeatures<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 TensorPolynomialFeatures<Trained>

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

Transform the input data