Struct ElasticNet 

#[non_exhaustive]
pub struct ElasticNet { /* private fields */ }

ElasticNet regression (mixed L1 + L2 regularization).

Uses coordinate descent to minimize:

(1 / 2n) ‖y − Xβ − β₀‖² + α × l1_ratio × ‖β‖₁ + α × (1 − l1_ratio) / 2 × ‖β‖²

Example

use scry_learn::dataset::Dataset;
use scry_learn::linear::ElasticNet;

let features = vec![vec![1.0, 2.0, 3.0, 4.0, 5.0]];
let target = vec![2.1, 4.0, 5.9, 8.1, 10.0];
let data = Dataset::new(features, target, vec!["x".into()], "y");

let mut model = ElasticNet::new().alpha(0.1).l1_ratio(0.5);
model.fit(&data).unwrap();
let preds = model.predict(&[vec![3.0]]).unwrap();

Implementations

impl ElasticNet

pub fn new() -> Self

Create a new ElasticNet with default parameters.

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

Set the overall regularization strength.

pub fn l1_ratio(self, r: f64) -> Self

Set the L1/L2 mix ratio (0.0 = Ridge, 1.0 = Lasso).

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

Set the maximum number of iterations.

pub fn tol(self, t: f64) -> Self

Set convergence tolerance.

pub fn fit(&mut self, data: &Dataset) -> Result<()>

Fit the ElasticNet model using coordinate descent.

pub fn predict(&self, features: &[Vec<f64>]) -> Result<Vec<f64>>

Predict target values for new samples.

features is row-major: features[sample_idx][feature_idx].

pub fn fit_sparse(&mut self, features: &CscMatrix, target: &[f64]) -> Result<()>

Fit on sparse features using coordinate descent.

pub fn predict_sparse(&self, features: &CsrMatrix) -> Result<Vec<f64>>

Predict from sparse features (CSR format).

pub fn coefficients(&self) -> &[f64]

Get learned coefficients.

pub fn intercept(&self) -> f64

Get learned intercept.

Trait Implementations

impl Clone for ElasticNet

fn clone(&self) -> ElasticNet

Returns a duplicate of the value.

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

Performs copy-assignment from source.

impl Debug for ElasticNet

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

Formats the value using the given formatter.

impl Default for ElasticNet

fn default() -> Self

Returns the “default value” for a type.

impl PipelineModel for ElasticNet

fn fit(&mut self, data: &Dataset) -> Result<()>

Train the model on a dataset.

fn predict(&self, features: &[Vec<f64>]) -> Result<Vec<f64>>

Predict on row-major feature matrix.

impl Tunable for ElasticNet

fn set_param(&mut self, name: &str, _value: ParamValue) -> Result<()>

Apply a named hyperparameter.

fn clone_box(&self) -> Box<dyn Tunable>

Clone this model into a boxed trait object.

fn fit(&mut self, data: &Dataset) -> Result<()>

Train on a dataset.

fn predict(&self, features: &[Vec<f64>]) -> Result<Vec<f64>>

Predict on row-major features.