Struct ElasticNet 

pub struct ElasticNet { /* private fields */ }

Elastic Net regression with combined L1 and L2 regularization.

Fits a linear model with both L1 and L2 penalties:

minimize ||y - Xβ||² + α * l1_ratio * ||β||₁ + α * (1 - l1_ratio) * ||β||²

Parameters

• alpha - Overall regularization strength
• l1_ratio - Mix between L1 and L2 (0.0 = Ridge, 1.0 = Lasso)

Solver

Uses coordinate descent with combined soft-thresholding and shrinkage.

When to use Elastic Net

• When you want both sparsity (L1) and grouping effect (L2)
• With correlated features where Lasso may be unstable
• When you don’t know which regularization type to use

Examples

use aprender::prelude::*;
use aprender::linear_model::ElasticNet;

let x = Matrix::from_vec(5, 2, vec![
    1.0, 2.0,
    2.0, 3.0,
    3.0, 4.0,
    4.0, 5.0,
    5.0, 6.0,
]).expect("Valid matrix dimensions");
let y = Vector::from_slice(&[5.0, 8.0, 11.0, 14.0, 17.0]);

// 50% L1, 50% L2
let mut model = ElasticNet::new(0.1, 0.5);
model.fit(&x, &y).expect("Fit should succeed with valid data");

let r2 = model.score(&x, &y);
assert!(r2 > 0.9);

Implementations

impl ElasticNet

pub fn new(alpha: f32, l1_ratio: f32) -> Self

Creates a new ElasticNet with the given parameters.

Arguments

• alpha - Overall regularization strength
• l1_ratio - Mix between L1 and L2 (0.0 = Ridge, 1.0 = Lasso)

pub fn with_intercept(self, fit_intercept: bool) -> Self

Sets whether to fit an intercept term.

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

Sets the maximum number of iterations.

pub fn with_tol(self, tol: f32) -> Self

Sets the convergence tolerance.

pub fn alpha(&self) -> f32

Returns the regularization strength (alpha).

pub fn l1_ratio(&self) -> f32

Returns the L1/L2 ratio.

pub fn coefficients(&self) -> &Vector<f32>

Returns the coefficients (excluding intercept).

Panics

Panics if model is not fitted.

pub fn intercept(&self) -> f32

Returns the intercept term.

pub fn is_fitted(&self) -> bool

Returns true if the model has been fitted.

pub fn save<P: AsRef<Path>>(&self, path: P) -> Result<(), String>

Saves the model to a binary file.

Errors

Returns an error if serialization or file writing fails.

pub fn load<P: AsRef<Path>>(path: P) -> Result<Self, String>

Loads a model from a binary file.

Errors

Returns an error if file reading or deserialization fails.

pub fn save_safetensors<P: AsRef<Path>>(&self, path: P) -> Result<(), String>

Saves the model to SafeTensors format.

SafeTensors format is compatible with:

• HuggingFace ecosystem
• Ollama (can convert to GGUF)
• PyTorch, TensorFlow
• realizar inference engine

Errors

Returns an error if:

• Model is not fitted
• Serialization fails
• File writing fails

pub fn load_safetensors<P: AsRef<Path>>(path: P) -> Result<Self, String>

Loads a model from SafeTensors format.

Errors

Returns an error if:

• File reading fails
• SafeTensors format is invalid
• Required tensors are missing

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<'de> Deserialize<'de> for ElasticNet

fn deserialize<__D>(__deserializer: __D) -> Result<Self, __D::Error>

where __D: Deserializer<'de>,

Deserialize this value from the given Serde deserializer.

impl Estimator for ElasticNet

fn fit(&mut self, x: &Matrix<f32>, y: &Vector<f32>) -> Result<()>

Fits the Elastic Net model using coordinate descent.

Errors

Returns an error if input dimensions don’t match.

fn predict(&self, x: &Matrix<f32>) -> Vector<f32>

Predicts target values for input data.

Panics

Panics if model is not fitted.

fn score(&self, x: &Matrix<f32>, y: &Vector<f32>) -> f32

Computes the R² score.

impl Serialize for ElasticNet

fn serialize<__S>(&self, __serializer: __S) -> Result<__S::Ok, __S::Error>

where __S: Serializer,

Serialize this value into the given Serde serializer.