Type Alias linfa_elasticnet::ElasticNetParams

pub type ElasticNetParams<F> = ElasticNetParamsBase<F, false>;

A hyper-parameter set for Elastic-Net

Configures and minimizes the following objective function:

1 / (2 * n_samples) * ||y - Xw||^2_2
    + penalty * l1_ratio * ||w||_1
    + 0.5 * penalty * (1 - l1_ratio) * ||w||^2_2

The parameter set can be verified into a ElasticNetValidParams by calling ParamGuard::check. It is also possible to directly fit a model with Fit::fit which implicitely verifies the parameter set prior to the model estimation and forwards any error.

Parameters

Name	Default	Purpose	Range
penalty	1.0	Overall parameter penalty	[0, inf)
l1_ratio	0.5	Distribution of penalty to L1 and L2 regularizations	[0.0, 1.0]
with_intercept	true	Enable intercept	false, true
tolerance	1e-4	Absolute change of any of the parameters	(0, inf)
max_iterations	1000	Maximum number of iterations	[1, inf)

Errors

The following errors can come from invalid hyper-parameters:

Returns InvalidPenalty if the penalty is negative.

Returns InvalidL1Ratio if the L1 ratio is not in unit. range

Returns InvalidTolerance if the tolerance is negative.

Example

use linfa_elasticnet::{ElasticNetParams, ElasticNetError};
use linfa::prelude::*;
use ndarray::array;

let ds = Dataset::new(array![[1.0, 0.0], [0.0, 1.0]], array![3.0, 2.0]);

// create a new parameter set with penalty equals `1e-5`
let unchecked_params = ElasticNetParams::new()
    .penalty(1e-5);

// fit model with unchecked parameter set
let model = unchecked_params.fit(&ds)?;

// transform into a verified parameter set
let checked_params = unchecked_params.check()?;

// Regenerate model with the verified parameters, this only returns
// errors originating from the fitting process
let model = checked_params.fit(&ds)?;

Aliased Type

struct ElasticNetParams<F>(/* private fields */);