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make default params available to serde (#167)

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* add seed param to search params

* make default params available to serde

* lints

* create defaults for enums

* lint
This commit is contained in:
Montana Low
2022-09-21 19:48:31 -07:00
committed by GitHub
parent 403d3f2348
commit 764309e313
22 changed files with 175 additions and 18 deletions
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src/linear/elastic_net.rs
+10
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@@ -71,16 +71,21 @@ use crate::linear::lasso_optimizer::InteriorPointOptimizer;
#[cfg_attr(feature = "serde", derive(Serialize, Deserialize))]
#[derive(Debug, Clone)]
pub struct ElasticNetParameters<T: RealNumber> {
#[cfg_attr(feature = "serde", serde(default))]
/// Regularization parameter.
pub alpha: T,
#[cfg_attr(feature = "serde", serde(default))]
/// The elastic net mixing parameter, with 0 <= l1_ratio <= 1.
/// For l1_ratio = 0 the penalty is an L2 penalty.
/// For l1_ratio = 1 it is an L1 penalty. For 0 < l1_ratio < 1, the penalty is a combination of L1 and L2.
pub l1_ratio: T,
#[cfg_attr(feature = "serde", serde(default))]
/// If True, the regressors X will be normalized before regression by subtracting the mean and dividing by the standard deviation.
pub normalize: bool,
#[cfg_attr(feature = "serde", serde(default))]
/// The tolerance for the optimization
pub tol: T,
#[cfg_attr(feature = "serde", serde(default))]
/// The maximum number of iterations
pub max_iter: usize,
}
@@ -139,16 +144,21 @@ impl<T: RealNumber> Default for ElasticNetParameters<T> {
#[cfg_attr(feature = "serde", derive(Serialize, Deserialize))]
#[derive(Debug, Clone)]
pub struct ElasticNetSearchParameters<T: RealNumber> {
#[cfg_attr(feature = "serde", serde(default))]
/// Regularization parameter.
pub alpha: Vec<T>,
#[cfg_attr(feature = "serde", serde(default))]
/// The elastic net mixing parameter, with 0 <= l1_ratio <= 1.
/// For l1_ratio = 0 the penalty is an L2 penalty.
/// For l1_ratio = 1 it is an L1 penalty. For 0 < l1_ratio < 1, the penalty is a combination of L1 and L2.
pub l1_ratio: Vec<T>,
#[cfg_attr(feature = "serde", serde(default))]
/// If True, the regressors X will be normalized before regression by subtracting the mean and dividing by the standard deviation.
pub normalize: Vec<bool>,
#[cfg_attr(feature = "serde", serde(default))]
/// The tolerance for the optimization
pub tol: Vec<T>,
#[cfg_attr(feature = "serde", serde(default))]
/// The maximum number of iterations
pub max_iter: Vec<usize>,
}