make default params available to serde (#167)

* add seed param to search params

* make default params available to serde

* lints

* create defaults for enums

* lint

src/linear/elastic_net.rs

@@ -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>,
 }

src/linear/lasso.rs

@@ -38,13 +38,17 @@ use crate::math::num::RealNumber;
 #[cfg_attr(feature = "serde", derive(Serialize, Deserialize))]
 #[derive(Debug, Clone)]
 pub struct LassoParameters<T: RealNumber> {
+    #[cfg_attr(feature = "serde", serde(default))]
     /// Controls the strength of the penalty to the loss function.
     pub alpha: 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,
 }
@@ -116,13 +120,17 @@ impl<T: RealNumber, M: Matrix<T>> Predictor<M, M::RowVector> for Lasso<T, M> {
 #[cfg_attr(feature = "serde", derive(Serialize, Deserialize))]
 #[derive(Debug, Clone)]
 pub struct LassoSearchParameters<T: RealNumber> {
+    #[cfg_attr(feature = "serde", serde(default))]
     /// Controls the strength of the penalty to the loss function.
     pub alpha: 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>,
 }

src/linear/linear_regression.rs

@@ -71,19 +71,21 @@ use crate::linalg::Matrix;
 use crate::math::num::RealNumber;
 
 #[cfg_attr(feature = "serde", derive(Serialize, Deserialize))]
-#[derive(Debug, Clone, Eq, PartialEq)]
+#[derive(Debug, Default, Clone, Eq, PartialEq)]
 /// Approach to use for estimation of regression coefficients. QR is more efficient but SVD is more stable.
 pub enum LinearRegressionSolverName {
     /// QR decomposition, see [QR](../../linalg/qr/index.html)
     QR,
+    #[default]
     /// SVD decomposition, see [SVD](../../linalg/svd/index.html)
     SVD,
 }
 
 /// Linear Regression parameters
 #[cfg_attr(feature = "serde", derive(Serialize, Deserialize))]
-#[derive(Debug, Clone)]
+#[derive(Debug, Default, Clone)]
 pub struct LinearRegressionParameters {
+    #[cfg_attr(feature = "serde", serde(default))]
     /// Solver to use for estimation of regression coefficients.
     pub solver: LinearRegressionSolverName,
 }
@@ -105,18 +107,11 @@ impl LinearRegressionParameters {
     }
 }
 
-impl Default for LinearRegressionParameters {
-    fn default() -> Self {
-        LinearRegressionParameters {
-            solver: LinearRegressionSolverName::SVD,
-        }
-    }
-}
-
 /// Linear Regression grid search parameters
 #[cfg_attr(feature = "serde", derive(Serialize, Deserialize))]
 #[derive(Debug, Clone)]
 pub struct LinearRegressionSearchParameters {
+    #[cfg_attr(feature = "serde", serde(default))]
     /// Solver to use for estimation of regression coefficients.
     pub solver: Vec<LinearRegressionSolverName>,
 }
@@ -353,5 +348,9 @@ mod tests {
             serde_json::from_str(&serde_json::to_string(&lr).unwrap()).unwrap();
 
         assert_eq!(lr, deserialized_lr);
+
+        let default = LinearRegressionParameters::default();
+        let parameters: LinearRegressionParameters = serde_json::from_str("{}").unwrap();
+        assert_eq!(parameters.solver, default.solver);
     }
 }

src/linear/logistic_regression.rs

@@ -75,12 +75,20 @@ pub enum LogisticRegressionSolverName {
     LBFGS,
 }
 
+impl Default for LogisticRegressionSolverName {
+    fn default() -> Self {
+        LogisticRegressionSolverName::LBFGS
+    }
+}
+
 /// Logistic Regression parameters
 #[cfg_attr(feature = "serde", derive(Serialize, Deserialize))]
 #[derive(Debug, Clone)]
 pub struct LogisticRegressionParameters<T: RealNumber> {
+    #[cfg_attr(feature = "serde", serde(default))]
     /// Solver to use for estimation of regression coefficients.
     pub solver: LogisticRegressionSolverName,
+    #[cfg_attr(feature = "serde", serde(default))]
     /// Regularization parameter.
     pub alpha: T,
 }
@@ -89,8 +97,10 @@ pub struct LogisticRegressionParameters<T: RealNumber> {
 #[cfg_attr(feature = "serde", derive(Serialize, Deserialize))]
 #[derive(Debug, Clone)]
 pub struct LogisticRegressionSearchParameters<T: RealNumber> {
+    #[cfg_attr(feature = "serde", serde(default))]
     /// Solver to use for estimation of regression coefficients.
     pub solver: Vec<LogisticRegressionSolverName>,
+    #[cfg_attr(feature = "serde", serde(default))]
     /// Regularization parameter.
     pub alpha: Vec<T>,
 }
@@ -204,7 +214,7 @@ impl<T: RealNumber> LogisticRegressionParameters<T> {
 impl<T: RealNumber> Default for LogisticRegressionParameters<T> {
     fn default() -> Self {
         LogisticRegressionParameters {
-            solver: LogisticRegressionSolverName::LBFGS,
+            solver: LogisticRegressionSolverName::default(),
             alpha: T::zero(),
         }
     }

src/linear/ridge_regression.rs

@@ -77,6 +77,12 @@ pub enum RidgeRegressionSolverName {
     SVD,
 }
 
+impl Default for RidgeRegressionSolverName {
+    fn default() -> Self {
+        RidgeRegressionSolverName::Cholesky
+    }
+}
+
 /// Ridge Regression parameters
 #[cfg_attr(feature = "serde", derive(Serialize, Deserialize))]
 #[derive(Debug, Clone)]
@@ -94,10 +100,13 @@ pub struct RidgeRegressionParameters<T: RealNumber> {
 #[cfg_attr(feature = "serde", derive(Serialize, Deserialize))]
 #[derive(Debug, Clone)]
 pub struct RidgeRegressionSearchParameters<T: RealNumber> {
+    #[cfg_attr(feature = "serde", serde(default))]
     /// Solver to use for estimation of regression coefficients.
     pub solver: Vec<RidgeRegressionSolverName>,
+    #[cfg_attr(feature = "serde", serde(default))]
     /// Regularization parameter.
     pub alpha: 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>,
@@ -204,7 +213,7 @@ impl<T: RealNumber> RidgeRegressionParameters<T> {
 impl<T: RealNumber> Default for RidgeRegressionParameters<T> {
     fn default() -> Self {
         RidgeRegressionParameters {
-            solver: RidgeRegressionSolverName::Cholesky,
+            solver: RidgeRegressionSolverName::default(),
             alpha: T::one(),
             normalize: true,
         }