add serde support for XGRegressor (#337)

* add serde support for XGBoostRegressor
* add traits to dependent structs

src/xgboost/xgb_regressor.rs

@@ -53,10 +53,14 @@ use crate::{
     rand_custom::get_rng_impl,
 };
 
+#[cfg(feature = "serde")]
+use serde::{Deserialize, Serialize};
+
 /// Defines the objective function to be optimized.
 /// The objective function provides the loss, gradient (first derivative), and
 /// hessian (second derivative) required for the XGBoost algorithm.
 #[derive(Clone, Debug)]
+#[cfg_attr(feature = "serde", derive(Serialize, Deserialize))]
 pub enum Objective {
     /// The objective for regression tasks using Mean Squared Error.
     /// Loss: 0.5 * (y_true - y_pred)^2
@@ -122,6 +126,8 @@ impl Objective {
 /// This is a recursive data structure where each `TreeRegressor` is a node
 /// that can have a left and a right child, also of type `TreeRegressor`.
 #[allow(dead_code)]
+#[cfg_attr(feature = "serde", derive(Serialize, Deserialize))]
+#[derive(Debug)]
 struct TreeRegressor<TX: Number + PartialOrd, TY: Number, X: Array2<TX>, Y: Array1<TY>> {
     left: Option<Box<TreeRegressor<TX, TY, X, Y>>>,
     right: Option<Box<TreeRegressor<TX, TY, X, Y>>>,
@@ -374,6 +380,7 @@ impl<TX: Number + PartialOrd, TY: Number, X: Array2<TX>, Y: Array1<TY>>
 /// Parameters for the `jRegressor` model.
 ///
 /// This struct holds all the hyperparameters that control the training process.
+#[cfg_attr(feature = "serde", derive(Serialize, Deserialize))]
 #[derive(Clone, Debug)]
 pub struct XGRegressorParameters {
     /// The number of boosting rounds or trees to build.
@@ -494,6 +501,8 @@ impl XGRegressorParameters {
 }
 
 /// An Extreme Gradient Boosting (XGBoost) model for regression and classification tasks.
+#[cfg_attr(feature = "serde", derive(Serialize, Deserialize))]
+#[derive(Debug)]
 pub struct XGRegressor<TX: Number + PartialOrd, TY: Number, X: Array2<TX>, Y: Array1<TY>> {
     regressors: Option<Vec<TreeRegressor<TX, TY, X, Y>>>,
     parameters: Option<XGRegressorParameters>,