feat: Make SerDe optional

src/neighbors/knn_classifier.rs

@@ -33,7 +33,7 @@
 //!
 use std::marker::PhantomData;
 
-use serde::{Deserialize, Serialize};
+#[cfg(feature = "serde")] use serde::{Deserialize, Serialize};
 
 use crate::algorithm::neighbour::{KNNAlgorithm, KNNAlgorithmName};
 use crate::api::{Predictor, SupervisedEstimator};
@@ -45,7 +45,8 @@ use crate::math::num::RealNumber;
 use crate::neighbors::KNNWeightFunction;
 
 /// `KNNClassifier` parameters. Use `Default::default()` for default values.
-#[derive(Serialize, Deserialize, Debug, Clone)]
+#[cfg_attr(feature = "serde", derive(Serialize, Deserialize))]
+#[derive(Debug, Clone)]
 pub struct KNNClassifierParameters<T: RealNumber, D: Distance<Vec<T>, T>> {
     /// a function that defines a distance between each pair of point in training data.
     /// This function should extend [`Distance`](../../math/distance/trait.Distance.html) trait.
@@ -62,7 +63,8 @@ pub struct KNNClassifierParameters<T: RealNumber, D: Distance<Vec<T>, T>> {
 }
 
 /// K Nearest Neighbors Classifier
-#[derive(Serialize, Deserialize, Debug)]
+#[cfg_attr(feature = "serde", derive(Serialize, Deserialize))]
+#[derive(Debug)]
 pub struct KNNClassifier<T: RealNumber, D: Distance<Vec<T>, T>> {
     classes: Vec<T>,
     y: Vec<usize>,

src/neighbors/knn_regressor.rs

@@ -36,7 +36,7 @@
 //!
 use std::marker::PhantomData;
 
-use serde::{Deserialize, Serialize};
+#[cfg(feature = "serde")] use serde::{Deserialize, Serialize};
 
 use crate::algorithm::neighbour::{KNNAlgorithm, KNNAlgorithmName};
 use crate::api::{Predictor, SupervisedEstimator};
@@ -48,7 +48,8 @@ use crate::math::num::RealNumber;
 use crate::neighbors::KNNWeightFunction;
 
 /// `KNNRegressor` parameters. Use `Default::default()` for default values.
-#[derive(Serialize, Deserialize, Debug, Clone)]
+#[cfg_attr(feature = "serde", derive(Serialize, Deserialize))]
+#[derive(Debug, Clone)]
 pub struct KNNRegressorParameters<T: RealNumber, D: Distance<Vec<T>, T>> {
     /// a function that defines a distance between each pair of point in training data.
     /// This function should extend [`Distance`](../../math/distance/trait.Distance.html) trait.
@@ -65,7 +66,8 @@ pub struct KNNRegressorParameters<T: RealNumber, D: Distance<Vec<T>, T>> {
 }
 
 /// K Nearest Neighbors Regressor
-#[derive(Serialize, Deserialize, Debug)]
+#[cfg_attr(feature = "serde", derive(Serialize, Deserialize))]
+#[derive(Debug)]
 pub struct KNNRegressor<T: RealNumber, D: Distance<Vec<T>, T>> {
     y: Vec<T>,
     knn_algorithm: KNNAlgorithm<T, D>,

src/neighbors/mod.rs

@@ -33,7 +33,7 @@
 //! <script id="MathJax-script" async src="https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js"></script>
 
 use crate::math::num::RealNumber;
-use serde::{Deserialize, Serialize};
+#[cfg(feature = "serde")] use serde::{Deserialize, Serialize};
 
 /// K Nearest Neighbors Classifier
 pub mod knn_classifier;
@@ -48,7 +48,8 @@ pub mod knn_regressor;
 pub type KNNAlgorithmName = crate::algorithm::neighbour::KNNAlgorithmName;
 
 /// Weight function that is used to determine estimated value.
-#[derive(Serialize, Deserialize, Debug, Clone)]
+#[cfg_attr(feature = "serde", derive(Serialize, Deserialize))]
+#[derive(Debug, Clone)]
 pub enum KNNWeightFunction {
     /// All k nearest points are weighted equally
     Uniform,