feat: Make SerDe optional

src/naive_bayes/bernoulli.rs

@@ -42,10 +42,11 @@ use crate::math::num::RealNumber;
 use crate::math::vector::RealNumberVector;
 use crate::naive_bayes::{BaseNaiveBayes, NBDistribution};
 
-use serde::{Deserialize, Serialize};
+#[cfg(feature = "serde")] use serde::{Deserialize, Serialize};
 
 /// Naive Bayes classifier for Bearnoulli features
-#[derive(Serialize, Deserialize, Debug, PartialEq)]
+#[cfg_attr(feature = "serde", derive(Serialize, Deserialize))]
+#[derive(Debug, PartialEq)]
 struct BernoulliNBDistribution<T: RealNumber> {
     /// class labels known to the classifier
     class_labels: Vec<T>,
@@ -77,7 +78,8 @@ impl<T: RealNumber, M: Matrix<T>> NBDistribution<T, M> for BernoulliNBDistributi
 }
 
 /// `BernoulliNB` parameters. Use `Default::default()` for default values.
-#[derive(Serialize, Deserialize, Debug, Clone)]
+#[cfg_attr(feature = "serde", derive(Serialize, Deserialize))]
+#[derive(Debug, Clone)]
 pub struct BernoulliNBParameters<T: RealNumber> {
     /// Additive (Laplace/Lidstone) smoothing parameter (0 for no smoothing).
     pub alpha: T,
@@ -202,7 +204,8 @@ impl<T: RealNumber> BernoulliNBDistribution<T> {
 }
 
 /// BernoulliNB implements the categorical naive Bayes algorithm for categorically distributed data.
-#[derive(Serialize, Deserialize, Debug, PartialEq)]
+#[cfg_attr(feature = "serde", derive(Serialize, Deserialize))]
+#[derive(Debug, PartialEq)]
 pub struct BernoulliNB<T: RealNumber, M: Matrix<T>> {
     inner: BaseNaiveBayes<T, M, BernoulliNBDistribution<T>>,
     binarize: Option<T>,

src/naive_bayes/categorical.rs

@@ -36,10 +36,11 @@ use crate::linalg::BaseVector;
 use crate::linalg::Matrix;
 use crate::math::num::RealNumber;
 use crate::naive_bayes::{BaseNaiveBayes, NBDistribution};
-use serde::{Deserialize, Serialize};
+#[cfg(feature = "serde")] use serde::{Deserialize, Serialize};
 
 /// Naive Bayes classifier for categorical features
-#[derive(Serialize, Deserialize, Debug)]
+#[cfg_attr(feature = "serde", derive(Serialize, Deserialize))]
+#[derive(Debug)]
 struct CategoricalNBDistribution<T: RealNumber> {
     class_labels: Vec<T>,
     class_priors: Vec<T>,
@@ -216,7 +217,8 @@ impl<T: RealNumber> CategoricalNBDistribution<T> {
 }
 
 /// `CategoricalNB` parameters. Use `Default::default()` for default values.
-#[derive(Serialize, Deserialize, Debug, Clone)]
+#[cfg_attr(feature = "serde", derive(Serialize, Deserialize))]
+#[derive(Debug, Clone)]
 pub struct CategoricalNBParameters<T: RealNumber> {
     /// Additive (Laplace/Lidstone) smoothing parameter (0 for no smoothing).
     pub alpha: T,
@@ -237,7 +239,8 @@ impl<T: RealNumber> Default for CategoricalNBParameters<T> {
 }
 
 /// CategoricalNB implements the categorical naive Bayes algorithm for categorically distributed data.
-#[derive(Serialize, Deserialize, Debug, PartialEq)]
+#[cfg_attr(feature = "serde", derive(Serialize, Deserialize))]
+#[derive(Debug, PartialEq)]
 pub struct CategoricalNB<T: RealNumber, M: Matrix<T>> {
     inner: BaseNaiveBayes<T, M, CategoricalNBDistribution<T>>,
 }

src/naive_bayes/gaussian.rs

@@ -30,10 +30,11 @@ use crate::linalg::Matrix;
 use crate::math::num::RealNumber;
 use crate::math::vector::RealNumberVector;
 use crate::naive_bayes::{BaseNaiveBayes, NBDistribution};
-use serde::{Deserialize, Serialize};
+#[cfg(feature = "serde")] use serde::{Deserialize, Serialize};
 
 /// Naive Bayes classifier for categorical features
-#[derive(Serialize, Deserialize, Debug, PartialEq)]
+#[cfg_attr(feature = "serde", derive(Serialize, Deserialize))]
+#[derive(Debug, PartialEq)]
 struct GaussianNBDistribution<T: RealNumber> {
     /// class labels known to the classifier
     class_labels: Vec<T>,
@@ -75,7 +76,8 @@ impl<T: RealNumber, M: Matrix<T>> NBDistribution<T, M> for GaussianNBDistributio
 }
 
 /// `GaussianNB` parameters. Use `Default::default()` for default values.
-#[derive(Serialize, Deserialize, Debug, Default, Clone)]
+#[cfg_attr(feature = "serde", derive(Serialize, Deserialize))]
+#[derive(Debug, Default, Clone)]
 pub struct GaussianNBParameters<T: RealNumber> {
     /// Prior probabilities of the classes. If specified the priors are not adjusted according to the data
     pub priors: Option<Vec<T>>,
@@ -178,7 +180,8 @@ impl<T: RealNumber> GaussianNBDistribution<T> {
 }
 
 /// GaussianNB implements the categorical naive Bayes algorithm for categorically distributed data.
-#[derive(Serialize, Deserialize, Debug, PartialEq)]
+#[cfg_attr(feature = "serde", derive(Serialize, Deserialize))]
+#[derive(Debug, PartialEq)]
 pub struct GaussianNB<T: RealNumber, M: Matrix<T>> {
     inner: BaseNaiveBayes<T, M, GaussianNBDistribution<T>>,
 }

src/naive_bayes/mod.rs

@@ -39,7 +39,7 @@ use crate::error::Failed;
 use crate::linalg::BaseVector;
 use crate::linalg::Matrix;
 use crate::math::num::RealNumber;
-use serde::{Deserialize, Serialize};
+#[cfg(feature = "serde")] use serde::{Deserialize, Serialize};
 use std::marker::PhantomData;
 
 /// Distribution used in the Naive Bayes classifier.
@@ -55,7 +55,8 @@ pub(crate) trait NBDistribution<T: RealNumber, M: Matrix<T>> {
 }
 
 /// Base struct for the Naive Bayes classifier.
-#[derive(Serialize, Deserialize, Debug, PartialEq)]
+#[cfg_attr(feature = "serde", derive(Serialize, Deserialize))]
+#[derive(Debug, PartialEq)]
 pub(crate) struct BaseNaiveBayes<T: RealNumber, M: Matrix<T>, D: NBDistribution<T, M>> {
     distribution: D,
     _phantom_t: PhantomData<T>,

src/naive_bayes/multinomial.rs

@@ -42,10 +42,11 @@ use crate::math::num::RealNumber;
 use crate::math::vector::RealNumberVector;
 use crate::naive_bayes::{BaseNaiveBayes, NBDistribution};
 
-use serde::{Deserialize, Serialize};
+#[cfg(feature = "serde")] use serde::{Deserialize, Serialize};
 
 /// Naive Bayes classifier for Multinomial features
-#[derive(Serialize, Deserialize, Debug, PartialEq)]
+#[cfg_attr(feature = "serde", derive(Serialize, Deserialize))]
+#[derive(Debug, PartialEq)]
 struct MultinomialNBDistribution<T: RealNumber> {
     /// class labels known to the classifier
     class_labels: Vec<T>,
@@ -73,7 +74,8 @@ impl<T: RealNumber, M: Matrix<T>> NBDistribution<T, M> for MultinomialNBDistribu
 }
 
 /// `MultinomialNB` parameters. Use `Default::default()` for default values.
-#[derive(Serialize, Deserialize, Debug, Clone)]
+#[cfg_attr(feature = "serde", derive(Serialize, Deserialize))]
+#[derive(Debug, Clone)]
 pub struct MultinomialNBParameters<T: RealNumber> {
     /// Additive (Laplace/Lidstone) smoothing parameter (0 for no smoothing).
     pub alpha: T,
@@ -189,7 +191,8 @@ impl<T: RealNumber> MultinomialNBDistribution<T> {
 }
 
 /// MultinomialNB implements the categorical naive Bayes algorithm for categorically distributed data.
-#[derive(Serialize, Deserialize, Debug, PartialEq)]
+#[cfg_attr(feature = "serde", derive(Serialize, Deserialize))]
+#[derive(Debug, PartialEq)]
 pub struct MultinomialNB<T: RealNumber, M: Matrix<T>> {
     inner: BaseNaiveBayes<T, M, MultinomialNBDistribution<T>>,
 }