Merge pull request #67 from ssorc3/development

Make SerDe Optional

.circleci/config.yml

@@ -23,10 +23,10 @@ jobs:
           command: cargo fmt -- --check
       - run:
           name: Stable Build
-          command: cargo build --features "nalgebra-bindings ndarray-bindings"
+          command: cargo build --all-features
       - run:
           name: Test
-          command: cargo test --features "nalgebra-bindings ndarray-bindings"
+          command: cargo test --all-features
       - save_cache:
           key: project-cache
           paths:

Cargo.toml

@@ -25,8 +25,7 @@ num-traits = "0.2.12"
 num = "0.3.0"
 rand = "0.7.3"
 rand_distr = "0.3.0"
-serde = { version = "1.0.115", features = ["derive"] }
+serde = { version = "1.0.115", features = ["derive"], optional = true }
-serde_derive = "1.0.115"
 
 [dev-dependencies]
 criterion = "0.3"

src/algorithm/neighbour/cover_tree.rs

@@ -24,6 +24,7 @@
 //! ```
 use std::fmt::Debug;
 
+#[cfg(feature = "serde")]
 use serde::{Deserialize, Serialize};
 
 use crate::algorithm::sort::heap_select::HeapSelection;
@@ -32,7 +33,8 @@ use crate::math::distance::Distance;
 use crate::math::num::RealNumber;
 
 /// Implements Cover Tree algorithm
-#[derive(Serialize, Deserialize, Debug)]
+#[cfg_attr(feature = "serde", derive(Serialize, Deserialize))]
+#[derive(Debug)]
 pub struct CoverTree<T, F: RealNumber, D: Distance<T, F>> {
     base: F,
     inv_log_base: F,
@@ -56,7 +58,8 @@ impl<T, F: RealNumber, D: Distance<T, F>> PartialEq for CoverTree<T, F, D> {
     }
 }
 
-#[derive(Debug, Serialize, Deserialize)]
+#[cfg_attr(feature = "serde", derive(Serialize, Deserialize))]
+#[derive(Debug)]
 struct Node<F: RealNumber> {
     idx: usize,
     max_dist: F,
@@ -65,7 +68,7 @@ struct Node<F: RealNumber> {
     scale: i64,
 }
 
-#[derive(Debug, Serialize, Deserialize)]
+#[derive(Debug)]
 struct DistanceSet<F: RealNumber> {
     idx: usize,
     dist: Vec<F>,
@@ -454,7 +457,8 @@ mod tests {
     use super::*;
     use crate::math::distance::Distances;
 
-    #[derive(Debug, Serialize, Deserialize, Clone)]
+    #[cfg_attr(feature = "serde", derive(Serialize, Deserialize))]
+    #[derive(Debug, Clone)]
     struct SimpleDistance {}
 
     impl Distance<i32, f64> for SimpleDistance {
@@ -500,6 +504,7 @@ mod tests {
     }
 
     #[test]
+    #[cfg(feature = "serde")]
     fn serde() {
         let data = vec![1, 2, 3, 4, 5, 6, 7, 8, 9];
 

src/algorithm/neighbour/linear_search.rs

@@ -22,6 +22,7 @@
 //!
 //! ```
 
+#[cfg(feature = "serde")]
 use serde::{Deserialize, Serialize};
 use std::cmp::{Ordering, PartialOrd};
 use std::marker::PhantomData;
@@ -32,7 +33,8 @@ use crate::math::distance::Distance;
 use crate::math::num::RealNumber;
 
 /// Implements Linear Search algorithm, see [KNN algorithms](../index.html)
-#[derive(Serialize, Deserialize, Debug)]
+#[cfg_attr(feature = "serde", derive(Serialize, Deserialize))]
+#[derive(Debug)]
 pub struct LinearKNNSearch<T, F: RealNumber, D: Distance<T, F>> {
     distance: D,
     data: Vec<T>,
@@ -138,7 +140,8 @@ mod tests {
     use super::*;
     use crate::math::distance::Distances;
 
-    #[derive(Debug, Serialize, Deserialize, Clone)]
+    #[cfg_attr(feature = "serde", derive(Serialize, Deserialize))]
+    #[derive(Debug, Clone)]
     struct SimpleDistance {}
 
     impl Distance<i32, f64> for SimpleDistance {

src/algorithm/neighbour/mod.rs

@@ -35,6 +35,7 @@ use crate::algorithm::neighbour::linear_search::LinearKNNSearch;
 use crate::error::Failed;
 use crate::math::distance::Distance;
 use crate::math::num::RealNumber;
+#[cfg(feature = "serde")]
 use serde::{Deserialize, Serialize};
 
 pub(crate) mod bbd_tree;
@@ -45,7 +46,8 @@ pub mod linear_search;
 
 /// Both, KNN classifier and regressor benefits from underlying search algorithms that helps to speed up queries.
 /// `KNNAlgorithmName` maintains a list of supported search algorithms, see [KNN algorithms](../algorithm/neighbour/index.html)
-#[derive(Serialize, Deserialize, Debug, Clone)]
+#[cfg_attr(feature = "serde", derive(Serialize, Deserialize))]
+#[derive(Debug, Clone)]
 pub enum KNNAlgorithmName {
     /// Heap Search algorithm, see [`LinearSearch`](../algorithm/neighbour/linear_search/index.html)
     LinearSearch,
@@ -53,7 +55,8 @@ pub enum KNNAlgorithmName {
     CoverTree,
 }
 
-#[derive(Serialize, Deserialize, Debug)]
+#[cfg_attr(feature = "serde", derive(Serialize, Deserialize))]
+#[derive(Debug)]
 pub(crate) enum KNNAlgorithm<T: RealNumber, D: Distance<Vec<T>, T>> {
     LinearSearch(LinearKNNSearch<Vec<T>, T, D>),
     CoverTree(CoverTree<Vec<T>, T, D>),

src/cluster/dbscan.rs

@@ -43,6 +43,7 @@
 use std::fmt::Debug;
 use std::iter::Sum;
 
+#[cfg(feature = "serde")]
 use serde::{Deserialize, Serialize};
 
 use crate::algorithm::neighbour::{KNNAlgorithm, KNNAlgorithmName};
@@ -55,7 +56,8 @@ use crate::math::num::RealNumber;
 use crate::tree::decision_tree_classifier::which_max;
 
 /// DBSCAN clustering algorithm
-#[derive(Serialize, Deserialize, Debug)]
+#[cfg_attr(feature = "serde", derive(Serialize, Deserialize))]
+#[derive(Debug)]
 pub struct DBSCAN<T: RealNumber, D: Distance<Vec<T>, T>> {
     cluster_labels: Vec<i16>,
     num_classes: usize,
@@ -263,6 +265,7 @@ impl<T: RealNumber + Sum, D: Distance<Vec<T>, T>> DBSCAN<T, D> {
 mod tests {
     use super::*;
     use crate::linalg::naive::dense_matrix::DenseMatrix;
+    #[cfg(feature = "serde")]
     use crate::math::distance::euclidian::Euclidian;
 
     #[test]
@@ -297,6 +300,7 @@ mod tests {
     }
 
     #[test]
+    #[cfg(feature = "serde")]
     fn serde() {
         let x = DenseMatrix::from_2d_array(&[
             &[5.1, 3.5, 1.4, 0.2],

src/cluster/kmeans.rs

@@ -56,6 +56,7 @@ use rand::Rng;
 use std::fmt::Debug;
 use std::iter::Sum;
 
+#[cfg(feature = "serde")]
 use serde::{Deserialize, Serialize};
 
 use crate::algorithm::neighbour::bbd_tree::BBDTree;
@@ -66,7 +67,8 @@ use crate::math::distance::euclidian::*;
 use crate::math::num::RealNumber;
 
 /// K-Means clustering algorithm
-#[derive(Serialize, Deserialize, Debug)]
+#[cfg_attr(feature = "serde", derive(Serialize, Deserialize))]
+#[derive(Debug)]
 pub struct KMeans<T: RealNumber> {
     k: usize,
     y: Vec<usize>,
@@ -345,6 +347,7 @@ mod tests {
     }
 
     #[test]
+    #[cfg(feature = "serde")]
     fn serde() {
         let x = DenseMatrix::from_2d_array(&[
             &[5.1, 3.5, 1.4, 0.2],

src/decomposition/pca.rs

@@ -47,6 +47,7 @@
 //! <script id="MathJax-script" async src="https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js"></script>
 use std::fmt::Debug;
 
+#[cfg(feature = "serde")]
 use serde::{Deserialize, Serialize};
 
 use crate::api::{Transformer, UnsupervisedEstimator};
@@ -55,7 +56,8 @@ use crate::linalg::Matrix;
 use crate::math::num::RealNumber;
 
 /// Principal components analysis algorithm
-#[derive(Serialize, Deserialize, Debug)]
+#[cfg_attr(feature = "serde", derive(Serialize, Deserialize))]
+#[derive(Debug)]
 pub struct PCA<T: RealNumber, M: Matrix<T>> {
     eigenvectors: M,
     eigenvalues: Vec<T>,
@@ -565,6 +567,7 @@ mod tests {
     }
 
     #[test]
+    #[cfg(feature = "serde")]
     fn serde() {
         let iris = DenseMatrix::from_2d_array(&[
             &[5.1, 3.5, 1.4, 0.2],

src/decomposition/svd.rs

@@ -46,6 +46,7 @@
 use std::fmt::Debug;
 use std::marker::PhantomData;
 
+#[cfg(feature = "serde")]
 use serde::{Deserialize, Serialize};
 
 use crate::api::{Transformer, UnsupervisedEstimator};
@@ -54,7 +55,8 @@ use crate::linalg::Matrix;
 use crate::math::num::RealNumber;
 
 /// SVD
-#[derive(Serialize, Deserialize, Debug)]
+#[cfg_attr(feature = "serde", derive(Serialize, Deserialize))]
+#[derive(Debug)]
 pub struct SVD<T: RealNumber, M: Matrix<T>> {
     components: M,
     phantom: PhantomData<T>,
@@ -226,6 +228,7 @@ mod tests {
     }
 
     #[test]
+    #[cfg(feature = "serde")]
     fn serde() {
         let iris = DenseMatrix::from_2d_array(&[
             &[5.1, 3.5, 1.4, 0.2],

src/ensemble/random_forest_classifier.rs

@@ -49,6 +49,7 @@ use std::default::Default;
 use std::fmt::Debug;
 
 use rand::Rng;
+#[cfg(feature = "serde")]
 use serde::{Deserialize, Serialize};
 
 use crate::api::{Predictor, SupervisedEstimator};
@@ -61,7 +62,8 @@ use crate::tree::decision_tree_classifier::{
 
 /// Parameters of the Random Forest algorithm.
 /// Some parameters here are passed directly into base estimator.
-#[derive(Serialize, Deserialize, Debug, Clone)]
+#[cfg_attr(feature = "serde", derive(Serialize, Deserialize))]
+#[derive(Debug, Clone)]
 pub struct RandomForestClassifierParameters {
     /// Split criteria to use when building a tree. See [Decision Tree Classifier](../../tree/decision_tree_classifier/index.html)
     pub criterion: SplitCriterion,
@@ -78,7 +80,8 @@ pub struct RandomForestClassifierParameters {
 }
 
 /// Random Forest Classifier
-#[derive(Serialize, Deserialize, Debug)]
+#[cfg_attr(feature = "serde", derive(Serialize, Deserialize))]
+#[derive(Debug)]
 pub struct RandomForestClassifier<T: RealNumber> {
     parameters: RandomForestClassifierParameters,
     trees: Vec<DecisionTreeClassifier<T>>,
@@ -322,6 +325,7 @@ mod tests {
     }
 
     #[test]
+    #[cfg(feature = "serde")]
     fn serde() {
         let x = DenseMatrix::from_2d_array(&[
             &[5.1, 3.5, 1.4, 0.2],

src/ensemble/random_forest_regressor.rs

@@ -47,6 +47,7 @@ use std::default::Default;
 use std::fmt::Debug;
 
 use rand::Rng;
+#[cfg(feature = "serde")]
 use serde::{Deserialize, Serialize};
 
 use crate::api::{Predictor, SupervisedEstimator};
@@ -57,7 +58,8 @@ use crate::tree::decision_tree_regressor::{
     DecisionTreeRegressor, DecisionTreeRegressorParameters,
 };
 
-#[derive(Serialize, Deserialize, Debug, Clone)]
+#[cfg_attr(feature = "serde", derive(Serialize, Deserialize))]
+#[derive(Debug, Clone)]
 /// Parameters of the Random Forest Regressor
 /// Some parameters here are passed directly into base estimator.
 pub struct RandomForestRegressorParameters {
@@ -74,7 +76,8 @@ pub struct RandomForestRegressorParameters {
 }
 
 /// Random Forest Regressor
-#[derive(Serialize, Deserialize, Debug)]
+#[cfg_attr(feature = "serde", derive(Serialize, Deserialize))]
+#[derive(Debug)]
 pub struct RandomForestRegressor<T: RealNumber> {
     parameters: RandomForestRegressorParameters,
     trees: Vec<DecisionTreeRegressor<T>>,
@@ -271,6 +274,7 @@ mod tests {
     }
 
     #[test]
+    #[cfg(feature = "serde")]
     fn serde() {
         let x = DenseMatrix::from_2d_array(&[
             &[234.289, 235.6, 159., 107.608, 1947., 60.323],

src/error/mod.rs

@@ -2,10 +2,12 @@
 use std::error::Error;
 use std::fmt;
 
+#[cfg(feature = "serde")]
 use serde::{Deserialize, Serialize};
 
 /// Generic error to be raised when something goes wrong.
-#[derive(Debug, Serialize, Deserialize)]
+#[cfg_attr(feature = "serde", derive(Serialize, Deserialize))]
+#[derive(Debug)]
 pub struct Failed {
     err: FailedError,
     msg: String,
@@ -13,7 +15,8 @@ pub struct Failed {
 
 /// Type of error
 #[non_exhaustive]
-#[derive(Copy, Clone, Debug, Serialize, Deserialize)]
+#[cfg_attr(feature = "serde", derive(Serialize, Deserialize))]
+#[derive(Copy, Clone, Debug)]
 pub enum FailedError {
     /// Can't fit algorithm to data
     FitFailed = 1,

src/linalg/naive/dense_matrix.rs

@@ -1,11 +1,15 @@
 #![allow(clippy::ptr_arg)]
 use std::fmt;
 use std::fmt::Debug;
+#[cfg(feature = "serde")]
 use std::marker::PhantomData;
 use std::ops::Range;
 
+#[cfg(feature = "serde")]
 use serde::de::{Deserializer, MapAccess, SeqAccess, Visitor};
+#[cfg(feature = "serde")]
 use serde::ser::{SerializeStruct, Serializer};
+#[cfg(feature = "serde")]
 use serde::{Deserialize, Serialize};
 
 use crate::linalg::cholesky::CholeskyDecomposableMatrix;
@@ -349,6 +353,7 @@ impl<'a, T: RealNumber> Iterator for DenseMatrixIterator<'a, T> {
     }
 }
 
+#[cfg(feature = "serde")]
 impl<'de, T: RealNumber + fmt::Debug + Deserialize<'de>> Deserialize<'de> for DenseMatrix<T> {
     fn deserialize<D>(deserializer: D) -> Result<Self, D::Error>
     where
@@ -434,6 +439,7 @@ impl<'de, T: RealNumber + fmt::Debug + Deserialize<'de>> Deserialize<'de> for De
     }
 }
 
+#[cfg(feature = "serde")]
 impl<T: RealNumber + fmt::Debug + Serialize> Serialize for DenseMatrix<T> {
     fn serialize<S>(&self, serializer: S) -> Result<S::Ok, S::Error>
     where
@@ -1306,6 +1312,7 @@ mod tests {
     }
 
     #[test]
+    #[cfg(feature = "serde")]
     fn to_from_json() {
         let a = DenseMatrix::from_2d_array(&[&[0.9, 0.4, 0.7], &[0.4, 0.5, 0.3], &[0.7, 0.3, 0.8]]);
         let deserialized_a: DenseMatrix<f64> =
@@ -1314,6 +1321,7 @@ mod tests {
     }
 
     #[test]
+    #[cfg(feature = "serde")]
     fn to_from_bincode() {
         let a = DenseMatrix::from_2d_array(&[&[0.9, 0.4, 0.7], &[0.4, 0.5, 0.3], &[0.7, 0.3, 0.8]]);
         let deserialized_a: DenseMatrix<f64> =

src/linear/elastic_net.rs

@@ -56,6 +56,7 @@
 //! <script id="MathJax-script" async src="https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js"></script>
 use std::fmt::Debug;
 
+#[cfg(feature = "serde")]
 use serde::{Deserialize, Serialize};
 
 use crate::api::{Predictor, SupervisedEstimator};
@@ -67,7 +68,8 @@ use crate::math::num::RealNumber;
 use crate::linear::lasso_optimizer::InteriorPointOptimizer;
 
 /// Elastic net parameters
-#[derive(Serialize, Deserialize, Debug, Clone)]
+#[cfg_attr(feature = "serde", derive(Serialize, Deserialize))]
+#[derive(Debug, Clone)]
 pub struct ElasticNetParameters<T: RealNumber> {
     /// Regularization parameter.
     pub alpha: T,
@@ -84,7 +86,8 @@ pub struct ElasticNetParameters<T: RealNumber> {
 }
 
 /// Elastic net
-#[derive(Serialize, Deserialize, Debug)]
+#[cfg_attr(feature = "serde", derive(Serialize, Deserialize))]
+#[derive(Debug)]
 pub struct ElasticNet<T: RealNumber, M: Matrix<T>> {
     coefficients: M,
     intercept: T,
@@ -398,6 +401,7 @@ mod tests {
     }
 
     #[test]
+    #[cfg(feature = "serde")]
     fn serde() {
         let x = DenseMatrix::from_2d_array(&[
             &[234.289, 235.6, 159.0, 107.608, 1947., 60.323],

src/linear/lasso.rs

@@ -24,6 +24,7 @@
 //! <script id="MathJax-script" async src="https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js"></script>
 use std::fmt::Debug;
 
+#[cfg(feature = "serde")]
 use serde::{Deserialize, Serialize};
 
 use crate::api::{Predictor, SupervisedEstimator};
@@ -34,7 +35,8 @@ use crate::linear::lasso_optimizer::InteriorPointOptimizer;
 use crate::math::num::RealNumber;
 
 /// Lasso regression parameters
-#[derive(Serialize, Deserialize, Debug, Clone)]
+#[cfg_attr(feature = "serde", derive(Serialize, Deserialize))]
+#[derive(Debug, Clone)]
 pub struct LassoParameters<T: RealNumber> {
     /// Controls the strength of the penalty to the loss function.
     pub alpha: T,
@@ -47,7 +49,8 @@ pub struct LassoParameters<T: RealNumber> {
     pub max_iter: usize,
 }
 
-#[derive(Serialize, Deserialize, Debug)]
+#[cfg_attr(feature = "serde", derive(Serialize, Deserialize))]
+#[derive(Debug)]
 /// Lasso regressor
 pub struct Lasso<T: RealNumber, M: Matrix<T>> {
     coefficients: M,
@@ -272,6 +275,7 @@ mod tests {
     }
 
     #[test]
+    #[cfg(feature = "serde")]
     fn serde() {
         let x = DenseMatrix::from_2d_array(&[
             &[234.289, 235.6, 159.0, 107.608, 1947., 60.323],

src/linear/linear_regression.rs

@@ -62,6 +62,7 @@
 //! <script id="MathJax-script" async src="https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js"></script>
 use std::fmt::Debug;
 
+#[cfg(feature = "serde")]
 use serde::{Deserialize, Serialize};
 
 use crate::api::{Predictor, SupervisedEstimator};
@@ -69,7 +70,8 @@ use crate::error::Failed;
 use crate::linalg::Matrix;
 use crate::math::num::RealNumber;
 
-#[derive(Serialize, Deserialize, Debug, Clone)]
+#[cfg_attr(feature = "serde", derive(Serialize, Deserialize))]
+#[derive(Debug, Clone)]
 /// 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)
@@ -79,14 +81,16 @@ pub enum LinearRegressionSolverName {
 }
 
 /// Linear Regression parameters
-#[derive(Serialize, Deserialize, Debug, Clone)]
+#[cfg_attr(feature = "serde", derive(Serialize, Deserialize))]
+#[derive(Debug, Clone)]
 pub struct LinearRegressionParameters {
     /// Solver to use for estimation of regression coefficients.
     pub solver: LinearRegressionSolverName,
 }
 
 /// Linear Regression
-#[derive(Serialize, Deserialize, Debug)]
+#[cfg_attr(feature = "serde", derive(Serialize, Deserialize))]
+#[derive(Debug)]
 pub struct LinearRegression<T: RealNumber, M: Matrix<T>> {
     coefficients: M,
     intercept: T,
@@ -247,6 +251,7 @@ mod tests {
     }
 
     #[test]
+    #[cfg(feature = "serde")]
     fn serde() {
         let x = DenseMatrix::from_2d_array(&[
             &[234.289, 235.6, 159.0, 107.608, 1947., 60.323],

src/linear/logistic_regression.rs

@@ -56,6 +56,7 @@ use std::cmp::Ordering;
 use std::fmt::Debug;
 use std::marker::PhantomData;
 
+#[cfg(feature = "serde")]
 use serde::{Deserialize, Serialize};
 
 use crate::api::{Predictor, SupervisedEstimator};
@@ -68,11 +69,13 @@ use crate::optimization::line_search::Backtracking;
 use crate::optimization::FunctionOrder;
 
 /// Logistic Regression parameters
-#[derive(Serialize, Deserialize, Debug, Clone)]
+#[cfg_attr(feature = "serde", derive(Serialize, Deserialize))]
+#[derive(Debug, Clone)]
 pub struct LogisticRegressionParameters {}
 
 /// Logistic Regression
-#[derive(Serialize, Deserialize, Debug)]
+#[cfg_attr(feature = "serde", derive(Serialize, Deserialize))]
+#[derive(Debug)]
 pub struct LogisticRegression<T: RealNumber, M: Matrix<T>> {
     coefficients: M,
     intercept: M,
@@ -540,6 +543,7 @@ mod tests {
     }
 
     #[test]
+    #[cfg(feature = "serde")]
     fn serde() {
         let x = DenseMatrix::from_2d_array(&[
             &[1., -5.],

src/linear/ridge_regression.rs

@@ -58,6 +58,7 @@
 //! <script id="MathJax-script" async src="https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js"></script>
 use std::fmt::Debug;
 
+#[cfg(feature = "serde")]
 use serde::{Deserialize, Serialize};
 
 use crate::api::{Predictor, SupervisedEstimator};
@@ -66,7 +67,8 @@ use crate::linalg::BaseVector;
 use crate::linalg::Matrix;
 use crate::math::num::RealNumber;
 
-#[derive(Serialize, Deserialize, Debug, Clone)]
+#[cfg_attr(feature = "serde", derive(Serialize, Deserialize))]
+#[derive(Debug, Clone)]
 /// Approach to use for estimation of regression coefficients. Cholesky is more efficient but SVD is more stable.
 pub enum RidgeRegressionSolverName {
     /// Cholesky decomposition, see [Cholesky](../../linalg/cholesky/index.html)
@@ -76,7 +78,8 @@ pub enum RidgeRegressionSolverName {
 }
 
 /// Ridge Regression parameters
-#[derive(Serialize, Deserialize, Debug, Clone)]
+#[cfg_attr(feature = "serde", derive(Serialize, Deserialize))]
+#[derive(Debug, Clone)]
 pub struct RidgeRegressionParameters<T: RealNumber> {
     /// Solver to use for estimation of regression coefficients.
     pub solver: RidgeRegressionSolverName,
@@ -88,7 +91,8 @@ pub struct RidgeRegressionParameters<T: RealNumber> {
 }
 
 /// Ridge regression
-#[derive(Serialize, Deserialize, Debug)]
+#[cfg_attr(feature = "serde", derive(Serialize, Deserialize))]
+#[derive(Debug)]
 pub struct RidgeRegression<T: RealNumber, M: Matrix<T>> {
     coefficients: M,
     intercept: T,
@@ -326,6 +330,7 @@ mod tests {
     }
 
     #[test]
+    #[cfg(feature = "serde")]
     fn serde() {
         let x = DenseMatrix::from_2d_array(&[
             &[234.289, 235.6, 159.0, 107.608, 1947., 60.323],

src/math/distance/euclidian.rs

@@ -18,6 +18,7 @@
 //!
 //! <script src="https://polyfill.io/v3/polyfill.min.js?features=es6"></script>
 //! <script id="MathJax-script" async src="https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js"></script>
+#[cfg(feature = "serde")]
 use serde::{Deserialize, Serialize};
 
 use crate::math::num::RealNumber;
@@ -25,7 +26,8 @@ use crate::math::num::RealNumber;
 use super::Distance;
 
 /// Euclidean distance is a measure of the true straight line distance between two points in Euclidean n-space.
-#[derive(Serialize, Deserialize, Debug, Clone)]
+#[cfg_attr(feature = "serde", derive(Serialize, Deserialize))]
+#[derive(Debug, Clone)]
 pub struct Euclidian {}
 
 impl Euclidian {

src/math/distance/hamming.rs

@@ -19,6 +19,7 @@
 //! <script src="https://polyfill.io/v3/polyfill.min.js?features=es6"></script>
 //! <script id="MathJax-script" async src="https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js"></script>
 
+#[cfg(feature = "serde")]
 use serde::{Deserialize, Serialize};
 
 use crate::math::num::RealNumber;
@@ -26,7 +27,8 @@ use crate::math::num::RealNumber;
 use super::Distance;
 
 /// While comparing two integer-valued vectors of equal length, Hamming distance is the number of bit positions in which the two bits are different
-#[derive(Serialize, Deserialize, Debug, Clone)]
+#[cfg_attr(feature = "serde", derive(Serialize, Deserialize))]
+#[derive(Debug, Clone)]
 pub struct Hamming {}
 
 impl<T: PartialEq, F: RealNumber> Distance<Vec<T>, F> for Hamming {

src/math/distance/mahalanobis.rs

@@ -44,6 +44,7 @@
 
 use std::marker::PhantomData;
 
+#[cfg(feature = "serde")]
 use serde::{Deserialize, Serialize};
 
 use crate::math::num::RealNumber;
@@ -52,7 +53,8 @@ use super::Distance;
 use crate::linalg::Matrix;
 
 /// Mahalanobis distance.
-#[derive(Serialize, Deserialize, Debug, Clone)]
+#[cfg_attr(feature = "serde", derive(Serialize, Deserialize))]
+#[derive(Debug, Clone)]
 pub struct Mahalanobis<T: RealNumber, M: Matrix<T>> {
     /// covariance matrix of the dataset
     pub sigma: M,

src/math/distance/manhattan.rs

@@ -17,6 +17,7 @@
 //! ```
 //! <script src="https://polyfill.io/v3/polyfill.min.js?features=es6"></script>
 //! <script id="MathJax-script" async src="https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js"></script>
+#[cfg(feature = "serde")]
 use serde::{Deserialize, Serialize};
 
 use crate::math::num::RealNumber;
@@ -24,7 +25,8 @@ use crate::math::num::RealNumber;
 use super::Distance;
 
 /// Manhattan distance
-#[derive(Serialize, Deserialize, Debug, Clone)]
+#[cfg_attr(feature = "serde", derive(Serialize, Deserialize))]
+#[derive(Debug, Clone)]
 pub struct Manhattan {}
 
 impl<T: RealNumber> Distance<Vec<T>, T> for Manhattan {

src/math/distance/minkowski.rs

@@ -21,6 +21,7 @@
 //! <script src="https://polyfill.io/v3/polyfill.min.js?features=es6"></script>
 //! <script id="MathJax-script" async src="https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js"></script>
 
+#[cfg(feature = "serde")]
 use serde::{Deserialize, Serialize};
 
 use crate::math::num::RealNumber;
@@ -28,7 +29,8 @@ use crate::math::num::RealNumber;
 use super::Distance;
 
 /// Defines the Minkowski distance of order `p`
-#[derive(Serialize, Deserialize, Debug, Clone)]
+#[cfg_attr(feature = "serde", derive(Serialize, Deserialize))]
+#[derive(Debug, Clone)]
 pub struct Minkowski {
     /// order, integer
     pub p: u16,

src/metrics/accuracy.rs

@@ -16,13 +16,15 @@
 //!
 //! <script src="https://polyfill.io/v3/polyfill.min.js?features=es6"></script>
 //! <script id="MathJax-script" async src="https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js"></script>
+#[cfg(feature = "serde")]
 use serde::{Deserialize, Serialize};
 
 use crate::linalg::BaseVector;
 use crate::math::num::RealNumber;
 
 /// Accuracy metric.
-#[derive(Serialize, Deserialize, Debug)]
+#[cfg_attr(feature = "serde", derive(Serialize, Deserialize))]
+#[derive(Debug)]
 pub struct Accuracy {}
 
 impl Accuracy {

src/metrics/auc.rs

@@ -20,6 +20,7 @@
 //! * ["The ROC-AUC and the Mann-Whitney U-test", Haupt, J.](https://johaupt.github.io/roc-auc/model%20evaluation/Area_under_ROC_curve.html)
 #![allow(non_snake_case)]
 
+#[cfg(feature = "serde")]
 use serde::{Deserialize, Serialize};
 
 use crate::algorithm::sort::quick_sort::QuickArgSort;
@@ -27,7 +28,8 @@ use crate::linalg::BaseVector;
 use crate::math::num::RealNumber;
 
 /// Area Under the Receiver Operating Characteristic Curve (ROC AUC)
-#[derive(Serialize, Deserialize, Debug)]
+#[cfg_attr(feature = "serde", derive(Serialize, Deserialize))]
+#[derive(Debug)]
 pub struct AUC {}
 
 impl AUC {

src/metrics/cluster_hcv.rs

@@ -1,10 +1,12 @@
+#[cfg(feature = "serde")]
 use serde::{Deserialize, Serialize};
 
 use crate::linalg::BaseVector;
 use crate::math::num::RealNumber;
 use crate::metrics::cluster_helpers::*;
 
-#[derive(Serialize, Deserialize, Debug)]
+#[cfg_attr(feature = "serde", derive(Serialize, Deserialize))]
+#[derive(Debug)]
 /// Homogeneity, completeness and V-Measure scores.
 pub struct HCVScore {}
 

src/metrics/f1.rs

@@ -18,6 +18,7 @@
 //!
 //! <script src="https://polyfill.io/v3/polyfill.min.js?features=es6"></script>
 //! <script id="MathJax-script" async src="https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js"></script>
+#[cfg(feature = "serde")]
 use serde::{Deserialize, Serialize};
 
 use crate::linalg::BaseVector;
@@ -26,7 +27,8 @@ use crate::metrics::precision::Precision;
 use crate::metrics::recall::Recall;
 
 /// F-measure
-#[derive(Serialize, Deserialize, Debug)]
+#[cfg_attr(feature = "serde", derive(Serialize, Deserialize))]
+#[derive(Debug)]
 pub struct F1<T: RealNumber> {
     /// a positive real factor
     pub beta: T,

src/metrics/mean_absolute_error.rs

@@ -18,12 +18,14 @@
 //!
 //! <script src="https://polyfill.io/v3/polyfill.min.js?features=es6"></script>
 //! <script id="MathJax-script" async src="https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js"></script>
+#[cfg(feature = "serde")]
 use serde::{Deserialize, Serialize};
 
 use crate::linalg::BaseVector;
 use crate::math::num::RealNumber;
 
-#[derive(Serialize, Deserialize, Debug)]
+#[cfg_attr(feature = "serde", derive(Serialize, Deserialize))]
+#[derive(Debug)]
 /// Mean Absolute Error
 pub struct MeanAbsoluteError {}
 

src/metrics/mean_squared_error.rs

@@ -18,12 +18,14 @@
 //!
 //! <script src="https://polyfill.io/v3/polyfill.min.js?features=es6"></script>
 //! <script id="MathJax-script" async src="https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js"></script>
+#[cfg(feature = "serde")]
 use serde::{Deserialize, Serialize};
 
 use crate::linalg::BaseVector;
 use crate::math::num::RealNumber;
 
-#[derive(Serialize, Deserialize, Debug)]
+#[cfg_attr(feature = "serde", derive(Serialize, Deserialize))]
+#[derive(Debug)]
 /// Mean Squared Error
 pub struct MeanSquareError {}
 

src/metrics/precision.rs

@@ -18,13 +18,15 @@
 //!
 //! <script src="https://polyfill.io/v3/polyfill.min.js?features=es6"></script>
 //! <script id="MathJax-script" async src="https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js"></script>
+#[cfg(feature = "serde")]
 use serde::{Deserialize, Serialize};
 
 use crate::linalg::BaseVector;
 use crate::math::num::RealNumber;
 
 /// Precision metric.
-#[derive(Serialize, Deserialize, Debug)]
+#[cfg_attr(feature = "serde", derive(Serialize, Deserialize))]
+#[derive(Debug)]
 pub struct Precision {}
 
 impl Precision {

src/metrics/r2.rs

@@ -18,13 +18,15 @@
 //!
 //! <script src="https://polyfill.io/v3/polyfill.min.js?features=es6"></script>
 //! <script id="MathJax-script" async src="https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js"></script>
+#[cfg(feature = "serde")]
 use serde::{Deserialize, Serialize};
 
 use crate::linalg::BaseVector;
 use crate::math::num::RealNumber;
 
 /// Coefficient of Determination (R2)
-#[derive(Serialize, Deserialize, Debug)]
+#[cfg_attr(feature = "serde", derive(Serialize, Deserialize))]
+#[derive(Debug)]
 pub struct R2 {}
 
 impl R2 {

src/metrics/recall.rs

@@ -18,13 +18,15 @@
 //!
 //! <script src="https://polyfill.io/v3/polyfill.min.js?features=es6"></script>
 //! <script id="MathJax-script" async src="https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js"></script>
+#[cfg(feature = "serde")]
 use serde::{Deserialize, Serialize};
 
 use crate::linalg::BaseVector;
 use crate::math::num::RealNumber;
 
 /// Recall metric.
-#[derive(Serialize, Deserialize, Debug)]
+#[cfg_attr(feature = "serde", derive(Serialize, Deserialize))]
+#[derive(Debug)]
 pub struct Recall {}
 
 impl Recall {

src/naive_bayes/bernoulli.rs

@@ -42,10 +42,12 @@ use crate::math::num::RealNumber;
 use crate::math::vector::RealNumberVector;
 use crate::naive_bayes::{BaseNaiveBayes, NBDistribution};
 
+#[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 +79,8 @@ 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 +205,8 @@ 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>,
@@ -347,6 +351,7 @@ mod tests {
     }
 
     #[test]
+    #[cfg(feature = "serde")]
     fn serde() {
         let x = DenseMatrix::<f64>::from_2d_array(&[
             &[1., 1., 0., 0., 0., 0.],

src/naive_bayes/categorical.rs

@@ -36,10 +36,12 @@ use crate::linalg::BaseVector;
 use crate::linalg::Matrix;
 use crate::math::num::RealNumber;
 use crate::naive_bayes::{BaseNaiveBayes, NBDistribution};
+#[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 +218,8 @@ 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 +240,8 @@ 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>>,
 }
@@ -345,6 +349,7 @@ mod tests {
     }
 
     #[test]
+    #[cfg(feature = "serde")]
     fn serde() {
         let x = DenseMatrix::<f64>::from_2d_array(&[
             &[3., 4., 0., 1.],

src/naive_bayes/gaussian.rs

@@ -30,10 +30,12 @@ use crate::linalg::Matrix;
 use crate::math::num::RealNumber;
 use crate::math::vector::RealNumberVector;
 use crate::naive_bayes::{BaseNaiveBayes, NBDistribution};
+#[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 +77,8 @@ 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 +181,8 @@ 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>>,
 }
@@ -277,6 +281,7 @@ mod tests {
     }
 
     #[test]
+    #[cfg(feature = "serde")]
     fn serde() {
         let x = DenseMatrix::<f64>::from_2d_array(&[
             &[-1., -1.],

src/naive_bayes/mod.rs

@@ -39,6 +39,7 @@ use crate::error::Failed;
 use crate::linalg::BaseVector;
 use crate::linalg::Matrix;
 use crate::math::num::RealNumber;
+#[cfg(feature = "serde")]
 use serde::{Deserialize, Serialize};
 use std::marker::PhantomData;
 
@@ -55,7 +56,8 @@ 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 +42,12 @@ use crate::math::num::RealNumber;
 use crate::math::vector::RealNumberVector;
 use crate::naive_bayes::{BaseNaiveBayes, NBDistribution};
 
+#[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 +75,8 @@ 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 +192,8 @@ 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>>,
 }
@@ -320,6 +324,7 @@ mod tests {
         ));
     }
     #[test]
+    #[cfg(feature = "serde")]
     fn serde() {
         let x = DenseMatrix::<f64>::from_2d_array(&[
             &[1., 1., 0., 0., 0., 0.],

src/neighbors/knn_classifier.rs

@@ -33,6 +33,7 @@
 //!
 use std::marker::PhantomData;
 
+#[cfg(feature = "serde")]
 use serde::{Deserialize, Serialize};
 
 use crate::algorithm::neighbour::{KNNAlgorithm, KNNAlgorithmName};
@@ -45,7 +46,8 @@ 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 +64,8 @@ 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>,
@@ -277,6 +280,7 @@ mod tests {
     }
 
     #[test]
+    #[cfg(feature = "serde")]
     fn serde() {
         let x =
             DenseMatrix::from_2d_array(&[&[1., 2.], &[3., 4.], &[5., 6.], &[7., 8.], &[9., 10.]]);

src/neighbors/knn_regressor.rs

@@ -36,6 +36,7 @@
 //!
 use std::marker::PhantomData;
 
+#[cfg(feature = "serde")]
 use serde::{Deserialize, Serialize};
 
 use crate::algorithm::neighbour::{KNNAlgorithm, KNNAlgorithmName};
@@ -48,7 +49,8 @@ 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 +67,8 @@ 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>,
@@ -266,6 +269,7 @@ mod tests {
     }
 
     #[test]
+    #[cfg(feature = "serde")]
     fn serde() {
         let x =
             DenseMatrix::from_2d_array(&[&[1., 2.], &[3., 4.], &[5., 6.], &[7., 8.], &[9., 10.]]);

src/neighbors/mod.rs

@@ -33,6 +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;
+#[cfg(feature = "serde")]
 use serde::{Deserialize, Serialize};
 
 /// K Nearest Neighbors Classifier
@@ -48,7 +49,8 @@ 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,

src/svm/mod.rs

@@ -26,6 +26,7 @@
 pub mod svc;
 pub mod svr;
 
+#[cfg(feature = "serde")]
 use serde::{Deserialize, Serialize};
 
 use crate::linalg::BaseVector;
@@ -93,18 +94,21 @@ impl Kernels {
 }
 
 /// Linear Kernel
-#[derive(Serialize, Deserialize, Debug, Clone)]
+#[cfg_attr(feature = "serde", derive(Serialize, Deserialize))]
+#[derive(Debug, Clone)]
 pub struct LinearKernel {}
 
 /// Radial basis function (Gaussian) kernel
-#[derive(Serialize, Deserialize, Debug, Clone)]
+#[cfg_attr(feature = "serde", derive(Serialize, Deserialize))]
+#[derive(Debug, Clone)]
 pub struct RBFKernel<T: RealNumber> {
     /// kernel coefficient
     pub gamma: T,
 }
 
 /// Polynomial kernel
-#[derive(Serialize, Deserialize, Debug, Clone)]
+#[cfg_attr(feature = "serde", derive(Serialize, Deserialize))]
+#[derive(Debug, Clone)]
 pub struct PolynomialKernel<T: RealNumber> {
     /// degree of the polynomial
     pub degree: T,
@@ -115,7 +119,8 @@ pub struct PolynomialKernel<T: RealNumber> {
 }
 
 /// Sigmoid (hyperbolic tangent) kernel
-#[derive(Serialize, Deserialize, Debug, Clone)]
+#[cfg_attr(feature = "serde", derive(Serialize, Deserialize))]
+#[derive(Debug, Clone)]
 pub struct SigmoidKernel<T: RealNumber> {
     /// kernel coefficient
     pub gamma: T,

src/svm/svc.rs

@@ -76,6 +76,7 @@ use std::marker::PhantomData;
 
 use rand::seq::SliceRandom;
 
+#[cfg(feature = "serde")]
 use serde::{Deserialize, Serialize};
 
 use crate::api::{Predictor, SupervisedEstimator};
@@ -85,7 +86,8 @@ use crate::linalg::Matrix;
 use crate::math::num::RealNumber;
 use crate::svm::{Kernel, Kernels, LinearKernel};
 
-#[derive(Serialize, Deserialize, Debug, Clone)]
+#[cfg_attr(feature = "serde", derive(Serialize, Deserialize))]
+#[derive(Debug, Clone)]
 /// SVC Parameters
 pub struct SVCParameters<T: RealNumber, M: Matrix<T>, K: Kernel<T, M::RowVector>> {
     /// Number of epochs.
@@ -100,11 +102,15 @@ pub struct SVCParameters<T: RealNumber, M: Matrix<T>, K: Kernel<T, M::RowVector>
     m: PhantomData<M>,
 }
 
-#[derive(Serialize, Deserialize, Debug)]
+#[cfg_attr(feature = "serde", derive(Serialize, Deserialize))]
-#[serde(bound(
+#[derive(Debug)]
+#[cfg_attr(
+    feature = "serde",
+    serde(bound(
         serialize = "M::RowVector: Serialize, K: Serialize, T: Serialize",
         deserialize = "M::RowVector: Deserialize<'de>, K: Deserialize<'de>, T: Deserialize<'de>",
-))]
+    ))
+)]
 /// Support Vector Classifier
 pub struct SVC<T: RealNumber, M: Matrix<T>, K: Kernel<T, M::RowVector>> {
     classes: Vec<T>,
@@ -114,7 +120,8 @@ pub struct SVC<T: RealNumber, M: Matrix<T>, K: Kernel<T, M::RowVector>> {
     b: T,
 }
 
-#[derive(Serialize, Deserialize, Debug)]
+#[cfg_attr(feature = "serde", derive(Serialize, Deserialize))]
+#[derive(Debug)]
 struct SupportVector<T: RealNumber, V: BaseVector<T>> {
     index: usize,
     x: V,
@@ -719,6 +726,7 @@ mod tests {
     use super::*;
     use crate::linalg::naive::dense_matrix::*;
     use crate::metrics::accuracy;
+    #[cfg(feature = "serde")]
     use crate::svm::*;
 
     #[test]
@@ -807,6 +815,7 @@ mod tests {
     }
 
     #[test]
+    #[cfg(feature = "serde")]
     fn svc_serde() {
         let x = DenseMatrix::from_2d_array(&[
             &[5.1, 3.5, 1.4, 0.2],

src/svm/svr.rs

@@ -68,6 +68,7 @@ use std::cell::{Ref, RefCell};
 use std::fmt::Debug;
 use std::marker::PhantomData;
 
+#[cfg(feature = "serde")]
 use serde::{Deserialize, Serialize};
 
 use crate::api::{Predictor, SupervisedEstimator};
@@ -77,7 +78,8 @@ use crate::linalg::Matrix;
 use crate::math::num::RealNumber;
 use crate::svm::{Kernel, Kernels, LinearKernel};
 
-#[derive(Serialize, Deserialize, Debug, Clone)]
+#[cfg_attr(feature = "serde", derive(Serialize, Deserialize))]
+#[derive(Debug, Clone)]
 /// SVR Parameters
 pub struct SVRParameters<T: RealNumber, M: Matrix<T>, K: Kernel<T, M::RowVector>> {
     /// Epsilon in the epsilon-SVR model.
@@ -92,11 +94,15 @@ pub struct SVRParameters<T: RealNumber, M: Matrix<T>, K: Kernel<T, M::RowVector>
     m: PhantomData<M>,
 }
 
-#[derive(Serialize, Deserialize, Debug)]
+#[cfg_attr(feature = "serde", derive(Serialize, Deserialize))]
-#[serde(bound(
+#[derive(Debug)]
+#[cfg_attr(
+    feature = "serde",
+    serde(bound(
         serialize = "M::RowVector: Serialize, K: Serialize, T: Serialize",
         deserialize = "M::RowVector: Deserialize<'de>, K: Deserialize<'de>, T: Deserialize<'de>",
-))]
+    ))
+)]
 
 /// Epsilon-Support Vector Regression
 pub struct SVR<T: RealNumber, M: Matrix<T>, K: Kernel<T, M::RowVector>> {
@@ -106,7 +112,8 @@ pub struct SVR<T: RealNumber, M: Matrix<T>, K: Kernel<T, M::RowVector>> {
     b: T,
 }
 
-#[derive(Serialize, Deserialize, Debug)]
+#[cfg_attr(feature = "serde", derive(Serialize, Deserialize))]
+#[derive(Debug)]
 struct SupportVector<T: RealNumber, V: BaseVector<T>> {
     index: usize,
     x: V,
@@ -526,6 +533,7 @@ mod tests {
     use super::*;
     use crate::linalg::naive::dense_matrix::*;
     use crate::metrics::mean_squared_error;
+    #[cfg(feature = "serde")]
     use crate::svm::*;
 
     #[test]
@@ -562,6 +570,7 @@ mod tests {
     }
 
     #[test]
+    #[cfg(feature = "serde")]
     fn svr_serde() {
         let x = DenseMatrix::from_2d_array(&[
             &[234.289, 235.6, 159.0, 107.608, 1947., 60.323],

src/tree/decision_tree_classifier.rs

@@ -68,6 +68,7 @@ use std::fmt::Debug;
 use std::marker::PhantomData;
 
 use rand::seq::SliceRandom;
+#[cfg(feature = "serde")]
 use serde::{Deserialize, Serialize};
 
 use crate::algorithm::sort::quick_sort::QuickArgSort;
@@ -76,7 +77,8 @@ use crate::error::Failed;
 use crate::linalg::Matrix;
 use crate::math::num::RealNumber;
 
-#[derive(Serialize, Deserialize, Debug, Clone)]
+#[cfg_attr(feature = "serde", derive(Serialize, Deserialize))]
+#[derive(Debug, Clone)]
 /// Parameters of Decision Tree
 pub struct DecisionTreeClassifierParameters {
     /// Split criteria to use when building a tree.
@@ -90,7 +92,8 @@ pub struct DecisionTreeClassifierParameters {
 }
 
 /// Decision Tree
-#[derive(Serialize, Deserialize, Debug)]
+#[cfg_attr(feature = "serde", derive(Serialize, Deserialize))]
+#[derive(Debug)]
 pub struct DecisionTreeClassifier<T: RealNumber> {
     nodes: Vec<Node<T>>,
     parameters: DecisionTreeClassifierParameters,
@@ -100,7 +103,8 @@ pub struct DecisionTreeClassifier<T: RealNumber> {
 }
 
 /// The function to measure the quality of a split.
-#[derive(Serialize, Deserialize, Debug, Clone)]
+#[cfg_attr(feature = "serde", derive(Serialize, Deserialize))]
+#[derive(Debug, Clone)]
 pub enum SplitCriterion {
     /// [Gini index](../decision_tree_classifier/index.html)
     Gini,
@@ -110,7 +114,8 @@ pub enum SplitCriterion {
     ClassificationError,
 }
 
-#[derive(Serialize, Deserialize, Debug)]
+#[cfg_attr(feature = "serde", derive(Serialize, Deserialize))]
+#[derive(Debug)]
 struct Node<T: RealNumber> {
     index: usize,
     output: usize,
@@ -740,6 +745,7 @@ mod tests {
     }
 
     #[test]
+    #[cfg(feature = "serde")]
     fn serde() {
         let x = DenseMatrix::from_2d_array(&[
             &[1., 1., 1., 0.],

src/tree/decision_tree_regressor.rs

@@ -63,6 +63,7 @@ use std::default::Default;
 use std::fmt::Debug;
 
 use rand::seq::SliceRandom;
+#[cfg(feature = "serde")]
 use serde::{Deserialize, Serialize};
 
 use crate::algorithm::sort::quick_sort::QuickArgSort;
@@ -71,7 +72,8 @@ use crate::error::Failed;
 use crate::linalg::Matrix;
 use crate::math::num::RealNumber;
 
-#[derive(Serialize, Deserialize, Debug, Clone)]
+#[cfg_attr(feature = "serde", derive(Serialize, Deserialize))]
+#[derive(Debug, Clone)]
 /// Parameters of Regression Tree
 pub struct DecisionTreeRegressorParameters {
     /// The maximum depth of the tree.
@@ -83,14 +85,16 @@ pub struct DecisionTreeRegressorParameters {
 }
 
 /// Regression Tree
-#[derive(Serialize, Deserialize, Debug)]
+#[cfg_attr(feature = "serde", derive(Serialize, Deserialize))]
+#[derive(Debug)]
 pub struct DecisionTreeRegressor<T: RealNumber> {
     nodes: Vec<Node<T>>,
     parameters: DecisionTreeRegressorParameters,
     depth: u16,
 }
 
-#[derive(Serialize, Deserialize, Debug)]
+#[cfg_attr(feature = "serde", derive(Serialize, Deserialize))]
+#[derive(Debug)]
 struct Node<T: RealNumber> {
     index: usize,
     output: T,
@@ -577,6 +581,7 @@ mod tests {
     }
 
     #[test]
+    #[cfg(feature = "serde")]
     fn serde() {
         let x = DenseMatrix::from_2d_array(&[
             &[234.289, 235.6, 159., 107.608, 1947., 60.323],