feat: Make SerDe optional

Author: ssorc3

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_derive = "1.0.115"
+serde = { version = "1.0.115", features = ["derive"], optional = true }
 
 [dev-dependencies]
 criterion = "0.3"

src/algorithm/neighbour/cover_tree.rs

@@ -24,15 +24,16 @@
 //! ```
 use std::fmt::Debug;
 
-use serde::{Deserialize, Serialize};
+#[cfg(feature = "serde")] use serde::{Deserialize, Serialize};
 
 use crate::algorithm::sort::heap_select::HeapSelection;
 use crate::error::{Failed, FailedError};
 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 +57,8 @@ 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 +67,7 @@ struct Node<F: RealNumber> {
     scale: i64,
 }
 
-#[derive(Debug, Serialize, Deserialize)]
+#[derive(Debug)]
 struct DistanceSet<F: RealNumber> {
     idx: usize,
     dist: Vec<F>,

src/algorithm/neighbour/linear_search.rs

@@ -22,7 +22,7 @@
 //!
 //! ```
 
-use serde::{Deserialize, Serialize};
+#[cfg(feature = "serde")] use serde::{Deserialize, Serialize};
 use std::cmp::{Ordering, PartialOrd};
 use std::marker::PhantomData;
 
@@ -32,7 +32,8 @@ 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>,

src/algorithm/neighbour/mod.rs

@@ -35,7 +35,7 @@ use crate::algorithm::neighbour::linear_search::LinearKNNSearch;
 use crate::error::Failed;
 use crate::math::distance::Distance;
 use crate::math::num::RealNumber;
-use serde::{Deserialize, Serialize};
+#[cfg(feature = "serde")] use serde::{Deserialize, Serialize};
 
 pub(crate) mod bbd_tree;
 /// tree data structure for fast nearest neighbor search
@@ -45,15 +45,17 @@ 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,
     /// Cover Tree Search algorithm, see [`CoverTree`](../algorithm/neighbour/cover_tree/index.html)
     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

@@ -43,7 +43,7 @@
 use std::fmt::Debug;
 use std::iter::Sum;
 
-use serde::{Deserialize, Serialize};
+#[cfg(feature = "serde")] use serde::{Deserialize, Serialize};
 
 use crate::algorithm::neighbour::{KNNAlgorithm, KNNAlgorithmName};
 use crate::api::{Predictor, UnsupervisedEstimator};
@@ -55,7 +55,8 @@ 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,

src/cluster/kmeans.rs

@@ -56,7 +56,7 @@ use rand::Rng;
 use std::fmt::Debug;
 use std::iter::Sum;
 
-use serde::{Deserialize, Serialize};
+#[cfg(feature = "serde")] use serde::{Deserialize, Serialize};
 
 use crate::algorithm::neighbour::bbd_tree::BBDTree;
 use crate::api::{Predictor, UnsupervisedEstimator};
@@ -66,7 +66,8 @@ 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>,

src/decomposition/pca.rs

@@ -47,15 +47,16 @@
 //! <script id="MathJax-script" async src="https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js"></script>
 use std::fmt::Debug;
 
-use serde::{Deserialize, Serialize};
+#[cfg(feature = "serde")] use serde::{Deserialize, Serialize};
 
 use crate::api::{Transformer, UnsupervisedEstimator};
 use crate::error::Failed;
 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>,

src/decomposition/svd.rs

@@ -46,15 +46,16 @@
 use std::fmt::Debug;
 use std::marker::PhantomData;
 
-use serde::{Deserialize, Serialize};
+#[cfg(feature = "serde")] use serde::{Deserialize, Serialize};
 
 use crate::api::{Transformer, UnsupervisedEstimator};
 use crate::error::Failed;
 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>,

src/ensemble/random_forest_classifier.rs

@@ -49,7 +49,7 @@ use std::default::Default;
 use std::fmt::Debug;
 
 use rand::Rng;
-use serde::{Deserialize, Serialize};
+#[cfg(feature = "serde")] use serde::{Deserialize, Serialize};
 
 use crate::api::{Predictor, SupervisedEstimator};
 use crate::error::Failed;
@@ -61,7 +61,8 @@ 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 +79,8 @@ 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>>,

src/ensemble/random_forest_regressor.rs

@@ -47,7 +47,7 @@ use std::default::Default;
 use std::fmt::Debug;
 
 use rand::Rng;
-use serde::{Deserialize, Serialize};
+#[cfg(feature = "serde")] use serde::{Deserialize, Serialize};
 
 use crate::api::{Predictor, SupervisedEstimator};
 use crate::error::Failed;
@@ -57,7 +57,8 @@ 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 +75,8 @@ 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>>,