Merge pull request #67 from ssorc3/development

Make SerDe Optional

Author: VolodymyrOrlov

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

src/algorithm/neighbour/cover_tree.rs

@@ -24,6 +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;
@@ -45,15 +46,17 @@ 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

@@ -43,6 +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],