feat: serialization/deserialization with Serde

Cargo.toml

@@ -9,10 +9,13 @@ ndarray = "0.13"
 num-traits = "0.2.11"
 num = "0.2.1"
 rand = "0.7.3"
+serde = { version = "1.0.105", features = ["derive"] }
+serde_derive = "1.0.105"
 
 [dev-dependencies]
-ndarray = "0.13"
 criterion = "0.3"
+serde_json = "1.0"
+bincode = "1.2.1"
 
 [[bench]]
 name = "distance"

src/cluster/kmeans.rs

@@ -4,12 +4,14 @@ use rand::Rng;
 use std::iter::Sum;
 use std::fmt::Debug;
 
+use serde::{Serialize, Deserialize};
+
 use crate::math::num::FloatExt;
 use crate::linalg::Matrix;
 use crate::math::distance::euclidian;
 use crate::algorithm::neighbour::bbd_tree::BBDTree;
 
-#[derive(Debug)]
+#[derive(Serialize, Deserialize, Debug)]
 pub struct KMeans<T: FloatExt> {    
     k: usize,
     y: Vec<usize>,
@@ -18,6 +20,29 @@ pub struct KMeans<T: FloatExt> {
     centroids: Vec<Vec<T>>
 }
 
+impl<T: FloatExt> PartialEq for KMeans<T> {    
+    fn eq(&self, other: &Self) -> bool {
+        if self.k != other.k ||
+            self.size != other.size ||
+            self.centroids.len() != other.centroids.len() {
+            false
+        } else {
+            let n_centroids = self.centroids.len();
+            for i in 0..n_centroids{
+                if self.centroids[i].len() != other.centroids[i].len(){
+                    return false
+                }
+                for j in 0..self.centroids[i].len() {
+                    if (self.centroids[i][j] - other.centroids[i][j]).abs() > T::epsilon() {
+                        return false
+                    }
+                }
+            }
+            true
+        }
+    }
+}
+
 #[derive(Debug, Clone)]
 pub struct KMeansParameters {  
     pub max_iter: usize
@@ -210,5 +235,37 @@ mod tests {
         }        
         
     }
+
+    #[test]
+    fn serde() {  
+        let x = DenseMatrix::from_array(&[
+            &[5.1, 3.5, 1.4, 0.2],
+            &[4.9, 3.0, 1.4, 0.2],
+            &[4.7, 3.2, 1.3, 0.2],
+            &[4.6, 3.1, 1.5, 0.2],
+            &[5.0, 3.6, 1.4, 0.2],
+            &[5.4, 3.9, 1.7, 0.4],
+            &[4.6, 3.4, 1.4, 0.3],
+            &[5.0, 3.4, 1.5, 0.2],
+            &[4.4, 2.9, 1.4, 0.2],
+            &[4.9, 3.1, 1.5, 0.1],
+            &[7.0, 3.2, 4.7, 1.4],
+            &[6.4, 3.2, 4.5, 1.5],
+            &[6.9, 3.1, 4.9, 1.5],
+            &[5.5, 2.3, 4.0, 1.3],
+            &[6.5, 2.8, 4.6, 1.5],
+            &[5.7, 2.8, 4.5, 1.3],
+            &[6.3, 3.3, 4.7, 1.6],
+            &[4.9, 2.4, 3.3, 1.0],
+            &[6.6, 2.9, 4.6, 1.3],
+            &[5.2, 2.7, 3.9, 1.4]]);                
+
+        let kmeans = KMeans::new(&x, 2, Default::default());
+
+        let deserialized_kmeans: KMeans<f64> = serde_json::from_str(&serde_json::to_string(&kmeans).unwrap()).unwrap();
+
+        assert_eq!(kmeans, deserialized_kmeans);       
+        
+    }
     
 }

src/linalg/mod.rs

@@ -5,7 +5,7 @@ pub mod evd;
 pub mod ndarray_bindings;
 
 use std::ops::Range;
-use std::fmt::Debug;
+use std::fmt::{Debug, Display};
 use std::marker::PhantomData;
 
 use crate::math::num::FloatExt;
@@ -13,7 +13,7 @@ use svd::SVDDecomposableMatrix;
 use evd::EVDDecomposableMatrix;
 use qr::QRDecomposableMatrix;
 
-pub trait BaseMatrix<T: FloatExt + Debug>: Clone + Debug {  
+pub trait BaseMatrix<T: FloatExt>: Clone + Debug {  
 
     type RowVector: Clone + Debug;    
 
@@ -175,9 +175,9 @@ pub trait BaseMatrix<T: FloatExt + Debug>: Clone + Debug {
 
 }
 
-pub trait Matrix<T: FloatExt + Debug>: BaseMatrix<T> + SVDDecomposableMatrix<T> + EVDDecomposableMatrix<T> + QRDecomposableMatrix<T> {}
+pub trait Matrix<T: FloatExt>: BaseMatrix<T> + SVDDecomposableMatrix<T> + EVDDecomposableMatrix<T> + QRDecomposableMatrix<T> + PartialEq + Display {}
 
-pub fn row_iter<F: FloatExt + Debug, M: Matrix<F>>(m: &M) -> RowIter<F, M> {
+pub fn row_iter<F: FloatExt, M: BaseMatrix<F>>(m: &M) -> RowIter<F, M> {
     RowIter{
         m: m,
         pos: 0,
@@ -186,14 +186,14 @@ pub fn row_iter<F: FloatExt + Debug, M: Matrix<F>>(m: &M) -> RowIter<F, M> {
     }
 }
 
-pub struct RowIter<'a, T: FloatExt + Debug, M: Matrix<T>> {
+pub struct RowIter<'a, T: FloatExt, M: BaseMatrix<T>> {
     m: &'a M,
     pos: usize,
     max_pos: usize,
     phantom: PhantomData<&'a T>
 }
 
-impl<'a, T: FloatExt + Debug, M: Matrix<T>> Iterator for RowIter<'a, T, M> {
+impl<'a, T: FloatExt, M: BaseMatrix<T>> Iterator for RowIter<'a, T, M> {
 
     type Item = Vec<T>;
 

src/linalg/naive/dense_matrix.rs

@@ -2,6 +2,11 @@ extern crate num;
 use std::ops::Range;
 use std::fmt;
 use std::fmt::Debug;
+use std::marker::PhantomData;
+
+use serde::{Serialize, Deserialize};
+use serde::ser::{Serializer, SerializeStruct};
+use serde::de::{Deserializer, Visitor, SeqAccess, MapAccess};
 
 use crate::linalg::Matrix;
 pub use crate::linalg::BaseMatrix;
@@ -87,6 +92,96 @@ impl<T: FloatExt + Debug> DenseMatrix<T> {
     
 }
 
+impl<'de, T: FloatExt + fmt::Debug + Deserialize<'de>> Deserialize<'de> for DenseMatrix<T> {
+    fn deserialize<D>(deserializer: D) -> Result<Self, D::Error>
+    where
+        D: Deserializer<'de>,
+    {
+
+        #[derive(Deserialize)]
+        #[serde(field_identifier, rename_all = "lowercase")]
+        enum Field { NRows, NCols, Values }        
+
+        struct DenseMatrixVisitor<T: FloatExt + fmt::Debug>{            
+            t: PhantomData<T>      
+        }
+        
+        impl<'a, T: FloatExt + fmt::Debug + Deserialize<'a>> Visitor<'a> for DenseMatrixVisitor<T> {
+            type Value = DenseMatrix<T>;
+
+            fn expecting(&self, formatter: &mut fmt::Formatter) -> fmt::Result {
+                formatter.write_str("struct DenseMatrix")
+            }     
+            
+            fn visit_seq<V>(self, mut seq: V) -> Result<DenseMatrix<T>, V::Error>
+            where
+                V: SeqAccess<'a>,
+            {
+                let nrows = seq.next_element()?
+                    .ok_or_else(|| serde::de::Error::invalid_length(0, &self))?;
+                let ncols = seq.next_element()?
+                    .ok_or_else(|| serde::de::Error::invalid_length(1, &self))?;
+                let values = seq.next_element()?
+                    .ok_or_else(|| serde::de::Error::invalid_length(2, &self))?;
+                Ok(DenseMatrix::new(nrows, ncols, values))
+            }
+
+            fn visit_map<V>(self, mut map: V) -> Result<DenseMatrix<T>, V::Error>
+            where
+                V: MapAccess<'a>,
+            {
+                let mut nrows = None;
+                let mut ncols = None;
+                let mut values = None;
+                while let Some(key) = map.next_key()? {
+                    match key {
+                        Field::NRows => {
+                            if nrows.is_some() {
+                                return Err(serde::de::Error::duplicate_field("nrows"));
+                            }
+                            nrows = Some(map.next_value()?);
+                        }
+                        Field::NCols => {
+                            if ncols.is_some() {
+                                return Err(serde::de::Error::duplicate_field("ncols"));
+                            }
+                            ncols = Some(map.next_value()?);
+                        }
+                        Field::Values => {
+                            if values.is_some() {
+                                return Err(serde::de::Error::duplicate_field("values"));
+                            }
+                            values = Some(map.next_value()?);
+                        }
+                    }
+                }
+                let nrows = nrows.ok_or_else(|| serde::de::Error::missing_field("nrows"))?;
+                let ncols = ncols.ok_or_else(|| serde::de::Error::missing_field("ncols"))?;
+                let values = values.ok_or_else(|| serde::de::Error::missing_field("values"))?;
+                Ok(DenseMatrix::new(nrows, ncols, values))
+            }
+        }
+
+        const FIELDS: &'static [&'static str] = &["nrows", "ncols", "values"];        
+        deserializer.deserialize_struct("DenseMatrix", FIELDS, DenseMatrixVisitor {
+            t: PhantomData            
+        })
+    }
+}
+
+impl<T: FloatExt + fmt::Debug + Serialize> Serialize for DenseMatrix<T> {
+
+    fn serialize<S>(&self, serializer: S) -> Result<S::Ok, S::Error> where
+    S: Serializer { 
+        let (nrows, ncols) = self.shape();
+        let mut state = serializer.serialize_struct("DenseMatrix", 3)?;
+        state.serialize_field("nrows", &nrows)?;
+        state.serialize_field("ncols", &ncols)?;    
+        state.serialize_field("values", &self.values)?;
+        state.end()   
+    }
+}
+
 impl<T: FloatExt + Debug> SVDDecomposableMatrix<T> for DenseMatrix<T> {}
 
 impl<T: FloatExt + Debug> EVDDecomposableMatrix<T> for DenseMatrix<T> {}
@@ -772,4 +867,24 @@ mod tests {
         assert_eq!(res, a);
     }
 
+    #[test]
+    fn to_from_json() { 
+            let a = DenseMatrix::from_array(&[&[0.9, 0.4, 0.7], &[0.4, 0.5, 0.3], &[0.7, 0.3, 0.8]]);
+            let deserialized_a: DenseMatrix<f64> = serde_json::from_str(&serde_json::to_string(&a).unwrap()).unwrap();
+            assert_eq!(a, deserialized_a);            
+    }
+
+    #[test]
+    fn to_from_bincode() { 
+            let a = DenseMatrix::from_array(&[&[0.9, 0.4, 0.7], &[0.4, 0.5, 0.3], &[0.7, 0.3, 0.8]]);
+            let deserialized_a: DenseMatrix<f64> = bincode::deserialize(&bincode::serialize(&a).unwrap()).unwrap();
+            assert_eq!(a, deserialized_a);            
+    }
+
+    #[test]
+    fn to_string() { 
+            let a = DenseMatrix::from_array(&[&[0.9, 0.4, 0.7], &[0.4, 0.5, 0.3], &[0.7, 0.3, 0.8]]);
+            assert_eq!(format!("{}", a), "[[0.9, 0.4, 0.7], [0.4, 0.5, 0.3], [0.7, 0.3, 0.8]]");            
+    }
+
 }

src/linalg/ndarray_bindings.rs

@@ -1,5 +1,4 @@
 use std::ops::Range;
-use std::fmt::Debug;
 use std::iter::Sum;
 use std::ops::AddAssign;
 use std::ops::SubAssign;
@@ -17,7 +16,7 @@ use crate::linalg::evd::EVDDecomposableMatrix;
 use crate::linalg::qr::QRDecomposableMatrix;
 
 
-impl<T: FloatExt + Debug + ScalarOperand + AddAssign + SubAssign + MulAssign + DivAssign + Sum> BaseMatrix<T> for ArrayBase<OwnedRepr<T>, Ix2>
+impl<T: FloatExt + ScalarOperand + AddAssign + SubAssign + MulAssign + DivAssign + Sum> BaseMatrix<T> for ArrayBase<OwnedRepr<T>, Ix2>
 {
     type RowVector = ArrayBase<OwnedRepr<T>, Ix1>;
 
@@ -286,13 +285,13 @@ impl<T: FloatExt + Debug + ScalarOperand + AddAssign + SubAssign + MulAssign + D
 
 }
 
-impl<T: FloatExt + Debug + ScalarOperand + AddAssign + SubAssign + MulAssign + DivAssign + Sum> SVDDecomposableMatrix<T> for ArrayBase<OwnedRepr<T>, Ix2> {}
+impl<T: FloatExt + ScalarOperand + AddAssign + SubAssign + MulAssign + DivAssign + Sum> SVDDecomposableMatrix<T> for ArrayBase<OwnedRepr<T>, Ix2> {}
 
-impl<T: FloatExt + Debug + ScalarOperand + AddAssign + SubAssign + MulAssign + DivAssign + Sum> EVDDecomposableMatrix<T> for ArrayBase<OwnedRepr<T>, Ix2> {}
+impl<T: FloatExt + ScalarOperand + AddAssign + SubAssign + MulAssign + DivAssign + Sum> EVDDecomposableMatrix<T> for ArrayBase<OwnedRepr<T>, Ix2> {}
 
-impl<T: FloatExt + Debug + ScalarOperand + AddAssign + SubAssign + MulAssign + DivAssign + Sum> QRDecomposableMatrix<T> for ArrayBase<OwnedRepr<T>, Ix2> {}
+impl<T: FloatExt + ScalarOperand + AddAssign + SubAssign + MulAssign + DivAssign + Sum> QRDecomposableMatrix<T> for ArrayBase<OwnedRepr<T>, Ix2> {}
 
-impl<T: FloatExt + Debug + ScalarOperand + AddAssign + SubAssign + MulAssign + DivAssign + Sum> Matrix<T> for ArrayBase<OwnedRepr<T>, Ix2> {}
+impl<T: FloatExt + ScalarOperand + AddAssign + SubAssign + MulAssign + DivAssign + Sum> Matrix<T> for ArrayBase<OwnedRepr<T>, Ix2> {}
 
 #[cfg(test)]
 mod tests {    

src/linear/linear_regression.rs

@@ -1,22 +1,31 @@
 use std::fmt::Debug;
 
+use serde::{Serialize, Deserialize};
+
 use crate::math::num::FloatExt;
 use crate::linalg::Matrix;
 
-#[derive(Debug)]
+#[derive(Serialize, Deserialize, Debug)]
 pub enum LinearRegressionSolver {
     QR,
     SVD
 }
 
-#[derive(Debug)]
-pub struct LinearRegression<T: FloatExt + Debug, M: Matrix<T>> {    
+#[derive(Serialize, Deserialize, Debug)]
+pub struct LinearRegression<T: FloatExt, M: Matrix<T>> {    
     coefficients: M,
     intercept: T,
     solver: LinearRegressionSolver
 }
 
-impl<T: FloatExt + Debug, M: Matrix<T>> LinearRegression<T, M> {
+impl<T: FloatExt, M: Matrix<T>> PartialEq for LinearRegression<T, M> {
+    fn eq(&self, other: &Self) -> bool {
+        self.coefficients == other.coefficients &&
+        self.intercept == other.intercept        
+    }
+}
+
+impl<T: FloatExt, M: Matrix<T>> LinearRegression<T, M> {
 
     pub fn fit(x: &M, y: &M, solver: LinearRegressionSolver) -> LinearRegression<T, M>{
 
@@ -90,4 +99,33 @@ mod tests {
 
 
     }
+
+    #[test]
+    fn serde(){
+        let x = DenseMatrix::from_array(&[
+            &[234.289, 235.6, 159.0, 107.608, 1947., 60.323],
+            &[259.426, 232.5, 145.6, 108.632, 1948., 61.122],
+            &[258.054, 368.2, 161.6, 109.773, 1949., 60.171],
+            &[284.599, 335.1, 165.0, 110.929, 1950., 61.187],
+            &[328.975, 209.9, 309.9, 112.075, 1951., 63.221],
+            &[346.999, 193.2, 359.4, 113.270, 1952., 63.639],
+            &[365.385, 187.0, 354.7, 115.094, 1953., 64.989],
+            &[363.112, 357.8, 335.0, 116.219, 1954., 63.761],
+            &[397.469, 290.4, 304.8, 117.388, 1955., 66.019],
+            &[419.180, 282.2, 285.7, 118.734, 1956., 67.857],
+            &[442.769, 293.6, 279.8, 120.445, 1957., 68.169],
+            &[444.546, 468.1, 263.7, 121.950, 1958., 66.513],
+            &[482.704, 381.3, 255.2, 123.366, 1959., 68.655],
+            &[502.601, 393.1, 251.4, 125.368, 1960., 69.564],
+            &[518.173, 480.6, 257.2, 127.852, 1961., 69.331],
+            &[554.894, 400.7, 282.7, 130.081, 1962., 70.551]]);
+    
+        let y = DenseMatrix::from_array(&[&[83.0,  88.5,  88.2,  89.5,  96.2,  98.1,  99.0, 100.0, 101.2, 104.6, 108.4, 110.8, 112.6, 114.2, 115.7, 116.9]]);  
+
+        let lr = LinearRegression::fit(&x, &y, LinearRegressionSolver::QR);
+        
+        let deserialized_lr: LinearRegression<f64, DenseMatrix<f64>> = serde_json::from_str(&serde_json::to_string(&lr).unwrap()).unwrap();
+
+        assert_eq!(lr, deserialized_lr);        
+    }
 }

src/linear/logistic_regression.rs

@@ -1,6 +1,8 @@
 use std::fmt::Debug;
 use std::marker::PhantomData;
 
+use serde::{Serialize, Deserialize};
+
 use crate::math::num::FloatExt;
 use crate::linalg::Matrix;
 use crate::optimization::FunctionOrder;
@@ -8,15 +10,15 @@ use crate::optimization::first_order::{FirstOrderOptimizer, OptimizerResult};
 use crate::optimization::line_search::Backtracking;
 use crate::optimization::first_order::lbfgs::LBFGS;
 
-#[derive(Debug)]
-pub struct LogisticRegression<T: FloatExt + Debug, M: Matrix<T>> {    
+#[derive(Serialize, Deserialize, Debug)]
+pub struct LogisticRegression<T: FloatExt, M: Matrix<T>> {    
     weights: M,     
     classes: Vec<T>,
     num_attributes: usize,
     num_classes: usize
 }
 
-trait ObjectiveFunction<T: FloatExt + Debug, M: Matrix<T>> {
+trait ObjectiveFunction<T: FloatExt, M: Matrix<T>> {
     fn f(&self, w_bias: &M) -> T;
     fn df(&self, g: &mut M, w_bias: &M);
 
@@ -31,13 +33,24 @@ trait ObjectiveFunction<T: FloatExt + Debug, M: Matrix<T>> {
     }
 }
 
-struct BinaryObjectiveFunction<'a, T: FloatExt + Debug, M: Matrix<T>> {
+struct BinaryObjectiveFunction<'a, T: FloatExt, M: Matrix<T>> {
     x: &'a M,
     y: Vec<usize>,
     phantom: PhantomData<&'a T>
 } 
 
-impl<'a, T: FloatExt + Debug, M: Matrix<T>> ObjectiveFunction<T, M> for BinaryObjectiveFunction<'a, T, M> {    
+impl<T: FloatExt, M: Matrix<T>> PartialEq for LogisticRegression<T, M> {
+    fn eq(&self, other: &Self) -> bool {
+
+        self.num_classes == other.num_classes &&
+        self.classes == other.classes &&
+        self.num_attributes == other.num_attributes &&
+        self.weights == other.weights
+        
+    }
+}
+
+impl<'a, T: FloatExt, M: Matrix<T>> ObjectiveFunction<T, M> for BinaryObjectiveFunction<'a, T, M> {    
 
     fn f(&self, w_bias: &M) -> T {                
         let mut f = T::zero();        
@@ -72,14 +85,14 @@ impl<'a, T: FloatExt + Debug, M: Matrix<T>> ObjectiveFunction<T, M> for BinaryOb
 
 }
 
-struct MultiClassObjectiveFunction<'a, T: FloatExt + Debug, M: Matrix<T>> {
+struct MultiClassObjectiveFunction<'a, T: FloatExt, M: Matrix<T>> {
     x: &'a M,
     y: Vec<usize>,
     k: usize,
     phantom: PhantomData<&'a T>
 }
 
-impl<'a, T: FloatExt + Debug, M: Matrix<T>> ObjectiveFunction<T, M> for MultiClassObjectiveFunction<'a, T, M> {    
+impl<'a, T: FloatExt, M: Matrix<T>> ObjectiveFunction<T, M> for MultiClassObjectiveFunction<'a, T, M> {    
 
     fn f(&self, w_bias: &M) -> T {                
         let mut f = T::zero();
@@ -125,7 +138,7 @@ impl<'a, T: FloatExt + Debug, M: Matrix<T>> ObjectiveFunction<T, M> for MultiCla
 
 }
 
-impl<T: FloatExt + Debug, M: Matrix<T>> LogisticRegression<T, M> {
+impl<T: FloatExt, M: Matrix<T>> LogisticRegression<T, M> {
 
     pub fn fit(x: &M, y: &M::RowVector) -> LogisticRegression<T, M>{
 
@@ -371,6 +384,33 @@ mod tests {
 
     }    
 
+    #[test]
+    fn serde(){
+        let x = DenseMatrix::from_array(&[
+            &[1., -5.],
+            &[ 2.,  5.],
+            &[ 3., -2.],
+            &[ 1.,  2.],
+            &[ 2.,  0.],
+            &[ 6., -5.],
+            &[ 7.,  5.],
+            &[ 6., -2.],
+            &[ 7.,  2.],
+            &[ 6.,  0.],
+            &[ 8., -5.],
+            &[ 9.,  5.],
+            &[10., -2.],
+            &[ 8.,  2.],
+            &[ 9.,  0.]]);
+        let y: Vec<f64> = vec![0., 0., 1., 1., 2., 1., 1., 0., 0., 2., 1., 1., 0., 0., 1.];
+
+        let lr = LogisticRegression::fit(&x, &y);
+        
+        let deserialized_lr: LogisticRegression<f64, DenseMatrix<f64>> = serde_json::from_str(&serde_json::to_string(&lr).unwrap()).unwrap();
+
+        assert_eq!(lr, deserialized_lr);        
+    }
+
     #[test]
     fn lr_fit_predict_iris() { 
         let x = arr2(&[
@@ -396,7 +436,7 @@ mod tests {
             [5.2, 2.7, 3.9, 1.4]]);
         let y = arr1(&[0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.]);        
 
-        let lr = LogisticRegression::fit(&x, &y);         
+        let lr = LogisticRegression::fit(&x, &y);
 
         let y_hat = lr.predict(&x);                        
 

src/math/num.rs

@@ -1,7 +1,8 @@
+use std::fmt::{Debug, Display};
 use num_traits::{Float, FromPrimitive};
 use rand::prelude::*;
 
-pub trait FloatExt: Float + FromPrimitive {
+pub trait FloatExt: Float + FromPrimitive + Debug + Display {
 
     fn copysign(self, sign: Self) -> Self;