feat: serialization/deserialization with Serde

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);