Add serde for StandardScaler

src/preprocessing/numerical.rs

@@ -32,7 +32,11 @@ use crate::error::{Failed, FailedError};
 use crate::linalg::Matrix;
 use crate::math::num::RealNumber;
 
+#[cfg(feature = "serde")]
+use serde::{Deserialize, Serialize};
+
 /// Configure Behaviour of `StandardScaler`.
+#[cfg_attr(feature = "serde", derive(Serialize, Deserialize))]
 #[derive(Clone, Debug, Copy, Eq, PartialEq)]
 pub struct StandardScalerParameters {
     /// Optionaly adjust mean to be zero.
@@ -54,6 +58,7 @@ impl Default for StandardScalerParameters {
 /// deviation of one. This can improve model training for
 /// scaling sensitive models like neural network or nearest
 /// neighbors based models.
+#[cfg_attr(feature = "serde", derive(Serialize, Deserialize))]
 #[derive(Clone, Debug, Default, Eq, PartialEq)]
 pub struct StandardScaler<T: RealNumber> {
     means: Vec<T>,
@@ -400,5 +405,43 @@ mod tests {
                 Ok(DenseMatrix::from_2d_array(&[&[0.0, 3.0], &[2.0, 4.0]]))
             )
         }
+
+        /// Same as `fit_for_random_values` test, but using a `StandardScaler` that has been
+        /// serialized and deserialized.
+        #[cfg_attr(target_arch = "wasm32", wasm_bindgen_test::wasm_bindgen_test)]
+        #[test]
+        #[cfg(feature = "serde")]
+        fn serde_fit_for_random_values() {
+            let fitted_scaler = StandardScaler::fit(
+                &DenseMatrix::from_2d_array(&[
+                    &[0.1004222429, 0.2194113576, 0.9310663354, 0.3313593793],
+                    &[0.2045493861, 0.1683865411, 0.5071506765, 0.7257355264],
+                    &[0.5708488802, 0.1846414616, 0.9590802982, 0.5591871046],
+                    &[0.8387612750, 0.5754861361, 0.5537109852, 0.1077646442],
+                ]),
+                StandardScalerParameters::default(),
+            )
+            .unwrap();
+
+            let deserialized_scaler: StandardScaler<f64> =
+                serde_json::from_str(&serde_json::to_string(&fitted_scaler).unwrap()).unwrap();
+
+            assert_eq!(
+                deserialized_scaler.means,
+                vec![0.42864544605, 0.2869813741, 0.737752073825, 0.431011663625],
+            );
+
+            assert!(
+                &DenseMatrix::from_2d_vec(&vec![deserialized_scaler.stds]).approximate_eq(
+                    &DenseMatrix::from_2d_array(&[&[
+                        0.29426447500954,
+                        0.16758497615485,
+                        0.20820945786863,
+                        0.23329718831165
+                    ],]),
+                    0.00000000000001
+                )
+            )
+        }
     }
 }