feat: adds 3 new regression metrics

src/metrics/mod.rs

@@ -1,7 +1,10 @@
 pub mod accuracy;
 pub mod auc;
 pub mod f1;
+pub mod mean_absolute_error;
+pub mod mean_squared_error;
 pub mod precision;
+pub mod r2;
 pub mod recall;
 
 use crate::linalg::BaseVector;
@@ -9,6 +12,8 @@ use crate::math::num::FloatExt;
 
 pub struct ClassificationMetrics {}
 
+pub struct RegressionMetrics {}
+
 impl ClassificationMetrics {
     pub fn accuracy() -> accuracy::Accuracy {
         accuracy::Accuracy {}
@@ -31,6 +36,20 @@ impl ClassificationMetrics {
     }
 }
 
+impl RegressionMetrics {
+    pub fn mean_squared_error() -> mean_squared_error::MeanSquareError {
+        mean_squared_error::MeanSquareError {}
+    }
+
+    pub fn mean_absolute_error() -> mean_absolute_error::MeanAbsoluteError {
+        mean_absolute_error::MeanAbsoluteError {}
+    }
+
+    pub fn r2() -> r2::R2 {
+        r2::R2 {}
+    }
+}
+
 pub fn accuracy<T: FloatExt, V: BaseVector<T>>(y_true: &V, y_pred: &V) -> T {
     ClassificationMetrics::accuracy().get_score(y_true, y_pred)
 }
@@ -50,3 +69,15 @@ pub fn f1<T: FloatExt, V: BaseVector<T>>(y_true: &V, y_pred: &V) -> T {
 pub fn roc_auc_score<T: FloatExt, V: BaseVector<T>>(y_true: &V, y_pred_probabilities: &V) -> T {
     ClassificationMetrics::roc_auc_score().get_score(y_true, y_pred_probabilities)
 }
+
+pub fn mean_squared_error<T: FloatExt, V: BaseVector<T>>(y_true: &V, y_pred: &V) -> T {
+    RegressionMetrics::mean_squared_error().get_score(y_true, y_pred)
+}
+
+pub fn mean_absolute_error<T: FloatExt, V: BaseVector<T>>(y_true: &V, y_pred: &V) -> T {
+    RegressionMetrics::mean_absolute_error().get_score(y_true, y_pred)
+}
+
+pub fn r2<T: FloatExt, V: BaseVector<T>>(y_true: &V, y_pred: &V) -> T {
+    RegressionMetrics::r2().get_score(y_true, y_pred)
+}