feat: adds 3 new regression metrics

src/math/num.rs

@@ -14,6 +14,10 @@ pub trait FloatExt: Float + FromPrimitive + Debug + Display + Copy {
     fn two() -> Self;
 
     fn half() -> Self;
+
+    fn square(self) -> Self {
+        self * self
+    }
 }
 
 impl FloatExt for f64 {

src/metrics/mean_absolute_error.rs

@@ -0,0 +1,46 @@
+use serde::{Deserialize, Serialize};
+
+use crate::linalg::BaseVector;
+use crate::math::num::FloatExt;
+
+#[derive(Serialize, Deserialize, Debug)]
+pub struct MeanAbsoluteError {}
+
+impl MeanAbsoluteError {
+    pub fn get_score<T: FloatExt, V: BaseVector<T>>(&self, y_true: &V, y_pred: &V) -> T {
+        if y_true.len() != y_pred.len() {
+            panic!(
+                "The vector sizes don't match: {} != {}",
+                y_true.len(),
+                y_pred.len()
+            );
+        }
+
+        let n = y_true.len();
+        let mut ras = T::zero();
+        for i in 0..n {
+            ras = ras + (y_true.get(i) - y_pred.get(i)).abs();
+        }
+
+        ras / T::from_usize(n).unwrap()
+    }
+}
+
+#[cfg(test)]
+mod tests {
+    use super::*;
+
+    #[test]
+    fn mean_absolute_error() {
+        let y_true: Vec<f64> = vec![3., -0.5, 2., 7.];
+        let y_pred: Vec<f64> = vec![2.5, 0.0, 2., 8.];
+
+        let score1: f64 = MeanAbsoluteError {}.get_score(&y_pred, &y_true);
+        let score2: f64 = MeanAbsoluteError {}.get_score(&y_true, &y_true);
+
+        println!("{}", score1);
+
+        assert!((score1 - 0.5).abs() < 1e-8);
+        assert!((score2 - 0.0).abs() < 1e-8);
+    }
+}

src/metrics/mean_squared_error.rs

@@ -0,0 +1,46 @@
+use serde::{Deserialize, Serialize};
+
+use crate::linalg::BaseVector;
+use crate::math::num::FloatExt;
+
+#[derive(Serialize, Deserialize, Debug)]
+pub struct MeanSquareError {}
+
+impl MeanSquareError {
+    pub fn get_score<T: FloatExt, V: BaseVector<T>>(&self, y_true: &V, y_pred: &V) -> T {
+        if y_true.len() != y_pred.len() {
+            panic!(
+                "The vector sizes don't match: {} != {}",
+                y_true.len(),
+                y_pred.len()
+            );
+        }
+
+        let n = y_true.len();
+        let mut rss = T::zero();
+        for i in 0..n {
+            rss = rss + (y_true.get(i) - y_pred.get(i)).square();
+        }
+
+        rss / T::from_usize(n).unwrap()
+    }
+}
+
+#[cfg(test)]
+mod tests {
+    use super::*;
+
+    #[test]
+    fn mean_squared_error() {
+        let y_true: Vec<f64> = vec![3., -0.5, 2., 7.];
+        let y_pred: Vec<f64> = vec![2.5, 0.0, 2., 8.];
+
+        let score1: f64 = MeanSquareError {}.get_score(&y_pred, &y_true);
+        let score2: f64 = MeanSquareError {}.get_score(&y_true, &y_true);
+
+        println!("{}", score1);
+
+        assert!((score1 - 0.375).abs() < 1e-8);
+        assert!((score2 - 0.0).abs() < 1e-8);
+    }
+}

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)
+}

src/metrics/r2.rs

@@ -0,0 +1,58 @@
+use serde::{Deserialize, Serialize};
+
+use crate::linalg::BaseVector;
+use crate::math::num::FloatExt;
+
+#[derive(Serialize, Deserialize, Debug)]
+pub struct R2 {}
+
+impl R2 {
+    pub fn get_score<T: FloatExt, V: BaseVector<T>>(&self, y_true: &V, y_pred: &V) -> T {
+        if y_true.len() != y_pred.len() {
+            panic!(
+                "The vector sizes don't match: {} != {}",
+                y_true.len(),
+                y_pred.len()
+            );
+        }
+
+        let n = y_true.len();
+
+        let mut mean = T::zero();
+
+        for i in 0..n {
+            mean = mean + y_true.get(i);
+        }
+
+        mean = mean / T::from_usize(n).unwrap();
+
+        let mut ss_tot = T::zero();
+        let mut ss_res = T::zero();
+
+        for i in 0..n {
+            let y_i = y_true.get(i);
+            let f_i = y_pred.get(i);
+            ss_tot = ss_tot + (y_i - mean).square();
+            ss_res = ss_res + (y_i - f_i).square();
+        }
+
+        T::one() - (ss_res / ss_tot)
+    }
+}
+
+#[cfg(test)]
+mod tests {
+    use super::*;
+
+    #[test]
+    fn r2() {
+        let y_true: Vec<f64> = vec![3., -0.5, 2., 7.];
+        let y_pred: Vec<f64> = vec![2.5, 0.0, 2., 8.];
+
+        let score1: f64 = R2 {}.get_score(&y_true, &y_pred);
+        let score2: f64 = R2 {}.get_score(&y_true, &y_true);
+
+        assert!((score1 - 0.948608137).abs() < 1e-8);
+        assert!((score2 - 1.0).abs() < 1e-8);
+    }
+}