feat: adds accuracy, recall and precision metrics

src/metrics/accuracy.rs

@@ -0,0 +1,45 @@
+use serde::{Serialize, Deserialize};
+
+use crate::math::num::FloatExt;
+use crate::linalg::BaseVector;
+
+#[derive(Serialize, Deserialize, Debug)]
+pub struct Accuracy{}
+
+impl Accuracy {
+    pub fn get_score<T: FloatExt, V: BaseVector<T>>(&self, y_true: &V, y_prod: &V) -> T {
+        if y_true.len() != y_prod.len() {
+            panic!("The vector sizes don't match: {} != {}", y_true.len(), y_prod.len());
+        }
+
+        let n = y_true.len();
+
+        let mut positive = 0;
+        for i in 0..n {
+            if y_true.get(i) == y_prod.get(i) {
+                positive += 1;
+            }
+        }
+
+        T::from_i64(positive).unwrap() / T::from_usize(n).unwrap()
+    }
+    
+}
+
+#[cfg(test)]
+mod tests {
+    use super::*;    
+
+    #[test]
+    fn accuracy() {
+        let y_pred: Vec<f64> = vec![0., 2., 1., 3.];
+        let y_true: Vec<f64> = vec![0., 1., 2., 3.];
+        
+        let score1: f64 = Accuracy{}.get_score(&y_pred, &y_true);
+        let score2: f64 = Accuracy{}.get_score(&y_true, &y_true);
+
+        assert!((score1 - 0.5).abs() < 1e-8);
+        assert!((score2 - 1.0).abs() < 1e-8);
+    }    
+
+}

src/metrics/mod.rs

@@ -0,0 +1,34 @@
+pub mod accuracy;
+pub mod recall;
+pub mod precision;
+
+use crate::math::num::FloatExt;
+use crate::linalg::BaseVector;
+
+pub struct ClassificationMetrics{}
+
+impl ClassificationMetrics {
+    pub fn accuracy() -> accuracy::Accuracy{
+        accuracy::Accuracy {}
+    }
+    
+    pub fn recall() -> recall::Recall{
+        recall::Recall {}
+    }
+
+    pub fn precision() -> precision::Precision{
+        precision::Precision {}
+    }
+}
+
+pub fn accuracy<T: FloatExt, V: BaseVector<T>>(y_true: &V, y_prod: &V) -> T{
+    ClassificationMetrics::accuracy().get_score(y_true, y_prod)
+}
+
+pub fn recall<T: FloatExt, V: BaseVector<T>>(y_true: &V, y_prod: &V) -> T{
+    ClassificationMetrics::recall().get_score(y_true, y_prod)
+}
+
+pub fn precision<T: FloatExt, V: BaseVector<T>>(y_true: &V, y_prod: &V) -> T{
+    ClassificationMetrics::precision().get_score(y_true, y_prod)
+}

src/metrics/precision.rs

@@ -0,0 +1,57 @@
+use serde::{Serialize, Deserialize};
+
+use crate::math::num::FloatExt;
+use crate::linalg::BaseVector;
+
+#[derive(Serialize, Deserialize, Debug)]
+pub struct Precision{}
+
+impl Precision {
+    pub fn get_score<T: FloatExt, V: BaseVector<T>>(&self, y_true: &V, y_prod: &V) -> T {
+        if y_true.len() != y_prod.len() {
+            panic!("The vector sizes don't match: {} != {}", y_true.len(), y_prod.len());
+        }
+
+        let mut tp = 0;
+        let mut p = 0;
+        let n = y_true.len();
+        for i in 0..n {
+            if y_true.get(i) != T::zero() && y_true.get(i) != T::one() {
+                panic!("Precision can only be applied to binary classification: {}", y_true.get(i));
+            }
+
+            if y_prod.get(i) != T::zero() && y_prod.get(i) != T::one() {
+                panic!("Precision can only be applied to binary classification: {}", y_prod.get(i));
+            }
+
+            if y_prod.get(i) == T::one() {
+                p += 1;
+
+                if y_true.get(i) == T::one() {
+                    tp += 1;
+                }
+            }
+        }        
+
+        T::from_i64(tp).unwrap() / T::from_i64(p).unwrap()
+    }
+    
+}
+
+#[cfg(test)]
+mod tests {
+    use super::*;    
+
+    #[test]
+    fn precision() {
+        let y_true: Vec<f64> = vec![0., 1., 1., 0.];
+        let y_pred: Vec<f64> = vec![0., 0., 1., 1.];        
+        
+        let score1: f64 = Precision{}.get_score(&y_pred, &y_true);
+        let score2: f64 = Precision{}.get_score(&y_pred, &y_pred);
+
+        assert!((score1 - 0.5).abs() < 1e-8);        
+        assert!((score2 - 1.0).abs() < 1e-8);
+    }    
+
+}

src/metrics/recall.rs

@@ -0,0 +1,57 @@
+use serde::{Serialize, Deserialize};
+
+use crate::math::num::FloatExt;
+use crate::linalg::BaseVector;
+
+#[derive(Serialize, Deserialize, Debug)]
+pub struct Recall{}
+
+impl Recall {
+    pub fn get_score<T: FloatExt, V: BaseVector<T>>(&self, y_true: &V, y_prod: &V) -> T {
+        if y_true.len() != y_prod.len() {
+            panic!("The vector sizes don't match: {} != {}", y_true.len(), y_prod.len());
+        }
+
+        let mut tp = 0;
+        let mut p = 0;
+        let n = y_true.len();
+        for i in 0..n {
+            if y_true.get(i) != T::zero() && y_true.get(i) != T::one() {
+                panic!("Recall can only be applied to binary classification: {}", y_true.get(i));
+            }
+
+            if y_prod.get(i) != T::zero() && y_prod.get(i) != T::one() {
+                panic!("Recall can only be applied to binary classification: {}", y_prod.get(i));
+            }
+
+            if y_true.get(i) == T::one() {
+                p += 1;
+
+                if y_prod.get(i) == T::one() {
+                    tp += 1;
+                }
+            }
+        }        
+
+        T::from_i64(tp).unwrap() / T::from_i64(p).unwrap()
+    }
+    
+}
+
+#[cfg(test)]
+mod tests {
+    use super::*;    
+
+    #[test]
+    fn recall() {
+        let y_true: Vec<f64> = vec![0., 1., 1., 0.];
+        let y_pred: Vec<f64> = vec![0., 0., 1., 1.];        
+        
+        let score1: f64 = Recall{}.get_score(&y_pred, &y_true);
+        let score2: f64 = Recall{}.get_score(&y_pred, &y_pred);
+
+        assert!((score1 - 0.5).abs() < 1e-8);        
+        assert!((score2 - 1.0).abs() < 1e-8);
+    }    
+
+}