feat: adds F1 and roc_auc_score

src/metrics/auc.rs

@@ -0,0 +1,81 @@
+#![allow(non_snake_case)]
+
+use serde::{Deserialize, Serialize};
+
+use crate::algorithm::sort::quick_sort::QuickArgSort;
+use crate::linalg::BaseVector;
+use crate::math::num::FloatExt;
+
+#[derive(Serialize, Deserialize, Debug)]
+pub struct AUC {}
+
+impl AUC {
+    pub fn get_score<T: FloatExt, V: BaseVector<T>>(&self, y_true: &V, y_pred_prob: &V) -> T {
+        let mut pos = T::zero();
+        let mut neg = T::zero();
+
+        let n = y_true.len();
+
+        for i in 0..n {
+            if y_true.get(i) == T::zero() {
+                neg = neg + T::one();
+            } else if y_true.get(i) == T::one() {
+                pos = pos + T::one();
+            } else {
+                panic!(
+                    "AUC is only for binary classification. Invalid label: {}",
+                    y_true.get(i)
+                );
+            }
+        }
+
+        let mut y_pred = y_pred_prob.to_vec();
+
+        let label_idx = y_pred.quick_argsort_mut();
+
+        let mut rank = vec![T::zero(); n];
+        let mut i = 0;
+        while i < n {
+            if i == n - 1 || y_pred[i] != y_pred[i + 1] {
+                rank[i] = T::from_usize(i + 1).unwrap();
+            } else {
+                let mut j = i + 1;
+                while j < n && y_pred[j] == y_pred[i] {
+                    j += 1;
+                }
+                let r = T::from_usize(i + 1 + j).unwrap() / T::two();
+                for k in i..j {
+                    rank[k] = r;
+                }
+                i = j - 1;
+            }
+            i += 1;
+        }
+
+        let mut auc = T::zero();
+        for i in 0..n {
+            if y_true.get(label_idx[i]) == T::one() {
+                auc = auc + rank[i];
+            }
+        }
+
+        (auc - (pos * (pos + T::one()) / T::two())) / (pos * neg)
+    }
+}
+
+#[cfg(test)]
+mod tests {
+    use super::*;
+
+    #[test]
+    fn auc() {
+        let y_true: Vec<f64> = vec![0., 0., 1., 1.];
+        let y_pred: Vec<f64> = vec![0.1, 0.4, 0.35, 0.8];
+
+        let score1: f64 = AUC {}.get_score(&y_true, &y_pred);
+        let score2: f64 = AUC {}.get_score(&y_true, &y_true);
+
+        assert!((score1 - 0.75).abs() < 1e-8);
+        assert!((score2 - 1.0).abs() < 1e-8);
+    }
+}

src/metrics/f1.rs

@@ -0,0 +1,44 @@
+use serde::{Deserialize, Serialize};
+
+use crate::linalg::BaseVector;
+use crate::math::num::FloatExt;
+use crate::metrics::precision::Precision;
+use crate::metrics::recall::Recall;
+
+#[derive(Serialize, Deserialize, Debug)]
+pub struct F1 {}
+
+impl F1 {
+    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 beta2 = T::one();
+
+        let p = Precision {}.get_score(y_true, y_prod);
+        let r = Recall {}.get_score(y_true, y_prod);
+
+        (T::one() + beta2) * (p * r) / (beta2 * p + r)
+    }
+}
+
+#[cfg(test)]
+mod tests {
+    use super::*;
+
+    #[test]
+    fn f1() {
+        let y_pred: Vec<f64> = vec![0., 0., 1., 1., 1., 1.];
+        let y_true: Vec<f64> = vec![0., 1., 1., 0., 1., 0.];
+
+        let score1: f64 = F1 {}.get_score(&y_pred, &y_true);
+        let score2: f64 = F1 {}.get_score(&y_true, &y_true);
+
+        assert!((score1 - 0.57142857).abs() < 1e-8);
+        assert!((score2 - 1.0).abs() < 1e-8);
+    }
+}

src/metrics/mod.rs

@@ -1,4 +1,6 @@
 pub mod accuracy;
+pub mod auc;
+pub mod f1;
 pub mod precision;
 pub mod recall;