fix: cargo fmt

src/metrics/precision.rs

@@ -1,15 +1,19 @@
-use serde::{Serialize, Deserialize};
+use serde::{Deserialize, Serialize};
 
-use crate::math::num::FloatExt;
 use crate::linalg::BaseVector;
+use crate::math::num::FloatExt;
 
 #[derive(Serialize, Deserialize, Debug)]
-pub struct Precision{}
+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());
+            panic!(
+                "The vector sizes don't match: {} != {}",
+                y_true.len(),
+                y_prod.len()
+            );
         }
 
         let mut tp = 0;
@@ -17,11 +21,17 @@ impl Precision {
         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));
+                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));
+                panic!(
+                    "Precision can only be applied to binary classification: {}",
+                    y_prod.get(i)
+                );
             }
 
             if y_prod.get(i) == T::one() {
@@ -31,27 +41,25 @@ impl Precision {
                     tp += 1;
                 }
             }
-        }        
+        }
 
         T::from_i64(tp).unwrap() / T::from_i64(p).unwrap()
     }
-    
 }
 
 #[cfg(test)]
 mod tests {
-    use super::*;    
+    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);
+        let y_pred: Vec<f64> = vec![0., 0., 1., 1.];
 
-        assert!((score1 - 0.5).abs() < 1e-8);        
+        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);
-    }    
-
-}
+    }
+}