diff --git a/src/algorithm/neighbour/fastpair.rs b/src/algorithm/neighbour/fastpair.rs
index d676460..bea438e 100644
--- a/src/algorithm/neighbour/fastpair.rs
+++ b/src/algorithm/neighbour/fastpair.rs
@@ -174,7 +174,7 @@ impl<'a, T: RealNumber + FloatNumber, M: Array2<T>> FastPair<'a, T, M> {
     ///
     /// Brute force algorithm, used only for comparison and testing
     ///
-    #[cfg(feature = "fp_bench")]
+    #[allow(dead_code)]
     pub fn closest_pair_brute(&self) -> PairwiseDistance<T> {
         use itertools::Itertools;
         let m = self.samples.shape().0;
diff --git a/src/metrics/auc.rs b/src/metrics/auc.rs
index a94f3a3..e8d02b2 100644
--- a/src/metrics/auc.rs
+++ b/src/metrics/auc.rs
@@ -26,8 +26,8 @@ use std::marker::PhantomData;
 #[cfg(feature = "serde")]
 use serde::{Deserialize, Serialize};
 
-use crate::linalg::basic::arrays::{Array1, ArrayView1, MutArrayView1};
-use crate::numbers::basenum::Number;
+use crate::linalg::basic::arrays::{Array1, ArrayView1};
+use crate::numbers::floatnum::FloatNumber;
 
 use crate::metrics::Metrics;
 
@@ -38,14 +38,14 @@ pub struct AUC<T> {
     _phantom: PhantomData<T>,
 }
 
-impl<T: Number + Ord> Metrics<T> for AUC<T> {
+impl<T: FloatNumber + PartialOrd> Metrics<T> for AUC<T> {
     /// create a typed object to call AUC functions
     fn new() -> Self {
         Self {
             _phantom: PhantomData,
         }
     }
-    fn new_with(_parameter: T) -> Self {
+    fn new_with(_parameter: f64) -> Self {
         Self {
             _phantom: PhantomData,
         }
@@ -53,11 +53,7 @@ impl<T: Number + Ord> Metrics<T> for AUC<T> {
     /// AUC score.
     /// * `y_true` - ground truth (correct) labels.
     /// * `y_pred_prob` - probability estimates, as returned by a classifier.
-    fn get_score(
-        &self,
-        y_true: &dyn ArrayView1<T>,
-        y_pred_prob: &dyn ArrayView1<T>,
-    ) -> f64 {
+    fn get_score(&self, y_true: &dyn ArrayView1<T>, y_pred_prob: &dyn ArrayView1<T>) -> f64 {
         let mut pos = T::zero();
         let mut neg = T::zero();
 
@@ -76,9 +72,10 @@ impl<T: Number + Ord> Metrics<T> for AUC<T> {
             }
         }
 
-        let y_pred = y_pred_prob.clone();
-
-        let label_idx = y_pred.argsort();
+        let y_pred: Vec<T> =
+            Array1::<T>::from_iterator(y_pred_prob.iterator(0).copied(), y_pred_prob.shape());
+        // TODO: try to use `crate::algorithm::sort::quick_sort` here
+        let label_idx: Vec<usize> = y_pred.argsort();
 
         let mut rank = vec![0f64; n];
         let mut i = 0;
@@ -108,7 +105,7 @@ impl<T: Number + Ord> Metrics<T> for AUC<T> {
         let pos = pos.to_f64().unwrap();
         let neg = neg.to_f64().unwrap();
 
-        T::from(auc - (pos * (pos + 1f64) / 2.0)).unwrap() / T::from(pos * neg).unwrap()
+        (auc - (pos * (pos + 1f64) / 2f64)) / (pos * neg)
     }
 }
 
diff --git a/src/metrics/mod.rs b/src/metrics/mod.rs
index 503391c..25cffa3 100644
--- a/src/metrics/mod.rs
+++ b/src/metrics/mod.rs
@@ -55,7 +55,7 @@
 pub mod accuracy;
 // TODO: reimplement AUC
 // /// Computes Area Under the Receiver Operating Characteristic Curve (ROC AUC) from prediction scores.
-// pub mod auc;
+pub mod auc;
 /// Compute the homogeneity, completeness and V-Measure scores.
 pub mod cluster_hcv;
 pub(crate) mod cluster_helpers;