Implement AUC-ROC in Kaun metrics

CHANGES.md

@@ -46,6 +46,7 @@ All notable changes to this project will be documented in this file.
 - Allow metric history to tolerate metrics that appear or disappear between epochs so dynamic metric sets no longer raise during training (@tmattio)
 - Make `Optimizer.clip_by_global_norm` robust to zero gradients and empty parameter trees to avoid NaNs during training (@tmattio)
 - Split CSV loader into `from_csv` and `from_csv_with_labels` to retain labels when requested (#114, @Satarupa22-SD)
+- Implement AUC-ROC in Kaun metrics and simplify its API (#109 @Shocker444)
 
 ### Talon
 

kaun/lib/kaun/metrics.ml

@@ -296,16 +296,100 @@ let f1_score ?(threshold = 0.5) ?(averaging = Micro) ?(beta = 1.0) () =
     ~reset:(fun _ -> [])
 
 (* Placeholder implementations for complex metrics *)
-let auc_roc ?(num_thresholds = 200) ?(curve = false) () =
-  let _ = num_thresholds in
-  let _ = curve in
+let auc_roc () =
   create_custom ~name:"auc_roc"
     ~init:(fun () -> [])
-    ~update:(fun state ~predictions:_ ~targets:_ ?weights:_ () -> state)
-    ~compute:(fun _ ->
-      failwith "AUC-ROC not yet implemented - requires trapezoid integration")
+    ~update:(fun state ~predictions ~targets ?weights () ->
+      let predictions = Rune.reshape [| -1 |] predictions in
+      let targets = Rune.reshape [| -1 |] targets in
+      let dtype =
+        match state with
+        | [ preds_acc; _; _ ] -> Rune.dtype preds_acc
+        | _ -> Rune.dtype predictions
+      in
+      let predictions = Rune.cast dtype predictions in
+      let targets = Rune.cast dtype targets in
+      let weights =
+        match weights with
+        | Some w -> Rune.cast dtype (Rune.reshape [| -1 |] w)
+        | None -> Rune.ones dtype (Rune.shape predictions)
+      in
+      match state with
+      | [] -> [ predictions; targets; weights ]
+      | [ preds_acc; targets_acc; weights_acc ] ->
+          let preds_acc =
+            Rune.concatenate ~axis:0 [ preds_acc; predictions ]
+          in
+          let targets_acc =
+            Rune.concatenate ~axis:0 [ targets_acc; targets ]
+          in
+          let weights_acc =
+            Rune.concatenate ~axis:0 [ weights_acc; weights ]
+          in
+          [ preds_acc; targets_acc; weights_acc ]
+      | _ -> failwith "Invalid auc_roc state")
+    ~compute:(fun state ->
+      match state with
+      | [ preds; targets; weights ] ->
+          let dtype = Rune.dtype preds in
+          let ones = Rune.ones dtype (Rune.shape targets) in
+          let sorted_idx =
+            Rune.argsort ~axis:0 ~descending:true preds
+          in
+          let sorted_targets =
+            Rune.take_along_axis ~axis:0 sorted_idx targets
+          in
+          let sorted_weights =
+            Rune.take_along_axis ~axis:0 sorted_idx weights
+          in
+
+          let positives = Rune.mul sorted_targets sorted_weights in
+          let negatives =
+            Rune.mul (Rune.sub ones sorted_targets) sorted_weights
+          in
+
+          let cum_tp = Rune.cumsum ~axis:0 positives in
+          let cum_fp = Rune.cumsum ~axis:0 negatives in
+          let zero = scalar_tensor dtype 0.0 in
+          let cum_tp =
+            Rune.concatenate ~axis:0 [ Rune.reshape [| 1 |] zero; cum_tp ]
+          in
+          let cum_fp =
+            Rune.concatenate ~axis:0 [ Rune.reshape [| 1 |] zero; cum_fp ]
+          in
+
+          let total_pos = Rune.item [] (Rune.sum positives) in
+          let total_neg = Rune.item [] (Rune.sum negatives) in
+
+          let ratio cumulative total =
+            if Float.abs total < 1e-12 then
+              Rune.zeros dtype (Rune.shape cumulative)
+            else
+              let total_tensor = scalar_tensor dtype total in
+              Rune.div cumulative total_tensor
+          in
+
+          let tpr = ratio cum_tp total_pos in
+          let fpr = ratio cum_fp total_neg in
+
+          let n = Rune.size tpr in
+          if n < 2 then scalar_tensor dtype 0.0
+          else
+            let tail_fpr = Rune.slice [ Rune.R (1, n) ] fpr in
+            let head_fpr = Rune.slice [ Rune.R (0, n - 1) ] fpr in
+            let dx = Rune.sub tail_fpr head_fpr in
+
+            let tail_tpr = Rune.slice [ Rune.R (1, n) ] tpr in
+            let head_tpr = Rune.slice [ Rune.R (0, n - 1) ] tpr in
+            let avg_tpr =
+              Rune.mul (scalar_tensor dtype 0.5)
+                (Rune.add tail_tpr head_tpr)
+            in
+            Rune.sum (Rune.mul dx avg_tpr)
+      | _ -> failwith "Invalid auc_roc state")
     ~reset:(fun _ -> [])
 
+
 let auc_pr ?(num_thresholds = 200) ?(curve = false) () =
   let _ = num_thresholds in
   let _ = curve in

kaun/lib/kaun/metrics.mli

@@ -114,14 +114,13 @@ val f1_score :
     @param averaging Multi-class averaging strategy (default: Micro)
     @param beta Weight of recall vs precision (default: 1.0 for F1) *)
 
-val auc_roc : ?num_thresholds:int -> ?curve:bool -> unit -> 'layout t
-(** [auc_roc ?num_thresholds ?curve ()] creates an AUC-ROC metric.
+val auc_roc : unit -> 'layout t
+(** [auc_roc ()] creates an AUC-ROC metric.
 
     Area Under the Receiver Operating Characteristic Curve.
 
-    @param num_thresholds
-      Number of thresholds for curve computation (default: 200)
-    @param curve If true, also return the ROC curve points (default: false) *)
+    Computes the exact ROC integral by sorting predictions and accumulating
+    true/false positive rates across all seen batches. *)
 
 val auc_pr : ?num_thresholds:int -> ?curve:bool -> unit -> 'layout t
 (** [auc_pr ?num_thresholds ?curve ()] creates an AUC-PR metric.

kaun/test/test_metrics.ml

@@ -78,6 +78,41 @@ let test_f1_score () =
   let expected = Rune.scalar dtype 0.8 in
   check (tensor_testable 1e-5) "f1 score" expected result
 
+let test_auc_roc () = 
+  let dtype = Rune.float32 in
+
+  let predictions = Rune.create dtype [| 4 |] [| 0.8; 0.7; 0.6; 0.3 |] in
+  let targets = Rune.create dtype [| 4 |] [| 1.; 1.; 0.; 0. |] in
+
+  let auc = Metrics.auc_roc () in
+  Metrics.update auc ~predictions ~targets ();
+  let result = Metrics.compute auc in
+  (* For perfectly separable predictions, AUC should be 1.0 *)
+  let expected = Rune.scalar dtype 1.0 in
+  check (tensor_testable 1e-5) "auc roc" expected result
+
+let test_auc_roc_multiple_updates () =
+  let dtype = Rune.float32 in
+
+  let predictions_full = Rune.create dtype [| 4 |] [| 0.8; 0.7; 0.6; 0.3 |] in
+  let targets_full = Rune.create dtype [| 4 |] [| 1.; 1.; 0.; 0. |] in
+
+  let auc_single = Metrics.auc_roc () in
+  Metrics.update auc_single ~predictions:predictions_full ~targets:targets_full ();
+  let full_result = Metrics.compute auc_single in
+
+  let auc_chunked = Metrics.auc_roc () in
+  let predictions_1 = Rune.create dtype [| 2 |] [| 0.8; 0.7 |] in
+  let targets_1 = Rune.create dtype [| 2 |] [| 1.; 1. |] in
+  Metrics.update auc_chunked ~predictions:predictions_1 ~targets:targets_1 ();
+  let predictions_2 = Rune.create dtype [| 2 |] [| 0.6; 0.3 |] in
+  let targets_2 = Rune.create dtype [| 2 |] [| 0.; 0. |] in
+  Metrics.update auc_chunked ~predictions:predictions_2 ~targets:targets_2 ();
+  let chunked_result = Metrics.compute auc_chunked in
+
+  check (tensor_testable 1e-5) "auc roc incremental"
+    full_result chunked_result
+
 let test_confusion_matrix () =
   let dtype = Rune.float32 in
 
@@ -376,6 +411,8 @@ let () =
           test_case "accuracy" `Quick test_accuracy;
           test_case "precision_recall" `Quick test_precision_recall;
           test_case "f1_score" `Quick test_f1_score;
+          test_case "auc_roc" `Quick test_auc_roc;
+          test_case "auc_roc_multiple_updates" `Quick test_auc_roc_multiple_updates;
           test_case "confusion_matrix" `Quick test_confusion_matrix;
         ] );
       ( "regression",