Merge pull request #356 from ntumlgroup/fix_metric_k

Fix top_k in metrics.py

Author: Eleven1Liu

libmultilabel/nn/metrics.py

@@ -217,19 +217,17 @@ def get_metrics(metric_threshold, monitor_metrics, num_classes, top_k=None):
 
         if match_top_k:
             metric_abbr = match_top_k.group(1)  # P, R, PR, or nDCG
-            top_k = int(match_top_k.group(2))
-            if top_k >= num_classes:
-                raise ValueError(
-                    f"Invalid metric: {metric}. top_k ({top_k}) is greater than num_classes({num_classes})."
-                )
+            k = int(match_top_k.group(2))
+            if k >= num_classes:
+                raise ValueError(f"Invalid metric: {metric}. k ({k}) is greater than num_classes({num_classes}).")
             if metric_abbr == "P":
-                metrics[metric] = Precision(num_classes, average="samples", top_k=top_k)
+                metrics[metric] = Precision(num_classes, average="samples", top_k=k)
             elif metric_abbr == "R":
-                metrics[metric] = Recall(num_classes, average="samples", top_k=top_k)
+                metrics[metric] = Recall(num_classes, average="samples", top_k=k)
             elif metric_abbr == "RP":
-                metrics[metric] = RPrecision(top_k=top_k)
+                metrics[metric] = RPrecision(top_k=k)
             elif metric_abbr == "nDCG":
-                metrics[metric] = NDCG(top_k=top_k)
+                metrics[metric] = NDCG(top_k=k)
                 # The implementation in torchmetrics stores the prediction/target of all batches,
                 # which can lead to CUDA out of memory.
                 # metrics[metric] = RetrievalNormalizedDCG(k=top_k)

setup.cfg

@@ -1,6 +1,6 @@
 [metadata]
 name = libmultilabel
-version = 0.5.1
+version = 0.5.2
 author = LibMultiLabel Team
 license = MIT License
 license_file = LICENSE