Enhancement: Add explicit logging warning for NaN classes in macro-averaged metrics

perceptionmetrics/utils/segmentation_metrics.py

@@ -1,4 +1,5 @@
 from collections import defaultdict
+import logging
 import math
 from typing import List, Optional, Union
 
@@ -255,7 +256,18 @@ def get_averaged_metric(
         """
         metric = getattr(self, f"get_{metric_name}")
         if method == "macro":
-            return float(np.nanmean(metric(per_class=True)))
+            per_class_values = metric(per_class=True)
+            nan_mask = np.isnan(per_class_values)
+            missing_count = int(np.sum(nan_mask))
+
+            if missing_count > 0:
+                # Use implicit string concatenation to keep lines short
+                msg = (
+                    f"Warning: {missing_count} class(es) were missing from the confusion matrix. "
+                    f"Their {metric_name.upper()} evaluated to NaN and will be ignored in the macro-average."
+                )
+                logging.warning(msg)
+            return float(np.nanmean(per_class_values))
         if method == "micro":
             return float(metric(per_class=False))
         if method == "weighted":