Minor fixes

src/batdetect2/data/conditions/sound_events.py

@@ -1,4 +1,6 @@
+import operator
 from collections.abc import Callable, Sequence
+from functools import partial
 from typing import Annotated, Literal
 
 from pydantic import Field
@@ -78,25 +80,23 @@ class DurationConfig(BaseConfig):
     seconds: float
 
 
-def _build_comparator(
-    operator: Operator, value: float
-) -> Callable[[float], bool]:
-    if operator == "gt":
-        return lambda x: x > value
+def _build_comparator(op: Operator, value: float) -> Callable[[float], bool]:
+    if op == "gt":
+        return partial(operator.gt, value)
 
-    if operator == "gte":
-        return lambda x: x >= value
+    if op == "gte":
+        return partial(operator.ge, value)
 
-    if operator == "lt":
-        return lambda x: x < value
+    if op == "lt":
+        return partial(operator.lt, value)
 
-    if operator == "lte":
-        return lambda x: x <= value
+    if op == "lte":
+        return partial(operator.le, value)
 
-    if operator == "eq":
-        return lambda x: x == value
+    if op == "eq":
+        return partial(operator.eq, value)
 
-    raise ValueError(f"Invalid operator {operator}")
+    raise ValueError(f"Invalid operator {op}")
 
 
 class Duration:

src/batdetect2/evaluate/metrics/classification.py

@@ -204,15 +204,19 @@ class ClassificationROCAUC(BaseClassificationMetric):
             ignore_generic=self.ignore_generic,
         )
 
-        class_scores = {
-            class_name: float(
+        class_scores = {}
+
+        for class_name in self.targets.class_names:
+            if len(y_true[class_name]) == 0:
+                class_scores[class_name] = np.nan
+                continue
+
+            class_scores[class_name] = float(
                 metrics.roc_auc_score(
                     y_true[class_name],
                     y_score[class_name],
                 )
             )
-            for class_name in self.targets.class_names
-        }
 
         mean_score = float(
             np.mean([v for v in class_scores.values() if v != np.nan])

src/batdetect2/evaluate/metrics/detection.py

@@ -133,6 +133,9 @@ class DetectionROCAUC:
                 y_true.append(m.is_ground_truth)
                 y_score.append(m.score)
 
+        if len(y_true) == 0:
+            return {self.label: np.nan}
+
         score = float(metrics.roc_auc_score(y_true, y_score))
         return {self.label: score}