update to have metrics calculated at the answer level

Author: jtsodapop

labelbox/data/metrics/group.py

@@ -3,13 +3,15 @@
 """
 from collections import defaultdict
 from typing import Dict, List, Tuple, Union
+
+from labelbox.data.annotation_types.annotation import ClassificationAnnotation, Checklist, Radio
 try:
     from typing import Literal
 except ImportError:
     from typing_extensions import Literal
 
 from ..annotation_types.feature import FeatureSchema
-from ..annotation_types import ObjectAnnotation, Label, LabelList
+from ..annotation_types import ObjectAnnotation, ClassificationAnnotation, Label, LabelList
 
 
 def get_identifying_key(
@@ -56,6 +58,14 @@ def all_have_key(features: List[FeatureSchema]) -> Tuple[bool, bool]:
     all_names = True
     all_schemas = True
     for feature in features:
+        if isinstance(feature, ClassificationAnnotation):
+            if isinstance(feature.value, Checklist):
+                all_names, all_schemas = all_have_key(feature.value.answer)
+            else:
+                if feature.value.answer.name is None:
+                    all_names = False
+                if feature.value.answer.feature_schema_id is None:
+                    all_schemas = False
         if feature.name is None:
             all_names = False
         if feature.feature_schema_id is None:
@@ -155,7 +165,17 @@ def _create_feature_lookup(features: List[FeatureSchema],
     """
     grouped_features = defaultdict(list)
     for feature in features:
-        grouped_features[getattr(feature, key)].append(feature)
+        if isinstance(feature, ClassificationAnnotation):
+            #checklists
+            if isinstance(feature.value, Checklist):
+                for answer in feature.value.answer:
+                    new_feature = Radio(answer=answer)
+                    grouped_features[getattr(answer, key)] = new_feature
+            else:
+                grouped_features[getattr(feature.value.answer,
+                                         key)].append(feature)
+        else:
+            grouped_features[getattr(feature, key)].append(feature)
     return grouped_features