Fix lint

Author: kozikkamil

labelbox/data/annotation_types/classification/classification.py

@@ -93,7 +93,7 @@ def __init__(self, **data: Any):
                       "removed in a future release")
 
 
-class ClassificationAnnotation(BaseAnnotation, ConfidenceMixin):
+class ClassificationAnnotation(BaseAnnotation, ConfidenceMixin, CustomMetricsMixin):
     """Classification annotations (non localized)
 
     >>> ClassificationAnnotation(

labelbox/data/mixins.py

@@ -55,8 +55,8 @@ class CustomMetricsMixin(BaseModel):
     def dict(self, *args, **kwargs):
         res = super().dict(*args, **kwargs)
 
-        if "customMetrics" in res and res["customMetrics"] is None:
-            res.pop("customMetrics")
+        if "custom_metrics" in res and res["custom_metrics"] is None:
+            res.pop("custom_metrics")
 
         return res
 

tests/data/annotation_types/classification/test_classification.py

@@ -13,11 +13,16 @@ def test_classification_answer():
     feature_schema_id = "schema_id"
     name = "my_feature"
     confidence = 0.9
-    answer = ClassificationAnswer(name=name, confidence=confidence)
+    custom_metrics = [{ 'name': 'metric1', 'value': 2 }]
+    answer = ClassificationAnswer(
+        name=name,
+        confidence=confidence,
+        custom_metrics=custom_metrics)
 
     assert answer.feature_schema_id is None
     assert answer.name == name
     assert answer.confidence == confidence
+    assert answer.custom_metrics == custom_metrics
 
     answer = ClassificationAnswer(feature_schema_id=feature_schema_id,
                                   name=name)
@@ -50,7 +55,7 @@ def test_subclass():
         'feature_schema_id': None,
         'extra': {},
         'value': {
-            'answer': answer
+            'answer': answer,
         },
         'message_id': None,
     }
@@ -63,7 +68,7 @@ def test_subclass():
         'feature_schema_id': feature_schema_id,
         'extra': {},
         'value': {
-            'answer': answer
+            'answer': answer,
         },
         'name': name,
         'message_id': None,
@@ -77,14 +82,17 @@ def test_subclass():
         'feature_schema_id': feature_schema_id,
         'extra': {},
         'value': {
-            'answer': answer
+            'answer': answer,
         },
         'message_id': None,
     }
 
 
 def test_radio():
-    answer = ClassificationAnswer(name="1", confidence=0.81)
+    answer = ClassificationAnswer(
+        name="1",
+        confidence=0.81,
+        custom_metrics=[{ 'name': 'metric1', 'value': 0.99 }])
     feature_schema_id = "feature_schema_id"
     name = "my_feature"
 
@@ -101,30 +109,34 @@ def test_radio():
             'feature_schema_id': None,
             'extra': {},
             'confidence': 0.81,
+            'custom_metrics': [{ 'name': 'metric1', 'value': 0.99 }],
         }
     }
     classification = ClassificationAnnotation(
         value=Radio(answer=answer),
         feature_schema_id=feature_schema_id,
-        name=name)
+        name=name,
+        custom_metrics=[{ 'name': 'metric1', 'value': 0.99 }])
     assert classification.dict() == {
         'name': name,
         'feature_schema_id': feature_schema_id,
         'extra': {},
+        'custom_metrics': [{ 'name': 'metric1', 'value': 0.99 }],
         'value': {
             'answer': {
                 'name': answer.name,
                 'feature_schema_id': None,
                 'extra': {},
-                'confidence': 0.81
-            }
+                'confidence': 0.81,
+                'custom_metrics': [{ 'name': 'metric1', 'value': 0.99 }]
+            },
         },
         'message_id': None,
     }
 
 
 def test_checklist():
-    answer = ClassificationAnswer(name="1", confidence=0.99)
+    answer = ClassificationAnswer(name="1", confidence=0.99, custom_metrics=[{ 'name': 'metric1', 'value': 2 }])
     feature_schema_id = "feature_schema_id"
     name = "my_feature"
 
@@ -140,7 +152,8 @@ def test_checklist():
             'name': answer.name,
             'feature_schema_id': None,
             'extra': {},
-            'confidence': 0.99
+            'confidence': 0.99,
+            'custom_metrics': [{ 'name': 'metric1', 'value': 2 }],
         }]
     }
     classification = ClassificationAnnotation(
@@ -157,7 +170,8 @@ def test_checklist():
                 'name': answer.name,
                 'feature_schema_id': None,
                 'extra': {},
-                'confidence': 0.99
+                'confidence': 0.99,
+                'custom_metrics': [{ 'name': 'metric1', 'value': 2 }],
             }]
         },
         'message_id': None,