feat: add error handling for validation of class files in score calibration creation and modification

Author: bencap

src/mavedb/routers/score_calibrations.py

@@ -26,6 +26,7 @@
 from mavedb.lib.score_sets import csv_data_to_df
 from mavedb.lib.validation.constants.general import calibration_class_column_name, calibration_variant_column_name
 from mavedb.lib.validation.dataframe.calibration import validate_and_standardize_calibration_classes_dataframe
+from mavedb.lib.validation.exceptions import ValidationError
 from mavedb.models.score_calibration import ScoreCalibration
 from mavedb.models.score_set import ScoreSet
 from mavedb.view_models import score_calibration
@@ -290,10 +291,16 @@ async def create_score_calibration_route(
                 status_code=400, detail=f"Error decoding file: {e}. Ensure the file has correct values."
             )
 
-        standardized_classes_df = validate_and_standardize_calibration_classes_dataframe(
-            db, score_set, calibration, classes_df
-        )
-        variant_classes = variant_classification_df_to_dict(standardized_classes_df)
+        try:
+            standardized_classes_df = validate_and_standardize_calibration_classes_dataframe(
+                db, score_set, calibration, classes_df
+            )
+            variant_classes = variant_classification_df_to_dict(standardized_classes_df)
+        except ValidationError as e:
+            raise HTTPException(
+                status_code=422,
+                detail=[{"loc": [e.custom_loc or "classesFile"], "msg": str(e), "type": "value_error"}],
+            )
 
     created_calibration = await create_score_calibration_in_score_set(
         db, calibration, user_data.user, variant_classes if classes_file else None
@@ -438,10 +445,16 @@ async def modify_score_calibration_route(
                 status_code=400, detail=f"Error decoding file: {e}. Ensure the file has correct values."
             )
 
-        standardized_classes_df = validate_and_standardize_calibration_classes_dataframe(
-            db, score_set, calibration_update, classes_df
-        )
-        variant_classes = variant_classification_df_to_dict(standardized_classes_df)
+        try:
+            standardized_classes_df = validate_and_standardize_calibration_classes_dataframe(
+                db, score_set, calibration_update, classes_df
+            )
+            variant_classes = variant_classification_df_to_dict(standardized_classes_df)
+        except ValidationError as e:
+            raise HTTPException(
+                status_code=422,
+                detail=[{"loc": [e.custom_loc or "classesFile"], "msg": str(e), "type": "value_error"}],
+            )
 
     updated_calibration = await modify_score_calibration(
         db, item, calibration_update, user_data.user, variant_classes if classes_file else None

tests/validation/dataframe/test_calibration.py

@@ -80,15 +80,15 @@ def test_validate_and_standardize_calibration_classes_dataframe_success(self, mo
         mock_dependencies["validate_data_column"].assert_called_once()
 
     def test_validate_and_standardize_calibration_classes_dataframe_not_class_based(self):
-        """Test ValueError when calibration is not class-based."""
+        """Test ValidationError when calibration is not class-based."""
         mock_db = Mock()
         mock_score_set = Mock()
         mock_calibration = Mock()
         mock_calibration.class_based = False
         input_df = pd.DataFrame({"variant": ["var1"], "class": ["A"]})
 
         with pytest.raises(
-            ValueError,
+            ValidationError,
             match="Calibration classes file can only be provided for functional classification calibrations.",
         ):
             validate_and_standardize_calibration_classes_dataframe(mock_db, mock_score_set, mock_calibration, input_df)
@@ -170,7 +170,7 @@ def test_validate_and_standardize_calibration_classes_dataframe_invalid_classes(
         mock_calibration.functional_classifications = None
 
         with pytest.raises(
-            ValueError, match="Calibration must have functional classifications defined for class validation."
+            ValidationError, match="Calibration must have functional classifications defined for class validation."
         ):
             validate_and_standardize_calibration_classes_dataframe(mock_db, mock_score_set, mock_calibration, input_df)
 
@@ -634,24 +634,24 @@ def test_validate_calibration_classes_success(self):
         validate_calibration_classes(calibration, classes)
 
     def test_validate_calibration_classes_no_functional_classifications(self):
-        """Test ValueError when calibration has no functional classifications."""
+        """Test ValidationError when calibration has no functional classifications."""
         calibration = Mock(spec=score_calibration.ScoreCalibrationCreate)
         calibration.functional_classifications = None
         classes = pd.Series(["class_a", "class_b"])
 
         with pytest.raises(
-            ValueError, match="Calibration must have functional classifications defined for class validation."
+            ValidationError, match="Calibration must have functional classifications defined for class validation."
         ):
             validate_calibration_classes(calibration, classes)
 
     def test_validate_calibration_classes_empty_functional_classifications(self):
-        """Test ValueError when calibration has empty functional classifications."""
+        """Test ValidationError when calibration has empty functional classifications."""
         calibration = Mock(spec=score_calibration.ScoreCalibrationCreate)
         calibration.functional_classifications = []
         classes = pd.Series(["class_a", "class_b"])
 
         with pytest.raises(
-            ValueError, match="Calibration must have functional classifications defined for class validation."
+            ValidationError, match="Calibration must have functional classifications defined for class validation."
         ):
             validate_calibration_classes(calibration, classes)