Add warning level to DataValidator

nomenclature/processor/data_validator.py

@@ -1,5 +1,6 @@
 import logging
 import textwrap
+from enum import Enum
 from pathlib import Path
 
 import yaml
@@ -16,7 +17,18 @@
 logger = logging.getLogger(__name__)
 
 
-class DataValidationCriteriaValue(IamcDataFilter):
+class WarningEnum(str, Enum):
+    high = "high"
+    medium = "medium"
+    low = "low"
+    error = "error"
+
+
+class DataValidationCriteria(IamcDataFilter):
+    warning_level: WarningEnum = WarningEnum.error
+
+
+class DataValidationCriteriaValue(DataValidationCriteria):
     value: float
     rtol: float = 0.0
     atol: float = 0.0
@@ -38,19 +50,19 @@ def validation_args(self):
         return self.model_dump(
             exclude_none=True,
             exclude_unset=True,
-            exclude=["value", "rtol", "atol"],
+            exclude=["warning_level", "value", "rtol", "atol"],
         )
 
     @property
     def criteria(self):
         return self.model_dump(
             exclude_none=True,
             exclude_unset=True,
-            exclude=["lower_bound", "upper_bound"],
+            exclude=["warning_level", "lower_bound", "upper_bound"],
         )
 
 
-class DataValidationCriteriaBounds(IamcDataFilter):
+class DataValidationCriteriaBounds(DataValidationCriteria):
     upper_bound: float | None = None
     lower_bound: float | None = None
 
@@ -64,6 +76,14 @@ def check_validation_criteria_exist(self):
     def validation_args(self):
         return self.criteria
 
+    @property
+    def criteria(self):
+        return self.model_dump(
+            exclude_none=True,
+            exclude_unset=True,
+            exclude=["warning_level"],
+        )
+
 
 class DataValidator(Processor):
     """Processor for validating IAMC datapoints"""
@@ -89,31 +109,39 @@ def from_file(cls, file: Path | str) -> "DataValidator":
         return cls(file=file, criteria_items=content)
 
     def apply(self, df: IamDataFrame) -> IamDataFrame:
-        error_list = []
+        fail_list = []
+        error = False
 
         with adjust_log_level():
             for item in self.criteria_items:
                 failed_validation = df.validate(**item.validation_args)
                 if failed_validation is not None:
-                    error_list.append(
-                        "  Criteria: "
-                        + ", ".join(
-                            [f"{key}: {value}" for key, value in item.criteria.items()]
-                        )
+                    criteria_msg = "  Criteria: " + ", ".join(
+                        [f"{key}: {value}" for key, value in item.criteria.items()]
                     )
-                    error_list.append(
+                    failed_validation["warning_level"] = item.warning_level.value
+                    if item.warning_level == WarningEnum.error:
+                        error = True
+                    fail_list.append(criteria_msg)
+                    fail_list.append(
                         textwrap.indent(str(failed_validation), prefix="    ") + "\n"
                     )
-
-            if error_list:
-                logger.error(
-                    "Failed data validation (file %s):\n%s",
-                    get_relative_path(self.file),
-                    "\n".join(error_list),
+            fail_msg = "(file %s):\n" % get_relative_path(self.file)
+            if error:
+                fail_msg = (
+                    "Data validation with error(s)/warning(s) "
+                    + fail_msg
+                    + "\n".join(fail_list)
                 )
+                logger.error(fail_msg)
                 raise ValueError(
                     "Data validation failed. Please check the log for details."
                 )
+            if fail_list:
+                fail_msg = (
+                    "Data validation with warning(s) " + fail_msg + "\n".join(fail_list)
+                )
+                logger.warning(fail_msg)
         return df
 
     def validate_with_definition(self, dsd: DataStructureDefinition) -> None:

tests/data/validation/validate_data/validate_warning.yaml

@@ -0,0 +1,13 @@
+ - variable: Primary Energy
+   year: 2010
+   upper_bound: 2.5
+   lower_bound: 1
+   warning_level: low
+ - variable: Primary Energy
+   year: 2010
+   upper_bound: 5
+   lower_bound: 1
+ - variable: Primary Energy|Coal
+   year: 2010
+   upper_bound: 5
+   lower_bound: 1

tests/test_validate_data.py

@@ -102,23 +102,53 @@ def test_DataValidator_apply_fails(simple_df, file, item_1, item_2, item_3, capl
     data_file = DATA_VALIDATION_TEST_DIR / f"validate_data_fails_{file}.yaml"
     data_validator = DataValidator.from_file(data_file)
 
-    failed_validation_message = f"""Failed data validation (file {data_file.relative_to(Path.cwd())}):
+    failed_validation_message = (
+        "Data validation with error(s)/warning(s) "
+        f"""(file {data_file.relative_to(Path.cwd())}):
   Criteria: variable: ['Primary Energy'], {item_1}
-         model scenario region        variable   unit  year  value
-    0  model_a   scen_a  World  Primary Energy  EJ/yr  2010    6.0
-    1  model_a   scen_b  World  Primary Energy  EJ/yr  2010    7.0
+         model scenario region        variable   unit  year  value warning_level
+    0  model_a   scen_a  World  Primary Energy  EJ/yr  2010    6.0         error
+    1  model_a   scen_b  World  Primary Energy  EJ/yr  2010    7.0         error
 
   Criteria: variable: ['Primary Energy|Coal'], {item_2}
-         model scenario region             variable   unit  year  value
-    0  model_a   scen_a  World  Primary Energy|Coal  EJ/yr  2005    0.5
+         model scenario region  ...  year value  warning_level
+    0  model_a   scen_a  World  ...  2005   0.5          error
+
+    [1 rows x 8 columns]
 
   Criteria: variable: ['Primary Energy'], year: [2005], {item_3}
-         model scenario region        variable   unit  year  value
-    0  model_a   scen_a  World  Primary Energy  EJ/yr  2005    1.0
-    1  model_a   scen_b  World  Primary Energy  EJ/yr  2005    2.0"""
+         model scenario region        variable   unit  year  value warning_level
+    0  model_a   scen_a  World  Primary Energy  EJ/yr  2005    1.0         error
+    1  model_a   scen_b  World  Primary Energy  EJ/yr  2005    2.0         error"""
+    )
 
     with pytest.raises(ValueError, match="Data validation failed"):
         data_validator.apply(simple_df)
 
     # check if the log message contains the correct information
     assert failed_validation_message in caplog.text
+
+
+def test_DataValidator_validate_with_warning(simple_df, caplog):
+    data_validator = DataValidator.from_file(
+        DATA_VALIDATION_TEST_DIR / "validate_warning.yaml"
+    )
+    with pytest.raises(ValueError, match="Data validation failed"):
+        data_validator.apply(simple_df)
+
+    failed_validation_message = (
+        "Data validation with error(s)/warning(s) "
+        f"""(file {(DATA_VALIDATION_TEST_DIR / "validate_warning.yaml").relative_to(Path.cwd())}):
+  Criteria: variable: ['Primary Energy'], year: [2010], upper_bound: 2.5, lower_bound: 1.0
+         model scenario region        variable   unit  year  value warning_level
+    0  model_a   scen_a  World  Primary Energy  EJ/yr  2010    6.0           low
+    1  model_a   scen_b  World  Primary Energy  EJ/yr  2010    7.0           low
+
+  Criteria: variable: ['Primary Energy'], year: [2010], upper_bound: 5.0, lower_bound: 1.0
+         model scenario region        variable   unit  year  value warning_level
+    0  model_a   scen_a  World  Primary Energy  EJ/yr  2010    6.0         error
+    1  model_a   scen_b  World  Primary Energy  EJ/yr  2010    7.0         error"""
+    )
+
+    # only prints two of three criteria in df to be validated
+    assert failed_validation_message in caplog.text