Extended operator errors and added forecast specific exceptions

ads/opctl/operator/common/errors.py

@@ -7,6 +7,16 @@
 from ads.opctl.operator import __operators__
 
 
+class OperatorSchemaYamlError(Exception):
+ """Exception raised when there is an issue with the schema."""
+ def __init__(self, error: str):
+ super().__init__(
+ "Invalid operator specification. Check the YAML structure and ensure it "
+ "complies with the required schema for the operator. \n"
+ f"{error}"
+ )
+
+
 class OperatorNotFoundError(Exception):
  def __init__(self, operator: str):
  super().__init__(

ads/opctl/operator/common/operator_config.py

@@ -12,7 +12,8 @@
 
 from ads.common.serializer import DataClassSerializable
 
-from .utils import OperatorValidator
+from ads.opctl.operator.common.utils import OperatorValidator
+from ads.opctl.operator.common.errors import OperatorSchemaYamlError
 
 
 @dataclass(repr=True)
@@ -52,19 +53,15 @@ def _validate_dict(cls, obj_dict: Dict) -> bool:
 
  Raises
  ------
- ValueError
+ ForecastSchemaYamlError
  In case of wrong specification format.
  """
  schema = cls._load_schema()
  validator = OperatorValidator(schema)
  result = validator.validate(obj_dict)
 
  if not result:
- raise ValueError(
- "Invalid operator specification. Check the YAML structure and ensure it "
- "complies with the required schema for the operator. \n"
- f"{json.dumps(validator.errors, indent=2)}"
- )
+ raise OperatorSchemaYamlError(json.dumps(validator.errors, indent=2))
  return True
 
  @classmethod

ads/opctl/operator/lowcode/forecast/errors.py

@@ -0,0 +1,26 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*--
+
+# Copyright (c) 2023 Oracle and/or its affiliates.
+# Licensed under the Universal Permissive License v 1.0 as shown at https://oss.oracle.com/licenses/upl/
+
+class ForecastSchemaYamlError(Exception):
+ """Exception raised when there is an issue with the schema."""
+
+ def __init__(self, error: str):
+ super().__init__(
+ "Invalid forecast operator specification. Check the YAML structure and ensure it "
+ "complies with the required schema for forecast operator. \n"
+ f"{error}"
+ )
+
+
+class ForecastInputDataError(Exception):
+ """Exception raised when there is an issue with input data."""
+
+ def __init__(self, error: str):
+ super().__init__(
+ "Invalid input data. Check the input data and ensure it "
+ "complies with the validation criteria. \n"
+ f"{error}"
+ )

ads/opctl/operator/lowcode/forecast/schema.yaml

@@ -217,7 +217,7 @@ spec:
 
  target_category_columns:
  type: list
- required: false
+ required: true
  schema:
  type: string
 

ads/opctl/operator/lowcode/forecast/utils.py

@@ -22,7 +22,7 @@
 
 from ads.dataset.label_encoder import DataFrameLabelEncoder
 from .const import SupportedModels, MAX_COLUMNS_AUTOMLX
-
+from .errors import ForecastInputDataError, ForecastSchemaYamlError
 
 def _label_encode_dataframe(df, no_encode=set()):
  df_to_encode = df[list(set(df.columns) - no_encode)]
@@ -135,7 +135,7 @@ def _load_data(filename, format, storage_options, columns, **kwargs):
  # keep only these columns, done after load because only CSV supports stream filtering
  data = data[columns]
  return data
- raise ValueError(f"Unrecognized format: {format}")
+ raise ForecastInputDataError(f"Unrecognized format: {format}")
 
 
 def _write_data(data, filename, format, storage_options, index=False, **kwargs):
@@ -147,7 +147,7 @@ def _write_data(data, filename, format, storage_options, index=False, **kwargs):
  return _call_pandas_fsspec(
  write_fn, filename, index=index, storage_options=storage_options
  )
- raise ValueError(f"Unrecognized format: {format}")
+ raise ForecastInputDataError(f"Unrecognized format: {format}")
 
 
 def _merge_category_columns(data, target_category_columns):
@@ -178,7 +178,7 @@ def _clean_data(data, target_column, datetime_column, target_category_columns=No
 
  return df.fillna(0), new_target_columns
 
- raise ValueError(
+ raise ForecastSchemaYamlError(
  f"Either target_columns, target_category_columns, or datetime_column not specified."
  )
 
@@ -297,7 +297,7 @@ def _build_indexed_datasets(
  new_target_columns = list(df_by_target.keys())
  remaining_categories = set(unique_categories) - set(invalid_categories)
  if not len(remaining_categories):
- raise ValueError(
+ raise ForecastInputDataError(
  "Stopping forecast operator as there is no data that meets the validation criteria."
  )
  return df_by_target, new_target_columns, remaining_categories

ads/opctl/operator/runtime/runtime.py

@@ -15,7 +15,7 @@
 from ads.common.extended_enum import ExtendedEnum
 from ads.common.serializer import DataClassSerializable
 from ads.opctl.operator.common.utils import _load_yaml_from_uri
-
+from ads.opctl.operator.common.errors import OperatorSchemaYamlError
 
 class OPERATOR_LOCAL_RUNTIME_TYPE(ExtendedEnum):
  PYTHON = "python"
@@ -56,11 +56,7 @@ def _validate_dict(cls, obj_dict: Dict) -> bool:
  result = validator.validate(obj_dict)
 
  if not result:
- raise ValueError(
- "Invalid runtime specification. Check the YAML structure and ensure it "
- "complies with the required schema for the runtime. \n"
- f"{json.dumps(validator.errors, indent=2)}"
- )
+ raise OperatorSchemaYamlError(json.dumps(validator.errors, indent=2))
  return True