chore(components/google-cloud): Update args and location of ForecastingPreprocessingOp and ForecastingValidationOp

components/google-cloud/google_cloud_pipeline_components/experimental/__init__.py

@@ -13,67 +13,5 @@
 # limitations under the License.
 """Google Cloud Pipeline Experimental Components."""
 
-import os
 from .custom_job.custom_job import run_as_vertex_ai_custom_job
-from kfp.components import load_component_from_file
 from .tensorflow_probability.anomaly_detection import tfp_anomaly_detection
-
-__all__ = [
-    'ForecastingPreprocessingOp',
-    'ForecastingValidationOp',
-]
-
-
-def ForecastingPreprocessingOp(**kwargs):
-  """Preprocesses BigQuery tables for training or prediction.
-
-  Creates a BigQuery table for training or prediction based on the input tables.
-  For training, a primary table is required. Optionally, you can include some
-  attribute tables. For prediction, you need to include all the tables that were
-  used in the training, plus a plan table.
-
-  Accepted kwargs:
-      input_tables (str): Serialized Json array that specifies input BigQuery
-      tables and specs.
-      preprocess_metadata (str): Path to a file used by the component to save
-      the output BigQuery table uri and column metadata. Do not set value to
-      this arg. The value will be automatically overriden by Managed Pipeline.
-
-  Args:
-    **kwargs: See Accepted kwargs.
-
-  Returns:
-    None
-  """
-  # TODO(yzhaozh): update the documentation with Json object reference and
-  # example.
-  return load_component_from_file(
-      os.path.join(
-          os.path.dirname(__file__),
-          'forecasting/preprocess/component.yaml'))(**kwargs)
-
-
-def ForecastingValidationOp(**kwargs):
-  """Validates BigQuery tables for training or prediction.
-
-  Validates BigQuery tables for training or prediction based on predefined
-  requirements. For training, a primary table is required. Optionally, you
-  can include some attribute tables. For prediction, you need to include all
-  the tables that were used in the training, plus a plan table.
-
-  Accepted kwargs:
-      input_tables (str): Serialized Json array that specifies input BigQuery
-      tables and specs.
-
-  Args:
-    **kwargs: See Accepted kwargs.
-
-  Returns:
-    None
-  """
-  # TODO(yzhaozh): update the documentation with Json object reference, example
-  # and predefined validation requirements.
-  return load_component_from_file(
-      os.path.join(
-          os.path.dirname(__file__),
-          'forecasting/validate/component.yaml'))(**kwargs)

components/google-cloud/google_cloud_pipeline_components/experimental/forecasting/__init__.py

@@ -0,0 +1,70 @@
+# Copyright 2021 The Kubeflow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Google Cloud Pipeline Experimental Forecasting Components."""
+
+import os
+from kfp.components import load_component_from_file
+
+__all__ = [
+    'ForecastingPreprocessingOp',
+    'ForecastingValidationOp',
+]
+
+
+def ForecastingPreprocessingOp(input_tables: str):
+  """Preprocesses BigQuery tables for training or prediction.
+
+  Creates a BigQuery table for training or prediction based on the input tables.
+  For training, a primary table is required. Optionally, you can include some
+  attribute tables. For prediction, you need to include all the tables that were
+  used in the training, plus a plan table.
+
+  Args:
+    input_tables (str): Serialized Json array that specifies input BigQuery
+    tables and specs.
+
+  Returns:
+    None
+  """
+  # TODO(yzhaozh): update the documentation with Json object reference and
+  # example.
+  return load_component_from_file(
+      os.path.join(
+          os.path.dirname(__file__), 'preprocess/component.yaml'))(
+              input_tables=input_tables)
+
+
+def ForecastingValidationOp(input_tables: str, validation_theme: str):
+  """Validates BigQuery tables for training or prediction.
+
+  Validates BigQuery tables for training or prediction based on predefined
+  requirements. For training, a primary table is required. Optionally, you
+  can include some attribute tables. For prediction, you need to include all
+  the tables that were used in the training, plus a plan table.
+
+  Args:
+    input_tables (str): Serialized Json array that specifies input BigQuery
+    tables and specs.
+    validation_theme (str): Theme to use for validating the BigQuery tables.
+    Acceptable values are FORECASTING_TRAINING and FORECASTING_PREDICTION.
+
+  Returns:
+    None
+  """
+  # TODO(yzhaozh): update the documentation with Json object reference, example
+  # and predefined validation requirements.
+  return load_component_from_file(
+      os.path.join(
+          os.path.dirname(__file__), 'validate/component.yaml'))(
+              input_tables=input_tables, validation_theme=validation_theme)