google_cloud_pipeline_components.experimental.forecasting package

Google Cloud Pipeline Experimental Forecasting Components.

google_cloud_pipeline_components.experimental.forecasting.ForecastingPrepareDataForTrainOp(input_tables: str, preprocess_metadata: str, model_feature_columns: str = None)

Prepare data for train Prepares the parameters for the training step.

google_cloud_pipeline_components.experimental.forecasting.ForecastingPreprocessingOp()

Forecasting Preprocessing 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:

project (str):

The GCP project id that runs the pipeline.

input_tables (str):

Serialized Json array that specifies input BigQuery tables and specs.

preprocessing_bigquery_dataset (str):

Optional BigQuery dataset to save the preprocessing result BigQuery table. If not present, a new dataset will be created by the component.

Returns:

preprocess_metadata (str)

google_cloud_pipeline_components.experimental.forecasting.ForecastingValidationOp()

Forecasting Validation 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