AutoML Forecasting¶

Experimental AutoML forecasting components.

Components:

`ForecastingEnsembleOp`(project, location, ...)	Ensembles AutoML Forecasting models.
`ForecastingStage1TunerOp`(project, location, ...)	Searches AutoML Forecasting architectures and selects the top trials.
`ForecastingStage2TunerOp`(project, location, ...)	Tunes AutoML Forecasting models and selects top trials.

Functions:

`get_learn_to_learn_forecasting_pipeline_and_parameters`(*, ...)	Returns l2l_forecasting pipeline and formatted parameters.
`get_sequence_to_sequence_forecasting_pipeline_and_parameters`(*, ...)	Returns seq2seq forecasting pipeline and formatted parameters.
`get_temporal_fusion_transformer_forecasting_pipeline_and_parameters`(*, ...)	Returns tft_forecasting pipeline and formatted parameters.
`get_time_series_dense_encoder_forecasting_pipeline_and_parameters`(*, ...)	Returns timeseries_dense_encoder_forecasting pipeline and parameters.

preview.automl.forecasting.ForecastingEnsembleOp(project: str, location: str, root_dir: str, transform_output: dsl.Input[system.Artifact], metadata: dsl.Input[system.Artifact], tuning_result_input: dsl.Input[system.Artifact], instance_baseline: dsl.Input[system.Artifact], instance_schema_path: dsl.Input[system.Artifact], prediction_image_uri: str, gcp_resources: dsl.OutputPath(str), model_architecture: dsl.Output[system.Artifact], example_instance: dsl.Output[system.Artifact], unmanaged_container_model: dsl.Output[google.UnmanagedContainerModel], explanation_metadata: dsl.OutputPath(dict), explanation_metadata_artifact: dsl.Output[system.Artifact], explanation_parameters: dsl.OutputPath(dict), encryption_spec_key_name: str | None = '')¶

Ensembles AutoML Forecasting models.

Parameters¶:

project: str¶: Project to run the job in.
location: str¶: Region to run the job in.
root_dir: str¶: The Cloud Storage path to store the output.
transform_output: dsl.Input[system.Artifact]¶: The transform output artifact.
metadata: dsl.Input[system.Artifact]¶: The tabular example gen metadata.
tuning_result_input: dsl.Input[system.Artifact]¶: AutoML Tabular tuning result.
instance_baseline: dsl.Input[system.Artifact]¶: The instance baseline used to calculate explanations.
instance_schema_path: dsl.Input[system.Artifact]¶: The path to the instance schema, describing the input data for the tf_model at serving time.
encryption_spec_key_name: str | None = ''¶: Customer-managed encryption key.
prediction_image_uri: str¶: URI of the Docker image to be used as the container for serving predictions. This URI must identify an image in Artifact Registry or Container Registry.

Returns¶:

gcp_resources: dsl.OutputPath(str): GCP resources created by this component. For more details, see https://github.com/kubeflow/pipelines/blob/master/components/google-cloud/google_cloud_pipeline_components/proto/README.md.
model_architecture: dsl.Output[system.Artifact]: The architecture of the output model.
nmanaged_container_model: dsl.Output[google.UnmanagedContainerModel]: Model information needed to perform batch prediction.
explanation_metadata: dsl.OutputPath(dict): The explanation metadata used by Vertex online and batch explanations.
explanation_metadata_artifact: dsl.Output[system.Artifact]: The explanation metadata used by Vertex online and batch explanations in the format of a KFP Artifact.
explanation_parameters: dsl.OutputPath(dict): The explanation parameters used by Vertex online and batch explanations.
example_instance: dsl.Output[system.Artifact]: An example instance which may be used as an input for predictions.

preview.automl.forecasting.ForecastingStage1TunerOp(project: str, location: str, root_dir: str, num_selected_trials: int, deadline_hours: float, num_parallel_trials: int, single_run_max_secs: int, metadata: dsl.Input[system.Artifact], transform_output: dsl.Input[system.Artifact], materialized_train_split: dsl.Input[system.Artifact], materialized_eval_split: dsl.Input[system.Artifact], gcp_resources: dsl.OutputPath(str), tuning_result_output: dsl.Output[system.Artifact], study_spec_parameters_override: list | None = [], worker_pool_specs_override_json: list | None = [], reduce_search_space_mode: str | None = 'regular', encryption_spec_key_name: str | None = '')¶

Searches AutoML Forecasting architectures and selects the top trials.

Parameters¶:

project: str¶: Project to run hyperparameter tuning.
location: str¶: Location for running the hyperparameter tuning.
root_dir: str¶: The Cloud Storage location to store the output.
study_spec_parameters_override: list | None = []¶: JSON study spec. E.g., [{“parameter_id”: “activation”,”categorical_value_spec”: {“values”: [“tanh”]}}]
worker_pool_specs_override_json: list | None = []¶: JSON worker pool specs. E.g., [{“machine_spec”: {“machine_type”: “n1-standard-16”}},{},{},{“machine_spec”: {“machine_type”: “n1-standard-16”}}]
reduce_search_space_mode: str | None = 'regular'¶: The reduce search space mode. Possible values: “regular” (default), “minimal”, “full”.
num_selected_trials: int¶: Number of selected trials. The number of weak learners in the final model is 5 * num_selected_trials.
deadline_hours: float¶: Number of hours the hyperparameter tuning should run.
num_parallel_trials: int¶: Number of parallel training trials.
single_run_max_secs: int¶: Max number of seconds each training trial runs.
metadata: dsl.Input[system.Artifact]¶: The tabular example gen metadata.
transform_output: dsl.Input[system.Artifact]¶: The transform output artifact.
materialized_train_split: dsl.Input[system.Artifact]¶: The materialized train split.
materialized_eval_split: dsl.Input[system.Artifact]¶: The materialized eval split.
encryption_spec_key_name: str | None = ''¶: Customer-managed encryption key.

Returns¶:

gcp_resources: dsl.OutputPath(str): GCP resources created by this component. For more details, see https://github.com/kubeflow/pipelines/blob/master/components/google-cloud/google_cloud_pipeline_components/proto/README.md.
ning_result_output: dsl.Output[system.Artifact]: The trained model and architectures.

preview.automl.forecasting.ForecastingStage2TunerOp(project: str, location: str, root_dir: str, num_selected_trials: int, deadline_hours: float, num_parallel_trials: int, single_run_max_secs: int, metadata: dsl.Input[system.Artifact], transform_output: dsl.Input[system.Artifact], materialized_train_split: dsl.Input[system.Artifact], materialized_eval_split: dsl.Input[system.Artifact], tuning_result_input_path: dsl.Input[system.Artifact], gcp_resources: dsl.OutputPath(str), tuning_result_output: dsl.Output[system.Artifact], worker_pool_specs_override_json: list | None = [], encryption_spec_key_name: str | None = '')¶

Tunes AutoML Forecasting models and selects top trials.

Parameters¶:

project: str¶: Project to run stage 2 tuner.
location: str¶: Cloud region for running the component: us-central1).
root_dir: str¶: The Cloud Storage location to store the output.
worker_pool_specs_override_json: list | None = []¶: JSON worker pool specs. E.g., [{“machine_spec”: {“machine_type”: “n1-standard-16”}},{},{},{“machine_spec”: {“machine_type”: “n1-standard-16”}}]
num_selected_trials: int¶: Number of selected trials. The number of weak learners in the final model.
deadline_hours: float¶: Number of hours the cross-validation trainer should run.
num_parallel_trials: int¶: Number of parallel training trials.
single_run_max_secs: int¶: Max number of seconds each training trial runs.
metadata: dsl.Input[system.Artifact]¶: The forecasting example gen metadata.
transform_output: dsl.Input[system.Artifact]¶: The transform output artifact.
materialized_train_split: dsl.Input[system.Artifact]¶: The materialized train split.
materialized_eval_split: dsl.Input[system.Artifact]¶: The materialized eval split.
encryption_spec_key_name: str | None = ''¶: Customer-managed encryption key.
tuning_result_input_path: dsl.Input[system.Artifact]¶: Path to the json of hyperparameter tuning results to use when evaluating models.

Returns¶:

gcp_resources: dsl.OutputPath(str): GCP resources created by this component. For more details, see https://github.com/kubeflow/pipelines/blob/master/components/google-cloud/google_cloud_pipeline_components/proto/README.md.
ning_result_output: dsl.Output[system.Artifact]: The trained (private) model artifact paths and their hyperparameters.

Returns l2l_forecasting pipeline and formatted parameters.

Parameters¶:

project: str¶: The GCP project that runs the pipeline components.
location: str¶: The GCP region that runs the pipeline components.
root_dir: str¶: The root GCS directory for the pipeline components.
target_column: str¶: The target column name.
optimization_objective: str¶: “minimize-rmse”, “minimize-mae”, “minimize-rmsle”, “minimize-rmspe”, “minimize-wape-mae”, “minimize-mape”, or “minimize-quantile-loss”.
transformations: dict[str, list[str]]¶: Dict mapping auto and/or type-resolutions to feature columns. The supported types are: auto, categorical, numeric, text, and timestamp.
train_budget_milli_node_hours: float¶: The train budget of creating this model, expressed in milli node hours i.e. 1,000 value in this field means 1 node hour.
time_column: str¶: The column that indicates the time.
time_series_identifier_columns: list[str]¶: The columns which distinguish different time series.
time_series_identifier_column: str | None = None¶: [Deprecated] The column which distinguishes different time series.
time_series_attribute_columns: list[str] | None = None¶: The columns that are invariant across the same time series.
available_at_forecast_columns: list[str] | None = None¶: The columns that are available at the forecast time.
unavailable_at_forecast_columns: list[str] | None = None¶: The columns that are unavailable at the forecast time.
forecast_horizon: int | None = None¶: The length of the horizon.
context_window: int | None = None¶: The length of the context window.
evaluated_examples_bigquery_path: str | None = None¶: The bigquery dataset to write the predicted examples into for evaluation, in the format bq://project.dataset.
window_predefined_column: str | None = None¶: The column that indicate the start of each window.
window_stride_length: int | None = None¶: The stride length to generate the window.
window_max_count: int | None = None¶: The maximum number of windows that will be generated.
holiday_regions: list[str] | None = None¶: The geographical regions where the holiday effect is applied in modeling.
stage_1_num_parallel_trials: int | None = None¶: Number of parallel trails for stage 1.
stage_1_tuning_result_artifact_uri: str | None = None¶: The stage 1 tuning result artifact GCS URI.
stage_2_num_parallel_trials: int | None = None¶: Number of parallel trails for stage 2.
num_selected_trials: int | None = None¶: Number of selected trails.
data_source_csv_filenames: str | None = None¶: A string that represents a list of comma separated CSV filenames.
data_source_bigquery_table_path: str | None = None¶: The BigQuery table path of format bq://bq_project.bq_dataset.bq_table
predefined_split_key: str | None = None¶: The predefined_split column name.
training_fraction: float | None = None¶: The training fraction.
validation_fraction: float | None = None¶: The validation fraction.
test_fraction: float | None = None¶: The test fraction.
weight_column: str | None = None¶: The weight column name.
dataflow_service_account: str | None = None¶: The full service account name.
dataflow_subnetwork: str | None = None¶: The dataflow subnetwork.
dataflow_use_public_ips: bool = True¶: True to enable dataflow public IPs.
feature_transform_engine_bigquery_staging_full_dataset_id: str = ''¶: The full id of the feature transform engine staging dataset.
feature_transform_engine_dataflow_machine_type: str = 'n1-standard-16'¶: The dataflow machine type of the feature transform engine.
feature_transform_engine_dataflow_max_num_workers: int = 10¶: The max number of dataflow workers of the feature transform engine.
feature_transform_engine_dataflow_disk_size_gb: int = 40¶: The disk size of the dataflow workers of the feature transform engine.
evaluation_batch_predict_machine_type: str = 'n1-standard-16'¶: Machine type for the batch prediction job in evaluation, such as ‘n1-standard-16’.
evaluation_batch_predict_starting_replica_count: int = 25¶: Number of replicas to use in the batch prediction cluster at startup time.
evaluation_batch_predict_max_replica_count: int = 25¶: The maximum count of replicas the batch prediction job can scale to.
evaluation_dataflow_machine_type: str = 'n1-standard-16'¶: Machine type for the dataflow job in evaluation, such as ‘n1-standard-16’.
evaluation_dataflow_max_num_workers: int = 25¶: Maximum number of dataflow workers.
evaluation_dataflow_disk_size_gb: int = 50¶: The disk space in GB for dataflow.
study_spec_parameters_override: list[dict[str, Any]] | None = None¶: The list for overriding study spec.
stage_1_tuner_worker_pool_specs_override: dict[str, Any] | None = None¶: The dictionary for overriding stage 1 tuner worker pool spec.
stage_2_trainer_worker_pool_specs_override: dict[str, Any] | None = None¶: The dictionary for overriding stage 2 trainer worker pool spec.
enable_probabilistic_inference: bool = False¶: If probabilistic inference is enabled, the model will fit a distribution that captures the uncertainty of a prediction. If quantiles are specified, then the quantiles of the distribution are also returned.
quantiles: list[float] | None = None¶: Quantiles to use for probabilistic inference. Up to 5 quantiles are allowed of values between 0 and 1, exclusive. Represents the quantiles to use for that objective. Quantiles must be unique.
encryption_spec_key_name: str | None = None¶: The KMS key name.
model_display_name: str | None = None¶: Optional display name for model.
model_description: str | None = None¶: Optional description.
run_evaluation: bool = True¶: True to evaluate the ensembled model on the test split.
group_columns: list[str] | None = None¶: A list of time series attribute column names that define the time series hierarchy.
group_total_weight: float = 0.0¶: The weight of the loss for predictions aggregated over time series in the same group.
temporal_total_weight: float = 0.0¶: The weight of the loss for predictions aggregated over the horizon for a single time series.
group_temporal_total_weight: float = 0.0¶: The weight of the loss for predictions aggregated over both the horizon and time series in the same hierarchy group.

Returns¶:

Tuple of pipeline_definition_path and parameter_values.

Returns seq2seq forecasting pipeline and formatted parameters.

Parameters¶:

project: str¶: The GCP project that runs the pipeline components.
location: str¶: The GCP region that runs the pipeline components.
root_dir: str¶: The root GCS directory for the pipeline components.
target_column: str¶: The target column name.
optimization_objective: str¶: “minimize-rmse”, “minimize-mae”, “minimize-rmsle”, “minimize-rmspe”, “minimize-wape-mae”, “minimize-mape”, or “minimize-quantile-loss”.
transformations: dict[str, list[str]]¶: Dict mapping auto and/or type-resolutions to feature columns. The supported types are: auto, categorical, numeric, text, and timestamp.
train_budget_milli_node_hours: float¶: The train budget of creating this model, expressed in milli node hours i.e. 1,000 value in this field means 1 node hour.
time_column: str¶: The column that indicates the time.
time_series_identifier_columns: list[str]¶: The columns which distinguish different time series.
time_series_identifier_column: str | None = None¶: [Deprecated] The column which distinguishes different time series.
time_series_attribute_columns: list[str] | None = None¶: The columns that are invariant across the same time series.
available_at_forecast_columns: list[str] | None = None¶: The columns that are available at the forecast time.
unavailable_at_forecast_columns: list[str] | None = None¶: The columns that are unavailable at the forecast time.
forecast_horizon: int | None = None¶: The length of the horizon.
context_window: int | None = None¶: The length of the context window.
evaluated_examples_bigquery_path: str | None = None¶: The bigquery dataset to write the predicted examples into for evaluation, in the format bq://project.dataset.
window_predefined_column: str | None = None¶: The column that indicate the start of each window.
window_stride_length: int | None = None¶: The stride length to generate the window.
window_max_count: int | None = None¶: The maximum number of windows that will be generated.
holiday_regions: list[str] | None = None¶: The geographical regions where the holiday effect is applied in modeling.
stage_1_num_parallel_trials: int | None = None¶: Number of parallel trails for stage 1.
stage_1_tuning_result_artifact_uri: str | None = None¶: The stage 1 tuning result artifact GCS URI.
stage_2_num_parallel_trials: int | None = None¶: Number of parallel trails for stage 2.
num_selected_trials: int | None = None¶: Number of selected trails.
data_source_csv_filenames: str | None = None¶: A string that represents a list of comma separated CSV filenames.
data_source_bigquery_table_path: str | None = None¶: The BigQuery table path of format bq://bq_project.bq_dataset.bq_table
predefined_split_key: str | None = None¶: The predefined_split column name.
training_fraction: float | None = None¶: The training fraction.
validation_fraction: float | None = None¶: The validation fraction.
test_fraction: float | None = None¶: The test fraction.
weight_column: str | None = None¶: The weight column name.
dataflow_service_account: str | None = None¶: The full service account name.
dataflow_subnetwork: str | None = None¶: The dataflow subnetwork.
dataflow_use_public_ips: bool = True¶: True to enable dataflow public IPs.
feature_transform_engine_bigquery_staging_full_dataset_id: str = ''¶: The full id of the feature transform engine staging dataset.
feature_transform_engine_dataflow_machine_type: str = 'n1-standard-16'¶: The dataflow machine type of the feature transform engine.
feature_transform_engine_dataflow_max_num_workers: int = 10¶: The max number of dataflow workers of the feature transform engine.
feature_transform_engine_dataflow_disk_size_gb: int = 40¶: The disk size of the dataflow workers of the feature transform engine.
evaluation_batch_predict_machine_type: str = 'n1-standard-16'¶: Machine type for the batch prediction job in evaluation, such as ‘n1-standard-16’.
evaluation_batch_predict_starting_replica_count: int = 25¶: Number of replicas to use in the batch prediction cluster at startup time.
evaluation_batch_predict_max_replica_count: int = 25¶: The maximum count of replicas the batch prediction job can scale to.
evaluation_dataflow_machine_type: str = 'n1-standard-16'¶: Machine type for the dataflow job in evaluation, such as ‘n1-standard-16’.
evaluation_dataflow_max_num_workers: int = 25¶: Maximum number of dataflow workers.
evaluation_dataflow_disk_size_gb: int = 50¶: The disk space in GB for dataflow.
study_spec_parameters_override: list[dict[str, Any]] | None = None¶: The list for overriding study spec.
stage_1_tuner_worker_pool_specs_override: dict[str, Any] | None = None¶: The dictionary for overriding stage 1 tuner worker pool spec.
stage_2_trainer_worker_pool_specs_override: dict[str, Any] | None = None¶: The dictionary for overriding stage 2 trainer worker pool spec.
encryption_spec_key_name: str | None = None¶: The KMS key name.
model_display_name: str | None = None¶: Optional display name for model.
model_description: str | None = None¶: Optional description.
run_evaluation: bool = True¶: True to evaluate the ensembled model on the test split.

Returns¶:

Tuple of pipeline_definition_path and parameter_values.

Returns tft_forecasting pipeline and formatted parameters.

Parameters¶:

project: str¶: The GCP project that runs the pipeline components.
location: str¶: The GCP region that runs the pipeline components.
root_dir: str¶: The root GCS directory for the pipeline components.
target_column: str¶: The target column name.
optimization_objective: str¶: “minimize-rmse”, “minimize-mae”, “minimize-rmsle”, “minimize-rmspe”, “minimize-wape-mae”, “minimize-mape”, or “minimize-quantile-loss”.
transformations: dict[str, list[str]]¶: Dict mapping auto and/or type-resolutions to feature columns. The supported types are: auto, categorical, numeric, text, and timestamp.
train_budget_milli_node_hours: float¶: The train budget of creating this model, expressed in milli node hours i.e. 1,000 value in this field means 1 node hour.
time_column: str¶: The column that indicates the time.
time_series_identifier_columns: list[str]¶: The columns which distinguish different time series.
time_series_identifier_column: str | None = None¶: [Deprecated] The column which distinguishes different time series.
time_series_attribute_columns: list[str] | None = None¶: The columns that are invariant across the same time series.
available_at_forecast_columns: list[str] | None = None¶: The columns that are available at the forecast time.
unavailable_at_forecast_columns: list[str] | None = None¶: The columns that are unavailable at the forecast time.
forecast_horizon: int | None = None¶: The length of the horizon.
context_window: int | None = None¶: The length of the context window.
evaluated_examples_bigquery_path: str | None = None¶: The bigquery dataset to write the predicted examples into for evaluation, in the format bq://project.dataset.
window_predefined_column: str | None = None¶: The column that indicate the start of each window.
window_stride_length: int | None = None¶: The stride length to generate the window.
window_max_count: int | None = None¶: The maximum number of windows that will be generated.
holiday_regions: list[str] | None = None¶: The geographical regions where the holiday effect is applied in modeling.
stage_1_num_parallel_trials: int | None = None¶: Number of parallel trails for stage 1.
stage_1_tuning_result_artifact_uri: str | None = None¶: The stage 1 tuning result artifact GCS URI.
stage_2_num_parallel_trials: int | None = None¶: Number of parallel trails for stage 2.
data_source_csv_filenames: str | None = None¶: A string that represents a list of comma separated CSV filenames.
data_source_bigquery_table_path: str | None = None¶: The BigQuery table path of format bq://bq_project.bq_dataset.bq_table
predefined_split_key: str | None = None¶: The predefined_split column name.
training_fraction: float | None = None¶: The training fraction.
validation_fraction: float | None = None¶: The validation fraction.
test_fraction: float | None = None¶: The test fraction.
weight_column: str | None = None¶: The weight column name.
dataflow_service_account: str | None = None¶: The full service account name.
dataflow_subnetwork: str | None = None¶: The dataflow subnetwork.
dataflow_use_public_ips: bool = True¶: True to enable dataflow public IPs.
feature_transform_engine_bigquery_staging_full_dataset_id: str = ''¶: The full id of the feature transform engine staging dataset.
feature_transform_engine_dataflow_machine_type: str = 'n1-standard-16'¶: The dataflow machine type of the feature transform engine.
feature_transform_engine_dataflow_max_num_workers: int = 10¶: The max number of dataflow workers of the feature transform engine.
feature_transform_engine_dataflow_disk_size_gb: int = 40¶: The disk size of the dataflow workers of the feature transform engine.
evaluation_batch_predict_machine_type: str = 'n1-standard-16'¶: Machine type for the batch prediction job in evaluation, such as ‘n1-standard-16’.
evaluation_batch_predict_starting_replica_count: int = 25¶: Number of replicas to use in the batch prediction cluster at startup time.
evaluation_batch_predict_max_replica_count: int = 25¶: The maximum count of replicas the batch prediction job can scale to.
evaluation_dataflow_machine_type: str = 'n1-standard-16'¶: Machine type for the dataflow job in evaluation, such as ‘n1-standard-16’.
evaluation_dataflow_max_num_workers: int = 25¶: Maximum number of dataflow workers.
evaluation_dataflow_disk_size_gb: int = 50¶: The disk space in GB for dataflow.
study_spec_parameters_override: list[dict[str, Any]] | None = None¶: The list for overriding study spec.
stage_1_tuner_worker_pool_specs_override: dict[str, Any] | None = None¶: The dictionary for overriding stage 1 tuner worker pool spec.
stage_2_trainer_worker_pool_specs_override: dict[str, Any] | None = None¶: The dictionary for overriding stage 2 trainer worker pool spec.
encryption_spec_key_name: str | None = None¶: The KMS key name.
model_display_name: str | None = None¶: Optional display name for model.
model_description: str | None = None¶: Optional description.
run_evaluation: bool = True¶: True to evaluate the ensembled model on the test split.

Returns¶:

Tuple of pipeline_definition_path and parameter_values.

Returns timeseries_dense_encoder_forecasting pipeline and parameters.

Parameters¶:

project: str¶: The GCP project that runs the pipeline components.
location: str¶: The GCP region that runs the pipeline components.
root_dir: str¶: The root GCS directory for the pipeline components.
target_column: str¶: The target column name.
optimization_objective: str¶: “minimize-rmse”, “minimize-mae”, “minimize-rmsle”, “minimize-rmspe”, “minimize-wape-mae”, “minimize-mape”, or “minimize-quantile-loss”.
transformations: dict[str, list[str]]¶: Dict mapping auto and/or type-resolutions to feature columns. The supported types are: auto, categorical, numeric, text, and timestamp.
train_budget_milli_node_hours: float¶: The train budget of creating this model, expressed in milli node hours i.e. 1,000 value in this field means 1 node hour.
time_column: str¶: The column that indicates the time.
time_series_identifier_columns: list[str]¶: The columns which distinguish different time series.
time_series_identifier_column: str | None = None¶: [Deprecated] The column which distinguishes different time series.
time_series_attribute_columns: list[str] | None = None¶: The columns that are invariant across the same time series.
available_at_forecast_columns: list[str] | None = None¶: The columns that are available at the forecast time.
unavailable_at_forecast_columns: list[str] | None = None¶: The columns that are unavailable at the forecast time.
forecast_horizon: int | None = None¶: The length of the horizon.
context_window: int | None = None¶: The length of the context window.
evaluated_examples_bigquery_path: str | None = None¶: The bigquery dataset to write the predicted examples into for evaluation, in the format bq://project.dataset.
window_predefined_column: str | None = None¶: The column that indicate the start of each window.
window_stride_length: int | None = None¶: The stride length to generate the window.
window_max_count: int | None = None¶: The maximum number of windows that will be generated.
holiday_regions: list[str] | None = None¶: The geographical regions where the holiday effect is applied in modeling.
stage_1_num_parallel_trials: int | None = None¶: Number of parallel trails for stage 1.
stage_1_tuning_result_artifact_uri: str | None = None¶: The stage 1 tuning result artifact GCS URI.
stage_2_num_parallel_trials: int | None = None¶: Number of parallel trails for stage 2.
num_selected_trials: int | None = None¶: Number of selected trails.
data_source_csv_filenames: str | None = None¶: A string that represents a list of comma separated CSV filenames.
data_source_bigquery_table_path: str | None = None¶: The BigQuery table path of format bq://bq_project.bq_dataset.bq_table
predefined_split_key: str | None = None¶: The predefined_split column name.
training_fraction: float | None = None¶: The training fraction.
validation_fraction: float | None = None¶: The validation fraction.
test_fraction: float | None = None¶: The test fraction.
weight_column: str | None = None¶: The weight column name.
dataflow_service_account: str | None = None¶: The full service account name.
dataflow_subnetwork: str | None = None¶: The dataflow subnetwork.
dataflow_use_public_ips: bool = True¶: True to enable dataflow public IPs.
feature_transform_engine_bigquery_staging_full_dataset_id: str = ''¶: The full id of the feature transform engine staging dataset.
feature_transform_engine_dataflow_machine_type: str = 'n1-standard-16'¶: The dataflow machine type of the feature transform engine.
feature_transform_engine_dataflow_max_num_workers: int = 10¶: The max number of dataflow workers of the feature transform engine.
feature_transform_engine_dataflow_disk_size_gb: int = 40¶: The disk size of the dataflow workers of the feature transform engine.
evaluation_batch_predict_machine_type: str = 'n1-standard-16'¶: Machine type for the batch prediction job in evaluation, such as ‘n1-standard-16’.
evaluation_batch_predict_starting_replica_count: int = 25¶: Number of replicas to use in the batch prediction cluster at startup time.
evaluation_batch_predict_max_replica_count: int = 25¶: The maximum count of replicas the batch prediction job can scale to.
evaluation_dataflow_machine_type: str = 'n1-standard-16'¶: Machine type for the dataflow job in evaluation, such as ‘n1-standard-16’.
evaluation_dataflow_max_num_workers: int = 25¶: Maximum number of dataflow workers.
evaluation_dataflow_disk_size_gb: int = 50¶: The disk space in GB for dataflow.
study_spec_parameters_override: list[dict[str, Any]] | None = None¶: The list for overriding study spec.
stage_1_tuner_worker_pool_specs_override: dict[str, Any] | None = None¶: The dictionary for overriding stage 1 tuner worker pool spec.
stage_2_trainer_worker_pool_specs_override: dict[str, Any] | None = None¶: The dictionary for overriding stage 2 trainer worker pool spec.
enable_probabilistic_inference: bool = False¶: If probabilistic inference is enabled, the model will fit a distribution that captures the uncertainty of a prediction. If quantiles are specified, then the quantiles of the distribution are also returned.
quantiles: list[float] | None = None¶: Quantiles to use for probabilistic inference. Up to 5 quantiles are allowed of values between 0 and 1, exclusive. Represents the quantiles to use for that objective. Quantiles must be unique.
encryption_spec_key_name: str | None = None¶: The KMS key name.
model_display_name: str | None = None¶: Optional display name for model.
model_description: str | None = None¶: Optional description.
run_evaluation: bool = True¶: True to evaluate the ensembled model on the test split.
group_columns: list[str] | None = None¶: A list of time series attribute column names that define the time series hierarchy.
group_total_weight: float = 0.0¶: The weight of the loss for predictions aggregated over time series in the same group.
temporal_total_weight: float = 0.0¶: The weight of the loss for predictions aggregated over the horizon for a single time series.
group_temporal_total_weight: float = 0.0¶: The weight of the loss for predictions aggregated over both the horizon and time series in the same hierarchy group.

Returns¶:

Tuple of pipeline_definition_path and parameter_values.