Class AutoMLForecastingTrainingJob (1.3.0)

Version 1.3.0

• 1.73.0 (latest)
• 1.72.0
• 1.71.1
• 1.70.0
• 1.69.0
• 1.68.0
• 1.67.1
• 1.66.0
• 1.65.0
• 1.63.0
• 1.62.0
• 1.60.0
• 1.59.0
• 1.58.0
• 1.57.0
• 1.56.0
• 1.55.0
• 1.54.1
• 1.53.0
• 1.52.0
• 1.51.0
• 1.50.0
• 1.49.0
• 1.48.0
• 1.47.0
• 1.46.0
• 1.45.0
• 1.44.0
• 1.43.0
• 1.39.0
• 1.38.1
• 1.37.0
• 1.36.4
• 1.35.0
• 1.34.0
• 1.33.1
• 1.32.0
• 1.31.1
• 1.30.1
• 1.29.0
• 1.28.1
• 1.27.1
• 1.26.1
• 1.25.0
• 1.24.1
• 1.23.0
• 1.22.1
• 1.21.0
• 1.20.0
• 1.19.1
• 1.18.3
• 1.17.1
• 1.16.1
• 1.15.1
• 1.14.0
• 1.13.1
• 1.12.1
• 1.11.0
• 1.10.0
• 1.9.0
• 1.8.1
• 1.7.1
• 1.6.2
• 1.5.0
• 1.4.3
• 1.3.0
• 1.2.0
• 1.1.1
• 1.0.1
• 0.9.0
• 0.8.0
• 0.7.1
• 0.6.0
• 0.5.1
• 0.4.0
• 0.3.1

AutoMLForecastingTrainingJob(
    display_name: str,
    optimization_objective: Optional[str] = None,
    column_transformations: Optional[Union[Dict, List[Dict]]] = None,
    project: Optional[str] = None,
    location: Optional[str] = None,
    credentials: Optional[google.auth.credentials.Credentials] = None,
)

Constructs a AutoML Forecasting Training Job.

Parameters

Name	Description
display_name	str Required. The user-defined name of this TrainingPipeline.
optimization_objective	str Optional. Objective function the model is to be optimized towards. The training process creates a Model that optimizes the value of the objective function over the validation set. The supported optimization objectives: "minimize-rmse" (default) - Minimize root-mean-squared error (RMSE). "minimize-mae" - Minimize mean-absolute error (MAE). "minimize-rmsle" - Minimize root-mean-squared log error (RMSLE). "minimize-rmspe" - Minimize root-mean-squared percentage error (RMSPE). "minimize-wape-mae" - Minimize the combination of weighted absolute percentage error (WAPE) and mean-absolute-error (MAE). "minimize-quantile-loss" - Minimize the quantile loss at the defined quantiles. (Set this objective to build quantile forecasts.)
column_transformations	Optional[Union[Dict, List[Dict]]] Optional. Transformations to apply to the input columns (i.e. columns other than the targetColumn). Each transformation may produce multiple result values from the column's value, and all are used for training. When creating transformation for BigQuery Struct column, the column should be flattened using "." as the delimiter. If an input column has no transformations on it, such a column is ignored by the training, except for the targetColumn, which should have no transformations defined on.
project	str Optional. Project to run training in. Overrides project set in aiplatform.init.
location	str Optional. Location to run training in. Overrides location set in aiplatform.init.
credentials	auth_credentials.Credentials Optional. Custom credentials to use to run call training service. Overrides credentials set in aiplatform.init.

Inheritance

builtins.object > google.cloud.aiplatform.base.VertexAiResourceNoun > builtins.object > google.cloud.aiplatform.base.FutureManager > google.cloud.aiplatform.base.VertexAiResourceNounWithFutureManager > google.cloud.aiplatform.training_jobs._TrainingJob > AutoMLForecastingTrainingJob

Methods

run

run(
    dataset: google.cloud.aiplatform.datasets.time_series_dataset.TimeSeriesDataset,
    target_column: str,
    time_column: str,
    time_series_identifier_column: str,
    unavailable_at_forecast_columns: List[str],
    available_at_forecast_columns: List[str],
    forecast_horizon: int,
    data_granularity_unit: str,
    data_granularity_count: int,
    predefined_split_column_name: Optional[str] = None,
    weight_column: Optional[str] = None,
    time_series_attribute_columns: Optional[List[str]] = None,
    context_window: Optional[int] = None,
    export_evaluated_data_items: bool = False,
    export_evaluated_data_items_bigquery_destination_uri: Optional[str] = None,
    export_evaluated_data_items_override_destination: bool = False,
    quantiles: Optional[List[float]] = None,
    validation_options: Optional[str] = None,
    budget_milli_node_hours: int = 1000,
    model_display_name: Optional[str] = None,
    sync: bool = True,
)

Runs the training job and returns a model.

The training data splits are set by default: Roughly 80% will be used for training, 10% for validation, and 10% for test.

Parameters

Name	Description
dataset	datasets.Dataset Required. The dataset within the same Project from which data will be used to train the Model. The Dataset must use schema compatible with Model being trained, and what is compatible should be described in the used TrainingPipeline's [training_task_definition] [google.cloud.aiplatform.v1beta1.TrainingPipeline.training_task_definition]. For time series Datasets, all their data is exported to training, to pick and choose from.
target_column	str Required. Name of the column that the Model is to predict values for.
time_column	str Required. Name of the column that identifies time order in the time series.
time_series_identifier_column	str Required. Name of the column that identifies the time series.
unavailable_at_forecast_columns	List[str] Required. Column names of columns that are unavailable at forecast. Each column contains information for the given entity (identified by the [time_series_identifier_column]) that is unknown before the forecast (e.g. population of a city in a given year, or weather on a given day).
available_at_forecast_columns	List[str] Required. Column names of columns that are available at forecast. Each column contains information for the given entity (identified by the [time_series_identifier_column]) that is known at forecast.
data_granularity_unit	str Required. The data granularity unit. Accepted values are minute, hour, day, week, month, year.
data_granularity_count	int Required. The number of data granularity units between data points in the training data. If [data_granularity_unit] is minute, can be 1, 5, 10, 15, or 30. For all other values of [data_granularity_unit], must be 1.
predefined_split_column_name	str Optional. The key is a name of one of the Dataset's data columns. The value of the key (either the label's value or value in the column) must be one of {TRAIN, VALIDATE, TEST}, and it defines to which set the given piece of data is assigned. If for a piece of data the key is not present or has an invalid value, that piece is ignored by the pipeline. Supported only for tabular and time series Datasets.
weight_column	str Optional. Name of the column that should be used as the weight column. Higher values in this column give more importance to the row during Model training. The column must have numeric values between 0 and 10000 inclusively, and 0 value means that the row is ignored. If the weight column field is not set, then all rows are assumed to have equal weight of 1.
time_series_attribute_columns	List[str] Optional. Column names that should be used as attribute columns. Each column is constant within a time series.
context_window	int Optional. The amount of time into the past training and prediction data is used for model training and prediction respectively. Expressed in number of units defined by the [data_granularity_unit] and [data_granularity_count] fields. When not provided uses the default value of 0 which means the model sets each series context window to be 0 (also known as "cold start"). Inclusive.
export_evaluated_data_items	bool Whether to export the test set predictions to a BigQuery table. If False, then the export is not performed.
export_evaluated_data_items_bigquery_destination_uri	string Optional. URI of desired destination BigQuery table for exported test set predictions. Expected format: bq://<project_id>:<dataset_id>:<table> If not specified, then results are exported to the following auto-created BigQuery table: <project_id>:export_evaluated_examples_<model_name>_<yyyy_MM_dd'T'HH_mm_ss_SSS'Z'>.evaluated_examples Applies only if [export_evaluated_data_items] is True.
export_evaluated_data_items_override_destination	bool Whether to override the contents of [export_evaluated_data_items_bigquery_destination_uri], if the table exists, for exported test set predictions. If False, and the table exists, then the training job will fail. Applies only if [export_evaluated_data_items] is True and [export_evaluated_data_items_bigquery_destination_uri] is specified.
quantiles	List[float] Quantiles to use for the minizmize-quantile-loss [AutoMLForecastingTrainingJob.optimization_objective]. This argument is required in this case. Accepts up to 5 quantiles in the form of a double from 0 to 1, exclusive. Each quantile must be unique.
validation_options	str Validation options for the data validation component. The available options are: "fail-pipeline" - (default), will validate against the validation and fail the pipeline if it fails. "ignore-validation" - ignore the results of the validation and continue the pipeline
budget_milli_node_hours	int Optional. The train budget of creating this Model, expressed in milli node hours i.e. 1,000 value in this field means 1 node hour. The training cost of the model will not exceed this budget. The final cost will be attempted to be close to the budget, though may end up being (even) noticeably smaller - at the backend's discretion. This especially may happen when further model training ceases to provide any improvements. If the budget is set to a value known to be insufficient to train a Model for the given training set, the training won't be attempted and will error. The minimum value is 1000 and the maximum is 72000.
model_display_name	str Optional. If the script produces a managed Vertex AI Model. The display name of the Model. The name can be up to 128 characters long and can be consist of any UTF-8 characters. If not provided upon creation, the job's display_name is used.
sync	bool Whether to execute this method synchronously. If False, this method will be executed in concurrent Future and any downstream object will be immediately returned and synced when the Future has completed.

Exceptions

Type	Description
RuntimeErro	if Training job has already been run or is waiting to run.:

Returns

Type	Description
model	The trained Vertex AI Model resource or None if training did not produce a Vertex AI Model.