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AutoMLForecastingTrainingJob(
display_name: Optional[str] = None,
optimization_objective: Optional[str] = None,
column_specs: Optional[Dict[str, str]] = None,
column_transformations: Optional[List[Dict[str, Dict[str, str]]]] = None,
project: Optional[str] = None,
location: Optional[str] = None,
credentials: Optional[google.auth.credentials.Credentials] = None,
labels: Optional[Dict[str, str]] = None,
training_encryption_spec_key_name: Optional[str] = None,
model_encryption_spec_key_name: Optional[str] = None,
)
Constructs a Forecasting Training Job.
Parameters
Name | Description |
display_name |
str
Optional. 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_specs |
Dict[str, str]
Optional. Alternative to column_transformations where the keys of the dict are column names and their respective values are one of AutoMLTabularTrainingJob.column_data_types. When creating transformation for BigQuery Struct column, the column should be flattened using "." as the delimiter. Only columns with no child should have a transformation. 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. Only one of column_transformations or column_specs should be passed. |
column_transformations |
List[Dict[str, Dict[str, str]]]
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. Only columns with no child should have a transformation. 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. Only one of column_transformations or column_specs should be passed. Consider using column_specs as column_transformations will be deprecated eventually. |
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. |
labels |
Dict[str, str]
Optional. The labels with user-defined metadata to organize TrainingPipelines. Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. See https://goo.gl/xmQnxf for more information and examples of labels. |
training_encryption_spec_key_name |
Optional[str]
Optional. The Cloud KMS resource identifier of the customer managed encryption key used to protect the training pipeline. Has the form: |
model_encryption_spec_key_name |
Optional[str]
Optional. The Cloud KMS resource identifier of the customer managed encryption key used to protect the model. Has the form: |
Inheritance
builtins.object > google.cloud.aiplatform.base.VertexAiResourceNoun > builtins.object > google.cloud.aiplatform.base.FutureManager > google.cloud.aiplatform.base.VertexAiResourceNounWithFutureManager > builtins.object > abc.ABC > google.cloud.aiplatform.base.DoneMixin > google.cloud.aiplatform.base.StatefulResource > google.cloud.aiplatform.base.VertexAiStatefulResource > google.cloud.aiplatform.training_jobs._TrainingJob > google.cloud.aiplatform.training_jobs._ForecastingTrainingJob > AutoMLForecastingTrainingJobMethods
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,
training_fraction_split: Optional[float] = None,
validation_fraction_split: Optional[float] = None,
test_fraction_split: Optional[float] = None,
predefined_split_column_name: Optional[str] = None,
timestamp_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,
model_labels: Optional[Dict[str, str]] = None,
model_id: Optional[str] = None,
parent_model: Optional[str] = None,
is_default_version: Optional[bool] = True,
model_version_aliases: Optional[Sequence[str]] = None,
model_version_description: Optional[str] = None,
additional_experiments: Optional[List[str]] = None,
hierarchy_group_columns: Optional[List[str]] = None,
hierarchy_group_total_weight: Optional[float] = None,
hierarchy_temporal_total_weight: Optional[float] = None,
hierarchy_group_temporal_total_weight: Optional[float] = None,
window_column: Optional[str] = None,
window_stride_length: Optional[int] = None,
window_max_count: Optional[int] = None,
holiday_regions: Optional[List[str]] = None,
sync: bool = True,
create_request_timeout: Optional[float] = None,
)
Runs the training job and returns a model.
If training on a Vertex AI dataset, you can use one of the following split configurations:
Data fraction splits:
Any of training_fraction_split
, validation_fraction_split
and
test_fraction_split
may optionally be provided, they must sum to up to 1. If
the provided ones sum to less than 1, the remainder is assigned to sets as
decided by Vertex AI. If none of the fractions are set, by default roughly 80%
of data will be used for training, 10% for validation, and 10% for test.
Predefined splits:
Assigns input data to training, validation, and test sets based on the value of a provided key.
If using predefined splits, ``predefined_split_column_name`` must be provided.
Supported only for tabular Datasets.
Timestamp splits:
Assigns input data to training, validation, and test sets
based on a provided timestamps. The youngest data pieces are
assigned to training set, next to validation set, and the oldest
to the test set.
Supported only for tabular Datasets.
Name | Description |
dataset |
datasets.TimeSeriesDataset
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. This column must be unavailable at forecast. |
time_column |
str
Required. Name of the column that identifies time order in the time series. This column must be available at forecast. |
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 |
data_granularity_count |
int
Required. The number of data granularity units between data points in the training data. If [data_granularity_unit] is |
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 { |
timestamp_split_column_name |
str
Optional. The key is a name of one of the Dataset's data columns. The value of the key values of the key (the values in the column) must be in RFC 3339 |
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. This column must be available at forecast. |
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: |
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 |
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. |
model_labels |
Dict[str, str]
Optional. The labels with user-defined metadata to organize your Models. Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. See https://goo.gl/xmQnxf for more information and examples of labels. |
model_id |
str
Optional. The ID to use for the Model produced by this job, which will become the final component of the model resource name. This value may be up to 63 characters, and valid characters are |
parent_model |
str
Optional. The resource name or model ID of an existing model. The new model uploaded by this job will be a version of |
is_default_version |
bool
Optional. When set to True, the newly uploaded model version will automatically have alias "default" included. Subsequent uses of the model produced by this job without a version specified will use this "default" version. When set to False, the "default" alias will not be moved. Actions targeting the model version produced by this job will need to specifically reference this version by ID or alias. New model uploads, i.e. version 1, will always be "default" aliased. |
model_version_aliases |
Sequence[str]
Optional. User provided version aliases so that the model version uploaded by this job can be referenced via alias instead of auto-generated version ID. A default version alias will be created for the first version of the model. The format is |
model_version_description |
str
Optional. The description of the model version being uploaded by this job. |
additional_experiments |
List[str]
Optional. Additional experiment flags for the time series forcasting training. |
hierarchy_group_columns |
List[str]
Optional. A list of time series attribute column names that define the time series hierarchy. Only one level of hierarchy is supported, ex. |
hierarchy_group_total_weight |
float
Optional. The weight of the loss for predictions aggregated over time series in the same hierarchy group. |
hierarchy_temporal_total_weight |
float
Optional. The weight of the loss for predictions aggregated over the horizon for a single time series. |
hierarchy_group_temporal_total_weight |
float
Optional. The weight of the loss for predictions aggregated over both the horizon and time series in the same hierarchy group. |
window_column |
str
Optional. Name of the column that should be used to filter input rows. The column should contain either booleans or string booleans; if the value of the row is True, generate a sliding window from that row. |
window_stride_length |
int
Optional. Step length used to generate input examples. Every |
window_max_count |
int
Optional. Number of rows that should be used to generate input examples. If the total row count is larger than this number, the input data will be randomly sampled to hit the count. |
holiday_regions |
List[str]
Optional. The geographical regions to use when creating holiday features. This option is only allowed when data_granularity_unit is |
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. |
create_request_timeout |
float
Optional. The timeout for the create request in seconds. |
Type | Description |
RuntimeError | If Training job has already been run or is waiting to run. |
Type | Description |
model | The trained Vertex AI Model resource or None if training did not produce a Vertex AI Model. |