class_docstr: Hook for Google Cloud Vertex AI Auto ML APIs.
class_docstr_tok: ["Hook","for","Google","Cloud","Vertex","AI","Auto","ML","APIs","."]
docstr: Create an AutoML Tabular Training Job.
  
  :param project_id: Required. Project to run training in.
  :param region: Required. Location to run training in.
  :param display_name: Required. The user-defined name of this TrainingPipeline.
  :param 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 tabular
      Datasets, all their data is exported to training, to pick and choose from.
  :param target_column: Required. The name of the column values of which the Model is to predict.
  :param parent_model: Optional. The resource name or model ID of an existing model.
      The new model uploaded by this job will be a version of `parent_model`.
      Only set this field when training a new version of an existing model.
  :param is_default_version: 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.
  :param model_version_aliases: 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 [a-z][a-zA-Z0-9-]{0,126}[a-z0-9]
  :param model_version_description: Optional. The description of the model version
      being uploaded by this job.
  :param optimization_prediction_type: The type of prediction the Model is to produce.
      "classification" - Predict one out of multiple target values is picked for each row.
      "regression" - Predict a value based on its relation to other values. This type is available only
      to columns that contain semantically numeric values, i.e. integers or floating point number, even
      if stored as e.g. strings.
  :param optimization_objective: Optional. Objective function the Model is to be optimized towards.
      The training task creates a Model that maximizes/minimizes the value of the objective function
      over the validation set.
  
      The supported optimization objectives depend on the prediction type, and in the case of
      classification also the number of distinct values in the target column (two distinct values
      -> binary, 3 or more distinct values -> multi class). If the field is not set, the default
      objective function is used.
  
      Classification (binary):
      "maximize-au-roc" (default) - Maximize the area under the receiver operating characteristic (ROC)
      curve.
      "minimize-log-loss" - Minimize log loss.
      "maximize-au-prc" - Maximize the area under the precision-recall curve.
      "maximize-precision-at-recall" - Maximize precision for a specified recall value.
      "maximize-recall-at-precision" - Maximize recall for a specified precision value.
  
      Classification (multi class):
      "minimize-log-loss" (default) - Minimize log loss.
  
      Regression:
      "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).
  :param column_specs: 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.
  :param column_transformations: 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.
  :param optimization_objective_recall_value: Optional. Required when maximize-precision-at-recall
      optimizationObjective was picked, represents the recall value at which the optimization is done.
      The minimum value is 0 and the maximum is 1.0.
  :param optimization_objective_precision_value: Optional. Required when maximize-recall-at-precision
      optimizationObjective was picked, represents the precision value at which the optimization is
      done.
      The minimum value is 0 and the maximum is 1.0.
  :param labels: 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.
  :param training_encryption_spec_key_name: Optional. The Cloud KMS resource identifier of the customer
      managed encryption key used to protect the training pipeline. Has the form:
      ``projects/my-project/locations/my-region/keyRings/my-kr/cryptoKeys/my-key``. The key needs to be
      in the same region as where the compute resource is created. If set, this TrainingPipeline will
      be secured by this key.
      Note: Model trained by this TrainingPipeline is also secured by this key if ``model_to_upload``
      is not set separately.
  :param model_encryption_spec_key_name: Optional. The Cloud KMS resource identifier of the customer
      managed encryption key used to protect the model. Has the form:
      ``projects/my-project/locations/my-region/keyRings/my-kr/cryptoKeys/my-key``. The key needs to be
      in the same region as where the compute resource is created. If set, the trained Model will be
      secured by this key.
  :param training_fraction_split: Optional. The fraction of the input data that is to be used to train
      the Model. This is ignored if Dataset is not provided.
  :param validation_fraction_split: Optional. The fraction of the input data that is to be used to
      validate the Model. This is ignored if Dataset is not provided.
  :param test_fraction_split: Optional. The fraction of the input data that is to be used to evaluate
      the Model. This is ignored if Dataset is not provided.
  :param predefined_split_column_name: 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
      {``training``, ``validation``, ``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.
  :param timestamp_split_column_name: 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 `date-time`
      format, where `time-offset` = `"Z"` (e.g. 1985-04-12T23:20:50.52Z). 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. This parameter must be used with training_fraction_split,
      validation_fraction_split and test_fraction_split.
  :param weight_column: 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.
  :param 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.
  :param model_display_name: 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.
  :param model_labels: 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.
  :param disable_early_stopping: Required. If true, the entire budget is used. This disables the early
      stopping feature. By default, the early stopping feature is enabled, which means that training
      might stop before the entire training budget has been used, if further training does no longer
      brings significant improvement to the model.
  :param export_evaluated_data_items: Whether to export the test set predictions to a BigQuery table.
      If False, then the export is not performed.
  :param export_evaluated_data_items_bigquery_destination_uri: 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.
  :param export_evaluated_data_items_override_destination: 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.
  :param sync: 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.
docstr_tok: 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code: def create_auto_ml_tabular_training_job(
      self,
      project_id: str,
      region: str,
      display_name: str,
      dataset: datasets.TabularDataset,
      target_column: str,
      optimization_prediction_type: str,
      optimization_objective: str | None = None,
      column_specs: dict[str, str] | None = None,
      column_transformations: list[dict[str, dict[str, str]]]
      | None = None,
      optimization_objective_recall_value: float | None = None,
      optimization_objective_precision_value: float | None = None,
      labels: dict[str, str] | None = None,
      training_encryption_spec_key_name: str | None = None,
      model_encryption_spec_key_name: str | None = None,
      training_fraction_split: float | None = None,
      validation_fraction_split: float | None = None,
      test_fraction_split: float | None = None,
      predefined_split_column_name: str | None = None,
      timestamp_split_column_name: str | None = None,
      weight_column: str | None = None,
      budget_milli_node_hours: int = 1000,
      model_display_name: str | None = None,
      model_labels: dict[str, str] | None = None,
      disable_early_stopping: bool = False,
      export_evaluated_data_items: bool = False,
      export_evaluated_data_items_bigquery_destination_uri: str
      | None = None,
      export_evaluated_data_items_override_destination: bool = False,
      sync: bool = True,
      parent_model: str | None = None,
      is_default_version: bool | None = None,
      model_version_aliases: list[str] | None = None,
      model_version_description: str | None = None,
  ) -> tuple[models.Model | None, str]:
      """
      Create an AutoML Tabular Training Job.
  
      :param project_id: Required. Project to run training in.
      :param region: Required. Location to run training in.
      :param display_name: Required. The user-defined name of this TrainingPipeline.
      :param 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 tabular
          Datasets, all their data is exported to training, to pick and choose from.
      :param target_column: Required. The name of the column values of which the Model is to predict.
      :param parent_model: Optional. The resource name or model ID of an existing model.
          The new model uploaded by this job will be a version of `parent_model`.
          Only set this field when training a new version of an existing model.
      :param is_default_version: 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.
      :param model_version_aliases: 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 [a-z][a-zA-Z0-9-]{0,126}[a-z0-9]
      :param model_version_description: Optional. The description of the model version
          being uploaded by this job.
      :param optimization_prediction_type: The type of prediction the Model is to produce.
          "classification" - Predict one out of multiple target values is picked for each row.
          "regression" - Predict a value based on its relation to other values. This type is available only
          to columns that contain semantically numeric values, i.e. integers or floating point number, even
          if stored as e.g. strings.
      :param optimization_objective: Optional. Objective function the Model is to be optimized towards.
          The training task creates a Model that maximizes/minimizes the value of the objective function
          over the validation set.
  
          The supported optimization objectives depend on the prediction type, and in the case of
          classification also the number of distinct values in the target column (two distinct values
          -> binary, 3 or more distinct values -> multi class). If the field is not set, the default
          objective function is used.
  
          Classification (binary):
          "maximize-au-roc" (default) - Maximize the area under the receiver operating characteristic (ROC)
          curve.
          "minimize-log-loss" - Minimize log loss.
          "maximize-au-prc" - Maximize the area under the precision-recall curve.
          "maximize-precision-at-recall" - Maximize precision for a specified recall value.
          "maximize-recall-at-precision" - Maximize recall for a specified precision value.
  
          Classification (multi class):
          "minimize-log-loss" (default) - Minimize log loss.
  
          Regression:
          "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).
      :param column_specs: 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.
      :param column_transformations: 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.
      :param optimization_objective_recall_value: Optional. Required when maximize-precision-at-recall
          optimizationObjective was picked, represents the recall value at which the optimization is done.
          The minimum value is 0 and the maximum is 1.0.
      :param optimization_objective_precision_value: Optional. Required when maximize-recall-at-precision
          optimizationObjective was picked, represents the precision value at which the optimization is
          done.
          The minimum value is 0 and the maximum is 1.0.
      :param labels: 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.
      :param training_encryption_spec_key_name: Optional. The Cloud KMS resource identifier of the customer
          managed encryption key used to protect the training pipeline. Has the form:
          ``projects/my-project/locations/my-region/keyRings/my-kr/cryptoKeys/my-key``. The key needs to be
          in the same region as where the compute resource is created. If set, this TrainingPipeline will
          be secured by this key.
          Note: Model trained by this TrainingPipeline is also secured by this key if ``model_to_upload``
          is not set separately.
      :param model_encryption_spec_key_name: Optional. The Cloud KMS resource identifier of the customer
          managed encryption key used to protect the model. Has the form:
          ``projects/my-project/locations/my-region/keyRings/my-kr/cryptoKeys/my-key``. The key needs to be
          in the same region as where the compute resource is created. If set, the trained Model will be
          secured by this key.
      :param training_fraction_split: Optional. The fraction of the input data that is to be used to train
          the Model. This is ignored if Dataset is not provided.
      :param validation_fraction_split: Optional. The fraction of the input data that is to be used to
          validate the Model. This is ignored if Dataset is not provided.
      :param test_fraction_split: Optional. The fraction of the input data that is to be used to evaluate
          the Model. This is ignored if Dataset is not provided.
      :param predefined_split_column_name: 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
          {``training``, ``validation``, ``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.
      :param timestamp_split_column_name: 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 `date-time`
          format, where `time-offset` = `"Z"` (e.g. 1985-04-12T23:20:50.52Z). 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. This parameter must be used with training_fraction_split,
          validation_fraction_split and test_fraction_split.
      :param weight_column: 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.
      :param 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.
      :param model_display_name: 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.
      :param model_labels: 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.
      :param disable_early_stopping: Required. If true, the entire budget is used. This disables the early
          stopping feature. By default, the early stopping feature is enabled, which means that training
          might stop before the entire training budget has been used, if further training does no longer
          brings significant improvement to the model.
      :param export_evaluated_data_items: Whether to export the test set predictions to a BigQuery table.
          If False, then the export is not performed.
      :param export_evaluated_data_items_bigquery_destination_uri: 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.
      :param export_evaluated_data_items_override_destination: 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.
      :param sync: 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.
      """
      if column_transformations:
          warnings.warn(
              "Consider using column_specs as column_transformations will be deprecated eventually.",
              AirflowProviderDeprecationWarning,
              stacklevel=2,
          )
  
      self._job = self.get_auto_ml_tabular_training_job(
          project=project_id,
          location=region,
          display_name=display_name,
          optimization_prediction_type=optimization_prediction_type,
          optimization_objective=optimization_objective,
          column_specs=column_specs,
          column_transformations=column_transformations,
          optimization_objective_recall_value=optimization_objective_recall_value,
          optimization_objective_precision_value=optimization_objective_precision_value,
          labels=labels,
          training_encryption_spec_key_name=training_encryption_spec_key_name,
          model_encryption_spec_key_name=model_encryption_spec_key_name,
      )
  
      if not self._job:
          raise AirflowException(
              "AutoMLTabularTrainingJob was not created"
          )
  
      model = self._job.run(
          dataset=dataset,
          target_column=target_column,
          training_fraction_split=training_fraction_split,
          validation_fraction_split=validation_fraction_split,
          test_fraction_split=test_fraction_split,
          predefined_split_column_name=predefined_split_column_name,
          timestamp_split_column_name=timestamp_split_column_name,
          weight_column=weight_column,
          budget_milli_node_hours=budget_milli_node_hours,
          model_display_name=model_display_name,
          model_labels=model_labels,
          disable_early_stopping=disable_early_stopping,
          export_evaluated_data_items=export_evaluated_data_items,
          export_evaluated_data_items_bigquery_destination_uri=(
              export_evaluated_data_items_bigquery_destination_uri
          ),
          export_evaluated_data_items_override_destination=export_evaluated_data_items_override_destination,
          sync=sync,
          parent_model=parent_model,
          is_default_version=is_default_version,
          model_version_aliases=model_version_aliases,
          model_version_description=model_version_description,
      )
      training_id = self.extract_training_id(self._job.resource_name)
      if model:
          model.wait()
      else:
          self.log.warning(
              "Training did not produce a Managed Model returning None. Training Pipeline is not "
              "configured to upload a Model."
          )
      return model, training_id
  
imports: from __future__ import annotations
  import warnings
  from typing import TYPE_CHECKING, Sequence
  from google.api_core.client_options import ClientOptions
  from google.api_core.gapic_v1.method import DEFAULT, _MethodDefault
  from google.cloud.aiplatform import AutoMLForecastingTrainingJob, AutoMLImageTrainingJob, AutoMLTabularTrainingJob, AutoMLTextTrainingJob, AutoMLVideoTrainingJob, datasets, models
  from google.cloud.aiplatform_v1 import JobServiceClient, PipelineServiceClient
  from airflow.exceptions import AirflowException, AirflowProviderDeprecationWarning
  from airflow.providers.google.common.hooks.base_google import GoogleBaseHook
label_desc: Use image node_id 13 for calling the AutoMLHook obj's underlying member method code with example usage: obj.create_auto_ml_tabular_training_job(project_id, region, display_name, dataset, target_column, optimization_prediction_type, optimization_objective, column_specs, column_transformations, optimization_objective_recall_value, optimization_objective_precision_value, labels, training_encryption_spec_key_name, model_encryption_spec_key_name, training_fraction_split, validation_fraction_split, test_fraction_split, predefined_split_column_name, timestamp_split_column_name, weight_column, budget_milli_node_hours, model_display_name, model_labels, disable_early_stopping, export_evaluated_data_items, export_evaluated_data_items_bigquery_destination_uri, export_evaluated_data_items_override_destination, sync, parent_model, is_default_version, model_version_aliases, model_version_description) and returns: model,  training_id