Package software.amazon.awssdk.services.sagemaker.model

Interface CandidateGenerationConfig.Builder

All Superinterfaces:

Buildable, CopyableBuilder<CandidateGenerationConfig.Builder,CandidateGenerationConfig>, SdkBuilder<CandidateGenerationConfig.Builder,CandidateGenerationConfig>, SdkPojo

Enclosing class:

CandidateGenerationConfig

public static interface CandidateGenerationConfig.Builder
extends SdkPojo, CopyableBuilder<CandidateGenerationConfig.Builder,CandidateGenerationConfig>

Method Summary

Modifier and Type	Method	Description
CandidateGenerationConfig.Builder	algorithmsConfig(Collection<AutoMLAlgorithmConfig> algorithmsConfig)	Your Autopilot job trains a default set of algorithms on your dataset.
CandidateGenerationConfig.Builder	algorithmsConfig(Consumer<AutoMLAlgorithmConfig.Builder>... algorithmsConfig)	Your Autopilot job trains a default set of algorithms on your dataset.
CandidateGenerationConfig.Builder	algorithmsConfig(AutoMLAlgorithmConfig... algorithmsConfig)	Your Autopilot job trains a default set of algorithms on your dataset.

Methods inherited from interface software.amazon.awssdk.utils.builder.CopyableBuilder

copy

Methods inherited from interface software.amazon.awssdk.utils.builder.SdkBuilder

applyMutation, build

Methods inherited from interface software.amazon.awssdk.core.SdkPojo

equalsBySdkFields, sdkFieldNameToField, sdkFields

Method Detail

algorithmsConfig

CandidateGenerationConfig.Builder algorithmsConfig(Collection<AutoMLAlgorithmConfig> algorithmsConfig)

Your Autopilot job trains a default set of algorithms on your dataset. For tabular and time-series data, you can customize the algorithm list by selecting a subset of algorithms for your problem type.

AlgorithmsConfig stores the customized selection of algorithms to train on your data.

• For the tabular problem type TabularJobConfig, the list of available algorithms to choose from depends on the training mode set in AutoMLJobConfig.Mode .

  • AlgorithmsConfig should not be set when the training mode AutoMLJobConfig.Mode is set to AUTO.

  • When AlgorithmsConfig is provided, one AutoMLAlgorithms attribute must be set and one only.

    If the list of algorithms provided as values for AutoMLAlgorithms is empty, CandidateGenerationConfig uses the full set of algorithms for the given training mode.

  • When AlgorithmsConfig is not provided, CandidateGenerationConfig uses the full set of algorithms for the given training mode.

  For the list of all algorithms per training mode, see AlgorithmConfig.

  For more information on each algorithm, see the Algorithm support section in the Autopilot developer guide.

• For the time-series forecasting problem type TimeSeriesForecastingJobConfig, choose your algorithms from the list provided in AlgorithmConfig.

  For more information on each algorithm, see the Algorithms support for time-series forecasting section in the Autopilot developer guide.

  • When AlgorithmsConfig is provided, one AutoMLAlgorithms attribute must be set and one only.

    If the list of algorithms provided as values for AutoMLAlgorithms is empty, CandidateGenerationConfig uses the full set of algorithms for time-series forecasting.

  • When AlgorithmsConfig is not provided, CandidateGenerationConfig uses the full set of algorithms for time-series forecasting.

Parameters:

algorithmsConfig - Your Autopilot job trains a default set of algorithms on your dataset. For tabular and time-series data, you can customize the algorithm list by selecting a subset of algorithms for your problem type.

AlgorithmsConfig stores the customized selection of algorithms to train on your data.

• For the tabular problem type TabularJobConfig, the list of available algorithms to choose from depends on the training mode set in AutoMLJobConfig.Mode .

  • AlgorithmsConfig should not be set when the training mode AutoMLJobConfig.Mode is set to AUTO.

  • When AlgorithmsConfig is provided, one AutoMLAlgorithms attribute must be set and one only.

    If the list of algorithms provided as values for AutoMLAlgorithms is empty, CandidateGenerationConfig uses the full set of algorithms for the given training mode.

  • When AlgorithmsConfig is not provided, CandidateGenerationConfig uses the full set of algorithms for the given training mode.

  For the list of all algorithms per training mode, see AlgorithmConfig.

  For more information on each algorithm, see the Algorithm support section in the Autopilot developer guide.

• For the time-series forecasting problem type TimeSeriesForecastingJobConfig, choose your algorithms from the list provided in AlgorithmConfig.

  For more information on each algorithm, see the Algorithms support for time-series forecasting section in the Autopilot developer guide.

  • When AlgorithmsConfig is provided, one AutoMLAlgorithms attribute must be set and one only.

    If the list of algorithms provided as values for AutoMLAlgorithms is empty, CandidateGenerationConfig uses the full set of algorithms for time-series forecasting.

  • When AlgorithmsConfig is not provided, CandidateGenerationConfig uses the full set of algorithms for time-series forecasting.

Returns:

Returns a reference to this object so that method calls can be chained together.

algorithmsConfig

CandidateGenerationConfig.Builder algorithmsConfig(AutoMLAlgorithmConfig... algorithmsConfig)

Your Autopilot job trains a default set of algorithms on your dataset. For tabular and time-series data, you can customize the algorithm list by selecting a subset of algorithms for your problem type.

AlgorithmsConfig stores the customized selection of algorithms to train on your data.

• For the tabular problem type TabularJobConfig, the list of available algorithms to choose from depends on the training mode set in AutoMLJobConfig.Mode .

  • AlgorithmsConfig should not be set when the training mode AutoMLJobConfig.Mode is set to AUTO.

  • When AlgorithmsConfig is provided, one AutoMLAlgorithms attribute must be set and one only.

    If the list of algorithms provided as values for AutoMLAlgorithms is empty, CandidateGenerationConfig uses the full set of algorithms for the given training mode.

  • When AlgorithmsConfig is not provided, CandidateGenerationConfig uses the full set of algorithms for the given training mode.

  For the list of all algorithms per training mode, see AlgorithmConfig.

  For more information on each algorithm, see the Algorithm support section in the Autopilot developer guide.

• For the time-series forecasting problem type TimeSeriesForecastingJobConfig, choose your algorithms from the list provided in AlgorithmConfig.

  For more information on each algorithm, see the Algorithms support for time-series forecasting section in the Autopilot developer guide.

  • When AlgorithmsConfig is provided, one AutoMLAlgorithms attribute must be set and one only.

    If the list of algorithms provided as values for AutoMLAlgorithms is empty, CandidateGenerationConfig uses the full set of algorithms for time-series forecasting.

  • When AlgorithmsConfig is not provided, CandidateGenerationConfig uses the full set of algorithms for time-series forecasting.

Parameters:

algorithmsConfig - Your Autopilot job trains a default set of algorithms on your dataset. For tabular and time-series data, you can customize the algorithm list by selecting a subset of algorithms for your problem type.

AlgorithmsConfig stores the customized selection of algorithms to train on your data.

• For the tabular problem type TabularJobConfig, the list of available algorithms to choose from depends on the training mode set in AutoMLJobConfig.Mode .

  • AlgorithmsConfig should not be set when the training mode AutoMLJobConfig.Mode is set to AUTO.

  • When AlgorithmsConfig is provided, one AutoMLAlgorithms attribute must be set and one only.

    If the list of algorithms provided as values for AutoMLAlgorithms is empty, CandidateGenerationConfig uses the full set of algorithms for the given training mode.

  • When AlgorithmsConfig is not provided, CandidateGenerationConfig uses the full set of algorithms for the given training mode.

  For the list of all algorithms per training mode, see AlgorithmConfig.

  For more information on each algorithm, see the Algorithm support section in the Autopilot developer guide.

• For the time-series forecasting problem type TimeSeriesForecastingJobConfig, choose your algorithms from the list provided in AlgorithmConfig.

  For more information on each algorithm, see the Algorithms support for time-series forecasting section in the Autopilot developer guide.

  • When AlgorithmsConfig is provided, one AutoMLAlgorithms attribute must be set and one only.

    If the list of algorithms provided as values for AutoMLAlgorithms is empty, CandidateGenerationConfig uses the full set of algorithms for time-series forecasting.

  • When AlgorithmsConfig is not provided, CandidateGenerationConfig uses the full set of algorithms for time-series forecasting.

Returns:

Returns a reference to this object so that method calls can be chained together.

algorithmsConfig

CandidateGenerationConfig.Builder algorithmsConfig(Consumer<AutoMLAlgorithmConfig.Builder>... algorithmsConfig)

Your Autopilot job trains a default set of algorithms on your dataset. For tabular and time-series data, you can customize the algorithm list by selecting a subset of algorithms for your problem type.

AlgorithmsConfig stores the customized selection of algorithms to train on your data.

• For the tabular problem type TabularJobConfig, the list of available algorithms to choose from depends on the training mode set in AutoMLJobConfig.Mode .

  • AlgorithmsConfig should not be set when the training mode AutoMLJobConfig.Mode is set to AUTO.

  • When AlgorithmsConfig is provided, one AutoMLAlgorithms attribute must be set and one only.

    If the list of algorithms provided as values for AutoMLAlgorithms is empty, CandidateGenerationConfig uses the full set of algorithms for the given training mode.

  • When AlgorithmsConfig is not provided, CandidateGenerationConfig uses the full set of algorithms for the given training mode.

  For the list of all algorithms per training mode, see AlgorithmConfig.

  For more information on each algorithm, see the Algorithm support section in the Autopilot developer guide.

• For the time-series forecasting problem type TimeSeriesForecastingJobConfig, choose your algorithms from the list provided in AlgorithmConfig.

  For more information on each algorithm, see the Algorithms support for time-series forecasting section in the Autopilot developer guide.

  • When AlgorithmsConfig is provided, one AutoMLAlgorithms attribute must be set and one only.

    If the list of algorithms provided as values for AutoMLAlgorithms is empty, CandidateGenerationConfig uses the full set of algorithms for time-series forecasting.

  • When AlgorithmsConfig is not provided, CandidateGenerationConfig uses the full set of algorithms for time-series forecasting.

This is a convenience method that creates an instance of the AutoMLAlgorithmConfig.Builder avoiding the need to create one manually via AutoMLAlgorithmConfig.builder().

When the Consumer completes, SdkBuilder.build() is called immediately and its result is passed to #algorithmsConfig(List).

Parameters:

algorithmsConfig - a consumer that will call methods on AutoMLAlgorithmConfig.Builder

Returns:

Returns a reference to this object so that method calls can be chained together.

See Also:

#algorithmsConfig(java.util.Collection)