com.amazonaws.services.sagemaker.model

Class CandidateGenerationConfig

java.lang.Object

com.amazonaws.services.sagemaker.model.CandidateGenerationConfig

All Implemented Interfaces:

StructuredPojo, Serializable, Cloneable

@Generated(value="com.amazonaws:aws-java-sdk-code-generator")
public class CandidateGenerationConfig
extends Object
implements Serializable, Cloneable, StructuredPojo

Stores the configuration information for how model candidates are generated using an AutoML job V2.

See Also:

AWS API Documentation, Serialized Form

Constructor Summary

Constructors

Constructor and Description
CandidateGenerationConfig()

Method Summary

Modifier and Type	Method and Description
CandidateGenerationConfig	clone()
boolean	equals(Object obj)
List<AutoMLAlgorithmConfig>	getAlgorithmsConfig() Your Autopilot job trains a default set of algorithms on your dataset.
int	hashCode()
void	marshall(ProtocolMarshaller protocolMarshaller) Marshalls this structured data using the given ProtocolMarshaller.
void	setAlgorithmsConfig(Collection<AutoMLAlgorithmConfig> algorithmsConfig) Your Autopilot job trains a default set of algorithms on your dataset.
String	toString() Returns a string representation of this object.
CandidateGenerationConfig	withAlgorithmsConfig(AutoMLAlgorithmConfig... algorithmsConfig) Your Autopilot job trains a default set of algorithms on your dataset.
CandidateGenerationConfig	withAlgorithmsConfig(Collection<AutoMLAlgorithmConfig> algorithmsConfig) Your Autopilot job trains a default set of algorithms on your dataset.

Methods inherited from class java.lang.Object

getClass, notify, notifyAll, wait, wait, wait

Constructor Detail

CandidateGenerationConfig

public CandidateGenerationConfig()

Method Detail

getAlgorithmsConfig

public List<AutoMLAlgorithmConfig> getAlgorithmsConfig()

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:

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.

setAlgorithmsConfig

public void setAlgorithmsConfig(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.

withAlgorithmsConfig

public CandidateGenerationConfig withAlgorithmsConfig(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.

NOTE: This method appends the values to the existing list (if any). Use setAlgorithmsConfig(java.util.Collection) or withAlgorithmsConfig(java.util.Collection) if you want to override the existing values.

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.

withAlgorithmsConfig

public CandidateGenerationConfig withAlgorithmsConfig(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.

toString

public String toString()

Returns a string representation of this object. This is useful for testing and debugging. Sensitive data will be redacted from this string using a placeholder value.

Overrides:

toString in class Object

Returns:

A string representation of this object.

See Also:

Object.toString()

equals

public boolean equals(Object obj)

Overrides:

equals in class Object

hashCode

public int hashCode()

Overrides:

hashCode in class Object

clone

public CandidateGenerationConfig clone()

Overrides:

clone in class Object

marshall

public void marshall(ProtocolMarshaller protocolMarshaller)

Description copied from interface: StructuredPojo

Marshalls this structured data using the given ProtocolMarshaller.

Specified by:

marshall in interface StructuredPojo

Parameters:

protocolMarshaller - Implementation of ProtocolMarshaller used to marshall this object's data.