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

Class CandidateGenerationConfig

java.lang.Object

software.amazon.awssdk.services.sagemaker.model.CandidateGenerationConfig

All Implemented Interfaces:

Serializable, SdkPojo, ToCopyableBuilder<CandidateGenerationConfig.Builder,CandidateGenerationConfig>

@Generated("software.amazon.awssdk:codegen")
public final class CandidateGenerationConfig
extends Object
implements SdkPojo, Serializable, ToCopyableBuilder<CandidateGenerationConfig.Builder,CandidateGenerationConfig>

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

See Also:

Serialized Form

Nested Class Summary

Nested Classes

Modifier and Type	Class	Description
static interface	CandidateGenerationConfig.Builder

Method Summary

Modifier and Type	Method	Description
List<AutoMLAlgorithmConfig>	algorithmsConfig()	Your Autopilot job trains a default set of algorithms on your dataset.
static CandidateGenerationConfig.Builder	builder()
boolean	equals(Object obj)
boolean	equalsBySdkFields(Object obj)
<T> Optional<T>	getValueForField(String fieldName, Class<T> clazz)
boolean	hasAlgorithmsConfig()	For responses, this returns true if the service returned a value for the AlgorithmsConfig property.
int	hashCode()
Map<String,SdkField<?>>	sdkFieldNameToField()
List<SdkField<?>>	sdkFields()
static Class<? extends CandidateGenerationConfig.Builder>	serializableBuilderClass()
CandidateGenerationConfig.Builder	toBuilder()
String	toString()	Returns a string representation of this object.

Methods inherited from class java.lang.Object

clone, finalize, getClass, notify, notifyAll, wait, wait, wait

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

copy

Method Detail

hasAlgorithmsConfig

public final boolean hasAlgorithmsConfig()

For responses, this returns true if the service returned a value for the AlgorithmsConfig property. This DOES NOT check that the value is non-empty (for which, you should check the isEmpty() method on the property). This is useful because the SDK will never return a null collection or map, but you may need to differentiate between the service returning nothing (or null) and the service returning an empty collection or map. For requests, this returns true if a value for the property was specified in the request builder, and false if a value was not specified.

algorithmsConfig

public final List<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.

Attempts to modify the collection returned by this method will result in an UnsupportedOperationException.

This method will never return null. If you would like to know whether the service returned this field (so that you can differentiate between null and empty), you can use the hasAlgorithmsConfig() method.

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.

toBuilder

public CandidateGenerationConfig.Builder toBuilder()

Specified by:

toBuilder in interface ToCopyableBuilder<CandidateGenerationConfig.Builder,CandidateGenerationConfig>

builder

public static CandidateGenerationConfig.Builder builder()

serializableBuilderClass

public static Class<? extends CandidateGenerationConfig.Builder> serializableBuilderClass()

hashCode

public final int hashCode()

Overrides:

hashCode in class Object

equals

public final boolean equals(Object obj)

Overrides:

equals in class Object

equalsBySdkFields

public final boolean equalsBySdkFields(Object obj)

Specified by:

equalsBySdkFields in interface SdkPojo

toString

public final 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

getValueForField

public final <T> Optional<T> getValueForField(String fieldName,
                                              Class<T> clazz)

sdkFields

public final List<SdkField<?>> sdkFields()

Specified by:

sdkFields in interface SdkPojo

sdkFieldNameToField

public final Map<String,SdkField<?>> sdkFieldNameToField()

Specified by:

sdkFieldNameToField in interface SdkPojo