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

Class AutoMLAlgorithmConfig

java.lang.Object

software.amazon.awssdk.services.sagemaker.model.AutoMLAlgorithmConfig

All Implemented Interfaces:

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

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

The selection of algorithms trained on your dataset to generate the model candidates for an Autopilot job.

See Also:

Serialized Form

Nested Class Summary

Nested Classes

Modifier and Type	Class	Description
static interface	AutoMLAlgorithmConfig.Builder

Method Summary

Modifier and Type	Method	Description
List<AutoMLAlgorithm>	autoMLAlgorithms()	The selection of algorithms trained on your dataset to generate the model candidates for an Autopilot job.
List<String>	autoMLAlgorithmsAsStrings()	The selection of algorithms trained on your dataset to generate the model candidates for an Autopilot job.
static AutoMLAlgorithmConfig.Builder	builder()
boolean	equals(Object obj)
boolean	equalsBySdkFields(Object obj)
<T> Optional<T>	getValueForField(String fieldName, Class<T> clazz)
boolean	hasAutoMLAlgorithms()	For responses, this returns true if the service returned a value for the AutoMLAlgorithms property.
int	hashCode()
Map<String,SdkField<?>>	sdkFieldNameToField()
List<SdkField<?>>	sdkFields()
static Class<? extends AutoMLAlgorithmConfig.Builder>	serializableBuilderClass()
AutoMLAlgorithmConfig.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

autoMLAlgorithms

public final List<AutoMLAlgorithm> autoMLAlgorithms()

The selection of algorithms trained on your dataset to generate the model candidates for an Autopilot job.

• For the tabular problem type TabularJobConfig:

  Selected algorithms must belong to the list corresponding to the training mode set in AutoMLJobConfig.Mode (ENSEMBLING or HYPERPARAMETER_TUNING). Choose a minimum of 1 algorithm.

  • In ENSEMBLING mode:

    • "catboost"

    • "extra-trees"

    • "fastai"

    • "lightgbm"

    • "linear-learner"

    • "nn-torch"

    • "randomforest"

    • "xgboost"

  • In HYPERPARAMETER_TUNING mode:

    • "linear-learner"

    • "mlp"

    • "xgboost"

• For the time-series forecasting problem type TimeSeriesForecastingJobConfig:

  • Choose your algorithms from this list.

    • "cnn-qr"

    • "deepar"

    • "prophet"

    • "arima"

    • "npts"

    • "ets"

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 hasAutoMLAlgorithms() method.

Returns:

The selection of algorithms trained on your dataset to generate the model candidates for an Autopilot job.

• For the tabular problem type TabularJobConfig:

  Selected algorithms must belong to the list corresponding to the training mode set in AutoMLJobConfig.Mode (ENSEMBLING or HYPERPARAMETER_TUNING). Choose a minimum of 1 algorithm.

  • In ENSEMBLING mode:

    • "catboost"

    • "extra-trees"

    • "fastai"

    • "lightgbm"

    • "linear-learner"

    • "nn-torch"

    • "randomforest"

    • "xgboost"

  • In HYPERPARAMETER_TUNING mode:

    • "linear-learner"

    • "mlp"

    • "xgboost"

• For the time-series forecasting problem type TimeSeriesForecastingJobConfig:

  • Choose your algorithms from this list.

    • "cnn-qr"

    • "deepar"

    • "prophet"

    • "arima"

    • "npts"

    • "ets"

hasAutoMLAlgorithms

public final boolean hasAutoMLAlgorithms()

For responses, this returns true if the service returned a value for the AutoMLAlgorithms 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.

autoMLAlgorithmsAsStrings

public final List<String> autoMLAlgorithmsAsStrings()

The selection of algorithms trained on your dataset to generate the model candidates for an Autopilot job.

• For the tabular problem type TabularJobConfig:

  Selected algorithms must belong to the list corresponding to the training mode set in AutoMLJobConfig.Mode (ENSEMBLING or HYPERPARAMETER_TUNING). Choose a minimum of 1 algorithm.

  • In ENSEMBLING mode:

    • "catboost"

    • "extra-trees"

    • "fastai"

    • "lightgbm"

    • "linear-learner"

    • "nn-torch"

    • "randomforest"

    • "xgboost"

  • In HYPERPARAMETER_TUNING mode:

    • "linear-learner"

    • "mlp"

    • "xgboost"

• For the time-series forecasting problem type TimeSeriesForecastingJobConfig:

  • Choose your algorithms from this list.

    • "cnn-qr"

    • "deepar"

    • "prophet"

    • "arima"

    • "npts"

    • "ets"

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 hasAutoMLAlgorithms() method.

Returns:

The selection of algorithms trained on your dataset to generate the model candidates for an Autopilot job.

• For the tabular problem type TabularJobConfig:

  Selected algorithms must belong to the list corresponding to the training mode set in AutoMLJobConfig.Mode (ENSEMBLING or HYPERPARAMETER_TUNING). Choose a minimum of 1 algorithm.

  • In ENSEMBLING mode:

    • "catboost"

    • "extra-trees"

    • "fastai"

    • "lightgbm"

    • "linear-learner"

    • "nn-torch"

    • "randomforest"

    • "xgboost"

  • In HYPERPARAMETER_TUNING mode:

    • "linear-learner"

    • "mlp"

    • "xgboost"

• For the time-series forecasting problem type TimeSeriesForecastingJobConfig:

  • Choose your algorithms from this list.

    • "cnn-qr"

    • "deepar"

    • "prophet"

    • "arima"

    • "npts"

    • "ets"

toBuilder

public AutoMLAlgorithmConfig.Builder toBuilder()

Specified by:

toBuilder in interface ToCopyableBuilder<AutoMLAlgorithmConfig.Builder,AutoMLAlgorithmConfig>

builder

public static AutoMLAlgorithmConfig.Builder builder()

serializableBuilderClass

public static Class<? extends AutoMLAlgorithmConfig.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