com.amazonaws.services.sagemaker.model

Class AutoMLAlgorithmConfig

java.lang.Object

com.amazonaws.services.sagemaker.model.AutoMLAlgorithmConfig

All Implemented Interfaces:

StructuredPojo, Serializable, Cloneable

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

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

See Also:

AWS API Documentation, Serialized Form

Constructor Summary

Constructors

Constructor and Description
AutoMLAlgorithmConfig()

Method Summary

Modifier and Type	Method and Description
AutoMLAlgorithmConfig	clone()
boolean	equals(Object obj)
List<String>	getAutoMLAlgorithms() The selection of algorithms trained on your dataset to generate the model candidates for an Autopilot job.
int	hashCode()
void	marshall(ProtocolMarshaller protocolMarshaller) Marshalls this structured data using the given ProtocolMarshaller.
void	setAutoMLAlgorithms(Collection<String> autoMLAlgorithms) The selection of algorithms trained on your dataset to generate the model candidates for an Autopilot job.
String	toString() Returns a string representation of this object.
AutoMLAlgorithmConfig	withAutoMLAlgorithms(AutoMLAlgorithm... autoMLAlgorithms) The selection of algorithms trained on your dataset to generate the model candidates for an Autopilot job.
AutoMLAlgorithmConfig	withAutoMLAlgorithms(Collection<String> autoMLAlgorithms) The selection of algorithms trained on your dataset to generate the model candidates for an Autopilot job.
AutoMLAlgorithmConfig	withAutoMLAlgorithms(String... autoMLAlgorithms) The selection of algorithms trained on your dataset to generate the model candidates for an Autopilot job.

Methods inherited from class java.lang.Object

getClass, notify, notifyAll, wait, wait, wait

Constructor Detail

AutoMLAlgorithmConfig

public AutoMLAlgorithmConfig()

Method Detail

getAutoMLAlgorithms

public List<String> getAutoMLAlgorithms()

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"

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"

See Also:

AutoMLAlgorithm

setAutoMLAlgorithms

public void setAutoMLAlgorithms(Collection<String> 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"

Parameters:

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"

See Also:

AutoMLAlgorithm

withAutoMLAlgorithms

public AutoMLAlgorithmConfig withAutoMLAlgorithms(String... 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"

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

Parameters:

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"

Returns:

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

See Also:

AutoMLAlgorithm

withAutoMLAlgorithms

public AutoMLAlgorithmConfig withAutoMLAlgorithms(Collection<String> 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"

Parameters:

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"

Returns:

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

See Also:

AutoMLAlgorithm

withAutoMLAlgorithms

public AutoMLAlgorithmConfig withAutoMLAlgorithms(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"

Parameters:

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"

Returns:

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

See Also:

AutoMLAlgorithm

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 AutoMLAlgorithmConfig 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.