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:

  • Method Details

    • 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()
      Description copied from interface: ToCopyableBuilder
      Take this object and create a builder that contains all of the current property values of this object.
      Specified by:
      toBuilder in interface ToCopyableBuilder<CandidateGenerationConfig.Builder,CandidateGenerationConfig>
      Returns:
      a builder for type T

    • 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)
      Description copied from interface: SdkPojo
      Indicates whether some other object is "equal to" this one by SDK fields. An SDK field is a modeled, non-inherited field in an SdkPojo class, and is generated based on a service model.

      If an SdkPojo class does not have any inherited fields, equalsBySdkFields and equals are essentially the same.

      Specified by:
      equalsBySdkFields in interface SdkPojo
      Parameters:
      obj - the object to be compared with
      Returns:
      true if the other object equals to this object by sdk fields, false otherwise.

    • 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
      Returns:
      List of SdkField in this POJO. May be empty list but should never be null.