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:

  • Method Details

    • 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()
      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<AutoMLAlgorithmConfig.Builder,AutoMLAlgorithmConfig>
      Returns:
      a builder for type T

    • 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)
      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.