Class TabularJobConfig

java.lang.Object
software.amazon.awssdk.services.sagemaker.model.TabularJobConfig
All Implemented Interfaces:
Serializable, SdkPojo, ToCopyableBuilder<TabularJobConfig.Builder,TabularJobConfig>
@Generated("software.amazon.awssdk:codegen") public final class TabularJobConfig extends Object implements SdkPojo, Serializable, ToCopyableBuilder<TabularJobConfig.Builder,TabularJobConfig>

The collection of settings used by an AutoML job V2 for the tabular problem type.

See Also:

  • Method Details

    • candidateGenerationConfig

      public final CandidateGenerationConfig candidateGenerationConfig()

      The configuration information of how model candidates are generated.

      Returns:
      The configuration information of how model candidates are generated.

    • completionCriteria

      public final AutoMLJobCompletionCriteria completionCriteria()
      Returns the value of the CompletionCriteria property for this object.
      Returns:
      The value of the CompletionCriteria property for this object.

    • featureSpecificationS3Uri

      public final String featureSpecificationS3Uri()

      A URL to the Amazon S3 data source containing selected features from the input data source to run an Autopilot job V2. You can input FeatureAttributeNames (optional) in JSON format as shown below:

      { "FeatureAttributeNames":["col1", "col2", ...] }.

      You can also specify the data type of the feature (optional) in the format shown below:

      { "FeatureDataTypes":{"col1":"numeric", "col2":"categorical" ... } }

      These column keys may not include the target column.

      In ensembling mode, Autopilot only supports the following data types: numeric, categorical, text, and datetime. In HPO mode, Autopilot can support numeric, categorical, text, datetime, and sequence.

      If only FeatureDataTypes is provided, the column keys (col1, col2,..) should be a subset of the column names in the input data.

      If both FeatureDataTypes and FeatureAttributeNames are provided, then the column keys should be a subset of the column names provided in FeatureAttributeNames.

      The key name FeatureAttributeNames is fixed. The values listed in ["col1", "col2", ...] are case sensitive and should be a list of strings containing unique values that are a subset of the column names in the input data. The list of columns provided must not include the target column.

      Returns:
      A URL to the Amazon S3 data source containing selected features from the input data source to run an Autopilot job V2. You can input FeatureAttributeNames (optional) in JSON format as shown below:

      { "FeatureAttributeNames":["col1", "col2", ...] }.

      You can also specify the data type of the feature (optional) in the format shown below:

      { "FeatureDataTypes":{"col1":"numeric", "col2":"categorical" ... } }

      These column keys may not include the target column.

      In ensembling mode, Autopilot only supports the following data types: numeric, categorical, text, and datetime. In HPO mode, Autopilot can support numeric, categorical, text, datetime, and sequence.

      If only FeatureDataTypes is provided, the column keys (col1, col2 ,..) should be a subset of the column names in the input data.

      If both FeatureDataTypes and FeatureAttributeNames are provided, then the column keys should be a subset of the column names provided in FeatureAttributeNames.

      The key name FeatureAttributeNames is fixed. The values listed in ["col1", "col2", ...] are case sensitive and should be a list of strings containing unique values that are a subset of the column names in the input data. The list of columns provided must not include the target column.

    • mode

      public final AutoMLMode mode()

      The method that Autopilot uses to train the data. You can either specify the mode manually or let Autopilot choose for you based on the dataset size by selecting AUTO. In AUTO mode, Autopilot chooses ENSEMBLING for datasets smaller than 100 MB, and HYPERPARAMETER_TUNING for larger ones.

      The ENSEMBLING mode uses a multi-stack ensemble model to predict classification and regression tasks directly from your dataset. This machine learning mode combines several base models to produce an optimal predictive model. It then uses a stacking ensemble method to combine predictions from contributing members. A multi-stack ensemble model can provide better performance over a single model by combining the predictive capabilities of multiple models. See Autopilot algorithm support for a list of algorithms supported by ENSEMBLING mode.

      The HYPERPARAMETER_TUNING (HPO) mode uses the best hyperparameters to train the best version of a model. HPO automatically selects an algorithm for the type of problem you want to solve. Then HPO finds the best hyperparameters according to your objective metric. See Autopilot algorithm support for a list of algorithms supported by HYPERPARAMETER_TUNING mode.

      If the service returns an enum value that is not available in the current SDK version, mode will return AutoMLMode.UNKNOWN_TO_SDK_VERSION. The raw value returned by the service is available from modeAsString().

      Returns:
      The method that Autopilot uses to train the data. You can either specify the mode manually or let Autopilot choose for you based on the dataset size by selecting AUTO. In AUTO mode, Autopilot chooses ENSEMBLING for datasets smaller than 100 MB, and HYPERPARAMETER_TUNING for larger ones.

      The ENSEMBLING mode uses a multi-stack ensemble model to predict classification and regression tasks directly from your dataset. This machine learning mode combines several base models to produce an optimal predictive model. It then uses a stacking ensemble method to combine predictions from contributing members. A multi-stack ensemble model can provide better performance over a single model by combining the predictive capabilities of multiple models. See Autopilot algorithm support for a list of algorithms supported by ENSEMBLING mode.

      The HYPERPARAMETER_TUNING (HPO) mode uses the best hyperparameters to train the best version of a model. HPO automatically selects an algorithm for the type of problem you want to solve. Then HPO finds the best hyperparameters according to your objective metric. See Autopilot algorithm support for a list of algorithms supported by HYPERPARAMETER_TUNING mode.

      See Also:

    • modeAsString

      public final String modeAsString()

      The method that Autopilot uses to train the data. You can either specify the mode manually or let Autopilot choose for you based on the dataset size by selecting AUTO. In AUTO mode, Autopilot chooses ENSEMBLING for datasets smaller than 100 MB, and HYPERPARAMETER_TUNING for larger ones.

      The ENSEMBLING mode uses a multi-stack ensemble model to predict classification and regression tasks directly from your dataset. This machine learning mode combines several base models to produce an optimal predictive model. It then uses a stacking ensemble method to combine predictions from contributing members. A multi-stack ensemble model can provide better performance over a single model by combining the predictive capabilities of multiple models. See Autopilot algorithm support for a list of algorithms supported by ENSEMBLING mode.

      The HYPERPARAMETER_TUNING (HPO) mode uses the best hyperparameters to train the best version of a model. HPO automatically selects an algorithm for the type of problem you want to solve. Then HPO finds the best hyperparameters according to your objective metric. See Autopilot algorithm support for a list of algorithms supported by HYPERPARAMETER_TUNING mode.

      If the service returns an enum value that is not available in the current SDK version, mode will return AutoMLMode.UNKNOWN_TO_SDK_VERSION. The raw value returned by the service is available from modeAsString().

      Returns:
      The method that Autopilot uses to train the data. You can either specify the mode manually or let Autopilot choose for you based on the dataset size by selecting AUTO. In AUTO mode, Autopilot chooses ENSEMBLING for datasets smaller than 100 MB, and HYPERPARAMETER_TUNING for larger ones.

      The ENSEMBLING mode uses a multi-stack ensemble model to predict classification and regression tasks directly from your dataset. This machine learning mode combines several base models to produce an optimal predictive model. It then uses a stacking ensemble method to combine predictions from contributing members. A multi-stack ensemble model can provide better performance over a single model by combining the predictive capabilities of multiple models. See Autopilot algorithm support for a list of algorithms supported by ENSEMBLING mode.

      The HYPERPARAMETER_TUNING (HPO) mode uses the best hyperparameters to train the best version of a model. HPO automatically selects an algorithm for the type of problem you want to solve. Then HPO finds the best hyperparameters according to your objective metric. See Autopilot algorithm support for a list of algorithms supported by HYPERPARAMETER_TUNING mode.

      See Also:

    • generateCandidateDefinitionsOnly

      public final Boolean generateCandidateDefinitionsOnly()

      Generates possible candidates without training the models. A model candidate is a combination of data preprocessors, algorithms, and algorithm parameter settings.

      Returns:
      Generates possible candidates without training the models. A model candidate is a combination of data preprocessors, algorithms, and algorithm parameter settings.

    • problemType

      public final ProblemType problemType()

      The type of supervised learning problem available for the model candidates of the AutoML job V2. For more information, see SageMaker Autopilot problem types.

      You must either specify the type of supervised learning problem in ProblemType and provide the AutoMLJobObjective metric, or none at all.

      If the service returns an enum value that is not available in the current SDK version, problemType will return ProblemType.UNKNOWN_TO_SDK_VERSION. The raw value returned by the service is available from problemTypeAsString().

      Returns:
      The type of supervised learning problem available for the model candidates of the AutoML job V2. For more information, see SageMaker Autopilot problem types.

      You must either specify the type of supervised learning problem in ProblemType and provide the AutoMLJobObjective metric, or none at all.

      See Also:

    • problemTypeAsString

      public final String problemTypeAsString()

      The type of supervised learning problem available for the model candidates of the AutoML job V2. For more information, see SageMaker Autopilot problem types.

      You must either specify the type of supervised learning problem in ProblemType and provide the AutoMLJobObjective metric, or none at all.

      If the service returns an enum value that is not available in the current SDK version, problemType will return ProblemType.UNKNOWN_TO_SDK_VERSION. The raw value returned by the service is available from problemTypeAsString().

      Returns:
      The type of supervised learning problem available for the model candidates of the AutoML job V2. For more information, see SageMaker Autopilot problem types.

      You must either specify the type of supervised learning problem in ProblemType and provide the AutoMLJobObjective metric, or none at all.

      See Also:

    • targetAttributeName

      public final String targetAttributeName()

      The name of the target variable in supervised learning, usually represented by 'y'.

      Returns:
      The name of the target variable in supervised learning, usually represented by 'y'.

    • sampleWeightAttributeName

      public final String sampleWeightAttributeName()

      If specified, this column name indicates which column of the dataset should be treated as sample weights for use by the objective metric during the training, evaluation, and the selection of the best model. This column is not considered as a predictive feature. For more information on Autopilot metrics, see Metrics and validation.

      Sample weights should be numeric, non-negative, with larger values indicating which rows are more important than others. Data points that have invalid or no weight value are excluded.

      Support for sample weights is available in Ensembling mode only.

      Returns:
      If specified, this column name indicates which column of the dataset should be treated as sample weights for use by the objective metric during the training, evaluation, and the selection of the best model. This column is not considered as a predictive feature. For more information on Autopilot metrics, see Metrics and validation.

      Sample weights should be numeric, non-negative, with larger values indicating which rows are more important than others. Data points that have invalid or no weight value are excluded.

      Support for sample weights is available in Ensembling mode only.

    • toBuilder

      public TabularJobConfig.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<TabularJobConfig.Builder,TabularJobConfig>
      Returns:
      a builder for type T

    • builder

      public static TabularJobConfig.Builder builder()

    • serializableBuilderClass

      public static Class<? extends TabularJobConfig.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.