Package org.apache.spark.ml.tuning

Class TrainValidationSplit

Object
org.apache.spark.ml.PipelineStage
org.apache.spark.ml.Estimator<TrainValidationSplitModel>
org.apache.spark.ml.tuning.TrainValidationSplit
All Implemented Interfaces:
Serializable, org.apache.spark.internal.Logging, Params, HasCollectSubModels, org.apache.spark.ml.param.shared.HasParallelism, HasSeed, org.apache.spark.ml.tuning.TrainValidationSplitParams, org.apache.spark.ml.tuning.ValidatorParams, Identifiable, MLWritable
public class TrainValidationSplit extends Estimator<TrainValidationSplitModel> implements org.apache.spark.ml.tuning.TrainValidationSplitParams, org.apache.spark.ml.param.shared.HasParallelism, HasCollectSubModels, MLWritable, org.apache.spark.internal.Logging
Validation for hyper-parameter tuning. Randomly splits the input dataset into train and validation sets, and uses evaluation metric on the validation set to select the best model. Similar to CrossValidator, but only splits the set once.
See Also:

  • Constructor Details

    • TrainValidationSplit

      public TrainValidationSplit(String uid)

    • TrainValidationSplit

      public TrainValidationSplit()

  • Method Details

    • read

      public static MLReader<TrainValidationSplit> read()

    • load

      public static TrainValidationSplit load(String path)

    • collectSubModels

      public final BooleanParam collectSubModels()
      Description copied from interface: HasCollectSubModels
      Param for whether to collect a list of sub-models trained during tuning. If set to false, then only the single best sub-model will be available after fitting. If set to true, then all sub-models will be available. Warning: For large models, collecting all sub-models can cause OOMs on the Spark driver.
      Specified by:
      collectSubModels in interface HasCollectSubModels
      Returns:
      (undocumented)

    • parallelism

      public IntParam parallelism()
      Specified by:
      parallelism in interface org.apache.spark.ml.param.shared.HasParallelism

    • trainRatio

      public DoubleParam trainRatio()
      Specified by:
      trainRatio in interface org.apache.spark.ml.tuning.TrainValidationSplitParams

    • estimator

      public Param<Estimator<?>> estimator()
      Specified by:
      estimator in interface org.apache.spark.ml.tuning.ValidatorParams

    • estimatorParamMaps

      public Param<ParamMap[]> estimatorParamMaps()
      Specified by:
      estimatorParamMaps in interface org.apache.spark.ml.tuning.ValidatorParams

    • evaluator

      public Param<Evaluator> evaluator()
      Specified by:
      evaluator in interface org.apache.spark.ml.tuning.ValidatorParams

    • seed

      public final LongParam seed()
      Description copied from interface: HasSeed
      Param for random seed.
      Specified by:
      seed in interface HasSeed
      Returns:
      (undocumented)

    • uid

      public String uid()
      Description copied from interface: Identifiable
      An immutable unique ID for the object and its derivatives.
      Specified by:
      uid in interface Identifiable
      Returns:
      (undocumented)

    • setEstimator

      public TrainValidationSplit setEstimator(Estimator<?> value)

    • setEstimatorParamMaps

      public TrainValidationSplit setEstimatorParamMaps(ParamMap[] value)

    • setEvaluator

      public TrainValidationSplit setEvaluator(Evaluator value)

    • setTrainRatio

      public TrainValidationSplit setTrainRatio(double value)

    • setSeed

      public TrainValidationSplit setSeed(long value)

    • setParallelism

      public TrainValidationSplit setParallelism(int value)
      Set the maximum level of parallelism to evaluate models in parallel. Default is 1 for serial evaluation

      Parameters:
      value - (undocumented)
      Returns:
      (undocumented)

    • setCollectSubModels

      public TrainValidationSplit setCollectSubModels(boolean value)
      Whether to collect submodels when fitting. If set, we can get submodels from the returned model.

      Note: If set this param, when you save the returned model, you can set an option "persistSubModels" to be "true" before saving, in order to save these submodels. You can check documents of TrainValidationSplitModel.TrainValidationSplitModelWriter for more information.

      Parameters:
      value - (undocumented)
      Returns:
      (undocumented)

    • fit

      public TrainValidationSplitModel fit(Dataset<?> dataset)
      Description copied from class: Estimator
      Fits a model to the input data.
      Specified by:
      fit in class Estimator<TrainValidationSplitModel>
      Parameters:
      dataset - (undocumented)
      Returns:
      (undocumented)

    • transformSchema

      public StructType transformSchema(StructType schema)
      Description copied from class: PipelineStage
      Check transform validity and derive the output schema from the input schema.

      We check validity for interactions between parameters during transformSchema and raise an exception if any parameter value is invalid. Parameter value checks which do not depend on other parameters are handled by Param.validate().

      Typical implementation should first conduct verification on schema change and parameter validity, including complex parameter interaction checks.

      Specified by:
      transformSchema in class PipelineStage
      Parameters:
      schema - (undocumented)
      Returns:
      (undocumented)

    • copy

      public TrainValidationSplit copy(ParamMap extra)
      Description copied from interface: Params
      Creates a copy of this instance with the same UID and some extra params. Subclasses should implement this method and set the return type properly. See defaultCopy().
      Specified by:
      copy in interface Params
      Specified by:
      copy in class Estimator<TrainValidationSplitModel>
      Parameters:
      extra - (undocumented)
      Returns:
      (undocumented)

    • write

      public MLWriter write()
      Description copied from interface: MLWritable
      Returns an MLWriter instance for this ML instance.
      Specified by:
      write in interface MLWritable
      Returns:
      (undocumented)