Package org.apache.spark.ml.tuning

Class CrossValidator

Object
org.apache.spark.ml.PipelineStage
org.apache.spark.ml.Estimator<CrossValidatorModel>
org.apache.spark.ml.tuning.CrossValidator
All Implemented Interfaces:
Serializable, org.apache.spark.internal.Logging, Params, HasCollectSubModels, HasParallelism, HasSeed, CrossValidatorParams, ValidatorParams, Identifiable, MLWritable
public class CrossValidator extends Estimator<CrossValidatorModel> implements CrossValidatorParams, HasParallelism, HasCollectSubModels, MLWritable, org.apache.spark.internal.Logging
K-fold cross validation performs model selection by splitting the dataset into a set of non-overlapping randomly partitioned folds which are used as separate training and test datasets e.g., with k=3 folds, K-fold cross validation will generate 3 (training, test) dataset pairs, each of which uses 2/3 of the data for training and 1/3 for testing. Each fold is used as the test set exactly once.
See Also:

  • Constructor Details

    • CrossValidator

      public CrossValidator(String uid)

    • CrossValidator

      public CrossValidator()

  • Method Details

    • read

      public static MLReader<CrossValidator> read()

    • load

      public static CrossValidator 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()
      Description copied from interface: HasParallelism
      The number of threads to use when running parallel algorithms. Default is 1 for serial execution

      Specified by:
      parallelism in interface HasParallelism
      Returns:
      (undocumented)

    • numFolds

      public IntParam numFolds()
      Description copied from interface: CrossValidatorParams
      Param for number of folds for cross validation. Must be &gt;= 2. Default: 3

      Specified by:
      numFolds in interface CrossValidatorParams
      Returns:
      (undocumented)

    • foldCol

      public Param<String> foldCol()
      Description copied from interface: CrossValidatorParams
      Param for the column name of user specified fold number. Once this is specified, CrossValidator won't do random k-fold split. Note that this column should be integer type with range [0, numFolds) and Spark will throw exception on out-of-range fold numbers.
      Specified by:
      foldCol in interface CrossValidatorParams
      Returns:
      (undocumented)

    • estimator

      public Param<Estimator<?>> estimator()
      Description copied from interface: ValidatorParams
      param for the estimator to be validated

      Specified by:
      estimator in interface ValidatorParams
      Returns:
      (undocumented)

    • estimatorParamMaps

      public Param<ParamMap[]> estimatorParamMaps()
      Description copied from interface: ValidatorParams
      param for estimator param maps

      Specified by:
      estimatorParamMaps in interface ValidatorParams
      Returns:
      (undocumented)

    • evaluator

      public Param<Evaluator> evaluator()
      Description copied from interface: ValidatorParams
      param for the evaluator used to select hyper-parameters that maximize the validated metric

      Specified by:
      evaluator in interface ValidatorParams
      Returns:
      (undocumented)

    • 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 CrossValidator setEstimator(Estimator<?> value)

    • setEstimatorParamMaps

      public CrossValidator setEstimatorParamMaps(ParamMap[] value)

    • setEvaluator

      public CrossValidator setEvaluator(Evaluator value)

    • setNumFolds

      public CrossValidator setNumFolds(int value)

    • setSeed

      public CrossValidator setSeed(long value)

    • setFoldCol

      public CrossValidator setFoldCol(String value)

    • setParallelism

      public CrossValidator 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 CrossValidator 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 CrossValidatorModel.CrossValidatorModelWriter for more information.

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

    • fit

      public CrossValidatorModel fit(Dataset<?> dataset)
      Description copied from class: Estimator
      Fits a model to the input data.
      Specified by:
      fit in class Estimator<CrossValidatorModel>
      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 CrossValidator 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<CrossValidatorModel>
      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)