Package org.apache.spark.ml.tuning

Class CrossValidatorModel

Object
org.apache.spark.ml.PipelineStage
org.apache.spark.ml.Transformer
org.apache.spark.ml.Model<CrossValidatorModel>
org.apache.spark.ml.tuning.CrossValidatorModel
All Implemented Interfaces:
Serializable, org.apache.spark.internal.Logging, Params, HasSeed, CrossValidatorParams, ValidatorParams, Identifiable, MLWritable, scala.Serializable
public class CrossValidatorModel extends Model<CrossValidatorModel> implements CrossValidatorParams, MLWritable
CrossValidatorModel contains the model with the highest average cross-validation metric across folds and uses this model to transform input data. CrossValidatorModel also tracks the metrics for each param map evaluated.

param: bestModel The best model selected from k-fold cross validation. param: avgMetrics Average cross-validation metrics for each paramMap in CrossValidator.estimatorParamMaps, in the corresponding order.

See Also:

  • Method Details

    • read

      public static MLReader<CrossValidatorModel> read()

    • load

      public static CrossValidatorModel load(String path)

    • 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)

    • bestModel

      public Model<?> bestModel()

    • avgMetrics

      public double[] avgMetrics()

    • subModels

      public Model<?>[][] subModels()
      Returns:
      submodels represented in two dimension array. The index of outer array is the fold index, and the index of inner array corresponds to the ordering of estimatorParamMaps
      Throws:
      IllegalArgumentException - if subModels are not available. To retrieve subModels, make sure to set collectSubModels to true before fitting.

    • hasSubModels

      public boolean hasSubModels()

    • transform

      public Dataset<Row> transform(Dataset<?> dataset)
      Description copied from class: Transformer
      Transforms the input dataset.
      Specified by:
      transform in class Transformer
      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 CrossValidatorModel 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 Model<CrossValidatorModel>
      Parameters:
      extra - (undocumented)
      Returns:
      (undocumented)

    • write

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

    • toString

      public String toString()
      Specified by:
      toString in interface Identifiable
      Overrides:
      toString in class Object