Packages

class TrainValidationSplitModel extends Model[TrainValidationSplitModel] with TrainValidationSplitParams with MLWritable

Model from train validation split.

Annotations
@Since( "1.5.0" )
Source
TrainValidationSplit.scala
Linear Supertypes
MLWritable, TrainValidationSplitParams, ValidatorParams, HasSeed, Model[TrainValidationSplitModel], Transformer, PipelineStage, Logging, Params, Serializable, Serializable, Identifiable, AnyRef, Any
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Inherited
  1. TrainValidationSplitModel
  2. MLWritable
  3. TrainValidationSplitParams
  4. ValidatorParams
  5. HasSeed
  6. Model
  7. Transformer
  8. PipelineStage
  9. Logging
  10. Params
  11. Serializable
  12. Serializable
  13. Identifiable
  14. AnyRef
  15. Any
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Visibility
  1. Public
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Parameters

A list of (hyper-)parameter keys this algorithm can take. Users can set and get the parameter values through setters and getters, respectively.

  1. val estimator: Param[Estimator[_]]

    param for the estimator to be validated

    param for the estimator to be validated

    Definition Classes
    ValidatorParams
  2. val estimatorParamMaps: Param[Array[ParamMap]]

    param for estimator param maps

    param for estimator param maps

    Definition Classes
    ValidatorParams
  3. val evaluator: Param[Evaluator]

    param for the evaluator used to select hyper-parameters that maximize the validated metric

    param for the evaluator used to select hyper-parameters that maximize the validated metric

    Definition Classes
    ValidatorParams
  4. final val seed: LongParam

    Param for random seed.

    Param for random seed.

    Definition Classes
    HasSeed
  5. val trainRatio: DoubleParam

    Param for ratio between train and validation data.

    Param for ratio between train and validation data. Must be between 0 and 1. Default: 0.75

    Definition Classes
    TrainValidationSplitParams

Members

  1. val bestModel: Model[_]
    Annotations
    @Since( "1.5.0" )
  2. final def clear(param: Param[_]): TrainValidationSplitModel.this.type

    Clears the user-supplied value for the input param.

    Clears the user-supplied value for the input param.

    Definition Classes
    Params
  3. def copy(extra: ParamMap): TrainValidationSplitModel

    Creates a copy of this instance with the same UID and some extra 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().

    Definition Classes
    TrainValidationSplitModelModelTransformerPipelineStageParams
    Annotations
    @Since( "1.5.0" )
  4. def explainParam(param: Param[_]): String

    Explains a param.

    Explains a param.

    param

    input param, must belong to this instance.

    returns

    a string that contains the input param name, doc, and optionally its default value and the user-supplied value

    Definition Classes
    Params
  5. def explainParams(): String

    Explains all params of this instance.

    Explains all params of this instance. See explainParam().

    Definition Classes
    Params
  6. final def extractParamMap(): ParamMap

    extractParamMap with no extra values.

    extractParamMap with no extra values.

    Definition Classes
    Params
  7. final def extractParamMap(extra: ParamMap): ParamMap

    Extracts the embedded default param values and user-supplied values, and then merges them with extra values from input into a flat param map, where the latter value is used if there exist conflicts, i.e., with ordering: default param values less than user-supplied values less than extra.

    Extracts the embedded default param values and user-supplied values, and then merges them with extra values from input into a flat param map, where the latter value is used if there exist conflicts, i.e., with ordering: default param values less than user-supplied values less than extra.

    Definition Classes
    Params
  8. final def get[T](param: Param[T]): Option[T]

    Optionally returns the user-supplied value of a param.

    Optionally returns the user-supplied value of a param.

    Definition Classes
    Params
  9. final def getDefault[T](param: Param[T]): Option[T]

    Gets the default value of a parameter.

    Gets the default value of a parameter.

    Definition Classes
    Params
  10. final def getOrDefault[T](param: Param[T]): T

    Gets the value of a param in the embedded param map or its default value.

    Gets the value of a param in the embedded param map or its default value. Throws an exception if neither is set.

    Definition Classes
    Params
  11. def getParam(paramName: String): Param[Any]

    Gets a param by its name.

    Gets a param by its name.

    Definition Classes
    Params
  12. final def hasDefault[T](param: Param[T]): Boolean

    Tests whether the input param has a default value set.

    Tests whether the input param has a default value set.

    Definition Classes
    Params
  13. def hasParam(paramName: String): Boolean

    Tests whether this instance contains a param with a given name.

    Tests whether this instance contains a param with a given name.

    Definition Classes
    Params
  14. def hasParent: Boolean

    Indicates whether this Model has a corresponding parent.

    Indicates whether this Model has a corresponding parent.

    Definition Classes
    Model
  15. def hasSubModels: Boolean
    Annotations
    @Since( "2.3.0" )
  16. final def isDefined(param: Param[_]): Boolean

    Checks whether a param is explicitly set or has a default value.

    Checks whether a param is explicitly set or has a default value.

    Definition Classes
    Params
  17. final def isSet(param: Param[_]): Boolean

    Checks whether a param is explicitly set.

    Checks whether a param is explicitly set.

    Definition Classes
    Params
  18. lazy val params: Array[Param[_]]

    Returns all params sorted by their names.

    Returns all params sorted by their names. The default implementation uses Java reflection to list all public methods that have no arguments and return Param.

    Definition Classes
    Params
    Note

    Developer should not use this method in constructor because we cannot guarantee that this variable gets initialized before other params.

  19. var parent: Estimator[TrainValidationSplitModel]

    The parent estimator that produced this model.

    The parent estimator that produced this model.

    Definition Classes
    Model
    Note

    For ensembles' component Models, this value can be null.

  20. def save(path: String): Unit

    Saves this ML instance to the input path, a shortcut of write.save(path).

    Saves this ML instance to the input path, a shortcut of write.save(path).

    Definition Classes
    MLWritable
    Annotations
    @Since( "1.6.0" ) @throws( ... )
  21. final def set[T](param: Param[T], value: T): TrainValidationSplitModel.this.type

    Sets a parameter in the embedded param map.

    Sets a parameter in the embedded param map.

    Definition Classes
    Params
  22. def setParent(parent: Estimator[TrainValidationSplitModel]): TrainValidationSplitModel

    Sets the parent of this model (Java API).

    Sets the parent of this model (Java API).

    Definition Classes
    Model
  23. def subModels: Array[Model[_]]

    returns

    submodels represented in array. The index of array corresponds to the ordering of estimatorParamMaps

    Annotations
    @Since( "2.3.0" )
    Exceptions thrown

    IllegalArgumentException if subModels are not available. To retrieve subModels, make sure to set collectSubModels to true before fitting.

  24. def toString(): String
    Definition Classes
    TrainValidationSplitModelIdentifiable → AnyRef → Any
    Annotations
    @Since( "3.0.0" )
  25. def transform(dataset: Dataset[_]): DataFrame

    Transforms the input dataset.

    Transforms the input dataset.

    Definition Classes
    TrainValidationSplitModelTransformer
    Annotations
    @Since( "2.0.0" )
  26. def transform(dataset: Dataset[_], paramMap: ParamMap): DataFrame

    Transforms the dataset with provided parameter map as additional parameters.

    Transforms the dataset with provided parameter map as additional parameters.

    dataset

    input dataset

    paramMap

    additional parameters, overwrite embedded params

    returns

    transformed dataset

    Definition Classes
    Transformer
    Annotations
    @Since( "2.0.0" )
  27. def transform(dataset: Dataset[_], firstParamPair: ParamPair[_], otherParamPairs: ParamPair[_]*): DataFrame

    Transforms the dataset with optional parameters

    Transforms the dataset with optional parameters

    dataset

    input dataset

    firstParamPair

    the first param pair, overwrite embedded params

    otherParamPairs

    other param pairs, overwrite embedded params

    returns

    transformed dataset

    Definition Classes
    Transformer
    Annotations
    @Since( "2.0.0" ) @varargs()
  28. def transformSchema(schema: StructType): StructType

    Check transform validity and derive the output schema from the input schema.

    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.

    Definition Classes
    TrainValidationSplitModelPipelineStage
    Annotations
    @Since( "1.5.0" )
  29. val uid: String

    An immutable unique ID for the object and its derivatives.

    An immutable unique ID for the object and its derivatives.

    Definition Classes
    TrainValidationSplitModelIdentifiable
    Annotations
    @Since( "1.5.0" )
  30. val validationMetrics: Array[Double]
    Annotations
    @Since( "1.5.0" )
  31. def write: TrainValidationSplitModelWriter

    Returns an MLWriter instance for this ML instance.

    Returns an MLWriter instance for this ML instance.

    Definition Classes
    TrainValidationSplitModelMLWritable
    Annotations
    @Since( "2.0.0" )

Parameter getters

  1. def getEstimator: Estimator[_]

    Definition Classes
    ValidatorParams
  2. def getEstimatorParamMaps: Array[ParamMap]

    Definition Classes
    ValidatorParams
  3. def getEvaluator: Evaluator

    Definition Classes
    ValidatorParams
  4. final def getSeed: Long

    Definition Classes
    HasSeed
  5. def getTrainRatio: Double

    Definition Classes
    TrainValidationSplitParams