Packages

class TrainValidationSplit extends Estimator[TrainValidationSplitModel] with TrainValidationSplitParams with HasParallelism with HasCollectSubModels with MLWritable with 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.

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

Instance Constructors

  1. new TrainValidationSplit()
    Annotations
    @Since( "1.5.0" )
  2. new TrainValidationSplit(uid: String)
    Annotations
    @Since( "1.5.0" )

Value Members

  1. final def !=(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  2. final def ##(): Int
    Definition Classes
    AnyRef → Any
  3. final def $[T](param: Param[T]): T

    An alias for getOrDefault().

    An alias for getOrDefault().

    Attributes
    protected
    Definition Classes
    Params
  4. final def ==(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  5. final def asInstanceOf[T0]: T0
    Definition Classes
    Any
  6. final def clear(param: Param[_]): TrainValidationSplit.this.type

    Clears the user-supplied value for the input param.

    Clears the user-supplied value for the input param.

    Definition Classes
    Params
  7. def clone(): AnyRef
    Attributes
    protected[lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( ... ) @native()
  8. final val collectSubModels: BooleanParam

    Param for whether to collect a list of sub-models trained during tuning.

    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.

    Definition Classes
    HasCollectSubModels
  9. def copy(extra: ParamMap): TrainValidationSplit

    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
    TrainValidationSplit → Estimator → PipelineStage → Params
    Annotations
    @Since( "1.5.0" )
  10. def copyValues[T <: Params](to: T, extra: ParamMap = ParamMap.empty): T

    Copies param values from this instance to another instance for params shared by them.

    Copies param values from this instance to another instance for params shared by them.

    This handles default Params and explicitly set Params separately. Default Params are copied from and to defaultParamMap, and explicitly set Params are copied from and to paramMap. Warning: This implicitly assumes that this Params instance and the target instance share the same set of default Params.

    to

    the target instance, which should work with the same set of default Params as this source instance

    extra

    extra params to be copied to the target's paramMap

    returns

    the target instance with param values copied

    Attributes
    protected
    Definition Classes
    Params
  11. final def defaultCopy[T <: Params](extra: ParamMap): T

    Default implementation of copy with extra params.

    Default implementation of copy with extra params. It tries to create a new instance with the same UID. Then it copies the embedded and extra parameters over and returns the new instance.

    Attributes
    protected
    Definition Classes
    Params
  12. final def eq(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  13. def equals(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  14. val estimator: Param[Estimator[_]]

    param for the estimator to be validated

    param for the estimator to be validated

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

    param for estimator param maps

    param for estimator param maps

    Definition Classes
    ValidatorParams
  16. 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
  17. 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
  18. def explainParams(): String

    Explains all params of this instance.

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

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

    extractParamMap with no extra values.

    extractParamMap with no extra values.

    Definition Classes
    Params
  20. 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
  21. def finalize(): Unit
    Attributes
    protected[lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( classOf[java.lang.Throwable] )
  22. def fit(dataset: Dataset[_]): TrainValidationSplitModel

    Fits a model to the input data.

    Fits a model to the input data.

    Definition Classes
    TrainValidationSplit → Estimator
    Annotations
    @Since( "2.0.0" )
  23. def fit(dataset: Dataset[_], paramMaps: Array[ParamMap]): Seq[TrainValidationSplitModel]

    Fits multiple models to the input data with multiple sets of parameters.

    Fits multiple models to the input data with multiple sets of parameters. The default implementation uses a for loop on each parameter map. Subclasses could override this to optimize multi-model training.

    dataset

    input dataset

    paramMaps

    An array of parameter maps. These values override any specified in this Estimator's embedded ParamMap.

    returns

    fitted models, matching the input parameter maps

    Definition Classes
    Estimator
    Annotations
    @Since( "2.0.0" )
  24. def fit(dataset: Dataset[_], paramMap: ParamMap): TrainValidationSplitModel

    Fits a single model to the input data with provided parameter map.

    Fits a single model to the input data with provided parameter map.

    dataset

    input dataset

    paramMap

    Parameter map. These values override any specified in this Estimator's embedded ParamMap.

    returns

    fitted model

    Definition Classes
    Estimator
    Annotations
    @Since( "2.0.0" )
  25. def fit(dataset: Dataset[_], firstParamPair: ParamPair[_], otherParamPairs: ParamPair[_]*): TrainValidationSplitModel

    Fits a single model to the input data with optional parameters.

    Fits a single model to the input data with optional parameters.

    dataset

    input dataset

    firstParamPair

    the first param pair, overrides embedded params

    otherParamPairs

    other param pairs. These values override any specified in this Estimator's embedded ParamMap.

    returns

    fitted model

    Definition Classes
    Estimator
    Annotations
    @Since( "2.0.0" ) @varargs()
  26. 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
  27. final def getClass(): Class[_]
    Definition Classes
    AnyRef → Any
    Annotations
    @native()
  28. final def getCollectSubModels: Boolean

    Definition Classes
    HasCollectSubModels
  29. 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
  30. def getEstimator: Estimator[_]

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

    Definition Classes
    ValidatorParams
  32. def getEvaluator: Evaluator

    Definition Classes
    ValidatorParams
  33. 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
  34. def getParallelism: Int

    Definition Classes
    HasParallelism
  35. def getParam(paramName: String): Param[Any]

    Gets a param by its name.

    Gets a param by its name.

    Definition Classes
    Params
  36. final def getSeed: Long

    Definition Classes
    HasSeed
  37. def getTrainRatio: Double

    Definition Classes
    TrainValidationSplitParams
  38. 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
  39. 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
  40. def hashCode(): Int
    Definition Classes
    AnyRef → Any
    Annotations
    @native()
  41. def initializeLogIfNecessary(isInterpreter: Boolean, silent: Boolean): Boolean
    Attributes
    protected
    Definition Classes
    Logging
  42. def initializeLogIfNecessary(isInterpreter: Boolean): Unit
    Attributes
    protected
    Definition Classes
    Logging
  43. 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
  44. final def isInstanceOf[T0]: Boolean
    Definition Classes
    Any
  45. final def isSet(param: Param[_]): Boolean

    Checks whether a param is explicitly set.

    Checks whether a param is explicitly set.

    Definition Classes
    Params
  46. def isTraceEnabled(): Boolean
    Attributes
    protected
    Definition Classes
    Logging
  47. def log: Logger
    Attributes
    protected
    Definition Classes
    Logging
  48. def logDebug(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  49. def logDebug(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  50. def logError(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  51. def logError(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  52. def logInfo(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  53. def logInfo(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  54. def logName: String
    Attributes
    protected
    Definition Classes
    Logging
  55. def logTrace(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  56. def logTrace(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  57. def logTuningParams(instrumentation: Instrumentation): Unit

    Instrumentation logging for tuning params including the inner estimator and evaluator info.

    Instrumentation logging for tuning params including the inner estimator and evaluator info.

    Attributes
    protected
    Definition Classes
    ValidatorParams
  58. def logWarning(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  59. def logWarning(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  60. final def ne(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  61. final def notify(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native()
  62. final def notifyAll(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native()
  63. val parallelism: IntParam

    The number of threads to use when running parallel algorithms.

    The number of threads to use when running parallel algorithms. Default is 1 for serial execution

    Definition Classes
    HasParallelism
  64. 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.

  65. 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( ... )
  66. final val seed: LongParam

    Param for random seed.

    Param for random seed.

    Definition Classes
    HasSeed
  67. final def set(paramPair: ParamPair[_]): TrainValidationSplit.this.type

    Sets a parameter in the embedded param map.

    Sets a parameter in the embedded param map.

    Attributes
    protected
    Definition Classes
    Params
  68. final def set(param: String, value: Any): TrainValidationSplit.this.type

    Sets a parameter (by name) in the embedded param map.

    Sets a parameter (by name) in the embedded param map.

    Attributes
    protected
    Definition Classes
    Params
  69. final def set[T](param: Param[T], value: T): TrainValidationSplit.this.type

    Sets a parameter in the embedded param map.

    Sets a parameter in the embedded param map.

    Definition Classes
    Params
  70. def setCollectSubModels(value: Boolean): TrainValidationSplit.this.type

    Whether to collect submodels when fitting.

    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 org.apache.spark.ml.tuning.TrainValidationSplitModel.TrainValidationSplitModelWriter for more information.

    Annotations
    @Since( "2.3.0" )
  71. final def setDefault(paramPairs: ParamPair[_]*): TrainValidationSplit.this.type

    Sets default values for a list of params.

    Sets default values for a list of params.

    Note: Java developers should use the single-parameter setDefault. Annotating this with varargs can cause compilation failures due to a Scala compiler bug. See SPARK-9268.

    paramPairs

    a list of param pairs that specify params and their default values to set respectively. Make sure that the params are initialized before this method gets called.

    Attributes
    protected
    Definition Classes
    Params
  72. final def setDefault[T](param: Param[T], value: T): TrainValidationSplit.this.type

    Sets a default value for a param.

    Sets a default value for a param.

    param

    param to set the default value. Make sure that this param is initialized before this method gets called.

    value

    the default value

    Attributes
    protected
    Definition Classes
    Params
  73. def setEstimator(value: Estimator[_]): TrainValidationSplit.this.type

    Annotations
    @Since( "1.5.0" )
  74. def setEstimatorParamMaps(value: Array[ParamMap]): TrainValidationSplit.this.type

    Annotations
    @Since( "1.5.0" )
  75. def setEvaluator(value: Evaluator): TrainValidationSplit.this.type

    Annotations
    @Since( "1.5.0" )
  76. def setParallelism(value: Int): TrainValidationSplit.this.type

    Set the maximum level of parallelism to evaluate models in parallel.

    Set the maximum level of parallelism to evaluate models in parallel. Default is 1 for serial evaluation

    Annotations
    @Since( "2.3.0" )
  77. def setSeed(value: Long): TrainValidationSplit.this.type

    Annotations
    @Since( "2.0.0" )
  78. def setTrainRatio(value: Double): TrainValidationSplit.this.type

    Annotations
    @Since( "1.5.0" )
  79. final def synchronized[T0](arg0: ⇒ T0): T0
    Definition Classes
    AnyRef
  80. def toString(): String
    Definition Classes
    Identifiable → AnyRef → Any
  81. 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
  82. 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
    TrainValidationSplit → PipelineStage
    Annotations
    @Since( "1.5.0" )
  83. def transformSchema(schema: StructType, logging: Boolean): StructType

    :: DeveloperApi ::

    :: DeveloperApi ::

    Derives the output schema from the input schema and parameters, optionally with logging.

    This should be optimistic. If it is unclear whether the schema will be valid, then it should be assumed valid until proven otherwise.

    Attributes
    protected
    Definition Classes
    PipelineStage
    Annotations
    @DeveloperApi()
  84. def transformSchemaImpl(schema: StructType): StructType
    Attributes
    protected
    Definition Classes
    ValidatorParams
  85. 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
    TrainValidationSplit → Identifiable
    Annotations
    @Since( "1.5.0" )
  86. final def wait(): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  87. final def wait(arg0: Long, arg1: Int): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  88. final def wait(arg0: Long): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... ) @native()
  89. def write: MLWriter

    Returns an MLWriter instance for this ML instance.

    Returns an MLWriter instance for this ML instance.

    Definition Classes
    TrainValidationSplit → MLWritable
    Annotations
    @Since( "2.0.0" )

Inherited from MLWritable

Inherited from HasCollectSubModels

Inherited from HasParallelism

Inherited from TrainValidationSplitParams

Inherited from ValidatorParams

Inherited from HasSeed

Inherited from Estimator[TrainValidationSplitModel]

Inherited from PipelineStage

Inherited from Logging

Inherited from Params

Inherited from Serializable

Inherited from Serializable

Inherited from Identifiable

Inherited from AnyRef

Inherited from Any

Parameters

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

Members

Parameter setters

Parameter getters

(expert-only) Parameters

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

(expert-only) Parameter setters

(expert-only) Parameter getters