Class/Object

com.salesforce.op.stages.impl.tuning

DataSplitter

Related Docs: object DataSplitter | package tuning

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class DataSplitter extends Splitter with SplitterParams

Instance that will split the data into training and holdout for regressions

Linear Supertypes
Splitter, SplitterParams, Params, Serializable, Serializable, Identifiable, AnyRef, Any
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Inherited
  1. DataSplitter
  2. Splitter
  3. SplitterParams
  4. Params
  5. Serializable
  6. Serializable
  7. Identifiable
  8. AnyRef
  9. Any
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Visibility
  1. Public
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Instance Constructors

  1. new DataSplitter(uid: String = UID[DataSplitter])

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Value Members

  1. final def !=(arg0: Any): Boolean

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    Definition Classes
    AnyRef → Any

  2. final def ##(): Int

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    Definition Classes
    AnyRef → Any

  3. final def $[T](param: Param[T]): T

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    Attributes
    protected
    Definition Classes
    Params

  4. final def ==(arg0: Any): Boolean

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    Definition Classes
    AnyRef → Any

  5. final def asInstanceOf[T0]: T0

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    Definition Classes
    Any

  6. def checkPreconditions(): Unit

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    Attributes
    protected
    Definition Classes
    Splitter

  7. final def clear(param: Param[_]): DataSplitter.this.type

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    Definition Classes
    Params

  8. def clone(): AnyRef

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    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

  9. def copy(extra: ParamMap): DataSplitter

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    Definition Classes
    DataSplitter → Params

  10. def copyValues[T <: Params](to: T, extra: ParamMap): T

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    Attributes
    protected
    Definition Classes
    Params

  11. final def defaultCopy[T <: Params](extra: ParamMap): T

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    Attributes
    protected
    Definition Classes
    Params

  12. final def eq(arg0: AnyRef): Boolean

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    Definition Classes
    AnyRef

  13. def equals(arg0: Any): Boolean

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    Definition Classes
    AnyRef → Any

  14. def explainParam(param: Param[_]): String

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    Definition Classes
    Params

  15. def explainParams(): String

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    Definition Classes
    Params

  16. final def extractParamMap(): ParamMap

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    Definition Classes
    Params

  17. final def extractParamMap(extra: ParamMap): ParamMap

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    Definition Classes
    Params

  18. def finalize(): Unit

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    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( classOf[java.lang.Throwable] )

  19. final def get[T](param: Param[T]): Option[T]

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    Definition Classes
    Params

  20. final def getClass(): Class[_]

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    Definition Classes
    AnyRef → Any

  21. final def getDefault[T](param: Param[T]): Option[T]

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    Definition Classes
    Params

  22. def getMaxTrainingSample: Int

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    Definition Classes
    SplitterParams

  23. final def getOrDefault[T](param: Param[T]): T

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    Definition Classes
    Params

  24. def getParam(paramName: String): Param[Any]

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    Definition Classes
    Params

  25. def getReserveTestFraction: Double

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    Definition Classes
    SplitterParams

  26. def getSeed: Long

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    Definition Classes
    SplitterParams

  27. final def hasDefault[T](param: Param[T]): Boolean

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    Definition Classes
    Params

  28. def hasParam(paramName: String): Boolean

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    Definition Classes
    Params

  29. def hashCode(): Int

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    Definition Classes
    AnyRef → Any

  30. final def isDefined(param: Param[_]): Boolean

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    Definition Classes
    Params

  31. final def isInstanceOf[T0]: Boolean

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    Definition Classes
    Any

  32. final def isSet(param: Param[_]): Boolean

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    Definition Classes
    Params

  33. final val labelColumnName: Param[String]

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    Definition Classes
    SplitterParams

  34. final val maxTrainingSample: IntParam

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    Maximum size of dataset want to train on.

    Maximum size of dataset want to train on. Value should be > 0. Default is 1000000.

    Definition Classes
    SplitterParams

  35. final def ne(arg0: AnyRef): Boolean

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    Definition Classes
    AnyRef

  36. final def notify(): Unit

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    Definition Classes
    AnyRef

  37. final def notifyAll(): Unit

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    Definition Classes
    AnyRef

  38. lazy val params: Array[Param[_]]

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    Definition Classes
    Params

  39. def preValidationPrepare(data: Dataset[Row]): PrevalidationVal

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    Function to set the down sampling fraction and parameters before passing into the validation step

    Function to set the down sampling fraction and parameters before passing into the validation step

    returns

    Parameters set in examining data

    Definition Classes
    DataSplitter → Splitter

  40. final val reserveTestFraction: DoubleParam

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    Fraction of data to reserve for test Default is 0.1

    Fraction of data to reserve for test Default is 0.1

    Definition Classes
    SplitterParams

  41. final val seed: LongParam

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    Seed for data splitting

    Seed for data splitting

    Definition Classes
    SplitterParams

  42. final def set(paramPair: ParamPair[_]): DataSplitter.this.type

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    Attributes
    protected
    Definition Classes
    Params

  43. final def set(param: String, value: Any): DataSplitter.this.type

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    Attributes
    protected
    Definition Classes
    Params

  44. final def set[T](param: Param[T], value: T): DataSplitter.this.type

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    Definition Classes
    Params

  45. final def setDefault(paramPairs: ParamPair[_]*): DataSplitter.this.type

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    Attributes
    protected
    Definition Classes
    Params

  46. final def setDefault[T](param: Param[T], value: T): DataSplitter.this.type

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    Attributes
    protected
    Definition Classes
    Params

  47. def setMaxTrainingSample(value: Int): DataSplitter.this.type

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    Definition Classes
    SplitterParams

  48. def setReserveTestFraction(value: Double): DataSplitter.this.type

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    Definition Classes
    SplitterParams

  49. def setSeed(value: Long): DataSplitter.this.type

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    Definition Classes
    SplitterParams

  50. def split[T](data: Dataset[T]): (Dataset[T], Dataset[T])

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    Function to use to create the training set and test set.

    Function to use to create the training set and test set.

    returns

    (dataTrain, dataTest)

    Definition Classes
    Splitter

  51. final def synchronized[T0](arg0: ⇒ T0): T0

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    Definition Classes
    AnyRef

  52. def toString(): String

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    Definition Classes
    Identifiable → AnyRef → Any

  53. val uid: String

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    Definition Classes
    Splitter → Identifiable

  54. def validationPrepare(data: Dataset[Row]): Dataset[Row]

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    Rebalance the training data within the validation step

    Rebalance the training data within the validation step

    data

    to prepare for model training. first column must be the label as a double

    returns

    balanced training set and a test set

    Definition Classes
    DataSplitter → Splitter

  55. final def wait(): Unit

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    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

  56. final def wait(arg0: Long, arg1: Int): Unit

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    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

  57. final def wait(arg0: Long): Unit

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    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

  58. def withLabelColumnName(label: String): Splitter

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    Add a splitter parameter to name the label column

    Add a splitter parameter to name the label column

    Definition Classes
    Splitter

Inherited from Splitter

Inherited from SplitterParams

Inherited from Params

Inherited from Serializable

Inherited from Serializable

Inherited from Identifiable

Inherited from AnyRef

Inherited from Any

param

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