DataBalancer

Value Members

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

   

   Definition Classes

   AnyRef → Any

2. final def ##(): Int

   

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

   

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

   

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5. final def asInstanceOf[T0]: T0

   

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6. final def clear(param: Param[_]): DataBalancer.this.type

   

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7. def clone(): AnyRef

   

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   protected[java.lang]

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   @throws( ... )

8. def copy(extra: ParamMap): DataBalancer

   

   Definition Classes

   DataBalancer → Params

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

   

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

    

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

    

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12. def equals(arg0: Any): Boolean

    

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

    

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14. def explainParams(): String

    

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15. final def extractParamMap(): ParamMap

    

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

    

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17. def finalize(): Unit

    

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    protected[java.lang]

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

    

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

    

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

    

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21. def getMaxTrainingSample: Int

    

    Definition Classes

    DataBalancerParams

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

    

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

    

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24. def getProportions(smallCount: Double, bigCount: Double, sampleF: Double, maxTrainingSample: Int): (Double, Double)

    

    Computes the upSample and downSample proportions.

    Computes the upSample and downSample proportions.

    smallCount

    size of minority class data

    bigCount

    size of majority class data

    sampleF

    targeted fraction of small data

    maxTrainingSample

    maximum training size

    returns

    downSample & upSample proportions

25. def getReserveTestFraction: Double

    

    Definition Classes

    SplitterParams

26. def getSampleFraction: Double

    

    Definition Classes

    DataBalancerParams

27. def getSeed: Long

    

    Definition Classes

    SplitterParams

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

    

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29. def hasParam(paramName: String): Boolean

    

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30. def hashCode(): Int

    

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

    

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32. final def isInstanceOf[T0]: Boolean

    

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

    

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34. final val maxTrainingSample: IntParam

    

    Maximum size of dataset want to train on.

    Maximum size of dataset want to train on. Value should be > 0. Default is 5000.

    Definition Classes

    DataBalancerParams

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

    

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36. final def notify(): Unit

    

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37. final def notifyAll(): Unit

    

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

    

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39. def prepare(data: Dataset[Row]): ModelData

    

    Split into a training set and a test set and balance the training set

    Split into a training set and a test set and balance the training set

    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

    DataBalancer → Splitter

40. final val reserveTestFraction: DoubleParam

    

    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 sampleFraction: DoubleParam

    

    Targeted sample fraction for the class in minority.

    Targeted sample fraction for the class in minority. Value should be in ]0.0, 1.0[ Default is 0.1.

    Definition Classes

    DataBalancerParams

42. final val seed: LongParam

    

    Seed for data splitting

    Seed for data splitting

    Definition Classes

    SplitterParams

43. final def set(paramPair: ParamPair[_]): DataBalancer.this.type

    

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44. final def set(param: String, value: Any): DataBalancer.this.type

    

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

    

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46. final def setDefault(paramPairs: ParamPair[_]*): DataBalancer.this.type

    

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

    

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48. def setMaxTrainingSample(value: Int): DataBalancer.this.type

    

    Definition Classes

    DataBalancerParams

49. def setReserveTestFraction(value: Double): DataBalancer.this.type

    

    Definition Classes

    SplitterParams

50. def setSampleFraction(value: Double): DataBalancer.this.type

    

    Definition Classes

    DataBalancerParams

51. def setSeed(value: Long): DataBalancer.this.type

    

    Definition Classes

    SplitterParams

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

    

    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

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

    

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54. def toString(): String

    

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    Identifiable → AnyRef → Any

55. val uid: String

    

    Definition Classes

    Splitter → Identifiable

56. final def wait(): Unit

    

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

    

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

    

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