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. def checkPreconditions(): Unit

   

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   Splitter

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

   

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8. def clone(): AnyRef

   

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   protected[java.lang]

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9. def copy(extra: ParamMap): DataBalancer

   

   Definition Classes

   DataBalancer → Params

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

    

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

    

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

    

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13. def equals(arg0: Any): Boolean

    

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

    

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15. def explainParams(): String

    

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16. final def extractParamMap(): ParamMap

    

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

    

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18. def finalize(): Unit

    

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

    

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

    

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

    

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22. def getMaxTrainingSample: Int

    

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

    

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

    

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25. 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

26. def getReserveTestFraction: Double

    

    Definition Classes

    SplitterParams

27. def getSampleFraction: Double

    

    Definition Classes

    DataBalancerParams

28. def getSeed: Long

    

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

    

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30. def hasParam(paramName: String): Boolean

    

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31. def hashCode(): Int

    

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

    

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33. final def isInstanceOf[T0]: Boolean

    

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

    

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35. final val labelColumnName: Param[String]

    

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36. 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 1000000.

    Definition Classes

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

    

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38. final def notify(): Unit

    

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39. final def notifyAll(): Unit

    

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

    

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

    

    Function to set parameters before passing into the validation step eg - do data balancing or dropping based on the labels

    Function to set parameters before passing into the validation step eg - do data balancing or dropping based on the labels

    returns

    Parameters set in examining data

    Definition Classes

    DataBalancer → Splitter

42. 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

43. 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

44. final val seed: LongParam

    

    Seed for data splitting

    Seed for data splitting

    Definition Classes

    SplitterParams

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

    

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

    

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

    

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

    

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

    

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

    

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

    

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

    

    Definition Classes

    DataBalancerParams

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

    

    Definition Classes

    SplitterParams

54. 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

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

    

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56. def toString(): String

    

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    Identifiable → AnyRef → Any

57. val uid: String

    

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    Splitter → Identifiable

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

    

    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

    DataBalancer → Splitter

59. final def wait(): Unit

    

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

    

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

    

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62. def withLabelColumnName(label: String): Splitter

    

    Add a splitter parameter to name the label column

    Add a splitter parameter to name the label column

    Definition Classes

    Splitter