Class

com.salesforce.op.stages.sparkwrappers.specific

OpBinaryEstimatorWrapper

Related Doc: package specific

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class OpBinaryEstimatorWrapper[I1 <: FeatureType, I2 <: FeatureType, O <: FeatureType, E <: Estimator[M], M <: Model[M]] extends SwBinaryEstimator[I1, I2, O, M, E]

Wraps a spark ML estimator. This class is meant for Predictor-like Spark algorithms, but which don't inherit from Predictor, for whatever reason. Examples of which include: IsotonicRegression, AFTSurvivalRegression, OneVsRest. Their defining characteristic is that they output a model which takes in 2 columns as input (labels and features) and output one column as result, but don't inherit from Predictor (if it did, should use OpPredictorWrapper instead).

I1

first input feature type

I2

second input feature type

O

output feature type

E

spark estimator to wrap

M

spark model type returned by spark estimator wrapped

Linear Supertypes
SwBinaryEstimator[I1, I2, O, M, E], SparkWrapperParams[E], OpPipelineStage2[I1, I2, O], HasOut[O], HasIn2, HasIn1, OpPipelineStage[O], OpPipelineStageBase, MLWritable, OpPipelineStageParams, InputParams, Estimator[SwBinaryModel[I1, I2, O, M]], PipelineStage, Logging, Params, Serializable, Serializable, Identifiable, AnyRef, Any
Known Subclasses
Ordering
  1. Alphabetic
  2. By Inheritance
Inherited
  1. OpBinaryEstimatorWrapper
  2. SwBinaryEstimator
  3. SparkWrapperParams
  4. OpPipelineStage2
  5. HasOut
  6. HasIn2
  7. HasIn1
  8. OpPipelineStage
  9. OpPipelineStageBase
  10. MLWritable
  11. OpPipelineStageParams
  12. InputParams
  13. Estimator
  14. PipelineStage
  15. Logging
  16. Params
  17. Serializable
  18. Serializable
  19. Identifiable
  20. AnyRef
  21. Any
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Visibility
  1. Public
  2. All

Instance Constructors

  1. new OpBinaryEstimatorWrapper(estimator: E, uid: String = ...)(implicit tti1: scala.reflect.api.JavaUniverse.TypeTag[I1], tti2: scala.reflect.api.JavaUniverse.TypeTag[I2], tto: scala.reflect.api.JavaUniverse.TypeTag[O], ttov: scala.reflect.api.JavaUniverse.TypeTag[O.Value])

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    estimator

    spark estimator to wrap

    uid

    stage uid

    tti1

    type tag for first input

    tti2

    type tag for second input

    tto

    type tag for input

Type Members

  1. final type InputFeatures = (FeatureLike[I1], FeatureLike[I2])

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    Input Features type

    Input Features type

    Definition Classes
    OpPipelineStage2OpPipelineStageInputParams
  2. final type OutputFeatures = FeatureLike[O]

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    Definition Classes
    OpPipelineStageOpPipelineStageBase

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. final def checkInputLength(features: Array[_]): Boolean

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    Checks the input length

    Checks the input length

    features

    input features

    returns

    true is input size as expected, false otherwise

    Definition Classes
    OpPipelineStage2InputParams
  7. def checkSerializable: Try[Unit]

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    Check if the stage is serializable

    Check if the stage is serializable

    returns

    Failure if not serializable

    Definition Classes
    OpPipelineStageBase
  8. final def clear(param: Param[_]): OpBinaryEstimatorWrapper.this.type

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    Definition Classes
    Params
  9. def clone(): AnyRef

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    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  10. final def copy(extra: ParamMap): OpBinaryEstimatorWrapper.this.type

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    This method is used to make a copy of the instance with new parameters in several methods in spark internals Default will find the constructor and make a copy for any class (AS LONG AS ALL CONSTRUCTOR PARAMS ARE VALS, this is why type tags are written as implicit vals in base classes).

    This method is used to make a copy of the instance with new parameters in several methods in spark internals Default will find the constructor and make a copy for any class (AS LONG AS ALL CONSTRUCTOR PARAMS ARE VALS, this is why type tags are written as implicit vals in base classes).

    Note: that the convention in spark is to have the uid be a constructor argument, so that copies will share a uid with the original (developers should follow this convention).

    extra

    new parameters want to add to instance

    returns

    a new instance with the same uid

    Definition Classes
    OpPipelineStageBase → Params
  11. def copyValues[T <: Params](to: T, extra: ParamMap): T

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

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    Attributes
    protected
    Definition Classes
    Params
  13. final def eq(arg0: AnyRef): Boolean

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    Definition Classes
    AnyRef
  14. def equals(arg0: Any): Boolean

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    Definition Classes
    AnyRef → Any
  15. val estimator: E

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    spark estimator to wrap

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

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    Definition Classes
    Params
  17. def explainParams(): String

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

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

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    Definition Classes
    Params
  20. def finalize(): Unit

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    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( classOf[java.lang.Throwable] )
  21. def fit(dataset: Dataset[_]): SwBinaryModel[I1, I2, O, M]

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    Definition Classes
    SwBinaryEstimator → Estimator
  22. def fit(dataset: Dataset[_], paramMaps: Array[ParamMap]): Seq[SwBinaryModel[I1, I2, O, M]]

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    Definition Classes
    Estimator
    Annotations
    @Since( "2.0.0" )
  23. def fit(dataset: Dataset[_], paramMap: ParamMap): SwBinaryModel[I1, I2, O, M]

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    Definition Classes
    Estimator
    Annotations
    @Since( "2.0.0" )
  24. def fit(dataset: Dataset[_], firstParamPair: ParamPair[_], otherParamPairs: ParamPair[_]*): SwBinaryModel[I1, I2, O, M]

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    Definition Classes
    Estimator
    Annotations
    @Since( "2.0.0" ) @varargs()
  25. final def get[T](param: Param[T]): Option[T]

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    Definition Classes
    Params
  26. final def getClass(): Class[_]

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    Definition Classes
    AnyRef → Any
  27. final def getDefault[T](param: Param[T]): Option[T]

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    Definition Classes
    Params
  28. def getInputColParamNames(): Array[String]

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    Gets names of parameters that control input columns for Spark stage

    Gets names of parameters that control input columns for Spark stage

    Definition Classes
    SparkWrapperParams
  29. final def getInputFeature[T <: FeatureType](i: Int): Option[FeatureLike[T]]

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    Gets an input feature Note: this method IS NOT safe to use outside the driver, please use getTransientFeature method instead

    Gets an input feature Note: this method IS NOT safe to use outside the driver, please use getTransientFeature method instead

    returns

    array of features

    Definition Classes
    InputParams
    Exceptions thrown

    NoSuchElementException if the features are not set

    RuntimeException in case one of the features is null

  30. final def getInputFeatures(): Array[OPFeature]

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    Gets the input features Note: this method IS NOT safe to use outside the driver, please use getTransientFeatures method instead

    Gets the input features Note: this method IS NOT safe to use outside the driver, please use getTransientFeatures method instead

    returns

    array of features

    Definition Classes
    InputParams
    Exceptions thrown

    NoSuchElementException if the features are not set

    RuntimeException in case one of the features is null

  31. final def getInputSchema(): StructType

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    Definition Classes
    OpPipelineStageParams
  32. final def getMetadata(): Metadata

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    Definition Classes
    OpPipelineStageParams
  33. final def getOrDefault[T](param: Param[T]): T

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    Definition Classes
    Params
  34. def getOutput(): FeatureLike[O]

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    Output features that will be created by this stage

    Output features that will be created by this stage

    returns

    feature of type OutputFeatures

    Definition Classes
    HasOut → OpPipelineStageBase
  35. def getOutputColParamNames(): Array[String]

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    Gets names of parameters that control output columns for Spark stage

    Gets names of parameters that control output columns for Spark stage

    Definition Classes
    SparkWrapperParams
  36. final def getOutputFeatureName: String

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    Name of output feature (i.e.

    Name of output feature (i.e. column created by this stage)

    Definition Classes
    OpPipelineStage
  37. def getParam(paramName: String): Param[Any]

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    Definition Classes
    Params
  38. def getSparkMlStage(): Option[E]

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    Method to access the spark stage being wrapped

    Method to access the spark stage being wrapped

    returns

    Option of spark ml stage

    Definition Classes
    SparkWrapperParams
  39. final def getSparkStage: E

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    Attributes
    protected
  40. def getStageSavePath(): Option[String]

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    Gets a save path for wrapped spark stage

    Gets a save path for wrapped spark stage

    Definition Classes
    SparkWrapperParams
  41. final def getTransientFeature(i: Int): Option[TransientFeature]

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    Gets an input feature at index i

    Gets an input feature at index i

    i

    input index

    returns

    maybe an input feature

    Definition Classes
    InputParams
  42. final def getTransientFeatures(): Array[TransientFeature]

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    Gets the input Features

    Gets the input Features

    returns

    input features

    Definition Classes
    InputParams
  43. final def hasDefault[T](param: Param[T]): Boolean

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

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    Definition Classes
    Params
  45. def hashCode(): Int

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    Definition Classes
    AnyRef → Any
  46. final def in1: TransientFeature

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    Attributes
    protected
    Definition Classes
    HasIn1
  47. final def in2: TransientFeature

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    Attributes
    protected
    Definition Classes
    HasIn2
  48. def initializeLogIfNecessary(isInterpreter: Boolean, silent: Boolean): Boolean

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    Attributes
    protected
    Definition Classes
    Logging
  49. def initializeLogIfNecessary(isInterpreter: Boolean): Unit

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    Attributes
    protected
    Definition Classes
    Logging
  50. final def inputAsArray(in: InputFeatures): Array[OPFeature]

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    Function to convert InputFeatures to an Array of FeatureLike

    Function to convert InputFeatures to an Array of FeatureLike

    returns

    an Array of FeatureLike

    Definition Classes
    OpPipelineStage2InputParams
  51. val inputParam1Name: String

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    name of spark parameter that sets the first input column

    name of spark parameter that sets the first input column

    Definition Classes
    SwBinaryEstimator
  52. val inputParam2Name: String

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    name of spark parameter that sets the second input column

    name of spark parameter that sets the second input column

    Definition Classes
    SwBinaryEstimator
  53. final def isDefined(param: Param[_]): Boolean

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    Definition Classes
    Params
  54. final def isInstanceOf[T0]: Boolean

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    Definition Classes
    Any
  55. final def isSet(param: Param[_]): Boolean

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    Definition Classes
    Params
  56. def isTraceEnabled(): Boolean

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    Attributes
    protected
    Definition Classes
    Logging
  57. def log: Logger

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    Attributes
    protected
    Definition Classes
    Logging
  58. def logDebug(msg: ⇒ String, throwable: Throwable): Unit

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    Attributes
    protected
    Definition Classes
    Logging
  59. def logDebug(msg: ⇒ String): Unit

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    Attributes
    protected
    Definition Classes
    Logging
  60. def logError(msg: ⇒ String, throwable: Throwable): Unit

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    Attributes
    protected
    Definition Classes
    Logging
  61. def logError(msg: ⇒ String): Unit

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    Attributes
    protected
    Definition Classes
    Logging
  62. def logInfo(msg: ⇒ String, throwable: Throwable): Unit

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    Attributes
    protected
    Definition Classes
    Logging
  63. def logInfo(msg: ⇒ String): Unit

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    Attributes
    protected
    Definition Classes
    Logging
  64. def logName: String

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    Attributes
    protected
    Definition Classes
    Logging
  65. def logTrace(msg: ⇒ String, throwable: Throwable): Unit

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    Attributes
    protected
    Definition Classes
    Logging
  66. def logTrace(msg: ⇒ String): Unit

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    Attributes
    protected
    Definition Classes
    Logging
  67. def logWarning(msg: ⇒ String, throwable: Throwable): Unit

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    Attributes
    protected
    Definition Classes
    Logging
  68. def logWarning(msg: ⇒ String): Unit

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    Attributes
    protected
    Definition Classes
    Logging
  69. final def ne(arg0: AnyRef): Boolean

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    Definition Classes
    AnyRef
  70. final def notify(): Unit

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    Definition Classes
    AnyRef
  71. final def notifyAll(): Unit

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    Definition Classes
    AnyRef
  72. def onGetMetadata(): Unit

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    Function to be called on getMetadata

    Function to be called on getMetadata

    Attributes
    protected
    Definition Classes
    OpPipelineStageParams
  73. def onSetInput(): Unit

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    Function to be called on setInput

    Function to be called on setInput

    Attributes
    protected
    Definition Classes
    InputParams
  74. val operationName: String

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    unique name of the operation this stage performs

    unique name of the operation this stage performs

    Definition Classes
    SwBinaryEstimatorOpPipelineStageBase
  75. final def outputAsArray(out: OutputFeatures): Array[OPFeature]

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    Function to convert OutputFeatures to an Array of FeatureLike

    Function to convert OutputFeatures to an Array of FeatureLike

    returns

    an Array of FeatureLike

    Definition Classes
    OpPipelineStageOpPipelineStageBase
  76. def outputFeatureUid: String

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    Attributes
    protected[com.salesforce.op]
    Definition Classes
    OpPipelineStage2OpPipelineStage
  77. def outputIsResponse: Boolean

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    Should output feature be a response? Yes, if any of the input features are.

    Should output feature be a response? Yes, if any of the input features are.

    returns

    true if the the output feature should be a response

    Definition Classes
    OpPipelineStage
  78. val outputParamName: String

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    name of spark parameter that sets the first output column

    name of spark parameter that sets the first output column

    Definition Classes
    SwBinaryEstimator
  79. lazy val params: Array[Param[_]]

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    Definition Classes
    Params
  80. def save(path: String): Unit

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    Definition Classes
    MLWritable
    Annotations
    @Since( "1.6.0" ) @throws( ... )
  81. final def set(paramPair: ParamPair[_]): OpBinaryEstimatorWrapper.this.type

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

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

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    Definition Classes
    Params
  84. final def setDefault(paramPairs: ParamPair[_]*): OpBinaryEstimatorWrapper.this.type

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

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    Attributes
    protected
    Definition Classes
    Params
  86. final def setInput(features: InputFeatures): OpBinaryEstimatorWrapper.this.type

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    Input features that will be used by the stage

    Input features that will be used by the stage

    returns

    feature of type InputFeatures

    Definition Classes
    OpPipelineStageBase
  87. final def setInputFeatures[S <: OPFeature](features: Array[S]): OpBinaryEstimatorWrapper.this.type

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    Sets input features

    Sets input features

    S

    feature like type

    features

    array of input features

    returns

    this stage

    Attributes
    protected
    Definition Classes
    InputParams
  88. final def setMetadata(m: Metadata): OpBinaryEstimatorWrapper.this.type

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    Definition Classes
    OpPipelineStageParams
  89. def setOutputFeatureName(name: String): OpBinaryEstimatorWrapper.this.type

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    Definition Classes
    OpPipelineStage
  90. def setSparkMlStage(stage: Option[E]): OpBinaryEstimatorWrapper.this.type

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    Attributes
    protected
    Definition Classes
    SparkWrapperParams
  91. def setStageSavePath(path: String): OpBinaryEstimatorWrapper.this.type

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    Sets a save path for wrapped spark stage

    Sets a save path for wrapped spark stage

    Definition Classes
    SparkWrapperParams
  92. final val sparkInputColParamNames: StringArrayParam

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    Definition Classes
    SparkWrapperParams
  93. final val sparkMlStage: SparkStageParam[E]

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    Definition Classes
    SparkWrapperParams
  94. final val sparkOutputColParamNames: StringArrayParam

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    Definition Classes
    SparkWrapperParams
  95. final def stageName: String

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    Stage unique name consisting of the stage operation name and uid

    Stage unique name consisting of the stage operation name and uid

    returns

    stage name

    Definition Classes
    OpPipelineStageBase
  96. final def synchronized[T0](arg0: ⇒ T0): T0

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    Definition Classes
    AnyRef
  97. def toString(): String

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    Definition Classes
    Identifiable → AnyRef → Any
  98. final def transformSchema(schema: StructType): StructType

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    This function translates the input and output features into spark schema checks and changes that will occur on the underlying data frame

    This function translates the input and output features into spark schema checks and changes that will occur on the underlying data frame

    schema

    schema of the input data frame

    returns

    a new schema with the output features added

    Definition Classes
    OpPipelineStageBase
  99. def transformSchema(schema: StructType, logging: Boolean): StructType

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    Attributes
    protected
    Definition Classes
    PipelineStage
    Annotations
    @DeveloperApi()
  100. implicit val tti1: scala.reflect.api.JavaUniverse.TypeTag[I1]

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    type tag for first input

    type tag for first input

    Definition Classes
    SwBinaryEstimator
  101. implicit val tti2: scala.reflect.api.JavaUniverse.TypeTag[I2]

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    type tag for second input

    type tag for second input

    Definition Classes
    SwBinaryEstimator
  102. implicit val tto: scala.reflect.api.JavaUniverse.TypeTag[O]

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    type tag for output

    type tag for output

    Definition Classes
    SwBinaryEstimator → HasOut
  103. implicit val ttov: scala.reflect.api.JavaUniverse.TypeTag[O.Value]

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    type tag for output value

    type tag for output value

    Definition Classes
    SwBinaryEstimator → HasOut
  104. val uid: String

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    stage uid

    stage uid

    Definition Classes
    SwBinaryEstimator → Identifiable
  105. final def wait(): Unit

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    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  106. final def wait(arg0: Long, arg1: Int): Unit

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    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  107. final def wait(arg0: Long): Unit

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    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  108. final def write: MLWriter

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    Definition Classes
    OpPipelineStageBase → MLWritable

Inherited from SwBinaryEstimator[I1, I2, O, M, E]

Inherited from SparkWrapperParams[E]

Inherited from OpPipelineStage2[I1, I2, O]

Inherited from HasOut[O]

Inherited from HasIn2

Inherited from HasIn1

Inherited from OpPipelineStage[O]

Inherited from OpPipelineStageBase

Inherited from MLWritable

Inherited from OpPipelineStageParams

Inherited from InputParams

Inherited from Estimator[SwBinaryModel[I1, I2, O, M]]

Inherited from PipelineStage

Inherited from Logging

Inherited from Params

Inherited from Serializable

Inherited from Serializable

Inherited from Identifiable

Inherited from AnyRef

Inherited from Any

Ungrouped