Class

com.salesforce.op.stages.impl.regression

OpRandomForestRegressor

Related Doc: package regression

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class OpRandomForestRegressor extends OpPredictorWrapper[RandomForestRegressor, RandomForestRegressionModel] with OpRandomForestRegressorParams

Wrapper around sparj Random Forest Regressor org.apache.spark.ml.regression.RandomForestRegressor

Linear Supertypes
OpRandomForestRegressorParams, RandomForestRegressorParams, TreeRegressorParams, RandomForestParams, TreeEnsembleParams, DecisionTreeParams, HasSeed, HasCheckpointInterval, PredictorParams, HasPredictionCol, HasFeaturesCol, HasLabelCol, OpPredictorWrapper[RandomForestRegressor, RandomForestRegressionModel], SparkWrapperParams[RandomForestRegressor], OpPipelineStage2[RealNN, OPVector, Prediction], HasIn2, HasIn1, OpPipelineStage[Prediction], OpPipelineStageBase, MLWritable, OpPipelineStageParams, InputParams, Estimator[OpPredictorWrapperModel[RandomForestRegressionModel]], PipelineStage, Logging, Params, Serializable, Serializable, Identifiable, AnyRef, Any
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Inherited
  1. OpRandomForestRegressor
  2. OpRandomForestRegressorParams
  3. RandomForestRegressorParams
  4. TreeRegressorParams
  5. RandomForestParams
  6. TreeEnsembleParams
  7. DecisionTreeParams
  8. HasSeed
  9. HasCheckpointInterval
  10. PredictorParams
  11. HasPredictionCol
  12. HasFeaturesCol
  13. HasLabelCol
  14. OpPredictorWrapper
  15. SparkWrapperParams
  16. OpPipelineStage2
  17. HasIn2
  18. HasIn1
  19. OpPipelineStage
  20. OpPipelineStageBase
  21. MLWritable
  22. OpPipelineStageParams
  23. InputParams
  24. Estimator
  25. PipelineStage
  26. Logging
  27. Params
  28. Serializable
  29. Serializable
  30. Identifiable
  31. AnyRef
  32. Any
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Visibility
  1. Public
  2. All

Instance Constructors

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

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    uid

    stage uid

Type Members

  1. final type InputFeatures = (FeatureLike[RealNN], FeatureLike[OPVector])

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    Input Features type

    Input Features type

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

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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 val cacheNodeIds: BooleanParam

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    Definition Classes
    DecisionTreeParams
  7. 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
  8. 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
  9. final val checkpointInterval: IntParam

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    Definition Classes
    HasCheckpointInterval
  10. final def clear(param: Param[_]): OpRandomForestRegressor.this.type

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    Definition Classes
    Params
  11. def clone(): AnyRef

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    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  12. final def copy(extra: ParamMap): OpRandomForestRegressor.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
  13. def copyValues[T <: Params](to: T, extra: ParamMap): T

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

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

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

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    Definition Classes
    AnyRef → Any
  17. def explainParam(param: Param[_]): String

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    Definition Classes
    Params
  18. def explainParams(): String

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

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

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    Definition Classes
    Params
  21. final val featureSubsetStrategy: Param[String]

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    Definition Classes
    TreeEnsembleParams
  22. final val featuresCol: Param[String]

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    Definition Classes
    HasFeaturesCol
  23. def finalize(): Unit

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    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( classOf[java.lang.Throwable] )
  24. def fit(dataset: Dataset[_]): OpPredictorWrapperModel[RandomForestRegressionModel]

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    Function that fits the binary model

    Function that fits the binary model

    Definition Classes
    OpPredictorWrapper → Estimator
  25. def fit(dataset: Dataset[_], paramMaps: Array[ParamMap]): Seq[OpPredictorWrapperModel[RandomForestRegressionModel]]

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

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

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

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    Definition Classes
    Params
  29. final def getCacheNodeIds: Boolean

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    Definition Classes
    DecisionTreeParams
  30. final def getCheckpointInterval: Int

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    Definition Classes
    HasCheckpointInterval
  31. final def getClass(): Class[_]

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

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    Definition Classes
    Params
  33. final def getFeatureSubsetStrategy: String

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    Definition Classes
    TreeEnsembleParams
  34. final def getFeaturesCol: String

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    Definition Classes
    HasFeaturesCol
  35. final def getImpurity: String

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    Definition Classes
    TreeRegressorParams
  36. 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

  37. 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

  38. final def getInputSchema(): StructType

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    Definition Classes
    OpPipelineStageParams
  39. final def getLabelCol: String

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    Definition Classes
    HasLabelCol
  40. final def getMaxBins: Int

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    Definition Classes
    DecisionTreeParams
  41. final def getMaxDepth: Int

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    Definition Classes
    DecisionTreeParams
  42. final def getMaxMemoryInMB: Int

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    Definition Classes
    DecisionTreeParams
  43. final def getMetadata(): Metadata

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    Definition Classes
    OpPipelineStageParams
  44. final def getMinInfoGain: Double

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    Definition Classes
    DecisionTreeParams
  45. final def getMinInstancesPerNode: Int

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    Definition Classes
    DecisionTreeParams
  46. final def getNumTrees: Int

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    Definition Classes
    RandomForestParams
  47. final def getOrDefault[T](param: Param[T]): T

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    Definition Classes
    Params
  48. def getOutput(): FeatureLike[Prediction]

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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
    OpPipelineStage2OpPipelineStageBase
  49. 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
  50. def getParam(paramName: String): Param[Any]

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    Definition Classes
    Params
  51. final def getPredictionCol: String

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    Definition Classes
    HasPredictionCol
  52. final def getSeed: Long

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    Definition Classes
    HasSeed
  53. def getSparkMlStage(): Option[RandomForestRegressor]

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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
  54. 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
  55. final def getSubsamplingRate: Double

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    Definition Classes
    TreeEnsembleParams
  56. 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
  57. final def getTransientFeatures(): Array[TransientFeature]

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    Gets the input Features

    Gets the input Features

    returns

    input features

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

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

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    Definition Classes
    Params
  60. def hashCode(): Int

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    Definition Classes
    AnyRef → Any
  61. final val impurity: Param[String]

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    Definition Classes
    TreeRegressorParams
  62. final def in1: TransientFeature

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

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

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

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    Attributes
    protected
    Definition Classes
    Logging
  66. 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
  67. val inputParam1Name: String

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    Definition Classes
    OpPredictorWrapper
  68. val inputParam2Name: String

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    Definition Classes
    OpPredictorWrapper
  69. final def isDefined(param: Param[_]): Boolean

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

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

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    Definition Classes
    Params
  72. def isTraceEnabled(): Boolean

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    Attributes
    protected
    Definition Classes
    Logging
  73. final val labelCol: Param[String]

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    Definition Classes
    HasLabelCol
  74. def log: Logger

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

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

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

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

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

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

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    Attributes
    protected
    Definition Classes
    Logging
  81. def logName: String

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

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

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

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

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    Attributes
    protected
    Definition Classes
    Logging
  86. final val maxBins: IntParam

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    Definition Classes
    DecisionTreeParams
  87. final val maxDepth: IntParam

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    Definition Classes
    DecisionTreeParams
  88. final val maxMemoryInMB: IntParam

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    Definition Classes
    DecisionTreeParams
  89. final val minInfoGain: DoubleParam

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    Definition Classes
    DecisionTreeParams
  90. final val minInstancesPerNode: IntParam

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    Definition Classes
    DecisionTreeParams
  91. final def ne(arg0: AnyRef): Boolean

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

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

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    Definition Classes
    AnyRef
  94. final val numTrees: IntParam

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    Definition Classes
    RandomForestParams
  95. def onGetMetadata(): Unit

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    Function to be called on getMetadata

    Function to be called on getMetadata

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

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    Function to be called on setInput

    Function to be called on setInput

    Attributes
    protected
    Definition Classes
    OpRandomForestRegressorOpPipelineStageBase
  97. val operationName: String

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    Short unique name of the operation this stage performs

    Short unique name of the operation this stage performs

    returns

    operation name

    Definition Classes
    OpPredictorWrapperOpPipelineStageBase
  98. 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
  99. def outputFeatureUid: String

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    Attributes
    protected[com.salesforce.op]
    Definition Classes
    OpPipelineStage2OpPipelineStage
  100. 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
  101. val outputParamName: String

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    Definition Classes
    OpPredictorWrapper
  102. lazy val params: Array[Param[_]]

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    Definition Classes
    Params
  103. final val predictionCol: Param[String]

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    Definition Classes
    HasPredictionCol
  104. val predictor: RandomForestRegressor

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    the predictor to wrap

    the predictor to wrap

    Definition Classes
    OpPredictorWrapper
  105. def save(path: String): Unit

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    Definition Classes
    MLWritable
    Annotations
    @Since( "1.6.0" ) @throws( ... )
  106. final val seed: LongParam

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

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

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

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    Definition Classes
    Params
  110. def setCacheNodeIds(value: Boolean): OpRandomForestRegressor.this.type

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    Definition Classes
    OpRandomForestRegressor → DecisionTreeParams
  111. def setCheckpointInterval(value: Int): OpRandomForestRegressor.this.type

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    Specifies how often to checkpoint the cached node IDs.

    Specifies how often to checkpoint the cached node IDs. E.g. 10 means that the cache will get checkpointed every 10 iterations. This is only used if cacheNodeIds is true and if the checkpoint directory is set in org.apache.spark.SparkContext. Must be at least 1. (default = 10)

    Definition Classes
    OpRandomForestRegressor → DecisionTreeParams
  112. final def setDefault(paramPairs: ParamPair[_]*): OpRandomForestRegressor.this.type

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

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    Attributes
    protected
    Definition Classes
    Params
  114. def setFeatureSubsetStrategy(value: String): OpRandomForestRegressor.this.type

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    Definition Classes
    OpRandomForestRegressor → TreeEnsembleParams
  115. def setImpurity(value: String): OpRandomForestRegressor.this.type

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    Definition Classes
    OpRandomForestRegressor → TreeRegressorParams
  116. final def setInput(features: InputFeatures): OpRandomForestRegressor.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
  117. final def setInputFeatures[S <: OPFeature](features: Array[S]): OpRandomForestRegressor.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
  118. def setMaxBins(value: Int): OpRandomForestRegressor.this.type

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    Definition Classes
    OpRandomForestRegressor → DecisionTreeParams
  119. def setMaxDepth(value: Int): OpRandomForestRegressor.this.type

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    Definition Classes
    OpRandomForestRegressor → DecisionTreeParams
  120. def setMaxMemoryInMB(value: Int): OpRandomForestRegressor.this.type

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    Definition Classes
    OpRandomForestRegressor → DecisionTreeParams
  121. final def setMetadata(m: Metadata): OpRandomForestRegressor.this.type

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    Definition Classes
    OpPipelineStageParams
  122. def setMinInfoGain(value: Double): OpRandomForestRegressor.this.type

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    Definition Classes
    OpRandomForestRegressor → DecisionTreeParams
  123. def setMinInstancesPerNode(value: Int): OpRandomForestRegressor.this.type

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    Definition Classes
    OpRandomForestRegressor → DecisionTreeParams
  124. def setNumTrees(value: Int): OpRandomForestRegressor.this.type

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    Definition Classes
    OpRandomForestRegressor → RandomForestParams
  125. def setOutputFeatureName(name: String): OpRandomForestRegressor.this.type

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    Definition Classes
    OpPipelineStage
  126. def setSeed(value: Long): OpRandomForestRegressor.this.type

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    Definition Classes
    OpRandomForestRegressor → DecisionTreeParams
  127. def setSparkMlStage(stage: Option[RandomForestRegressor]): OpRandomForestRegressor.this.type

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    Attributes
    protected
    Definition Classes
    SparkWrapperParams
  128. def setStageSavePath(path: String): OpRandomForestRegressor.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
  129. def setSubsamplingRate(value: Double): OpRandomForestRegressor.this.type

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    Definition Classes
    OpRandomForestRegressor → TreeEnsembleParams
  130. final val sparkInputColParamNames: StringArrayParam

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    Definition Classes
    SparkWrapperParams
  131. final val sparkMlStage: SparkStageParam[RandomForestRegressor]

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    Definition Classes
    SparkWrapperParams
  132. final val sparkOutputColParamNames: StringArrayParam

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    Definition Classes
    SparkWrapperParams
  133. 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
  134. final val subsamplingRate: DoubleParam

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    Definition Classes
    TreeEnsembleParams
  135. final def synchronized[T0](arg0: ⇒ T0): T0

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    Definition Classes
    AnyRef
  136. def toString(): String

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    Definition Classes
    Identifiable → AnyRef → Any
  137. 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
  138. def transformSchema(schema: StructType, logging: Boolean): StructType

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

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    Definition Classes
    OpPredictorWrapper
  140. implicit val tti2: scala.reflect.api.JavaUniverse.TypeTag[OPVector]

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    Definition Classes
    OpPredictorWrapper
  141. implicit val tto: scala.reflect.api.JavaUniverse.TypeTag[Prediction]

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    Definition Classes
    OpPredictorWrapperOpPipelineStage2
  142. implicit val ttov: scala.reflect.api.JavaUniverse.TypeTag[Map[String, Double]]

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    Definition Classes
    OpPredictorWrapperOpPipelineStage2
  143. val uid: String

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    stage uid

    stage uid

    Definition Classes
    OpPredictorWrapper → Identifiable
  144. def validateAndTransformSchema(schema: StructType, fitting: Boolean, featuresDataType: DataType): StructType

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    Attributes
    protected
    Definition Classes
    PredictorParams
  145. final def wait(): Unit

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

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

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

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    Definition Classes
    OpPipelineStageBase → MLWritable

Inherited from RandomForestRegressorParams

Inherited from TreeRegressorParams

Inherited from RandomForestParams

Inherited from TreeEnsembleParams

Inherited from DecisionTreeParams

Inherited from HasSeed

Inherited from HasCheckpointInterval

Inherited from PredictorParams

Inherited from HasPredictionCol

Inherited from HasFeaturesCol

Inherited from HasLabelCol

Inherited from OpPredictorWrapper[RandomForestRegressor, RandomForestRegressionModel]

Inherited from SparkWrapperParams[RandomForestRegressor]

Inherited from HasIn2

Inherited from HasIn1

Inherited from OpPipelineStage[Prediction]

Inherited from OpPipelineStageBase

Inherited from MLWritable

Inherited from OpPipelineStageParams

Inherited from InputParams

Inherited from Estimator[OpPredictorWrapperModel[RandomForestRegressionModel]]

Inherited from PipelineStage

Inherited from Logging

Inherited from Params

Inherited from Serializable

Inherited from Serializable

Inherited from Identifiable

Inherited from AnyRef

Inherited from Any

expertSetParam

setParam

Ungrouped