Class

com.salesforce.op.stages.impl.regression

OpGeneralizedLinearRegression

Related Doc: package regression

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class OpGeneralizedLinearRegression extends OpPredictorWrapper[GeneralizedLinearRegression, GeneralizedLinearRegressionModel] with OpGeneralizedLinearRegressionParams

Wrapper for spark Generalized Regression org.apache.spark.ml.regression.GeneralizedLinearRegression

Linear Supertypes
OpGeneralizedLinearRegressionParams, GeneralizedLinearRegressionBase, HasSolver, HasWeightCol, HasRegParam, HasTol, HasMaxIter, HasFitIntercept, PredictorParams, HasPredictionCol, HasFeaturesCol, HasLabelCol, OpPredictorWrapper[GeneralizedLinearRegression, GeneralizedLinearRegressionModel], SparkWrapperParams[GeneralizedLinearRegression], OpPipelineStage2[RealNN, OPVector, Prediction], HasIn2, HasIn1, OpPipelineStage[Prediction], OpPipelineStageBase, MLWritable, OpPipelineStageParams, InputParams, Estimator[OpPredictorWrapperModel[GeneralizedLinearRegressionModel]], PipelineStage, Logging, Params, Serializable, Serializable, Identifiable, AnyRef, Any
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Inherited
  1. OpGeneralizedLinearRegression
  2. OpGeneralizedLinearRegressionParams
  3. GeneralizedLinearRegressionBase
  4. HasSolver
  5. HasWeightCol
  6. HasRegParam
  7. HasTol
  8. HasMaxIter
  9. HasFitIntercept
  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
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Instance Constructors

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

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

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    Definition Classes
    Params
  16. def explainParams(): String

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

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

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    Definition Classes
    Params
  19. final val family: Param[String]

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    Definition Classes
    GeneralizedLinearRegressionBase
    Annotations
    @Since( "2.0.0" )
  20. final val featuresCol: Param[String]

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    Definition Classes
    HasFeaturesCol
  21. def finalize(): Unit

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

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    Function that fits the binary model

    Function that fits the binary model

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

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

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

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    Definition Classes
    Estimator
    Annotations
    @Since( "2.0.0" ) @varargs()
  26. final val fitIntercept: BooleanParam

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    Definition Classes
    HasFitIntercept
  27. final def get[T](param: Param[T]): Option[T]

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

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

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    Definition Classes
    Params
  30. def getFamily: String

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    Definition Classes
    GeneralizedLinearRegressionBase
    Annotations
    @Since( "2.0.0" )
  31. final def getFeaturesCol: String

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    Definition Classes
    HasFeaturesCol
  32. final def getFitIntercept: Boolean

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    Definition Classes
    HasFitIntercept
  33. 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

  34. 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

  35. final def getInputSchema(): StructType

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    Definition Classes
    OpPipelineStageParams
  36. final def getLabelCol: String

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    Definition Classes
    HasLabelCol
  37. def getLink: String

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    Definition Classes
    GeneralizedLinearRegressionBase
    Annotations
    @Since( "2.0.0" )
  38. def getLinkPower: Double

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    Definition Classes
    GeneralizedLinearRegressionBase
    Annotations
    @Since( "2.2.0" )
  39. def getLinkPredictionCol: String

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    Definition Classes
    GeneralizedLinearRegressionBase
    Annotations
    @Since( "2.0.0" )
  40. final def getMaxIter: Int

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    Definition Classes
    HasMaxIter
  41. final def getMetadata(): Metadata

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    Definition Classes
    OpPipelineStageParams
  42. def getOffsetCol: String

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    Definition Classes
    GeneralizedLinearRegressionBase
    Annotations
    @Since( "2.3.0" )
  43. final def getOrDefault[T](param: Param[T]): T

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    Definition Classes
    Params
  44. 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
  45. 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
  46. def getParam(paramName: String): Param[Any]

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    Definition Classes
    Params
  47. final def getPredictionCol: String

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    Definition Classes
    HasPredictionCol
  48. final def getRegParam: Double

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    Definition Classes
    HasRegParam
  49. final def getSolver: String

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    Definition Classes
    HasSolver
  50. def getSparkMlStage(): Option[GeneralizedLinearRegression]

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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
  51. 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
  52. final def getTol: Double

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    Definition Classes
    HasTol
  53. 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
  54. final def getTransientFeatures(): Array[TransientFeature]

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    Gets the input Features

    Gets the input Features

    returns

    input features

    Definition Classes
    InputParams
  55. def getVariancePower: Double

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    Definition Classes
    GeneralizedLinearRegressionBase
    Annotations
    @Since( "2.2.0" )
  56. final def getWeightCol: String

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    Definition Classes
    HasWeightCol
  57. final def hasDefault[T](param: Param[T]): Boolean

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

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    Definition Classes
    Params
  59. def hashCode(): Int

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

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

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

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

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    Attributes
    protected
    Definition Classes
    Logging
  64. 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
  65. val inputParam1Name: String

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    Definition Classes
    OpPredictorWrapper
  66. val inputParam2Name: String

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

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

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

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    Definition Classes
    Params
  70. def isTraceEnabled(): Boolean

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

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    Definition Classes
    HasLabelCol
  72. final val link: Param[String]

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    Definition Classes
    GeneralizedLinearRegressionBase
    Annotations
    @Since( "2.0.0" )
  73. final val linkPower: DoubleParam

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    Definition Classes
    GeneralizedLinearRegressionBase
    Annotations
    @Since( "2.2.0" )
  74. final val linkPredictionCol: Param[String]

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    Definition Classes
    GeneralizedLinearRegressionBase
    Annotations
    @Since( "2.0.0" )
  75. def log: Logger

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

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

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

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

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

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

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    Attributes
    protected
    Definition Classes
    Logging
  82. def logName: String

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

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

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

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

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    Attributes
    protected
    Definition Classes
    Logging
  87. final val maxIter: IntParam

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    Definition Classes
    HasMaxIter
  88. final def ne(arg0: AnyRef): Boolean

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

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

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    Definition Classes
    AnyRef
  91. final val offsetCol: Param[String]

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    Definition Classes
    GeneralizedLinearRegressionBase
    Annotations
    @Since( "2.3.0" )
  92. def onGetMetadata(): Unit

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    Function to be called on getMetadata

    Function to be called on getMetadata

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

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    Function to be called on setInput

    Function to be called on setInput

    Attributes
    protected
    Definition Classes
    OpGeneralizedLinearRegressionOpPipelineStageBase
  94. 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
  95. 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
  96. def outputFeatureUid: String

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    Attributes
    protected[com.salesforce.op]
    Definition Classes
    OpPipelineStage2OpPipelineStage
  97. 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
  98. val outputParamName: String

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

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

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    Definition Classes
    HasPredictionCol
  101. val predictor: GeneralizedLinearRegression

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    the predictor to wrap

    the predictor to wrap

    Definition Classes
    OpPredictorWrapper
  102. final val regParam: DoubleParam

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    Definition Classes
    HasRegParam
  103. def save(path: String): Unit

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

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

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

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

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

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    Attributes
    protected
    Definition Classes
    Params
  109. def setFamily(value: String): OpGeneralizedLinearRegression.this.type

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    Sets the value of param family.

    Sets the value of param family. Default is "gaussian".

  110. def setFitIntercept(value: Boolean): OpGeneralizedLinearRegression.this.type

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    Sets if we should fit the intercept.

    Sets if we should fit the intercept. Default is true.

  111. final def setInput(features: InputFeatures): OpGeneralizedLinearRegression.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
  112. final def setInputFeatures[S <: OPFeature](features: Array[S]): OpGeneralizedLinearRegression.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
  113. def setLink(value: String): OpGeneralizedLinearRegression.this.type

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    Sets the value of param link.

    Sets the value of param link. Used only when family is not "tweedie".

  114. def setLinkPower(value: Double): OpGeneralizedLinearRegression.this.type

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    Sets the value of param linkPower.

    Sets the value of param linkPower. Used only when family is "tweedie".

  115. def setLinkPredictionCol(value: String): OpGeneralizedLinearRegression.this.type

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    Sets the link prediction (linear predictor) column name.

  116. def setMaxIter(value: Int): OpGeneralizedLinearRegression.this.type

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    Sets the maximum number of iterations (applicable for solver "irls").

    Sets the maximum number of iterations (applicable for solver "irls"). Default is 25.

  117. final def setMetadata(m: Metadata): OpGeneralizedLinearRegression.this.type

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    Definition Classes
    OpPipelineStageParams
  118. def setOutputFeatureName(name: String): OpGeneralizedLinearRegression.this.type

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    Definition Classes
    OpPipelineStage
  119. def setRegParam(value: Double): OpGeneralizedLinearRegression.this.type

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    Sets the regularization parameter for L2 regularization.

    Sets the regularization parameter for L2 regularization. The regularization term is

    $$ 0.5 * regParam * L2norm(coefficients)^2 $$
    Default is 0.0.

  120. def setSolver(value: String): OpGeneralizedLinearRegression.this.type

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    Sets the solver algorithm used for optimization.

    Sets the solver algorithm used for optimization. Currently only supports "irls" which is also the default solver.

  121. def setSparkMlStage(stage: Option[GeneralizedLinearRegression]): OpGeneralizedLinearRegression.this.type

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    Attributes
    protected
    Definition Classes
    SparkWrapperParams
  122. def setStageSavePath(path: String): OpGeneralizedLinearRegression.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
  123. def setTol(value: Double): OpGeneralizedLinearRegression.this.type

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    Sets the convergence tolerance of iterations.

    Sets the convergence tolerance of iterations. Smaller value will lead to higher accuracy with the cost of more iterations. Default is 1E-6.

  124. def setVariancePower(value: Double): OpGeneralizedLinearRegression.this.type

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    Sets the value of param variancePower.

    Sets the value of param variancePower. Used only when family is "tweedie". Default is 0.0, which corresponds to the "gaussian" family.

  125. def setWeightCol(value: String): OpGeneralizedLinearRegression.this.type

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    Sets the value of param weightCol.

    Sets the value of param weightCol. If this is not set or empty, we treat all instance weights as 1.0. Default is not set, so all instances have weight one. In the Binomial family, weights correspond to number of trials and should be integer. Non-integer weights are rounded to integer in AIC calculation.

  126. final val solver: Param[String]

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    Definition Classes
    GeneralizedLinearRegressionBase → HasSolver
    Annotations
    @Since( "2.0.0" )
  127. final val sparkInputColParamNames: StringArrayParam

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    Definition Classes
    SparkWrapperParams
  128. final val sparkMlStage: SparkStageParam[GeneralizedLinearRegression]

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    Definition Classes
    SparkWrapperParams
  129. final val sparkOutputColParamNames: StringArrayParam

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    Definition Classes
    SparkWrapperParams
  130. 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
  131. final def synchronized[T0](arg0: ⇒ T0): T0

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    Definition Classes
    AnyRef
  132. def toString(): String

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    Definition Classes
    Identifiable → AnyRef → Any
  133. final val tol: DoubleParam

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    Definition Classes
    HasTol
  134. 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
  135. def transformSchema(schema: StructType, logging: Boolean): StructType

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

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

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

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

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    Definition Classes
    OpPredictorWrapperOpPipelineStage2
  140. val uid: String

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    stage uid

    stage uid

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

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    Definition Classes
    GeneralizedLinearRegressionBase → PredictorParams
    Annotations
    @Since( "2.0.0" )
  142. final val variancePower: DoubleParam

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    Definition Classes
    GeneralizedLinearRegressionBase
    Annotations
    @Since( "2.2.0" )
  143. final def wait(): Unit

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

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

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    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  146. final val weightCol: Param[String]

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    Definition Classes
    HasWeightCol
  147. final def write: MLWriter

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    Definition Classes
    OpPipelineStageBase → MLWritable

Inherited from GeneralizedLinearRegressionBase

Inherited from HasSolver

Inherited from HasWeightCol

Inherited from HasRegParam

Inherited from HasTol

Inherited from HasMaxIter

Inherited from HasFitIntercept

Inherited from PredictorParams

Inherited from HasPredictionCol

Inherited from HasFeaturesCol

Inherited from HasLabelCol

Inherited from OpPredictorWrapper[GeneralizedLinearRegression, GeneralizedLinearRegressionModel]

Inherited from SparkWrapperParams[GeneralizedLinearRegression]

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[GeneralizedLinearRegressionModel]]

Inherited from PipelineStage

Inherited from Logging

Inherited from Params

Inherited from Serializable

Inherited from Serializable

Inherited from Identifiable

Inherited from AnyRef

Inherited from Any

setParam

Ungrouped