Class

com.salesforce.op.stages.impl.classification

OpMultilayerPerceptronClassifier

Related Doc: package classification

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class OpMultilayerPerceptronClassifier extends OpPredictorWrapper[MultilayerPerceptronClassifier, MultilayerPerceptronClassificationModel] with OpMultilayerPerceptronClassifierParams

Wrapper for spark MultiLayerPerceptronClassifier org.apache.spark.ml.classification.MultilayerPerceptronClassifier

Linear Supertypes
OpMultilayerPerceptronClassifierParams, MultilayerPerceptronParams, HasSolver, HasStepSize, HasTol, HasMaxIter, HasSeed, ProbabilisticClassifierParams, HasThresholds, HasProbabilityCol, ClassifierParams, HasRawPredictionCol, PredictorParams, HasPredictionCol, HasFeaturesCol, HasLabelCol, OpPredictorWrapper[MultilayerPerceptronClassifier, MultilayerPerceptronClassificationModel], SparkWrapperParams[MultilayerPerceptronClassifier], OpPipelineStage2[RealNN, OPVector, Prediction], HasOut[Prediction], HasIn2, HasIn1, OpPipelineStage[Prediction], OpPipelineStageBase, MLWritable, OpPipelineStageParams, InputParams, Estimator[OpPredictorWrapperModel[MultilayerPerceptronClassificationModel]], PipelineStage, Logging, Params, Serializable, Serializable, Identifiable, AnyRef, Any
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Inherited
  1. OpMultilayerPerceptronClassifier
  2. OpMultilayerPerceptronClassifierParams
  3. MultilayerPerceptronParams
  4. HasSolver
  5. HasStepSize
  6. HasTol
  7. HasMaxIter
  8. HasSeed
  9. ProbabilisticClassifierParams
  10. HasThresholds
  11. HasProbabilityCol
  12. ClassifierParams
  13. HasRawPredictionCol
  14. PredictorParams
  15. HasPredictionCol
  16. HasFeaturesCol
  17. HasLabelCol
  18. OpPredictorWrapper
  19. SparkWrapperParams
  20. OpPipelineStage2
  21. HasOut
  22. HasIn2
  23. HasIn1
  24. OpPipelineStage
  25. OpPipelineStageBase
  26. MLWritable
  27. OpPipelineStageParams
  28. InputParams
  29. Estimator
  30. PipelineStage
  31. Logging
  32. Params
  33. Serializable
  34. Serializable
  35. Identifiable
  36. AnyRef
  37. Any
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Visibility
  1. Public
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Instance Constructors

  1. new OpMultilayerPerceptronClassifier(uid: String = ...)

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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 blockSize: IntParam

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    Definition Classes
    MultilayerPerceptronParams
    Annotations
    @Since( "1.5.0" )
  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 def clear(param: Param[_]): OpMultilayerPerceptronClassifier.this.type

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    Definition Classes
    Params
  10. def clone(): AnyRef

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

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

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

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

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    Definition Classes
    AnyRef → Any
  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. 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[MultilayerPerceptronClassificationModel]

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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[MultilayerPerceptronClassificationModel]]

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

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

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

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    Definition Classes
    Params
  27. final def getBlockSize: Int

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    Definition Classes
    MultilayerPerceptronParams
    Annotations
    @Since( "1.5.0" )
  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. final def getFeaturesCol: String

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    Definition Classes
    HasFeaturesCol
  31. final def getInitialWeights: Vector

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    Definition Classes
    MultilayerPerceptronParams
    Annotations
    @Since( "2.0.0" )
  32. 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
  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. final def getLayers: Array[Int]

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    Definition Classes
    MultilayerPerceptronParams
    Annotations
    @Since( "1.5.0" )
  38. final def getMaxIter: Int

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

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

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    Definition Classes
    Params
  41. 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
    HasOut → OpPipelineStageBase
  42. 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
  43. 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
  44. def getParam(paramName: String): Param[Any]

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    Definition Classes
    Params
  45. final def getPredictionCol: String

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    Definition Classes
    HasPredictionCol
  46. final def getProbabilityCol: String

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    Definition Classes
    HasProbabilityCol
  47. final def getRawPredictionCol: String

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    Definition Classes
    HasRawPredictionCol
  48. final def getSeed: Long

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    Definition Classes
    HasSeed
  49. final def getSolver: String

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

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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 getStepSize: Double

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    Definition Classes
    HasStepSize
  53. def getThresholds: Array[Double]

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    Definition Classes
    HasThresholds
  54. final def getTol: Double

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    Definition Classes
    HasTol
  55. 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
  56. final def getTransientFeatures(): Array[TransientFeature]

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    Gets the input Features

    Gets the input Features

    returns

    input features

    Definition Classes
    InputParams
  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. final val initialWeights: Param[Vector]

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    Definition Classes
    MultilayerPerceptronParams
    Annotations
    @Since( "2.0.0" )
  63. def initializeLogIfNecessary(isInterpreter: Boolean, silent: Boolean): Boolean

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

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

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    Definition Classes
    OpPredictorWrapper
  67. val inputParam2Name: String

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

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

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

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    Definition Classes
    Params
  71. def isTraceEnabled(): Boolean

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

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    Definition Classes
    HasLabelCol
  73. final val layers: IntArrayParam

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    Definition Classes
    MultilayerPerceptronParams
    Annotations
    @Since( "1.5.0" )
  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 maxIter: IntParam

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

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

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

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    Definition Classes
    AnyRef
  90. def onGetMetadata(): Unit

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    Function to be called on getMetadata

    Function to be called on getMetadata

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

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    Function to be called on setInput

    Function to be called on setInput

    Attributes
    protected
    Definition Classes
    OpMultilayerPerceptronClassifierInputParams
  92. 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
  93. 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
  94. def outputFeatureUid: String

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    Attributes
    protected[com.salesforce.op]
    Definition Classes
    OpPipelineStage2OpPipelineStage
  95. 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
  96. val outputParamName: String

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

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

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    Definition Classes
    HasPredictionCol
  99. val predictor: MultilayerPerceptronClassifier

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    the predictor to wrap

    the predictor to wrap

    Definition Classes
    OpPredictorWrapper
  100. final val probabilityCol: Param[String]

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    Definition Classes
    HasProbabilityCol
  101. final val rawPredictionCol: Param[String]

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    Definition Classes
    HasRawPredictionCol
  102. def save(path: String): Unit

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

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

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

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

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    Definition Classes
    Params
  107. def setBlockSize(value: Int): OpMultilayerPerceptronClassifier.this.type

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    Sets the value of param blockSize.

    Sets the value of param blockSize. Default is 128.

  108. final def setDefault(paramPairs: ParamPair[_]*): OpMultilayerPerceptronClassifier.this.type

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

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    Attributes
    protected
    Definition Classes
    Params
  110. def setInitialWeights(value: Vector): OpMultilayerPerceptronClassifier.this.type

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    Sets the value of param initialWeights.

  111. final def setInput(features: InputFeatures): OpMultilayerPerceptronClassifier.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]): OpMultilayerPerceptronClassifier.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 setLayers(value: Array[Int]): OpMultilayerPerceptronClassifier.this.type

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    Sets the value of param layers.

  114. def setMaxIter(value: Int): OpMultilayerPerceptronClassifier.this.type

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    Set the maximum number of iterations.

    Set the maximum number of iterations. Default is 100.

  115. final def setMetadata(m: Metadata): OpMultilayerPerceptronClassifier.this.type

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    Definition Classes
    OpPipelineStageParams
  116. def setOutputFeatureName(name: String): OpMultilayerPerceptronClassifier.this.type

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    Definition Classes
    OpPipelineStage
  117. def setSeed(value: Long): OpMultilayerPerceptronClassifier.this.type

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    Set the seed for weights initialization if weights are not set

  118. def setSolver(value: String): OpMultilayerPerceptronClassifier.this.type

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    Sets the value of param solver.

    Sets the value of param solver. Default is "l-bfgs".

  119. def setSparkMlStage(stage: Option[MultilayerPerceptronClassifier]): OpMultilayerPerceptronClassifier.this.type

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    Attributes
    protected
    Definition Classes
    SparkWrapperParams
  120. def setStageSavePath(path: String): OpMultilayerPerceptronClassifier.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
  121. def setStepSize(value: Double): OpMultilayerPerceptronClassifier.this.type

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    Sets the value of param stepSize (applicable only for solver "gd").

    Sets the value of param stepSize (applicable only for solver "gd"). Default is 0.03.

  122. def setTol(value: Double): OpMultilayerPerceptronClassifier.this.type

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    Set the convergence tolerance of iterations.

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

  123. final val solver: Param[String]

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

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    Definition Classes
    SparkWrapperParams
  125. final val sparkMlStage: SparkStageParam[MultilayerPerceptronClassifier]

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    Definition Classes
    SparkWrapperParams
  126. final val sparkOutputColParamNames: StringArrayParam

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    Definition Classes
    SparkWrapperParams
  127. 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
  128. val stepSize: DoubleParam

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    Definition Classes
    HasStepSize
  129. final def synchronized[T0](arg0: ⇒ T0): T0

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    Definition Classes
    AnyRef
  130. final val thresholds: DoubleArrayParam

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    Definition Classes
    HasThresholds
  131. def toString(): String

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

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    Definition Classes
    HasTol
  133. 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
  134. def transformSchema(schema: StructType, logging: Boolean): StructType

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

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

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

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    Type tag of the output

    Type tag of the output

    Definition Classes
    OpPredictorWrapper → HasOut
  138. implicit val ttov: scala.reflect.api.JavaUniverse.TypeTag[Map[String, Double]]

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    Type tag of the output value

    Type tag of the output value

    Definition Classes
    OpPredictorWrapper → HasOut
  139. val uid: String

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    stage uid

    stage uid

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

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    Attributes
    protected
    Definition Classes
    ProbabilisticClassifierParams → ClassifierParams → PredictorParams
  141. final def wait(): Unit

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

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

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

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    Definition Classes
    OpPipelineStageBase → MLWritable

Inherited from MultilayerPerceptronParams

Inherited from HasSolver

Inherited from HasStepSize

Inherited from HasTol

Inherited from HasMaxIter

Inherited from HasSeed

Inherited from ProbabilisticClassifierParams

Inherited from HasThresholds

Inherited from HasProbabilityCol

Inherited from ClassifierParams

Inherited from HasRawPredictionCol

Inherited from PredictorParams

Inherited from HasPredictionCol

Inherited from HasFeaturesCol

Inherited from HasLabelCol

Inherited from OpPredictorWrapper[MultilayerPerceptronClassifier, MultilayerPerceptronClassificationModel]

Inherited from SparkWrapperParams[MultilayerPerceptronClassifier]

Inherited from HasOut[Prediction]

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[MultilayerPerceptronClassificationModel]]

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