Class

com.alpine.model.pack.multiple

PipelineCategoricalTransformer

Related Doc: package multiple

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class PipelineCategoricalTransformer[R <: CategoricalResult] extends PipelineMLTransformer[R] with CategoricalTransformer[R]

Linear Supertypes
CategoricalTransformer[R], PipelineMLTransformer[R], MLTransformer[R], PipelineTransformer, Transformer, Serializable, Serializable, AnyRef, Any
Known Subclasses
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Inherited
  1. PipelineCategoricalTransformer
  2. CategoricalTransformer
  3. PipelineMLTransformer
  4. MLTransformer
  5. PipelineTransformer
  6. Transformer
  7. Serializable
  8. Serializable
  9. AnyRef
  10. Any
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Visibility
  1. Public
  2. All

Instance Constructors

  1. new PipelineCategoricalTransformer(preProcessors: List[_ <: Transformer], finalTransformer: CategoricalTransformer[R], subModels: Seq[RowModel])

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Type Members

  1. type Row = Seq[Any]

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    Shorthand for the input / output type of the apply method.

    Shorthand for the input / output type of the apply method. Equivalent to Seq[Any].

    Definition Classes
    Transformer

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 ==(arg0: Any): Boolean

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    Definition Classes
    AnyRef → Any
  4. def allowNullValues: Boolean

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    Allows the transformer to specify if the apply method can handle null values in the input row.

    Allows the transformer to specify if the apply method can handle null values in the input row.

    e.g. a Null value replacement transformer, or a Naive Bayes transformer would naturally handle null values, but a Linear Regression transformer would not.

    Default value is false.

    returns

    Boolean indicating tolerance for null values in input.

    Definition Classes
    Transformer
  5. def apply(row: Row): Row

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    This method should be implemented with speed and garbage collection in mind, as it called once per row on potentially huge data-sets.

    This method should be implemented with speed and garbage collection in mind, as it called once per row on potentially huge data-sets.

    This method is not required to be thread-safe.

    row

    The row of input to be scored.

    returns

    The result from applying the trained model to the row.

    Definition Classes
    PipelineCategoricalTransformerCategoricalTransformerPipelineTransformerTransformer
  6. def apply(row: Row, tList: List[Transformer], reordering: List[Option[Array[Int]]]): Row

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    Attributes
    protected
    Definition Classes
    PipelineTransformer
  7. final def asInstanceOf[T0]: T0

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    Definition Classes
    Any
  8. def classLabels: Seq[String]

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    The result must always return the labels in the order specified here.

    The result must always return the labels in the order specified here.

    returns

    The class labels in the order that they will be returned by the result.

    Definition Classes
    PipelineCategoricalTransformerCategoricalTransformer
  9. def clone(): AnyRef

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    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  10. final def eq(arg0: AnyRef): Boolean

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

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    AnyRef → Any
  12. def finalize(): Unit

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    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( classOf[java.lang.Throwable] )
  13. final def getClass(): Class[_]

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    Definition Classes
    AnyRef → Any
  14. def hashCode(): Int

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    Definition Classes
    AnyRef → Any
  15. def inputRowForLastModel(row: Row): Row

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    Attributes
    protected
    Definition Classes
    PipelineMLTransformer
  16. final def isInstanceOf[T0]: Boolean

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    Definition Classes
    Any
  17. val lastReordering: Option[Array[Int]]

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    Attributes
    protected
    Definition Classes
    PipelineTransformer
  18. final def ne(arg0: AnyRef): Boolean

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

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

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    Definition Classes
    AnyRef
  21. val reorderingIndices: List[Option[Array[Int]]]

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    Attributes
    protected
    Definition Classes
    PipelineTransformer
  22. def score(row: Row): R

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    Definition Classes
    PipelineMLTransformerMLTransformer
  23. final def synchronized[T0](arg0: ⇒ T0): T0

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    Definition Classes
    AnyRef
  24. def toString(): String

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    Definition Classes
    AnyRef → Any
  25. final def wait(): Unit

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

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

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    Definition Classes
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    Annotations
    @throws( ... )

Inherited from CategoricalTransformer[R]

Inherited from PipelineMLTransformer[R]

Inherited from MLTransformer[R]

Inherited from PipelineTransformer

Inherited from Transformer

Inherited from Serializable

Inherited from Serializable

Inherited from AnyRef

Inherited from Any

Ungrouped