Class

com.salesforce.op.dsl.RichSetFeature

RichOPSetFeature

Related Doc: package RichSetFeature

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implicit class RichOPSetFeature[T <: OPSet[_]] extends AnyRef

Enrichment functions for OPSet Feature

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Instance Constructors

  1. new RichOPSetFeature(f: FeatureLike[T])(implicit arg0: scala.reflect.api.JavaUniverse.TypeTag[T], ttiv: scala.reflect.api.JavaUniverse.TypeTag[T.Value])

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    f

    OPSet Feature

Value Members

  1. final def !=(arg0: Any): Boolean

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  2. final def ##(): Int

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  3. final def ==(arg0: Any): Boolean

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  4. final def asInstanceOf[T0]: T0

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  5. def clone(): AnyRef

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  6. final def eq(arg0: AnyRef): Boolean

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  7. def equals(arg0: Any): Boolean

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  8. val f: FeatureLike[T]

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    OPSet Feature

  9. def finalize(): Unit

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  10. final def getClass(): Class[_]

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  11. def hashCode(): Int

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  12. final def isInstanceOf[T0]: Boolean

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  13. final def ne(arg0: AnyRef): Boolean

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  14. final def notify(): Unit

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  15. final def notifyAll(): Unit

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  16. def pivot(others: Array[FeatureLike[T]] = Array.empty, topK: Int = TransmogrifierDefaults.TopK, minSupport: Int = TransmogrifierDefaults.MinSupport, cleanText: Boolean = TransmogrifierDefaults.CleanText, trackNulls: Boolean = TransmogrifierDefaults.TrackNulls): FeatureLike[OPVector]

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    Converts a sequence of OPSet features into a vector keeping the top K most common occurrences of each OPSet feature (ie the final vector has length k * number of OPSet inputs).

    Converts a sequence of OPSet features into a vector keeping the top K most common occurrences of each OPSet feature (ie the final vector has length k * number of OPSet inputs). Plus an additional column for "other" values - which will capture values that do not make the cut or values not seen in training

    others

    other features to include in the pivot

    topK

    keep topK values

    minSupport

    min occurrences to keep a value

    cleanText

    if true ignores capitalization and punctuations when grouping categories

    trackNulls

    keep a count of nulls

  17. final def synchronized[T0](arg0: ⇒ T0): T0

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  18. def toString(): String

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  19. def vectorize(topK: Int, minSupport: Int, cleanText: Boolean, trackNulls: Boolean = TransmogrifierDefaults.TrackNulls, others: Array[FeatureLike[T]] = Array.empty): FeatureLike[OPVector]

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    Converts a sequence of OPSet features into a vector keeping the top K most common occurrences of each OPSet feature (ie the final vector has length k * number of OPSet inputs).

    Converts a sequence of OPSet features into a vector keeping the top K most common occurrences of each OPSet feature (ie the final vector has length k * number of OPSet inputs). Plus an additional column for "other" values - which will capture values that do not make the cut or values not seen in training

    topK

    keep topK values

    minSupport

    min occurrences to keep a value

    cleanText

    if true ignores capitalization and punctuations when grouping categories

    trackNulls

    keep a count of nulls

    others

    other features to include in the pivot

  20. final def wait(): Unit

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  21. final def wait(arg0: Long, arg1: Int): Unit

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  22. final def wait(arg0: Long): Unit

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