Class

com.salesforce.op.dsl.RichMapFeature

RichStringMapFeature

Related Doc: package RichMapFeature

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implicit class RichStringMapFeature[T <: OPMap[String]] extends AnyRef

Enrichment functions for OPMap Features with String values. All are pivoted by default except TextMap and TextAreaMap which are defined specially below.

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Instance Constructors

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

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    f

    FeatureLike

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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  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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    FeatureLike

  9. def finalize(): Unit

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

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  14. final def notify(): Unit

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  15. final def notifyAll(): Unit

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

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  17. def toString(): String

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  18. implicit val ttiv: scala.reflect.api.JavaUniverse.TypeTag[Map[String, String]]

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  19. def vectorize(topK: Int, minSupport: Int, cleanText: Boolean, cleanKeys: Boolean = TransmogrifierDefaults.CleanKeys, whiteListKeys: Array[String] = Array.empty, blackListKeys: Array[String] = Array.empty, trackNulls: Boolean = TransmogrifierDefaults.TrackNulls, others: Array[FeatureLike[T]] = Array.empty, maxPctCardinality: Double = ...): FeatureLike[OPVector]

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    Apply TextMapPivotVectorizer on any OPMap that has string values

    Apply TextMapPivotVectorizer on any OPMap that has string values

    topK

    number of values to keep for each key

    minSupport

    min times a value must occur to be retained in pivot

    cleanText

    clean text before pivoting

    cleanKeys

    clean map keys before pivoting

    whiteListKeys

    keys to whitelist

    blackListKeys

    keys to blacklist

    trackNulls

    option to keep track of values that were missing

    others

    other features of the same type

    maxPctCardinality

    max percentage of distinct values a categorical feature can have (between 0.0 and 1.00)

    returns

    an OPVector feature

  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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