Class

com.salesforce.op.dsl.RichNumericFeature

RichIntegralFeature

Related Doc: package RichNumericFeature

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implicit class RichIntegralFeature[T <: Integral] extends AnyRef

Enrichment functions for Integral Feature

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Instance Constructors

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

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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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  5. def autoBucketize(label: FeatureLike[RealNN], trackNulls: Boolean, trackInvalid: Boolean = TransmogrifierDefaults.TrackInvalid, minInfoGain: Double = ...): FeatureLike[OPVector]

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    Apply a smart bucketizer transformer

    Apply a smart bucketizer transformer

    label

    label feature

    trackNulls

    option to keep track of values that were missing

    trackInvalid

    option to keep track of invalid values, eg. NaN, -/+Inf or values that fall outside the buckets

    minInfoGain

    minimum info gain, one of the stopping criteria of the Decision Tree

  6. def bucketize(trackNulls: Boolean, trackInvalid: Boolean = TransmogrifierDefaults.TrackInvalid, splits: Array[Double] = NumericBucketizer.Splits, splitInclusion: Inclusion = NumericBucketizer.SplitInclusion, bucketLabels: Option[Array[String]] = None): FeatureLike[OPVector]

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    Apply NumericBucketizer transformer shortcut function

    Apply NumericBucketizer transformer shortcut function

    trackNulls

    option to keep track of values that were missing

    trackInvalid

    option to keep track of invalid values, eg. NaN, -/+Inf or values that fall outside the buckets

    splits

    sorted list of split points for bucketizing

    splitInclusion

    should the splits be left or right inclusive. Meaning if x1 and x2 are split points, then for Left the bucket interval is [x1, x2) and for Right the bucket interval is (x1, x2].

    bucketLabels

    sorted list of labels for the buckets

  7. def clone(): AnyRef

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

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  9. def equals(arg0: Any): Boolean

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  10. val f: FeatureLike[T]

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    FeatureLike

  11. def fillMissingWithMean(default: Double = 0.0): FeatureLike[RealNN]

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    Fill missing values with mean

    Fill missing values with mean

    default

    default value is the whole feature is filled with missing values

    returns

    transformed feature of type RealNN

  12. def finalize(): Unit

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

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  14. def hashCode(): Int

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  15. final def isInstanceOf[T0]: Boolean

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

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  17. final def notify(): Unit

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  18. final def notifyAll(): Unit

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

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  20. def toString(): String

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  21. implicit val ttiv: scala.reflect.api.JavaUniverse.TypeTag[Option[Long]]

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  22. def vectorize(fillValue: Long, fillWithMode: Boolean, trackNulls: Boolean, others: Array[FeatureLike[T]] = Array.empty, trackInvalid: Boolean = TransmogrifierDefaults.TrackInvalid, minInfoGain: Double = TransmogrifierDefaults.MinInfoGain, label: Option[FeatureLike[RealNN]] = None): FeatureLike[OPVector]

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    Apply integral vectorizer: Converts a sequence of Integral features into a vector feature.

    Apply integral vectorizer: Converts a sequence of Integral features into a vector feature.

    fillValue

    value to pull in place of nulls

    fillWithMode

    replace missing values with mode (as apposed to constant provided in fillValue)

    trackNulls

    keep tract of when nulls occur by adding a second column to the vector with a null indicator

    others

    other features of same type

    trackInvalid

    option to keep track of invalid values, eg. NaN, -/+Inf or values that fall outside the buckets

    minInfoGain

    minimum info gain, one of the stopping criteria of the Decision Tree for the autoBucketizer

    label

    optional label column to be passed into autoBucketizer if present

    returns

    a vector feature containing the raw Features with filled missing values and the bucketized features if a label argument is passed

  23. final def wait(): Unit

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

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

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