Class

com.salesforce.op.dsl.RichNumericFeature

RichRealFeature

Related Doc: package RichNumericFeature

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implicit class RichRealFeature[T <: Real] extends AnyRef

Enrichment functions for Real Feature

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Instance Constructors

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

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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. def descale[I <: Real, O <: Real](scaledFeature: FeatureLike[I])(implicit arg0: scala.reflect.api.JavaUniverse.TypeTag[I], arg1: scala.reflect.api.JavaUniverse.TypeTag[O]): FeatureLike[O]

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    Apply DescalerTransformer shortcut.

    Apply DescalerTransformer shortcut. Applies the inverse of the scaling function found in the metadata of the the input feature: scaledFeature

    I

    feature type of the input feature: scaledFeature

    O

    output feature type

    scaledFeature

    the feature containing metadata for constructing the scaling used to make this column

    returns

    the scaled input cast to type O

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

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  10. def equals(arg0: Any): Boolean

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  11. val f: FeatureLike[T]

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    FeatureLike

  12. 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

  13. def finalize(): Unit

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

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  15. def hashCode(): Int

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  16. final def isInstanceOf[T0]: Boolean

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

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  18. final def notify(): Unit

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  19. final def notifyAll(): Unit

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  20. def scale[O <: Real](scalingType: ScalingType, scalingArgs: ScalingArgs)(implicit arg0: scala.reflect.api.JavaUniverse.TypeTag[O]): FeatureLike[O]

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    Apply ScalerTransformer shortcut.

    Apply ScalerTransformer shortcut. Applies the scaling function defined by the scalingType and scalingArg params

    O

    Output feature type

    scalingType

    type of scaling function

    scalingArgs

    arguments to define the scaling function

    returns

    the descaled input cast to type O

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

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  22. def toString(): String

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  23. implicit val ttiv: scala.reflect.api.JavaUniverse.TypeTag[Option[Double]]

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  24. def vectorize(fillValue: Double, fillWithMean: 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 real vectorizer: Converts a sequence of Real features into a vector feature.

    Apply real vectorizer: Converts a sequence of Real features into a vector feature.

    fillValue

    value to pull in place of nulls

    fillWithMean

    replace missing values with mean (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

  25. final def wait(): Unit

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

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

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