RichRealFeature

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]

   

   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]

   

   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]

    

    FeatureLike

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

    

    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[Double]]

    

22. 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]

    

    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

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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