RichIntegralMapFeature

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 = ..., cleanKeys: Boolean = TransmogrifierDefaults.CleanKeys, whiteListKeys: Array[String] = Array.empty, blackListKeys: Array[String] = Array.empty): 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

   cleanKeys

   clean text before pivoting

   whiteListKeys

   keys to whitelist

   blackListKeys

   keys to blacklist

6. def clone(): AnyRef

   

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

   

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8. def equals(arg0: Any): Boolean

   

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9. val f: FeatureLike[T]

   

   FeatureLike

10. def finalize(): Unit

    

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

    

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12. def hashCode(): Int

    

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13. final def isInstanceOf[T0]: Boolean

    

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

    

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15. final def notify(): Unit

    

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16. final def notifyAll(): Unit

    

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

    

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18. def toString(): String

    

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

    

20. def vectorize(defaultValue: Double, fillWithMode: Boolean = TransmogrifierDefaults.FillWithMode, cleanKeys: Boolean = TransmogrifierDefaults.CleanKeys, whiteListKeys: Array[String] = Array.empty, blackListKeys: Array[String] = Array.empty, others: Array[FeatureLike[T]] = Array.empty, trackNulls: Boolean = TransmogrifierDefaults.TrackNulls, trackInvalid: Boolean = TransmogrifierDefaults.TrackInvalid, minInfoGain: Double = TransmogrifierDefaults.MinInfoGain, label: Option[FeatureLike[RealNN]] = None): FeatureLike[OPVector]

    

    Apply IntegralMapVectorizer or auto bucketizer (when label is present) on any OPMap that has long values

    Apply IntegralMapVectorizer or auto bucketizer (when label is present) on any OPMap that has long values

    defaultValue

    value to give missing keys on pivot

    cleanKeys

    clean text before pivoting

    whiteListKeys

    keys to whitelist

    blackListKeys

    keys to blacklist

    others

    other features of the same type

    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

    label

    optional label column to be passed into autoBucketizer if present

    returns

    an OPVector feature

21. final def wait(): Unit

    

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

    

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

    

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