RichRealNNFeature

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 clone(): AnyRef

   

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6. def deindexed(labels: Array[String] = Array.empty, unseenName: String = ..., handleInvalid: IndexToStringHandleInvalid = IndexToStringHandleInvalid.NoFilter): FeatureLike[Text]

   

   Apply OpIndexToStringNoFilter transformer.

   Apply OpIndexToStringNoFilter transformer.

   A transformer that maps a feature of indices back to a new feature of corresponding text values. The index-string mapping is either from the ML attributes of the input feature, or from user-supplied labels (which take precedence over ML attributes).

   labels

   Optional array of labels specifying index-string mapping. If not provided or if empty, then metadata from input feature is used instead.

   unseenName

   name to give strings that appear in transform but not in fit

   handleInvalid

   how to transform values not seen in fitting

   returns

   deindexed text feature

   See also

   OpStringIndexerNoFilter for converting text into indices

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[RealNN]

   

   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. def sanityCheck(featureVector: FeatureLike[OPVector], checkSample: Double = SanityChecker.CheckSample, sampleSeed: Long = SanityChecker.SampleSeed, sampleLowerLimit: Int = SanityChecker.SampleLowerLimit, sampleUpperLimit: Int = SanityChecker.SampleUpperLimit, maxCorrelation: Double = SanityChecker.MaxCorrelation, minCorrelation: Double = SanityChecker.MinCorrelation, maxCramersV: Double = SanityChecker.MaxCramersV, correlationType: CorrelationType = ..., minVariance: Double = SanityChecker.MinVariance, removeBadFeatures: Boolean = SanityChecker.RemoveBadFeatures, removeFeatureGroup: Boolean = SanityChecker.RemoveFeatureGroup, protectTextSharedHash: Boolean = SanityChecker.ProtectTextSharedHash, maxRuleConfidence: Double = SanityChecker.MaxRuleConfidence, minRequiredRuleSupport: Double = ..., featureLabelCorrOnly: Boolean = SanityChecker.FeatureLabelCorrOnly, correlationExclusion: CorrelationExclusion = ..., categoricalLabel: Option[Boolean] = None): FeatureLike[OPVector]

    

    Apply SanityChecker estimator.

    Apply SanityChecker estimator. It checks for potential problems with computed features in a supervized learning setting.

    featureVector

    feature vector

    checkSample

    Rate to downsample the data for statistical calculations (note: actual sampling will not be exact due to Spark's dataset sampling behavior)

    sampleSeed

    Seed to use when sampling

    sampleLowerLimit

    Lower limit on number of samples in downsampled data set (note: sample limit will not be exact, due to Spark's dataset sampling behavior)

    sampleUpperLimit

    Upper limit on number of samples in downsampled data set (note: sample limit will not be exact, due to Spark's dataset sampling behavior)

    maxCorrelation

    Maximum correlation (absolute value) allowed between a feature in the feature vector and the label

    minCorrelation

    Minimum correlation (absolute value) allowed between a feature in the feature vector and the label

    correlationType

    Which coefficient to use for computing correlation

    minVariance

    Minimum amount of variance allowed for each feature and label

    removeBadFeatures

    If set to true, this will automatically remove all the bad features from the feature vector

    removeFeatureGroup

    remove all features descended from a parent feature

    protectTextSharedHash

    protect text shared hash from related null indicators and other hashes

    maxRuleConfidence

    Maximum allowed confidence of association rules in categorical variables. A categorical variable will be removed if there is a choice where the maximum confidence is above this threshold, and the support for that choice is above the min rule support parameter, defined below.

    minRequiredRuleSupport

    Categoricals can be removed if an association rule is found between one of the choices and a categorical label where the confidence of that rule is above maxRuleConfidence and the support fraction of that choice is above minRuleSupport.

    featureLabelCorrOnly

    If true, then only calculate correlations between features and label instead of the entire correlation matrix which includes all feature-feature correlations

    correlationExclusion

    Setting for what categories of feature vector columns to exclude from the correlation calculation (eg. hashed text features)

    categoricalLabel

    If true, treat label as categorical. If not set, check number of distinct labels to decide whether a label should be treated categorical.

    returns

    sanity checked feature vector

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

    

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19. def toIsotonicCalibrated(label: FeatureLike[RealNN], isIsotonic: Boolean = true): FeatureLike[RealNN]

    

    Apply standard isotonic regression transformer shortcut function.

    Apply standard isotonic regression transformer shortcut function.

    label

    feature to calibrate against

    isIsotonic

    increasing default true or decreasing

    returns

    recalibrated feature

20. def toPercentile(buckets: Int = 100): FeatureLike[RealNN]

    

    Apply PercentileBucketizer transformer shortcut function.

    Apply PercentileBucketizer transformer shortcut function. Will rescale values into the specified number of bins (default it 100)

    buckets

    number of bins to scale into

21. def toString(): String

    

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22. def vectorize(others: Array[FeatureLike[RealNN]] = Array.empty): FeatureLike[OPVector]

    

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

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

    others

    other features of same type

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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26. def zNormalize(): FeatureLike[RealNN]

    

    Z-normalization shortcut function using OpStandardScaler.