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com.spotify.featran

FeatureSpec

Related Docs: object FeatureSpec | package featran

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class FeatureSpec[T] extends AnyRef

Encapsulate specification for feature extraction and transformation.

T

input record type to extract features from

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  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 compose[S](spec: FeatureSpec[S])(f: (T) ⇒ S): FeatureSpec[T]

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    Compose with another spec by applying a prepare function to input records first.

    Compose with another spec by applying a prepare function to input records first.

    Useful for reusing an existing spec for a different input record type.

    S

    input record type of the other spec

    spec

    spec to compose with

    f

    function to prepare input records for the other spec

  7. def cross(k: (String, String))(f: (Double, Double) ⇒ Double): FeatureSpec[T]

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    Cross feature values of two underlying transformers.

    Cross feature values of two underlying transformers.

    k

    names of transformers to be crossed

    f

    function to cross feature value pairs

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

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  9. def equals(arg0: Any): Boolean

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  10. def extract[M[_]](input: M[T])(implicit arg0: CollectionType[M]): FeatureExtractor[M, T]

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    Extract features from an input collection.

    Extract features from an input collection.

    This is done in two steps, a reduce step over the collection to aggregate feature summary, and a map step to transform values using the summary.

    M

    input collection type, e.g. Array, List

    input

    input collection

  11. def extractWithSettings[F](settings: String)(implicit arg0: FeatureBuilder[F], arg1: ClassTag[F]): RecordExtractor[T, F]

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    Extract features from individual records using settings from a previous session.

    Extract features from individual records using settings from a previous session. Since the settings are parsed only once, this is more efficient and is recommended when the input is from an unbounded source, e.g. a stream of events or a backend service.

    This bypasses the reduce step in extract and uses feature summary from settings exported in a previous session.

    settings

    JSON settings from a previous session

  12. def extractWithSettings[M[_]](input: M[T], settings: M[String])(implicit arg0: CollectionType[M]): FeatureExtractor[M, T]

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    Extract features from an input collection using settings from a previous session.

    Extract features from an input collection using settings from a previous session.

    This bypasses the reduce step in extract and uses feature summary from settings exported in a previous session.

    M

    input collection type, e.g. Array, List

    input

    input collection

    settings

    JSON settings from a previous session

  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 optional[A](f: (T) ⇒ Option[A], default: Option[A] = None)(t: Transformer[A, _, _]): FeatureSpec[T]

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    Add an optional field specification.

    Add an optional field specification.

    A

    extracted feature type

    f

    function to extract feature Option[A] from record T

    default

    default for missing values

    t

    Transformer for extracted feature A

  21. def required[A](f: (T) ⇒ A)(t: Transformer[A, _, _]): FeatureSpec[T]

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    Add a required field specification.

    Add a required field specification.

    A

    extracted feature type

    f

    function to extract feature A from record T

    t

    Transformer for extracted feature A

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

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  23. def toString(): String

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  24. final def wait(): Unit

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

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

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