keystoneml.workflow

FittedPipeline

class FittedPipeline[A, B] extends Chainable[A, B] with Serializable

This is the result of fitting a Pipeline. It is logically equivalent to the Pipeline it is produced by, but with all Estimators pre-fit, and only containing Transformers in the underlying graph. Applying a FittedPipeline to new data does not trigger any new optimization or estimator fitting.

Unlike normal Pipelines, FittedPipelines are serializable and may be written to and from disk.

A

type of the data this FittedPipeline expects as input

B

type of the data this FittedPipeline outputs

Linear Supertypes
Serializable, Serializable, Chainable[A, B], AnyRef, Any
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  1. final def !=(arg0: AnyRef): Boolean

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  2. final def !=(arg0: Any): Boolean

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  3. final def ##(): Int

    Definition Classes
    AnyRef → Any

  4. final def ==(arg0: AnyRef): Boolean

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  5. final def ==(arg0: Any): Boolean

    Definition Classes
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  6. final def andThen[C, L](est: LabelEstimator[B, C, L], data: PipelineDataset[A], labels: PipelineDataset[L]): Pipeline[A, C]

    Chains a label estimator onto the end of this pipeline, producing a new pipeline.

    Chains a label estimator onto the end of this pipeline, producing a new pipeline. If this pipeline has already been executed, it will not need to be fit again.

    est

    The estimator to chain onto the end of this pipeline

    data

    The training data to use (the estimator will be fit on the result of passing this data through the current pipeline)

    labels

    The labels to use when fitting the LabelEstimator. Must be zippable with the training data.

    Definition Classes
    Chainable

  7. final def andThen[C, L](est: LabelEstimator[B, C, L], data: RDD[A], labels: PipelineDataset[L]): Pipeline[A, C]

    Chains a label estimator onto the end of this pipeline, producing a new pipeline.

    Chains a label estimator onto the end of this pipeline, producing a new pipeline. If this pipeline has already been executed, it will not need to be fit again.

    est

    The estimator to chain onto the end of this pipeline

    data

    The training data to use (the estimator will be fit on the result of passing this data through the current pipeline)

    labels

    The labels to use when fitting the LabelEstimator. Must be zippable with the training data.

    Definition Classes
    Chainable

  8. final def andThen[C, L](est: LabelEstimator[B, C, L], data: PipelineDataset[A], labels: RDD[L]): Pipeline[A, C]

    Chains a label estimator onto the end of this pipeline, producing a new pipeline.

    Chains a label estimator onto the end of this pipeline, producing a new pipeline. If this pipeline has already been executed, it will not need to be fit again.

    est

    The estimator to chain onto the end of this pipeline

    data

    The training data to use (the estimator will be fit on the result of passing this data through the current pipeline)

    labels

    The labels to use when fitting the LabelEstimator. Must be zippable with the training data.

    Definition Classes
    Chainable

  9. final def andThen[C, L](est: LabelEstimator[B, C, L], data: RDD[A], labels: RDD[L]): Pipeline[A, C]

    Chains a label estimator onto the end of this pipeline, producing a new pipeline.

    Chains a label estimator onto the end of this pipeline, producing a new pipeline. If this pipeline has already been executed, it will not need to be fit again.

    est

    The estimator to chain onto the end of this pipeline

    data

    The training data to use (the estimator will be fit on the result of passing this data through the current pipeline)

    labels

    The labels to use when fitting the LabelEstimator. Must be zippable with the training data.

    Definition Classes
    Chainable

  10. final def andThen[C](est: Estimator[B, C], data: PipelineDataset[A]): Pipeline[A, C]

    Chains an estimator onto the end of this pipeline, producing a new pipeline.

    Chains an estimator onto the end of this pipeline, producing a new pipeline. If this pipeline has already been executed, it will not need to be fit again.

    est

    The estimator to chain onto the end of this pipeline

    data

    The training data to use (the estimator will be fit on the result of passing this data through the current pipeline)

    Definition Classes
    Chainable

  11. final def andThen[C](est: Estimator[B, C], data: RDD[A]): Pipeline[A, C]

    Chains an estimator onto the end of this pipeline, producing a new pipeline.

    Chains an estimator onto the end of this pipeline, producing a new pipeline. If this pipeline has already been executed, it will not need to be fit again.

    est

    The estimator to chain onto the end of this pipeline

    data

    The training data to use (the estimator will be fit on the result of passing this data through the current pipeline)

    Definition Classes
    Chainable

  12. final def andThen[C](next: Chainable[B, C]): Pipeline[A, C]

    Chains a pipeline onto the end of this one, producing a new pipeline.

    Chains a pipeline onto the end of this one, producing a new pipeline. If either this pipeline or the following has already been executed, it will not need to be fit again.

    next

    the pipeline to chain

    Definition Classes
    Chainable

  13. def apply(in: RDD[A]): RDD[B]

    The application of this FittedPipeline to an RDD of input items.

    The application of this FittedPipeline to an RDD of input items.

    in

    The RDD input to pass into this transformer

    returns

    The RDD output for the given input

  14. def apply(in: A): B

    The application of this FittedPipeline to a single input item.

    The application of this FittedPipeline to a single input item.

    in

    The input item to pass into this transformer

    returns

    The output value

  15. final def asInstanceOf[T0]: T0

    Definition Classes
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  16. def clone(): AnyRef

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    protected[java.lang]
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    @throws( ... )

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

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  18. def equals(arg0: Any): Boolean

    Definition Classes
    AnyRef → Any

  19. def finalize(): Unit

    Attributes
    protected[java.lang]
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    @throws( classOf[java.lang.Throwable] )

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

    Definition Classes
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  21. def hashCode(): Int

    Definition Classes
    AnyRef → Any

  22. final def isInstanceOf[T0]: Boolean

    Definition Classes
    Any

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

    Definition Classes
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  24. final def notify(): Unit

    Definition Classes
    AnyRef

  25. final def notifyAll(): Unit

    Definition Classes
    AnyRef

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

    Definition Classes
    AnyRef

  27. def toPipeline: Pipeline[A, B]

    Converts this FittedPipeline back into a Pipeline.

    Converts this FittedPipeline back into a Pipeline.

    Definition Classes
    FittedPipeline → Chainable

  28. def toString(): String

    Definition Classes
    AnyRef → Any

  29. final def wait(): Unit

    Definition Classes
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    @throws( ... )

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

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

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Inherited from Serializable

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Inherited from Chainable[A, B]

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