keystoneml.workflow

Chainable

trait Chainable[A, B] extends AnyRef

This trait provides methods to chain an object with Estimators, LabelEstimators, and other Chainables to construct Pipelines. To extend this trait, a class must implement the toPipeline method, which converts an object of the class into a Pipeline.

A

type of the data this Chainable expects as input

B

type of the data this Chainable outputs

Linear Supertypes
AnyRef, Any
Known Subclasses
BatchPCATransformer, BatchSignedHellingerMapper, BlockLinearMapper, Cacher, ClassLabelIndicatorsFromIntArrayLabels, ClassLabelIndicatorsFromIntLabels, ColumnSampler, Convolver, CoreNLPFeatureExtractor, CosineRandomFeatures, Cropper, DaisyExtractor, Densify, FisherVector, FisherVector, FisherVectorInterface, FittedPipeline, FloatToDouble, GaussianMixtureModel, GrayScaler, HashingTF, HogExtractor, Identity, Identity, ImageExtractor, ImageVectorizer, KMeansModel, KernelBlockLinearMapper, LCSExtractor, LabelExtractor, LinearMapper, LinearRectifier, LogisticRegressionModel, LowerCase, MatrixVectorizer, MaxClassifier, MultiLabelExtractor, MultiLabeledImageExtractor, NGramsFeaturizer, NGramsHashingTF, NaiveBayesModel, NormalizeRows, OptimizableTransformer, PCATransformer, PaddedFFT, Pipeline, PixelScaler, Pooler, RandomImageTransformer, RandomSignNode, SIFTExtractor, SIFTExtractorInterface, Shuffler, SignedHellingerMapper, SparseFeatureVectorizer, SparseLinearMapper, Sparsify, StandardScalerModel, StupidBackoffModel, SymmetricRectifier, TermFrequency, Tokenizer, TopKClassifier, Transformer, TransformerChain, Trim, VectorCombiner, WordFrequencyTransformer, ZCAWhitener
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Abstract Value Members

  1. abstract def toPipeline: Pipeline[A, B]

    A method that converts this object into a Pipeline.

    A method that converts this object into a Pipeline. Must be implemented by anything that extends Chainable.

Concrete Value Members

  1. final def !=(arg0: AnyRef): Boolean

    Definition Classes
    AnyRef

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

    Definition Classes
    Any

  3. final def ##(): Int

    Definition Classes
    AnyRef → Any

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

    Definition Classes
    AnyRef

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

    Definition Classes
    Any

  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.

  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.

  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.

  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.

  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)

  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)

  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

  13. final def asInstanceOf[T0]: T0

    Definition Classes
    Any

  14. def clone(): AnyRef

    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

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

    Definition Classes
    AnyRef

  16. def equals(arg0: Any): Boolean

    Definition Classes
    AnyRef → Any

  17. def finalize(): Unit

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

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

    Definition Classes
    AnyRef → Any

  19. def hashCode(): Int

    Definition Classes
    AnyRef → Any

  20. final def isInstanceOf[T0]: Boolean

    Definition Classes
    Any

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

    Definition Classes
    AnyRef

  22. final def notify(): Unit

    Definition Classes
    AnyRef

  23. final def notifyAll(): Unit

    Definition Classes
    AnyRef

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

    Definition Classes
    AnyRef

  25. def toString(): String

    Definition Classes
    AnyRef → Any

  26. final def wait(): Unit

    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

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

    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

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

    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

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