keystoneml.nodes.util

Cacher

case class Cacher[T](name: Option[String] = scala.None)(implicit evidence$1: ClassTag[T]) extends Transformer[T, T] with Logging with Product with Serializable

Caches an RDD at a given point within a Pipeline. Follows Spark's lazy evaluation conventions.

T

Type of the input to cache.

name

An optional name to set on the cached output. Useful for debugging.

Linear Supertypes
Product, Equals, Logging, Transformer[T, T], Chainable[T, T], TransformerOperator, Serializable, Serializable, Operator, AnyRef, Any
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Inherited
  1. Cacher
  2. Product
  3. Equals
  4. Logging
  5. Transformer
  6. Chainable
  7. TransformerOperator
  8. Serializable
  9. Serializable
  10. Operator
  11. AnyRef
  12. Any
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Instance Constructors

  1. new Cacher(name: Option[String] = scala.None)(implicit arg0: ClassTag[T])

    name

    An optional name to set on the cached output. Useful for debugging.

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[T, C, L], data: PipelineDataset[T], labels: PipelineDataset[L]): Pipeline[T, 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[T, C, L], data: RDD[T], labels: PipelineDataset[L]): Pipeline[T, 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[T, C, L], data: PipelineDataset[T], labels: RDD[L]): Pipeline[T, 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[T, C, L], data: RDD[T], labels: RDD[L]): Pipeline[T, 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[T, C], data: PipelineDataset[T]): Pipeline[T, 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[T, C], data: RDD[T]): Pipeline[T, 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[T, C]): Pipeline[T, 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: T): T

    The application of this Transformer to a single input item.

    The application of this Transformer to a single input item. This method MUST be overridden by ML developers.

    in

    The input item to pass into this transformer

    returns

    The output value

    Definition Classes
    CacherTransformer
  14. def apply(in: RDD[T]): RDD[T]

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

    The application of this Transformer to an RDD of input items. This method may optionally be overridden by ML developers.

    in

    The bulk RDD input to pass into this transformer

    returns

    The bulk RDD output for the given input

    Definition Classes
    CacherTransformer
  15. final def asInstanceOf[T0]: T0

    Definition Classes
    Any
  16. def clone(): AnyRef

    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  17. final def eq(arg0: AnyRef): Boolean

    Definition Classes
    AnyRef
  18. def execute(deps: Seq[Expression]): Expression

    Definition Classes
    TransformerOperator → Operator
  19. def finalize(): Unit

    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( classOf[java.lang.Throwable] )
  20. final def getClass(): Class[_]

    Definition Classes
    AnyRef → Any
  21. final def isInstanceOf[T0]: Boolean

    Definition Classes
    Any
  22. def label: String

    Definition Classes
    Operator
  23. def log: Logger

    Attributes
    protected
    Definition Classes
    Logging
  24. def logDebug(msg: ⇒ String, throwable: Throwable): Unit

    Attributes
    protected
    Definition Classes
    Logging
  25. def logDebug(msg: ⇒ String): Unit

    Attributes
    protected
    Definition Classes
    Logging
  26. def logError(msg: ⇒ String, throwable: Throwable): Unit

    Attributes
    protected
    Definition Classes
    Logging
  27. def logError(msg: ⇒ String): Unit

    Attributes
    protected
    Definition Classes
    Logging
  28. def logInfo(msg: ⇒ String, throwable: Throwable): Unit

    Attributes
    protected
    Definition Classes
    Logging
  29. def logInfo(msg: ⇒ String): Unit

    Attributes
    protected
    Definition Classes
    Logging
  30. def logTrace(msg: ⇒ String, throwable: Throwable): Unit

    Attributes
    protected
    Definition Classes
    Logging
  31. def logTrace(msg: ⇒ String): Unit

    Attributes
    protected
    Definition Classes
    Logging
  32. def logWarning(msg: ⇒ String, throwable: Throwable): Unit

    Attributes
    protected
    Definition Classes
    Logging
  33. def logWarning(msg: ⇒ String): Unit

    Attributes
    protected
    Definition Classes
    Logging
  34. val name: Option[String]

    An optional name to set on the cached output.

    An optional name to set on the cached output. Useful for debugging.

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

    Definition Classes
    AnyRef
  36. final def notify(): Unit

    Definition Classes
    AnyRef
  37. final def notifyAll(): Unit

    Definition Classes
    AnyRef
  38. final def synchronized[T0](arg0: ⇒ T0): T0

    Definition Classes
    AnyRef
  39. def toPipeline: Pipeline[T, T]

    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.

    Definition Classes
    TransformerChainable
  40. final def wait(): Unit

    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  41. final def wait(arg0: Long, arg1: Int): Unit

    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  42. final def wait(arg0: Long): Unit

    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

Inherited from Product

Inherited from Equals

Inherited from Logging

Inherited from Transformer[T, T]

Inherited from Chainable[T, T]

Inherited from TransformerOperator

Inherited from Serializable

Inherited from Serializable

Inherited from Operator

Inherited from AnyRef

Inherited from Any

Ungrouped