keystoneml.workflow

TransformerLabelEstimatorChain

case class TransformerLabelEstimatorChain[A, B, C, L](first: Transformer[A, B], second: LabelEstimator[B, C, L])(implicit evidence$3: ClassTag[C]) extends LabelEstimator[A, C, L] with Product with Serializable

A chain of a Transformer followed by a LabelEstimator (as a LabelEstimator)

first
second

Linear Supertypes
Product, Equals, LabelEstimator[A, C, L], EstimatorOperator, Serializable, Serializable, Operator, AnyRef, Any
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Inherited
  1. TransformerLabelEstimatorChain
  2. Product
  3. Equals
  4. LabelEstimator
  5. EstimatorOperator
  6. Serializable
  7. Serializable
  8. Operator
  9. AnyRef
  10. Any
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Visibility
  1. Public
  2. All

Instance Constructors

  1. new TransformerLabelEstimatorChain(first: Transformer[A, B], second: LabelEstimator[B, C, L])(implicit arg0: ClassTag[C])

    first
    second

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 asInstanceOf[T0]: T0

    Definition Classes
    Any
  7. def clone(): AnyRef

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

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

    Definition Classes
    EstimatorOperator → Operator
  10. def finalize(): Unit

    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( classOf[java.lang.Throwable] )
  11. val first: Transformer[A, B]

  12. def fit(data: RDD[A], labels: RDD[L]): Transformer[A, C]

    The type-safe method that ML developers need to implement when writing new Estimators.

    The type-safe method that ML developers need to implement when writing new Estimators.

    data

    The estimator's training data.

    labels

    The estimator's training labels

    returns

    A new transformer

    Definition Classes
    TransformerLabelEstimatorChainLabelEstimator
  13. final def getClass(): Class[_]

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

    Definition Classes
    Any
  15. def label: String

    Definition Classes
    Operator
  16. final def ne(arg0: AnyRef): Boolean

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

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

    Definition Classes
    AnyRef
  19. val second: LabelEstimator[B, C, L]

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

    Definition Classes
    AnyRef
  21. final def wait(): Unit

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

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

    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  24. final def withData(data: PipelineDataset[A], labels: PipelineDataset[L]): Pipeline[A, C]

    Constructs a pipeline that fits this label estimator to training data and labels, then applies the resultant transformer to the Pipeline input.

    Constructs a pipeline that fits this label estimator to training data and labels, then applies the resultant transformer to the Pipeline input.

    data

    The training data

    labels

    The training labels

    returns

    A pipeline that fits this label estimator and applies the result to inputs.

    Definition Classes
    LabelEstimator
  25. final def withData(data: RDD[A], labels: RDD[L]): Pipeline[A, C]

    Constructs a pipeline that fits this label estimator to training data and labels, then applies the resultant transformer to the Pipeline input.

    Constructs a pipeline that fits this label estimator to training data and labels, then applies the resultant transformer to the Pipeline input.

    data

    The training data

    labels

    The training labels

    returns

    A pipeline that fits this label estimator and applies the result to inputs.

    Definition Classes
    LabelEstimator
  26. final def withData(data: PipelineDataset[A], labels: RDD[L]): Pipeline[A, C]

    Constructs a pipeline that fits this label estimator to training data and labels, then applies the resultant transformer to the Pipeline input.

    Constructs a pipeline that fits this label estimator to training data and labels, then applies the resultant transformer to the Pipeline input.

    data

    The training data

    labels

    The training labels

    returns

    A pipeline that fits this label estimator and applies the result to inputs.

    Definition Classes
    LabelEstimator
  27. final def withData(data: RDD[A], labels: PipelineDataset[L]): Pipeline[A, C]

    Constructs a pipeline that fits this label estimator to training data and labels, then applies the resultant transformer to the Pipeline input.

    Constructs a pipeline that fits this label estimator to training data and labels, then applies the resultant transformer to the Pipeline input.

    data

    The training data

    labels

    The training labels

    returns

    A pipeline that fits this label estimator and applies the result to inputs.

    Definition Classes
    LabelEstimator

Inherited from Product

Inherited from Equals

Inherited from LabelEstimator[A, C, L]

Inherited from EstimatorOperator

Inherited from Serializable

Inherited from Serializable

Inherited from Operator

Inherited from AnyRef

Inherited from Any

Ungrouped