keystoneml.workflow

OptimizableLabelEstimator

abstract class OptimizableLabelEstimator[A, B, L] extends LabelEstimator[A, B, L] with Optimizable

Represents a node-level optimizable LabelEstimator and its optimization rules

Linear Supertypes
Optimizable, LabelEstimator[A, B, L], EstimatorOperator, Serializable, Serializable, Operator, AnyRef, Any
Known Subclasses
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Inherited
  1. OptimizableLabelEstimator
  2. Optimizable
  3. LabelEstimator
  4. EstimatorOperator
  5. Serializable
  6. Serializable
  7. Operator
  8. AnyRef
  9. Any
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Instance Constructors

  1. new OptimizableLabelEstimator()

Abstract Value Members

  1. abstract val default: LabelEstimator[A, B, L]

  2. abstract def optimize(sample: RDD[A], sampleLabels: RDD[L], numPerPartition: Map[Int, Int]): LabelEstimator[A, B, L]

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 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 equals(arg0: Any): Boolean

    Definition Classes
    AnyRef → Any
  10. def execute(deps: Seq[Expression]): TransformerExpression

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

    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( classOf[java.lang.Throwable] )
  12. def fit(data: RDD[A], labels: RDD[L]): Transformer[A, B]

    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
    OptimizableLabelEstimatorLabelEstimator
  13. final def getClass(): Class[_]

    Definition Classes
    AnyRef → Any
  14. def hashCode(): Int

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

    Definition Classes
    Any
  16. def label: String

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

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

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

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

    Definition Classes
    AnyRef
  21. def toString(): String

    Definition Classes
    AnyRef → Any
  22. final def wait(): Unit

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

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

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

    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: RDD[A], labels: RDD[L]): Pipeline[A, B]

    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: PipelineDataset[A], labels: RDD[L]): Pipeline[A, B]

    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
  28. final def withData(data: RDD[A], labels: PipelineDataset[L]): Pipeline[A, B]

    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 Optimizable

Inherited from LabelEstimator[A, B, L]

Inherited from EstimatorOperator

Inherited from Serializable

Inherited from Serializable

Inherited from Operator

Inherited from AnyRef

Inherited from Any

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