keystoneml.workflow

LabelEstimator

abstract class LabelEstimator[A, B, L] extends EstimatorOperator

A LabelEstimator has a fitRDDs method which takes input data and input labels, and emits a Transformer.

A

The type of input data this estimator (and the resulting Transformer) takes

B

The output type of the Transformer this estimator produces when being fit

L

The type of input labels this estimator takes at training time

Linear Supertypes
EstimatorOperator, Serializable, Serializable, Operator, AnyRef, Any
Known Subclasses
BlockLeastSquaresEstimator, BlockWeightedLeastSquaresEstimator, DenseLBFGSwithL2, KernelRidgeRegression, LeastSquaresEstimator, LinearDiscriminantAnalysis, LinearMapEstimator, LocalLeastSquaresEstimator, LogisticRegressionEstimator, NaiveBayesEstimator, OptimizableLabelEstimator, PerClassWeightedLeastSquaresEstimator, SparseLBFGSwithL2, TransformerLabelEstimatorChain
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Inherited
  1. LabelEstimator
  2. EstimatorOperator
  3. Serializable
  4. Serializable
  5. Operator
  6. AnyRef
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Instance Constructors

  1. new LabelEstimator()

Abstract Value Members

  1. abstract 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

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. final def getClass(): Class[_]

    Definition Classes
    AnyRef → Any

  13. def hashCode(): Int

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

    Definition Classes
    AnyRef

  20. def toString(): String

    Definition Classes
    AnyRef → Any

  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, 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.

  25. 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.

  26. 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.

  27. 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.

Inherited from EstimatorOperator

Inherited from Serializable

Inherited from Serializable

Inherited from Operator

Inherited from AnyRef

Inherited from Any

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