org.apache.predictionio.controller

MetricEvaluator

class MetricEvaluator[EI, Q, P, A, R] extends BaseEvaluator[EI, Q, P, A, MetricEvaluatorResult[R]]

:: DeveloperApi :: Do no use this directly. Use MetricEvaluator$ instead. This is an implementation of org.apache.predictionio.core.BaseEvaluator that evaluates prediction performance based on metric scores.

EI

Evaluation information type

Q

Query class

P

Predicted result class

A

Actual result class

R

Metric result class

Annotations
@DeveloperApi()
Linear Supertypes
BaseEvaluator[EI, Q, P, A, MetricEvaluatorResult[R]], AbstractDoer, Serializable, Serializable, AnyRef, Any
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  2. BaseEvaluator
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Instance Constructors

  1. new MetricEvaluator(metric: Metric[EI, Q, P, A, R], otherMetrics: Seq[Metric[EI, Q, P, A, _]], outputPath: Option[String])

    metric

    Primary metric

    otherMetrics

    Other metrics

    outputPath

    Optional output path to save evaluation results

Value Members

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

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

    Definition Classes
    Any
  3. final def ##(): Int

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    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
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  7. def clone(): AnyRef

    Attributes
    protected[java.lang]
    Definition Classes
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    Annotations
    @throws( ... )
  8. val engineInstances: EngineInstances

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

    Definition Classes
    AnyRef
  10. def equals(arg0: Any): Boolean

    Definition Classes
    AnyRef → Any
  11. def evaluateBase(sc: SparkContext, evaluation: Evaluation, engineEvalDataSet: Seq[(EngineParams, Seq[(EI, RDD[(Q, P, A)])])], params: WorkflowParams): MetricEvaluatorResult[R]

    :: DeveloperApi :: Engine developers should not use this directly.

    :: DeveloperApi :: Engine developers should not use this directly. This is called by evaluation workflow to perform evaluation.

    sc

    Spark context

    evaluation

    Evaluation to run

    engineEvalDataSet

    Sets of engine parameters and data for evaluation

    params

    Evaluation workflow parameters

    returns

    Evaluation result

    Definition Classes
    MetricEvaluatorBaseEvaluator
  12. def finalize(): Unit

    Attributes
    protected[java.lang]
    Definition Classes
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    Annotations
    @throws( classOf[java.lang.Throwable] )
  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. lazy val logger: Logger

  17. val metric: Metric[EI, Q, P, A, R]

    Primary metric

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

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

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

    Definition Classes
    AnyRef
  21. val otherMetrics: Seq[Metric[EI, Q, P, A, _]]

    Other metrics

  22. val outputPath: Option[String]

    Optional output path to save evaluation results

  23. def saveEngineJson(evaluation: Evaluation, engineParams: EngineParams, outputPath: String): Unit

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

    Definition Classes
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  25. def toString(): String

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

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

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

    Definition Classes
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    @throws( ... )

Inherited from BaseEvaluator[EI, Q, P, A, MetricEvaluatorResult[R]]

Inherited from AbstractDoer

Inherited from Serializable

Inherited from Serializable

Inherited from AnyRef

Inherited from Any

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