Evaluator

Value Members

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

   

   Definition Classes

   AnyRef → Any

2. final def ##(): Int

   

   Definition Classes

   AnyRef → Any

3. final def ==(arg0: Any): Boolean

   

   Definition Classes

   AnyRef → Any

4. final def asInstanceOf[T0]: T0

   

   Definition Classes

   Any

5. def calculateAccuracy[L](scores: Seq[(L, L)]): Float

   

6. def calculatePerformance[L](scores: Seq[LabelPair[L]]): Seq[Performance[L]]

   

   Calculate precision, recall, and f1 for each label base on scores of form (gold, predicted)

   Calculate precision, recall, and f1 for each label base on scores of form (gold, predicted)

   returns

   Map from label to Performance

7. def clone(): AnyRef

   

   Attributes

   protected[java.lang]

   Definition Classes

   AnyRef

   Annotations

   @throws( ... )

8. def crossValidate(dataset: RVFDataset[String, String], clfType: String): Seq[(String, String)]

   

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

   

   Definition Classes

   AnyRef

10. def equals(arg0: Any): Boolean

    

    Definition Classes

    AnyRef → Any

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 mkStratifiedFolds[L, F](numFolds: Int, dataset: Dataset[L, F], seed: Int): Iterable[DatasetStratifiedFold]

    

    Creates dataset folds to be used for cross validation

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. def stratifiedCrossValidate[L, F](dataset: Dataset[L, F], classifierFactory: () ⇒ Classifier[L, F], numFolds: Int = 5, seed: Int = 42): Seq[LabelPair[L]]

    

    Implements stratified cross validation; producing pairs of gold/predicted labels across the training dataset.

    Implements stratified cross validation; producing pairs of gold/predicted labels across the training dataset. Each fold is as balanced as possible by label L.

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. def writeScoresToTSV(scores: Seq[LabelPair[String]], outFile: String): Unit