Evaluator

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

   

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2. final def ##(): Int

   

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

   

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4. final def asInstanceOf[T0]: T0

   

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

   

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

   L

   scores

   returns

   Map from label to Performance

7. def clone(): AnyRef

   

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

   

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

   

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10. def equals(arg0: Any): Boolean

    

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11. def finalize(): Unit

    

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12. final def getClass(): Class[_]

    

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13. def hashCode(): Int

    

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14. final def isInstanceOf[T0]: Boolean

    

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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

    

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17. final def notify(): Unit

    

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18. final def notifyAll(): Unit

    

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19. def stratifiedCrossValidate[L, F](dataset: Dataset[L, F], classifierFactory: () ⇒ Classifier[L, F], numFolds: Int = 5, seed: Int = 73): Seq[(L, 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

    

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21. def toString(): String

    

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22. final def wait(): Unit

    

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23. final def wait(arg0: Long, arg1: Int): Unit

    

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24. final def wait(arg0: Long): Unit

    

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