org.apache.spark.mllib.evaluation

RankingMetrics

class RankingMetrics[T] extends Logging with Serializable

::Experimental:: Evaluator for ranking algorithms.

Java users should use RankingMetrics$.of to create a RankingMetrics instance.

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@Since( "1.2.0" ) @Experimental()
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Instance Constructors

  1. new RankingMetrics(predictionAndLabels: RDD[(Array[T], Array[T])])(implicit arg0: ClassTag[T])

    predictionAndLabels

    an RDD of (predicted ranking, ground truth set) pairs.

Value Members

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

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

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  3. final def ##(): Int

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  7. def clone(): AnyRef

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  10. def finalize(): Unit

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

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  13. final def isInstanceOf[T0]: Boolean

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  14. def isTraceEnabled(): Boolean

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  15. def log: Logger

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  16. def logDebug(msg: ⇒ String, throwable: Throwable): Unit

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  17. def logDebug(msg: ⇒ String): Unit

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  18. def logError(msg: ⇒ String, throwable: Throwable): Unit

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  19. def logError(msg: ⇒ String): Unit

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  20. def logInfo(msg: ⇒ String, throwable: Throwable): Unit

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  21. def logInfo(msg: ⇒ String): Unit

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  22. def logName: String

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  23. def logTrace(msg: ⇒ String, throwable: Throwable): Unit

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  24. def logTrace(msg: ⇒ String): Unit

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  25. def logWarning(msg: ⇒ String, throwable: Throwable): Unit

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  26. def logWarning(msg: ⇒ String): Unit

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  27. lazy val meanAveragePrecision: Double

    Returns the mean average precision (MAP) of all the queries.

    Returns the mean average precision (MAP) of all the queries. If a query has an empty ground truth set, the average precision will be zero and a log warining is generated.

  28. def ndcgAt(k: Int): Double

    Compute the average NDCG value of all the queries, truncated at ranking position k.

    Compute the average NDCG value of all the queries, truncated at ranking position k. The discounted cumulative gain at position k is computed as: sumi=1k (2{relevance of ith item} - 1) / log(i + 1), and the NDCG is obtained by dividing the DCG value on the ground truth set. In the current implementation, the relevance value is binary.

    If a query has an empty ground truth set, zero will be used as ndcg together with a log warning.

    See the following paper for detail:

    IR evaluation methods for retrieving highly relevant documents. K. Jarvelin and J. Kekalainen

    k

    the position to compute the truncated ndcg, must be positive

    returns

    the average ndcg at the first k ranking positions

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    @Since( "1.2.0" )

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

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  30. final def notify(): Unit

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  31. final def notifyAll(): Unit

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  32. def precisionAt(k: Int): Double

    Compute the average precision of all the queries, truncated at ranking position k.

    Compute the average precision of all the queries, truncated at ranking position k.

    If for a query, the ranking algorithm returns n (n < k) results, the precision value will be computed as #(relevant items retrieved) / k. This formula also applies when the size of the ground truth set is less than k.

    If a query has an empty ground truth set, zero will be used as precision together with a log warning.

    See the following paper for detail:

    IR evaluation methods for retrieving highly relevant documents. K. Jarvelin and J. Kekalainen

    k

    the position to compute the truncated precision, must be positive

    returns

    the average precision at the first k ranking positions

    Annotations
    @Since( "1.2.0" )

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

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  34. def toString(): String

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  35. final def wait(): Unit

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

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