Class/Object

org.apache.spark.mllib.evaluation

RankingMetrics

Related Docs: object RankingMetrics | package evaluation

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class RankingMetrics[T] extends Logging with Serializable

Evaluator for ranking algorithms.

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

Annotations
@Since( "1.2.0" )
Source
RankingMetrics.scala
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Serializable, Serializable, Logging, AnyRef, Any
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Instance Constructors

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

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    predictionAndLabels

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

Value Members

  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 clone(): AnyRef

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

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  7. def equals(arg0: Any): Boolean

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  8. def finalize(): Unit

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

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  10. def hashCode(): Int

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  11. def initializeLogIfNecessary(isInterpreter: Boolean): Unit

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    Logging
  12. final def isInstanceOf[T0]: Boolean

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  13. def isTraceEnabled(): Boolean

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  14. def log: Logger

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

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

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

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

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

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

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  21. def logName: String

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

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

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

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

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  26. lazy val meanAveragePrecision: Double

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    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 warning is generated.

  27. def ndcgAt(k: Int): Double

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    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

    Annotations
    @Since( "1.2.0" )
  28. final def ne(arg0: AnyRef): Boolean

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  29. final def notify(): Unit

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  30. final def notifyAll(): Unit

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  31. def precisionAt(k: Int): Double

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    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 is less than 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" )
  32. final def synchronized[T0](arg0: ⇒ T0): T0

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  33. def toString(): String

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  34. final def wait(): Unit

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

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

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Inherited from Serializable

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Inherited from Logging

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