org.apache.spark.mllib.evaluation

RankingMetrics

class RankingMetrics[T] extends Logging with Serializable

::Experimental:: Evaluator for ranking algorithms.

Annotations
@Experimental()
Linear Supertypes
Serializable, Serializable, Logging, AnyRef, Any
Ordering
  1. Alphabetic
  2. By inheritance
Inherited
  1. RankingMetrics
  2. Serializable
  3. Serializable
  4. Logging
  5. AnyRef
  6. Any
  1. Hide All
  2. Show all
Learn more about member selection
Visibility
  1. Public
  2. All

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

    Definition Classes
    AnyRef

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

    Definition Classes
    Any

  3. final def ##(): Int

    Definition Classes
    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
    Any

  7. def clone(): AnyRef

    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

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

    Definition Classes
    AnyRef

  9. def equals(arg0: Any): Boolean

    Definition Classes
    AnyRef → Any

  10. def finalize(): Unit

    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( classOf[java.lang.Throwable] )

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

    Definition Classes
    AnyRef → Any

  12. def hashCode(): Int

    Definition Classes
    AnyRef → Any

  13. final def isInstanceOf[T0]: Boolean

    Definition Classes
    Any

  14. def isTraceEnabled(): Boolean

    Attributes
    protected
    Definition Classes
    Logging

  15. def log: Logger

    Attributes
    protected
    Definition Classes
    Logging

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

    Attributes
    protected
    Definition Classes
    Logging

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

    Attributes
    protected
    Definition Classes
    Logging

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

    Attributes
    protected
    Definition Classes
    Logging

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

    Attributes
    protected
    Definition Classes
    Logging

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

    Attributes
    protected
    Definition Classes
    Logging

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

    Attributes
    protected
    Definition Classes
    Logging

  22. def logName: String

    Attributes
    protected
    Definition Classes
    Logging

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

    Attributes
    protected
    Definition Classes
    Logging

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

    Attributes
    protected
    Definition Classes
    Logging

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

    Attributes
    protected
    Definition Classes
    Logging

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

    Attributes
    protected
    Definition Classes
    Logging

  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

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

    Definition Classes
    AnyRef

  30. final def notify(): Unit

    Definition Classes
    AnyRef

  31. final def notifyAll(): Unit

    Definition Classes
    AnyRef

  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

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

    Definition Classes
    AnyRef

  34. def toString(): String

    Definition Classes
    AnyRef → Any

  35. final def wait(): Unit

    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

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

    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

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

    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

Inherited from Serializable

Inherited from Serializable

Inherited from Logging

Inherited from AnyRef

Inherited from Any

Ungrouped