Packages

  • package root
    Definition Classes
    root
  • package org
    Definition Classes
    root
  • package apache
    Definition Classes
    org
  • package spark

    Core Spark functionality.

    Core Spark functionality. org.apache.spark.SparkContext serves as the main entry point to Spark, while org.apache.spark.rdd.RDD is the data type representing a distributed collection, and provides most parallel operations.

    In addition, org.apache.spark.rdd.PairRDDFunctions contains operations available only on RDDs of key-value pairs, such as groupByKey and join; org.apache.spark.rdd.DoubleRDDFunctions contains operations available only on RDDs of Doubles; and org.apache.spark.rdd.SequenceFileRDDFunctions contains operations available on RDDs that can be saved as SequenceFiles. These operations are automatically available on any RDD of the right type (e.g. RDD[(Int, Int)] through implicit conversions.

    Java programmers should reference the org.apache.spark.api.java package for Spark programming APIs in Java.

    Classes and methods marked with Experimental are user-facing features which have not been officially adopted by the Spark project. These are subject to change or removal in minor releases.

    Classes and methods marked with Developer API are intended for advanced users want to extend Spark through lower level interfaces. These are subject to changes or removal in minor releases.

    Definition Classes
    apache
  • package mllib

    RDD-based machine learning APIs (in maintenance mode).

    RDD-based machine learning APIs (in maintenance mode).

    The spark.mllib package is in maintenance mode as of the Spark 2.0.0 release to encourage migration to the DataFrame-based APIs under the org.apache.spark.ml package. While in maintenance mode,

    • no new features in the RDD-based spark.mllib package will be accepted, unless they block implementing new features in the DataFrame-based spark.ml package;
    • bug fixes in the RDD-based APIs will still be accepted.

    The developers will continue adding more features to the DataFrame-based APIs in the 2.x series to reach feature parity with the RDD-based APIs. And once we reach feature parity, this package will be deprecated.

    Definition Classes
    spark
    See also

    SPARK-4591 to track the progress of feature parity

  • package evaluation
    Definition Classes
    mllib
  • BinaryClassificationMetrics
  • MulticlassMetrics
  • MultilabelMetrics
  • RankingMetrics
  • RegressionMetrics

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
Linear Supertypes
Serializable, Serializable, Logging, AnyRef, Any
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  1. RankingMetrics
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Instance Constructors

  1. new RankingMetrics(predictionAndLabels: RDD[_ <: Product])(implicit arg0: ClassTag[T])

    predictionAndLabels

    an RDD of (predicted ranking, ground truth set) pair or (predicted ranking, ground truth set, . relevance value of ground truth set). Since 3.4.0, it supports ndcg evaluation with relevance value.

    Annotations
    @Since( "1.2.0" )

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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    @throws( ... ) @native()
  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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    @throws( classOf[java.lang.Throwable] )
  9. final def getClass(): Class[_]
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  10. def hashCode(): Int
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    @native()
  11. def initializeLogIfNecessary(isInterpreter: Boolean, silent: Boolean): Boolean
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    protected
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    Logging
  12. def initializeLogIfNecessary(isInterpreter: Boolean): Unit
    Attributes
    protected
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    Logging
  13. final def isInstanceOf[T0]: Boolean
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    Any
  14. def isTraceEnabled(): Boolean
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    Logging
  15. def log: Logger
    Attributes
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    Logging
  16. def logDebug(msg: ⇒ String, throwable: Throwable): Unit
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    Logging
  17. def logDebug(msg: ⇒ String): Unit
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  18. def logError(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
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  19. def logError(msg: ⇒ String): Unit
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    protected
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    Logging
  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
    Attributes
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    Logging
  24. def logTrace(msg: ⇒ String): Unit
    Attributes
    protected
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    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 warning is generated.

    Annotations
    @Since( "1.2.0" )
  28. def meanAveragePrecisionAt(k: Int): Double

    Returns the mean average precision (MAP) at ranking position k of all the queries.

    Returns the mean average precision (MAP) at ranking position k 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.

    k

    the position to compute the truncated precision, must be positive

    returns

    the mean average precision at first k ranking positions

    Annotations
    @Since( "3.0.0" )
  29. 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 the relevance value is empty.

    If the relevance value is not empty but its size doesn't match the ground truth set size, a log warning is generated.

    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" )
  30. final def ne(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  31. final def notify(): Unit
    Definition Classes
    AnyRef
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    @native()
  32. final def notifyAll(): Unit
    Definition Classes
    AnyRef
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    @native()
  33. 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 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" )
  34. def recallAt(k: Int): Double

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

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

    If for a query, the ranking algorithm returns n results, the recall value will be computed as #(relevant items retrieved) / #(ground truth set). 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 recall 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 recall, must be positive

    returns

    the average recall at the first k ranking positions

    Annotations
    @Since( "3.0.0" )
  35. final def synchronized[T0](arg0: ⇒ T0): T0
    Definition Classes
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  36. def toString(): String
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  37. final def wait(): Unit
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  38. final def wait(arg0: Long, arg1: Int): Unit
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  39. final def wait(arg0: Long): Unit
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Inherited from Serializable

Inherited from Serializable

Inherited from Logging

Inherited from AnyRef

Inherited from Any

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