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  • package root
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    root
  • package org
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    root
  • package apache
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    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
c

org.apache.spark.mllib.evaluation

MultilabelMetrics

class MultilabelMetrics extends AnyRef

Evaluator for multilabel classification.

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@Since( "1.2.0" )
Source
MultilabelMetrics.scala
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Instance Constructors

  1. new MultilabelMetrics(predictionAndLabels: RDD[(Array[Double], Array[Double])])

    predictionAndLabels

    an RDD of (predictions, labels) pairs, both are non-null Arrays, each with unique elements.

    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
    Definition Classes
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  4. val accuracy: Double

    Returns accuracy

    Returns accuracy

    Annotations
    @Since( "1.2.0" )
  5. final def asInstanceOf[T0]: T0
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    Any
  6. def clone(): AnyRef
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    protected[lang]
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    @throws( ... ) @native() @IntrinsicCandidate()
  7. final def eq(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  8. def equals(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  9. def f1Measure(label: Double): Double

    Returns f1-measure for a given label (category)

    Returns f1-measure for a given label (category)

    label

    the label.

    Annotations
    @Since( "1.2.0" )
  10. val f1Measure: Double

    Returns document-based f1-measure averaged by the number of documents

    Returns document-based f1-measure averaged by the number of documents

    Annotations
    @Since( "1.2.0" )
  11. final def getClass(): Class[_]
    Definition Classes
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    Annotations
    @native() @IntrinsicCandidate()
  12. val hammingLoss: Double

    Returns Hamming-loss

    Returns Hamming-loss

    Annotations
    @Since( "1.2.0" )
  13. def hashCode(): Int
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    @native() @IntrinsicCandidate()
  14. final def isInstanceOf[T0]: Boolean
    Definition Classes
    Any
  15. lazy val labels: Array[Double]

    Returns the sequence of labels in ascending order

    Returns the sequence of labels in ascending order

    Annotations
    @Since( "1.2.0" )
  16. lazy val microF1Measure: Double

    Returns micro-averaged label-based f1-measure (equals to micro-averaged document-based f1-measure)

    Returns micro-averaged label-based f1-measure (equals to micro-averaged document-based f1-measure)

    Annotations
    @Since( "1.2.0" )
  17. lazy val microPrecision: Double

    Returns micro-averaged label-based precision (equals to micro-averaged document-based precision)

    Returns micro-averaged label-based precision (equals to micro-averaged document-based precision)

    Annotations
    @Since( "1.2.0" )
  18. lazy val microRecall: Double

    Returns micro-averaged label-based recall (equals to micro-averaged document-based recall)

    Returns micro-averaged label-based recall (equals to micro-averaged document-based recall)

    Annotations
    @Since( "1.2.0" )
  19. final def ne(arg0: AnyRef): Boolean
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  20. final def notify(): Unit
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    @native() @IntrinsicCandidate()
  21. final def notifyAll(): Unit
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    @native() @IntrinsicCandidate()
  22. def precision(label: Double): Double

    Returns precision for a given label (category)

    Returns precision for a given label (category)

    label

    the label.

    Annotations
    @Since( "1.2.0" )
  23. val precision: Double

    Returns document-based precision averaged by the number of documents

    Returns document-based precision averaged by the number of documents

    Annotations
    @Since( "1.2.0" )
  24. def recall(label: Double): Double

    Returns recall for a given label (category)

    Returns recall for a given label (category)

    label

    the label.

    Annotations
    @Since( "1.2.0" )
  25. val recall: Double

    Returns document-based recall averaged by the number of documents

    Returns document-based recall averaged by the number of documents

    Annotations
    @Since( "1.2.0" )
  26. val subsetAccuracy: Double

    Returns subset accuracy (for equal sets of labels)

    Returns subset accuracy (for equal sets of labels)

    Annotations
    @Since( "1.2.0" )
  27. final def synchronized[T0](arg0: ⇒ T0): T0
    Definition Classes
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  28. def toString(): String
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  29. final def wait(arg0: Long, arg1: Int): Unit
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  30. final def wait(arg0: Long): Unit
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  31. final def wait(): Unit
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    @throws( ... )

Deprecated Value Members

  1. def finalize(): Unit
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    Deprecated

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