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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

BinaryClassificationMetrics

class BinaryClassificationMetrics extends Logging

Evaluator for binary classification.

Annotations
@Since("1.0.0")
Source
BinaryClassificationMetrics.scala
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Logging, AnyRef, Any
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Instance Constructors

  1. new BinaryClassificationMetrics(scoreAndLabels: RDD[(Double, Double)])

    Defaults numBins to 0.

    Defaults numBins to 0.

    Annotations
    @Since("1.0.0")
  2. new BinaryClassificationMetrics(scoreAndLabels: RDD[_ <: Product], numBins: Int = 1000)

    scoreAndLabels

    an RDD of (score, label) or (score, label, weight) tuples.

    numBins

    if greater than 0, then the curves (ROC curve, PR curve) computed internally will be down-sampled to this many "bins". If 0, no down-sampling will occur. This is useful because the curve contains a point for each distinct score in the input, and this could be as large as the input itself -- millions of points or more, when thousands may be entirely sufficient to summarize the curve. After down-sampling, the curves will instead be made of approximately numBins points instead. Points are made from bins of equal numbers of consecutive points. The size of each bin is floor(scoreAndLabels.count() / numBins), which means the resulting number of bins may not exactly equal numBins. The last bin in each partition may be smaller as a result, meaning there may be an extra sample at partition boundaries.

    Annotations
    @Since("3.0.0")

Type Members

  1. implicit class LogStringContext extends AnyRef
    Definition Classes
    Logging

Value Members

  1. final def !=(arg0: Any): Boolean
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    AnyRef → Any
  2. final def ##: Int
    Definition Classes
    AnyRef → Any
  3. final def ==(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  4. def areaUnderPR(): Double

    Computes the area under the precision-recall curve.

    Computes the area under the precision-recall curve.

    Annotations
    @Since("1.0.0")
  5. def areaUnderROC(): Double

    Computes the area under the receiver operating characteristic (ROC) curve.

    Computes the area under the receiver operating characteristic (ROC) curve.

    Annotations
    @Since("1.0.0")
  6. final def asInstanceOf[T0]: T0
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  7. def clone(): AnyRef
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    protected[lang]
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    @throws(classOf[java.lang.CloneNotSupportedException]) @IntrinsicCandidate() @native()
  8. final def eq(arg0: AnyRef): Boolean
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  9. def equals(arg0: AnyRef): Boolean
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    AnyRef → Any
  10. def fMeasureByThreshold(): RDD[(Double, Double)]

    Returns the (threshold, F-Measure) curve with beta = 1.0.

    Returns the (threshold, F-Measure) curve with beta = 1.0.

    Annotations
    @Since("1.0.0")
  11. def fMeasureByThreshold(beta: Double): RDD[(Double, Double)]

    Returns the (threshold, F-Measure) curve.

    Returns the (threshold, F-Measure) curve.

    beta

    the beta factor in F-Measure computation.

    returns

    an RDD of (threshold, F-Measure) pairs.

    Annotations
    @Since("1.0.0")
    See also

    F1 score (Wikipedia)

  12. final def getClass(): Class[_ <: AnyRef]
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    @IntrinsicCandidate() @native()
  13. def hashCode(): Int
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    @IntrinsicCandidate() @native()
  14. def initializeLogIfNecessary(isInterpreter: Boolean, silent: Boolean): Boolean
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    Logging
  15. def initializeLogIfNecessary(isInterpreter: Boolean): Unit
    Attributes
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  16. final def isInstanceOf[T0]: Boolean
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  17. def isTraceEnabled(): Boolean
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  18. def log: Logger
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  19. def logDebug(msg: => String, throwable: Throwable): Unit
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  20. def logDebug(entry: LogEntry, throwable: Throwable): Unit
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  21. def logDebug(entry: LogEntry): Unit
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  22. def logDebug(msg: => String): Unit
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  23. def logError(msg: => String, throwable: Throwable): Unit
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  24. def logError(entry: LogEntry, throwable: Throwable): Unit
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  25. def logError(entry: LogEntry): Unit
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  26. def logError(msg: => String): Unit
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  27. def logInfo(msg: => String, throwable: Throwable): Unit
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  28. def logInfo(entry: LogEntry, throwable: Throwable): Unit
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  29. def logInfo(entry: LogEntry): Unit
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  30. def logInfo(msg: => String): Unit
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  31. def logName: String
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  32. def logTrace(msg: => String, throwable: Throwable): Unit
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  33. def logTrace(entry: LogEntry, throwable: Throwable): Unit
    Attributes
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  34. def logTrace(entry: LogEntry): Unit
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  35. def logTrace(msg: => String): Unit
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  36. def logWarning(msg: => String, throwable: Throwable): Unit
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  37. def logWarning(entry: LogEntry, throwable: Throwable): Unit
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  38. def logWarning(entry: LogEntry): Unit
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    protected
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    Logging
  39. def logWarning(msg: => String): Unit
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    protected
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    Logging
  40. final def ne(arg0: AnyRef): Boolean
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  41. final def notify(): Unit
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    @IntrinsicCandidate() @native()
  42. final def notifyAll(): Unit
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    @IntrinsicCandidate() @native()
  43. val numBins: Int
    Annotations
    @Since("1.3.0")
  44. def pr(): RDD[(Double, Double)]

    Returns the precision-recall curve, which is an RDD of (recall, precision), NOT (precision, recall), with (0.0, p) prepended to it, where p is the precision associated with the lowest recall on the curve.

    Returns the precision-recall curve, which is an RDD of (recall, precision), NOT (precision, recall), with (0.0, p) prepended to it, where p is the precision associated with the lowest recall on the curve.

    Annotations
    @Since("1.0.0")
    See also

    Precision and recall (Wikipedia)

  45. def precisionByThreshold(): RDD[(Double, Double)]

    Returns the (threshold, precision) curve.

    Returns the (threshold, precision) curve.

    Annotations
    @Since("1.0.0")
  46. def recallByThreshold(): RDD[(Double, Double)]

    Returns the (threshold, recall) curve.

    Returns the (threshold, recall) curve.

    Annotations
    @Since("1.0.0")
  47. def roc(): RDD[(Double, Double)]

    Returns the receiver operating characteristic (ROC) curve, which is an RDD of (false positive rate, true positive rate) with (0.0, 0.0) prepended and (1.0, 1.0) appended to it.

    Returns the receiver operating characteristic (ROC) curve, which is an RDD of (false positive rate, true positive rate) with (0.0, 0.0) prepended and (1.0, 1.0) appended to it.

    Annotations
    @Since("1.0.0")
    See also

    Receiver operating characteristic (Wikipedia)

  48. val scoreAndLabels: RDD[_ <: Product]
    Annotations
    @Since("1.3.0")
  49. final def synchronized[T0](arg0: => T0): T0
    Definition Classes
    AnyRef
  50. def thresholds(): RDD[Double]

    Returns thresholds in descending order.

    Returns thresholds in descending order.

    Annotations
    @Since("1.0.0")
  51. def toString(): String
    Definition Classes
    AnyRef → Any
  52. def unpersist(): Unit

    Unpersist intermediate RDDs used in the computation.

    Unpersist intermediate RDDs used in the computation.

    Annotations
    @Since("1.0.0")
  53. final def wait(arg0: Long, arg1: Int): Unit
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    @throws(classOf[java.lang.InterruptedException])
  54. final def wait(arg0: Long): Unit
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    @throws(classOf[java.lang.InterruptedException]) @native()
  55. final def wait(): Unit
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    @throws(classOf[java.lang.InterruptedException])
  56. def withLogContext(context: HashMap[String, String])(body: => Unit): Unit
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    Logging

Deprecated Value Members

  1. def finalize(): Unit
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    @throws(classOf[java.lang.Throwable]) @Deprecated
    Deprecated

    (Since version 9)

Inherited from Logging

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