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
c

org.apache.spark.mllib.evaluation

BinaryClassificationMetrics

class BinaryClassificationMetrics extends Logging

Evaluator for binary classification.

Annotations
@Since( "1.0.0" )
Source
BinaryClassificationMetrics.scala
Linear Supertypes
Logging, AnyRef, Any
Ordering
  1. Alphabetic
  2. By Inheritance
Inherited
  1. BinaryClassificationMetrics
  2. Logging
  3. AnyRef
  4. Any
  1. Hide All
  2. Show All
Visibility
  1. Public
  2. All

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" )

Value Members

  1. final def !=(arg0: Any): Boolean
    Definition Classes
    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
    Definition Classes
    Any
  7. def clone(): AnyRef
    Attributes
    protected[lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( ... ) @native() @IntrinsicCandidate()
  8. final def eq(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  9. def equals(arg0: Any): Boolean
    Definition Classes
    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[_]
    Definition Classes
    AnyRef → Any
    Annotations
    @native() @IntrinsicCandidate()
  13. def hashCode(): Int
    Definition Classes
    AnyRef → Any
    Annotations
    @native() @IntrinsicCandidate()
  14. def initializeLogIfNecessary(isInterpreter: Boolean, silent: Boolean): Boolean
    Attributes
    protected
    Definition Classes
    Logging
  15. def initializeLogIfNecessary(isInterpreter: Boolean): Unit
    Attributes
    protected
    Definition Classes
    Logging
  16. final def isInstanceOf[T0]: Boolean
    Definition Classes
    Any
  17. def isTraceEnabled(): Boolean
    Attributes
    protected
    Definition Classes
    Logging
  18. def log: Logger
    Attributes
    protected
    Definition Classes
    Logging
  19. def logDebug(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  20. def logDebug(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  21. def logError(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  22. def logError(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  23. def logInfo(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  24. def logInfo(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  25. def logName: String
    Attributes
    protected
    Definition Classes
    Logging
  26. def logTrace(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  27. def logTrace(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  28. def logWarning(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  29. def logWarning(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  30. final def ne(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  31. final def notify(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native() @IntrinsicCandidate()
  32. final def notifyAll(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native() @IntrinsicCandidate()
  33. val numBins: Int
    Annotations
    @Since( "1.3.0" )
  34. 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)

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

    Returns the (threshold, precision) curve.

    Returns the (threshold, precision) curve.

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

    Returns the (threshold, recall) curve.

    Returns the (threshold, recall) curve.

    Annotations
    @Since( "1.0.0" )
  37. 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)

  38. val scoreAndLabels: RDD[_ <: Product]
    Annotations
    @Since( "1.3.0" )
  39. final def synchronized[T0](arg0: ⇒ T0): T0
    Definition Classes
    AnyRef
  40. def thresholds(): RDD[Double]

    Returns thresholds in descending order.

    Returns thresholds in descending order.

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

    Unpersist intermediate RDDs used in the computation.

    Unpersist intermediate RDDs used in the computation.

    Annotations
    @Since( "1.0.0" )
  43. final def wait(arg0: Long, arg1: Int): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  44. final def wait(arg0: Long): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... ) @native()
  45. final def wait(): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

Deprecated Value Members

  1. def finalize(): Unit
    Attributes
    protected[lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( classOf[java.lang.Throwable] ) @Deprecated
    Deprecated
  2. val scoreLabelsWeight: RDD[(Double, (Double, Double))]
    Annotations
    @deprecated
    Deprecated

    (Since version 3.4.0)

Inherited from Logging

Inherited from AnyRef

Inherited from Any

Ungrouped