org.apache.spark.mllib.stat.test

StreamingTest

class StreamingTest extends Logging with Serializable

:: Experimental :: Performs online 2-sample significance testing for a stream of (Boolean, Double) pairs. The Boolean identifies which sample each observation comes from, and the Double is the numeric value of the observation.

To address novelty affects, the peacePeriod specifies a set number of initial org.apache.spark.rdd.RDD batches of the DStream to be dropped from significance testing.

The windowSize sets the number of batches each significance test is to be performed over. The window is sliding with a stride length of 1 batch. Setting windowSize to 0 will perform cumulative processing, using all batches seen so far.

Different tests may be used for assessing statistical significance depending on assumptions satisfied by data. For more details, see StreamingTestMethod. The testMethod specifies which test will be used.

Use a builder pattern to construct a streaming test in an application, for example:

val model = new StreamingTest()
.setPeacePeriod(10)
.setWindowSize(0)
.setTestMethod("welch")
.registerStream(DStream)
Annotations
@Experimental() @Since( "1.6.0" )
Source
StreamingTest.scala
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Instance Constructors

  1. new StreamingTest()

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    @Since( "1.6.0" )

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  1. final def !=(arg0: AnyRef): Boolean

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

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  10. def finalize(): Unit

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  14. def isTraceEnabled(): Boolean

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  15. def log: Logger

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

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

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

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

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  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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  28. final def notify(): Unit

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  29. final def notifyAll(): Unit

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  30. def registerStream(data: JavaDStream[BinarySample]): JavaDStream[StreamingTestResult]

    Register a JavaDStream of values for significance testing.

    Register a JavaDStream of values for significance testing.

    data

    stream of BinarySample(isExperiment,value) pairs where the isExperiment denotes group (true = experiment, false = control) and the value is the numerical metric to test for significance

    returns

    stream of significance testing results

    Annotations
    @Since( "1.6.0" )

  31. def registerStream(data: DStream[BinarySample]): DStream[StreamingTestResult]

    Register a DStream of values for significance testing.

    Register a DStream of values for significance testing.

    data

    stream of BinarySample(key,value) pairs where the key denotes group membership (true = experiment, false = control) and the value is the numerical metric to test for significance

    returns

    stream of significance testing results

    Annotations
    @Since( "1.6.0" )

  32. def setPeacePeriod(peacePeriod: Int): StreamingTest.this.type

    Set the number of initial batches to ignore.

    Set the number of initial batches to ignore. Default: 0.

    Annotations
    @Since( "1.6.0" )

  33. def setTestMethod(method: String): StreamingTest.this.type

    Set the statistical method used for significance testing.

    Set the statistical method used for significance testing. Default: "welch"

    Annotations
    @Since( "1.6.0" )

  34. def setWindowSize(windowSize: Int): StreamingTest.this.type

    Set the number of batches to compute significance tests over.

    Set the number of batches to compute significance tests over. Default: 0. A value of 0 will use all batches seen so far.

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    @Since( "1.6.0" )

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

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