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 stat
    Definition Classes
    mllib
  • package test
    Definition Classes
    stat
  • BinarySample
  • ChiSqTestResult
  • KolmogorovSmirnovTestResult
  • StreamingTest
  • TestResult
c

org.apache.spark.mllib.stat.test

StreamingTest

class StreamingTest extends Logging with Serializable

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
@Since( "1.6.0" )
Source
StreamingTest.scala
Linear Supertypes
Serializable, Serializable, Logging, AnyRef, Any
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  1. StreamingTest
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Visibility
  1. Public
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Instance Constructors

  1. new StreamingTest()
    Annotations
    @Since( "1.6.0" )

Value Members

  1. 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" )
  2. 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" )
  3. 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" )
  4. 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" )
  5. 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.

    Annotations
    @Since( "1.6.0" )