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  • 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.

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  • package ml

    DataFrame-based machine learning APIs to let users quickly assemble and configure practical machine learning pipelines.

    DataFrame-based machine learning APIs to let users quickly assemble and configure practical machine learning pipelines.

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  • package distribution
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  • ChiSquareTest
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  • KolmogorovSmirnovTest
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o

org.apache.spark.ml.stat

KolmogorovSmirnovTest

object KolmogorovSmirnovTest

Conduct the two-sided Kolmogorov Smirnov (KS) test for data sampled from a continuous distribution. By comparing the largest difference between the empirical cumulative distribution of the sample data and the theoretical distribution we can provide a test for the the null hypothesis that the sample data comes from that theoretical distribution. For more information on KS Test:

Annotations
@Since("2.4.0")
Source
KolmogorovSmirnovTest.scala
See also

Kolmogorov-Smirnov test (Wikipedia)

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Value Members

  1. def test(dataset: Dataset[_], sampleCol: String, distName: String, params: Double*): DataFrame

    Convenience function to conduct a one-sample, two-sided Kolmogorov-Smirnov test for probability distribution equality.

    Convenience function to conduct a one-sample, two-sided Kolmogorov-Smirnov test for probability distribution equality. Currently supports the normal distribution, taking as parameters the mean and standard deviation.

    dataset

    A Dataset or a DataFrame containing the sample of data to test

    sampleCol

    Name of sample column in dataset, of any numerical type

    distName

    a String name for a theoretical distribution, currently only support "norm".

    params

    Double* specifying the parameters to be used for the theoretical distribution. For "norm" distribution, the parameters includes mean and variance.

    returns

    DataFrame containing the test result for the input sampled data. This DataFrame will contain a single Row with the following fields:

    • pValue: Double
    • statistic: Double
    Annotations
    @Since("2.4.0") @varargs()
  2. def test(dataset: Dataset[_], sampleCol: String, cdf: Function[Double, Double]): DataFrame

    Java-friendly version of test(dataset: DataFrame, sampleCol: String, cdf: Double => Double)

    Java-friendly version of test(dataset: DataFrame, sampleCol: String, cdf: Double => Double)

    Annotations
    @Since("2.4.0")
  3. def test(dataset: Dataset[_], sampleCol: String, cdf: (Double) => Double): DataFrame

    Conduct the two-sided Kolmogorov-Smirnov (KS) test for data sampled from a continuous distribution.

    Conduct the two-sided Kolmogorov-Smirnov (KS) test for data sampled from a continuous distribution. By comparing the largest difference between the empirical cumulative distribution of the sample data and the theoretical distribution we can provide a test for the the null hypothesis that the sample data comes from that theoretical distribution.

    dataset

    A Dataset or a DataFrame containing the sample of data to test

    sampleCol

    Name of sample column in dataset, of any numerical type

    cdf

    a Double => Double function to calculate the theoretical CDF at a given value

    returns

    DataFrame containing the test result for the input sampled data. This DataFrame will contain a single Row with the following fields:

    • pValue: Double
    • statistic: Double
    Annotations
    @Since("2.4.0")