Package org.apache.spark.ml.stat

Class KolmogorovSmirnovTest

Object

org.apache.spark.ml.stat.KolmogorovSmirnovTest

public class KolmogorovSmirnovTest
extends Object

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:

See Also:

• Kolmogorov-Smirnov test (Wikipedia)

Constructor Summary

Constructors

Constructor	Description
KolmogorovSmirnovTest()

Method Summary

Modifier and Type	Method	Description
static Dataset<Row>	test(Dataset<?> dataset, String sampleCol, String distName, double... params)	Convenience function to conduct a one-sample, two-sided Kolmogorov-Smirnov test for probability distribution equality.
static Dataset<Row>	test(Dataset<?> dataset, String sampleCol, String distName, scala.collection.Seq<Object> params)
static Dataset<Row>	test(Dataset<?> dataset, String sampleCol, Function<Double,Double> cdf)
static Dataset<Row>	test(Dataset<?> dataset, String sampleCol, scala.Function1<Object,Object> cdf)

Methods inherited from class java.lang.Object

equals, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Constructor Details

KolmogorovSmirnovTest

public KolmogorovSmirnovTest()

Method Details

test

public static Dataset<Row> test(Dataset<?> dataset,
 String sampleCol,
 String distName,
 double... params)

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.

Parameters:

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

test

public static Dataset<Row> test(Dataset<?> dataset,
 String sampleCol,
 scala.Function1<Object,Object> cdf)

test

public static Dataset<Row> test(Dataset<?> dataset,
 String sampleCol,
 Function<Double,Double> cdf)

test

public static Dataset<Row> test(Dataset<?> dataset,
 String sampleCol,
 String distName,
 scala.collection.Seq<Object> params)