Github user sryza commented on a diff in the pull request:
https://github.com/apache/spark/pull/6994#discussion_r33189747
--- Diff: mllib/src/main/scala/org/apache/spark/mllib/stat/Statistics.scala
---
@@ -158,4 +158,44 @@ object Statistics {
def chiSqTest(data: RDD[LabeledPoint]): Array[ChiSqTestResult] = {
ChiSqTest.chiSquaredFeatures(data)
}
+
+ /**
+ * Conduct a one-sample, two sided Kolmogorov Smirnov test for
probability distribution equality
+ * @param data an `RDD[Double]` containing the sample of data to test
+ * @param cdf a `Double => Double` function to calculate the theoretical
CDF at a given value
+ * @return KSTestResult object containing test statistic, p-value, and
null hypothesis.
+ */
+ def ksTest(data: RDD[Double], cdf: Double => Double): KSTestResult = {
+ KSTest.testOneSample(data, cdf)
+ }
+
+ /**
+ * Conduct a one-sample, two sided Kolmogorov Smirnov test for
probability distribution equality,
+ * which creates only 1 distribution object per partition (useful in
conjunction with Apache
+ * Commons Math distributions)
+ * @param data an `RDD[Double]` containing the sample of data to test
+ * @param distCalc a `Iterator[(Double, Double, Double)] =>
Iterator[Double]` function, to
+ * calculate the distance between empirical values and
theoretical values of
+ * a distribution. The first element corresponds to the
value x, the second
+ * element is the lower bound of the empirical CDF,
while the third element is
+ * the upper bound. Thus if we call triple associated
with an observation T, the
+ * KS distance at that point is max(Pr[X <= T._1] -
T._2, T._3 - Pr[X <= T._1])
+ * @return KSTestResult object containing test statistic, p-value, and
null hypothesis.
+ */
+ def ksTestOpt(data: RDD[Double],
+ distCalc: Iterator[(Double, Double, Double)] => Iterator[Double])
+ : KSTestResult = {
+ KSTest.testOneSampleOpt(data, distCalc)
+ }
+
+ /**
+ * A convenience method to conduct a one-sample, two sided Kolmogorov
Smirnov test for probability
+ * distribution equality
+ * @param data an `RDD[Double]` containing the sample of data to test
+ * @param name a `String` name for a theoretical distribution
+ * @return KSTestResult object containing test statistic, p-value, and
null hypothesis.
+ */
+ def ksTest(data: RDD[Double], name: String): KSTestResult = {
--- End diff --
Put this up below the other `ksTest` method
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