Github user srowen commented on a diff in the pull request:
https://github.com/apache/spark/pull/6994#discussion_r34217201
 Diff: mllib/src/main/scala/org/apache/spark/mllib/stat/Statistics.scala 
@@ 158,4 +158,47 @@ object Statistics {
def chiSqTest(data: RDD[LabeledPoint]): Array[ChiSqTestResult] = {
ChiSqTest.chiSquaredFeatures(data)
}
+
+ /**
+ * Conduct the twosided Kolmogorov Smirnov 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 [[https://en.wikipedia.org/wiki/Kolmogorov%E2%80%93Smirnov_test]]
+ *
+ * Implementation note: We seek to implement the KS test with a minimal number of distributed
+ * passes. We sort the RDD, and then perform the following operations on a perpartition
basis:
+ * calculate an empirical cumulative distribution value for each observation, and a
theoretical
+ * cumulative distribution value. We know the latter to be correct, while the former
will be off
+ * by a constant (how large the constant is depends on how many values precede it in
other
+ * partitions).However, given that this constant simply shifts the ECDF upwards, but
doesn't
+ * change its shape, and furthermore, that constant is the same within a given partition,
we can
+ * pick 2 values in each partition that can potentially resolve to the largest global
distance.
+ * Namely, we pick the minimum distance and the maximum distance. Additionally, we
keep track of
+ * how many elements are in each partition. Once these three values have been returned
for every
+ * partition, we can collect and operate locally. Locally, we can now adjust each distance
by the
+ * appropriate constant (the cumulative sum of # of elements in the prior partitions
divided by
+ * the data set size). Finally, we take the maximum absolute value, and this is the
statistic.
+ * @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, pvalue, and null hypothesis.
+ */
+ def ksTest(data: RDD[Double], cdf: Double => Double): KSTestResult = {
+ KSTest.testOneSample(data, cdf)
+ }
+
+ /**
+ * Convenience function to conduct a onesample, two sided Kolmogorov Smirnov test
for probability
+ * distribution equality. Currently supports the normal distribution, taking as parameters
+ * the mean and standard deviation.
+ * (distName = "norm")
+ * @param data an `RDD[Double]` containing the sample of data to test
+ * @param distName a `String` name for a theoretical distribution
+ * @param params `Double*` specifying the parameters to be used for the theoretical
distribution
+ * @return KSTestResult object containing test statistic, pvalue, and null hypothesis.
+ */
+ def ksTest(data: RDD[Double], distName: String, params: Double*): KSTestResult = {
 End diff 
What about renaming ksTest > kolmogorovSmirnovTest? Obviously I prefer less terse
names in modern languages but am aware that at times these are meant to mirror old R packages
and such.

If your project is set up for it, you can reply to this email and have your
reply appear on GitHub as well. If your project does not have this feature
enabled and wishes so, or if the feature is enabled but not working, please
contact infrastructure at infrastructure@apache.org or file a JIRA ticket
with INFRA.


To unsubscribe, email: reviewsunsubscribe@spark.apache.org
For additional commands, email: reviewshelp@spark.apache.org
