Github user sryza commented on a diff in the pull request:
https://github.com/apache/spark/pull/7075#discussion_r35939573
--- Diff:
mllib/src/main/scala/org/apache/spark/mllib/stat/test/KolmogorovSmirnovTest.scala
---
@@ -190,5 +191,93 @@ private[stat] object KolmogorovSmirnovTest extends
Logging {
val pval = 1 - new KolmogorovSmirnovTest().cdf(ksStat, n.toInt)
new KolmogorovSmirnovTestResult(pval, ksStat,
NullHypothesis.OneSampleTwoSided.toString)
}
+
+ /**
+ * Implements a two-sample, two-sided Kolmogorov-Smirnov test, which
tests if the 2 samples
+ * come from the same distribution
+ * @param data1 `RDD[Double]` first sample of data
+ * @param data2 `RDD[Double]` second sample of data
+ * @return
[[org.apache.spark.mllib.stat.test.KolmogorovSmirnovTestResult]] with the test
+ * statistic, p-value, and appropriate null hypothesis
+ */
+ def testTwoSamples(data1: RDD[Double], data2: RDD[Double]):
KolmogorovSmirnovTestResult = {
+ val n1 = data1.count().toDouble
+ val n2 = data2.count().toDouble
+ val isSample1 = true // identifier for sample 1, needed after co-sort
+ // combine identified samples
+ val joinedData = data1.map(x => (x, isSample1)) ++ data2.map(x => (x,
!isSample1))
+ // co-sort and operate on each partition
+ val localData = joinedData.sortBy { case (v, id) => v }.mapPartitions
{ part =>
+ searchTwoSampleCandidates(part, n1, n2) // local extrema
+ }.collect()
+ val ksStat = searchTwoSampleStatistic(localData, n1 * n2) // result:
global extreme
+ evalTwoSampleP(ksStat, n1.toInt, n2.toInt)
+ }
+
+ /**
+ * Calculates maximum distance candidates and counts from each sample
within one partition for
+ * the two-sample, two-sided Kolmogorov-Smirnov test implementation
+ * @param partData `Iterator[(Double, Boolean)]` the data in 1 partition
of the co-sorted RDDs,
+ * each element is additionally tagged with a boolean
flag for sample 1 membership
+ * @param n1 `Double` sample 1 size
+ * @param n2 `Double` sample 2 size
+ * @return `Iterator[(Double, Double, Double)]` where the first element
is an unadjusted minimum
+ * distance , the second is an unadjusted maximum distance (both
of which will later
+ * be adjusted by a constant to account for elements in prior
partitions), and a
+ * count corresponding to the numerator of the adjustment
constant coming from this
+ * partition
+ */
+ private def searchTwoSampleCandidates(
+ partData: Iterator[(Double, Boolean)],
+ n1: Double,
+ n2: Double)
+ : Iterator[(Double, Double, Double)] = {
--- End diff --
This can go on the line above
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