Github user josepablocam commented on a diff in the pull request:
https://github.com/apache/spark/pull/7075#discussion_r36148400
--- Diff:
mllib/src/main/scala/org/apache/spark/mllib/stat/test/KolmogorovSmirnovTest.scala
---
@@ -190,5 +191,104 @@ private[stat] object KolmogorovSmirnovTest extends
Logging {
val pval = 1 - new CommonMathKolmogorovSmirnovTest().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
+ // identifier for sample 1, needed after co-sort
+ val isSample1 = true
+ // combine identified samples
+ val unionedData = data1.map((_, isSample1)).union(data2.map((_,
!isSample1)))
+ // co-sort and operate on each partition, returning local extrema to
the driver
+ val localData = unionedData.sortByKey().mapPartitions(
+ searchTwoSampleCandidates(_, n1, n2)
+ ).collect()
+ // result: global extreme
+ val ksStat = searchTwoSampleStatistic(localData, n1 * n2)
+ evalTwoSampleP(ksStat, n1.toInt, n2.toInt)
+ }
+
+ /**
+ * Calculates maximum distance candidates and counts of elements 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 the third is
+ * a count corresponding to the numerator of the adjustment
constant coming from this
--- End diff --
done, thanks for feedback
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