Github user josepablocam commented on a diff in the pull request:
https://github.com/apache/spark/pull/7075#discussion_r35942574
--- 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)] = {
+ // fold accumulator: local minimum, local maximum, index for sample 1,
index for sample2
+ case class KS2Acc(min: Double, max: Double, ix1: Int, ix2: Int)
+ val initAcc = KS2Acc(Double.MaxValue, Double.MinValue, 0, 0)
+ // traverse the data in partition and calculate distances and counts
+ val pResults = partData.foldLeft(initAcc) { case (acc: KS2Acc, (v,
isSample1)) =>
+ val (add1, add2) = if (isSample1) (1, 0) else (0, 1)
+ val cdf1 = (acc.ix1 + add1) / n1
+ val cdf2 = (acc.ix2 + add2) / n2
+ val dist = cdf1 - cdf2
+ KS2Acc(math.min(acc.min, dist), math.max(acc.max, dist), acc.ix1 +
add1, acc.ix2 + add2)
+ }
+ val results = if (pResults == initAcc) {
--- End diff --
Things go bad. The accumulator for the fold has Double.MaxValue and
Double.MinValue, to allow proper accumulator of minimums and maximums,
respectively. If the partition is empty, the foldLeft still returns the
accumulator. So if we don't check and replace with an empty array (on line
249), then the statistic is ruined by the max double.
---
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