Github user mengxr commented on a diff in the pull request:
https://github.com/apache/spark/pull/6994#discussion_r34226093
--- 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 two-sided 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 per-partition 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, p-value, and
null hypothesis.
+ */
+ def ksTest(data: RDD[Double], cdf: Double => Double): KSTestResult = {
+ KSTest.testOneSample(data, cdf)
+ }
+
+ /**
+ * Convenience function to conduct a one-sample, 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, p-value, and
null hypothesis.
+ */
+ def ksTest(data: RDD[Double], distName: String, params: Double*):
KSTestResult = {
--- End diff --
The issue with overloading the name would show up in the Python API,
because you cannot declare two methods with the same name. Then under this
method, you cannot call the second argument `distName` or `data2`, which has to
be more general like `y`. This is R's doc for the second arg:
~~~
y either a numeric vector of data values, or a character string naming a
cumulative distribution function or an actual cumulative distribution
function such as pnorm. Only continuous CDFs are valid.
~~~
MATLAB uses `kstest2`. We can discuss more in the 2-sample test PR.
@srowen This is mostly mirroring R's API. No strong preference, but I would
never type `kolmogorovSmirnovTest` without auto-completion. (Well, I just typed
it ...)
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