Github user jkbradley commented on a diff in the pull request:
https://github.com/apache/spark/pull/15965#discussion_r92527086
--- Diff: mllib/src/main/scala/org/apache/spark/ml/clustering/KMeans.scala
---
@@ -306,12 +307,20 @@ class KMeans @Since("1.5.0") (
@Since("2.0.0")
override def fit(dataset: Dataset[_]): KMeansModel = {
+ val handlePersistence = dataset.rdd.getStorageLevel ==
StorageLevel.NONE
+ fit(dataset, handlePersistence)
+ }
+
+ @Since("2.2.0")
+ protected def fit(dataset: Dataset[_], handlePersistence: Boolean):
KMeansModel = {
--- End diff --
I like the change in terms of eliminating the extra fit() method (which is
not done anywhere else in spark.ml), but I think you'll need to keep the
original test for storage level ```val handlePersistence =
dataset.rdd.getStorageLevel == StorageLevel.NONE```. In the current change,
the RDD being tested is different from ```dataset.rdd```, so handlePersistence
would always be true regardless of the original storage level. Could you
please send a little PR which merges the 2 fit() methods? Thanks!
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