Github user keypointt commented on a diff in the pull request:
https://github.com/apache/spark/pull/12432#discussion_r59957731
--- Diff: mllib/src/main/scala/org/apache/spark/ml/clustering/KMeans.scala
---
@@ -264,6 +264,9 @@ class KMeans @Since("1.5.0") (
override def fit(dataset: Dataset[_]): KMeansModel = {
val rdd = dataset.select(col($(featuresCol))).rdd.map { case
Row(point: Vector) => point }
+ val instr = Instrumentation.create(this, rdd)
+ instr.logParams(featuresCol, predictionCol, k, initMode, initSteps,
maxIter, seed, tol)
+
val algo = new MLlibKMeans()
--- End diff --
Thanks Timothy. I'm a starter on Spark sorry for being naive.
I just want to confirm with you that I understand correctly.
1. for creating a new method `algo.run(rdd, instr)`, I just find I also
need to create another method `runAlgorithm(zippedData, instr)` to take `instr`
as a parameter
https://github.com/apache/spark/blob/master/mllib/src/main/scala/org/apache/spark/mllib/clustering/KMeans.scala#L241
, since inside 'runAlgorithm' is the dimension we want
https://github.com/apache/spark/blob/master/mllib/src/main/scala/org/apache/spark/mllib/clustering/KMeans.scala#L295
1. class 'Instrumentation' is private and in ml package, so it cannot be
accessed from mllib package. So I have to change it to be public by removing
`private[ml] `?
https://github.com/apache/spark/blob/master/mllib/src/main/scala/org/apache/spark/ml/util/Instrumentation.scala#L42
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