Github user thunterdb commented on a diff in the pull request:
https://github.com/apache/spark/pull/12432#discussion_r59950837
--- 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 --
one statistic that is usually very useful to get is the dimension of the
vectors (`numFeatures`). One way to get it is to pass the instrumentation
instance to `algo.run(rdd)` below, and mark this new method as `private[spark]`.
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