Github user dbtsai commented on a diff in the pull request:
https://github.com/apache/spark/pull/17078#discussion_r103342093
--- Diff:
mllib/src/main/scala/org/apache/spark/ml/classification/LogisticRegression.scala
---
@@ -1447,7 +1447,7 @@ private class LogisticAggregator(
label: Double): Unit = {
val localFeaturesStd = bcFeaturesStd.value
- val localCoefficients = bcCoefficients.value
+ val localCoefficients = bcCoefficients.value.toArray
--- End diff --
My concern is that if `coefficients` is sparse, we are not just doing the
pointer indirection but creating a new dense array from sparse matrix. I know
we always pass in a dense matrix so this will not be an issue now, but being
said that, in the following code, if we call `compress` in the `coefficients`,
we may be able to broadcast a smaller object when L1 is applied or in the
initial iteration that most of the elements in `coefficients` are zero.
https://github.com/sethah/spark/blob/3bea389f6780e1fd0385fbe26954fa4f59b69e37/mllib/src/main/scala/org/apache/spark/ml/classification/LogisticRegression.scala#L1674
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