srowen commented on a change in pull request #24963: [SPARK-28159][ML] Make the
transform natively in ml framework to avoid extra conversion
URL: https://github.com/apache/spark/pull/24963#discussion_r299526026
##########
File path:
mllib/src/main/scala/org/apache/spark/mllib/clustering/LDAOptimizer.scala
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@@ -642,6 +639,20 @@ private[clustering] object OnlineLDAOptimizer {
}
val sstatsd = expElogthetad.asDenseMatrix.t * (ctsVector /:/
phiNorm).asDenseMatrix
- (gammad, sstatsd, ids)
+ (gammad, sstatsd, indices)
+ }
+
+ private[clustering] def variationalTopicInference(
+ termCounts: Vector,
+ expElogbeta: BDM[Double],
+ alpha: breeze.linalg.Vector[Double],
+ gammaShape: Double,
+ k: Int,
+ seed: Long): (BDV[Double], BDM[Double], List[Int]) = {
+ val (ids: List[Int], cts: Array[Double]) = termCounts match {
+ case v: DenseVector => ((0 until v.size).toList, v.values)
Review comment:
Here and elsewhere, as an optimization, can we avoid `(0 until
v.size).toList)`? pass an empty list in this case or something, and then deduce
that the indices are just the same length as the values?
You're generally solving this with separate sparse/dense methods which could
be fine too if it doesn't result in too much code duplication and improves
performance in the dense case.
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