Github user karlhigley commented on a diff in the pull request:
https://github.com/apache/spark/pull/9843#discussion_r46051533
--- Diff: mllib/src/main/scala/org/apache/spark/mllib/feature/IDF.scala ---
@@ -211,14 +213,17 @@ private object IDFModel {
val n = v.size
v match {
case SparseVector(size, indices, values) =>
+ val newElements = new ArrayBuffer[(Int, Double)]
val nnz = indices.size
- val newValues = new Array[Double](nnz)
var k = 0
while (k < nnz) {
- newValues(k) = values(k) * idf(indices(k))
+ val newValue = values(k) * idf(indices(k))
--- End diff --
This change doesn't affect the `idf` vector -- it's still always dense. The
existing `transform` code was producing `SparseVector`s containing explicit
zeros. This change removes the explicit zeros, but only when the input to
`transform` (not the `idf`) is a `SparseVector`.
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