srowen commented on a change in pull request #25178: [SPARK-28421][ML]
SparseVector.apply performance optimization
URL: https://github.com/apache/spark/pull/25178#discussion_r305084255
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File path: mllib-local/src/main/scala/org/apache/spark/ml/linalg/Vectors.scala
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@@ -603,6 +603,19 @@ class SparseVector @Since("2.0.0") (
private[spark] override def asBreeze: BV[Double] = new BSV[Double](indices,
values, size)
+ override def apply(i: Int): Double = {
+ if (i < 0 || i >= size) {
+ throw new IndexOutOfBoundsException(s"Index $i out of bounds [0, $size)")
+ }
+
+ if (indices.isEmpty || i < indices(0) || i > indices(indices.length - 1)) {
Review comment:
Yeah, but you're also always paying the cost of these two checks. It depends
on the access pattern, but assuming pretty uniform distribution, the check will
rarely save checks and always add a few. It seems simpler to avoid it unless
there's a clear case it's a win.
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