zhengruifeng commented on a change in pull request #31588:
URL: https://github.com/apache/spark/pull/31588#discussion_r585441622
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File path: mllib/src/main/scala/org/apache/spark/ml/feature/VectorSlicer.scala
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@@ -110,22 +110,21 @@ final class VectorSlicer @Since("1.5.0") (@Since("1.5.0")
override val uid: Stri
}
// Prepare output attributes
- val inds = getSelectedFeatureIndices(dataset.schema)
- val selectedAttrs = inputAttr.attributes.map { attrs =>
- inds.map(index => attrs(index))
- }
+ val selectedIndices = getSelectedFeatureIndices(dataset.schema)
+ val selectedAttrs = inputAttr.attributes.map { attrs =>
selectedIndices.map(attrs.apply) }
val outputAttr = selectedAttrs match {
case Some(attrs) => new AttributeGroup($(outputCol), attrs)
- case None => new AttributeGroup($(outputCol), inds.length)
+ case None => new AttributeGroup($(outputCol), selectedIndices.length)
}
- // Select features
+ val sorted = selectedIndices.length > 1 &&
selectedIndices.sliding(2).forall(t => t(1) > t(0))
Review comment:
> I just have a moderately strong preference for not putting this logic
in N callers if possible.
I am not sure how to impl it.
> Is the runtime check for sortedness just too expensive and/or not reusable
in other functions?
Checking for sortedness maybe O(n), mayebe too expensive. The sortedness is
only used in slicing for now.
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