[GitHub] [spark] srowen commented on a change in pull request #31588: [SPARK-34470][ML] VectorSlicer utilize ordering if possible

[GitBox <gi...@apache.org>]

srowen commented on a change in pull request #31588:
URL: https://github.com/apache/spark/pull/31588#discussion_r584715387



##########
File path: mllib/src/main/scala/org/apache/spark/ml/feature/VectorSlicer.scala
##########
@@ -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:
       Not quite sure what you're proposing; I just have a moderately strong preference for not putting this logic in N callers if possible. OK, factoring it out to a utility class is a little better. Is the runtime check for sortedness just too expensive and/or not reusable in other functions?




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