Github user VinceShieh commented on a diff in the pull request:
https://github.com/apache/spark/pull/14858#discussion_r77465278
--- Diff:
mllib/src/main/scala/org/apache/spark/ml/feature/QuantileDiscretizer.scala ---
@@ -114,10 +115,10 @@ final class QuantileDiscretizer @Since("1.6.0")
(@Since("1.6.0") override val ui
splits(0) = Double.NegativeInfinity
splits(splits.length - 1) = Double.PositiveInfinity
- val distinctSplits = splits.distinct
+ val distinctSplits = splits.filter(!_.isNaN).distinct
--- End diff --
@srowen then maybe we should, as we discussed earlier on JIRA, align with
R, by having a NaN checker in approxQuantile, that is, having a NaN filter
inside of approxQuantile, rather than ahead of calling approxQuantile.
We can also have a same flag for user to choose to either remove NaN values
or throw an error when there is NaN in data, although, this API change will
introduce collateral impact on several existing function calls.
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