Github user srowen commented on a diff in the pull request:
https://github.com/apache/spark/pull/14858#discussion_r78518465
--- Diff:
mllib/src/main/scala/org/apache/spark/ml/feature/QuantileDiscretizer.scala ---
@@ -109,7 +114,7 @@ final class QuantileDiscretizer @Since("1.6.0")
(@Since("1.6.0") override val ui
@Since("2.0.0")
override def fit(dataset: Dataset[_]): Bucketizer = {
transformSchema(dataset.schema, logging = true)
- val splits = dataset.stat.approxQuantile($(inputCol),
+ val splits =
dataset.select($(inputCol)).na.drop().stat.approxQuantile($(inputCol),
--- End diff --
Yes, I agree that a similar argument applies for approxQuantile methods. I
think the most reasonable semantics are to ignore NaN as well.
QuantileSummaries should probably reject insertion of NaN too.
I'd support making that change as well here, and expanding the scope
accordingly.
---
If your project is set up for it, you can reply to this email and have your
reply appear on GitHub as well. If your project does not have this feature
enabled and wishes so, or if the feature is enabled but not working, please
contact infrastructure at infrastruct...@apache.org or file a JIRA ticket
with INFRA.
---
---------------------------------------------------------------------
To unsubscribe, e-mail: reviews-unsubscr...@spark.apache.org
For additional commands, e-mail: reviews-h...@spark.apache.org
