Github user viirya commented on a diff in the pull request:
https://github.com/apache/spark/pull/20594#discussion_r167812275
--- Diff: mllib/src/main/scala/org/apache/spark/ml/feature/Bucketizer.scala
---
@@ -290,6 +293,27 @@ object Bucketizer extends
DefaultParamsReadable[Bucketizer] {
}
}
+
+ private[Bucketizer] class BucketizerWriter(instance: Bucketizer) extends
MLWriter {
+
+ override protected def saveImpl(path: String): Unit = {
+ // SPARK-23377: The default params will be saved and loaded as
user-supplied params.
+ // Once `inputCols` is set, the default value of `outputCol` param
causes the error
+ // when checking exclusive params. As a temporary to fix it, we
remove the default
+ // value of `outputCol` if `inputCols` is set before saving.
+ // TODO: If we modify the persistence mechanism later to better
handle default params,
+ // we can get rid of this.
+ var removedOutputCol: Option[String] = None
--- End diff --
I was thinking about this too. But looks like we don't add lock to the
places we change params in ML. I guess that we assume the usage of ML models is
single-threaded. So I leave it as this. Will add it if others think this is
required too.
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