Github user imatiach-msft commented on a diff in the pull request:
https://github.com/apache/spark/pull/17123#discussion_r106339731
--- Diff: mllib/src/main/scala/org/apache/spark/ml/feature/Bucketizer.scala
---
@@ -105,20 +106,21 @@ final class Bucketizer @Since("1.4.0")
(@Since("1.4.0") override val uid: String
transformSchema(dataset.schema)
val (filteredDataset, keepInvalid) = {
if (getHandleInvalid == Bucketizer.SKIP_INVALID) {
- // "skip" NaN option is set, will filter out NaN values in the
dataset
+ // "skip" NaN/NULL option is set, will filter out NaN/NULL values
in the dataset
(dataset.na.drop().toDF(), false)
} else {
(dataset.toDF(), getHandleInvalid == Bucketizer.KEEP_INVALID)
}
}
- val bucketizer: UserDefinedFunction = udf { (feature: Double) =>
+ val bucketizer: UserDefinedFunction = udf { (row: Row) =>
--- End diff --
I believe you should try to avoid using a udf on a row because the
serialization costs will be more expensive... hmm how could we make this
perform well and handle nulls? Does it work with Option[Double] instead of Row?
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