Github user crackcell commented on a diff in the pull request:
https://github.com/apache/spark/pull/17123#discussion_r103954857
--- Diff: mllib/src/main/scala/org/apache/spark/ml/feature/Bucketizer.scala
---
@@ -105,20 +106,24 @@ 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) =>
- Bucketizer.binarySearchForBuckets($(splits), feature, keepInvalid)
+ val bucketizer: UserDefinedFunction = udf { (row: Row) =>
+ Bucketizer.binarySearchForBuckets(
+ $(splits),
+ row.getAs[java.lang.Double]($(inputCol)),
--- End diff --
Hi, Scala's Double will convert null to zero. Say:
> scala> val a: Double = null.asInstanceOf[Double]
> a: Double = 0.0
So I use Java's Double instead to hold NULLs. I feel it a litte ugly, any
better way?
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