Bucketizer.transform incorrectly drops row containing NaN

import org.apache.spark.ml.feature._

val df = spark.createDataFrame(Seq((2.3, 3.0), (Double.NaN, 3.0), (6.7, Double.NaN))).toDF("a", "b")

val splits = Array(Double.NegativeInfinity, 3.0, Double.PositiveInfinity)

val bucketizer: Bucketizer = new Bucketizer().setInputCol("a").setOutputCol("aa").setSplits(splits)

bucketizer.setHandleInvalid("skip")

scala> df.show
+---+---+
|  a|  b|
+---+---+
|2.3|3.0|
|NaN|3.0|
|6.7|NaN|
+---+---+

scala> bucketizer.transform(df).show
+---+---+---+
|  a|  b| aa|
+---+---+---+
|2.3|3.0|0.0|
+---+---+---+