Menglong TAN created SPARK-19781:
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Summary: Bucketizer's handleInvalid leave null values untouched
unlike the NaNs
Key: SPARK-19781
URL: https://issues.apache.org/jira/browse/SPARK-19781
Project: Spark
Issue Type: Improvement
Components: MLlib
Affects Versions: 2.1.0
Reporter: Menglong TAN
Priority: Minor
Bucketizer can put NaN values into a special bucket when handleInvalid is on.
but leave null values untouched.
import org.apache.spark.ml.feature.Bucketizer
val data = sc.parallelize(Seq(("crackcell",
null.asInstanceOf[java.lang.Double]))).toDF("name", "number")
val bucketizer = new
Bucketizer().setInputCol("number").setOutputCol("number_output").setSplits(Array(Double.NegativeInfinity,
0, 10, Double.PositiveInfinity)).setHandleInvalid("keep")
val res = bucketizer.transform(data)
res.show(1)
will output:
+---------+------+-------------+
| name|number|number_output|
+---------+------+-------------+
|crackcell| null| null|
+---------+------+-------------+
If we change null to NaN:
val data2 = sc.parallelize(Seq(("crackcell", Double.NaN))).toDF("name",
"number")
data2: org.apache.spark.sql.DataFrame = [name: string, number: double]
bucketizer.transform(data2).show(1)
will output:
+---------+------+-------------+
| name|number|number_output|
+---------+------+-------------+
|crackcell| NaN| 3.0|
+---------+------+-------------+
Maybe we should unify the behaviours? Is it resonable to process nulls as well?
If so, maybe my code can help. :-)
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