[jira] [Resolved] (SPARK-19714) Clarify Bucketizer handling of invalid input

["Sean Owen (JIRA)" <ji...@apache.org>]

     [ https://issues.apache.org/jira/browse/SPARK-19714?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]

Sean Owen resolved SPARK-19714.
-------------------------------
       Resolution: Fixed
    Fix Version/s: 3.0.0

Issue resolved by pull request 23003
[https://github.com/apache/spark/pull/23003]

> Clarify Bucketizer handling of invalid input
> --------------------------------------------
>
>                 Key: SPARK-19714
>                 URL: https://issues.apache.org/jira/browse/SPARK-19714
>             Project: Spark
>          Issue Type: Improvement
>          Components: ML, MLlib
>    Affects Versions: 2.1.0
>            Reporter: Bill Chambers
>            Assignee: Wojciech Szymanski
>            Priority: Minor
>             Fix For: 3.0.0
>
>
> {code}
> contDF = spark.range(500).selectExpr("cast(id as double) as id")
> import org.apache.spark.ml.feature.Bucketizer
> val splits = Array(5.0, 10.0, 250.0, 500.0)
> val bucketer = new Bucketizer()
>   .setSplits(splits)
>   .setInputCol("id")
>   .setHandleInvalid("skip")
> bucketer.transform(contDF).show()
> {code}
> You would expect that this would handle the invalid buckets. However it fails
> {code}
> Caused by: org.apache.spark.SparkException: Feature value 0.0 out of Bucketizer bounds [5.0, 500.0].  Check your features, or loosen the lower/upper bound constraints.
> {code} 
> It seems strange that handleInvalud doesn't actually handleInvalid inputs.
> Thoughts anyone?



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