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From "Bill Chambers (JIRA)" <j...@apache.org>
Subject [jira] [Commented] (SPARK-19714) Bucketizer Bug Regarding Handling Unbucketed Inputs
Date Mon, 27 Feb 2017 16:53:45 GMT

    [ https://issues.apache.org/jira/browse/SPARK-19714?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15886109#comment-15886109
] 

Bill Chambers commented on SPARK-19714:
---------------------------------------

Agree with your first and second paragraphs.

Regarding the third, it's worth a discussion certainly, but it's a pretty big departure from
the current definition which is worrisome.

> Bucketizer Bug Regarding Handling Unbucketed Inputs
> ---------------------------------------------------
>
>                 Key: SPARK-19714
>                 URL: https://issues.apache.org/jira/browse/SPARK-19714
>             Project: Spark
>          Issue Type: Bug
>          Components: ML, MLlib
>    Affects Versions: 2.1.0
>            Reporter: Bill Chambers
>
> {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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