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https://issues.apache.org/jira/browse/SPARK-19714?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15893821#comment-15893821
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Nick Pentreath commented on SPARK-19714:
----------------------------------------
If you feel that handling values outside the bucket ranges as "invalid" is
reasonable - specifically including them in the special "invalid" bucket - then
we can discuss if and how that could be implemented.
I agree it's quite a large departure, but we could support it with a further
param value such as "keepAll" which keeps both {{NaN}} and values outside of
range in the special bucket.
I don't see a compelling reason that this is a bug, so if you want to motivate
for a change then propose an approach.
I do think we should update the doc for {{handleInvalid}} - [~wojtek-szymanski]
feel free to open a PR for that.
> 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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