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https://issues.apache.org/jira/browse/SPARK-19714?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15882224#comment-15882224
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Nick Pentreath commented on SPARK-19714:
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Another alternative is that we do expand the "invalid" handling to include
anything that falls outside of the splits provided. For {{QuantileDiscretizer}}
it would have no effect but it would provide further flexibility to users.
> 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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