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https://issues.apache.org/jira/browse/SPARK-19781?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Hyukjin Kwon resolved SPARK-19781.
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Resolution: Incomplete
> 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
> Labels: MLlib, bulk-closed
> Original Estimate: 2h
> Remaining Estimate: 2h
>
> Bucketizer can put NaN values into a special bucket when handleInvalid is on.
> but leave null values untouched.
> {code}
> 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)
> {code}
> will output:
> {quote}
> +---------+------+-------------+
> | name|number|number_output|
> +---------+------+-------------+
> |crackcell| null| null|
> +---------+------+-------------+
> {quote}
> If we change null to NaN:
> {code}
> 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)
> {code}
> will output:
> {quote}
> +---------+------+-------------+
> | name|number|number_output|
> +---------+------+-------------+
> |crackcell| NaN| 3.0|
> +---------+------+-------------+
> {quote}
> 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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