[jira] [Assigned] (SPARK-20574) Allow Bucketizer to handle non-Double column

["Apache Spark (JIRA)" <ji...@apache.org>]

     [ https://issues.apache.org/jira/browse/SPARK-20574?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]

Apache Spark reassigned SPARK-20574:
------------------------------------

    Assignee: Apache Spark

> Allow Bucketizer to handle non-Double column
> --------------------------------------------
>
>                 Key: SPARK-20574
>                 URL: https://issues.apache.org/jira/browse/SPARK-20574
>             Project: Spark
>          Issue Type: Bug
>          Components: ML
>    Affects Versions: 2.1.0
>            Reporter: Wayne Zhang
>            Assignee: Apache Spark
>
> Bucketizer currently requires input column to be Double, but the logic should work on any numeric data types. Many practical problems have integer/float data types, and it could get very tedious to manually cast them into Double before calling bucketizer. This transformer could be extended to handle all numeric types.  
> The example below shows failure of Bucketizer on integer data. 
> {code}
> val splits = Array(-3.0, 0.0, 3.0)
> val data: Array[Int] = Array(-2, -1, 0, 1, 2)
> val expectedBuckets = Array(0.0, 0.0, 1.0, 1.0, 1.0)
> val dataFrame = data.zip(expectedBuckets).toSeq.toDF("feature", "expected")
> val bucketizer = new Bucketizer()
>   .setInputCol("feature")
>   .setOutputCol("result")
>   .setSplits(splits)
> bucketizer.transform(dataFrame)  
> java.lang.IllegalArgumentException: requirement failed: Column feature must be of type DoubleType but was actually IntegerType.
> {code}



--
This message was sent by Atlassian JIRA
(v6.3.15#6346)

---------------------------------------------------------------------
To unsubscribe, e-mail: issues-unsubscribe@spark.apache.org
For additional commands, e-mail: issues-help@spark.apache.org