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Posted to issues@spark.apache.org by "Hyukjin Kwon (Jira)" <ji...@apache.org> on 2019/10/08 05:45:09 UTC
[jira] [Resolved] (SPARK-24406) Exposing custom spark scala ml
transformers in pyspark
[ https://issues.apache.org/jira/browse/SPARK-24406?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Hyukjin Kwon resolved SPARK-24406.
----------------------------------
Resolution: Incomplete
> Exposing custom spark scala ml transformers in pyspark
> -------------------------------------------------------
>
> Key: SPARK-24406
> URL: https://issues.apache.org/jira/browse/SPARK-24406
> Project: Spark
> Issue Type: Question
> Components: ML, MLlib
> Affects Versions: 2.3.0
> Reporter: Pratyush Sharma
> Priority: Minor
> Labels: bulk-closed
>
> How can I use a custom transformer written in scala in a pyspark pipeline.
> {code:java}
> class UpperTransformer(override val uid: String)
> extends UnaryTransformer[String, String, UpperTransformer] {
>
> def this() = this(Identifiable.randomUID("upper"))
>
> override protected def validateInputType(inputType: DataType): Unit = {
> require(inputType == StringType)
> }
>
> protected def createTransformFunc: String => String = {
> _.toUpperCase
> }
>
> protected def outputDataType: DataType = StringType
> }{code}
>
> Use this transformer in pyspark pipeline.
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