[jira] [Commented] (SPARK-24632) Allow 3rd-party libraries to use pyspark.ml abstractions for Java wrappers for persistence

["Bryan Cutler (JIRA)" <ji...@apache.org>]

    [ https://issues.apache.org/jira/browse/SPARK-24632?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16544703#comment-16544703 ] 

Bryan Cutler commented on SPARK-24632:
--------------------------------------

Hi [~josephkb], would you mind clarifying why there needs to be an additional trait in Scala to point to Python class paths, instead of something to override the line
{code:java}
stage_name = java_stage.getClass().getName().replace("org.apache.spark", "pyspark")
{code}
in wrapper.py?  Ideally the Scala classes should not be aware of the Python, and when loading, the Python esitmators/models should be able to create the Java object and wrap it as long as the line above has the correct class prefix?  Thanks!

> Allow 3rd-party libraries to use pyspark.ml abstractions for Java wrappers for persistence
> ------------------------------------------------------------------------------------------
>
>                 Key: SPARK-24632
>                 URL: https://issues.apache.org/jira/browse/SPARK-24632
>             Project: Spark
>          Issue Type: Improvement
>          Components: ML, PySpark
>    Affects Versions: 2.4.0
>            Reporter: Joseph K. Bradley
>            Assignee: Joseph K. Bradley
>            Priority: Major
>
> This is a follow-up for [SPARK-17025], which allowed users to implement Python PipelineStages in 3rd-party libraries, include them in Pipelines, and use Pipeline persistence.  This task is to make it easier for 3rd-party libraries to have PipelineStages written in Java and then to use pyspark.ml abstractions to create wrappers around those Java classes.  This is currently possible, except that users hit bugs around persistence.
> I spent a bit thinking about this and wrote up thoughts and a proposal in the doc linked below.  Summary of proposal:
> Require that 3rd-party libraries with Java classes with Python wrappers implement a trait which provides the corresponding Python classpath in some field:
> {code}
> trait PythonWrappable {
>   def pythonClassPath: String = …
> }
> MyJavaType extends PythonWrappable
> {code}
> This will not be required for MLlib wrappers, which we can handle specially.
> One issue for this task will be that we may have trouble writing unit tests.  They would ideally test a Java class + Python wrapper class pair sitting outside of pyspark.



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