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From "Xusen Yin (JIRA)" <j...@apache.org>
Subject [jira] [Commented] (SPARK-15574) Python meta-algorithms in Scala
Date Wed, 15 Jun 2016 20:21:09 GMT

    [ https://issues.apache.org/jira/browse/SPARK-15574?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15332475#comment-15332475
] 

Xusen Yin commented on SPARK-15574:
-----------------------------------

I just finished the prototype of PythonTransformer in Scala as the transformer wrapper of
pure Python transformers. It works well if I run it alone from Scala side. But if I chained
the PythonTransformer with other transformers/estimators in Pipeline, it fails for lacking
of transformSchema in Python side. AFAIK, we need to add transformSchema in Python ML for
pure Python PipelineStages. [~josephkb] [~mengxr]

> Python meta-algorithms in Scala
> -------------------------------
>
>                 Key: SPARK-15574
>                 URL: https://issues.apache.org/jira/browse/SPARK-15574
>             Project: Spark
>          Issue Type: Improvement
>          Components: ML, PySpark
>            Reporter: Joseph K. Bradley
>
> This is an experimental idea for implementing Python ML meta-algorithms (CrossValidator,
TrainValidationSplit, Pipeline, OneVsRest, etc.) in Scala.  This would require a Scala wrapper
for algorithms implemented in Python, somewhat analogous to Python UDFs.
> The benefit of this change would be that we could avoid currently awkward conversions
between Scala/Python meta-algorithms required for persistence.  It would let us have full
support for Python persistence and would generally simplify the implementation within MLlib.



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