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