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https://issues.apache.org/jira/browse/SPARK-13068?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15131344#comment-15131344
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holdenk commented on SPARK-13068:
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This seems like a good direction, the current approach only works for params
which are inside of Spark since it isn't really extensible. That being said
there probably aren't going to be a lot of people making custom scala
estimators wanting to expose them in Python.
Right now these validators that your talking about are just looking at type,
but on the Scala side the validators are also able to check range and similar
things. Might be worth looking at params.scala for ideas.
> Extend pyspark ml paramtype conversion to support lists
> -------------------------------------------------------
>
> Key: SPARK-13068
> URL: https://issues.apache.org/jira/browse/SPARK-13068
> Project: Spark
> Issue Type: Improvement
> Components: ML, PySpark
> Reporter: holdenk
> Priority: Trivial
>
> In SPARK-7675 we added type conversion for PySpark ML params. We should
> follow up and support param type conversion for lists and nested structures
> as required. This blocks having all PySpark ML params having type information.
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