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https://issues.apache.org/jira/browse/SPARK-13068?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15178794#comment-15178794
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Joseph K. Bradley commented on SPARK-13068:
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I like the idea of automatic casting when possible. A few comments:
* I don't think we should add special subclasses of Param for each type. We
had to do that in Scala to make it Java-friendly. Instead, the casting could
be handled by a single method which takes the value and the expected type.
* I don't want to go overboard on conversions. E.g., if a {{str}} is required,
I don't think we should try casting to a string. (If a user calls
{{setInputCol([1,2,3])}}, then they are probably making a mistake.)
* I don't think we should add ParamValidators in Python at this time. We could
consider doing it later, but it's really a separate issue. I'm also worried
about having validation in 2 places since they could get out of synch.
How does that sound?
> 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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