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https://issues.apache.org/jira/browse/SPARK-7675?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14997814#comment-14997814
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Apache Spark commented on SPARK-7675:
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User 'holdenk' has created a pull request for this issue:
https://github.com/apache/spark/pull/9581
> PySpark spark.ml Params type conversions
> ----------------------------------------
>
> Key: SPARK-7675
> URL: https://issues.apache.org/jira/browse/SPARK-7675
> Project: Spark
> Issue Type: Improvement
> Components: ML, PySpark
> Reporter: Joseph K. Bradley
> Priority: Minor
>
> Currently, PySpark wrappers for spark.ml Scala classes are brittle when
> accepting Param types. E.g., Normalizer's "p" param cannot be set to "2" (an
> integer); it must be set to "2.0" (a float). Fixing this is not trivial
> since there does not appear to be a natural place to insert the conversion
> before Python wrappers call Java's Params setter method.
> A possible fix will be to include a method "_checkType" to PySpark's Param
> class which checks the type, prints an error if needed, and converts types
> when relevant (e.g., int to float, or scipy matrix to array). The Java
> wrapper method which copies params to Scala can call this method when
> available.
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