[jira] [Updated] (SPARK-7675) PySpark spark.ml Params type conversions

["Joseph K. Bradley (JIRA)" <ji...@apache.org>]

     [ https://issues.apache.org/jira/browse/SPARK-7675?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]

Joseph K. Bradley updated SPARK-7675:
-------------------------------------
            Shepherd: Joseph K. Bradley
            Assignee: holdenk
    Target Version/s: 2.0.0

> 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
>            Assignee: holdenk
>            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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