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From "Joseph K. Bradley (JIRA)" <j...@apache.org>
Subject [jira] [Updated] (SPARK-7675) PySpark spark.ml Params type conversions
Date Thu, 13 Aug 2015 01:29:46 GMT

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

Joseph K. Bradley updated SPARK-7675:
-------------------------------------
    Target Version/s: 1.6.0  (was: 1.5.0)

> 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
>            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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