[GitHub] spark pull request #14725: [SPARK-17161] [PYSPARK][ML] Add PySpark-ML JavaWr...

[holdenk <gi...@git.apache.org>]

Github user holdenk commented on a diff in the pull request:

    https://github.com/apache/spark/pull/14725#discussion_r94691274
  
    --- Diff: python/pyspark/ml/wrapper.py ---
    @@ -59,6 +63,32 @@ def _new_java_obj(java_class, *args):
             java_args = [_py2java(sc, arg) for arg in args]
             return java_obj(*java_args)
     
    +    @staticmethod
    +    def _new_java_primitive_array(pylist):
    +        if not pylist:
    +            raise ValueError("Unable to convert an empty list to Java array")
    --- End diff --
    
    So I understand we can't look at the types on an empty list, but using this seems like a potential headache that might not be worth it - in at least some of the places where we take an array we will want to take an empty array.
    
    Also I forsee this possibly leading to all kinds of fun problems with numeric types :(
    
    What about if we kept `_new_java_array` and just required in our code we specify the type? It should still be simpler when porting new algorithms to Python but give us more flexibility.


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