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From jkbradley <...@git.apache.org>
Subject [GitHub] spark pull request: [SPARK-13068][PYSPARK][ML] Type conversion for...
Date Tue, 22 Mar 2016 16:58:22 GMT
Github user jkbradley commented on a diff in the pull request:

    https://github.com/apache/spark/pull/11663#discussion_r57024837
  
    --- Diff: python/pyspark/ml/param/__init__.py ---
    @@ -65,6 +75,146 @@ def __eq__(self, other):
                 return False
     
     
    +class TypeConverters(object):
    +    """
    +    .. note:: DeveloperApi
    +
    +    Factory methods for common type conversion functions for `Param.typeConverter`.
    +
    +    .. versionadded:: 2.0.0
    +    """
    +
    +    @staticmethod
    +    def _is_numeric(value):
    +        vtype = type(value)
    +        return vtype in [int, float, np.float64, np.int64] or vtype.__name__ == 'long'
    +
    +    @staticmethod
    +    def _is_integer(value):
    +        if TypeConverters._is_numeric(value):
    +            value = float(value)
    +            return value.is_integer()
    +        else:
    +            return False
    +
    +    @staticmethod
    +    def _can_convert_to_list(value):
    +        vtype = type(value)
    +        return vtype == list or vtype == np.ndarray or isinstance(value, Vector)
    +
    +    @staticmethod
    +    def _is_string(value):
    --- End diff --
    
    I'd follow existing conventions.  Look at, e.g., the beginning of file feature.py:
    ```
    import sys
    if sys.version > '3':
        basestring = str
    ```
    Then you can use ```isinstance(blah, basestring)```


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