[GitHub] [spark] srowen commented on a change in pull request #11663: [SPARK-13068][PYSPARK][ML] Type conversion for Pyspark params

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srowen commented on a change in pull request #11663: [SPARK-13068][PYSPARK][ML] Type conversion for Pyspark params
URL: https://github.com/apache/spark/pull/11663#discussion_r388382703
 
 

 ##########
 File path: python/pyspark/ml/param/__init__.py
 ##########
 @@ -65,6 +80,144 @@ 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):
+        return TypeConverters._is_numeric(value) and float(value).is_integer()
+
+    @staticmethod
+    def _can_convert_to_list(value):
+        vtype = type(value)
+        return vtype in [list, np.ndarray, tuple, xrange, array.array] or isinstance(value, Vector)
+
+    @staticmethod
+    def _can_convert_to_string(value):
 
 Review comment:
   @sethah @jkbradley sorry for a question on an old bit of code, but hope this is a quick one:
   These converters don't allow None. Some parameters can be set to None. Should they actually all just return None if their value is None? any harm in that?

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