Github user jkbradley commented on a diff in the pull request:
https://github.com/apache/spark/pull/11663#discussion_r57084301
--- Diff: 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):
--- End diff --
I hadn't thought about this before, but we actually should support unicode.
The main use case is StringIndexer, which might be used to index unicode. For
that, we'd want to pass an array of unicode and probably avoid converting it to
str types.
Java/Scala should already handle this since java.lang.String handles
unicode.
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