Github user sethah commented on a diff in the pull request:
https://github.com/apache/spark/pull/10962#discussion_r51076979
--- Diff: python/pyspark/ml/param/__init__.py ---
@@ -152,13 +152,17 @@ def isDefined(self, param):
return self.isSet(param) or self.hasDefault(param)
@since("1.4.0")
- def hasParam(self, paramName):
+ def hasParam(self, param):
"""
- Tests whether this instance contains a param with a given
- (string) name.
+ Tests whether this instance contains a param.
"""
- param = self._resolveParam(paramName)
- return param in self.params
+ if isinstance(param, Param):
+ return hasattr(self, param.name)
--- End diff --
I tend to agree that we should only accept string types for this function.
The reason I have included `Param` type is because in the current ml param
tests, there is a check where `hasParam` is called by passing a `Param`
instance instead of a string, so this test would fail ([see
here](https://github.com/apache/spark/blob/master/python/pyspark/ml/tests.py#L216)).
It is odd that the test passes a `Param` instance and not a string, since the
function describes itself as accepting strings, but, in an odd twist, the check
works anyway.
If we do accept `Param` type, we can't call `_shouldOwn` because it throws
an error instead of returning `False` (by design?). At any rate, I vote to
accept only strings and change the test to pass in the param name instead of
the param.
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