srowen commented on a change in pull request #26527: [SPARK-29691][ML][PYTHON]
ensure Param objects are valid in fit, transform
URL: https://github.com/apache/spark/pull/26527#discussion_r346961279
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File path: python/pyspark/ml/param/__init__.py
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@@ -484,8 +484,19 @@ def _copyValues(self, to, extra=None):
:return: the target instance with param values copied
"""
paramMap = self._paramMap.copy()
- if extra is not None:
- paramMap.update(extra)
+ if isinstance(extra, dict):
+ for param, value in extra.items():
+ if isinstance(param, Param):
+ paramMap[param] = value
+ elif self.hasParam(param):
+ # set value by name if vaiid
+ paramMap[self.getParam(param)] = value
Review comment:
Hm yeah I don't know if there's a use case for passing parameters to
elements of a Pipeline this way. It might work, not sure. Anyway I think
argument from consistency probably still stands.
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