viirya commented on a change in pull request #29445:
URL: https://github.com/apache/spark/pull/29445#discussion_r475044767
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File path: python/pyspark/ml/tuning.py
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@@ -536,7 +536,7 @@ def copy(self, extra=None):
bestModel = self.bestModel.copy(extra)
avgMetrics = self.avgMetrics
subModels = self.subModels
- return CrossValidatorModel(bestModel, avgMetrics, subModels)
+ return self._copyValues(CrossValidatorModel(bestModel, avgMetrics,
subModels), extra=extra)
Review comment:
I think I get your point above. You meant if we just shallow copy the
model itself, reassigning of element in `avgMetrics` will be propagated to the
`avgMetrics` in copied model. Because two models use the same `avgMetrics`
reference.
You want to shallow copy `avgMetrics` itself. So two models have difference
`avgMetrics` references. Because `avgMetrics` is a list of float, it is no
matter shallow copy or deep copy.
> By shallow copy I mean copy.copy() in python, which makes re-assigning
cvModel.avgMetrics[0] not being propagated to cvModelCopied.avgMetrics[0].
No matter deep copy or shallow copy, I think reassigning `avgMetrics[0]`
won't propagate to the `avgMetrics[0]` of copied model. Shallow copy copies
object references, reassigning changes references, so won't propagate. Deep
copy copies object instance, reassigning changes references too, of course
won't propagate either.
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