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Fix numpy 1.25 deprecation warnings #372

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merged 4 commits into from
Dec 18, 2023
Merged

Fix numpy 1.25 deprecation warnings #372

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e8e1113
fix deprecation warnings
eirikurj Dec 12, 2023
cb19f2b
adding disallow_deprecations to tests
eirikurj Dec 12, 2023
ab4df5d
address comments
eirikurj Dec 13, 2023
436319e
Merge branch 'main' into fixNumpyDeprecationWarnings
eirikurj Dec 14, 2023
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2 changes: 1 addition & 1 deletion .github/test_real.sh
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Expand Up @@ -11,4 +11,4 @@ cd tests
# we have to copy over the coveragerc file to make sure it's in the
# same directory where codecov is run
cp ../.coveragerc .
testflo --pre_announce -v --coverage --coverpkg pyoptsparse $EXTRA_FLAGS
testflo --pre_announce --disallow_deprecations -v --coverage --coverpkg pyoptsparse $EXTRA_FLAGS
4 changes: 2 additions & 2 deletions pyoptsparse/pyOpt_solution.py
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Expand Up @@ -50,8 +50,8 @@ def __init__(self, optProb, xStar, fStar, lambdaStar, optInform, info):

# Now set the f-values
if isinstance(fStar, float) or len(fStar) == 1:
self.objectives[list(self.objectives.keys())[0]].value = float(fStar)
fStar = float(fStar)
self.objectives[list(self.objectives.keys())[0]].value = float(fStar.item())
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The .item() only works when fStar is a numpy array right? But based on the check above, it may be a float? I'm not entirely sure what the type is (since we don't really have good type coverage)

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So I guess

  • if float, don't do any casting
  • if singleton array, use fStar.item() without float() which is unnecessary

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ahh right, I dont think we need the float check. It should be a numpy array coming into this function (_mapObjtoUser in _createSolution will cast it to a numpy array before creating the solution object). I can push some updates.

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Pushed some changes. Thinking about this a second time though, for multiobjective problems this will be a dict, so it would make sense to just always use a dict instead of an array. That way we dont need any of this logic and there is no difference in the way you access your fStar. Maybe not convenient, and strictly is a breaking change. Let me know what you think. This would be a separate PR.

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I have thought about it too, and I think @marcomangano suggested the same before. But given that almost all of our users do single-objective optimization, I hesitate to unify the variable type for consistency. Happy to discuss further (maybe in an issue?) and yes I would vote for separate PR + probably a minor version bump if we do go for it.

fStar = float(fStar.item())
else:
for f_name, f in self.objectives.items():
f.value = fStar[f_name]
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