Comments (6)
@gcoon151 I pinned networkx
to fix a hyperopt
bug, though possibly it's been fixed on the latest version of hyperopt
. I'll check and see if it can be updated. For now, if you just pip install bbopt --no-deps
and then install the dependencies you want manually it should work.
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@gcoon151 This should be fixed now on the latest version.
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Sadly the same thing is happening again. It depends on networkx<2.0,>=1.0
, but networkx is now version 2.4. Could the restrictions be loosened a bit?
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@PureTryOut What Python version are you using? The current requirements should be set to networkx<2.0,>=1.0
on Python 2 but networkx>=2.2
on Python 3.
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Python 3.8, so not sure how that is happening then
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@PureTryOut Are you sure you're using the latest version of bbopt
? If you run pip install -U bbopt>=1.1.9
, does that fix the problem?
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Related Issues (16)
- Not able to run the optimizer more than 10 times for Keras implementation HOT 3
- run_backend with Mixture option HOT 8
- Add a framework for dealing with converting old bbopt data to new bbopt data
- I wish i'd seen this... HOT 3
- Add pySOT backend
- Add meta backend HOT 1
- Compatibility with Colab? HOT 4
- [FEATURE]: Run Id should be available in a process HOT 1
- [BUG]: `numpy.random.mtrand.RandomState` object has no attribute `integers` -> switch to `np.random.Generator` HOT 1
- Prevent regenerating the model with each run HOT 1
- Add bbopt endpoint which runs many iterations
- [Errno 9] Bad file descriptor HOT 2
- TypeError: Cannot cast array data from dtype('<U4') to dtype('int64') according to the rule 'safe' HOT 4
- Support all scikit-optimize visualization methods HOT 1
- ValueError: 16 is not in list HOT 1
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