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Enabling candidate generation with both non_linear_constraints
and fixed_features
#1912
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8026af1
remove guarding for nonlinear constraints
jduerholt f86c27e
remove failing test
jduerholt 3cf28b1
add test
jduerholt a4445be
Update test/optim/test_optimize.py
jduerholt f30d81e
implement reduced
jduerholt 82bcab1
Merge branch 'non_linear_and_fixed' of github.com:jduerholt/botorch i…
jduerholt e4346ce
fix flake8
jduerholt 2593b81
fix bug
jduerholt d550f15
fix test
jduerholt fa4d4cb
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black it
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flake it
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add test
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Hmm so with this setup this test may pass even if fixing the features doesn't work (given that it only narrows this to a specific solution of the previous solution set). Can you use a different value here? I might also make sense to use a different mock acquisition function so that the optimizer is unique...
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I will have a deeper look to the test. All this mocking is sometimes a bit hard for me to understand ;)
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I don't think there is actually any mocking going on here - it's just that
mock_acq_function
is aSquaredAcquisitionFunction
rather than a true acquisition function.If you don't want to change that, one thing you could do would be to just replace one of the constraints to result in a different optimum / optimizer.