Comments (6)
@Irynei, this is intentional behavior –– the evaluation function is strictly typed, and it's return type is a union of a float, a tuple of two floats, and a dictionary of a string to such tuples, as documented in SimpleExperiment
. We use python builtins for primitive types and convert from numpy primitives where necessary. However, you're right that for the evaluation function, we should allow numpy types –– will add that support shortly.
Is it much trouble to convert back from numpy to python primitive in the evaluation function, for now? You could use our utility ax.utils.common.typeutils.numpy_type_to_python_type
, if you'd like.
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@lena-kashtelyan, thanks for your explanation. I was expecting the evaluation function to work with numpy types, so for me, that was a surprising behavior.
No, It's not hard to convert them for now, but thanks for the suggestion.
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Great! I'll let you know once the fix for this is on master.
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Fix is in the latest version!
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Interesting. Have you tried with torch.Tensor data types?
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Cool, thank you!
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Related Issues (20)
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