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)
- Ax pulling numpy 2.0 with breaking changes HOT 3
- How to set the beta coefficient of generation strategy? HOT 6
- Numpy 2.0 Compatibility Issue HOT 1
- save the state HOT 9
- Defining a Model class for ModelListGP HOT 2
- Error : Try again with more data HOT 26
- Using nonlinear constraints with boolean masks HOT 5
- input data is not standardized (mean = tensor([0.] HOT 9
- nonlinear constarined in evaluate paramter instead add to the experiment HOT 2
- Ax 0.4.0 Causing Segmentation Fault When Calling `.get_next_trial()` HOT 2
- Defining Metric in Ax Service HOT 2
- Hierarchical Search Spaces with Multiple Independent Search Spaces HOT 3
- Services API :×1-1.5*×2>=0 HOT 4
- primary_objective and secondary_objective HOT 1
- Comparison of multi-objective acquistion functions HOT 14
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- attach trials HOT 8
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- Question : Generation 12 trials HOT 8
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