Comments (4)
Now that you are able to plot the outcomes, do the results look like they make more sense, @glenn-jocher?
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@lena-kashtelyan ah, unfortunately the plotting problem #83 persists (it seems to not be numpy related, which was solved in #82). You can reproduce the plotting problem directly by running the tutorial notebook, which I have in a Google Colab:
https://colab.research.google.com/drive/1P6TvA9UZDtLf9dMFTcYWm_RUBW0wpsiV#scrollTo=DMsfBROgGSBK
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@glenn-jocher, let's wait until we have the plots of this? I think it's just fine that the results are different, since optimization is stochastic and there is likely more than one optimal hyperparameter setting in your setup.
from ax.
@lena-kashtelyan yes, sure I agree. Hyperparameter tuning will always be a bit mystifying I'm afraid, at least for the foreseeable future.
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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
- plot_pareto_frontier HOT 6
- attach trials HOT 8
- Multi-task BO with Service API HOT 2
- constrains HOT 17
- Question : Generation 12 trials HOT 8
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