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ENH: Custom Optimizers for Hyperparameter Optimization #204
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Jun 6, 2024
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- Introduces custom optimization methods for hyperparameter tuning in Gaussian Process models, inspired by Andersson et al. (2015):
- Negative Log-Likelihood Optimization
- Leave-One-Out Cross-Validation (LOO-CV)
- Includes test with standard kernels from scikit-learn.
The import of scipy in optimizers.py causes an error during the documentation build process. The issue seems to be related to the handling of dependencies or environment configuration in the Sphinx build. Has anyone encountered a similar problem, or does anyone have suggestions on how to resolve this? (Maybe @effigies @jhlegarreta ) |
You can probably fix this by adding scipy to the mocked imports: Lines 45 to 65 in 2b07b0e
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Thanks a lot, it worked! |
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Thanks for doing this.
Made some comments about docstrings. Have not checked the details, or have not thought how this can be tested more thoroughly. @oesteban any thought on this?
If we think this is enough, it can be merged as is.
Thanks for your comments @jhlegarreta, I have addressed all of them in commit 34670bb |