Support combined kernels

[till-m]

The previous method of constructing the WrappedKernel didn't allow for combined kernels, this PR fixes that.

Summary by CodeRabbit

• Bug Fixes

  • Improved kernel state preservation during wrapping to maintain internal configuration and transform functions correctly.

• Tests

  • Added validation test for combined kernel compatibility with the wrapped kernel workflow.

_{✏️ Tip: You can customize this high-level summary in your review settings.}

[coderabbitai]

📝 Walkthrough

Walkthrough

The changes modify the wrap_kernel function in bayes_opt/parameter.py to preserve a kernel's internal state and transform function by copying attributes directly via __dict__.update, rather than reconstructing from parameters. A new test validates this wrapping behavior with composite kernels (kernel sums) in GP fitting workflows.

Changes

Cohort / File(s)	Summary
Kernel Wrapping Implementation bayes_opt/parameter.py	Modified wrap_kernel to instantiate WrappedKernel without calling __init__, copy all attributes via __dict__.update, and explicitly set _transform. Changes state preservation strategy from parameter-based reconstruction to direct attribute copying.
Kernel Wrapping Validation tests/test_parameter.py	Added test_combined_wrapped_kernel_fit() to validate wrapped kernel behavior with composite kernels (Matern + WhiteKernel sum) in GaussianProcessRegressor fitting, complementing existing wrapped kernel tests.

Estimated code review effort

🎯 3 (Moderate) | ⏱️ ~20 minutes

Poem

🐰 A kernel wrapped with care so true,
State preserved in every view,
Transform intact, composites blessed,
Our wrapped kernels pass the test! ✨

Pre-merge checks and finishing touches

❌ Failed checks (1 warning)

Check name	Status	Explanation	Resolution
Docstring Coverage	⚠️ Warning	Docstring coverage is 40.00% which is insufficient. The required threshold is 80.00%.	You can run @coderabbitai generate docstrings to improve docstring coverage.

✅ Passed checks (2 passed)

Check name	Status	Explanation
Description Check	✅ Passed	Check skipped - CodeRabbit’s high-level summary is enabled.
Title check	✅ Passed	The title 'Support combined kernels' directly and specifically summarizes the main change - enabling combined kernels in the WrappedKernel construction method.

✨ Finishing touches

• 📝 Generate docstrings

🧪 Generate unit tests (beta)

• Create PR with unit tests
• Post copyable unit tests in a comment

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[codecov]

Codecov Report

✅ All modified and coverable lines are covered by tests.
✅ Project coverage is 97.89%. Comparing base (c410d51) to head (350bfa5).
⚠️ Report is 3 commits behind head on master.

Additional details and impacted files

@@           Coverage Diff           @@
##           master     #597   +/-   ##
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  Coverage   97.89%   97.89%           
=======================================
  Files          10       10           
  Lines        1185     1188    +3     
=======================================
+ Hits         1160     1163    +3     
  Misses         25       25           

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[coderabbitai]

Actionable comments posted: 0

🧹 Nitpick comments (2)

tests/test_parameter.py (1)

249-260: Consider adding assertions to validate kernel behavior.

The test successfully validates that combined kernels can be wrapped and fitted without errors. However, it could be more thorough by adding assertions similar to the existing test_wrapped_kernel_fit test (line 246), which verifies that the kernel's parameters changed after fitting.

🔎 Suggested assertion to validate the kernel behavior

     gp.fit(space.params, space.target)
+
+    # Verify that hyperparameters were optimized (not just using initial values)
+    assert gp.kernel_.k1.length_scale != 1e5
+    assert gp.kernel_.k2.noise_level != 1.0

bayes_opt/parameter.py (1)

492-495: Document why shallow copy and __new__ are necessary for combined kernels.

The implementation uses __new__ and __dict__.update() to preserve the internal state of combined kernels (e.g., Matern + WhiteKernel), which cannot be reconstructed from get_params() alone. This approach is necessary and works correctly with sklearn kernels, which have simple attribute structures that are safe to shallow-copy after initialization.

Add a comment explaining that this pattern is specifically required to support combined kernels and that the shallow copy is safe because sklearn kernels do not modify their attributes after construction.

📜 Review details

Configuration used: defaults

Review profile: CHILL

Plan: Pro

📥 Commits

Reviewing files that changed from the base of the PR and between c410d51 and 350bfa5.

📒 Files selected for processing (2)

• bayes_opt/parameter.py
• tests/test_parameter.py

🧰 Additional context used

🧬 Code graph analysis (1)

tests/test_parameter.py (2)

bayes_opt/target_space.py (3)

• kernel_transform (340-347)
• params (149-156)
• target (159-166)

bayes_opt/parameter.py (5)

• wrap_kernel (457-495)
• kernel_transform (115-126)
• kernel_transform (222-234)
• kernel_transform (308-320)
• kernel_transform (434-449)

⏰ Context from checks skipped due to timeout of 90000ms. You can increase the timeout in your CodeRabbit configuration to a maximum of 15 minutes (900000ms). (9)

• GitHub Check: Python 3.10 - numpy >=1.25,<2
• GitHub Check: Python 3.12 - numpy >=1.25,<2
• GitHub Check: Python 3.12 - numpy >=2
• GitHub Check: Python 3.11 - numpy >=2
• GitHub Check: Python 3.11 - numpy >=1.25,<2
• GitHub Check: Python 3.9 - numpy >=1.25,<2
• GitHub Check: Python 3.13 - numpy >=2
• GitHub Check: Python 3.10 - numpy >=2
• GitHub Check: Python 3.9 - numpy >=2