Add `.predict` function to optimizer

[till-m]

This introduces a user-facing .predict() method to the optimizer to simplify making predictions with the GP. I think that with the support for optimization over non-float parameters, calling .predict() on the GP now requires a bit too much parameter transformation and mangling. Rather than forcing users to handle this complexity themselves, this change provides an interface that handles all necessary conversions internally.

Summary by CodeRabbit

• New Features

  • Added a predict() method to generate predictions for specified parameters with optional standard deviation or covariance outputs.

• Documentation

  • Updated maximize() documentation to reference the new prediction capability.

[codecov[bot]]

Codecov Report

:x: Patch coverage is 92.59259% with 2 lines in your changes missing coverage. Please review. :white_check_mark: Project coverage is 97.77%. Comparing base (c410d51) to head (1e51b28).

Files with missing lines	Patch %	Lines
bayes_opt/bayesian_optimization.py	92.59%	2 Missing :warning:

Additional details and impacted files

@@            Coverage Diff             @@
##           master     #593      +/-   ##
==========================================
- Coverage   97.89%   97.77%   -0.12%     
==========================================
  Files          10       10              
  Lines        1185     1212      +27     
==========================================
+ Hits         1160     1185      +25     
- Misses         25       27       +2     

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[till-m]

@coderabbitai full review

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✅ Actions performed

Full review triggered.

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Walkthrough

A new predict() method was added to BayesianOptimization for computing predictions on given parameters. It accepts single or multiple parameter inputs, optionally fits the GP, and returns mean with optional uncertainty estimates. Documentation in maximize() was updated. Test utilities including a custom parameter class and triangle area function were added.

Changes

Cohort / File(s)	Summary
Core Feature bayes_opt/bayesian_optimization.py	Added public method predict(params, return_std=False, return_cov=False, fit_gp=True) that accepts single dict or list of dicts, optionally fits GP, normalizes inputs to 2D array, and returns predictions with optional std/covariance. Updated maximize() documentation to reference the new prediction capability.
Test Utilities tests/test_bayesian_optimization.py	Added helper class FixedPerimeterTriangleParameter (subclass of BayesParameter) for continuous sampling via Dirichlet distributions with perimeter constraints. Added standalone function area_of_triangle(sides) computing area via Heron's formula.

Sequence Diagram

sequenceDiagram
    participant User
    participant BO as BayesianOptimization
    participant GP as GaussianProcess
    
    User->>BO: predict(params, fit_gp=True)
    
    alt fit_gp is True
        BO->>BO: Check if observations exist
        alt No observations
            BO-->>User: RuntimeError
        else Observations exist
            BO->>GP: Fit GP via _fit_gp()
            GP-->>BO: GP fitted
        end
    end
    
    BO->>BO: Normalize params to 2D array
    BO->>GP: Predict mean (and std/cov if requested)
    GP-->>BO: Predictions returned
    
    BO->>BO: Denormalize output shape
    
    alt return_std or return_cov
        BO-->>User: (mean, uncertainty)
    else Neither requested
        BO-->>User: mean only
    end

Estimated code review effort

🎯 3 (Moderate) | ⏱️ ~22 minutes

• Input validation & normalization logic: Verify parameter conversion from single dict/list of dicts to 2D array and shape consistency
• GP fitting pathway: Ensure fit_gp conditional logic correctly delegates to _fit_gp() and handles edge cases (zero observations)
• Return shape handling: Validate that output shapes differ correctly between single vs. multiple inputs and with/without uncertainty estimates
• Test coverage: Review test utilities and their integration with the new prediction functionality

Poem

🐰 A prediction method hops in with grace,
Parameters fed, predictions embrace,
Uncertainty whispered with std or cov,
Triangles tested with Heron's beloved,
The Bayesian warren grows strong, here's the trove! 🥕✨

Pre-merge checks and finishing touches

✅ Passed checks (3 passed)

Check name	Status	Explanation
Description Check	✅ Passed	Check skipped - CodeRabbit’s high-level summary is enabled.
Title check	✅ Passed	The title directly and accurately describes the main change: adding a new .predict() method to the BayesianOptimization optimizer class.
Docstring Coverage	✅ Passed	No functions found in the changed files to evaluate docstring coverage. Skipping docstring coverage check.

✨ Finishing touches

• [ ] 📝 Generate docstrings

🧪 Generate unit tests (beta)

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

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