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name: advanced-domain-features description: "Use BayesianOptimization advanced domain features: constraints, typed parameters, custom parameter classes, TargetSpace conversions, and sequential domain reduction." disable-model-invocation: true metadata: disco-role: operating license: MIT

Advanced Domain Features

Use this sub-skill when a task involves constrained optimization, non-float search variables, custom parameter encodings, low-level TargetSpace conversion/debugging, or sequential domain reduction in the bayesian-optimization package.

Route here for

• SciPy NonlinearConstraint setup with BayesianOptimization(..., constraint=...).
• ConstraintModel behavior: eval, fit, predict, approx, allowed, multiple constraints, and registering known constrained observations.
• Integer, categorical, float, or preconstructed BayesParameter entries in pbounds.
• Custom BayesParameter subclasses and kernel-space transforms.
• TargetSpace.params_to_array, array_to_params, masks, bounds, and typed conversion semantics.
• SequentialDomainReductionTransformer setup, validation, and limitations.

Route elsewhere

• Basic optimizer lifecycle, maximize, ask-tell loops, state save/load, predict, and ordinary HPO recipes: ../optimizer-workflows/SKILL.md
• Acquisition selection, exploration/exploitation tuning, custom acquisition functions, Constant Liar, or GPHedge: ../acquisition-control/SKILL.md
• Editing the repository, running native test selections, docs, lint, CI, or release-maintenance checks: ../repo-maintenance/SKILL.md

Start with the relevant reference

• Read references/constraints.md when the user needs expensive/learned constraints, multi-output constraints, feasibility debugging, or manual registration of constrained observations.
• Read references/parameter-types.md when pbounds includes integers, categories, preconstructed parameters, or custom domain encodings.
• Read references/domain-reduction.md when bounds should shrink during optimization or a bounds transformer fails.
• Read references/troubleshooting.md when errors mention mismatched constraint arguments, no allowed points, unsupported constrained acquisitions, invalid categorical categories, key/dimension mismatches, experimental non-float warnings, or domain reduction with typed parameters.

Safe smoke check

Run scripts/advanced_features_smoke.py only after the package and its normal runtime dependencies are importable:

python scripts/advanced_features_smoke.py --check all

The helper is deterministic, uses no network and no plots, and validates tiny constraint, typed-parameter, and domain-reduction behavior. Use python scripts/advanced_features_smoke.py --help to list narrower checks.

Working rules for future agents

1. Treat constraints and typed parameters as part of the domain definition, not as after-the-fact filters. The optimizer wraps SciPy constraints in a ConstraintModel, and the internal GP kernels use the TargetSpace transform for typed domains.
2. Keep objective and constraint keyword names identical. TargetSpace calls both functions with the same array_to_params(...) result.
3. For known observations in a constrained optimizer, always register the raw constraint value with constraint_value; otherwise the target and constraint histories cannot stay aligned.
4. Expect a warning for non-float parameters. Integer/categorical optimization is useful, but still marked experimental by the package.
5. Do not combine sequential domain reduction with integer, categorical, or custom non-FloatParameter domains in the current implementation.
6. If a task asks both for advanced domains and acquisition strategy design, handle the domain facts here, then route acquisition-specific choices to the acquisition-control sub-skill.