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name: bayesian-optimization description: "Guides modAL BayesianOptimizer and PI/EI/UCB acquisition workflows for expensive scalar-function optimization with GP-style regressors." disable-model-invocation: true metadata: disco-role: operating license: MIT

Bayesian Optimization

Use this sub-skill when a task asks for Bayesian optimization with modAL: build a BayesianOptimizer, choose PI/EI/UCB acquisition behavior, run a bounded objective-evaluation loop, inspect acquisition scores, or debug get_max()/y_max tracking.

Read or run first

• Read references/workflows.md when you need an end-to-end optimizer loop, shape conventions, 1-D or multidimensional candidate grids, or evaluation-budget guidance.
• Read references/acquisition-reference.md when choosing among max_PI, max_EI, max_UCB or inspecting optimizer_PI, optimizer_EI, optimizer_UCB score arrays.
• Read references/troubleshooting.md when teach, query, acquisition utilities, predict(return_std=True), or get_max() behave unexpectedly.
• Run scripts/bayesian_optimizer_smoke.py to check that the installed modAL package can execute a deterministic Bayesian optimization loop and print PASS with get_max() results.

Route boundaries

• For general ActiveLearner, Committee, query, teach, fit, return_metrics, bagging, or estimator lifecycle questions, route to ../learners-and-committees/SKILL.md.
• For generic query strategy selection, custom strategy combinators, uncertainty/disagreement sampling, ranked batch, density, expected-error, or multilabel strategies, route to ../query-strategies/SKILL.md.
• Keep this sub-skill focused on scalar-objective maximization with regressors that can provide both predictive mean and predictive standard deviation.

Minimal operating checklist

1. Define a finite candidate pool X_pool with shape (n_candidates, n_features).
2. Seed BayesianOptimizer with at least one evaluated point: X_training has matching feature columns and y_training contains numeric scalar objective values.
3. Use a regressor whose predict(X, return_std=True) returns (mean, std); GaussianProcessRegressor is the usual modAL-backed choice.
4. Query before evaluating the expensive objective: query_idx, query_inst = optimizer.query(X_pool).
5. Evaluate only query_inst, normalize the result to one scalar per queried row, and call optimizer.teach(query_inst, y_new).
6. Call optimizer.get_max() for the best observed location/value so far; do not recompute the maximum from unevaluated candidate predictions.