---

name: acquisition-control description: "Select, tune, debug, and extend BayesianOptimization acquisition functions including UCB, EI, PI, Constant Liar, GPHedge, custom acquisitions, and constraint compatibility." disable-model-invocation: true metadata: disco-role: operating license: MIT

Acquisition Control

Use this sub-skill when the task is to choose, configure, debug, serialize, or extend acquisition functions for bayesian-optimization v3.3.x.

Route here for

• Choosing UCB, Expected Improvement, Probability of Improvement, Constant Liar, or GPHedge.
• Tuning exploration/exploitation knobs: kappa, xi, exploration_decay, and exploration_decay_delay.
• Using lower-level acquisition suggest(...) controls such as n_random, n_smart, fit_gp, and random_state.
• Running asynchronous ask-tell loops with ConstantLiar dummy suggestions.
• Building portfolio acquisition strategies with GPHedge.
• Writing custom AcquisitionFunction subclasses and making them compatible with optimizer save_state / load_state.
• Diagnosing constraint compatibility, empty target-space errors, duplicate suggestions, stale UtilityFunction snippets, and custom state-method failures.

Do not use this sub-skill for

• Basic optimizer lifecycle, maximize, probe, registration, or state management beyond acquisition-specific state. Use optimizer workflows.
• Detailed constraints, typed parameters, domain reduction, or parameter-bound transformations beyond acquisition compatibility notes. Use advanced domain features.
• Repository maintenance, tests, releases, or contribution workflows. Use repo maintenance.

Read first

1. API reference for class signatures, parameter constraints, state parameters, constraint support, and common errors.
2. Workflows for strategy selection, exploration tuning, Constant Liar ask-tell, GPHedge portfolios, custom acquisitions, and save/load patterns.
3. Troubleshooting when suggestions fail, constraints are rejected, parameters validate incorrectly, duplicates appear, old UtilityFunction code is encountered, or custom acquisitions fail persistence.
4. Acquisition probe script for a deterministic tiny check that compares built-in acquisition suggestions and optionally exercises Constant Liar and GPHedge.

Minimal setup snippets

from bayes_opt import BayesianOptimization, acquisition

optimizer = BayesianOptimization(
    f=None,
    pbounds={"x": (-2.0, 4.0)},
    acquisition_function=acquisition.UpperConfidenceBound(kappa=2.576),
    random_state=7,
    verbose=0,
)

# Seed observations before acquisition-driven suggestions.
optimizer.register({"x": -1.0}, target=-4.29)
optimizer.register({"x": 0.0}, target=-0.69)
optimizer.register({"x": 2.5}, target=-0.44)
next_point = optimizer.suggest()

If no acquisition_function is passed, BayesianOptimization chooses UCB for unconstrained problems and Expected Improvement for constrained problems. The optimizer-level suggest() method returns a random point while the target space is empty; lower-level acquisition suggest(...) requires at least one registered observation.

Quick validation

After installing bayesian-optimization with its normal dependencies, run:

python scripts/acquisition_probe.py --help
python scripts/acquisition_probe.py --include-constant-liar --include-gphedge

The script performs no network access, no plotting, and no writes. It prints concise PASS lines after validating that suggested points remain inside bounds.