Module bayesian_optimization 

Bayesian Optimization for Hyperparameter Tuning

This module implements Bayesian optimization algorithms for efficient optimization of expensive black-box functions. It’s particularly useful for hyperparameter optimization where function evaluations are costly.

Key features:

• Gaussian Process surrogate models
• Various acquisition functions (EI, PI, UCB, etc.)
• Sequential model-based optimization
• Multi-objective optimization support
• Constraint handling
• Parallel evaluation support

Modules

test_functions

Example objective functions for testing

Structs

BayesianOptConfig

Configuration for Bayesian optimization

BayesianOptResult

Result from Bayesian optimization

BayesianOptimizer

Bayesian optimization algorithm

DataPoint

Data point in the optimization history

GaussianProcess

Simplified Gaussian Process implementation

GaussianProcessConfig

Gaussian Process configuration

Enums

AcquisitionFunction

Available acquisition functions

KernelType

Available kernel types

Traits

ObjectiveFunction

Trait for objective functions in Bayesian optimization