Abstract Standard Bayesian Optimization assumes homoscedastic (constant) noise across the entire search space. When standard Gaussian Process (GP) models encounter a highly noisy localized region, the Marginal Log-Likelihood optimization is forced to absorb that localized variance into a single global noise parameter. This inflates uncertainty across the entire surrogate model, causing the optimizer to over-explore and waste search budget.
The HeteroscedasticGPSampler natively supports input-dependent observation noise. By explicitly passing the known or estimated noise variance of a trial via a user-defined noise_func, this sampler maps and isolates high-variance regions.
Class or Function Names plot_hypervolume_history Example mod = optunahub.load_module("visualization/plot_hypervolume_history_with_rp") mod.plot_hypervolume_history(study, reference_point) See example.py for more details. The example of generated image is as follows.
Class or Function Names SimulatedAnnealingSampler Example mod = optunahub.load_module("samplers/simulated_annealing") sampler = mod.SimulatedAnnealingSampler() See example.py for more details. You can run the example in Google Colab.
Others This package provides a sampler based on Simulated Annealing algorithm. For more details, see the documentation.