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 LevyFlightSampler Overview This sampler proposes new hyperparameter candidates by taking a Lévy-flight step from the current best trial. A Lévy flight is a random walk whose step lengths follow a heavy-tailed (Lévy stable) distribution: most steps are small, enabling fine local search, but occasional large steps jump far away, helping to escape shallow local optima.
The step is computed via the Mantegna algorithm, an efficient closed-form approximation of the Lévy stable distribution that requires only standard Gaussian random variates.
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.