Abstract This package implements a modified Constrained Adversarially Robust Bayesian Optimization (CARBO) sampler based on the paper Constrained robust Bayesian optimization of expensive noisyblack-box functions with guaranteed regret bounds. This sampler robustly optimizes a function along with inequality constraints that incurs a noise in its input. The algorithm details are described in the Others section.
APIs CARBOSampler(*, seed: int | None = None, independent_sampler: BaseSampler | None = None, n_startup_trials: int = 10, deterministic_objective: bool = False, constraints_func: Callable[[FrozenTrial], Sequence[float]] | None = None, rho: float = 1e3, beta: float = 4.
Abstract This package provides a parallel coordinate plot for Optuna studies. It follows Optuna’s visualization module layout: plot_parallel_coordinate returns a Plotly figure, and matplotlib.plot_parallel_coordinate returns a Matplotlib axes object.
The current implementation adds:
Conditional parameter support: if a trial does not contain a selected parameter, the line is connected to a special None tick below the valid values. Multi-objective support: objective axes are shown side by side, and trial colors are ordered by Pareto rank.
Abstract SafeCMASampler provides an implementation of SafeCMA, a variant of CMA-ES that incorporates safety constraints. This sampler extends the standard CMA-ES algorithm to handle constrained optimization problems where certain regions of the search space should be avoided. SafeCMA uses Gaussian Process models to estimate Lipschitz constants and manage trust regions, ensuring that the optimization process respects safety constraints while exploring the search space efficiently. Please refer to the original paper for more details.
Abstract This package provides a wrapper of the COCO experiments libarary’s bbob-constrained test suite.
APIs class Problem(function_id: int, dimension: int, instance_id: int = 1) function_id: ID of the bbob-constrained benchmark function to use. It must be in the range of [1, 54]. dimension: Dimension of the benchmark function. It must be in [2, 3, 5, 10, 20, 40]. instance_id: ID of the instance of the benchmark function. It must be in the range of [1, 15].