`bayes_opt.SequentialDomainReductionTransformer`¶

See the Sequential Domain Reduction notebook for a complete example.

class bayes_opt.SequentialDomainReductionTransformer(parameters: Iterable[str] | None = None, gamma_osc: float = 0.7, gamma_pan: float = 1.0, eta: float = 0.9, minimum_window: NDArray[Float] | collections.abc.Sequence[float] | Mapping[str, float] | float = 0.0) → None¶

Reduce the searchable space.

A sequential domain reduction transformer based on the work by Stander, N. and Craig, K: “On the robustness of a simple domain reduction scheme for simulation-based optimization”

Parameters:¶

gamma_osc : float, default=0.7¶: Parameter used to scale (typically dampen) oscillations.
gamma_pan : float, default=1.0¶: Parameter used to scale (typically unitary) panning.
eta : float, default=0.9¶: Zooming parameter used to shrink the region of interest.
minimum_window : float or np.ndarray or dict, default=0.0¶: Minimum window size for each parameter. If a float is provided, the same value is used for all parameters.
parameters : Iterable[str] | None¶