saealib.RBFSurrogate

class saealib.RBFSurrogate(kernel, polynomial_degree='auto', solver='lstsq', alpha=1e-08)

ベースクラス: RegressionSurrogate

Radial Basis Function interpolation surrogate model.

Supports multi-objective problems with one independent model per objective. The number of objectives is inferred from train_y on the first call to fit.

パラメータ:

• kernel (RBFKernel)

• polynomial_degree (Literal['auto', 0, 1] | None)

• solver (str)

• alpha (float)

kernel

Kernel object (e.g. GaussianKernel()). Reassigning this property invalidates any fitted state; call fit() again before the next predict().

Type:

RBFKernel

n_features_in_

Number of input features seen by the most recent successful fit(); None before the first successful fit.

Type:

int or None

n_obj

Number of objectives. Set on first fit call.

Type:

int or None

polynomial_degree

Configured polynomial degree. "auto" resolves from kernel.min_polynomial_degree and None disables the polynomial term.

Type:

{"auto", None, 0, 1}

resolved_polynomial_degree

Concrete polynomial degree used by the next or most recent fit.

Type:

int or None

resolved_kernel

Kernel instance used by the most recent successful fit, or None before fitting.

Type:

RBFKernel or None

solver

Linear system solver used during fitting.

Type:

str

alpha

Tikhonov regularization strength.

Type:

float

参照

Origin of RBF interpolation: Hardy (1971).

[16]: Gutmann, H.-M. (2001). A radial basis function method for global optimization. Journal of Global Optimization, 19(3), 201-227.

[17]: Regis, R. G., & Shoemaker, C. A. (2005). Constrained global optimization of expensive black box functions using radial basis functions. Journal of Global Optimization, 31(1), 153-171.

[18]: Rasmussen, C. E., & Williams, C. K. I. (2006). Gaussian processes for machine learning. MIT Press.

Methods

__init__
contract	Return the surrogate model contract.
fit	Fit the surrogate model.
post_fit	Post-fit lifecycle hook; override to inject custom processing.
predict	Predict using the surrogate model.
with_post_fit	Return a copy of this surrogate with fn appended to the post-fit hook.

Method Details

RBFSurrogate.__init__(kernel, polynomial_degree='auto', solver='lstsq', alpha=1e-08)

パラメータ:

• kernel (RBFKernel)

• polynomial_degree (Literal['auto', 0, 1] | None)

• solver (str)

• alpha (float)

戻り値の型:

None

RBFSurrogate.contract()

Return the surrogate model contract.

戻り値の型:

ComponentContract

RBFSurrogate.fit(train_x, train_y)

Fit the surrogate model.

パラメータ:

• train_x (np.ndarray) -- Training input data. shape: (n_samples, n_features)

• train_y (np.ndarray) -- Training output data. shape: (n_samples, n_obj) or (n_samples,). 1-D input is treated as single-objective: shape (n_samples, 1).

戻り値の型:

None

RBFSurrogate.post_fit(train_x, train_y, ctx=None)

Post-fit lifecycle hook; override to inject custom processing.

パラメータ:

• train_x (np.ndarray) -- Training input data used for this fit. shape: (n_samples, n_features)

• train_y (np.ndarray) -- Training output data used for this fit. shape: (n_samples, n_obj)

• ctx (OptimizationState or None, optional) -- Current optimization context.

戻り値の型:

None

RBFSurrogate.predict(test_x)

Predict using the surrogate model.

パラメータ:

test_x (np.ndarray) -- Input data. shape: (n_samples, n_features) or (n_features,)

戻り値:

prediction.value shape: (n_samples, n_obj) prediction.std is None (RBF interpolation provides no uncertainty) prediction.x holds the (n_samples, n_features) query points passed to this call (candidates when called from a prediction stage, holdout/archive points when called for accuracy evaluation), needed by acquisition functions that have no other channel to the points being scored (e.g. CORSDistance).

戻り値の型:

SurrogatePrediction

例外:

RuntimeError -- If the surrogate has not been fitted, or kernel was reassigned since the last fit() call.

RBFSurrogate.with_post_fit(fn)

Return a copy of this surrogate with fn appended to the post-fit hook.

パラメータ:

fn (callable) -- fn(train_x, train_y, ctx) -> None

戻り値:

Shallow copy with the hook registered.

戻り値の型:

Surrogate