saealib.SklearnGPRSurrogate

class saealib.SklearnGPRSurrogate(**kwargs)

Bases: SklearnSurrogate

Gaussian Process Regressor surrogate.

Convenience wrapper around SklearnSurrogate using sklearn.gaussian_process.GaussianProcessRegressor. Provides uncertainty estimates via SurrogatePrediction.std, enabling compatibility with acquisition functions such as EI, LCB, PoF, and MaxUncertainty.

Parameters:

**kwargs (object) – Keyword arguments forwarded to sklearn.gaussian_process.GaussianProcessRegressor (e.g. kernel, alpha, n_restarts_optimizer).

References

[19]: Sacks, J., Welch, W. J., Mitchell, T. J., & Wynn, H. P. (1989). Design and analysis of computer experiments. Statistical Science, 4(4), 409-423.

[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 mean and standard deviation using the GP models.
with_post_fit	Return a copy of this surrogate with fn appended to the post-fit hook.

Method Details

SklearnGPRSurrogate.__init__(**kwargs)

Parameters:

kwargs (object)

Return type:

None

SklearnGPRSurrogate.contract()

Return the surrogate model contract.

Return type:

ComponentContract

SklearnGPRSurrogate.fit(train_x, train_y)

Fit the surrogate model.

Parameters:

• 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,).

Return type:

None

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

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

Parameters:

• 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.

Return type:

None

SklearnGPRSurrogate.predict(test_x)

Predict mean and standard deviation using the GP models.

Parameters:

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

Returns:

prediction.value shape: (n_samples, n_obj) prediction.std shape: (n_samples, n_obj)

Return type:

SurrogatePrediction

SklearnGPRSurrogate.with_post_fit(fn)

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

Parameters:

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

Returns:

Shallow copy with the hook registered.

Return type:

Surrogate