smac.model.gaussian_process.gaussian_process

Classes

GaussianProcess(configspace, kernel[, ...])

Implementation of Gaussian process model.

Interfaces

class smac.model.gaussian_process.gaussian_process.GaussianProcess(configspace, kernel, n_restarts=10, normalize_y=True, instance_features=None, pca_components=7, seed=0)[source]

Bases: AbstractGaussianProcess

Implementation of Gaussian process model. The Gaussian process hyperparameters are obtained by optimizing the marginal log likelihood.

This code is based on the implementation of RoBO: Klein, A. and Falkner, S. and Mansur, N. and Hutter, F. RoBO: A Flexible and Robust Bayesian Optimization Framework in Python In: NIPS 2017 Bayesian Optimization Workshop

Parameters:

  • configspace (ConfigurationSpace) –

  • kernel (Kernel) – Kernel which is used for the Gaussian process.

  • n_restarts (int, defaults to 10) – Number of restarts for the Gaussian process hyperparameter optimization.

  • normalize_y (bool, defaults to True) – Zero mean unit variance normalization of the output values.

  • instance_features (dict[str, list[int | float]] | None, defaults to None) – Features (list of int or floats) of the instances (str). The features are incorporated into the X data, on which the model is trained on.

  • pca_components (float, defaults to 7) – Number of components to keep when using PCA to reduce dimensionality of instance features.

  • seed (int) –

property meta: dict[str, Any]

Returns the meta data of the created object.

sample_functions(X_test, n_funcs=1)[source]

Samples F function values from the current posterior at the N specified test points.

Parameters:

  • X (np.ndarray [#samples, #hyperparameters + #features]) – Input data points.

  • n_funcs (int) – Number of function values that are drawn at each test point.

Returns:

function_samples – The F function values drawn at the N test points.

Return type:

np.ndarray

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