smac.epm.gaussian_process.gp

Classes

GaussianProcess(configspace, types, bounds, ...)

Gaussian process model.
class smac.epm.gaussian_process.gp.GaussianProcess(configspace, types, bounds, seed, kernel, normalize_y=True, n_opt_restarts=10, instance_features=None, pca_components=None)[source]

Bases: smac.epm.gaussian_process.BaseModel

Gaussian process model.

The GP hyperparameterŝ 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

  • types (List[int]) – Specifies the number of categorical values of an input dimension where the i-th entry corresponds to the i-th input dimension. Let’s say we have 2 dimension where the first dimension consists of 3 different categorical choices and the second dimension is continuous than we have to pass [3, 0]. Note that we count starting from 0.

  • bounds (List[Tuple[float, float]]) – bounds of input dimensions: (lower, uppper) for continuous dims; (n_cat, np.nan) for categorical dims

  • seed (int) – Model seed.

  • kernel (george kernel object) – Specifies the kernel that is used for all Gaussian Process

  • prior (prior object) – Defines a prior for the hyperparameters of the GP. Make sure that it implements the Prior interface.

  • normalize_y (bool) – Zero mean unit variance normalization of the output values

  • n_opt_restart (int) – Number of restarts for GP hyperparameter optimization

  • instance_features (np.ndarray (I, K)) – Contains the K dimensional instance features of the I different instances

  • pca_components (float) – Number of components to keep when using PCA to reduce dimensionality of instance features. Requires to set n_feats (> pca_dims).

sample_functions(X_test, n_funcs=1)[source]

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

Parameters

  • X_test (np.ndarray (N, D)) – Input test 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.array(N, F)