smac.model.gaussian_process.gaussian_process¶
Classes¶
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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