emukit.bayesian_optimization.interfaces package

Submodules

class emukit.bayesian_optimization.interfaces.models.IEntropySearchModel

Interface containing abstract methods that need to be implemented if using entropy search Bayesian optimization acquisition function.

predict_covariance(X, with_noise=True)

Parameters:

X (ndarray) – Numpy array of shape (n_points, n_features) of test input locations
with_noise (bool) – Whether to include likelihood noise term in covariance

Return type:

ndarray

Returns:

Posterior covariance matrix which has shape (n_points x n_points)

get_covariance_between_points(X1, X2)

Calculate posterior covariance between one point in X1 and all points in X2

Parameters:

X1 (ndarray) – An array of shape 1 x n_dimensions that contains a data single point. It is the first argument of the posterior covariance function
X2 (ndarray) – An array of shape n_points x n_dimensions that may contain multiple data points. This is the second argument to the posterior covariance function.

Return type:

ndarray

Returns:

An array of shape n_points x 1 of posterior covariances between X1 and X2