bayeso.utils.utils_bo¶

bayeso.utils.utils_bo.get_next_best_acquisition(arr_points, arr_acquisitions, cur_points)¶

It returns the next best acquired example.

Parameters:	arr_points (numpy.ndarray) – inputs for acquisition function. Shape: (n, d). arr_acquisitions (numpy.ndarray) – acquisition function values over arr_points. Shape: (n, ). cur_points (numpy.ndarray) – examples evaluated so far. Shape: (m, d).
Returns:	next best acquired point. Shape: (d, ).
Return type:	numpy.ndarray
Raises:	AssertionError

bayeso.utils.utils_bo.optimize_many(model_bo, fun_target, X_train, int_iter, str_initial_method_ao='uniform', int_samples_ao=100, str_mlm_method='regular')¶

It optimizes fun_target for int_iter iterations with given model_bo and initial inputs X_train. It returns the optimization results and execution times.

Parameters:	model_bo (bayeso.bo.BO) – Bayesian optimization model. fun_target (function) – a target function. X_train (numpy.ndarray) – initial inputs. Shape: (n, d) or (n, m, d). int_iter (int.) – the number of iterations for Bayesian optimization. str_initial_method_ao (str., optional) – the name of initialization method for acquisition function optimization. int_samples_ao (int., optional) – the number of samples for acquisition function optimization. If L-BFGS-B is used as an acquisition function optimization method, it is employed. str_mlm_method (str., optional) – the name of marginal likelihood maximization method for Gaussian process regression.
Returns:	tuple of acquired examples, their function values, overall execution times per iteration, execution time consumed in Gaussian process regression, and execution time consumed in acquisition function optimization. Shape: ((n + int_iter, d), (n + int_iter, 1), (n + int_iter, ), (int_iter, ), (int_iter, )), or ((n + int_iter, m, d), (n + int_iter, m, 1), (n + int_iter, ), (int_iter, ), (int_iter, )).
Return type:	(numpy.ndarray, numpy.ndarray, numpy.ndarray, numpy.ndarray, numpy.ndarray)
Raises:	AssertionError

bayeso.utils.utils_bo.optimize_many_(model_bo, fun_target, X_train, Y_train, int_iter, str_initial_method_ao='uniform', int_samples_ao=100, str_mlm_method='regular')¶

It optimizes fun_target for int_iter iterations with given model_bo. It returns the optimization results and execution times.

Parameters:	model_bo (bayeso.bo.BO) – Bayesian optimization model. fun_target (function) – a target function. X_train (numpy.ndarray) – initial inputs. Shape: (n, d) or (n, m, d). Y_train (numpy.ndarray) – initial outputs. Shape: (n, 1). int_iter (int.) – the number of iterations for Bayesian optimization. str_initial_method_ao (str., optional) – the name of initialization method for acquisition function optimization. int_samples_ao (int., optional) – the number of samples for acquisition function optimization. If L-BFGS-B is used as an acquisition function optimization method, it is employed. str_mlm_method (str., optional) – the name of marginal likelihood maximization method for Gaussian process regression.
Returns:	tuple of acquired examples, their function values, overall execution times per iteration, execution time consumed in Gaussian process regression, and execution time consumed in acquisition function optimization. Shape: ((n + int_iter, d), (n + int_iter, 1), (int_iter, ), (int_iter, ), (int_iter, )), or ((n + int_iter, m, d), (n + int_iter, m, 1), (int_iter, ), (int_iter, ), (int_iter, )).
Return type:	(numpy.ndarray, numpy.ndarray, numpy.ndarray, numpy.ndarray, numpy.ndarray)
Raises:	AssertionError

bayeso.utils.utils_bo.optimize_many_with_random_init(model_bo, fun_target, int_init, int_iter, str_initial_method_bo='uniform', str_initial_method_ao='uniform', int_samples_ao=100, str_mlm_method='regular', int_seed=None)¶

It optimizes fun_target for int_iter iterations with given model_bo and int_init initial examples. Initial examples are sampled by get_initial method in model_bo. It returns the optimization results and execution times.

Parameters:	model_bo (bayeso.bo.BO) – Bayesian optimization model. fun_target (function) – a target function. int_init (int.) – the number of initial examples for Bayesian optimization. int_iter (int.) – the number of iterations for Bayesian optimization. str_initial_method_bo (str., optional) – the name of initialization method for sampling initial examples in Bayesian optimization. str_initial_method_ao (str., optional) – the name of initialization method for acquisition function optimization. int_samples_ao (int., optional) – the number of samples for acquisition function optimization. If L-BFGS-B is used as an acquisition function optimization method, it is employed. str_mlm_method (str., optional) – the name of marginal likelihood maximization method for Gaussian process regression. int_seed (NoneType or int., optional) – None, or random seed.
Returns:	tuple of acquired examples, their function values, overall execution times per iteration, execution time consumed in Gaussian process regression, and execution time consumed in acquisition function optimization. Shape: ((int_init + int_iter, d), (int_init + int_iter, 1), (int_init + int_iter, ), (int_iter, ), (int_iter, )), or ((int_init + int_iter, m, d), (int_init + int_iter, m, 1), (int_init + int_iter, ), (int_iter, ), (int_iter, )), where d is a dimensionality of the problem we are solving and m is a cardinality of sets.
Return type:	(numpy.ndarray, numpy.ndarray, numpy.ndarray, numpy.ndarray, numpy.ndarray)
Raises:	AssertionError