bolero.optimizer.BIPOPCMAESOptimizer¶

class bolero.optimizer.BIPOPCMAESOptimizer(initial_params=None, variance=1.0, covariance=None, n_samples_per_update=None, active=False, bounds=None, maximize=True, min_variance=9.8607613152626476e-32, min_fitness_dist=4.4408920985006262e-16, max_condition=10000000.0, log_to_file=False, log_to_stdout=False, random_state=None)[source]¶

BI-population CMA-ES.

After each restart, the population size will be increased or decreased. For details, see the paper.

Parameters:

initial_params : array-like, shape = (n_params,), optional (default: 0s) Initial parameter vector. variance : float, optional (default: 1.0) Initial exploration variance. covariance : array-like, optional (default: None) Either a diagonal (with shape (n_params,)) or a full covariance matrix (with shape (n_params, n_params)). A full covariance can contain information about the correlation of variables. n_samples_per_update : integer, optional (default: 4+int(3*log(n_params))) Number of roll-outs that are required for a parameter update. active : bool, optional (default: False) Active CMA-ES (aCMA-ES) with negative weighted covariance matrix update bounds : array-like, shape (n_samples, 2), optional (default: None) Upper and lower bounds for each parameter. maximize : optional, boolean (default: True) Maximize return or minimize cost? min_variance : float, optional (default: 2 * np.finfo(np.float).eps ** 2) Minimum variance before restart min_fitness_dist : float, optional (default: 2 * np.finfo(np.float).eps) Minimum distance between fitness values before restart max_condition : float optional (default: 1e7) Maximum condition of covariance matrix log_to_file: optional, boolean or string (default: False) Log results to given file, it will be located in the $BL_LOG_PATH log_to_stdout: optional, boolean (default: False) Log to standard output random_state : optional, int Seed for the random number generator.

__init__(initial_params=None, variance=1.0, covariance=None, n_samples_per_update=None, active=False, bounds=None, maximize=True, min_variance=9.8607613152626476e-32, min_fitness_dist=4.4408920985006262e-16, max_condition=10000000.0, log_to_file=False, log_to_stdout=False, random_state=None)[source]¶

get_args()¶

Get parameters for this estimator.

Returns:	params : mapping of string to any Parameter names mapped to their values.

get_best_fitness()¶

Get the best observed fitness.

Returns:	best_fitness : float Best fitness (sum of feedbacks) so far. Corresponds to the parameters obtained by get_best_parameters(method=’best’). For maximize=True, this is the highest observed fitness, and for maximize=False, this is the lowest observed fitness.

get_best_parameters(method='best')¶

Get the best parameters.

Parameters:	method : string, optional (default: ‘best’) Either ‘best’ or ‘mean’
Returns:	best_params : array-like, shape (n_params,) Best parameters

get_next_parameters(params)¶

Get next individual/parameter vector for evaluation.

Parameters:	params : array_like, shape (n_params,) Parameter vector, will be modified

init(n_params)¶

Initialize the behavior search.

Parameters:	n_params : int dimension of the parameter vector

is_behavior_learning_done()¶

Returns false because we will restart and not stop.

Returns:	finished : bool Is the learning of a behavior finished?

set_evaluation_feedback(feedback)¶

Set feedbacks for the parameter vector.

Parameters:	feedback : list of float feedbacks for each step or for the episode, depends on the problem