specs = SpecService.generate_dict_multiple_spec(
n_specs=n_specs,
problem_names=problem_name,
dim_xs=dim_x,
bounds_domain_xs=bounds_domain_x,
training_names=training_name,
type_kernels=type_kernel,
dimensionss=dimensions,
bounds_domains=bounds_domain,
number_points_each_dimensions=number_points_each_dimension,
n_trainings=n_training,
random_seeds=random_seed,
type_boundss=type_bounds,
x_domains=x_domain,
mles=mle,
distributions=distribution,
parallels=parallel,
thinnings=thinning,
n_burnings=n_burning,
max_steps_outs=max_steps_out,
n_iterationss=n_iterations,
same_correlations=same_correlation,
debugs=debug,
number_points_each_dimension_debugs=number_points_each_dimension_debug,
monte_carlo_sbos=monte_carlo_sbo,
n_samples_mcs=n_samples_mc,
n_restarts_mcs=n_restarts_mc,
factr_mcs=factr_mc,
maxiter_mcs=maxiter_mc,
n_restartss=n_restarts,
n_best_restartss=n_best_restarts,
use_only_training_pointss=use_only_training_points,
method_optimizations=method_optimization,
n_samples_parameterss=n_samples_parameters,
n_restarts_means=n_restarts_mean,
n_best_restarts_means=n_best_restarts_mean,
n_best_restarts_mcs=n_best_restarts_mc,
maxepochs=maxepoch,
method_opt_mcs=method_opt_mc,
n_samples_parameters_means=n_samples_parameters_mean,
maxepoch_means=maxepoch_mean,
threshold_sbos=threshold_sbo,
parallel_trainings=parallel_training,
noises=noises,
n_sampless=n_sampless,
parameters_distributions=parameters_distributions,
optimize_only_posterior_means=optimize_only_posterior_means,
start_optimize_posterior_means=start_optimize_posterior_means,
simplex_domain=simplex_domain)
def test_generate_dic_specs(self):
specs = SpecService.generate_dict_multiple_spec(
1, self.problem_names, self.dim_xs, self.bounds_domain_xs,
[self.training_name], [self.type_kernel], [self.dimensions])
assert specs == {
'problem_names': self.problem_names,
'dim_xs': self.dim_xs,
'bounds_domain_xs': self.bounds_domain_xs,
'training_names': [self.training_name],
'bounds_domains': self.bounds_domain_xs,
'number_points_each_dimensions': [[10]],
'choose_noises': [True],
'method_optimizations': ['sbo'],
'type_boundss': [[0]],
'n_trainings': [10],
'pointss': [[]],
'noises': [False],
'n_sampless': [0],
'random_seeds': [1],
'parallels': [True],
'type_kernels': [[SCALED_KERNEL, MATERN52_NAME]],
'dimensionss': [[1]],
'name_models': ['gp_fitting_gaussian'],
'mles': [True],
'thinnings': [0],
'n_burnings': [0],
'max_steps_outs': [1],
'training_datas': [{}],
'distributions': [UNIFORM_FINITE],
'minimizes': [False],
'n_iterationss': [5],
'parameters_distributions': [{}],
'x_domains': [[]],
'kernel_valuess': [[]],
'mean_values': [[]],
'var_noise_values': [[]],
}
specs_ = SpecService.generate_dict_multiple_spec(
2, ['toy', 'toy_2'], [1, 2], [[[2, 3]], [[5, 8]]],
[self.training_name, 'test_spec_2'], [self.type_kernel],
[self.dimensions])
assert specs_ == {
'problem_names': ['toy', 'toy_2'],
'dim_xs': [1, 2],
'bounds_domain_xs': [[[2, 3]], [[5, 8]]],
'training_names': [self.training_name, 'test_spec_2'],
'bounds_domains': [[[2, 3]], [[5, 8]]],
'number_points_each_dimensions': [[10], [10, 10]],
'choose_noises': [True, True],
'method_optimizations': ['sbo', 'sbo'],
'type_boundss': [[0], [0]],
'n_trainings': [10, 10],
'pointss': [[], []],
'noises': [False, False],
'n_sampless': [0, 0],
'random_seeds': [1, 1],
'parallels': [True, True],
'type_kernels': [[SCALED_KERNEL, MATERN52_NAME],
[SCALED_KERNEL, MATERN52_NAME]],
'dimensionss': [[1], [1]],
'name_models': ['gp_fitting_gaussian', 'gp_fitting_gaussian'],
'mles': [True, True],
'thinnings': [0, 0],
'n_burnings': [0, 0],
'max_steps_outs': [1, 1],
'training_datas': [{}, {}],
'distributions': [UNIFORM_FINITE, UNIFORM_FINITE],
'minimizes': [False, False],
'n_iterationss': [5, 5],
'parameters_distributions': [{}, {}],
'x_domains': [[], []],
'kernel_valuess': [[], []],
'mean_values': [[], []],
'var_noise_values': [[], []],
}