def get_method_options(prob, capital_type):
""" Returns the method options. """
methods = prob.methods
all_method_options = {}
for meth in methods:
if meth == 'rand':
curr_options = load_options(cp_random_optimiser_args)
elif meth == 'ga':
curr_options = load_options(cpga_opt_args)
elif meth.startswith('dragonfly'):
if meth.startswith('dragonfly-mf'):
curr_options = load_options(get_all_mf_cp_gp_bandit_args())
else:
curr_options = load_options(get_all_cp_gp_bandit_args())
meth_parts = meth.split('_')
if len(meth_parts) == 2:
curr_options.acq_opt_method = meth_parts[-1]
elif meth in ['hyperopt', 'gpyopt', 'smac']:
curr_options = load_options(blackbox_opt_args)
curr_options = Namespace(redo_evals_for_true_val=False)
else:
raise ValueError('Unknown method %s.'%(meth))
curr_options.capital_type = capital_type
all_method_options[meth] = curr_options
return all_method_options
def get_method_options(prob, capital_type):
""" Returns the method options. """
methods = prob.methods
all_method_options = {}
for meth in methods:
if meth == 'rand':
curr_options = load_options(cp_random_optimiser_args)
elif meth == 'ga':
curr_options = load_options(cpga_opt_args)
elif meth.startswith('dragonfly'):
if meth.startswith('dragonfly-mf'):
curr_options = load_options(get_all_mf_cp_gp_bandit_args())
else:
curr_options = load_options(get_all_cp_gp_bandit_args())
meth_parts = meth.split('_')
if len(meth_parts) == 2:
curr_options.acq_opt_method = meth_parts[-1]
elif meth in ['hyperopt', 'gpyopt', 'smac', 'spearmint']:
curr_options = load_options(blackbox_opt_args)
curr_options = Namespace(redo_evals_for_true_val=True)
else:
raise ValueError('Unknown method %s.' % (meth))
# Some additional data we will need for Spearmint
if meth == 'spearmint':
# Swap in the following after you download and install Spearmint
curr_options.exp_dir = '/home/karun/boss/e3_cp/Spearmint/' + \
prob.study_name.split('-')[0]
curr_options.pkg_dir = '/home/karun/Spearmint/spearmint'
curr_options.capital_type = capital_type
all_method_options[meth] = curr_options
return all_method_options
def get_command_line_args():
""" Returns all arguments for the command line. """
ret = dragonfly_args + \
ga_opt_args + \
euclidean_random_optimiser_args + cp_random_optimiser_args + \
mf_euclidean_random_optimiser_args + mf_cp_random_optimiser_args + \
get_all_euc_gp_bandit_args() + get_all_cp_gp_bandit_args() + \
get_all_mf_euc_gp_bandit_args() + get_all_mf_cp_gp_bandit_args() + \
euclidean_random_multiobjective_optimiser_args + \
cp_random_multiobjective_optimiser_args + \
get_all_euc_moo_gp_bandit_args() + get_all_cp_moo_gp_bandit_args()
return get_unique_list_of_option_args(ret)
def prepare_chemist_options(self, chemist_args, domain_config):
""" Resets default gp_bandit options with chemist arguments """
dflt_list_of_options = get_all_cp_gp_bandit_args()
list_of_options = self.reset_default_options(dflt_list_of_options,
chemist_args)
for name, value in domain_config.items():
list_of_options += [{'name': name, 'default': value}]
options = load_options(list_of_options, reporter=self.reporter)
if self.mf_strategy is not None:
options.mf_strategy = self.mf_strategy
if isinstance(self.worker_manager, RealWorkerManager):
options.capital_type = 'realtime'
elif isinstance(self.worker_manager, SyntheticWorkerManager):
options.capital_type = 'return_value'
options.get_initial_qinfos = \
lambda num: get_cp_domain_initial_qinfos(self.func_caller.domain, num)
return options
def main():
""" Main function. """
# First load arguments
all_args = dragonfly_args + get_all_euc_gp_bandit_args() + get_all_cp_gp_bandit_args() \
+ get_all_mf_euc_gp_bandit_args() + get_all_mf_cp_gp_bandit_args() \
+ get_all_euc_moo_gp_bandit_args() + get_all_cp_moo_gp_bandit_args()
all_args = get_unique_list_of_option_args(all_args)
options = load_options(all_args, cmd_line=True)
# Load domain and objective
config = load_config_file(options.config)
if hasattr(config, 'fidel_space'):
is_mf = True
else:
is_mf = False
expt_dir = os.path.dirname(
os.path.abspath(os.path.realpath(options.config)))
if not os.path.exists(expt_dir):
raise ValueError("Experiment directory does not exist.")
objective_file_name = config.name
obj_module = imp.load_source(
objective_file_name, os.path.join(expt_dir,
objective_file_name + '.py'))
# Set capital
options.capital_type = 'return_value'
if options.budget < 0:
budget = options.max_capital
else:
budget = options.budget
if budget < 0:
raise ValueError(
'Specify the budget via argument budget or max_capital.')
options.max_capital = budget
# Call optimiser
_print_prefix = 'Maximising' if options.max_or_min == 'max' else 'Minimising'
call_to_optimise = {
'single': {
'max': maximise_function,
'min': minimise_function
},
'single_mf': {
'max': maximise_multifidelity_function,
'min': minimise_multifidelity_function
},
'multi': {
'max': multiobjective_maximise_functions,
'min': multiobjective_minimise_functions
},
}
if not options.is_multi_objective:
if is_mf:
print('%s function on fidel_space: %s, domain %s.' %
(_print_prefix, config.fidel_space, config.domain))
opt_val, opt_pt, history = call_to_optimise['single_mf'][
options.max_or_min](obj_module.objective,
domain=None,
fidel_space=None,
fidel_to_opt=config.fidel_to_opt,
fidel_cost_func=obj_module.cost,
max_capital=options.max_capital,
config=config,
options=options)
else:
print('%s function on domain %s.' % (_print_prefix, config.domain))
opt_val, opt_pt, history = call_to_optimise['single'][
options.max_or_min](obj_module.objective,
domain=None,
max_capital=options.max_capital,
config=config,
options=options)
print('Optimum Value in %d evals: %0.4f' %
(len(history.curr_opt_points), opt_val))
print('Optimum Point: %s.' % (opt_pt))
else:
if is_mf:
raise ValueError(
'Multi-objective multi-fidelity optimisation has not been ' +
'implemented yet.')
else:
print(
'%s multiobjective functions on domain %s with %d functions.' %
(_print_prefix, config.domain, len(obj_module.objectives)))
pareto_values, pareto_points, history = \
call_to_optimise['multi'][options.max_or_min](obj_module.objectives,
domain=None, max_capital=options.max_capital, config=config, options=options)
num_pareto_points = len(pareto_points)
print('Found %d Pareto Points: %s.' %
(num_pareto_points, pareto_points))
print('Corresponding Pareto Values: %s.' % (pareto_values))
