def minimize(func_name):
config = configparser.ConfigParser()
config_name = os.path.join(project_dir,
'objective_function/config/noisy.ini')
config.read(config_name, encoding='utf-8')
print(config.sections())
optimal_position_address = os.path.join(
project_dir, config.get(obj_name, 'optimal_position_address'))
dim_size = config.getint(obj_name, 'dim_size')
dim_regs = eval(config.get(obj_name, 'dim_regs'))
budget = config.getint(obj_name, 'budget')
set_optimal_position(optimal_position_address)
seed = 0
random.seed(seed)
np.random.seed(seed)
repeat = 10
for _ in range(repeat):
x = fmin(function_dict[func_name],
space=[
hp.uniform(str(dim), dim_regs[0], dim_regs[1])
for dim in range(dim_size)
],
algo=tpe.suggest,
max_evals=budget)
epoch_first_items(budget)
append_all_epoch()
all_epoch = np.array(get_all_epoch())
log_address = os.path.join(project_dir, 'hyperopt_exp/log/noisy/')
file_name = os.path.join(log_address,
'{}_{}.txt'.format(obj_name, dim_size))
os.makedirs(log_address, exist_ok=True)
np.savetxt(file_name, all_epoch)
print(all_epoch.shape)
def minimize(func_name):
config = configparser.ConfigParser()
config_name = os.path.join(project_dir, 'objective_function/config/noisy.ini')
config.read(config_name, encoding='utf-8')
print(config.sections())
optimal_position_address = os.path.join(project_dir, config.get(obj_name, 'optimal_position_address'))
dim_size = config.getint(obj_name, 'dim_size')
dim_regs = eval(config.get(obj_name, 'dim_regs'))
budget = config.getint(obj_name, 'budget')
set_optimal_position(optimal_position_address)
func = noisy_function_dict[func_name]
repeat = 30
budget_list = np.arange(0, budget, budget/10) + budget/10
real_value = np.zeros((repeat, budget_list.shape[0]))
seed = 0
random.seed(seed)
np.random.seed(seed)
for i in range(repeat):
for j in range(len(budget_list)):
x = optimize(budget_list[j], func, dim_regs, dim_size)
real_value[i][j] = base_function_dict[args.objective](x)
log_address = os.path.join(project_dir, 'DEAP_exp/log/noisy/')
file_name = os.path.join(log_address, '{}_{}.txt'.format(obj_name, dim_size))
os.makedirs(log_address, exist_ok=True)
np.savetxt(file_name, real_value)
print(real_value[0])
def minimize(func_name):
config = configparser.ConfigParser()
config_name = os.path.join(project_dir, 'objective_function/config/scale.ini')
config.read(config_name, encoding='utf-8')
print(config.sections())
optimal_position_address_dir = os.path.join(project_dir, "objective_function/optimal_position")
dim_list = eval(config.get(func_name, 'dim_list'))
dim_regs = eval(config.get(func_name, 'dim_regs'))
repeat = 30
values = np.zeros((repeat, len(dim_list)))
seed = 0
random.seed(seed)
np.random.seed(seed)
speed_lim = dim_regs[1]/5
for i in range(repeat):
for j in range(len(dim_list)):
dim_size = dim_list[j]
creator.create("FitnessMin", base.Fitness, weights=(-1.0,))
creator.create("Particle", list, fitness=creator.FitnessMin, speed=list, smin=None, smax=None, best=None)
toolbox = base.Toolbox()
toolbox.register("particle", generate, size=dim_size, pmin=dim_regs[0], pmax=dim_regs[1], smin=-speed_lim, smax=speed_lim)
toolbox.register("population", tools.initRepeat, list, toolbox.particle)
toolbox.register("update", updateParticle, phi1=2.0, phi2=2.0)
toolbox.register("evaluate", lambda x: (function_dict[func_name](x), ))
optimal_position_address = os.path.join(optimal_position_address_dir, func_name, "{}_{}.txt".format(func_name, dim_size))
budget = dim_size * 100
set_optimal_position(optimal_position_address)
population = 10
pop = toolbox.population(n=population)
best = None
while get_cnt() < budget:
for part in pop:
part.fitness.values = toolbox.evaluate(part)
if not part.best or part.best.fitness < part.fitness:
part.best = creator.Particle(part)
part.best.fitness.values = part.fitness.values
if not best or best.fitness < part.fitness:
best = creator.Particle(part)
best.fitness.values = part.fitness.values
for part in pop:
toolbox.update(part, best)
values[i, j] = best.fitness.values[0]
clear_epoch()
print("finist repeat: {}, dim: {}, best f: {}".format(i, dim_size, best.fitness.values))
log_address = os.path.join(project_dir, 'DEAP_exp/log/scale/')
file_name = os.path.join(log_address, '{}.txt'.format(obj_name))
os.makedirs(log_address, exist_ok=True)
np.savetxt(file_name, values)
def minimize(func_name):
config = configparser.ConfigParser()
config_name = os.path.join(project_dir, 'objective_function/config/scale.ini')
config.read(config_name, encoding='utf-8')
print(config.sections())
optimal_position_address_dir = os.path.join(project_dir, "objective_function/optimal_position")
dim_list = eval(config.get(func_name, 'dim_list'))
dim_regs = eval(config.get(func_name, 'dim_regs'))
repeat = 30
values = np.zeros((repeat, len(dim_list)))
seed = 0
random.seed(seed)
np.random.seed(seed)
def fitness_func(solution, solution_idx):
# Calculating the fitness value of each solution in the current population.
output = -function_dict[obj_name](solution)
return output
fitness_function = fitness_func
for i in range(repeat):
for j in range(len(dim_list)):
num_genes = dim_size = dim_list[j]
gene_space = [{'low': dim_regs[0], 'high': dim_regs[1]}] * dim_size
if dim_list[j] == 20:
sol_per_pop = 20
num_parents_mating = 5
else:
sol_per_pop = 50
num_parents_mating = 7
budget = 100 * dim_size
num_generations = int(budget // sol_per_pop)
optimal_position_address = os.path.join(optimal_position_address_dir, func_name, "{}_{}.txt".format(func_name, dim_size))
set_optimal_position(optimal_position_address)
ga_instance = pygad.GA(num_generations=num_generations,
num_parents_mating=num_parents_mating,
fitness_func=fitness_function,
sol_per_pop=sol_per_pop,
gene_space=gene_space,
num_genes=num_genes)
ga_instance.run()
result = get_best_result()
clear_epoch()
print("finist repeat: {}, dim: {}, best f: {}".format(i, dim_size, result))
values[i, j] = result
log_address = os.path.join(project_dir, 'pygad_exp/log/scale/')
file_name = os.path.join(log_address, '{}.txt'.format(obj_name))
os.makedirs(log_address, exist_ok=True)
np.savetxt(file_name, values)
print(values)
def minimize(func_name):
config = configparser.ConfigParser()
config_name = os.path.join(project_dir,
'objective_function/config/low_dim.ini')
config.read(config_name, encoding='utf-8')
print(config.sections())
optimal_position_address = os.path.join(
project_dir, config.get(func_name, 'optimal_position_address'))
dim_size = config.getint(func_name, 'dim_size')
dim_regs = eval(config.get(func_name, 'dim_regs'))
budget = config.getint(func_name, 'budget')
repeat = 30
set_optimal_position(optimal_position_address)
seed = 0
random.seed(seed)
np.random.seed(seed)
for i in range(repeat):
init_pos = [
np.random.uniform(dim_regs[0], dim_regs[1])
for _ in range(dim_size)
]
es = cma.CMAEvolutionStrategy(init_pos, 0.5) # doctest: +ELLIPSIS
while get_cnt() < budget:
solutions = es.ask()
es.tell(solutions,
[function_cmaes_dict[func_name](x) for x in solutions])
es.logger.add()
epoch_first_items(budget)
append_all_epoch()
sol = es.result_pretty()
es.result_pretty()
all_epoch = np.array(get_all_epoch())
log_address = os.path.join(project_dir, 'pycma_exp/log/low_dim/')
file_name = os.path.join(log_address,
'{}_{}.txt'.format(obj_name, dim_size))
os.makedirs(log_address, exist_ok=True)
np.savetxt(file_name, all_epoch)
print(all_epoch.shape)
def minimize(func_name):
config = configparser.ConfigParser()
config_name = os.path.join(project_dir, 'objective_function/config/scale.ini')
config.read(config_name, encoding='utf-8')
print(config.sections())
optimal_position_address_dir = os.path.join(project_dir, "objective_function/optimal_position")
dim_list = eval(config.get(func_name, 'dim_list'))
dim_regs = eval(config.get(func_name, 'dim_regs'))
repeat = 20
values = np.zeros((repeat, len(dim_list)))
seed = 0
random.seed(seed)
np.random.seed(seed)
for i in range(repeat):
for j in range(len(dim_list)):
dim_size = dim_list[j]
optimal_position_address = os.path.join(optimal_position_address_dir, func_name, "{}_{}.txt".format(func_name, dim_size))
budget = dim_size * 100
set_optimal_position(optimal_position_address)
init_pos = [np.random.uniform(dim_regs[0], dim_regs[1]) for _ in range(dim_size)]
es = cma.CMAEvolutionStrategy(init_pos, 0.3) # doctest: +ELLIPSIS
while get_cnt() < budget:
print(get_cnt())
solutions = es.ask()
es.tell(solutions, [function_cmaes_dict[func_name](x) for x in solutions])
es.logger.add()
clear_epoch()
print("finist repeat: {}, dim: {}, best f: {}".format(i, dim_size, es.best.f))
values[i, j] = es.best.f
log_address = os.path.join(project_dir, 'pycma_exp/log/scale/')
file_name = os.path.join(log_address, '{}_2.txt'.format(obj_name))
os.makedirs(log_address, exist_ok=True)
np.savetxt(file_name, values)
print(values)
obj_name = args.objective
config = configparser.ConfigParser()
config_name = os.path.join(project_dir,
'objective_function/config/noisy.ini')
config.read(config_name, encoding='utf-8')
print(config.sections())
optimal_position_address = os.path.join(
project_dir, config.get(obj_name, 'optimal_position_address'))
dim_size = config.getint(obj_name, 'dim_size')
dim_regs = eval(config.get(obj_name, 'dim_regs'))
budget = config.getint(obj_name, 'budget')
repeat = 30
set_optimal_position(optimal_position_address)
seed = 0
budget_list = np.arange(0, budget, budget / 10) + budget / 10
random.seed(seed)
np.random.seed(seed)
real_value = np.zeros((repeat, budget_list.shape[0]))
for i in range(repeat):
for j in range(len(budget_list)):
func = noisy_function_cmaes_dict[obj_name]
if args.noise_handling is True:
best_x = optimize_with_noise_handler(budget_list[j], func,
dim_regs, dim_size)
else:
best_x = optimize_without_noise_handler(
budget_list[j], func, dim_regs, dim_size)
real_value[i][j] = base_function_dict[args.objective](best_x)
def minimize(func_name):
config = configparser.ConfigParser()
config_name = os.path.join(project_dir,
'objective_function/config/low_dim.ini')
config.read(config_name, encoding='utf-8')
print(config.sections())
optimal_position_address = os.path.join(
project_dir, config.get(obj_name, 'optimal_position_address'))
dim_size = config.getint(obj_name, 'dim_size')
dim_regs = eval(config.get(obj_name, 'dim_regs'))
budget = config.getint(obj_name, 'budget')
set_optimal_position(optimal_position_address)
repeat = 30
seed = 0
random.seed(seed)
np.random.seed(seed)
speed_lim = dim_regs[1] / 5
creator.create("FitnessMin", base.Fitness, weights=(-1.0, ))
creator.create("Particle",
list,
fitness=creator.FitnessMin,
speed=list,
smin=None,
smax=None,
best=None)
toolbox = base.Toolbox()
toolbox.register("particle",
generate,
size=dim_size,
pmin=dim_regs[0],
pmax=dim_regs[1],
smin=-speed_lim,
smax=speed_lim)
toolbox.register("population", tools.initRepeat, list, toolbox.particle)
toolbox.register("update", updateParticle, phi1=2.0, phi2=2.0)
toolbox.register("evaluate", lambda x: (function_dict[func_name](x), ))
for i in range(repeat):
fmin = []
population = 10
pop = toolbox.population(n=population)
best = None
i = 0
while get_cnt() < budget:
for part in pop:
part.fitness.values = toolbox.evaluate(part)
if not part.best or part.best.fitness < part.fitness:
part.best = creator.Particle(part)
part.best.fitness.values = part.fitness.values
if not best or best.fitness < part.fitness:
best = creator.Particle(part)
best.fitness.values = part.fitness.values
for part in pop:
toolbox.update(part, best)
epoch_first_items(budget)
append_all_epoch()
all_epoch = np.array(get_all_epoch())
log_address = os.path.join(project_dir, 'DEAP_exp/log/low_dim/')
file_name = os.path.join(log_address,
'{}_{}.txt'.format(obj_name, dim_size))
os.makedirs(log_address, exist_ok=True)
np.savetxt(file_name, all_epoch)
print(all_epoch.shape)
