def test_judge_match(self):
size = 3
regs = [[1, 5], [-1, 1], [1, 2]]
tys = [True, True, True]
assert Dimension2.judge_match(size, regs, tys) == True
tys = [True, True]
assert Dimension2.judge_match(size, regs, tys) == False
def test_set_regions(self):
dim = Dimension2([(ValueType.CONTINUOUS, [1, 2], 1e-6),
(ValueType.CONTINUOUS, [2, 3], 1e-6)])
dim.set_regions([[-1, 1], [-1, 1]], [True, True])
assert dim.equal(
Dimension2([(ValueType.CONTINUOUS, [-1, 1], 1e-6),
(ValueType.CONTINUOUS, [-1, 1], 1e-6)]))
def test_limited_space(self):
dim1 = Dimension2([(ValueType.CONTINUOUS, [-1, 1], 1e-6),
(ValueType.CONTINUOUS, [-1, 1], 1e-6)])
limited, number = dim1.limited_space()
assert limited is False and number == 0
dim2 = Dimension2([(ValueType.DISCRETE, [-1, 1], False),
(ValueType.DISCRETE, [-1, 1], False)])
limited, number = dim2.limited_space()
assert limited is True and number == 9
def test_merge_dim(self):
dim_list1 = [(ValueType.CONTINUOUS, [1, 2], 1e-6)]
dim_list2 = [(ValueType.CONTINUOUS, [1, 2], 1e-6),
(ValueType.CONTINUOUS, [2, 3], 1e-6)]
dim1 = Dimension2(dim_list1)
dim2 = Dimension2(dim_list2)
dim3 = Dimension2.merge_dim(dim1, dim2)
assert dim3.equal(
Dimension2([(ValueType.CONTINUOUS, [1, 2], 1e-6),
(ValueType.CONTINUOUS, [1, 2], 1e-6),
(ValueType.CONTINUOUS, [2, 3], 1e-6)]))
def test_performance(self):
ackley_noise_func = ackley_noise_creator(0, 0.1)
dim_size = 100 # dimensions
one_dim = (ValueType.CONTINUOUS, [-1, 1], 1e-6)
dim_list = [(one_dim)] * dim_size
dim = Dimension2(dim_list) # form up the dimension object
objective = Objective(ackley_noise_func,
dim) # form up the objective function
budget = 20000 # 20*dim_size # number of calls to the objective function
# suppression=True means optimize with value suppression, which is a noise handling method
# resampling=True means optimize with re-sampling, which is another common used noise handling method
# non_update_allowed=500 and resample_times=100 means if the best solution doesn't change for 500 budgets,
# the best solution will be evaluated repeatedly for 100 times
# balance_rate is a parameter for exponential weight average of several evaluations of one sample.
parameter = Parameter(budget=budget,
noise_handling=True,
suppression=True,
non_update_allowed=200,
resample_times=50,
balance_rate=0.5)
# parameter = Parameter(budget=budget, noise_handling=True, resampling=True, resample_times=10)
parameter.set_positive_size(5)
sol = Opt.min(objective, parameter)
assert sol.get_value() < 4
def test_racos_performance2(self):
# continuous
dim = 100 # dimension
one_dim = (ValueType.CONTINUOUS, [-1, 1], 1e-6)
dim_list = [(one_dim)] * dim
objective = Objective(ackley, Dimension2(dim_list)) # setup objective
parameter = Parameter(budget=100 * dim, sequential=False, seed=1)
solution = ExpOpt.min(objective, parameter)[0]
assert solution.get_value() < 0.2
dim = 500
dim_list = [(one_dim)] * dim
objective = Objective(ackley, Dimension2(dim_list)) # setup objective
parameter = Parameter(budget=10000, sequential=False, seed=1)
sol = Opt.min(objective, parameter)
sol.print_solution()
assert solution.get_value() < 2
# discrete
# setcover
problem = SetCover()
dim_size = 20
one_dim = (ValueType.DISCRETE, [0, 1], False)
dim_list = [(one_dim)] * dim_size
dim = Dimension2(dim_list) # the dim is prepared by the class
objective = Objective(problem.fx,
dim) # form up the objective function
budget = 100 * dim.get_size(
) # number of calls to the objective function
parameter = Parameter(budget=budget, sequential=False, seed=777)
sol = Opt.min(objective, parameter)
sol.print_solution()
assert sol.get_value() < 2
# sphere
dim_size = 100 # dimensions
one_dim = (ValueType.DISCRETE, [-10, 10], True)
dim_list = [(one_dim)] * dim_size
dim = Dimension2(dim_list) # form up the dimension object
objective = Objective(sphere_discrete_order,
dim) # form up the objective function
parameter = Parameter(budget=10000, sequential=False, seed=77)
sol = Opt.min(objective, parameter)
sol.print_solution()
assert sol.get_value() < 200
def __init__(self,
algo="asracos",
budget=None,
dim_dict=None,
max_concurrent=10,
metric="episode_reward_mean",
mode="min",
**kwargs):
assert budget is not None, "`budget` should not be None!"
assert dim_dict is not None, "`dim_list` should not be None!"
assert type(max_concurrent) is int and max_concurrent > 0
assert mode in ["min", "max"], "`mode` must be 'min' or 'max'!"
_algo = algo.lower()
assert _algo in ["asracos", "sracos"
], "`algo` must be in ['asracos', 'sracos'] currently"
self._max_concurrent = max_concurrent
self._metric = metric
if mode == "max":
self._metric_op = -1.
elif mode == "min":
self._metric_op = 1.
self._live_trial_mapping = {}
self._dim_keys = []
_dim_list = []
for k in dim_dict:
self._dim_keys.append(k)
_dim_list.append(dim_dict[k])
dim = Dimension2(_dim_list)
par = Parameter(budget=budget)
if _algo == "sracos" or _algo == "asracos":
self.optimizer = SRacosTune(dimension=dim, parameter=par)
self.solution_dict = {}
self.best_solution_list = []
super(ZOOptSearch, self).__init__(
metric=self._metric, mode=mode, **kwargs)
'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')
repeat = 30
set_optimal_position(optimal_position_address)
seed = 0
gl.set_seed(seed)
for i in range(repeat):
dim = Dimension2([(ValueType.CONTINUOUS, dim_regs, 1e-6)] * dim_size)
# dim = Dimension(dim_size, [dim_regs] * dim_size, [True] * dim_size)
objective = Objective(lambda sol: function_dict[obj_name](sol.get_x()),
dim) # form up the objective function
parameter = Parameter(budget=budget,
intermediate_result=True,
intermediate_freq=100)
sol = Opt.min(objective, parameter)
append_all_epoch()
all_epoch = np.array(get_all_epoch())
log_address = os.path.join(project_dir, 'ZOOpt_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 test_is_discrete(self):
dim1 = Dimension2([(ValueType.CONTINUOUS, [-1, 1], 1e-6),
(ValueType.DISCRETE, [-1, 1], False)])
assert dim1.is_discrete() is False
def test_copy_region(self):
dim1 = Dimension2([(ValueType.CONTINUOUS, [-1, 1], 1e-6),
(ValueType.CONTINUOUS, [-1, 1], 1e-6)])
region = dim1.copy_region()
assert region == [[-1, 1], [-1, 1]]
def test_deep_copy(self):
dim1 = Dimension2([(ValueType.CONTINUOUS, [-1, 1], 1e-6),
(ValueType.CONTINUOUS, [-1, 1], 1e-6)])
dim2 = dim1.deep_copy()
assert dim1.equal(dim2)