def maximize(self,
runhistory: HistoryContainer,
initial_config=None,
**kwargs) -> List[Tuple[float, Configuration]]:
def negative_acquisition(x):
# shape of x = (d,)
return -self.acquisition_function(x,
convert=False)[0] # shape=(1,)
acq_configs = []
result = scipy.optimize.differential_evolution(
func=negative_acquisition, bounds=self.bounds)
if not result.success:
self.logger.debug(
'Scipy differential evolution optimizer failed. Info:\n%s' %
(result, ))
try:
config = Configuration(self.config_space, vector=result.x)
acq = self.acquisition_function(result.x, convert=False)
acq_configs.append((acq, config))
except Exception:
pass
if not acq_configs: # empty
self.logger.warning(
'Scipy differential evolution optimizer failed. Return empty config list. Info:\n%s'
% (result, ))
challengers = ChallengerList([config for _, config in acq_configs],
self.config_space, self.random_chooser)
self.random_chooser.next_smbo_iteration()
return challengers
def evaluate(mth, run_i, seed):
print(mth, run_i, seed, '===== start =====', flush=True)
def objective_function(config):
y = problem.evaluate_config(config)
return y
from cma import CMAEvolutionStrategy
from litebo.utils.util_funcs import get_types
from litebo.utils.config_space import Configuration
types, bounds = get_types(cs)
assert all(types == 0)
# Check Constant Hyperparameter
const_idx = list()
for i, bound in enumerate(bounds):
if np.isnan(bound[1]):
const_idx.append(i)
hp_num = len(bounds) - len(const_idx)
es = CMAEvolutionStrategy(hp_num * [0], 0.99, inopts={'bounds': [0, 1], 'seed': seed})
global_start_time = time.time()
global_trial_counter = 0
config_list = []
perf_list = []
time_list = []
eval_num = 0
while eval_num < max_runs:
X = es.ask(number=es.popsize)
_X = X.copy()
for i in range(len(_X)):
for index in const_idx:
_X[i] = np.insert(_X[i], index, 0) # np.insert returns a copy
# _X = np.asarray(_X)
values = []
for xi in _X:
# convert array to Configuration
config = Configuration(cs, vector=xi)
perf = objective_function(config)
global_time = time.time() - global_start_time
global_trial_counter += 1
values.append(perf)
print('=== CMAES Trial %d: %s perf=%f global_time=%f' % (global_trial_counter, config, perf, global_time))
config_list.append(config)
perf_list.append(perf)
time_list.append(global_time)
values = np.reshape(values, (-1,))
es.tell(X, values)
eval_num += es.popsize
print('===== Total evaluation times=%d. Truncate to max_runs=%d.' % (eval_num, max_runs))
config_list = config_list[:max_runs]
perf_list = perf_list[:max_runs]
time_list = time_list[:max_runs]
return config_list, perf_list, time_list
def maximize(self, runhistory: HistoryContainer, num_points: int,
**kwargs) -> Iterable[Tuple[float, Configuration]]:
try:
from cma import CMAEvolutionStrategy
except ImportError:
raise ImportError("Package cma is not installed!")
types, bounds = get_types(self.config_space)
assert all(types == 0)
# Check Constant Hyperparameter
const_idx = list()
for i, bound in enumerate(bounds):
if np.isnan(bound[1]):
const_idx.append(i)
hp_num = len(bounds) - len(const_idx)
es = CMAEvolutionStrategy(hp_num * [0],
0.99,
inopts={'bounds': [0, 1]})
eval_num = 0
next_configs_by_acq_value = list()
while eval_num < num_points:
X = es.ask(number=es.popsize)
_X = X.copy()
for i in range(len(_X)):
for index in const_idx:
_X[i] = np.insert(_X[i], index, 0)
_X = np.asarray(_X)
values = self.acquisition_function._compute(_X)
values = np.reshape(values, (-1, ))
es.tell(X, values)
next_configs_by_acq_value.extend([(values[i], _X[i])
for i in range(es.popsize)])
eval_num += es.popsize
next_configs_by_acq_value.sort(reverse=True, key=lambda x: x[0])
next_configs_by_acq_value = [_[1] for _ in next_configs_by_acq_value]
next_configs_by_acq_value = [
Configuration(self.config_space, vector=array)
for array in next_configs_by_acq_value
]
challengers = ChallengerList(next_configs_by_acq_value,
self.config_space, self.random_chooser)
self.random_chooser.next_smbo_iteration()
return challengers
def maximize(
self,
runhistory: HistoryContainer,
num_points: int, # useless in USeMO
**kwargs) -> Iterable[Configuration]:
"""Maximize acquisition function using ``_maximize``.
Parameters
----------
runhistory: ~litebo.utils.history_container.HistoryContainer
runhistory object
num_points: int
number of points to be sampled
**kwargs
passed to acquisition function
Returns
-------
Iterable[Configuration]
to be concrete: ~litebo.ei_optimization.ChallengerList
"""
acq_vals = np.asarray(self.acquisition_function.uncertainties)
candidates = np.asarray(self.acquisition_function.candidates)
assert len(acq_vals.shape) == 1 and len(candidates.shape) == 2 \
and acq_vals.shape[0] == candidates.shape[0]
acq_configs = []
for i in range(acq_vals.shape[0]):
# convert array to Configuration todo
config = Configuration(self.config_space, vector=candidates[i])
acq_configs.append((acq_vals[i], config))
# shuffle for random tie-break
self.rng.shuffle(acq_configs)
# sort according to acq value
acq_configs.sort(reverse=True, key=lambda x: x[0])
configs = [_[1] for _ in acq_configs]
challengers = ChallengerList(configs, self.config_space,
self.random_chooser)
self.random_chooser.next_smbo_iteration()
return challengers
def generate(self, return_config=True):
"""
Create samples in the domain specified during construction.
Returns
-------
configs : list
List of N sampled configurations within domain. (return_config is True)
X : array, shape (N, D)
Design matrix X in the specified domain. (return_config is False)
"""
X = self._generate()
X = self.lower_bounds + (self.upper_bounds - self.lower_bounds) * X
if return_config:
configs = [Configuration(self.config_space, vector=x) for x in X]
return configs
else:
return X
def update_observation(request):
"""
Update observation in config advisor.
Parameters
----------
request : a dict
Returns
-------
a readable information string in HttpResponse form
"""
if request.method == 'POST':
if request.POST:
task_id = request.POST.get('task_id')
config_advisor = advisor_dict[task_id]
config_dict = json.loads(request.POST.get('config'))
config = Configuration(config_advisor.config_space, config_dict)
trial_state = int(request.POST.get('trial_state'))
constraints = json.loads(request.POST.get('constraints'))
objs = json.loads(request.POST.get('objs'))
observation = Observation(config, trial_state, constraints, objs)
config_advisor.update_observation(observation)
config_advisor.save_history()
# print('config history', config_advisor.load_history_from_json())
print('-' * 21)
print('Update observation')
print(observation)
print('-' * 21)
return HttpResponse('[bo_advice/views.py] update SUCCESS')
else:
return HttpResponse('[bo_advice/views.py] error3')
else:
return HttpResponse('[bo_advice/views.py] error4')
def maximize(
self,
runhistory: HistoryContainer,
num_points: int, # todo useless
**kwargs) -> Iterable[Configuration]:
"""Maximize acquisition function using ``_maximize``.
Parameters
----------
runhistory: ~litebo.utils.history_container.HistoryContainer
runhistory object
num_points: int
number of points to be sampled
**kwargs
passed to acquisition function
Returns
-------
Iterable[Configuration]
to be concrete: ~litebo.ei_optimization.ChallengerList
"""
def inverse_acquisition(x):
# shape of x = (d,)
return -self.acquisition_function(x,
convert=False)[0] # shape=(1,)
d = len(self.config_space.get_hyperparameters())
bound = (0.0, 1.0) # todo only on continuous dims (int, float) now
bounds = [bound] * d
acq_configs = []
# MC
x_tries = self.rng.uniform(bound[0], bound[1], size=(self.num_mc, d))
acq_tries = self.acquisition_function(x_tries, convert=False)
for i in range(x_tries.shape[0]):
# convert array to Configuration
config = Configuration(self.config_space, vector=x_tries[i])
config.origin = 'Random Search'
acq_configs.append((acq_tries[i], config))
# L-BFGS-B
x_seed = self.rng.uniform(low=bound[0],
high=bound[1],
size=(self.num_opt, d))
for i in range(x_seed.shape[0]):
x0 = x_seed[i].reshape(1, -1)
result = self.minimizer(inverse_acquisition,
x0=x0,
method='L-BFGS-B',
bounds=bounds)
if not result.success:
continue
# convert array to Configuration
config = Configuration(self.config_space, vector=result.x)
config.origin = 'Scipy'
acq_val = self.acquisition_function(result.x, convert=False) # [0]
acq_configs.append((acq_val, config))
# shuffle for random tie-break
self.rng.shuffle(acq_configs)
# sort according to acq value
acq_configs.sort(reverse=True, key=lambda x: x[0])
configs = [_[1] for _ in acq_configs]
challengers = ChallengerList(configs, self.config_space,
self.random_chooser)
self.random_chooser.next_smbo_iteration()
return challengers
def maximize(
self,
runhistory: HistoryContainer,
num_points: int, # todo useless
**kwargs) -> List[Tuple[float, Configuration]]:
# print('start optimize') # todo remove
# import time
# t0 = time.time()
acq_configs = []
# random points
random_points = self.rng.uniform(self.bound[0],
self.bound[1],
size=(self.num_random, self.dim))
acq_random = self.acquisition_function(random_points, convert=False)
for i in range(random_points.shape[0]):
# convert array to Configuration
config = Configuration(self.config_space, vector=random_points[i])
config.origin = 'Random Search'
acq_configs.append((acq_random[i], config))
# scipy points
initial_points = self.gen_initial_points(
num_restarts=self.num_restarts, raw_samples=self.raw_samples)
for start_idx in range(0, self.num_restarts, self.batch_limit):
end_idx = min(start_idx + self.batch_limit, self.num_restarts)
# optimize using random restart optimization
scipy_points = self.gen_batch_scipy_points(
initial_points[start_idx:end_idx])
if scipy_points is None:
continue
acq_scipy = self.acquisition_function(scipy_points, convert=False)
for i in range(scipy_points.shape[0]):
# convert array to Configuration
config = Configuration(self.config_space,
vector=scipy_points[i])
config.origin = 'Batch Scipy'
acq_configs.append((acq_scipy[i], config))
# shuffle for random tie-break
self.rng.shuffle(acq_configs)
# sort according to acq value
acq_configs.sort(reverse=True, key=lambda x: x[0])
configs = [_[1] for _ in acq_configs]
challengers = ChallengerList(configs, self.config_space,
self.random_chooser)
self.random_chooser.next_smbo_iteration()
# t1 = time.time() # todo remove
# print('==time total=%.2f' % (t1-t0,))
# for x1 in np.linspace(0, 1, 20):
# optimal_point = np.array([x1.item()] + [0.5] * (self.dim-1))
# print('optimal_point acq=', self.acquisition_function(optimal_point, convert=False))
# print('best point acq=', acq_configs[0])
# time.sleep(2)
return challengers
# Send task id and config space at register
task_id = time.time()
townsend_params = {'float': {'x1': (-2.25, 2.5, 0), 'x2': (-2.5, 1.75, 0)}}
townsend_cs = ConfigurationSpace()
townsend_cs.add_hyperparameters([
UniformFloatHyperparameter(e, *townsend_params['float'][e])
for e in townsend_params['float']
])
# Create remote advisor
config_advisor = RemoteAdvisor(townsend_cs,
'127.0.0.1',
8000,
num_constraints=1,
random_state=1)
# Simulate 50 iterations
for _ in range(20):
config_dict = config_advisor.get_suggestion()
config = Configuration(config_advisor.config_space, config_dict)
obs = townsend(config)
config_advisor.update_observation(config_dict,
obs['objs'],
obs['constraints'],
trial_state=SUCCESS)
incumbents, history = config_advisor.get_result()
print(incumbents)
seed = 123
# Evaluate mth
X_init = np.array([
[6.66666667e-01, 3.33333333e-01],
[3.33333333e-01, 6.66666667e-01],
[2.22222222e-01, 2.22222222e-01],
[7.77777778e-01, 7.77777778e-01],
[5.55555556e-01, 0],
[0, 5.55555556e-01],
[1.00000000e+00, 4.44444444e-01],
[4.44444444e-01, 1.00000000e+00],
[8.88888889e-01, 1.11111111e-01],
[1.11111111e-01, 8.88888889e-01],
]) # use latin hypercube
X_init = [Configuration(cs, vector=X_init[i]) for i in range(X_init.shape[0])]
bo = SMBO(
multi_objective_func,
cs,
num_objs=num_objs,
max_runs=max_runs,
# surrogate_type='gp_rbf', # use default
acq_type=mth,
# initial_configurations=X_init, initial_runs=10,
time_limit_per_trial=60,
task_id='mo',
random_state=seed)
bo.config_advisor.optimizer.random_chooser.prob = rand_prob # set rand_prob, default 0
bo.config_advisor.acquisition_function.sample_num = sample_num # set sample_num
bo.config_advisor.acquisition_function.random_state = seed # set random_state
