def fetch_candidate_configurations(self, num_config):
if len(self.target_y[self.iterate_r[-1]]) == 0:
return sample_configurations(self.config_space, num_config)
incumbent = dict()
max_r = self.iterate_r[-1]
# The lower, the better.
best_index = np.argmin(self.target_y[max_r])
incumbent['config'] = self.target_x[max_r][best_index]
approximate_obj = self.weighted_surrogate.predict(convert_configurations_to_array([incumbent['config']]))[0]
incumbent['obj'] = approximate_obj
self.weighted_acquisition_func.update(model=self.weighted_surrogate, eta=incumbent)
config_candidates = self.weighted_acq_optimizer.maximize(batch_size=num_config)
p_threshold = 0.3
n_acq = self.eta * self.eta
if num_config <= n_acq:
return config_candidates
candidates = config_candidates[: n_acq]
idx_acq = n_acq
for _id in range(num_config - n_acq):
if rd.random() < p_threshold:
config = sample_configurations(self.config_space, 1)[0]
else:
config = config_candidates[idx_acq]
idx_acq += 1
candidates.append(config)
return candidates
def maximize(self, batch_size=1):
"""
Maximizes the given acquisition function.
Parameters
----------
batch_size: number of maximizer returned.
Returns
-------
np.ndarray(N,D)
Point with highest acquisition value.
"""
incs_configs = list(
get_one_exchange_neighbourhood(self.objective_func.eta['config'],
seed=self.rng.randint(int(1e6))))
configs_list = list(incs_configs)
rand_incs = convert_configurations_to_array(configs_list)
# Sample random points uniformly over the whole space
rand_configs = sample_configurations(
self.config_space, self.n_samples - rand_incs.shape[0])
rand = convert_configurations_to_array(rand_configs)
configs_list.extend(rand_configs)
X = np.concatenate((rand_incs, rand), axis=0)
y = self.objective_func(X).flatten()
candidate_idxs = list(np.argsort(-y)[:batch_size])
# print(candidate_idxs)
# print(type(candidate_idxs))
# print(configs_list[:5])
return [configs_list[idx] for idx in candidate_idxs]
def _iterate(self, s, budget=MAX_INT, skip_last=0):
# Set initial number of configurations
n = int(ceil(self.B / self.R / (s + 1) * self.eta**s))
# initial number of iterations per config
r = int(self.R * self.eta**(-s))
# Choose a batch of configurations in different mechanisms.
start_time = time.time()
T = sample_configurations(self.config_space, n)
time_elapsed = time.time() - start_time
self.logger.info(
"Choosing next batch of configurations took %.2f sec." %
time_elapsed)
with ParallelProcessEvaluator(self.eval_func,
n_worker=self.n_workers) as executor:
for i in range((s + 1) - int(skip_last)): # changed from s + 1
if time.time() >= budget + start_time:
break
# Run each of the n configs for <iterations>
# and keep best (n_configs / eta) configurations
n_configs = n * self.eta**(-i)
n_resource = r * self.eta**i
self.logger.info(
"MFSE: %d configurations x size %d / %d each" %
(int(n_configs), n_resource, self.R))
val_losses = executor.parallel_execute(
T,
resource_ratio=float(n_resource / self.R),
eta=self.eta,
first_iter=(i == 0))
for _id, _val_loss in enumerate(val_losses):
if np.isfinite(_val_loss):
self.target_x[int(n_resource)].append(T[_id])
self.target_y[int(n_resource)].append(_val_loss)
self.exp_output[time.time()] = (int(n_resource), T, val_losses)
if int(n_resource) == self.R:
self.incumbent_configs.extend(T)
self.incumbent_perfs.extend(val_losses)
# Select a number of best configurations for the next loop.
# Filter out early stops, if any.
indices = np.argsort(val_losses)
if len(T) >= self.eta:
T = [T[i] for i in indices]
reduced_num = int(n_configs / self.eta)
T = T[0:reduced_num]
else:
T = [T[indices[0]]]
def choose_next(self, X: np.ndarray, Y: np.ndarray):
_config_num = X.shape[0]
if _config_num < self.init_num:
if self.initial_configurations is None:
if _config_num == 0:
return self.config_space.get_default_configuration()
else:
return sample_configurations(self.config_space, 1)[0]
else:
return self.initial_configurations[_config_num]
else:
if self.surrogate_model == 'tpe':
config, _ = self.model.get_config()
elif self.surrogate_model == 'random_search':
config = sample_configurations(self.config_space, 1)[0]
else:
raise ValueError('Invalid surrogate model - %s.' %
self.surrogate_model)
return config
def smac_get_candidate_configurations(self, num_config):
if len(self.target_y[self.iterate_r[-1]]) <= 3:
return sample_configurations(self.config_space, num_config)
incumbent = dict()
max_r = self.iterate_r[-1]
best_index = np.argmin(self.target_y[max_r])
incumbent['config'] = self.target_x[max_r][best_index]
incumbent['obj'] = self.target_y[max_r][best_index]
self.acquisition_func.update(model=self.surrogate, eta=incumbent)
config_candidates = self.acq_optimizer.maximize(batch_size=num_config)
p_threshold = 0.3
candidates = list()
idx_acq = 0
for _id in range(num_config):
if rd.random() < p_threshold or _id >= len(config_candidates):
config = sample_configurations(self.config_space, 1)[0]
else:
config = config_candidates[idx_acq]
idx_acq += 1
candidates.append(config)
return candidates
def baseline_get_candidate_configurations(self, num_config):
config_candidates = list()
config_left = num_config
while config_left:
config = self.config_gen.get_config()[0]
if config in config_candidates:
continue
config_candidates.append(config)
config_left -= 1
p_threshold = 0.3
candidates = list()
idx_acq = 0
for _id in range(num_config):
if rd.random() < p_threshold:
config = sample_configurations(self.config_space, 1)[0]
else:
config = config_candidates[idx_acq]
idx_acq += 1
candidates.append(config)
return candidates
def sample_configs_for_archs(self,
include_architectures,
N,
sampling_strategy='uniform'):
configs = list()
for _arch in include_architectures:
_cs = self.get_model_config_space(_arch)
if sampling_strategy == 'uniform':
configs.append(_cs.get_default_configuration())
configs.extend(sample_configurations(_cs, N - 1))
elif sampling_strategy == 'bohb':
if _arch not in self.tpe_config_gen:
self.tpe_config_gen[_arch] = TPE(_cs)
config_candidates = list()
config_left = N
while config_left:
config = self.tpe_config_gen[_arch].get_config()[0]
if config in config_candidates:
continue
config_candidates.append(config)
config_left -= 1
p_threshold = 0.3
idx_acq = 0
for _id in range(N):
if rd.random() < p_threshold:
config = sample_configurations(_cs, 1)[0]
else:
config = config_candidates[idx_acq]
idx_acq += 1
configs.append(config)
else: # mfse
if _arch not in self.mfse_config_gen:
types, bounds = get_types(_cs)
init_weight = [1. / self.s_max] * self.s_max + [0.]
self.mfse_config_gen[_arch] = dict()
self.mfse_config_gen[_arch][
'surrogate'] = WeightedRandomForestCluster(
types, bounds, self.s_max, self.eta, init_weight,
'gpoe')
acq_func = EI(
model=self.mfse_config_gen[_arch]['surrogate'])
self.mfse_config_gen[_arch][
'acq_optimizer'] = RandomSampling(
acq_func,
_cs,
n_samples=2000,
rng=np.random.RandomState(1))
if self.R not in self.eval_hist_perfs[_arch] or len(
self.eval_hist_perfs[_arch][self.R]) == 0:
configs.extend(sample_configurations(_cs, N))
continue
incumbent = dict()
max_r = self.R
# The lower, the better.
best_index = np.argmin(self.eval_hist_perfs[_arch][max_r])
incumbent['config'] = self.eval_hist_configs[_arch][max_r][
best_index]
approximate_obj = self.mfse_config_gen[_arch][
'surrogate'].predict(
convert_configurations_to_array([incumbent['config']
]))[0]
incumbent['obj'] = approximate_obj
self.mfse_config_gen[_arch]['acq_optimizer'].update(
model=self.mfse_config_gen[_arch]['surrogate'],
eta=incumbent)
config_candidates = self.mfse_config_gen[_arch][
'acq_optimizer'].maximize(batch_size=N)
p_threshold = 0.3
n_acq = self.eta * self.eta
if N <= n_acq:
return config_candidates
candidates = config_candidates[:n_acq]
idx_acq = n_acq
for _id in range(N - n_acq):
if rd.random() < p_threshold:
config = sample_configurations(_cs, 1)[0]
else:
config = config_candidates[idx_acq]
idx_acq += 1
candidates.append(config)
return candidates
return configs
def sample_configs_for_archs(self, include_architectures, N):
configs = list()
for _arch in include_architectures:
_cs = self.get_model_config_space(_arch)
configs.extend(sample_configurations(_cs, N))
return configs