def rollout_with_model_performance(self, history=[]):
value_to_choose = []
list_configuration_to_choose = []
list_rollout = []
st_time = time.time()
try:
configs = self.config_space.sample_partial_configuration(
history, 500)
list_configuration_to_choose = [
np.nan_to_num(c.get_array()) for c in configs
]
except Exception as e:
pass
mu, sigma = self.score_model.get_mu_sigma_from_rf(
np.array(list_configuration_to_choose), self.score_model.model)
ei_values = expected_improvement(mu, sigma,
self.bestconfig["validation_score"])
mu_time, sigma_time = self.score_model.get_mu_sigma_from_rf(
np.array(list_configuration_to_choose),
self.score_model.model_of_time)
ei_values = [
ei if mu_t < (self.cpu_time_in_s) else -1000
for ei, mu_t in zip(ei_values, mu_time)
]
id_max = np.argmax(ei_values)
return configs[id_max]
def rollout_in_expert_neighborhood(self, history=[]):
try:
expert, _ = self.experts[tuple(history)]
except:
expert = self.config_space.sample_partial_configuration_with_default(
history)
try:
#configs = [c_f for c_f in
# [get_one_exchange_neighbourhood_with_history(c_i, self.seed, history) for c_i in
# [c for c in get_one_exchange_neighbourhood_with_history(expert, self.seed, history)]
#]]
configs = []
# print("[Rollout] Neighborhood on", expert.get_dictionary())
for c in get_one_exchange_neighbourhood_with_history(
expert, self.seed, history):
tmp_ = list(
get_one_exchange_neighbourhood_with_history(
c, self.seed, history))
configs.extend(tmp_)
configs.extend(
self.config_space.sample_partial_configuration(history, 1000))
list_configuration_to_choose = np.nan_to_num(
np.array([c.get_array() for c in configs]))
except Exception as e:
raise (e)
mu_loc, sigma_loc = self.score_model.get_mu_sigma_from_rf(
np.array(list_configuration_to_choose), "local")
ei_values = expected_improvement(mu_loc, sigma_loc,
self.bestconfig["validation_score"])
#mu_time, sigma_time = self.score_model.get_mu_sigma_from_rf(np.array(list_configuration_to_choose), "time")
#if self.is_metalearning:
# beta = self.get_beta()
# mu_gen, sigma_gen = self.score_model.get_mu_sigma_from_rf(np.array(list_configuration_to_choose), "general")
#ei_values = np.array([mu_gener * beta + (1 - beta) * mu_local for mu_gener, mu_local in zip(mu, mu_loc)])
id_max = (-ei_values).argsort()[:3]
return [configs[np.random.choice(id_max)] for _ in range(3)]