def target(learning_rate_power, dropout, loss_function):
global bo
if bo != -1:
pickle.dump(bo, open(bo_path, "wb"))
# return (1 - (learning_rate_power - .6) ** 2) * (1 - (dropout - .2) ** 2) * (1 - (art_fraction - .2) ** 2)
domains = {
# 'unet_depth': (3, 5),
'learning_rate_power': (-5, -3),
# 'patch_size_factor': (1, 6),
'dropout': (0, 1),
# 'art_fraction': (0, 1),
# 'feature_map_inc_rate': (1., 2.),
'loss_function': (0, 1)
}
# print(domains.keys())
hp = {}
for k in domains.keys():
mx = domains[k][1]
mn = domains[k][0]
new_value = mn + (mx - mn) * eval(k)
hp[k] = new_value
print(' '.join(list(hp.keys())))
print(' '.join([str(i) for i in list(hp.values())]))
# return hp['unet_depth'] * hp['learning_rate_power'] * hp['patch_size_factor'] * hp['dropout'] * \
# hp['feature_map_inc_rate'] * -1 * hp['loss_function']
s = Settings()
# hp['unet_depth'] = int(round(hp['unet_depth']))
# hp['patch_size_factor'] = int(round(hp['patch_size_factor']))
loc = locals()
args_name = [arg for arg in inspect.getfullargspec(target).args]
model_nr = pickle.load(open(nr_steps_path, "rb")) + 1
s.MODEL_NAME = MAIN_FOLDER + str(model_nr)
s.VALTEST_MODEL_NAMES = [s.MODEL_NAME]
s.DROPOUT = hp['dropout']
s.LEARNING_RATE = math.pow(10, hp['learning_rate_power'])
# s.ART_FRACTION = hp['art_fraction']
# s.UNET_DEPTH = hp['unet_depth']
# s.PATCH_SIZE = (1, hp['patch_size_factor'] * 64, hp['patch_size_factor'] * 64)
# s.FEATURE_MAP_INC_RATE = hp['feature_map_inc_rate']
s.LOSS_FUNCTION = 'dice' if hp['loss_function'] < .5 else 'weighted_binary_cross_entropy'
# s.NR_DIM = int(round(hp['nr_dim']))
# if s.NR_DIM == 3:
# s.PATCH_SIZE = (3, hp['patch_size_factor'] * 32, hp['patch_size_factor'] * 32)
# elif s.NR_DIM == 2:
# s.PATCH_SIZE = (1, hp['patch_size_factor'] * 64, hp['patch_size_factor'] * 64)
# else:
# raise Exception('Wrong number of dimensions: {}'.format(s.NR_DIM))
with suppress_stdout():
h = Helper(s)
Train(s, h).train()
metric_means, metric_sds = Test(s, h).test()
pickle.dump(model_nr, open(nr_steps_path, "wb"))
return metric_means[s.MODEL_NAME]['Dice']
