available_gpus = gp.getAvailable(limit=5)
#try:
#available_gpus.remove(0)#CHRIS gpu 0 and 5 are differen gpu types on duranium since they are faster, timing will be unreliable, so remove them from list
#except:
#pass
#try:
#available_gpus.remove(5)
#except:
#pass
print(available_gpus)
n_job = max(min(5, len(available_gpus)), 1)
# use random forest as the surrogate model
#CHRIS two surrogate models are needed
model = RandomForest(levels=search_space.levels, n_estimators=100)
opt = mipego(search_space,
objective,
model,
ftarget=None,
minimize=True,
noisy=False,
max_eval=None,
max_iter=n_step,
infill='MGFI',
n_init_sample=n_init_sample,
n_point=1,
n_job=n_job,
n_restart=None,
max_infill_eval=None,
wait_iter=3,
eval_n += 1
x_r, x_i, x_d = np.array([x['C_0'], x['C_1']]), x['I'], x['N']
if x_d == 'OK':
tmp = 0
else:
tmp = 1
return np.sum(x_r**2.) + abs(x_i - 10) / 123. + tmp * 2.
C = ContinuousSpace([-5, 5], 'C') * 2
I = OrdinalSpace([-100, 100], 'I')
N = NominalSpace(['OK', 'A', 'B', 'C', 'D', 'E'], 'N')
search_space = C * I * N
model = RandomForest(levels=search_space.levels)
# model = RrandomForest(levels=search_space.levels, seed=1, max_features='sqrt')
opt = mipego(
search_space,
obj_func,
model,
max_iter=n_step,
random_seed=None,
n_init_sample=n_init_sample,
minimize=True,
log_file="test.log",
verbose=True,
optimizer='MIES',
infill='EI',
available_gpus=[1, 2],
