def test_noisy_expected_improvement(self, cuda=False):
for dtype in (torch.float, torch.double):
model = self._get_model(cuda=cuda, dtype=dtype)
X_observed = model.train_inputs[0]
nEI = NoisyExpectedImprovement(model, X_observed, num_fantasies=5)
X_test = torch.tensor(
[[[0.25]], [[0.75]]],
device=X_observed.device,
dtype=dtype,
requires_grad=True,
)
val = nEI(X_test)
# test basics
self.assertEqual(val.dtype, dtype)
self.assertEqual(val.device.type, X_observed.device.type)
self.assertEqual(val.shape, torch.Size([2]))
# test values
self.assertGreater(val[0].item(), 1e-4)
self.assertLess(val[1].item(), 1e-6)
# test gradient
val.sum().backward()
self.assertGreater(X_test.grad[0].abs().item(), 1e-4)
# test without gradient
with torch.no_grad():
nEI(X_test)
# test non-FixedNoiseGP model
other_model = SingleTaskGP(X_observed, model.train_targets)
with self.assertRaises(UnsupportedError):
NoisyExpectedImprovement(other_model, X_observed, num_fantasies=5)
# Test with minimize
nEI = NoisyExpectedImprovement(
model, X_observed, num_fantasies=5, maximize=False
)
def test_noisy_expected_improvement(self, cuda=False):
for dtype in (torch.float, torch.double):
model = self._get_model(cuda=cuda, dtype=dtype)
X_observed = model.train_inputs[0]
nEI = NoisyExpectedImprovement(model, X_observed, num_fantasies=5)
X_test = torch.tensor(
[[[0.25]], [[0.75]]],
device=X_observed.device,
dtype=dtype,
requires_grad=True,
)
val = nEI(X_test)
# test basics
self.assertEqual(val.dtype, dtype)
self.assertEqual(val.device.type, X_observed.device.type)
self.assertEqual(val.shape, torch.Size([2]))
# test values
self.assertGreater(val[0].item(), 1e-4)
self.assertLess(val[1].item(), 1e-6)
# test gradient
val.sum().backward()
self.assertGreater(X_test.grad[0].abs().item(), 1e-4)
# test without gradient
with torch.no_grad():
nEI(X_test)
def main(argv):
dataset = 1
try:
opts, args = getopt.getopt(argv, "hd:", ["dataset="])
except getopt.GetoptError:
print ('random parallel with input dataset')
sys.exit(2)
for opt, arg in opts:
if opt == '-h':
print ('random parallel with input dataset')
sys.exit()
elif opt in ("-d", "--dataset"):
dataset = int(arg)
# average over multiple trials
for trial in range(3, N_TRIALS + 1):
print(f"\nTrial {trial:>2} of {N_TRIALS} ", end="")
best_observed_ei, best_observed_nei = [], []
# call helper functions to generate initial training data and initialize model
train_x_ei, train_obj_ei, best_observed_value_ei, current_best_config = generate_initial_data(dataset)
train_x_nei, train_obj_nei = train_x_ei, train_obj_ei
best_observed_value_nei = best_observed_value_ei
mll_nei, model_nei = initialize_model(train_x_nei, train_obj_nei)
best_observed_nei.append(best_observed_value_nei)
# run N_BATCH rounds of BayesOpt after the initial random batch
for iteration in range(1, N_BATCH + 1):
# fit the models
fit_gpytorch_model(mll_nei)
# define the qEI and qNEI acquisition modules using a QMC sampler
qmc_sampler = SobolQMCNormalSampler(num_samples=MC_SAMPLES)
# for best_f, we use the best observed noisy values as an approximation
NEI = NoisyExpectedImprovement(
model=model_nei,
X_observed=train_x_nei,
)
# optimize and get new observation
new_x_nei, new_obj_nei = optimize_acqf_and_get_observation(NEI, dataset)
# update training points
train_x_nei = torch.cat([train_x_nei, new_x_nei])
train_obj_nei = torch.cat([train_obj_nei, new_obj_nei])
# update progress
best_value_nei = train_obj_nei.max().item()
best_observed_nei.append(best_value_nei)
# reinitialize the models so they are ready for fitting on next iteration
# use the current state dict to speed up fitting
mll_nei, model_nei = initialize_model(
train_x_nei,
train_obj_nei,
model_nei.state_dict(),
)
# return the best configuration
best_tensor_nei, indices_nei = torch.max(train_obj_nei, 0)
train_best_x_nei = train_x_nei[indices_nei].cpu().numpy()
from botorch.acquisition import PosteriorMean
argmax_pmean_nei, max_pmean_nei = optimize_acqf(
acq_function=PosteriorMean(model_nei),
bounds=bounds,
q=1,
num_restarts=20,
raw_samples=2048,
)
csv_file_name = '/home/junjie/modes/botorch/' + folder_name + '/modes-b/hp-ngp-nei-dataset-' + str(
dataset) + '-trail' + str(trial) + '.csv'
with open(csv_file_name, 'w') as csvFile:
writer = csv.writer(csvFile)
writer.writerow([str(argmax_pmean_nei.cpu().numpy()), str(max_pmean_nei.cpu().numpy())]) # nei prediction
writer.writerow([str(train_best_x_nei), str(best_tensor_nei.cpu().numpy())]) # nei observation
csvFile.close()