def main():
if args.cuda and torch.cuda.is_available():
device = torch.device("cuda")
else:
device = torch.device("cpu")
print("Using device", type(device))
algorithmopts = yaml.safe_load(open(args.algorithm_specs, "r"))
testcase = load_example(algorithmopts["example"])
# start_time = time.time() #######
model, likelihood = init_gp(testcase, algorithmopts,
algorithmopts["ninit"], device)
# time1 = time.time() ####
# print("--- init_gp %s seconds ---" % (time1 - start_time)) ###
estimator = ExcursionSetEstimator(testcase, algorithmopts, model,
likelihood, device)
timestampStr = datetime.datetime.now().strftime("%d-%b-%Y_%H:%M:%S") + "/"
os.mkdir(args.outputfolder + timestampStr)
while estimator.this_iteration < algorithmopts["nupdates"]:
time2 = time.time()
estimator.step(testcase, algorithmopts, model, likelihood)
time3 = time.time() ####
print("--- step %s seconds ---" % (time3 - time2)) ###
model = estimator.update_posterior(testcase, algorithmopts, model,
likelihood)
time4 = time.time() ####
print("--- posterior %s seconds ---" % (time4 - time3)) ###
estimator.plot_status(testcase, algorithmopts, model,
estimator.acq_values,
args.outputfolder + timestampStr)
estimator.get_diagnostics(testcase, model, likelihood)
time5 = time.time() ####
print("--- get_diagnostics %s seconds ---" % (time5 - time4)) ###
estimator.print_results(args.outputfolder + timestampStr, testcase,
algorithmopts)
def test_init_gp():
device = torch.device("cpu")
ninit = 1
algorithmopts = yaml.safe_load(open("testing/algorithm_specs.yaml", "r"))
# three toy examples
for example in ["1Dtoyanalysis", "2Dtoyanalysis", "3Dtoyanalysis"]:
print(example)
testcase = load_example(example)
gp, likelihood = init_gp(testcase, algorithmopts, ninit, device)
assert type(gp.train_inputs[0]) != type(None)
assert type(gp.train_targets) != type(None)
def test_init_excursion():
device = torch.device("cpu")
ninit = 1
algorithmopts = yaml.safe_load(open("testing/algorithm_specs.yaml", "r"))
# three toy examples
for example in ["1Dtoyanalysis", "2Dtoyanalysis", "3Dtoyanalysis"]:
testcase = load_example(example)
gp, likelihood = init_gp(testcase, algorithmopts, ninit, device)
estimator = ExcursionSetEstimator(testcase, algorithmopts, gp,
likelihood, device)
assert type(estimator) != type(None)
assert type(estimator._X_grid) != type(None)
assert type(estimator._n_dims) != type(None)
assert type(estimator._acq_type) != type(None)
def test_full_simple():
tol = 1e-6
device = torch.device("cpu")
ninit = 1
algorithmopts = yaml.safe_load(
open("testing/algorithm_specs_full_test.yaml", "r"))
# three toy examples
for example in ["1D_test"]:
testcase = load_example(example)
model, likelihood = init_gp(testcase, algorithmopts, ninit, device)
estimator = ExcursionSetEstimator(testcase, algorithmopts, model,
likelihood, device)
while estimator.this_iteration < algorithmopts["nupdates"]:
estimator.step(testcase, algorithmopts, model, likelihood)
model = estimator.update_posterior(testcase, algorithmopts, model,
likelihood)
assert type(torch.abs(model.train_targets) <= tol) != type(None)
def test_step_excursion():
device = torch.device("cpu")
ninit = 1
algorithmopts = yaml.safe_load(open("testing/algorithm_specs.yaml", "r"))
# three toy examples
for example in ["1Dtoyanalysis", "2Dtoyanalysis", "3Dtoyanalysis"]:
testcase = load_example(example)
gp, likelihood = init_gp(testcase, algorithmopts, ninit, device)
estimator = ExcursionSetEstimator(testcase, algorithmopts, gp,
likelihood, device)
# one iteration only to test
estimator.step(testcase, algorithmopts, gp, likelihood)
assert type(estimator) != type(None)
assert type(estimator.x_new) != type(None)
assert type(estimator.y_new) != type(None)
assert estimator.walltime_step != 0.0
assert estimator.this_iteration == 1