def test_emulation():
emulator = Emulator(dataset='hplc', model='BayesNeuralNet')
values = emulator.run([0, 0, 0, 0, 0, 0], return_paramvector=True)
# values is a list of ParamVectors. Each ParamVector for a sample. We have only one sample unless we probe
# batches of parameters. A ParamVector can then have multiple keys if we have multiple objectives.
assert len(values) == 1 # we expect a list with only 1 ParamVector
assert len(values[0].to_array()) == 1 # we expect only 1 objective
value = values[0].to_array()[0]
assert isinstance(value, float)
assert value >= 0
def test_init_with_objects():
dataset = Dataset(kind='hplc', test_frac=0.3)
model = BayesNeuralNet(max_epochs=1000)
_ = Emulator(dataset=dataset,
model=model,
feature_transform='standardize',
target_transform='mean')
def test_cross_validate():
# only tests if the code runs
dataset = Dataset(kind='hplc', num_folds=5)
model = BayesNeuralNet(scope='hplc', max_epochs=2)
emulator = Emulator(dataset=dataset, model=model)
emulator.cross_validate()
emulator.save('emulator_test')
shutil.rmtree('emulator_test')
def test_train_nn():
# only tests if the code
dataset = Dataset(kind='hplc', num_folds=5)
model = NeuralNet(scope='hplc', max_epochs=2)
emulator = Emulator(dataset=dataset, model=model)
emulator.train()
emulator.save('emulator_test')
shutil.rmtree('emulator_test')
def test_init_existing():
emulator = Emulator(dataset='hplc', model='BayesNeuralNet')
assert emulator.dataset.kind == 'hplc'
assert emulator.model.kind == 'BayesNeuralNet'
def test_loading_all_emulators():
models_list = get_models_list()
for dataset in datasets_list:
for model in ['BayesNeuralNet', 'NeuralNet']:
_ = Emulator(dataset=dataset, model=model)
def test_init_with_nones():
emulator_0 = Emulator(dataset=None, model='BayesNeuralNet')
emulator_1 = Emulator(dataset='hplc', model=None)