class TestOptimizerCreation(unittest.TestCase):
def __init__(self, *args, **kwargs):
super(TestOptimizerCreation, self).__init__(*args, **kwargs)
self.space = [
{'name': 'var_1', 'type': 'continuous', 'domain': (-1, 1), 'dimensionality': 1},
{'name': 'var_2', 'type': 'continuous', 'domain': (-1, 1), 'dimensionality': 1}
]
self.design_space = Design_space(self.space)
self.f = lambda x: np.sum(np.sin(x))
def test_invalid_optimizer_name_raises_error(self):
self.assertRaises(InvalidVariableNameError,
choose_optimizer, 'asd', None)
def test_create_lbfgs_optimizer(self):
optimizer = choose_optimizer('lbfgs', self.design_space.get_bounds())
self.assertIsNotNone(optimizer)
def test_create_direct_optimizer(self):
optimizer = choose_optimizer('DIRECT', self.design_space.get_bounds())
self.assertIsNotNone(optimizer)
def test_create_cma_optimizer(self):
optimizer = choose_optimizer('CMA', self.design_space.get_bounds())
self.assertIsNotNone(optimizer)
def setUp(self):
np.random.seed(123)
domain = [{
'name': 'var1',
'type': 'continuous',
'domain': (-5, 5),
'dimensionality': 5
}]
space = Design_space(domain)
func = alpine1(input_dim=5, bounds=space.get_bounds())
bo = BayesianOptimization(f=func.f, domain=domain)
context = {'var1_1': 0.3, 'var1_2': 0.4}
context_manager = ContextManager(space, context)
x0 = np.array([[0, 0, 0, 0, 0]])
# initialize the model in a least intrusive way possible
bo.suggest_next_locations()
f = bo.acquisition.acquisition_function
f_df = bo.acquisition.acquisition_function_withGradients
self.problem_with_context = OptimizationWithContext(
x0=x0, f=f, df=None, f_df=f_df, context_manager=context_manager)
self.x = np.array([[3, -3, 3]])