def test_branin(self):
branin = Branin()
# Check batch computation
n_points = 10
X = np.random.rand(n_points, branin.n_dims)
X[:, 0] = X[:, 0].dot(branin.X_upper[0] - branin.X_lower[0]) + branin.X_lower[0]
X[:, 1] = X[:, 1].dot(branin.X_upper[1] - branin.X_lower[1]) + branin.X_lower[1]
y = branin.evaluate(X)
assert len(y.shape) == 2
assert y.shape[0] == n_points
assert y.shape[1] == 1
# Check single computation
X = np.array([np.random.rand(branin.n_dims)])
X[:, 0] = X[:, 0].dot(branin.X_upper[0] - branin.X_lower[0]) + branin.X_lower[0]
X[:, 1] = X[:, 1].dot(branin.X_upper[1] - branin.X_lower[1]) + branin.X_lower[1]
y = branin.evaluate(X)
assert y.shape[0] == 1
# Check optimas
X = branin.opt
y = branin.evaluate(X)
assert np.all(np.round(y, 6) == np.array([branin.fopt]))
class TestMaximizers2D(unittest.TestCase):
def setUp(self):
self.branin = Branin()
n_points = 5
rng = np.random.RandomState(42)
self.X = init_random_uniform(self.branin.X_lower,
self.branin.X_upper,
n_points,
rng=rng)
self.Y = self.branin.evaluate(self.X)
kernel = GPy.kern.Matern52(input_dim=self.branin.n_dims)
self.model = GPyModel(kernel, optimize=True,
noise_variance=1e-4,
num_restarts=10)
self.model.train(self.X, self.Y)
self.acquisition_func = EI(self.model,
X_upper=self.branin.X_upper,
X_lower=self.branin.X_lower,
par=0.1)
def test_direct(self):
maximizer = Direct(self.acquisition_func,
self.branin.X_lower,
self.branin.X_upper)
x = maximizer.maximize()
assert x.shape[0] == 1
assert x.shape[1] == self.branin.n_dims
assert np.all(x[:, 0] >= self.branin.X_lower[0])
assert np.all(x[:, 1] >= self.branin.X_lower[1])
assert np.all(x[:, 0] <= self.branin.X_upper[0])
assert np.all(x[:, 1] <= self.branin.X_upper[1])
assert np.all(x < self.branin.X_upper)
def test_stochastic_local_search(self):
maximizer = StochasticLocalSearch(self.acquisition_func,
self.branin.X_lower,
self.branin.X_upper)
x = maximizer.maximize()
assert x.shape[0] == 1
assert x.shape[1] == self.branin.n_dims
assert np.all(x[:, 0] >= self.branin.X_lower[0])
assert np.all(x[:, 1] >= self.branin.X_lower[1])
assert np.all(x[:, 0] <= self.branin.X_upper[0])
assert np.all(x[:, 1] <= self.branin.X_upper[1])
assert np.all(x < self.branin.X_upper)
def test_cmaes(self):
maximizer = CMAES(self.acquisition_func,
self.branin.X_lower,
self.branin.X_upper)
x = maximizer.maximize()
assert x.shape[0] == 1
assert x.shape[1] == self.branin.n_dims
assert np.all(x[:, 0] >= self.branin.X_lower[0])
assert np.all(x[:, 1] >= self.branin.X_lower[1])
assert np.all(x[:, 0] <= self.branin.X_upper[0])
assert np.all(x[:, 1] <= self.branin.X_upper[1])
assert np.all(x < self.branin.X_upper)
class TestMaximizers2D(unittest.TestCase):
def setUp(self):
self.branin = Branin()
n_points = 5
rng = np.random.RandomState(42)
self.X = init_random_uniform(self.branin.X_lower,
self.branin.X_upper,
n_points,
rng=rng)
self.Y = self.branin.evaluate(self.X)
kernel = GPy.kern.Matern52(input_dim=self.branin.n_dims)
self.model = GPyModel(kernel,
optimize=True,
noise_variance=1e-4,
num_restarts=10)
self.model.train(self.X, self.Y)
self.acquisition_func = EI(self.model,
X_upper=self.branin.X_upper,
X_lower=self.branin.X_lower,
par=0.1)
def test_direct(self):
maximizer = Direct(self.acquisition_func, self.branin.X_lower,
self.branin.X_upper)
x = maximizer.maximize()
assert x.shape[0] == 1
assert x.shape[1] == self.branin.n_dims
assert np.all(x[:, 0] >= self.branin.X_lower[0])
assert np.all(x[:, 1] >= self.branin.X_lower[1])
assert np.all(x[:, 0] <= self.branin.X_upper[0])
assert np.all(x[:, 1] <= self.branin.X_upper[1])
assert np.all(x < self.branin.X_upper)
def test_stochastic_local_search(self):
maximizer = StochasticLocalSearch(self.acquisition_func,
self.branin.X_lower,
self.branin.X_upper)
x = maximizer.maximize()
assert x.shape[0] == 1
assert x.shape[1] == self.branin.n_dims
assert np.all(x[:, 0] >= self.branin.X_lower[0])
assert np.all(x[:, 1] >= self.branin.X_lower[1])
assert np.all(x[:, 0] <= self.branin.X_upper[0])
assert np.all(x[:, 1] <= self.branin.X_upper[1])
assert np.all(x < self.branin.X_upper)
def test_cmaes(self):
maximizer = CMAES(self.acquisition_func, self.branin.X_lower,
self.branin.X_upper)
x = maximizer.maximize()
assert x.shape[0] == 1
assert x.shape[1] == self.branin.n_dims
assert np.all(x[:, 0] >= self.branin.X_lower[0])
assert np.all(x[:, 1] >= self.branin.X_lower[1])
assert np.all(x[:, 0] <= self.branin.X_upper[0])
assert np.all(x[:, 1] <= self.branin.X_upper[1])
assert np.all(x < self.branin.X_upper)