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 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]))
def test_branin(self):
task = Branin()
# Check batch computation
n_points = 10
X = np.random.rand(n_points, task.n_dims)
X[:, 0] = X[:, 0].dot(task.X_upper[0] - task.X_lower[0]) + task.X_lower[0]
X[:, 1] = X[:, 1].dot(task.X_upper[1] - task.X_lower[1]) + task.X_lower[1]
y = task.evaluate(X)
assert y.shape[0] == n_points
assert y.shape[1] == 1
# Check single computation
X = np.array([np.random.rand(task.n_dims)])
X[:, 0] = X[:, 0].dot(task.X_upper[0] - task.X_lower[0]) + task.X_lower[0]
X[:, 1] = X[:, 1].dot(task.X_upper[1] - task.X_lower[1]) + task.X_lower[1]
y = task.evaluate(X)
assert y.shape[0] == 1
# Check optimas
X = np.array([[-np.pi, 12.275], [np.pi, 2.275], [9.42478, 2.475]])
y = task.evaluate(X)
assert np.all(np.round(y, 6) == 0.397887) == True
class EnvBranin(BaseTask):
def __init__(self):
self.branin = Branin()
X_lower = np.concatenate((self.branin.original_X_lower, np.array([0])))
X_upper = np.concatenate((self.branin.original_X_upper, np.array([1])))
self.is_env = np.array([0, 0, 1])
super(EnvBranin, self).__init__(X_lower, X_upper)
def objective_function(self, x):
y = self.branin.objective_function(x[:, :-1]) * np.exp(-(x[0, -1] - 1))
return y
def objective_function_test(self, x):
return self.branin.objective_function(x[:, :-1])
class TestMaximizers2D(unittest.TestCase):
def setUp(self):
self.branin = Branin()
n_points = 5
self.X = np.random.rand(n_points, self.branin.n_dims)
self.X[:, 0] = self.X[:, 0].dot(self.branin.X_upper[0] - self.branin.X_lower[0]) + self.branin.X_lower[0]
self.X[:, 1] = self.X[:, 1].dot(self.branin.X_upper[1] - self.branin.X_lower[1]) + self.branin.X_lower[1]
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,
compute_incumbent=compute_incumbent,
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(verbose=False)
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 BraninInBillionDims(REMBO):
def __init__(self):
self.b = Branin()
X_lower = np.concatenate((self.b.X_lower, np.zeros([999998])))
X_upper = np.concatenate((self.b.X_upper, np.ones([999998])))
super(BraninInBillionDims, self).__init__(X_lower, X_upper, d=2)
def objective_function(self, x):
return self.b.objective_function(x[:, :2])
def setUp(self):
self.branin = Branin()
n_points = 5
self.X = np.random.rand(n_points, self.branin.n_dims)
self.X[:, 0] = self.X[:, 0].dot(self.branin.X_upper[0] - self.branin.X_lower[0]) + self.branin.X_lower[0]
self.X[:, 1] = self.X[:, 1].dot(self.branin.X_upper[1] - self.branin.X_lower[1]) + self.branin.X_lower[1]
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,
compute_incumbent=compute_incumbent,
par=0.1,
)
def __init__(self):
self.branin = Branin()
X_lower = np.concatenate((self.branin.original_X_lower, np.array([0])))
X_upper = np.concatenate((self.branin.original_X_upper, np.array([1])))
self.is_env = np.array([0, 0, 1])
super(EnvBranin, self).__init__(X_lower, X_upper)
def __init__(self):
self.b = Branin()
X_lower = np.concatenate((self.b.X_lower, np.zeros([999998])))
X_upper = np.concatenate((self.b.X_upper, np.ones([999998])))
super(BraninInBillionDims, self).__init__(X_lower, X_upper, d=2)
