class TestEI(unittest.TestCase):
def setUp(self):
self.X = np.random.rand(10, 2)
self.y = np.sinc(self.X * 10 - 5).sum(axis=1)
self.model = DemoModel()
self.model.train(self.X, self.y)
def test_compute(self):
ei = EI(self.model)
X_test = np.random.rand(5, 2)
a = ei.compute(X_test, derivative=False)
assert a.shape[0] == X_test.shape[0]
assert len(a.shape) == 1
class TestEI(unittest.TestCase):
def setUp(self):
self.X = np.random.rand(10, 2)
self.y = np.sinc(self.X * 10 - 5).sum(axis=1)
self.model = DemoModel()
self.model.train(self.X, self.y)
def test_compute(self):
ei = EI(self.model)
X_test = np.random.rand(5, 2)
a = ei.compute(X_test, derivative=False)
assert a.shape[0] == X_test.shape[0]
assert len(a.shape) == 1
class TestLCB(unittest.TestCase):
def setUp(self):
self.X = np.random.rand(10, 2)
self.y = np.sinc(self.X * 10 - 5).sum(axis=1)
self.model = DemoModel()
self.model.train(self.X, self.y)
def test_compute(self):
lcb = LCB(self.model)
X_test = np.random.rand(5, 2)
a = lcb.compute(X_test, derivative=False)
assert a.shape[0] == X_test.shape[0]
assert len(a.shape) == 1
np.testing.assert_almost_equal(a, np.ones(X_test.shape[0]) * (- np.mean(self.y) + np.std(self.y)), decimal=3)
class TestLCB(unittest.TestCase):
def setUp(self):
self.X = np.random.rand(10, 2)
self.y = np.sinc(self.X * 10 - 5).sum(axis=1)
self.model = DemoModel()
self.model.train(self.X, self.y)
def test_compute(self):
lcb = LCB(self.model)
X_test = np.random.rand(5, 2)
a = lcb.compute(X_test, derivative=False)
assert a.shape[0] == X_test.shape[0]
assert len(a.shape) == 1
np.testing.assert_almost_equal(a,
np.ones(X_test.shape[0]) *
(-np.mean(self.y) + np.std(self.y)),
decimal=3)
def setUp(self):
self.X = np.random.rand(10, 2)
self.y = np.sinc(self.X * 10 - 5).sum(axis=1)
self.model = DemoModel()
self.model.train(self.X, self.y)
def setUp(self):
self.X = np.random.rand(10, 2)
self.y = np.sinc(self.X * 10 - 5).sum(axis=1)
self.model = DemoModel()
self.model.train(self.X, self.y)