def test_learn(self, mocked_gp):
"""Test the learning function."""
# initialize
mocked_env = mock.MagicMock(name='env', return_value="Batch")
n = 5
batch_size = 3
coeffs = np.arange(n)
meshgrid = np.meshgrid(coeffs, coeffs)
subject = ucb.BatchGPUCB(batch_size, meshgrid, mocked_env, beta=1)
# set up mocked functions
subject.batch_sample = mock.MagicMock(name='batch sample')
mocked_gp.return_value = mock.MagicMock(name='Mocked GP')
mocked_gp.return_value.predict.return_value = ('Mu', 'Sigma')
subject.learn()
# test GP correctly called
self.assertTrue(mocked_gp.return_value.fit.called)
self.assertEqual(subject.mu, 'Mu')
self.assertEqual(subject.sigma, 'Sigma')
# check time step increase
self.assertEqual(subject.T, 1)
# test second time step
subject = ucb.BatchGPUCB(batch_size, meshgrid, mocked_env, beta=1)
subject.T = 1
subject.learn()
# check time step increase
self.assertEqual(subject.T, 2)
def test___init__(self):
"""Test initialization of the Batch GP UCB class."""
# initialize
mocked_env = mock.MagicMock(name='env')
n = 5
batch_size = 3
coeffs = np.arange(n)
meshgrid = np.meshgrid(coeffs, coeffs)
subject = ucb.BatchGPUCB(batch_size, meshgrid, mocked_env, beta=1)
# test assignment of additional attribute
self.assertTrue(hasattr(subject, 'batch_size'))
self.assertEqual(subject.batch_size, batch_size)
def test_argsort_ucb(self):
"""Test getting the argsort of the UCB."""
# initialize
mocked_env = mock.MagicMock(name='env', return_value="Batch")
n = 5
batch_size = 3
coeffs = np.arange(n)
meshgrid = np.meshgrid(coeffs, coeffs)
subject = ucb.BatchGPUCB(batch_size, meshgrid, mocked_env, beta=1)
# making the max occur at starting at index 2 for an entire batch
subject.mu[2:2 + batch_size] = 1
return_val = subject.argsort_ucb()
expected_val = np.arange(2, 2 + batch_size)
np.testing.assert_array_equal(return_val, expected_val)
def test_batch_sample(self):
"""Test the environment sampling."""
# initialize
mocked_env = mock.MagicMock(name='env')
mocked_env.sample = mock.MagicMock(name='env_sample',
return_value="Batch")
n = 5
batch_size = 3
coeffs = np.arange(n)
meshgrid = np.meshgrid(coeffs, coeffs)
subject = ucb.BatchGPUCB(batch_size, meshgrid, mocked_env, beta=1)
indices = [2, 4, 5]
subject.batch_sample(indices)
self.assertEqual(subject.Y[0], ["Batch", "Batch", "Batch"])
def test_latin_hypercube_sample(self, mocked_lhs):
"""Test the Latin hypercube sampling on first iteration of learn."""
mocked_env = mock.MagicMock(name='env')
mocked_env.sample = mock.MagicMock(name='env_sample',
return_value="Batch")
mocked_lhs.return_value = mock.MagicMock(name='LH_sample',
return_value=[[3.0, 2.1],
[0.4, 1.1],
[0.0, 1.9]])
n = 5
batch_size = 3
coeffs = np.arange(n)
meshgrid = np.meshgrid(coeffs, coeffs)
subject = ucb.BatchGPUCB(batch_size, meshgrid, mocked_env, beta=1)
subject.latin_hypercube_sample()
self.assertEqual(list(subject.X[0][0]), [3, 2])
self.assertEqual(list(subject.X[0][1]), [0, 1])
self.assertEqual(list(subject.X[0][2]), [0, 2])
name, args, kwargs = mocked_lhs.mock_calls[0]
self.assertEqual(kwargs['xlimits'].tolist(), [[0, n - 1], [0, n - 1]])