def testGetLastPoint(self):
generated_points = np.array([[1, 2, 3], [4, 5, 6]])
RandomModelWithPoints = RandomModel(generated_points=generated_points)
result = RandomModelWithPoints._get_last_point()
expected = torch.tensor([[4], [5], [6]])
comparison = result == expected
self.assertEqual(comparison.any(), True)
class RandomModelTest(TestCase):
def setUp(self):
self.random_model = RandomModel()
def testRandomModelGenSamples(self):
with self.assertRaises(NotImplementedError):
self.random_model._gen_samples(n=1, tunable_d=1)
def testRandomModelGenUnconstrained(self):
with self.assertRaises(NotImplementedError):
self.random_model._gen_unconstrained(
n=1, d=2, tunable_feature_indices=np.array([]))
def testConvertEqualityConstraints(self):
fixed_features = {3: 0.7, 1: 0.5}
d = 4
C, c = self.random_model._convert_equality_constraints(
d, fixed_features)
c_expected = torch.tensor([[0.5], [0.7]], dtype=torch.double)
C_expected = torch.tensor([[0, 1, 0, 0], [0, 0, 0, 1]],
dtype=torch.double)
c_comparison = c == c_expected
C_comparison = C == C_expected
self.assertEqual(c_comparison.any(), True)
self.assertEqual(C_comparison.any(), True)
self.assertEqual(
self.random_model._convert_equality_constraints(d, None), None)
def testConvertInequalityConstraints(self):
A = np.array([[1, 2], [3, 4]])
b = np.array([[5], [6]])
A_result, b_result = self.random_model._convert_inequality_constraints(
(A, b))
A_expected = torch.tensor([[1, 2], [3, 4]], dtype=torch.double)
b_expected = torch.tensor([[5], [6]], dtype=torch.double)
A_comparison = A_result == A_expected
b_comparison = b_result == b_expected
self.assertEqual(A_comparison.any(), True)
self.assertEqual(b_comparison.any(), True)
self.assertEqual(
self.random_model._convert_inequality_constraints(None), None)
def testConvertBounds(self):
bounds = [(1, 2), (3, 4), (5, 6)]
bounds_result = self.random_model._convert_bounds(bounds)
bounds_expected = torch.tensor([[1, 3, 5], [2, 4, 6]],
dtype=torch.double)
bounds_comparison = bounds_result == bounds_expected
self.assertEqual(bounds_comparison.any(), True)
self.assertEqual(self.random_model._convert_bounds(None), None)
def testGetLastPoint(self):
generated_points = np.array([[1, 2, 3], [4, 5, 6]])
RandomModelWithPoints = RandomModel(generated_points=generated_points)
result = RandomModelWithPoints._get_last_point()
expected = torch.tensor([[4], [5], [6]])
comparison = result == expected
self.assertEqual(comparison.any(), True)
class RandomModelTest(TestCase):
def setUp(self):
self.random_model = RandomModel()
def testRandomModelGenSamples(self):
with self.assertRaises(NotImplementedError):
self.random_model._gen_samples(n=1, tunable_d=1)
def testRandomModelGenUnconstrained(self):
with self.assertRaises(NotImplementedError):
self.random_model._gen_unconstrained(
n=1, d=2, tunable_feature_indices=np.array([]))
def testGen(self, mock_init, mock_gen):
# Test with constraints
modelbridge = RandomModelBridge()
modelbridge.parameters = ["x", "y", "z"]
modelbridge.transforms = OrderedDict()
modelbridge.model = RandomModel()
observation_features, weights, best_obsf, _ = modelbridge._gen(
n=3,
search_space=self.search_space,
pending_observations={},
fixed_features=ObservationFeatures({"z": 3.0}),
optimization_config=None,
model_gen_options=self.model_gen_options,
)
gen_args = mock_gen.mock_calls[0][2]
self.assertEqual(gen_args["n"], 3)
self.assertEqual(gen_args["bounds"], [(0.0, 1.0), (1.0, 2.0),
(0.0, 5.0)])
self.assertTrue(
np.array_equal(
gen_args["linear_constraints"][0],
np.array([[1.0, -1, 0.0], [-1.0, 0.0, -1.0]]),
))
self.assertTrue(
np.array_equal(gen_args["linear_constraints"][1],
np.array([[0.0], [-3.5]])))
self.assertEqual(gen_args["fixed_features"], {2: 3.0})
self.assertEqual(gen_args["model_gen_options"], {"option": "yes"})
self.assertEqual(observation_features[0].parameters, {
"x": 1.0,
"y": 2.0,
"z": 3.0
})
self.assertEqual(observation_features[1].parameters, {
"x": 3.0,
"y": 4.0,
"z": 3.0
})
self.assertTrue(np.array_equal(weights, np.array([1.0, 2.0])))
# Test with no constraints, no fixed feature, no pending observations
search_space = SearchSpace(self.parameters[:2])
modelbridge.parameters = ["x", "y"]
modelbridge._gen(
n=3,
search_space=search_space,
pending_observations={},
fixed_features=ObservationFeatures({}),
optimization_config=None,
model_gen_options=self.model_gen_options,
)
gen_args = mock_gen.mock_calls[1][2]
self.assertEqual(gen_args["bounds"], [(0.0, 1.0), (1.0, 2.0)])
self.assertIsNone(gen_args["linear_constraints"])
self.assertIsNone(gen_args["fixed_features"])
def setUp(self):
self.random_model = RandomModel()