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 testCrossValidate(self, mock_init):
modelbridge = RandomModelBridge()
modelbridge.transforms = OrderedDict()
modelbridge.parameters = ["x", "y", "z"]
with self.assertRaises(NotImplementedError):
modelbridge._cross_validate([], [], [])
def testPredict(self, mock_init):
modelbridge = RandomModelBridge()
modelbridge.transforms = OrderedDict()
modelbridge.parameters = ["x", "y", "z"]
with self.assertRaises(NotImplementedError):
modelbridge._predict([])
