def test_deduplicate(self):
sobol = RandomModelBridge(
search_space=get_small_discrete_search_space(),
model=SobolGenerator(deduplicate=True),
transforms=Cont_X_trans,
)
for _ in range(4): # Search space is {[0, 1], {"red", "panda"}}
self.assertEqual(len(sobol.gen(1).arms), 1)
with self.assertRaises(SearchSpaceExhausted):
sobol.gen(1)
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 get_ALEBOInitializer(search_space: SearchSpace, B: np.ndarray,
**model_kwargs: Any) -> RandomModelBridge:
return RandomModelBridge(
search_space=search_space,
model=ALEBOInitializer(B=B, **model_kwargs),
transforms=ALEBO_X_trans, # pyre-ignore
)
def get_sobol(
search_space: SearchSpace,
seed: Optional[int] = None,
deduplicate: bool = False,
init_position: int = 0,
scramble: bool = True,
) -> RandomModelBridge:
"""Instantiates a Sobol sequence quasi-random generator.
Args:
search_space: Sobol generator search space.
kwargs: Custom args for sobol generator.
Returns:
RandomModelBridge, with SobolGenerator as model.
"""
return RandomModelBridge(
search_space=search_space,
model=SobolGenerator(
seed=seed,
deduplicate=deduplicate,
init_position=init_position,
scramble=scramble,
),
transforms=Cont_X_trans,
)
def get_ALEBOInitializer(
search_space: SearchSpace, B: np.ndarray, **model_kwargs: Any
) -> RandomModelBridge:
return RandomModelBridge(
search_space=search_space,
model=ALEBOInitializer(B=B, **model_kwargs),
transforms=[CenteredUnitX],
)
def get_uniform(search_space: SearchSpace,
deduplicate: bool = False,
seed: Optional[int] = None) -> RandomModelBridge:
"""Instantiate uniform generator.
Args:
search_space: Uniform generator search space.
kwargs: Custom args for uniform generator.
Returns:
RandomModelBridge, with UniformGenerator as model.
"""
return RandomModelBridge(
search_space=search_space,
model=UniformGenerator(deduplicate=deduplicate, seed=seed),
transforms=Cont_X_trans,
)
def get_rembo_initializer(
search_space: SearchSpace,
A: np.ndarray,
bounds_d: List[Tuple[float, float]],
**kwargs: Any,
) -> RandomModelBridge:
"""Instantiates a uniform random generator.
Args:
search_space: Search space.
A: Projection matrix.
bounds_d: Bounds in low-d space.
kwargs: kwargs
Returns:
RandomModelBridge, with REMBOInitializer as model.
"""
return RandomModelBridge(
search_space=search_space,
model=REMBOInitializer(A=A, bounds_d=bounds_d, **kwargs),
transforms=[CenteredUnitX],
)
def get_sobol(search_space: SearchSpace) -> RandomModelBridge:
return RandomModelBridge(
search_space=search_space,
model=SobolGenerator(),
transforms=Cont_X_trans,
)
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([])
def testFit(self, mock_init):
modelbridge = RandomModelBridge()
model = mock.create_autospec(RandomModel, instance=True)
modelbridge._fit(model, self.search_space, None, None)
self.assertEqual(modelbridge.parameters, ["x", "y", "z"])
self.assertTrue(isinstance(modelbridge.model, RandomModel))
