def state_dict(self):
"""Return a state dict that can be used to reset the state of the algorithm."""
return {
'optimizer_rng_state':
self.optimizer.rng.get_state(),
'estimator_rng_state':
check_random_state(
self.optimizer.base_estimator_.random_state).get_state()
}
def test_sample(self):
"""Check whether sampling works correctly."""
seed = 5
space = Space()
probs = (0.1, 0.2, 0.3, 0.4)
categories = ("asdfa", 2, 3, 4)
dim1 = Categorical("yolo", OrderedDict(zip(categories, probs)), shape=(2, 2))
space.register(dim1)
dim2 = Integer("yolo2", "uniform", -3, 6)
space.register(dim2)
dim3 = Real("yolo3", "norm", 0.9)
space.register(dim3)
point = space.sample(seed=seed)
rng = check_random_state(seed)
test_point = [
dict(
yolo=dim1.sample(seed=rng)[0],
yolo2=dim2.sample(seed=rng)[0],
yolo3=dim3.sample(seed=rng)[0],
)
]
assert len(point) == len(test_point) == 1
assert len(point[0].params) == len(test_point[0]) == 3
assert np.all(point[0].params["yolo"] == test_point[0]["yolo"])
assert point[0].params["yolo2"] == test_point[0]["yolo2"]
assert point[0].params["yolo3"] == test_point[0]["yolo3"]
points = space.sample(2, seed=seed)
rng = check_random_state(seed)
points1 = dim1.sample(2, seed=rng)
points2 = dim2.sample(2, seed=rng)
points3 = dim3.sample(2, seed=rng)
test_points = [
dict(yolo=points1[0], yolo2=points2[0], yolo3=points3[0]),
dict(yolo=points1[1], yolo2=points2[1], yolo3=points3[1]),
]
assert len(points) == len(test_points) == 2
for i in range(2):
assert len(points[i].params) == len(test_points[i]) == 3
assert np.all(points[i].params["yolo"] == test_points[i]["yolo"])
assert points[i].params["yolo2"] == test_points[i]["yolo2"]
assert points[i].params["yolo3"] == test_points[i]["yolo3"]
def state_dict(self):
"""Return a state dict that can be used to reset the state of the algorithm."""
state_dict = super(BayesianOptimizer, self).state_dict
if self.optimizer is None:
return state_dict
state_dict.update(
{
"optimizer_rng_state": self.optimizer.rng.get_state(),
"estimator_rng_state": check_random_state(
self.optimizer.base_estimator_.random_state
).get_state(),
"Xi": self.optimizer.Xi,
"yi": self.optimizer.yi,
# pylint: disable = protected-access
"_n_initial_points": self.optimizer._n_initial_points,
"gains_": getattr(self.optimizer, "gains_", None),
"models": self.optimizer.models,
"_next_x": getattr(self.optimizer, "_next_x", None),
}
)
return state_dict
def test_rng_invalid_value(self):
"""Test that passing int returns RandomState"""
with pytest.raises(ValueError) as exc:
check_random_state('oh_no_oh_no')
assert '\'oh_no_oh_no\' cannot be used to seed' in str(exc.value)
def test_rng_tuple(self):
"""Test that passing int returns RandomState"""
rng = check_random_state((1, 12, 123))
assert isinstance(rng, np.random.RandomState)
assert rng is not np.random.mtrand._rand
def test_rng_random_state(self):
"""Test that passing RandomState returns itself"""
rng = np.random.RandomState(1)
assert check_random_state(rng) is rng
def test_rng_null(self):
"""Test that passing None returns numpy._rand"""
assert check_random_state(None) is np.random.mtrand._rand
