def test_lbfgs_with_gradient_and_context(lbfgs_context, objective, gradient,
space):
context = ContextManager(space, {'x': 0.5})
x0 = np.array([1, 1])
x, f = apply_optimizer(lbfgs_context, x0, space, objective, gradient, None,
context)
assert np.all(np.isclose(x, np.array([0.5, 0])))
def optimize(self,
acquisition: Acquisition,
context: Optional = None) -> Tuple[np.ndarray, np.ndarray]:
"""
Optimizes the acquisition function.
:param acquisition: The acquisition function to be optimized
:param context: Optimization context.
Determines whether any variable values should be fixed during the optimization
:return: Tuple of (location of maximum, acquisition value at maximizer)
"""
if context is None:
context = dict()
else:
self._validate_context_parameters(context)
context_manager = ContextManager(self.space, context)
max_x, max_value = self._optimize(acquisition, context_manager)
# Optimization might not match any encoding exactly
# Rounding operation here finds the closest encoding
rounded_max_x = np.concatenate(tuple(
self.space.round(max_x[:, i * self.batch_size:(i + 1) *
self.batch_size])
for i in range(self.batch_size)),
axis=1)
return max_x, max_value
def test_context_manager_catg_expand_vector(catg_space, catg_context):
context_manager = ContextManager(catg_space, catg_context)
x = np.array([[4.0, -1], [3, 0.0]])
x_expanded = context_manager.expand_vector(x)
assert context_manager.space.dimensionality == 11
assert x_expanded.shape == (2, context_manager.space.dimensionality)
assert np.array_equal(
x_expanded,
np.array([[4.0, 1, 0, 0, 0, 0, 0, 1, 0, 0, -1.0],
[3, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0.0]]))
def test_context_manager_incorrect_category(catg_space):
erroneous_context = {"x2": 0, "x3": 3}
with pytest.raises(ValueError):
context_manager = ContextManager(catg_space, erroneous_context)
def test_context_manager_expand_vector(space, context):
context_manager = ContextManager(space, context)
x = np.array([[0.5]])
x_expanded = context_manager.expand_vector(x)
assert x_expanded.ndim == 2
assert np.array_equal(x_expanded, np.array([[0.3, 0.5]]))
def test_context_manager_idxs(space, context):
context_manager = ContextManager(space, context)
assert context_manager.non_context_idxs == [1]
assert context_manager.context_idxs == [0]
assert context_manager.context_values == [0.3]