def test_acquisition_gradient_cookbook():
rng = np.random.RandomState(0)
X = rng.randn(20, 5)
y = rng.randn(20)
X_new = rng.randn(5)
gpr = cook_estimator("GP", Space(((-5.0, 5.0), )), random_state=0)
gpr.fit(X, y)
for acq_func in ["LCB", "PI", "EI"]:
check_gradient_correctness(X_new, gpr, acq_func, np.max(y))
def test_acquisition_per_second_gradient(acq_func):
rng = np.random.RandomState(0)
X = rng.randn(20, 10)
# Make the second component large, so that mean_grad and std_grad
# do not become zero.
y = np.vstack((X[:, 0], np.abs(X[:, 0])**3)).T
for X_new in [rng.randn(10), rng.randn(10)]:
gpr = cook_estimator("GP", Space(((-5.0, 5.0), )), random_state=0)
mor = MultiOutputRegressor(gpr)
mor.fit(X, y)
check_gradient_correctness(X_new, mor, acq_func, 1.5)
def fit(self, X, y):
"""
The first estimator returns a constant value.
The second estimator is a gaussian process regressor that
models the logarithm of the time.
"""
X = np.array(X)
gpr = cook_estimator("GP", self.space, normalize_y=False)
gpr.fit(X, np.log(np.ravel(X)))
self.estimators_ = []
self.estimators_.append(ConstSurrogate())
self.estimators_.append(gpr)
return self
def test_use_given_estimator():
"""Test that gp_minimize does not use default estimator if one is passed
in explicitly."""
domain = [(1.0, 2.0), (3.0, 4.0)]
noise_correct = 1e5
noise_fake = 1e-10
estimator = cook_estimator("GP", domain, noise=noise_correct)
res = gp_minimize(
branin,
domain,
n_calls=4,
n_initial_points=2,
base_estimator=estimator,
noise=noise_fake,
)
assert res["models"][-1].noise == noise_correct
def test_categorical_gp_has_gradients():
space = Space([("a", "b")])
assert not has_gradients(cook_estimator("GP", space=space))
def test_has_gradients(estimator, gradients):
space = Space([(-2.0, 2.0)])
assert has_gradients(cook_estimator(estimator, space=space)) == gradients