def suggest(self, utility_function):
"""Most promissing point to probe next"""
if len(self._space) == 0:
continuous_sample = self._space.random_sample()
discrete_sample = np.array([
CustomBayesianOptimization.round_to_step(
x, self.parameter_step_sizes[i])
for i, x in enumerate(continuous_sample)
])
return self._space.array_to_params(discrete_sample)
# Sklearn's GP throws a large number of warnings at times, but
# we don't really need to see them here.
with warnings.catch_warnings():
warnings.simplefilter("ignore")
self._gp.fit(self._space.params, self._space.target)
try:
# Finding argmax of the acquisition function.
continuous_suggestion = acq_max(ac=utility_function.utility,
gp=self._gp,
y_max=self._space.target.max(),
bounds=self._space.bounds,
random_state=self._random_state)
# transform each value in suggestion into the
# discrete value closed to the defined step size
discrete_suggestion: np.ndarray = np.array([
CustomBayesianOptimization.round_to_step(
x, self.parameter_step_sizes[i])
for i, x in enumerate(continuous_suggestion)
])
if discrete_suggestion in self._space:
raise KeyError("Already probed suggestion.")
return self._space.array_to_params(discrete_suggestion)
except:
number_retries = 0
max_number_retries = 20
while (number_retries < max_number_retries):
try:
continuous_sample = self._space.random_sample()
discrete_sample = np.array([
CustomBayesianOptimization.round_to_step(
x, self.parameter_step_sizes[i])
for i, x in enumerate(continuous_sample)
])
if discrete_sample in self._space:
raise KeyError("Already probed suggestion.")
return self._space.array_to_params(discrete_sample)
except:
number_retries += 1
raise KeyError(
"Even with retries no unique sample could be probed.")
def test_acq_with_poi():
util = UtilityFunction(kind="poi", kappa=1.0, xi=1e-4)
episilon = 1e-2
y_max = 2.0
max_arg = acq_max(
util.utility,
GP,
y_max,
bounds=np.array([[0, 1], [0, 1]]),
random_state=ensure_rng(0),
n_iter=200,
)
_, brute_max_arg = brute_force_maximum(MESH, GP, kind='poi', kappa=1.0, xi=1e-4)
assert all(abs(brute_max_arg - max_arg) < episilon)
def test_acq_with_ucb():
util = UtilityFunction(kind="ucb", kappa=1.0, xi=1.0)
episilon = 1e-2
y_max = 2.0
max_arg = acq_max(util.utility,
GP,
y_max,
bounds=np.array([[0, 1], [0, 1]]),
btypes=[float, float],
random_state=ensure_rng(0),
n_iter=20)
_, brute_max_arg = brute_force_maximum(MESH,
GP,
kind='ucb',
kappa=1.0,
xi=1.0)
assert all(abs(brute_max_arg - max_arg) < episilon)
