Skip to content

Remove error_message_searchspace_fully_observed message in BO #338

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Merged
merged 1 commit into from
Oct 14, 2025
Merged

Remove error_message_searchspace_fully_observed message in BO #338

Changes from all commits
Commits
Show all changes
1 commit
Select commit
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
11 changes: 5 additions & 6 deletions kernel_tuner/strategies/bayes_opt.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -268,7 +268,6 @@ def get_hyperparam(name: str, default, supported_values=list()):
self.__valid_observations = list()
self.unvisited_cache = self.unvisited()
time_setup = time.perf_counter_ns()
self.error_message_searchspace_fully_observed = "The search space has been fully observed"

# take initial sample
if self.num_initial_samples > 0:
Expand Down Expand Up @@ -569,7 +568,7 @@ def __optimize(self, max_fevals):
"""Find the next best candidate configuration(s), evaluate those and update the model accordingly."""
while self.fevals < max_fevals:
if self.__visited_num >= self.searchspace_size:
raise ValueError(self.error_message_searchspace_fully_observed)
break
predictions, _, std = self.predict_list(self.unvisited_cache)
hyperparam = self.contextual_variance(std)
list_of_acquisition_values = self.__af(predictions, hyperparam)
Expand Down Expand Up @@ -608,7 +607,7 @@ def __optimize_multi(self, max_fevals):
predictions, _, std = self.predict_list(self.unvisited_cache)
hyperparam = self.contextual_variance(std)
if self.__visited_num >= self.searchspace_size:
raise ValueError(self.error_message_searchspace_fully_observed)
break
time_predictions = time.perf_counter_ns()
actual_candidate_params = list()
actual_candidate_indices = list()
Expand Down Expand Up @@ -724,7 +723,7 @@ def __optimize_multi_advanced(self, max_fevals, increase_precision=False):
if single_af:
return self.__optimize(max_fevals)
if self.__visited_num >= self.searchspace_size:
raise ValueError(self.error_message_searchspace_fully_observed)
break
observations_median = np.median(self.__valid_observations)
if increase_precision is False:
predictions, _, std = self.predict_list(self.unvisited_cache)
Expand Down Expand Up @@ -832,7 +831,7 @@ def __optimize_multi_fast(self, max_fevals):
predictions, _, std = self.predict_list(self.unvisited_cache)
hyperparam = self.contextual_variance(std)
if self.__visited_num >= self.searchspace_size:
raise ValueError(self.error_message_searchspace_fully_observed)
break
for af in aqfs:
if self.__visited_num >= self.searchspace_size or self.fevals >= max_fevals:
break
Expand Down Expand Up @@ -873,7 +872,7 @@ def __optimize_multi_ultrafast(self, max_fevals, predict_eval_ratio=5):
eval_start = time.perf_counter()
hyperparam = self.contextual_variance(std)
if self.__visited_num >= self.searchspace_size:
raise ValueError(self.error_message_searchspace_fully_observed)
break
for af in aqfs:
if self.__visited_num >= self.searchspace_size or self.fevals >= max_fevals:
break
Expand Down