Skip to content
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

Add seed option to SMAC #193

Merged
merged 2 commits into from
Dec 4, 2024
Merged

Add seed option to SMAC #193

Changes from 1 commit
Commits
Show all changes
2 commits
Select commit Hold shift + click to select a range
81fa083
Add seed option to SMAC
nabenabe0928 Dec 2, 2024
2733097
Add a description of seed
nabenabe0928 Dec 3, 2024
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
3 changes: 2 additions & 1 deletion package/samplers/smac_sampler/sampler.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -99,6 +99,7 @@ def __init__(
self,
search_space: dict[str, BaseDistribution],
n_trials: int = 100,
seed: int = -1,
Copy link
Contributor

Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

if seed is set as -1, SMAC will generate a random seed and we cannot guarantee the reproducibility. This argument here is not so intuitive to users, so it would be best if we can also mark this in the docs (such that users will change this to another value if they require reproducibility)

*,
surrogate_model_type: str = "rf",
acq_func_type: str = "ei_log",
Expand All @@ -113,7 +114,7 @@ def __init__(
) -> None:
super().__init__(search_space)
self._cs, self._hp_scale_value = self._convert_to_config_space_design_space(search_space)
scenario = Scenario(configspace=self._cs, deterministic=True, n_trials=n_trials)
scenario = Scenario(configspace=self._cs, deterministic=True, n_trials=n_trials, seed=seed)
surrogate_model = self._get_surrogate_model(
scenario,
surrogate_model_type,
Expand Down