Add adaptive stdev scaling to local search neighborhood sampling #954#1303
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mwever merged 2 commits intodevelopmentfrom Mar 19, 2026
Merged
Add adaptive stdev scaling to local search neighborhood sampling #954#1303mwever merged 2 commits intodevelopmentfrom
mwever merged 2 commits intodevelopmentfrom
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Mar 10, 2026
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Please fix the comments made below.
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| hp_names = list(start_points[0].config_space.keys()) | ||
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| # Initial standard deviation used when sampling continuous neighbors. |
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Please, no magic constants in our code :-)
Can you please add these parameters as actual parameters for this class? You can still set default values, no problem, but these parameters should not just simply pop up within an internal method.
| improvement_count[i] = 0 | ||
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| # Increase exploration radius if the search improves consistently. | ||
| if improvement_count[i] >= 3 and n_no_plateau_walk[i] == 0: |
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Also here please no magic constants in between. The limit for scaling out or zooming in should be a parameter of this class.
| n_no_plateau_walk[i] += 1 | ||
| # Reduce exploration radius during plateau walking to refine the | ||
| # search locally around the current candidate. | ||
| if n_no_plateau_walk[i] % 3 == 0: |
`_stdev_init`, `_stdev_min`, `_stdev_max`, `_upscale_thresh`, `_downscale_interval` - `_stdev_max` can be None, in which case it is computed heuristically
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This PR adds adaptive scaling of the neighborhood sampling standard deviation for continuous hyperparameters based on local search progress.
The scaling follows these rules:
These changes were benchmarked in the SMAC3-Benchmarking repository. The repository also contains additional details explaining the design and implementation choices.