fix(steady-state): store evaluated points in natural scale#81
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`update_storage_steady` stored points in internal (transformed) scale, unlike the sequential `update_storage` which inverse-transforms first. With a `log10`-transformed variable this left `X_` in log space; the surrogate refit then re-applied `transform_X`, taking `log10` of a negative value -> NaN -> `ValueError: Input X contains NaN` in the GP fit. Factor variables were likewise stored as numeric codes rather than the natural-scale representation the sequential path produces. Apply `inverse_transform_X` in `update_storage_steady`, mirroring `spotoptim.core.storage.update_storage`, so `X_` and `best_x_` hold user-facing natural-scale values for every `n_jobs`. This unblocks parallel optimization of any search space containing a transformed hyperparameter (e.g. spotforecast2's `learning_rate`/`alpha`). Add regression tests: a parallel run over a `log10` + factor search space no longer crashes and stores natural-scale `X_`, and the seeded initial design matches the sequential path. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
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update_storage_steadystored points in internal (transformed) scale, unlike the sequentialupdate_storagewhich inverse-transforms first. With alog10-transformed variable this leftX_in log space; the surrogate refit then re-appliedtransform_X, takinglog10of a negative value -> NaN ->ValueError: Input X contains NaNin the GP fit. Factor variables were likewise stored as numeric codes rather than the natural-scale representation the sequential path produces.Apply
inverse_transform_Xinupdate_storage_steady, mirroringspotoptim.core.storage.update_storage, soX_andbest_x_hold user-facing natural-scale values for everyn_jobs. This unblocks parallel optimization of any search space containing a transformed hyperparameter (e.g. spotforecast2'slearning_rate/alpha).Add regression tests: a parallel run over a
log10+ factor search space no longer crashes and stores natural-scaleX_, and the seeded initial design matches the sequential path.