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… feature/batch_bo_issue_1229
dengdifan
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Jun 12, 2025
smac/main/config_selector.py
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| self._model, GaussianProcess | ||
| ), "Sample based estimate strategy only allows GP as surrogate model!" | ||
| return self._model.sample_functions(X_test=X_running, n_funcs=1) | ||
| elif estimation_strategy == "q_ei": |
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Hmm... I think if we use q_ei, there is no need to estimate the running configurations. i.e., we use q_ei to provide multiple candidates at once.
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I removed this part.
dengdifan
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Jun 12, 2025
| if not self._log: | ||
| f_samples = normal_samples # in original (normal) space | ||
| f_min_sample = np.min(f_samples, axis=1) | ||
| improvement = np.maximum(self._eta - self._xi - f_min_sample, 0.0) |
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Not quite sure here, but if the surrogate model is a GP, we can directly sample from the joint posterior distribution. As shown in botorch: https://botorch.readthedocs.io/en/stable/acquisition.html#botorch.acquisition.monte_carlo.qExpectedImprovement
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I replaced it with the surrogate model, if it is a GP.
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I am not sure if it is correctly implemented. Please check!