Batch sampling improvement #1154
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benjamc
requested changes
Dec 4, 2024
| Y_estimated = self.estimate_running_config_costs( | ||
| X_running, Y, self._batch_sampling_estimation_strategy | ||
| ) | ||
| if Y_estimated is not None: |
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in what cases could this be None?
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ah when there are no running configs
smac/main/config_selector.py
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| a np array with size (n_evaluated_configs, n_obj) that records the costs of all the previous evaluated | ||
| configurations | ||
| estimation_strategy: str | ||
| how do we estimate the target y_running values |
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- copy docstring from above to add more info about the estimation strategy here
smac/main/config_selector.py
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| Y_evaluated: np.ndarray, | ||
| estimation_strategy: str = 'CL_max'): | ||
| """ | ||
| This function is implemented to estimate the still pending/ running configurations |
smac/main/config_selector.py
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| Y_estimated = np.nanmin(Y_evaluated, axis=0, keepdims=True) | ||
| return np.repeat(Y_estimated, n_running_points, 0) | ||
| elif estimation_strategy == 'CL_mean': | ||
| # constant liar min, we take the mean values of all the evaluated Y and apply them to the running X |
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should be constant liar mean instead of min
| # gaussian process | ||
| assert isinstance(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) |
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Why cannot we sample from the random forest?
| trial: self.runhistory[trial] | ||
| for trial in self.runhistory | ||
| if self.runhistory[trial].status == StatusType.RUNNING | ||
| # and runhistory.data[run].time >= self._algorithm_walltime_limit # type: ignore |
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why is this commented out / why would we need this?
If it should stay there commented, please explain why
| trial: self.runhistory[trial] | ||
| for trial in self.runhistory | ||
| if self.runhistory[trial].status == StatusType.RUNNING | ||
| # and runhistory.data[run].time >= self._algorithm_walltime_limit # type: ignore |
| @@ -211,6 +234,13 @@ def _get_timeout_trials( | |||
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| return trials | |||
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| def _convert_config_ids_to_array(self, | |||
| config_ids: Iterable[int]) -> np.ndarray: | |||
| """extract the configurations from rh and transform them into np array""" | |||
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- write proper docstring with Parameters and return values
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Closing #1152
This is the first step towards solving the batch sampling
TODO: