Multi obj epm #837
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Multi obj epm #837
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benjamc
reviewed
May 12, 2022
| """Wrapper for the surrogate models to predict multiple targets. | ||
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| Only a list with the target names and the types array for the | ||
| underlying forest model are mandatory. All other hyperparameters to |
| model_kwargs = {} | ||
| self.target_names = target_names | ||
| self.num_targets = len(self.target_names) | ||
| print(seed, model_kwargs) |
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Should we keep the print statement?
| raise NotImplementedError | ||
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| def _train(self, X: np.ndarray, Y: np.ndarray) -> "UncorrelatedMultiObjectiveModel": | ||
| """Trains the random forest on X and y. |
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Adjust to "trains the surrogate model on X and y"?
smac/runhistory/runhistory.py
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| # (incremental updates are used to save time as getting the cost for > 100 instances is high) | ||
| # assumes an average across runs as cost function aggregation, this is used for | ||
| # algorithm configuration (incremental updates are used to save time as getting | ||
| # Zthe cost for > 100 instances is high) |
| # (incremental updates are used to save time as getting the cost for > 100 instances is high) | ||
| # assumes an average across runs as cost function aggregation, this is used for | ||
| # algorithm configuration (incremental updates are used to save time as getting | ||
| # Zthe cost for > 100 instances is high) | ||
| self.incremental_update_cost(self.ids_config[k.config_id], v.cost) | ||
| else: |
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So the cost in runhistory will be still averaged for the new uncorrelated MO?
smac/runhistory/runhistory2epm.py
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| @@ -725,5 +725,5 @@ def transform_response_values(self, values: np.ndarray) -> np.ndarray: | |||
| ------- | |||
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We should create an acquisition function that works with this type of surrogate model :) |
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This PR makes it easier for developing new Multi-Objective models. Also, it will explicitly raise an error if the acquisition function is incompatible with the epm models