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# Author: Simon Blanke |
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# Email: [email protected] |
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# License: MIT License |
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from .dictionary import DictClass |
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def gfo2hyper(search_space, para): |
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values_dict = {} |
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for i, key in enumerate(search_space.keys()): |
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pos_ = int(para[key]) |
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values_dict[key] = search_space[key][pos_] |
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return values_dict |
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class ObjectiveFunction(DictClass): |
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def __init__(self, objective_function, optimizer, nth_process): |
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super().__init__() |
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self.objective_function = objective_function |
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self.optimizer = optimizer |
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self.nth_process = nth_process |
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def __call__(self, search_space, data_c): |
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# wrapper for GFOs |
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def _model(para): |
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para = gfo2hyper(search_space, para) |
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self.para_dict = para |
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results = self.objective_function(self) |
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if data_c: |
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progress_dict = para |
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if isinstance(results, tuple): |
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score = results[0] |
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results_dict = results[1] |
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else: |
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score = results |
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results_dict = {} |
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results_dict["score"] = score |
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progress_dict.update(results_dict) |
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progress_dict["score_best"] = self.optimizer.best_score |
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progress_dict["nth_iter"] = self.optimizer.nth_iter |
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progress_dict["nth_process"] = self.optimizer.nth_process |
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data_c.append(progress_dict) |
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# ltm save after iteration |
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# self.ltm.ltm_obj_func_wrapper(results, para, nth_process) |
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return results |
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_model.__name__ = self.objective_function.__name__ |
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return _model |
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