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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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import numpy as np |
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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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self.best = 0 |
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self.nth_iter = -1 |
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self.best_para = None |
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self.best_score = -np.inf |
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def get_best(self, score, para): |
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self.nth_iter += 1 |
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if score > self.best_score: |
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self.best_score = score |
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self.best_para = para |
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self.best = 1 |
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else: |
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self.best = 0 |
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def __call__(self, search_space, progress_collector): |
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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 progress_collector: |
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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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# keep track on best score and para |
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self.get_best(score, para) |
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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.best_score |
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progress_dict["nth_iter"] = self.nth_iter |
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progress_dict["best"] = self.best |
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progress_dict["nth_process"] = self.optimizer.nth_process |
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progress_collector.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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SimonBlanke /
Hyperactive
