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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 .search_space import SearchSpace |
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from .model import Model |
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from .init_position import InitSearchPosition |
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class Candidate: |
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def __init__(self, obj_func, func_para, search_space, init_para, memory, verb): |
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self.obj_func = obj_func |
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self.func_para = func_para |
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self.search_space = search_space |
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self.memory = memory |
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self.verb = verb |
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self.space = SearchSpace(search_space, verb) |
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self.model = Model(obj_func, func_para, verb) |
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self.init = InitSearchPosition(init_para, self.space, verb) |
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self.memory_dict = {} |
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self.memory_dict_new = {} |
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self._score = -np.inf |
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self._pos = None |
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self.score_best = -np.inf |
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self.pos_best = None |
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self.score_list = [] |
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self.pos_list = [] |
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self.eval_time = [] |
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self.iter_times = [] |
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if not memory: |
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self.mem = None |
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self.eval_pos = self.eval_pos_noMem |
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else: |
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self.mem = None |
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self.eval_pos = self.eval_pos_Mem |
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@property |
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def score(self): |
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return self._score |
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@score.setter |
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def score(self, value): |
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self.score_list.append(value) |
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self._score = value |
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@property |
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def pos(self): |
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return self._score |
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@pos.setter |
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def pos(self, value): |
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self.pos_list.append(value) |
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self._pos = value |
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def base_eval(self, pos, nth_iter): |
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para = self.space.pos2para(pos) |
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para["iteration"] = nth_iter |
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results = self.model.eval(para) |
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if results["score"] > self.score_best: |
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self.score_best = results["score"] |
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self.pos_best = pos |
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self.verb.p_bar.best_since_iter = nth_iter |
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return results |
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def eval_pos_noMem(self, pos, nth_iter): |
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results = self.base_eval(pos, nth_iter) |
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return results["score"] |
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def eval_pos_Mem(self, pos, nth_iter, force_eval=False): |
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pos.astype(int) |
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pos_tuple = tuple(pos) |
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if pos_tuple in self.memory_dict and not force_eval: |
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return self.memory_dict[pos_tuple]["score"] |
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else: |
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results = self.base_eval(pos, nth_iter) |
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self.memory_dict[pos_tuple] = results |
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self.memory_dict_new[pos_tuple] = results |
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return results["score"] |
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def get_score(self, pos_new, nth_iter): |
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score_new = self.eval_pos(pos_new, nth_iter) |
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self.verb.p_bar.update_p_bar(1, self.score_best) |
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if score_new > self.score_best: |
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self.score = score_new |
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self.pos = pos_new |
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return score_new |
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SimonBlanke /
Hyperactive
