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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 ..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, nth_process, _core_): |
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self.iter = 0 |
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self.search_config = _core_.search_config |
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self.memory = _core_.memory |
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self._score_best = -1000 |
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self.pos_best = None |
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self.model = None |
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self.nth_process = nth_process |
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model_nr = nth_process % _core_.n_models |
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self.func_ = list(_core_.search_config.keys())[model_nr] |
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self._space_ = SearchSpace(_core_, model_nr) |
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self.func_name = str(self.func_).split(" ")[1] |
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self._space_.create_searchspace() |
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self._model_ = Model(self.func_, nth_process) |
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self._init_ = InitSearchPosition( |
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self._space_, self._model_, _core_.warm_start, _core_.scatter_init |
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) |
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self.eval_time_sum = 0 |
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def _get_warm_start(self): |
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return self._space_.pos2para(self.pos_best) |
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@property |
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def score_best(self): |
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return self._score_best |
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@score_best.setter |
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def score_best(self, value): |
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self.model_best = self.model |
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self._score_best = value |
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def eval_pos(self, pos, X, y, force_eval=False): |
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pos_str = pos.tostring() |
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if pos_str in self._space_.memory and self.memory and not force_eval: |
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return self._space_.memory[pos_str] |
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else: |
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para = self._space_.pos2para(pos) |
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score, eval_time, self.model = self._model_.train_model(para, X, y) |
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self._space_.memory[pos_str] = score |
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self.eval_time_sum = self.eval_time_sum + eval_time |
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return score |
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SimonBlanke /
Hyperactive
