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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 time |
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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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from .extensions.memory import ShortTermMemory, LongTermMemory |
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class Candidate: |
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def __init__(self, nth_process, _main_args_, _info_): |
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self.start_time = time.time() |
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self.i = 0 |
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self._main_args_ = _main_args_ |
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self.memory = _main_args_.memory |
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self._info_ = _info_() |
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self._score_best = -np.inf |
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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 % _main_args_.n_models |
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self.func_ = list(_main_args_.search_config.keys())[model_nr] |
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self._space_ = SearchSpace(_main_args_, model_nr) |
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self.func_name = str(self.func_).split(" ")[1] |
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self._model_ = Model(self.func_, nth_process, _main_args_) |
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self._init_ = InitSearchPosition(self._space_, self._model_, _main_args_) |
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self.eval_time = [] |
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if not self.memory: |
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self.mem = None |
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self.eval_pos = self.eval_pos_noMem |
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elif self.memory == "short": |
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self.mem = ShortTermMemory(self._space_, _main_args_, self) |
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self.eval_pos = self.eval_pos_Mem |
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elif self.memory == "long": |
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self.mem = LongTermMemory(self._space_, _main_args_, self) |
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self.eval_pos = self.eval_pos_Mem |
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self.mem.load_memory(self, self._info_) |
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else: |
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print("Warning: Memory not defined") |
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self.mem = None |
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self.eval_pos = self.eval_pos_noMem |
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if self.mem: |
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if self.mem.meta_data_found: |
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self.pos_best = self.mem.pos_best |
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self.score_best = self.mem.score_best |
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self.pos_best = self._init_._set_start_pos(self._info_) |
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self.score_best = self.eval_pos(self.pos_best) |
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def _get_warm_start(self): |
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return self._space_.pos2para(self.pos_best) |
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def _process_results(self, _opt_args_): |
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self.total_time = time.time() - self.start_time |
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start_point = self._info_.print_start_point(self) |
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if self._main_args_.memory == "long": |
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self.mem.save_memory(self._main_args_, _opt_args_, self) |
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return start_point |
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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 base_eval(self, pos): |
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para = self._space_.pos2para(pos) |
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para["iteration"] = self.i |
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score, eval_time, self.model = self._model_.train_model(para) |
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self.eval_time.append(eval_time) |
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return score |
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def eval_pos_noMem(self, pos): |
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score = self.base_eval(pos) |
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return score |
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def eval_pos_Mem(self, pos, force_eval=False): |
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pos.astype(int) |
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pos_str = pos.tostring() |
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if pos_str in self.mem.memory_dict and not force_eval: |
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return self.mem.memory_dict[pos_str] |
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else: |
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score = self.base_eval(pos) |
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self.mem.memory_dict[pos_str] = score |
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return score |
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SimonBlanke /
Hyperactive
