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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 os |
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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 hypermemory import Hypermemory |
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def meta_data_path(): |
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current_path = os.path.realpath(__file__) |
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return current_path.rsplit("/", 1)[0] + "/meta_data/" |
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class ShortTermMemory: |
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def __init__(self, _space_, _main_args_, _cand_): |
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self._space_ = _space_ |
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self._main_args_ = _main_args_ |
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self.pos_best = None |
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self.score_best = -np.inf |
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self.memory_type = _main_args_.memory |
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self.memory_dict = {} |
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self.memory_dict_new = {} |
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self.meta_data_found = False |
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self.n_dims = None |
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class SearchProcess: |
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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.memory_dict = {} |
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self.memory_dict_new = {} |
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self._info_ = _info_() |
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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.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.search_space = _main_args_.search_config[self.func_] |
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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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self.iter_times = [] |
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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 = None |
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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 = Hypermemory( |
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_main_args_.X, |
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_main_args_.y, |
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self.func_, |
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self.search_space, |
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path=meta_data_path(), |
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) |
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self.eval_pos = self.eval_pos_Mem |
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self.memory_dict = self.mem.load() |
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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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def _init_eval(self): |
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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): |
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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.dump(self.memory_dict_new, main_args=self._main_args_) |
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return start_point |
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def init_pos(self, n_positions): |
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init_pos_list = [] |
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for i in range(n_positions): |
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init_pos = self._init_._set_start_pos(self._info_) |
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init_pos_list.append(init_pos) |
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return init_pos_list |
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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, p_bar, nth_iter): |
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para = self._space_.pos2para(pos) |
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para["iteration"] = self.i |
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results = self._model_.train_model(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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p_bar.best_since_iter = nth_iter |
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return results |
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def eval_pos_noMem(self, pos, p_bar, nth_iter): |
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results = self.base_eval(pos, p_bar, nth_iter) |
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return results["score"] |
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def eval_pos_Mem(self, pos, p_bar, nth_iter, 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.memory_dict and not force_eval: |
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return self.memory_dict[pos_str]["score"] |
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else: |
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results = self.base_eval(pos, p_bar, nth_iter) |
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self.memory_dict[pos_str] = results |
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self.memory_dict_new[pos_str] = results |
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return results["score"] |
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SimonBlanke /
Hyperactive
