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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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from ._progress_bar import ProgressBarLVL0, ProgressBarLVL1 |
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from ._times_tracker import TimesTracker |
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from ._search_statistics import SearchStatistics |
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from ._memory import Memory |
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from ._print_info import print_info |
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from ._stop_run import StopRun |
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from ._results_manager import ResultsManager |
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class Search(TimesTracker, SearchStatistics): |
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def __init__(self): |
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super().__init__() |
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self.optimizers = [] |
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self.new_results_list = [] |
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self.all_results_list = [] |
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self.score_l = [] |
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self.pos_l = [] |
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self.random_seed = None |
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self.results_mang = ResultsManager() |
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@TimesTracker.eval_time |
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def _score(self, pos): |
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return self.score(pos) |
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@TimesTracker.iter_time |
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def _initialization(self): |
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self.best_score = self.p_bar.score_best |
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init_pos = self.init_pos() |
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score_new = self._score(init_pos) |
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self.evaluate_init(score_new) |
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self.pos_l.append(init_pos) |
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self.score_l.append(score_new) |
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self.p_bar.update(score_new, init_pos, self.nth_iter) |
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self.n_init_total += 1 |
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self.n_init_search += 1 |
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self.stop.update(self.p_bar.score_best, self.score_l) |
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@TimesTracker.iter_time |
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def _iteration(self): |
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self.best_score = self.p_bar.score_best |
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pos_new = self.iterate() |
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score_new = self._score(pos_new) |
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self.evaluate(score_new) |
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self.pos_l.append(pos_new) |
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self.score_l.append(score_new) |
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self.p_bar.update(score_new, pos_new, self.nth_iter) |
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self.n_iter_total += 1 |
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self.n_iter_search += 1 |
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self.stop.update(self.p_bar.score_best, self.score_l) |
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def search( |
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self, |
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objective_function, |
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n_iter, |
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max_time=None, |
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max_score=None, |
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early_stopping=None, |
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memory=True, |
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memory_warm_start=None, |
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verbosity=["progress_bar", "print_results", "print_times"], |
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optimum = "maximum", |
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): |
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self.optimum = optimum |
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self.init_search( |
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objective_function, |
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n_iter, |
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max_time, |
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max_score, |
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early_stopping, |
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memory, |
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memory_warm_start, |
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verbosity, |
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) |
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for nth_trial in range(n_iter): |
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self.search_step(nth_trial) |
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if self.stop.check(): |
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break |
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self.finish_search() |
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@SearchStatistics.init_stats |
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def init_search( |
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self, |
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objective_function, |
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n_iter, |
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max_time, |
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max_score, |
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early_stopping, |
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memory, |
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memory_warm_start, |
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verbosity, |
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): |
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if getattr(self, "optimum", "maximum") == "minimum": |
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self.objective_function = lambda pos: -objective_function(pos) |
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else: |
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self.objective_function = objective_function |
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self.n_iter = n_iter |
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self.max_time = max_time |
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self.max_score = max_score |
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self.early_stopping = early_stopping |
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self.memory = memory |
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self.memory_warm_start = memory_warm_start |
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self.verbosity = verbosity |
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self.results_mang.conv = self.conv |
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if self.verbosity is False: |
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self.verbosity = [] |
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start_time = time.time() |
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self.stop = StopRun( |
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start_time, self.max_time, self.max_score, self.early_stopping |
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) |
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if "progress_bar" in self.verbosity: |
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self.p_bar = ProgressBarLVL1( |
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self.nth_process, self.n_iter, self.objective_function |
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) |
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else: |
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self.p_bar = ProgressBarLVL0( |
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self.nth_process, self.n_iter, self.objective_function |
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) |
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self.mem = Memory(self.memory_warm_start, self.conv, memory=self.memory) |
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if self.memory not in [False, None]: |
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self.score = self.results_mang.score( |
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self.mem.memory(self.objective_function) |
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) |
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else: |
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self.score = self.results_mang.score(self.objective_function) |
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self.n_inits_norm = min( |
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(self.init.n_inits - self.n_init_total), self.n_iter |
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) |
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def finish_search(self): |
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self.search_data = self.results_mang.search_data |
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self.best_score = self.p_bar.score_best |
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self.best_value = self.conv.position2value(self.p_bar.pos_best) |
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self.best_para = self.conv.value2para(self.best_value) |
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if self.memory not in [False, None]: |
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self.memory_dict = self.mem.memory_dict |
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else: |
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self.memory_dict = {} |
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self.p_bar.close() |
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print_info( |
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self.verbosity, |
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self.objective_function, |
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self.best_score, |
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self.best_para, |
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self.eval_times, |
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self.iter_times, |
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self.n_iter, |
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self.random_seed, |
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) |
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def search_step(self, nth_iter): |
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self.nth_iter = nth_iter |
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if self.nth_iter < self.n_inits_norm: |
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self._initialization() |
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if self.nth_iter == self.n_init_search: |
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self.finish_initialization() |
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if self.n_init_search <= self.nth_iter < self.n_iter: |
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self._iteration() |
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SimonBlanke /
Gradient-Free-Optimizers
