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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 random |
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import numpy as np |
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import pandas as pd |
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from .init_positions import Initializer |
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from .progress_bar import ProgressBarLVL0, ProgressBarLVL1 |
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from .times_tracker import TimesTracker |
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from .memory import Memory |
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from .print_info import print_info |
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def time_exceeded(start_time, max_time): |
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run_time = time.time() - start_time |
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return max_time and run_time > max_time |
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def score_exceeded(score_best, max_score): |
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return max_score and score_best >= max_score |
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def set_random_seed(nth_process, random_state): |
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""" |
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Sets the random seed separately for each thread |
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(to avoid getting the same results in each thread) |
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""" |
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if nth_process is None: |
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nth_process = 0 |
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if random_state is None: |
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random_state = np.random.randint(0, high=2 ** 32 - 2) |
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random.seed(random_state + nth_process) |
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np.random.seed(random_state + nth_process) |
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class Search(TimesTracker): |
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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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@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, init_pos, nth_iter): |
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self.init_pos(init_pos) |
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score_new = self._score(init_pos) |
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self.evaluate(score_new) |
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self.p_bar.update(score_new, init_pos, nth_iter) |
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@TimesTracker.iter_time |
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def _iteration(self, nth_iter): |
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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.p_bar.update(score_new, pos_new, nth_iter) |
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def _init_search(self): |
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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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set_random_seed(self.nth_process, self.random_state) |
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# get init positions |
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init = Initializer(self.conv) |
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init_positions = init.set_pos(self.initialize) |
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return init_positions |
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def _early_stop(self): |
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if time_exceeded(self.start_time, self.max_time): |
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return True |
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elif score_exceeded(self.p_bar.score_best, self.max_score): |
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return True |
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else: |
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return False |
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def print_info(self, *args): |
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print_info(*args) |
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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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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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random_state=None, |
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nth_process=None, |
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): |
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self.start_time = time.time() |
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if verbosity is False: |
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verbosity = [] |
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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.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.random_state = random_state |
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self.nth_process = nth_process |
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init_positions = self._init_search() |
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if memory is True: |
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mem = Memory(memory_warm_start, self.conv) |
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self.score = self.results_mang.score(mem.memory(objective_function)) |
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else: |
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self.score = self.results_mang.score(objective_function) |
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# loop to initialize N positions |
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for init_pos, nth_iter in zip(init_positions, range(n_iter)): |
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if self._early_stop(): |
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break |
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self._initialization(init_pos, nth_iter) |
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# loop to do the iterations |
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for nth_iter in range(len(init_positions), n_iter): |
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if self._early_stop(): |
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break |
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self._iteration(nth_iter) |
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self.results = pd.DataFrame(self.results_mang.results_list) |
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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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self.results["eval_time"] = self.eval_times |
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self.results["iter_time"] = self.iter_times |
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if memory is not False: |
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self.memory_dict = 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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self.print_info( |
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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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) |
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SimonBlanke /
Gradient-Free-Optimizers
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