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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 copy |
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import random |
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import numpy as np |
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from .base_population_optimizer import BasePopulationOptimizer |
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from ..local_opt import SimulatedAnnealingOptimizer |
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class ParallelTemperingOptimizer(BasePopulationOptimizer): |
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name = "Parallel Tempering" |
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_name_ = "parallel_tempering" |
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__name__ = "ParallelTemperingOptimizer" |
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optimizer_type = "population" |
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computationally_expensive = False |
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def __init__( |
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self, |
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search_space, |
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initialize={"grid": 4, "random": 2, "vertices": 4}, |
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constraints=[], |
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random_state=None, |
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rand_rest_p=0, |
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nth_process=None, |
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population=5, |
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n_iter_swap=5, |
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): |
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super().__init__( |
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search_space=search_space, |
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initialize=initialize, |
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constraints=constraints, |
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random_state=random_state, |
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rand_rest_p=rand_rest_p, |
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nth_process=nth_process, |
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) |
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self.population = population |
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self.n_iter_swap = n_iter_swap |
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self.systems = self._create_population(SimulatedAnnealingOptimizer) |
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for system in self.systems: |
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system.temp = 1.1 ** random.uniform(0, 25) |
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self.optimizers = self.systems |
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def _swap_pos(self): |
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for _p1_ in self.systems: |
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_systems_temp = copy.copy(self.systems) |
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if len(_systems_temp) > 1: |
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_systems_temp.remove(_p1_) |
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rand = random.uniform(0, 1) * 100 |
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_p2_ = np.random.choice(_systems_temp) |
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p_accept = self._accept_swap(_p1_, _p2_) |
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if p_accept > rand: |
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_p1_.temp, _p2_.temp = (_p2_.temp, _p1_.temp) |
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def _accept_swap(self, _p1_, _p2_): |
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denom = _p1_.score_current + _p2_.score_current |
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if denom == 0: |
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return 100 |
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elif abs(denom) == np.inf: |
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return 0 |
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else: |
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score_diff_norm = (_p1_.score_current - _p2_.score_current) / denom |
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temp = (1 / _p1_.temp) - (1 / _p2_.temp) |
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return np.exp(score_diff_norm * temp) * 100 |
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@BasePopulationOptimizer.track_new_pos |
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def init_pos(self): |
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nth_pop = self.nth_trial % len(self.systems) |
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self.p_current = self.systems[nth_pop] |
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return self.p_current.init_pos() |
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@BasePopulationOptimizer.track_new_pos |
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def iterate(self): |
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self.p_current = self.systems[self.nth_trial % len(self.systems)] |
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return self.p_current.iterate() |
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@BasePopulationOptimizer.track_new_score |
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def evaluate(self, score_new): |
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notZero = self.n_iter_swap != 0 |
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modZero = self.nth_trial % self.n_iter_swap == 0 |
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if notZero and modZero: |
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self._swap_pos() |
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self.p_current.evaluate(score_new) |
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SimonBlanke /
Gradient-Free-Optimizers
