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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 numpy as np |
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from random import random |
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from . import HillClimbingOptimizer |
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from ..core_optimizer.parameter_tracker.stochastic_hill_climbing import ( |
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ParameterTracker, |
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) |
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class StochasticHillClimbingOptimizer(HillClimbingOptimizer, ParameterTracker): |
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name = "Stochastic Hill Climbing" |
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_name_ = "stochastic_hill_climbing" |
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__name__ = "StochasticHillClimbingOptimizer" |
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optimizer_type = "local" |
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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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epsilon=0.03, |
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distribution="normal", |
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n_neighbours=3, |
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p_accept=0.5, |
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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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epsilon=epsilon, |
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distribution=distribution, |
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n_neighbours=n_neighbours, |
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) |
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self.p_accept = p_accept |
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self.temp = 1 |
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@ParameterTracker.considered_transitions |
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def _consider(self, p_accept): |
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if p_accept >= random(): |
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self._execute_transition() |
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@ParameterTracker.transitions |
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def _execute_transition(self): |
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self._new2current() |
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@property |
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def _normalized_energy_state(self): |
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denom = self.score_current + self.score_new |
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if denom == 0: |
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return 1 |
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elif abs(denom) == np.inf: |
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return 0 |
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else: |
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return (self.score_new - self.score_current) / denom |
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@property |
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def _exponent(self): |
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if self.temp == 0: |
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return -np.inf |
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else: |
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return self._normalized_energy_state / self.temp |
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def _p_accept_default(self): |
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return self.p_accept * 2 / (1 + np.exp(self._exponent)) |
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@HillClimbingOptimizer.track_new_score |
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def _transition(self, score_new): |
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p_accept = self._p_accept_default() |
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self._consider(p_accept) |
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def evaluate(self, score_new): |
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if score_new <= self.score_current: |
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self._transition(score_new) |
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else: |
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HillClimbingOptimizer.evaluate(self, score_new) |
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SimonBlanke /
Gradient-Free-Optimizers
