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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 scipy.stats import norm |
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from .smbo import SMBO |
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from .surrogate_models import ( |
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GPR_linear, |
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GPR, |
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) |
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from .acquisition_function import ExpectedImprovement |
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gaussian_process = {"gp_nonlinear": GPR(), "gp_linear": GPR_linear()} |
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def normalize(array): |
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array_min = array.min() |
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array_max = array.max() |
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range_ = array_max - array_min |
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if range_ == 0: |
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return np.random.random_sample(array.shape) |
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else: |
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return (array - array_min) / range_ |
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class BayesianOptimizer(SMBO): |
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name = "Bayesian Optimization" |
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_name_ = "bayesian_optimization" |
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__name__ = "BayesianOptimizer" |
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optimizer_type = "sequential" |
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computationally_expensive = True |
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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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warm_start_smbo=None, |
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max_sample_size=10000000, |
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sampling={"random": 1000000}, |
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replacement=True, |
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gpr=gaussian_process["gp_nonlinear"], |
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xi=0.03, |
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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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warm_start_smbo=warm_start_smbo, |
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max_sample_size=max_sample_size, |
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sampling=sampling, |
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replacement=replacement, |
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) |
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self.gpr = gpr |
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self.regr = gpr |
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self.xi = xi |
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def finish_initialization(self): |
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self.all_pos_comb = self._all_possible_pos() |
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return super().finish_initialization() |
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def _expected_improvement(self): |
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self.pos_comb = self._sampling(self.all_pos_comb) |
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acqu_func = ExpectedImprovement(self.regr, self.pos_comb, self.xi) |
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return acqu_func.calculate(self.X_sample, self.Y_sample) |
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def _training(self): |
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X_sample = np.array(self.X_sample) |
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Y_sample = np.array(self.Y_sample) |
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Y_sample = normalize(Y_sample).reshape(-1, 1) |
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self.regr.fit(X_sample, Y_sample) |
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SimonBlanke /
Gradient-Free-Optimizers
