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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 ..smb_opt.smbo import SMBO |
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from ..smb_opt.surrogate_models import EnsembleRegressor |
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from ..smb_opt.acquisition_function import ExpectedImprovement |
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from sklearn.tree import DecisionTreeRegressor |
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from sklearn.ensemble import GradientBoostingRegressor |
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from sklearn.svm import SVR |
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from sklearn.gaussian_process import GaussianProcessRegressor |
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from sklearn.neural_network import MLPRegressor |
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def normalize(array): |
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num = array - array.min() |
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den = array.max() - array.min() |
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if den == 0: |
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return np.random.random_sample(array.shape) |
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else: |
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return ((num / den) + 0) / 1 |
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class EnsembleOptimizer(SMBO): |
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name = "Ensemble Optimizer" |
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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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estimators=[ |
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GradientBoostingRegressor(n_estimators=5), |
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# DecisionTreeRegressor(), |
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# MLPRegressor(), |
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GaussianProcessRegressor(), |
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], |
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xi=0.01, |
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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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warnings=100000000, |
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**kwargs, |
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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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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.estimators = estimators |
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self.regr = EnsembleRegressor(estimators) |
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self.xi = xi |
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self.warm_start_smbo = warm_start_smbo |
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self.max_sample_size = max_sample_size |
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self.sampling = sampling |
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self.warnings = warnings |
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self.init_warm_start_smbo() |
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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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if len(Y_sample) == 0: |
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return self.move_random() |
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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
