SimonBlanke /
Gradient-Free-Optimizers
| Total Complexity | 1 |
| Total Lines | 46 |
| Duplicated Lines | 0 % |
| Changes | 0 | ||
| 1 | # Author: Simon Blanke |
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| 2 | # Email: [email protected] |
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| 3 | # License: MIT License |
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| 4 | |||
| 5 | |||
| 6 | from ..smb_opt.exp_imp_based_opt import ExpectedImprovementBasedOptimization |
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| 7 | from ..smb_opt.surrogate_models import EnsembleRegressor |
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| 8 | |||
| 9 | |||
| 10 | from sklearn.tree import DecisionTreeRegressor |
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| 11 | from sklearn.ensemble import GradientBoostingRegressor |
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| 12 | from sklearn.svm import SVR |
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| 13 | from sklearn.gaussian_process import GaussianProcessRegressor |
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| 14 | from sklearn.neural_network import MLPRegressor |
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| 15 | |||
| 16 | |||
| 17 | class EnsembleOptimizer(ExpectedImprovementBasedOptimization): |
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| 18 | name = "Ensemble Optimizer" |
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| 19 | |||
| 20 | def __init__( |
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| 21 | self, |
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| 22 | *args, |
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| 23 | estimators=[ |
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| 24 | GradientBoostingRegressor(n_estimators=5), |
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| 25 | # DecisionTreeRegressor(), |
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| 26 | # MLPRegressor(), |
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| 27 | GaussianProcessRegressor(), |
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| 28 | ], |
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| 29 | xi=0.01, |
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| 30 | warm_start_smbo=None, |
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| 31 | max_sample_size=10000000, |
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| 32 | sampling={"random": 1000000}, |
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| 33 | warnings=100000000, |
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| 34 | **kwargs, |
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| 35 | ): |
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| 36 | super().__init__(*args, **kwargs) |
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| 37 | self.estimators = estimators |
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| 38 | self.regr = EnsembleRegressor(estimators) |
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| 39 | self.xi = xi |
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| 40 | self.warm_start_smbo = warm_start_smbo |
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| 41 | self.max_sample_size = max_sample_size |
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| 42 | self.sampling = sampling |
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| 43 | self.warnings = warnings |
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| 44 | |||
| 45 | self.init_warm_start_smbo() |
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| 46 |
