SimonBlanke /
Gradient-Free-Optimizers
| Total Complexity | 7 |
| Total Lines | 56 |
| 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 | import numpy as np |
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| 7 | |||
| 8 | np.seterr(divide="ignore", invalid="ignore") |
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| 9 | |||
| 10 | from ..base_optimizer import BaseOptimizer |
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| 11 | from ...search import Search |
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| 12 | |||
| 13 | |||
| 14 | class SMBO(BaseOptimizer, Search): |
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| 15 | def __init__( |
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| 16 | self, |
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| 17 | search_space, |
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| 18 | initialize={"grid": 4, "random": 2, "vertices": 4}, |
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| 19 | warm_start_smbo=None, |
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| 20 | ): |
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| 21 | super().__init__(search_space, initialize) |
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| 22 | self.warm_start_smbo = warm_start_smbo |
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| 23 | |||
| 24 | self.X_sample = [] |
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| 25 | self.Y_sample = [] |
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| 26 | |||
| 27 | self.all_pos_comb = self._all_possible_pos() |
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| 28 | |||
| 29 | def init_warm_start_smbo(self): |
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| 30 | if self.warm_start_smbo is not None: |
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| 31 | (X_sample_values, Y_sample) = self.warm_start_smbo |
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| 32 | |||
| 33 | self.X_sample = self.conv.values2positions(X_sample_values) |
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| 34 | self.Y_sample = list(Y_sample) |
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| 35 | |||
| 36 | def track_X_sample(func): |
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| 37 | def wrapper(self, *args, **kwargs): |
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| 38 | pos = func(self, *args, **kwargs) |
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| 39 | self.X_sample.append(pos) |
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| 40 | return pos |
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| 41 | |||
| 42 | return wrapper |
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| 43 | |||
| 44 | def _all_possible_pos(self): |
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| 45 | pos_space = [] |
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| 46 | for dim_ in self.conv.max_positions: |
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| 47 | pos_space.append(np.arange(dim_)) |
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| 48 | |||
| 49 | n_dim = len(pos_space) |
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| 50 | return np.array(np.meshgrid(*pos_space)).T.reshape(-1, n_dim) |
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| 51 | |||
| 52 | @track_X_sample |
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| 53 | def init_pos(self, pos): |
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| 54 | super().init_pos(pos) |
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| 55 | return pos |
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| 56 |
