Total Complexity | 9 |
Total Lines | 63 |
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 | import random |
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6 | import numpy as np |
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7 | |||
8 | |||
9 | class SearchSpace: |
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10 | def __init__(self, _core_, model_nr): |
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11 | self.search_config = _core_.search_config |
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12 | self.warm_start = _core_.warm_start |
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13 | self.scatter_init = _core_.scatter_init |
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14 | self.model_nr = model_nr |
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15 | |||
16 | self.memory = {} |
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17 | |||
18 | def pos_space_limit(self): |
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19 | dim = [] |
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20 | |||
21 | for pos_key in self.para_space: |
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22 | dim.append(len(self.para_space[pos_key]) - 1) |
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23 | |||
24 | self.dim = np.array(dim) |
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25 | |||
26 | def create_searchspace(self): |
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27 | """ |
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28 | para_space = {} |
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29 | |||
30 | for para_key in search_config_temp.keys(): |
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31 | |||
32 | for param_str in search_config_temp[para_key].keys(): |
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33 | new_param_str = para_key + "." + param_str |
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34 | |||
35 | para_space[new_param_str] = search_config_temp[para_key][param_str] |
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36 | |||
37 | """ |
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38 | |||
39 | self.para_space = self.search_config[list(self.search_config)[self.model_nr]] |
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40 | |||
41 | self.pos_space_limit() |
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42 | |||
43 | def get_random_pos(self): |
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44 | pos_new = np.random.uniform(np.zeros(self.dim.shape), self.dim, self.dim.shape) |
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45 | pos = np.rint(pos_new) |
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46 | |||
47 | return pos |
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48 | |||
49 | def get_random_pos_scalar(self, hyperpara_name): |
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50 | n_para_values = len(self.para_space[hyperpara_name]) |
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51 | pos = random.randint(0, n_para_values - 1) |
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52 | |||
53 | return pos |
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54 | |||
55 | def pos2para(self, pos): |
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56 | if len(self.para_space.keys()) == pos.size: |
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57 | values_dict = {} |
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58 | for i, key in enumerate(self.para_space.keys()): |
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59 | pos_ = int(pos[i]) |
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60 | values_dict[key] = list(self.para_space[key])[pos_] |
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61 | |||
62 | return values_dict |
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63 |