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1
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import time |
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2
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import pytest |
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3
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import numpy as np |
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4
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5
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from ._parametrize import optimizers |
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6
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7
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8
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def objective_function(para): |
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9
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score = -para["x1"] * para["x1"] |
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10
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return score |
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11
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12
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13
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search_space = { |
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14
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"x1": np.arange(-100, 100, 0.1), |
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15
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} |
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16
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17
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18
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@pytest.mark.parametrize(*optimizers) |
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19
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def test_random_seed_0(Optimizer): |
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20
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opt = Optimizer(search_space, initialize={"random": 3}) |
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opt.search(objective_function, n_iter=5) |
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22
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23
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assert opt.random_seed is not None |
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24
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25
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26
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@pytest.mark.parametrize(*optimizers) |
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27
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def test_random_seed_1(Optimizer): |
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28
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opt = Optimizer(search_space) |
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29
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print("opt.random_seed", opt.random_seed) |
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30
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31
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assert opt.random_seed is not None |
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32
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33
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34
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@pytest.mark.parametrize(*optimizers) |
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35
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def test_random_seed_2(Optimizer): |
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36
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random_state = 42 |
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37
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opt = Optimizer(search_space, initialize={"random": 3}, random_state=random_state) |
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38
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opt.search(objective_function, n_iter=5) |
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39
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40
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assert opt.random_seed == random_state |
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41
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42
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43
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@pytest.mark.parametrize(*optimizers) |
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44
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def test_random_seed_3(Optimizer): |
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45
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random_state = 42 |
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46
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opt = Optimizer(search_space, random_state=random_state) |
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47
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48
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assert opt.random_seed == random_state |
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49
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50
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51
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@pytest.mark.parametrize(*optimizers) |
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52
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def test_random_seed_4(Optimizer): |
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53
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n_iter = 10 |
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54
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55
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opt1 = Optimizer(search_space, initialize={"random": 3}) |
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56
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opt1.search(objective_function, n_iter=n_iter) |
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57
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58
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best_score1 = opt1.best_score |
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59
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60
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opt2 = Optimizer( |
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61
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search_space, initialize={"random": 3}, random_state=opt1.random_seed |
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62
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) |
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63
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opt2.search(objective_function, n_iter=n_iter) |
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64
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65
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best_score2 = opt2.best_score |
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66
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67
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print("opt1.random_seed", opt1.random_seed) |
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68
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print("opt2.random_seed", opt2.random_seed) |
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69
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70
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print("\n opt1.search_data \n", opt1.search_data) |
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71
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print("\n opt2.search_data \n", opt2.search_data) |
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72
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73
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assert best_score1 == best_score2 |
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74
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SimonBlanke /
Gradient-Free-Optimizers
