@@ 63-79 (lines=17) @@ | ||
60 | assert abs(opt0.best_score - opt1.best_score) < err |
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61 | ||
62 | ||
63 | @pytest.mark.parametrize(*optimizers) |
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64 | def test_random_state_2(Optimizer): |
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65 | opt0 = Optimizer(search_space, initialize={"random": 1}) |
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66 | opt0.search( |
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67 | objective_function, |
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68 | n_iter=n_iter, |
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69 | random_state=1, |
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70 | ) |
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71 | ||
72 | opt1 = Optimizer(search_space, initialize={"random": 1}) |
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73 | opt1.search( |
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74 | objective_function, |
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75 | n_iter=n_iter, |
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76 | random_state=10, |
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77 | ) |
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78 | ||
79 | assert abs(opt0.best_score - opt1.best_score) > err |
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80 | ||
81 | ||
82 | @pytest.mark.parametrize(*optimizers) |
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@@ 44-60 (lines=17) @@ | ||
41 | assert abs(opt0.best_score - opt1.best_score) < err |
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42 | ||
43 | ||
44 | @pytest.mark.parametrize(*optimizers) |
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45 | def test_random_state_1(Optimizer): |
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46 | opt0 = Optimizer(search_space, initialize={"random": 1}) |
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47 | opt0.search( |
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48 | objective_function, |
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49 | n_iter=n_iter, |
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50 | random_state=10, |
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51 | ) |
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52 | ||
53 | opt1 = Optimizer(search_space, initialize={"random": 1}) |
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54 | opt1.search( |
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55 | objective_function, |
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56 | n_iter=n_iter, |
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57 | random_state=10, |
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58 | ) |
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59 | ||
60 | assert abs(opt0.best_score - opt1.best_score) < err |
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61 | ||
62 | ||
63 | @pytest.mark.parametrize(*optimizers) |
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@@ 25-41 (lines=17) @@ | ||
22 | n_iter = 5 |
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23 | ||
24 | ||
25 | @pytest.mark.parametrize(*optimizers) |
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26 | def test_random_state_0(Optimizer): |
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27 | opt0 = Optimizer(search_space, initialize={"random": 1}) |
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28 | opt0.search( |
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29 | objective_function, |
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30 | n_iter=n_iter, |
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31 | random_state=1, |
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32 | ) |
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33 | ||
34 | opt1 = Optimizer(search_space, initialize={"random": 1}) |
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35 | opt1.search( |
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36 | objective_function, |
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37 | n_iter=n_iter, |
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38 | random_state=1, |
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39 | ) |
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40 | ||
41 | assert abs(opt0.best_score - opt1.best_score) < err |
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42 | ||
43 | ||
44 | @pytest.mark.parametrize(*optimizers) |