| @@ 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) |
|