SimonBlanke /
Hyperactive
| @@ 92-124 (lines=33) @@ | ||
| 89 | assert optimizer1.best_score <= optimizer2.best_score |
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| 90 | ||
| 91 | ||
| 92 | @pytest.mark.parametrize(*optimizers) |
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| 93 | @pytest.mark.parametrize(*optimizers_strat) |
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| 94 | def test_strategy_combinations_2(Optimizer, Optimizer_strat): |
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| 95 | optimizer1 = Optimizer() |
|
| 96 | optimizer2 = Optimizer_strat() |
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| 97 | ||
| 98 | opt_strat = CustomOptimizationStrategy() |
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| 99 | opt_strat.add_optimizer(optimizer1, duration=0.9) |
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| 100 | opt_strat.add_optimizer(optimizer2, duration=0.1) |
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| 101 | ||
| 102 | n_iter = 10 |
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| 103 | ||
| 104 | hyper = Hyperactive() |
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| 105 | hyper.add_search( |
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| 106 | objective_function, |
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| 107 | search_space, |
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| 108 | optimizer=opt_strat, |
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| 109 | n_iter=n_iter, |
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| 110 | memory=False, |
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| 111 | ) |
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| 112 | hyper.run() |
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| 113 | ||
| 114 | search_data = hyper.search_data(objective_function) |
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| 115 | ||
| 116 | optimizer1 = hyper.opt_pros[0].optimizer_setup_l[0]["optimizer"] |
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| 117 | optimizer2 = hyper.opt_pros[0].optimizer_setup_l[1]["optimizer"] |
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| 118 | ||
| 119 | assert len(search_data) == n_iter |
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| 120 | ||
| 121 | assert len(optimizer1.search_data) == 9 |
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| 122 | assert len(optimizer2.search_data) == 1 |
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| 123 | ||
| 124 | assert optimizer1.best_score <= optimizer2.best_score |
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| 125 | ||
| 126 | ||
| 127 | @pytest.mark.parametrize(*optimizers) |
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| @@ 57-89 (lines=33) @@ | ||
| 54 | assert optimizer1.best_score <= optimizer2.best_score |
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| 55 | ||
| 56 | ||
| 57 | @pytest.mark.parametrize(*optimizers) |
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| 58 | @pytest.mark.parametrize(*optimizers_strat) |
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| 59 | def test_strategy_combinations_1(Optimizer, Optimizer_strat): |
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| 60 | optimizer1 = Optimizer() |
|
| 61 | optimizer2 = Optimizer_strat() |
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| 62 | ||
| 63 | opt_strat = CustomOptimizationStrategy() |
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| 64 | opt_strat.add_optimizer(optimizer1, duration=0.1) |
|
| 65 | opt_strat.add_optimizer(optimizer2, duration=0.9) |
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| 66 | ||
| 67 | n_iter = 10 |
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| 68 | ||
| 69 | hyper = Hyperactive() |
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| 70 | hyper.add_search( |
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| 71 | objective_function, |
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| 72 | search_space, |
|
| 73 | optimizer=opt_strat, |
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| 74 | n_iter=n_iter, |
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| 75 | memory=False, |
|
| 76 | ) |
|
| 77 | hyper.run() |
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| 78 | ||
| 79 | search_data = hyper.search_data(objective_function) |
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| 80 | ||
| 81 | optimizer1 = hyper.opt_pros[0].optimizer_setup_l[0]["optimizer"] |
|
| 82 | optimizer2 = hyper.opt_pros[0].optimizer_setup_l[1]["optimizer"] |
|
| 83 | ||
| 84 | assert len(search_data) == n_iter |
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| 85 | ||
| 86 | assert len(optimizer1.search_data) == 1 |
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| 87 | assert len(optimizer2.search_data) == 9 |
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| 88 | ||
| 89 | assert optimizer1.best_score <= optimizer2.best_score |
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| 90 | ||
| 91 | ||
| 92 | @pytest.mark.parametrize(*optimizers) |
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| @@ 22-54 (lines=33) @@ | ||
| 19 | } |
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| 20 | ||
| 21 | ||
| 22 | @pytest.mark.parametrize(*optimizers) |
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| 23 | @pytest.mark.parametrize(*optimizers_strat) |
|
| 24 | def test_strategy_combinations_0(Optimizer, Optimizer_strat): |
|
| 25 | optimizer1 = Optimizer() |
|
| 26 | optimizer2 = Optimizer_strat() |
|
| 27 | ||
| 28 | opt_strat = CustomOptimizationStrategy() |
|
| 29 | opt_strat.add_optimizer(optimizer1, duration=0.5) |
|
| 30 | opt_strat.add_optimizer(optimizer2, duration=0.5) |
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| 31 | ||
| 32 | n_iter = 30 |
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| 33 | ||
| 34 | hyper = Hyperactive() |
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| 35 | hyper.add_search( |
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| 36 | objective_function, |
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| 37 | search_space, |
|
| 38 | optimizer=opt_strat, |
|
| 39 | n_iter=n_iter, |
|
| 40 | memory=False, |
|
| 41 | ) |
|
| 42 | hyper.run() |
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| 43 | ||
| 44 | search_data = hyper.search_data(objective_function) |
|
| 45 | ||
| 46 | optimizer1 = hyper.opt_pros[0].optimizer_setup_l[0]["optimizer"] |
|
| 47 | optimizer2 = hyper.opt_pros[0].optimizer_setup_l[1]["optimizer"] |
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| 48 | ||
| 49 | assert len(search_data) == n_iter |
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| 50 | ||
| 51 | assert len(optimizer1.search_data) == 15 |
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| 52 | assert len(optimizer2.search_data) == 15 |
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| 53 | ||
| 54 | assert optimizer1.best_score <= optimizer2.best_score |
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| 55 | ||
| 56 | ||
| 57 | @pytest.mark.parametrize(*optimizers) |
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| @@ 23-55 (lines=33) @@ | ||
| 20 | } |
|
| 21 | ||
| 22 | ||
| 23 | @pytest.mark.parametrize(*optimizers) |
|
| 24 | def test_strategy_combinations_0(Optimizer): |
|
| 25 | optimizer1 = Optimizer() |
|
| 26 | optimizer2 = HillClimbingOptimizer() |
|
| 27 | ||
| 28 | opt_strat = CustomOptimizationStrategy() |
|
| 29 | opt_strat.add_optimizer(optimizer1, duration=0.5) |
|
| 30 | opt_strat.add_optimizer(optimizer2, duration=0.5) |
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| 31 | ||
| 32 | n_iter = 4 |
|
| 33 | ||
| 34 | hyper = Hyperactive() |
|
| 35 | hyper.add_search( |
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| 36 | objective_function, |
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| 37 | search_space, |
|
| 38 | optimizer=opt_strat, |
|
| 39 | n_iter=n_iter, |
|
| 40 | memory=False, |
|
| 41 | initialize={"random": 1}, |
|
| 42 | ) |
|
| 43 | hyper.run() |
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| 44 | ||
| 45 | search_data = hyper.search_data(objective_function) |
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| 46 | ||
| 47 | optimizer1 = hyper.opt_pros[0].optimizer_setup_l[0]["optimizer"] |
|
| 48 | optimizer2 = hyper.opt_pros[0].optimizer_setup_l[1]["optimizer"] |
|
| 49 | ||
| 50 | assert len(search_data) == n_iter |
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| 51 | ||
| 52 | assert len(optimizer1.search_data) == 2 |
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| 53 | assert len(optimizer2.search_data) == 2 |
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| 54 | ||
| 55 | assert optimizer1.best_score <= optimizer2.best_score |
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| 56 | ||
| @@ 24-55 (lines=32) @@ | ||
| 21 | } |
|
| 22 | ||
| 23 | ||
| 24 | @pytest.mark.parametrize(*optimizers_smbo) |
|
| 25 | def test_memory_Warm_start_smbo_0(Optimizer_smbo): |
|
| 26 | optimizer1 = GridSearchOptimizer() |
|
| 27 | optimizer2 = Optimizer_smbo() |
|
| 28 | ||
| 29 | opt_strat = CustomOptimizationStrategy() |
|
| 30 | opt_strat.add_optimizer(optimizer1, duration=0.8) |
|
| 31 | opt_strat.add_optimizer(optimizer2, duration=0.2) |
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| 32 | ||
| 33 | n_iter = 100 |
|
| 34 | ||
| 35 | hyper = Hyperactive() |
|
| 36 | hyper.add_search( |
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| 37 | objective_function, |
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| 38 | search_space, |
|
| 39 | optimizer=opt_strat, |
|
| 40 | n_iter=n_iter, |
|
| 41 | memory=True, |
|
| 42 | ) |
|
| 43 | hyper.run() |
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| 44 | ||
| 45 | search_data = hyper.search_data(objective_function) |
|
| 46 | ||
| 47 | optimizer1 = hyper.opt_pros[0].optimizer_setup_l[0]["optimizer"] |
|
| 48 | optimizer2 = hyper.opt_pros[0].optimizer_setup_l[1]["optimizer"] |
|
| 49 | ||
| 50 | assert len(search_data) == n_iter |
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| 51 | ||
| 52 | assert len(optimizer1.search_data) == 80 |
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| 53 | assert len(optimizer2.search_data) == 20 |
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| 54 | ||
| 55 | assert optimizer1.best_score <= optimizer2.best_score |
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| 56 | ||
