1 | import pytest |
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2 | import numpy as np |
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3 | |||
4 | |||
5 | from hyperactive import Hyperactive |
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6 | from hyperactive.optimizers.strategies import CustomOptimizationStrategy |
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7 | |||
8 | from ._parametrize import optimizers, optimizers_strat |
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9 | |||
10 | |||
11 | def objective_function(opt): |
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12 | score = -(opt["x1"] * opt["x1"] + opt["x2"] * opt["x2"]) |
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13 | return score |
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14 | |||
15 | |||
16 | search_space = { |
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17 | "x1": list(np.arange(-3, 3, 1)), |
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18 | "x2": list(np.arange(-3, 3, 1)), |
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19 | } |
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20 | |||
21 | |||
22 | View Code Duplication | @pytest.mark.parametrize(*optimizers) |
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23 | @pytest.mark.parametrize(*optimizers_strat) |
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24 | def test_strategy_combinations_0(Optimizer, Optimizer_strat): |
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25 | optimizer1 = Optimizer() |
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26 | optimizer2 = Optimizer_strat() |
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27 | |||
28 | opt_strat = CustomOptimizationStrategy() |
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29 | opt_strat.add_optimizer(optimizer1, duration=0.5) |
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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, |
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38 | optimizer=opt_strat, |
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39 | n_iter=n_iter, |
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40 | memory=False, |
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41 | ) |
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42 | hyper.run() |
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43 | |||
44 | search_data = hyper.search_data(objective_function) |
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45 | |||
46 | optimizer1 = hyper.opt_pros[0].optimizer_setup_l[0]["optimizer"] |
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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 | View Code Duplication | @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() |
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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) |
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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, |
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73 | optimizer=opt_strat, |
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74 | n_iter=n_iter, |
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75 | memory=False, |
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76 | ) |
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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"] |
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82 | optimizer2 = hyper.opt_pros[0].optimizer_setup_l[1]["optimizer"] |
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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 | View Code Duplication | @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() |
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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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128 | @pytest.mark.parametrize(*optimizers_strat) |
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129 | def test_strategy_combinations_3(Optimizer, Optimizer_strat): |
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130 | optimizer1 = Optimizer() |
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131 | optimizer2 = Optimizer_strat() |
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132 | optimizer3 = Optimizer_strat() |
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133 | |||
134 | opt_strat = CustomOptimizationStrategy() |
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135 | opt_strat.add_optimizer(optimizer1, duration=10) |
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136 | opt_strat.add_optimizer(optimizer2, duration=20) |
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137 | opt_strat.add_optimizer(optimizer3, duration=30) |
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138 | |||
139 | n_iter = 100 |
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140 | |||
141 | hyper = Hyperactive() |
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142 | hyper.add_search( |
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143 | objective_function, |
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144 | search_space, |
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145 | optimizer=opt_strat, |
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146 | n_iter=n_iter, |
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147 | memory=False, |
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148 | ) |
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149 | hyper.run() |
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150 | |||
151 | search_data = hyper.search_data(objective_function) |
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152 | |||
153 | optimizer1 = hyper.opt_pros[0].optimizer_setup_l[0]["optimizer"] |
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154 | optimizer2 = hyper.opt_pros[0].optimizer_setup_l[1]["optimizer"] |
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155 | optimizer3 = hyper.opt_pros[0].optimizer_setup_l[2]["optimizer"] |
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156 | |||
157 | assert len(search_data) == n_iter |
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158 | |||
159 | assert len(optimizer1.search_data) == round(n_iter * 10 / 60) |
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160 | assert len(optimizer2.search_data) == round(n_iter * 20 / 60) |
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161 | assert len(optimizer3.search_data) == round(n_iter * 30 / 60) |
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162 | |||
163 | assert optimizer1.best_score <= optimizer2.best_score <= optimizer3.best_score |
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164 |