1 | import numpy as np |
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2 | from hyperactive import Hyperactive |
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3 | |||
4 | |||
5 | def objective_function(opt): |
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6 | score = -opt["x1"] * opt["x1"] |
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7 | return score |
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8 | |||
9 | |||
10 | search_space = { |
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11 | "x1": np.arange(-100, 101, 1), |
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12 | } |
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13 | |||
14 | |||
15 | def test_initialize_warm_start_0(): |
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16 | init = { |
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17 | "x1": 0, |
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18 | } |
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19 | |||
20 | initialize = {"warm_start": [init]} |
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21 | |||
22 | hyper = Hyperactive() |
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23 | hyper.add_search( |
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24 | objective_function, search_space, n_iter=1, initialize=initialize, |
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25 | ) |
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26 | hyper.run() |
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27 | |||
28 | assert abs(hyper.best_score(objective_function)) < 0.001 |
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29 | |||
30 | |||
31 | def test_initialize_warm_start_1(): |
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32 | search_space = { |
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33 | "x1": np.arange(-10, 10, 1), |
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34 | } |
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35 | init = { |
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36 | "x1": -10, |
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37 | } |
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38 | |||
39 | initialize = {"warm_start": [init]} |
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40 | |||
41 | hyper = Hyperactive() |
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42 | hyper.add_search( |
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43 | objective_function, search_space, n_iter=1, initialize=initialize, |
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44 | ) |
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45 | hyper.run() |
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46 | |||
47 | assert hyper.best_para(objective_function) == init |
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48 | |||
49 | |||
50 | def test_initialize_vertices(): |
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51 | initialize = {"vertices": 2} |
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52 | |||
53 | hyper = Hyperactive() |
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54 | hyper.add_search( |
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55 | objective_function, search_space, n_iter=2, initialize=initialize, |
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56 | ) |
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57 | hyper.run() |
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58 | |||
59 | assert abs(hyper.best_score(objective_function)) - 10000 < 0.001 |
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60 | |||
61 | |||
62 | View Code Duplication | def test_initialize_grid_0(): |
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63 | search_space = { |
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64 | "x1": np.arange(-1, 2, 1), |
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65 | } |
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66 | initialize = {"grid": 1} |
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67 | |||
68 | hyper = Hyperactive() |
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69 | hyper.add_search( |
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70 | objective_function, search_space, n_iter=1, initialize=initialize, |
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71 | ) |
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72 | hyper.run() |
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73 | |||
74 | assert abs(hyper.best_score(objective_function)) < 0.001 |
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75 | |||
76 | |||
77 | View Code Duplication | def test_initialize_grid_1(): |
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78 | search_space = { |
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79 | "x1": np.arange(-2, 3, 1), |
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80 | } |
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81 | |||
82 | initialize = {"grid": 1} |
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83 | |||
84 | hyper = Hyperactive() |
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85 | hyper.add_search( |
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86 | objective_function, search_space, n_iter=1, initialize=initialize, |
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87 | ) |
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88 | hyper.run() |
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89 | |||
90 | assert abs(hyper.best_score(objective_function)) - 1 < 0.001 |
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91 | |||
92 | |||
93 | def test_initialize_all_0(): |
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94 | search_space = { |
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95 | "x1": np.arange(-2, 3, 1), |
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96 | } |
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97 | |||
98 | initialize = {"grid": 100, "vertices": 100, "random": 100} |
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99 | |||
100 | hyper = Hyperactive() |
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101 | hyper.add_search( |
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102 | objective_function, search_space, n_iter=300, initialize=initialize, |
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103 | ) |
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104 | hyper.run() |
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105 | |||
106 |