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import numpy as np |
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3
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from hyperactive import Hyperactive |
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4
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5
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6
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def test_constr_opt_0(): |
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7
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def objective_function(para): |
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score = -para["x1"] * para["x1"] |
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return score |
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10
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11
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search_space = { |
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"x1": list(np.arange(-15, 15, 1)), |
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} |
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15
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def constraint_1(para): |
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print(" para", para) |
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return para["x1"] > -5 |
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constraints_list = [constraint_1] |
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hyper = Hyperactive() |
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hyper.add_search( |
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objective_function, |
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search_space, |
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n_iter=50, |
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constraints=constraints_list, |
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) |
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hyper.run() |
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search_data = hyper.search_data(objective_function) |
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x0_values = search_data["x1"].values |
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print("\n search_data \n", search_data, "\n") |
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assert np.all(x0_values > -5) |
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39
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def test_constr_opt_1(): |
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def objective_function(para): |
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score = -(para["x1"] * para["x1"] + para["x2"] * para["x2"]) |
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return score |
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search_space = { |
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"x1": list(np.arange(-10, 10, 1)), |
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"x2": list(np.arange(-10, 10, 1)), |
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} |
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49
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def constraint_1(para): |
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50
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return para["x1"] > -5 |
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52
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constraints_list = [constraint_1] |
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hyper = Hyperactive() |
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hyper.add_search( |
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objective_function, |
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search_space, |
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n_iter=50, |
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constraints=constraints_list, |
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) |
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hyper.run() |
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search_data = hyper.search_data(objective_function) |
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x0_values = search_data["x1"].values |
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66
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print("\n search_data \n", search_data, "\n") |
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assert np.all(x0_values > -5) |
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69
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70
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71
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def test_constr_opt_2(): |
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n_iter = 50 |
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74
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def objective_function(para): |
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score = -para["x1"] * para["x1"] |
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return score |
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search_space = { |
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"x1": list(np.arange(-10, 10, 0.1)), |
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} |
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81
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82
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def constraint_1(para): |
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return para["x1"] > -5 |
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85
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def constraint_2(para): |
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return para["x1"] < 5 |
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88
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constraints_list = [constraint_1, constraint_2] |
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90
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hyper = Hyperactive() |
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91
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hyper.add_search( |
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92
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objective_function, |
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search_space, |
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n_iter=50, |
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95
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constraints=constraints_list, |
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96
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) |
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97
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hyper.run() |
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99
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search_data = hyper.search_data(objective_function) |
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x0_values = search_data["x1"].values |
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102
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print("\n search_data \n", search_data, "\n") |
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104
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assert np.all(x0_values > -5) |
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assert np.all(x0_values < 5) |
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106
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107
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n_new_positions = 0 |
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108
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n_new_scores = 0 |
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109
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110
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n_current_positions = 0 |
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n_current_scores = 0 |
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112
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113
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n_best_positions = 0 |
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114
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n_best_scores = 0 |
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115
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116
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for hyper_optimizer in hyper.opt_pros.values(): |
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optimizer = hyper_optimizer.gfo_optimizer |
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119
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n_new_positions = n_new_positions + len(optimizer.pos_new_list) |
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n_new_scores = n_new_scores + len(optimizer.score_new_list) |
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122
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n_current_positions = n_current_positions + len(optimizer.pos_current_list) |
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n_current_scores = n_current_scores + len(optimizer.score_current_list) |
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125
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n_best_positions = n_best_positions + len(optimizer.pos_best_list) |
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n_best_scores = n_best_scores + len(optimizer.score_best_list) |
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127
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128
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print("\n optimizer", optimizer) |
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print(" n_new_positions", optimizer.pos_new_list) |
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130
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print(" n_new_scores", optimizer.score_new_list) |
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assert n_new_positions == n_iter |
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assert n_new_scores == n_iter |
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135
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assert n_current_positions == n_current_scores |
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assert n_current_positions <= n_new_positions |
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138
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assert n_best_positions == n_best_scores |
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assert n_best_positions <= n_new_positions |
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140
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141
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assert n_new_positions == n_new_scores |
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142
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SimonBlanke /
Hyperactive
