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import numpy as np |
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import pytest |
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from ._parametrize import optimizers |
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6
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7
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@pytest.mark.parametrize(*optimizers) |
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def test_constr_opt_0(Optimizer): |
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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": np.arange(-15, 15, 1), |
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15
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} |
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def constraint_1(para): |
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return para["x1"] > -5 |
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constraints_list = [constraint_1] |
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opt = Optimizer(search_space, constraints=constraints_list) |
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opt.search(objective_function, n_iter=20) |
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search_data = opt.search_data |
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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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@pytest.mark.parametrize(*optimizers) |
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def test_constr_opt_1(Optimizer): |
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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": np.arange(-10, 10, 1), |
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"x2": np.arange(-10, 10, 1), |
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} |
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def constraint_1(para): |
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return para["x1"] > -5 |
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constraints_list = [constraint_1] |
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opt = Optimizer(search_space, constraints=constraints_list) |
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opt.search(objective_function, n_iter=50) |
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search_data = opt.search_data |
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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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60
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@pytest.mark.parametrize(*optimizers) |
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def test_constr_opt_2(Optimizer): |
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n_iter = 50 |
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64
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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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68
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search_space = { |
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"x1": np.arange(-10, 10, 0.1), |
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} |
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def constraint_1(para): |
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return para["x1"] > -5 |
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75
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def constraint_2(para): |
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return para["x1"] < 5 |
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constraints_list = [constraint_1, constraint_2] |
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80
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opt = Optimizer(search_space, constraints=constraints_list) |
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opt.search(objective_function, n_iter=n_iter) |
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search_data = opt.search_data |
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x0_values = search_data["x1"].values |
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86
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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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assert np.all(x0_values < 5) |
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90
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91
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n_new_positions = 0 |
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92
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n_new_scores = 0 |
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94
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n_current_positions = 0 |
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95
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n_current_scores = 0 |
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96
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97
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n_best_positions = 0 |
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98
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n_best_scores = 0 |
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100
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for optimizer in opt.optimizers: |
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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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104
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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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107
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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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110
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print("\n optimizer", optimizer) |
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print(" n_new_positions", optimizer.pos_new_list) |
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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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117
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assert n_current_positions == n_current_scores |
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118
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assert n_current_positions <= n_new_positions |
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119
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120
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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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122
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123
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assert n_new_positions == n_new_scores |
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124
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SimonBlanke /
Gradient-Free-Optimizers
