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import numpy as np |
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import pandas as pd |
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from gradient_free_optimizers import RandomSearchOptimizer |
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5
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6
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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": np.arange(0, 100000, 0.1), |
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} |
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def test_attributes_results_0(): |
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opt = RandomSearchOptimizer(search_space) |
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opt.search(objective_function, n_iter=100) |
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assert isinstance(opt.search_data, pd.DataFrame) |
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def test_attributes_results_1(): |
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opt = RandomSearchOptimizer(search_space) |
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opt.search(objective_function, n_iter=100) |
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assert set(search_space.keys()) < set(opt.search_data.columns) |
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def test_attributes_results_2(): |
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opt = RandomSearchOptimizer(search_space) |
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opt.search(objective_function, n_iter=100) |
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assert "x1" in list(opt.search_data.columns) |
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def test_attributes_results_3(): |
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opt = RandomSearchOptimizer(search_space) |
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opt.search(objective_function, n_iter=100) |
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assert "score" in list(opt.search_data.columns) |
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def test_attributes_results_4(): |
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opt = RandomSearchOptimizer(search_space, initialize={"warm_start": [{"x1": 0}]}) |
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opt.search(objective_function, n_iter=1) |
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assert 0 in list(opt.search_data["x1"].values) |
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51
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def test_attributes_results_5(): |
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opt = RandomSearchOptimizer(search_space, initialize={"warm_start": [{"x1": 10}]}) |
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opt.search(objective_function, n_iter=1) |
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assert 10 in list(opt.search_data["x1"].values) |
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def test_attributes_results_6(): |
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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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63
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search_space = { |
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"x1": np.arange(0, 10, 1), |
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} |
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67
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opt = RandomSearchOptimizer(search_space, initialize={"random": 1}) |
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opt.search(objective_function, n_iter=20, memory=False) |
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x1_results = list(opt.search_data["x1"].values) |
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print("\n x1_results \n", x1_results) |
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assert len(set(x1_results)) < len(x1_results) |
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""" |
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def test_attributes_results_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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83
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search_space = { |
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"x1": np.arange(0, 10, 1), |
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} |
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86
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87
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opt = RandomSearchOptimizer(search_space) |
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opt.search( |
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objective_function, n_iter=20, initialize={"random": 1}, memory=True |
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) |
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92
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x1_results = list(opt.search_data["x1"].values) |
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print("\n x1_results \n", x1_results) |
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96
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assert len(set(x1_results)) == len(x1_results) |
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98
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99
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def test_attributes_results_8(): |
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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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103
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104
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search_space = { |
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105
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"x1": np.arange(-10, 11, 1), |
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106
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} |
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107
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108
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results = pd.DataFrame(np.arange(-10, 10, 1), columns=["x1"]) |
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results["score"] = 0 |
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111
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opt = RandomSearchOptimizer(search_space) |
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opt.search( |
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objective_function, |
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n_iter=100, |
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initialize={}, |
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memory=True, |
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memory_warm_start=results, |
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) |
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120
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print("\n opt.search_data \n", opt.search_data) |
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122
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x1_results = list(opt.search_data["x1"].values) |
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123
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assert 10 == x1_results[0] |
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""" |
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126
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SimonBlanke /
Gradient-Free-Optimizers
