SimonBlanke /
Gradient-Free-Optimizers
| @@ 203-257 (lines=55) @@ | ||
| 200 | assert n_performed_iter == (n_iter_no_change + 1) |
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| 201 | ||
| 202 | ||
| 203 | def test_early_stop_6(): |
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| 204 | def objective_function(para): |
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| 205 | return para["x1"] |
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| 206 | ||
| 207 | search_space = { |
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| 208 | "x1": np.arange(0, 100, 0.01), |
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| 209 | } |
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| 210 | ||
| 211 | n_iter_no_change = 5 |
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| 212 | early_stopping = { |
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| 213 | "n_iter_no_change": 5, |
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| 214 | "tol_abs": None, |
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| 215 | "tol_rel": 10, |
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| 216 | } |
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| 217 | ||
| 218 | start1 = {"x1": 1} |
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| 219 | start2 = {"x1": 1.1} |
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| 220 | start3 = {"x1": 1.22} |
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| 221 | start4 = {"x1": 1.35} |
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| 222 | start5 = {"x1": 1.48} |
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| 223 | ||
| 224 | warm_start_l = [ |
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| 225 | start1, |
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| 226 | start1, |
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| 227 | start1, |
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| 228 | start1, |
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| 229 | start1, |
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| 230 | start2, |
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| 231 | start2, |
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| 232 | start2, |
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| 233 | start3, |
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| 234 | start3, |
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| 235 | start3, |
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| 236 | start4, |
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| 237 | start4, |
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| 238 | start4, |
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| 239 | start5, |
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| 240 | start5, |
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| 241 | start5, |
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| 242 | ] |
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| 243 | n_iter = len(warm_start_l) |
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| 244 | ||
| 245 | opt = HillClimbingOptimizer(search_space, initialize={"warm_start": warm_start_l}) |
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| 246 | opt.search( |
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| 247 | objective_function, |
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| 248 | n_iter=n_iter, |
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| 249 | early_stopping=early_stopping, |
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| 250 | ) |
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| 251 | search_data = opt.results |
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| 252 | n_performed_iter = len(search_data) |
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| 253 | ||
| 254 | print("\n n_performed_iter \n", n_performed_iter) |
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| 255 | print("\n n_iter_no_change \n", n_iter_no_change) |
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| 256 | ||
| 257 | assert n_performed_iter == n_iter |
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| 258 | ||
| 259 | ||
| 260 | def test_early_stop_7(): |
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| @@ 97-151 (lines=55) @@ | ||
| 94 | assert n_performed_iter == (n_iter_no_change + 1) |
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| 95 | ||
| 96 | ||
| 97 | def test_early_stop_4(): |
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| 98 | def objective_function(para): |
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| 99 | return para["x1"] |
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| 100 | ||
| 101 | search_space = { |
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| 102 | "x1": np.arange(0, 100, 0.1), |
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| 103 | } |
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| 104 | ||
| 105 | n_iter_no_change = 5 |
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| 106 | early_stopping = { |
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| 107 | "n_iter_no_change": 5, |
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| 108 | "tol_abs": 0.1, |
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| 109 | "tol_rel": None, |
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| 110 | } |
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| 111 | ||
| 112 | start1 = {"x1": 0} |
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| 113 | start2 = {"x1": 0.1} |
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| 114 | start3 = {"x1": 0.2} |
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| 115 | start4 = {"x1": 0.3} |
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| 116 | start5 = {"x1": 0.4} |
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| 117 | ||
| 118 | warm_start_l = [ |
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| 119 | start1, |
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| 120 | start1, |
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| 121 | start1, |
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| 122 | start1, |
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| 123 | start1, |
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| 124 | start2, |
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| 125 | start2, |
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| 126 | start2, |
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| 127 | start3, |
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| 128 | start3, |
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| 129 | start3, |
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| 130 | start4, |
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| 131 | start4, |
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| 132 | start4, |
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| 133 | start5, |
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| 134 | start5, |
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| 135 | start5, |
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| 136 | ] |
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| 137 | n_iter = len(warm_start_l) |
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| 138 | ||
| 139 | opt = HillClimbingOptimizer(search_space, initialize={"warm_start": warm_start_l}) |
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| 140 | opt.search( |
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| 141 | objective_function, |
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| 142 | n_iter=n_iter, |
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| 143 | early_stopping=early_stopping, |
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| 144 | ) |
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| 145 | search_data = opt.results |
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| 146 | n_performed_iter = len(search_data) |
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| 147 | ||
| 148 | print("\n n_performed_iter \n", n_performed_iter) |
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| 149 | print("\n n_iter_no_change \n", n_iter_no_change) |
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| 150 | ||
| 151 | assert n_performed_iter == n_iter |
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| 152 | ||
| 153 | ||
| 154 | def test_early_stop_5(): |
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