SimonBlanke /
Hyperactive
| @@ 285-304 (lines=20) @@ | ||
| 282 | assert opt0._optimizer_.score_best < opt1._optimizer_.score_best |
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| 283 | ||
| 284 | ||
| 285 | def test_Bayesian(): |
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| 286 | opt0 = Hyperactive( |
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| 287 | search_config, |
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| 288 | optimizer="Bayesian", |
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| 289 | n_iter=n_iter_min, |
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| 290 | random_state=random_state, |
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| 291 | warm_start=warm_start, |
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| 292 | ) |
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| 293 | opt0.fit(X, y) |
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| 294 | ||
| 295 | opt1 = Hyperactive( |
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| 296 | search_config, |
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| 297 | optimizer="Bayesian", |
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| 298 | n_iter=n_iter_max, |
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| 299 | random_state=random_state, |
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| 300 | warm_start=warm_start, |
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| 301 | ) |
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| 302 | opt1.fit(X, y) |
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| 303 | ||
| 304 | assert opt0._optimizer_.score_best < opt1._optimizer_.score_best |
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| 305 | ||
| @@ 263-282 (lines=20) @@ | ||
| 260 | assert opt0._optimizer_.score_best < opt1._optimizer_.score_best |
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| 261 | ||
| 262 | ||
| 263 | def test_EvolutionStrategy(): |
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| 264 | opt0 = Hyperactive( |
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| 265 | search_config, |
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| 266 | optimizer="EvolutionStrategy", |
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| 267 | n_iter=n_iter_min, |
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| 268 | random_state=random_state, |
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| 269 | warm_start=warm_start, |
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| 270 | ) |
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| 271 | opt0.fit(X, y) |
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| 272 | ||
| 273 | opt1 = Hyperactive( |
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| 274 | search_config, |
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| 275 | optimizer="EvolutionStrategy", |
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| 276 | n_iter=n_iter_max, |
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| 277 | random_state=random_state, |
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| 278 | warm_start=warm_start, |
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| 279 | ) |
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| 280 | opt1.fit(X, y) |
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| 281 | ||
| 282 | assert opt0._optimizer_.score_best < opt1._optimizer_.score_best |
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| 283 | ||
| 284 | ||
| 285 | def test_Bayesian(): |
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| @@ 241-260 (lines=20) @@ | ||
| 238 | assert opt0._optimizer_.score_best < opt1._optimizer_.score_best |
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| 239 | ||
| 240 | ||
| 241 | def test_ParticleSwarm(): |
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| 242 | opt0 = Hyperactive( |
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| 243 | search_config, |
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| 244 | optimizer="ParticleSwarm", |
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| 245 | n_iter=n_iter_min, |
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| 246 | random_state=random_state, |
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| 247 | warm_start=warm_start, |
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| 248 | ) |
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| 249 | opt0.fit(X, y) |
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| 250 | ||
| 251 | opt1 = Hyperactive( |
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| 252 | search_config, |
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| 253 | optimizer="ParticleSwarm", |
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| 254 | n_iter=n_iter_max, |
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| 255 | random_state=random_state, |
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| 256 | warm_start=warm_start, |
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| 257 | ) |
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| 258 | opt1.fit(X, y) |
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| 259 | ||
| 260 | assert opt0._optimizer_.score_best < opt1._optimizer_.score_best |
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| 261 | ||
| 262 | ||
| 263 | def test_EvolutionStrategy(): |
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| @@ 219-238 (lines=20) @@ | ||
| 216 | assert opt0._optimizer_.score_best < opt1._optimizer_.score_best |
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| 217 | ||
| 218 | ||
| 219 | def test_ParallelTempering(): |
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| 220 | opt0 = Hyperactive( |
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| 221 | search_config, |
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| 222 | optimizer="ParallelTempering", |
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| 223 | n_iter=n_iter_min, |
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| 224 | random_state=random_state, |
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| 225 | warm_start=warm_start, |
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| 226 | ) |
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| 227 | opt0.fit(X, y) |
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| 228 | ||
| 229 | opt1 = Hyperactive( |
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| 230 | search_config, |
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| 231 | optimizer="ParallelTempering", |
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| 232 | n_iter=n_iter_max, |
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| 233 | random_state=random_state, |
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| 234 | warm_start=warm_start, |
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| 235 | ) |
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| 236 | opt1.fit(X, y) |
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| 237 | ||
| 238 | assert opt0._optimizer_.score_best < opt1._optimizer_.score_best |
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| 239 | ||
| 240 | ||
| 241 | def test_ParticleSwarm(): |
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| @@ 197-216 (lines=20) @@ | ||
| 194 | assert opt0._optimizer_.score_best < opt1._optimizer_.score_best |
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| 195 | ||
| 196 | ||
| 197 | def test_StochasticTunneling(): |
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| 198 | opt0 = Hyperactive( |
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| 199 | search_config, |
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| 200 | optimizer="StochasticTunneling", |
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| 201 | n_iter=n_iter_min, |
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| 202 | random_state=random_state, |
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| 203 | warm_start=warm_start, |
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| 204 | ) |
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| 205 | opt0.fit(X, y) |
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| 206 | ||
| 207 | opt1 = Hyperactive( |
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| 208 | search_config, |
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| 209 | optimizer="StochasticTunneling", |
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| 210 | n_iter=n_iter_max, |
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| 211 | random_state=random_state, |
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| 212 | warm_start=warm_start, |
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| 213 | ) |
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| 214 | opt1.fit(X, y) |
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| 215 | ||
| 216 | assert opt0._optimizer_.score_best < opt1._optimizer_.score_best |
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| 217 | ||
| 218 | ||
| 219 | def test_ParallelTempering(): |
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| @@ 175-194 (lines=20) @@ | ||
| 172 | assert opt0._optimizer_.score_best < opt1._optimizer_.score_best |
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| 173 | ||
| 174 | ||
| 175 | def test_SimulatedAnnealing(): |
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| 176 | opt0 = Hyperactive( |
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| 177 | search_config, |
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| 178 | optimizer="SimulatedAnnealing", |
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| 179 | n_iter=n_iter_min, |
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| 180 | random_state=random_state, |
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| 181 | warm_start=warm_start, |
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| 182 | ) |
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| 183 | opt0.fit(X, y) |
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| 184 | ||
| 185 | opt1 = Hyperactive( |
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| 186 | search_config, |
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| 187 | optimizer="SimulatedAnnealing", |
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| 188 | n_iter=n_iter_max, |
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| 189 | random_state=random_state, |
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| 190 | warm_start=warm_start, |
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| 191 | ) |
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| 192 | opt1.fit(X, y) |
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| 193 | ||
| 194 | assert opt0._optimizer_.score_best < opt1._optimizer_.score_best |
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| 195 | ||
| 196 | ||
| 197 | def test_StochasticTunneling(): |
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| @@ 153-172 (lines=20) @@ | ||
| 150 | assert opt0._optimizer_.score_best < opt1._optimizer_.score_best |
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| 151 | ||
| 152 | ||
| 153 | def test_RandomAnnealing(): |
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| 154 | opt0 = Hyperactive( |
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| 155 | search_config, |
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| 156 | optimizer="RandomAnnealing", |
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| 157 | n_iter=n_iter_min, |
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| 158 | random_state=random_state, |
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| 159 | warm_start=warm_start, |
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| 160 | ) |
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| 161 | opt0.fit(X, y) |
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| 162 | ||
| 163 | opt1 = Hyperactive( |
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| 164 | search_config, |
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| 165 | optimizer="RandomAnnealing", |
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| 166 | n_iter=n_iter_max, |
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| 167 | random_state=random_state, |
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| 168 | warm_start=warm_start, |
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| 169 | ) |
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| 170 | opt1.fit(X, y) |
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| 171 | ||
| 172 | assert opt0._optimizer_.score_best < opt1._optimizer_.score_best |
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| 173 | ||
| 174 | ||
| 175 | def test_SimulatedAnnealing(): |
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| @@ 131-150 (lines=20) @@ | ||
| 128 | assert opt0._optimizer_.score_best < opt1._optimizer_.score_best |
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| 129 | ||
| 130 | ||
| 131 | def test_RandomRestartHillClimbing(): |
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| 132 | opt0 = Hyperactive( |
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| 133 | search_config, |
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| 134 | optimizer="RandomRestartHillClimbing", |
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| 135 | n_iter=n_iter_min, |
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| 136 | random_state=random_state, |
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| 137 | warm_start=warm_start, |
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| 138 | ) |
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| 139 | opt0.fit(X, y) |
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| 140 | ||
| 141 | opt1 = Hyperactive( |
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| 142 | search_config, |
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| 143 | optimizer="RandomRestartHillClimbing", |
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| 144 | n_iter=n_iter_max, |
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| 145 | random_state=random_state, |
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| 146 | warm_start=warm_start, |
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| 147 | ) |
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| 148 | opt1.fit(X, y) |
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| 149 | ||
| 150 | assert opt0._optimizer_.score_best < opt1._optimizer_.score_best |
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| 151 | ||
| 152 | ||
| 153 | def test_RandomAnnealing(): |
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| @@ 109-128 (lines=20) @@ | ||
| 106 | assert opt0._optimizer_.score_best < opt1._optimizer_.score_best |
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| 107 | ||
| 108 | ||
| 109 | def test_RandomSearch(): |
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| 110 | opt0 = Hyperactive( |
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| 111 | search_config, |
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| 112 | optimizer="RandomSearch", |
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| 113 | n_iter=n_iter_min, |
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| 114 | random_state=random_state, |
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| 115 | warm_start=warm_start, |
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| 116 | ) |
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| 117 | opt0.fit(X, y) |
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| 118 | ||
| 119 | opt1 = Hyperactive( |
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| 120 | search_config, |
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| 121 | optimizer="RandomSearch", |
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| 122 | n_iter=n_iter_max, |
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| 123 | random_state=random_state, |
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| 124 | warm_start=warm_start, |
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| 125 | ) |
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| 126 | opt1.fit(X, y) |
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| 127 | ||
| 128 | assert opt0._optimizer_.score_best < opt1._optimizer_.score_best |
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| 129 | ||
| 130 | ||
| 131 | def test_RandomRestartHillClimbing(): |
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| @@ 87-106 (lines=20) @@ | ||
| 84 | assert opt0._optimizer_.score_best < opt1._optimizer_.score_best |
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| 85 | ||
| 86 | ||
| 87 | def test_TabuOptimizer(): |
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| 88 | opt0 = Hyperactive( |
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| 89 | search_config, |
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| 90 | optimizer="TabuSearch", |
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| 91 | n_iter=n_iter_min, |
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| 92 | random_state=random_state, |
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| 93 | warm_start=warm_start, |
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| 94 | ) |
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| 95 | opt0.fit(X, y) |
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| 96 | ||
| 97 | opt1 = Hyperactive( |
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| 98 | search_config, |
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| 99 | optimizer="TabuSearch", |
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| 100 | n_iter=n_iter_max, |
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| 101 | random_state=random_state, |
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| 102 | warm_start=warm_start, |
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| 103 | ) |
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| 104 | opt1.fit(X, y) |
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| 105 | ||
| 106 | assert opt0._optimizer_.score_best < opt1._optimizer_.score_best |
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| 107 | ||
| 108 | ||
| 109 | def test_RandomSearch(): |
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| @@ 65-84 (lines=20) @@ | ||
| 62 | assert opt0._optimizer_.score_best < opt1._optimizer_.score_best |
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| 63 | ||
| 64 | ||
| 65 | def test_StochasticHillClimbing(): |
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| 66 | opt0 = Hyperactive( |
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| 67 | search_config, |
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| 68 | optimizer="StochasticHillClimbing", |
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| 69 | n_iter=n_iter_min, |
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| 70 | random_state=random_state, |
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| 71 | warm_start=warm_start, |
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| 72 | ) |
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| 73 | opt0.fit(X, y) |
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| 74 | ||
| 75 | opt1 = Hyperactive( |
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| 76 | search_config, |
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| 77 | optimizer="StochasticHillClimbing", |
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| 78 | n_iter=n_iter_max, |
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| 79 | random_state=random_state, |
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| 80 | warm_start=warm_start, |
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| 81 | ) |
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| 82 | opt1.fit(X, y) |
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| 83 | ||
| 84 | assert opt0._optimizer_.score_best < opt1._optimizer_.score_best |
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| 85 | ||
| 86 | ||
| 87 | def test_TabuOptimizer(): |
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| @@ 43-62 (lines=20) @@ | ||
| 40 | warm_start = {model: {"max_depth": [1]}} |
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| 41 | ||
| 42 | ||
| 43 | def test_HillClimbing(): |
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| 44 | opt0 = Hyperactive( |
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| 45 | search_config, |
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| 46 | optimizer="HillClimbing", |
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| 47 | n_iter=n_iter_min, |
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| 48 | random_state=random_state, |
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| 49 | warm_start=warm_start, |
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| 50 | ) |
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| 51 | opt0.fit(X, y) |
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| 52 | ||
| 53 | opt1 = Hyperactive( |
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| 54 | search_config, |
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| 55 | optimizer="HillClimbing", |
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| 56 | n_iter=n_iter_max, |
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| 57 | random_state=random_state, |
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| 58 | warm_start=warm_start, |
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| 59 | ) |
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| 60 | opt1.fit(X, y) |
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| 61 | ||
| 62 | assert opt0._optimizer_.score_best < opt1._optimizer_.score_best |
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| 63 | ||
| 64 | ||
| 65 | def test_StochasticHillClimbing(): |
|
