SimonBlanke /
Gradient-Free-Optimizers
| @@ 264-311 (lines=48) @@ | ||
| 261 | assert n_performed_iter == n_iter |
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| 262 | ||
| 263 | ||
| 264 | @pytest.mark.parametrize(*optimizers) |
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| 265 | def test_early_stop_7(Optimizer): |
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| 266 | def objective_function(para): |
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| 267 | return para["x1"] |
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| 268 | ||
| 269 | search_space = { |
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| 270 | "x1": np.arange(0, 100, 0.01), |
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| 271 | } |
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| 272 | ||
| 273 | n_iter_no_change = 5 |
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| 274 | early_stopping = { |
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| 275 | "n_iter_no_change": n_iter_no_change, |
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| 276 | "tol_abs": None, |
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| 277 | "tol_rel": 10, |
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| 278 | } |
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| 279 | ||
| 280 | start1 = {"x1": 1} |
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| 281 | start2 = {"x1": 1.09} |
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| 282 | start3 = {"x1": 1.20} |
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| 283 | ||
| 284 | warm_start_l = [ |
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| 285 | start1, |
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| 286 | start1, |
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| 287 | start1, |
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| 288 | start1, |
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| 289 | start1, |
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| 290 | start2, |
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| 291 | start2, |
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| 292 | start2, |
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| 293 | start3, |
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| 294 | start3, |
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| 295 | start3, |
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| 296 | ] |
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| 297 | n_iter = len(warm_start_l) |
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| 298 | ||
| 299 | opt = Optimizer(search_space, initialize={"warm_start": warm_start_l}) |
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| 300 | opt.search( |
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| 301 | objective_function, |
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| 302 | n_iter=n_iter, |
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| 303 | early_stopping=early_stopping, |
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| 304 | ) |
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| 305 | search_data = opt.search_data |
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| 306 | n_performed_iter = len(search_data) |
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| 307 | ||
| 308 | print("\n n_performed_iter \n", n_performed_iter) |
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| 309 | print("\n n_iter_no_change \n", n_iter_no_change) |
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| 310 | ||
| 311 | assert n_performed_iter == (n_iter_no_change + 1) |
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| 312 | ||
| @@ 156-203 (lines=48) @@ | ||
| 153 | assert n_performed_iter == n_iter |
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| 154 | ||
| 155 | ||
| 156 | @pytest.mark.parametrize(*optimizers) |
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| 157 | def test_early_stop_5(Optimizer): |
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| 158 | def objective_function(para): |
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| 159 | return para["x1"] |
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| 160 | ||
| 161 | search_space = { |
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| 162 | "x1": np.arange(0, 100, 0.01), |
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| 163 | } |
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| 164 | ||
| 165 | n_iter_no_change = 5 |
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| 166 | early_stopping = { |
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| 167 | "n_iter_no_change": n_iter_no_change, |
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| 168 | "tol_abs": 0.1, |
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| 169 | "tol_rel": None, |
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| 170 | } |
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| 171 | ||
| 172 | start1 = {"x1": 0} |
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| 173 | start2 = {"x1": 0.09} |
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| 174 | start3 = {"x1": 0.20} |
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| 175 | ||
| 176 | warm_start_l = [ |
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| 177 | start1, |
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| 178 | start1, |
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| 179 | start1, |
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| 180 | start1, |
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| 181 | start1, |
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| 182 | start2, |
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| 183 | start2, |
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| 184 | start2, |
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| 185 | start3, |
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| 186 | start3, |
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| 187 | start3, |
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| 188 | ] |
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| 189 | n_iter = len(warm_start_l) |
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| 190 | ||
| 191 | opt = Optimizer(search_space, initialize={"warm_start": warm_start_l}) |
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| 192 | opt.search( |
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| 193 | objective_function, |
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| 194 | n_iter=n_iter, |
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| 195 | early_stopping=early_stopping, |
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| 196 | ) |
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| 197 | search_data = opt.search_data |
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| 198 | n_performed_iter = len(search_data) |
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| 199 | ||
| 200 | print("\n n_performed_iter \n", n_performed_iter) |
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| 201 | print("\n n_iter_no_change \n", n_iter_no_change) |
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| 202 | ||
| 203 | assert n_performed_iter == (n_iter_no_change + 1) |
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| 204 | ||
| 205 | ||
| 206 | @pytest.mark.parametrize(*optimizers) |
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| @@ 260-306 (lines=47) @@ | ||
| 257 | assert n_performed_iter == n_iter |
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| 258 | ||
| 259 | ||
| 260 | def test_early_stop_7(): |
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| 261 | def objective_function(para): |
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| 262 | return para["x1"] |
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| 263 | ||
| 264 | search_space = { |
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| 265 | "x1": np.arange(0, 100, 0.01), |
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| 266 | } |
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| 267 | ||
| 268 | n_iter_no_change = 5 |
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| 269 | early_stopping = { |
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| 270 | "n_iter_no_change": n_iter_no_change, |
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| 271 | "tol_abs": None, |
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| 272 | "tol_rel": 10, |
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| 273 | } |
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| 274 | ||
| 275 | start1 = {"x1": 1} |
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| 276 | start2 = {"x1": 1.09} |
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| 277 | start3 = {"x1": 1.20} |
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| 278 | ||
| 279 | warm_start_l = [ |
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| 280 | start1, |
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| 281 | start1, |
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| 282 | start1, |
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| 283 | start1, |
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| 284 | start1, |
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| 285 | start2, |
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| 286 | start2, |
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| 287 | start2, |
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| 288 | start3, |
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| 289 | start3, |
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| 290 | start3, |
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| 291 | ] |
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| 292 | n_iter = len(warm_start_l) |
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| 293 | ||
| 294 | opt = HillClimbingOptimizer(search_space, initialize={"warm_start": warm_start_l}) |
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| 295 | opt.search( |
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| 296 | objective_function, |
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| 297 | n_iter=n_iter, |
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| 298 | early_stopping=early_stopping, |
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| 299 | ) |
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| 300 | search_data = opt.search_data |
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| 301 | n_performed_iter = len(search_data) |
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| 302 | ||
| 303 | print("\n n_performed_iter \n", n_performed_iter) |
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| 304 | print("\n n_iter_no_change \n", n_iter_no_change) |
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| 305 | ||
| 306 | assert n_performed_iter == (n_iter_no_change + 1) |
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| 307 | ||
| @@ 154-200 (lines=47) @@ | ||
| 151 | assert n_performed_iter == n_iter |
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| 152 | ||
| 153 | ||
| 154 | def test_early_stop_5(): |
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| 155 | def objective_function(para): |
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| 156 | return para["x1"] |
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| 157 | ||
| 158 | search_space = { |
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| 159 | "x1": np.arange(0, 100, 0.01), |
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| 160 | } |
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| 161 | ||
| 162 | n_iter_no_change = 5 |
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| 163 | early_stopping = { |
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| 164 | "n_iter_no_change": n_iter_no_change, |
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| 165 | "tol_abs": 10, |
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| 166 | "tol_rel": None, |
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| 167 | } |
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| 168 | ||
| 169 | start1 = {"x1": 0} |
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| 170 | start2 = {"x1": 9} |
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| 171 | start3 = {"x1": 20} |
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| 172 | ||
| 173 | warm_start_l = [ |
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| 174 | start1, |
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| 175 | start1, |
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| 176 | start1, |
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| 177 | start1, |
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| 178 | start1, |
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| 179 | start2, |
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| 180 | start2, |
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| 181 | start2, |
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| 182 | start3, |
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| 183 | start3, |
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| 184 | start3, |
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| 185 | ] |
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| 186 | n_iter = len(warm_start_l) |
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| 187 | ||
| 188 | opt = HillClimbingOptimizer(search_space, initialize={"warm_start": warm_start_l}) |
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| 189 | opt.search( |
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| 190 | objective_function, |
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| 191 | n_iter=n_iter, |
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| 192 | early_stopping=early_stopping, |
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| 193 | ) |
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| 194 | search_data = opt.search_data |
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| 195 | n_performed_iter = len(search_data) |
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| 196 | ||
| 197 | print("\n n_performed_iter \n", n_performed_iter) |
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| 198 | print("\n n_iter_no_change \n", n_iter_no_change) |
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| 199 | ||
| 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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