/////////////////////////////////////////// // Running convGAN-proximary-5 on imblearn_protein_homo /////////////////////////////////////////// Load 'data_input/imblearn_protein_homo' from imblearn Data loaded. -> Shuffling data ### Start exercise for synthetic point generator ====== Step 1/5 ======= -> Shuffling data -> Spliting data to slices ------ Step 1/5: Slice 1/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 114528 synthetic samples -> test with GAN.predict GAN tn, fp: 26787, 2104 GAN fn, tp: 32, 228 GAN f1 score: 0.176 GAN cohens kappa score: 0.162 -> test with 'LR' LR tn, fp: 27679, 1212 LR fn, tp: 16, 244 LR f1 score: 0.284 LR cohens kappa score: 0.273 LR average precision score: 0.857 -> test with 'GB' GB tn, fp: 28403, 488 GB fn, tp: 18, 242 GB f1 score: 0.489 GB cohens kappa score: 0.482 -> test with 'KNN' KNN tn, fp: 28545, 346 KNN fn, tp: 96, 164 KNN f1 score: 0.426 KNN cohens kappa score: 0.419 ------ Step 1/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 114528 synthetic samples -> test with GAN.predict GAN tn, fp: 26737, 2154 GAN fn, tp: 17, 243 GAN f1 score: 0.183 GAN cohens kappa score: 0.170 -> test with 'LR' LR tn, fp: 27751, 1140 LR fn, tp: 13, 247 LR f1 score: 0.300 LR cohens kappa score: 0.289 LR average precision score: 0.884 -> test with 'GB' GB tn, fp: 28426, 465 GB fn, tp: 16, 244 GB f1 score: 0.504 GB cohens kappa score: 0.497 -> test with 'KNN' KNN tn, fp: 28528, 363 KNN fn, tp: 81, 179 KNN f1 score: 0.446 KNN cohens kappa score: 0.440 ------ Step 1/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 114528 synthetic samples -> test with GAN.predict GAN tn, fp: 28682, 209 GAN fn, tp: 85, 175 GAN f1 score: 0.543 GAN cohens kappa score: 0.539 -> test with 'LR' LR tn, fp: 27759, 1132 LR fn, tp: 8, 252 LR f1 score: 0.307 LR cohens kappa score: 0.296 LR average precision score: 0.887 -> test with 'GB' GB tn, fp: 28377, 514 GB fn, tp: 10, 250 GB f1 score: 0.488 GB cohens kappa score: 0.481 -> test with 'KNN' KNN tn, fp: 28122, 769 KNN fn, tp: 97, 163 KNN f1 score: 0.273 KNN cohens kappa score: 0.263 ------ Step 1/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 114528 synthetic samples -> test with GAN.predict GAN tn, fp: 26613, 2278 GAN fn, tp: 30, 230 GAN f1 score: 0.166 GAN cohens kappa score: 0.152 -> test with 'LR' LR tn, fp: 27745, 1146 LR fn, tp: 14, 246 LR f1 score: 0.298 LR cohens kappa score: 0.287 LR average precision score: 0.856 -> test with 'GB' GB tn, fp: 28431, 460 GB fn, tp: 20, 240 GB f1 score: 0.500 GB cohens kappa score: 0.493 -> test with 'KNN' KNN tn, fp: 28499, 392 KNN fn, tp: 94, 166 KNN f1 score: 0.406 KNN cohens kappa score: 0.399 ------ Step 1/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 114524 synthetic samples -> test with GAN.predict GAN tn, fp: 27153, 1738 GAN fn, tp: 36, 220 GAN f1 score: 0.199 GAN cohens kappa score: 0.186 -> test with 'LR' LR tn, fp: 27842, 1049 LR fn, tp: 22, 234 LR f1 score: 0.304 LR cohens kappa score: 0.294 LR average precision score: 0.817 -> test with 'GB' GB tn, fp: 28515, 376 GB fn, tp: 26, 230 GB f1 score: 0.534 GB cohens kappa score: 0.528 -> test with 'KNN' KNN tn, fp: 28504, 387 KNN fn, tp: 113, 143 KNN f1 score: 0.364 KNN cohens kappa score: 0.356 ====== Step 2/5 ======= -> Shuffling data -> Spliting data to slices ------ Step 2/5: Slice 1/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 114528 synthetic samples -> test with GAN.predict GAN tn, fp: 26830, 2061 GAN fn, tp: 28, 232 GAN f1 score: 0.182 GAN cohens kappa score: 0.168 -> test with 'LR' LR tn, fp: 27753, 1138 LR fn, tp: 11, 249 LR f1 score: 0.302 LR cohens kappa score: 0.292 LR average precision score: 0.866 -> test with 'GB' GB tn, fp: 28453, 438 GB fn, tp: 17, 243 GB f1 score: 0.516 GB cohens kappa score: 0.510 -> test with 'KNN' KNN tn, fp: 28549, 342 KNN fn, tp: 100, 160 KNN f1 score: 0.420 KNN cohens kappa score: 0.413 ------ Step 2/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 114528 synthetic samples -> test with GAN.predict GAN tn, fp: 27290, 1601 GAN fn, tp: 27, 233 GAN f1 score: 0.223 GAN cohens kappa score: 0.210 -> test with 'LR' LR tn, fp: 27734, 1157 LR fn, tp: 13, 247 LR f1 score: 0.297 LR cohens kappa score: 0.286 LR average precision score: 0.891 -> test with 'GB' GB tn, fp: 28380, 511 GB fn, tp: 18, 242 GB f1 score: 0.478 GB cohens kappa score: 0.471 -> test with 'KNN' KNN tn, fp: 28527, 364 KNN fn, tp: 93, 167 KNN f1 score: 0.422 KNN cohens kappa score: 0.415 ------ Step 2/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 114528 synthetic samples -> test with GAN.predict GAN tn, fp: 26904, 1987 GAN fn, tp: 37, 223 GAN f1 score: 0.181 GAN cohens kappa score: 0.167 -> test with 'LR' LR tn, fp: 27726, 1165 LR fn, tp: 17, 243 LR f1 score: 0.291 LR cohens kappa score: 0.281 LR average precision score: 0.833 -> test with 'GB' GB tn, fp: 28431, 460 GB fn, tp: 23, 237 GB f1 score: 0.495 GB cohens kappa score: 0.489 -> test with 'KNN' KNN tn, fp: 28495, 396 KNN fn, tp: 99, 161 KNN f1 score: 0.394 KNN cohens kappa score: 0.387 ------ Step 2/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 114528 synthetic samples -> test with GAN.predict GAN tn, fp: 26919, 1972 GAN fn, tp: 25, 235 GAN f1 score: 0.191 GAN cohens kappa score: 0.177 -> test with 'LR' LR tn, fp: 27704, 1187 LR fn, tp: 15, 245 LR f1 score: 0.290 LR cohens kappa score: 0.279 LR average precision score: 0.861 -> test with 'GB' GB tn, fp: 28422, 469 GB fn, tp: 15, 245 GB f1 score: 0.503 GB cohens kappa score: 0.496 -> test with 'KNN' KNN tn, fp: 28502, 389 KNN fn, tp: 90, 170 KNN f1 score: 0.415 KNN cohens kappa score: 0.408 ------ Step 2/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 114524 synthetic samples -> test with GAN.predict GAN tn, fp: 27373, 1518 GAN fn, tp: 34, 222 GAN f1 score: 0.222 GAN cohens kappa score: 0.210 -> test with 'LR' LR tn, fp: 27641, 1250 LR fn, tp: 13, 243 LR f1 score: 0.278 LR cohens kappa score: 0.267 LR average precision score: 0.844 -> test with 'GB' GB tn, fp: 28374, 517 GB fn, tp: 20, 236 GB f1 score: 0.468 GB cohens kappa score: 0.461 -> test with 'KNN' KNN tn, fp: 28517, 374 KNN fn, tp: 97, 159 KNN f1 score: 0.403 KNN cohens kappa score: 0.396 ====== Step 3/5 ======= -> Shuffling data -> Spliting data to slices ------ Step 3/5: Slice 1/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 114528 synthetic samples -> test with GAN.predict GAN tn, fp: 27072, 1819 GAN fn, tp: 26, 234 GAN f1 score: 0.202 GAN cohens kappa score: 0.190 -> test with 'LR' LR tn, fp: 27791, 1100 LR fn, tp: 17, 243 LR f1 score: 0.303 LR cohens kappa score: 0.293 LR average precision score: 0.869 -> test with 'GB' GB tn, fp: 28459, 432 GB fn, tp: 20, 240 GB f1 score: 0.515 GB cohens kappa score: 0.509 -> test with 'KNN' KNN tn, fp: 28501, 390 KNN fn, tp: 91, 169 KNN f1 score: 0.413 KNN cohens kappa score: 0.405 ------ Step 3/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 114528 synthetic samples -> test with GAN.predict GAN tn, fp: 25799, 3092 GAN fn, tp: 22, 238 GAN f1 score: 0.133 GAN cohens kappa score: 0.118 -> test with 'LR' LR tn, fp: 27731, 1160 LR fn, tp: 15, 245 LR f1 score: 0.294 LR cohens kappa score: 0.284 LR average precision score: 0.864 -> test with 'GB' GB tn, fp: 28418, 473 GB fn, tp: 18, 242 GB f1 score: 0.496 GB cohens kappa score: 0.490 -> test with 'KNN' KNN tn, fp: 28227, 664 KNN fn, tp: 91, 169 KNN f1 score: 0.309 KNN cohens kappa score: 0.300 ------ Step 3/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 114528 synthetic samples -> test with GAN.predict GAN tn, fp: 26408, 2483 GAN fn, tp: 30, 230 GAN f1 score: 0.155 GAN cohens kappa score: 0.141 -> test with 'LR' LR tn, fp: 27734, 1157 LR fn, tp: 16, 244 LR f1 score: 0.294 LR cohens kappa score: 0.283 LR average precision score: 0.831 -> test with 'GB' GB tn, fp: 28442, 449 GB fn, tp: 24, 236 GB f1 score: 0.499 GB cohens kappa score: 0.493 -> test with 'KNN' KNN tn, fp: 28523, 368 KNN fn, tp: 101, 159 KNN f1 score: 0.404 KNN cohens kappa score: 0.397 ------ Step 3/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 114528 synthetic samples -> test with GAN.predict GAN tn, fp: 27430, 1461 GAN fn, tp: 44, 216 GAN f1 score: 0.223 GAN cohens kappa score: 0.211 -> test with 'LR' LR tn, fp: 27682, 1209 LR fn, tp: 11, 249 LR f1 score: 0.290 LR cohens kappa score: 0.279 LR average precision score: 0.865 -> test with 'GB' GB tn, fp: 28401, 490 GB fn, tp: 13, 247 GB f1 score: 0.495 GB cohens kappa score: 0.489 -> test with 'KNN' KNN tn, fp: 28495, 396 KNN fn, tp: 99, 161 KNN f1 score: 0.394 KNN cohens kappa score: 0.387 ------ Step 3/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 114524 synthetic samples -> test with GAN.predict GAN tn, fp: 27162, 1729 GAN fn, tp: 22, 234 GAN f1 score: 0.211 GAN cohens kappa score: 0.198 -> test with 'LR' LR tn, fp: 27670, 1221 LR fn, tp: 13, 243 LR f1 score: 0.283 LR cohens kappa score: 0.272 LR average precision score: 0.882 -> test with 'GB' GB tn, fp: 28397, 494 GB fn, tp: 14, 242 GB f1 score: 0.488 GB cohens kappa score: 0.481 -> test with 'KNN' KNN tn, fp: 28414, 477 KNN fn, tp: 88, 168 KNN f1 score: 0.373 KNN cohens kappa score: 0.365 ====== Step 4/5 ======= -> Shuffling data -> Spliting data to slices ------ Step 4/5: Slice 1/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 114528 synthetic samples -> test with GAN.predict GAN tn, fp: 27177, 1714 GAN fn, tp: 27, 233 GAN f1 score: 0.211 GAN cohens kappa score: 0.199 -> test with 'LR' LR tn, fp: 27698, 1193 LR fn, tp: 13, 247 LR f1 score: 0.291 LR cohens kappa score: 0.280 LR average precision score: 0.873 -> test with 'GB' GB tn, fp: 28433, 458 GB fn, tp: 16, 244 GB f1 score: 0.507 GB cohens kappa score: 0.501 -> test with 'KNN' KNN tn, fp: 28514, 377 KNN fn, tp: 96, 164 KNN f1 score: 0.409 KNN cohens kappa score: 0.402 ------ Step 4/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 114528 synthetic samples -> test with GAN.predict GAN tn, fp: 27168, 1723 GAN fn, tp: 34, 226 GAN f1 score: 0.205 GAN cohens kappa score: 0.192 -> test with 'LR' LR tn, fp: 27762, 1129 LR fn, tp: 16, 244 LR f1 score: 0.299 LR cohens kappa score: 0.288 LR average precision score: 0.839 -> test with 'GB' GB tn, fp: 28405, 486 GB fn, tp: 20, 240 GB f1 score: 0.487 GB cohens kappa score: 0.480 -> test with 'KNN' KNN tn, fp: 28518, 373 KNN fn, tp: 105, 155 KNN f1 score: 0.393 KNN cohens kappa score: 0.386 ------ Step 4/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 114528 synthetic samples -> test with GAN.predict GAN tn, fp: 28015, 876 GAN fn, tp: 27, 233 GAN f1 score: 0.340 GAN cohens kappa score: 0.331 -> test with 'LR' LR tn, fp: 27740, 1151 LR fn, tp: 18, 242 LR f1 score: 0.293 LR cohens kappa score: 0.282 LR average precision score: 0.855 -> test with 'GB' GB tn, fp: 28433, 458 GB fn, tp: 20, 240 GB f1 score: 0.501 GB cohens kappa score: 0.494 -> test with 'KNN' KNN tn, fp: 28501, 390 KNN fn, tp: 89, 171 KNN f1 score: 0.417 KNN cohens kappa score: 0.409 ------ Step 4/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 114528 synthetic samples -> test with GAN.predict GAN tn, fp: 26083, 2808 GAN fn, tp: 20, 240 GAN f1 score: 0.145 GAN cohens kappa score: 0.131 -> test with 'LR' LR tn, fp: 27727, 1164 LR fn, tp: 11, 249 LR f1 score: 0.298 LR cohens kappa score: 0.287 LR average precision score: 0.878 -> test with 'GB' GB tn, fp: 28475, 416 GB fn, tp: 15, 245 GB f1 score: 0.532 GB cohens kappa score: 0.526 -> test with 'KNN' KNN tn, fp: 28523, 368 KNN fn, tp: 93, 167 KNN f1 score: 0.420 KNN cohens kappa score: 0.413 ------ Step 4/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 114524 synthetic samples -> test with GAN.predict GAN tn, fp: 27049, 1842 GAN fn, tp: 29, 227 GAN f1 score: 0.195 GAN cohens kappa score: 0.182 -> test with 'LR' LR tn, fp: 27746, 1145 LR fn, tp: 16, 240 LR f1 score: 0.293 LR cohens kappa score: 0.282 LR average precision score: 0.840 -> test with 'GB' GB tn, fp: 28403, 488 GB fn, tp: 17, 239 GB f1 score: 0.486 GB cohens kappa score: 0.480 -> test with 'KNN' KNN tn, fp: 28492, 399 KNN fn, tp: 89, 167 KNN f1 score: 0.406 KNN cohens kappa score: 0.399 ====== Step 5/5 ======= -> Shuffling data -> Spliting data to slices ------ Step 5/5: Slice 1/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 114528 synthetic samples -> test with GAN.predict GAN tn, fp: 25883, 3008 GAN fn, tp: 16, 244 GAN f1 score: 0.139 GAN cohens kappa score: 0.124 -> test with 'LR' LR tn, fp: 27840, 1051 LR fn, tp: 13, 247 LR f1 score: 0.317 LR cohens kappa score: 0.307 LR average precision score: 0.865 -> test with 'GB' GB tn, fp: 28426, 465 GB fn, tp: 18, 242 GB f1 score: 0.501 GB cohens kappa score: 0.494 -> test with 'KNN' KNN tn, fp: 28142, 749 KNN fn, tp: 89, 171 KNN f1 score: 0.290 KNN cohens kappa score: 0.280 ------ Step 5/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 114528 synthetic samples -> test with GAN.predict GAN tn, fp: 26648, 2243 GAN fn, tp: 33, 227 GAN f1 score: 0.166 GAN cohens kappa score: 0.153 -> test with 'LR' LR tn, fp: 27748, 1143 LR fn, tp: 14, 246 LR f1 score: 0.298 LR cohens kappa score: 0.288 LR average precision score: 0.867 -> test with 'GB' GB tn, fp: 28428, 463 GB fn, tp: 19, 241 GB f1 score: 0.500 GB cohens kappa score: 0.493 -> test with 'KNN' KNN tn, fp: 28518, 373 KNN fn, tp: 100, 160 KNN f1 score: 0.404 KNN cohens kappa score: 0.396 ------ Step 5/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 114528 synthetic samples -> test with GAN.predict GAN tn, fp: 26641, 2250 GAN fn, tp: 29, 231 GAN f1 score: 0.169 GAN cohens kappa score: 0.155 -> test with 'LR' LR tn, fp: 27677, 1214 LR fn, tp: 18, 242 LR f1 score: 0.282 LR cohens kappa score: 0.271 LR average precision score: 0.854 -> test with 'GB' GB tn, fp: 28472, 419 GB fn, tp: 17, 243 GB f1 score: 0.527 GB cohens kappa score: 0.521 -> test with 'KNN' KNN tn, fp: 28515, 376 KNN fn, tp: 105, 155 KNN f1 score: 0.392 KNN cohens kappa score: 0.385 ------ Step 5/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 114528 synthetic samples -> test with GAN.predict GAN tn, fp: 27392, 1499 GAN fn, tp: 33, 227 GAN f1 score: 0.229 GAN cohens kappa score: 0.216 -> test with 'LR' LR tn, fp: 27795, 1096 LR fn, tp: 11, 249 LR f1 score: 0.310 LR cohens kappa score: 0.300 LR average precision score: 0.863 -> test with 'GB' GB tn, fp: 28420, 471 GB fn, tp: 19, 241 GB f1 score: 0.496 GB cohens kappa score: 0.489 -> test with 'KNN' KNN tn, fp: 28256, 635 KNN fn, tp: 87, 173 KNN f1 score: 0.324 KNN cohens kappa score: 0.315 ------ Step 5/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 114524 synthetic samples -> test with GAN.predict GAN tn, fp: 26974, 1917 GAN fn, tp: 23, 233 GAN f1 score: 0.194 GAN cohens kappa score: 0.181 -> test with 'LR' LR tn, fp: 27744, 1147 LR fn, tp: 16, 240 LR f1 score: 0.292 LR cohens kappa score: 0.281 LR average precision score: 0.848 -> test with 'GB' GB tn, fp: 28398, 493 GB fn, tp: 15, 241 GB f1 score: 0.487 GB cohens kappa score: 0.480 -> test with 'KNN' KNN tn, fp: 28519, 372 KNN fn, tp: 91, 165 KNN f1 score: 0.416 KNN cohens kappa score: 0.409 ### Exercise is done. -----[ LR ]----- maximum: LR tn, fp: 27842, 1250 LR fn, tp: 22, 252 LR f1 score: 0.317 LR cohens kappa score: 0.307 LR average precision score: 0.891 average: LR tn, fp: 27736.76, 1154.24 LR fn, tp: 14.4, 244.8 LR f1 score: 0.295 LR cohens kappa score: 0.285 LR average precision score: 0.860 minimum: LR tn, fp: 27641, 1049 LR fn, tp: 8, 234 LR f1 score: 0.278 LR cohens kappa score: 0.267 LR average precision score: 0.817 -----[ GB ]----- maximum: GB tn, fp: 28515, 517 GB fn, tp: 26, 250 GB f1 score: 0.534 GB cohens kappa score: 0.528 average: GB tn, fp: 28424.88, 466.12 GB fn, tp: 17.92, 241.28 GB f1 score: 0.500 GB cohens kappa score: 0.493 minimum: GB tn, fp: 28374, 376 GB fn, tp: 10, 230 GB f1 score: 0.468 GB cohens kappa score: 0.461 -----[ KNN ]----- maximum: KNN tn, fp: 28549, 769 KNN fn, tp: 113, 179 KNN f1 score: 0.446 KNN cohens kappa score: 0.440 average: KNN tn, fp: 28457.84, 433.16 KNN fn, tp: 94.96, 164.24 KNN f1 score: 0.389 KNN cohens kappa score: 0.382 minimum: KNN tn, fp: 28122, 342 KNN fn, tp: 81, 143 KNN f1 score: 0.273 KNN cohens kappa score: 0.263 -----[ GAN ]----- maximum: GAN tn, fp: 28682, 3092 GAN fn, tp: 85, 244 GAN f1 score: 0.543 GAN cohens kappa score: 0.539 average: GAN tn, fp: 26967.56, 1923.44 GAN fn, tp: 30.64, 228.56 GAN f1 score: 0.207 GAN cohens kappa score: 0.195 minimum: GAN tn, fp: 25799, 209 GAN fn, tp: 16, 175 GAN f1 score: 0.133 GAN cohens kappa score: 0.118