/////////////////////////////////////////// // Running convGAN 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 'LR' LR tn, fp: 27668, 1223 LR fn, tp: 17, 243 LR f1 score: 0.282 LR cohens kappa score: 0.271 LR average precision score: 0.857 -> test with 'GB' GB tn, fp: 28379, 512 GB fn, tp: 19, 241 GB f1 score: 0.476 GB cohens kappa score: 0.469 -> test with 'KNN' KNN tn, fp: 28546, 345 KNN fn, tp: 96, 164 KNN f1 score: 0.427 KNN cohens kappa score: 0.420 ------ Step 1/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 114528 synthetic samples -> test with 'LR' LR tn, fp: 27748, 1143 LR fn, tp: 13, 247 LR f1 score: 0.299 LR cohens kappa score: 0.289 LR average precision score: 0.883 -> test with 'GB' GB tn, fp: 28404, 487 GB fn, tp: 14, 246 GB f1 score: 0.495 GB cohens kappa score: 0.489 -> test with 'KNN' KNN tn, fp: 28529, 362 KNN fn, tp: 81, 179 KNN f1 score: 0.447 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 'LR' LR tn, fp: 27639, 1252 LR fn, tp: 7, 253 LR f1 score: 0.287 LR cohens kappa score: 0.276 LR average precision score: 0.887 -> test with 'GB' GB tn, fp: 28387, 504 GB fn, tp: 10, 250 GB f1 score: 0.493 GB cohens kappa score: 0.486 -> test with 'KNN' KNN tn, fp: 28493, 398 KNN fn, tp: 110, 150 KNN f1 score: 0.371 KNN cohens kappa score: 0.364 ------ Step 1/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 114528 synthetic samples -> test with 'LR' LR tn, fp: 27753, 1138 LR fn, tp: 14, 246 LR f1 score: 0.299 LR cohens kappa score: 0.289 LR average precision score: 0.856 -> 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: 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 'LR' LR tn, fp: 27804, 1087 LR fn, tp: 21, 235 LR f1 score: 0.298 LR cohens kappa score: 0.287 LR average precision score: 0.817 -> test with 'GB' GB tn, fp: 28503, 388 GB fn, tp: 26, 230 GB f1 score: 0.526 GB cohens kappa score: 0.520 -> test with 'KNN' KNN tn, fp: 28504, 387 KNN fn, tp: 111, 145 KNN f1 score: 0.368 KNN cohens kappa score: 0.360 ====== 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 'LR' LR tn, fp: 27768, 1123 LR fn, tp: 11, 249 LR f1 score: 0.305 LR cohens kappa score: 0.295 LR average precision score: 0.866 -> test with 'GB' GB tn, fp: 28467, 424 GB fn, tp: 18, 242 GB f1 score: 0.523 GB cohens kappa score: 0.516 -> 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 'LR' LR tn, fp: 27742, 1149 LR fn, tp: 13, 247 LR f1 score: 0.298 LR cohens kappa score: 0.288 LR average precision score: 0.890 -> test with 'GB' GB tn, fp: 28395, 496 GB fn, tp: 17, 243 GB f1 score: 0.486 GB cohens kappa score: 0.480 -> test with 'KNN' KNN tn, fp: 28524, 367 KNN fn, tp: 93, 167 KNN f1 score: 0.421 KNN cohens kappa score: 0.414 ------ Step 2/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 114528 synthetic samples -> test with 'LR' LR tn, fp: 27715, 1176 LR fn, tp: 17, 243 LR f1 score: 0.289 LR cohens kappa score: 0.279 LR average precision score: 0.832 -> test with 'GB' GB tn, fp: 28433, 458 GB fn, tp: 22, 238 GB f1 score: 0.498 GB cohens kappa score: 0.491 -> test with 'KNN' KNN tn, fp: 28497, 394 KNN fn, tp: 99, 161 KNN f1 score: 0.395 KNN cohens kappa score: 0.388 ------ Step 2/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 114528 synthetic samples -> test with 'LR' LR tn, fp: 27699, 1192 LR fn, tp: 13, 247 LR f1 score: 0.291 LR cohens kappa score: 0.280 LR average precision score: 0.863 -> test with 'GB' GB tn, fp: 28404, 487 GB fn, tp: 17, 243 GB f1 score: 0.491 GB cohens kappa score: 0.484 -> 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 'LR' LR tn, fp: 27642, 1249 LR fn, tp: 12, 244 LR f1 score: 0.279 LR cohens kappa score: 0.268 LR average precision score: 0.843 -> test with 'GB' GB tn, fp: 28404, 487 GB fn, tp: 18, 238 GB f1 score: 0.485 GB cohens kappa score: 0.478 -> test with 'KNN' KNN tn, fp: 28518, 373 KNN fn, tp: 97, 159 KNN f1 score: 0.404 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 'LR' LR tn, fp: 27788, 1103 LR fn, tp: 17, 243 LR f1 score: 0.303 LR cohens kappa score: 0.292 LR average precision score: 0.868 -> test with 'GB' GB tn, fp: 28458, 433 GB fn, tp: 19, 241 GB f1 score: 0.516 GB cohens kappa score: 0.510 -> 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 'LR' LR tn, fp: 27767, 1124 LR fn, tp: 14, 246 LR f1 score: 0.302 LR cohens kappa score: 0.291 LR average precision score: 0.863 -> test with 'GB' GB tn, fp: 28392, 499 GB fn, tp: 18, 242 GB f1 score: 0.484 GB cohens kappa score: 0.477 -> test with 'KNN' KNN tn, fp: 28333, 558 KNN fn, tp: 98, 162 KNN f1 score: 0.331 KNN cohens kappa score: 0.322 ------ Step 3/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 114528 synthetic samples -> test with 'LR' LR tn, fp: 27747, 1144 LR fn, tp: 16, 244 LR f1 score: 0.296 LR cohens kappa score: 0.285 LR average precision score: 0.831 -> test with 'GB' GB tn, fp: 28427, 464 GB fn, tp: 23, 237 GB f1 score: 0.493 GB cohens kappa score: 0.487 -> test with 'KNN' KNN tn, fp: 28520, 371 KNN fn, tp: 101, 159 KNN f1 score: 0.403 KNN cohens kappa score: 0.395 ------ Step 3/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 114528 synthetic samples -> test with 'LR' LR tn, fp: 27672, 1219 LR fn, tp: 12, 248 LR f1 score: 0.287 LR cohens kappa score: 0.276 LR average precision score: 0.865 -> test with 'GB' GB tn, fp: 28413, 478 GB fn, tp: 13, 247 GB f1 score: 0.502 GB cohens kappa score: 0.495 -> 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 'LR' LR tn, fp: 27699, 1192 LR fn, tp: 13, 243 LR f1 score: 0.287 LR cohens kappa score: 0.277 LR average precision score: 0.883 -> test with 'GB' GB tn, fp: 28398, 493 GB fn, tp: 16, 240 GB f1 score: 0.485 GB cohens kappa score: 0.479 -> test with 'KNN' KNN tn, fp: 28505, 386 KNN fn, tp: 92, 164 KNN f1 score: 0.407 KNN cohens kappa score: 0.400 ====== 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 'LR' LR tn, fp: 27722, 1169 LR fn, tp: 14, 246 LR f1 score: 0.294 LR cohens kappa score: 0.283 LR average precision score: 0.872 -> test with 'GB' GB tn, fp: 28402, 489 GB fn, tp: 15, 245 GB f1 score: 0.493 GB cohens kappa score: 0.486 -> 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 'LR' LR tn, fp: 27757, 1134 LR fn, tp: 15, 245 LR f1 score: 0.299 LR cohens kappa score: 0.288 LR average precision score: 0.840 -> test with 'GB' GB tn, fp: 28392, 499 GB fn, tp: 22, 238 GB f1 score: 0.477 GB cohens kappa score: 0.470 -> test with 'KNN' KNN tn, fp: 28527, 364 KNN fn, tp: 105, 155 KNN f1 score: 0.398 KNN cohens kappa score: 0.391 ------ Step 4/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 114528 synthetic samples -> test with 'LR' LR tn, fp: 27735, 1156 LR fn, tp: 17, 243 LR f1 score: 0.293 LR cohens kappa score: 0.282 LR average precision score: 0.855 -> test with 'GB' GB tn, fp: 28445, 446 GB fn, tp: 20, 240 GB f1 score: 0.507 GB cohens kappa score: 0.501 -> 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 'LR' LR tn, fp: 27719, 1172 LR fn, tp: 11, 249 LR f1 score: 0.296 LR cohens kappa score: 0.285 LR average precision score: 0.879 -> test with 'GB' GB tn, fp: 28487, 404 GB fn, tp: 15, 245 GB f1 score: 0.539 GB cohens kappa score: 0.533 -> 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 'LR' LR tn, fp: 27754, 1137 LR fn, tp: 16, 240 LR f1 score: 0.294 LR cohens kappa score: 0.283 LR average precision score: 0.839 -> test with 'GB' GB tn, fp: 28415, 476 GB fn, tp: 15, 241 GB f1 score: 0.495 GB cohens kappa score: 0.489 -> 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 'LR' LR tn, fp: 27746, 1145 LR fn, tp: 13, 247 LR f1 score: 0.299 LR cohens kappa score: 0.288 LR average precision score: 0.863 -> test with 'GB' GB tn, fp: 28425, 466 GB fn, tp: 18, 242 GB f1 score: 0.500 GB cohens kappa score: 0.493 -> test with 'KNN' KNN tn, fp: 28548, 343 KNN fn, tp: 100, 160 KNN f1 score: 0.419 KNN cohens kappa score: 0.412 ------ Step 5/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 114528 synthetic samples -> test with 'LR' LR tn, fp: 27736, 1155 LR fn, tp: 14, 246 LR f1 score: 0.296 LR cohens kappa score: 0.285 LR average precision score: 0.867 -> test with 'GB' GB tn, fp: 28461, 430 GB fn, tp: 19, 241 GB f1 score: 0.518 GB cohens kappa score: 0.511 -> test with 'KNN' KNN tn, fp: 28520, 371 KNN fn, tp: 100, 160 KNN f1 score: 0.405 KNN cohens kappa score: 0.397 ------ Step 5/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 114528 synthetic samples -> test with 'LR' LR tn, fp: 27717, 1174 LR fn, tp: 18, 242 LR f1 score: 0.289 LR cohens kappa score: 0.278 LR average precision score: 0.854 -> test with 'GB' GB tn, fp: 28430, 461 GB fn, tp: 16, 244 GB f1 score: 0.506 GB cohens kappa score: 0.499 -> test with 'KNN' KNN tn, fp: 28223, 668 KNN fn, tp: 98, 162 KNN f1 score: 0.297 KNN cohens kappa score: 0.288 ------ Step 5/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 114528 synthetic samples -> test with 'LR' LR tn, fp: 27720, 1171 LR fn, tp: 10, 250 LR f1 score: 0.297 LR cohens kappa score: 0.287 LR average precision score: 0.863 -> 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: 28522, 369 KNN fn, tp: 95, 165 KNN f1 score: 0.416 KNN cohens kappa score: 0.409 ------ Step 5/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 114524 synthetic samples -> test with 'LR' LR tn, fp: 27732, 1159 LR fn, tp: 14, 242 LR f1 score: 0.292 LR cohens kappa score: 0.281 LR average precision score: 0.849 -> test with 'GB' GB tn, fp: 28405, 486 GB fn, tp: 15, 241 GB f1 score: 0.490 GB cohens kappa score: 0.484 -> test with 'KNN' KNN tn, fp: 28520, 371 KNN fn, tp: 91, 165 KNN f1 score: 0.417 KNN cohens kappa score: 0.410 ### Exercise is done. -----[ LR ]----- maximum: LR tn, fp: 27804, 1252 LR fn, tp: 21, 253 LR f1 score: 0.305 LR cohens kappa score: 0.295 LR average precision score: 0.890 average: LR tn, fp: 27727.56, 1163.44 LR fn, tp: 14.08, 245.12 LR f1 score: 0.294 LR cohens kappa score: 0.283 LR average precision score: 0.859 minimum: LR tn, fp: 27639, 1087 LR fn, tp: 7, 235 LR f1 score: 0.279 LR cohens kappa score: 0.268 LR average precision score: 0.817 -----[ GB ]----- maximum: GB tn, fp: 28503, 512 GB fn, tp: 26, 250 GB f1 score: 0.539 GB cohens kappa score: 0.533 average: GB tn, fp: 28423.4, 467.6 GB fn, tp: 17.72, 241.48 GB f1 score: 0.499 GB cohens kappa score: 0.493 minimum: GB tn, fp: 28379, 388 GB fn, tp: 10, 230 GB f1 score: 0.476 GB cohens kappa score: 0.469 -----[ KNN ]----- maximum: KNN tn, fp: 28549, 668 KNN fn, tp: 111, 179 KNN f1 score: 0.447 KNN cohens kappa score: 0.440 average: KNN tn, fp: 28496.2, 394.8 KNN fn, tp: 96.32, 162.88 KNN f1 score: 0.401 KNN cohens kappa score: 0.394 minimum: KNN tn, fp: 28223, 342 KNN fn, tp: 81, 145 KNN f1 score: 0.297 KNN cohens kappa score: 0.288