/////////////////////////////////////////// // Running convGAN on folding_yeast6 /////////////////////////////////////////// Load 'data_input/folding_yeast6' from pickle file 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 1131 synthetic samples -> test with 'LR' LR tn, fp: 265, 25 LR fn, tp: 0, 7 LR f1 score: 0.359 LR cohens kappa score: 0.333 LR average precision score: 0.654 -> test with 'GB' GB tn, fp: 287, 3 GB fn, tp: 4, 3 GB f1 score: 0.462 GB cohens kappa score: 0.450 -> test with 'KNN' KNN tn, fp: 275, 15 KNN fn, tp: 1, 6 KNN f1 score: 0.429 KNN cohens kappa score: 0.408 ------ Step 1/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1131 synthetic samples -> test with 'LR' LR tn, fp: 264, 26 LR fn, tp: 2, 5 LR f1 score: 0.263 LR cohens kappa score: 0.234 LR average precision score: 0.427 -> test with 'GB' GB tn, fp: 286, 4 GB fn, tp: 3, 4 GB f1 score: 0.533 GB cohens kappa score: 0.521 -> test with 'KNN' KNN tn, fp: 268, 22 KNN fn, tp: 2, 5 KNN f1 score: 0.294 KNN cohens kappa score: 0.267 ------ Step 1/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1131 synthetic samples -> test with 'LR' LR tn, fp: 257, 33 LR fn, tp: 1, 6 LR f1 score: 0.261 LR cohens kappa score: 0.230 LR average precision score: 0.270 -> test with 'GB' GB tn, fp: 290, 0 GB fn, tp: 5, 2 GB f1 score: 0.444 GB cohens kappa score: 0.439 -> test with 'KNN' KNN tn, fp: 265, 25 KNN fn, tp: 1, 6 KNN f1 score: 0.316 KNN cohens kappa score: 0.288 ------ Step 1/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1131 synthetic samples -> test with 'LR' LR tn, fp: 269, 21 LR fn, tp: 2, 5 LR f1 score: 0.303 LR cohens kappa score: 0.276 LR average precision score: 0.554 -> test with 'GB' GB tn, fp: 287, 3 GB fn, tp: 4, 3 GB f1 score: 0.462 GB cohens kappa score: 0.450 -> test with 'KNN' KNN tn, fp: 273, 17 KNN fn, tp: 1, 6 KNN f1 score: 0.400 KNN cohens kappa score: 0.378 ------ Step 1/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1132 synthetic samples -> test with 'LR' LR tn, fp: 242, 47 LR fn, tp: 0, 7 LR f1 score: 0.230 LR cohens kappa score: 0.196 LR average precision score: 0.599 -> test with 'GB' GB tn, fp: 289, 0 GB fn, tp: 3, 4 GB f1 score: 0.727 GB cohens kappa score: 0.722 -> test with 'KNN' KNN tn, fp: 264, 25 KNN fn, tp: 0, 7 KNN f1 score: 0.359 KNN cohens kappa score: 0.333 ====== Step 2/5 ======= -> Shuffling data -> Spliting data to slices ------ Step 2/5: Slice 1/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1131 synthetic samples -> test with 'LR' LR tn, fp: 258, 32 LR fn, tp: 0, 7 LR f1 score: 0.304 LR cohens kappa score: 0.275 LR average precision score: 0.667 -> test with 'GB' GB tn, fp: 287, 3 GB fn, tp: 4, 3 GB f1 score: 0.462 GB cohens kappa score: 0.450 -> test with 'KNN' KNN tn, fp: 269, 21 KNN fn, tp: 1, 6 KNN f1 score: 0.353 KNN cohens kappa score: 0.328 ------ Step 2/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1131 synthetic samples -> test with 'LR' LR tn, fp: 253, 37 LR fn, tp: 0, 7 LR f1 score: 0.275 LR cohens kappa score: 0.244 LR average precision score: 0.227 -> test with 'GB' GB tn, fp: 288, 2 GB fn, tp: 4, 3 GB f1 score: 0.500 GB cohens kappa score: 0.490 -> test with 'KNN' KNN tn, fp: 262, 28 KNN fn, tp: 0, 7 KNN f1 score: 0.333 KNN cohens kappa score: 0.306 ------ Step 2/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1131 synthetic samples -> test with 'LR' LR tn, fp: 260, 30 LR fn, tp: 1, 6 LR f1 score: 0.279 LR cohens kappa score: 0.249 LR average precision score: 0.550 -> test with 'GB' GB tn, fp: 288, 2 GB fn, tp: 4, 3 GB f1 score: 0.500 GB cohens kappa score: 0.490 -> test with 'KNN' KNN tn, fp: 268, 22 KNN fn, tp: 1, 6 KNN f1 score: 0.343 KNN cohens kappa score: 0.317 ------ Step 2/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1131 synthetic samples -> test with 'LR' LR tn, fp: 254, 36 LR fn, tp: 2, 5 LR f1 score: 0.208 LR cohens kappa score: 0.175 LR average precision score: 0.530 -> test with 'GB' GB tn, fp: 287, 3 GB fn, tp: 5, 2 GB f1 score: 0.333 GB cohens kappa score: 0.320 -> test with 'KNN' KNN tn, fp: 266, 24 KNN fn, tp: 2, 5 KNN f1 score: 0.278 KNN cohens kappa score: 0.249 ------ Step 2/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1132 synthetic samples -> test with 'LR' LR tn, fp: 268, 21 LR fn, tp: 1, 6 LR f1 score: 0.353 LR cohens kappa score: 0.328 LR average precision score: 0.555 -> test with 'GB' GB tn, fp: 288, 1 GB fn, tp: 6, 1 GB f1 score: 0.222 GB cohens kappa score: 0.214 -> test with 'KNN' KNN tn, fp: 273, 16 KNN fn, tp: 3, 4 KNN f1 score: 0.296 KNN cohens kappa score: 0.271 ====== Step 3/5 ======= -> Shuffling data -> Spliting data to slices ------ Step 3/5: Slice 1/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1131 synthetic samples -> test with 'LR' LR tn, fp: 263, 27 LR fn, tp: 1, 6 LR f1 score: 0.300 LR cohens kappa score: 0.272 LR average precision score: 0.659 -> test with 'GB' GB tn, fp: 288, 2 GB fn, tp: 3, 4 GB f1 score: 0.615 GB cohens kappa score: 0.607 -> test with 'KNN' KNN tn, fp: 268, 22 KNN fn, tp: 1, 6 KNN f1 score: 0.343 KNN cohens kappa score: 0.317 ------ Step 3/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1131 synthetic samples -> test with 'LR' LR tn, fp: 247, 43 LR fn, tp: 0, 7 LR f1 score: 0.246 LR cohens kappa score: 0.213 LR average precision score: 0.802 -> test with 'GB' GB tn, fp: 290, 0 GB fn, tp: 4, 3 GB f1 score: 0.600 GB cohens kappa score: 0.594 -> test with 'KNN' KNN tn, fp: 258, 32 KNN fn, tp: 0, 7 KNN f1 score: 0.304 KNN cohens kappa score: 0.275 ------ Step 3/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1131 synthetic samples -> test with 'LR' LR tn, fp: 267, 23 LR fn, tp: 2, 5 LR f1 score: 0.286 LR cohens kappa score: 0.258 LR average precision score: 0.397 -> test with 'GB' GB tn, fp: 288, 2 GB fn, tp: 5, 2 GB f1 score: 0.364 GB cohens kappa score: 0.353 -> test with 'KNN' KNN tn, fp: 275, 15 KNN fn, tp: 2, 5 KNN f1 score: 0.370 KNN cohens kappa score: 0.348 ------ Step 3/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1131 synthetic samples -> test with 'LR' LR tn, fp: 261, 29 LR fn, tp: 1, 6 LR f1 score: 0.286 LR cohens kappa score: 0.257 LR average precision score: 0.423 -> test with 'GB' GB tn, fp: 285, 5 GB fn, tp: 3, 4 GB f1 score: 0.500 GB cohens kappa score: 0.486 -> test with 'KNN' KNN tn, fp: 260, 30 KNN fn, tp: 1, 6 KNN f1 score: 0.279 KNN cohens kappa score: 0.249 ------ Step 3/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1132 synthetic samples -> test with 'LR' LR tn, fp: 268, 21 LR fn, tp: 1, 6 LR f1 score: 0.353 LR cohens kappa score: 0.328 LR average precision score: 0.420 -> test with 'GB' GB tn, fp: 288, 1 GB fn, tp: 7, 0 GB f1 score: 0.000 GB cohens kappa score: -0.006 -> test with 'KNN' KNN tn, fp: 275, 14 KNN fn, tp: 1, 6 KNN f1 score: 0.444 KNN cohens kappa score: 0.424 ====== Step 4/5 ======= -> Shuffling data -> Spliting data to slices ------ Step 4/5: Slice 1/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1131 synthetic samples -> test with 'LR' LR tn, fp: 271, 19 LR fn, tp: 1, 6 LR f1 score: 0.375 LR cohens kappa score: 0.351 LR average precision score: 0.676 -> test with 'GB' GB tn, fp: 289, 1 GB fn, tp: 2, 5 GB f1 score: 0.769 GB cohens kappa score: 0.764 -> test with 'KNN' KNN tn, fp: 270, 20 KNN fn, tp: 1, 6 KNN f1 score: 0.364 KNN cohens kappa score: 0.339 ------ Step 4/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1131 synthetic samples -> test with 'LR' LR tn, fp: 258, 32 LR fn, tp: 0, 7 LR f1 score: 0.304 LR cohens kappa score: 0.275 LR average precision score: 0.222 -> test with 'GB' GB tn, fp: 286, 4 GB fn, tp: 5, 2 GB f1 score: 0.308 GB cohens kappa score: 0.292 -> test with 'KNN' KNN tn, fp: 270, 20 KNN fn, tp: 2, 5 KNN f1 score: 0.312 KNN cohens kappa score: 0.286 ------ Step 4/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1131 synthetic samples -> test with 'LR' LR tn, fp: 247, 43 LR fn, tp: 1, 6 LR f1 score: 0.214 LR cohens kappa score: 0.180 LR average precision score: 0.550 -> test with 'GB' GB tn, fp: 287, 3 GB fn, tp: 3, 4 GB f1 score: 0.571 GB cohens kappa score: 0.561 -> test with 'KNN' KNN tn, fp: 255, 35 KNN fn, tp: 0, 7 KNN f1 score: 0.286 KNN cohens kappa score: 0.256 ------ Step 4/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1131 synthetic samples -> test with 'LR' LR tn, fp: 262, 28 LR fn, tp: 1, 6 LR f1 score: 0.293 LR cohens kappa score: 0.264 LR average precision score: 0.635 -> test with 'GB' GB tn, fp: 288, 2 GB fn, tp: 4, 3 GB f1 score: 0.500 GB cohens kappa score: 0.490 -> test with 'KNN' KNN tn, fp: 270, 20 KNN fn, tp: 2, 5 KNN f1 score: 0.312 KNN cohens kappa score: 0.286 ------ Step 4/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1132 synthetic samples -> test with 'LR' LR tn, fp: 268, 21 LR fn, tp: 2, 5 LR f1 score: 0.303 LR cohens kappa score: 0.276 LR average precision score: 0.677 -> test with 'GB' GB tn, fp: 288, 1 GB fn, tp: 4, 3 GB f1 score: 0.545 GB cohens kappa score: 0.537 -> test with 'KNN' KNN tn, fp: 275, 14 KNN fn, tp: 2, 5 KNN f1 score: 0.385 KNN cohens kappa score: 0.363 ====== Step 5/5 ======= -> Shuffling data -> Spliting data to slices ------ Step 5/5: Slice 1/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1131 synthetic samples -> test with 'LR' LR tn, fp: 251, 39 LR fn, tp: 0, 7 LR f1 score: 0.264 LR cohens kappa score: 0.233 LR average precision score: 0.514 -> test with 'GB' GB tn, fp: 287, 3 GB fn, tp: 3, 4 GB f1 score: 0.571 GB cohens kappa score: 0.561 -> test with 'KNN' KNN tn, fp: 259, 31 KNN fn, tp: 1, 6 KNN f1 score: 0.273 KNN cohens kappa score: 0.243 ------ Step 5/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1131 synthetic samples -> test with 'LR' LR tn, fp: 268, 22 LR fn, tp: 3, 4 LR f1 score: 0.242 LR cohens kappa score: 0.213 LR average precision score: 0.216 -> test with 'GB' GB tn, fp: 288, 2 GB fn, tp: 4, 3 GB f1 score: 0.500 GB cohens kappa score: 0.490 -> test with 'KNN' KNN tn, fp: 272, 18 KNN fn, tp: 3, 4 KNN f1 score: 0.276 KNN cohens kappa score: 0.249 ------ Step 5/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1131 synthetic samples -> test with 'LR' LR tn, fp: 259, 31 LR fn, tp: 0, 7 LR f1 score: 0.311 LR cohens kappa score: 0.283 LR average precision score: 0.754 -> test with 'GB' GB tn, fp: 289, 1 GB fn, tp: 1, 6 GB f1 score: 0.857 GB cohens kappa score: 0.854 -> test with 'KNN' KNN tn, fp: 263, 27 KNN fn, tp: 0, 7 KNN f1 score: 0.341 KNN cohens kappa score: 0.315 ------ Step 5/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1131 synthetic samples -> test with 'LR' LR tn, fp: 256, 34 LR fn, tp: 1, 6 LR f1 score: 0.255 LR cohens kappa score: 0.224 LR average precision score: 0.295 -> test with 'GB' GB tn, fp: 289, 1 GB fn, tp: 6, 1 GB f1 score: 0.222 GB cohens kappa score: 0.214 -> test with 'KNN' KNN tn, fp: 266, 24 KNN fn, tp: 1, 6 KNN f1 score: 0.324 KNN cohens kappa score: 0.297 ------ Step 5/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1132 synthetic samples -> test with 'LR' LR tn, fp: 270, 19 LR fn, tp: 2, 5 LR f1 score: 0.323 LR cohens kappa score: 0.297 LR average precision score: 0.372 -> test with 'GB' GB tn, fp: 288, 1 GB fn, tp: 5, 2 GB f1 score: 0.400 GB cohens kappa score: 0.391 -> test with 'KNN' KNN tn, fp: 273, 16 KNN fn, tp: 2, 5 KNN f1 score: 0.357 KNN cohens kappa score: 0.334 ### Exercise is done. -----[ LR ]----- maximum: LR tn, fp: 271, 47 LR fn, tp: 3, 7 LR f1 score: 0.375 LR cohens kappa score: 0.351 LR average precision score: 0.802 average: LR tn, fp: 260.24, 29.56 LR fn, tp: 1.0, 6.0 LR f1 score: 0.288 LR cohens kappa score: 0.259 LR average precision score: 0.506 minimum: LR tn, fp: 242, 19 LR fn, tp: 0, 4 LR f1 score: 0.208 LR cohens kappa score: 0.175 LR average precision score: 0.216 -----[ GB ]----- maximum: GB tn, fp: 290, 5 GB fn, tp: 7, 6 GB f1 score: 0.857 GB cohens kappa score: 0.854 average: GB tn, fp: 287.8, 2.0 GB fn, tp: 4.04, 2.96 GB f1 score: 0.479 GB cohens kappa score: 0.469 minimum: GB tn, fp: 285, 0 GB fn, tp: 1, 0 GB f1 score: 0.000 GB cohens kappa score: -0.006 -----[ KNN ]----- maximum: KNN tn, fp: 275, 35 KNN fn, tp: 3, 7 KNN f1 score: 0.444 KNN cohens kappa score: 0.424 average: KNN tn, fp: 267.68, 22.12 KNN fn, tp: 1.24, 5.76 KNN f1 score: 0.335 KNN cohens kappa score: 0.309 minimum: KNN tn, fp: 255, 14 KNN fn, tp: 0, 4 KNN f1 score: 0.273 KNN cohens kappa score: 0.243