/////////////////////////////////////////// // Running convGAN-full on folding_yeast4 /////////////////////////////////////////// Load 'data_input/folding_yeast4' 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 1106 synthetic samples -> test with 'LR' LR tn, fp: 255, 32 LR fn, tp: 2, 9 LR f1 score: 0.346 LR cohens kappa score: 0.306 LR average precision score: 0.383 -> test with 'GB' GB tn, fp: 287, 0 GB fn, tp: 10, 1 GB f1 score: 0.167 GB cohens kappa score: 0.162 -> test with 'KNN' KNN tn, fp: 261, 26 KNN fn, tp: 2, 9 KNN f1 score: 0.391 KNN cohens kappa score: 0.355 ------ Step 1/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1106 synthetic samples -> test with 'LR' LR tn, fp: 239, 48 LR fn, tp: 1, 10 LR f1 score: 0.290 LR cohens kappa score: 0.243 LR average precision score: 0.615 -> test with 'GB' GB tn, fp: 282, 5 GB fn, tp: 6, 5 GB f1 score: 0.476 GB cohens kappa score: 0.457 -> test with 'KNN' KNN tn, fp: 254, 33 KNN fn, tp: 1, 10 KNN f1 score: 0.370 KNN cohens kappa score: 0.331 ------ Step 1/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1106 synthetic samples -> test with 'LR' LR tn, fp: 249, 38 LR fn, tp: 1, 10 LR f1 score: 0.339 LR cohens kappa score: 0.297 LR average precision score: 0.243 -> test with 'GB' GB tn, fp: 285, 2 GB fn, tp: 10, 1 GB f1 score: 0.143 GB cohens kappa score: 0.129 -> test with 'KNN' KNN tn, fp: 257, 30 KNN fn, tp: 4, 7 KNN f1 score: 0.292 KNN cohens kappa score: 0.249 ------ Step 1/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1106 synthetic samples -> test with 'LR' LR tn, fp: 256, 31 LR fn, tp: 6, 5 LR f1 score: 0.213 LR cohens kappa score: 0.166 LR average precision score: 0.243 -> test with 'GB' GB tn, fp: 281, 6 GB fn, tp: 8, 3 GB f1 score: 0.300 GB cohens kappa score: 0.276 -> test with 'KNN' KNN tn, fp: 264, 23 KNN fn, tp: 4, 7 KNN f1 score: 0.341 KNN cohens kappa score: 0.304 ------ Step 1/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1104 synthetic samples -> test with 'LR' LR tn, fp: 240, 45 LR fn, tp: 1, 6 LR f1 score: 0.207 LR cohens kappa score: 0.172 LR average precision score: 0.396 -> test with 'GB' GB tn, fp: 284, 1 GB fn, tp: 6, 1 GB f1 score: 0.222 GB cohens kappa score: 0.214 -> test with 'KNN' KNN tn, fp: 261, 24 KNN fn, tp: 1, 6 KNN f1 score: 0.324 KNN cohens kappa score: 0.297 ====== Step 2/5 ======= -> Shuffling data -> Spliting data to slices ------ Step 2/5: Slice 1/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1106 synthetic samples -> test with 'LR' LR tn, fp: 250, 37 LR fn, tp: 2, 9 LR f1 score: 0.316 LR cohens kappa score: 0.272 LR average precision score: 0.344 -> test with 'GB' GB tn, fp: 282, 5 GB fn, tp: 7, 4 GB f1 score: 0.400 GB cohens kappa score: 0.379 -> test with 'KNN' KNN tn, fp: 262, 25 KNN fn, tp: 3, 8 KNN f1 score: 0.364 KNN cohens kappa score: 0.326 ------ Step 2/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1106 synthetic samples -> test with 'LR' LR tn, fp: 242, 45 LR fn, tp: 3, 8 LR f1 score: 0.250 LR cohens kappa score: 0.201 LR average precision score: 0.457 -> test with 'GB' GB tn, fp: 284, 3 GB fn, tp: 7, 4 GB f1 score: 0.444 GB cohens kappa score: 0.428 -> test with 'KNN' KNN tn, fp: 236, 51 KNN fn, tp: 2, 9 KNN f1 score: 0.254 KNN cohens kappa score: 0.204 ------ Step 2/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1106 synthetic samples -> test with 'LR' LR tn, fp: 252, 35 LR fn, tp: 4, 7 LR f1 score: 0.264 LR cohens kappa score: 0.218 LR average precision score: 0.387 -> test with 'GB' GB tn, fp: 284, 3 GB fn, tp: 8, 3 GB f1 score: 0.353 GB cohens kappa score: 0.336 -> test with 'KNN' KNN tn, fp: 251, 36 KNN fn, tp: 2, 9 KNN f1 score: 0.321 KNN cohens kappa score: 0.279 ------ Step 2/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1106 synthetic samples -> test with 'LR' LR tn, fp: 250, 37 LR fn, tp: 2, 9 LR f1 score: 0.316 LR cohens kappa score: 0.272 LR average precision score: 0.299 -> test with 'GB' GB tn, fp: 285, 2 GB fn, tp: 9, 2 GB f1 score: 0.267 GB cohens kappa score: 0.252 -> test with 'KNN' KNN tn, fp: 267, 20 KNN fn, tp: 2, 9 KNN f1 score: 0.450 KNN cohens kappa score: 0.419 ------ Step 2/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1104 synthetic samples -> test with 'LR' LR tn, fp: 242, 43 LR fn, tp: 1, 6 LR f1 score: 0.214 LR cohens kappa score: 0.180 LR average precision score: 0.348 -> test with 'GB' GB tn, fp: 284, 1 GB fn, tp: 6, 1 GB f1 score: 0.222 GB cohens kappa score: 0.214 -> test with 'KNN' KNN tn, fp: 262, 23 KNN fn, tp: 1, 6 KNN f1 score: 0.333 KNN cohens kappa score: 0.307 ====== Step 3/5 ======= -> Shuffling data -> Spliting data to slices ------ Step 3/5: Slice 1/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1106 synthetic samples -> test with 'LR' LR tn, fp: 248, 39 LR fn, tp: 3, 8 LR f1 score: 0.276 LR cohens kappa score: 0.230 LR average precision score: 0.415 -> test with 'GB' GB tn, fp: 284, 3 GB fn, tp: 9, 2 GB f1 score: 0.250 GB cohens kappa score: 0.232 -> test with 'KNN' KNN tn, fp: 262, 25 KNN fn, tp: 3, 8 KNN f1 score: 0.364 KNN cohens kappa score: 0.326 ------ Step 3/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1106 synthetic samples -> test with 'LR' LR tn, fp: 251, 36 LR fn, tp: 2, 9 LR f1 score: 0.321 LR cohens kappa score: 0.279 LR average precision score: 0.384 -> test with 'GB' GB tn, fp: 285, 2 GB fn, tp: 10, 1 GB f1 score: 0.143 GB cohens kappa score: 0.129 -> test with 'KNN' KNN tn, fp: 260, 27 KNN fn, tp: 1, 10 KNN f1 score: 0.417 KNN cohens kappa score: 0.381 ------ Step 3/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1106 synthetic samples -> test with 'LR' LR tn, fp: 246, 41 LR fn, tp: 3, 8 LR f1 score: 0.267 LR cohens kappa score: 0.220 LR average precision score: 0.240 -> test with 'GB' GB tn, fp: 283, 4 GB fn, tp: 10, 1 GB f1 score: 0.125 GB cohens kappa score: 0.104 -> test with 'KNN' KNN tn, fp: 258, 29 KNN fn, tp: 3, 8 KNN f1 score: 0.333 KNN cohens kappa score: 0.293 ------ Step 3/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1106 synthetic samples -> test with 'LR' LR tn, fp: 246, 41 LR fn, tp: 2, 9 LR f1 score: 0.295 LR cohens kappa score: 0.250 LR average precision score: 0.522 -> test with 'GB' GB tn, fp: 284, 3 GB fn, tp: 9, 2 GB f1 score: 0.250 GB cohens kappa score: 0.232 -> test with 'KNN' KNN tn, fp: 256, 31 KNN fn, tp: 4, 7 KNN f1 score: 0.286 KNN cohens kappa score: 0.242 ------ Step 3/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1104 synthetic samples -> test with 'LR' LR tn, fp: 246, 39 LR fn, tp: 2, 5 LR f1 score: 0.196 LR cohens kappa score: 0.161 LR average precision score: 0.395 -> test with 'GB' GB tn, fp: 284, 1 GB fn, tp: 4, 3 GB f1 score: 0.545 GB cohens kappa score: 0.537 -> test with 'KNN' KNN tn, fp: 254, 31 KNN fn, tp: 1, 6 KNN f1 score: 0.273 KNN cohens kappa score: 0.242 ====== Step 4/5 ======= -> Shuffling data -> Spliting data to slices ------ Step 4/5: Slice 1/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1106 synthetic samples -> test with 'LR' LR tn, fp: 263, 24 LR fn, tp: 4, 7 LR f1 score: 0.333 LR cohens kappa score: 0.295 LR average precision score: 0.479 -> test with 'GB' GB tn, fp: 286, 1 GB fn, tp: 10, 1 GB f1 score: 0.154 GB cohens kappa score: 0.144 -> test with 'KNN' KNN tn, fp: 269, 18 KNN fn, tp: 5, 6 KNN f1 score: 0.343 KNN cohens kappa score: 0.308 ------ Step 4/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1106 synthetic samples -> test with 'LR' LR tn, fp: 247, 40 LR fn, tp: 2, 9 LR f1 score: 0.300 LR cohens kappa score: 0.255 LR average precision score: 0.306 -> test with 'GB' GB tn, fp: 283, 4 GB fn, tp: 8, 3 GB f1 score: 0.333 GB cohens kappa score: 0.314 -> test with 'KNN' KNN tn, fp: 256, 31 KNN fn, tp: 2, 9 KNN f1 score: 0.353 KNN cohens kappa score: 0.313 ------ Step 4/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1106 synthetic samples -> test with 'LR' LR tn, fp: 238, 49 LR fn, tp: 2, 9 LR f1 score: 0.261 LR cohens kappa score: 0.212 LR average precision score: 0.248 -> test with 'GB' GB tn, fp: 282, 5 GB fn, tp: 9, 2 GB f1 score: 0.222 GB cohens kappa score: 0.199 -> test with 'KNN' KNN tn, fp: 254, 33 KNN fn, tp: 1, 10 KNN f1 score: 0.370 KNN cohens kappa score: 0.331 ------ Step 4/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1106 synthetic samples -> test with 'LR' LR tn, fp: 243, 44 LR fn, tp: 3, 8 LR f1 score: 0.254 LR cohens kappa score: 0.206 LR average precision score: 0.286 -> test with 'GB' GB tn, fp: 284, 3 GB fn, tp: 10, 1 GB f1 score: 0.133 GB cohens kappa score: 0.116 -> test with 'KNN' KNN tn, fp: 259, 28 KNN fn, tp: 3, 8 KNN f1 score: 0.340 KNN cohens kappa score: 0.301 ------ Step 4/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1104 synthetic samples -> test with 'LR' LR tn, fp: 248, 37 LR fn, tp: 2, 5 LR f1 score: 0.204 LR cohens kappa score: 0.170 LR average precision score: 0.501 -> test with 'GB' GB tn, fp: 278, 7 GB fn, tp: 4, 3 GB f1 score: 0.353 GB cohens kappa score: 0.334 -> test with 'KNN' KNN tn, fp: 260, 25 KNN fn, tp: 2, 5 KNN f1 score: 0.270 KNN cohens kappa score: 0.241 ====== Step 5/5 ======= -> Shuffling data -> Spliting data to slices ------ Step 5/5: Slice 1/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1106 synthetic samples -> test with 'LR' LR tn, fp: 252, 35 LR fn, tp: 3, 8 LR f1 score: 0.296 LR cohens kappa score: 0.252 LR average precision score: 0.226 -> test with 'GB' GB tn, fp: 284, 3 GB fn, tp: 11, 0 GB f1 score: 0.000 GB cohens kappa score: -0.016 -> test with 'KNN' KNN tn, fp: 263, 24 KNN fn, tp: 2, 9 KNN f1 score: 0.409 KNN cohens kappa score: 0.374 ------ Step 5/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1106 synthetic samples -> test with 'LR' LR tn, fp: 237, 50 LR fn, tp: 2, 9 LR f1 score: 0.257 LR cohens kappa score: 0.208 LR average precision score: 0.495 -> test with 'GB' GB tn, fp: 285, 2 GB fn, tp: 8, 3 GB f1 score: 0.375 GB cohens kappa score: 0.360 -> test with 'KNN' KNN tn, fp: 256, 31 KNN fn, tp: 1, 10 KNN f1 score: 0.385 KNN cohens kappa score: 0.347 ------ Step 5/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1106 synthetic samples -> test with 'LR' LR tn, fp: 255, 32 LR fn, tp: 3, 8 LR f1 score: 0.314 LR cohens kappa score: 0.272 LR average precision score: 0.530 -> test with 'GB' GB tn, fp: 287, 0 GB fn, tp: 8, 3 GB f1 score: 0.429 GB cohens kappa score: 0.419 -> test with 'KNN' KNN tn, fp: 255, 32 KNN fn, tp: 4, 7 KNN f1 score: 0.280 KNN cohens kappa score: 0.236 ------ Step 5/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1106 synthetic samples -> test with 'LR' LR tn, fp: 246, 41 LR fn, tp: 1, 10 LR f1 score: 0.323 LR cohens kappa score: 0.279 LR average precision score: 0.534 -> test with 'GB' GB tn, fp: 281, 6 GB fn, tp: 9, 2 GB f1 score: 0.211 GB cohens kappa score: 0.185 -> test with 'KNN' KNN tn, fp: 256, 31 KNN fn, tp: 3, 8 KNN f1 score: 0.320 KNN cohens kappa score: 0.278 ------ Step 5/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1104 synthetic samples -> test with 'LR' LR tn, fp: 240, 45 LR fn, tp: 3, 4 LR f1 score: 0.143 LR cohens kappa score: 0.105 LR average precision score: 0.124 -> test with 'GB' GB tn, fp: 281, 4 GB fn, tp: 5, 2 GB f1 score: 0.308 GB cohens kappa score: 0.292 -> test with 'KNN' KNN tn, fp: 264, 21 KNN fn, tp: 1, 6 KNN f1 score: 0.353 KNN cohens kappa score: 0.327 ### Exercise is done. -----[ LR ]----- maximum: LR tn, fp: 263, 50 LR fn, tp: 6, 10 LR f1 score: 0.346 LR cohens kappa score: 0.306 LR average precision score: 0.615 average: LR tn, fp: 247.24, 39.36 LR fn, tp: 2.4, 7.8 LR f1 score: 0.272 LR cohens kappa score: 0.229 LR average precision score: 0.376 minimum: LR tn, fp: 237, 24 LR fn, tp: 1, 4 LR f1 score: 0.143 LR cohens kappa score: 0.105 LR average precision score: 0.124 -----[ GB ]----- maximum: GB tn, fp: 287, 7 GB fn, tp: 11, 5 GB f1 score: 0.545 GB cohens kappa score: 0.537 average: GB tn, fp: 283.56, 3.04 GB fn, tp: 8.04, 2.16 GB f1 score: 0.273 GB cohens kappa score: 0.257 minimum: GB tn, fp: 278, 0 GB fn, tp: 4, 0 GB f1 score: 0.000 GB cohens kappa score: -0.016 -----[ KNN ]----- maximum: KNN tn, fp: 269, 51 KNN fn, tp: 5, 10 KNN f1 score: 0.450 KNN cohens kappa score: 0.419 average: KNN tn, fp: 258.28, 28.32 KNN fn, tp: 2.32, 7.88 KNN f1 score: 0.341 KNN cohens kappa score: 0.304 minimum: KNN tn, fp: 236, 18 KNN fn, tp: 1, 5 KNN f1 score: 0.254 KNN cohens kappa score: 0.204