/////////////////////////////////////////// // Running ProWRAS 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: 239, 48 LR fn, tp: 2, 9 LR f1 score: 0.265 LR cohens kappa score: 0.216 LR average precision score: 0.426 -> test with 'GB' GB tn, fp: 282, 5 GB fn, tp: 8, 3 GB f1 score: 0.316 GB cohens kappa score: 0.294 -> test with 'KNN' KNN tn, fp: 272, 15 KNN fn, tp: 5, 6 KNN f1 score: 0.375 KNN cohens kappa score: 0.343 ------ Step 1/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1106 synthetic samples -> test with 'LR' LR tn, fp: 231, 56 LR fn, tp: 1, 10 LR f1 score: 0.260 LR cohens kappa score: 0.210 LR average precision score: 0.659 -> test with 'GB' GB tn, fp: 280, 7 GB fn, tp: 3, 8 GB f1 score: 0.615 GB cohens kappa score: 0.598 -> test with 'KNN' KNN tn, fp: 273, 14 KNN fn, tp: 4, 7 KNN f1 score: 0.437 KNN cohens kappa score: 0.409 ------ Step 1/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1106 synthetic samples -> test with 'LR' LR tn, fp: 236, 51 LR fn, tp: 1, 10 LR f1 score: 0.278 LR cohens kappa score: 0.230 LR average precision score: 0.268 -> test with 'GB' GB tn, fp: 282, 5 GB fn, tp: 8, 3 GB f1 score: 0.316 GB cohens kappa score: 0.294 -> test with 'KNN' KNN tn, fp: 268, 19 KNN fn, tp: 5, 6 KNN f1 score: 0.333 KNN cohens kappa score: 0.297 ------ Step 1/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1106 synthetic samples -> test with 'LR' LR tn, fp: 253, 34 LR fn, tp: 6, 5 LR f1 score: 0.200 LR cohens kappa score: 0.151 LR average precision score: 0.201 -> test with 'GB' GB tn, fp: 276, 11 GB fn, tp: 8, 3 GB f1 score: 0.240 GB cohens kappa score: 0.207 -> test with 'KNN' KNN tn, fp: 270, 17 KNN fn, tp: 7, 4 KNN f1 score: 0.250 KNN cohens kappa score: 0.212 ------ Step 1/5: 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: 0.379 -> test with 'GB' GB tn, fp: 280, 5 GB fn, tp: 5, 2 GB f1 score: 0.286 GB cohens kappa score: 0.268 -> test with 'KNN' KNN tn, fp: 265, 20 KNN fn, tp: 1, 6 KNN f1 score: 0.364 KNN cohens kappa score: 0.339 ====== 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: 244, 43 LR fn, tp: 2, 9 LR f1 score: 0.286 LR cohens kappa score: 0.239 LR average precision score: 0.344 -> test with 'GB' GB tn, fp: 280, 7 GB fn, tp: 8, 3 GB f1 score: 0.286 GB cohens kappa score: 0.260 -> test with 'KNN' KNN tn, fp: 278, 9 KNN fn, tp: 5, 6 KNN f1 score: 0.462 KNN cohens kappa score: 0.438 ------ Step 2/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1106 synthetic samples -> test with 'LR' LR tn, fp: 234, 53 LR fn, tp: 3, 8 LR f1 score: 0.222 LR cohens kappa score: 0.170 LR average precision score: 0.472 -> test with 'GB' GB tn, fp: 278, 9 GB fn, tp: 6, 5 GB f1 score: 0.400 GB cohens kappa score: 0.374 -> test with 'KNN' KNN tn, fp: 255, 32 KNN fn, tp: 3, 8 KNN f1 score: 0.314 KNN cohens kappa score: 0.272 ------ Step 2/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1106 synthetic samples -> test with 'LR' LR tn, fp: 244, 43 LR fn, tp: 4, 7 LR f1 score: 0.230 LR cohens kappa score: 0.180 LR average precision score: 0.354 -> 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: 270, 17 KNN fn, tp: 6, 5 KNN f1 score: 0.303 KNN cohens kappa score: 0.267 ------ Step 2/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1106 synthetic samples -> test with 'LR' LR tn, fp: 245, 42 LR fn, tp: 3, 8 LR f1 score: 0.262 LR cohens kappa score: 0.215 LR average precision score: 0.326 -> test with 'GB' GB tn, fp: 278, 9 GB fn, tp: 5, 6 GB f1 score: 0.462 GB cohens kappa score: 0.438 -> test with 'KNN' KNN tn, fp: 274, 13 KNN fn, tp: 4, 7 KNN f1 score: 0.452 KNN cohens kappa score: 0.424 ------ Step 2/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1104 synthetic samples -> test with 'LR' LR tn, fp: 231, 54 LR fn, tp: 1, 6 LR f1 score: 0.179 LR cohens kappa score: 0.142 LR average precision score: 0.423 -> test with 'GB' GB tn, fp: 281, 4 GB fn, tp: 6, 1 GB f1 score: 0.167 GB cohens kappa score: 0.150 -> test with 'KNN' KNN tn, fp: 274, 11 KNN fn, tp: 3, 4 KNN f1 score: 0.364 KNN cohens kappa score: 0.342 ====== 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: 242, 45 LR fn, tp: 3, 8 LR f1 score: 0.250 LR cohens kappa score: 0.201 LR average precision score: 0.416 -> test with 'GB' GB tn, fp: 282, 5 GB fn, tp: 8, 3 GB f1 score: 0.316 GB cohens kappa score: 0.294 -> test with 'KNN' KNN tn, fp: 271, 16 KNN fn, tp: 5, 6 KNN f1 score: 0.364 KNN cohens kappa score: 0.331 ------ Step 3/5: 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: 2, 9 LR f1 score: 0.265 LR cohens kappa score: 0.216 LR average precision score: 0.372 -> test with 'GB' GB tn, fp: 283, 4 GB fn, tp: 9, 2 GB f1 score: 0.235 GB cohens kappa score: 0.215 -> test with 'KNN' KNN tn, fp: 275, 12 KNN fn, tp: 5, 6 KNN f1 score: 0.414 KNN cohens kappa score: 0.386 ------ Step 3/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1106 synthetic samples -> test with 'LR' LR tn, fp: 247, 40 LR fn, tp: 3, 8 LR f1 score: 0.271 LR cohens kappa score: 0.225 LR average precision score: 0.255 -> test with 'GB' GB tn, fp: 280, 7 GB fn, tp: 5, 6 GB f1 score: 0.500 GB cohens kappa score: 0.479 -> test with 'KNN' KNN tn, fp: 270, 17 KNN fn, tp: 2, 9 KNN f1 score: 0.486 KNN cohens kappa score: 0.458 ------ Step 3/5: Slice 4/5 ------- -> 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: 0.522 -> test with 'GB' GB tn, fp: 278, 9 GB fn, tp: 3, 8 GB f1 score: 0.571 GB cohens kappa score: 0.551 -> test with 'KNN' KNN tn, fp: 273, 14 KNN fn, tp: 5, 6 KNN f1 score: 0.387 KNN cohens kappa score: 0.356 ------ Step 3/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1104 synthetic samples -> test with 'LR' LR tn, fp: 239, 46 LR fn, tp: 2, 5 LR f1 score: 0.172 LR cohens kappa score: 0.136 LR average precision score: 0.454 -> test with 'GB' GB tn, fp: 276, 9 GB fn, tp: 5, 2 GB f1 score: 0.222 GB cohens kappa score: 0.199 -> test with 'KNN' KNN tn, fp: 269, 16 KNN fn, tp: 3, 4 KNN f1 score: 0.296 KNN cohens kappa score: 0.270 ====== 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: 253, 34 LR fn, tp: 3, 8 LR f1 score: 0.302 LR cohens kappa score: 0.259 LR average precision score: 0.489 -> test with 'GB' GB tn, fp: 283, 4 GB fn, tp: 7, 4 GB f1 score: 0.421 GB cohens kappa score: 0.402 -> test with 'KNN' KNN tn, fp: 276, 11 KNN fn, tp: 6, 5 KNN f1 score: 0.370 KNN cohens kappa score: 0.342 ------ Step 4/5: 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: 2, 9 LR f1 score: 0.265 LR cohens kappa score: 0.216 LR average precision score: 0.345 -> 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: 264, 23 KNN fn, tp: 5, 6 KNN f1 score: 0.300 KNN cohens kappa score: 0.260 ------ Step 4/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1106 synthetic samples -> test with 'LR' LR tn, fp: 235, 52 LR fn, tp: 3, 8 LR f1 score: 0.225 LR cohens kappa score: 0.174 LR average precision score: 0.298 -> test with 'GB' GB tn, fp: 281, 6 GB fn, tp: 7, 4 GB f1 score: 0.381 GB cohens kappa score: 0.358 -> test with 'KNN' KNN tn, fp: 267, 20 KNN fn, tp: 4, 7 KNN f1 score: 0.368 KNN cohens kappa score: 0.333 ------ Step 4/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1106 synthetic samples -> test with 'LR' LR tn, fp: 244, 43 LR fn, tp: 4, 7 LR f1 score: 0.230 LR cohens kappa score: 0.180 LR average precision score: 0.289 -> test with 'GB' GB tn, fp: 279, 8 GB fn, tp: 6, 5 GB f1 score: 0.417 GB cohens kappa score: 0.392 -> test with 'KNN' KNN tn, fp: 269, 18 KNN fn, tp: 4, 7 KNN f1 score: 0.389 KNN cohens kappa score: 0.356 ------ Step 4/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1104 synthetic samples -> test with 'LR' LR tn, fp: 237, 48 LR fn, tp: 2, 5 LR f1 score: 0.167 LR cohens kappa score: 0.130 LR average precision score: 0.563 -> test with 'GB' GB tn, fp: 277, 8 GB fn, tp: 2, 5 GB f1 score: 0.500 GB cohens kappa score: 0.484 -> test with 'KNN' KNN tn, fp: 269, 16 KNN fn, tp: 2, 5 KNN f1 score: 0.357 KNN cohens kappa score: 0.333 ====== 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: 251, 36 LR fn, tp: 3, 8 LR f1 score: 0.291 LR cohens kappa score: 0.246 LR average precision score: 0.227 -> test with 'GB' GB tn, fp: 280, 7 GB fn, tp: 9, 2 GB f1 score: 0.200 GB cohens kappa score: 0.173 -> test with 'KNN' KNN tn, fp: 270, 17 KNN fn, tp: 4, 7 KNN f1 score: 0.400 KNN cohens kappa score: 0.368 ------ Step 5/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1106 synthetic samples -> test with 'LR' LR tn, fp: 229, 58 LR fn, tp: 2, 9 LR f1 score: 0.231 LR cohens kappa score: 0.179 LR average precision score: 0.535 -> test with 'GB' GB tn, fp: 277, 10 GB fn, tp: 7, 4 GB f1 score: 0.320 GB cohens kappa score: 0.291 -> test with 'KNN' KNN tn, fp: 266, 21 KNN fn, tp: 4, 7 KNN f1 score: 0.359 KNN cohens kappa score: 0.323 ------ Step 5/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1106 synthetic samples -> test with 'LR' LR tn, fp: 247, 40 LR fn, tp: 3, 8 LR f1 score: 0.271 LR cohens kappa score: 0.225 LR average precision score: 0.554 -> 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: 275, 12 KNN fn, tp: 6, 5 KNN f1 score: 0.357 KNN cohens kappa score: 0.327 ------ Step 5/5: 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: 1, 10 LR f1 score: 0.308 LR cohens kappa score: 0.262 LR average precision score: 0.509 -> test with 'GB' GB tn, fp: 276, 11 GB fn, tp: 5, 6 GB f1 score: 0.429 GB cohens kappa score: 0.402 -> test with 'KNN' KNN tn, fp: 272, 15 KNN fn, tp: 7, 4 KNN f1 score: 0.267 KNN cohens kappa score: 0.231 ------ Step 5/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1104 synthetic samples -> test with 'LR' LR tn, fp: 241, 44 LR fn, tp: 3, 4 LR f1 score: 0.145 LR cohens kappa score: 0.108 LR average precision score: 0.122 -> test with 'GB' GB tn, fp: 280, 5 GB fn, tp: 5, 2 GB f1 score: 0.286 GB cohens kappa score: 0.268 -> test with 'KNN' KNN tn, fp: 276, 9 KNN fn, tp: 3, 4 KNN f1 score: 0.400 KNN cohens kappa score: 0.381 ### Exercise is done. -----[ LR ]----- maximum: LR tn, fp: 253, 58 LR fn, tp: 6, 10 LR f1 score: 0.308 LR cohens kappa score: 0.262 LR average precision score: 0.659 average: LR tn, fp: 240.96, 45.64 LR fn, tp: 2.44, 7.76 LR f1 score: 0.243 LR cohens kappa score: 0.197 LR average precision score: 0.392 minimum: LR tn, fp: 229, 34 LR fn, tp: 1, 4 LR f1 score: 0.145 LR cohens kappa score: 0.108 LR average precision score: 0.122 -----[ GB ]----- maximum: GB tn, fp: 285, 11 GB fn, tp: 9, 8 GB f1 score: 0.615 GB cohens kappa score: 0.598 average: GB tn, fp: 279.92, 6.68 GB fn, tp: 6.4, 3.8 GB f1 score: 0.352 GB cohens kappa score: 0.330 minimum: GB tn, fp: 276, 2 GB fn, tp: 2, 1 GB f1 score: 0.167 GB cohens kappa score: 0.150 -----[ KNN ]----- maximum: KNN tn, fp: 278, 32 KNN fn, tp: 7, 9 KNN f1 score: 0.486 KNN cohens kappa score: 0.458 average: KNN tn, fp: 270.44, 16.16 KNN fn, tp: 4.32, 5.88 KNN f1 score: 0.367 KNN cohens kappa score: 0.336 minimum: KNN tn, fp: 255, 9 KNN fn, tp: 1, 4 KNN f1 score: 0.250 KNN cohens kappa score: 0.212