/////////////////////////////////////////// // 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: 235, 52 LR fn, tp: 2, 9 LR f1 score: 0.250 LR cohens kappa score: 0.200 LR average precision score: 0.474 -> test with 'RF' RF tn, fp: 284, 3 RF fn, tp: 8, 3 RF f1 score: 0.353 RF cohens kappa score: 0.336 -> 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: 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: 229, 58 LR fn, tp: 1, 10 LR f1 score: 0.253 LR cohens kappa score: 0.202 LR average precision score: 0.675 -> test with 'RF' RF tn, fp: 284, 3 RF fn, tp: 7, 4 RF f1 score: 0.444 RF cohens kappa score: 0.428 -> test with 'GB' GB tn, fp: 282, 5 GB fn, tp: 3, 8 GB f1 score: 0.667 GB cohens kappa score: 0.653 -> test with 'KNN' KNN tn, fp: 272, 15 KNN fn, tp: 4, 7 KNN f1 score: 0.424 KNN cohens kappa score: 0.394 ------ Step 1/5: 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: 1, 10 LR f1 score: 0.286 LR cohens kappa score: 0.238 LR average precision score: 0.269 -> test with 'RF' RF tn, fp: 286, 1 RF fn, tp: 7, 4 RF f1 score: 0.500 RF cohens kappa score: 0.488 -> 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: 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: 254, 33 LR fn, tp: 6, 5 LR f1 score: 0.204 LR cohens kappa score: 0.156 LR average precision score: 0.212 -> test with 'RF' RF tn, fp: 282, 5 RF fn, tp: 9, 2 RF f1 score: 0.222 RF cohens kappa score: 0.199 -> test with 'GB' GB tn, fp: 275, 12 GB fn, tp: 8, 3 GB f1 score: 0.231 GB cohens kappa score: 0.197 -> 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: 238, 47 LR fn, tp: 1, 6 LR f1 score: 0.200 LR cohens kappa score: 0.165 LR average precision score: 0.381 -> test with 'RF' RF tn, fp: 282, 3 RF fn, tp: 5, 2 RF f1 score: 0.333 RF cohens kappa score: 0.320 -> test with 'GB' GB tn, fp: 280, 5 GB fn, tp: 4, 3 GB f1 score: 0.400 GB cohens kappa score: 0.384 -> 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: 248, 39 LR fn, tp: 2, 9 LR f1 score: 0.305 LR cohens kappa score: 0.261 LR average precision score: 0.274 -> test with 'RF' RF tn, fp: 285, 2 RF fn, tp: 8, 3 RF f1 score: 0.375 RF cohens kappa score: 0.360 -> 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: 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: 233, 54 LR fn, tp: 3, 8 LR f1 score: 0.219 LR cohens kappa score: 0.167 LR average precision score: 0.471 -> test with 'RF' RF tn, fp: 282, 5 RF fn, tp: 7, 4 RF f1 score: 0.400 RF cohens kappa score: 0.379 -> 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: 256, 31 KNN fn, tp: 3, 8 KNN f1 score: 0.320 KNN cohens kappa score: 0.278 ------ 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: 0.356 -> test with 'RF' RF tn, fp: 286, 1 RF fn, tp: 9, 2 RF f1 score: 0.286 RF cohens kappa score: 0.274 -> 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: 247, 40 LR fn, tp: 3, 8 LR f1 score: 0.271 LR cohens kappa score: 0.225 LR average precision score: 0.338 -> test with 'RF' RF tn, fp: 283, 4 RF fn, tp: 5, 6 RF f1 score: 0.571 RF cohens kappa score: 0.556 -> test with 'GB' GB tn, fp: 277, 10 GB fn, tp: 5, 6 GB f1 score: 0.444 GB cohens kappa score: 0.419 -> 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: 230, 55 LR fn, tp: 1, 6 LR f1 score: 0.176 LR cohens kappa score: 0.139 LR average precision score: 0.409 -> test with 'RF' RF tn, fp: 283, 2 RF fn, tp: 6, 1 RF f1 score: 0.200 RF cohens kappa score: 0.188 -> test with 'GB' GB tn, fp: 279, 6 GB fn, tp: 5, 2 GB f1 score: 0.267 GB cohens kappa score: 0.247 -> 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: 241, 46 LR fn, tp: 2, 9 LR f1 score: 0.273 LR cohens kappa score: 0.225 LR average precision score: 0.428 -> test with 'RF' RF tn, fp: 281, 6 RF fn, tp: 9, 2 RF f1 score: 0.211 RF cohens kappa score: 0.185 -> 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: 270, 17 KNN fn, tp: 5, 6 KNN f1 score: 0.353 KNN cohens kappa score: 0.319 ------ 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: 0.379 -> test with 'RF' RF tn, fp: 287, 0 RF fn, tp: 10, 1 RF f1 score: 0.167 RF cohens kappa score: 0.162 -> 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: 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: 0.245 -> test with 'RF' RF tn, fp: 284, 3 RF fn, tp: 8, 3 RF f1 score: 0.353 RF cohens kappa score: 0.336 -> 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: 240, 47 LR fn, tp: 1, 10 LR f1 score: 0.294 LR cohens kappa score: 0.248 LR average precision score: 0.526 -> test with 'RF' RF tn, fp: 281, 6 RF fn, tp: 5, 6 RF f1 score: 0.522 RF cohens kappa score: 0.503 -> test with 'GB' GB tn, fp: 279, 8 GB fn, tp: 3, 8 GB f1 score: 0.593 GB cohens kappa score: 0.574 -> 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: 0.452 -> test with 'RF' RF tn, fp: 281, 4 RF fn, tp: 5, 2 RF f1 score: 0.308 RF cohens kappa score: 0.292 -> test with 'GB' GB tn, fp: 274, 11 GB fn, tp: 4, 3 GB f1 score: 0.286 GB cohens kappa score: 0.262 -> 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: 254, 33 LR fn, tp: 3, 8 LR f1 score: 0.308 LR cohens kappa score: 0.265 LR average precision score: 0.485 -> test with 'RF' RF tn, fp: 287, 0 RF fn, tp: 8, 3 RF f1 score: 0.429 RF cohens kappa score: 0.419 -> 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: 275, 12 KNN fn, tp: 6, 5 KNN f1 score: 0.357 KNN cohens kappa score: 0.327 ------ Step 4/5: Slice 2/5 ------- -> 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: 0.380 -> test with 'RF' RF tn, fp: 286, 1 RF fn, tp: 10, 1 RF f1 score: 0.154 RF cohens kappa score: 0.144 -> 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: 236, 51 LR fn, tp: 3, 8 LR f1 score: 0.229 LR cohens kappa score: 0.177 LR average precision score: 0.306 -> test with 'RF' RF tn, fp: 283, 4 RF fn, tp: 10, 1 RF f1 score: 0.125 RF cohens kappa score: 0.104 -> 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: 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: 245, 42 LR fn, tp: 4, 7 LR f1 score: 0.233 LR cohens kappa score: 0.184 LR average precision score: 0.291 -> test with 'RF' RF tn, fp: 282, 5 RF fn, tp: 8, 3 RF f1 score: 0.316 RF cohens kappa score: 0.294 -> 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: 240, 45 LR fn, tp: 2, 5 LR f1 score: 0.175 LR cohens kappa score: 0.139 LR average precision score: 0.523 -> test with 'RF' RF tn, fp: 281, 4 RF fn, tp: 3, 4 RF f1 score: 0.533 RF cohens kappa score: 0.521 -> test with 'GB' GB tn, fp: 276, 9 GB fn, tp: 3, 4 GB f1 score: 0.400 GB cohens kappa score: 0.381 -> 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: 0.224 -> test with 'RF' RF tn, fp: 285, 2 RF fn, tp: 10, 1 RF f1 score: 0.143 RF cohens kappa score: 0.129 -> 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: 235, 52 LR fn, tp: 2, 9 LR f1 score: 0.250 LR cohens kappa score: 0.200 LR average precision score: 0.538 -> test with 'RF' RF tn, fp: 282, 5 RF fn, tp: 9, 2 RF f1 score: 0.222 RF cohens kappa score: 0.199 -> 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: 252, 35 LR fn, tp: 3, 8 LR f1 score: 0.296 LR cohens kappa score: 0.252 LR average precision score: 0.560 -> test with 'RF' RF tn, fp: 284, 3 RF fn, tp: 9, 2 RF f1 score: 0.250 RF cohens kappa score: 0.232 -> 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: 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: 240, 47 LR fn, tp: 1, 10 LR f1 score: 0.294 LR cohens kappa score: 0.248 LR average precision score: 0.505 -> test with 'RF' RF tn, fp: 283, 4 RF fn, tp: 7, 4 RF f1 score: 0.421 RF cohens kappa score: 0.402 -> test with 'GB' GB tn, fp: 275, 12 GB fn, tp: 7, 4 GB f1 score: 0.296 GB cohens kappa score: 0.264 -> 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: 240, 45 LR fn, tp: 3, 4 LR f1 score: 0.143 LR cohens kappa score: 0.105 LR average precision score: 0.119 -> test with 'RF' RF tn, fp: 284, 1 RF fn, tp: 5, 2 RF f1 score: 0.400 RF cohens kappa score: 0.391 -> 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: 254, 58 LR fn, tp: 6, 10 LR f1 score: 0.308 LR cohens kappa score: 0.265 LR average precision score: 0.675 average: LR tn, fp: 241.64, 44.96 LR fn, tp: 2.4, 7.8 LR f1 score: 0.247 LR cohens kappa score: 0.202 LR average precision score: 0.393 minimum: LR tn, fp: 229, 33 LR fn, tp: 1, 4 LR f1 score: 0.143 LR cohens kappa score: 0.105 LR average precision score: 0.119 -----[ RF ]----- maximum: RF tn, fp: 287, 6 RF fn, tp: 10, 6 RF f1 score: 0.571 RF cohens kappa score: 0.556 average: RF tn, fp: 283.52, 3.08 RF fn, tp: 7.48, 2.72 RF f1 score: 0.329 RF cohens kappa score: 0.314 minimum: RF tn, fp: 281, 0 RF fn, tp: 3, 1 RF f1 score: 0.125 RF cohens kappa score: 0.104 -----[ GB ]----- maximum: GB tn, fp: 285, 12 GB fn, tp: 9, 8 GB f1 score: 0.667 GB cohens kappa score: 0.653 average: GB tn, fp: 279.64, 6.96 GB fn, tp: 6.4, 3.8 GB f1 score: 0.354 GB cohens kappa score: 0.331 minimum: GB tn, fp: 274, 2 GB fn, tp: 3, 2 GB f1 score: 0.200 GB cohens kappa score: 0.173 -----[ KNN ]----- maximum: KNN tn, fp: 278, 31 KNN fn, tp: 7, 9 KNN f1 score: 0.486 KNN cohens kappa score: 0.458 average: KNN tn, fp: 270.36, 16.24 KNN fn, tp: 4.32, 5.88 KNN f1 score: 0.365 KNN cohens kappa score: 0.335 minimum: KNN tn, fp: 256, 9 KNN fn, tp: 1, 4 KNN f1 score: 0.250 KNN cohens kappa score: 0.212