/////////////////////////////////////////// // Running ctGAN 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: 274, 16 LR fn, tp: 3, 4 LR f1 score: 0.296 LR cohens kappa score: 0.271 LR average precision score: 0.356 -> test with 'RF' RF tn, fp: 286, 4 RF fn, tp: 3, 4 RF f1 score: 0.533 RF cohens kappa score: 0.521 -> test with 'GB' GB tn, fp: 284, 6 GB fn, tp: 4, 3 GB f1 score: 0.375 GB cohens kappa score: 0.358 -> test with 'KNN' KNN tn, fp: 279, 11 KNN fn, tp: 1, 6 KNN f1 score: 0.500 KNN cohens kappa score: 0.483 ------ Step 1/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1131 synthetic samples -> test with 'LR' LR tn, fp: 235, 55 LR fn, tp: 2, 5 LR f1 score: 0.149 LR cohens kappa score: 0.112 LR average precision score: 0.215 -> test with 'RF' RF tn, fp: 274, 16 RF fn, tp: 4, 3 RF f1 score: 0.231 RF cohens kappa score: 0.203 -> test with 'GB' GB tn, fp: 277, 13 GB fn, tp: 4, 3 GB f1 score: 0.261 GB cohens kappa score: 0.236 -> test with 'KNN' KNN tn, fp: 258, 32 KNN fn, tp: 2, 5 KNN f1 score: 0.227 KNN cohens kappa score: 0.195 ------ Step 1/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1131 synthetic samples -> test with 'LR' LR tn, fp: 279, 11 LR fn, tp: 2, 5 LR f1 score: 0.435 LR cohens kappa score: 0.416 LR average precision score: 0.309 -> test with 'RF' RF tn, fp: 283, 7 RF fn, tp: 3, 4 RF f1 score: 0.444 RF cohens kappa score: 0.428 -> 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: 282, 8 KNN fn, tp: 3, 4 KNN f1 score: 0.421 KNN cohens kappa score: 0.403 ------ Step 1/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1131 synthetic samples -> test with 'LR' LR tn, fp: 279, 11 LR fn, tp: 2, 5 LR f1 score: 0.435 LR cohens kappa score: 0.416 LR average precision score: 0.384 -> test with 'RF' RF tn, fp: 282, 8 RF fn, tp: 4, 3 RF f1 score: 0.333 RF cohens kappa score: 0.314 -> test with 'GB' GB tn, fp: 284, 6 GB fn, tp: 5, 2 GB f1 score: 0.267 GB cohens kappa score: 0.248 -> test with 'KNN' KNN tn, fp: 282, 8 KNN fn, tp: 1, 6 KNN f1 score: 0.571 KNN cohens kappa score: 0.558 ------ 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: 0.353 -> test with 'RF' RF tn, fp: 273, 16 RF fn, tp: 1, 6 RF f1 score: 0.414 RF cohens kappa score: 0.392 -> test with 'GB' GB tn, fp: 285, 4 GB fn, tp: 2, 5 GB f1 score: 0.625 GB cohens kappa score: 0.615 -> test with 'KNN' KNN tn, fp: 273, 16 KNN fn, tp: 0, 7 KNN f1 score: 0.467 KNN cohens kappa score: 0.447 ====== 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: 264, 26 LR fn, tp: 1, 6 LR f1 score: 0.308 LR cohens kappa score: 0.280 LR average precision score: 0.398 -> test with 'RF' RF tn, fp: 278, 12 RF fn, tp: 3, 4 RF f1 score: 0.348 RF cohens kappa score: 0.326 -> test with 'GB' GB tn, fp: 282, 8 GB fn, tp: 2, 5 GB f1 score: 0.500 GB cohens kappa score: 0.484 -> test with 'KNN' KNN tn, fp: 276, 14 KNN fn, tp: 1, 6 KNN f1 score: 0.444 KNN cohens kappa score: 0.424 ------ Step 2/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1131 synthetic samples -> test with 'LR' LR tn, fp: 267, 23 LR fn, tp: 0, 7 LR f1 score: 0.378 LR cohens kappa score: 0.354 LR average precision score: 0.242 -> test with 'RF' RF tn, fp: 283, 7 RF fn, tp: 1, 6 RF f1 score: 0.600 RF cohens kappa score: 0.587 -> test with 'GB' GB tn, fp: 284, 6 GB fn, tp: 3, 4 GB f1 score: 0.471 GB cohens kappa score: 0.455 -> test with 'KNN' KNN tn, fp: 281, 9 KNN fn, tp: 0, 7 KNN f1 score: 0.609 KNN cohens kappa score: 0.595 ------ Step 2/5: Slice 3/5 ------- -> 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: 0.340 -> test with 'RF' RF tn, fp: 282, 8 RF fn, tp: 4, 3 RF f1 score: 0.333 RF cohens kappa score: 0.314 -> test with 'GB' GB tn, fp: 283, 7 GB fn, tp: 4, 3 GB f1 score: 0.353 GB cohens kappa score: 0.334 -> test with 'KNN' KNN tn, fp: 276, 14 KNN fn, tp: 2, 5 KNN f1 score: 0.385 KNN cohens kappa score: 0.363 ------ Step 2/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1131 synthetic samples -> test with 'LR' LR tn, fp: 266, 24 LR fn, tp: 3, 4 LR f1 score: 0.229 LR cohens kappa score: 0.198 LR average precision score: 0.318 -> test with 'RF' RF tn, fp: 283, 7 RF fn, tp: 4, 3 RF f1 score: 0.353 RF cohens kappa score: 0.334 -> test with 'GB' GB tn, fp: 281, 9 GB fn, tp: 4, 3 GB f1 score: 0.316 GB cohens kappa score: 0.295 -> test with 'KNN' KNN tn, fp: 274, 16 KNN fn, tp: 2, 5 KNN f1 score: 0.357 KNN cohens kappa score: 0.334 ------ Step 2/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1132 synthetic samples -> test with 'LR' LR tn, fp: 231, 58 LR fn, tp: 1, 6 LR f1 score: 0.169 LR cohens kappa score: 0.132 LR average precision score: 0.495 -> test with 'RF' RF tn, fp: 281, 8 RF fn, tp: 4, 3 RF f1 score: 0.333 RF cohens kappa score: 0.313 -> test with 'GB' GB tn, fp: 280, 9 GB fn, tp: 5, 2 GB f1 score: 0.222 GB cohens kappa score: 0.199 -> test with 'KNN' KNN tn, fp: 268, 21 KNN fn, tp: 3, 4 KNN f1 score: 0.250 KNN cohens kappa score: 0.221 ====== 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: 235, 55 LR fn, tp: 1, 6 LR f1 score: 0.176 LR cohens kappa score: 0.140 LR average precision score: 0.180 -> test with 'RF' RF tn, fp: 280, 10 RF fn, tp: 4, 3 RF f1 score: 0.300 RF cohens kappa score: 0.278 -> test with 'GB' GB tn, fp: 283, 7 GB fn, tp: 4, 3 GB f1 score: 0.353 GB cohens kappa score: 0.334 -> test with 'KNN' KNN tn, fp: 276, 14 KNN fn, tp: 3, 4 KNN f1 score: 0.320 KNN cohens kappa score: 0.296 ------ Step 3/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1131 synthetic samples -> test with 'LR' LR tn, fp: 263, 27 LR fn, tp: 0, 7 LR f1 score: 0.341 LR cohens kappa score: 0.315 LR average precision score: 0.391 -> test with 'RF' RF tn, fp: 283, 7 RF fn, tp: 1, 6 RF f1 score: 0.600 RF cohens kappa score: 0.587 -> test with 'GB' GB tn, fp: 283, 7 GB fn, tp: 1, 6 GB f1 score: 0.600 GB cohens kappa score: 0.587 -> test with 'KNN' KNN tn, fp: 277, 13 KNN fn, tp: 0, 7 KNN f1 score: 0.519 KNN cohens kappa score: 0.501 ------ Step 3/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1131 synthetic samples -> test with 'LR' LR tn, fp: 218, 72 LR fn, tp: 4, 3 LR f1 score: 0.073 LR cohens kappa score: 0.031 LR average precision score: 0.045 -> test with 'RF' RF tn, fp: 278, 12 RF fn, tp: 4, 3 RF f1 score: 0.273 RF cohens kappa score: 0.249 -> test with 'GB' GB tn, fp: 282, 8 GB fn, tp: 7, 0 GB f1 score: 0.000 GB cohens kappa score: -0.026 -> test with 'KNN' KNN tn, fp: 268, 22 KNN fn, tp: 3, 4 KNN f1 score: 0.242 KNN cohens kappa score: 0.213 ------ Step 3/5: 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: 4, 3 LR f1 score: 0.130 LR cohens kappa score: 0.094 LR average precision score: 0.074 -> test with 'RF' RF tn, fp: 271, 19 RF fn, tp: 4, 3 RF f1 score: 0.207 RF cohens kappa score: 0.177 -> test with 'GB' GB tn, fp: 276, 14 GB fn, tp: 5, 2 GB f1 score: 0.174 GB cohens kappa score: 0.146 -> test with 'KNN' KNN tn, fp: 261, 29 KNN fn, tp: 2, 5 KNN f1 score: 0.244 KNN cohens kappa score: 0.213 ------ Step 3/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1132 synthetic samples -> test with 'LR' LR tn, fp: 275, 14 LR fn, tp: 2, 5 LR f1 score: 0.385 LR cohens kappa score: 0.363 LR average precision score: 0.344 -> test with 'RF' RF tn, fp: 284, 5 RF fn, tp: 4, 3 RF f1 score: 0.400 RF cohens kappa score: 0.384 -> test with 'GB' GB tn, fp: 286, 3 GB fn, tp: 4, 3 GB f1 score: 0.462 GB cohens kappa score: 0.450 -> test with 'KNN' KNN tn, fp: 284, 5 KNN fn, tp: 4, 3 KNN f1 score: 0.400 KNN cohens kappa score: 0.384 ====== 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: 259, 31 LR fn, tp: 2, 5 LR f1 score: 0.233 LR cohens kappa score: 0.201 LR average precision score: 0.248 -> test with 'RF' RF tn, fp: 286, 4 RF fn, tp: 3, 4 RF f1 score: 0.533 RF cohens kappa score: 0.521 -> 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: 275, 15 KNN fn, tp: 0, 7 KNN f1 score: 0.483 KNN cohens kappa score: 0.464 ------ Step 4/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1131 synthetic samples -> test with 'LR' LR tn, fp: 202, 88 LR fn, tp: 2, 5 LR f1 score: 0.100 LR cohens kappa score: 0.059 LR average precision score: 0.063 -> test with 'RF' RF tn, fp: 279, 11 RF fn, tp: 6, 1 RF f1 score: 0.105 RF cohens kappa score: 0.078 -> test with 'GB' GB tn, fp: 280, 10 GB fn, tp: 5, 2 GB f1 score: 0.211 GB cohens kappa score: 0.186 -> 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 4/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1131 synthetic samples -> test with 'LR' LR tn, fp: 238, 52 LR fn, tp: 0, 7 LR f1 score: 0.212 LR cohens kappa score: 0.177 LR average precision score: 0.260 -> test with 'RF' RF tn, fp: 267, 23 RF fn, tp: 1, 6 RF f1 score: 0.333 RF cohens kappa score: 0.307 -> test with 'GB' GB tn, fp: 276, 14 GB fn, tp: 4, 3 GB f1 score: 0.250 GB cohens kappa score: 0.224 -> 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 4/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1131 synthetic samples -> test with 'LR' LR tn, fp: 275, 15 LR fn, tp: 1, 6 LR f1 score: 0.429 LR cohens kappa score: 0.408 LR average precision score: 0.635 -> test with 'RF' RF tn, fp: 283, 7 RF fn, tp: 4, 3 RF f1 score: 0.353 RF cohens kappa score: 0.334 -> test with 'GB' GB tn, fp: 282, 8 GB fn, tp: 4, 3 GB f1 score: 0.333 GB cohens kappa score: 0.314 -> test with 'KNN' KNN tn, fp: 278, 12 KNN fn, tp: 1, 6 KNN f1 score: 0.480 KNN cohens kappa score: 0.462 ------ Step 4/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1132 synthetic samples -> test with 'LR' LR tn, fp: 237, 52 LR fn, tp: 2, 5 LR f1 score: 0.156 LR cohens kappa score: 0.119 LR average precision score: 0.189 -> test with 'RF' RF tn, fp: 282, 7 RF fn, tp: 4, 3 RF f1 score: 0.353 RF cohens kappa score: 0.334 -> test with 'GB' GB tn, fp: 285, 4 GB fn, tp: 4, 3 GB f1 score: 0.429 GB cohens kappa score: 0.415 -> test with 'KNN' KNN tn, fp: 270, 19 KNN fn, tp: 2, 5 KNN f1 score: 0.323 KNN cohens kappa score: 0.297 ====== 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: 274, 16 LR fn, tp: 2, 5 LR f1 score: 0.357 LR cohens kappa score: 0.334 LR average precision score: 0.288 -> test with 'RF' RF tn, fp: 279, 11 RF fn, tp: 3, 4 RF f1 score: 0.364 RF cohens kappa score: 0.343 -> test with 'GB' GB tn, fp: 279, 11 GB fn, tp: 3, 4 GB f1 score: 0.364 GB cohens kappa score: 0.343 -> test with 'KNN' KNN tn, fp: 274, 16 KNN fn, tp: 2, 5 KNN f1 score: 0.357 KNN cohens kappa score: 0.334 ------ Step 5/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1131 synthetic samples -> test with 'LR' LR tn, fp: 235, 55 LR fn, tp: 1, 6 LR f1 score: 0.176 LR cohens kappa score: 0.140 LR average precision score: 0.171 -> test with 'RF' RF tn, fp: 280, 10 RF fn, tp: 3, 4 RF f1 score: 0.381 RF cohens kappa score: 0.361 -> test with 'GB' GB tn, fp: 283, 7 GB fn, tp: 7, 0 GB f1 score: 0.000 GB cohens kappa score: -0.024 -> test with 'KNN' KNN tn, fp: 271, 19 KNN fn, tp: 1, 6 KNN f1 score: 0.375 KNN cohens kappa score: 0.351 ------ Step 5/5: 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: 0, 7 LR f1 score: 0.318 LR cohens kappa score: 0.290 LR average precision score: 0.700 -> test with 'RF' RF tn, fp: 276, 14 RF fn, tp: 1, 6 RF f1 score: 0.444 RF cohens kappa score: 0.424 -> test with 'GB' GB tn, fp: 281, 9 GB fn, tp: 1, 6 GB f1 score: 0.545 GB cohens kappa score: 0.530 -> test with 'KNN' KNN tn, fp: 273, 17 KNN fn, tp: 0, 7 KNN f1 score: 0.452 KNN cohens kappa score: 0.431 ------ Step 5/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1131 synthetic samples -> test with 'LR' LR tn, fp: 273, 17 LR fn, tp: 2, 5 LR f1 score: 0.345 LR cohens kappa score: 0.321 LR average precision score: 0.317 -> test with 'RF' RF tn, fp: 286, 4 RF fn, tp: 3, 4 RF f1 score: 0.533 RF cohens kappa score: 0.521 -> 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: 280, 10 KNN fn, tp: 3, 4 KNN f1 score: 0.381 KNN cohens kappa score: 0.361 ------ Step 5/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1132 synthetic samples -> test with 'LR' LR tn, fp: 279, 10 LR fn, tp: 2, 5 LR f1 score: 0.455 LR cohens kappa score: 0.436 LR average precision score: 0.374 -> test with 'RF' RF tn, fp: 285, 4 RF fn, tp: 4, 3 RF f1 score: 0.429 RF cohens kappa score: 0.415 -> test with 'GB' GB tn, fp: 282, 7 GB fn, tp: 4, 3 GB f1 score: 0.353 GB cohens kappa score: 0.334 -> test with 'KNN' KNN tn, fp: 282, 7 KNN fn, tp: 3, 4 KNN f1 score: 0.444 KNN cohens kappa score: 0.428 ### Exercise is done. -----[ LR ]----- maximum: LR tn, fp: 279, 88 LR fn, tp: 4, 7 LR f1 score: 0.455 LR cohens kappa score: 0.436 LR average precision score: 0.700 average: LR tn, fp: 255.0, 34.8 LR fn, tp: 1.6, 5.4 LR f1 score: 0.272 LR cohens kappa score: 0.242 LR average precision score: 0.308 minimum: LR tn, fp: 202, 10 LR fn, tp: 0, 3 LR f1 score: 0.073 LR cohens kappa score: 0.031 LR average precision score: 0.045 -----[ RF ]----- maximum: RF tn, fp: 286, 23 RF fn, tp: 6, 6 RF f1 score: 0.600 RF cohens kappa score: 0.587 average: RF tn, fp: 280.16, 9.64 RF fn, tp: 3.2, 3.8 RF f1 score: 0.381 RF cohens kappa score: 0.362 minimum: RF tn, fp: 267, 4 RF fn, tp: 1, 1 RF f1 score: 0.105 RF cohens kappa score: 0.078 -----[ GB ]----- maximum: GB tn, fp: 287, 14 GB fn, tp: 7, 6 GB f1 score: 0.625 GB cohens kappa score: 0.615 average: GB tn, fp: 282.28, 7.52 GB fn, tp: 3.88, 3.12 GB f1 score: 0.355 GB cohens kappa score: 0.336 minimum: GB tn, fp: 276, 3 GB fn, tp: 1, 0 GB f1 score: 0.000 GB cohens kappa score: -0.026 -----[ KNN ]----- maximum: KNN tn, fp: 284, 32 KNN fn, tp: 4, 7 KNN f1 score: 0.609 KNN cohens kappa score: 0.595 average: KNN tn, fp: 274.12, 15.68 KNN fn, tp: 1.64, 5.36 KNN f1 score: 0.397 KNN cohens kappa score: 0.375 minimum: KNN tn, fp: 258, 5 KNN fn, tp: 0, 3 KNN f1 score: 0.227 KNN cohens kappa score: 0.195