/////////////////////////////////////////// // Running convGAN-majority-full 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 GAN.predict GAN tn, fp: 278, 12 GAN fn, tp: 1, 6 GAN f1 score: 0.480 GAN cohens kappa score: 0.462 -> test with 'LR' LR tn, fp: 272, 18 LR fn, tp: 1, 6 LR f1 score: 0.387 LR cohens kappa score: 0.364 LR average precision score: 0.629 -> 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: 274, 16 KNN fn, tp: 1, 6 KNN f1 score: 0.414 KNN cohens kappa score: 0.392 ------ Step 1/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1131 synthetic samples -> test with GAN.predict GAN tn, fp: 273, 17 GAN fn, tp: 3, 4 GAN f1 score: 0.286 GAN cohens kappa score: 0.260 -> 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: 0.425 -> 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: 274, 16 KNN fn, tp: 3, 4 KNN f1 score: 0.296 KNN cohens kappa score: 0.271 ------ Step 1/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1131 synthetic samples -> test with GAN.predict GAN tn, fp: 274, 16 GAN fn, tp: 1, 6 GAN f1 score: 0.414 GAN cohens kappa score: 0.392 -> test with 'LR' LR tn, fp: 267, 23 LR fn, tp: 1, 6 LR f1 score: 0.333 LR cohens kappa score: 0.307 LR average precision score: 0.335 -> test with 'GB' GB tn, fp: 290, 0 GB fn, tp: 3, 4 GB f1 score: 0.727 GB cohens kappa score: 0.723 -> 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 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1131 synthetic samples -> test with GAN.predict GAN tn, fp: 282, 8 GAN fn, tp: 1, 6 GAN f1 score: 0.571 GAN cohens kappa score: 0.558 -> 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: 0.619 -> 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: 278, 12 KNN fn, tp: 1, 6 KNN f1 score: 0.480 KNN cohens kappa score: 0.462 ------ Step 1/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1132 synthetic samples -> test with GAN.predict GAN tn, fp: 272, 17 GAN fn, tp: 1, 6 GAN f1 score: 0.400 GAN cohens kappa score: 0.377 -> test with 'LR' LR tn, fp: 258, 31 LR fn, tp: 0, 7 LR f1 score: 0.311 LR cohens kappa score: 0.282 LR average precision score: 0.604 -> test with 'GB' GB tn, fp: 286, 3 GB fn, tp: 2, 5 GB f1 score: 0.667 GB cohens kappa score: 0.658 -> test with 'KNN' KNN tn, fp: 270, 19 KNN fn, tp: 1, 6 KNN f1 score: 0.375 KNN cohens kappa score: 0.351 ====== 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 GAN.predict GAN tn, fp: 276, 14 GAN fn, tp: 1, 6 GAN f1 score: 0.444 GAN cohens kappa score: 0.424 -> test with 'LR' LR tn, fp: 270, 20 LR fn, tp: 1, 6 LR f1 score: 0.364 LR cohens kappa score: 0.339 LR average precision score: 0.679 -> 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: 272, 18 KNN fn, tp: 1, 6 KNN f1 score: 0.387 KNN cohens kappa score: 0.364 ------ Step 2/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1131 synthetic samples -> test with GAN.predict GAN tn, fp: 272, 18 GAN fn, tp: 0, 7 GAN f1 score: 0.438 GAN cohens kappa score: 0.416 -> test with 'LR' LR tn, fp: 255, 35 LR fn, tp: 0, 7 LR f1 score: 0.286 LR cohens kappa score: 0.256 LR average precision score: 0.246 -> test with 'GB' GB tn, fp: 286, 4 GB fn, tp: 2, 5 GB f1 score: 0.625 GB cohens kappa score: 0.615 -> test with 'KNN' KNN tn, fp: 269, 21 KNN fn, tp: 0, 7 KNN f1 score: 0.400 KNN cohens kappa score: 0.376 ------ Step 2/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1131 synthetic samples -> test with GAN.predict GAN tn, fp: 275, 15 GAN fn, tp: 2, 5 GAN f1 score: 0.370 GAN cohens kappa score: 0.348 -> test with 'LR' LR tn, fp: 263, 27 LR fn, tp: 2, 5 LR f1 score: 0.256 LR cohens kappa score: 0.226 LR average precision score: 0.512 -> 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: 267, 23 KNN fn, tp: 2, 5 KNN f1 score: 0.286 KNN cohens kappa score: 0.258 ------ Step 2/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1131 synthetic samples -> test with GAN.predict GAN tn, fp: 278, 12 GAN fn, tp: 2, 5 GAN f1 score: 0.417 GAN cohens kappa score: 0.397 -> test with 'LR' LR tn, fp: 266, 24 LR fn, tp: 2, 5 LR f1 score: 0.278 LR cohens kappa score: 0.249 LR average precision score: 0.596 -> 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: 273, 17 KNN fn, tp: 2, 5 KNN f1 score: 0.345 KNN cohens kappa score: 0.321 ------ Step 2/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1132 synthetic samples -> test with GAN.predict GAN tn, fp: 280, 9 GAN fn, tp: 2, 5 GAN f1 score: 0.476 GAN cohens kappa score: 0.459 -> test with 'LR' LR tn, fp: 274, 15 LR fn, tp: 1, 6 LR f1 score: 0.429 LR cohens kappa score: 0.408 LR average precision score: 0.492 -> 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: 281, 8 KNN fn, tp: 2, 5 KNN f1 score: 0.500 KNN cohens kappa score: 0.484 ====== 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 GAN.predict GAN tn, fp: 279, 11 GAN fn, tp: 1, 6 GAN f1 score: 0.500 GAN cohens kappa score: 0.483 -> test with 'LR' LR tn, fp: 269, 21 LR fn, tp: 1, 6 LR f1 score: 0.353 LR cohens kappa score: 0.328 LR average precision score: 0.652 -> 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: 278, 12 KNN fn, tp: 1, 6 KNN f1 score: 0.480 KNN cohens kappa score: 0.462 ------ Step 3/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1131 synthetic samples -> test with GAN.predict GAN tn, fp: 270, 20 GAN fn, tp: 0, 7 GAN f1 score: 0.412 GAN cohens kappa score: 0.389 -> test with 'LR' LR tn, fp: 256, 34 LR fn, tp: 0, 7 LR f1 score: 0.292 LR cohens kappa score: 0.262 LR average precision score: 0.689 -> test with 'GB' GB tn, fp: 287, 3 GB fn, tp: 2, 5 GB f1 score: 0.667 GB cohens kappa score: 0.658 -> 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 3/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1131 synthetic samples -> test with GAN.predict GAN tn, fp: 280, 10 GAN fn, tp: 3, 4 GAN f1 score: 0.381 GAN cohens kappa score: 0.361 -> test with 'LR' LR tn, fp: 277, 13 LR fn, tp: 2, 5 LR f1 score: 0.400 LR cohens kappa score: 0.379 LR average precision score: 0.420 -> test with 'GB' GB tn, fp: 288, 2 GB fn, tp: 6, 1 GB f1 score: 0.200 GB cohens kappa score: 0.189 -> test with 'KNN' KNN tn, fp: 279, 11 KNN fn, tp: 3, 4 KNN f1 score: 0.364 KNN cohens kappa score: 0.343 ------ Step 3/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1131 synthetic samples -> test with GAN.predict GAN tn, fp: 277, 13 GAN fn, tp: 2, 5 GAN f1 score: 0.400 GAN cohens kappa score: 0.379 -> test with 'LR' LR tn, fp: 265, 25 LR fn, tp: 1, 6 LR f1 score: 0.316 LR cohens kappa score: 0.288 LR average precision score: 0.417 -> test with 'GB' GB tn, fp: 280, 10 GB fn, tp: 3, 4 GB f1 score: 0.381 GB cohens kappa score: 0.361 -> test with 'KNN' KNN tn, fp: 271, 19 KNN fn, tp: 2, 5 KNN f1 score: 0.323 KNN cohens kappa score: 0.297 ------ Step 3/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1132 synthetic samples -> test with GAN.predict GAN tn, fp: 283, 6 GAN fn, tp: 2, 5 GAN f1 score: 0.556 GAN cohens kappa score: 0.542 -> 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: 0.448 -> 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: 282, 7 KNN fn, tp: 1, 6 KNN f1 score: 0.600 KNN cohens kappa score: 0.587 ====== 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 GAN.predict GAN tn, fp: 277, 13 GAN fn, tp: 1, 6 GAN f1 score: 0.462 GAN cohens kappa score: 0.442 -> test with 'LR' LR tn, fp: 276, 14 LR fn, tp: 1, 6 LR f1 score: 0.444 LR cohens kappa score: 0.424 LR average precision score: 0.750 -> test with 'GB' GB tn, fp: 287, 3 GB fn, tp: 1, 6 GB f1 score: 0.750 GB cohens kappa score: 0.743 -> 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 4/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1131 synthetic samples -> test with GAN.predict GAN tn, fp: 279, 11 GAN fn, tp: 1, 6 GAN f1 score: 0.500 GAN cohens kappa score: 0.483 -> test with 'LR' LR tn, fp: 268, 22 LR fn, tp: 0, 7 LR f1 score: 0.389 LR cohens kappa score: 0.365 LR average precision score: 0.289 -> test with 'GB' GB tn, fp: 286, 4 GB fn, tp: 4, 3 GB f1 score: 0.429 GB cohens kappa score: 0.415 -> 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 4/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1131 synthetic samples -> test with GAN.predict GAN tn, fp: 274, 16 GAN fn, tp: 1, 6 GAN f1 score: 0.414 GAN cohens kappa score: 0.392 -> 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: 0.634 -> test with 'GB' GB tn, fp: 287, 3 GB fn, tp: 2, 5 GB f1 score: 0.667 GB cohens kappa score: 0.658 -> 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 4/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1131 synthetic samples -> test with GAN.predict GAN tn, fp: 276, 14 GAN fn, tp: 2, 5 GAN f1 score: 0.385 GAN cohens kappa score: 0.363 -> 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: 0.672 -> 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: 280, 10 KNN fn, tp: 1, 6 KNN f1 score: 0.522 KNN cohens kappa score: 0.506 ------ Step 4/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1132 synthetic samples -> test with GAN.predict GAN tn, fp: 275, 14 GAN fn, tp: 2, 5 GAN f1 score: 0.385 GAN cohens kappa score: 0.363 -> 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: 0.629 -> 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: 279, 10 KNN fn, tp: 2, 5 KNN f1 score: 0.455 KNN cohens kappa score: 0.436 ====== 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 GAN.predict GAN tn, fp: 267, 23 GAN fn, tp: 1, 6 GAN f1 score: 0.333 GAN cohens kappa score: 0.307 -> 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: 0.503 -> test with 'GB' GB tn, fp: 285, 5 GB fn, tp: 2, 5 GB f1 score: 0.588 GB cohens kappa score: 0.576 -> test with 'KNN' KNN tn, fp: 267, 23 KNN fn, tp: 1, 6 KNN f1 score: 0.333 KNN cohens kappa score: 0.307 ------ Step 5/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1131 synthetic samples -> test with GAN.predict GAN tn, fp: 281, 9 GAN fn, tp: 3, 4 GAN f1 score: 0.400 GAN cohens kappa score: 0.381 -> test with 'LR' LR tn, fp: 273, 17 LR fn, tp: 3, 4 LR f1 score: 0.286 LR cohens kappa score: 0.260 LR average precision score: 0.233 -> 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: 276, 14 KNN fn, tp: 3, 4 KNN f1 score: 0.320 KNN cohens kappa score: 0.296 ------ Step 5/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1131 synthetic samples -> test with GAN.predict GAN tn, fp: 273, 17 GAN fn, tp: 0, 7 GAN f1 score: 0.452 GAN cohens kappa score: 0.431 -> 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: 0.773 -> test with 'GB' GB tn, fp: 286, 4 GB fn, tp: 0, 7 GB f1 score: 0.778 GB cohens kappa score: 0.771 -> test with 'KNN' KNN tn, fp: 270, 20 KNN fn, tp: 0, 7 KNN f1 score: 0.412 KNN cohens kappa score: 0.389 ------ Step 5/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1131 synthetic samples -> test with GAN.predict GAN tn, fp: 271, 19 GAN fn, tp: 2, 5 GAN f1 score: 0.323 GAN cohens kappa score: 0.297 -> 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: 0.353 -> test with 'GB' GB tn, fp: 289, 1 GB fn, tp: 5, 2 GB f1 score: 0.400 GB cohens kappa score: 0.391 -> 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 5/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1132 synthetic samples -> test with GAN.predict GAN tn, fp: 274, 15 GAN fn, tp: 2, 5 GAN f1 score: 0.370 GAN cohens kappa score: 0.348 -> test with 'LR' LR tn, fp: 271, 18 LR fn, tp: 2, 5 LR f1 score: 0.333 LR cohens kappa score: 0.308 LR average precision score: 0.423 -> test with 'GB' GB tn, fp: 284, 5 GB fn, tp: 5, 2 GB f1 score: 0.286 GB cohens kappa score: 0.268 -> test with 'KNN' KNN tn, fp: 269, 20 KNN fn, tp: 2, 5 KNN f1 score: 0.312 KNN cohens kappa score: 0.286 ### Exercise is done. -----[ LR ]----- maximum: LR tn, fp: 277, 35 LR fn, tp: 3, 7 LR f1 score: 0.444 LR cohens kappa score: 0.424 LR average precision score: 0.773 average: LR tn, fp: 267.6, 22.2 LR fn, tp: 1.12, 5.88 LR f1 score: 0.342 LR cohens kappa score: 0.316 LR average precision score: 0.521 minimum: LR tn, fp: 255, 13 LR fn, tp: 0, 4 LR f1 score: 0.256 LR cohens kappa score: 0.226 LR average precision score: 0.233 -----[ GB ]----- maximum: GB tn, fp: 290, 10 GB fn, tp: 6, 7 GB f1 score: 0.778 GB cohens kappa score: 0.771 average: GB tn, fp: 286.72, 3.08 GB fn, tp: 3.2, 3.8 GB f1 score: 0.540 GB cohens kappa score: 0.530 minimum: GB tn, fp: 280, 0 GB fn, tp: 0, 1 GB f1 score: 0.200 GB cohens kappa score: 0.189 -----[ KNN ]----- maximum: KNN tn, fp: 282, 28 KNN fn, tp: 3, 7 KNN f1 score: 0.600 KNN cohens kappa score: 0.587 average: KNN tn, fp: 273.8, 16.0 KNN fn, tp: 1.4, 5.6 KNN f1 score: 0.402 KNN cohens kappa score: 0.380 minimum: KNN tn, fp: 262, 7 KNN fn, tp: 0, 4 KNN f1 score: 0.286 KNN cohens kappa score: 0.258 -----[ GAN ]----- maximum: GAN tn, fp: 283, 23 GAN fn, tp: 3, 7 GAN f1 score: 0.571 GAN cohens kappa score: 0.558 average: GAN tn, fp: 275.84, 13.96 GAN fn, tp: 1.48, 5.52 GAN f1 score: 0.423 GAN cohens kappa score: 0.402 minimum: GAN tn, fp: 267, 6 GAN fn, tp: 0, 4 GAN f1 score: 0.286 GAN cohens kappa score: 0.260