/////////////////////////////////////////// // Running convGAN-proxymary-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: 284, 6 GAN fn, tp: 2, 5 GAN f1 score: 0.556 GAN cohens kappa score: 0.542 -> test with 'LR' LR tn, fp: 268, 22 LR fn, tp: 1, 6 LR f1 score: 0.343 LR cohens kappa score: 0.317 LR average precision score: 0.689 -> test with 'GB' GB tn, fp: 287, 3 GB fn, tp: 4, 3 GB f1 score: 0.462 GB cohens kappa score: 0.450 -> 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 1/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1131 synthetic samples -> test with GAN.predict GAN tn, fp: 274, 16 GAN fn, tp: 2, 5 GAN f1 score: 0.357 GAN cohens kappa score: 0.334 -> test with 'LR' LR tn, fp: 268, 22 LR fn, tp: 2, 5 LR f1 score: 0.294 LR cohens kappa score: 0.267 LR average precision score: 0.428 -> 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: 273, 17 KNN fn, tp: 3, 4 KNN f1 score: 0.286 KNN cohens kappa score: 0.260 ------ 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: 279, 11 GAN fn, tp: 6, 1 GAN f1 score: 0.105 GAN cohens kappa score: 0.078 -> test with 'LR' LR tn, fp: 263, 27 LR fn, tp: 1, 6 LR f1 score: 0.300 LR cohens kappa score: 0.272 LR average precision score: 0.305 -> test with 'GB' GB tn, fp: 290, 0 GB fn, tp: 5, 2 GB f1 score: 0.444 GB cohens kappa score: 0.439 -> test with 'KNN' KNN tn, fp: 277, 13 KNN fn, tp: 1, 6 KNN f1 score: 0.462 KNN cohens kappa score: 0.442 ------ 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: 268, 22 GAN fn, tp: 3, 4 GAN f1 score: 0.242 GAN cohens kappa score: 0.213 -> test with 'LR' LR tn, fp: 271, 19 LR fn, tp: 2, 5 LR f1 score: 0.323 LR cohens kappa score: 0.297 LR average precision score: 0.614 -> test with 'GB' GB tn, fp: 287, 3 GB fn, tp: 4, 3 GB f1 score: 0.462 GB cohens kappa score: 0.450 -> test with 'KNN' KNN tn, fp: 281, 9 KNN fn, tp: 1, 6 KNN f1 score: 0.545 KNN cohens kappa score: 0.530 ------ 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: 275, 14 GAN fn, tp: 2, 5 GAN f1 score: 0.385 GAN cohens kappa score: 0.363 -> test with 'LR' LR tn, fp: 254, 35 LR fn, tp: 0, 7 LR f1 score: 0.286 LR cohens kappa score: 0.256 LR average precision score: 0.704 -> test with 'GB' GB tn, fp: 289, 0 GB fn, tp: 3, 4 GB f1 score: 0.727 GB cohens kappa score: 0.722 -> test with 'KNN' KNN tn, fp: 265, 24 KNN fn, tp: 0, 7 KNN f1 score: 0.368 KNN cohens kappa score: 0.343 ====== 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: 264, 26 GAN fn, tp: 2, 5 GAN f1 score: 0.263 GAN cohens kappa score: 0.234 -> 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: 0.669 -> 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 2/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1131 synthetic samples -> test with GAN.predict GAN tn, fp: 275, 15 GAN fn, tp: 3, 4 GAN f1 score: 0.308 GAN cohens kappa score: 0.283 -> test with 'LR' LR tn, fp: 262, 28 LR fn, tp: 0, 7 LR f1 score: 0.333 LR cohens kappa score: 0.306 LR average precision score: 0.346 -> 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: 268, 22 KNN fn, tp: 0, 7 KNN f1 score: 0.389 KNN cohens kappa score: 0.365 ------ 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: 281, 9 GAN fn, tp: 2, 5 GAN f1 score: 0.476 GAN cohens kappa score: 0.459 -> 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: 0.525 -> test with 'GB' GB tn, fp: 287, 3 GB fn, tp: 4, 3 GB f1 score: 0.462 GB cohens kappa score: 0.450 -> 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 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1131 synthetic samples -> test with GAN.predict GAN tn, fp: 279, 11 GAN fn, tp: 3, 4 GAN f1 score: 0.364 GAN cohens kappa score: 0.343 -> test with 'LR' LR tn, fp: 261, 29 LR fn, tp: 2, 5 LR f1 score: 0.244 LR cohens kappa score: 0.213 LR average precision score: 0.594 -> 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: 272, 18 KNN fn, tp: 2, 5 KNN f1 score: 0.333 KNN cohens kappa score: 0.308 ------ 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: 4, 3 GAN f1 score: 0.316 GAN cohens kappa score: 0.295 -> test with 'LR' LR tn, fp: 272, 17 LR fn, tp: 1, 6 LR f1 score: 0.400 LR cohens kappa score: 0.377 LR average precision score: 0.524 -> test with 'GB' GB tn, fp: 289, 0 GB fn, tp: 6, 1 GB f1 score: 0.250 GB cohens kappa score: 0.246 -> test with 'KNN' KNN tn, fp: 279, 10 KNN fn, tp: 3, 4 KNN f1 score: 0.381 KNN cohens kappa score: 0.361 ====== 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: 274, 16 GAN fn, tp: 5, 2 GAN f1 score: 0.160 GAN cohens kappa score: 0.130 -> test with 'LR' LR tn, fp: 268, 22 LR fn, tp: 1, 6 LR f1 score: 0.343 LR cohens kappa score: 0.317 LR average precision score: 0.648 -> test with 'GB' GB tn, fp: 289, 1 GB fn, tp: 3, 4 GB f1 score: 0.667 GB cohens kappa score: 0.660 -> 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 3/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1131 synthetic samples -> test with GAN.predict GAN tn, fp: 283, 7 GAN fn, tp: 4, 3 GAN f1 score: 0.353 GAN cohens kappa score: 0.334 -> test with 'LR' LR tn, fp: 259, 31 LR fn, tp: 0, 7 LR f1 score: 0.311 LR cohens kappa score: 0.283 LR average precision score: 0.782 -> test with 'GB' GB tn, fp: 285, 5 GB fn, tp: 1, 6 GB f1 score: 0.667 GB cohens kappa score: 0.657 -> test with 'KNN' KNN tn, fp: 264, 26 KNN fn, tp: 0, 7 KNN f1 score: 0.350 KNN cohens kappa score: 0.324 ------ 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: 272, 18 GAN fn, tp: 4, 3 GAN f1 score: 0.214 GAN cohens kappa score: 0.185 -> 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: 0.448 -> test with 'GB' GB tn, fp: 288, 2 GB fn, tp: 5, 2 GB f1 score: 0.364 GB cohens kappa score: 0.353 -> test with 'KNN' KNN tn, fp: 277, 13 KNN fn, tp: 3, 4 KNN f1 score: 0.333 KNN cohens kappa score: 0.310 ------ 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: 266, 24 GAN fn, tp: 2, 5 GAN f1 score: 0.278 GAN cohens kappa score: 0.249 -> test with 'LR' LR tn, fp: 260, 30 LR fn, tp: 1, 6 LR f1 score: 0.279 LR cohens kappa score: 0.249 LR average precision score: 0.420 -> 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: 268, 22 KNN fn, tp: 1, 6 KNN f1 score: 0.343 KNN cohens kappa score: 0.317 ------ 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: 281, 8 GAN fn, tp: 3, 4 GAN f1 score: 0.421 GAN cohens kappa score: 0.403 -> test with 'LR' LR tn, fp: 274, 15 LR fn, tp: 2, 5 LR f1 score: 0.370 LR cohens kappa score: 0.348 LR average precision score: 0.456 -> test with 'GB' GB tn, fp: 288, 1 GB fn, tp: 7, 0 GB f1 score: 0.000 GB cohens kappa score: -0.006 -> test with 'KNN' KNN tn, fp: 281, 8 KNN fn, tp: 1, 6 KNN f1 score: 0.571 KNN cohens kappa score: 0.557 ====== 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: 280, 10 GAN fn, tp: 2, 5 GAN f1 score: 0.455 GAN cohens kappa score: 0.436 -> 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: 0.662 -> test with 'GB' GB tn, fp: 289, 1 GB fn, tp: 2, 5 GB f1 score: 0.769 GB cohens kappa score: 0.764 -> 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 4/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1131 synthetic samples -> test with GAN.predict GAN tn, fp: 275, 15 GAN fn, tp: 3, 4 GAN f1 score: 0.308 GAN cohens kappa score: 0.283 -> test with 'LR' LR tn, fp: 264, 26 LR fn, tp: 0, 7 LR f1 score: 0.350 LR cohens kappa score: 0.324 LR average precision score: 0.248 -> test with 'GB' GB tn, fp: 286, 4 GB fn, tp: 5, 2 GB f1 score: 0.308 GB cohens kappa score: 0.292 -> test with 'KNN' KNN tn, fp: 275, 15 KNN fn, tp: 1, 6 KNN f1 score: 0.429 KNN cohens kappa score: 0.408 ------ 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: 279, 11 GAN fn, tp: 2, 5 GAN f1 score: 0.435 GAN cohens kappa score: 0.416 -> test with 'LR' LR tn, fp: 260, 30 LR fn, tp: 1, 6 LR f1 score: 0.279 LR cohens kappa score: 0.249 LR average precision score: 0.549 -> 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: 270, 20 KNN fn, tp: 1, 6 KNN f1 score: 0.364 KNN cohens kappa score: 0.339 ------ 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: 280, 10 GAN fn, tp: 3, 4 GAN f1 score: 0.381 GAN cohens kappa score: 0.361 -> test with 'LR' LR tn, fp: 268, 22 LR fn, tp: 1, 6 LR f1 score: 0.343 LR cohens kappa score: 0.317 LR average precision score: 0.640 -> 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: 279, 11 KNN fn, tp: 2, 5 KNN f1 score: 0.435 KNN cohens kappa score: 0.416 ------ 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: 280, 9 GAN fn, tp: 4, 3 GAN f1 score: 0.316 GAN cohens kappa score: 0.295 -> test with 'LR' LR tn, fp: 274, 15 LR fn, tp: 2, 5 LR f1 score: 0.370 LR cohens kappa score: 0.348 LR average precision score: 0.675 -> test with 'GB' GB tn, fp: 287, 2 GB fn, tp: 4, 3 GB f1 score: 0.500 GB cohens kappa score: 0.490 -> test with 'KNN' KNN tn, fp: 280, 9 KNN fn, tp: 2, 5 KNN f1 score: 0.476 KNN cohens kappa score: 0.459 ====== 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: 280, 10 GAN fn, tp: 2, 5 GAN f1 score: 0.455 GAN cohens kappa score: 0.436 -> 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: 0.504 -> 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: 272, 18 KNN fn, tp: 1, 6 KNN f1 score: 0.387 KNN cohens kappa score: 0.364 ------ 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: 278, 12 GAN fn, tp: 3, 4 GAN f1 score: 0.348 GAN cohens kappa score: 0.326 -> test with 'LR' LR tn, fp: 270, 20 LR fn, tp: 3, 4 LR f1 score: 0.258 LR cohens kappa score: 0.230 LR average precision score: 0.223 -> test with 'GB' GB tn, fp: 289, 1 GB fn, tp: 4, 3 GB f1 score: 0.545 GB cohens kappa score: 0.538 -> test with 'KNN' KNN tn, fp: 275, 15 KNN fn, tp: 3, 4 KNN f1 score: 0.308 KNN cohens kappa score: 0.283 ------ 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: 279, 11 GAN fn, tp: 0, 7 GAN f1 score: 0.560 GAN cohens kappa score: 0.545 -> 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: 0.754 -> test with 'GB' GB tn, fp: 288, 2 GB fn, tp: 1, 6 GB f1 score: 0.800 GB cohens kappa score: 0.795 -> 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 5/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1131 synthetic samples -> test with GAN.predict GAN tn, fp: 247, 43 GAN fn, tp: 3, 4 GAN f1 score: 0.148 GAN cohens kappa score: 0.112 -> test with 'LR' LR tn, fp: 266, 24 LR fn, tp: 1, 6 LR f1 score: 0.324 LR cohens kappa score: 0.297 LR average precision score: 0.543 -> test with 'GB' GB tn, fp: 289, 1 GB fn, tp: 6, 1 GB f1 score: 0.222 GB cohens kappa score: 0.214 -> 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 5/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1132 synthetic samples -> test with GAN.predict GAN tn, fp: 278, 11 GAN fn, tp: 4, 3 GAN f1 score: 0.286 GAN cohens kappa score: 0.262 -> test with 'LR' LR tn, fp: 273, 16 LR fn, tp: 2, 5 LR f1 score: 0.357 LR cohens kappa score: 0.334 LR average precision score: 0.441 -> test with 'GB' GB tn, fp: 288, 1 GB fn, tp: 5, 2 GB f1 score: 0.400 GB cohens kappa score: 0.391 -> test with 'KNN' KNN tn, fp: 276, 13 KNN fn, tp: 2, 5 KNN f1 score: 0.400 KNN cohens kappa score: 0.379 ### Exercise is done. -----[ LR ]----- maximum: LR tn, fp: 275, 35 LR fn, tp: 3, 7 LR f1 score: 0.429 LR cohens kappa score: 0.408 LR average precision score: 0.782 average: LR tn, fp: 267.04, 22.76 LR fn, tp: 1.12, 5.88 LR f1 score: 0.334 LR cohens kappa score: 0.308 LR average precision score: 0.536 minimum: LR tn, fp: 254, 15 LR fn, tp: 0, 4 LR f1 score: 0.244 LR cohens kappa score: 0.213 LR average precision score: 0.223 -----[ GB ]----- maximum: GB tn, fp: 290, 5 GB fn, tp: 7, 6 GB f1 score: 0.800 GB cohens kappa score: 0.795 average: GB tn, fp: 287.48, 2.32 GB fn, tp: 3.84, 3.16 GB f1 score: 0.484 GB cohens kappa score: 0.475 minimum: GB tn, fp: 285, 0 GB fn, tp: 1, 0 GB f1 score: 0.000 GB cohens kappa score: -0.006 -----[ KNN ]----- maximum: KNN tn, fp: 281, 26 KNN fn, tp: 3, 7 KNN f1 score: 0.571 KNN cohens kappa score: 0.557 average: KNN tn, fp: 274.16, 15.64 KNN fn, tp: 1.36, 5.64 KNN f1 score: 0.406 KNN cohens kappa score: 0.384 minimum: KNN tn, fp: 264, 8 KNN fn, tp: 0, 4 KNN f1 score: 0.286 KNN cohens kappa score: 0.260 -----[ GAN ]----- maximum: GAN tn, fp: 284, 43 GAN fn, tp: 6, 7 GAN f1 score: 0.560 GAN cohens kappa score: 0.545 average: GAN tn, fp: 275.64, 14.16 GAN fn, tp: 2.92, 4.08 GAN f1 score: 0.340 GAN cohens kappa score: 0.317 minimum: GAN tn, fp: 247, 6 GAN fn, tp: 0, 1 GAN f1 score: 0.105 GAN cohens kappa score: 0.078