/////////////////////////////////////////// // Running convGAN-proximary-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: 280, 10 GAN fn, tp: 1, 6 GAN f1 score: 0.522 GAN cohens kappa score: 0.506 -> 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: 0.692 -> 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: 1, 6 KNN f1 score: 0.400 KNN cohens kappa score: 0.378 ------ 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: 272, 18 GAN fn, tp: 3, 4 GAN f1 score: 0.276 GAN cohens kappa score: 0.249 -> 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: 0.428 -> 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: 273, 17 KNN fn, tp: 2, 5 KNN f1 score: 0.345 KNN cohens kappa score: 0.321 ------ 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: 2, 5 GAN f1 score: 0.357 GAN cohens kappa score: 0.334 -> 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: 0.336 -> 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 1/5: 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: 273, 17 LR fn, tp: 1, 6 LR f1 score: 0.400 LR cohens kappa score: 0.378 LR average precision score: 0.613 -> 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 1/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1132 synthetic samples -> test with GAN.predict GAN tn, fp: 273, 16 GAN fn, tp: 1, 6 GAN f1 score: 0.414 GAN cohens kappa score: 0.392 -> test with 'LR' LR tn, fp: 253, 36 LR fn, tp: 0, 7 LR f1 score: 0.280 LR cohens kappa score: 0.249 LR average precision score: 0.634 -> 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: 263, 26 KNN fn, tp: 0, 7 KNN f1 score: 0.350 KNN cohens kappa score: 0.324 ====== 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: 274, 16 GAN fn, tp: 1, 6 GAN f1 score: 0.414 GAN cohens kappa score: 0.392 -> test with 'LR' LR tn, fp: 273, 17 LR fn, tp: 1, 6 LR f1 score: 0.400 LR cohens kappa score: 0.378 LR average precision score: 0.666 -> 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: 265, 25 GAN fn, tp: 2, 5 GAN f1 score: 0.270 GAN cohens kappa score: 0.241 -> test with 'LR' LR tn, fp: 251, 39 LR fn, tp: 0, 7 LR f1 score: 0.264 LR cohens kappa score: 0.233 LR average precision score: 0.250 -> 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: 271, 19 KNN fn, tp: 0, 7 KNN f1 score: 0.424 KNN cohens kappa score: 0.402 ------ 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: 271, 19 GAN fn, tp: 1, 6 GAN f1 score: 0.375 GAN cohens kappa score: 0.351 -> 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: 0.529 -> 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: 269, 21 KNN fn, tp: 1, 6 KNN f1 score: 0.353 KNN cohens kappa score: 0.328 ------ 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: 277, 13 GAN fn, tp: 2, 5 GAN f1 score: 0.400 GAN cohens kappa score: 0.379 -> 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: 0.529 -> 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: 279, 10 GAN fn, tp: 3, 4 GAN f1 score: 0.381 GAN cohens kappa score: 0.361 -> test with 'LR' LR tn, fp: 268, 21 LR fn, tp: 1, 6 LR f1 score: 0.353 LR cohens kappa score: 0.328 LR average precision score: 0.507 -> 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: 274, 15 KNN fn, tp: 2, 5 KNN f1 score: 0.370 KNN cohens kappa score: 0.348 ====== 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: 265, 25 GAN fn, tp: 1, 6 GAN f1 score: 0.316 GAN cohens kappa score: 0.288 -> 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: 0.636 -> 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: 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: 270, 20 GAN fn, tp: 0, 7 GAN f1 score: 0.412 GAN cohens kappa score: 0.389 -> 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: 0.743 -> 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: 268, 22 KNN fn, tp: 0, 7 KNN f1 score: 0.389 KNN cohens kappa score: 0.365 ------ 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: 274, 16 GAN fn, tp: 3, 4 GAN f1 score: 0.296 GAN cohens kappa score: 0.271 -> test with 'LR' LR tn, fp: 270, 20 LR fn, tp: 2, 5 LR f1 score: 0.312 LR cohens kappa score: 0.286 LR average precision score: 0.424 -> 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: 275, 15 KNN fn, tp: 2, 5 KNN f1 score: 0.370 KNN cohens kappa score: 0.348 ------ 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: 274, 16 GAN fn, tp: 2, 5 GAN f1 score: 0.357 GAN cohens kappa score: 0.334 -> 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: 0.374 -> test with 'GB' GB tn, fp: 281, 9 GB fn, tp: 3, 4 GB f1 score: 0.400 GB cohens kappa score: 0.381 -> 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 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: 276, 13 LR fn, tp: 2, 5 LR f1 score: 0.400 LR cohens kappa score: 0.379 LR average precision score: 0.514 -> test with 'GB' GB tn, fp: 288, 1 GB fn, tp: 3, 4 GB f1 score: 0.667 GB cohens kappa score: 0.660 -> 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: 282, 8 GAN fn, tp: 3, 4 GAN f1 score: 0.421 GAN cohens kappa score: 0.403 -> test with 'LR' LR tn, fp: 274, 16 LR fn, tp: 1, 6 LR f1 score: 0.414 LR cohens kappa score: 0.392 LR average precision score: 0.682 -> 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: 273, 17 KNN fn, tp: 1, 6 KNN f1 score: 0.400 KNN cohens kappa score: 0.378 ------ 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: 283, 7 GAN fn, tp: 1, 6 GAN f1 score: 0.600 GAN cohens kappa score: 0.587 -> 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: 0.288 -> 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: 272, 18 KNN fn, tp: 1, 6 KNN f1 score: 0.387 KNN cohens kappa score: 0.364 ------ 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: 281, 9 GAN fn, tp: 1, 6 GAN f1 score: 0.545 GAN cohens kappa score: 0.530 -> test with 'LR' LR tn, fp: 259, 31 LR fn, tp: 1, 6 LR f1 score: 0.273 LR cohens kappa score: 0.243 LR average precision score: 0.551 -> test with 'GB' GB tn, fp: 286, 4 GB fn, tp: 1, 6 GB f1 score: 0.706 GB cohens kappa score: 0.697 -> 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 4/5: Slice 4/5 ------- -> 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: 271, 19 LR fn, tp: 1, 6 LR f1 score: 0.375 LR cohens kappa score: 0.351 LR average precision score: 0.649 -> 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: 2, 5 KNN f1 score: 0.417 KNN cohens kappa score: 0.397 ------ 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: 282, 7 GAN fn, tp: 2, 5 GAN f1 score: 0.526 GAN cohens kappa score: 0.512 -> 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: 0.648 -> 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: 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: 274, 16 GAN fn, tp: 1, 6 GAN f1 score: 0.414 GAN cohens kappa score: 0.392 -> test with 'LR' LR tn, fp: 269, 21 LR fn, tp: 0, 7 LR f1 score: 0.400 LR cohens kappa score: 0.376 LR average precision score: 0.514 -> 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 5/5: 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: 3, 4 GAN f1 score: 0.276 GAN cohens kappa score: 0.249 -> test with 'LR' LR tn, fp: 263, 27 LR fn, tp: 3, 4 LR f1 score: 0.211 LR cohens kappa score: 0.179 LR average precision score: 0.228 -> 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: 274, 16 KNN fn, tp: 3, 4 KNN f1 score: 0.296 KNN cohens kappa score: 0.271 ------ 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: 269, 21 GAN fn, tp: 0, 7 GAN f1 score: 0.400 GAN cohens kappa score: 0.376 -> 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: 0.698 -> 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: 269, 21 KNN fn, tp: 0, 7 KNN f1 score: 0.400 KNN cohens kappa score: 0.376 ------ 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: 277, 13 GAN fn, tp: 4, 3 GAN f1 score: 0.261 GAN cohens kappa score: 0.236 -> 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: 0.325 -> 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: 276, 14 KNN fn, tp: 1, 6 KNN f1 score: 0.444 KNN cohens kappa score: 0.424 ------ 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: 270, 19 GAN fn, tp: 5, 2 GAN f1 score: 0.143 GAN cohens kappa score: 0.111 -> 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: 0.429 -> 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: 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: 276, 39 LR fn, tp: 3, 7 LR f1 score: 0.414 LR cohens kappa score: 0.392 LR average precision score: 0.743 average: LR tn, fp: 265.68, 24.12 LR fn, tp: 1.08, 5.92 LR f1 score: 0.326 LR cohens kappa score: 0.300 LR average precision score: 0.515 minimum: LR tn, fp: 251, 13 LR fn, tp: 0, 4 LR f1 score: 0.211 LR cohens kappa score: 0.179 LR average precision score: 0.228 -----[ GB ]----- maximum: GB tn, fp: 289, 9 GB fn, tp: 5, 7 GB f1 score: 0.778 GB cohens kappa score: 0.771 average: GB tn, fp: 286.48, 3.32 GB fn, tp: 3.2, 3.8 GB f1 score: 0.535 GB cohens kappa score: 0.524 minimum: GB tn, fp: 281, 1 GB fn, tp: 0, 2 GB f1 score: 0.333 GB cohens kappa score: 0.320 -----[ KNN ]----- maximum: KNN tn, fp: 282, 26 KNN fn, tp: 3, 7 KNN f1 score: 0.600 KNN cohens kappa score: 0.587 average: KNN tn, fp: 272.88, 16.92 KNN fn, tp: 1.24, 5.76 KNN f1 score: 0.394 KNN cohens kappa score: 0.371 minimum: KNN tn, fp: 263, 7 KNN fn, tp: 0, 4 KNN f1 score: 0.296 KNN cohens kappa score: 0.271 -----[ GAN ]----- maximum: GAN tn, fp: 283, 25 GAN fn, tp: 5, 7 GAN f1 score: 0.600 GAN cohens kappa score: 0.587 average: GAN tn, fp: 275.12, 14.68 GAN fn, tp: 1.96, 5.04 GAN f1 score: 0.388 GAN cohens kappa score: 0.366 minimum: GAN tn, fp: 265, 7 GAN fn, tp: 0, 2 GAN f1 score: 0.143 GAN cohens kappa score: 0.111