/////////////////////////////////////////// // Running convGAN-proximary-5 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: 274, 16 GAN fn, tp: 1, 6 GAN f1 score: 0.414 GAN cohens kappa score: 0.392 -> 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: 0.661 -> 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: 262, 28 GAN fn, tp: 3, 4 GAN f1 score: 0.205 GAN cohens kappa score: 0.173 -> 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: 0.428 -> 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: 265, 25 KNN fn, tp: 2, 5 KNN f1 score: 0.270 KNN cohens kappa score: 0.241 ------ 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: 245, 45 GAN fn, tp: 2, 5 GAN f1 score: 0.175 GAN cohens kappa score: 0.140 -> 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: 0.265 -> 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: 267, 23 KNN fn, tp: 1, 6 KNN f1 score: 0.333 KNN cohens kappa score: 0.307 ------ 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: 251, 39 GAN fn, tp: 3, 4 GAN f1 score: 0.160 GAN cohens kappa score: 0.125 -> 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: 0.583 -> 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: 271, 19 KNN fn, tp: 1, 6 KNN f1 score: 0.375 KNN cohens kappa score: 0.351 ------ 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: 218, 71 GAN fn, tp: 2, 5 GAN f1 score: 0.120 GAN cohens kappa score: 0.081 -> test with 'LR' LR tn, fp: 245, 44 LR fn, tp: 0, 7 LR f1 score: 0.241 LR cohens kappa score: 0.208 LR average precision score: 0.547 -> 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: 264, 25 KNN fn, tp: 0, 7 KNN f1 score: 0.359 KNN cohens kappa score: 0.333 ====== 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: 215, 75 GAN fn, tp: 2, 5 GAN f1 score: 0.115 GAN cohens kappa score: 0.075 -> 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: 0.610 -> 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: 266, 24 KNN fn, tp: 1, 6 KNN f1 score: 0.324 KNN cohens kappa score: 0.297 ------ 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: 254, 36 GAN fn, tp: 1, 6 GAN f1 score: 0.245 GAN cohens kappa score: 0.213 -> test with 'LR' LR tn, fp: 252, 38 LR fn, tp: 0, 7 LR f1 score: 0.269 LR cohens kappa score: 0.238 LR average precision score: 0.246 -> 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: 257, 33 KNN fn, tp: 0, 7 KNN f1 score: 0.298 KNN cohens kappa score: 0.269 ------ 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: 264, 26 GAN fn, tp: 4, 3 GAN f1 score: 0.167 GAN cohens kappa score: 0.134 -> 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: 0.528 -> 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: 266, 24 KNN fn, tp: 2, 5 KNN f1 score: 0.278 KNN cohens kappa score: 0.249 ------ 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: 282, 8 GAN fn, tp: 3, 4 GAN f1 score: 0.421 GAN cohens kappa score: 0.403 -> test with 'LR' LR tn, fp: 258, 32 LR fn, tp: 2, 5 LR f1 score: 0.227 LR cohens kappa score: 0.195 LR average precision score: 0.575 -> 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: 269, 21 KNN fn, tp: 2, 5 KNN f1 score: 0.303 KNN cohens kappa score: 0.276 ------ 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: 4, 3 GAN f1 score: 0.300 GAN cohens kappa score: 0.278 -> 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: 0.493 -> 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: 273, 16 KNN fn, tp: 3, 4 KNN f1 score: 0.296 KNN cohens kappa score: 0.271 ====== 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: 249, 41 GAN fn, tp: 3, 4 GAN f1 score: 0.154 GAN cohens kappa score: 0.118 -> 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: 0.623 -> 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: 271, 19 KNN fn, tp: 1, 6 KNN f1 score: 0.375 KNN cohens kappa score: 0.351 ------ 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: 278, 12 GAN fn, tp: 2, 5 GAN f1 score: 0.417 GAN cohens kappa score: 0.397 -> test with 'LR' LR tn, fp: 250, 40 LR fn, tp: 0, 7 LR f1 score: 0.259 LR cohens kappa score: 0.228 LR average precision score: 0.800 -> 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: 257, 33 KNN fn, tp: 0, 7 KNN f1 score: 0.298 KNN cohens kappa score: 0.269 ------ 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: 266, 24 GAN fn, tp: 2, 5 GAN f1 score: 0.278 GAN cohens kappa score: 0.249 -> 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: 0.413 -> 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: 271, 19 KNN fn, tp: 2, 5 KNN f1 score: 0.323 KNN cohens kappa score: 0.297 ------ 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: 236, 54 GAN fn, tp: 2, 5 GAN f1 score: 0.152 GAN cohens kappa score: 0.114 -> test with 'LR' LR tn, fp: 254, 36 LR fn, tp: 1, 6 LR f1 score: 0.245 LR cohens kappa score: 0.213 LR average precision score: 0.381 -> 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: 264, 26 KNN fn, tp: 1, 6 KNN f1 score: 0.308 KNN cohens kappa score: 0.280 ------ 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: 249, 40 GAN fn, tp: 3, 4 GAN f1 score: 0.157 GAN cohens kappa score: 0.121 -> 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: 0.411 -> 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: 273, 16 KNN fn, tp: 1, 6 KNN f1 score: 0.414 KNN cohens kappa score: 0.392 ====== 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: 268, 22 GAN fn, tp: 1, 6 GAN f1 score: 0.343 GAN cohens kappa score: 0.317 -> 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: 0.731 -> 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: 274, 16 KNN fn, tp: 1, 6 KNN f1 score: 0.414 KNN cohens kappa score: 0.392 ------ 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: 260, 30 GAN fn, tp: 5, 2 GAN f1 score: 0.103 GAN cohens kappa score: 0.066 -> test with 'LR' LR tn, fp: 257, 33 LR fn, tp: 0, 7 LR f1 score: 0.298 LR cohens kappa score: 0.269 LR average precision score: 0.252 -> 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: 271, 19 KNN fn, tp: 1, 6 KNN f1 score: 0.375 KNN cohens kappa score: 0.351 ------ 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: 272, 18 GAN fn, tp: 3, 4 GAN f1 score: 0.276 GAN cohens kappa score: 0.249 -> 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: 0.548 -> test with 'GB' GB tn, fp: 288, 2 GB fn, tp: 2, 5 GB f1 score: 0.714 GB cohens kappa score: 0.707 -> test with 'KNN' KNN tn, fp: 265, 25 KNN fn, tp: 2, 5 KNN f1 score: 0.270 KNN cohens kappa score: 0.241 ------ 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: 247, 43 GAN fn, tp: 2, 5 GAN f1 score: 0.182 GAN cohens kappa score: 0.147 -> test with 'LR' LR tn, fp: 262, 28 LR fn, tp: 1, 6 LR f1 score: 0.293 LR cohens kappa score: 0.264 LR average precision score: 0.639 -> 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: 270, 20 KNN fn, tp: 1, 6 KNN f1 score: 0.364 KNN cohens kappa score: 0.339 ------ 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: 268, 21 GAN fn, tp: 5, 2 GAN f1 score: 0.133 GAN cohens kappa score: 0.101 -> test with 'LR' LR tn, fp: 268, 21 LR fn, tp: 2, 5 LR f1 score: 0.303 LR cohens kappa score: 0.276 LR average precision score: 0.661 -> 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: 276, 13 KNN fn, tp: 2, 5 KNN f1 score: 0.400 KNN cohens kappa score: 0.379 ====== 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: 233, 57 GAN fn, tp: 3, 4 GAN f1 score: 0.118 GAN cohens kappa score: 0.079 -> test with 'LR' LR tn, fp: 250, 40 LR fn, tp: 0, 7 LR f1 score: 0.259 LR cohens kappa score: 0.228 LR average precision score: 0.514 -> 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: 259, 31 KNN fn, tp: 1, 6 KNN f1 score: 0.273 KNN cohens kappa score: 0.243 ------ 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: 274, 16 GAN fn, tp: 3, 4 GAN f1 score: 0.296 GAN cohens kappa score: 0.271 -> test with 'LR' LR tn, fp: 268, 22 LR fn, tp: 3, 4 LR f1 score: 0.242 LR cohens kappa score: 0.213 LR average precision score: 0.230 -> 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: 273, 17 KNN fn, tp: 3, 4 KNN f1 score: 0.286 KNN cohens kappa score: 0.260 ------ 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: 217, 73 GAN fn, tp: 0, 7 GAN f1 score: 0.161 GAN cohens kappa score: 0.123 -> 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: 289, 1 GB fn, tp: 1, 6 GB f1 score: 0.857 GB cohens kappa score: 0.854 -> test with 'KNN' KNN tn, fp: 265, 25 KNN fn, tp: 0, 7 KNN f1 score: 0.359 KNN cohens kappa score: 0.333 ------ 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: 234, 56 GAN fn, tp: 2, 5 GAN f1 score: 0.147 GAN cohens kappa score: 0.109 -> test with 'LR' LR tn, fp: 257, 33 LR fn, tp: 0, 7 LR f1 score: 0.298 LR cohens kappa score: 0.269 LR average precision score: 0.342 -> 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: 274, 16 KNN fn, tp: 1, 6 KNN f1 score: 0.414 KNN cohens kappa score: 0.392 ------ 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: 279, 10 GAN fn, tp: 4, 3 GAN f1 score: 0.300 GAN cohens kappa score: 0.278 -> test with 'LR' LR tn, fp: 270, 19 LR fn, tp: 2, 5 LR f1 score: 0.323 LR cohens kappa score: 0.297 LR average precision score: 0.439 -> test with 'GB' GB tn, fp: 287, 2 GB fn, tp: 5, 2 GB f1 score: 0.364 GB cohens kappa score: 0.353 -> test with 'KNN' KNN tn, fp: 274, 15 KNN fn, tp: 2, 5 KNN f1 score: 0.370 KNN cohens kappa score: 0.348 ### Exercise is done. -----[ LR ]----- maximum: LR tn, fp: 272, 44 LR fn, tp: 3, 7 LR f1 score: 0.400 LR cohens kappa score: 0.377 LR average precision score: 0.800 average: LR tn, fp: 260.88, 28.92 LR fn, tp: 0.96, 6.04 LR f1 score: 0.293 LR cohens kappa score: 0.264 LR average precision score: 0.505 minimum: LR tn, fp: 245, 17 LR fn, tp: 0, 4 LR f1 score: 0.227 LR cohens kappa score: 0.195 LR average precision score: 0.230 -----[ GB ]----- maximum: GB tn, fp: 290, 4 GB fn, tp: 7, 6 GB f1 score: 0.857 GB cohens kappa score: 0.854 average: GB tn, fp: 287.88, 1.92 GB fn, tp: 4.0, 3.0 GB f1 score: 0.485 GB cohens kappa score: 0.476 minimum: GB tn, fp: 286, 0 GB fn, tp: 1, 0 GB f1 score: 0.000 GB cohens kappa score: -0.006 -----[ KNN ]----- maximum: KNN tn, fp: 276, 33 KNN fn, tp: 3, 7 KNN f1 score: 0.414 KNN cohens kappa score: 0.392 average: KNN tn, fp: 268.28, 21.52 KNN fn, tp: 1.28, 5.72 KNN f1 score: 0.339 KNN cohens kappa score: 0.313 minimum: KNN tn, fp: 257, 13 KNN fn, tp: 0, 4 KNN f1 score: 0.270 KNN cohens kappa score: 0.241 -----[ GAN ]----- maximum: GAN tn, fp: 282, 75 GAN fn, tp: 5, 7 GAN f1 score: 0.421 GAN cohens kappa score: 0.403 average: GAN tn, fp: 254.96, 34.84 GAN fn, tp: 2.6, 4.4 GAN f1 score: 0.221 GAN cohens kappa score: 0.190 minimum: GAN tn, fp: 215, 8 GAN fn, tp: 0, 2 GAN f1 score: 0.103 GAN cohens kappa score: 0.066