/////////////////////////////////////////// // 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: 265, 25 LR fn, tp: 1, 6 LR f1 score: 0.316 LR cohens kappa score: 0.288 LR average precision score: 0.445 -> 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: 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 'LR' LR tn, fp: 248, 42 LR fn, tp: 3, 4 LR f1 score: 0.151 LR cohens kappa score: 0.115 LR average precision score: 0.286 -> 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: 272, 18 KNN fn, tp: 2, 5 KNN f1 score: 0.333 KNN cohens kappa score: 0.308 ------ Step 1/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1131 synthetic samples -> test with 'LR' LR tn, fp: 250, 40 LR fn, tp: 5, 2 LR f1 score: 0.082 LR cohens kappa score: 0.043 LR average precision score: 0.083 -> test with 'GB' GB tn, fp: 285, 5 GB fn, tp: 4, 3 GB f1 score: 0.400 GB cohens kappa score: 0.385 -> test with 'KNN' KNN tn, fp: 277, 13 KNN fn, tp: 5, 2 KNN f1 score: 0.182 KNN cohens kappa score: 0.155 ------ Step 1/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1131 synthetic samples -> test with 'LR' LR tn, fp: 244, 46 LR fn, tp: 1, 6 LR f1 score: 0.203 LR cohens kappa score: 0.169 LR average precision score: 0.369 -> test with 'GB' GB tn, fp: 283, 7 GB fn, tp: 5, 2 GB f1 score: 0.250 GB cohens kappa score: 0.230 -> 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 1/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1132 synthetic samples -> test with 'LR' LR tn, fp: 206, 83 LR fn, tp: 0, 7 LR f1 score: 0.144 LR cohens kappa score: 0.105 LR average precision score: 0.195 -> 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: 259, 30 KNN fn, tp: 0, 7 KNN f1 score: 0.318 KNN cohens kappa score: 0.290 ====== 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: 256, 34 LR fn, tp: 0, 7 LR f1 score: 0.292 LR cohens kappa score: 0.262 LR average precision score: 0.685 -> test with 'GB' GB tn, fp: 283, 7 GB fn, tp: 3, 4 GB f1 score: 0.444 GB cohens kappa score: 0.428 -> 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 'LR' LR tn, fp: 260, 30 LR fn, tp: 7, 0 LR f1 score: 0.000 LR cohens kappa score: -0.040 LR average precision score: 0.045 -> test with 'GB' GB tn, fp: 286, 4 GB fn, tp: 6, 1 GB f1 score: 0.167 GB cohens kappa score: 0.150 -> 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 2/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1131 synthetic samples -> test with 'LR' LR tn, fp: 255, 35 LR fn, tp: 1, 6 LR f1 score: 0.250 LR cohens kappa score: 0.219 LR average precision score: 0.265 -> test with 'GB' GB tn, fp: 279, 11 GB fn, tp: 5, 2 GB f1 score: 0.200 GB cohens kappa score: 0.175 -> 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 2/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1131 synthetic samples -> 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: 0.140 -> test with 'GB' GB tn, fp: 286, 4 GB fn, tp: 6, 1 GB f1 score: 0.167 GB cohens kappa score: 0.150 -> 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 'LR' LR tn, fp: 269, 20 LR fn, tp: 2, 5 LR f1 score: 0.312 LR cohens kappa score: 0.286 LR average precision score: 0.295 -> test with 'GB' GB tn, fp: 283, 6 GB fn, tp: 5, 2 GB f1 score: 0.267 GB cohens kappa score: 0.248 -> test with 'KNN' KNN tn, fp: 277, 12 KNN fn, tp: 3, 4 KNN f1 score: 0.348 KNN cohens kappa score: 0.326 ====== 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: 274, 16 LR fn, tp: 2, 5 LR f1 score: 0.357 LR cohens kappa score: 0.334 LR average precision score: 0.411 -> 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: 285, 5 KNN fn, tp: 3, 4 KNN f1 score: 0.500 KNN cohens kappa score: 0.486 ------ Step 3/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1131 synthetic samples -> test with 'LR' LR tn, fp: 225, 65 LR fn, tp: 0, 7 LR f1 score: 0.177 LR cohens kappa score: 0.140 LR average precision score: 0.218 -> test with 'GB' GB tn, fp: 285, 5 GB fn, tp: 5, 2 GB f1 score: 0.286 GB cohens kappa score: 0.268 -> test with 'KNN' KNN tn, fp: 259, 31 KNN fn, tp: 0, 7 KNN f1 score: 0.311 KNN cohens kappa score: 0.283 ------ Step 3/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1131 synthetic samples -> test with 'LR' LR tn, fp: 278, 12 LR fn, tp: 4, 3 LR f1 score: 0.273 LR cohens kappa score: 0.249 LR average precision score: 0.284 -> test with 'GB' GB tn, fp: 284, 6 GB fn, tp: 6, 1 GB f1 score: 0.143 GB cohens kappa score: 0.122 -> test with 'KNN' KNN tn, fp: 281, 9 KNN fn, tp: 3, 4 KNN f1 score: 0.400 KNN cohens kappa score: 0.381 ------ Step 3/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1131 synthetic samples -> test with 'LR' LR tn, fp: 234, 56 LR fn, tp: 1, 6 LR f1 score: 0.174 LR cohens kappa score: 0.137 LR average precision score: 0.355 -> 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: 260, 30 KNN fn, tp: 1, 6 KNN f1 score: 0.279 KNN cohens kappa score: 0.249 ------ Step 3/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1132 synthetic samples -> test with 'LR' LR tn, fp: 272, 17 LR fn, tp: 5, 2 LR f1 score: 0.154 LR cohens kappa score: 0.124 LR average precision score: 0.238 -> 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: 279, 10 KNN fn, tp: 5, 2 KNN f1 score: 0.211 KNN cohens kappa score: 0.186 ====== 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: 282, 8 LR fn, tp: 1, 6 LR f1 score: 0.571 LR cohens kappa score: 0.558 LR average precision score: 0.769 -> 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: 282, 8 KNN fn, tp: 1, 6 KNN f1 score: 0.571 KNN cohens kappa score: 0.558 ------ Step 4/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1131 synthetic samples -> test with 'LR' LR tn, fp: 264, 26 LR fn, tp: 2, 5 LR f1 score: 0.263 LR cohens kappa score: 0.234 LR average precision score: 0.173 -> 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: 275, 15 KNN fn, tp: 2, 5 KNN f1 score: 0.370 KNN cohens kappa score: 0.348 ------ Step 4/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1131 synthetic samples -> test with 'LR' LR tn, fp: 241, 49 LR fn, tp: 1, 6 LR f1 score: 0.194 LR cohens kappa score: 0.158 LR average precision score: 0.518 -> test with 'GB' GB tn, fp: 282, 8 GB fn, tp: 3, 4 GB f1 score: 0.421 GB cohens kappa score: 0.403 -> test with 'KNN' KNN tn, fp: 265, 25 KNN fn, tp: 1, 6 KNN f1 score: 0.316 KNN cohens kappa score: 0.288 ------ Step 4/5: 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: 2, 5 LR f1 score: 0.278 LR cohens kappa score: 0.249 LR average precision score: 0.317 -> 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: 281, 9 KNN fn, tp: 3, 4 KNN f1 score: 0.400 KNN cohens kappa score: 0.381 ------ Step 4/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1132 synthetic samples -> test with 'LR' LR tn, fp: 248, 41 LR fn, tp: 3, 4 LR f1 score: 0.154 LR cohens kappa score: 0.118 LR average precision score: 0.409 -> test with 'GB' GB tn, fp: 284, 5 GB fn, tp: 4, 3 GB f1 score: 0.400 GB cohens kappa score: 0.384 -> test with 'KNN' KNN tn, fp: 275, 14 KNN fn, tp: 2, 5 KNN f1 score: 0.385 KNN cohens kappa score: 0.363 ====== 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: 3, 4 LR f1 score: 0.296 LR cohens kappa score: 0.271 LR average precision score: 0.331 -> 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: 279, 11 KNN fn, tp: 3, 4 KNN f1 score: 0.364 KNN cohens kappa score: 0.343 ------ Step 5/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1131 synthetic samples -> test with 'LR' LR tn, fp: 257, 33 LR fn, tp: 3, 4 LR f1 score: 0.182 LR cohens kappa score: 0.148 LR average precision score: 0.151 -> test with 'GB' GB tn, fp: 287, 3 GB fn, tp: 6, 1 GB f1 score: 0.182 GB cohens kappa score: 0.168 -> 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 'LR' LR tn, fp: 219, 71 LR fn, tp: 6, 1 LR f1 score: 0.025 LR cohens kappa score: -0.018 LR average precision score: 0.026 -> 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: 268, 22 KNN fn, tp: 3, 4 KNN f1 score: 0.242 KNN cohens kappa score: 0.213 ------ Step 5/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1131 synthetic samples -> test with 'LR' LR tn, fp: 276, 14 LR fn, tp: 4, 3 LR f1 score: 0.250 LR cohens kappa score: 0.224 LR average precision score: 0.279 -> test with 'GB' GB tn, fp: 285, 5 GB fn, tp: 5, 2 GB f1 score: 0.286 GB cohens kappa score: 0.268 -> test with 'KNN' KNN tn, fp: 278, 12 KNN fn, tp: 3, 4 KNN f1 score: 0.348 KNN cohens kappa score: 0.326 ------ Step 5/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1132 synthetic samples -> 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: 0.301 -> 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: 273, 16 KNN fn, tp: 2, 5 KNN f1 score: 0.357 KNN cohens kappa score: 0.334 ### Exercise is done. -----[ LR ]----- maximum: LR tn, fp: 282, 83 LR fn, tp: 7, 7 LR f1 score: 0.571 LR cohens kappa score: 0.558 LR average precision score: 0.769 average: LR tn, fp: 255.64, 34.16 LR fn, tp: 2.44, 4.56 LR f1 score: 0.226 LR cohens kappa score: 0.195 LR average precision score: 0.303 minimum: LR tn, fp: 206, 8 LR fn, tp: 0, 0 LR f1 score: 0.000 LR cohens kappa score: -0.040 LR average precision score: 0.026 -----[ GB ]----- maximum: GB tn, fp: 287, 11 GB fn, tp: 7, 6 GB f1 score: 0.667 GB cohens kappa score: 0.657 average: GB tn, fp: 283.8, 6.0 GB fn, tp: 4.2, 2.8 GB f1 score: 0.346 GB cohens kappa score: 0.329 minimum: GB tn, fp: 279, 3 GB fn, tp: 1, 0 GB f1 score: 0.000 GB cohens kappa score: -0.026 -----[ KNN ]----- maximum: KNN tn, fp: 285, 31 KNN fn, tp: 5, 7 KNN f1 score: 0.571 KNN cohens kappa score: 0.558 average: KNN tn, fp: 272.68, 17.12 KNN fn, tp: 2.16, 4.84 KNN f1 score: 0.343 KNN cohens kappa score: 0.319 minimum: KNN tn, fp: 259, 5 KNN fn, tp: 0, 2 KNN f1 score: 0.182 KNN cohens kappa score: 0.155