/////////////////////////////////////////// // Running ctGAN on folding_yeast4 /////////////////////////////////////////// Load 'data_input/folding_yeast4' 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 1106 synthetic samples -> test with 'LR' LR tn, fp: 270, 17 LR fn, tp: 3, 8 LR f1 score: 0.444 LR cohens kappa score: 0.414 LR average precision score: 0.264 -> test with 'RF' RF tn, fp: 278, 9 RF fn, tp: 6, 5 RF f1 score: 0.400 RF cohens kappa score: 0.374 -> test with 'GB' GB tn, fp: 280, 7 GB fn, tp: 7, 4 GB f1 score: 0.364 GB cohens kappa score: 0.339 -> test with 'KNN' KNN tn, fp: 283, 4 KNN fn, tp: 6, 5 KNN f1 score: 0.500 KNN cohens kappa score: 0.483 ------ Step 1/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1106 synthetic samples -> test with 'LR' LR tn, fp: 259, 28 LR fn, tp: 4, 7 LR f1 score: 0.304 LR cohens kappa score: 0.263 LR average precision score: 0.255 -> test with 'RF' RF tn, fp: 270, 17 RF fn, tp: 4, 7 RF f1 score: 0.400 RF cohens kappa score: 0.368 -> test with 'GB' GB tn, fp: 271, 16 GB fn, tp: 7, 4 GB f1 score: 0.258 GB cohens kappa score: 0.221 -> test with 'KNN' KNN tn, fp: 278, 9 KNN fn, tp: 5, 6 KNN f1 score: 0.462 KNN cohens kappa score: 0.438 ------ Step 1/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1106 synthetic samples -> test with 'LR' LR tn, fp: 260, 27 LR fn, tp: 6, 5 LR f1 score: 0.233 LR cohens kappa score: 0.188 LR average precision score: 0.134 -> test with 'RF' RF tn, fp: 280, 7 RF fn, tp: 7, 4 RF f1 score: 0.364 RF cohens kappa score: 0.339 -> test with 'GB' GB tn, fp: 281, 6 GB fn, tp: 9, 2 GB f1 score: 0.211 GB cohens kappa score: 0.185 -> test with 'KNN' KNN tn, fp: 273, 14 KNN fn, tp: 6, 5 KNN f1 score: 0.333 KNN cohens kappa score: 0.301 ------ Step 1/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1106 synthetic samples -> test with 'LR' LR tn, fp: 270, 17 LR fn, tp: 7, 4 LR f1 score: 0.250 LR cohens kappa score: 0.212 LR average precision score: 0.214 -> test with 'RF' RF tn, fp: 277, 10 RF fn, tp: 6, 5 RF f1 score: 0.385 RF cohens kappa score: 0.357 -> test with 'GB' GB tn, fp: 279, 8 GB fn, tp: 7, 4 GB f1 score: 0.348 GB cohens kappa score: 0.322 -> test with 'KNN' KNN tn, fp: 279, 8 KNN fn, tp: 8, 3 KNN f1 score: 0.273 KNN cohens kappa score: 0.245 ------ Step 1/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1104 synthetic samples -> test with 'LR' LR tn, fp: 252, 33 LR fn, tp: 0, 7 LR f1 score: 0.298 LR cohens kappa score: 0.268 LR average precision score: 0.205 -> test with 'RF' RF tn, fp: 278, 7 RF fn, tp: 4, 3 RF f1 score: 0.353 RF cohens kappa score: 0.334 -> test with 'GB' GB tn, fp: 281, 4 GB fn, tp: 4, 3 GB f1 score: 0.429 GB cohens kappa score: 0.415 -> test with 'KNN' KNN tn, fp: 272, 13 KNN fn, tp: 3, 4 KNN f1 score: 0.333 KNN cohens kappa score: 0.310 ====== Step 2/5 ======= -> Shuffling data -> Spliting data to slices ------ Step 2/5: Slice 1/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1106 synthetic samples -> test with 'LR' LR tn, fp: 272, 15 LR fn, tp: 6, 5 LR f1 score: 0.323 LR cohens kappa score: 0.289 LR average precision score: 0.230 -> test with 'RF' RF tn, fp: 275, 12 RF fn, tp: 5, 6 RF f1 score: 0.414 RF cohens kappa score: 0.386 -> test with 'GB' GB tn, fp: 278, 9 GB fn, tp: 7, 4 GB f1 score: 0.333 GB cohens kappa score: 0.306 -> test with 'KNN' KNN tn, fp: 278, 9 KNN fn, tp: 8, 3 KNN f1 score: 0.261 KNN cohens kappa score: 0.231 ------ Step 2/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1106 synthetic samples -> test with 'LR' LR tn, fp: 236, 51 LR fn, tp: 6, 5 LR f1 score: 0.149 LR cohens kappa score: 0.093 LR average precision score: 0.091 -> test with 'RF' RF tn, fp: 269, 18 RF fn, tp: 5, 6 RF f1 score: 0.343 RF cohens kappa score: 0.308 -> test with 'GB' GB tn, fp: 270, 17 GB fn, tp: 8, 3 GB f1 score: 0.194 GB cohens kappa score: 0.153 -> test with 'KNN' KNN tn, fp: 262, 25 KNN fn, tp: 4, 7 KNN f1 score: 0.326 KNN cohens kappa score: 0.286 ------ Step 2/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1106 synthetic samples -> test with 'LR' LR tn, fp: 261, 26 LR fn, tp: 5, 6 LR f1 score: 0.279 LR cohens kappa score: 0.237 LR average precision score: 0.179 -> test with 'RF' RF tn, fp: 274, 13 RF fn, tp: 3, 8 RF f1 score: 0.500 RF cohens kappa score: 0.475 -> test with 'GB' GB tn, fp: 279, 8 GB fn, tp: 7, 4 GB f1 score: 0.348 GB cohens kappa score: 0.322 -> test with 'KNN' KNN tn, fp: 274, 13 KNN fn, tp: 5, 6 KNN f1 score: 0.400 KNN cohens kappa score: 0.371 ------ Step 2/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1106 synthetic samples -> test with 'LR' LR tn, fp: 264, 23 LR fn, tp: 3, 8 LR f1 score: 0.381 LR cohens kappa score: 0.345 LR average precision score: 0.222 -> test with 'RF' RF tn, fp: 278, 9 RF fn, tp: 7, 4 RF f1 score: 0.333 RF cohens kappa score: 0.306 -> test with 'GB' GB tn, fp: 279, 8 GB fn, tp: 8, 3 GB f1 score: 0.273 GB cohens kappa score: 0.245 -> test with 'KNN' KNN tn, fp: 279, 8 KNN fn, tp: 7, 4 KNN f1 score: 0.348 KNN cohens kappa score: 0.322 ------ Step 2/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1104 synthetic samples -> test with 'LR' LR tn, fp: 253, 32 LR fn, tp: 6, 1 LR f1 score: 0.050 LR cohens kappa score: 0.011 LR average precision score: 0.071 -> test with 'RF' RF tn, fp: 276, 9 RF fn, tp: 6, 1 RF f1 score: 0.118 RF cohens kappa score: 0.092 -> test with 'GB' GB tn, fp: 280, 5 GB fn, tp: 7, 0 GB f1 score: 0.000 GB cohens kappa score: -0.020 -> test with 'KNN' KNN tn, fp: 278, 7 KNN fn, tp: 7, 0 KNN f1 score: 0.000 KNN cohens kappa score: -0.025 ====== Step 3/5 ======= -> Shuffling data -> Spliting data to slices ------ Step 3/5: Slice 1/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1106 synthetic samples -> test with 'LR' LR tn, fp: 268, 19 LR fn, tp: 6, 5 LR f1 score: 0.286 LR cohens kappa score: 0.248 LR average precision score: 0.191 -> test with 'RF' RF tn, fp: 279, 8 RF fn, tp: 7, 4 RF f1 score: 0.348 RF cohens kappa score: 0.322 -> test with 'GB' GB tn, fp: 279, 8 GB fn, tp: 8, 3 GB f1 score: 0.273 GB cohens kappa score: 0.245 -> test with 'KNN' KNN tn, fp: 274, 13 KNN fn, tp: 4, 7 KNN f1 score: 0.452 KNN cohens kappa score: 0.424 ------ Step 3/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1106 synthetic samples -> test with 'LR' LR tn, fp: 270, 17 LR fn, tp: 3, 8 LR f1 score: 0.444 LR cohens kappa score: 0.414 LR average precision score: 0.345 -> test with 'RF' RF tn, fp: 273, 14 RF fn, tp: 4, 7 RF f1 score: 0.437 RF cohens kappa score: 0.409 -> test with 'GB' GB tn, fp: 275, 12 GB fn, tp: 7, 4 GB f1 score: 0.296 GB cohens kappa score: 0.264 -> test with 'KNN' KNN tn, fp: 277, 10 KNN fn, tp: 7, 4 KNN f1 score: 0.320 KNN cohens kappa score: 0.291 ------ Step 3/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1106 synthetic samples -> test with 'LR' LR tn, fp: 254, 33 LR fn, tp: 7, 4 LR f1 score: 0.167 LR cohens kappa score: 0.116 LR average precision score: 0.169 -> test with 'RF' RF tn, fp: 277, 10 RF fn, tp: 7, 4 RF f1 score: 0.320 RF cohens kappa score: 0.291 -> test with 'GB' GB tn, fp: 279, 8 GB fn, tp: 8, 3 GB f1 score: 0.273 GB cohens kappa score: 0.245 -> test with 'KNN' KNN tn, fp: 280, 7 KNN fn, tp: 7, 4 KNN f1 score: 0.364 KNN cohens kappa score: 0.339 ------ Step 3/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1106 synthetic samples -> test with 'LR' LR tn, fp: 237, 50 LR fn, tp: 6, 5 LR f1 score: 0.152 LR cohens kappa score: 0.096 LR average precision score: 0.091 -> test with 'RF' RF tn, fp: 270, 17 RF fn, tp: 5, 6 RF f1 score: 0.353 RF cohens kappa score: 0.319 -> test with 'GB' GB tn, fp: 274, 13 GB fn, tp: 8, 3 GB f1 score: 0.222 GB cohens kappa score: 0.187 -> test with 'KNN' KNN tn, fp: 263, 24 KNN fn, tp: 5, 6 KNN f1 score: 0.293 KNN cohens kappa score: 0.252 ------ Step 3/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1104 synthetic samples -> test with 'LR' LR tn, fp: 240, 45 LR fn, tp: 5, 2 LR f1 score: 0.074 LR cohens kappa score: 0.034 LR average precision score: 0.044 -> test with 'RF' RF tn, fp: 275, 10 RF fn, tp: 3, 4 RF f1 score: 0.381 RF cohens kappa score: 0.361 -> test with 'GB' GB tn, fp: 280, 5 GB fn, tp: 5, 2 GB f1 score: 0.286 GB cohens kappa score: 0.268 -> test with 'KNN' KNN tn, fp: 266, 19 KNN fn, tp: 3, 4 KNN f1 score: 0.267 KNN cohens kappa score: 0.239 ====== Step 4/5 ======= -> Shuffling data -> Spliting data to slices ------ Step 4/5: Slice 1/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1106 synthetic samples -> test with 'LR' LR tn, fp: 220, 67 LR fn, tp: 1, 10 LR f1 score: 0.227 LR cohens kappa score: 0.174 LR average precision score: 0.285 -> test with 'RF' RF tn, fp: 271, 16 RF fn, tp: 8, 3 RF f1 score: 0.200 RF cohens kappa score: 0.161 -> test with 'GB' GB tn, fp: 276, 11 GB fn, tp: 9, 2 GB f1 score: 0.167 GB cohens kappa score: 0.132 -> test with 'KNN' KNN tn, fp: 278, 9 KNN fn, tp: 8, 3 KNN f1 score: 0.261 KNN cohens kappa score: 0.231 ------ Step 4/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1106 synthetic samples -> test with 'LR' LR tn, fp: 259, 28 LR fn, tp: 10, 1 LR f1 score: 0.050 LR cohens kappa score: -0.004 LR average precision score: 0.047 -> test with 'RF' RF tn, fp: 278, 9 RF fn, tp: 7, 4 RF f1 score: 0.333 RF cohens kappa score: 0.306 -> test with 'GB' GB tn, fp: 279, 8 GB fn, tp: 8, 3 GB f1 score: 0.273 GB cohens kappa score: 0.245 -> test with 'KNN' KNN tn, fp: 277, 10 KNN fn, tp: 9, 2 KNN f1 score: 0.174 KNN cohens kappa score: 0.141 ------ Step 4/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1106 synthetic samples -> test with 'LR' LR tn, fp: 258, 29 LR fn, tp: 7, 4 LR f1 score: 0.182 LR cohens kappa score: 0.134 LR average precision score: 0.098 -> test with 'RF' RF tn, fp: 271, 16 RF fn, tp: 9, 2 RF f1 score: 0.138 RF cohens kappa score: 0.097 -> test with 'GB' GB tn, fp: 275, 12 GB fn, tp: 7, 4 GB f1 score: 0.296 GB cohens kappa score: 0.264 -> test with 'KNN' KNN tn, fp: 275, 12 KNN fn, tp: 9, 2 KNN f1 score: 0.160 KNN cohens kappa score: 0.124 ------ Step 4/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1106 synthetic samples -> test with 'LR' LR tn, fp: 258, 29 LR fn, tp: 6, 5 LR f1 score: 0.222 LR cohens kappa score: 0.176 LR average precision score: 0.156 -> test with 'RF' RF tn, fp: 271, 16 RF fn, tp: 6, 5 RF f1 score: 0.312 RF cohens kappa score: 0.277 -> test with 'GB' GB tn, fp: 271, 16 GB fn, tp: 7, 4 GB f1 score: 0.258 GB cohens kappa score: 0.221 -> test with 'KNN' KNN tn, fp: 270, 17 KNN fn, tp: 7, 4 KNN f1 score: 0.250 KNN cohens kappa score: 0.212 ------ Step 4/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1104 synthetic samples -> test with 'LR' LR tn, fp: 229, 56 LR fn, tp: 5, 2 LR f1 score: 0.062 LR cohens kappa score: 0.020 LR average precision score: 0.036 -> test with 'RF' RF tn, fp: 279, 6 RF fn, tp: 3, 4 RF f1 score: 0.471 RF cohens kappa score: 0.455 -> test with 'GB' GB tn, fp: 282, 3 GB fn, tp: 4, 3 GB f1 score: 0.462 GB cohens kappa score: 0.449 -> test with 'KNN' KNN tn, fp: 276, 9 KNN fn, tp: 4, 3 KNN f1 score: 0.316 KNN cohens kappa score: 0.294 ====== Step 5/5 ======= -> Shuffling data -> Spliting data to slices ------ Step 5/5: Slice 1/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1106 synthetic samples -> test with 'LR' LR tn, fp: 270, 17 LR fn, tp: 6, 5 LR f1 score: 0.303 LR cohens kappa score: 0.267 LR average precision score: 0.172 -> test with 'RF' RF tn, fp: 278, 9 RF fn, tp: 8, 3 RF f1 score: 0.261 RF cohens kappa score: 0.231 -> test with 'GB' GB tn, fp: 283, 4 GB fn, tp: 9, 2 GB f1 score: 0.235 GB cohens kappa score: 0.215 -> test with 'KNN' KNN tn, fp: 279, 8 KNN fn, tp: 6, 5 KNN f1 score: 0.417 KNN cohens kappa score: 0.392 ------ Step 5/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1106 synthetic samples -> test with 'LR' LR tn, fp: 262, 25 LR fn, tp: 7, 4 LR f1 score: 0.200 LR cohens kappa score: 0.155 LR average precision score: 0.149 -> test with 'RF' RF tn, fp: 278, 9 RF fn, tp: 3, 8 RF f1 score: 0.571 RF cohens kappa score: 0.551 -> test with 'GB' GB tn, fp: 280, 7 GB fn, tp: 5, 6 GB f1 score: 0.500 GB cohens kappa score: 0.479 -> test with 'KNN' KNN tn, fp: 275, 12 KNN fn, tp: 3, 8 KNN f1 score: 0.516 KNN cohens kappa score: 0.492 ------ Step 5/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1106 synthetic samples -> test with 'LR' LR tn, fp: 232, 55 LR fn, tp: 3, 8 LR f1 score: 0.216 LR cohens kappa score: 0.164 LR average precision score: 0.414 -> test with 'RF' RF tn, fp: 275, 12 RF fn, tp: 7, 4 RF f1 score: 0.296 RF cohens kappa score: 0.264 -> test with 'GB' GB tn, fp: 273, 14 GB fn, tp: 9, 2 GB f1 score: 0.148 GB cohens kappa score: 0.109 -> test with 'KNN' KNN tn, fp: 275, 12 KNN fn, tp: 4, 7 KNN f1 score: 0.467 KNN cohens kappa score: 0.441 ------ Step 5/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1106 synthetic samples -> test with 'LR' LR tn, fp: 244, 43 LR fn, tp: 2, 9 LR f1 score: 0.286 LR cohens kappa score: 0.239 LR average precision score: 0.187 -> test with 'RF' RF tn, fp: 272, 15 RF fn, tp: 7, 4 RF f1 score: 0.267 RF cohens kappa score: 0.231 -> test with 'GB' GB tn, fp: 276, 11 GB fn, tp: 10, 1 GB f1 score: 0.087 GB cohens kappa score: 0.050 -> test with 'KNN' KNN tn, fp: 269, 18 KNN fn, tp: 9, 2 KNN f1 score: 0.129 KNN cohens kappa score: 0.085 ------ Step 5/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1104 synthetic samples -> test with 'LR' LR tn, fp: 241, 44 LR fn, tp: 2, 5 LR f1 score: 0.179 LR cohens kappa score: 0.143 LR average precision score: 0.118 -> test with 'RF' RF tn, fp: 275, 10 RF fn, tp: 1, 6 RF f1 score: 0.522 RF cohens kappa score: 0.505 -> test with 'GB' GB tn, fp: 274, 11 GB fn, tp: 3, 4 GB f1 score: 0.364 GB cohens kappa score: 0.342 -> test with 'KNN' KNN tn, fp: 273, 12 KNN fn, tp: 3, 4 KNN f1 score: 0.348 KNN cohens kappa score: 0.325 ### Exercise is done. -----[ LR ]----- maximum: LR tn, fp: 272, 67 LR fn, tp: 10, 10 LR f1 score: 0.444 LR cohens kappa score: 0.414 LR average precision score: 0.414 average: LR tn, fp: 253.56, 33.04 LR fn, tp: 4.88, 5.32 LR f1 score: 0.230 LR cohens kappa score: 0.188 LR average precision score: 0.175 minimum: LR tn, fp: 220, 15 LR fn, tp: 0, 1 LR f1 score: 0.050 LR cohens kappa score: -0.004 LR average precision score: 0.036 -----[ RF ]----- maximum: RF tn, fp: 280, 18 RF fn, tp: 9, 8 RF f1 score: 0.571 RF cohens kappa score: 0.551 average: RF tn, fp: 275.08, 11.52 RF fn, tp: 5.52, 4.68 RF f1 score: 0.353 RF cohens kappa score: 0.325 minimum: RF tn, fp: 269, 6 RF fn, tp: 1, 1 RF f1 score: 0.118 RF cohens kappa score: 0.092 -----[ GB ]----- maximum: GB tn, fp: 283, 17 GB fn, tp: 10, 6 GB f1 score: 0.500 GB cohens kappa score: 0.479 average: GB tn, fp: 277.36, 9.24 GB fn, tp: 7.12, 3.08 GB f1 score: 0.276 GB cohens kappa score: 0.248 minimum: GB tn, fp: 270, 3 GB fn, tp: 3, 0 GB f1 score: 0.000 GB cohens kappa score: -0.020 -----[ KNN ]----- maximum: KNN tn, fp: 283, 25 KNN fn, tp: 9, 8 KNN f1 score: 0.516 KNN cohens kappa score: 0.492 average: KNN tn, fp: 274.52, 12.08 KNN fn, tp: 5.88, 4.32 KNN f1 score: 0.319 KNN cohens kappa score: 0.290 minimum: KNN tn, fp: 262, 4 KNN fn, tp: 3, 0 KNN f1 score: 0.000 KNN cohens kappa score: -0.025