/////////////////////////////////////////// // 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: 260, 27 LR fn, tp: 2, 9 LR f1 score: 0.383 LR cohens kappa score: 0.346 LR average precision score: 0.233 -> 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: 5, 6 KNN f1 score: 0.500 KNN cohens kappa score: 0.479 ------ Step 1/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1106 synthetic samples -> test with 'LR' LR tn, fp: 247, 40 LR fn, tp: 7, 4 LR f1 score: 0.145 LR cohens kappa score: 0.092 LR average precision score: 0.071 -> test with 'GB' GB tn, fp: 276, 11 GB fn, tp: 7, 4 GB f1 score: 0.308 GB cohens kappa score: 0.277 -> test with 'KNN' KNN tn, fp: 274, 13 KNN fn, tp: 6, 5 KNN f1 score: 0.345 KNN cohens kappa score: 0.313 ------ Step 1/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1106 synthetic samples -> test with 'LR' LR tn, fp: 257, 30 LR fn, tp: 4, 7 LR f1 score: 0.292 LR cohens kappa score: 0.249 LR average precision score: 0.178 -> test with 'GB' GB tn, fp: 281, 6 GB fn, tp: 8, 3 GB f1 score: 0.300 GB cohens kappa score: 0.276 -> test with 'KNN' KNN tn, fp: 271, 16 KNN fn, tp: 6, 5 KNN f1 score: 0.312 KNN cohens kappa score: 0.277 ------ Step 1/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1106 synthetic samples -> test with 'LR' LR tn, fp: 269, 18 LR fn, tp: 7, 4 LR f1 score: 0.242 LR cohens kappa score: 0.203 LR average precision score: 0.173 -> test with 'GB' GB tn, fp: 276, 11 GB fn, tp: 8, 3 GB f1 score: 0.240 GB cohens kappa score: 0.207 -> test with 'KNN' KNN tn, fp: 274, 13 KNN fn, tp: 8, 3 KNN f1 score: 0.222 KNN cohens kappa score: 0.187 ------ Step 1/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1104 synthetic samples -> test with 'LR' LR tn, fp: 260, 25 LR fn, tp: 4, 3 LR f1 score: 0.171 LR cohens kappa score: 0.138 LR average precision score: 0.147 -> test with 'GB' GB tn, fp: 280, 5 GB fn, tp: 4, 3 GB f1 score: 0.400 GB cohens kappa score: 0.384 -> test with 'KNN' KNN tn, fp: 279, 6 KNN fn, tp: 5, 2 KNN f1 score: 0.267 KNN cohens kappa score: 0.247 ====== 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: 266, 21 LR fn, tp: 6, 5 LR f1 score: 0.270 LR cohens kappa score: 0.230 LR average precision score: 0.165 -> test with 'GB' GB tn, fp: 278, 9 GB fn, tp: 6, 5 GB f1 score: 0.400 GB cohens kappa score: 0.374 -> test with 'KNN' KNN tn, fp: 274, 13 KNN fn, tp: 9, 2 KNN f1 score: 0.154 KNN cohens kappa score: 0.116 ------ Step 2/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1106 synthetic samples -> test with 'LR' LR tn, fp: 233, 54 LR fn, tp: 2, 9 LR f1 score: 0.243 LR cohens kappa score: 0.192 LR average precision score: 0.288 -> test with 'GB' GB tn, fp: 273, 14 GB fn, tp: 8, 3 GB f1 score: 0.214 GB cohens kappa score: 0.177 -> test with 'KNN' KNN tn, fp: 260, 27 KNN fn, tp: 3, 8 KNN f1 score: 0.348 KNN cohens kappa score: 0.309 ------ Step 2/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1106 synthetic samples -> test with 'LR' LR tn, fp: 266, 21 LR fn, tp: 6, 5 LR f1 score: 0.270 LR cohens kappa score: 0.230 LR average precision score: 0.337 -> test with 'GB' GB tn, fp: 281, 6 GB fn, tp: 7, 4 GB f1 score: 0.381 GB cohens kappa score: 0.358 -> test with 'KNN' KNN tn, fp: 280, 7 KNN fn, tp: 6, 5 KNN f1 score: 0.435 KNN cohens kappa score: 0.412 ------ Step 2/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1106 synthetic samples -> test with 'LR' LR tn, fp: 268, 19 LR fn, tp: 5, 6 LR f1 score: 0.333 LR cohens kappa score: 0.297 LR average precision score: 0.241 -> test with 'GB' GB tn, fp: 278, 9 GB fn, tp: 8, 3 GB f1 score: 0.261 GB cohens kappa score: 0.231 -> test with 'KNN' KNN tn, fp: 282, 5 KNN fn, tp: 8, 3 KNN f1 score: 0.316 KNN cohens kappa score: 0.294 ------ Step 2/5: 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: 2, 5 LR f1 score: 0.147 LR cohens kappa score: 0.109 LR average precision score: 0.247 -> test with 'GB' GB tn, fp: 273, 12 GB fn, tp: 6, 1 GB f1 score: 0.100 GB cohens kappa score: 0.071 -> test with 'KNN' KNN tn, fp: 265, 20 KNN fn, tp: 4, 3 KNN f1 score: 0.200 KNN cohens kappa score: 0.169 ====== 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: 247, 40 LR fn, tp: 8, 3 LR f1 score: 0.111 LR cohens kappa score: 0.056 LR average precision score: 0.167 -> test with 'GB' GB tn, fp: 277, 10 GB fn, tp: 9, 2 GB f1 score: 0.174 GB cohens kappa score: 0.141 -> test with 'KNN' KNN tn, fp: 276, 11 KNN fn, tp: 7, 4 KNN f1 score: 0.308 KNN cohens kappa score: 0.277 ------ Step 3/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1106 synthetic samples -> test with 'LR' LR tn, fp: 266, 21 LR fn, tp: 3, 8 LR f1 score: 0.400 LR cohens kappa score: 0.366 LR average precision score: 0.245 -> test with 'GB' GB tn, fp: 274, 13 GB fn, tp: 9, 2 GB f1 score: 0.154 GB cohens kappa score: 0.116 -> test with 'KNN' KNN tn, fp: 275, 12 KNN fn, tp: 10, 1 KNN f1 score: 0.083 KNN cohens kappa score: 0.045 ------ Step 3/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1106 synthetic samples -> test with 'LR' LR tn, fp: 241, 46 LR fn, tp: 4, 7 LR f1 score: 0.219 LR cohens kappa score: 0.168 LR average precision score: 0.197 -> test with 'GB' GB tn, fp: 282, 5 GB fn, tp: 8, 3 GB f1 score: 0.316 GB cohens kappa score: 0.294 -> test with 'KNN' KNN tn, fp: 276, 11 KNN fn, tp: 7, 4 KNN f1 score: 0.308 KNN cohens kappa score: 0.277 ------ Step 3/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1106 synthetic samples -> test with 'LR' LR tn, fp: 239, 48 LR fn, tp: 3, 8 LR f1 score: 0.239 LR cohens kappa score: 0.189 LR average precision score: 0.239 -> test with 'GB' GB tn, fp: 277, 10 GB fn, tp: 8, 3 GB f1 score: 0.250 GB cohens kappa score: 0.219 -> 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 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1104 synthetic samples -> test with 'LR' LR tn, fp: 242, 43 LR fn, tp: 5, 2 LR f1 score: 0.077 LR cohens kappa score: 0.037 LR average precision score: 0.052 -> test with 'GB' GB tn, fp: 276, 9 GB fn, tp: 4, 3 GB f1 score: 0.316 GB cohens kappa score: 0.294 -> test with 'KNN' KNN tn, fp: 268, 17 KNN fn, tp: 3, 4 KNN f1 score: 0.286 KNN cohens kappa score: 0.259 ====== 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: 253, 34 LR fn, tp: 5, 6 LR f1 score: 0.235 LR cohens kappa score: 0.188 LR average precision score: 0.236 -> test with 'GB' GB tn, fp: 280, 7 GB fn, tp: 9, 2 GB f1 score: 0.200 GB cohens kappa score: 0.173 -> 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: 256, 31 LR fn, tp: 6, 5 LR f1 score: 0.213 LR cohens kappa score: 0.166 LR average precision score: 0.161 -> test with 'GB' GB tn, fp: 283, 4 GB fn, tp: 10, 1 GB f1 score: 0.125 GB cohens kappa score: 0.104 -> test with 'KNN' KNN tn, fp: 271, 16 KNN fn, tp: 5, 6 KNN f1 score: 0.364 KNN cohens kappa score: 0.331 ------ Step 4/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1106 synthetic samples -> test with 'LR' LR tn, fp: 265, 22 LR fn, tp: 6, 5 LR f1 score: 0.263 LR cohens kappa score: 0.222 LR average precision score: 0.134 -> test with 'GB' GB tn, fp: 276, 11 GB fn, tp: 8, 3 GB f1 score: 0.240 GB cohens kappa score: 0.207 -> test with 'KNN' KNN tn, fp: 271, 16 KNN fn, tp: 9, 2 KNN f1 score: 0.138 KNN cohens kappa score: 0.097 ------ Step 4/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1106 synthetic samples -> test with 'LR' LR tn, fp: 209, 78 LR fn, tp: 2, 9 LR f1 score: 0.184 LR cohens kappa score: 0.126 LR average precision score: 0.166 -> test with 'GB' GB tn, fp: 265, 22 GB fn, tp: 8, 3 GB f1 score: 0.167 GB cohens kappa score: 0.122 -> test with 'KNN' KNN tn, fp: 255, 32 KNN fn, tp: 4, 7 KNN f1 score: 0.280 KNN cohens kappa score: 0.236 ------ Step 4/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1104 synthetic samples -> test with 'LR' LR tn, fp: 246, 39 LR fn, tp: 3, 4 LR f1 score: 0.160 LR cohens kappa score: 0.124 LR average precision score: 0.081 -> test with 'GB' GB tn, fp: 273, 12 GB fn, tp: 3, 4 GB f1 score: 0.348 GB cohens kappa score: 0.325 -> test with 'KNN' KNN tn, fp: 273, 12 KNN fn, tp: 3, 4 KNN f1 score: 0.348 KNN cohens kappa score: 0.325 ====== 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: 251, 36 LR fn, tp: 6, 5 LR f1 score: 0.192 LR cohens kappa score: 0.142 LR average precision score: 0.116 -> test with 'GB' GB tn, fp: 275, 12 GB fn, tp: 11, 0 GB f1 score: 0.000 GB cohens kappa score: -0.040 -> test with 'KNN' KNN tn, fp: 278, 9 KNN fn, tp: 7, 4 KNN f1 score: 0.333 KNN cohens kappa score: 0.306 ------ Step 5/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1106 synthetic samples -> test with 'LR' LR tn, fp: 264, 23 LR fn, tp: 4, 7 LR f1 score: 0.341 LR cohens kappa score: 0.304 LR average precision score: 0.502 -> test with 'GB' GB tn, fp: 276, 11 GB fn, tp: 5, 6 GB f1 score: 0.429 GB cohens kappa score: 0.402 -> 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 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1106 synthetic samples -> test with 'LR' LR tn, fp: 212, 75 LR fn, tp: 5, 6 LR f1 score: 0.130 LR cohens kappa score: 0.070 LR average precision score: 0.190 -> test with 'GB' GB tn, fp: 274, 13 GB fn, tp: 9, 2 GB f1 score: 0.154 GB cohens kappa score: 0.116 -> test with 'KNN' KNN tn, fp: 260, 27 KNN fn, tp: 6, 5 KNN f1 score: 0.233 KNN cohens kappa score: 0.188 ------ Step 5/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1106 synthetic samples -> test with 'LR' LR tn, fp: 252, 35 LR fn, tp: 1, 10 LR f1 score: 0.357 LR cohens kappa score: 0.317 LR average precision score: 0.288 -> test with 'GB' GB tn, fp: 272, 15 GB fn, tp: 6, 5 GB f1 score: 0.323 GB cohens kappa score: 0.289 -> 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 5/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1104 synthetic samples -> test with 'LR' LR tn, fp: 270, 15 LR fn, tp: 5, 2 LR f1 score: 0.167 LR cohens kappa score: 0.137 LR average precision score: 0.112 -> 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: 280, 5 KNN fn, tp: 5, 2 KNN f1 score: 0.286 KNN cohens kappa score: 0.268 ### Exercise is done. -----[ LR ]----- maximum: LR tn, fp: 270, 78 LR fn, tp: 8, 10 LR f1 score: 0.400 LR cohens kappa score: 0.366 LR average precision score: 0.502 average: LR tn, fp: 250.72, 35.88 LR fn, tp: 4.44, 5.76 LR f1 score: 0.231 LR cohens kappa score: 0.188 LR average precision score: 0.199 minimum: LR tn, fp: 209, 15 LR fn, tp: 1, 2 LR f1 score: 0.077 LR cohens kappa score: 0.037 LR average precision score: 0.052 -----[ GB ]----- maximum: GB tn, fp: 283, 22 GB fn, tp: 11, 6 GB f1 score: 0.429 GB cohens kappa score: 0.402 average: GB tn, fp: 276.6, 10.0 GB fn, tp: 7.28, 2.92 GB f1 score: 0.254 GB cohens kappa score: 0.225 minimum: GB tn, fp: 265, 4 GB fn, tp: 3, 0 GB f1 score: 0.000 GB cohens kappa score: -0.040 -----[ KNN ]----- maximum: KNN tn, fp: 282, 32 KNN fn, tp: 10, 8 KNN f1 score: 0.500 KNN cohens kappa score: 0.479 average: KNN tn, fp: 273.0, 13.6 KNN fn, tp: 6.04, 4.16 KNN f1 score: 0.298 KNN cohens kappa score: 0.267 minimum: KNN tn, fp: 255, 5 KNN fn, tp: 3, 1 KNN f1 score: 0.083 KNN cohens kappa score: 0.045