/////////////////////////////////////////// // Running convGAN 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: 241, 46 LR fn, tp: 2, 9 LR f1 score: 0.273 LR cohens kappa score: 0.225 LR average precision score: 0.390 -> test with 'GB' GB tn, fp: 286, 1 GB fn, tp: 10, 1 GB f1 score: 0.154 GB cohens kappa score: 0.144 -> test with 'KNN' KNN tn, fp: 257, 30 KNN fn, tp: 3, 8 KNN f1 score: 0.327 KNN cohens kappa score: 0.286 ------ Step 1/5: 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: 1, 10 LR f1 score: 0.267 LR cohens kappa score: 0.217 LR average precision score: 0.615 -> test with 'GB' GB tn, fp: 284, 3 GB fn, tp: 6, 5 GB f1 score: 0.526 GB cohens kappa score: 0.511 -> 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 1/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1106 synthetic samples -> test with 'LR' LR tn, fp: 242, 45 LR fn, tp: 1, 10 LR f1 score: 0.303 LR cohens kappa score: 0.257 LR average precision score: 0.269 -> test with 'GB' GB tn, fp: 286, 1 GB fn, tp: 10, 1 GB f1 score: 0.154 GB cohens kappa score: 0.144 -> test with 'KNN' KNN tn, fp: 258, 29 KNN fn, tp: 5, 6 KNN f1 score: 0.261 KNN cohens kappa score: 0.217 ------ Step 1/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1106 synthetic samples -> test with 'LR' LR tn, fp: 248, 39 LR fn, tp: 6, 5 LR f1 score: 0.182 LR cohens kappa score: 0.130 LR average precision score: 0.168 -> 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: 262, 25 KNN fn, tp: 4, 7 KNN f1 score: 0.326 KNN cohens kappa score: 0.286 ------ Step 1/5: 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: 1, 6 LR f1 score: 0.214 LR cohens kappa score: 0.180 LR average precision score: 0.400 -> test with 'GB' GB tn, fp: 284, 1 GB fn, tp: 6, 1 GB f1 score: 0.222 GB cohens kappa score: 0.214 -> test with 'KNN' KNN tn, fp: 261, 24 KNN fn, tp: 1, 6 KNN f1 score: 0.324 KNN cohens kappa score: 0.297 ====== 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: 245, 42 LR fn, tp: 2, 9 LR f1 score: 0.290 LR cohens kappa score: 0.244 LR average precision score: 0.320 -> test with 'GB' GB tn, fp: 282, 5 GB fn, tp: 10, 1 GB f1 score: 0.118 GB cohens kappa score: 0.094 -> test with 'KNN' KNN tn, fp: 265, 22 KNN fn, tp: 3, 8 KNN f1 score: 0.390 KNN cohens kappa score: 0.355 ------ Step 2/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1106 synthetic samples -> test with 'LR' LR tn, fp: 243, 44 LR fn, tp: 3, 8 LR f1 score: 0.254 LR cohens kappa score: 0.206 LR average precision score: 0.457 -> test with 'GB' GB tn, fp: 284, 3 GB fn, tp: 7, 4 GB f1 score: 0.444 GB cohens kappa score: 0.428 -> test with 'KNN' KNN tn, fp: 239, 48 KNN fn, tp: 3, 8 KNN f1 score: 0.239 KNN cohens kappa score: 0.189 ------ Step 2/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1106 synthetic samples -> test with 'LR' LR tn, fp: 240, 47 LR fn, tp: 4, 7 LR f1 score: 0.215 LR cohens kappa score: 0.164 LR average precision score: 0.375 -> test with 'GB' GB tn, fp: 285, 2 GB fn, tp: 8, 3 GB f1 score: 0.375 GB cohens kappa score: 0.360 -> test with 'KNN' KNN tn, fp: 252, 35 KNN fn, tp: 3, 8 KNN f1 score: 0.296 KNN cohens kappa score: 0.252 ------ Step 2/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1106 synthetic samples -> test with 'LR' LR tn, fp: 250, 37 LR fn, tp: 3, 8 LR f1 score: 0.286 LR cohens kappa score: 0.241 LR average precision score: 0.298 -> test with 'GB' GB tn, fp: 285, 2 GB fn, tp: 9, 2 GB f1 score: 0.267 GB cohens kappa score: 0.252 -> test with 'KNN' KNN tn, fp: 267, 20 KNN fn, tp: 3, 8 KNN f1 score: 0.410 KNN cohens kappa score: 0.377 ------ Step 2/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1104 synthetic samples -> test with 'LR' LR tn, fp: 224, 61 LR fn, tp: 1, 6 LR f1 score: 0.162 LR cohens kappa score: 0.124 LR average precision score: 0.401 -> test with 'GB' GB tn, fp: 284, 1 GB fn, tp: 6, 1 GB f1 score: 0.222 GB cohens kappa score: 0.214 -> test with 'KNN' KNN tn, fp: 262, 23 KNN fn, tp: 1, 6 KNN f1 score: 0.333 KNN cohens kappa score: 0.307 ====== 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: 236, 51 LR fn, tp: 1, 10 LR f1 score: 0.278 LR cohens kappa score: 0.230 LR average precision score: 0.403 -> test with 'GB' GB tn, fp: 284, 3 GB fn, tp: 10, 1 GB f1 score: 0.133 GB cohens kappa score: 0.116 -> test with 'KNN' KNN tn, fp: 263, 24 KNN fn, tp: 2, 9 KNN f1 score: 0.409 KNN cohens kappa score: 0.374 ------ Step 3/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1106 synthetic samples -> test with 'LR' LR tn, fp: 238, 49 LR fn, tp: 2, 9 LR f1 score: 0.261 LR cohens kappa score: 0.212 LR average precision score: 0.400 -> test with 'GB' GB tn, fp: 286, 1 GB fn, tp: 10, 1 GB f1 score: 0.154 GB cohens kappa score: 0.144 -> test with 'KNN' KNN tn, fp: 258, 29 KNN fn, tp: 1, 10 KNN f1 score: 0.400 KNN cohens kappa score: 0.363 ------ Step 3/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1106 synthetic samples -> test with 'LR' LR tn, fp: 245, 42 LR fn, tp: 3, 8 LR f1 score: 0.262 LR cohens kappa score: 0.215 LR average precision score: 0.228 -> 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: 257, 30 KNN fn, tp: 2, 9 KNN f1 score: 0.360 KNN cohens kappa score: 0.321 ------ Step 3/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1106 synthetic samples -> test with 'LR' LR tn, fp: 243, 44 LR fn, tp: 3, 8 LR f1 score: 0.254 LR cohens kappa score: 0.206 LR average precision score: 0.532 -> test with 'GB' GB tn, fp: 284, 3 GB fn, tp: 9, 2 GB f1 score: 0.250 GB cohens kappa score: 0.232 -> test with 'KNN' KNN tn, fp: 259, 28 KNN fn, tp: 5, 6 KNN f1 score: 0.267 KNN cohens kappa score: 0.223 ------ 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: 2, 5 LR f1 score: 0.182 LR cohens kappa score: 0.146 LR average precision score: 0.413 -> test with 'GB' GB tn, fp: 284, 1 GB fn, tp: 4, 3 GB f1 score: 0.545 GB cohens kappa score: 0.537 -> test with 'KNN' KNN tn, fp: 255, 30 KNN fn, tp: 1, 6 KNN f1 score: 0.279 KNN cohens kappa score: 0.249 ====== 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: 255, 32 LR fn, tp: 4, 7 LR f1 score: 0.280 LR cohens kappa score: 0.236 LR average precision score: 0.474 -> test with 'GB' GB tn, fp: 286, 1 GB fn, tp: 9, 2 GB f1 score: 0.286 GB cohens kappa score: 0.274 -> test with 'KNN' KNN tn, fp: 267, 20 KNN fn, tp: 6, 5 KNN f1 score: 0.278 KNN cohens kappa score: 0.239 ------ Step 4/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1106 synthetic samples -> test with 'LR' LR tn, fp: 243, 44 LR fn, tp: 2, 9 LR f1 score: 0.281 LR cohens kappa score: 0.234 LR average precision score: 0.350 -> test with 'GB' GB tn, fp: 285, 2 GB fn, tp: 8, 3 GB f1 score: 0.375 GB cohens kappa score: 0.360 -> test with 'KNN' KNN tn, fp: 256, 31 KNN fn, tp: 2, 9 KNN f1 score: 0.353 KNN cohens kappa score: 0.313 ------ Step 4/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1106 synthetic samples -> test with 'LR' LR tn, fp: 230, 57 LR fn, tp: 2, 9 LR f1 score: 0.234 LR cohens kappa score: 0.182 LR average precision score: 0.257 -> test with 'GB' GB tn, fp: 282, 5 GB fn, tp: 9, 2 GB f1 score: 0.222 GB cohens kappa score: 0.199 -> test with 'KNN' KNN tn, fp: 249, 38 KNN fn, tp: 1, 10 KNN f1 score: 0.339 KNN cohens kappa score: 0.297 ------ Step 4/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1106 synthetic samples -> test with 'LR' LR tn, fp: 243, 44 LR fn, tp: 3, 8 LR f1 score: 0.254 LR cohens kappa score: 0.206 LR average precision score: 0.295 -> test with 'GB' GB tn, fp: 284, 3 GB fn, tp: 10, 1 GB f1 score: 0.133 GB cohens kappa score: 0.116 -> test with 'KNN' KNN tn, fp: 254, 33 KNN fn, tp: 4, 7 KNN f1 score: 0.275 KNN cohens kappa score: 0.230 ------ Step 4/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1104 synthetic samples -> test with 'LR' LR tn, fp: 243, 42 LR fn, tp: 2, 5 LR f1 score: 0.185 LR cohens kappa score: 0.150 LR average precision score: 0.511 -> test with 'GB' GB tn, fp: 282, 3 GB fn, tp: 5, 2 GB f1 score: 0.333 GB cohens kappa score: 0.320 -> test with 'KNN' KNN tn, fp: 255, 30 KNN fn, tp: 2, 5 KNN f1 score: 0.238 KNN cohens kappa score: 0.206 ====== 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: 253, 34 LR fn, tp: 3, 8 LR f1 score: 0.302 LR cohens kappa score: 0.259 LR average precision score: 0.241 -> test with 'GB' GB tn, fp: 284, 3 GB fn, tp: 11, 0 GB f1 score: 0.000 GB cohens kappa score: -0.016 -> test with 'KNN' KNN tn, fp: 268, 19 KNN fn, tp: 1, 10 KNN f1 score: 0.500 KNN cohens kappa score: 0.472 ------ Step 5/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1106 synthetic samples -> test with 'LR' LR tn, fp: 230, 57 LR fn, tp: 2, 9 LR f1 score: 0.234 LR cohens kappa score: 0.182 LR average precision score: 0.486 -> test with 'GB' GB tn, fp: 286, 1 GB fn, tp: 8, 3 GB f1 score: 0.400 GB cohens kappa score: 0.388 -> test with 'KNN' KNN tn, fp: 257, 30 KNN fn, tp: 1, 10 KNN f1 score: 0.392 KNN cohens kappa score: 0.355 ------ Step 5/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1106 synthetic samples -> test with 'LR' LR tn, fp: 240, 47 LR fn, tp: 3, 8 LR f1 score: 0.242 LR cohens kappa score: 0.193 LR average precision score: 0.531 -> test with 'GB' GB tn, fp: 287, 0 GB fn, tp: 10, 1 GB f1 score: 0.167 GB cohens kappa score: 0.162 -> test with 'KNN' KNN tn, fp: 260, 27 KNN fn, tp: 5, 6 KNN f1 score: 0.273 KNN cohens kappa score: 0.230 ------ Step 5/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1106 synthetic samples -> test with 'LR' LR tn, fp: 242, 45 LR fn, tp: 1, 10 LR f1 score: 0.303 LR cohens kappa score: 0.257 LR average precision score: 0.515 -> 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: 256, 31 KNN fn, tp: 2, 9 KNN f1 score: 0.353 KNN cohens kappa score: 0.313 ------ Step 5/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1104 synthetic samples -> test with 'LR' LR tn, fp: 231, 54 LR fn, tp: 2, 5 LR f1 score: 0.152 LR cohens kappa score: 0.114 LR average precision score: 0.130 -> test with 'GB' GB tn, fp: 281, 4 GB fn, tp: 5, 2 GB f1 score: 0.308 GB cohens kappa score: 0.292 -> test with 'KNN' KNN tn, fp: 266, 19 KNN fn, tp: 2, 5 KNN f1 score: 0.323 KNN cohens kappa score: 0.296 ### Exercise is done. -----[ LR ]----- maximum: LR tn, fp: 255, 61 LR fn, tp: 6, 10 LR f1 score: 0.303 LR cohens kappa score: 0.259 LR average precision score: 0.615 average: LR tn, fp: 240.88, 45.72 LR fn, tp: 2.36, 7.84 LR f1 score: 0.246 LR cohens kappa score: 0.200 LR average precision score: 0.378 minimum: LR tn, fp: 224, 32 LR fn, tp: 1, 5 LR f1 score: 0.152 LR cohens kappa score: 0.114 LR average precision score: 0.130 -----[ GB ]----- maximum: GB tn, fp: 287, 6 GB fn, tp: 11, 5 GB f1 score: 0.545 GB cohens kappa score: 0.537 average: GB tn, fp: 284.0, 2.6 GB fn, tp: 8.28, 1.92 GB f1 score: 0.257 GB cohens kappa score: 0.242 minimum: GB tn, fp: 281, 0 GB fn, tp: 4, 0 GB f1 score: 0.000 GB cohens kappa score: -0.016 -----[ KNN ]----- maximum: KNN tn, fp: 268, 48 KNN fn, tp: 6, 10 KNN f1 score: 0.500 KNN cohens kappa score: 0.472 average: KNN tn, fp: 258.52, 28.08 KNN fn, tp: 2.64, 7.56 KNN f1 score: 0.332 KNN cohens kappa score: 0.294 minimum: KNN tn, fp: 239, 19 KNN fn, tp: 1, 5 KNN f1 score: 0.238 KNN cohens kappa score: 0.189