/////////////////////////////////////////// // Running convGAN on imblearn_ozone_level /////////////////////////////////////////// Load 'data_input/imblearn_ozone_level' from imblearn 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 1912 synthetic samples -> test with 'LR' LR tn, fp: 434, 59 LR fn, tp: 2, 13 LR f1 score: 0.299 LR cohens kappa score: 0.263 LR average precision score: 0.379 -> test with 'GB' GB tn, fp: 479, 14 GB fn, tp: 8, 7 GB f1 score: 0.389 GB cohens kappa score: 0.367 -> test with 'KNN' KNN tn, fp: 408, 85 KNN fn, tp: 9, 6 KNN f1 score: 0.113 KNN cohens kappa score: 0.066 ------ Step 1/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1912 synthetic samples -> test with 'LR' LR tn, fp: 435, 58 LR fn, tp: 4, 11 LR f1 score: 0.262 LR cohens kappa score: 0.224 LR average precision score: 0.210 -> test with 'GB' GB tn, fp: 483, 10 GB fn, tp: 6, 9 GB f1 score: 0.529 GB cohens kappa score: 0.513 -> test with 'KNN' KNN tn, fp: 399, 94 KNN fn, tp: 7, 8 KNN f1 score: 0.137 KNN cohens kappa score: 0.090 ------ Step 1/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1912 synthetic samples -> test with 'LR' LR tn, fp: 438, 55 LR fn, tp: 6, 9 LR f1 score: 0.228 LR cohens kappa score: 0.189 LR average precision score: 0.125 -> test with 'GB' GB tn, fp: 473, 20 GB fn, tp: 8, 7 GB f1 score: 0.333 GB cohens kappa score: 0.307 -> test with 'KNN' KNN tn, fp: 409, 84 KNN fn, tp: 10, 5 KNN f1 score: 0.096 KNN cohens kappa score: 0.048 ------ Step 1/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1912 synthetic samples -> test with 'LR' LR tn, fp: 428, 65 LR fn, tp: 5, 10 LR f1 score: 0.222 LR cohens kappa score: 0.182 LR average precision score: 0.218 -> test with 'GB' GB tn, fp: 476, 17 GB fn, tp: 8, 7 GB f1 score: 0.359 GB cohens kappa score: 0.335 -> test with 'KNN' KNN tn, fp: 420, 73 KNN fn, tp: 8, 7 KNN f1 score: 0.147 KNN cohens kappa score: 0.103 ------ Step 1/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1912 synthetic samples -> test with 'LR' LR tn, fp: 430, 61 LR fn, tp: 3, 10 LR f1 score: 0.238 LR cohens kappa score: 0.203 LR average precision score: 0.180 -> test with 'GB' GB tn, fp: 478, 13 GB fn, tp: 8, 5 GB f1 score: 0.323 GB cohens kappa score: 0.302 -> test with 'KNN' KNN tn, fp: 388, 103 KNN fn, tp: 7, 6 KNN f1 score: 0.098 KNN cohens kappa score: 0.055 ====== Step 2/5 ======= -> Shuffling data -> Spliting data to slices ------ Step 2/5: Slice 1/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1912 synthetic samples -> test with 'LR' LR tn, fp: 425, 68 LR fn, tp: 5, 10 LR f1 score: 0.215 LR cohens kappa score: 0.174 LR average precision score: 0.191 -> test with 'GB' GB tn, fp: 479, 14 GB fn, tp: 11, 4 GB f1 score: 0.242 GB cohens kappa score: 0.217 -> test with 'KNN' KNN tn, fp: 399, 94 KNN fn, tp: 10, 5 KNN f1 score: 0.088 KNN cohens kappa score: 0.038 ------ Step 2/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1912 synthetic samples -> test with 'LR' LR tn, fp: 444, 49 LR fn, tp: 5, 10 LR f1 score: 0.270 LR cohens kappa score: 0.234 LR average precision score: 0.210 -> test with 'GB' GB tn, fp: 478, 15 GB fn, tp: 8, 7 GB f1 score: 0.378 GB cohens kappa score: 0.356 -> test with 'KNN' KNN tn, fp: 398, 95 KNN fn, tp: 5, 10 KNN f1 score: 0.167 KNN cohens kappa score: 0.121 ------ Step 2/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1912 synthetic samples -> test with 'LR' LR tn, fp: 432, 61 LR fn, tp: 1, 14 LR f1 score: 0.311 LR cohens kappa score: 0.275 LR average precision score: 0.487 -> test with 'GB' GB tn, fp: 481, 12 GB fn, tp: 7, 8 GB f1 score: 0.457 GB cohens kappa score: 0.438 -> test with 'KNN' KNN tn, fp: 385, 108 KNN fn, tp: 9, 6 KNN f1 score: 0.093 KNN cohens kappa score: 0.043 ------ Step 2/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1912 synthetic samples -> test with 'LR' LR tn, fp: 436, 57 LR fn, tp: 5, 10 LR f1 score: 0.244 LR cohens kappa score: 0.206 LR average precision score: 0.153 -> test with 'GB' GB tn, fp: 475, 18 GB fn, tp: 11, 4 GB f1 score: 0.216 GB cohens kappa score: 0.188 -> test with 'KNN' KNN tn, fp: 437, 56 KNN fn, tp: 11, 4 KNN f1 score: 0.107 KNN cohens kappa score: 0.062 ------ Step 2/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1912 synthetic samples -> test with 'LR' LR tn, fp: 421, 70 LR fn, tp: 3, 10 LR f1 score: 0.215 LR cohens kappa score: 0.179 LR average precision score: 0.202 -> test with 'GB' GB tn, fp: 477, 14 GB fn, tp: 6, 7 GB f1 score: 0.412 GB cohens kappa score: 0.392 -> test with 'KNN' KNN tn, fp: 432, 59 KNN fn, tp: 7, 6 KNN f1 score: 0.154 KNN cohens kappa score: 0.116 ====== Step 3/5 ======= -> Shuffling data -> Spliting data to slices ------ Step 3/5: Slice 1/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1912 synthetic samples -> test with 'LR' LR tn, fp: 427, 66 LR fn, tp: 3, 12 LR f1 score: 0.258 LR cohens kappa score: 0.219 LR average precision score: 0.311 -> test with 'GB' GB tn, fp: 473, 20 GB fn, tp: 8, 7 GB f1 score: 0.333 GB cohens kappa score: 0.307 -> test with 'KNN' KNN tn, fp: 415, 78 KNN fn, tp: 7, 8 KNN f1 score: 0.158 KNN cohens kappa score: 0.114 ------ Step 3/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1912 synthetic samples -> test with 'LR' LR tn, fp: 438, 55 LR fn, tp: 4, 11 LR f1 score: 0.272 LR cohens kappa score: 0.235 LR average precision score: 0.147 -> test with 'GB' GB tn, fp: 480, 13 GB fn, tp: 10, 5 GB f1 score: 0.303 GB cohens kappa score: 0.280 -> test with 'KNN' KNN tn, fp: 413, 80 KNN fn, tp: 9, 6 KNN f1 score: 0.119 KNN cohens kappa score: 0.072 ------ Step 3/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1912 synthetic samples -> test with 'LR' LR tn, fp: 444, 49 LR fn, tp: 4, 11 LR f1 score: 0.293 LR cohens kappa score: 0.258 LR average precision score: 0.177 -> test with 'GB' GB tn, fp: 480, 13 GB fn, tp: 8, 7 GB f1 score: 0.400 GB cohens kappa score: 0.379 -> test with 'KNN' KNN tn, fp: 416, 77 KNN fn, tp: 10, 5 KNN f1 score: 0.103 KNN cohens kappa score: 0.056 ------ Step 3/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1912 synthetic samples -> test with 'LR' LR tn, fp: 430, 63 LR fn, tp: 5, 10 LR f1 score: 0.227 LR cohens kappa score: 0.187 LR average precision score: 0.167 -> test with 'GB' GB tn, fp: 480, 13 GB fn, tp: 7, 8 GB f1 score: 0.444 GB cohens kappa score: 0.425 -> test with 'KNN' KNN tn, fp: 423, 70 KNN fn, tp: 12, 3 KNN f1 score: 0.068 KNN cohens kappa score: 0.020 ------ Step 3/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1912 synthetic samples -> test with 'LR' LR tn, fp: 425, 66 LR fn, tp: 2, 11 LR f1 score: 0.244 LR cohens kappa score: 0.210 LR average precision score: 0.379 -> test with 'GB' GB tn, fp: 474, 17 GB fn, tp: 6, 7 GB f1 score: 0.378 GB cohens kappa score: 0.357 -> test with 'KNN' KNN tn, fp: 364, 127 KNN fn, tp: 3, 10 KNN f1 score: 0.133 KNN cohens kappa score: 0.090 ====== Step 4/5 ======= -> Shuffling data -> Spliting data to slices ------ Step 4/5: Slice 1/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1912 synthetic samples -> test with 'LR' LR tn, fp: 418, 75 LR fn, tp: 3, 12 LR f1 score: 0.235 LR cohens kappa score: 0.195 LR average precision score: 0.296 -> test with 'GB' GB tn, fp: 476, 17 GB fn, tp: 7, 8 GB f1 score: 0.400 GB cohens kappa score: 0.377 -> test with 'KNN' KNN tn, fp: 419, 74 KNN fn, tp: 11, 4 KNN f1 score: 0.086 KNN cohens kappa score: 0.038 ------ Step 4/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1912 synthetic samples -> test with 'LR' LR tn, fp: 437, 56 LR fn, tp: 2, 13 LR f1 score: 0.310 LR cohens kappa score: 0.274 LR average precision score: 0.225 -> test with 'GB' GB tn, fp: 480, 13 GB fn, tp: 7, 8 GB f1 score: 0.444 GB cohens kappa score: 0.425 -> test with 'KNN' KNN tn, fp: 427, 66 KNN fn, tp: 11, 4 KNN f1 score: 0.094 KNN cohens kappa score: 0.048 ------ Step 4/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1912 synthetic samples -> test with 'LR' LR tn, fp: 441, 52 LR fn, tp: 3, 12 LR f1 score: 0.304 LR cohens kappa score: 0.269 LR average precision score: 0.204 -> test with 'GB' GB tn, fp: 482, 11 GB fn, tp: 8, 7 GB f1 score: 0.424 GB cohens kappa score: 0.405 -> test with 'KNN' KNN tn, fp: 396, 97 KNN fn, tp: 7, 8 KNN f1 score: 0.133 KNN cohens kappa score: 0.086 ------ Step 4/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1912 synthetic samples -> test with 'LR' LR tn, fp: 424, 69 LR fn, tp: 3, 12 LR f1 score: 0.250 LR cohens kappa score: 0.211 LR average precision score: 0.272 -> test with 'GB' GB tn, fp: 479, 14 GB fn, tp: 8, 7 GB f1 score: 0.389 GB cohens kappa score: 0.367 -> test with 'KNN' KNN tn, fp: 393, 100 KNN fn, tp: 9, 6 KNN f1 score: 0.099 KNN cohens kappa score: 0.050 ------ Step 4/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1912 synthetic samples -> test with 'LR' LR tn, fp: 423, 68 LR fn, tp: 3, 10 LR f1 score: 0.220 LR cohens kappa score: 0.184 LR average precision score: 0.224 -> test with 'GB' GB tn, fp: 478, 13 GB fn, tp: 5, 8 GB f1 score: 0.471 GB cohens kappa score: 0.453 -> test with 'KNN' KNN tn, fp: 395, 96 KNN fn, tp: 9, 4 KNN f1 score: 0.071 KNN cohens kappa score: 0.026 ====== Step 5/5 ======= -> Shuffling data -> Spliting data to slices ------ Step 5/5: Slice 1/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1912 synthetic samples -> test with 'LR' LR tn, fp: 444, 49 LR fn, tp: 3, 12 LR f1 score: 0.316 LR cohens kappa score: 0.282 LR average precision score: 0.262 -> test with 'GB' GB tn, fp: 480, 13 GB fn, tp: 4, 11 GB f1 score: 0.564 GB cohens kappa score: 0.548 -> test with 'KNN' KNN tn, fp: 369, 124 KNN fn, tp: 6, 9 KNN f1 score: 0.122 KNN cohens kappa score: 0.072 ------ Step 5/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1912 synthetic samples -> test with 'LR' LR tn, fp: 424, 69 LR fn, tp: 3, 12 LR f1 score: 0.250 LR cohens kappa score: 0.211 LR average precision score: 0.139 -> test with 'GB' GB tn, fp: 478, 15 GB fn, tp: 9, 6 GB f1 score: 0.333 GB cohens kappa score: 0.310 -> test with 'KNN' KNN tn, fp: 404, 89 KNN fn, tp: 12, 3 KNN f1 score: 0.056 KNN cohens kappa score: 0.006 ------ Step 5/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1912 synthetic samples -> test with 'LR' LR tn, fp: 449, 44 LR fn, tp: 6, 9 LR f1 score: 0.265 LR cohens kappa score: 0.229 LR average precision score: 0.177 -> test with 'GB' GB tn, fp: 484, 9 GB fn, tp: 8, 7 GB f1 score: 0.452 GB cohens kappa score: 0.434 -> test with 'KNN' KNN tn, fp: 426, 67 KNN fn, tp: 8, 7 KNN f1 score: 0.157 KNN cohens kappa score: 0.114 ------ Step 5/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1912 synthetic samples -> test with 'LR' LR tn, fp: 427, 66 LR fn, tp: 3, 12 LR f1 score: 0.258 LR cohens kappa score: 0.219 LR average precision score: 0.238 -> test with 'GB' GB tn, fp: 479, 14 GB fn, tp: 6, 9 GB f1 score: 0.474 GB cohens kappa score: 0.454 -> test with 'KNN' KNN tn, fp: 429, 64 KNN fn, tp: 8, 7 KNN f1 score: 0.163 KNN cohens kappa score: 0.120 ------ Step 5/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1912 synthetic samples -> test with 'LR' LR tn, fp: 422, 69 LR fn, tp: 3, 10 LR f1 score: 0.217 LR cohens kappa score: 0.181 LR average precision score: 0.338 -> test with 'GB' GB tn, fp: 482, 9 GB fn, tp: 9, 4 GB f1 score: 0.308 GB cohens kappa score: 0.289 -> test with 'KNN' KNN tn, fp: 418, 73 KNN fn, tp: 10, 3 KNN f1 score: 0.067 KNN cohens kappa score: 0.024 ### Exercise is done. -----[ LR ]----- maximum: LR tn, fp: 449, 75 LR fn, tp: 6, 14 LR f1 score: 0.316 LR cohens kappa score: 0.282 LR average precision score: 0.487 average: LR tn, fp: 431.84, 60.76 LR fn, tp: 3.56, 11.04 LR f1 score: 0.257 LR cohens kappa score: 0.220 LR average precision score: 0.236 minimum: LR tn, fp: 418, 44 LR fn, tp: 1, 9 LR f1 score: 0.215 LR cohens kappa score: 0.174 LR average precision score: 0.125 -----[ GB ]----- maximum: GB tn, fp: 484, 20 GB fn, tp: 11, 11 GB f1 score: 0.564 GB cohens kappa score: 0.548 average: GB tn, fp: 478.56, 14.04 GB fn, tp: 7.64, 6.96 GB f1 score: 0.390 GB cohens kappa score: 0.369 minimum: GB tn, fp: 473, 9 GB fn, tp: 4, 4 GB f1 score: 0.216 GB cohens kappa score: 0.188 -----[ KNN ]----- maximum: KNN tn, fp: 437, 127 KNN fn, tp: 12, 10 KNN f1 score: 0.167 KNN cohens kappa score: 0.121 average: KNN tn, fp: 407.28, 85.32 KNN fn, tp: 8.6, 6.0 KNN f1 score: 0.113 KNN cohens kappa score: 0.067 minimum: KNN tn, fp: 364, 56 KNN fn, tp: 3, 3 KNN f1 score: 0.056 KNN cohens kappa score: 0.006