/////////////////////////////////////////// // Running convGAN-proximary-5 on kaggle_creditcard /////////////////////////////////////////// Load 'data_input/kaggle_creditcard' 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 227059 synthetic samples -> test with GAN.predict GAN tn, fp: 34356, 22507 GAN fn, tp: 4, 95 GAN f1 score: 0.008 GAN cohens kappa score: 0.005 -> test with 'LR' LR tn, fp: 54228, 2635 LR fn, tp: 17, 82 LR f1 score: 0.058 LR cohens kappa score: 0.055 LR average precision score: 0.548 -> test with 'GB' GB tn, fp: 56650, 213 GB fn, tp: 19, 80 GB f1 score: 0.408 GB cohens kappa score: 0.407 -> test with 'KNN' KNN tn, fp: 56611, 252 KNN fn, tp: 79, 20 KNN f1 score: 0.108 KNN cohens kappa score: 0.106 ------ Step 1/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 227059 synthetic samples -> test with GAN.predict GAN tn, fp: 14063, 42800 GAN fn, tp: 1, 98 GAN f1 score: 0.005 GAN cohens kappa score: 0.001 -> test with 'LR' LR tn, fp: 54148, 2715 LR fn, tp: 6, 93 LR f1 score: 0.064 LR cohens kappa score: 0.061 LR average precision score: 0.725 -> test with 'GB' GB tn, fp: 56631, 232 GB fn, tp: 10, 89 GB f1 score: 0.424 GB cohens kappa score: 0.422 -> test with 'KNN' KNN tn, fp: 56682, 181 KNN fn, tp: 79, 20 KNN f1 score: 0.133 KNN cohens kappa score: 0.131 ------ Step 1/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 227059 synthetic samples -> test with GAN.predict GAN tn, fp: 56736, 127 GAN fn, tp: 47, 52 GAN f1 score: 0.374 GAN cohens kappa score: 0.373 -> test with 'LR' LR tn, fp: 54573, 2290 LR fn, tp: 9, 90 LR f1 score: 0.073 LR cohens kappa score: 0.070 LR average precision score: 0.663 -> test with 'GB' GB tn, fp: 56540, 323 GB fn, tp: 13, 86 GB f1 score: 0.339 GB cohens kappa score: 0.337 -> test with 'KNN' KNN tn, fp: 56565, 298 KNN fn, tp: 77, 22 KNN f1 score: 0.105 KNN cohens kappa score: 0.103 ------ Step 1/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 227059 synthetic samples -> test with GAN.predict GAN tn, fp: 56733, 130 GAN fn, tp: 33, 66 GAN f1 score: 0.447 GAN cohens kappa score: 0.446 -> test with 'LR' LR tn, fp: 55214, 1649 LR fn, tp: 7, 92 LR f1 score: 0.100 LR cohens kappa score: 0.097 LR average precision score: 0.751 -> test with 'GB' GB tn, fp: 56500, 363 GB fn, tp: 7, 92 GB f1 score: 0.332 GB cohens kappa score: 0.330 -> test with 'KNN' KNN tn, fp: 56575, 288 KNN fn, tp: 73, 26 KNN f1 score: 0.126 KNN cohens kappa score: 0.124 ------ Step 1/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 227056 synthetic samples -> test with GAN.predict GAN tn, fp: 56708, 155 GAN fn, tp: 23, 73 GAN f1 score: 0.451 GAN cohens kappa score: 0.449 -> test with 'LR' LR tn, fp: 55107, 1756 LR fn, tp: 7, 89 LR f1 score: 0.092 LR cohens kappa score: 0.089 LR average precision score: 0.857 -> test with 'GB' GB tn, fp: 56479, 384 GB fn, tp: 9, 87 GB f1 score: 0.307 GB cohens kappa score: 0.305 -> test with 'KNN' KNN tn, fp: 56549, 314 KNN fn, tp: 70, 26 KNN f1 score: 0.119 KNN cohens kappa score: 0.117 ====== Step 2/5 ======= -> Shuffling data -> Spliting data to slices ------ Step 2/5: Slice 1/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 227059 synthetic samples -> test with GAN.predict GAN tn, fp: 56555, 308 GAN fn, tp: 29, 70 GAN f1 score: 0.294 GAN cohens kappa score: 0.292 -> test with 'LR' LR tn, fp: 54544, 2319 LR fn, tp: 7, 92 LR f1 score: 0.073 LR cohens kappa score: 0.070 LR average precision score: 0.750 -> test with 'GB' GB tn, fp: 56467, 396 GB fn, tp: 10, 89 GB f1 score: 0.305 GB cohens kappa score: 0.303 -> test with 'KNN' KNN tn, fp: 56641, 222 KNN fn, tp: 78, 21 KNN f1 score: 0.123 KNN cohens kappa score: 0.121 ------ Step 2/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 227059 synthetic samples -> test with GAN.predict GAN tn, fp: 56756, 107 GAN fn, tp: 48, 51 GAN f1 score: 0.397 GAN cohens kappa score: 0.396 -> test with 'LR' LR tn, fp: 54054, 2809 LR fn, tp: 12, 87 LR f1 score: 0.058 LR cohens kappa score: 0.055 LR average precision score: 0.608 -> test with 'GB' GB tn, fp: 56422, 441 GB fn, tp: 9, 90 GB f1 score: 0.286 GB cohens kappa score: 0.284 -> test with 'KNN' KNN tn, fp: 56512, 351 KNN fn, tp: 69, 30 KNN f1 score: 0.125 KNN cohens kappa score: 0.123 ------ Step 2/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 227059 synthetic samples -> test with GAN.predict GAN tn, fp: 52985, 3878 GAN fn, tp: 8, 91 GAN f1 score: 0.045 GAN cohens kappa score: 0.041 -> test with 'LR' LR tn, fp: 54553, 2310 LR fn, tp: 10, 89 LR f1 score: 0.071 LR cohens kappa score: 0.068 LR average precision score: 0.663 -> test with 'GB' GB tn, fp: 56604, 259 GB fn, tp: 12, 87 GB f1 score: 0.391 GB cohens kappa score: 0.389 -> test with 'KNN' KNN tn, fp: 56523, 340 KNN fn, tp: 67, 32 KNN f1 score: 0.136 KNN cohens kappa score: 0.134 ------ Step 2/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 227059 synthetic samples -> test with GAN.predict GAN tn, fp: 56661, 202 GAN fn, tp: 54, 45 GAN f1 score: 0.260 GAN cohens kappa score: 0.258 -> test with 'LR' LR tn, fp: 55078, 1785 LR fn, tp: 9, 90 LR f1 score: 0.091 LR cohens kappa score: 0.088 LR average precision score: 0.673 -> test with 'GB' GB tn, fp: 56636, 227 GB fn, tp: 13, 86 GB f1 score: 0.417 GB cohens kappa score: 0.416 -> test with 'KNN' KNN tn, fp: 56592, 271 KNN fn, tp: 77, 22 KNN f1 score: 0.112 KNN cohens kappa score: 0.110 ------ Step 2/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 227056 synthetic samples -> test with GAN.predict GAN tn, fp: 56727, 136 GAN fn, tp: 27, 69 GAN f1 score: 0.458 GAN cohens kappa score: 0.457 -> test with 'LR' LR tn, fp: 54621, 2242 LR fn, tp: 13, 83 LR f1 score: 0.069 LR cohens kappa score: 0.066 LR average precision score: 0.737 -> test with 'GB' GB tn, fp: 56472, 391 GB fn, tp: 16, 80 GB f1 score: 0.282 GB cohens kappa score: 0.280 -> test with 'KNN' KNN tn, fp: 56609, 254 KNN fn, tp: 75, 21 KNN f1 score: 0.113 KNN cohens kappa score: 0.111 ====== Step 3/5 ======= -> Shuffling data -> Spliting data to slices ------ Step 3/5: Slice 1/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 227059 synthetic samples -> test with GAN.predict GAN tn, fp: 55865, 998 GAN fn, tp: 15, 84 GAN f1 score: 0.142 GAN cohens kappa score: 0.140 -> test with 'LR' LR tn, fp: 55632, 1231 LR fn, tp: 10, 89 LR f1 score: 0.125 LR cohens kappa score: 0.123 LR average precision score: 0.665 -> test with 'GB' GB tn, fp: 56688, 175 GB fn, tp: 15, 84 GB f1 score: 0.469 GB cohens kappa score: 0.468 -> test with 'KNN' KNN tn, fp: 56287, 576 KNN fn, tp: 76, 23 KNN f1 score: 0.066 KNN cohens kappa score: 0.063 ------ Step 3/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 227059 synthetic samples -> test with GAN.predict GAN tn, fp: 4941, 51922 GAN fn, tp: 0, 99 GAN f1 score: 0.004 GAN cohens kappa score: 0.000 -> test with 'LR' LR tn, fp: 54257, 2606 LR fn, tp: 7, 92 LR f1 score: 0.066 LR cohens kappa score: 0.063 LR average precision score: 0.638 -> test with 'GB' GB tn, fp: 56482, 381 GB fn, tp: 10, 89 GB f1 score: 0.313 GB cohens kappa score: 0.311 -> test with 'KNN' KNN tn, fp: 56687, 176 KNN fn, tp: 78, 21 KNN f1 score: 0.142 KNN cohens kappa score: 0.140 ------ Step 3/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 227059 synthetic samples -> test with GAN.predict GAN tn, fp: 2800, 54063 GAN fn, tp: 0, 99 GAN f1 score: 0.004 GAN cohens kappa score: 0.000 -> test with 'LR' LR tn, fp: 54766, 2097 LR fn, tp: 10, 89 LR f1 score: 0.078 LR cohens kappa score: 0.075 LR average precision score: 0.722 -> test with 'GB' GB tn, fp: 56595, 268 GB fn, tp: 13, 86 GB f1 score: 0.380 GB cohens kappa score: 0.378 -> test with 'KNN' KNN tn, fp: 56578, 285 KNN fn, tp: 77, 22 KNN f1 score: 0.108 KNN cohens kappa score: 0.106 ------ Step 3/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 227059 synthetic samples -> test with GAN.predict GAN tn, fp: 3270, 53593 GAN fn, tp: 1, 98 GAN f1 score: 0.004 GAN cohens kappa score: 0.000 -> test with 'LR' LR tn, fp: 54299, 2564 LR fn, tp: 9, 90 LR f1 score: 0.065 LR cohens kappa score: 0.062 LR average precision score: 0.745 -> test with 'GB' GB tn, fp: 56540, 323 GB fn, tp: 9, 90 GB f1 score: 0.352 GB cohens kappa score: 0.350 -> test with 'KNN' KNN tn, fp: 56682, 181 KNN fn, tp: 79, 20 KNN f1 score: 0.133 KNN cohens kappa score: 0.131 ------ Step 3/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 227056 synthetic samples -> test with GAN.predict GAN tn, fp: 47210, 9653 GAN fn, tp: 8, 88 GAN f1 score: 0.018 GAN cohens kappa score: 0.015 -> test with 'LR' LR tn, fp: 55383, 1480 LR fn, tp: 8, 88 LR f1 score: 0.106 LR cohens kappa score: 0.103 LR average precision score: 0.745 -> test with 'GB' GB tn, fp: 56536, 327 GB fn, tp: 11, 85 GB f1 score: 0.335 GB cohens kappa score: 0.333 -> test with 'KNN' KNN tn, fp: 56423, 440 KNN fn, tp: 67, 29 KNN f1 score: 0.103 KNN cohens kappa score: 0.100 ====== Step 4/5 ======= -> Shuffling data -> Spliting data to slices ------ Step 4/5: Slice 1/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 227059 synthetic samples -> test with GAN.predict GAN tn, fp: 56263, 600 GAN fn, tp: 14, 85 GAN f1 score: 0.217 GAN cohens kappa score: 0.214 -> test with 'LR' LR tn, fp: 54684, 2179 LR fn, tp: 3, 96 LR f1 score: 0.081 LR cohens kappa score: 0.078 LR average precision score: 0.708 -> test with 'GB' GB tn, fp: 56502, 361 GB fn, tp: 7, 92 GB f1 score: 0.333 GB cohens kappa score: 0.331 -> test with 'KNN' KNN tn, fp: 56547, 316 KNN fn, tp: 79, 20 KNN f1 score: 0.092 KNN cohens kappa score: 0.090 ------ Step 4/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 227059 synthetic samples -> test with GAN.predict GAN tn, fp: 56725, 138 GAN fn, tp: 46, 53 GAN f1 score: 0.366 GAN cohens kappa score: 0.364 -> test with 'LR' LR tn, fp: 54793, 2070 LR fn, tp: 10, 89 LR f1 score: 0.079 LR cohens kappa score: 0.076 LR average precision score: 0.617 -> test with 'GB' GB tn, fp: 56571, 292 GB fn, tp: 13, 86 GB f1 score: 0.361 GB cohens kappa score: 0.359 -> test with 'KNN' KNN tn, fp: 56584, 279 KNN fn, tp: 81, 18 KNN f1 score: 0.091 KNN cohens kappa score: 0.089 ------ Step 4/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 227059 synthetic samples -> test with GAN.predict GAN tn, fp: 12795, 44068 GAN fn, tp: 2, 97 GAN f1 score: 0.004 GAN cohens kappa score: 0.001 -> test with 'LR' LR tn, fp: 54916, 1947 LR fn, tp: 11, 88 LR f1 score: 0.082 LR cohens kappa score: 0.079 LR average precision score: 0.712 -> test with 'GB' GB tn, fp: 56534, 329 GB fn, tp: 13, 86 GB f1 score: 0.335 GB cohens kappa score: 0.333 -> test with 'KNN' KNN tn, fp: 56686, 177 KNN fn, tp: 77, 22 KNN f1 score: 0.148 KNN cohens kappa score: 0.146 ------ Step 4/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 227059 synthetic samples -> test with GAN.predict GAN tn, fp: 56655, 208 GAN fn, tp: 21, 78 GAN f1 score: 0.405 GAN cohens kappa score: 0.404 -> test with 'LR' LR tn, fp: 54798, 2065 LR fn, tp: 8, 91 LR f1 score: 0.081 LR cohens kappa score: 0.078 LR average precision score: 0.766 -> test with 'GB' GB tn, fp: 56494, 369 GB fn, tp: 11, 88 GB f1 score: 0.317 GB cohens kappa score: 0.315 -> test with 'KNN' KNN tn, fp: 56532, 331 KNN fn, tp: 64, 35 KNN f1 score: 0.151 KNN cohens kappa score: 0.148 ------ Step 4/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 227056 synthetic samples -> test with GAN.predict GAN tn, fp: 56750, 113 GAN fn, tp: 49, 47 GAN f1 score: 0.367 GAN cohens kappa score: 0.366 -> test with 'LR' LR tn, fp: 55501, 1362 LR fn, tp: 11, 85 LR f1 score: 0.110 LR cohens kappa score: 0.107 LR average precision score: 0.716 -> test with 'GB' GB tn, fp: 56658, 205 GB fn, tp: 14, 82 GB f1 score: 0.428 GB cohens kappa score: 0.427 -> test with 'KNN' KNN tn, fp: 56454, 409 KNN fn, tp: 72, 24 KNN f1 score: 0.091 KNN cohens kappa score: 0.088 ====== Step 5/5 ======= -> Shuffling data -> Spliting data to slices ------ Step 5/5: Slice 1/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 227059 synthetic samples -> test with GAN.predict GAN tn, fp: 56685, 178 GAN fn, tp: 44, 55 GAN f1 score: 0.331 GAN cohens kappa score: 0.330 -> test with 'LR' LR tn, fp: 55050, 1813 LR fn, tp: 15, 84 LR f1 score: 0.084 LR cohens kappa score: 0.081 LR average precision score: 0.652 -> test with 'GB' GB tn, fp: 56633, 230 GB fn, tp: 19, 80 GB f1 score: 0.391 GB cohens kappa score: 0.390 -> test with 'KNN' KNN tn, fp: 56440, 423 KNN fn, tp: 72, 27 KNN f1 score: 0.098 KNN cohens kappa score: 0.096 ------ Step 5/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 227059 synthetic samples -> test with GAN.predict GAN tn, fp: 56704, 159 GAN fn, tp: 28, 71 GAN f1 score: 0.432 GAN cohens kappa score: 0.430 -> test with 'LR' LR tn, fp: 54787, 2076 LR fn, tp: 5, 94 LR f1 score: 0.083 LR cohens kappa score: 0.080 LR average precision score: 0.767 -> test with 'GB' GB tn, fp: 56528, 335 GB fn, tp: 8, 91 GB f1 score: 0.347 GB cohens kappa score: 0.345 -> test with 'KNN' KNN tn, fp: 56461, 402 KNN fn, tp: 69, 30 KNN f1 score: 0.113 KNN cohens kappa score: 0.110 ------ Step 5/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 227059 synthetic samples -> test with GAN.predict GAN tn, fp: 3197, 53666 GAN fn, tp: 0, 99 GAN f1 score: 0.004 GAN cohens kappa score: 0.000 -> test with 'LR' LR tn, fp: 55134, 1729 LR fn, tp: 12, 87 LR f1 score: 0.091 LR cohens kappa score: 0.088 LR average precision score: 0.691 -> test with 'GB' GB tn, fp: 56505, 358 GB fn, tp: 13, 86 GB f1 score: 0.317 GB cohens kappa score: 0.315 -> test with 'KNN' KNN tn, fp: 56427, 436 KNN fn, tp: 73, 26 KNN f1 score: 0.093 KNN cohens kappa score: 0.090 ------ Step 5/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 227059 synthetic samples -> test with GAN.predict GAN tn, fp: 56450, 413 GAN fn, tp: 13, 86 GAN f1 score: 0.288 GAN cohens kappa score: 0.286 -> test with 'LR' LR tn, fp: 54893, 1970 LR fn, tp: 9, 90 LR f1 score: 0.083 LR cohens kappa score: 0.080 LR average precision score: 0.768 -> test with 'GB' GB tn, fp: 56469, 394 GB fn, tp: 9, 90 GB f1 score: 0.309 GB cohens kappa score: 0.307 -> test with 'KNN' KNN tn, fp: 56459, 404 KNN fn, tp: 77, 22 KNN f1 score: 0.084 KNN cohens kappa score: 0.081 ------ Step 5/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 227056 synthetic samples -> test with GAN.predict GAN tn, fp: 51540, 5323 GAN fn, tp: 6, 90 GAN f1 score: 0.033 GAN cohens kappa score: 0.029 -> test with 'LR' LR tn, fp: 54706, 2157 LR fn, tp: 11, 85 LR f1 score: 0.073 LR cohens kappa score: 0.070 LR average precision score: 0.663 -> test with 'GB' GB tn, fp: 56556, 307 GB fn, tp: 10, 86 GB f1 score: 0.352 GB cohens kappa score: 0.350 -> test with 'KNN' KNN tn, fp: 56520, 343 KNN fn, tp: 73, 23 KNN f1 score: 0.100 KNN cohens kappa score: 0.097 ### Exercise is done. -----[ LR ]----- maximum: LR tn, fp: 55632, 2809 LR fn, tp: 17, 96 LR f1 score: 0.125 LR cohens kappa score: 0.123 LR average precision score: 0.857 average: LR tn, fp: 54788.76, 2074.24 LR fn, tp: 9.44, 88.96 LR f1 score: 0.081 LR cohens kappa score: 0.078 LR average precision score: 0.702 minimum: LR tn, fp: 54054, 1231 LR fn, tp: 3, 82 LR f1 score: 0.058 LR cohens kappa score: 0.055 LR average precision score: 0.548 -----[ GB ]----- maximum: GB tn, fp: 56688, 441 GB fn, tp: 19, 92 GB f1 score: 0.469 GB cohens kappa score: 0.468 average: GB tn, fp: 56547.68, 315.32 GB fn, tp: 11.72, 86.68 GB f1 score: 0.353 GB cohens kappa score: 0.351 minimum: GB tn, fp: 56422, 175 GB fn, tp: 7, 80 GB f1 score: 0.282 GB cohens kappa score: 0.280 -----[ KNN ]----- maximum: KNN tn, fp: 56687, 576 KNN fn, tp: 81, 35 KNN f1 score: 0.151 KNN cohens kappa score: 0.148 average: KNN tn, fp: 56545.04, 317.96 KNN fn, tp: 74.32, 24.08 KNN f1 score: 0.112 KNN cohens kappa score: 0.110 minimum: KNN tn, fp: 56287, 176 KNN fn, tp: 64, 18 KNN f1 score: 0.066 KNN cohens kappa score: 0.063 -----[ GAN ]----- maximum: GAN tn, fp: 56756, 54063 GAN fn, tp: 54, 99 GAN f1 score: 0.458 GAN cohens kappa score: 0.457 average: GAN tn, fp: 43045.2, 13817.8 GAN fn, tp: 20.84, 77.56 GAN f1 score: 0.214 GAN cohens kappa score: 0.212 minimum: GAN tn, fp: 2800, 107 GAN fn, tp: 0, 45 GAN f1 score: 0.004 GAN cohens kappa score: 0.000