/////////////////////////////////////////// // Running Repeater 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 'LR' LR tn, fp: 53336, 3527 LR fn, tp: 16, 83 LR f1 score: 0.045 LR cohens kappa score: 0.042 LR average precision score: 0.562 -> test with 'GB' GB tn, fp: 56582, 281 GB fn, tp: 19, 80 GB f1 score: 0.348 GB cohens kappa score: 0.346 -> test with 'KNN' KNN tn, fp: 56663, 200 KNN fn, tp: 79, 20 KNN f1 score: 0.125 KNN cohens kappa score: 0.123 ------ Step 1/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 227059 synthetic samples -> test with 'LR' LR tn, fp: 54088, 2775 LR fn, tp: 6, 93 LR f1 score: 0.063 LR cohens kappa score: 0.060 LR average precision score: 0.738 -> test with 'GB' GB tn, fp: 56485, 378 GB fn, tp: 8, 91 GB f1 score: 0.320 GB cohens kappa score: 0.318 -> test with 'KNN' KNN tn, fp: 56680, 183 KNN fn, tp: 81, 18 KNN f1 score: 0.120 KNN cohens kappa score: 0.118 ------ Step 1/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 227059 synthetic samples -> test with 'LR' LR tn, fp: 54602, 2261 LR fn, tp: 8, 91 LR f1 score: 0.074 LR cohens kappa score: 0.071 LR average precision score: 0.685 -> test with 'GB' GB tn, fp: 56497, 366 GB fn, tp: 11, 88 GB f1 score: 0.318 GB cohens kappa score: 0.316 -> test with 'KNN' KNN tn, fp: 56697, 166 KNN fn, tp: 78, 21 KNN f1 score: 0.147 KNN cohens kappa score: 0.145 ------ Step 1/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 227059 synthetic samples -> test with 'LR' LR tn, fp: 54722, 2141 LR fn, tp: 6, 93 LR f1 score: 0.080 LR cohens kappa score: 0.077 LR average precision score: 0.754 -> test with 'GB' GB tn, fp: 56421, 442 GB fn, tp: 6, 93 GB f1 score: 0.293 GB cohens kappa score: 0.291 -> test with 'KNN' KNN tn, fp: 56708, 155 KNN fn, tp: 73, 26 KNN f1 score: 0.186 KNN cohens kappa score: 0.184 ------ Step 1/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 227056 synthetic samples -> test with 'LR' LR tn, fp: 54488, 2375 LR fn, tp: 9, 87 LR f1 score: 0.068 LR cohens kappa score: 0.065 LR average precision score: 0.794 -> test with 'GB' GB tn, fp: 56372, 491 GB fn, tp: 9, 87 GB f1 score: 0.258 GB cohens kappa score: 0.256 -> test with 'KNN' KNN tn, fp: 56693, 170 KNN fn, tp: 73, 23 KNN f1 score: 0.159 KNN cohens kappa score: 0.157 ====== 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 'LR' LR tn, fp: 53491, 3372 LR fn, tp: 6, 93 LR f1 score: 0.052 LR cohens kappa score: 0.049 LR average precision score: 0.737 -> test with 'GB' GB tn, fp: 56455, 408 GB fn, tp: 10, 89 GB f1 score: 0.299 GB cohens kappa score: 0.297 -> test with 'KNN' KNN tn, fp: 56652, 211 KNN fn, tp: 78, 21 KNN f1 score: 0.127 KNN cohens kappa score: 0.125 ------ Step 2/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 227059 synthetic samples -> test with 'LR' LR tn, fp: 53916, 2947 LR fn, tp: 9, 90 LR f1 score: 0.057 LR cohens kappa score: 0.054 LR average precision score: 0.642 -> test with 'GB' GB tn, fp: 56360, 503 GB fn, tp: 9, 90 GB f1 score: 0.260 GB cohens kappa score: 0.258 -> test with 'KNN' KNN tn, fp: 56682, 181 KNN fn, tp: 74, 25 KNN f1 score: 0.164 KNN cohens kappa score: 0.162 ------ Step 2/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 227059 synthetic samples -> test with 'LR' LR tn, fp: 54424, 2439 LR fn, tp: 9, 90 LR f1 score: 0.068 LR cohens kappa score: 0.065 LR average precision score: 0.717 -> test with 'GB' GB tn, fp: 56506, 357 GB fn, tp: 11, 88 GB f1 score: 0.324 GB cohens kappa score: 0.322 -> test with 'KNN' KNN tn, fp: 56698, 165 KNN fn, tp: 69, 30 KNN f1 score: 0.204 KNN cohens kappa score: 0.202 ------ Step 2/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 227059 synthetic samples -> test with 'LR' LR tn, fp: 54803, 2060 LR fn, tp: 8, 91 LR f1 score: 0.081 LR cohens kappa score: 0.078 LR average precision score: 0.735 -> test with 'GB' GB tn, fp: 56506, 357 GB fn, tp: 11, 88 GB f1 score: 0.324 GB cohens kappa score: 0.322 -> test with 'KNN' KNN tn, fp: 56681, 182 KNN fn, tp: 78, 21 KNN f1 score: 0.139 KNN cohens kappa score: 0.137 ------ Step 2/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 227056 synthetic samples -> test with 'LR' LR tn, fp: 54675, 2188 LR fn, tp: 9, 87 LR f1 score: 0.073 LR cohens kappa score: 0.070 LR average precision score: 0.744 -> test with 'GB' GB tn, fp: 56426, 437 GB fn, tp: 15, 81 GB f1 score: 0.264 GB cohens kappa score: 0.262 -> test with 'KNN' KNN tn, fp: 56696, 167 KNN fn, tp: 76, 20 KNN f1 score: 0.141 KNN cohens kappa score: 0.139 ====== 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 'LR' LR tn, fp: 53273, 3590 LR fn, tp: 8, 91 LR f1 score: 0.048 LR cohens kappa score: 0.045 LR average precision score: 0.673 -> test with 'GB' GB tn, fp: 56427, 436 GB fn, tp: 12, 87 GB f1 score: 0.280 GB cohens kappa score: 0.278 -> test with 'KNN' KNN tn, fp: 56662, 201 KNN fn, tp: 77, 22 KNN f1 score: 0.137 KNN cohens kappa score: 0.135 ------ Step 3/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 227059 synthetic samples -> test with 'LR' LR tn, fp: 54063, 2800 LR fn, tp: 7, 92 LR f1 score: 0.062 LR cohens kappa score: 0.058 LR average precision score: 0.644 -> test with 'GB' GB tn, fp: 56441, 422 GB fn, tp: 11, 88 GB f1 score: 0.289 GB cohens kappa score: 0.287 -> test with 'KNN' KNN tn, fp: 56691, 172 KNN fn, tp: 78, 21 KNN f1 score: 0.144 KNN cohens kappa score: 0.142 ------ Step 3/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 227059 synthetic samples -> test with 'LR' LR tn, fp: 54655, 2208 LR fn, tp: 10, 89 LR f1 score: 0.074 LR cohens kappa score: 0.071 LR average precision score: 0.709 -> test with 'GB' GB tn, fp: 56473, 390 GB fn, tp: 13, 86 GB f1 score: 0.299 GB cohens kappa score: 0.297 -> test with 'KNN' KNN tn, fp: 56727, 136 KNN fn, tp: 80, 19 KNN f1 score: 0.150 KNN cohens kappa score: 0.148 ------ Step 3/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 227059 synthetic samples -> test with 'LR' LR tn, fp: 54008, 2855 LR fn, tp: 8, 91 LR f1 score: 0.060 LR cohens kappa score: 0.057 LR average precision score: 0.745 -> test with 'GB' GB tn, fp: 56442, 421 GB fn, tp: 8, 91 GB f1 score: 0.298 GB cohens kappa score: 0.296 -> 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 'LR' LR tn, fp: 54921, 1942 LR fn, tp: 7, 89 LR f1 score: 0.084 LR cohens kappa score: 0.081 LR average precision score: 0.750 -> test with 'GB' GB tn, fp: 56501, 362 GB fn, tp: 12, 84 GB f1 score: 0.310 GB cohens kappa score: 0.308 -> test with 'KNN' KNN tn, fp: 56701, 162 KNN fn, tp: 70, 26 KNN f1 score: 0.183 KNN cohens kappa score: 0.181 ====== 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 'LR' LR tn, fp: 53074, 3789 LR fn, tp: 5, 94 LR f1 score: 0.047 LR cohens kappa score: 0.044 LR average precision score: 0.672 -> test with 'GB' GB tn, fp: 56468, 395 GB fn, tp: 7, 92 GB f1 score: 0.314 GB cohens kappa score: 0.312 -> test with 'KNN' KNN tn, fp: 56696, 167 KNN fn, tp: 81, 18 KNN f1 score: 0.127 KNN cohens kappa score: 0.125 ------ Step 4/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 227059 synthetic samples -> test with 'LR' LR tn, fp: 54055, 2808 LR fn, tp: 11, 88 LR f1 score: 0.059 LR cohens kappa score: 0.056 LR average precision score: 0.647 -> test with 'GB' GB tn, fp: 56513, 350 GB fn, tp: 13, 86 GB f1 score: 0.321 GB cohens kappa score: 0.320 -> test with 'KNN' KNN tn, fp: 56692, 171 KNN fn, tp: 84, 15 KNN f1 score: 0.105 KNN cohens kappa score: 0.103 ------ Step 4/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 227059 synthetic samples -> test with 'LR' LR tn, fp: 54609, 2254 LR fn, tp: 10, 89 LR f1 score: 0.073 LR cohens kappa score: 0.070 LR average precision score: 0.720 -> test with 'GB' GB tn, fp: 56525, 338 GB fn, tp: 12, 87 GB f1 score: 0.332 GB cohens kappa score: 0.330 -> test with 'KNN' KNN tn, fp: 56689, 174 KNN fn, tp: 77, 22 KNN f1 score: 0.149 KNN cohens kappa score: 0.147 ------ Step 4/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 227059 synthetic samples -> test with 'LR' LR tn, fp: 54164, 2699 LR fn, tp: 9, 90 LR f1 score: 0.062 LR cohens kappa score: 0.059 LR average precision score: 0.758 -> test with 'GB' GB tn, fp: 56367, 496 GB fn, tp: 11, 88 GB f1 score: 0.258 GB cohens kappa score: 0.255 -> test with 'KNN' KNN tn, fp: 56690, 173 KNN fn, tp: 67, 32 KNN f1 score: 0.211 KNN cohens kappa score: 0.209 ------ Step 4/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 227056 synthetic samples -> test with 'LR' LR tn, fp: 54833, 2030 LR fn, tp: 8, 88 LR f1 score: 0.079 LR cohens kappa score: 0.077 LR average precision score: 0.692 -> test with 'GB' GB tn, fp: 56490, 373 GB fn, tp: 15, 81 GB f1 score: 0.295 GB cohens kappa score: 0.293 -> test with 'KNN' KNN tn, fp: 56695, 168 KNN fn, tp: 74, 22 KNN f1 score: 0.154 KNN cohens kappa score: 0.152 ====== 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 'LR' LR tn, fp: 54233, 2630 LR fn, tp: 10, 89 LR f1 score: 0.063 LR cohens kappa score: 0.060 LR average precision score: 0.630 -> test with 'GB' GB tn, fp: 56542, 321 GB fn, tp: 17, 82 GB f1 score: 0.327 GB cohens kappa score: 0.325 -> test with 'KNN' KNN tn, fp: 56702, 161 KNN fn, tp: 77, 22 KNN f1 score: 0.156 KNN cohens kappa score: 0.154 ------ Step 5/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 227059 synthetic samples -> test with 'LR' LR tn, fp: 54380, 2483 LR fn, tp: 4, 95 LR f1 score: 0.071 LR cohens kappa score: 0.068 LR average precision score: 0.767 -> test with 'GB' GB tn, fp: 56450, 413 GB fn, tp: 7, 92 GB f1 score: 0.305 GB cohens kappa score: 0.303 -> test with 'KNN' KNN tn, fp: 56690, 173 KNN fn, tp: 75, 24 KNN f1 score: 0.162 KNN cohens kappa score: 0.160 ------ Step 5/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 227059 synthetic samples -> test with 'LR' LR tn, fp: 54385, 2478 LR fn, tp: 11, 88 LR f1 score: 0.066 LR cohens kappa score: 0.063 LR average precision score: 0.673 -> test with 'GB' GB tn, fp: 56462, 401 GB fn, tp: 12, 87 GB f1 score: 0.296 GB cohens kappa score: 0.294 -> test with 'KNN' KNN tn, fp: 56727, 136 KNN fn, tp: 76, 23 KNN f1 score: 0.178 KNN cohens kappa score: 0.177 ------ Step 5/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 227059 synthetic samples -> test with 'LR' LR tn, fp: 54436, 2427 LR fn, tp: 6, 93 LR f1 score: 0.071 LR cohens kappa score: 0.068 LR average precision score: 0.751 -> test with 'GB' GB tn, fp: 56433, 430 GB fn, tp: 10, 89 GB f1 score: 0.288 GB cohens kappa score: 0.286 -> test with 'KNN' KNN tn, fp: 56676, 187 KNN fn, tp: 78, 21 KNN f1 score: 0.137 KNN cohens kappa score: 0.135 ------ Step 5/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 227056 synthetic samples -> test with 'LR' LR tn, fp: 54576, 2287 LR fn, tp: 5, 91 LR f1 score: 0.074 LR cohens kappa score: 0.071 LR average precision score: 0.650 -> test with 'GB' GB tn, fp: 56468, 395 GB fn, tp: 9, 87 GB f1 score: 0.301 GB cohens kappa score: 0.299 -> test with 'KNN' KNN tn, fp: 56695, 168 KNN fn, tp: 73, 23 KNN f1 score: 0.160 KNN cohens kappa score: 0.158 ### Exercise is done. -----[ LR ]----- maximum: LR tn, fp: 54921, 3789 LR fn, tp: 16, 95 LR f1 score: 0.084 LR cohens kappa score: 0.081 LR average precision score: 0.794 average: LR tn, fp: 54248.4, 2614.6 LR fn, tp: 8.2, 90.2 LR f1 score: 0.066 LR cohens kappa score: 0.063 LR average precision score: 0.704 minimum: LR tn, fp: 53074, 1942 LR fn, tp: 4, 83 LR f1 score: 0.045 LR cohens kappa score: 0.042 LR average precision score: 0.562 -----[ GB ]----- maximum: GB tn, fp: 56582, 503 GB fn, tp: 19, 93 GB f1 score: 0.348 GB cohens kappa score: 0.346 average: GB tn, fp: 56464.48, 398.52 GB fn, tp: 11.12, 87.28 GB f1 score: 0.301 GB cohens kappa score: 0.299 minimum: GB tn, fp: 56360, 281 GB fn, tp: 6, 80 GB f1 score: 0.258 GB cohens kappa score: 0.255 -----[ KNN ]----- maximum: KNN tn, fp: 56727, 211 KNN fn, tp: 84, 32 KNN f1 score: 0.211 KNN cohens kappa score: 0.209 average: KNN tn, fp: 56690.6, 172.4 KNN fn, tp: 76.2, 22.2 KNN f1 score: 0.152 KNN cohens kappa score: 0.150 minimum: KNN tn, fp: 56652, 136 KNN fn, tp: 67, 15 KNN f1 score: 0.105 KNN cohens kappa score: 0.103