/////////////////////////////////////////// // Running convGAN-proximary-full 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: 52781, 4082 GAN fn, tp: 13, 86 GAN f1 score: 0.040 GAN cohens kappa score: 0.037 -> test with 'LR' LR tn, fp: 53586, 3277 LR fn, tp: 16, 83 LR f1 score: 0.048 LR cohens kappa score: 0.045 LR average precision score: 0.563 -> test with 'GB' GB tn, fp: 56611, 252 GB fn, tp: 19, 80 GB f1 score: 0.371 GB cohens kappa score: 0.370 -> test with 'KNN' KNN tn, fp: 55607, 1256 KNN fn, tp: 77, 22 KNN f1 score: 0.032 KNN cohens kappa score: 0.029 ------ 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: 56311, 552 GAN fn, tp: 13, 86 GAN f1 score: 0.233 GAN cohens kappa score: 0.231 -> test with 'LR' LR tn, fp: 53657, 3206 LR fn, tp: 6, 93 LR f1 score: 0.055 LR cohens kappa score: 0.052 LR average precision score: 0.734 -> test with 'GB' GB tn, fp: 56464, 399 GB fn, tp: 8, 91 GB f1 score: 0.309 GB cohens kappa score: 0.307 -> test with 'KNN' KNN tn, fp: 56363, 500 KNN fn, tp: 77, 22 KNN f1 score: 0.071 KNN cohens kappa score: 0.068 ------ 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: 56461, 402 GAN fn, tp: 15, 84 GAN f1 score: 0.287 GAN cohens kappa score: 0.285 -> test with 'LR' LR tn, fp: 54172, 2691 LR fn, tp: 9, 90 LR f1 score: 0.062 LR cohens kappa score: 0.059 LR average precision score: 0.658 -> test with 'GB' GB tn, fp: 56516, 347 GB fn, tp: 12, 87 GB f1 score: 0.326 GB cohens kappa score: 0.325 -> test with 'KNN' KNN tn, fp: 55941, 922 KNN fn, tp: 72, 27 KNN f1 score: 0.052 KNN cohens kappa score: 0.049 ------ 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: 43317, 13546 GAN fn, tp: 3, 96 GAN f1 score: 0.014 GAN cohens kappa score: 0.011 -> test with 'LR' LR tn, fp: 55008, 1855 LR fn, tp: 6, 93 LR f1 score: 0.091 LR cohens kappa score: 0.088 LR average precision score: 0.791 -> test with 'GB' GB tn, fp: 56502, 361 GB fn, tp: 8, 91 GB f1 score: 0.330 GB cohens kappa score: 0.328 -> test with 'KNN' KNN tn, fp: 55804, 1059 KNN fn, tp: 69, 30 KNN f1 score: 0.051 KNN cohens kappa score: 0.047 ------ 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: 51762, 5101 GAN fn, tp: 6, 90 GAN f1 score: 0.034 GAN cohens kappa score: 0.031 -> test with 'LR' LR tn, fp: 54715, 2148 LR fn, tp: 9, 87 LR f1 score: 0.075 LR cohens kappa score: 0.072 LR average precision score: 0.793 -> test with 'GB' GB tn, fp: 56391, 472 GB fn, tp: 9, 87 GB f1 score: 0.266 GB cohens kappa score: 0.264 -> test with 'KNN' KNN tn, fp: 56285, 578 KNN fn, tp: 70, 26 KNN f1 score: 0.074 KNN cohens kappa score: 0.072 ====== 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: 55210, 1653 GAN fn, tp: 12, 87 GAN f1 score: 0.095 GAN cohens kappa score: 0.092 -> test with 'LR' LR tn, fp: 52822, 4041 LR fn, tp: 12, 87 LR f1 score: 0.041 LR cohens kappa score: 0.038 LR average precision score: 0.701 -> test with 'GB' GB tn, fp: 56487, 376 GB fn, tp: 10, 89 GB f1 score: 0.316 GB cohens kappa score: 0.314 -> test with 'KNN' KNN tn, fp: 55426, 1437 KNN fn, tp: 72, 27 KNN f1 score: 0.035 KNN cohens kappa score: 0.031 ------ 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: 52251, 4612 GAN fn, tp: 7, 92 GAN f1 score: 0.038 GAN cohens kappa score: 0.035 -> test with 'LR' LR tn, fp: 53964, 2899 LR fn, tp: 9, 90 LR f1 score: 0.058 LR cohens kappa score: 0.055 LR average precision score: 0.643 -> test with 'GB' GB tn, fp: 56403, 460 GB fn, tp: 10, 89 GB f1 score: 0.275 GB cohens kappa score: 0.273 -> test with 'KNN' KNN tn, fp: 56508, 355 KNN fn, tp: 71, 28 KNN f1 score: 0.116 KNN cohens kappa score: 0.114 ------ 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: 51974, 4889 GAN fn, tp: 4, 95 GAN f1 score: 0.037 GAN cohens kappa score: 0.034 -> test with 'LR' LR tn, fp: 54274, 2589 LR fn, tp: 10, 89 LR f1 score: 0.064 LR cohens kappa score: 0.061 LR average precision score: 0.659 -> test with 'GB' GB tn, fp: 56521, 342 GB fn, tp: 11, 88 GB f1 score: 0.333 GB cohens kappa score: 0.331 -> test with 'KNN' KNN tn, fp: 56110, 753 KNN fn, tp: 68, 31 KNN f1 score: 0.070 KNN cohens kappa score: 0.067 ------ 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: 51109, 5754 GAN fn, tp: 6, 93 GAN f1 score: 0.031 GAN cohens kappa score: 0.028 -> test with 'LR' LR tn, fp: 54871, 1992 LR fn, tp: 8, 91 LR f1 score: 0.083 LR cohens kappa score: 0.080 LR average precision score: 0.737 -> test with 'GB' GB tn, fp: 56509, 354 GB fn, tp: 10, 89 GB f1 score: 0.328 GB cohens kappa score: 0.326 -> test with 'KNN' KNN tn, fp: 56552, 311 KNN fn, tp: 78, 21 KNN f1 score: 0.097 KNN cohens kappa score: 0.095 ------ 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: 56454, 409 GAN fn, tp: 16, 80 GAN f1 score: 0.274 GAN cohens kappa score: 0.271 -> test with 'LR' LR tn, fp: 54634, 2229 LR fn, tp: 12, 84 LR f1 score: 0.070 LR cohens kappa score: 0.067 LR average precision score: 0.759 -> test with 'GB' GB tn, fp: 56431, 432 GB fn, tp: 15, 81 GB f1 score: 0.266 GB cohens kappa score: 0.264 -> test with 'KNN' KNN tn, fp: 56517, 346 KNN fn, tp: 76, 20 KNN f1 score: 0.087 KNN cohens kappa score: 0.084 ====== 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: 52135, 4728 GAN fn, tp: 6, 93 GAN f1 score: 0.038 GAN cohens kappa score: 0.035 -> test with 'LR' LR tn, fp: 53496, 3367 LR fn, tp: 9, 90 LR f1 score: 0.051 LR cohens kappa score: 0.047 LR average precision score: 0.673 -> test with 'GB' GB tn, fp: 56438, 425 GB fn, tp: 14, 85 GB f1 score: 0.279 GB cohens kappa score: 0.277 -> test with 'KNN' KNN tn, fp: 55897, 966 KNN fn, tp: 72, 27 KNN f1 score: 0.049 KNN cohens kappa score: 0.046 ------ 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: 55694, 1169 GAN fn, tp: 12, 87 GAN f1 score: 0.128 GAN cohens kappa score: 0.126 -> test with 'LR' LR tn, fp: 54640, 2223 LR fn, tp: 7, 92 LR f1 score: 0.076 LR cohens kappa score: 0.073 LR average precision score: 0.686 -> test with 'GB' GB tn, fp: 56465, 398 GB fn, tp: 10, 89 GB f1 score: 0.304 GB cohens kappa score: 0.302 -> test with 'KNN' KNN tn, fp: 56045, 818 KNN fn, tp: 70, 29 KNN f1 score: 0.061 KNN cohens kappa score: 0.058 ------ 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: 51836, 5027 GAN fn, tp: 7, 92 GAN f1 score: 0.035 GAN cohens kappa score: 0.032 -> test with 'LR' LR tn, fp: 54671, 2192 LR fn, tp: 11, 88 LR f1 score: 0.074 LR cohens kappa score: 0.071 LR average precision score: 0.698 -> test with 'GB' GB tn, fp: 56516, 347 GB fn, tp: 14, 85 GB f1 score: 0.320 GB cohens kappa score: 0.318 -> test with 'KNN' KNN tn, fp: 56040, 823 KNN fn, tp: 75, 24 KNN f1 score: 0.051 KNN cohens kappa score: 0.048 ------ 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: 56227, 636 GAN fn, tp: 20, 79 GAN f1 score: 0.194 GAN cohens kappa score: 0.192 -> test with 'LR' LR tn, fp: 54738, 2125 LR fn, tp: 8, 91 LR f1 score: 0.079 LR cohens kappa score: 0.076 LR average precision score: 0.788 -> test with 'GB' GB tn, fp: 56437, 426 GB fn, tp: 10, 89 GB f1 score: 0.290 GB cohens kappa score: 0.288 -> test with 'KNN' KNN tn, fp: 56028, 835 KNN fn, tp: 70, 29 KNN f1 score: 0.060 KNN cohens kappa score: 0.057 ------ 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: 46516, 10347 GAN fn, tp: 4, 92 GAN f1 score: 0.017 GAN cohens kappa score: 0.014 -> test with 'LR' LR tn, fp: 54971, 1892 LR fn, tp: 5, 91 LR f1 score: 0.088 LR cohens kappa score: 0.085 LR average precision score: 0.710 -> test with 'GB' GB tn, fp: 56528, 335 GB fn, tp: 12, 84 GB f1 score: 0.326 GB cohens kappa score: 0.324 -> test with 'KNN' KNN tn, fp: 56065, 798 KNN fn, tp: 67, 29 KNN f1 score: 0.063 KNN cohens kappa score: 0.060 ====== 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: 25153, 31710 GAN fn, tp: 2, 97 GAN f1 score: 0.006 GAN cohens kappa score: 0.003 -> test with 'LR' LR tn, fp: 54080, 2783 LR fn, tp: 3, 96 LR f1 score: 0.064 LR cohens kappa score: 0.061 LR average precision score: 0.680 -> 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: 55887, 976 KNN fn, tp: 72, 27 KNN f1 score: 0.049 KNN cohens kappa score: 0.046 ------ 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: 56570, 293 GAN fn, tp: 22, 77 GAN f1 score: 0.328 GAN cohens kappa score: 0.327 -> test with 'LR' LR tn, fp: 54746, 2117 LR fn, tp: 11, 88 LR f1 score: 0.076 LR cohens kappa score: 0.073 LR average precision score: 0.672 -> test with 'GB' GB tn, fp: 56506, 357 GB fn, tp: 13, 86 GB f1 score: 0.317 GB cohens kappa score: 0.315 -> test with 'KNN' KNN tn, fp: 56042, 821 KNN fn, tp: 77, 22 KNN f1 score: 0.047 KNN cohens kappa score: 0.044 ------ 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: 53376, 3487 GAN fn, tp: 11, 88 GAN f1 score: 0.048 GAN cohens kappa score: 0.045 -> test with 'LR' LR tn, fp: 54690, 2173 LR fn, tp: 10, 89 LR f1 score: 0.075 LR cohens kappa score: 0.072 LR average precision score: 0.723 -> test with 'GB' GB tn, fp: 56522, 341 GB fn, tp: 11, 88 GB f1 score: 0.333 GB cohens kappa score: 0.331 -> test with 'KNN' KNN tn, fp: 56189, 674 KNN fn, tp: 74, 25 KNN f1 score: 0.063 KNN cohens kappa score: 0.060 ------ 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: 12505, 44358 GAN fn, tp: 1, 98 GAN f1 score: 0.004 GAN cohens kappa score: 0.001 -> test with 'LR' LR tn, fp: 54384, 2479 LR fn, tp: 9, 90 LR f1 score: 0.067 LR cohens kappa score: 0.064 LR average precision score: 0.751 -> test with 'GB' GB tn, fp: 56419, 444 GB fn, tp: 11, 88 GB f1 score: 0.279 GB cohens kappa score: 0.277 -> test with 'KNN' KNN tn, fp: 56198, 665 KNN fn, tp: 64, 35 KNN f1 score: 0.088 KNN cohens kappa score: 0.085 ------ 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: 55163, 1700 GAN fn, tp: 13, 83 GAN f1 score: 0.088 GAN cohens kappa score: 0.085 -> test with 'LR' LR tn, fp: 54890, 1973 LR fn, tp: 11, 85 LR f1 score: 0.079 LR cohens kappa score: 0.076 LR average precision score: 0.720 -> test with 'GB' GB tn, fp: 56487, 376 GB fn, tp: 14, 82 GB f1 score: 0.296 GB cohens kappa score: 0.294 -> test with 'KNN' KNN tn, fp: 56260, 603 KNN fn, tp: 70, 26 KNN f1 score: 0.072 KNN cohens kappa score: 0.069 ====== 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: 52566, 4297 GAN fn, tp: 12, 87 GAN f1 score: 0.039 GAN cohens kappa score: 0.036 -> 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: 0.630 -> test with 'GB' GB tn, fp: 56585, 278 GB fn, tp: 16, 83 GB f1 score: 0.361 GB cohens kappa score: 0.359 -> test with 'KNN' KNN tn, fp: 56156, 707 KNN fn, tp: 75, 24 KNN f1 score: 0.058 KNN cohens kappa score: 0.055 ------ 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: 51554, 5309 GAN fn, tp: 3, 96 GAN f1 score: 0.035 GAN cohens kappa score: 0.032 -> test with 'LR' LR tn, fp: 54103, 2760 LR fn, tp: 5, 94 LR f1 score: 0.064 LR cohens kappa score: 0.061 LR average precision score: 0.773 -> test with 'GB' GB tn, fp: 56491, 372 GB fn, tp: 7, 92 GB f1 score: 0.327 GB cohens kappa score: 0.325 -> test with 'KNN' KNN tn, fp: 56163, 700 KNN fn, tp: 71, 28 KNN f1 score: 0.068 KNN cohens kappa score: 0.065 ------ 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: 55413, 1450 GAN fn, tp: 14, 85 GAN f1 score: 0.104 GAN cohens kappa score: 0.101 -> test with 'LR' LR tn, fp: 54887, 1976 LR fn, tp: 11, 88 LR f1 score: 0.081 LR cohens kappa score: 0.078 LR average precision score: 0.688 -> test with 'GB' GB tn, fp: 56491, 372 GB fn, tp: 13, 86 GB f1 score: 0.309 GB cohens kappa score: 0.307 -> test with 'KNN' KNN tn, fp: 56159, 704 KNN fn, tp: 74, 25 KNN f1 score: 0.060 KNN cohens kappa score: 0.058 ------ 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: 56099, 764 GAN fn, tp: 11, 88 GAN f1 score: 0.185 GAN cohens kappa score: 0.183 -> test with 'LR' LR tn, fp: 54530, 2333 LR fn, tp: 7, 92 LR f1 score: 0.073 LR cohens kappa score: 0.070 LR average precision score: 0.767 -> test with 'GB' GB tn, fp: 56486, 377 GB fn, tp: 9, 90 GB f1 score: 0.318 GB cohens kappa score: 0.316 -> test with 'KNN' KNN tn, fp: 55906, 957 KNN fn, tp: 66, 33 KNN f1 score: 0.061 KNN cohens kappa score: 0.058 ------ 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: 55476, 1387 GAN fn, tp: 12, 84 GAN f1 score: 0.107 GAN cohens kappa score: 0.104 -> test with 'LR' LR tn, fp: 54225, 2638 LR fn, tp: 9, 87 LR f1 score: 0.062 LR cohens kappa score: 0.059 LR average precision score: 0.653 -> test with 'GB' GB tn, fp: 56478, 385 GB fn, tp: 9, 87 GB f1 score: 0.306 GB cohens kappa score: 0.304 -> test with 'KNN' KNN tn, fp: 56508, 355 KNN fn, tp: 73, 23 KNN f1 score: 0.097 KNN cohens kappa score: 0.095 ### Exercise is done. -----[ LR ]----- maximum: LR tn, fp: 55008, 4041 LR fn, tp: 16, 96 LR f1 score: 0.091 LR cohens kappa score: 0.088 LR average precision score: 0.793 average: LR tn, fp: 54365.56, 2497.44 LR fn, tp: 8.96, 89.44 LR f1 score: 0.069 LR cohens kappa score: 0.066 LR average precision score: 0.706 minimum: LR tn, fp: 52822, 1855 LR fn, tp: 3, 83 LR f1 score: 0.041 LR cohens kappa score: 0.038 LR average precision score: 0.563 -----[ GB ]----- maximum: GB tn, fp: 56611, 472 GB fn, tp: 19, 92 GB f1 score: 0.371 GB cohens kappa score: 0.370 average: GB tn, fp: 56487.36, 375.64 GB fn, tp: 11.28, 87.12 GB f1 score: 0.313 GB cohens kappa score: 0.311 minimum: GB tn, fp: 56391, 252 GB fn, tp: 7, 80 GB f1 score: 0.266 GB cohens kappa score: 0.264 -----[ KNN ]----- maximum: KNN tn, fp: 56552, 1437 KNN fn, tp: 78, 35 KNN f1 score: 0.116 KNN cohens kappa score: 0.114 average: KNN tn, fp: 56106.24, 756.76 KNN fn, tp: 72.0, 26.4 KNN f1 score: 0.065 KNN cohens kappa score: 0.062 minimum: KNN tn, fp: 55426, 311 KNN fn, tp: 64, 20 KNN f1 score: 0.032 KNN cohens kappa score: 0.029 -----[ GAN ]----- maximum: GAN tn, fp: 56570, 44358 GAN fn, tp: 22, 98 GAN f1 score: 0.328 GAN cohens kappa score: 0.327 average: GAN tn, fp: 50556.52, 6306.48 GAN fn, tp: 9.8, 88.6 GAN f1 score: 0.098 GAN cohens kappa score: 0.095 minimum: GAN tn, fp: 12505, 293 GAN fn, tp: 1, 77 GAN f1 score: 0.004 GAN cohens kappa score: 0.001