/////////////////////////////////////////// // Running convGAN-proximary-5 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 GAN.predict GAN tn, fp: 394, 99 GAN fn, tp: 3, 12 GAN f1 score: 0.190 GAN cohens kappa score: 0.146 -> test with 'LR' LR tn, fp: 425, 68 LR fn, tp: 1, 14 LR f1 score: 0.289 LR cohens kappa score: 0.251 LR average precision score: 0.349 -> test with 'GB' GB tn, fp: 478, 15 GB fn, tp: 7, 8 GB f1 score: 0.421 GB cohens kappa score: 0.400 -> test with 'KNN' KNN tn, fp: 391, 102 KNN fn, tp: 9, 6 KNN f1 score: 0.098 KNN cohens kappa score: 0.048 ------ Step 1/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1912 synthetic samples -> test with GAN.predict GAN tn, fp: 484, 9 GAN fn, tp: 15, 0 GAN f1 score: 0.000 GAN cohens kappa score: -0.023 -> test with 'LR' LR tn, fp: 432, 61 LR fn, tp: 4, 11 LR f1 score: 0.253 LR cohens kappa score: 0.214 LR average precision score: 0.202 -> test with 'GB' GB tn, fp: 485, 8 GB fn, tp: 7, 8 GB f1 score: 0.516 GB cohens kappa score: 0.501 -> test with 'KNN' KNN tn, fp: 446, 47 KNN fn, tp: 13, 2 KNN f1 score: 0.062 KNN cohens kappa score: 0.018 ------ Step 1/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1912 synthetic samples -> test with GAN.predict GAN tn, fp: 492, 1 GAN fn, tp: 15, 0 GAN f1 score: 0.000 GAN cohens kappa score: -0.004 -> test with 'LR' LR tn, fp: 432, 61 LR fn, tp: 4, 11 LR f1 score: 0.253 LR cohens kappa score: 0.214 LR average precision score: 0.119 -> test with 'GB' GB tn, fp: 478, 15 GB fn, tp: 7, 8 GB f1 score: 0.421 GB cohens kappa score: 0.400 -> test with 'KNN' KNN tn, fp: 426, 67 KNN fn, tp: 11, 4 KNN f1 score: 0.093 KNN cohens kappa score: 0.047 ------ Step 1/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1912 synthetic samples -> test with GAN.predict GAN tn, fp: 387, 106 GAN fn, tp: 4, 11 GAN f1 score: 0.167 GAN cohens kappa score: 0.121 -> test with 'LR' LR tn, fp: 432, 61 LR fn, tp: 5, 10 LR f1 score: 0.233 LR cohens kappa score: 0.193 LR average precision score: 0.202 -> test with 'GB' GB tn, fp: 485, 8 GB fn, tp: 9, 6 GB f1 score: 0.414 GB cohens kappa score: 0.397 -> test with 'KNN' KNN tn, fp: 430, 63 KNN fn, tp: 10, 5 KNN f1 score: 0.120 KNN cohens kappa score: 0.076 ------ Step 1/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1912 synthetic samples -> test with GAN.predict GAN tn, fp: 163, 328 GAN fn, tp: 1, 12 GAN f1 score: 0.068 GAN cohens kappa score: 0.019 -> test with 'LR' LR tn, fp: 427, 64 LR fn, tp: 3, 10 LR f1 score: 0.230 LR cohens kappa score: 0.195 LR average precision score: 0.180 -> test with 'GB' GB tn, fp: 475, 16 GB fn, tp: 9, 4 GB f1 score: 0.242 GB cohens kappa score: 0.218 -> test with 'KNN' KNN tn, fp: 383, 108 KNN fn, tp: 5, 8 KNN f1 score: 0.124 KNN cohens kappa score: 0.081 ====== 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 GAN.predict GAN tn, fp: 39, 454 GAN fn, tp: 0, 15 GAN f1 score: 0.062 GAN cohens kappa score: 0.005 -> 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: 0.308 -> test with 'GB' GB tn, fp: 483, 10 GB fn, tp: 12, 3 GB f1 score: 0.214 GB cohens kappa score: 0.192 -> test with 'KNN' KNN tn, fp: 415, 78 KNN fn, tp: 10, 5 KNN f1 score: 0.102 KNN cohens kappa score: 0.055 ------ Step 2/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1912 synthetic samples -> test with GAN.predict GAN tn, fp: 341, 152 GAN fn, tp: 7, 8 GAN f1 score: 0.091 GAN cohens kappa score: 0.040 -> 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: 0.214 -> 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: 396, 97 KNN fn, tp: 6, 9 KNN f1 score: 0.149 KNN cohens kappa score: 0.102 ------ Step 2/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1912 synthetic samples -> test with GAN.predict GAN tn, fp: 72, 421 GAN fn, tp: 0, 15 GAN f1 score: 0.067 GAN cohens kappa score: 0.010 -> test with 'LR' LR tn, fp: 426, 67 LR fn, tp: 1, 14 LR f1 score: 0.292 LR cohens kappa score: 0.255 LR average precision score: 0.413 -> 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: 394, 99 KNN fn, tp: 11, 4 KNN f1 score: 0.068 KNN cohens kappa score: 0.017 ------ Step 2/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1912 synthetic samples -> test with GAN.predict GAN tn, fp: 450, 43 GAN fn, tp: 13, 2 GAN f1 score: 0.067 GAN cohens kappa score: 0.023 -> test with 'LR' LR tn, fp: 440, 53 LR fn, tp: 5, 10 LR f1 score: 0.256 LR cohens kappa score: 0.219 LR average precision score: 0.155 -> test with 'GB' GB tn, fp: 477, 16 GB fn, tp: 11, 4 GB f1 score: 0.229 GB cohens kappa score: 0.202 -> test with 'KNN' KNN tn, fp: 432, 61 KNN fn, tp: 11, 4 KNN f1 score: 0.100 KNN cohens kappa score: 0.055 ------ Step 2/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1912 synthetic samples -> test with GAN.predict GAN tn, fp: 473, 18 GAN fn, tp: 11, 2 GAN f1 score: 0.121 GAN cohens kappa score: 0.093 -> 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: 0.195 -> test with 'GB' GB tn, fp: 479, 12 GB fn, tp: 7, 6 GB f1 score: 0.387 GB cohens kappa score: 0.368 -> test with 'KNN' KNN tn, fp: 377, 114 KNN fn, tp: 2, 11 KNN f1 score: 0.159 KNN cohens kappa score: 0.118 ====== 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 GAN.predict GAN tn, fp: 379, 114 GAN fn, tp: 8, 7 GAN f1 score: 0.103 GAN cohens kappa score: 0.053 -> test with 'LR' LR tn, fp: 431, 62 LR fn, tp: 3, 12 LR f1 score: 0.270 LR cohens kappa score: 0.232 LR average precision score: 0.302 -> test with 'GB' GB tn, fp: 475, 18 GB fn, tp: 10, 5 GB f1 score: 0.263 GB cohens kappa score: 0.236 -> test with 'KNN' KNN tn, fp: 395, 98 KNN fn, tp: 9, 6 KNN f1 score: 0.101 KNN cohens kappa score: 0.052 ------ Step 3/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1912 synthetic samples -> test with GAN.predict GAN tn, fp: 463, 30 GAN fn, tp: 11, 4 GAN f1 score: 0.163 GAN cohens kappa score: 0.128 -> test with 'LR' LR tn, fp: 430, 63 LR fn, tp: 3, 12 LR f1 score: 0.267 LR cohens kappa score: 0.229 LR average precision score: 0.147 -> 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: 391, 102 KNN fn, tp: 7, 8 KNN f1 score: 0.128 KNN cohens kappa score: 0.080 ------ Step 3/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1912 synthetic samples -> test with GAN.predict GAN tn, fp: 411, 82 GAN fn, tp: 5, 10 GAN f1 score: 0.187 GAN cohens kappa score: 0.143 -> test with 'LR' LR tn, fp: 449, 44 LR fn, tp: 4, 11 LR f1 score: 0.314 LR cohens kappa score: 0.281 LR average precision score: 0.188 -> test with 'GB' GB tn, fp: 477, 16 GB fn, tp: 9, 6 GB f1 score: 0.324 GB cohens kappa score: 0.300 -> test with 'KNN' KNN tn, fp: 398, 95 KNN fn, tp: 10, 5 KNN f1 score: 0.087 KNN cohens kappa score: 0.038 ------ Step 3/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1912 synthetic samples -> test with GAN.predict GAN tn, fp: 482, 11 GAN fn, tp: 15, 0 GAN f1 score: 0.000 GAN cohens kappa score: -0.026 -> test with 'LR' LR tn, fp: 448, 45 LR fn, tp: 5, 10 LR f1 score: 0.286 LR cohens kappa score: 0.251 LR average precision score: 0.165 -> test with 'GB' GB tn, fp: 477, 16 GB fn, tp: 9, 6 GB f1 score: 0.324 GB cohens kappa score: 0.300 -> test with 'KNN' KNN tn, fp: 394, 99 KNN fn, tp: 9, 6 KNN f1 score: 0.100 KNN cohens kappa score: 0.051 ------ Step 3/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1912 synthetic samples -> test with GAN.predict GAN tn, fp: 143, 348 GAN fn, tp: 0, 13 GAN f1 score: 0.070 GAN cohens kappa score: 0.021 -> test with 'LR' LR tn, fp: 424, 67 LR fn, tp: 2, 11 LR f1 score: 0.242 LR cohens kappa score: 0.207 LR average precision score: 0.367 -> 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: 396, 95 KNN fn, tp: 3, 10 KNN f1 score: 0.169 KNN cohens kappa score: 0.130 ====== 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 GAN.predict GAN tn, fp: 490, 3 GAN fn, tp: 15, 0 GAN f1 score: 0.000 GAN cohens kappa score: -0.010 -> test with 'LR' LR tn, fp: 432, 61 LR fn, tp: 4, 11 LR f1 score: 0.253 LR cohens kappa score: 0.214 LR average precision score: 0.270 -> test with 'GB' GB tn, fp: 478, 15 GB fn, tp: 10, 5 GB f1 score: 0.286 GB cohens kappa score: 0.261 -> test with 'KNN' KNN tn, fp: 399, 94 KNN fn, tp: 9, 6 KNN f1 score: 0.104 KNN cohens kappa score: 0.056 ------ Step 4/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1912 synthetic samples -> test with GAN.predict GAN tn, fp: 456, 37 GAN fn, tp: 11, 4 GAN f1 score: 0.143 GAN cohens kappa score: 0.104 -> test with 'LR' LR tn, fp: 439, 54 LR fn, tp: 4, 11 LR f1 score: 0.275 LR cohens kappa score: 0.238 LR average precision score: 0.238 -> test with 'GB' GB tn, fp: 478, 15 GB fn, tp: 7, 8 GB f1 score: 0.421 GB cohens kappa score: 0.400 -> test with 'KNN' KNN tn, fp: 406, 87 KNN fn, tp: 9, 6 KNN f1 score: 0.111 KNN cohens kappa score: 0.063 ------ Step 4/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1912 synthetic samples -> test with GAN.predict GAN tn, fp: 452, 41 GAN fn, tp: 10, 5 GAN f1 score: 0.164 GAN cohens kappa score: 0.125 -> test with 'LR' LR tn, fp: 433, 60 LR fn, tp: 3, 12 LR f1 score: 0.276 LR cohens kappa score: 0.239 LR average precision score: 0.193 -> 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: 412, 81 KNN fn, tp: 11, 4 KNN f1 score: 0.080 KNN cohens kappa score: 0.031 ------ Step 4/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1912 synthetic samples -> test with GAN.predict GAN tn, fp: 449, 44 GAN fn, tp: 9, 6 GAN f1 score: 0.185 GAN cohens kappa score: 0.146 -> test with 'LR' LR tn, fp: 435, 58 LR fn, tp: 3, 12 LR f1 score: 0.282 LR cohens kappa score: 0.246 LR average precision score: 0.283 -> test with 'GB' GB tn, fp: 477, 16 GB fn, tp: 8, 7 GB f1 score: 0.368 GB cohens kappa score: 0.345 -> test with 'KNN' KNN tn, fp: 395, 98 KNN fn, tp: 9, 6 KNN f1 score: 0.101 KNN cohens kappa score: 0.052 ------ Step 4/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1912 synthetic samples -> test with GAN.predict GAN tn, fp: 338, 153 GAN fn, tp: 4, 9 GAN f1 score: 0.103 GAN cohens kappa score: 0.058 -> test with 'LR' LR tn, fp: 422, 69 LR fn, tp: 4, 9 LR f1 score: 0.198 LR cohens kappa score: 0.161 LR average precision score: 0.212 -> test with 'GB' GB tn, fp: 474, 17 GB fn, tp: 7, 6 GB f1 score: 0.333 GB cohens kappa score: 0.311 -> test with 'KNN' KNN tn, fp: 393, 98 KNN fn, tp: 6, 7 KNN f1 score: 0.119 KNN cohens kappa score: 0.076 ====== 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 GAN.predict GAN tn, fp: 17, 476 GAN fn, tp: 0, 15 GAN f1 score: 0.059 GAN cohens kappa score: 0.002 -> test with 'LR' LR tn, fp: 441, 52 LR fn, tp: 2, 13 LR f1 score: 0.325 LR cohens kappa score: 0.291 LR average precision score: 0.266 -> 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: 383, 110 KNN fn, tp: 11, 4 KNN f1 score: 0.062 KNN cohens kappa score: 0.010 ------ Step 5/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1912 synthetic samples -> test with GAN.predict GAN tn, fp: 423, 70 GAN fn, tp: 4, 11 GAN f1 score: 0.229 GAN cohens kappa score: 0.189 -> test with 'LR' LR tn, fp: 422, 71 LR fn, tp: 3, 12 LR f1 score: 0.245 LR cohens kappa score: 0.205 LR average precision score: 0.159 -> test with 'GB' GB tn, fp: 476, 17 GB fn, tp: 10, 5 GB f1 score: 0.270 GB cohens kappa score: 0.244 -> test with 'KNN' KNN tn, fp: 410, 83 KNN fn, tp: 11, 4 KNN f1 score: 0.078 KNN cohens kappa score: 0.030 ------ Step 5/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1912 synthetic samples -> test with GAN.predict GAN tn, fp: 455, 38 GAN fn, tp: 9, 6 GAN f1 score: 0.203 GAN cohens kappa score: 0.167 -> test with 'LR' LR tn, fp: 455, 38 LR fn, tp: 6, 9 LR f1 score: 0.290 LR cohens kappa score: 0.257 LR average precision score: 0.191 -> test with 'GB' GB tn, fp: 482, 11 GB fn, tp: 11, 4 GB f1 score: 0.267 GB cohens kappa score: 0.244 -> test with 'KNN' KNN tn, fp: 363, 130 KNN fn, tp: 5, 10 KNN f1 score: 0.129 KNN cohens kappa score: 0.080 ------ Step 5/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1912 synthetic samples -> test with GAN.predict GAN tn, fp: 387, 106 GAN fn, tp: 3, 12 GAN f1 score: 0.180 GAN cohens kappa score: 0.135 -> test with 'LR' LR tn, fp: 425, 68 LR fn, tp: 2, 13 LR f1 score: 0.271 LR cohens kappa score: 0.233 LR average precision score: 0.242 -> test with 'GB' GB tn, fp: 474, 19 GB fn, tp: 6, 9 GB f1 score: 0.419 GB cohens kappa score: 0.395 -> test with 'KNN' KNN tn, fp: 404, 89 KNN fn, tp: 8, 7 KNN f1 score: 0.126 KNN cohens kappa score: 0.079 ------ Step 5/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1912 synthetic samples -> test with GAN.predict GAN tn, fp: 232, 259 GAN fn, tp: 0, 13 GAN f1 score: 0.091 GAN cohens kappa score: 0.044 -> test with 'LR' LR tn, fp: 421, 70 LR fn, tp: 2, 11 LR f1 score: 0.234 LR cohens kappa score: 0.198 LR average precision score: 0.277 -> test with 'GB' GB tn, fp: 476, 15 GB fn, tp: 9, 4 GB f1 score: 0.250 GB cohens kappa score: 0.226 -> test with 'KNN' KNN tn, fp: 383, 108 KNN fn, tp: 6, 7 KNN f1 score: 0.109 KNN cohens kappa score: 0.066 ### Exercise is done. -----[ LR ]----- maximum: LR tn, fp: 455, 71 LR fn, tp: 6, 14 LR f1 score: 0.325 LR cohens kappa score: 0.291 LR average precision score: 0.413 average: LR tn, fp: 432.8, 59.8 LR fn, tp: 3.44, 11.16 LR f1 score: 0.262 LR cohens kappa score: 0.225 LR average precision score: 0.233 minimum: LR tn, fp: 421, 38 LR fn, tp: 1, 9 LR f1 score: 0.198 LR cohens kappa score: 0.161 LR average precision score: 0.119 -----[ GB ]----- maximum: GB tn, fp: 485, 19 GB fn, tp: 12, 9 GB f1 score: 0.516 GB cohens kappa score: 0.501 average: GB tn, fp: 478.44, 14.16 GB fn, tp: 8.4, 6.2 GB f1 score: 0.353 GB cohens kappa score: 0.331 minimum: GB tn, fp: 474, 8 GB fn, tp: 6, 3 GB f1 score: 0.214 GB cohens kappa score: 0.192 -----[ KNN ]----- maximum: KNN tn, fp: 446, 130 KNN fn, tp: 13, 11 KNN f1 score: 0.169 KNN cohens kappa score: 0.130 average: KNN tn, fp: 400.48, 92.12 KNN fn, tp: 8.44, 6.16 KNN f1 score: 0.107 KNN cohens kappa score: 0.060 minimum: KNN tn, fp: 363, 47 KNN fn, tp: 2, 2 KNN f1 score: 0.062 KNN cohens kappa score: 0.010 -----[ GAN ]----- maximum: GAN tn, fp: 492, 476 GAN fn, tp: 15, 15 GAN f1 score: 0.229 GAN cohens kappa score: 0.189 average: GAN tn, fp: 354.88, 137.72 GAN fn, tp: 6.92, 7.68 GAN f1 score: 0.109 GAN cohens kappa score: 0.068 minimum: GAN tn, fp: 17, 1 GAN fn, tp: 0, 0 GAN f1 score: 0.000 GAN cohens kappa score: -0.026