/////////////////////////////////////////// // Running convGAN-proximary-full 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: 399, 94 GAN fn, tp: 1, 14 GAN f1 score: 0.228 GAN cohens kappa score: 0.185 -> test with 'LR' LR tn, fp: 435, 58 LR fn, tp: 1, 14 LR f1 score: 0.322 LR cohens kappa score: 0.287 LR average precision score: 0.357 -> test with 'GB' GB tn, fp: 477, 16 GB fn, tp: 10, 5 GB f1 score: 0.278 GB cohens kappa score: 0.252 -> test with 'KNN' KNN tn, fp: 384, 109 KNN fn, tp: 9, 6 KNN f1 score: 0.092 KNN cohens kappa score: 0.042 ------ 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: 329, 164 GAN fn, tp: 4, 11 GAN f1 score: 0.116 GAN cohens kappa score: 0.065 -> test with 'LR' LR tn, fp: 436, 57 LR fn, tp: 4, 11 LR f1 score: 0.265 LR cohens kappa score: 0.228 LR average precision score: 0.222 -> test with 'GB' GB tn, fp: 487, 6 GB fn, tp: 7, 8 GB f1 score: 0.552 GB cohens kappa score: 0.539 -> 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 1/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1912 synthetic samples -> test with GAN.predict GAN tn, fp: 460, 33 GAN fn, tp: 8, 7 GAN f1 score: 0.255 GAN cohens kappa score: 0.221 -> test with 'LR' LR tn, fp: 437, 56 LR fn, tp: 5, 10 LR f1 score: 0.247 LR cohens kappa score: 0.209 LR average precision score: 0.136 -> test with 'GB' GB tn, fp: 477, 16 GB fn, tp: 10, 5 GB f1 score: 0.278 GB cohens kappa score: 0.252 -> 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 1/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1912 synthetic samples -> test with GAN.predict GAN tn, fp: 402, 91 GAN fn, tp: 3, 12 GAN f1 score: 0.203 GAN cohens kappa score: 0.160 -> test with 'LR' LR tn, fp: 427, 66 LR fn, tp: 5, 10 LR f1 score: 0.220 LR cohens kappa score: 0.179 LR average precision score: 0.219 -> test with 'GB' GB tn, fp: 475, 18 GB fn, tp: 8, 7 GB f1 score: 0.350 GB cohens kappa score: 0.325 -> test with 'KNN' KNN tn, fp: 403, 90 KNN fn, tp: 8, 7 KNN f1 score: 0.125 KNN cohens kappa score: 0.078 ------ 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: 376, 115 GAN fn, tp: 3, 10 GAN f1 score: 0.145 GAN cohens kappa score: 0.103 -> test with 'LR' LR tn, fp: 440, 51 LR fn, tp: 3, 10 LR f1 score: 0.270 LR cohens kappa score: 0.238 LR average precision score: 0.170 -> test with 'GB' GB tn, fp: 479, 12 GB fn, tp: 9, 4 GB f1 score: 0.276 GB cohens kappa score: 0.255 -> test with 'KNN' KNN tn, fp: 397, 94 KNN fn, tp: 9, 4 KNN f1 score: 0.072 KNN cohens kappa score: 0.028 ====== 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: 328, 165 GAN fn, tp: 2, 13 GAN f1 score: 0.135 GAN cohens kappa score: 0.085 -> test with 'LR' LR tn, fp: 442, 51 LR fn, tp: 5, 10 LR f1 score: 0.263 LR cohens kappa score: 0.226 LR average precision score: 0.355 -> test with 'GB' GB tn, fp: 484, 9 GB fn, tp: 12, 3 GB f1 score: 0.222 GB cohens kappa score: 0.201 -> test with 'KNN' KNN tn, fp: 400, 93 KNN fn, tp: 7, 8 KNN f1 score: 0.138 KNN cohens kappa score: 0.091 ------ 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: 324, 169 GAN fn, tp: 3, 12 GAN f1 score: 0.122 GAN cohens kappa score: 0.072 -> test with 'LR' LR tn, fp: 448, 45 LR fn, tp: 6, 9 LR f1 score: 0.261 LR cohens kappa score: 0.225 LR average precision score: 0.204 -> 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: 405, 88 KNN fn, tp: 7, 8 KNN f1 score: 0.144 KNN cohens kappa score: 0.098 ------ 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: 386, 107 GAN fn, tp: 0, 15 GAN f1 score: 0.219 GAN cohens kappa score: 0.176 -> test with 'LR' LR tn, fp: 434, 59 LR fn, tp: 1, 14 LR f1 score: 0.318 LR cohens kappa score: 0.283 LR average precision score: 0.442 -> test with 'GB' GB tn, fp: 486, 7 GB fn, tp: 8, 7 GB f1 score: 0.483 GB cohens kappa score: 0.468 -> test with 'KNN' KNN tn, fp: 396, 97 KNN fn, tp: 13, 2 KNN f1 score: 0.035 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: 384, 109 GAN fn, tp: 7, 8 GAN f1 score: 0.121 GAN cohens kappa score: 0.073 -> test with 'LR' LR tn, fp: 438, 55 LR fn, tp: 5, 10 LR f1 score: 0.250 LR cohens kappa score: 0.212 LR average precision score: 0.158 -> test with 'GB' GB tn, fp: 484, 9 GB fn, tp: 9, 6 GB f1 score: 0.400 GB cohens kappa score: 0.382 -> test with 'KNN' KNN tn, fp: 423, 70 KNN fn, tp: 11, 4 KNN f1 score: 0.090 KNN cohens kappa score: 0.043 ------ 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: 384, 107 GAN fn, tp: 0, 13 GAN f1 score: 0.195 GAN cohens kappa score: 0.156 -> test with 'LR' LR tn, fp: 432, 59 LR fn, tp: 3, 10 LR f1 score: 0.244 LR cohens kappa score: 0.210 LR average precision score: 0.210 -> test with 'GB' GB tn, fp: 477, 14 GB fn, tp: 8, 5 GB f1 score: 0.312 GB cohens kappa score: 0.291 -> test with 'KNN' KNN tn, fp: 398, 93 KNN fn, tp: 7, 6 KNN f1 score: 0.107 KNN cohens kappa score: 0.064 ====== 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: 340, 153 GAN fn, tp: 2, 13 GAN f1 score: 0.144 GAN cohens kappa score: 0.095 -> 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: 0.327 -> test with 'GB' GB tn, fp: 478, 15 GB fn, tp: 11, 4 GB f1 score: 0.235 GB cohens kappa score: 0.209 -> test with 'KNN' KNN tn, fp: 406, 87 KNN fn, tp: 12, 3 KNN f1 score: 0.057 KNN cohens kappa score: 0.007 ------ 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: 436, 57 GAN fn, tp: 4, 11 GAN f1 score: 0.265 GAN cohens kappa score: 0.228 -> test with 'LR' LR tn, fp: 441, 52 LR fn, tp: 5, 10 LR f1 score: 0.260 LR cohens kappa score: 0.223 LR average precision score: 0.135 -> test with 'GB' GB tn, fp: 479, 14 GB fn, tp: 9, 6 GB f1 score: 0.343 GB cohens kappa score: 0.320 -> test with 'KNN' KNN tn, fp: 413, 80 KNN fn, tp: 10, 5 KNN f1 score: 0.100 KNN cohens kappa score: 0.052 ------ 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: 470, 23 GAN fn, tp: 8, 7 GAN f1 score: 0.311 GAN cohens kappa score: 0.283 -> test with 'LR' LR tn, fp: 454, 39 LR fn, tp: 5, 10 LR f1 score: 0.312 LR cohens kappa score: 0.280 LR average precision score: 0.183 -> 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: 393, 100 KNN fn, tp: 7, 8 KNN f1 score: 0.130 KNN cohens kappa score: 0.083 ------ 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: 357, 136 GAN fn, tp: 2, 13 GAN f1 score: 0.159 GAN cohens kappa score: 0.111 -> test with 'LR' LR tn, fp: 437, 56 LR fn, tp: 4, 11 LR f1 score: 0.268 LR cohens kappa score: 0.231 LR average precision score: 0.189 -> test with 'GB' GB tn, fp: 483, 10 GB fn, tp: 9, 6 GB f1 score: 0.387 GB cohens kappa score: 0.368 -> test with 'KNN' KNN tn, fp: 401, 92 KNN fn, tp: 11, 4 KNN f1 score: 0.072 KNN cohens kappa score: 0.022 ------ 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: 403, 88 GAN fn, tp: 5, 8 GAN f1 score: 0.147 GAN cohens kappa score: 0.106 -> test with 'LR' LR tn, fp: 432, 59 LR fn, tp: 3, 10 LR f1 score: 0.244 LR cohens kappa score: 0.210 LR average precision score: 0.358 -> test with 'GB' GB tn, fp: 472, 19 GB fn, tp: 7, 6 GB f1 score: 0.316 GB cohens kappa score: 0.292 -> test with 'KNN' KNN tn, fp: 380, 111 KNN fn, tp: 7, 6 KNN f1 score: 0.092 KNN cohens kappa score: 0.048 ====== 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: 392, 101 GAN fn, tp: 4, 11 GAN f1 score: 0.173 GAN cohens kappa score: 0.128 -> test with 'LR' LR tn, fp: 426, 67 LR fn, tp: 3, 12 LR f1 score: 0.255 LR cohens kappa score: 0.216 LR average precision score: 0.282 -> test with 'GB' GB tn, fp: 472, 21 GB fn, tp: 9, 6 GB f1 score: 0.286 GB cohens kappa score: 0.258 -> test with 'KNN' KNN tn, fp: 391, 102 KNN fn, tp: 11, 4 KNN f1 score: 0.066 KNN cohens kappa score: 0.015 ------ 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: 322, 171 GAN fn, tp: 1, 14 GAN f1 score: 0.140 GAN cohens kappa score: 0.090 -> test with 'LR' LR tn, fp: 445, 48 LR fn, tp: 4, 11 LR f1 score: 0.297 LR cohens kappa score: 0.263 LR average precision score: 0.239 -> 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: 409, 84 KNN fn, tp: 7, 8 KNN f1 score: 0.150 KNN cohens kappa score: 0.104 ------ 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: 476, 17 GAN fn, tp: 12, 3 GAN f1 score: 0.171 GAN cohens kappa score: 0.142 -> test with 'LR' LR tn, fp: 446, 47 LR fn, tp: 3, 12 LR f1 score: 0.324 LR cohens kappa score: 0.291 LR average precision score: 0.193 -> test with 'GB' GB tn, fp: 483, 10 GB fn, tp: 7, 8 GB f1 score: 0.485 GB cohens kappa score: 0.468 -> test with 'KNN' KNN tn, fp: 402, 91 KNN fn, tp: 9, 6 KNN f1 score: 0.107 KNN cohens kappa score: 0.059 ------ 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: 437, 56 GAN fn, tp: 4, 11 GAN f1 score: 0.268 GAN cohens kappa score: 0.231 -> test with 'LR' LR tn, fp: 429, 64 LR fn, tp: 3, 12 LR f1 score: 0.264 LR cohens kappa score: 0.226 LR average precision score: 0.310 -> 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: 387, 106 KNN fn, tp: 8, 7 KNN f1 score: 0.109 KNN cohens kappa score: 0.060 ------ 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: 343, 148 GAN fn, tp: 4, 9 GAN f1 score: 0.106 GAN cohens kappa score: 0.061 -> test with 'LR' LR tn, fp: 432, 59 LR fn, tp: 5, 8 LR f1 score: 0.200 LR cohens kappa score: 0.164 LR average precision score: 0.185 -> 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: 390, 101 KNN fn, tp: 8, 5 KNN f1 score: 0.084 KNN cohens kappa score: 0.040 ====== 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: 451, 42 GAN fn, tp: 6, 9 GAN f1 score: 0.273 GAN cohens kappa score: 0.238 -> test with 'LR' LR tn, fp: 439, 54 LR fn, tp: 2, 13 LR f1 score: 0.317 LR cohens kappa score: 0.282 LR average precision score: 0.291 -> test with 'GB' GB tn, fp: 481, 12 GB fn, tp: 5, 10 GB f1 score: 0.541 GB cohens kappa score: 0.524 -> 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 5/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1912 synthetic samples -> test with GAN.predict GAN tn, fp: 438, 55 GAN fn, tp: 3, 12 GAN f1 score: 0.293 GAN cohens kappa score: 0.257 -> test with 'LR' LR tn, fp: 436, 57 LR fn, tp: 3, 12 LR f1 score: 0.286 LR cohens kappa score: 0.249 LR average precision score: 0.164 -> 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: 401, 92 KNN fn, tp: 11, 4 KNN f1 score: 0.072 KNN cohens kappa score: 0.022 ------ 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: 395, 98 GAN fn, tp: 4, 11 GAN f1 score: 0.177 GAN cohens kappa score: 0.132 -> test with 'LR' LR tn, fp: 453, 40 LR fn, tp: 6, 9 LR f1 score: 0.281 LR cohens kappa score: 0.247 LR average precision score: 0.206 -> test with 'GB' GB tn, fp: 484, 9 GB fn, tp: 12, 3 GB f1 score: 0.222 GB cohens kappa score: 0.201 -> test with 'KNN' KNN tn, fp: 407, 86 KNN fn, tp: 10, 5 KNN f1 score: 0.094 KNN cohens kappa score: 0.046 ------ 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: 373, 120 GAN fn, tp: 2, 13 GAN f1 score: 0.176 GAN cohens kappa score: 0.129 -> 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: 0.217 -> test with 'GB' GB tn, fp: 477, 16 GB fn, tp: 6, 9 GB f1 score: 0.450 GB cohens kappa score: 0.429 -> test with 'KNN' KNN tn, fp: 405, 88 KNN fn, tp: 7, 8 KNN f1 score: 0.144 KNN cohens kappa score: 0.098 ------ 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: 423, 68 GAN fn, tp: 4, 9 GAN f1 score: 0.200 GAN cohens kappa score: 0.163 -> test with 'LR' LR tn, fp: 433, 58 LR fn, tp: 3, 10 LR f1 score: 0.247 LR cohens kappa score: 0.213 LR average precision score: 0.248 -> test with 'GB' GB tn, fp: 483, 8 GB fn, tp: 11, 2 GB f1 score: 0.174 GB cohens kappa score: 0.155 -> test with 'KNN' KNN tn, fp: 412, 79 KNN fn, tp: 7, 6 KNN f1 score: 0.122 KNN cohens kappa score: 0.081 ### Exercise is done. -----[ LR ]----- maximum: LR tn, fp: 454, 67 LR fn, tp: 6, 14 LR f1 score: 0.324 LR cohens kappa score: 0.291 LR average precision score: 0.442 average: LR tn, fp: 437.44, 55.16 LR fn, tp: 3.68, 10.92 LR f1 score: 0.271 LR cohens kappa score: 0.235 LR average precision score: 0.240 minimum: LR tn, fp: 426, 39 LR fn, tp: 1, 8 LR f1 score: 0.200 LR cohens kappa score: 0.164 LR average precision score: 0.135 -----[ GB ]----- maximum: GB tn, fp: 487, 21 GB fn, tp: 12, 10 GB f1 score: 0.552 GB cohens kappa score: 0.539 average: GB tn, fp: 479.32, 13.28 GB fn, tp: 8.72, 5.88 GB f1 score: 0.345 GB cohens kappa score: 0.324 minimum: GB tn, fp: 472, 6 GB fn, tp: 5, 2 GB f1 score: 0.174 GB cohens kappa score: 0.155 -----[ KNN ]----- maximum: KNN tn, fp: 423, 111 KNN fn, tp: 13, 9 KNN f1 score: 0.150 KNN cohens kappa score: 0.104 average: KNN tn, fp: 400.32, 92.28 KNN fn, tp: 8.84, 5.76 KNN f1 score: 0.102 KNN cohens kappa score: 0.055 minimum: KNN tn, fp: 380, 70 KNN fn, tp: 6, 2 KNN f1 score: 0.035 KNN cohens kappa score: -0.017 -----[ GAN ]----- maximum: GAN tn, fp: 476, 171 GAN fn, tp: 12, 15 GAN f1 score: 0.311 GAN cohens kappa score: 0.283 average: GAN tn, fp: 393.12, 99.48 GAN fn, tp: 3.84, 10.76 GAN f1 score: 0.190 GAN cohens kappa score: 0.148 minimum: GAN tn, fp: 322, 17 GAN fn, tp: 0, 3 GAN f1 score: 0.106 GAN cohens kappa score: 0.061