/////////////////////////////////////////// // Running convGAN on folding_flare-F /////////////////////////////////////////// Load 'data_input/folding_flare-F' from pickle file non empty cut in data_input/folding_flare-F! (23 points) 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 784 synthetic samples -> test with 'LR' LR tn, fp: 174, 31 LR fn, tp: 6, 3 LR f1 score: 0.140 LR cohens kappa score: 0.078 LR average precision score: 0.088 -> test with 'GB' GB tn, fp: 198, 7 GB fn, tp: 8, 1 GB f1 score: 0.118 GB cohens kappa score: 0.081 -> test with 'KNN' KNN tn, fp: 179, 26 KNN fn, tp: 4, 5 KNN f1 score: 0.250 KNN cohens kappa score: 0.198 ------ Step 1/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 784 synthetic samples -> test with 'LR' LR tn, fp: 155, 50 LR fn, tp: 0, 9 LR f1 score: 0.265 LR cohens kappa score: 0.207 LR average precision score: 0.406 -> test with 'GB' GB tn, fp: 203, 2 GB fn, tp: 8, 1 GB f1 score: 0.167 GB cohens kappa score: 0.149 -> test with 'KNN' KNN tn, fp: 171, 34 KNN fn, tp: 3, 6 KNN f1 score: 0.245 KNN cohens kappa score: 0.189 ------ Step 1/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 784 synthetic samples -> test with 'LR' LR tn, fp: 176, 29 LR fn, tp: 4, 5 LR f1 score: 0.233 LR cohens kappa score: 0.178 LR average precision score: 0.275 -> test with 'GB' GB tn, fp: 203, 2 GB fn, tp: 7, 2 GB f1 score: 0.308 GB cohens kappa score: 0.289 -> test with 'KNN' KNN tn, fp: 177, 28 KNN fn, tp: 4, 5 KNN f1 score: 0.238 KNN cohens kappa score: 0.184 ------ Step 1/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 784 synthetic samples -> test with 'LR' LR tn, fp: 181, 24 LR fn, tp: 0, 9 LR f1 score: 0.429 LR cohens kappa score: 0.388 LR average precision score: 0.680 -> test with 'GB' GB tn, fp: 204, 1 GB fn, tp: 7, 2 GB f1 score: 0.333 GB cohens kappa score: 0.319 -> test with 'KNN' KNN tn, fp: 183, 22 KNN fn, tp: 3, 6 KNN f1 score: 0.324 KNN cohens kappa score: 0.278 ------ Step 1/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 784 synthetic samples -> test with 'LR' LR tn, fp: 170, 33 LR fn, tp: 2, 5 LR f1 score: 0.222 LR cohens kappa score: 0.176 LR average precision score: 0.205 -> test with 'GB' GB tn, fp: 200, 3 GB fn, tp: 6, 1 GB f1 score: 0.182 GB cohens kappa score: 0.161 -> test with 'KNN' KNN tn, fp: 173, 30 KNN fn, tp: 2, 5 KNN f1 score: 0.238 KNN cohens kappa score: 0.193 ====== Step 2/5 ======= -> Shuffling data -> Spliting data to slices ------ Step 2/5: Slice 1/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 784 synthetic samples -> test with 'LR' LR tn, fp: 168, 37 LR fn, tp: 2, 7 LR f1 score: 0.264 LR cohens kappa score: 0.209 LR average precision score: 0.410 -> test with 'GB' GB tn, fp: 203, 2 GB fn, tp: 8, 1 GB f1 score: 0.167 GB cohens kappa score: 0.149 -> test with 'KNN' KNN tn, fp: 177, 28 KNN fn, tp: 2, 7 KNN f1 score: 0.318 KNN cohens kappa score: 0.269 ------ Step 2/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 784 synthetic samples -> test with 'LR' LR tn, fp: 169, 36 LR fn, tp: 3, 6 LR f1 score: 0.235 LR cohens kappa score: 0.178 LR average precision score: 0.365 -> test with 'GB' GB tn, fp: 203, 2 GB fn, tp: 9, 0 GB f1 score: 0.000 GB cohens kappa score: -0.016 -> test with 'KNN' KNN tn, fp: 174, 31 KNN fn, tp: 2, 7 KNN f1 score: 0.298 KNN cohens kappa score: 0.247 ------ Step 2/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 784 synthetic samples -> test with 'LR' LR tn, fp: 175, 30 LR fn, tp: 3, 6 LR f1 score: 0.267 LR cohens kappa score: 0.214 LR average precision score: 0.265 -> test with 'GB' GB tn, fp: 203, 2 GB fn, tp: 8, 1 GB f1 score: 0.167 GB cohens kappa score: 0.149 -> test with 'KNN' KNN tn, fp: 181, 24 KNN fn, tp: 4, 5 KNN f1 score: 0.263 KNN cohens kappa score: 0.213 ------ Step 2/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 784 synthetic samples -> test with 'LR' LR tn, fp: 184, 21 LR fn, tp: 4, 5 LR f1 score: 0.286 LR cohens kappa score: 0.238 LR average precision score: 0.308 -> test with 'GB' GB tn, fp: 204, 1 GB fn, tp: 8, 1 GB f1 score: 0.182 GB cohens kappa score: 0.169 -> test with 'KNN' KNN tn, fp: 179, 26 KNN fn, tp: 3, 6 KNN f1 score: 0.293 KNN cohens kappa score: 0.243 ------ Step 2/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 784 synthetic samples -> test with 'LR' LR tn, fp: 173, 30 LR fn, tp: 0, 7 LR f1 score: 0.318 LR cohens kappa score: 0.278 LR average precision score: 0.371 -> test with 'GB' GB tn, fp: 201, 2 GB fn, tp: 6, 1 GB f1 score: 0.200 GB cohens kappa score: 0.184 -> test with 'KNN' KNN tn, fp: 172, 31 KNN fn, tp: 1, 6 KNN f1 score: 0.273 KNN cohens kappa score: 0.230 ====== Step 3/5 ======= -> Shuffling data -> Spliting data to slices ------ Step 3/5: Slice 1/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 784 synthetic samples -> test with 'LR' LR tn, fp: 185, 20 LR fn, tp: 2, 7 LR f1 score: 0.389 LR cohens kappa score: 0.348 LR average precision score: 0.734 -> test with 'GB' GB tn, fp: 205, 0 GB fn, tp: 8, 1 GB f1 score: 0.200 GB cohens kappa score: 0.193 -> test with 'KNN' KNN tn, fp: 193, 12 KNN fn, tp: 3, 6 KNN f1 score: 0.444 KNN cohens kappa score: 0.411 ------ Step 3/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 784 synthetic samples -> test with 'LR' LR tn, fp: 164, 41 LR fn, tp: 2, 7 LR f1 score: 0.246 LR cohens kappa score: 0.188 LR average precision score: 0.223 -> test with 'GB' GB tn, fp: 194, 11 GB fn, tp: 5, 4 GB f1 score: 0.333 GB cohens kappa score: 0.296 -> test with 'KNN' KNN tn, fp: 166, 39 KNN fn, tp: 4, 5 KNN f1 score: 0.189 KNN cohens kappa score: 0.128 ------ Step 3/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 784 synthetic samples -> test with 'LR' LR tn, fp: 169, 36 LR fn, tp: 2, 7 LR f1 score: 0.269 LR cohens kappa score: 0.215 LR average precision score: 0.447 -> test with 'GB' GB tn, fp: 204, 1 GB fn, tp: 9, 0 GB f1 score: 0.000 GB cohens kappa score: -0.008 -> test with 'KNN' KNN tn, fp: 177, 28 KNN fn, tp: 2, 7 KNN f1 score: 0.318 KNN cohens kappa score: 0.269 ------ Step 3/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 784 synthetic samples -> test with 'LR' LR tn, fp: 179, 26 LR fn, tp: 4, 5 LR f1 score: 0.250 LR cohens kappa score: 0.198 LR average precision score: 0.327 -> test with 'GB' GB tn, fp: 205, 0 GB fn, tp: 9, 0 GB f1 score: 0.000 GB cohens kappa score: 0.000 -> test with 'KNN' KNN tn, fp: 176, 29 KNN fn, tp: 3, 6 KNN f1 score: 0.273 KNN cohens kappa score: 0.221 ------ Step 3/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 784 synthetic samples -> test with 'LR' LR tn, fp: 159, 44 LR fn, tp: 1, 6 LR f1 score: 0.211 LR cohens kappa score: 0.161 LR average precision score: 0.239 -> test with 'GB' GB tn, fp: 197, 6 GB fn, tp: 6, 1 GB f1 score: 0.143 GB cohens kappa score: 0.113 -> test with 'KNN' KNN tn, fp: 179, 24 KNN fn, tp: 4, 3 KNN f1 score: 0.176 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 784 synthetic samples -> test with 'LR' LR tn, fp: 174, 31 LR fn, tp: 1, 8 LR f1 score: 0.333 LR cohens kappa score: 0.284 LR average precision score: 0.205 -> test with 'GB' GB tn, fp: 202, 3 GB fn, tp: 9, 0 GB f1 score: 0.000 GB cohens kappa score: -0.021 -> test with 'KNN' KNN tn, fp: 173, 32 KNN fn, tp: 4, 5 KNN f1 score: 0.217 KNN cohens kappa score: 0.161 ------ Step 4/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 784 synthetic samples -> test with 'LR' LR tn, fp: 178, 27 LR fn, tp: 2, 7 LR f1 score: 0.326 LR cohens kappa score: 0.278 LR average precision score: 0.534 -> test with 'GB' GB tn, fp: 205, 0 GB fn, tp: 9, 0 GB f1 score: 0.000 GB cohens kappa score: 0.000 -> test with 'KNN' KNN tn, fp: 180, 25 KNN fn, tp: 4, 5 KNN f1 score: 0.256 KNN cohens kappa score: 0.205 ------ Step 4/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 784 synthetic samples -> test with 'LR' LR tn, fp: 169, 36 LR fn, tp: 4, 5 LR f1 score: 0.200 LR cohens kappa score: 0.141 LR average precision score: 0.228 -> test with 'GB' GB tn, fp: 203, 2 GB fn, tp: 8, 1 GB f1 score: 0.167 GB cohens kappa score: 0.149 -> test with 'KNN' KNN tn, fp: 178, 27 KNN fn, tp: 4, 5 KNN f1 score: 0.244 KNN cohens kappa score: 0.191 ------ Step 4/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 784 synthetic samples -> test with 'LR' LR tn, fp: 170, 35 LR fn, tp: 0, 9 LR f1 score: 0.340 LR cohens kappa score: 0.290 LR average precision score: 0.359 -> test with 'GB' GB tn, fp: 202, 3 GB fn, tp: 7, 2 GB f1 score: 0.286 GB cohens kappa score: 0.264 -> test with 'KNN' KNN tn, fp: 177, 28 KNN fn, tp: 2, 7 KNN f1 score: 0.318 KNN cohens kappa score: 0.269 ------ Step 4/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 784 synthetic samples -> test with 'LR' LR tn, fp: 170, 33 LR fn, tp: 1, 6 LR f1 score: 0.261 LR cohens kappa score: 0.217 LR average precision score: 0.549 -> test with 'GB' GB tn, fp: 202, 1 GB fn, tp: 6, 1 GB f1 score: 0.222 GB cohens kappa score: 0.211 -> test with 'KNN' KNN tn, fp: 168, 35 KNN fn, tp: 3, 4 KNN f1 score: 0.174 KNN cohens kappa score: 0.124 ====== Step 5/5 ======= -> Shuffling data -> Spliting data to slices ------ Step 5/5: Slice 1/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 784 synthetic samples -> test with 'LR' LR tn, fp: 180, 25 LR fn, tp: 4, 5 LR f1 score: 0.256 LR cohens kappa score: 0.205 LR average precision score: 0.205 -> test with 'GB' GB tn, fp: 202, 3 GB fn, tp: 8, 1 GB f1 score: 0.154 GB cohens kappa score: 0.131 -> test with 'KNN' KNN tn, fp: 179, 26 KNN fn, tp: 3, 6 KNN f1 score: 0.293 KNN cohens kappa score: 0.243 ------ Step 5/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 784 synthetic samples -> test with 'LR' LR tn, fp: 173, 32 LR fn, tp: 2, 7 LR f1 score: 0.292 LR cohens kappa score: 0.240 LR average precision score: 0.485 -> test with 'GB' GB tn, fp: 205, 0 GB fn, tp: 9, 0 GB f1 score: 0.000 GB cohens kappa score: 0.000 -> test with 'KNN' KNN tn, fp: 176, 29 KNN fn, tp: 3, 6 KNN f1 score: 0.273 KNN cohens kappa score: 0.221 ------ Step 5/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 784 synthetic samples -> test with 'LR' LR tn, fp: 178, 27 LR fn, tp: 0, 9 LR f1 score: 0.400 LR cohens kappa score: 0.357 LR average precision score: 0.433 -> test with 'GB' GB tn, fp: 205, 0 GB fn, tp: 7, 2 GB f1 score: 0.364 GB cohens kappa score: 0.354 -> test with 'KNN' KNN tn, fp: 173, 32 KNN fn, tp: 3, 6 KNN f1 score: 0.255 KNN cohens kappa score: 0.201 ------ Step 5/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 784 synthetic samples -> test with 'LR' LR tn, fp: 178, 27 LR fn, tp: 4, 5 LR f1 score: 0.244 LR cohens kappa score: 0.191 LR average precision score: 0.205 -> test with 'GB' GB tn, fp: 203, 2 GB fn, tp: 9, 0 GB f1 score: 0.000 GB cohens kappa score: -0.016 -> test with 'KNN' KNN tn, fp: 188, 17 KNN fn, tp: 4, 5 KNN f1 score: 0.323 KNN cohens kappa score: 0.280 ------ Step 5/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 784 synthetic samples -> test with 'LR' LR tn, fp: 163, 40 LR fn, tp: 2, 5 LR f1 score: 0.192 LR cohens kappa score: 0.143 LR average precision score: 0.343 -> test with 'GB' GB tn, fp: 202, 1 GB fn, tp: 5, 2 GB f1 score: 0.400 GB cohens kappa score: 0.388 -> test with 'KNN' KNN tn, fp: 172, 31 KNN fn, tp: 3, 4 KNN f1 score: 0.190 KNN cohens kappa score: 0.143 ### Exercise is done. -----[ LR ]----- maximum: LR tn, fp: 185, 50 LR fn, tp: 6, 9 LR f1 score: 0.429 LR cohens kappa score: 0.388 LR average precision score: 0.734 average: LR tn, fp: 172.56, 32.04 LR fn, tp: 2.2, 6.4 LR f1 score: 0.275 LR cohens kappa score: 0.224 LR average precision score: 0.356 minimum: LR tn, fp: 155, 20 LR fn, tp: 0, 3 LR f1 score: 0.140 LR cohens kappa score: 0.078 LR average precision score: 0.088 -----[ GB ]----- maximum: GB tn, fp: 205, 11 GB fn, tp: 9, 4 GB f1 score: 0.400 GB cohens kappa score: 0.388 average: GB tn, fp: 202.32, 2.28 GB fn, tp: 7.56, 1.04 GB f1 score: 0.164 GB cohens kappa score: 0.148 minimum: GB tn, fp: 194, 0 GB fn, tp: 5, 0 GB f1 score: 0.000 GB cohens kappa score: -0.021 -----[ KNN ]----- maximum: KNN tn, fp: 193, 39 KNN fn, tp: 4, 7 KNN f1 score: 0.444 KNN cohens kappa score: 0.411 average: KNN tn, fp: 176.84, 27.76 KNN fn, tp: 3.08, 5.52 KNN f1 score: 0.267 KNN cohens kappa score: 0.218 minimum: KNN tn, fp: 166, 12 KNN fn, tp: 1, 3 KNN f1 score: 0.174 KNN cohens kappa score: 0.124