/////////////////////////////////////////// // Running ProWRAS 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: 178, 27 LR fn, tp: 6, 3 LR f1 score: 0.154 LR cohens kappa score: 0.095 LR average precision score: 0.093 -> test with 'RF' RF tn, fp: 196, 9 RF fn, tp: 7, 2 RF f1 score: 0.200 RF cohens kappa score: 0.161 -> test with 'GB' GB tn, fp: 197, 8 GB fn, tp: 8, 1 GB f1 score: 0.111 GB cohens kappa score: 0.072 -> test with 'KNN' KNN tn, fp: 184, 21 KNN fn, tp: 5, 4 KNN f1 score: 0.235 KNN cohens kappa score: 0.185 ------ Step 1/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 784 synthetic samples -> test with 'LR' LR tn, fp: 157, 48 LR fn, tp: 1, 8 LR f1 score: 0.246 LR cohens kappa score: 0.187 LR average precision score: 0.363 -> test with 'RF' RF tn, fp: 201, 4 RF fn, tp: 8, 1 RF f1 score: 0.143 RF cohens kappa score: 0.116 -> test with 'GB' GB tn, fp: 201, 4 GB fn, tp: 7, 2 GB f1 score: 0.267 GB cohens kappa score: 0.241 -> 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 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: 4, 5 LR f1 score: 0.244 LR cohens kappa score: 0.191 LR average precision score: 0.307 -> test with 'RF' RF tn, fp: 203, 2 RF fn, tp: 9, 0 RF f1 score: 0.000 RF cohens kappa score: -0.016 -> 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: 181, 24 KNN fn, tp: 5, 4 KNN f1 score: 0.216 KNN cohens kappa score: 0.163 ------ Step 1/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 784 synthetic samples -> test with 'LR' LR tn, fp: 183, 22 LR fn, tp: 0, 9 LR f1 score: 0.450 LR cohens kappa score: 0.412 LR average precision score: 0.684 -> test with 'RF' RF tn, fp: 204, 1 RF fn, tp: 9, 0 RF f1 score: 0.000 RF cohens kappa score: -0.008 -> 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: 189, 16 KNN fn, tp: 5, 4 KNN f1 score: 0.276 KNN cohens kappa score: 0.231 ------ Step 1/5: 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: 3, 4 LR f1 score: 0.195 LR cohens kappa score: 0.148 LR average precision score: 0.205 -> test with 'RF' RF tn, fp: 198, 5 RF fn, tp: 6, 1 RF f1 score: 0.154 RF cohens kappa score: 0.127 -> 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: 187, 16 KNN fn, tp: 2, 5 KNN f1 score: 0.357 KNN cohens kappa score: 0.323 ====== 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: 170, 35 LR fn, tp: 2, 7 LR f1 score: 0.275 LR cohens kappa score: 0.221 LR average precision score: 0.395 -> test with 'RF' RF tn, fp: 201, 4 RF fn, tp: 8, 1 RF f1 score: 0.143 RF cohens kappa score: 0.116 -> test with 'GB' GB tn, fp: 198, 7 GB fn, tp: 7, 2 GB f1 score: 0.222 GB cohens kappa score: 0.188 -> test with 'KNN' KNN tn, fp: 184, 21 KNN fn, tp: 2, 7 KNN f1 score: 0.378 KNN cohens kappa score: 0.336 ------ Step 2/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 784 synthetic samples -> test with 'LR' LR tn, fp: 172, 33 LR fn, tp: 3, 6 LR f1 score: 0.250 LR cohens kappa score: 0.195 LR average precision score: 0.399 -> test with 'RF' RF tn, fp: 202, 3 RF fn, tp: 9, 0 RF f1 score: 0.000 RF cohens kappa score: -0.021 -> 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: 183, 22 KNN fn, tp: 4, 5 KNN f1 score: 0.278 KNN cohens kappa score: 0.229 ------ Step 2/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 784 synthetic samples -> test with 'LR' LR tn, fp: 177, 28 LR fn, tp: 3, 6 LR f1 score: 0.279 LR cohens kappa score: 0.228 LR average precision score: 0.246 -> test with 'RF' RF tn, fp: 203, 2 RF fn, tp: 8, 1 RF f1 score: 0.167 RF cohens kappa score: 0.149 -> test with 'GB' GB tn, fp: 201, 4 GB fn, tp: 8, 1 GB f1 score: 0.143 GB cohens kappa score: 0.116 -> test with 'KNN' KNN tn, fp: 189, 16 KNN fn, tp: 5, 4 KNN f1 score: 0.276 KNN cohens kappa score: 0.231 ------ 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: 0.315 -> test with 'RF' RF tn, fp: 202, 3 RF fn, tp: 8, 1 RF f1 score: 0.154 RF cohens kappa score: 0.131 -> 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: 190, 15 KNN fn, tp: 6, 3 KNN f1 score: 0.222 KNN cohens kappa score: 0.176 ------ Step 2/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 784 synthetic samples -> test with 'LR' LR tn, fp: 171, 32 LR fn, tp: 0, 7 LR f1 score: 0.304 LR cohens kappa score: 0.263 LR average precision score: 0.287 -> test with 'RF' RF tn, fp: 198, 5 RF fn, tp: 7, 0 RF f1 score: 0.000 RF cohens kappa score: -0.029 -> test with 'GB' GB tn, fp: 199, 4 GB fn, tp: 6, 1 GB f1 score: 0.167 GB cohens kappa score: 0.143 -> test with 'KNN' KNN tn, fp: 184, 19 KNN fn, tp: 2, 5 KNN f1 score: 0.323 KNN cohens kappa score: 0.286 ====== 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: 188, 17 LR fn, tp: 2, 7 LR f1 score: 0.424 LR cohens kappa score: 0.387 LR average precision score: 0.739 -> test with 'RF' RF tn, fp: 204, 1 RF fn, tp: 9, 0 RF f1 score: 0.000 RF cohens kappa score: -0.008 -> 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: 196, 9 KNN fn, tp: 4, 5 KNN f1 score: 0.435 KNN cohens kappa score: 0.404 ------ Step 3/5: 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: 2, 7 LR f1 score: 0.269 LR cohens kappa score: 0.215 LR average precision score: 0.243 -> test with 'RF' RF tn, fp: 194, 11 RF fn, tp: 6, 3 RF f1 score: 0.261 RF cohens kappa score: 0.221 -> test with 'GB' GB tn, fp: 193, 12 GB fn, tp: 5, 4 GB f1 score: 0.320 GB cohens kappa score: 0.281 -> test with 'KNN' KNN tn, fp: 182, 23 KNN fn, tp: 4, 5 KNN f1 score: 0.270 KNN cohens kappa score: 0.221 ------ Step 3/5: 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: 0.498 -> test with 'RF' RF tn, fp: 204, 1 RF fn, tp: 9, 0 RF f1 score: 0.000 RF cohens kappa score: -0.008 -> 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: 182, 23 KNN fn, tp: 3, 6 KNN f1 score: 0.316 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: 182, 23 LR fn, tp: 4, 5 LR f1 score: 0.270 LR cohens kappa score: 0.221 LR average precision score: 0.313 -> test with 'RF' RF tn, fp: 203, 2 RF fn, tp: 8, 1 RF f1 score: 0.167 RF cohens kappa score: 0.149 -> 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: 189, 16 KNN fn, tp: 4, 5 KNN f1 score: 0.333 KNN cohens kappa score: 0.292 ------ Step 3/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 784 synthetic samples -> test with 'LR' LR tn, fp: 162, 41 LR fn, tp: 1, 6 LR f1 score: 0.222 LR cohens kappa score: 0.174 LR average precision score: 0.291 -> test with 'RF' RF tn, fp: 198, 5 RF fn, tp: 7, 0 RF f1 score: 0.000 RF cohens kappa score: -0.029 -> test with 'GB' GB tn, fp: 197, 6 GB fn, tp: 7, 0 GB f1 score: 0.000 GB cohens kappa score: -0.032 -> test with 'KNN' KNN tn, fp: 184, 19 KNN fn, tp: 3, 4 KNN f1 score: 0.267 KNN cohens kappa score: 0.227 ====== 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: 173, 32 LR fn, tp: 1, 8 LR f1 score: 0.327 LR cohens kappa score: 0.277 LR average precision score: 0.218 -> test with 'RF' RF tn, fp: 199, 6 RF fn, tp: 9, 0 RF f1 score: 0.000 RF cohens kappa score: -0.035 -> test with 'GB' GB tn, fp: 199, 6 GB fn, tp: 9, 0 GB f1 score: 0.000 GB cohens kappa score: -0.035 -> 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 4/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 784 synthetic samples -> test with 'LR' LR tn, fp: 183, 22 LR fn, tp: 2, 7 LR f1 score: 0.368 LR cohens kappa score: 0.325 LR average precision score: 0.545 -> test with 'RF' RF tn, fp: 202, 3 RF fn, tp: 9, 0 RF f1 score: 0.000 RF cohens kappa score: -0.021 -> 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: 187, 18 KNN fn, tp: 6, 3 KNN f1 score: 0.200 KNN cohens kappa score: 0.150 ------ Step 4/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 784 synthetic samples -> test with 'LR' LR tn, fp: 171, 34 LR fn, tp: 4, 5 LR f1 score: 0.208 LR cohens kappa score: 0.150 LR average precision score: 0.250 -> test with 'RF' RF tn, fp: 204, 1 RF fn, tp: 8, 1 RF f1 score: 0.182 RF cohens kappa score: 0.169 -> 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: 182, 23 KNN fn, tp: 3, 6 KNN f1 score: 0.316 KNN cohens kappa score: 0.269 ------ Step 4/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 784 synthetic samples -> test with 'LR' LR tn, fp: 171, 34 LR fn, tp: 1, 8 LR f1 score: 0.314 LR cohens kappa score: 0.263 LR average precision score: 0.431 -> test with 'RF' RF tn, fp: 199, 6 RF fn, tp: 7, 2 RF f1 score: 0.235 RF cohens kappa score: 0.204 -> test with 'GB' GB tn, fp: 199, 6 GB fn, tp: 5, 4 GB f1 score: 0.421 GB cohens kappa score: 0.394 -> test with 'KNN' KNN tn, fp: 190, 15 KNN fn, tp: 3, 6 KNN f1 score: 0.400 KNN cohens kappa score: 0.362 ------ Step 4/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 784 synthetic samples -> test with 'LR' LR tn, fp: 171, 32 LR fn, tp: 2, 5 LR f1 score: 0.227 LR cohens kappa score: 0.181 LR average precision score: 0.566 -> test with 'RF' RF tn, fp: 201, 2 RF fn, tp: 7, 0 RF f1 score: 0.000 RF cohens kappa score: -0.015 -> test with 'GB' GB tn, fp: 202, 1 GB fn, tp: 7, 0 GB f1 score: 0.000 GB cohens kappa score: -0.008 -> test with 'KNN' KNN tn, fp: 185, 18 KNN fn, tp: 3, 4 KNN f1 score: 0.276 KNN cohens kappa score: 0.237 ====== 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: 184, 21 LR fn, tp: 4, 5 LR f1 score: 0.286 LR cohens kappa score: 0.238 LR average precision score: 0.197 -> test with 'RF' RF tn, fp: 203, 2 RF fn, tp: 9, 0 RF f1 score: 0.000 RF cohens kappa score: -0.016 -> 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: 192, 13 KNN fn, tp: 6, 3 KNN f1 score: 0.240 KNN cohens kappa score: 0.197 ------ Step 5/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 784 synthetic samples -> test with 'LR' LR tn, fp: 174, 31 LR fn, tp: 2, 7 LR f1 score: 0.298 LR cohens kappa score: 0.247 LR average precision score: 0.434 -> test with 'RF' RF tn, fp: 204, 1 RF fn, tp: 9, 0 RF f1 score: 0.000 RF cohens kappa score: -0.008 -> 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: 183, 22 KNN fn, tp: 4, 5 KNN f1 score: 0.278 KNN cohens kappa score: 0.229 ------ Step 5/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 784 synthetic samples -> test with 'LR' LR tn, fp: 180, 25 LR fn, tp: 0, 9 LR f1 score: 0.419 LR cohens kappa score: 0.377 LR average precision score: 0.439 -> test with 'RF' RF tn, fp: 205, 0 RF fn, tp: 8, 1 RF f1 score: 0.200 RF cohens kappa score: 0.193 -> 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: 189, 16 KNN fn, tp: 2, 7 KNN f1 score: 0.438 KNN cohens kappa score: 0.401 ------ Step 5/5: 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: 6, 3 LR f1 score: 0.167 LR cohens kappa score: 0.111 LR average precision score: 0.187 -> test with 'RF' RF tn, fp: 202, 3 RF fn, tp: 9, 0 RF f1 score: 0.000 RF cohens kappa score: -0.021 -> 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: 194, 11 KNN fn, tp: 6, 3 KNN f1 score: 0.261 KNN cohens kappa score: 0.221 ------ Step 5/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 784 synthetic samples -> test with 'LR' LR tn, fp: 165, 38 LR fn, tp: 2, 5 LR f1 score: 0.200 LR cohens kappa score: 0.151 LR average precision score: 0.454 -> test with 'RF' RF tn, fp: 196, 7 RF fn, tp: 7, 0 RF f1 score: 0.000 RF cohens kappa score: -0.034 -> test with 'GB' GB tn, fp: 196, 7 GB fn, tp: 5, 2 GB f1 score: 0.250 GB cohens kappa score: 0.221 -> test with 'KNN' KNN tn, fp: 182, 21 KNN fn, tp: 4, 3 KNN f1 score: 0.194 KNN cohens kappa score: 0.150 ### Exercise is done. -----[ LR ]----- maximum: LR tn, fp: 188, 48 LR fn, tp: 6, 9 LR f1 score: 0.450 LR cohens kappa score: 0.412 LR average precision score: 0.739 average: LR tn, fp: 174.88, 29.72 LR fn, tp: 2.48, 6.12 LR f1 score: 0.278 LR cohens kappa score: 0.228 LR average precision score: 0.364 minimum: LR tn, fp: 157, 17 LR fn, tp: 0, 3 LR f1 score: 0.154 LR cohens kappa score: 0.095 LR average precision score: 0.093 -----[ RF ]----- maximum: RF tn, fp: 205, 11 RF fn, tp: 9, 3 RF f1 score: 0.261 RF cohens kappa score: 0.221 average: RF tn, fp: 201.04, 3.56 RF fn, tp: 8.0, 0.6 RF f1 score: 0.080 RF cohens kappa score: 0.059 minimum: RF tn, fp: 194, 0 RF fn, tp: 6, 0 RF f1 score: 0.000 RF cohens kappa score: -0.035 -----[ GB ]----- maximum: GB tn, fp: 205, 12 GB fn, tp: 9, 4 GB f1 score: 0.421 GB cohens kappa score: 0.394 average: GB tn, fp: 201.2, 3.4 GB fn, tp: 7.6, 1.0 GB f1 score: 0.133 GB cohens kappa score: 0.113 minimum: GB tn, fp: 193, 0 GB fn, tp: 5, 0 GB f1 score: 0.000 GB cohens kappa score: -0.035 -----[ KNN ]----- maximum: KNN tn, fp: 196, 26 KNN fn, tp: 6, 7 KNN f1 score: 0.438 KNN cohens kappa score: 0.404 average: KNN tn, fp: 186.0, 18.6 KNN fn, tp: 3.92, 4.68 KNN f1 score: 0.294 KNN cohens kappa score: 0.251 minimum: KNN tn, fp: 179, 9 KNN fn, tp: 2, 3 KNN f1 score: 0.194 KNN cohens kappa score: 0.150