/////////////////////////////////////////// // Running convGAN-proxymary-full 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 GAN.predict GAN tn, fp: 192, 13 GAN fn, tp: 5, 4 GAN f1 score: 0.308 GAN cohens kappa score: 0.267 -> test with 'LR' LR tn, fp: 181, 24 LR fn, tp: 8, 1 LR f1 score: 0.059 LR cohens kappa score: -0.003 LR average precision score: 0.074 -> test with 'GB' GB tn, fp: 200, 5 GB fn, tp: 7, 2 GB f1 score: 0.250 GB cohens kappa score: 0.221 -> test with 'KNN' KNN tn, fp: 183, 22 KNN fn, tp: 5, 4 KNN f1 score: 0.229 KNN cohens kappa score: 0.177 ------ Step 1/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 784 synthetic samples -> test with GAN.predict GAN tn, fp: 191, 14 GAN fn, tp: 3, 6 GAN f1 score: 0.414 GAN cohens kappa score: 0.378 -> test with 'LR' LR tn, fp: 165, 40 LR fn, tp: 1, 8 LR f1 score: 0.281 LR cohens kappa score: 0.226 LR average precision score: 0.341 -> 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: 166, 39 KNN fn, tp: 2, 7 KNN f1 score: 0.255 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 GAN.predict GAN tn, fp: 200, 5 GAN fn, tp: 5, 4 GAN f1 score: 0.444 GAN cohens kappa score: 0.420 -> 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: 0.401 -> 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: 176, 29 KNN fn, tp: 4, 5 KNN f1 score: 0.233 KNN cohens kappa score: 0.178 ------ Step 1/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 784 synthetic samples -> test with GAN.predict GAN tn, fp: 193, 12 GAN fn, tp: 6, 3 GAN f1 score: 0.250 GAN cohens kappa score: 0.208 -> test with 'LR' LR tn, fp: 185, 20 LR fn, tp: 0, 9 LR f1 score: 0.474 LR cohens kappa score: 0.438 LR average precision score: 0.747 -> 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: 186, 19 KNN fn, tp: 4, 5 KNN f1 score: 0.303 KNN cohens kappa score: 0.258 ------ Step 1/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 784 synthetic samples -> test with GAN.predict GAN tn, fp: 186, 17 GAN fn, tp: 4, 3 GAN f1 score: 0.222 GAN cohens kappa score: 0.182 -> test with 'LR' LR tn, fp: 179, 24 LR fn, tp: 3, 4 LR f1 score: 0.229 LR cohens kappa score: 0.185 LR average precision score: 0.273 -> test with 'GB' GB tn, fp: 201, 2 GB fn, tp: 7, 0 GB f1 score: 0.000 GB cohens kappa score: -0.015 -> test with 'KNN' KNN tn, fp: 170, 33 KNN fn, tp: 1, 6 KNN f1 score: 0.261 KNN cohens kappa score: 0.217 ====== 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 GAN.predict GAN tn, fp: 193, 12 GAN fn, tp: 4, 5 GAN f1 score: 0.385 GAN cohens kappa score: 0.349 -> 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: 0.487 -> 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: 175, 30 KNN fn, tp: 2, 7 KNN f1 score: 0.304 KNN cohens kappa score: 0.254 ------ Step 2/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 784 synthetic samples -> test with GAN.predict GAN tn, fp: 190, 15 GAN fn, tp: 7, 2 GAN f1 score: 0.154 GAN cohens kappa score: 0.105 -> test with 'LR' LR tn, fp: 174, 31 LR fn, tp: 3, 6 LR f1 score: 0.261 LR cohens kappa score: 0.207 LR average precision score: 0.425 -> 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: 181, 24 KNN fn, tp: 4, 5 KNN f1 score: 0.263 KNN cohens kappa score: 0.213 ------ Step 2/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 784 synthetic samples -> test with GAN.predict GAN tn, fp: 191, 14 GAN fn, tp: 7, 2 GAN f1 score: 0.160 GAN cohens kappa score: 0.112 -> 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: 0.378 -> 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: 180, 25 KNN fn, tp: 5, 4 KNN f1 score: 0.211 KNN cohens kappa score: 0.156 ------ Step 2/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 784 synthetic samples -> test with GAN.predict GAN tn, fp: 193, 12 GAN fn, tp: 5, 4 GAN f1 score: 0.320 GAN cohens kappa score: 0.281 -> test with 'LR' LR tn, fp: 192, 13 LR fn, tp: 4, 5 LR f1 score: 0.370 LR cohens kappa score: 0.333 LR average precision score: 0.286 -> 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: 191, 14 KNN fn, tp: 3, 6 KNN f1 score: 0.414 KNN cohens kappa score: 0.378 ------ Step 2/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 784 synthetic samples -> test with GAN.predict GAN tn, fp: 180, 23 GAN fn, tp: 4, 3 GAN f1 score: 0.182 GAN cohens kappa score: 0.136 -> 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: 0.359 -> 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: 173, 30 KNN fn, tp: 1, 6 KNN f1 score: 0.279 KNN cohens kappa score: 0.236 ====== 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 GAN.predict GAN tn, fp: 194, 11 GAN fn, tp: 7, 2 GAN f1 score: 0.182 GAN cohens kappa score: 0.139 -> test with 'LR' LR tn, fp: 190, 15 LR fn, tp: 2, 7 LR f1 score: 0.452 LR cohens kappa score: 0.417 LR average precision score: 0.762 -> 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: 190, 15 KNN fn, tp: 4, 5 KNN f1 score: 0.345 KNN cohens kappa score: 0.304 ------ Step 3/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 784 synthetic samples -> test with GAN.predict GAN tn, fp: 191, 14 GAN fn, tp: 6, 3 GAN f1 score: 0.231 GAN cohens kappa score: 0.186 -> 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: 0.270 -> test with 'GB' GB tn, fp: 195, 10 GB fn, tp: 5, 4 GB f1 score: 0.348 GB cohens kappa score: 0.313 -> 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 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 784 synthetic samples -> test with GAN.predict GAN tn, fp: 191, 14 GAN fn, tp: 6, 3 GAN f1 score: 0.231 GAN cohens kappa score: 0.186 -> test with 'LR' LR tn, fp: 185, 20 LR fn, tp: 3, 6 LR f1 score: 0.343 LR cohens kappa score: 0.299 LR average precision score: 0.436 -> 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: 172, 33 KNN fn, tp: 2, 7 KNN f1 score: 0.286 KNN cohens kappa score: 0.233 ------ Step 3/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 784 synthetic samples -> test with GAN.predict GAN tn, fp: 188, 17 GAN fn, tp: 6, 3 GAN f1 score: 0.207 GAN cohens kappa score: 0.158 -> test with 'LR' LR tn, fp: 183, 22 LR fn, tp: 4, 5 LR f1 score: 0.278 LR cohens kappa score: 0.229 LR average precision score: 0.265 -> 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: 179, 26 KNN fn, tp: 4, 5 KNN f1 score: 0.250 KNN cohens kappa score: 0.198 ------ Step 3/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 784 synthetic samples -> test with GAN.predict GAN tn, fp: 196, 7 GAN fn, tp: 5, 2 GAN f1 score: 0.250 GAN cohens kappa score: 0.221 -> test with 'LR' LR tn, fp: 168, 35 LR fn, tp: 1, 6 LR f1 score: 0.250 LR cohens kappa score: 0.205 LR average precision score: 0.299 -> 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: 185, 18 KNN fn, tp: 6, 1 KNN f1 score: 0.077 KNN cohens kappa score: 0.030 ====== 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 GAN.predict GAN tn, fp: 192, 13 GAN fn, tp: 6, 3 GAN f1 score: 0.240 GAN cohens kappa score: 0.197 -> test with 'LR' LR tn, fp: 179, 26 LR fn, tp: 2, 7 LR f1 score: 0.333 LR cohens kappa score: 0.286 LR average precision score: 0.193 -> 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: 179, 26 KNN fn, tp: 3, 6 KNN f1 score: 0.293 KNN cohens kappa score: 0.243 ------ Step 4/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 784 synthetic samples -> test with GAN.predict GAN tn, fp: 195, 10 GAN fn, tp: 6, 3 GAN f1 score: 0.273 GAN cohens kappa score: 0.235 -> test with 'LR' LR tn, fp: 187, 18 LR fn, tp: 4, 5 LR f1 score: 0.312 LR cohens kappa score: 0.268 LR average precision score: 0.582 -> 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 4/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 784 synthetic samples -> test with GAN.predict GAN tn, fp: 196, 9 GAN fn, tp: 4, 5 GAN f1 score: 0.435 GAN cohens kappa score: 0.404 -> test with 'LR' LR tn, fp: 173, 32 LR fn, tp: 4, 5 LR f1 score: 0.217 LR cohens kappa score: 0.161 LR average precision score: 0.270 -> 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: 179, 26 KNN fn, tp: 3, 6 KNN f1 score: 0.293 KNN cohens kappa score: 0.243 ------ Step 4/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 784 synthetic samples -> test with GAN.predict GAN tn, fp: 190, 15 GAN fn, tp: 4, 5 GAN f1 score: 0.345 GAN cohens kappa score: 0.304 -> test with 'LR' LR tn, fp: 178, 27 LR fn, tp: 1, 8 LR f1 score: 0.364 LR cohens kappa score: 0.318 LR average precision score: 0.377 -> test with 'GB' GB tn, fp: 201, 4 GB fn, tp: 6, 3 GB f1 score: 0.375 GB cohens kappa score: 0.351 -> test with 'KNN' KNN tn, fp: 184, 21 KNN fn, tp: 4, 5 KNN f1 score: 0.286 KNN cohens kappa score: 0.238 ------ Step 4/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 784 synthetic samples -> test with GAN.predict GAN tn, fp: 183, 20 GAN fn, tp: 4, 3 GAN f1 score: 0.200 GAN cohens kappa score: 0.157 -> test with 'LR' LR tn, fp: 177, 26 LR fn, tp: 2, 5 LR f1 score: 0.263 LR cohens kappa score: 0.221 LR average precision score: 0.404 -> 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: 167, 36 KNN fn, tp: 2, 5 KNN f1 score: 0.208 KNN cohens kappa score: 0.161 ====== 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 GAN.predict GAN tn, fp: 193, 12 GAN fn, tp: 4, 5 GAN f1 score: 0.385 GAN cohens kappa score: 0.349 -> 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: 0.268 -> 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 5/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 784 synthetic samples -> test with GAN.predict GAN tn, fp: 182, 23 GAN fn, tp: 6, 3 GAN f1 score: 0.171 GAN cohens kappa score: 0.116 -> 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: 0.478 -> 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: 169, 36 KNN fn, tp: 3, 6 KNN f1 score: 0.235 KNN cohens kappa score: 0.178 ------ Step 5/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 784 synthetic samples -> test with GAN.predict GAN tn, fp: 194, 11 GAN fn, tp: 6, 3 GAN f1 score: 0.261 GAN cohens kappa score: 0.221 -> test with 'LR' LR tn, fp: 177, 28 LR fn, tp: 0, 9 LR f1 score: 0.391 LR cohens kappa score: 0.347 LR average precision score: 0.499 -> 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: 174, 31 KNN fn, tp: 4, 5 KNN f1 score: 0.222 KNN cohens kappa score: 0.166 ------ Step 5/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 784 synthetic samples -> test with GAN.predict GAN tn, fp: 199, 6 GAN fn, tp: 7, 2 GAN f1 score: 0.235 GAN cohens kappa score: 0.204 -> test with 'LR' LR tn, fp: 182, 23 LR fn, tp: 5, 4 LR f1 score: 0.222 LR cohens kappa score: 0.170 LR average precision score: 0.225 -> 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: 6, 3 KNN f1 score: 0.207 KNN cohens kappa score: 0.158 ------ Step 5/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 784 synthetic samples -> test with GAN.predict GAN tn, fp: 189, 14 GAN fn, tp: 5, 2 GAN f1 score: 0.174 GAN cohens kappa score: 0.134 -> test with 'LR' LR tn, fp: 169, 34 LR fn, tp: 2, 5 LR f1 score: 0.217 LR cohens kappa score: 0.171 LR average precision score: 0.440 -> test with 'GB' GB tn, fp: 198, 5 GB fn, tp: 5, 2 GB f1 score: 0.286 GB cohens kappa score: 0.261 -> test with 'KNN' KNN tn, fp: 179, 24 KNN fn, tp: 3, 4 KNN f1 score: 0.229 KNN cohens kappa score: 0.185 ### Exercise is done. -----[ LR ]----- maximum: LR tn, fp: 192, 40 LR fn, tp: 8, 9 LR f1 score: 0.474 LR cohens kappa score: 0.438 LR average precision score: 0.762 average: LR tn, fp: 177.84, 26.76 LR fn, tp: 2.64, 5.96 LR f1 score: 0.290 LR cohens kappa score: 0.243 LR average precision score: 0.382 minimum: LR tn, fp: 165, 13 LR fn, tp: 0, 1 LR f1 score: 0.059 LR cohens kappa score: -0.003 LR average precision score: 0.074 -----[ GB ]----- maximum: GB tn, fp: 205, 10 GB fn, tp: 9, 4 GB f1 score: 0.375 GB cohens kappa score: 0.354 average: GB tn, fp: 202.12, 2.48 GB fn, tp: 7.6, 1.0 GB f1 score: 0.150 GB cohens kappa score: 0.133 minimum: GB tn, fp: 195, 0 GB fn, tp: 5, 0 GB f1 score: 0.000 GB cohens kappa score: -0.035 -----[ KNN ]----- maximum: KNN tn, fp: 191, 39 KNN fn, tp: 6, 7 KNN f1 score: 0.414 KNN cohens kappa score: 0.378 average: KNN tn, fp: 178.6, 26.0 KNN fn, tp: 3.4, 5.2 KNN f1 score: 0.261 KNN cohens kappa score: 0.212 minimum: KNN tn, fp: 166, 14 KNN fn, tp: 1, 1 KNN f1 score: 0.077 KNN cohens kappa score: 0.030 -----[ GAN ]----- maximum: GAN tn, fp: 200, 23 GAN fn, tp: 7, 6 GAN f1 score: 0.444 GAN cohens kappa score: 0.420 average: GAN tn, fp: 191.28, 13.32 GAN fn, tp: 5.28, 3.32 GAN f1 score: 0.266 GAN cohens kappa score: 0.226 minimum: GAN tn, fp: 180, 5 GAN fn, tp: 3, 2 GAN f1 score: 0.154 GAN cohens kappa score: 0.105