/////////////////////////////////////////// // Running convGAN-majority-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: 194, 11 GAN fn, tp: 8, 1 GAN f1 score: 0.095 GAN cohens kappa score: 0.050 -> 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: 0.084 -> test with 'GB' GB tn, fp: 200, 5 GB fn, tp: 8, 1 GB f1 score: 0.133 GB cohens kappa score: 0.103 -> 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 1/5: Slice 2/5 ------- -> 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: 162, 43 LR fn, tp: 1, 8 LR f1 score: 0.267 LR cohens kappa score: 0.210 LR average precision score: 0.348 -> 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: 165, 40 KNN fn, tp: 2, 7 KNN f1 score: 0.250 KNN cohens kappa score: 0.193 ------ 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: 197, 8 GAN fn, tp: 4, 5 GAN f1 score: 0.455 GAN cohens kappa score: 0.426 -> test with 'LR' LR tn, fp: 181, 24 LR fn, tp: 4, 5 LR f1 score: 0.263 LR cohens kappa score: 0.213 LR average precision score: 0.432 -> 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: 173, 32 KNN fn, tp: 4, 5 KNN f1 score: 0.217 KNN cohens kappa score: 0.161 ------ 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: 201, 4 GAN fn, tp: 8, 1 GAN f1 score: 0.143 GAN cohens kappa score: 0.116 -> test with 'LR' LR tn, fp: 187, 18 LR fn, tp: 0, 9 LR f1 score: 0.500 LR cohens kappa score: 0.466 LR average precision score: 0.740 -> 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: 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 GAN.predict GAN tn, fp: 189, 14 GAN fn, tp: 2, 5 GAN f1 score: 0.385 GAN cohens kappa score: 0.353 -> test with 'LR' LR tn, fp: 178, 25 LR fn, tp: 3, 4 LR f1 score: 0.222 LR cohens kappa score: 0.178 LR average precision score: 0.294 -> 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: 175, 28 KNN fn, tp: 2, 5 KNN f1 score: 0.250 KNN cohens kappa score: 0.206 ====== 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: 194, 11 GAN fn, tp: 2, 7 GAN f1 score: 0.519 GAN cohens kappa score: 0.490 -> test with 'LR' LR tn, fp: 177, 28 LR fn, tp: 2, 7 LR f1 score: 0.318 LR cohens kappa score: 0.269 LR average precision score: 0.482 -> 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: 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 GAN.predict GAN tn, fp: 191, 14 GAN fn, tp: 4, 5 GAN f1 score: 0.357 GAN cohens kappa score: 0.318 -> test with 'LR' LR tn, fp: 175, 30 LR fn, tp: 4, 5 LR f1 score: 0.227 LR cohens kappa score: 0.172 LR average precision score: 0.487 -> 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: 180, 25 KNN fn, tp: 2, 7 KNN f1 score: 0.341 KNN cohens kappa score: 0.295 ------ 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: 199, 6 GAN fn, tp: 7, 2 GAN f1 score: 0.235 GAN cohens kappa score: 0.204 -> test with 'LR' LR tn, fp: 176, 29 LR fn, tp: 3, 6 LR f1 score: 0.273 LR cohens kappa score: 0.221 LR average precision score: 0.387 -> 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: 5, 4 KNN f1 score: 0.222 KNN cohens kappa score: 0.170 ------ 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: 197, 8 GAN fn, tp: 6, 3 GAN f1 score: 0.300 GAN cohens kappa score: 0.266 -> 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: 0.295 -> 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: 178, 27 KNN fn, tp: 3, 6 KNN f1 score: 0.286 KNN cohens kappa score: 0.235 ------ 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: 189, 14 GAN fn, tp: 3, 4 GAN f1 score: 0.320 GAN cohens kappa score: 0.286 -> test with 'LR' LR tn, fp: 175, 28 LR fn, tp: 0, 7 LR f1 score: 0.333 LR cohens kappa score: 0.294 LR average precision score: 0.398 -> 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: 174, 29 KNN fn, tp: 1, 6 KNN f1 score: 0.286 KNN cohens kappa score: 0.244 ====== 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: 203, 2 GAN fn, tp: 5, 4 GAN f1 score: 0.533 GAN cohens kappa score: 0.517 -> 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: 0.770 -> 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: 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: 187, 18 GAN fn, tp: 5, 4 GAN f1 score: 0.258 GAN cohens kappa score: 0.211 -> test with 'LR' LR tn, fp: 172, 33 LR fn, tp: 2, 7 LR f1 score: 0.286 LR cohens kappa score: 0.233 LR average precision score: 0.271 -> 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: 175, 30 KNN fn, tp: 3, 6 KNN f1 score: 0.267 KNN cohens kappa score: 0.214 ------ 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: 202, 3 GAN fn, tp: 5, 4 GAN f1 score: 0.500 GAN cohens kappa score: 0.481 -> test with 'LR' LR tn, fp: 181, 24 LR fn, tp: 3, 6 LR f1 score: 0.308 LR cohens kappa score: 0.260 LR average precision score: 0.491 -> 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: 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: 193, 12 GAN fn, tp: 5, 4 GAN f1 score: 0.320 GAN cohens kappa score: 0.281 -> test with 'LR' LR tn, fp: 189, 16 LR fn, tp: 5, 4 LR f1 score: 0.276 LR cohens kappa score: 0.231 LR average precision score: 0.315 -> 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: 185, 20 KNN fn, tp: 4, 5 KNN f1 score: 0.294 KNN cohens kappa score: 0.248 ------ 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: 6, 1 GAN f1 score: 0.133 GAN cohens kappa score: 0.101 -> 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: 0.261 -> 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: 188, 15 KNN fn, tp: 6, 1 KNN f1 score: 0.087 KNN cohens kappa score: 0.043 ====== 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: 195, 10 GAN fn, tp: 6, 3 GAN f1 score: 0.273 GAN cohens kappa score: 0.235 -> test with 'LR' LR tn, fp: 180, 25 LR fn, tp: 2, 7 LR f1 score: 0.341 LR cohens kappa score: 0.295 LR average precision score: 0.192 -> 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: 182, 23 KNN fn, tp: 4, 5 KNN f1 score: 0.270 KNN cohens kappa score: 0.221 ------ 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: 203, 2 GAN fn, tp: 8, 1 GAN f1 score: 0.167 GAN cohens kappa score: 0.149 -> test with 'LR' LR tn, fp: 186, 19 LR fn, tp: 4, 5 LR f1 score: 0.303 LR cohens kappa score: 0.258 LR average precision score: 0.579 -> 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: 184, 21 KNN fn, tp: 4, 5 KNN f1 score: 0.286 KNN cohens kappa score: 0.238 ------ 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: 191, 14 GAN fn, tp: 4, 5 GAN f1 score: 0.357 GAN cohens kappa score: 0.318 -> 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: 0.311 -> 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: 3, 6 KNN f1 score: 0.308 KNN cohens kappa score: 0.260 ------ 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: 195, 10 GAN fn, tp: 7, 2 GAN f1 score: 0.190 GAN cohens kappa score: 0.150 -> 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: 0.384 -> 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: 184, 21 KNN fn, tp: 3, 6 KNN f1 score: 0.333 KNN cohens kappa score: 0.288 ------ 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: 197, 6 GAN fn, tp: 6, 1 GAN f1 score: 0.143 GAN cohens kappa score: 0.113 -> test with 'LR' LR tn, fp: 181, 22 LR fn, tp: 2, 5 LR f1 score: 0.294 LR cohens kappa score: 0.255 LR average precision score: 0.361 -> 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: 177, 26 KNN fn, tp: 3, 4 KNN f1 score: 0.216 KNN cohens kappa score: 0.171 ====== 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: 180, 25 LR fn, tp: 4, 5 LR f1 score: 0.256 LR cohens kappa score: 0.205 LR average precision score: 0.258 -> 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 GAN.predict GAN tn, fp: 196, 9 GAN fn, tp: 6, 3 GAN f1 score: 0.286 GAN cohens kappa score: 0.250 -> test with 'LR' LR tn, fp: 177, 28 LR fn, tp: 2, 7 LR f1 score: 0.318 LR cohens kappa score: 0.269 LR average precision score: 0.421 -> 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: 170, 35 KNN fn, tp: 3, 6 KNN f1 score: 0.240 KNN cohens kappa score: 0.184 ------ 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: 191, 14 GAN fn, tp: 5, 4 GAN f1 score: 0.296 GAN cohens kappa score: 0.254 -> 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: 0.490 -> 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: 177, 28 KNN fn, tp: 4, 5 KNN f1 score: 0.238 KNN cohens kappa score: 0.184 ------ 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: 202, 3 GAN fn, tp: 6, 3 GAN f1 score: 0.400 GAN cohens kappa score: 0.379 -> test with 'LR' LR tn, fp: 181, 24 LR fn, tp: 5, 4 LR f1 score: 0.216 LR cohens kappa score: 0.163 LR average precision score: 0.198 -> 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: 192, 11 GAN fn, tp: 5, 2 GAN f1 score: 0.200 GAN cohens kappa score: 0.164 -> test with 'LR' LR tn, fp: 175, 28 LR fn, tp: 2, 5 LR f1 score: 0.250 LR cohens kappa score: 0.206 LR average precision score: 0.439 -> test with 'GB' GB tn, fp: 197, 6 GB fn, tp: 5, 2 GB f1 score: 0.267 GB cohens kappa score: 0.240 -> test with 'KNN' KNN tn, fp: 178, 25 KNN fn, tp: 3, 4 KNN f1 score: 0.222 KNN cohens kappa score: 0.178 ### Exercise is done. -----[ LR ]----- maximum: LR tn, fp: 189, 43 LR fn, tp: 8, 9 LR f1 score: 0.500 LR cohens kappa score: 0.466 LR average precision score: 0.770 average: LR tn, fp: 178.68, 25.92 LR fn, tp: 2.76, 5.84 LR f1 score: 0.290 LR cohens kappa score: 0.242 LR average precision score: 0.388 minimum: LR tn, fp: 162, 16 LR fn, tp: 0, 1 LR f1 score: 0.059 LR cohens kappa score: -0.003 LR average precision score: 0.084 -----[ GB ]----- maximum: GB tn, fp: 205, 10 GB fn, tp: 9, 4 GB f1 score: 0.348 GB cohens kappa score: 0.319 average: GB tn, fp: 201.88, 2.72 GB fn, tp: 7.68, 0.92 GB f1 score: 0.134 GB cohens kappa score: 0.116 minimum: GB tn, fp: 195, 0 GB fn, tp: 5, 0 GB f1 score: 0.000 GB cohens kappa score: -0.021 -----[ KNN ]----- maximum: KNN tn, fp: 190, 40 KNN fn, tp: 6, 7 KNN f1 score: 0.345 KNN cohens kappa score: 0.304 average: KNN tn, fp: 179.32, 25.28 KNN fn, tp: 3.44, 5.16 KNN f1 score: 0.262 KNN cohens kappa score: 0.213 minimum: KNN tn, fp: 165, 15 KNN fn, tp: 1, 1 KNN f1 score: 0.087 KNN cohens kappa score: 0.043 -----[ GAN ]----- maximum: GAN tn, fp: 203, 18 GAN fn, tp: 8, 7 GAN f1 score: 0.533 GAN cohens kappa score: 0.517 average: GAN tn, fp: 195.2, 9.4 GAN fn, tp: 5.24, 3.36 GAN f1 score: 0.305 GAN cohens kappa score: 0.272 minimum: GAN tn, fp: 187, 2 GAN fn, tp: 2, 1 GAN f1 score: 0.095 GAN cohens kappa score: 0.050