/////////////////////////////////////////// // 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: 204, 1 GAN fn, tp: 9, 0 GAN f1 score: 0.000 GAN cohens kappa score: -0.008 -> test with 'LR' LR tn, fp: 180, 25 LR fn, tp: 8, 1 LR f1 score: 0.057 LR cohens kappa score: -0.006 LR average precision score: 0.081 -> 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: 185, 20 KNN fn, tp: 6, 3 KNN f1 score: 0.188 KNN cohens kappa score: 0.135 ------ 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: 190, 15 GAN fn, tp: 6, 3 GAN f1 score: 0.222 GAN cohens kappa score: 0.176 -> 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: 0.398 -> 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: 169, 36 KNN fn, tp: 2, 7 KNN f1 score: 0.269 KNN cohens kappa score: 0.215 ------ 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: 201, 4 GAN fn, tp: 7, 2 GAN f1 score: 0.267 GAN cohens kappa score: 0.241 -> test with 'LR' LR tn, fp: 187, 18 LR fn, tp: 3, 6 LR f1 score: 0.364 LR cohens kappa score: 0.322 LR average precision score: 0.506 -> 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: 188, 17 KNN fn, tp: 3, 6 KNN f1 score: 0.375 KNN cohens kappa score: 0.335 ------ 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: 202, 3 GAN fn, tp: 8, 1 GAN f1 score: 0.154 GAN cohens kappa score: 0.131 -> test with 'LR' LR tn, fp: 190, 15 LR fn, tp: 0, 9 LR f1 score: 0.545 LR cohens kappa score: 0.516 LR average precision score: 0.796 -> 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: 198, 7 KNN fn, tp: 6, 3 KNN f1 score: 0.316 KNN cohens kappa score: 0.284 ------ 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: 192, 11 GAN fn, tp: 5, 2 GAN f1 score: 0.200 GAN cohens kappa score: 0.164 -> test with 'LR' LR tn, fp: 181, 22 LR fn, tp: 3, 4 LR f1 score: 0.242 LR cohens kappa score: 0.200 LR average precision score: 0.236 -> test with 'GB' GB tn, fp: 199, 4 GB fn, tp: 4, 3 GB f1 score: 0.429 GB cohens kappa score: 0.409 -> test with 'KNN' KNN tn, fp: 182, 21 KNN fn, tp: 2, 5 KNN f1 score: 0.303 KNN cohens kappa score: 0.264 ====== 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: 203, 2 GAN fn, tp: 6, 3 GAN f1 score: 0.429 GAN cohens kappa score: 0.411 -> 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: 0.405 -> 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: 3, 6 KNN f1 score: 0.387 KNN cohens kappa score: 0.348 ------ 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: 200, 5 GAN fn, tp: 8, 1 GAN f1 score: 0.133 GAN cohens kappa score: 0.103 -> 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: 0.372 -> 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: 185, 20 KNN fn, tp: 4, 5 KNN f1 score: 0.294 KNN cohens kappa score: 0.248 ------ 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: 200, 5 GAN fn, tp: 7, 2 GAN f1 score: 0.250 GAN cohens kappa score: 0.221 -> test with 'LR' LR tn, fp: 179, 26 LR fn, tp: 3, 6 LR f1 score: 0.293 LR cohens kappa score: 0.243 LR average precision score: 0.389 -> 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: 188, 17 KNN fn, tp: 4, 5 KNN f1 score: 0.323 KNN cohens kappa score: 0.280 ------ 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: 198, 7 GAN fn, tp: 7, 2 GAN f1 score: 0.222 GAN cohens kappa score: 0.188 -> test with 'LR' LR tn, fp: 190, 15 LR fn, tp: 5, 4 LR f1 score: 0.286 LR cohens kappa score: 0.242 LR average precision score: 0.296 -> 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: 3, 6 KNN f1 score: 0.429 KNN cohens kappa score: 0.394 ------ 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: 186, 17 GAN fn, tp: 4, 3 GAN f1 score: 0.222 GAN cohens kappa score: 0.182 -> test with 'LR' LR tn, fp: 167, 36 LR fn, tp: 0, 7 LR f1 score: 0.280 LR cohens kappa score: 0.236 LR average precision score: 0.428 -> 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: 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: 201, 4 GAN fn, tp: 6, 3 GAN f1 score: 0.375 GAN cohens kappa score: 0.351 -> test with 'LR' LR tn, fp: 185, 20 LR fn, tp: 1, 8 LR f1 score: 0.432 LR cohens kappa score: 0.394 LR average precision score: 0.829 -> 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: 3, 6 KNN f1 score: 0.400 KNN cohens kappa score: 0.362 ------ 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: 190, 15 GAN fn, tp: 6, 3 GAN f1 score: 0.222 GAN cohens kappa score: 0.176 -> test with 'LR' LR tn, fp: 175, 30 LR fn, tp: 2, 7 LR f1 score: 0.304 LR cohens kappa score: 0.254 LR average precision score: 0.249 -> test with 'GB' GB tn, fp: 196, 9 GB fn, tp: 5, 4 GB f1 score: 0.364 GB cohens kappa score: 0.330 -> 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 3/5: 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: 8, 1 GAN f1 score: 0.105 GAN cohens kappa score: 0.064 -> test with 'LR' LR tn, fp: 179, 26 LR fn, tp: 3, 6 LR f1 score: 0.293 LR cohens kappa score: 0.243 LR average precision score: 0.367 -> 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: 175, 30 KNN fn, tp: 2, 7 KNN f1 score: 0.304 KNN cohens kappa score: 0.254 ------ 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: 205, 0 GAN fn, tp: 8, 1 GAN f1 score: 0.200 GAN cohens kappa score: 0.193 -> 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: 0.231 -> 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: 192, 13 KNN fn, tp: 4, 5 KNN f1 score: 0.370 KNN cohens kappa score: 0.333 ------ 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: 199, 4 GAN fn, tp: 6, 1 GAN f1 score: 0.167 GAN cohens kappa score: 0.143 -> test with 'LR' LR tn, fp: 183, 20 LR fn, tp: 2, 5 LR f1 score: 0.312 LR cohens kappa score: 0.275 LR average precision score: 0.274 -> test with 'GB' GB tn, fp: 198, 5 GB fn, tp: 6, 1 GB f1 score: 0.154 GB cohens kappa score: 0.127 -> test with 'KNN' KNN tn, fp: 191, 12 KNN fn, tp: 6, 1 KNN f1 score: 0.100 KNN cohens kappa score: 0.059 ====== 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: 7, 2 GAN f1 score: 0.190 GAN cohens kappa score: 0.150 -> test with 'LR' LR tn, fp: 184, 21 LR fn, tp: 3, 6 LR f1 score: 0.333 LR cohens kappa score: 0.288 LR average precision score: 0.174 -> 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 4/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 784 synthetic samples -> test with GAN.predict GAN tn, fp: 204, 1 GAN fn, tp: 8, 1 GAN f1 score: 0.182 GAN cohens kappa score: 0.169 -> test with 'LR' LR tn, fp: 188, 17 LR fn, tp: 4, 5 LR f1 score: 0.323 LR cohens kappa score: 0.280 LR average precision score: 0.592 -> 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: 189, 16 KNN fn, tp: 5, 4 KNN f1 score: 0.276 KNN cohens kappa score: 0.231 ------ 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: 202, 3 GAN fn, tp: 6, 3 GAN f1 score: 0.400 GAN cohens kappa score: 0.379 -> 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: 0.266 -> 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: 188, 17 KNN fn, tp: 5, 4 KNN f1 score: 0.267 KNN cohens kappa score: 0.221 ------ 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: 202, 3 GAN fn, tp: 7, 2 GAN f1 score: 0.286 GAN cohens kappa score: 0.264 -> test with 'LR' LR tn, fp: 187, 18 LR fn, tp: 2, 7 LR f1 score: 0.412 LR cohens kappa score: 0.373 LR average precision score: 0.402 -> 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: 193, 12 KNN fn, tp: 6, 3 KNN f1 score: 0.250 KNN cohens kappa score: 0.208 ------ 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: 199, 4 GAN fn, tp: 6, 1 GAN f1 score: 0.167 GAN cohens kappa score: 0.143 -> test with 'LR' LR tn, fp: 183, 20 LR fn, tp: 1, 6 LR f1 score: 0.364 LR cohens kappa score: 0.328 LR average precision score: 0.490 -> 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: 187, 16 KNN fn, tp: 4, 3 KNN f1 score: 0.231 KNN cohens kappa score: 0.191 ====== 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: 0.254 -> 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: 2, 7 KNN f1 score: 0.378 KNN cohens kappa score: 0.336 ------ 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: 201, 4 GAN fn, tp: 9, 0 GAN f1 score: 0.000 GAN cohens kappa score: -0.027 -> test with 'LR' LR tn, fp: 189, 16 LR fn, tp: 3, 6 LR f1 score: 0.387 LR cohens kappa score: 0.348 LR average precision score: 0.352 -> 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 5/5: Slice 3/5 ------- -> 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: 170, 35 LR fn, tp: 0, 9 LR f1 score: 0.340 LR cohens kappa score: 0.290 LR average precision score: 0.503 -> 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: 178, 27 KNN fn, tp: 2, 7 KNN f1 score: 0.326 KNN cohens kappa score: 0.278 ------ 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: 197, 8 GAN fn, tp: 6, 3 GAN f1 score: 0.300 GAN cohens kappa score: 0.266 -> test with 'LR' LR tn, fp: 191, 14 LR fn, tp: 5, 4 LR f1 score: 0.296 LR cohens kappa score: 0.254 LR average precision score: 0.207 -> 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: 195, 10 KNN fn, tp: 6, 3 KNN f1 score: 0.273 KNN cohens kappa score: 0.235 ------ 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: 190, 13 GAN fn, tp: 5, 2 GAN f1 score: 0.182 GAN cohens kappa score: 0.143 -> test with 'LR' LR tn, fp: 168, 35 LR fn, tp: 2, 5 LR f1 score: 0.213 LR cohens kappa score: 0.165 LR average precision score: 0.440 -> 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: 173, 30 KNN fn, tp: 3, 4 KNN f1 score: 0.195 KNN cohens kappa score: 0.148 ### Exercise is done. -----[ LR ]----- maximum: LR tn, fp: 191, 40 LR fn, tp: 8, 9 LR f1 score: 0.545 LR cohens kappa score: 0.516 LR average precision score: 0.829 average: LR tn, fp: 181.08, 23.52 LR fn, tp: 2.8, 5.8 LR f1 score: 0.309 LR cohens kappa score: 0.264 LR average precision score: 0.381 minimum: LR tn, fp: 165, 14 LR fn, tp: 0, 1 LR f1 score: 0.057 LR cohens kappa score: -0.006 LR average precision score: 0.081 -----[ GB ]----- maximum: GB tn, fp: 205, 9 GB fn, tp: 9, 4 GB f1 score: 0.429 GB cohens kappa score: 0.409 average: GB tn, fp: 202.08, 2.52 GB fn, tp: 7.56, 1.04 GB f1 score: 0.157 GB cohens kappa score: 0.140 minimum: GB tn, fp: 196, 0 GB fn, tp: 4, 0 GB f1 score: 0.000 GB cohens kappa score: -0.021 -----[ KNN ]----- maximum: KNN tn, fp: 198, 36 KNN fn, tp: 6, 7 KNN f1 score: 0.429 KNN cohens kappa score: 0.394 average: KNN tn, fp: 185.4, 19.2 KNN fn, tp: 3.76, 4.84 KNN f1 score: 0.296 KNN cohens kappa score: 0.253 minimum: KNN tn, fp: 169, 7 KNN fn, tp: 1, 1 KNN f1 score: 0.100 KNN cohens kappa score: 0.059 -----[ GAN ]----- maximum: GAN tn, fp: 205, 17 GAN fn, tp: 9, 5 GAN f1 score: 0.429 GAN cohens kappa score: 0.411 average: GAN tn, fp: 197.8, 6.8 GAN fn, tp: 6.6, 2.0 GAN f1 score: 0.221 GAN cohens kappa score: 0.192 minimum: GAN tn, fp: 186, 0 GAN fn, tp: 4, 0 GAN f1 score: 0.000 GAN cohens kappa score: -0.027