/////////////////////////////////////////// // Running convGAN-majority-5 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: 187, 18 GAN fn, tp: 7, 2 GAN f1 score: 0.138 GAN cohens kappa score: 0.085 -> test with 'LR' LR tn, fp: 176, 29 LR fn, tp: 6, 3 LR f1 score: 0.146 LR cohens kappa score: 0.086 LR average precision score: 0.096 -> 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: 182, 23 KNN fn, tp: 4, 5 KNN f1 score: 0.270 KNN cohens kappa score: 0.221 ------ 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: 178, 27 GAN fn, tp: 2, 7 GAN f1 score: 0.326 GAN cohens kappa score: 0.278 -> test with 'LR' LR tn, fp: 158, 47 LR fn, tp: 1, 8 LR f1 score: 0.250 LR cohens kappa score: 0.192 LR average precision score: 0.347 -> 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: 172, 33 KNN fn, tp: 3, 6 KNN f1 score: 0.250 KNN cohens kappa score: 0.195 ------ 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: 188, 17 GAN fn, tp: 4, 5 GAN f1 score: 0.323 GAN cohens kappa score: 0.280 -> 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: 0.311 -> 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: 178, 27 KNN fn, tp: 4, 5 KNN f1 score: 0.244 KNN cohens kappa score: 0.191 ------ 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: 3, 6 GAN f1 score: 0.444 GAN cohens kappa score: 0.411 -> test with 'LR' LR tn, fp: 182, 23 LR fn, tp: 0, 9 LR f1 score: 0.439 LR cohens kappa score: 0.400 LR average precision score: 0.726 -> 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: 186, 19 KNN fn, tp: 3, 6 KNN f1 score: 0.353 KNN cohens kappa score: 0.310 ------ 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: 175, 28 GAN fn, tp: 2, 5 GAN f1 score: 0.250 GAN cohens kappa score: 0.206 -> test with 'LR' LR tn, fp: 172, 31 LR fn, tp: 3, 4 LR f1 score: 0.190 LR cohens kappa score: 0.143 LR average precision score: 0.204 -> 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 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: 175, 30 GAN fn, tp: 2, 7 GAN f1 score: 0.304 GAN cohens kappa score: 0.254 -> 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: 0.321 -> 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: 2, 7 KNN f1 score: 0.350 KNN cohens kappa score: 0.305 ------ 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: 175, 30 GAN fn, tp: 3, 6 GAN f1 score: 0.267 GAN cohens kappa score: 0.214 -> test with 'LR' LR tn, fp: 170, 35 LR fn, tp: 3, 6 LR f1 score: 0.240 LR cohens kappa score: 0.184 LR average precision score: 0.408 -> 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: 178, 27 KNN fn, tp: 4, 5 KNN f1 score: 0.244 KNN cohens kappa score: 0.191 ------ 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: 181, 24 GAN fn, tp: 4, 5 GAN f1 score: 0.263 GAN cohens kappa score: 0.213 -> 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: 0.269 -> 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: 178, 27 KNN fn, tp: 4, 5 KNN f1 score: 0.244 KNN cohens kappa score: 0.191 ------ 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: 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: 4, 5 LR f1 score: 0.263 LR cohens kappa score: 0.213 LR average precision score: 0.306 -> 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 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 784 synthetic samples -> test with GAN.predict GAN tn, fp: 175, 28 GAN fn, tp: 1, 6 GAN f1 score: 0.293 GAN cohens kappa score: 0.251 -> test with 'LR' LR tn, fp: 164, 39 LR fn, tp: 0, 7 LR f1 score: 0.264 LR cohens kappa score: 0.219 LR average precision score: 0.403 -> 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: 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 GAN.predict GAN tn, fp: 195, 10 GAN fn, tp: 1, 8 GAN f1 score: 0.593 GAN cohens kappa score: 0.568 -> 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: 0.759 -> 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: 192, 13 KNN fn, tp: 3, 6 KNN f1 score: 0.429 KNN cohens kappa score: 0.394 ------ 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: 176, 29 GAN fn, tp: 3, 6 GAN f1 score: 0.273 GAN cohens kappa score: 0.221 -> test with 'LR' LR tn, fp: 163, 42 LR fn, tp: 2, 7 LR f1 score: 0.241 LR cohens kappa score: 0.183 LR average precision score: 0.222 -> 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: 164, 41 KNN fn, tp: 4, 5 KNN f1 score: 0.182 KNN cohens kappa score: 0.120 ------ 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: 188, 17 GAN fn, tp: 3, 6 GAN f1 score: 0.375 GAN cohens kappa score: 0.335 -> 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: 0.448 -> 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: 170, 35 KNN fn, tp: 2, 7 KNN f1 score: 0.275 KNN cohens kappa score: 0.221 ------ 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: 186, 19 GAN fn, tp: 4, 5 GAN f1 score: 0.303 GAN cohens kappa score: 0.258 -> 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: 0.244 -> 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: 178, 27 KNN fn, tp: 4, 5 KNN f1 score: 0.244 KNN cohens kappa score: 0.191 ------ 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: 180, 23 GAN fn, tp: 4, 3 GAN f1 score: 0.182 GAN cohens kappa score: 0.136 -> 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: 0.194 -> test with 'GB' GB tn, fp: 199, 4 GB fn, tp: 5, 2 GB f1 score: 0.308 GB cohens kappa score: 0.286 -> test with 'KNN' KNN tn, fp: 181, 22 KNN fn, tp: 3, 4 KNN f1 score: 0.242 KNN cohens kappa score: 0.200 ====== 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: 183, 22 GAN fn, tp: 2, 7 GAN f1 score: 0.368 GAN cohens kappa score: 0.325 -> test with 'LR' LR tn, fp: 169, 36 LR fn, tp: 1, 8 LR f1 score: 0.302 LR cohens kappa score: 0.249 LR average precision score: 0.222 -> test with 'GB' GB tn, fp: 198, 7 GB fn, tp: 9, 0 GB f1 score: 0.000 GB cohens kappa score: -0.038 -> test with 'KNN' KNN tn, fp: 170, 35 KNN fn, tp: 2, 7 KNN f1 score: 0.275 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: 198, 7 GAN fn, tp: 5, 4 GAN f1 score: 0.400 GAN cohens kappa score: 0.371 -> test with 'LR' LR tn, fp: 178, 27 LR fn, tp: 3, 6 LR f1 score: 0.286 LR cohens kappa score: 0.235 LR average precision score: 0.522 -> test with 'GB' GB tn, fp: 205, 0 GB fn, tp: 6, 3 GB f1 score: 0.500 GB cohens kappa score: 0.489 -> test with 'KNN' KNN tn, fp: 178, 27 KNN fn, tp: 5, 4 KNN f1 score: 0.200 KNN cohens kappa score: 0.144 ------ 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: 181, 24 GAN fn, tp: 3, 6 GAN f1 score: 0.308 GAN cohens kappa score: 0.260 -> test with 'LR' LR tn, fp: 168, 37 LR fn, tp: 4, 5 LR f1 score: 0.196 LR cohens kappa score: 0.136 LR average precision score: 0.241 -> 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: 176, 29 KNN fn, tp: 4, 5 KNN f1 score: 0.233 KNN cohens kappa score: 0.178 ------ 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: 183, 22 GAN fn, tp: 3, 6 GAN f1 score: 0.324 GAN cohens kappa score: 0.278 -> 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: 0.409 -> 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: 180, 25 KNN fn, tp: 2, 7 KNN f1 score: 0.341 KNN cohens kappa score: 0.295 ------ 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: 178, 25 GAN fn, tp: 2, 5 GAN f1 score: 0.270 GAN cohens kappa score: 0.229 -> 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: 0.496 -> 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: 169, 34 KNN fn, tp: 3, 4 KNN f1 score: 0.178 KNN cohens kappa score: 0.129 ====== 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: 186, 19 GAN fn, tp: 3, 6 GAN f1 score: 0.353 GAN cohens kappa score: 0.310 -> 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: 0.196 -> 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: 183, 22 KNN fn, tp: 4, 5 KNN f1 score: 0.278 KNN cohens kappa score: 0.229 ------ 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: 179, 26 GAN fn, tp: 3, 6 GAN f1 score: 0.293 GAN cohens kappa score: 0.243 -> 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: 0.465 -> 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: 3, 6 KNN f1 score: 0.255 KNN cohens kappa score: 0.201 ------ 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: 184, 21 GAN fn, tp: 1, 8 GAN f1 score: 0.421 GAN cohens kappa score: 0.381 -> test with 'LR' LR tn, fp: 175, 30 LR fn, tp: 1, 8 LR f1 score: 0.340 LR cohens kappa score: 0.292 LR average precision score: 0.447 -> 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: 174, 31 KNN fn, tp: 2, 7 KNN f1 score: 0.298 KNN cohens kappa score: 0.247 ------ 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: 196, 9 GAN fn, tp: 6, 3 GAN f1 score: 0.286 GAN cohens kappa score: 0.250 -> 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: 0.177 -> 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: 5, 4 KNN f1 score: 0.242 KNN cohens kappa score: 0.193 ------ 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: 176, 27 GAN fn, tp: 4, 3 GAN f1 score: 0.162 GAN cohens kappa score: 0.114 -> test with 'LR' LR tn, fp: 162, 41 LR fn, tp: 2, 5 LR f1 score: 0.189 LR cohens kappa score: 0.139 LR average precision score: 0.387 -> test with 'GB' GB tn, fp: 198, 5 GB fn, tp: 4, 3 GB f1 score: 0.400 GB cohens kappa score: 0.378 -> test with 'KNN' KNN tn, fp: 171, 32 KNN fn, tp: 3, 4 KNN f1 score: 0.186 KNN cohens kappa score: 0.138 ### Exercise is done. -----[ LR ]----- maximum: LR tn, fp: 185, 47 LR fn, tp: 6, 9 LR f1 score: 0.439 LR cohens kappa score: 0.400 LR average precision score: 0.759 average: LR tn, fp: 172.12, 32.48 LR fn, tp: 2.32, 6.28 LR f1 score: 0.267 LR cohens kappa score: 0.216 LR average precision score: 0.353 minimum: LR tn, fp: 158, 20 LR fn, tp: 0, 3 LR f1 score: 0.146 LR cohens kappa score: 0.086 LR average precision score: 0.096 -----[ GB ]----- maximum: GB tn, fp: 205, 7 GB fn, tp: 9, 3 GB f1 score: 0.500 GB cohens kappa score: 0.489 average: GB tn, fp: 202.68, 1.92 GB fn, tp: 7.36, 1.24 GB f1 score: 0.205 GB cohens kappa score: 0.190 minimum: GB tn, fp: 198, 0 GB fn, tp: 4, 0 GB f1 score: 0.000 GB cohens kappa score: -0.038 -----[ KNN ]----- maximum: KNN tn, fp: 192, 41 KNN fn, tp: 5, 7 KNN f1 score: 0.429 KNN cohens kappa score: 0.394 average: KNN tn, fp: 176.96, 27.64 KNN fn, tp: 3.2, 5.4 KNN f1 score: 0.263 KNN cohens kappa score: 0.213 minimum: KNN tn, fp: 164, 13 KNN fn, tp: 1, 4 KNN f1 score: 0.178 KNN cohens kappa score: 0.120 -----[ GAN ]----- maximum: GAN tn, fp: 198, 30 GAN fn, tp: 7, 8 GAN f1 score: 0.593 GAN cohens kappa score: 0.568 average: GAN tn, fp: 183.52, 21.08 GAN fn, tp: 3.2, 5.4 GAN f1 score: 0.313 GAN cohens kappa score: 0.270 minimum: GAN tn, fp: 175, 7 GAN fn, tp: 1, 2 GAN f1 score: 0.138 GAN cohens kappa score: 0.085