/////////////////////////////////////////// // Running convGAN-proximary-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: 177, 28 GAN fn, tp: 4, 5 GAN f1 score: 0.238 GAN cohens kappa score: 0.184 -> test with 'LR' LR tn, fp: 174, 31 LR fn, tp: 7, 2 LR f1 score: 0.095 LR cohens kappa score: 0.031 LR average precision score: 0.076 -> 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: 4, 5 KNN f1 score: 0.256 KNN cohens kappa score: 0.205 ------ 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: 184, 21 GAN fn, tp: 4, 5 GAN f1 score: 0.286 GAN cohens kappa score: 0.238 -> 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: 0.367 -> 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: 186, 19 KNN fn, tp: 3, 6 KNN f1 score: 0.353 KNN cohens kappa score: 0.310 ------ 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: 191, 14 GAN fn, tp: 6, 3 GAN f1 score: 0.231 GAN cohens kappa score: 0.186 -> 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: 0.524 -> 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: 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: 195, 10 GAN fn, tp: 5, 4 GAN f1 score: 0.348 GAN cohens kappa score: 0.313 -> 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: 0.793 -> 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: 196, 9 KNN fn, tp: 5, 4 KNN f1 score: 0.364 KNN cohens kappa score: 0.330 ------ 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: 183, 20 GAN fn, tp: 3, 4 GAN f1 score: 0.258 GAN cohens kappa score: 0.218 -> 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: 0.226 -> 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: 184, 19 KNN fn, tp: 3, 4 KNN f1 score: 0.267 KNN cohens kappa score: 0.227 ====== 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: 184, 21 GAN fn, tp: 5, 4 GAN f1 score: 0.235 GAN cohens kappa score: 0.185 -> 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: 0.410 -> 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: 190, 15 KNN fn, tp: 4, 5 KNN f1 score: 0.345 KNN cohens kappa score: 0.304 ------ 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: 182, 23 GAN fn, tp: 3, 6 GAN f1 score: 0.316 GAN cohens kappa score: 0.269 -> 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: 0.367 -> 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: 200, 5 GAN fn, tp: 7, 2 GAN f1 score: 0.250 GAN cohens kappa score: 0.221 -> 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: 0.396 -> 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: 5, 4 KNN f1 score: 0.296 KNN cohens kappa score: 0.254 ------ 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: 194, 11 GAN fn, tp: 6, 3 GAN f1 score: 0.261 GAN cohens kappa score: 0.221 -> test with 'LR' LR tn, fp: 190, 15 LR fn, tp: 4, 5 LR f1 score: 0.345 LR cohens kappa score: 0.304 LR average precision score: 0.295 -> 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: 1, 6 GAN f1 score: 0.333 GAN cohens kappa score: 0.295 -> test with 'LR' LR tn, fp: 173, 30 LR fn, tp: 0, 7 LR f1 score: 0.318 LR cohens kappa score: 0.278 LR average precision score: 0.440 -> 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: 184, 21 GAN fn, tp: 4, 5 GAN f1 score: 0.286 GAN cohens kappa score: 0.238 -> test with 'LR' LR tn, fp: 187, 18 LR fn, tp: 1, 8 LR f1 score: 0.457 LR cohens kappa score: 0.421 LR average precision score: 0.816 -> 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: 193, 12 KNN fn, tp: 3, 6 KNN f1 score: 0.444 KNN cohens kappa score: 0.411 ------ 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: 172, 33 GAN fn, tp: 5, 4 GAN f1 score: 0.174 GAN cohens kappa score: 0.114 -> 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: 0.247 -> test with 'GB' GB tn, fp: 198, 7 GB fn, tp: 5, 4 GB f1 score: 0.400 GB cohens kappa score: 0.371 -> test with 'KNN' KNN tn, fp: 177, 28 KNN fn, tp: 3, 6 KNN f1 score: 0.279 KNN cohens kappa score: 0.228 ------ 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: 175, 30 GAN fn, tp: 3, 6 GAN f1 score: 0.267 GAN cohens kappa score: 0.214 -> test with 'LR' LR tn, fp: 180, 25 LR fn, tp: 3, 6 LR f1 score: 0.300 LR cohens kappa score: 0.251 LR average precision score: 0.376 -> 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: 173, 32 KNN fn, tp: 2, 7 KNN f1 score: 0.292 KNN cohens kappa score: 0.240 ------ 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: 190, 15 GAN fn, tp: 3, 6 GAN f1 score: 0.400 GAN cohens kappa score: 0.362 -> 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: 0.202 -> 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: 193, 12 KNN fn, tp: 4, 5 KNN f1 score: 0.385 KNN cohens kappa score: 0.349 ------ 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: 177, 26 GAN fn, tp: 5, 2 GAN f1 score: 0.114 GAN cohens kappa score: 0.064 -> test with 'LR' LR tn, fp: 169, 34 LR fn, tp: 1, 6 LR f1 score: 0.255 LR cohens kappa score: 0.211 LR average precision score: 0.272 -> 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: 186, 17 KNN fn, tp: 6, 1 KNN f1 score: 0.080 KNN cohens kappa score: 0.034 ====== 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: 181, 24 GAN fn, tp: 4, 5 GAN f1 score: 0.263 GAN cohens kappa score: 0.213 -> test with 'LR' LR tn, fp: 183, 22 LR fn, tp: 3, 6 LR f1 score: 0.324 LR cohens kappa score: 0.278 LR average precision score: 0.181 -> 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: 186, 19 KNN fn, tp: 4, 5 KNN f1 score: 0.303 KNN cohens kappa score: 0.258 ------ 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: 187, 18 GAN fn, tp: 6, 3 GAN f1 score: 0.200 GAN cohens kappa score: 0.150 -> 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: 0.606 -> test with 'GB' GB tn, fp: 202, 3 GB fn, tp: 7, 2 GB f1 score: 0.286 GB cohens kappa score: 0.264 -> test with 'KNN' KNN tn, fp: 189, 16 KNN fn, tp: 6, 3 KNN f1 score: 0.214 KNN cohens kappa score: 0.167 ------ 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: 180, 25 GAN fn, tp: 3, 6 GAN f1 score: 0.300 GAN cohens kappa score: 0.251 -> 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: 0.268 -> 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: 4, 5 KNN f1 score: 0.270 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: 191, 14 GAN fn, tp: 4, 5 GAN f1 score: 0.357 GAN cohens kappa score: 0.318 -> 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: 0.397 -> 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: 188, 17 KNN fn, tp: 3, 6 KNN f1 score: 0.375 KNN cohens kappa score: 0.335 ------ 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: 182, 21 GAN fn, tp: 3, 4 GAN f1 score: 0.250 GAN cohens kappa score: 0.209 -> test with 'LR' LR tn, fp: 177, 26 LR fn, tp: 1, 6 LR f1 score: 0.308 LR cohens kappa score: 0.268 LR average precision score: 0.536 -> 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: 178, 25 KNN fn, tp: 3, 4 KNN f1 score: 0.222 KNN cohens kappa score: 0.178 ====== 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: 191, 14 GAN fn, tp: 4, 5 GAN f1 score: 0.357 GAN cohens kappa score: 0.318 -> test with 'LR' LR tn, fp: 189, 16 LR fn, tp: 6, 3 LR f1 score: 0.214 LR cohens kappa score: 0.167 LR average precision score: 0.253 -> 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: 192, 13 KNN fn, tp: 2, 7 KNN f1 score: 0.483 KNN cohens kappa score: 0.451 ------ 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: 192, 13 GAN fn, tp: 7, 2 GAN f1 score: 0.167 GAN cohens kappa score: 0.120 -> 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: 0.368 -> 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: 4, 5 KNN f1 score: 0.370 KNN cohens kappa score: 0.333 ------ 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: 166, 39 GAN fn, tp: 4, 5 GAN f1 score: 0.189 GAN cohens kappa score: 0.128 -> test with 'LR' LR tn, fp: 168, 37 LR fn, tp: 0, 9 LR f1 score: 0.327 LR cohens kappa score: 0.276 LR average precision score: 0.502 -> 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: 2, 7 KNN f1 score: 0.292 KNN cohens kappa score: 0.240 ------ 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: 3, 6 GAN f1 score: 0.500 GAN cohens kappa score: 0.472 -> test with 'LR' LR tn, fp: 196, 9 LR fn, tp: 5, 4 LR f1 score: 0.364 LR cohens kappa score: 0.330 LR average precision score: 0.212 -> 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: 198, 7 KNN fn, tp: 6, 3 KNN f1 score: 0.316 KNN cohens kappa score: 0.284 ------ 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: 181, 22 GAN fn, tp: 5, 2 GAN f1 score: 0.129 GAN cohens kappa score: 0.082 -> test with 'LR' LR tn, fp: 179, 24 LR fn, tp: 2, 5 LR f1 score: 0.278 LR cohens kappa score: 0.237 LR average precision score: 0.426 -> 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: 190, 13 KNN fn, tp: 4, 3 KNN f1 score: 0.261 KNN cohens kappa score: 0.225 ### Exercise is done. -----[ LR ]----- maximum: LR tn, fp: 196, 37 LR fn, tp: 7, 9 LR f1 score: 0.500 LR cohens kappa score: 0.466 LR average precision score: 0.816 average: LR tn, fp: 180.08, 24.52 LR fn, tp: 2.68, 5.92 LR f1 score: 0.306 LR cohens kappa score: 0.261 LR average precision score: 0.382 minimum: LR tn, fp: 168, 9 LR fn, tp: 0, 2 LR f1 score: 0.095 LR cohens kappa score: 0.031 LR average precision score: 0.076 -----[ GB ]----- maximum: GB tn, fp: 205, 7 GB fn, tp: 9, 4 GB f1 score: 0.400 GB cohens kappa score: 0.371 average: GB tn, fp: 202.24, 2.36 GB fn, tp: 7.6, 1.0 GB f1 score: 0.149 GB cohens kappa score: 0.133 minimum: GB tn, fp: 197, 0 GB fn, tp: 5, 0 GB f1 score: 0.000 GB cohens kappa score: -0.021 -----[ KNN ]----- maximum: KNN tn, fp: 198, 32 KNN fn, tp: 6, 7 KNN f1 score: 0.483 KNN cohens kappa score: 0.451 average: KNN tn, fp: 186.0, 18.6 KNN fn, tp: 3.64, 4.96 KNN f1 score: 0.312 KNN cohens kappa score: 0.270 minimum: KNN tn, fp: 173, 7 KNN fn, tp: 1, 1 KNN f1 score: 0.080 KNN cohens kappa score: 0.034 -----[ GAN ]----- maximum: GAN tn, fp: 200, 39 GAN fn, tp: 7, 6 GAN f1 score: 0.500 GAN cohens kappa score: 0.472 average: GAN tn, fp: 184.6, 20.0 GAN fn, tp: 4.28, 4.32 GAN f1 score: 0.268 GAN cohens kappa score: 0.223 minimum: GAN tn, fp: 166, 5 GAN fn, tp: 1, 2 GAN f1 score: 0.114 GAN cohens kappa score: 0.064