/////////////////////////////////////////// // Running convGAN-proximary-full on folding_yeast4 /////////////////////////////////////////// Load 'data_input/folding_yeast4' from pickle file 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 1106 synthetic samples -> test with GAN.predict GAN tn, fp: 264, 23 GAN fn, tp: 3, 8 GAN f1 score: 0.381 GAN cohens kappa score: 0.345 -> test with 'LR' LR tn, fp: 256, 31 LR fn, tp: 3, 8 LR f1 score: 0.320 LR cohens kappa score: 0.278 LR average precision score: 0.343 -> test with 'GB' GB tn, fp: 279, 8 GB fn, tp: 7, 4 GB f1 score: 0.348 GB cohens kappa score: 0.322 -> test with 'KNN' KNN tn, fp: 261, 26 KNN fn, tp: 3, 8 KNN f1 score: 0.356 KNN cohens kappa score: 0.317 ------ Step 1/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1106 synthetic samples -> test with GAN.predict GAN tn, fp: 241, 46 GAN fn, tp: 3, 8 GAN f1 score: 0.246 GAN cohens kappa score: 0.197 -> test with 'LR' LR tn, fp: 246, 41 LR fn, tp: 1, 10 LR f1 score: 0.323 LR cohens kappa score: 0.279 LR average precision score: 0.581 -> test with 'GB' GB tn, fp: 283, 4 GB fn, tp: 6, 5 GB f1 score: 0.500 GB cohens kappa score: 0.483 -> test with 'KNN' KNN tn, fp: 255, 32 KNN fn, tp: 2, 9 KNN f1 score: 0.346 KNN cohens kappa score: 0.306 ------ Step 1/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1106 synthetic samples -> test with GAN.predict GAN tn, fp: 247, 40 GAN fn, tp: 4, 7 GAN f1 score: 0.241 GAN cohens kappa score: 0.193 -> test with 'LR' LR tn, fp: 249, 38 LR fn, tp: 2, 9 LR f1 score: 0.310 LR cohens kappa score: 0.266 LR average precision score: 0.299 -> test with 'GB' GB tn, fp: 283, 4 GB fn, tp: 8, 3 GB f1 score: 0.333 GB cohens kappa score: 0.314 -> test with 'KNN' KNN tn, fp: 264, 23 KNN fn, tp: 3, 8 KNN f1 score: 0.381 KNN cohens kappa score: 0.345 ------ Step 1/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1106 synthetic samples -> test with GAN.predict GAN tn, fp: 262, 25 GAN fn, tp: 7, 4 GAN f1 score: 0.200 GAN cohens kappa score: 0.155 -> test with 'LR' LR tn, fp: 256, 31 LR fn, tp: 6, 5 LR f1 score: 0.213 LR cohens kappa score: 0.166 LR average precision score: 0.221 -> test with 'GB' GB tn, fp: 279, 8 GB fn, tp: 8, 3 GB f1 score: 0.273 GB cohens kappa score: 0.245 -> test with 'KNN' KNN tn, fp: 265, 22 KNN fn, tp: 5, 6 KNN f1 score: 0.308 KNN cohens kappa score: 0.269 ------ Step 1/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1104 synthetic samples -> test with GAN.predict GAN tn, fp: 246, 39 GAN fn, tp: 2, 5 GAN f1 score: 0.196 GAN cohens kappa score: 0.161 -> test with 'LR' LR tn, fp: 248, 37 LR fn, tp: 1, 6 LR f1 score: 0.240 LR cohens kappa score: 0.207 LR average precision score: 0.466 -> test with 'GB' GB tn, fp: 283, 2 GB fn, tp: 6, 1 GB f1 score: 0.200 GB cohens kappa score: 0.188 -> test with 'KNN' KNN tn, fp: 255, 30 KNN fn, tp: 1, 6 KNN f1 score: 0.279 KNN cohens kappa score: 0.249 ====== Step 2/5 ======= -> Shuffling data -> Spliting data to slices ------ Step 2/5: Slice 1/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1106 synthetic samples -> test with GAN.predict GAN tn, fp: 249, 38 GAN fn, tp: 4, 7 GAN f1 score: 0.250 GAN cohens kappa score: 0.203 -> test with 'LR' LR tn, fp: 254, 33 LR fn, tp: 3, 8 LR f1 score: 0.308 LR cohens kappa score: 0.265 LR average precision score: 0.345 -> test with 'GB' GB tn, fp: 280, 7 GB fn, tp: 6, 5 GB f1 score: 0.435 GB cohens kappa score: 0.412 -> test with 'KNN' KNN tn, fp: 264, 23 KNN fn, tp: 3, 8 KNN f1 score: 0.381 KNN cohens kappa score: 0.345 ------ Step 2/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1106 synthetic samples -> test with GAN.predict GAN tn, fp: 249, 38 GAN fn, tp: 4, 7 GAN f1 score: 0.250 GAN cohens kappa score: 0.203 -> test with 'LR' LR tn, fp: 249, 38 LR fn, tp: 2, 9 LR f1 score: 0.310 LR cohens kappa score: 0.266 LR average precision score: 0.438 -> test with 'GB' GB tn, fp: 270, 17 GB fn, tp: 5, 6 GB f1 score: 0.353 GB cohens kappa score: 0.319 -> test with 'KNN' KNN tn, fp: 246, 41 KNN fn, tp: 2, 9 KNN f1 score: 0.295 KNN cohens kappa score: 0.250 ------ Step 2/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1106 synthetic samples -> test with GAN.predict GAN tn, fp: 248, 39 GAN fn, tp: 5, 6 GAN f1 score: 0.214 GAN cohens kappa score: 0.165 -> test with 'LR' LR tn, fp: 249, 38 LR fn, tp: 4, 7 LR f1 score: 0.250 LR cohens kappa score: 0.203 LR average precision score: 0.339 -> test with 'GB' GB tn, fp: 282, 5 GB fn, tp: 7, 4 GB f1 score: 0.400 GB cohens kappa score: 0.379 -> test with 'KNN' KNN tn, fp: 256, 31 KNN fn, tp: 3, 8 KNN f1 score: 0.320 KNN cohens kappa score: 0.278 ------ Step 2/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1106 synthetic samples -> test with GAN.predict GAN tn, fp: 253, 34 GAN fn, tp: 3, 8 GAN f1 score: 0.302 GAN cohens kappa score: 0.259 -> test with 'LR' LR tn, fp: 249, 38 LR fn, tp: 3, 8 LR f1 score: 0.281 LR cohens kappa score: 0.235 LR average precision score: 0.351 -> test with 'GB' GB tn, fp: 274, 13 GB fn, tp: 5, 6 GB f1 score: 0.400 GB cohens kappa score: 0.371 -> test with 'KNN' KNN tn, fp: 268, 19 KNN fn, tp: 2, 9 KNN f1 score: 0.462 KNN cohens kappa score: 0.431 ------ Step 2/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1104 synthetic samples -> test with GAN.predict GAN tn, fp: 257, 28 GAN fn, tp: 2, 5 GAN f1 score: 0.250 GAN cohens kappa score: 0.219 -> test with 'LR' LR tn, fp: 248, 37 LR fn, tp: 1, 6 LR f1 score: 0.240 LR cohens kappa score: 0.207 LR average precision score: 0.419 -> test with 'GB' GB tn, fp: 274, 11 GB fn, tp: 4, 3 GB f1 score: 0.286 GB cohens kappa score: 0.262 -> test with 'KNN' KNN tn, fp: 265, 20 KNN fn, tp: 2, 5 KNN f1 score: 0.312 KNN cohens kappa score: 0.286 ====== Step 3/5 ======= -> Shuffling data -> Spliting data to slices ------ Step 3/5: Slice 1/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1106 synthetic samples -> test with GAN.predict GAN tn, fp: 254, 33 GAN fn, tp: 3, 8 GAN f1 score: 0.308 GAN cohens kappa score: 0.265 -> test with 'LR' LR tn, fp: 253, 34 LR fn, tp: 3, 8 LR f1 score: 0.302 LR cohens kappa score: 0.259 LR average precision score: 0.370 -> test with 'GB' GB tn, fp: 281, 6 GB fn, tp: 7, 4 GB f1 score: 0.381 GB cohens kappa score: 0.358 -> test with 'KNN' KNN tn, fp: 268, 19 KNN fn, tp: 4, 7 KNN f1 score: 0.378 KNN cohens kappa score: 0.344 ------ Step 3/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1106 synthetic samples -> test with GAN.predict GAN tn, fp: 273, 14 GAN fn, tp: 4, 7 GAN f1 score: 0.437 GAN cohens kappa score: 0.409 -> test with 'LR' LR tn, fp: 251, 36 LR fn, tp: 2, 9 LR f1 score: 0.321 LR cohens kappa score: 0.279 LR average precision score: 0.395 -> test with 'GB' GB tn, fp: 281, 6 GB fn, tp: 6, 5 GB f1 score: 0.455 GB cohens kappa score: 0.434 -> test with 'KNN' KNN tn, fp: 259, 28 KNN fn, tp: 2, 9 KNN f1 score: 0.375 KNN cohens kappa score: 0.337 ------ Step 3/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1106 synthetic samples -> test with GAN.predict GAN tn, fp: 269, 18 GAN fn, tp: 6, 5 GAN f1 score: 0.294 GAN cohens kappa score: 0.257 -> test with 'LR' LR tn, fp: 249, 38 LR fn, tp: 4, 7 LR f1 score: 0.250 LR cohens kappa score: 0.203 LR average precision score: 0.223 -> test with 'GB' GB tn, fp: 279, 8 GB fn, tp: 9, 2 GB f1 score: 0.190 GB cohens kappa score: 0.161 -> test with 'KNN' KNN tn, fp: 259, 28 KNN fn, tp: 4, 7 KNN f1 score: 0.304 KNN cohens kappa score: 0.263 ------ Step 3/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1106 synthetic samples -> test with GAN.predict GAN tn, fp: 269, 18 GAN fn, tp: 3, 8 GAN f1 score: 0.432 GAN cohens kappa score: 0.401 -> test with 'LR' LR tn, fp: 249, 38 LR fn, tp: 3, 8 LR f1 score: 0.281 LR cohens kappa score: 0.235 LR average precision score: 0.431 -> test with 'GB' GB tn, fp: 274, 13 GB fn, tp: 5, 6 GB f1 score: 0.400 GB cohens kappa score: 0.371 -> test with 'KNN' KNN tn, fp: 261, 26 KNN fn, tp: 5, 6 KNN f1 score: 0.279 KNN cohens kappa score: 0.237 ------ Step 3/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1104 synthetic samples -> test with GAN.predict GAN tn, fp: 259, 26 GAN fn, tp: 2, 5 GAN f1 score: 0.263 GAN cohens kappa score: 0.233 -> test with 'LR' LR tn, fp: 251, 34 LR fn, tp: 2, 5 LR f1 score: 0.217 LR cohens kappa score: 0.184 LR average precision score: 0.422 -> test with 'GB' GB tn, fp: 278, 7 GB fn, tp: 4, 3 GB f1 score: 0.353 GB cohens kappa score: 0.334 -> test with 'KNN' KNN tn, fp: 261, 24 KNN fn, tp: 2, 5 KNN f1 score: 0.278 KNN cohens kappa score: 0.249 ====== Step 4/5 ======= -> Shuffling data -> Spliting data to slices ------ Step 4/5: Slice 1/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1106 synthetic samples -> test with GAN.predict GAN tn, fp: 277, 10 GAN fn, tp: 5, 6 GAN f1 score: 0.444 GAN cohens kappa score: 0.419 -> test with 'LR' LR tn, fp: 260, 27 LR fn, tp: 5, 6 LR f1 score: 0.273 LR cohens kappa score: 0.230 LR average precision score: 0.438 -> test with 'GB' GB tn, fp: 280, 7 GB fn, tp: 6, 5 GB f1 score: 0.435 GB cohens kappa score: 0.412 -> test with 'KNN' KNN tn, fp: 272, 15 KNN fn, tp: 7, 4 KNN f1 score: 0.267 KNN cohens kappa score: 0.231 ------ Step 4/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1106 synthetic samples -> test with GAN.predict GAN tn, fp: 247, 40 GAN fn, tp: 3, 8 GAN f1 score: 0.271 GAN cohens kappa score: 0.225 -> test with 'LR' LR tn, fp: 252, 35 LR fn, tp: 2, 9 LR f1 score: 0.327 LR cohens kappa score: 0.285 LR average precision score: 0.375 -> test with 'GB' GB tn, fp: 276, 11 GB fn, tp: 4, 7 GB f1 score: 0.483 GB cohens kappa score: 0.458 -> test with 'KNN' KNN tn, fp: 260, 27 KNN fn, tp: 2, 9 KNN f1 score: 0.383 KNN cohens kappa score: 0.346 ------ Step 4/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1106 synthetic samples -> test with GAN.predict GAN tn, fp: 254, 33 GAN fn, tp: 5, 6 GAN f1 score: 0.240 GAN cohens kappa score: 0.194 -> test with 'LR' LR tn, fp: 244, 43 LR fn, tp: 2, 9 LR f1 score: 0.286 LR cohens kappa score: 0.239 LR average precision score: 0.239 -> test with 'GB' GB tn, fp: 281, 6 GB fn, tp: 9, 2 GB f1 score: 0.211 GB cohens kappa score: 0.185 -> test with 'KNN' KNN tn, fp: 257, 30 KNN fn, tp: 2, 9 KNN f1 score: 0.360 KNN cohens kappa score: 0.321 ------ Step 4/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1106 synthetic samples -> test with GAN.predict GAN tn, fp: 263, 24 GAN fn, tp: 4, 7 GAN f1 score: 0.333 GAN cohens kappa score: 0.295 -> test with 'LR' LR tn, fp: 246, 41 LR fn, tp: 3, 8 LR f1 score: 0.267 LR cohens kappa score: 0.220 LR average precision score: 0.291 -> test with 'GB' GB tn, fp: 274, 13 GB fn, tp: 6, 5 GB f1 score: 0.345 GB cohens kappa score: 0.313 -> test with 'KNN' KNN tn, fp: 256, 31 KNN fn, tp: 4, 7 KNN f1 score: 0.286 KNN cohens kappa score: 0.242 ------ Step 4/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1104 synthetic samples -> test with GAN.predict GAN tn, fp: 261, 24 GAN fn, tp: 3, 4 GAN f1 score: 0.229 GAN cohens kappa score: 0.198 -> test with 'LR' LR tn, fp: 248, 37 LR fn, tp: 2, 5 LR f1 score: 0.204 LR cohens kappa score: 0.170 LR average precision score: 0.447 -> test with 'GB' GB tn, fp: 276, 9 GB fn, tp: 3, 4 GB f1 score: 0.400 GB cohens kappa score: 0.381 -> test with 'KNN' KNN tn, fp: 261, 24 KNN fn, tp: 2, 5 KNN f1 score: 0.278 KNN cohens kappa score: 0.249 ====== Step 5/5 ======= -> Shuffling data -> Spliting data to slices ------ Step 5/5: Slice 1/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1106 synthetic samples -> test with GAN.predict GAN tn, fp: 266, 21 GAN fn, tp: 4, 7 GAN f1 score: 0.359 GAN cohens kappa score: 0.323 -> test with 'LR' LR tn, fp: 256, 31 LR fn, tp: 5, 6 LR f1 score: 0.250 LR cohens kappa score: 0.205 LR average precision score: 0.242 -> test with 'GB' GB tn, fp: 281, 6 GB fn, tp: 9, 2 GB f1 score: 0.211 GB cohens kappa score: 0.185 -> test with 'KNN' KNN tn, fp: 267, 20 KNN fn, tp: 4, 7 KNN f1 score: 0.368 KNN cohens kappa score: 0.333 ------ Step 5/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1106 synthetic samples -> test with GAN.predict GAN tn, fp: 246, 41 GAN fn, tp: 5, 6 GAN f1 score: 0.207 GAN cohens kappa score: 0.156 -> test with 'LR' LR tn, fp: 242, 45 LR fn, tp: 2, 9 LR f1 score: 0.277 LR cohens kappa score: 0.230 LR average precision score: 0.513 -> test with 'GB' GB tn, fp: 280, 7 GB fn, tp: 5, 6 GB f1 score: 0.500 GB cohens kappa score: 0.479 -> test with 'KNN' KNN tn, fp: 260, 27 KNN fn, tp: 1, 10 KNN f1 score: 0.417 KNN cohens kappa score: 0.381 ------ Step 5/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1106 synthetic samples -> test with GAN.predict GAN tn, fp: 260, 27 GAN fn, tp: 5, 6 GAN f1 score: 0.273 GAN cohens kappa score: 0.230 -> test with 'LR' LR tn, fp: 252, 35 LR fn, tp: 3, 8 LR f1 score: 0.296 LR cohens kappa score: 0.252 LR average precision score: 0.446 -> test with 'GB' GB tn, fp: 283, 4 GB fn, tp: 7, 4 GB f1 score: 0.421 GB cohens kappa score: 0.402 -> test with 'KNN' KNN tn, fp: 257, 30 KNN fn, tp: 5, 6 KNN f1 score: 0.255 KNN cohens kappa score: 0.211 ------ Step 5/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1106 synthetic samples -> test with GAN.predict GAN tn, fp: 267, 20 GAN fn, tp: 5, 6 GAN f1 score: 0.324 GAN cohens kappa score: 0.287 -> test with 'LR' LR tn, fp: 248, 39 LR fn, tp: 1, 10 LR f1 score: 0.333 LR cohens kappa score: 0.291 LR average precision score: 0.495 -> test with 'GB' GB tn, fp: 282, 5 GB fn, tp: 5, 6 GB f1 score: 0.545 GB cohens kappa score: 0.528 -> test with 'KNN' KNN tn, fp: 258, 29 KNN fn, tp: 3, 8 KNN f1 score: 0.333 KNN cohens kappa score: 0.293 ------ Step 5/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1104 synthetic samples -> test with GAN.predict GAN tn, fp: 269, 16 GAN fn, tp: 3, 4 GAN f1 score: 0.296 GAN cohens kappa score: 0.270 -> test with 'LR' LR tn, fp: 249, 36 LR fn, tp: 3, 4 LR f1 score: 0.170 LR cohens kappa score: 0.135 LR average precision score: 0.167 -> test with 'GB' GB tn, fp: 279, 6 GB fn, tp: 5, 2 GB f1 score: 0.267 GB cohens kappa score: 0.247 -> test with 'KNN' KNN tn, fp: 269, 16 KNN fn, tp: 2, 5 KNN f1 score: 0.357 KNN cohens kappa score: 0.333 ### Exercise is done. -----[ LR ]----- maximum: LR tn, fp: 260, 45 LR fn, tp: 6, 10 LR f1 score: 0.333 LR cohens kappa score: 0.291 LR average precision score: 0.581 average: LR tn, fp: 250.16, 36.44 LR fn, tp: 2.72, 7.48 LR f1 score: 0.274 LR cohens kappa score: 0.232 LR average precision score: 0.372 minimum: LR tn, fp: 242, 27 LR fn, tp: 1, 4 LR f1 score: 0.170 LR cohens kappa score: 0.135 LR average precision score: 0.167 -----[ GB ]----- maximum: GB tn, fp: 283, 17 GB fn, tp: 9, 7 GB f1 score: 0.545 GB cohens kappa score: 0.528 average: GB tn, fp: 278.88, 7.72 GB fn, tp: 6.08, 4.12 GB f1 score: 0.365 GB cohens kappa score: 0.342 minimum: GB tn, fp: 270, 2 GB fn, tp: 3, 1 GB f1 score: 0.190 GB cohens kappa score: 0.161 -----[ KNN ]----- maximum: KNN tn, fp: 272, 41 KNN fn, tp: 7, 10 KNN f1 score: 0.462 KNN cohens kappa score: 0.431 average: KNN tn, fp: 260.96, 25.64 KNN fn, tp: 3.0, 7.2 KNN f1 score: 0.334 KNN cohens kappa score: 0.298 minimum: KNN tn, fp: 246, 15 KNN fn, tp: 1, 4 KNN f1 score: 0.255 KNN cohens kappa score: 0.211 -----[ GAN ]----- maximum: GAN tn, fp: 277, 46 GAN fn, tp: 7, 8 GAN f1 score: 0.444 GAN cohens kappa score: 0.419 average: GAN tn, fp: 258.0, 28.6 GAN fn, tp: 3.88, 6.32 GAN f1 score: 0.290 GAN cohens kappa score: 0.250 minimum: GAN tn, fp: 241, 10 GAN fn, tp: 2, 4 GAN f1 score: 0.196 GAN cohens kappa score: 0.155