/////////////////////////////////////////// // Running convGAN-majority-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: 255, 32 GAN fn, tp: 2, 9 GAN f1 score: 0.346 GAN cohens kappa score: 0.306 -> 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: 0.403 -> test with 'GB' GB tn, fp: 286, 1 GB fn, tp: 10, 1 GB f1 score: 0.154 GB cohens kappa score: 0.144 -> test with 'KNN' KNN tn, fp: 264, 23 KNN fn, tp: 2, 9 KNN f1 score: 0.419 KNN cohens kappa score: 0.385 ------ 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: 253, 34 GAN fn, tp: 2, 9 GAN f1 score: 0.333 GAN cohens kappa score: 0.292 -> test with 'LR' LR tn, fp: 238, 49 LR fn, tp: 1, 10 LR f1 score: 0.286 LR cohens kappa score: 0.238 LR average precision score: 0.637 -> 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: 259, 28 KNN fn, tp: 2, 9 KNN f1 score: 0.375 KNN cohens kappa score: 0.337 ------ 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: 250, 37 GAN fn, tp: 2, 9 GAN f1 score: 0.316 GAN cohens kappa score: 0.272 -> 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: 0.267 -> test with 'GB' GB tn, fp: 285, 2 GB fn, tp: 10, 1 GB f1 score: 0.143 GB cohens kappa score: 0.129 -> test with 'KNN' KNN tn, fp: 257, 30 KNN fn, tp: 4, 7 KNN f1 score: 0.292 KNN cohens kappa score: 0.249 ------ 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: 263, 24 GAN fn, tp: 6, 5 GAN f1 score: 0.250 GAN cohens kappa score: 0.208 -> 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: 0.188 -> test with 'GB' GB tn, fp: 281, 6 GB fn, tp: 8, 3 GB f1 score: 0.300 GB cohens kappa score: 0.276 -> test with 'KNN' KNN tn, fp: 264, 23 KNN fn, tp: 5, 6 KNN f1 score: 0.300 KNN cohens kappa score: 0.260 ------ 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: 234, 51 GAN fn, tp: 1, 6 GAN f1 score: 0.188 GAN cohens kappa score: 0.151 -> test with 'LR' LR tn, fp: 246, 39 LR fn, tp: 1, 6 LR f1 score: 0.231 LR cohens kappa score: 0.197 LR average precision score: 0.381 -> test with 'GB' GB tn, fp: 284, 1 GB fn, tp: 6, 1 GB f1 score: 0.222 GB cohens kappa score: 0.214 -> test with 'KNN' KNN tn, fp: 263, 22 KNN fn, tp: 1, 6 KNN f1 score: 0.343 KNN cohens kappa score: 0.317 ====== 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: 262, 25 GAN fn, tp: 2, 9 GAN f1 score: 0.400 GAN cohens kappa score: 0.365 -> 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: 0.319 -> test with 'GB' GB tn, fp: 284, 3 GB fn, tp: 9, 2 GB f1 score: 0.250 GB cohens kappa score: 0.232 -> test with 'KNN' KNN tn, fp: 269, 18 KNN fn, tp: 3, 8 KNN f1 score: 0.432 KNN cohens kappa score: 0.401 ------ 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: 254, 33 GAN fn, tp: 3, 8 GAN f1 score: 0.308 GAN cohens kappa score: 0.265 -> test with 'LR' LR tn, fp: 242, 45 LR fn, tp: 3, 8 LR f1 score: 0.250 LR cohens kappa score: 0.201 LR average precision score: 0.453 -> test with 'GB' GB tn, fp: 285, 2 GB fn, tp: 7, 4 GB f1 score: 0.471 GB cohens kappa score: 0.456 -> test with 'KNN' KNN tn, fp: 236, 51 KNN fn, tp: 3, 8 KNN f1 score: 0.229 KNN cohens kappa score: 0.177 ------ 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: 261, 26 GAN fn, tp: 4, 7 GAN f1 score: 0.318 GAN cohens kappa score: 0.278 -> test with 'LR' LR tn, fp: 257, 30 LR fn, tp: 4, 7 LR f1 score: 0.292 LR cohens kappa score: 0.249 LR average precision score: 0.405 -> test with 'GB' GB tn, fp: 284, 3 GB fn, tp: 8, 3 GB f1 score: 0.353 GB cohens kappa score: 0.336 -> test with 'KNN' KNN tn, fp: 258, 29 KNN fn, tp: 4, 7 KNN f1 score: 0.298 KNN cohens kappa score: 0.256 ------ 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: 247, 40 GAN fn, tp: 2, 9 GAN f1 score: 0.300 GAN cohens kappa score: 0.255 -> 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: 0.300 -> test with 'GB' GB tn, fp: 285, 2 GB fn, tp: 9, 2 GB f1 score: 0.267 GB cohens kappa score: 0.252 -> test with 'KNN' KNN tn, fp: 264, 23 KNN fn, tp: 2, 9 KNN f1 score: 0.419 KNN cohens kappa score: 0.385 ------ 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: 245, 40 GAN fn, tp: 2, 5 GAN f1 score: 0.192 GAN cohens kappa score: 0.157 -> test with 'LR' LR tn, fp: 244, 41 LR fn, tp: 1, 6 LR f1 score: 0.222 LR cohens kappa score: 0.188 LR average precision score: 0.405 -> test with 'GB' GB tn, fp: 284, 1 GB fn, tp: 6, 1 GB f1 score: 0.222 GB cohens kappa score: 0.214 -> test with 'KNN' KNN tn, fp: 266, 19 KNN fn, tp: 1, 6 KNN f1 score: 0.375 KNN cohens kappa score: 0.351 ====== 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: 272, 15 GAN fn, tp: 5, 6 GAN f1 score: 0.375 GAN cohens kappa score: 0.343 -> test with 'LR' LR tn, fp: 248, 39 LR fn, tp: 3, 8 LR f1 score: 0.276 LR cohens kappa score: 0.230 LR average precision score: 0.374 -> test with 'GB' GB tn, fp: 284, 3 GB fn, tp: 9, 2 GB f1 score: 0.250 GB cohens kappa score: 0.232 -> test with 'KNN' KNN tn, fp: 265, 22 KNN fn, tp: 2, 9 KNN f1 score: 0.429 KNN cohens kappa score: 0.396 ------ 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: 265, 22 GAN fn, tp: 4, 7 GAN f1 score: 0.350 GAN cohens kappa score: 0.313 -> 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: 0.391 -> test with 'GB' GB tn, fp: 286, 1 GB fn, tp: 10, 1 GB f1 score: 0.154 GB cohens kappa score: 0.144 -> 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: 259, 28 GAN fn, tp: 5, 6 GAN f1 score: 0.267 GAN cohens kappa score: 0.223 -> test with 'LR' LR tn, fp: 248, 39 LR fn, tp: 3, 8 LR f1 score: 0.276 LR cohens kappa score: 0.230 LR average precision score: 0.250 -> test with 'GB' GB tn, fp: 284, 3 GB fn, tp: 9, 2 GB f1 score: 0.250 GB cohens kappa score: 0.232 -> test with 'KNN' KNN tn, fp: 263, 24 KNN fn, tp: 2, 9 KNN f1 score: 0.409 KNN cohens kappa score: 0.374 ------ 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: 258, 29 GAN fn, tp: 3, 8 GAN f1 score: 0.333 GAN cohens kappa score: 0.293 -> test with 'LR' LR tn, fp: 245, 42 LR fn, tp: 3, 8 LR f1 score: 0.262 LR cohens kappa score: 0.215 LR average precision score: 0.527 -> test with 'GB' GB tn, fp: 278, 9 GB fn, tp: 5, 6 GB f1 score: 0.462 GB cohens kappa score: 0.438 -> test with 'KNN' KNN tn, fp: 260, 27 KNN fn, tp: 3, 8 KNN f1 score: 0.348 KNN cohens kappa score: 0.309 ------ 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: 260, 25 GAN fn, tp: 2, 5 GAN f1 score: 0.270 GAN cohens kappa score: 0.241 -> test with 'LR' LR tn, fp: 251, 34 LR fn, tp: 3, 4 LR f1 score: 0.178 LR cohens kappa score: 0.143 LR average precision score: 0.397 -> test with 'GB' GB tn, fp: 283, 2 GB fn, tp: 5, 2 GB f1 score: 0.364 GB cohens kappa score: 0.352 -> test with 'KNN' KNN tn, fp: 257, 28 KNN fn, tp: 1, 6 KNN f1 score: 0.293 KNN cohens kappa score: 0.263 ====== 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: 270, 17 GAN fn, tp: 5, 6 GAN f1 score: 0.353 GAN cohens kappa score: 0.319 -> test with 'LR' LR tn, fp: 260, 27 LR fn, tp: 4, 7 LR f1 score: 0.311 LR cohens kappa score: 0.270 LR average precision score: 0.476 -> test with 'GB' GB tn, fp: 286, 1 GB fn, tp: 10, 1 GB f1 score: 0.154 GB cohens kappa score: 0.144 -> test with 'KNN' KNN tn, fp: 270, 17 KNN fn, tp: 5, 6 KNN f1 score: 0.353 KNN cohens kappa score: 0.319 ------ 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: 264, 23 GAN fn, tp: 5, 6 GAN f1 score: 0.300 GAN cohens kappa score: 0.260 -> 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: 0.320 -> test with 'GB' GB tn, fp: 285, 2 GB fn, tp: 7, 4 GB f1 score: 0.471 GB cohens kappa score: 0.456 -> test with 'KNN' KNN tn, fp: 253, 34 KNN fn, tp: 2, 9 KNN f1 score: 0.333 KNN cohens kappa score: 0.292 ------ 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: 260, 27 GAN fn, tp: 3, 8 GAN f1 score: 0.348 GAN cohens kappa score: 0.309 -> test with 'LR' LR tn, fp: 240, 47 LR fn, tp: 3, 8 LR f1 score: 0.242 LR cohens kappa score: 0.193 LR average precision score: 0.265 -> 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: 259, 28 KNN fn, tp: 1, 10 KNN f1 score: 0.408 KNN cohens kappa score: 0.372 ------ 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: 254, 33 GAN fn, tp: 3, 8 GAN f1 score: 0.308 GAN cohens kappa score: 0.265 -> test with 'LR' LR tn, fp: 246, 41 LR fn, tp: 4, 7 LR f1 score: 0.237 LR cohens kappa score: 0.189 LR average precision score: 0.295 -> test with 'GB' GB tn, fp: 285, 2 GB fn, tp: 11, 0 GB f1 score: 0.000 GB cohens kappa score: -0.011 -> test with 'KNN' KNN tn, fp: 257, 30 KNN fn, tp: 3, 8 KNN f1 score: 0.327 KNN cohens kappa score: 0.286 ------ 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: 257, 28 GAN fn, tp: 2, 5 GAN f1 score: 0.250 GAN cohens kappa score: 0.219 -> test with 'LR' LR tn, fp: 250, 35 LR fn, tp: 2, 5 LR f1 score: 0.213 LR cohens kappa score: 0.179 LR average precision score: 0.522 -> test with 'GB' GB tn, fp: 281, 4 GB fn, tp: 4, 3 GB f1 score: 0.429 GB cohens kappa score: 0.415 -> test with 'KNN' KNN tn, fp: 264, 21 KNN fn, tp: 2, 5 KNN f1 score: 0.303 KNN cohens kappa score: 0.276 ====== 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: 268, 19 GAN fn, tp: 4, 7 GAN f1 score: 0.378 GAN cohens kappa score: 0.344 -> test with 'LR' LR tn, fp: 257, 30 LR fn, tp: 4, 7 LR f1 score: 0.292 LR cohens kappa score: 0.249 LR average precision score: 0.229 -> test with 'GB' GB tn, fp: 285, 2 GB fn, tp: 11, 0 GB f1 score: 0.000 GB cohens kappa score: -0.011 -> 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 5/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1106 synthetic samples -> test with GAN.predict GAN tn, fp: 251, 36 GAN fn, tp: 2, 9 GAN f1 score: 0.321 GAN cohens kappa score: 0.279 -> test with 'LR' LR tn, fp: 235, 52 LR fn, tp: 2, 9 LR f1 score: 0.250 LR cohens kappa score: 0.200 LR average precision score: 0.503 -> test with 'GB' GB tn, fp: 285, 2 GB fn, tp: 8, 3 GB f1 score: 0.375 GB cohens kappa score: 0.360 -> test with 'KNN' KNN tn, fp: 259, 28 KNN fn, tp: 1, 10 KNN f1 score: 0.408 KNN cohens kappa score: 0.372 ------ 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: 269, 18 GAN fn, tp: 4, 7 GAN f1 score: 0.389 GAN cohens kappa score: 0.356 -> test with 'LR' LR tn, fp: 258, 29 LR fn, tp: 3, 8 LR f1 score: 0.333 LR cohens kappa score: 0.293 LR average precision score: 0.558 -> test with 'GB' GB tn, fp: 287, 0 GB fn, tp: 8, 3 GB f1 score: 0.429 GB cohens kappa score: 0.419 -> test with 'KNN' KNN tn, fp: 262, 25 KNN fn, tp: 3, 8 KNN f1 score: 0.364 KNN cohens kappa score: 0.326 ------ 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: 256, 31 GAN fn, tp: 1, 10 GAN f1 score: 0.385 GAN cohens kappa score: 0.347 -> test with 'LR' LR tn, fp: 250, 37 LR fn, tp: 1, 10 LR f1 score: 0.345 LR cohens kappa score: 0.303 LR average precision score: 0.523 -> test with 'GB' GB tn, fp: 280, 7 GB fn, tp: 9, 2 GB f1 score: 0.200 GB cohens kappa score: 0.173 -> test with 'KNN' KNN tn, fp: 250, 37 KNN fn, tp: 3, 8 KNN f1 score: 0.286 KNN cohens kappa score: 0.241 ------ 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: 262, 23 GAN fn, tp: 4, 3 GAN f1 score: 0.182 GAN cohens kappa score: 0.150 -> test with 'LR' LR tn, fp: 245, 40 LR fn, tp: 3, 4 LR f1 score: 0.157 LR cohens kappa score: 0.120 LR average precision score: 0.119 -> test with 'GB' GB tn, fp: 281, 4 GB fn, tp: 5, 2 GB f1 score: 0.308 GB cohens kappa score: 0.292 -> test with 'KNN' KNN tn, fp: 266, 19 KNN fn, tp: 1, 6 KNN f1 score: 0.375 KNN cohens kappa score: 0.351 ### Exercise is done. -----[ LR ]----- maximum: LR tn, fp: 260, 52 LR fn, tp: 6, 10 LR f1 score: 0.345 LR cohens kappa score: 0.303 LR average precision score: 0.637 average: LR tn, fp: 248.4, 38.2 LR fn, tp: 2.72, 7.48 LR f1 score: 0.267 LR cohens kappa score: 0.224 LR average precision score: 0.380 minimum: LR tn, fp: 235, 27 LR fn, tp: 1, 4 LR f1 score: 0.157 LR cohens kappa score: 0.120 LR average precision score: 0.119 -----[ GB ]----- maximum: GB tn, fp: 287, 9 GB fn, tp: 11, 6 GB f1 score: 0.471 GB cohens kappa score: 0.456 average: GB tn, fp: 283.76, 2.84 GB fn, tp: 7.96, 2.24 GB f1 score: 0.279 GB cohens kappa score: 0.264 minimum: GB tn, fp: 278, 0 GB fn, tp: 4, 0 GB f1 score: 0.000 GB cohens kappa score: -0.011 -----[ KNN ]----- maximum: KNN tn, fp: 270, 51 KNN fn, tp: 5, 10 KNN f1 score: 0.432 KNN cohens kappa score: 0.401 average: KNN tn, fp: 260.32, 26.28 KNN fn, tp: 2.44, 7.76 KNN f1 score: 0.355 KNN cohens kappa score: 0.319 minimum: KNN tn, fp: 236, 17 KNN fn, tp: 1, 5 KNN f1 score: 0.229 KNN cohens kappa score: 0.177 -----[ GAN ]----- maximum: GAN tn, fp: 272, 51 GAN fn, tp: 6, 10 GAN f1 score: 0.400 GAN cohens kappa score: 0.365 average: GAN tn, fp: 257.96, 28.64 GAN fn, tp: 3.12, 7.08 GAN f1 score: 0.310 GAN cohens kappa score: 0.272 minimum: GAN tn, fp: 234, 15 GAN fn, tp: 1, 3 GAN f1 score: 0.182 GAN cohens kappa score: 0.150