/////////////////////////////////////////// // Running convGAN-proxymary-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: 257, 30 GAN fn, tp: 5, 6 GAN f1 score: 0.255 GAN cohens kappa score: 0.211 -> test with 'LR' LR tn, fp: 248, 39 LR fn, tp: 2, 9 LR f1 score: 0.305 LR cohens kappa score: 0.261 LR average precision score: 0.397 -> 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: 261, 26 KNN fn, tp: 2, 9 KNN f1 score: 0.391 KNN cohens kappa score: 0.355 ------ 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: 237, 50 GAN fn, tp: 3, 8 GAN f1 score: 0.232 GAN cohens kappa score: 0.181 -> 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: 0.633 -> 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: 257, 30 KNN fn, tp: 1, 10 KNN f1 score: 0.392 KNN cohens kappa score: 0.355 ------ 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: 273, 14 GAN fn, tp: 5, 6 GAN f1 score: 0.387 GAN cohens kappa score: 0.356 -> test with 'LR' LR tn, fp: 248, 39 LR fn, tp: 2, 9 LR f1 score: 0.305 LR cohens kappa score: 0.261 LR average precision score: 0.274 -> 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: 253, 34 KNN fn, tp: 3, 8 KNN f1 score: 0.302 KNN cohens kappa score: 0.259 ------ 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: 277, 10 GAN fn, tp: 6, 5 GAN f1 score: 0.385 GAN cohens kappa score: 0.357 -> test with 'LR' LR tn, fp: 257, 30 LR fn, tp: 6, 5 LR f1 score: 0.217 LR cohens kappa score: 0.171 LR average precision score: 0.206 -> 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: 259, 28 KNN fn, tp: 5, 6 KNN f1 score: 0.267 KNN cohens kappa score: 0.223 ------ 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: 266, 19 GAN fn, tp: 2, 5 GAN f1 score: 0.323 GAN cohens kappa score: 0.296 -> test with 'LR' LR tn, fp: 242, 43 LR fn, tp: 1, 6 LR f1 score: 0.214 LR cohens kappa score: 0.180 LR average precision score: 0.410 -> 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: 261, 24 KNN fn, tp: 1, 6 KNN f1 score: 0.324 KNN cohens kappa score: 0.297 ====== 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: 266, 21 GAN fn, tp: 6, 5 GAN f1 score: 0.270 GAN cohens kappa score: 0.230 -> 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: 0.382 -> test with 'GB' GB tn, fp: 283, 4 GB fn, tp: 10, 1 GB f1 score: 0.125 GB cohens kappa score: 0.104 -> test with 'KNN' KNN tn, fp: 265, 22 KNN fn, tp: 4, 7 KNN f1 score: 0.350 KNN cohens kappa score: 0.313 ------ 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: 279, 8 GAN fn, tp: 4, 7 GAN f1 score: 0.538 GAN cohens kappa score: 0.518 -> 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: 0.452 -> test with 'GB' GB tn, fp: 284, 3 GB fn, tp: 6, 5 GB f1 score: 0.526 GB cohens kappa score: 0.511 -> test with 'KNN' KNN tn, fp: 236, 51 KNN fn, tp: 2, 9 KNN f1 score: 0.254 KNN cohens kappa score: 0.204 ------ 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: 271, 16 GAN fn, tp: 6, 5 GAN f1 score: 0.312 GAN cohens kappa score: 0.277 -> 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: 0.389 -> 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: 3, 8 KNN f1 score: 0.333 KNN cohens kappa score: 0.293 ------ 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: 277, 10 GAN fn, tp: 4, 7 GAN f1 score: 0.500 GAN cohens kappa score: 0.477 -> 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: 0.295 -> 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: 263, 24 KNN fn, tp: 3, 8 KNN f1 score: 0.372 KNN cohens kappa score: 0.336 ------ 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: 268, 17 GAN fn, tp: 2, 5 GAN f1 score: 0.345 GAN cohens kappa score: 0.320 -> test with 'LR' LR tn, fp: 243, 42 LR fn, tp: 1, 6 LR f1 score: 0.218 LR cohens kappa score: 0.184 LR average precision score: 0.392 -> test with 'GB' GB tn, fp: 279, 6 GB fn, tp: 6, 1 GB f1 score: 0.143 GB cohens kappa score: 0.122 -> test with 'KNN' KNN tn, fp: 267, 18 KNN fn, tp: 1, 6 KNN f1 score: 0.387 KNN cohens kappa score: 0.363 ====== 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: 269, 18 GAN fn, tp: 6, 5 GAN f1 score: 0.294 GAN cohens kappa score: 0.257 -> test with 'LR' LR tn, fp: 247, 40 LR fn, tp: 3, 8 LR f1 score: 0.271 LR cohens kappa score: 0.225 LR average precision score: 0.360 -> test with 'GB' GB tn, fp: 284, 3 GB fn, tp: 10, 1 GB f1 score: 0.133 GB cohens kappa score: 0.116 -> test with 'KNN' KNN tn, fp: 262, 25 KNN fn, tp: 2, 9 KNN f1 score: 0.400 KNN cohens kappa score: 0.365 ------ 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: 268, 19 GAN fn, tp: 7, 4 GAN f1 score: 0.235 GAN cohens kappa score: 0.195 -> test with 'LR' LR tn, fp: 255, 32 LR fn, tp: 2, 9 LR f1 score: 0.346 LR cohens kappa score: 0.306 LR average precision score: 0.401 -> 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: 262, 25 KNN fn, tp: 2, 9 KNN f1 score: 0.400 KNN cohens kappa score: 0.365 ------ 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: 276, 11 GAN fn, tp: 7, 4 GAN f1 score: 0.308 GAN cohens kappa score: 0.277 -> 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: 0.249 -> 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: 260, 27 KNN fn, tp: 2, 9 KNN f1 score: 0.383 KNN cohens kappa score: 0.346 ------ 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: 261, 26 GAN fn, tp: 5, 6 GAN f1 score: 0.279 GAN cohens kappa score: 0.237 -> 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: 0.528 -> 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: 261, 26 KNN fn, tp: 4, 7 KNN f1 score: 0.318 KNN cohens kappa score: 0.278 ------ 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: 274, 11 GAN fn, tp: 3, 4 GAN f1 score: 0.364 GAN cohens kappa score: 0.342 -> test with 'LR' LR tn, fp: 252, 33 LR fn, tp: 2, 5 LR f1 score: 0.222 LR cohens kappa score: 0.189 LR average precision score: 0.398 -> test with 'GB' GB tn, fp: 284, 1 GB fn, tp: 4, 3 GB f1 score: 0.545 GB cohens kappa score: 0.537 -> test with 'KNN' KNN tn, fp: 256, 29 KNN fn, tp: 1, 6 KNN f1 score: 0.286 KNN cohens kappa score: 0.256 ====== 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: 274, 13 GAN fn, tp: 7, 4 GAN f1 score: 0.286 GAN cohens kappa score: 0.252 -> test with 'LR' LR tn, fp: 265, 22 LR fn, tp: 5, 6 LR f1 score: 0.308 LR cohens kappa score: 0.269 LR average precision score: 0.461 -> 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: 273, 14 KNN fn, tp: 6, 5 KNN f1 score: 0.333 KNN cohens kappa score: 0.301 ------ 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: 270, 17 GAN fn, tp: 5, 6 GAN f1 score: 0.353 GAN cohens kappa score: 0.319 -> 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: 0.302 -> test with 'GB' GB tn, fp: 283, 4 GB fn, tp: 9, 2 GB f1 score: 0.235 GB cohens kappa score: 0.215 -> test with 'KNN' KNN tn, fp: 252, 35 KNN fn, tp: 3, 8 KNN f1 score: 0.296 KNN cohens kappa score: 0.252 ------ 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: 275, 12 GAN fn, tp: 6, 5 GAN f1 score: 0.357 GAN cohens kappa score: 0.327 -> test with 'LR' LR tn, fp: 238, 49 LR fn, tp: 3, 8 LR f1 score: 0.235 LR cohens kappa score: 0.185 LR average precision score: 0.292 -> test with 'GB' GB tn, fp: 282, 5 GB fn, tp: 11, 0 GB f1 score: 0.000 GB cohens kappa score: -0.024 -> 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 4/5: Slice 4/5 ------- -> 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: 248, 39 LR fn, tp: 4, 7 LR f1 score: 0.246 LR cohens kappa score: 0.198 LR average precision score: 0.276 -> 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: 256, 31 KNN fn, tp: 3, 8 KNN f1 score: 0.320 KNN cohens kappa score: 0.278 ------ 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: 270, 15 GAN fn, tp: 4, 3 GAN f1 score: 0.240 GAN cohens kappa score: 0.213 -> 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: 0.500 -> test with 'GB' GB tn, fp: 280, 5 GB fn, tp: 4, 3 GB f1 score: 0.400 GB cohens kappa score: 0.384 -> test with 'KNN' KNN tn, fp: 257, 28 KNN fn, tp: 2, 5 KNN f1 score: 0.250 KNN cohens kappa score: 0.219 ====== 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: 280, 7 GAN fn, tp: 8, 3 GAN f1 score: 0.286 GAN cohens kappa score: 0.260 -> test with 'LR' LR tn, fp: 255, 32 LR fn, tp: 4, 7 LR f1 score: 0.280 LR cohens kappa score: 0.236 LR average precision score: 0.223 -> test with 'GB' GB tn, fp: 284, 3 GB fn, tp: 11, 0 GB f1 score: 0.000 GB cohens kappa score: -0.016 -> test with 'KNN' KNN tn, fp: 265, 22 KNN fn, tp: 4, 7 KNN f1 score: 0.350 KNN cohens kappa score: 0.313 ------ 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: 275, 12 GAN fn, tp: 5, 6 GAN f1 score: 0.414 GAN cohens kappa score: 0.386 -> test with 'LR' LR tn, fp: 233, 54 LR fn, tp: 2, 9 LR f1 score: 0.243 LR cohens kappa score: 0.192 LR average precision score: 0.492 -> 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: 256, 31 KNN fn, tp: 1, 10 KNN f1 score: 0.385 KNN cohens kappa score: 0.347 ------ 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: 238, 49 GAN fn, tp: 3, 8 GAN f1 score: 0.235 GAN cohens kappa score: 0.185 -> test with 'LR' LR tn, fp: 260, 27 LR fn, tp: 3, 8 LR f1 score: 0.348 LR cohens kappa score: 0.309 LR average precision score: 0.544 -> test with 'GB' GB tn, fp: 287, 0 GB fn, tp: 9, 2 GB f1 score: 0.308 GB cohens kappa score: 0.300 -> test with 'KNN' KNN tn, fp: 268, 19 KNN fn, tp: 3, 8 KNN f1 score: 0.421 KNN cohens kappa score: 0.389 ------ 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: 261, 26 GAN fn, tp: 6, 5 GAN f1 score: 0.238 GAN cohens kappa score: 0.194 -> 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: 0.540 -> test with 'GB' GB tn, fp: 281, 6 GB fn, tp: 10, 1 GB f1 score: 0.111 GB cohens kappa score: 0.085 -> 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 5/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1104 synthetic samples -> test with GAN.predict GAN tn, fp: 266, 19 GAN fn, tp: 5, 2 GAN f1 score: 0.143 GAN cohens kappa score: 0.111 -> test with 'LR' LR tn, fp: 241, 44 LR fn, tp: 3, 4 LR f1 score: 0.145 LR cohens kappa score: 0.108 LR average precision score: 0.122 -> test with 'GB' GB tn, fp: 280, 5 GB fn, tp: 6, 1 GB f1 score: 0.154 GB cohens kappa score: 0.135 -> test with 'KNN' KNN tn, fp: 262, 23 KNN fn, tp: 1, 6 KNN f1 score: 0.333 KNN cohens kappa score: 0.307 ### Exercise is done. -----[ LR ]----- maximum: LR tn, fp: 265, 54 LR fn, tp: 6, 10 LR f1 score: 0.348 LR cohens kappa score: 0.309 LR average precision score: 0.633 average: LR tn, fp: 248.48, 38.12 LR fn, tp: 2.72, 7.48 LR f1 score: 0.268 LR cohens kappa score: 0.225 LR average precision score: 0.381 minimum: LR tn, fp: 233, 22 LR fn, tp: 1, 4 LR f1 score: 0.145 LR cohens kappa score: 0.108 LR average precision score: 0.122 -----[ GB ]----- maximum: GB tn, fp: 287, 8 GB fn, tp: 11, 5 GB f1 score: 0.545 GB cohens kappa score: 0.537 average: GB tn, fp: 283.2, 3.4 GB fn, tp: 8.2, 2.0 GB f1 score: 0.249 GB cohens kappa score: 0.232 minimum: GB tn, fp: 279, 0 GB fn, tp: 4, 0 GB f1 score: 0.000 GB cohens kappa score: -0.024 -----[ KNN ]----- maximum: KNN tn, fp: 273, 51 KNN fn, tp: 6, 10 KNN f1 score: 0.421 KNN cohens kappa score: 0.389 average: KNN tn, fp: 258.84, 27.76 KNN fn, tp: 2.52, 7.68 KNN f1 score: 0.340 KNN cohens kappa score: 0.303 minimum: KNN tn, fp: 236, 14 KNN fn, tp: 1, 5 KNN f1 score: 0.250 KNN cohens kappa score: 0.204 -----[ GAN ]----- maximum: GAN tn, fp: 280, 50 GAN fn, tp: 8, 8 GAN f1 score: 0.538 GAN cohens kappa score: 0.518 average: GAN tn, fp: 267.92, 18.68 GAN fn, tp: 5.0, 5.2 GAN f1 score: 0.317 GAN cohens kappa score: 0.284 minimum: GAN tn, fp: 237, 7 GAN fn, tp: 2, 2 GAN f1 score: 0.143 GAN cohens kappa score: 0.111