/////////////////////////////////////////// // 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: 254, 33 GAN fn, tp: 3, 8 GAN f1 score: 0.308 GAN cohens kappa score: 0.265 -> 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: 0.339 -> 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: 258, 29 KNN fn, tp: 2, 9 KNN f1 score: 0.367 KNN cohens kappa score: 0.329 ------ 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: 244, 43 GAN fn, tp: 1, 10 GAN f1 score: 0.312 GAN cohens kappa score: 0.268 -> test with 'LR' LR tn, fp: 245, 42 LR fn, tp: 2, 9 LR f1 score: 0.290 LR cohens kappa score: 0.244 LR average precision score: 0.610 -> test with 'GB' GB tn, fp: 280, 7 GB fn, tp: 7, 4 GB f1 score: 0.364 GB cohens kappa score: 0.339 -> 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 1/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1106 synthetic samples -> test with GAN.predict GAN tn, fp: 257, 30 GAN fn, tp: 4, 7 GAN f1 score: 0.292 GAN cohens kappa score: 0.249 -> test with 'LR' LR tn, fp: 250, 37 LR fn, tp: 2, 9 LR f1 score: 0.316 LR cohens kappa score: 0.272 LR average precision score: 0.309 -> 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: 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: 255, 32 GAN fn, tp: 7, 4 GAN f1 score: 0.170 GAN cohens kappa score: 0.120 -> 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: 0.220 -> 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: 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: 244, 41 GAN fn, tp: 1, 6 GAN f1 score: 0.222 GAN cohens kappa score: 0.188 -> 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: 0.475 -> 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: 257, 28 KNN fn, tp: 2, 5 KNN f1 score: 0.250 KNN cohens kappa score: 0.219 ====== 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: 258, 29 GAN fn, tp: 3, 8 GAN f1 score: 0.333 GAN cohens kappa score: 0.293 -> 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: 0.343 -> 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: 270, 17 KNN fn, tp: 4, 7 KNN f1 score: 0.400 KNN cohens kappa score: 0.368 ------ 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: 252, 35 GAN fn, tp: 3, 8 GAN f1 score: 0.296 GAN cohens kappa score: 0.252 -> test with 'LR' LR tn, fp: 250, 37 LR fn, tp: 3, 8 LR f1 score: 0.286 LR cohens kappa score: 0.241 LR average precision score: 0.438 -> test with 'GB' GB tn, fp: 275, 12 GB fn, tp: 6, 5 GB f1 score: 0.357 GB cohens kappa score: 0.327 -> test with 'KNN' KNN tn, fp: 246, 41 KNN fn, tp: 3, 8 KNN f1 score: 0.267 KNN cohens kappa score: 0.220 ------ 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: 255, 32 GAN fn, tp: 5, 6 GAN f1 score: 0.245 GAN cohens kappa score: 0.199 -> test with 'LR' LR tn, fp: 251, 36 LR fn, tp: 4, 7 LR f1 score: 0.259 LR cohens kappa score: 0.213 LR average precision score: 0.342 -> test with 'GB' GB tn, fp: 280, 7 GB fn, tp: 7, 4 GB f1 score: 0.364 GB cohens kappa score: 0.339 -> 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 2/5: 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: 4, 7 GAN f1 score: 0.298 GAN cohens kappa score: 0.256 -> test with 'LR' LR tn, fp: 250, 37 LR fn, tp: 3, 8 LR f1 score: 0.286 LR cohens kappa score: 0.241 LR average precision score: 0.319 -> test with 'GB' GB tn, fp: 281, 6 GB fn, tp: 4, 7 GB f1 score: 0.583 GB cohens kappa score: 0.566 -> 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: 1, 6 GAN f1 score: 0.293 GAN cohens kappa score: 0.263 -> test with 'LR' LR tn, fp: 251, 34 LR fn, tp: 1, 6 LR f1 score: 0.255 LR cohens kappa score: 0.224 LR average precision score: 0.427 -> test with 'GB' GB tn, fp: 278, 7 GB fn, tp: 5, 2 GB f1 score: 0.250 GB cohens kappa score: 0.229 -> test with 'KNN' KNN tn, fp: 267, 18 KNN fn, tp: 2, 5 KNN f1 score: 0.333 KNN cohens kappa score: 0.308 ====== 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: 251, 36 GAN fn, tp: 3, 8 GAN f1 score: 0.291 GAN cohens kappa score: 0.246 -> 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: 0.422 -> test with 'GB' GB tn, fp: 276, 11 GB fn, tp: 6, 5 GB f1 score: 0.370 GB cohens kappa score: 0.342 -> test with 'KNN' KNN tn, fp: 266, 21 KNN fn, tp: 3, 8 KNN f1 score: 0.400 KNN cohens kappa score: 0.366 ------ 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: 254, 33 GAN fn, tp: 4, 7 GAN f1 score: 0.275 GAN cohens kappa score: 0.230 -> test with 'LR' LR tn, fp: 250, 37 LR fn, tp: 2, 9 LR f1 score: 0.316 LR cohens kappa score: 0.272 LR average precision score: 0.388 -> test with 'GB' GB tn, fp: 277, 10 GB fn, tp: 6, 5 GB f1 score: 0.385 GB cohens kappa score: 0.357 -> 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: 256, 31 GAN fn, tp: 4, 7 GAN f1 score: 0.286 GAN cohens kappa score: 0.242 -> test with 'LR' LR tn, fp: 250, 37 LR fn, tp: 4, 7 LR f1 score: 0.255 LR cohens kappa score: 0.208 LR average precision score: 0.235 -> test with 'GB' GB tn, fp: 278, 9 GB fn, tp: 8, 3 GB f1 score: 0.261 GB cohens kappa score: 0.231 -> test with 'KNN' KNN tn, fp: 264, 23 KNN fn, tp: 4, 7 KNN f1 score: 0.341 KNN cohens kappa score: 0.304 ------ 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: 244, 43 GAN fn, tp: 2, 9 GAN f1 score: 0.286 GAN cohens kappa score: 0.239 -> test with 'LR' LR tn, fp: 251, 36 LR fn, tp: 3, 8 LR f1 score: 0.291 LR cohens kappa score: 0.246 LR average precision score: 0.444 -> test with 'GB' GB tn, fp: 276, 11 GB fn, tp: 5, 6 GB f1 score: 0.429 GB cohens kappa score: 0.402 -> 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 3/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1104 synthetic samples -> test with GAN.predict GAN tn, fp: 251, 34 GAN fn, tp: 2, 5 GAN f1 score: 0.217 GAN cohens kappa score: 0.184 -> 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: 0.407 -> 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: 259, 26 KNN fn, tp: 2, 5 KNN f1 score: 0.263 KNN cohens kappa score: 0.233 ====== 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: 261, 26 LR fn, tp: 5, 6 LR f1 score: 0.279 LR cohens kappa score: 0.237 LR average precision score: 0.435 -> 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: 238, 49 GAN fn, tp: 3, 8 GAN f1 score: 0.235 GAN cohens kappa score: 0.185 -> test with 'LR' LR tn, fp: 250, 37 LR fn, tp: 2, 9 LR f1 score: 0.316 LR cohens kappa score: 0.272 LR average precision score: 0.374 -> test with 'GB' GB tn, fp: 280, 7 GB fn, tp: 7, 4 GB f1 score: 0.364 GB cohens kappa score: 0.339 -> 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 4/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1106 synthetic samples -> test with GAN.predict GAN tn, fp: 249, 38 GAN fn, tp: 5, 6 GAN f1 score: 0.218 GAN cohens kappa score: 0.169 -> 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: 0.245 -> test with 'GB' GB tn, fp: 282, 5 GB fn, tp: 8, 3 GB f1 score: 0.316 GB cohens kappa score: 0.294 -> 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 4/5: 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: 3, 8 GAN f1 score: 0.320 GAN cohens kappa score: 0.278 -> test with 'LR' LR tn, fp: 244, 43 LR fn, tp: 3, 8 LR f1 score: 0.258 LR cohens kappa score: 0.210 LR average precision score: 0.290 -> test with 'GB' GB tn, fp: 275, 12 GB fn, tp: 6, 5 GB f1 score: 0.357 GB cohens kappa score: 0.327 -> 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 4/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1104 synthetic samples -> test with GAN.predict GAN tn, fp: 250, 35 GAN fn, tp: 1, 6 GAN f1 score: 0.250 GAN cohens kappa score: 0.218 -> 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: 0.443 -> test with 'GB' GB tn, fp: 274, 11 GB fn, tp: 3, 4 GB f1 score: 0.364 GB cohens kappa score: 0.342 -> test with 'KNN' KNN tn, fp: 260, 25 KNN fn, tp: 2, 5 KNN f1 score: 0.270 KNN cohens kappa score: 0.241 ====== 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: 251, 36 GAN fn, tp: 3, 8 GAN f1 score: 0.291 GAN cohens kappa score: 0.246 -> test with 'LR' LR tn, fp: 258, 29 LR fn, tp: 4, 7 LR f1 score: 0.298 LR cohens kappa score: 0.256 LR average precision score: 0.250 -> 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: 239, 48 GAN fn, tp: 3, 8 GAN f1 score: 0.239 GAN cohens kappa score: 0.189 -> test with 'LR' LR tn, fp: 240, 47 LR fn, tp: 2, 9 LR f1 score: 0.269 LR cohens kappa score: 0.221 LR average precision score: 0.511 -> 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: 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: 261, 26 GAN fn, tp: 3, 8 GAN f1 score: 0.356 GAN cohens kappa score: 0.317 -> 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: 0.455 -> 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: 259, 28 KNN fn, tp: 4, 7 KNN f1 score: 0.304 KNN cohens kappa score: 0.263 ------ 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: 254, 33 GAN fn, tp: 3, 8 GAN f1 score: 0.308 GAN cohens kappa score: 0.265 -> 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: 0.495 -> 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: 3, 8 KNN f1 score: 0.327 KNN cohens kappa score: 0.286 ------ 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: 256, 29 GAN fn, tp: 3, 4 GAN f1 score: 0.200 GAN cohens kappa score: 0.167 -> test with 'LR' LR tn, fp: 252, 33 LR fn, tp: 3, 4 LR f1 score: 0.182 LR cohens kappa score: 0.147 LR average precision score: 0.172 -> test with 'GB' GB tn, fp: 277, 8 GB fn, tp: 5, 2 GB f1 score: 0.235 GB cohens kappa score: 0.213 -> test with 'KNN' KNN tn, fp: 268, 17 KNN fn, tp: 2, 5 KNN f1 score: 0.345 KNN cohens kappa score: 0.320 ### Exercise is done. -----[ LR ]----- maximum: LR tn, fp: 261, 47 LR fn, tp: 6, 10 LR f1 score: 0.345 LR cohens kappa score: 0.303 LR average precision score: 0.610 average: LR tn, fp: 250.8, 35.8 LR fn, tp: 2.72, 7.48 LR f1 score: 0.277 LR cohens kappa score: 0.235 LR average precision score: 0.376 minimum: LR tn, fp: 240, 26 LR fn, tp: 1, 4 LR f1 score: 0.182 LR cohens kappa score: 0.147 LR average precision score: 0.172 -----[ GB ]----- maximum: GB tn, fp: 284, 12 GB fn, tp: 10, 7 GB f1 score: 0.583 GB cohens kappa score: 0.566 average: GB tn, fp: 278.92, 7.68 GB fn, tp: 6.32, 3.88 GB f1 score: 0.347 GB cohens kappa score: 0.323 minimum: GB tn, fp: 274, 3 GB fn, tp: 3, 1 GB f1 score: 0.133 GB cohens kappa score: 0.116 -----[ 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: 261.72, 24.88 KNN fn, tp: 3.04, 7.16 KNN f1 score: 0.339 KNN cohens kappa score: 0.303 minimum: KNN tn, fp: 246, 15 KNN fn, tp: 1, 4 KNN f1 score: 0.250 KNN cohens kappa score: 0.219 -----[ GAN ]----- maximum: GAN tn, fp: 270, 49 GAN fn, tp: 7, 10 GAN f1 score: 0.356 GAN cohens kappa score: 0.319 average: GAN tn, fp: 252.56, 34.04 GAN fn, tp: 3.16, 7.04 GAN f1 score: 0.276 GAN cohens kappa score: 0.234 minimum: GAN tn, fp: 238, 17 GAN fn, tp: 1, 4 GAN f1 score: 0.170 GAN cohens kappa score: 0.120