/////////////////////////////////////////// // Running Repeater 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 'LR' LR tn, fp: 234, 53 LR fn, tp: 2, 9 LR f1 score: 0.247 LR cohens kappa score: 0.196 LR average precision score: 0.371 -> 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: 3, 8 KNN f1 score: 0.381 KNN cohens kappa score: 0.345 ------ Step 1/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1106 synthetic samples -> test with 'LR' LR tn, fp: 232, 55 LR fn, tp: 0, 11 LR f1 score: 0.286 LR cohens kappa score: 0.237 LR average precision score: 0.529 -> test with 'GB' GB tn, fp: 271, 16 GB fn, tp: 3, 8 GB f1 score: 0.457 GB cohens kappa score: 0.428 -> 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 1/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1106 synthetic samples -> test with 'LR' LR tn, fp: 229, 58 LR fn, tp: 1, 10 LR f1 score: 0.253 LR cohens kappa score: 0.202 LR average precision score: 0.235 -> test with 'GB' GB tn, fp: 275, 12 GB fn, tp: 5, 6 GB f1 score: 0.414 GB cohens kappa score: 0.386 -> 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 1/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1106 synthetic samples -> test with 'LR' LR tn, fp: 240, 47 LR fn, tp: 6, 5 LR f1 score: 0.159 LR cohens kappa score: 0.104 LR average precision score: 0.164 -> test with 'GB' GB tn, fp: 276, 11 GB fn, tp: 7, 4 GB f1 score: 0.308 GB cohens kappa score: 0.277 -> test with 'KNN' KNN tn, fp: 263, 24 KNN fn, tp: 4, 7 KNN f1 score: 0.333 KNN cohens kappa score: 0.295 ------ Step 1/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1104 synthetic samples -> test with 'LR' LR tn, fp: 228, 57 LR fn, tp: 0, 7 LR f1 score: 0.197 LR cohens kappa score: 0.161 LR average precision score: 0.429 -> test with 'GB' GB tn, fp: 269, 16 GB fn, tp: 1, 6 GB f1 score: 0.414 GB cohens kappa score: 0.392 -> 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 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 'LR' LR tn, fp: 242, 45 LR fn, tp: 1, 10 LR f1 score: 0.303 LR cohens kappa score: 0.257 LR average precision score: 0.263 -> 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: 263, 24 KNN fn, tp: 3, 8 KNN f1 score: 0.372 KNN cohens kappa score: 0.336 ------ Step 2/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1106 synthetic samples -> test with 'LR' LR tn, fp: 224, 63 LR fn, tp: 1, 10 LR f1 score: 0.238 LR cohens kappa score: 0.186 LR average precision score: 0.418 -> test with 'GB' GB tn, fp: 267, 20 GB fn, tp: 4, 7 GB f1 score: 0.368 GB cohens kappa score: 0.333 -> test with 'KNN' KNN tn, fp: 247, 40 KNN fn, tp: 3, 8 KNN f1 score: 0.271 KNN cohens kappa score: 0.225 ------ Step 2/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1106 synthetic samples -> test with 'LR' LR tn, fp: 229, 58 LR fn, tp: 3, 8 LR f1 score: 0.208 LR cohens kappa score: 0.154 LR average precision score: 0.314 -> test with 'GB' GB tn, fp: 277, 10 GB fn, tp: 7, 4 GB f1 score: 0.320 GB cohens kappa score: 0.291 -> 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 2/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1106 synthetic samples -> test with 'LR' LR tn, fp: 239, 48 LR fn, tp: 2, 9 LR f1 score: 0.265 LR cohens kappa score: 0.216 LR average precision score: 0.248 -> test with 'GB' GB tn, fp: 272, 15 GB fn, tp: 3, 8 GB f1 score: 0.471 GB cohens kappa score: 0.443 -> test with 'KNN' KNN tn, fp: 271, 16 KNN fn, tp: 5, 6 KNN f1 score: 0.364 KNN cohens kappa score: 0.331 ------ Step 2/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1104 synthetic samples -> test with 'LR' LR tn, fp: 227, 58 LR fn, tp: 1, 6 LR f1 score: 0.169 LR cohens kappa score: 0.131 LR average precision score: 0.415 -> test with 'GB' GB tn, fp: 269, 16 GB fn, tp: 3, 4 GB f1 score: 0.296 GB cohens kappa score: 0.270 -> test with 'KNN' KNN tn, fp: 264, 21 KNN fn, tp: 3, 4 KNN f1 score: 0.250 KNN cohens kappa score: 0.221 ====== 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 'LR' LR tn, fp: 234, 53 LR fn, tp: 1, 10 LR f1 score: 0.270 LR cohens kappa score: 0.221 LR average precision score: 0.345 -> test with 'GB' GB tn, fp: 275, 12 GB fn, tp: 4, 7 GB f1 score: 0.467 GB cohens kappa score: 0.441 -> 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 'LR' LR tn, fp: 231, 56 LR fn, tp: 1, 10 LR f1 score: 0.260 LR cohens kappa score: 0.210 LR average precision score: 0.395 -> test with 'GB' GB tn, fp: 278, 9 GB fn, tp: 6, 5 GB f1 score: 0.400 GB cohens kappa score: 0.374 -> 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 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1106 synthetic samples -> test with 'LR' LR tn, fp: 239, 48 LR fn, tp: 3, 8 LR f1 score: 0.239 LR cohens kappa score: 0.189 LR average precision score: 0.219 -> test with 'GB' GB tn, fp: 269, 18 GB fn, tp: 6, 5 GB f1 score: 0.294 GB cohens kappa score: 0.257 -> test with 'KNN' KNN tn, fp: 260, 27 KNN fn, tp: 4, 7 KNN f1 score: 0.311 KNN cohens kappa score: 0.270 ------ Step 3/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1106 synthetic samples -> test with 'LR' LR tn, fp: 219, 68 LR fn, tp: 0, 11 LR f1 score: 0.244 LR cohens kappa score: 0.192 LR average precision score: 0.423 -> test with 'GB' GB tn, fp: 275, 12 GB fn, tp: 3, 8 GB f1 score: 0.516 GB cohens kappa score: 0.492 -> test with 'KNN' KNN tn, fp: 263, 24 KNN fn, tp: 5, 6 KNN f1 score: 0.293 KNN cohens kappa score: 0.252 ------ Step 3/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1104 synthetic samples -> test with 'LR' LR tn, fp: 235, 50 LR fn, tp: 1, 6 LR f1 score: 0.190 LR cohens kappa score: 0.154 LR average precision score: 0.384 -> test with 'GB' GB tn, fp: 268, 17 GB fn, tp: 3, 4 GB f1 score: 0.286 GB cohens kappa score: 0.259 -> 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 '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: 0.462 -> 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: 272, 15 KNN fn, tp: 8, 3 KNN f1 score: 0.207 KNN cohens kappa score: 0.169 ------ Step 4/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1106 synthetic samples -> test with 'LR' LR tn, fp: 235, 52 LR fn, tp: 1, 10 LR f1 score: 0.274 LR cohens kappa score: 0.225 LR average precision score: 0.308 -> test with 'GB' GB tn, fp: 269, 18 GB fn, tp: 4, 7 GB f1 score: 0.389 GB cohens kappa score: 0.356 -> 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 4/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1106 synthetic samples -> test with 'LR' LR tn, fp: 231, 56 LR fn, tp: 1, 10 LR f1 score: 0.260 LR cohens kappa score: 0.210 LR average precision score: 0.219 -> 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: 260, 27 KNN fn, tp: 2, 9 KNN f1 score: 0.383 KNN cohens kappa score: 0.346 ------ Step 4/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1106 synthetic samples -> test with 'LR' LR tn, fp: 226, 61 LR fn, tp: 3, 8 LR f1 score: 0.200 LR cohens kappa score: 0.146 LR average precision score: 0.274 -> test with 'GB' GB tn, fp: 273, 14 GB fn, tp: 5, 6 GB f1 score: 0.387 GB cohens kappa score: 0.356 -> 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 4/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1104 synthetic samples -> test with 'LR' LR tn, fp: 235, 50 LR fn, tp: 0, 7 LR f1 score: 0.219 LR cohens kappa score: 0.184 LR average precision score: 0.422 -> test with 'GB' GB tn, fp: 272, 13 GB fn, tp: 3, 4 GB f1 score: 0.333 GB cohens kappa score: 0.310 -> 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 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 '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: 0.204 -> test with 'GB' GB tn, fp: 276, 11 GB fn, tp: 7, 4 GB f1 score: 0.308 GB cohens kappa score: 0.277 -> test with 'KNN' KNN tn, fp: 267, 20 KNN fn, tp: 3, 8 KNN f1 score: 0.410 KNN cohens kappa score: 0.377 ------ Step 5/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1106 synthetic samples -> test with 'LR' LR tn, fp: 225, 62 LR fn, tp: 1, 10 LR f1 score: 0.241 LR cohens kappa score: 0.189 LR average precision score: 0.438 -> test with 'GB' GB tn, fp: 268, 19 GB fn, tp: 4, 7 GB f1 score: 0.378 GB cohens kappa score: 0.344 -> 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 '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: 0.431 -> test with 'GB' GB tn, fp: 273, 14 GB fn, tp: 5, 6 GB f1 score: 0.387 GB cohens kappa score: 0.356 -> test with 'KNN' KNN tn, fp: 263, 24 KNN fn, tp: 5, 6 KNN f1 score: 0.293 KNN cohens kappa score: 0.252 ------ Step 5/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1106 synthetic samples -> test with 'LR' LR tn, fp: 234, 53 LR fn, tp: 1, 10 LR f1 score: 0.270 LR cohens kappa score: 0.221 LR average precision score: 0.499 -> test with 'GB' GB tn, fp: 271, 16 GB fn, tp: 5, 6 GB f1 score: 0.364 GB cohens kappa score: 0.331 -> test with 'KNN' KNN tn, fp: 259, 28 KNN fn, tp: 6, 5 KNN f1 score: 0.227 KNN cohens kappa score: 0.182 ------ Step 5/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1104 synthetic samples -> test with 'LR' LR tn, fp: 231, 54 LR fn, tp: 1, 6 LR f1 score: 0.179 LR cohens kappa score: 0.142 LR average precision score: 0.130 -> test with 'GB' GB tn, fp: 270, 15 GB fn, tp: 4, 3 GB f1 score: 0.240 GB cohens kappa score: 0.213 -> 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: 246, 68 LR fn, tp: 6, 11 LR f1 score: 0.303 LR cohens kappa score: 0.257 LR average precision score: 0.529 average: LR tn, fp: 233.0, 53.6 LR fn, tp: 1.64, 8.56 LR f1 score: 0.236 LR cohens kappa score: 0.188 LR average precision score: 0.342 minimum: LR tn, fp: 219, 41 LR fn, tp: 0, 5 LR f1 score: 0.159 LR cohens kappa score: 0.104 LR average precision score: 0.130 -----[ GB ]----- maximum: GB tn, fp: 281, 20 GB fn, tp: 7, 8 GB f1 score: 0.516 GB cohens kappa score: 0.492 average: GB tn, fp: 273.0, 13.6 GB fn, tp: 4.64, 5.56 GB f1 score: 0.375 GB cohens kappa score: 0.347 minimum: GB tn, fp: 267, 6 GB fn, tp: 1, 3 GB f1 score: 0.240 GB cohens kappa score: 0.213 -----[ KNN ]----- maximum: KNN tn, fp: 272, 40 KNN fn, tp: 8, 10 KNN f1 score: 0.410 KNN cohens kappa score: 0.377 average: KNN tn, fp: 262.52, 24.08 KNN fn, tp: 3.4, 6.8 KNN f1 score: 0.329 KNN cohens kappa score: 0.293 minimum: KNN tn, fp: 247, 15 KNN fn, tp: 1, 3 KNN f1 score: 0.207 KNN cohens kappa score: 0.169