/////////////////////////////////////////// // Running SimpleGAN 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 Epoch 1/3 Epoch 2/3 Epoch 3/3 -> create 1106 synthetic samples -> test with 'LR' LR tn, fp: 285, 2 LR fn, tp: 8, 3 LR f1 score: 0.375 LR cohens kappa score: 0.360 LR average precision score: 0.428 -> 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: 287, 0 KNN fn, tp: 10, 1 KNN f1 score: 0.167 KNN cohens kappa score: 0.162 ------ Step 1/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples Epoch 1/3 Epoch 2/3 Epoch 3/3 -> create 1106 synthetic samples -> test with 'LR' LR tn, fp: 286, 1 LR fn, tp: 7, 4 LR f1 score: 0.500 LR cohens kappa score: 0.488 LR average precision score: 0.656 -> test with 'GB' GB tn, fp: 283, 4 GB fn, tp: 5, 6 GB f1 score: 0.571 GB cohens kappa score: 0.556 -> test with 'KNN' KNN tn, fp: 287, 0 KNN fn, tp: 11, 0 KNN f1 score: 0.000 KNN cohens kappa score: 0.000 ------ Step 1/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples Epoch 1/3 Epoch 2/3 Epoch 3/3 -> create 1106 synthetic samples -> test with 'LR' LR tn, fp: 285, 2 LR fn, tp: 10, 1 LR f1 score: 0.143 LR cohens kappa score: 0.129 LR average precision score: 0.390 -> test with 'GB' GB tn, fp: 286, 1 GB fn, tp: 8, 3 GB f1 score: 0.400 GB cohens kappa score: 0.388 -> test with 'KNN' KNN tn, fp: 287, 0 KNN fn, tp: 11, 0 KNN f1 score: 0.000 KNN cohens kappa score: 0.000 ------ Step 1/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples Epoch 1/3 Epoch 2/3 Epoch 3/3 -> create 1106 synthetic samples -> test with 'LR' LR tn, fp: 284, 3 LR fn, tp: 9, 2 LR f1 score: 0.250 LR cohens kappa score: 0.232 LR average precision score: 0.213 -> test with 'GB' GB tn, fp: 280, 7 GB fn, tp: 8, 3 GB f1 score: 0.286 GB cohens kappa score: 0.260 -> test with 'KNN' KNN tn, fp: 286, 1 KNN fn, tp: 11, 0 KNN f1 score: 0.000 KNN cohens kappa score: -0.006 ------ Step 1/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples Epoch 1/3 Epoch 2/3 Epoch 3/3 -> create 1104 synthetic samples -> test with 'LR' LR tn, fp: 282, 3 LR fn, tp: 5, 2 LR f1 score: 0.333 LR cohens kappa score: 0.320 LR average precision score: 0.273 -> test with 'GB' GB tn, fp: 284, 1 GB fn, tp: 5, 2 GB f1 score: 0.400 GB cohens kappa score: 0.391 -> test with 'KNN' KNN tn, fp: 284, 1 KNN fn, tp: 7, 0 KNN f1 score: 0.000 KNN cohens kappa score: -0.006 ====== Step 2/5 ======= -> Shuffling data -> Spliting data to slices ------ Step 2/5: Slice 1/5 ------- -> Reset the GAN -> Train generator for synthetic samples Epoch 1/3 Epoch 2/3 Epoch 3/3 -> create 1106 synthetic samples -> test with 'LR' LR tn, fp: 286, 1 LR fn, tp: 10, 1 LR f1 score: 0.154 LR cohens kappa score: 0.144 LR average precision score: 0.286 -> 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: 287, 0 KNN fn, tp: 11, 0 KNN f1 score: 0.000 KNN cohens kappa score: 0.000 ------ Step 2/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples Epoch 1/3 Epoch 2/3 Epoch 3/3 -> create 1106 synthetic samples -> test with 'LR' LR tn, fp: 284, 3 LR fn, tp: 5, 6 LR f1 score: 0.600 LR cohens kappa score: 0.586 LR average precision score: 0.473 -> test with 'GB' GB tn, fp: 283, 4 GB fn, tp: 5, 6 GB f1 score: 0.571 GB cohens kappa score: 0.556 -> test with 'KNN' KNN tn, fp: 286, 1 KNN fn, tp: 9, 2 KNN f1 score: 0.286 KNN cohens kappa score: 0.274 ------ Step 2/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples Epoch 1/3 Epoch 2/3 Epoch 3/3 -> create 1106 synthetic samples -> test with 'LR' LR tn, fp: 285, 2 LR fn, tp: 9, 2 LR f1 score: 0.267 LR cohens kappa score: 0.252 LR average precision score: 0.402 -> 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: 286, 1 KNN fn, tp: 11, 0 KNN f1 score: 0.000 KNN cohens kappa score: -0.006 ------ Step 2/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples Epoch 1/3 Epoch 2/3 Epoch 3/3 -> create 1106 synthetic samples -> test with 'LR' LR tn, fp: 286, 1 LR fn, tp: 10, 1 LR f1 score: 0.154 LR cohens kappa score: 0.144 LR average precision score: 0.374 -> 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: 287, 0 KNN fn, tp: 11, 0 KNN f1 score: 0.000 KNN cohens kappa score: 0.000 ------ Step 2/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples Epoch 1/3 Epoch 2/3 Epoch 3/3 -> create 1104 synthetic samples -> test with 'LR' LR tn, fp: 284, 1 LR fn, tp: 6, 1 LR f1 score: 0.222 LR cohens kappa score: 0.214 LR average precision score: 0.505 -> test with 'GB' GB tn, fp: 283, 2 GB fn, tp: 4, 3 GB f1 score: 0.500 GB cohens kappa score: 0.490 -> test with 'KNN' KNN tn, fp: 285, 0 KNN fn, tp: 7, 0 KNN f1 score: 0.000 KNN cohens kappa score: 0.000 ====== Step 3/5 ======= -> Shuffling data -> Spliting data to slices ------ Step 3/5: Slice 1/5 ------- -> Reset the GAN -> Train generator for synthetic samples Epoch 1/3 Epoch 2/3 Epoch 3/3 -> create 1106 synthetic samples -> test with 'LR' LR tn, fp: 285, 2 LR fn, tp: 8, 3 LR f1 score: 0.375 LR cohens kappa score: 0.360 LR average precision score: 0.408 -> 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: 286, 1 KNN fn, tp: 11, 0 KNN f1 score: 0.000 KNN cohens kappa score: -0.006 ------ Step 3/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples Epoch 1/3 Epoch 2/3 Epoch 3/3 -> create 1106 synthetic samples -> test with 'LR' LR tn, fp: 284, 3 LR fn, tp: 11, 0 LR f1 score: 0.000 LR cohens kappa score: -0.016 LR average precision score: 0.382 -> test with 'GB' GB tn, fp: 284, 3 GB fn, tp: 7, 4 GB f1 score: 0.444 GB cohens kappa score: 0.428 -> test with 'KNN' KNN tn, fp: 287, 0 KNN fn, tp: 11, 0 KNN f1 score: 0.000 KNN cohens kappa score: 0.000 ------ Step 3/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples Epoch 1/3 Epoch 2/3 Epoch 3/3 -> create 1106 synthetic samples -> test with 'LR' LR tn, fp: 283, 4 LR fn, tp: 10, 1 LR f1 score: 0.125 LR cohens kappa score: 0.104 LR average precision score: 0.247 -> test with 'GB' GB tn, fp: 282, 5 GB fn, tp: 9, 2 GB f1 score: 0.222 GB cohens kappa score: 0.199 -> test with 'KNN' KNN tn, fp: 287, 0 KNN fn, tp: 11, 0 KNN f1 score: 0.000 KNN cohens kappa score: 0.000 ------ Step 3/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples Epoch 1/3 Epoch 2/3 Epoch 3/3 -> create 1106 synthetic samples -> test with 'LR' LR tn, fp: 284, 3 LR fn, tp: 7, 4 LR f1 score: 0.444 LR cohens kappa score: 0.428 LR average precision score: 0.420 -> 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: 287, 0 KNN fn, tp: 10, 1 KNN f1 score: 0.167 KNN cohens kappa score: 0.162 ------ Step 3/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples Epoch 1/3 Epoch 2/3 Epoch 3/3 -> create 1104 synthetic samples -> test with 'LR' LR tn, fp: 285, 0 LR fn, tp: 5, 2 LR f1 score: 0.444 LR cohens kappa score: 0.438 LR average precision score: 0.444 -> test with 'GB' GB tn, fp: 283, 2 GB fn, tp: 4, 3 GB f1 score: 0.500 GB cohens kappa score: 0.490 -> test with 'KNN' KNN tn, fp: 285, 0 KNN fn, tp: 5, 2 KNN f1 score: 0.444 KNN cohens kappa score: 0.438 ====== Step 4/5 ======= -> Shuffling data -> Spliting data to slices ------ Step 4/5: Slice 1/5 ------- -> Reset the GAN -> Train generator for synthetic samples Epoch 1/3 Epoch 2/3 Epoch 3/3 -> create 1106 synthetic samples -> test with 'LR' LR tn, fp: 287, 0 LR fn, tp: 8, 3 LR f1 score: 0.429 LR cohens kappa score: 0.419 LR average precision score: 0.471 -> 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: 287, 0 KNN fn, tp: 10, 1 KNN f1 score: 0.167 KNN cohens kappa score: 0.162 ------ Step 4/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples Epoch 1/3 Epoch 2/3 Epoch 3/3 -> create 1106 synthetic samples -> test with 'LR' LR tn, fp: 287, 0 LR fn, tp: 11, 0 LR f1 score: 0.000 LR cohens kappa score: 0.000 LR average precision score: 0.436 -> 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: 286, 1 KNN fn, tp: 11, 0 KNN f1 score: 0.000 KNN cohens kappa score: -0.006 ------ Step 4/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples Epoch 1/3 Epoch 2/3 Epoch 3/3 -> create 1106 synthetic samples -> test with 'LR' LR tn, fp: 285, 2 LR fn, tp: 10, 1 LR f1 score: 0.143 LR cohens kappa score: 0.129 LR average precision score: 0.244 -> test with 'GB' GB tn, fp: 282, 5 GB fn, tp: 9, 2 GB f1 score: 0.222 GB cohens kappa score: 0.199 -> test with 'KNN' KNN tn, fp: 287, 0 KNN fn, tp: 11, 0 KNN f1 score: 0.000 KNN cohens kappa score: 0.000 ------ Step 4/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples Epoch 1/3 Epoch 2/3 Epoch 3/3 -> create 1106 synthetic samples -> test with 'LR' LR tn, fp: 283, 4 LR fn, tp: 8, 3 LR f1 score: 0.333 LR cohens kappa score: 0.314 LR average precision score: 0.306 -> 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: 286, 1 KNN fn, tp: 10, 1 KNN f1 score: 0.154 KNN cohens kappa score: 0.144 ------ Step 4/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples Epoch 1/3 Epoch 2/3 Epoch 3/3 -> create 1104 synthetic samples -> test with 'LR' LR tn, fp: 283, 2 LR fn, tp: 4, 3 LR f1 score: 0.500 LR cohens kappa score: 0.490 LR average precision score: 0.586 -> test with 'GB' GB tn, fp: 282, 3 GB fn, tp: 5, 2 GB f1 score: 0.333 GB cohens kappa score: 0.320 -> test with 'KNN' KNN tn, fp: 285, 0 KNN fn, tp: 5, 2 KNN f1 score: 0.444 KNN cohens kappa score: 0.438 ====== Step 5/5 ======= -> Shuffling data -> Spliting data to slices ------ Step 5/5: Slice 1/5 ------- -> Reset the GAN -> Train generator for synthetic samples Epoch 1/3 Epoch 2/3 Epoch 3/3 -> create 1106 synthetic samples -> test with 'LR' LR tn, fp: 283, 4 LR fn, tp: 9, 2 LR f1 score: 0.235 LR cohens kappa score: 0.215 LR average precision score: 0.264 -> 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: 287, 0 KNN fn, tp: 11, 0 KNN f1 score: 0.000 KNN cohens kappa score: 0.000 ------ Step 5/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples Epoch 1/3 Epoch 2/3 Epoch 3/3 -> create 1106 synthetic samples -> test with 'LR' LR tn, fp: 285, 2 LR fn, tp: 7, 4 LR f1 score: 0.471 LR cohens kappa score: 0.456 LR average precision score: 0.558 -> test with 'GB' GB tn, fp: 286, 1 GB fn, tp: 8, 3 GB f1 score: 0.400 GB cohens kappa score: 0.388 -> test with 'KNN' KNN tn, fp: 287, 0 KNN fn, tp: 9, 2 KNN f1 score: 0.308 KNN cohens kappa score: 0.300 ------ Step 5/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples Epoch 1/3 Epoch 2/3 Epoch 3/3 -> create 1106 synthetic samples -> test with 'LR' LR tn, fp: 287, 0 LR fn, tp: 10, 1 LR f1 score: 0.167 LR cohens kappa score: 0.162 LR average precision score: 0.548 -> 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: 287, 0 KNN fn, tp: 10, 1 KNN f1 score: 0.167 KNN cohens kappa score: 0.162 ------ Step 5/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples Epoch 1/3 Epoch 2/3 Epoch 3/3 -> create 1106 synthetic samples -> test with 'LR' LR tn, fp: 284, 3 LR fn, tp: 7, 4 LR f1 score: 0.444 LR cohens kappa score: 0.428 LR average precision score: 0.503 -> 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: 287, 0 KNN fn, tp: 11, 0 KNN f1 score: 0.000 KNN cohens kappa score: 0.000 ------ Step 5/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples Epoch 1/3 Epoch 2/3 Epoch 3/3 -> create 1104 synthetic samples -> test with 'LR' LR tn, fp: 283, 2 LR fn, tp: 6, 1 LR f1 score: 0.200 LR cohens kappa score: 0.188 LR average precision score: 0.180 -> 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: 285, 0 KNN fn, tp: 7, 0 KNN f1 score: 0.000 KNN cohens kappa score: 0.000 ### Exercise is done. -----[ LR ]----- maximum: LR tn, fp: 287, 4 LR fn, tp: 11, 6 LR f1 score: 0.600 LR cohens kappa score: 0.586 LR average precision score: 0.656 average: LR tn, fp: 284.6, 2.0 LR fn, tp: 8.0, 2.2 LR f1 score: 0.292 LR cohens kappa score: 0.279 LR average precision score: 0.400 minimum: LR tn, fp: 282, 0 LR fn, tp: 4, 0 LR f1 score: 0.000 LR cohens kappa score: -0.016 LR average precision score: 0.180 -----[ GB ]----- maximum: GB tn, fp: 287, 7 GB fn, tp: 10, 6 GB f1 score: 0.571 GB cohens kappa score: 0.556 average: GB tn, fp: 283.48, 3.12 GB fn, tp: 7.36, 2.84 GB f1 score: 0.352 GB cohens kappa score: 0.335 minimum: GB tn, fp: 280, 0 GB fn, tp: 4, 1 GB f1 score: 0.125 GB cohens kappa score: 0.104 -----[ KNN ]----- maximum: KNN tn, fp: 287, 1 KNN fn, tp: 11, 2 KNN f1 score: 0.444 KNN cohens kappa score: 0.438 average: KNN tn, fp: 286.32, 0.28 KNN fn, tp: 9.68, 0.52 KNN f1 score: 0.092 KNN cohens kappa score: 0.088 minimum: KNN tn, fp: 284, 0 KNN fn, tp: 5, 0 KNN f1 score: 0.000 KNN cohens kappa score: -0.006