/////////////////////////////////////////// // Running convGAN-majority-full on folding_abalone9-18 /////////////////////////////////////////// Load 'data_input/folding_abalone9-18' 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 518 synthetic samples -> retrain GAN for predict Epoch 1/10 1/56 [..............................] - ETA: 7s - loss: 0.0614 49/56 [=========================>....] - ETA: 0s - loss: 0.0987 56/56 [==============================] - 0s 1ms/step - loss: 0.1013 Epoch 2/10 1/56 [..............................] - ETA: 0s - loss: 0.2406 49/56 [=========================>....] - ETA: 0s - loss: 0.0972 56/56 [==============================] - 0s 1ms/step - loss: 0.0940 Epoch 3/10 1/56 [..............................] - ETA: 0s - loss: 0.2309 49/56 [=========================>....] - ETA: 0s - loss: 0.0894 56/56 [==============================] - 0s 1ms/step - loss: 0.0884 Epoch 4/10 1/56 [..............................] - ETA: 0s - loss: 0.1345 49/56 [=========================>....] - ETA: 0s - loss: 0.0725 56/56 [==============================] - 0s 1ms/step - loss: 0.0902 Epoch 5/10 1/56 [..............................] - ETA: 0s - loss: 0.0460 50/56 [=========================>....] - ETA: 0s - loss: 0.0806 56/56 [==============================] - 0s 1ms/step - loss: 0.0885 Epoch 6/10 1/56 [..............................] - ETA: 0s - loss: 0.0116 50/56 [=========================>....] - ETA: 0s - loss: 0.0884 56/56 [==============================] - 0s 1ms/step - loss: 0.0869 Epoch 7/10 1/56 [..............................] - ETA: 0s - loss: 0.0121 50/56 [=========================>....] - ETA: 0s - loss: 0.0811 56/56 [==============================] - 0s 1ms/step - loss: 0.0881 Epoch 8/10 1/56 [..............................] - ETA: 0s - loss: 0.0323 50/56 [=========================>....] - ETA: 0s - loss: 0.0903 56/56 [==============================] - 0s 1ms/step - loss: 0.0884 Epoch 9/10 1/56 [..............................] - ETA: 0s - loss: 0.0292 50/56 [=========================>....] - ETA: 0s - loss: 0.0871 56/56 [==============================] - 0s 1ms/step - loss: 0.0848 Epoch 10/10 1/56 [..............................] - ETA: 0s - loss: 0.0436 49/56 [=========================>....] - ETA: 0s - loss: 0.0805 56/56 [==============================] - 0s 1ms/step - loss: 0.0837 -> test with GAN.predict GAN tn, fp: 137, 1 GAN fn, tp: 3, 6 GAN f1 score: 0.750 GAN cohens kappa score: 0.736 -> test with 'LR' LR tn, fp: 127, 11 LR fn, tp: 1, 8 LR f1 score: 0.571 LR cohens kappa score: 0.533 LR average precision score: 0.920 -> test with 'RF' RF tn, fp: 138, 0 RF fn, tp: 6, 3 RF f1 score: 0.500 RF cohens kappa score: 0.484 -> test with 'GB' GB tn, fp: 137, 1 GB fn, tp: 6, 3 GB f1 score: 0.462 GB cohens kappa score: 0.440 -> test with 'KNN' KNN tn, fp: 137, 1 KNN fn, tp: 9, 0 KNN f1 score: 0.000 KNN cohens kappa score: -0.012 ------ Step 1/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 518 synthetic samples -> retrain GAN for predict Epoch 1/10 1/56 [..............................] - ETA: 7s - loss: 0.0052 48/56 [========================>.....] - ETA: 0s - loss: 0.1266 56/56 [==============================] - 0s 1ms/step - loss: 0.1203 Epoch 2/10 1/56 [..............................] - ETA: 0s - loss: 0.2734 47/56 [========================>.....] - ETA: 0s - loss: 0.1029 56/56 [==============================] - 0s 1ms/step - loss: 0.1054 Epoch 3/10 1/56 [..............................] - ETA: 0s - loss: 0.0378 31/56 [===============>..............] - ETA: 0s - loss: 0.1024 56/56 [==============================] - 0s 4ms/step - loss: 0.1057 Epoch 4/10 1/56 [..............................] - ETA: 0s - loss: 0.0308 49/56 [=========================>....] - ETA: 0s - loss: 0.1023 56/56 [==============================] - 0s 1ms/step - loss: 0.0979 Epoch 5/10 1/56 [..............................] - ETA: 0s - loss: 0.0124 49/56 [=========================>....] - ETA: 0s - loss: 0.1025 56/56 [==============================] - 0s 1ms/step - loss: 0.0968 Epoch 6/10 1/56 [..............................] - ETA: 0s - loss: 0.0798 50/56 [=========================>....] - ETA: 0s - loss: 0.0999 56/56 [==============================] - 0s 1ms/step - loss: 0.0980 Epoch 7/10 1/56 [..............................] - ETA: 0s - loss: 0.2325 49/56 [=========================>....] - ETA: 0s - loss: 0.0999 56/56 [==============================] - 0s 1ms/step - loss: 0.0937 Epoch 8/10 1/56 [..............................] - ETA: 0s - loss: 0.0378 50/56 [=========================>....] - ETA: 0s - loss: 0.0907 56/56 [==============================] - 0s 1ms/step - loss: 0.0938 Epoch 9/10 1/56 [..............................] - ETA: 0s - loss: 0.0535 49/56 [=========================>....] - ETA: 0s - loss: 0.0851 56/56 [==============================] - 0s 1ms/step - loss: 0.0921 Epoch 10/10 1/56 [..............................] - ETA: 0s - loss: 0.3573 49/56 [=========================>....] - ETA: 0s - loss: 0.0914 56/56 [==============================] - 0s 1ms/step - loss: 0.0915 -> test with GAN.predict GAN tn, fp: 129, 9 GAN fn, tp: 4, 5 GAN f1 score: 0.435 GAN cohens kappa score: 0.389 -> test with 'LR' LR tn, fp: 130, 8 LR fn, tp: 3, 6 LR f1 score: 0.522 LR cohens kappa score: 0.483 LR average precision score: 0.571 -> test with 'RF' RF tn, fp: 137, 1 RF fn, tp: 8, 1 RF f1 score: 0.182 RF cohens kappa score: 0.163 -> test with 'GB' GB tn, fp: 136, 2 GB fn, tp: 8, 1 GB f1 score: 0.167 GB cohens kappa score: 0.140 -> test with 'KNN' KNN tn, fp: 135, 3 KNN fn, tp: 8, 1 KNN f1 score: 0.154 KNN cohens kappa score: 0.121 ------ Step 1/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 518 synthetic samples -> retrain GAN for predict Epoch 1/10 1/56 [..............................] - ETA: 8s - loss: 0.0088 46/56 [=======================>......] - ETA: 0s - loss: 0.1133 56/56 [==============================] - 0s 1ms/step - loss: 0.1087 Epoch 2/10 1/56 [..............................] - ETA: 0s - loss: 0.0119 46/56 [=======================>......] - ETA: 0s - loss: 0.0879 56/56 [==============================] - 0s 1ms/step - loss: 0.0925 Epoch 3/10 1/56 [..............................] - ETA: 0s - loss: 0.0061 45/56 [=======================>......] - ETA: 0s - loss: 0.0930 56/56 [==============================] - 0s 1ms/step - loss: 0.0884 Epoch 4/10 1/56 [..............................] - ETA: 0s - loss: 0.0375 46/56 [=======================>......] - ETA: 0s - loss: 0.0834 56/56 [==============================] - 0s 1ms/step - loss: 0.0893 Epoch 5/10 1/56 [..............................] - ETA: 0s - loss: 0.0302 46/56 [=======================>......] - ETA: 0s - loss: 0.0829 56/56 [==============================] - 0s 1ms/step - loss: 0.0868 Epoch 6/10 1/56 [..............................] - ETA: 0s - loss: 0.0561 44/56 [======================>.......] - ETA: 0s - loss: 0.0869 56/56 [==============================] - 0s 1ms/step - loss: 0.0857 Epoch 7/10 1/56 [..............................] - ETA: 0s - loss: 0.0138 45/56 [=======================>......] - ETA: 0s - loss: 0.0824 56/56 [==============================] - 0s 1ms/step - loss: 0.0814 Epoch 8/10 1/56 [..............................] - ETA: 0s - loss: 0.0120 46/56 [=======================>......] - ETA: 0s - loss: 0.0839 56/56 [==============================] - 0s 1ms/step - loss: 0.0805 Epoch 9/10 1/56 [..............................] - ETA: 0s - loss: 0.0219 45/56 [=======================>......] - ETA: 0s - loss: 0.0755 56/56 [==============================] - 0s 1ms/step - loss: 0.0797 Epoch 10/10 1/56 [..............................] - ETA: 0s - loss: 0.0243 46/56 [=======================>......] - ETA: 0s - loss: 0.0820 56/56 [==============================] - 0s 1ms/step - loss: 0.0860 -> test with GAN.predict GAN tn, fp: 136, 2 GAN fn, tp: 4, 5 GAN f1 score: 0.625 GAN cohens kappa score: 0.604 -> test with 'LR' LR tn, fp: 132, 6 LR fn, tp: 1, 8 LR f1 score: 0.696 LR cohens kappa score: 0.671 LR average precision score: 0.799 -> test with 'RF' RF tn, fp: 136, 2 RF fn, tp: 7, 2 RF f1 score: 0.308 RF cohens kappa score: 0.281 -> test with 'GB' GB tn, fp: 137, 1 GB fn, tp: 8, 1 GB f1 score: 0.182 GB cohens kappa score: 0.163 -> test with 'KNN' KNN tn, fp: 138, 0 KNN fn, tp: 8, 1 KNN f1 score: 0.200 KNN cohens kappa score: 0.190 ------ Step 1/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 518 synthetic samples -> retrain GAN for predict Epoch 1/10 1/56 [..............................] - ETA: 7s - loss: 0.4065 42/56 [=====================>........] - ETA: 0s - loss: 0.1108 56/56 [==============================] - 0s 1ms/step - loss: 0.1076 Epoch 2/10 1/56 [..............................] - ETA: 0s - loss: 0.0198 50/56 [=========================>....] - ETA: 0s - loss: 0.0894 56/56 [==============================] - 0s 1ms/step - loss: 0.0919 Epoch 3/10 1/56 [..............................] - ETA: 0s - loss: 0.1713 49/56 [=========================>....] - ETA: 0s - loss: 0.0890 56/56 [==============================] - 0s 1ms/step - loss: 0.0931 Epoch 4/10 1/56 [..............................] - ETA: 0s - loss: 0.0488 50/56 [=========================>....] - ETA: 0s - loss: 0.0744 56/56 [==============================] - 0s 1ms/step - loss: 0.0844 Epoch 5/10 1/56 [..............................] - ETA: 0s - loss: 0.0318 49/56 [=========================>....] - ETA: 0s - loss: 0.0923 56/56 [==============================] - 0s 1ms/step - loss: 0.0866 Epoch 6/10 1/56 [..............................] - ETA: 0s - loss: 0.0301 49/56 [=========================>....] - ETA: 0s - loss: 0.0884 56/56 [==============================] - 0s 1ms/step - loss: 0.0823 Epoch 7/10 1/56 [..............................] - ETA: 0s - loss: 0.0970 50/56 [=========================>....] - ETA: 0s - loss: 0.0877 56/56 [==============================] - 0s 1ms/step - loss: 0.0821 Epoch 8/10 1/56 [..............................] - ETA: 0s - loss: 0.0395 50/56 [=========================>....] - ETA: 0s - loss: 0.0693 56/56 [==============================] - 0s 1ms/step - loss: 0.0796 Epoch 9/10 1/56 [..............................] - ETA: 0s - loss: 0.0239 49/56 [=========================>....] - ETA: 0s - loss: 0.0828 56/56 [==============================] - 0s 1ms/step - loss: 0.0807 Epoch 10/10 1/56 [..............................] - ETA: 0s - loss: 0.1084 49/56 [=========================>....] - ETA: 0s - loss: 0.0789 56/56 [==============================] - 0s 1ms/step - loss: 0.0819 -> test with GAN.predict GAN tn, fp: 136, 2 GAN fn, tp: 6, 3 GAN f1 score: 0.429 GAN cohens kappa score: 0.402 -> test with 'LR' LR tn, fp: 134, 4 LR fn, tp: 4, 5 LR f1 score: 0.556 LR cohens kappa score: 0.527 LR average precision score: 0.634 -> test with 'RF' RF tn, fp: 138, 0 RF fn, tp: 7, 2 RF f1 score: 0.364 RF cohens kappa score: 0.349 -> test with 'GB' GB tn, fp: 137, 1 GB fn, tp: 5, 4 GB f1 score: 0.571 GB cohens kappa score: 0.552 -> test with 'KNN' KNN tn, fp: 138, 0 KNN fn, tp: 8, 1 KNN f1 score: 0.200 KNN cohens kappa score: 0.190 ------ Step 1/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 516 synthetic samples -> retrain GAN for predict Epoch 1/10 1/56 [..............................] - ETA: 7s - loss: 0.0181 48/56 [========================>.....] - ETA: 0s - loss: 0.0913 56/56 [==============================] - 0s 1ms/step - loss: 0.0955 Epoch 2/10 1/56 [..............................] - ETA: 0s - loss: 0.0252 33/56 [================>.............] - ETA: 0s - loss: 0.0665 56/56 [==============================] - 0s 1ms/step - loss: 0.0864 Epoch 3/10 1/56 [..............................] - ETA: 0s - loss: 0.1864 38/56 [===================>..........] - ETA: 0s - loss: 0.0827 56/56 [==============================] - 0s 1ms/step - loss: 0.0965 Epoch 4/10 1/56 [..............................] - ETA: 0s - loss: 0.0117 41/56 [====================>.........] - ETA: 0s - loss: 0.0815 56/56 [==============================] - 0s 1ms/step - loss: 0.0906 Epoch 5/10 1/56 [..............................] - ETA: 0s - loss: 0.3460 46/56 [=======================>......] - ETA: 0s - loss: 0.1045 56/56 [==============================] - 0s 1ms/step - loss: 0.0970 Epoch 6/10 1/56 [..............................] - ETA: 0s - loss: 0.0051 41/56 [====================>.........] - ETA: 0s - loss: 0.0709 56/56 [==============================] - 0s 1ms/step - loss: 0.0875 Epoch 7/10 1/56 [..............................] - ETA: 0s - loss: 0.0269 45/56 [=======================>......] - ETA: 0s - loss: 0.0882 56/56 [==============================] - 0s 1ms/step - loss: 0.0876 Epoch 8/10 1/56 [..............................] - ETA: 0s - loss: 0.0200 43/56 [======================>.......] - ETA: 0s - loss: 0.0735 56/56 [==============================] - 0s 1ms/step - loss: 0.0899 Epoch 9/10 1/56 [..............................] - ETA: 0s - loss: 0.0074 44/56 [======================>.......] - ETA: 0s - loss: 0.0949 56/56 [==============================] - 0s 1ms/step - loss: 0.0870 Epoch 10/10 1/56 [..............................] - ETA: 0s - loss: 0.3021 45/56 [=======================>......] - ETA: 0s - loss: 0.0956 56/56 [==============================] - 0s 1ms/step - loss: 0.0914 -> test with GAN.predict GAN tn, fp: 135, 2 GAN fn, tp: 4, 2 GAN f1 score: 0.400 GAN cohens kappa score: 0.379 -> test with 'LR' LR tn, fp: 133, 4 LR fn, tp: 2, 4 LR f1 score: 0.571 LR cohens kappa score: 0.550 LR average precision score: 0.484 -> test with 'RF' RF tn, fp: 137, 0 RF fn, tp: 5, 1 RF f1 score: 0.286 RF cohens kappa score: 0.277 -> test with 'GB' GB tn, fp: 136, 1 GB fn, tp: 5, 1 GB f1 score: 0.250 GB cohens kappa score: 0.234 -> test with 'KNN' KNN tn, fp: 137, 0 KNN fn, tp: 5, 1 KNN f1 score: 0.286 KNN cohens kappa score: 0.277 ====== Step 2/5 ======= -> Shuffling data -> Spliting data to slices ------ Step 2/5: Slice 1/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 518 synthetic samples -> retrain GAN for predict Epoch 1/10 1/56 [..............................] - ETA: 8s - loss: 0.0605 49/56 [=========================>....] - ETA: 0s - loss: 0.1000 56/56 [==============================] - 0s 1ms/step - loss: 0.0955 Epoch 2/10 1/56 [..............................] - ETA: 0s - loss: 0.0445 49/56 [=========================>....] - ETA: 0s - loss: 0.0916 56/56 [==============================] - 0s 1ms/step - loss: 0.0920 Epoch 3/10 1/56 [..............................] - ETA: 0s - loss: 0.1234 49/56 [=========================>....] - ETA: 0s - loss: 0.0872 56/56 [==============================] - 0s 1ms/step - loss: 0.0878 Epoch 4/10 1/56 [..............................] - ETA: 0s - loss: 0.4762 49/56 [=========================>....] - ETA: 0s - loss: 0.0875 56/56 [==============================] - 0s 1ms/step - loss: 0.0852 Epoch 5/10 1/56 [..............................] - ETA: 0s - loss: 0.1695 49/56 [=========================>....] - ETA: 0s - loss: 0.0794 56/56 [==============================] - 0s 1ms/step - loss: 0.0823 Epoch 6/10 1/56 [..............................] - ETA: 0s - loss: 0.2331 49/56 [=========================>....] - ETA: 0s - loss: 0.0908 56/56 [==============================] - 0s 1ms/step - loss: 0.0875 Epoch 7/10 1/56 [..............................] - ETA: 0s - loss: 0.0096 49/56 [=========================>....] - ETA: 0s - loss: 0.0871 56/56 [==============================] - 0s 1ms/step - loss: 0.0887 Epoch 8/10 1/56 [..............................] - ETA: 0s - loss: 0.1408 48/56 [========================>.....] - ETA: 0s - loss: 0.0887 56/56 [==============================] - 0s 1ms/step - loss: 0.0857 Epoch 9/10 1/56 [..............................] - ETA: 0s - loss: 0.0821 49/56 [=========================>....] - ETA: 0s - loss: 0.0952 56/56 [==============================] - 0s 1ms/step - loss: 0.0903 Epoch 10/10 1/56 [..............................] - ETA: 0s - loss: 0.0236 49/56 [=========================>....] - ETA: 0s - loss: 0.0810 56/56 [==============================] - 0s 1ms/step - loss: 0.0832 -> test with GAN.predict GAN tn, fp: 135, 3 GAN fn, tp: 6, 3 GAN f1 score: 0.400 GAN cohens kappa score: 0.369 -> test with 'LR' LR tn, fp: 131, 7 LR fn, tp: 2, 7 LR f1 score: 0.609 LR cohens kappa score: 0.577 LR average precision score: 0.615 -> test with 'RF' RF tn, fp: 136, 2 RF fn, tp: 5, 4 RF f1 score: 0.533 RF cohens kappa score: 0.509 -> test with 'GB' GB tn, fp: 135, 3 GB fn, tp: 6, 3 GB f1 score: 0.400 GB cohens kappa score: 0.369 -> test with 'KNN' KNN tn, fp: 135, 3 KNN fn, tp: 8, 1 KNN f1 score: 0.154 KNN cohens kappa score: 0.121 ------ Step 2/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 518 synthetic samples -> retrain GAN for predict Epoch 1/10 1/56 [..............................] - ETA: 7s - loss: 0.0036 50/56 [=========================>....] - ETA: 0s - loss: 0.1100 56/56 [==============================] - 0s 1ms/step - loss: 0.1072 Epoch 2/10 1/56 [..............................] - ETA: 0s - loss: 0.1136 50/56 [=========================>....] - ETA: 0s - loss: 0.0969 56/56 [==============================] - 0s 1ms/step - loss: 0.0986 Epoch 3/10 1/56 [..............................] - ETA: 0s - loss: 0.1500 50/56 [=========================>....] - ETA: 0s - loss: 0.0874 56/56 [==============================] - 0s 1ms/step - loss: 0.0952 Epoch 4/10 1/56 [..............................] - ETA: 0s - loss: 0.0960 50/56 [=========================>....] - ETA: 0s - loss: 0.0974 56/56 [==============================] - 0s 1ms/step - loss: 0.0961 Epoch 5/10 1/56 [..............................] - ETA: 0s - loss: 0.1029 48/56 [========================>.....] - ETA: 0s - loss: 0.0929 56/56 [==============================] - 0s 1ms/step - loss: 0.0925 Epoch 6/10 1/56 [..............................] - ETA: 0s - loss: 0.1297 44/56 [======================>.......] - ETA: 0s - loss: 0.1033 56/56 [==============================] - 0s 1ms/step - loss: 0.0942 Epoch 7/10 1/56 [..............................] - ETA: 0s - loss: 0.0412 45/56 [=======================>......] - ETA: 0s - loss: 0.0909 56/56 [==============================] - 0s 1ms/step - loss: 0.0941 Epoch 8/10 1/56 [..............................] - ETA: 0s - loss: 0.0432 49/56 [=========================>....] - ETA: 0s - loss: 0.0952 56/56 [==============================] - 0s 1ms/step - loss: 0.0927 Epoch 9/10 1/56 [..............................] - ETA: 0s - loss: 0.0618 49/56 [=========================>....] - ETA: 0s - loss: 0.0932 56/56 [==============================] - 0s 1ms/step - loss: 0.0917 Epoch 10/10 1/56 [..............................] - ETA: 0s - loss: 0.0222 49/56 [=========================>....] - ETA: 0s - loss: 0.0830 56/56 [==============================] - 0s 1ms/step - loss: 0.0902 -> test with GAN.predict GAN tn, fp: 133, 5 GAN fn, tp: 3, 6 GAN f1 score: 0.600 GAN cohens kappa score: 0.571 -> test with 'LR' LR tn, fp: 132, 6 LR fn, tp: 2, 7 LR f1 score: 0.636 LR cohens kappa score: 0.608 LR average precision score: 0.779 -> test with 'RF' RF tn, fp: 136, 2 RF fn, tp: 7, 2 RF f1 score: 0.308 RF cohens kappa score: 0.281 -> test with 'GB' GB tn, fp: 137, 1 GB fn, tp: 8, 1 GB f1 score: 0.182 GB cohens kappa score: 0.163 -> test with 'KNN' KNN tn, fp: 137, 1 KNN fn, tp: 8, 1 KNN f1 score: 0.182 KNN cohens kappa score: 0.163 ------ Step 2/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 518 synthetic samples -> retrain GAN for predict Epoch 1/10 1/56 [..............................] - ETA: 7s - loss: 0.0121 46/56 [=======================>......] - ETA: 0s - loss: 0.1086 56/56 [==============================] - 0s 1ms/step - loss: 0.0980 Epoch 2/10 1/56 [..............................] - ETA: 0s - loss: 0.0175 50/56 [=========================>....] - ETA: 0s - loss: 0.0865 56/56 [==============================] - 0s 1ms/step - loss: 0.0849 Epoch 3/10 1/56 [..............................] - ETA: 0s - loss: 0.0288 48/56 [========================>.....] - ETA: 0s - loss: 0.0824 56/56 [==============================] - 0s 1ms/step - loss: 0.0813 Epoch 4/10 1/56 [..............................] - ETA: 0s - loss: 0.0158 50/56 [=========================>....] - ETA: 0s - loss: 0.0829 56/56 [==============================] - 0s 1ms/step - loss: 0.0799 Epoch 5/10 1/56 [..............................] - ETA: 0s - loss: 0.1422 49/56 [=========================>....] - ETA: 0s - loss: 0.0730 56/56 [==============================] - 0s 1ms/step - loss: 0.0779 Epoch 6/10 1/56 [..............................] - ETA: 0s - loss: 0.0437 50/56 [=========================>....] - ETA: 0s - loss: 0.0833 56/56 [==============================] - 0s 1ms/step - loss: 0.0808 Epoch 7/10 1/56 [..............................] - ETA: 0s - loss: 0.0993 50/56 [=========================>....] - ETA: 0s - loss: 0.0790 56/56 [==============================] - 0s 1ms/step - loss: 0.0783 Epoch 8/10 1/56 [..............................] - ETA: 0s - loss: 0.0115 50/56 [=========================>....] - ETA: 0s - loss: 0.0809 56/56 [==============================] - 0s 1ms/step - loss: 0.0764 Epoch 9/10 1/56 [..............................] - ETA: 0s - loss: 0.0151 49/56 [=========================>....] - ETA: 0s - loss: 0.0797 56/56 [==============================] - 0s 1ms/step - loss: 0.0752 Epoch 10/10 1/56 [..............................] - ETA: 0s - loss: 0.0830 49/56 [=========================>....] - ETA: 0s - loss: 0.0665 56/56 [==============================] - 0s 1ms/step - loss: 0.0733 -> test with GAN.predict GAN tn, fp: 137, 1 GAN fn, tp: 4, 5 GAN f1 score: 0.667 GAN cohens kappa score: 0.649 -> test with 'LR' LR tn, fp: 134, 4 LR fn, tp: 2, 7 LR f1 score: 0.700 LR cohens kappa score: 0.678 LR average precision score: 0.730 -> test with 'RF' RF tn, fp: 135, 3 RF fn, tp: 8, 1 RF f1 score: 0.154 RF cohens kappa score: 0.121 -> test with 'GB' GB tn, fp: 134, 4 GB fn, tp: 7, 2 GB f1 score: 0.267 GB cohens kappa score: 0.229 -> test with 'KNN' KNN tn, fp: 138, 0 KNN fn, tp: 9, 0 KNN f1 score: 0.000 KNN cohens kappa score: 0.000 ------ Step 2/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 518 synthetic samples -> retrain GAN for predict Epoch 1/10 1/56 [..............................] - ETA: 8s - loss: 0.7748 49/56 [=========================>....] - ETA: 0s - loss: 0.1241 56/56 [==============================] - 0s 1ms/step - loss: 0.1140 Epoch 2/10 1/56 [..............................] - ETA: 0s - loss: 0.2186 50/56 [=========================>....] - ETA: 0s - loss: 0.0996 56/56 [==============================] - 0s 1ms/step - loss: 0.1016 Epoch 3/10 1/56 [..............................] - ETA: 0s - loss: 0.3670 49/56 [=========================>....] - ETA: 0s - loss: 0.0849 56/56 [==============================] - 0s 1ms/step - loss: 0.0886 Epoch 4/10 1/56 [..............................] - ETA: 0s - loss: 0.0177 49/56 [=========================>....] - ETA: 0s - loss: 0.0941 56/56 [==============================] - 0s 1ms/step - loss: 0.0886 Epoch 5/10 1/56 [..............................] - ETA: 0s - loss: 0.0393 50/56 [=========================>....] - ETA: 0s - loss: 0.0822 56/56 [==============================] - 0s 1ms/step - loss: 0.0846 Epoch 6/10 1/56 [..............................] - ETA: 0s - loss: 0.1120 50/56 [=========================>....] - ETA: 0s - loss: 0.0901 56/56 [==============================] - 0s 1ms/step - loss: 0.0842 Epoch 7/10 1/56 [..............................] - ETA: 0s - loss: 0.0413 49/56 [=========================>....] - ETA: 0s - loss: 0.0812 56/56 [==============================] - 0s 1ms/step - loss: 0.0819 Epoch 8/10 1/56 [..............................] - ETA: 0s - loss: 0.0328 50/56 [=========================>....] - ETA: 0s - loss: 0.0721 56/56 [==============================] - 0s 1ms/step - loss: 0.0794 Epoch 9/10 1/56 [..............................] - ETA: 0s - loss: 0.0896 49/56 [=========================>....] - ETA: 0s - loss: 0.0794 56/56 [==============================] - 0s 1ms/step - loss: 0.0782 Epoch 10/10 1/56 [..............................] - ETA: 0s - loss: 0.0424 50/56 [=========================>....] - ETA: 0s - loss: 0.0752 56/56 [==============================] - 0s 1ms/step - loss: 0.0785 -> test with GAN.predict GAN tn, fp: 135, 3 GAN fn, tp: 6, 3 GAN f1 score: 0.400 GAN cohens kappa score: 0.369 -> test with 'LR' LR tn, fp: 133, 5 LR fn, tp: 3, 6 LR f1 score: 0.600 LR cohens kappa score: 0.571 LR average precision score: 0.740 -> test with 'RF' RF tn, fp: 134, 4 RF fn, tp: 7, 2 RF f1 score: 0.267 RF cohens kappa score: 0.229 -> test with 'GB' GB tn, fp: 135, 3 GB fn, tp: 6, 3 GB f1 score: 0.400 GB cohens kappa score: 0.369 -> test with 'KNN' KNN tn, fp: 137, 1 KNN fn, tp: 9, 0 KNN f1 score: 0.000 KNN cohens kappa score: -0.012 ------ Step 2/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 516 synthetic samples -> retrain GAN for predict Epoch 1/10 1/56 [..............................] - ETA: 8s - loss: 0.0221 46/56 [=======================>......] - ETA: 0s - loss: 0.0921 56/56 [==============================] - 0s 1ms/step - loss: 0.0939 Epoch 2/10 1/56 [..............................] - ETA: 0s - loss: 0.0277 46/56 [=======================>......] - ETA: 0s - loss: 0.0743 56/56 [==============================] - 0s 1ms/step - loss: 0.0918 Epoch 3/10 1/56 [..............................] - ETA: 0s - loss: 0.0452 45/56 [=======================>......] - ETA: 0s - loss: 0.0821 56/56 [==============================] - 0s 1ms/step - loss: 0.0879 Epoch 4/10 1/56 [..............................] - ETA: 0s - loss: 0.0281 45/56 [=======================>......] - ETA: 0s - loss: 0.0814 56/56 [==============================] - 0s 1ms/step - loss: 0.0933 Epoch 5/10 1/56 [..............................] - ETA: 0s - loss: 0.0390 45/56 [=======================>......] - ETA: 0s - loss: 0.0909 56/56 [==============================] - 0s 1ms/step - loss: 0.0874 Epoch 6/10 1/56 [..............................] - ETA: 0s - loss: 0.1012 45/56 [=======================>......] - ETA: 0s - loss: 0.0962 56/56 [==============================] - 0s 1ms/step - loss: 0.0895 Epoch 7/10 1/56 [..............................] - ETA: 0s - loss: 0.0472 43/56 [======================>.......] - ETA: 0s - loss: 0.0849 56/56 [==============================] - 0s 1ms/step - loss: 0.0852 Epoch 8/10 1/56 [..............................] - ETA: 0s - loss: 0.0049 42/56 [=====================>........] - ETA: 0s - loss: 0.0784 56/56 [==============================] - 0s 1ms/step - loss: 0.0865 Epoch 9/10 1/56 [..............................] - ETA: 0s - loss: 0.0194 42/56 [=====================>........] - ETA: 0s - loss: 0.0870 56/56 [==============================] - 0s 1ms/step - loss: 0.0863 Epoch 10/10 1/56 [..............................] - ETA: 0s - loss: 0.0064 46/56 [=======================>......] - ETA: 0s - loss: 0.0725 56/56 [==============================] - 0s 1ms/step - loss: 0.0838 -> test with GAN.predict GAN tn, fp: 136, 1 GAN fn, tp: 3, 3 GAN f1 score: 0.600 GAN cohens kappa score: 0.586 -> test with 'LR' LR tn, fp: 129, 8 LR fn, tp: 1, 5 LR f1 score: 0.526 LR cohens kappa score: 0.497 LR average precision score: 0.645 -> test with 'RF' RF tn, fp: 136, 1 RF fn, tp: 4, 2 RF f1 score: 0.444 RF cohens kappa score: 0.428 -> test with 'GB' GB tn, fp: 136, 1 GB fn, tp: 5, 1 GB f1 score: 0.250 GB cohens kappa score: 0.234 -> test with 'KNN' KNN tn, fp: 137, 0 KNN fn, tp: 5, 1 KNN f1 score: 0.286 KNN cohens kappa score: 0.277 ====== Step 3/5 ======= -> Shuffling data -> Spliting data to slices ------ Step 3/5: Slice 1/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 518 synthetic samples -> retrain GAN for predict Epoch 1/10 1/56 [..............................] - ETA: 9s - loss: 0.0056 49/56 [=========================>....] - ETA: 0s - loss: 0.0872 56/56 [==============================] - 0s 1ms/step - loss: 0.0860 Epoch 2/10 1/56 [..............................] - ETA: 0s - loss: 0.0131 49/56 [=========================>....] - ETA: 0s - loss: 0.0879 56/56 [==============================] - 0s 1ms/step - loss: 0.0843 Epoch 3/10 1/56 [..............................] - ETA: 0s - loss: 0.0308 49/56 [=========================>....] - ETA: 0s - loss: 0.0735 56/56 [==============================] - 0s 1ms/step - loss: 0.0800 Epoch 4/10 1/56 [..............................] - ETA: 0s - loss: 0.5603 49/56 [=========================>....] - ETA: 0s - loss: 0.0840 56/56 [==============================] - 0s 1ms/step - loss: 0.0763 Epoch 5/10 1/56 [..............................] - ETA: 0s - loss: 0.0168 49/56 [=========================>....] - ETA: 0s - loss: 0.0762 56/56 [==============================] - 0s 1ms/step - loss: 0.0783 Epoch 6/10 1/56 [..............................] - ETA: 0s - loss: 0.0297 49/56 [=========================>....] - ETA: 0s - loss: 0.0749 56/56 [==============================] - 0s 1ms/step - loss: 0.0732 Epoch 7/10 1/56 [..............................] - ETA: 0s - loss: 0.0157 49/56 [=========================>....] - ETA: 0s - loss: 0.0728 56/56 [==============================] - 0s 1ms/step - loss: 0.0711 Epoch 8/10 1/56 [..............................] - ETA: 0s - loss: 0.0199 45/56 [=======================>......] - ETA: 0s - loss: 0.0786 56/56 [==============================] - 0s 1ms/step - loss: 0.0770 Epoch 9/10 1/56 [..............................] - ETA: 0s - loss: 0.1037 44/56 [======================>.......] - ETA: 0s - loss: 0.0937 56/56 [==============================] - 0s 1ms/step - loss: 0.0830 Epoch 10/10 1/56 [..............................] - ETA: 0s - loss: 0.0279 48/56 [========================>.....] - ETA: 0s - loss: 0.0676 56/56 [==============================] - 0s 1ms/step - loss: 0.0717 -> test with GAN.predict GAN tn, fp: 135, 3 GAN fn, tp: 6, 3 GAN f1 score: 0.400 GAN cohens kappa score: 0.369 -> test with 'LR' LR tn, fp: 131, 7 LR fn, tp: 5, 4 LR f1 score: 0.400 LR cohens kappa score: 0.357 LR average precision score: 0.526 -> test with 'RF' RF tn, fp: 135, 3 RF fn, tp: 7, 2 RF f1 score: 0.286 RF cohens kappa score: 0.253 -> test with 'GB' GB tn, fp: 135, 3 GB fn, tp: 8, 1 GB f1 score: 0.154 GB cohens kappa score: 0.121 -> test with 'KNN' KNN tn, fp: 136, 2 KNN fn, tp: 8, 1 KNN f1 score: 0.167 KNN cohens kappa score: 0.140 ------ Step 3/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 518 synthetic samples -> retrain GAN for predict Epoch 1/10 1/56 [..............................] - ETA: 8s - loss: 0.0606 46/56 [=======================>......] - ETA: 0s - loss: 0.1043 56/56 [==============================] - 0s 1ms/step - loss: 0.1018 Epoch 2/10 1/56 [..............................] - ETA: 0s - loss: 0.0732 46/56 [=======================>......] - ETA: 0s - loss: 0.1025 56/56 [==============================] - 0s 1ms/step - loss: 0.0947 Epoch 3/10 1/56 [..............................] - ETA: 0s - loss: 0.0092 44/56 [======================>.......] - ETA: 0s - loss: 0.0896 56/56 [==============================] - 0s 1ms/step - loss: 0.0901 Epoch 4/10 1/56 [..............................] - ETA: 0s - loss: 0.0104 43/56 [======================>.......] - ETA: 0s - loss: 0.0936 56/56 [==============================] - 0s 1ms/step - loss: 0.0881 Epoch 5/10 1/56 [..............................] - ETA: 0s - loss: 0.0355 45/56 [=======================>......] - ETA: 0s - loss: 0.0861 56/56 [==============================] - 0s 1ms/step - loss: 0.0876 Epoch 6/10 1/56 [..............................] - ETA: 0s - loss: 0.0366 45/56 [=======================>......] - ETA: 0s - loss: 0.0811 56/56 [==============================] - 0s 1ms/step - loss: 0.0851 Epoch 7/10 1/56 [..............................] - ETA: 0s - loss: 0.0523 45/56 [=======================>......] - ETA: 0s - loss: 0.0792 56/56 [==============================] - 0s 1ms/step - loss: 0.0840 Epoch 8/10 1/56 [..............................] - ETA: 0s - loss: 0.0292 44/56 [======================>.......] - ETA: 0s - loss: 0.0841 56/56 [==============================] - 0s 1ms/step - loss: 0.0808 Epoch 9/10 1/56 [..............................] - ETA: 0s - loss: 0.0491 45/56 [=======================>......] - ETA: 0s - loss: 0.0911 56/56 [==============================] - 0s 1ms/step - loss: 0.0848 Epoch 10/10 1/56 [..............................] - ETA: 0s - loss: 0.0152 45/56 [=======================>......] - ETA: 0s - loss: 0.0816 56/56 [==============================] - 0s 1ms/step - loss: 0.0844 -> test with GAN.predict GAN tn, fp: 136, 2 GAN fn, tp: 4, 5 GAN f1 score: 0.625 GAN cohens kappa score: 0.604 -> test with 'LR' LR tn, fp: 134, 4 LR fn, tp: 0, 9 LR f1 score: 0.818 LR cohens kappa score: 0.804 LR average precision score: 0.825 -> test with 'RF' RF tn, fp: 136, 2 RF fn, tp: 8, 1 RF f1 score: 0.167 RF cohens kappa score: 0.140 -> test with 'GB' GB tn, fp: 136, 2 GB fn, tp: 7, 2 GB f1 score: 0.308 GB cohens kappa score: 0.281 -> test with 'KNN' KNN tn, fp: 137, 1 KNN fn, tp: 9, 0 KNN f1 score: 0.000 KNN cohens kappa score: -0.012 ------ Step 3/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 518 synthetic samples -> retrain GAN for predict Epoch 1/10 1/56 [..............................] - ETA: 8s - loss: 0.0240 48/56 [========================>.....] - ETA: 0s - loss: 0.1141 56/56 [==============================] - 0s 1ms/step - loss: 0.1064 Epoch 2/10 1/56 [..............................] - ETA: 0s - loss: 0.1729 48/56 [========================>.....] - ETA: 0s - loss: 0.1024 56/56 [==============================] - 0s 1ms/step - loss: 0.1030 Epoch 3/10 1/56 [..............................] - ETA: 0s - loss: 0.0297 47/56 [========================>.....] - ETA: 0s - loss: 0.1201 56/56 [==============================] - 0s 1ms/step - loss: 0.1129 Epoch 4/10 1/56 [..............................] - ETA: 0s - loss: 0.3140 49/56 [=========================>....] - ETA: 0s - loss: 0.1052 56/56 [==============================] - 0s 1ms/step - loss: 0.1011 Epoch 5/10 1/56 [..............................] - ETA: 0s - loss: 0.0717 49/56 [=========================>....] - ETA: 0s - loss: 0.1084 56/56 [==============================] - 0s 1ms/step - loss: 0.1024 Epoch 6/10 1/56 [..............................] - ETA: 0s - loss: 0.0318 50/56 [=========================>....] - ETA: 0s - loss: 0.1046 56/56 [==============================] - 0s 1ms/step - loss: 0.0998 Epoch 7/10 1/56 [..............................] - ETA: 0s - loss: 0.2862 49/56 [=========================>....] - ETA: 0s - loss: 0.1066 56/56 [==============================] - 0s 1ms/step - loss: 0.1005 Epoch 8/10 1/56 [..............................] - ETA: 0s - loss: 0.0723 50/56 [=========================>....] - ETA: 0s - loss: 0.0991 56/56 [==============================] - 0s 1ms/step - loss: 0.0966 Epoch 9/10 1/56 [..............................] - ETA: 0s - loss: 0.0981 49/56 [=========================>....] - ETA: 0s - loss: 0.0911 56/56 [==============================] - 0s 1ms/step - loss: 0.0994 Epoch 10/10 1/56 [..............................] - ETA: 0s - loss: 0.2255 50/56 [=========================>....] - ETA: 0s - loss: 0.1034 56/56 [==============================] - 0s 1ms/step - loss: 0.1040 -> test with GAN.predict GAN tn, fp: 131, 7 GAN fn, tp: 6, 3 GAN f1 score: 0.316 GAN cohens kappa score: 0.269 -> test with 'LR' LR tn, fp: 132, 6 LR fn, tp: 4, 5 LR f1 score: 0.500 LR cohens kappa score: 0.464 LR average precision score: 0.670 -> test with 'RF' RF tn, fp: 135, 3 RF fn, tp: 7, 2 RF f1 score: 0.286 RF cohens kappa score: 0.253 -> test with 'GB' GB tn, fp: 136, 2 GB fn, tp: 8, 1 GB f1 score: 0.167 GB cohens kappa score: 0.140 -> test with 'KNN' KNN tn, fp: 136, 2 KNN fn, tp: 9, 0 KNN f1 score: 0.000 KNN cohens kappa score: -0.023 ------ Step 3/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 518 synthetic samples -> retrain GAN for predict Epoch 1/10 1/56 [..............................] - ETA: 7s - loss: 0.1700 48/56 [========================>.....] - ETA: 0s - loss: 0.0881 56/56 [==============================] - 0s 1ms/step - loss: 0.0949 Epoch 2/10 1/56 [..............................] - ETA: 0s - loss: 0.2452 49/56 [=========================>....] - ETA: 0s - loss: 0.0910 56/56 [==============================] - 0s 1ms/step - loss: 0.0844 Epoch 3/10 1/56 [..............................] - ETA: 0s - loss: 0.0429 49/56 [=========================>....] - ETA: 0s - loss: 0.0829 56/56 [==============================] - 0s 1ms/step - loss: 0.0846 Epoch 4/10 1/56 [..............................] - ETA: 0s - loss: 0.0093 49/56 [=========================>....] - ETA: 0s - loss: 0.0783 56/56 [==============================] - 0s 1ms/step - loss: 0.0817 Epoch 5/10 1/56 [..............................] - ETA: 0s - loss: 0.0663 49/56 [=========================>....] - ETA: 0s - loss: 0.0827 56/56 [==============================] - 0s 1ms/step - loss: 0.0802 Epoch 6/10 1/56 [..............................] - ETA: 0s - loss: 0.0060 49/56 [=========================>....] - ETA: 0s - loss: 0.0699 56/56 [==============================] - 0s 1ms/step - loss: 0.0785 Epoch 7/10 1/56 [..............................] - ETA: 0s - loss: 0.0152 50/56 [=========================>....] - ETA: 0s - loss: 0.0796 56/56 [==============================] - 0s 1ms/step - loss: 0.0790 Epoch 8/10 1/56 [..............................] - ETA: 0s - loss: 0.0194 49/56 [=========================>....] - ETA: 0s - loss: 0.0776 56/56 [==============================] - 0s 1ms/step - loss: 0.0790 Epoch 9/10 1/56 [..............................] - ETA: 0s - loss: 0.0087 49/56 [=========================>....] - ETA: 0s - loss: 0.0814 56/56 [==============================] - 0s 1ms/step - loss: 0.0790 Epoch 10/10 1/56 [..............................] - ETA: 0s - loss: 0.1023 49/56 [=========================>....] - ETA: 0s - loss: 0.0733 56/56 [==============================] - 0s 1ms/step - loss: 0.0770 -> test with GAN.predict GAN tn, fp: 136, 2 GAN fn, tp: 4, 5 GAN f1 score: 0.625 GAN cohens kappa score: 0.604 -> test with 'LR' LR tn, fp: 128, 10 LR fn, tp: 2, 7 LR f1 score: 0.538 LR cohens kappa score: 0.498 LR average precision score: 0.668 -> test with 'RF' RF tn, fp: 138, 0 RF fn, tp: 6, 3 RF f1 score: 0.500 RF cohens kappa score: 0.484 -> test with 'GB' GB tn, fp: 138, 0 GB fn, tp: 5, 4 GB f1 score: 0.615 GB cohens kappa score: 0.600 -> test with 'KNN' KNN tn, fp: 138, 0 KNN fn, tp: 8, 1 KNN f1 score: 0.200 KNN cohens kappa score: 0.190 ------ Step 3/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 516 synthetic samples -> retrain GAN for predict Epoch 1/10 1/56 [..............................] - ETA: 8s - loss: 0.2589 49/56 [=========================>....] - ETA: 0s - loss: 0.1165 56/56 [==============================] - 0s 1ms/step - loss: 0.1265 Epoch 2/10 1/56 [..............................] - ETA: 0s - loss: 0.1902 47/56 [========================>.....] - ETA: 0s - loss: 0.1098 56/56 [==============================] - 0s 1ms/step - loss: 0.1055 Epoch 3/10 1/56 [..............................] - ETA: 0s - loss: 0.1600 43/56 [======================>.......] - ETA: 0s - loss: 0.0996 56/56 [==============================] - 0s 1ms/step - loss: 0.0958 Epoch 4/10 1/56 [..............................] - ETA: 0s - loss: 0.0085 46/56 [=======================>......] - ETA: 0s - loss: 0.0810 56/56 [==============================] - 0s 1ms/step - loss: 0.0850 Epoch 5/10 1/56 [..............................] - ETA: 0s - loss: 0.0064 49/56 [=========================>....] - ETA: 0s - loss: 0.0928 56/56 [==============================] - 0s 1ms/step - loss: 0.0875 Epoch 6/10 1/56 [..............................] - ETA: 0s - loss: 0.0136 50/56 [=========================>....] - ETA: 0s - loss: 0.0830 56/56 [==============================] - 0s 1ms/step - loss: 0.0860 Epoch 7/10 1/56 [..............................] - ETA: 0s - loss: 0.0279 48/56 [========================>.....] - ETA: 0s - loss: 0.0871 56/56 [==============================] - 0s 1ms/step - loss: 0.0879 Epoch 8/10 1/56 [..............................] - ETA: 0s - loss: 0.2052 47/56 [========================>.....] - ETA: 0s - loss: 0.0901 56/56 [==============================] - 0s 1ms/step - loss: 0.0849 Epoch 9/10 1/56 [..............................] - ETA: 0s - loss: 0.4856 49/56 [=========================>....] - ETA: 0s - loss: 0.0873 56/56 [==============================] - 0s 1ms/step - loss: 0.0843 Epoch 10/10 1/56 [..............................] - ETA: 0s - loss: 0.0050 50/56 [=========================>....] - ETA: 0s - loss: 0.0833 56/56 [==============================] - 0s 1ms/step - loss: 0.0863 -> test with GAN.predict GAN tn, fp: 133, 4 GAN fn, tp: 2, 4 GAN f1 score: 0.571 GAN cohens kappa score: 0.550 -> test with 'LR' LR tn, fp: 131, 6 LR fn, tp: 2, 4 LR f1 score: 0.500 LR cohens kappa score: 0.472 LR average precision score: 0.533 -> test with 'RF' RF tn, fp: 136, 1 RF fn, tp: 5, 1 RF f1 score: 0.250 RF cohens kappa score: 0.234 -> test with 'GB' GB tn, fp: 135, 2 GB fn, tp: 4, 2 GB f1 score: 0.400 GB cohens kappa score: 0.379 -> test with 'KNN' KNN tn, fp: 136, 1 KNN fn, tp: 6, 0 KNN f1 score: 0.000 KNN cohens kappa score: -0.012 ====== Step 4/5 ======= -> Shuffling data -> Spliting data to slices ------ Step 4/5: Slice 1/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 518 synthetic samples -> retrain GAN for predict Epoch 1/10 1/56 [..............................] - ETA: 8s - loss: 0.0188 48/56 [========================>.....] - ETA: 0s - loss: 0.1383 56/56 [==============================] - 0s 1ms/step - loss: 0.1387 Epoch 2/10 1/56 [..............................] - ETA: 0s - loss: 0.0497 49/56 [=========================>....] - ETA: 0s - loss: 0.1001 56/56 [==============================] - 0s 1ms/step - loss: 0.1207 Epoch 3/10 1/56 [..............................] - ETA: 0s - loss: 0.0496 49/56 [=========================>....] - ETA: 0s - loss: 0.1303 56/56 [==============================] - 0s 1ms/step - loss: 0.1214 Epoch 4/10 1/56 [..............................] - ETA: 0s - loss: 0.0644 49/56 [=========================>....] - ETA: 0s - loss: 0.1128 56/56 [==============================] - 0s 1ms/step - loss: 0.1138 Epoch 5/10 1/56 [..............................] - ETA: 0s - loss: 0.0493 49/56 [=========================>....] - ETA: 0s - loss: 0.1120 56/56 [==============================] - 0s 1ms/step - loss: 0.1108 Epoch 6/10 1/56 [..............................] - ETA: 0s - loss: 0.0123 49/56 [=========================>....] - ETA: 0s - loss: 0.1081 56/56 [==============================] - 0s 1ms/step - loss: 0.1026 Epoch 7/10 1/56 [..............................] - ETA: 0s - loss: 0.0280 50/56 [=========================>....] - ETA: 0s - loss: 0.1040 56/56 [==============================] - 0s 1ms/step - loss: 0.1019 Epoch 8/10 1/56 [..............................] - ETA: 0s - loss: 0.0286 49/56 [=========================>....] - ETA: 0s - loss: 0.0913 56/56 [==============================] - 0s 1ms/step - loss: 0.0981 Epoch 9/10 1/56 [..............................] - ETA: 0s - loss: 0.0505 49/56 [=========================>....] - ETA: 0s - loss: 0.1053 56/56 [==============================] - 0s 1ms/step - loss: 0.1007 Epoch 10/10 1/56 [..............................] - ETA: 0s - loss: 0.0474 46/56 [=======================>......] - ETA: 0s - loss: 0.0832 56/56 [==============================] - 0s 1ms/step - loss: 0.0979 -> test with GAN.predict GAN tn, fp: 128, 10 GAN fn, tp: 4, 5 GAN f1 score: 0.417 GAN cohens kappa score: 0.368 -> test with 'LR' LR tn, fp: 130, 8 LR fn, tp: 5, 4 LR f1 score: 0.381 LR cohens kappa score: 0.334 LR average precision score: 0.530 -> test with 'RF' RF tn, fp: 136, 2 RF fn, tp: 6, 3 RF f1 score: 0.429 RF cohens kappa score: 0.402 -> test with 'GB' GB tn, fp: 135, 3 GB fn, tp: 6, 3 GB f1 score: 0.400 GB cohens kappa score: 0.369 -> test with 'KNN' KNN tn, fp: 136, 2 KNN fn, tp: 8, 1 KNN f1 score: 0.167 KNN cohens kappa score: 0.140 ------ Step 4/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 518 synthetic samples -> retrain GAN for predict Epoch 1/10 1/56 [..............................] - ETA: 8s - loss: 0.0070 49/56 [=========================>....] - ETA: 0s - loss: 0.0864 56/56 [==============================] - 0s 1ms/step - loss: 0.0904 Epoch 2/10 1/56 [..............................] - ETA: 0s - loss: 0.2332 50/56 [=========================>....] - ETA: 0s - loss: 0.0809 56/56 [==============================] - 0s 1ms/step - loss: 0.0863 Epoch 3/10 1/56 [..............................] - ETA: 0s - loss: 0.0186 49/56 [=========================>....] - ETA: 0s - loss: 0.0927 56/56 [==============================] - 0s 1ms/step - loss: 0.0937 Epoch 4/10 1/56 [..............................] - ETA: 0s - loss: 0.1599 49/56 [=========================>....] - ETA: 0s - loss: 0.0676 56/56 [==============================] - 0s 1ms/step - loss: 0.0768 Epoch 5/10 1/56 [..............................] - ETA: 0s - loss: 0.0636 48/56 [========================>.....] - ETA: 0s - loss: 0.0890 56/56 [==============================] - 0s 1ms/step - loss: 0.0808 Epoch 6/10 1/56 [..............................] - ETA: 0s - loss: 0.1082 46/56 [=======================>......] - ETA: 0s - loss: 0.0741 56/56 [==============================] - 0s 1ms/step - loss: 0.0721 Epoch 7/10 1/56 [..............................] - ETA: 0s - loss: 0.0681 46/56 [=======================>......] - ETA: 0s - loss: 0.0761 56/56 [==============================] - 0s 1ms/step - loss: 0.0727 Epoch 8/10 1/56 [..............................] - ETA: 0s - loss: 0.0309 45/56 [=======================>......] - ETA: 0s - loss: 0.0817 56/56 [==============================] - 0s 1ms/step - loss: 0.0747 Epoch 9/10 1/56 [..............................] - ETA: 0s - loss: 0.0105 48/56 [========================>.....] - ETA: 0s - loss: 0.0806 56/56 [==============================] - 0s 1ms/step - loss: 0.0718 Epoch 10/10 1/56 [..............................] - ETA: 0s - loss: 0.1120 43/56 [======================>.......] - ETA: 0s - loss: 0.0843 56/56 [==============================] - 0s 1ms/step - loss: 0.0718 -> test with GAN.predict GAN tn, fp: 136, 2 GAN fn, tp: 5, 4 GAN f1 score: 0.533 GAN cohens kappa score: 0.509 -> test with 'LR' LR tn, fp: 131, 7 LR fn, tp: 3, 6 LR f1 score: 0.545 LR cohens kappa score: 0.510 LR average precision score: 0.688 -> test with 'RF' RF tn, fp: 135, 3 RF fn, tp: 6, 3 RF f1 score: 0.400 RF cohens kappa score: 0.369 -> test with 'GB' GB tn, fp: 129, 9 GB fn, tp: 5, 4 GB f1 score: 0.364 GB cohens kappa score: 0.314 -> test with 'KNN' KNN tn, fp: 138, 0 KNN fn, tp: 8, 1 KNN f1 score: 0.200 KNN cohens kappa score: 0.190 ------ Step 4/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 518 synthetic samples -> retrain GAN for predict Epoch 1/10 1/56 [..............................] - ETA: 8s - loss: 0.0670 49/56 [=========================>....] - ETA: 0s - loss: 0.1150 56/56 [==============================] - 0s 1ms/step - loss: 0.1145 Epoch 2/10 1/56 [..............................] - ETA: 0s - loss: 0.0688 49/56 [=========================>....] - ETA: 0s - loss: 0.1134 56/56 [==============================] - 0s 1ms/step - loss: 0.1191 Epoch 3/10 1/56 [..............................] - ETA: 0s - loss: 0.1365 48/56 [========================>.....] - ETA: 0s - loss: 0.1053 56/56 [==============================] - 0s 1ms/step - loss: 0.1059 Epoch 4/10 1/56 [..............................] - ETA: 0s - loss: 0.0178 49/56 [=========================>....] - ETA: 0s - loss: 0.1094 56/56 [==============================] - 0s 1ms/step - loss: 0.1083 Epoch 5/10 1/56 [..............................] - ETA: 0s - loss: 0.0226 49/56 [=========================>....] - ETA: 0s - loss: 0.1044 56/56 [==============================] - 0s 1ms/step - loss: 0.1046 Epoch 6/10 1/56 [..............................] - ETA: 0s - loss: 0.0962 46/56 [=======================>......] - ETA: 0s - loss: 0.0966 56/56 [==============================] - 0s 1ms/step - loss: 0.1032 Epoch 7/10 1/56 [..............................] - ETA: 0s - loss: 0.0266 41/56 [====================>.........] - ETA: 0s - loss: 0.0996 56/56 [==============================] - 0s 1ms/step - loss: 0.1050 Epoch 8/10 1/56 [..............................] - ETA: 0s - loss: 0.3545 47/56 [========================>.....] - ETA: 0s - loss: 0.1105 56/56 [==============================] - 0s 1ms/step - loss: 0.1080 Epoch 9/10 1/56 [..............................] - ETA: 0s - loss: 0.0386 49/56 [=========================>....] - ETA: 0s - loss: 0.1054 56/56 [==============================] - 0s 1ms/step - loss: 0.1051 Epoch 10/10 1/56 [..............................] - ETA: 0s - loss: 0.0943 49/56 [=========================>....] - ETA: 0s - loss: 0.1072 56/56 [==============================] - 0s 1ms/step - loss: 0.1067 -> test with GAN.predict GAN tn, fp: 137, 1 GAN fn, tp: 4, 5 GAN f1 score: 0.667 GAN cohens kappa score: 0.649 -> test with 'LR' LR tn, fp: 129, 9 LR fn, tp: 1, 8 LR f1 score: 0.615 LR cohens kappa score: 0.582 LR average precision score: 0.671 -> test with 'RF' RF tn, fp: 138, 0 RF fn, tp: 7, 2 RF f1 score: 0.364 RF cohens kappa score: 0.349 -> test with 'GB' GB tn, fp: 137, 1 GB fn, tp: 7, 2 GB f1 score: 0.333 GB cohens kappa score: 0.312 -> test with 'KNN' KNN tn, fp: 138, 0 KNN fn, tp: 8, 1 KNN f1 score: 0.200 KNN cohens kappa score: 0.190 ------ Step 4/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 518 synthetic samples -> retrain GAN for predict Epoch 1/10 1/56 [..............................] - ETA: 8s - loss: 0.5220 49/56 [=========================>....] - ETA: 0s - loss: 0.1154 56/56 [==============================] - 0s 1ms/step - loss: 0.1064 Epoch 2/10 1/56 [..............................] - ETA: 0s - loss: 0.0190 50/56 [=========================>....] - ETA: 0s - loss: 0.0955 56/56 [==============================] - 0s 1ms/step - loss: 0.1002 Epoch 3/10 1/56 [..............................] - ETA: 0s - loss: 0.0154 49/56 [=========================>....] - ETA: 0s - loss: 0.0739 56/56 [==============================] - 0s 1ms/step - loss: 0.0902 Epoch 4/10 1/56 [..............................] - ETA: 0s - loss: 0.3329 49/56 [=========================>....] - ETA: 0s - loss: 0.0872 56/56 [==============================] - 0s 1ms/step - loss: 0.0886 Epoch 5/10 1/56 [..............................] - ETA: 0s - loss: 0.1420 49/56 [=========================>....] - ETA: 0s - loss: 0.0828 56/56 [==============================] - 0s 1ms/step - loss: 0.0867 Epoch 6/10 1/56 [..............................] - ETA: 0s - loss: 0.1011 49/56 [=========================>....] - ETA: 0s - loss: 0.0884 56/56 [==============================] - 0s 1ms/step - loss: 0.0869 Epoch 7/10 1/56 [..............................] - ETA: 0s - loss: 0.0152 49/56 [=========================>....] - ETA: 0s - loss: 0.0685 56/56 [==============================] - 0s 1ms/step - loss: 0.0815 Epoch 8/10 1/56 [..............................] - ETA: 0s - loss: 0.0325 49/56 [=========================>....] - ETA: 0s - loss: 0.0834 56/56 [==============================] - 0s 1ms/step - loss: 0.0841 Epoch 9/10 1/56 [..............................] - ETA: 0s - loss: 0.1239 49/56 [=========================>....] - ETA: 0s - loss: 0.0843 56/56 [==============================] - 0s 1ms/step - loss: 0.0809 Epoch 10/10 1/56 [..............................] - ETA: 0s - loss: 0.0548 49/56 [=========================>....] - ETA: 0s - loss: 0.0864 56/56 [==============================] - 0s 1ms/step - loss: 0.0796 -> test with GAN.predict GAN tn, fp: 133, 5 GAN fn, tp: 6, 3 GAN f1 score: 0.353 GAN cohens kappa score: 0.313 -> test with 'LR' LR tn, fp: 133, 5 LR fn, tp: 0, 9 LR f1 score: 0.783 LR cohens kappa score: 0.765 LR average precision score: 0.897 -> test with 'RF' RF tn, fp: 138, 0 RF fn, tp: 7, 2 RF f1 score: 0.364 RF cohens kappa score: 0.349 -> test with 'GB' GB tn, fp: 134, 4 GB fn, tp: 8, 1 GB f1 score: 0.143 GB cohens kappa score: 0.104 -> test with 'KNN' KNN tn, fp: 135, 3 KNN fn, tp: 9, 0 KNN f1 score: 0.000 KNN cohens kappa score: -0.032 ------ Step 4/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 516 synthetic samples -> retrain GAN for predict Epoch 1/10 1/56 [..............................] - ETA: 8s - loss: 0.0097 49/56 [=========================>....] - ETA: 0s - loss: 0.0911 56/56 [==============================] - 0s 1ms/step - loss: 0.0873 Epoch 2/10 1/56 [..............................] - ETA: 0s - loss: 0.0335 49/56 [=========================>....] - ETA: 0s - loss: 0.0907 56/56 [==============================] - 0s 1ms/step - loss: 0.0851 Epoch 3/10 1/56 [..............................] - ETA: 0s - loss: 0.0028 49/56 [=========================>....] - ETA: 0s - loss: 0.0787 56/56 [==============================] - 0s 1ms/step - loss: 0.0784 Epoch 4/10 1/56 [..............................] - ETA: 0s - loss: 0.1885 49/56 [=========================>....] - ETA: 0s - loss: 0.0723 56/56 [==============================] - 0s 1ms/step - loss: 0.0758 Epoch 5/10 1/56 [..............................] - ETA: 0s - loss: 0.1350 49/56 [=========================>....] - ETA: 0s - loss: 0.0782 56/56 [==============================] - 0s 1ms/step - loss: 0.0782 Epoch 6/10 1/56 [..............................] - ETA: 0s - loss: 0.0289 49/56 [=========================>....] - ETA: 0s - loss: 0.0721 56/56 [==============================] - 0s 1ms/step - loss: 0.0793 Epoch 7/10 1/56 [..............................] - ETA: 0s - loss: 0.0465 44/56 [======================>.......] - ETA: 0s - loss: 0.0778 56/56 [==============================] - 0s 1ms/step - loss: 0.0737 Epoch 8/10 1/56 [..............................] - ETA: 0s - loss: 0.2821 44/56 [======================>.......] - ETA: 0s - loss: 0.0813 56/56 [==============================] - 0s 1ms/step - loss: 0.0754 Epoch 9/10 1/56 [..............................] - ETA: 0s - loss: 0.0175 47/56 [========================>.....] - ETA: 0s - loss: 0.0735 56/56 [==============================] - 0s 1ms/step - loss: 0.0747 Epoch 10/10 1/56 [..............................] - ETA: 0s - loss: 0.0493 49/56 [=========================>....] - ETA: 0s - loss: 0.0779 56/56 [==============================] - 0s 1ms/step - loss: 0.0724 -> test with GAN.predict GAN tn, fp: 136, 1 GAN fn, tp: 2, 4 GAN f1 score: 0.727 GAN cohens kappa score: 0.716 -> test with 'LR' LR tn, fp: 132, 5 LR fn, tp: 2, 4 LR f1 score: 0.533 LR cohens kappa score: 0.509 LR average precision score: 0.626 -> test with 'RF' RF tn, fp: 136, 1 RF fn, tp: 3, 3 RF f1 score: 0.600 RF cohens kappa score: 0.586 -> test with 'GB' GB tn, fp: 134, 3 GB fn, tp: 4, 2 GB f1 score: 0.364 GB cohens kappa score: 0.338 -> test with 'KNN' KNN tn, fp: 136, 1 KNN fn, tp: 5, 1 KNN f1 score: 0.250 KNN cohens kappa score: 0.234 ====== Step 5/5 ======= -> Shuffling data -> Spliting data to slices ------ Step 5/5: Slice 1/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 518 synthetic samples -> retrain GAN for predict Epoch 1/10 1/56 [..............................] - ETA: 8s - loss: 0.1947 49/56 [=========================>....] - ETA: 0s - loss: 0.1141 56/56 [==============================] - 0s 1ms/step - loss: 0.1097 Epoch 2/10 1/56 [..............................] - ETA: 0s - loss: 0.1009 50/56 [=========================>....] - ETA: 0s - loss: 0.1075 56/56 [==============================] - 0s 1ms/step - loss: 0.1085 Epoch 3/10 1/56 [..............................] - ETA: 0s - loss: 0.0159 50/56 [=========================>....] - ETA: 0s - loss: 0.1181 56/56 [==============================] - 0s 1ms/step - loss: 0.1158 Epoch 4/10 1/56 [..............................] - ETA: 0s - loss: 0.2182 49/56 [=========================>....] - ETA: 0s - loss: 0.1023 56/56 [==============================] - 0s 1ms/step - loss: 0.1067 Epoch 5/10 1/56 [..............................] - ETA: 0s - loss: 0.0502 50/56 [=========================>....] - ETA: 0s - loss: 0.1023 56/56 [==============================] - 0s 1ms/step - loss: 0.1028 Epoch 6/10 1/56 [..............................] - ETA: 0s - loss: 0.0281 49/56 [=========================>....] - ETA: 0s - loss: 0.1018 56/56 [==============================] - 0s 1ms/step - loss: 0.1044 Epoch 7/10 1/56 [..............................] - ETA: 0s - loss: 0.1079 50/56 [=========================>....] - ETA: 0s - loss: 0.1018 56/56 [==============================] - 0s 1ms/step - loss: 0.0998 Epoch 8/10 1/56 [..............................] - ETA: 0s - loss: 0.0598 50/56 [=========================>....] - ETA: 0s - loss: 0.1115 56/56 [==============================] - 0s 1ms/step - loss: 0.1048 Epoch 9/10 1/56 [..............................] - ETA: 0s - loss: 0.0074 50/56 [=========================>....] - ETA: 0s - loss: 0.1038 56/56 [==============================] - 0s 1ms/step - loss: 0.1056 Epoch 10/10 1/56 [..............................] - ETA: 0s - loss: 0.1623 50/56 [=========================>....] - ETA: 0s - loss: 0.1053 56/56 [==============================] - 0s 1ms/step - loss: 0.1046 -> test with GAN.predict GAN tn, fp: 132, 6 GAN fn, tp: 4, 5 GAN f1 score: 0.500 GAN cohens kappa score: 0.464 -> test with 'LR' LR tn, fp: 127, 11 LR fn, tp: 2, 7 LR f1 score: 0.519 LR cohens kappa score: 0.476 LR average precision score: 0.694 -> test with 'RF' RF tn, fp: 138, 0 RF fn, tp: 9, 0 RF f1 score: 0.000 RF cohens kappa score: 0.000 -> test with 'GB' GB tn, fp: 136, 2 GB fn, tp: 8, 1 GB f1 score: 0.167 GB cohens kappa score: 0.140 -> test with 'KNN' KNN tn, fp: 136, 2 KNN fn, tp: 9, 0 KNN f1 score: 0.000 KNN cohens kappa score: -0.023 ------ Step 5/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 518 synthetic samples -> retrain GAN for predict Epoch 1/10 1/56 [..............................] - ETA: 8s - loss: 0.0816 49/56 [=========================>....] - ETA: 0s - loss: 0.1233 56/56 [==============================] - 0s 1ms/step - loss: 0.1148 Epoch 2/10 1/56 [..............................] - ETA: 0s - loss: 0.0130 49/56 [=========================>....] - ETA: 0s - loss: 0.1105 56/56 [==============================] - 0s 1ms/step - loss: 0.1111 Epoch 3/10 1/56 [..............................] - ETA: 0s - loss: 0.0274 49/56 [=========================>....] - ETA: 0s - loss: 0.0999 56/56 [==============================] - 0s 1ms/step - loss: 0.1027 Epoch 4/10 1/56 [..............................] - ETA: 0s - loss: 0.1439 49/56 [=========================>....] - ETA: 0s - loss: 0.0986 56/56 [==============================] - 0s 1ms/step - loss: 0.0986 Epoch 5/10 1/56 [..............................] - ETA: 0s - loss: 0.0286 49/56 [=========================>....] - ETA: 0s - loss: 0.1038 56/56 [==============================] - 0s 1ms/step - loss: 0.0991 Epoch 6/10 1/56 [..............................] - ETA: 0s - loss: 0.0447 49/56 [=========================>....] - ETA: 0s - loss: 0.0974 56/56 [==============================] - 0s 1ms/step - loss: 0.0954 Epoch 7/10 1/56 [..............................] - ETA: 0s - loss: 0.1299 49/56 [=========================>....] - ETA: 0s - loss: 0.1020 56/56 [==============================] - 0s 1ms/step - loss: 0.0924 Epoch 8/10 1/56 [..............................] - ETA: 0s - loss: 0.2587 49/56 [=========================>....] - ETA: 0s - loss: 0.0888 56/56 [==============================] - 0s 1ms/step - loss: 0.0920 Epoch 9/10 1/56 [..............................] - ETA: 0s - loss: 0.0288 49/56 [=========================>....] - ETA: 0s - loss: 0.0879 56/56 [==============================] - 0s 1ms/step - loss: 0.0919 Epoch 10/10 1/56 [..............................] - ETA: 0s - loss: 0.0543 47/56 [========================>.....] - ETA: 0s - loss: 0.0722 56/56 [==============================] - 0s 1ms/step - loss: 0.0889 -> test with GAN.predict GAN tn, fp: 137, 1 GAN fn, tp: 5, 4 GAN f1 score: 0.571 GAN cohens kappa score: 0.552 -> test with 'LR' LR tn, fp: 130, 8 LR fn, tp: 2, 7 LR f1 score: 0.583 LR cohens kappa score: 0.549 LR average precision score: 0.689 -> test with 'RF' RF tn, fp: 138, 0 RF fn, tp: 7, 2 RF f1 score: 0.364 RF cohens kappa score: 0.349 -> test with 'GB' GB tn, fp: 136, 2 GB fn, tp: 6, 3 GB f1 score: 0.429 GB cohens kappa score: 0.402 -> test with 'KNN' KNN tn, fp: 138, 0 KNN fn, tp: 9, 0 KNN f1 score: 0.000 KNN cohens kappa score: 0.000 ------ Step 5/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 518 synthetic samples -> retrain GAN for predict Epoch 1/10 1/56 [..............................] - ETA: 9s - loss: 0.0306 48/56 [========================>.....] - ETA: 0s - loss: 0.0972 56/56 [==============================] - 0s 1ms/step - loss: 0.0944 Epoch 2/10 1/56 [..............................] - ETA: 0s - loss: 0.1812 49/56 [=========================>....] - ETA: 0s - loss: 0.0798 56/56 [==============================] - 0s 1ms/step - loss: 0.0749 Epoch 3/10 1/56 [..............................] - ETA: 0s - loss: 0.0362 49/56 [=========================>....] - ETA: 0s - loss: 0.0784 56/56 [==============================] - 0s 1ms/step - loss: 0.0721 Epoch 4/10 1/56 [..............................] - ETA: 0s - loss: 0.0121 47/56 [========================>.....] - ETA: 0s - loss: 0.0667 56/56 [==============================] - 0s 1ms/step - loss: 0.0671 Epoch 5/10 1/56 [..............................] - ETA: 0s - loss: 0.0041 49/56 [=========================>....] - ETA: 0s - loss: 0.0723 56/56 [==============================] - 0s 1ms/step - loss: 0.0734 Epoch 6/10 1/56 [..............................] - ETA: 0s - loss: 0.0187 49/56 [=========================>....] - ETA: 0s - loss: 0.0715 56/56 [==============================] - 0s 1ms/step - loss: 0.0691 Epoch 7/10 1/56 [..............................] - ETA: 0s - loss: 0.0171 49/56 [=========================>....] - ETA: 0s - loss: 0.0684 56/56 [==============================] - 0s 1ms/step - loss: 0.0720 Epoch 8/10 1/56 [..............................] - ETA: 0s - loss: 0.0200 49/56 [=========================>....] - ETA: 0s - loss: 0.0643 56/56 [==============================] - 0s 1ms/step - loss: 0.0658 Epoch 9/10 1/56 [..............................] - ETA: 0s - loss: 0.0744 50/56 [=========================>....] - ETA: 0s - loss: 0.0660 56/56 [==============================] - 0s 1ms/step - loss: 0.0630 Epoch 10/10 1/56 [..............................] - ETA: 0s - loss: 0.0060 50/56 [=========================>....] - ETA: 0s - loss: 0.0654 56/56 [==============================] - 0s 1ms/step - loss: 0.0631 -> test with GAN.predict GAN tn, fp: 135, 3 GAN fn, tp: 6, 3 GAN f1 score: 0.400 GAN cohens kappa score: 0.369 -> test with 'LR' LR tn, fp: 131, 7 LR fn, tp: 4, 5 LR f1 score: 0.476 LR cohens kappa score: 0.437 LR average precision score: 0.543 -> test with 'RF' RF tn, fp: 136, 2 RF fn, tp: 7, 2 RF f1 score: 0.308 RF cohens kappa score: 0.281 -> test with 'GB' GB tn, fp: 134, 4 GB fn, tp: 8, 1 GB f1 score: 0.143 GB cohens kappa score: 0.104 -> test with 'KNN' KNN tn, fp: 136, 2 KNN fn, tp: 8, 1 KNN f1 score: 0.167 KNN cohens kappa score: 0.140 ------ Step 5/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 518 synthetic samples -> retrain GAN for predict Epoch 1/10 1/56 [..............................] - ETA: 7s - loss: 0.0140 50/56 [=========================>....] - ETA: 0s - loss: 0.1184 56/56 [==============================] - 0s 1ms/step - loss: 0.1162 Epoch 2/10 1/56 [..............................] - ETA: 0s - loss: 0.0198 49/56 [=========================>....] - ETA: 0s - loss: 0.1106 56/56 [==============================] - 0s 1ms/step - loss: 0.1100 Epoch 3/10 1/56 [..............................] - ETA: 0s - loss: 0.0998 50/56 [=========================>....] - ETA: 0s - loss: 0.1170 56/56 [==============================] - 0s 1ms/step - loss: 0.1109 Epoch 4/10 1/56 [..............................] - ETA: 0s - loss: 0.4057 50/56 [=========================>....] - ETA: 0s - loss: 0.1111 56/56 [==============================] - 0s 1ms/step - loss: 0.1068 Epoch 5/10 1/56 [..............................] - ETA: 0s - loss: 0.1950 50/56 [=========================>....] - ETA: 0s - loss: 0.0987 56/56 [==============================] - 0s 1ms/step - loss: 0.1032 Epoch 6/10 1/56 [..............................] - ETA: 0s - loss: 0.0264 49/56 [=========================>....] - ETA: 0s - loss: 0.1079 56/56 [==============================] - 0s 1ms/step - loss: 0.0996 Epoch 7/10 1/56 [..............................] - ETA: 0s - loss: 0.0165 50/56 [=========================>....] - ETA: 0s - loss: 0.0982 56/56 [==============================] - 0s 1ms/step - loss: 0.1059 Epoch 8/10 1/56 [..............................] - ETA: 0s - loss: 0.0154 50/56 [=========================>....] - ETA: 0s - loss: 0.0967 56/56 [==============================] - 0s 1ms/step - loss: 0.0986 Epoch 9/10 1/56 [..............................] - ETA: 0s - loss: 0.0202 46/56 [=======================>......] - ETA: 0s - loss: 0.1043 56/56 [==============================] - 0s 1ms/step - loss: 0.0969 Epoch 10/10 1/56 [..............................] - ETA: 0s - loss: 0.0110 50/56 [=========================>....] - ETA: 0s - loss: 0.1080 56/56 [==============================] - 0s 1ms/step - loss: 0.1028 -> test with GAN.predict GAN tn, fp: 134, 4 GAN fn, tp: 1, 8 GAN f1 score: 0.762 GAN cohens kappa score: 0.744 -> test with 'LR' LR tn, fp: 131, 7 LR fn, tp: 1, 8 LR f1 score: 0.667 LR cohens kappa score: 0.639 LR average precision score: 0.882 -> test with 'RF' RF tn, fp: 136, 2 RF fn, tp: 6, 3 RF f1 score: 0.429 RF cohens kappa score: 0.402 -> test with 'GB' GB tn, fp: 137, 1 GB fn, tp: 7, 2 GB f1 score: 0.333 GB cohens kappa score: 0.312 -> test with 'KNN' KNN tn, fp: 136, 2 KNN fn, tp: 7, 2 KNN f1 score: 0.308 KNN cohens kappa score: 0.281 ------ Step 5/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 516 synthetic samples -> retrain GAN for predict Epoch 1/10 1/56 [..............................] - ETA: 7s - loss: 0.0751 50/56 [=========================>....] - ETA: 0s - loss: 0.1536 56/56 [==============================] - 0s 1ms/step - loss: 0.1428 Epoch 2/10 1/56 [..............................] - ETA: 0s - loss: 0.0951 49/56 [=========================>....] - ETA: 0s - loss: 0.1105 56/56 [==============================] - 0s 1ms/step - loss: 0.1228 Epoch 3/10 1/56 [..............................] - ETA: 0s - loss: 0.1758 50/56 [=========================>....] - ETA: 0s - loss: 0.1149 56/56 [==============================] - 0s 1ms/step - loss: 0.1131 Epoch 4/10 1/56 [..............................] - ETA: 0s - loss: 0.0759 50/56 [=========================>....] - ETA: 0s - loss: 0.1129 56/56 [==============================] - 0s 1ms/step - loss: 0.1104 Epoch 5/10 1/56 [..............................] - ETA: 0s - loss: 0.0277 50/56 [=========================>....] - ETA: 0s - loss: 0.1157 56/56 [==============================] - 0s 1ms/step - loss: 0.1117 Epoch 6/10 1/56 [..............................] - ETA: 0s - loss: 1.0189 48/56 [========================>.....] - ETA: 0s - loss: 0.0889 56/56 [==============================] - 0s 1ms/step - loss: 0.0987 Epoch 7/10 1/56 [..............................] - ETA: 0s - loss: 0.0293 50/56 [=========================>....] - ETA: 0s - loss: 0.0935 56/56 [==============================] - 0s 1ms/step - loss: 0.1014 Epoch 8/10 1/56 [..............................] - ETA: 0s - loss: 0.0671 50/56 [=========================>....] - ETA: 0s - loss: 0.0915 56/56 [==============================] - 0s 1ms/step - loss: 0.0987 Epoch 9/10 1/56 [..............................] - ETA: 0s - loss: 0.1939 48/56 [========================>.....] - ETA: 0s - loss: 0.1001 56/56 [==============================] - 0s 1ms/step - loss: 0.0957 Epoch 10/10 1/56 [..............................] - ETA: 0s - loss: 0.0082 50/56 [=========================>....] - ETA: 0s - loss: 0.0888 56/56 [==============================] - 0s 1ms/step - loss: 0.0999 -> test with GAN.predict GAN tn, fp: 137, 0 GAN fn, tp: 3, 3 GAN f1 score: 0.667 GAN cohens kappa score: 0.657 -> test with 'LR' LR tn, fp: 132, 5 LR fn, tp: 2, 4 LR f1 score: 0.533 LR cohens kappa score: 0.509 LR average precision score: 0.827 -> test with 'RF' RF tn, fp: 136, 1 RF fn, tp: 3, 3 RF f1 score: 0.600 RF cohens kappa score: 0.586 -> test with 'GB' GB tn, fp: 137, 0 GB fn, tp: 4, 2 GB f1 score: 0.500 GB cohens kappa score: 0.489 -> test with 'KNN' KNN tn, fp: 137, 0 KNN fn, tp: 5, 1 KNN f1 score: 0.286 KNN cohens kappa score: 0.277 ### Exercise is done. -----[ LR ]----- maximum: LR tn, fp: 134, 11 LR fn, tp: 5, 9 LR f1 score: 0.818 LR cohens kappa score: 0.804 LR average precision score: 0.920 average: LR tn, fp: 131.08, 6.72 LR fn, tp: 2.24, 6.16 LR f1 score: 0.575 LR cohens kappa score: 0.544 LR average precision score: 0.687 minimum: LR tn, fp: 127, 4 LR fn, tp: 0, 4 LR f1 score: 0.381 LR cohens kappa score: 0.334 LR average precision score: 0.484 -----[ RF ]----- maximum: RF tn, fp: 138, 4 RF fn, tp: 9, 4 RF f1 score: 0.600 RF cohens kappa score: 0.586 average: RF tn, fp: 136.4, 1.4 RF fn, tp: 6.32, 2.08 RF f1 score: 0.348 RF cohens kappa score: 0.326 minimum: RF tn, fp: 134, 0 RF fn, tp: 3, 0 RF f1 score: 0.000 RF cohens kappa score: 0.000 -----[ GB ]----- maximum: GB tn, fp: 138, 9 GB fn, tp: 8, 4 GB f1 score: 0.615 GB cohens kappa score: 0.600 average: GB tn, fp: 135.56, 2.24 GB fn, tp: 6.36, 2.04 GB f1 score: 0.318 GB cohens kappa score: 0.292 minimum: GB tn, fp: 129, 0 GB fn, tp: 4, 1 GB f1 score: 0.143 GB cohens kappa score: 0.104 -----[ KNN ]----- maximum: KNN tn, fp: 138, 3 KNN fn, tp: 9, 2 KNN f1 score: 0.308 KNN cohens kappa score: 0.281 average: KNN tn, fp: 136.72, 1.08 KNN fn, tp: 7.72, 0.68 KNN f1 score: 0.136 KNN cohens kappa score: 0.120 minimum: KNN tn, fp: 135, 0 KNN fn, tp: 5, 0 KNN f1 score: 0.000 KNN cohens kappa score: -0.032 -----[ GAN ]----- maximum: GAN tn, fp: 137, 10 GAN fn, tp: 6, 8 GAN f1 score: 0.762 GAN cohens kappa score: 0.744 average: GAN tn, fp: 134.6, 3.2 GAN fn, tp: 4.2, 4.2 GAN f1 score: 0.538 GAN cohens kappa score: 0.512 minimum: GAN tn, fp: 128, 0 GAN fn, tp: 1, 2 GAN f1 score: 0.316 GAN cohens kappa score: 0.269