/////////////////////////////////////////// // Running convGAN-proximary-5 on folding_yeast6 /////////////////////////////////////////// Load 'data_input/folding_yeast6' 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 1131 synthetic samples -> retrain GAN for predict Epoch 1/10 1/116 [..............................] - ETA: 17s - loss: 0.4931 41/116 [=========>....................] - ETA: 0s - loss: 0.3604  81/116 [===================>..........] - ETA: 0s - loss: 0.3124 116/116 [==============================] - 0s 1ms/step - loss: 0.2884 Epoch 2/10 1/116 [..............................] - ETA: 0s - loss: 0.3485 42/116 [=========>....................] - ETA: 0s - loss: 0.2352 81/116 [===================>..........] - ETA: 0s - loss: 0.2236 116/116 [==============================] - 0s 1ms/step - loss: 0.2290 Epoch 3/10 1/116 [..............................] - ETA: 0s - loss: 0.1518 39/116 [=========>....................] - ETA: 0s - loss: 0.1935 74/116 [==================>...........] - ETA: 0s - loss: 0.2003 104/116 [=========================>....] - ETA: 0s - loss: 0.2110 116/116 [==============================] - 0s 1ms/step - loss: 0.2124 Epoch 4/10 1/116 [..............................] - ETA: 0s - loss: 0.2238 40/116 [=========>....................] - ETA: 0s - loss: 0.1764 82/116 [====================>.........] - ETA: 0s - loss: 0.1970 116/116 [==============================] - 0s 1ms/step - loss: 0.2043 Epoch 5/10 1/116 [..............................] - ETA: 0s - loss: 0.1379 42/116 [=========>....................] - ETA: 0s - loss: 0.2055 83/116 [====================>.........] - ETA: 0s - loss: 0.1929 116/116 [==============================] - 0s 1ms/step - loss: 0.1982 Epoch 6/10 1/116 [..............................] - ETA: 0s - loss: 0.2845 43/116 [==========>...................] - ETA: 0s - loss: 0.1932 84/116 [====================>.........] - ETA: 0s - loss: 0.1879 116/116 [==============================] - 0s 1ms/step - loss: 0.1948 Epoch 7/10 1/116 [..............................] - ETA: 0s - loss: 0.1497 41/116 [=========>....................] - ETA: 0s - loss: 0.1760 83/116 [====================>.........] - ETA: 0s - loss: 0.1932 116/116 [==============================] - 0s 1ms/step - loss: 0.1904 Epoch 8/10 1/116 [..............................] - ETA: 0s - loss: 0.0777 38/116 [========>.....................] - ETA: 0s - loss: 0.1707 77/116 [==================>...........] - ETA: 0s - loss: 0.1766 116/116 [==============================] - 0s 1ms/step - loss: 0.1836 Epoch 9/10 1/116 [..............................] - ETA: 0s - loss: 0.1008 41/116 [=========>....................] - ETA: 0s - loss: 0.1854 81/116 [===================>..........] - ETA: 0s - loss: 0.1826 116/116 [==============================] - 0s 1ms/step - loss: 0.1794 Epoch 10/10 1/116 [..............................] - ETA: 0s - loss: 0.2045 41/116 [=========>....................] - ETA: 0s - loss: 0.1573 80/116 [===================>..........] - ETA: 0s - loss: 0.1751 116/116 [==============================] - 0s 1ms/step - loss: 0.1757 -> test with GAN.predict GAN tn, fp: 272, 18 GAN fn, tp: 1, 6 GAN f1 score: 0.387 GAN cohens kappa score: 0.364 -> test with 'LR' LR tn, fp: 262, 28 LR fn, tp: 0, 7 LR f1 score: 0.333 LR cohens kappa score: 0.306 LR average precision score: 0.684 -> test with 'RF' RF tn, fp: 288, 2 RF fn, tp: 4, 3 RF f1 score: 0.500 RF cohens kappa score: 0.490 -> test with 'GB' GB tn, fp: 287, 3 GB fn, tp: 4, 3 GB f1 score: 0.462 GB cohens kappa score: 0.450 -> test with 'KNN' KNN tn, fp: 267, 23 KNN fn, tp: 1, 6 KNN f1 score: 0.333 KNN cohens kappa score: 0.307 ------ Step 1/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1131 synthetic samples -> retrain GAN for predict Epoch 1/10 1/116 [..............................] - ETA: 17s - loss: 0.4839 41/116 [=========>....................] - ETA: 0s - loss: 0.3292  83/116 [====================>.........] - ETA: 0s - loss: 0.2970 116/116 [==============================] - 0s 1ms/step - loss: 0.2794 Epoch 2/10 1/116 [..............................] - ETA: 0s - loss: 0.2256 43/116 [==========>...................] - ETA: 0s - loss: 0.2304 85/116 [====================>.........] - ETA: 0s - loss: 0.2312 116/116 [==============================] - 0s 1ms/step - loss: 0.2285 Epoch 3/10 1/116 [..............................] - ETA: 0s - loss: 0.1380 43/116 [==========>...................] - ETA: 0s - loss: 0.1986 85/116 [====================>.........] - ETA: 0s - loss: 0.2139 116/116 [==============================] - 0s 1ms/step - loss: 0.2152 Epoch 4/10 1/116 [..............................] - ETA: 0s - loss: 0.2217 44/116 [==========>...................] - ETA: 0s - loss: 0.2120 85/116 [====================>.........] - ETA: 0s - loss: 0.2114 116/116 [==============================] - 0s 1ms/step - loss: 0.2062 Epoch 5/10 1/116 [..............................] - ETA: 0s - loss: 0.1556 42/116 [=========>....................] - ETA: 0s - loss: 0.1676 84/116 [====================>.........] - ETA: 0s - loss: 0.1927 116/116 [==============================] - 0s 1ms/step - loss: 0.1982 Epoch 6/10 1/116 [..............................] - ETA: 0s - loss: 0.2457 43/116 [==========>...................] - ETA: 0s - loss: 0.1706 85/116 [====================>.........] - ETA: 0s - loss: 0.1841 116/116 [==============================] - 0s 1ms/step - loss: 0.1928 Epoch 7/10 1/116 [..............................] - ETA: 0s - loss: 0.1352 43/116 [==========>...................] - ETA: 0s - loss: 0.1600 82/116 [====================>.........] - ETA: 0s - loss: 0.1843 116/116 [==============================] - 0s 1ms/step - loss: 0.1885 Epoch 8/10 1/116 [..............................] - ETA: 0s - loss: 0.1100 43/116 [==========>...................] - ETA: 0s - loss: 0.1878 84/116 [====================>.........] - ETA: 0s - loss: 0.1818 116/116 [==============================] - 0s 1ms/step - loss: 0.1801 Epoch 9/10 1/116 [..............................] - ETA: 0s - loss: 0.1294 41/116 [=========>....................] - ETA: 0s - loss: 0.1664 81/116 [===================>..........] - ETA: 0s - loss: 0.1826 116/116 [==============================] - 0s 1ms/step - loss: 0.1767 Epoch 10/10 1/116 [..............................] - ETA: 0s - loss: 0.0551 42/116 [=========>....................] - ETA: 0s - loss: 0.1691 83/116 [====================>.........] - ETA: 0s - loss: 0.1743 116/116 [==============================] - 0s 1ms/step - loss: 0.1707 -> test with GAN.predict GAN tn, fp: 261, 29 GAN fn, tp: 1, 6 GAN f1 score: 0.286 GAN cohens kappa score: 0.257 -> test with 'LR' LR tn, fp: 256, 34 LR fn, tp: 2, 5 LR f1 score: 0.217 LR cohens kappa score: 0.185 LR average precision score: 0.427 -> test with 'RF' RF tn, fp: 287, 3 RF fn, tp: 4, 3 RF f1 score: 0.462 RF cohens kappa score: 0.450 -> test with 'GB' GB tn, fp: 286, 4 GB fn, tp: 3, 4 GB f1 score: 0.533 GB cohens kappa score: 0.521 -> test with 'KNN' KNN tn, fp: 263, 27 KNN fn, tp: 2, 5 KNN f1 score: 0.256 KNN cohens kappa score: 0.226 ------ Step 1/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1131 synthetic samples -> retrain GAN for predict Epoch 1/10 1/116 [..............................] - ETA: 18s - loss: 0.2562 43/116 [==========>...................] - ETA: 0s - loss: 0.3046  85/116 [====================>.........] - ETA: 0s - loss: 0.2806 116/116 [==============================] - 0s 1ms/step - loss: 0.2634 Epoch 2/10 1/116 [..............................] - ETA: 0s - loss: 0.1331 44/116 [==========>...................] - ETA: 0s - loss: 0.2020 86/116 [=====================>........] - ETA: 0s - loss: 0.2060 116/116 [==============================] - 0s 1ms/step - loss: 0.2117 Epoch 3/10 1/116 [..............................] - ETA: 0s - loss: 0.1020 35/116 [========>.....................] - ETA: 0s - loss: 0.2115 69/116 [================>.............] - ETA: 0s - loss: 0.2024 111/116 [===========================>..] - ETA: 0s - loss: 0.1979 116/116 [==============================] - 0s 1ms/step - loss: 0.1963 Epoch 4/10 1/116 [..............................] - ETA: 0s - loss: 0.1439 41/116 [=========>....................] - ETA: 0s - loss: 0.2142 79/116 [===================>..........] - ETA: 0s - loss: 0.1930 116/116 [==============================] - 0s 1ms/step - loss: 0.1844 Epoch 5/10 1/116 [..............................] - ETA: 0s - loss: 0.1417 40/116 [=========>....................] - ETA: 0s - loss: 0.1835 82/116 [====================>.........] - ETA: 0s - loss: 0.1760 116/116 [==============================] - 0s 1ms/step - loss: 0.1786 Epoch 6/10 1/116 [..............................] - ETA: 0s - loss: 0.2404 43/116 [==========>...................] - ETA: 0s - loss: 0.1779 84/116 [====================>.........] - ETA: 0s - loss: 0.1686 116/116 [==============================] - 0s 1ms/step - loss: 0.1752 Epoch 7/10 1/116 [..............................] - ETA: 0s - loss: 0.1578 41/116 [=========>....................] - ETA: 0s - loss: 0.1788 81/116 [===================>..........] - ETA: 0s - loss: 0.1769 116/116 [==============================] - 0s 1ms/step - loss: 0.1708 Epoch 8/10 1/116 [..............................] - ETA: 0s - loss: 0.3369 43/116 [==========>...................] - ETA: 0s - loss: 0.1750 85/116 [====================>.........] - ETA: 0s - loss: 0.1697 116/116 [==============================] - 0s 1ms/step - loss: 0.1648 Epoch 9/10 1/116 [..............................] - ETA: 0s - loss: 0.0738 42/116 [=========>....................] - ETA: 0s - loss: 0.1565 84/116 [====================>.........] - ETA: 0s - loss: 0.1549 116/116 [==============================] - 0s 1ms/step - loss: 0.1608 Epoch 10/10 1/116 [..............................] - ETA: 0s - loss: 0.1021 43/116 [==========>...................] - ETA: 0s - loss: 0.1657 84/116 [====================>.........] - ETA: 0s - loss: 0.1603 116/116 [==============================] - 0s 1ms/step - loss: 0.1567 -> test with GAN.predict GAN tn, fp: 270, 20 GAN fn, tp: 1, 6 GAN f1 score: 0.364 GAN cohens kappa score: 0.339 -> test with 'LR' LR tn, fp: 252, 38 LR fn, tp: 1, 6 LR f1 score: 0.235 LR cohens kappa score: 0.203 LR average precision score: 0.229 -> test with 'RF' RF tn, fp: 290, 0 RF fn, tp: 3, 4 RF f1 score: 0.727 RF cohens kappa score: 0.723 -> test with 'GB' GB tn, fp: 290, 0 GB fn, tp: 5, 2 GB f1 score: 0.444 GB cohens kappa score: 0.439 -> test with 'KNN' KNN tn, fp: 267, 23 KNN fn, tp: 1, 6 KNN f1 score: 0.333 KNN cohens kappa score: 0.307 ------ Step 1/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1131 synthetic samples -> retrain GAN for predict Epoch 1/10 1/116 [..............................] - ETA: 18s - loss: 0.3467 41/116 [=========>....................] - ETA: 0s - loss: 0.3164  83/116 [====================>.........] - ETA: 0s - loss: 0.2711 116/116 [==============================] - 0s 1ms/step - loss: 0.2526 Epoch 2/10 1/116 [..............................] - ETA: 0s - loss: 0.1345 42/116 [=========>....................] - ETA: 0s - loss: 0.1990 83/116 [====================>.........] - ETA: 0s - loss: 0.1980 116/116 [==============================] - 0s 1ms/step - loss: 0.1905 Epoch 3/10 1/116 [..............................] - ETA: 0s - loss: 0.1291 43/116 [==========>...................] - ETA: 0s - loss: 0.1638 86/116 [=====================>........] - ETA: 0s - loss: 0.1762 116/116 [==============================] - 0s 1ms/step - loss: 0.1732 Epoch 4/10 1/116 [..............................] - ETA: 0s - loss: 0.2314 44/116 [==========>...................] - ETA: 0s - loss: 0.1752 86/116 [=====================>........] - ETA: 0s - loss: 0.1641 116/116 [==============================] - 0s 1ms/step - loss: 0.1649 Epoch 5/10 1/116 [..............................] - ETA: 0s - loss: 0.1365 43/116 [==========>...................] - ETA: 0s - loss: 0.1670 83/116 [====================>.........] - ETA: 0s - loss: 0.1623 116/116 [==============================] - 0s 1ms/step - loss: 0.1603 Epoch 6/10 1/116 [..............................] - ETA: 0s - loss: 0.2004 38/116 [========>.....................] - ETA: 0s - loss: 0.1608 73/116 [=================>............] - ETA: 0s - loss: 0.1621 109/116 [===========================>..] - ETA: 0s - loss: 0.1538 116/116 [==============================] - 0s 1ms/step - loss: 0.1572 Epoch 7/10 1/116 [..............................] - ETA: 0s - loss: 0.0592 43/116 [==========>...................] - ETA: 0s - loss: 0.1541 86/116 [=====================>........] - ETA: 0s - loss: 0.1516 116/116 [==============================] - 0s 1ms/step - loss: 0.1550 Epoch 8/10 1/116 [..............................] - ETA: 0s - loss: 0.0915 39/116 [=========>....................] - ETA: 0s - loss: 0.1744 79/116 [===================>..........] - ETA: 0s - loss: 0.1457 116/116 [==============================] - 0s 1ms/step - loss: 0.1543 Epoch 9/10 1/116 [..............................] - ETA: 0s - loss: 0.1507 43/116 [==========>...................] - ETA: 0s - loss: 0.1809 84/116 [====================>.........] - ETA: 0s - loss: 0.1472 116/116 [==============================] - 0s 1ms/step - loss: 0.1502 Epoch 10/10 1/116 [..............................] - ETA: 0s - loss: 0.1411 42/116 [=========>....................] - ETA: 0s - loss: 0.1618 84/116 [====================>.........] - ETA: 0s - loss: 0.1586 116/116 [==============================] - 0s 1ms/step - loss: 0.1503 -> test with GAN.predict GAN tn, fp: 270, 20 GAN fn, tp: 2, 5 GAN f1 score: 0.312 GAN cohens kappa score: 0.286 -> test with 'LR' LR tn, fp: 270, 20 LR fn, tp: 1, 6 LR f1 score: 0.364 LR cohens kappa score: 0.339 LR average precision score: 0.588 -> test with 'RF' RF tn, fp: 288, 2 RF fn, tp: 4, 3 RF f1 score: 0.500 RF cohens kappa score: 0.490 -> test with 'GB' GB tn, fp: 288, 2 GB fn, tp: 5, 2 GB f1 score: 0.364 GB cohens kappa score: 0.353 -> test with 'KNN' KNN tn, fp: 273, 17 KNN fn, tp: 1, 6 KNN f1 score: 0.400 KNN cohens kappa score: 0.378 ------ Step 1/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1132 synthetic samples -> retrain GAN for predict Epoch 1/10 1/116 [..............................] - ETA: 15s - loss: 0.3044 37/116 [========>.....................] - ETA: 0s - loss: 0.3987  75/116 [==================>...........] - ETA: 0s - loss: 0.3815 107/116 [==========================>...] - ETA: 0s - loss: 0.3629 116/116 [==============================] - 0s 1ms/step - loss: 0.3569 Epoch 2/10 1/116 [..............................] - ETA: 0s - loss: 0.2946 32/116 [=======>......................] - ETA: 0s - loss: 0.2690 69/116 [================>.............] - ETA: 0s - loss: 0.2624 109/116 [===========================>..] - ETA: 0s - loss: 0.2481 116/116 [==============================] - 0s 1ms/step - loss: 0.2502 Epoch 3/10 1/116 [..............................] - ETA: 0s - loss: 0.1354 42/116 [=========>....................] - ETA: 0s - loss: 0.2215 83/116 [====================>.........] - ETA: 0s - loss: 0.2115 116/116 [==============================] - 0s 1ms/step - loss: 0.2169 Epoch 4/10 1/116 [..............................] - ETA: 0s - loss: 0.0770 44/116 [==========>...................] - ETA: 0s - loss: 0.1986 83/116 [====================>.........] - ETA: 0s - loss: 0.2036 116/116 [==============================] - 0s 1ms/step - loss: 0.2030 Epoch 5/10 1/116 [..............................] - ETA: 0s - loss: 0.1661 44/116 [==========>...................] - ETA: 0s - loss: 0.2080 82/116 [====================>.........] - ETA: 0s - loss: 0.2004 116/116 [==============================] - 0s 1ms/step - loss: 0.1957 Epoch 6/10 1/116 [..............................] - ETA: 0s - loss: 0.2345 43/116 [==========>...................] - ETA: 0s - loss: 0.2077 88/116 [=====================>........] - ETA: 0s - loss: 0.1979 116/116 [==============================] - 0s 1ms/step - loss: 0.1907 Epoch 7/10 1/116 [..............................] - ETA: 0s - loss: 0.1079 41/116 [=========>....................] - ETA: 0s - loss: 0.1816 82/116 [====================>.........] - ETA: 0s - loss: 0.1837 116/116 [==============================] - 0s 1ms/step - loss: 0.1865 Epoch 8/10 1/116 [..............................] - ETA: 0s - loss: 0.0955 43/116 [==========>...................] - ETA: 0s - loss: 0.1564 86/116 [=====================>........] - ETA: 0s - loss: 0.1769 116/116 [==============================] - 0s 1ms/step - loss: 0.1823 Epoch 9/10 1/116 [..............................] - ETA: 0s - loss: 0.0928 46/116 [==========>...................] - ETA: 0s - loss: 0.1491 89/116 [======================>.......] - ETA: 0s - loss: 0.1716 116/116 [==============================] - 0s 1ms/step - loss: 0.1776 Epoch 10/10 1/116 [..............................] - ETA: 0s - loss: 0.2331 45/116 [==========>...................] - ETA: 0s - loss: 0.1931 87/116 [=====================>........] - ETA: 0s - loss: 0.1880 116/116 [==============================] - 0s 1ms/step - loss: 0.1754 -> test with GAN.predict GAN tn, fp: 260, 29 GAN fn, tp: 1, 6 GAN f1 score: 0.286 GAN cohens kappa score: 0.256 -> test with 'LR' LR tn, fp: 244, 45 LR fn, tp: 0, 7 LR f1 score: 0.237 LR cohens kappa score: 0.204 LR average precision score: 0.625 -> test with 'RF' RF tn, fp: 289, 0 RF fn, tp: 4, 3 RF f1 score: 0.600 RF cohens kappa score: 0.594 -> test with 'GB' GB tn, fp: 288, 1 GB fn, tp: 3, 4 GB f1 score: 0.667 GB cohens kappa score: 0.660 -> test with 'KNN' KNN tn, fp: 262, 27 KNN fn, tp: 1, 6 KNN f1 score: 0.300 KNN cohens kappa score: 0.272 ====== Step 2/5 ======= -> Shuffling data -> Spliting data to slices ------ Step 2/5: Slice 1/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1131 synthetic samples -> retrain GAN for predict Epoch 1/10 1/116 [..............................] - ETA: 19s - loss: 0.5174 43/116 [==========>...................] - ETA: 0s - loss: 0.3912  84/116 [====================>.........] - ETA: 0s - loss: 0.3534 116/116 [==============================] - 0s 1ms/step - loss: 0.3276 Epoch 2/10 1/116 [..............................] - ETA: 0s - loss: 0.1576 43/116 [==========>...................] - ETA: 0s - loss: 0.2441 84/116 [====================>.........] - ETA: 0s - loss: 0.2363 116/116 [==============================] - 0s 1ms/step - loss: 0.2305 Epoch 3/10 1/116 [..............................] - ETA: 0s - loss: 0.1740 43/116 [==========>...................] - ETA: 0s - loss: 0.1993 83/116 [====================>.........] - ETA: 0s - loss: 0.1966 116/116 [==============================] - 0s 1ms/step - loss: 0.2043 Epoch 4/10 1/116 [..............................] - ETA: 0s - loss: 0.1628 40/116 [=========>....................] - ETA: 0s - loss: 0.1945 81/116 [===================>..........] - ETA: 0s - loss: 0.1982 116/116 [==============================] - 0s 1ms/step - loss: 0.1926 Epoch 5/10 1/116 [..............................] - ETA: 0s - loss: 0.0611 43/116 [==========>...................] - ETA: 0s - loss: 0.1870 83/116 [====================>.........] - ETA: 0s - loss: 0.1877 116/116 [==============================] - 0s 1ms/step - loss: 0.1836 Epoch 6/10 1/116 [..............................] - ETA: 0s - loss: 0.0379 43/116 [==========>...................] - ETA: 0s - loss: 0.1578 84/116 [====================>.........] - ETA: 0s - loss: 0.1679 116/116 [==============================] - 0s 1ms/step - loss: 0.1771 Epoch 7/10 1/116 [..............................] - ETA: 0s - loss: 0.1941 43/116 [==========>...................] - ETA: 0s - loss: 0.1765 83/116 [====================>.........] - ETA: 0s - loss: 0.1632 116/116 [==============================] - 0s 1ms/step - loss: 0.1726 Epoch 8/10 1/116 [..............................] - ETA: 0s - loss: 0.2146 41/116 [=========>....................] - ETA: 0s - loss: 0.1749 81/116 [===================>..........] - ETA: 0s - loss: 0.1740 116/116 [==============================] - 0s 1ms/step - loss: 0.1682 Epoch 9/10 1/116 [..............................] - ETA: 0s - loss: 0.0888 42/116 [=========>....................] - ETA: 0s - loss: 0.1664 83/116 [====================>.........] - ETA: 0s - loss: 0.1743 116/116 [==============================] - 0s 1ms/step - loss: 0.1601 Epoch 10/10 1/116 [..............................] - ETA: 0s - loss: 0.0483 42/116 [=========>....................] - ETA: 0s - loss: 0.1625 84/116 [====================>.........] - ETA: 0s - loss: 0.1600 116/116 [==============================] - 0s 1ms/step - loss: 0.1580 -> test with GAN.predict GAN tn, fp: 277, 13 GAN fn, tp: 1, 6 GAN f1 score: 0.462 GAN cohens kappa score: 0.442 -> test with 'LR' LR tn, fp: 261, 29 LR fn, tp: 1, 6 LR f1 score: 0.286 LR cohens kappa score: 0.257 LR average precision score: 0.673 -> test with 'RF' RF tn, fp: 288, 2 RF fn, tp: 3, 4 RF f1 score: 0.615 RF cohens kappa score: 0.607 -> test with 'GB' GB tn, fp: 286, 4 GB fn, tp: 3, 4 GB f1 score: 0.533 GB cohens kappa score: 0.521 -> test with 'KNN' KNN tn, fp: 269, 21 KNN fn, tp: 1, 6 KNN f1 score: 0.353 KNN cohens kappa score: 0.328 ------ Step 2/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1131 synthetic samples -> retrain GAN for predict Epoch 1/10 1/116 [..............................] - ETA: 17s - loss: 0.4289 44/116 [==========>...................] - ETA: 0s - loss: 0.3494  81/116 [===================>..........] - ETA: 0s - loss: 0.3304 116/116 [==============================] - 0s 1ms/step - loss: 0.3216 Epoch 2/10 1/116 [..............................] - ETA: 0s - loss: 0.2145 43/116 [==========>...................] - ETA: 0s - loss: 0.2477 84/116 [====================>.........] - ETA: 0s - loss: 0.2559 116/116 [==============================] - 0s 1ms/step - loss: 0.2559 Epoch 3/10 1/116 [..............................] - ETA: 0s - loss: 0.3809 43/116 [==========>...................] - ETA: 0s - loss: 0.2508 84/116 [====================>.........] - ETA: 0s - loss: 0.2363 116/116 [==============================] - 0s 1ms/step - loss: 0.2338 Epoch 4/10 1/116 [..............................] - ETA: 0s - loss: 0.2007 42/116 [=========>....................] - ETA: 0s - loss: 0.2366 83/116 [====================>.........] - ETA: 0s - loss: 0.2281 116/116 [==============================] - 0s 1ms/step - loss: 0.2188 Epoch 5/10 1/116 [..............................] - ETA: 0s - loss: 0.2250 43/116 [==========>...................] - ETA: 0s - loss: 0.2125 85/116 [====================>.........] - ETA: 0s - loss: 0.2125 116/116 [==============================] - 0s 1ms/step - loss: 0.2086 Epoch 6/10 1/116 [..............................] - ETA: 0s - loss: 0.1141 41/116 [=========>....................] - ETA: 0s - loss: 0.1921 82/116 [====================>.........] - ETA: 0s - loss: 0.2012 116/116 [==============================] - 0s 1ms/step - loss: 0.2033 Epoch 7/10 1/116 [..............................] - ETA: 0s - loss: 0.2975 43/116 [==========>...................] - ETA: 0s - loss: 0.1980 85/116 [====================>.........] - ETA: 0s - loss: 0.1931 116/116 [==============================] - 0s 1ms/step - loss: 0.1999 Epoch 8/10 1/116 [..............................] - ETA: 0s - loss: 0.3985 40/116 [=========>....................] - ETA: 0s - loss: 0.1888 75/116 [==================>...........] - ETA: 0s - loss: 0.1873 109/116 [===========================>..] - ETA: 0s - loss: 0.1938 116/116 [==============================] - 0s 1ms/step - loss: 0.1960 Epoch 9/10 1/116 [..............................] - ETA: 0s - loss: 0.1619 42/116 [=========>....................] - ETA: 0s - loss: 0.1836 83/116 [====================>.........] - ETA: 0s - loss: 0.1830 116/116 [==============================] - 0s 1ms/step - loss: 0.1870 Epoch 10/10 1/116 [..............................] - ETA: 0s - loss: 0.0898 42/116 [=========>....................] - ETA: 0s - loss: 0.1819 80/116 [===================>..........] - ETA: 0s - loss: 0.1719 114/116 [============================>.] - ETA: 0s - loss: 0.1812 116/116 [==============================] - 0s 1ms/step - loss: 0.1835 -> test with GAN.predict GAN tn, fp: 263, 27 GAN fn, tp: 0, 7 GAN f1 score: 0.341 GAN cohens kappa score: 0.315 -> test with 'LR' LR tn, fp: 251, 39 LR fn, tp: 0, 7 LR f1 score: 0.264 LR cohens kappa score: 0.233 LR average precision score: 0.229 -> test with 'RF' RF tn, fp: 289, 1 RF fn, tp: 5, 2 RF f1 score: 0.400 RF cohens kappa score: 0.391 -> test with 'GB' GB tn, fp: 288, 2 GB fn, tp: 4, 3 GB f1 score: 0.500 GB cohens kappa score: 0.490 -> test with 'KNN' KNN tn, fp: 264, 26 KNN fn, tp: 0, 7 KNN f1 score: 0.350 KNN cohens kappa score: 0.324 ------ Step 2/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1131 synthetic samples -> retrain GAN for predict Epoch 1/10 1/116 [..............................] - ETA: 18s - loss: 0.4258 36/116 [========>.....................] - ETA: 0s - loss: 0.3212  72/116 [=================>............] - ETA: 0s - loss: 0.2899 110/116 [===========================>..] - ETA: 0s - loss: 0.2672 116/116 [==============================] - 0s 1ms/step - loss: 0.2649 Epoch 2/10 1/116 [..............................] - ETA: 0s - loss: 0.3471 35/116 [========>.....................] - ETA: 0s - loss: 0.1975 73/116 [=================>............] - ETA: 0s - loss: 0.1975 112/116 [===========================>..] - ETA: 0s - loss: 0.1960 116/116 [==============================] - 0s 1ms/step - loss: 0.1936 Epoch 3/10 1/116 [..............................] - ETA: 0s - loss: 0.1180 40/116 [=========>....................] - ETA: 0s - loss: 0.1755 76/116 [==================>...........] - ETA: 0s - loss: 0.1712 113/116 [============================>.] - ETA: 0s - loss: 0.1691 116/116 [==============================] - 0s 1ms/step - loss: 0.1700 Epoch 4/10 1/116 [..............................] - ETA: 0s - loss: 0.0547 34/116 [=======>......................] - ETA: 0s - loss: 0.1644 70/116 [=================>............] - ETA: 0s - loss: 0.1487 106/116 [==========================>...] - ETA: 0s - loss: 0.1523 116/116 [==============================] - 0s 1ms/step - loss: 0.1573 Epoch 5/10 1/116 [..............................] - ETA: 0s - loss: 0.1459 42/116 [=========>....................] - ETA: 0s - loss: 0.1623 82/116 [====================>.........] - ETA: 0s - loss: 0.1538 116/116 [==============================] - 0s 1ms/step - loss: 0.1516 Epoch 6/10 1/116 [..............................] - ETA: 0s - loss: 0.0911 32/116 [=======>......................] - ETA: 0s - loss: 0.1568 68/116 [================>.............] - ETA: 0s - loss: 0.1523 101/116 [=========================>....] - ETA: 0s - loss: 0.1456 116/116 [==============================] - 0s 2ms/step - loss: 0.1432 Epoch 7/10 1/116 [..............................] - ETA: 0s - loss: 0.0718 38/116 [========>.....................] - ETA: 0s - loss: 0.1556 72/116 [=================>............] - ETA: 0s - loss: 0.1453 108/116 [==========================>...] - ETA: 0s - loss: 0.1409 116/116 [==============================] - 0s 1ms/step - loss: 0.1403 Epoch 8/10 1/116 [..............................] - ETA: 0s - loss: 0.0489 37/116 [========>.....................] - ETA: 0s - loss: 0.1245 71/116 [=================>............] - ETA: 0s - loss: 0.1319 108/116 [==========================>...] - ETA: 0s - loss: 0.1338 116/116 [==============================] - 0s 1ms/step - loss: 0.1366 Epoch 9/10 1/116 [..............................] - ETA: 0s - loss: 0.0365 36/116 [========>.....................] - ETA: 0s - loss: 0.1381 67/116 [================>.............] - ETA: 0s - loss: 0.1368 100/116 [========================>.....] - ETA: 0s - loss: 0.1313 116/116 [==============================] - 0s 2ms/step - loss: 0.1325 Epoch 10/10 1/116 [..............................] - ETA: 0s - loss: 0.0745 38/116 [========>.....................] - ETA: 0s - loss: 0.1685 73/116 [=================>............] - ETA: 0s - loss: 0.1436 111/116 [===========================>..] - ETA: 0s - loss: 0.1287 116/116 [==============================] - 0s 1ms/step - loss: 0.1309 -> test with GAN.predict GAN tn, fp: 272, 18 GAN fn, tp: 2, 5 GAN f1 score: 0.333 GAN cohens kappa score: 0.308 -> test with 'LR' LR tn, fp: 262, 28 LR fn, tp: 1, 6 LR f1 score: 0.293 LR cohens kappa score: 0.264 LR average precision score: 0.420 -> test with 'RF' RF tn, fp: 289, 1 RF fn, tp: 5, 2 RF f1 score: 0.400 RF cohens kappa score: 0.391 -> test with 'GB' GB tn, fp: 288, 2 GB fn, tp: 4, 3 GB f1 score: 0.500 GB cohens kappa score: 0.490 -> test with 'KNN' KNN tn, fp: 271, 19 KNN fn, tp: 2, 5 KNN f1 score: 0.323 KNN cohens kappa score: 0.297 ------ Step 2/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1131 synthetic samples -> retrain GAN for predict Epoch 1/10 1/116 [..............................] - ETA: 20s - loss: 0.3021 42/116 [=========>....................] - ETA: 0s - loss: 0.2460  83/116 [====================>.........] - ETA: 0s - loss: 0.2300 116/116 [==============================] - 0s 1ms/step - loss: 0.2198 Epoch 2/10 1/116 [..............................] - ETA: 0s - loss: 0.1576 41/116 [=========>....................] - ETA: 0s - loss: 0.1599 80/116 [===================>..........] - ETA: 0s - loss: 0.1717 116/116 [==============================] - 0s 1ms/step - loss: 0.1779 Epoch 3/10 1/116 [..............................] - ETA: 0s - loss: 0.2547 35/116 [========>.....................] - ETA: 0s - loss: 0.1852 70/116 [=================>............] - ETA: 0s - loss: 0.1767 105/116 [==========================>...] - ETA: 0s - loss: 0.1645 116/116 [==============================] - 0s 1ms/step - loss: 0.1637 Epoch 4/10 1/116 [..............................] - ETA: 0s - loss: 0.1413 42/116 [=========>....................] - ETA: 0s - loss: 0.1790 81/116 [===================>..........] - ETA: 0s - loss: 0.1639 116/116 [==============================] - 0s 1ms/step - loss: 0.1566 Epoch 5/10 1/116 [..............................] - ETA: 0s - loss: 0.0388 42/116 [=========>....................] - ETA: 0s - loss: 0.1369 84/116 [====================>.........] - ETA: 0s - loss: 0.1502 116/116 [==============================] - 0s 1ms/step - loss: 0.1523 Epoch 6/10 1/116 [..............................] - ETA: 0s - loss: 0.1055 40/116 [=========>....................] - ETA: 0s - loss: 0.1593 81/116 [===================>..........] - ETA: 0s - loss: 0.1515 116/116 [==============================] - 0s 1ms/step - loss: 0.1487 Epoch 7/10 1/116 [..............................] - ETA: 0s - loss: 0.0492 42/116 [=========>....................] - ETA: 0s - loss: 0.1538 84/116 [====================>.........] - ETA: 0s - loss: 0.1474 116/116 [==============================] - 0s 1ms/step - loss: 0.1449 Epoch 8/10 1/116 [..............................] - ETA: 0s - loss: 0.0883 42/116 [=========>....................] - ETA: 0s - loss: 0.1641 84/116 [====================>.........] - ETA: 0s - loss: 0.1498 116/116 [==============================] - 0s 1ms/step - loss: 0.1444 Epoch 9/10 1/116 [..............................] - ETA: 0s - loss: 0.2343 42/116 [=========>....................] - ETA: 0s - loss: 0.1347 83/116 [====================>.........] - ETA: 0s - loss: 0.1456 116/116 [==============================] - 0s 1ms/step - loss: 0.1389 Epoch 10/10 1/116 [..............................] - ETA: 0s - loss: 0.2105 42/116 [=========>....................] - ETA: 0s - loss: 0.1576 82/116 [====================>.........] - ETA: 0s - loss: 0.1441 116/116 [==============================] - 0s 1ms/step - loss: 0.1378 -> test with GAN.predict GAN tn, fp: 268, 22 GAN fn, tp: 2, 5 GAN f1 score: 0.294 GAN cohens kappa score: 0.267 -> test with 'LR' LR tn, fp: 262, 28 LR fn, tp: 2, 5 LR f1 score: 0.250 LR cohens kappa score: 0.220 LR average precision score: 0.558 -> test with 'RF' RF tn, fp: 289, 1 RF fn, tp: 5, 2 RF f1 score: 0.400 RF cohens kappa score: 0.391 -> test with 'GB' GB tn, fp: 286, 4 GB fn, tp: 5, 2 GB f1 score: 0.308 GB cohens kappa score: 0.292 -> test with 'KNN' KNN tn, fp: 268, 22 KNN fn, tp: 2, 5 KNN f1 score: 0.294 KNN cohens kappa score: 0.267 ------ Step 2/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1132 synthetic samples -> retrain GAN for predict Epoch 1/10 1/116 [..............................] - ETA: 15s - loss: 0.4644 45/116 [==========>...................] - ETA: 0s - loss: 0.3142  90/116 [======================>.......] - ETA: 0s - loss: 0.2834 116/116 [==============================] - 0s 1ms/step - loss: 0.2719 Epoch 2/10 1/116 [..............................] - ETA: 0s - loss: 0.0738 46/116 [==========>...................] - ETA: 0s - loss: 0.2189 85/116 [====================>.........] - ETA: 0s - loss: 0.2156 116/116 [==============================] - 0s 1ms/step - loss: 0.2100 Epoch 3/10 1/116 [..............................] - ETA: 0s - loss: 0.2406 44/116 [==========>...................] - ETA: 0s - loss: 0.1804 85/116 [====================>.........] - ETA: 0s - loss: 0.1825 116/116 [==============================] - 0s 1ms/step - loss: 0.1929 Epoch 4/10 1/116 [..............................] - ETA: 0s - loss: 0.1650 44/116 [==========>...................] - ETA: 0s - loss: 0.1890 85/116 [====================>.........] - ETA: 0s - loss: 0.1963 116/116 [==============================] - 0s 1ms/step - loss: 0.1883 Epoch 5/10 1/116 [..............................] - ETA: 0s - loss: 0.1306 36/116 [========>.....................] - ETA: 0s - loss: 0.1782 73/116 [=================>............] - ETA: 0s - loss: 0.1847 116/116 [==============================] - 0s 1ms/step - loss: 0.1796 Epoch 6/10 1/116 [..............................] - ETA: 0s - loss: 0.0649 45/116 [==========>...................] - ETA: 0s - loss: 0.1967 88/116 [=====================>........] - ETA: 0s - loss: 0.1814 116/116 [==============================] - 0s 1ms/step - loss: 0.1775 Epoch 7/10 1/116 [..............................] - ETA: 0s - loss: 0.0848 46/116 [==========>...................] - ETA: 0s - loss: 0.1637 89/116 [======================>.......] - ETA: 0s - loss: 0.1693 116/116 [==============================] - 0s 1ms/step - loss: 0.1741 Epoch 8/10 1/116 [..............................] - ETA: 0s - loss: 0.3022 46/116 [==========>...................] - ETA: 0s - loss: 0.1990 92/116 [======================>.......] - ETA: 0s - loss: 0.1729 116/116 [==============================] - 0s 1ms/step - loss: 0.1698 Epoch 9/10 1/116 [..............................] - ETA: 0s - loss: 0.0940 45/116 [==========>...................] - ETA: 0s - loss: 0.1731 90/116 [======================>.......] - ETA: 0s - loss: 0.1754 116/116 [==============================] - 0s 1ms/step - loss: 0.1674 Epoch 10/10 1/116 [..............................] - ETA: 0s - loss: 0.1259 46/116 [==========>...................] - ETA: 0s - loss: 0.1907 92/116 [======================>.......] - ETA: 0s - loss: 0.1645 116/116 [==============================] - 0s 1ms/step - loss: 0.1657 -> test with GAN.predict GAN tn, fp: 275, 14 GAN fn, tp: 1, 6 GAN f1 score: 0.444 GAN cohens kappa score: 0.424 -> test with 'LR' LR tn, fp: 267, 22 LR fn, tp: 1, 6 LR f1 score: 0.343 LR cohens kappa score: 0.317 LR average precision score: 0.560 -> test with 'RF' RF tn, fp: 289, 0 RF fn, tp: 5, 2 RF f1 score: 0.444 RF cohens kappa score: 0.439 -> test with 'GB' GB tn, fp: 289, 0 GB fn, tp: 6, 1 GB f1 score: 0.250 GB cohens kappa score: 0.246 -> test with 'KNN' KNN tn, fp: 274, 15 KNN fn, tp: 3, 4 KNN f1 score: 0.308 KNN cohens kappa score: 0.283 ====== Step 3/5 ======= -> Shuffling data -> Spliting data to slices ------ Step 3/5: Slice 1/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1131 synthetic samples -> retrain GAN for predict Epoch 1/10 1/116 [..............................] - ETA: 17s - loss: 0.3072 43/116 [==========>...................] - ETA: 0s - loss: 0.2552  84/116 [====================>.........] - ETA: 0s - loss: 0.2160 116/116 [==============================] - 0s 1ms/step - loss: 0.2005 Epoch 2/10 1/116 [..............................] - ETA: 0s - loss: 0.1715 42/116 [=========>....................] - ETA: 0s - loss: 0.1369 84/116 [====================>.........] - ETA: 0s - loss: 0.1418 116/116 [==============================] - 0s 1ms/step - loss: 0.1377 Epoch 3/10 1/116 [..............................] - ETA: 0s - loss: 0.1241 43/116 [==========>...................] - ETA: 0s - loss: 0.1183 84/116 [====================>.........] - ETA: 0s - loss: 0.1230 116/116 [==============================] - 0s 1ms/step - loss: 0.1198 Epoch 4/10 1/116 [..............................] - ETA: 0s - loss: 0.2198 41/116 [=========>....................] - ETA: 0s - loss: 0.1129 78/116 [===================>..........] - ETA: 0s - loss: 0.1133 116/116 [==============================] - 0s 1ms/step - loss: 0.1151 Epoch 5/10 1/116 [..............................] - ETA: 0s - loss: 0.0238 41/116 [=========>....................] - ETA: 0s - loss: 0.0987 82/116 [====================>.........] - ETA: 0s - loss: 0.1126 116/116 [==============================] - 0s 1ms/step - loss: 0.1087 Epoch 6/10 1/116 [..............................] - ETA: 0s - loss: 0.0485 43/116 [==========>...................] - ETA: 0s - loss: 0.1038 83/116 [====================>.........] - ETA: 0s - loss: 0.0968 116/116 [==============================] - 0s 1ms/step - loss: 0.1052 Epoch 7/10 1/116 [..............................] - ETA: 0s - loss: 0.1086 42/116 [=========>....................] - ETA: 0s - loss: 0.0924 83/116 [====================>.........] - ETA: 0s - loss: 0.1028 116/116 [==============================] - 0s 1ms/step - loss: 0.1045 Epoch 8/10 1/116 [..............................] - ETA: 0s - loss: 0.0399 42/116 [=========>....................] - ETA: 0s - loss: 0.1006 84/116 [====================>.........] - ETA: 0s - loss: 0.1018 116/116 [==============================] - 0s 1ms/step - loss: 0.1030 Epoch 9/10 1/116 [..............................] - ETA: 0s - loss: 0.0706 43/116 [==========>...................] - ETA: 0s - loss: 0.1010 84/116 [====================>.........] - ETA: 0s - loss: 0.1003 116/116 [==============================] - 0s 1ms/step - loss: 0.0980 Epoch 10/10 1/116 [..............................] - ETA: 0s - loss: 0.3166 33/116 [=======>......................] - ETA: 0s - loss: 0.0930 66/116 [================>.............] - ETA: 0s - loss: 0.0983 103/116 [=========================>....] - ETA: 0s - loss: 0.0954 116/116 [==============================] - 0s 1ms/step - loss: 0.0973 -> test with GAN.predict GAN tn, fp: 277, 13 GAN fn, tp: 3, 4 GAN f1 score: 0.333 GAN cohens kappa score: 0.310 -> test with 'LR' LR tn, fp: 269, 21 LR fn, tp: 1, 6 LR f1 score: 0.353 LR cohens kappa score: 0.328 LR average precision score: 0.598 -> test with 'RF' RF tn, fp: 289, 1 RF fn, tp: 4, 3 RF f1 score: 0.545 RF cohens kappa score: 0.538 -> test with 'GB' GB tn, fp: 289, 1 GB fn, tp: 3, 4 GB f1 score: 0.667 GB cohens kappa score: 0.660 -> test with 'KNN' KNN tn, fp: 272, 18 KNN fn, tp: 3, 4 KNN f1 score: 0.276 KNN cohens kappa score: 0.249 ------ Step 3/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1131 synthetic samples -> retrain GAN for predict Epoch 1/10 1/116 [..............................] - ETA: 17s - loss: 0.4285 38/116 [========>.....................] - ETA: 0s - loss: 0.3682  80/116 [===================>..........] - ETA: 0s - loss: 0.3374 114/116 [============================>.] - ETA: 0s - loss: 0.3224 116/116 [==============================] - 0s 1ms/step - loss: 0.3221 Epoch 2/10 1/116 [..............................] - ETA: 0s - loss: 0.3699 36/116 [========>.....................] - ETA: 0s - loss: 0.2636 70/116 [=================>............] - ETA: 0s - loss: 0.2676 110/116 [===========================>..] - ETA: 0s - loss: 0.2617 116/116 [==============================] - 0s 1ms/step - loss: 0.2565 Epoch 3/10 1/116 [..............................] - ETA: 0s - loss: 0.2426 40/116 [=========>....................] - ETA: 0s - loss: 0.2873 80/116 [===================>..........] - ETA: 0s - loss: 0.2525 116/116 [==============================] - 0s 1ms/step - loss: 0.2426 Epoch 4/10 1/116 [..............................] - ETA: 0s - loss: 0.1818 42/116 [=========>....................] - ETA: 0s - loss: 0.2408 83/116 [====================>.........] - ETA: 0s - loss: 0.2266 116/116 [==============================] - 0s 1ms/step - loss: 0.2304 Epoch 5/10 1/116 [..............................] - ETA: 0s - loss: 0.2660 42/116 [=========>....................] - ETA: 0s - loss: 0.2296 83/116 [====================>.........] - ETA: 0s - loss: 0.2334 116/116 [==============================] - 0s 1ms/step - loss: 0.2303 Epoch 6/10 1/116 [..............................] - ETA: 0s - loss: 0.1972 42/116 [=========>....................] - ETA: 0s - loss: 0.2354 84/116 [====================>.........] - ETA: 0s - loss: 0.2179 116/116 [==============================] - 0s 1ms/step - loss: 0.2175 Epoch 7/10 1/116 [..............................] - ETA: 0s - loss: 0.1260 43/116 [==========>...................] - ETA: 0s - loss: 0.1989 84/116 [====================>.........] - ETA: 0s - loss: 0.2150 116/116 [==============================] - 0s 1ms/step - loss: 0.2124 Epoch 8/10 1/116 [..............................] - ETA: 0s - loss: 0.0874 42/116 [=========>....................] - ETA: 0s - loss: 0.2074 82/116 [====================>.........] - ETA: 0s - loss: 0.2007 116/116 [==============================] - 0s 1ms/step - loss: 0.2106 Epoch 9/10 1/116 [..............................] - ETA: 0s - loss: 0.2802 43/116 [==========>...................] - ETA: 0s - loss: 0.2006 84/116 [====================>.........] - ETA: 0s - loss: 0.2075 116/116 [==============================] - 0s 1ms/step - loss: 0.2034 Epoch 10/10 1/116 [..............................] - ETA: 0s - loss: 0.1188 43/116 [==========>...................] - ETA: 0s - loss: 0.2163 83/116 [====================>.........] - ETA: 0s - loss: 0.2168 116/116 [==============================] - 0s 1ms/step - loss: 0.2003 -> test with GAN.predict GAN tn, fp: 272, 18 GAN fn, tp: 0, 7 GAN f1 score: 0.438 GAN cohens kappa score: 0.416 -> test with 'LR' LR tn, fp: 246, 44 LR fn, tp: 0, 7 LR f1 score: 0.241 LR cohens kappa score: 0.209 LR average precision score: 0.717 -> test with 'RF' RF tn, fp: 290, 0 RF fn, tp: 3, 4 RF f1 score: 0.727 RF cohens kappa score: 0.723 -> test with 'GB' GB tn, fp: 290, 0 GB fn, tp: 4, 3 GB f1 score: 0.600 GB cohens kappa score: 0.594 -> test with 'KNN' KNN tn, fp: 258, 32 KNN fn, tp: 0, 7 KNN f1 score: 0.304 KNN cohens kappa score: 0.275 ------ Step 3/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1131 synthetic samples -> retrain GAN for predict Epoch 1/10 1/116 [..............................] - ETA: 17s - loss: 0.5050 39/116 [=========>....................] - ETA: 0s - loss: 0.3237  74/116 [==================>...........] - ETA: 0s - loss: 0.2744 107/116 [==========================>...] - ETA: 0s - loss: 0.2556 116/116 [==============================] - 0s 1ms/step - loss: 0.2535 Epoch 2/10 1/116 [..............................] - ETA: 0s - loss: 0.1513 38/116 [========>.....................] - ETA: 0s - loss: 0.1950 79/116 [===================>..........] - ETA: 0s - loss: 0.2031 116/116 [==============================] - 0s 1ms/step - loss: 0.1890 Epoch 3/10 1/116 [..............................] - ETA: 0s - loss: 0.1316 42/116 [=========>....................] - ETA: 0s - loss: 0.1893 83/116 [====================>.........] - ETA: 0s - loss: 0.1769 116/116 [==============================] - 0s 1ms/step - loss: 0.1689 Epoch 4/10 1/116 [..............................] - ETA: 0s - loss: 0.1087 41/116 [=========>....................] - ETA: 0s - loss: 0.1413 80/116 [===================>..........] - ETA: 0s - loss: 0.1666 116/116 [==============================] - 0s 1ms/step - loss: 0.1625 Epoch 5/10 1/116 [..............................] - ETA: 0s - loss: 0.0904 41/116 [=========>....................] - ETA: 0s - loss: 0.1666 81/116 [===================>..........] - ETA: 0s - loss: 0.1535 116/116 [==============================] - 0s 1ms/step - loss: 0.1564 Epoch 6/10 1/116 [..............................] - ETA: 0s - loss: 0.2277 42/116 [=========>....................] - ETA: 0s - loss: 0.1420 84/116 [====================>.........] - ETA: 0s - loss: 0.1675 116/116 [==============================] - 0s 1ms/step - loss: 0.1569 Epoch 7/10 1/116 [..............................] - ETA: 0s - loss: 0.1459 42/116 [=========>....................] - ETA: 0s - loss: 0.1537 83/116 [====================>.........] - ETA: 0s - loss: 0.1549 116/116 [==============================] - 0s 1ms/step - loss: 0.1505 Epoch 8/10 1/116 [..............................] - ETA: 0s - loss: 0.1845 42/116 [=========>....................] - ETA: 0s - loss: 0.1662 83/116 [====================>.........] - ETA: 0s - loss: 0.1479 116/116 [==============================] - 0s 1ms/step - loss: 0.1477 Epoch 9/10 1/116 [..............................] - ETA: 0s - loss: 0.1360 41/116 [=========>....................] - ETA: 0s - loss: 0.1468 81/116 [===================>..........] - ETA: 0s - loss: 0.1415 116/116 [==============================] - 0s 1ms/step - loss: 0.1445 Epoch 10/10 1/116 [..............................] - ETA: 0s - loss: 0.1706 41/116 [=========>....................] - ETA: 0s - loss: 0.1591 82/116 [====================>.........] - ETA: 0s - loss: 0.1503 116/116 [==============================] - 0s 1ms/step - loss: 0.1420 -> test with GAN.predict GAN tn, fp: 277, 13 GAN fn, tp: 3, 4 GAN f1 score: 0.333 GAN cohens kappa score: 0.310 -> test with 'LR' LR tn, fp: 269, 21 LR fn, tp: 2, 5 LR f1 score: 0.303 LR cohens kappa score: 0.276 LR average precision score: 0.395 -> test with 'RF' RF tn, fp: 288, 2 RF fn, tp: 5, 2 RF f1 score: 0.364 RF cohens kappa score: 0.353 -> test with 'GB' GB tn, fp: 288, 2 GB fn, tp: 6, 1 GB f1 score: 0.200 GB cohens kappa score: 0.189 -> test with 'KNN' KNN tn, fp: 278, 12 KNN fn, tp: 2, 5 KNN f1 score: 0.417 KNN cohens kappa score: 0.397 ------ Step 3/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1131 synthetic samples -> retrain GAN for predict Epoch 1/10 1/116 [..............................] - ETA: 19s - loss: 0.5465 37/116 [========>.....................] - ETA: 0s - loss: 0.4478  78/116 [===================>..........] - ETA: 0s - loss: 0.3983 116/116 [==============================] - 0s 1ms/step - loss: 0.3705 Epoch 2/10 1/116 [..............................] - ETA: 0s - loss: 0.3108 43/116 [==========>...................] - ETA: 0s - loss: 0.2587 83/116 [====================>.........] - ETA: 0s - loss: 0.2665 116/116 [==============================] - 0s 1ms/step - loss: 0.2706 Epoch 3/10 1/116 [..............................] - ETA: 0s - loss: 0.5236 41/116 [=========>....................] - ETA: 0s - loss: 0.2625 82/116 [====================>.........] - ETA: 0s - loss: 0.2504 116/116 [==============================] - 0s 1ms/step - loss: 0.2425 Epoch 4/10 1/116 [..............................] - ETA: 0s - loss: 0.1895 42/116 [=========>....................] - ETA: 0s - loss: 0.2356 83/116 [====================>.........] - ETA: 0s - loss: 0.2312 116/116 [==============================] - 0s 1ms/step - loss: 0.2314 Epoch 5/10 1/116 [..............................] - ETA: 0s - loss: 0.4162 43/116 [==========>...................] - ETA: 0s - loss: 0.2170 84/116 [====================>.........] - ETA: 0s - loss: 0.2165 116/116 [==============================] - 0s 1ms/step - loss: 0.2192 Epoch 6/10 1/116 [..............................] - ETA: 0s - loss: 0.1860 42/116 [=========>....................] - ETA: 0s - loss: 0.1913 82/116 [====================>.........] - ETA: 0s - loss: 0.2023 116/116 [==============================] - 0s 1ms/step - loss: 0.2127 Epoch 7/10 1/116 [..............................] - ETA: 0s - loss: 0.0556 42/116 [=========>....................] - ETA: 0s - loss: 0.1880 82/116 [====================>.........] - ETA: 0s - loss: 0.2112 116/116 [==============================] - 0s 1ms/step - loss: 0.2077 Epoch 8/10 1/116 [..............................] - ETA: 0s - loss: 0.0349 42/116 [=========>....................] - ETA: 0s - loss: 0.2202 81/116 [===================>..........] - ETA: 0s - loss: 0.2106 116/116 [==============================] - 0s 1ms/step - loss: 0.1989 Epoch 9/10 1/116 [..............................] - ETA: 0s - loss: 0.1459 40/116 [=========>....................] - ETA: 0s - loss: 0.2066 81/116 [===================>..........] - ETA: 0s - loss: 0.1953 116/116 [==============================] - 0s 1ms/step - loss: 0.1955 Epoch 10/10 1/116 [..............................] - ETA: 0s - loss: 0.2971 41/116 [=========>....................] - ETA: 0s - loss: 0.1949 82/116 [====================>.........] - ETA: 0s - loss: 0.2029 116/116 [==============================] - 0s 1ms/step - loss: 0.1886 -> test with GAN.predict GAN tn, fp: 271, 19 GAN fn, tp: 2, 5 GAN f1 score: 0.323 GAN cohens kappa score: 0.297 -> test with 'LR' LR tn, fp: 255, 35 LR fn, tp: 0, 7 LR f1 score: 0.286 LR cohens kappa score: 0.256 LR average precision score: 0.400 -> test with 'RF' RF tn, fp: 286, 4 RF fn, tp: 3, 4 RF f1 score: 0.533 RF cohens kappa score: 0.521 -> test with 'GB' GB tn, fp: 285, 5 GB fn, tp: 3, 4 GB f1 score: 0.500 GB cohens kappa score: 0.486 -> test with 'KNN' KNN tn, fp: 262, 28 KNN fn, tp: 1, 6 KNN f1 score: 0.293 KNN cohens kappa score: 0.264 ------ Step 3/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1132 synthetic samples -> retrain GAN for predict Epoch 1/10 1/116 [..............................] - ETA: 14s - loss: 0.3241 45/116 [==========>...................] - ETA: 0s - loss: 0.3088  89/116 [======================>.......] - ETA: 0s - loss: 0.2846 116/116 [==============================] - 0s 1ms/step - loss: 0.2697 Epoch 2/10 1/116 [..............................] - ETA: 0s - loss: 0.1568 46/116 [==========>...................] - ETA: 0s - loss: 0.1828 89/116 [======================>.......] - ETA: 0s - loss: 0.1740 116/116 [==============================] - 0s 1ms/step - loss: 0.1757 Epoch 3/10 1/116 [..............................] - ETA: 0s - loss: 0.1616 46/116 [==========>...................] - ETA: 0s - loss: 0.1761 87/116 [=====================>........] - ETA: 0s - loss: 0.1549 116/116 [==============================] - 0s 1ms/step - loss: 0.1498 Epoch 4/10 1/116 [..............................] - ETA: 0s - loss: 0.1576 45/116 [==========>...................] - ETA: 0s - loss: 0.1215 86/116 [=====================>........] - ETA: 0s - loss: 0.1379 116/116 [==============================] - 0s 1ms/step - loss: 0.1391 Epoch 5/10 1/116 [..............................] - ETA: 0s - loss: 0.0541 43/116 [==========>...................] - ETA: 0s - loss: 0.1081 87/116 [=====================>........] - ETA: 0s - loss: 0.1313 116/116 [==============================] - 0s 1ms/step - loss: 0.1344 Epoch 6/10 1/116 [..............................] - ETA: 0s - loss: 0.0852 47/116 [===========>..................] - ETA: 0s - loss: 0.1361 88/116 [=====================>........] - ETA: 0s - loss: 0.1357 116/116 [==============================] - 0s 1ms/step - loss: 0.1285 Epoch 7/10 1/116 [..............................] - ETA: 0s - loss: 0.0900 46/116 [==========>...................] - ETA: 0s - loss: 0.1225 86/116 [=====================>........] - ETA: 0s - loss: 0.1262 116/116 [==============================] - 0s 1ms/step - loss: 0.1247 Epoch 8/10 1/116 [..............................] - ETA: 0s - loss: 0.1444 45/116 [==========>...................] - ETA: 0s - loss: 0.1364 88/116 [=====================>........] - ETA: 0s - loss: 0.1177 116/116 [==============================] - 0s 1ms/step - loss: 0.1241 Epoch 9/10 1/116 [..............................] - ETA: 0s - loss: 0.1417 43/116 [==========>...................] - ETA: 0s - loss: 0.1271 85/116 [====================>.........] - ETA: 0s - loss: 0.1233 116/116 [==============================] - 0s 1ms/step - loss: 0.1183 Epoch 10/10 1/116 [..............................] - ETA: 0s - loss: 0.0577 42/116 [=========>....................] - ETA: 0s - loss: 0.1023 85/116 [====================>.........] - ETA: 0s - loss: 0.1173 116/116 [==============================] - 0s 1ms/step - loss: 0.1177 -> test with GAN.predict GAN tn, fp: 280, 9 GAN fn, tp: 2, 5 GAN f1 score: 0.476 GAN cohens kappa score: 0.459 -> test with 'LR' LR tn, fp: 270, 19 LR fn, tp: 2, 5 LR f1 score: 0.323 LR cohens kappa score: 0.297 LR average precision score: 0.362 -> test with 'RF' RF tn, fp: 289, 0 RF fn, tp: 7, 0 RF f1 score: 0.000 RF cohens kappa score: 0.000 -> test with 'GB' GB tn, fp: 288, 1 GB fn, tp: 7, 0 GB f1 score: 0.000 GB cohens kappa score: -0.006 -> test with 'KNN' KNN tn, fp: 277, 12 KNN fn, tp: 1, 6 KNN f1 score: 0.480 KNN cohens kappa score: 0.462 ====== Step 4/5 ======= -> Shuffling data -> Spliting data to slices ------ Step 4/5: Slice 1/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1131 synthetic samples -> retrain GAN for predict Epoch 1/10 1/116 [..............................] - ETA: 18s - loss: 0.4760 42/116 [=========>....................] - ETA: 0s - loss: 0.4371  79/116 [===================>..........] - ETA: 0s - loss: 0.3877 114/116 [============================>.] - ETA: 0s - loss: 0.3562 116/116 [==============================] - 0s 1ms/step - loss: 0.3531 Epoch 2/10 1/116 [..............................] - ETA: 0s - loss: 0.3167 35/116 [========>.....................] - ETA: 0s - loss: 0.2476 76/116 [==================>...........] - ETA: 0s - loss: 0.2444 116/116 [==============================] - 0s 1ms/step - loss: 0.2290 Epoch 3/10 1/116 [..............................] - ETA: 0s - loss: 0.2655 43/116 [==========>...................] - ETA: 0s - loss: 0.2059 85/116 [====================>.........] - ETA: 0s - loss: 0.2094 116/116 [==============================] - 0s 1ms/step - loss: 0.1969 Epoch 4/10 1/116 [..............................] - ETA: 0s - loss: 0.3847 40/116 [=========>....................] - ETA: 0s - loss: 0.1777 81/116 [===================>..........] - ETA: 0s - loss: 0.1854 116/116 [==============================] - 0s 1ms/step - loss: 0.1847 Epoch 5/10 1/116 [..............................] - ETA: 0s - loss: 0.1577 43/116 [==========>...................] - ETA: 0s - loss: 0.1579 85/116 [====================>.........] - ETA: 0s - loss: 0.1667 116/116 [==============================] - 0s 1ms/step - loss: 0.1714 Epoch 6/10 1/116 [..............................] - ETA: 0s - loss: 0.2027 42/116 [=========>....................] - ETA: 0s - loss: 0.1570 85/116 [====================>.........] - ETA: 0s - loss: 0.1557 116/116 [==============================] - 0s 1ms/step - loss: 0.1639 Epoch 7/10 1/116 [..............................] - ETA: 0s - loss: 0.2187 43/116 [==========>...................] - ETA: 0s - loss: 0.1557 85/116 [====================>.........] - ETA: 0s - loss: 0.1597 116/116 [==============================] - 0s 1ms/step - loss: 0.1599 Epoch 8/10 1/116 [..............................] - ETA: 0s - loss: 0.0884 39/116 [=========>....................] - ETA: 0s - loss: 0.1700 80/116 [===================>..........] - ETA: 0s - loss: 0.1521 116/116 [==============================] - 0s 1ms/step - loss: 0.1564 Epoch 9/10 1/116 [..............................] - ETA: 0s - loss: 0.1745 43/116 [==========>...................] - ETA: 0s - loss: 0.1557 84/116 [====================>.........] - ETA: 0s - loss: 0.1521 116/116 [==============================] - 0s 1ms/step - loss: 0.1517 Epoch 10/10 1/116 [..............................] - ETA: 0s - loss: 0.1025 40/116 [=========>....................] - ETA: 0s - loss: 0.1596 82/116 [====================>.........] - ETA: 0s - loss: 0.1536 116/116 [==============================] - 0s 1ms/step - loss: 0.1487 -> test with GAN.predict GAN tn, fp: 276, 14 GAN fn, tp: 1, 6 GAN f1 score: 0.444 GAN cohens kappa score: 0.424 -> test with 'LR' LR tn, fp: 272, 18 LR fn, tp: 1, 6 LR f1 score: 0.387 LR cohens kappa score: 0.364 LR average precision score: 0.721 -> test with 'RF' RF tn, fp: 290, 0 RF fn, tp: 4, 3 RF f1 score: 0.600 RF cohens kappa score: 0.594 -> test with 'GB' GB tn, fp: 289, 1 GB fn, tp: 2, 5 GB f1 score: 0.769 GB cohens kappa score: 0.764 -> test with 'KNN' KNN tn, fp: 273, 17 KNN fn, tp: 1, 6 KNN f1 score: 0.400 KNN cohens kappa score: 0.378 ------ Step 4/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1131 synthetic samples -> retrain GAN for predict Epoch 1/10 1/116 [..............................] - ETA: 17s - loss: 0.5294 40/116 [=========>....................] - ETA: 0s - loss: 0.2943  79/116 [===================>..........] - ETA: 0s - loss: 0.2743 116/116 [==============================] - 0s 1ms/step - loss: 0.2638 Epoch 2/10 1/116 [..............................] - ETA: 0s - loss: 0.1092 40/116 [=========>....................] - ETA: 0s - loss: 0.2321 81/116 [===================>..........] - ETA: 0s - loss: 0.2164 116/116 [==============================] - 0s 1ms/step - loss: 0.2087 Epoch 3/10 1/116 [..............................] - ETA: 0s - loss: 0.4276 41/116 [=========>....................] - ETA: 0s - loss: 0.1918 81/116 [===================>..........] - ETA: 0s - loss: 0.1946 116/116 [==============================] - 0s 1ms/step - loss: 0.1965 Epoch 4/10 1/116 [..............................] - ETA: 0s - loss: 0.2578 42/116 [=========>....................] - ETA: 0s - loss: 0.1715 83/116 [====================>.........] - ETA: 0s - loss: 0.1733 116/116 [==============================] - 0s 1ms/step - loss: 0.1851 Epoch 5/10 1/116 [..............................] - ETA: 0s - loss: 0.0406 40/116 [=========>....................] - ETA: 0s - loss: 0.1733 81/116 [===================>..........] - ETA: 0s - loss: 0.1649 116/116 [==============================] - 0s 1ms/step - loss: 0.1789 Epoch 6/10 1/116 [..............................] - ETA: 0s - loss: 0.2648 39/116 [=========>....................] - ETA: 0s - loss: 0.1916 80/116 [===================>..........] - ETA: 0s - loss: 0.1747 116/116 [==============================] - 0s 1ms/step - loss: 0.1723 Epoch 7/10 1/116 [..............................] - ETA: 0s - loss: 0.0461 41/116 [=========>....................] - ETA: 0s - loss: 0.1706 82/116 [====================>.........] - ETA: 0s - loss: 0.1727 116/116 [==============================] - 0s 1ms/step - loss: 0.1692 Epoch 8/10 1/116 [..............................] - ETA: 0s - loss: 0.2510 41/116 [=========>....................] - ETA: 0s - loss: 0.1513 82/116 [====================>.........] - ETA: 0s - loss: 0.1543 116/116 [==============================] - 0s 1ms/step - loss: 0.1635 Epoch 9/10 1/116 [..............................] - ETA: 0s - loss: 0.1281 41/116 [=========>....................] - ETA: 0s - loss: 0.1571 82/116 [====================>.........] - ETA: 0s - loss: 0.1589 116/116 [==============================] - 0s 1ms/step - loss: 0.1612 Epoch 10/10 1/116 [..............................] - ETA: 0s - loss: 0.0560 37/116 [========>.....................] - ETA: 0s - loss: 0.1242 67/116 [================>.............] - ETA: 0s - loss: 0.1484 103/116 [=========================>....] - ETA: 0s - loss: 0.1470 116/116 [==============================] - 0s 1ms/step - loss: 0.1550 -> test with GAN.predict GAN tn, fp: 270, 20 GAN fn, tp: 0, 7 GAN f1 score: 0.412 GAN cohens kappa score: 0.389 -> test with 'LR' LR tn, fp: 260, 30 LR fn, tp: 0, 7 LR f1 score: 0.318 LR cohens kappa score: 0.290 LR average precision score: 0.294 -> test with 'RF' RF tn, fp: 288, 2 RF fn, tp: 5, 2 RF f1 score: 0.364 RF cohens kappa score: 0.353 -> test with 'GB' GB tn, fp: 286, 4 GB fn, tp: 4, 3 GB f1 score: 0.429 GB cohens kappa score: 0.415 -> test with 'KNN' KNN tn, fp: 270, 20 KNN fn, tp: 2, 5 KNN f1 score: 0.312 KNN cohens kappa score: 0.286 ------ Step 4/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1131 synthetic samples -> retrain GAN for predict Epoch 1/10 1/116 [..............................] - ETA: 17s - loss: 0.4312 41/116 [=========>....................] - ETA: 0s - loss: 0.3656  77/116 [==================>...........] - ETA: 0s - loss: 0.3230 112/116 [===========================>..] - ETA: 0s - loss: 0.2993 116/116 [==============================] - 0s 1ms/step - loss: 0.2977 Epoch 2/10 1/116 [..............................] - ETA: 0s - loss: 0.2111 39/116 [=========>....................] - ETA: 0s - loss: 0.2219 80/116 [===================>..........] - ETA: 0s - loss: 0.2499 116/116 [==============================] - 0s 1ms/step - loss: 0.2295 Epoch 3/10 1/116 [..............................] - ETA: 0s - loss: 0.1968 40/116 [=========>....................] - ETA: 0s - loss: 0.2161 79/116 [===================>..........] - ETA: 0s - loss: 0.2173 116/116 [==============================] - 0s 1ms/step - loss: 0.2112 Epoch 4/10 1/116 [..............................] - ETA: 0s - loss: 0.1783 42/116 [=========>....................] - ETA: 0s - loss: 0.2021 81/116 [===================>..........] - ETA: 0s - loss: 0.1989 116/116 [==============================] - 0s 1ms/step - loss: 0.2023 Epoch 5/10 1/116 [..............................] - ETA: 0s - loss: 0.1139 40/116 [=========>....................] - ETA: 0s - loss: 0.2011 82/116 [====================>.........] - ETA: 0s - loss: 0.2038 116/116 [==============================] - 0s 1ms/step - loss: 0.1953 Epoch 6/10 1/116 [..............................] - ETA: 0s - loss: 0.1949 42/116 [=========>....................] - ETA: 0s - loss: 0.1781 81/116 [===================>..........] - ETA: 0s - loss: 0.1901 116/116 [==============================] - 0s 1ms/step - loss: 0.1939 Epoch 7/10 1/116 [..............................] - ETA: 0s - loss: 0.1915 42/116 [=========>....................] - ETA: 0s - loss: 0.2006 83/116 [====================>.........] - ETA: 0s - loss: 0.1904 116/116 [==============================] - 0s 1ms/step - loss: 0.1930 Epoch 8/10 1/116 [..............................] - ETA: 0s - loss: 0.2446 41/116 [=========>....................] - ETA: 0s - loss: 0.2196 80/116 [===================>..........] - ETA: 0s - loss: 0.1982 116/116 [==============================] - 0s 1ms/step - loss: 0.1879 Epoch 9/10 1/116 [..............................] - ETA: 0s - loss: 0.3674 41/116 [=========>....................] - ETA: 0s - loss: 0.1813 81/116 [===================>..........] - ETA: 0s - loss: 0.1994 116/116 [==============================] - 0s 1ms/step - loss: 0.1843 Epoch 10/10 1/116 [..............................] - ETA: 0s - loss: 0.1133 42/116 [=========>....................] - ETA: 0s - loss: 0.1766 81/116 [===================>..........] - ETA: 0s - loss: 0.1816 116/116 [==============================] - 0s 1ms/step - loss: 0.1799 -> test with GAN.predict GAN tn, fp: 263, 27 GAN fn, tp: 1, 6 GAN f1 score: 0.300 GAN cohens kappa score: 0.272 -> test with 'LR' LR tn, fp: 250, 40 LR fn, tp: 1, 6 LR f1 score: 0.226 LR cohens kappa score: 0.193 LR average precision score: 0.553 -> test with 'RF' RF tn, fp: 288, 2 RF fn, tp: 2, 5 RF f1 score: 0.714 RF cohens kappa score: 0.707 -> test with 'GB' GB tn, fp: 287, 3 GB fn, tp: 2, 5 GB f1 score: 0.667 GB cohens kappa score: 0.658 -> test with 'KNN' KNN tn, fp: 257, 33 KNN fn, tp: 0, 7 KNN f1 score: 0.298 KNN cohens kappa score: 0.269 ------ Step 4/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1131 synthetic samples -> retrain GAN for predict Epoch 1/10 1/116 [..............................] - ETA: 17s - loss: 0.4122 41/116 [=========>....................] - ETA: 0s - loss: 0.3662  81/116 [===================>..........] - ETA: 0s - loss: 0.3206 116/116 [==============================] - 0s 1ms/step - loss: 0.3041 Epoch 2/10 1/116 [..............................] - ETA: 0s - loss: 0.2429 41/116 [=========>....................] - ETA: 0s - loss: 0.2241 79/116 [===================>..........] - ETA: 0s - loss: 0.2172 116/116 [==============================] - 0s 1ms/step - loss: 0.2125 Epoch 3/10 1/116 [..............................] - ETA: 0s - loss: 0.1027 41/116 [=========>....................] - ETA: 0s - loss: 0.1888 83/116 [====================>.........] - ETA: 0s - loss: 0.1924 116/116 [==============================] - 0s 1ms/step - loss: 0.1827 Epoch 4/10 1/116 [..............................] - ETA: 0s - loss: 0.1566 39/116 [=========>....................] - ETA: 0s - loss: 0.1859 79/116 [===================>..........] - ETA: 0s - loss: 0.1696 116/116 [==============================] - 0s 1ms/step - loss: 0.1702 Epoch 5/10 1/116 [..............................] - ETA: 0s - loss: 0.1734 41/116 [=========>....................] - ETA: 0s - loss: 0.1407 81/116 [===================>..........] - ETA: 0s - loss: 0.1540 116/116 [==============================] - 0s 1ms/step - loss: 0.1623 Epoch 6/10 1/116 [..............................] - ETA: 0s - loss: 0.0778 41/116 [=========>....................] - ETA: 0s - loss: 0.1520 83/116 [====================>.........] - ETA: 0s - loss: 0.1413 116/116 [==============================] - 0s 1ms/step - loss: 0.1533 Epoch 7/10 1/116 [..............................] - ETA: 0s - loss: 0.0652 40/116 [=========>....................] - ETA: 0s - loss: 0.1494 77/116 [==================>...........] - ETA: 0s - loss: 0.1488 116/116 [==============================] - 0s 1ms/step - loss: 0.1494 Epoch 8/10 1/116 [..............................] - ETA: 0s - loss: 0.0986 42/116 [=========>....................] - ETA: 0s - loss: 0.1317 82/116 [====================>.........] - ETA: 0s - loss: 0.1410 116/116 [==============================] - 0s 1ms/step - loss: 0.1451 Epoch 9/10 1/116 [..............................] - ETA: 0s - loss: 0.2405 43/116 [==========>...................] - ETA: 0s - loss: 0.1360 82/116 [====================>.........] - ETA: 0s - loss: 0.1393 116/116 [==============================] - 0s 1ms/step - loss: 0.1413 Epoch 10/10 1/116 [..............................] - ETA: 0s - loss: 0.0209 42/116 [=========>....................] - ETA: 0s - loss: 0.1317 84/116 [====================>.........] - ETA: 0s - loss: 0.1472 116/116 [==============================] - 0s 1ms/step - loss: 0.1449 -> test with GAN.predict GAN tn, fp: 274, 16 GAN fn, tp: 2, 5 GAN f1 score: 0.357 GAN cohens kappa score: 0.334 -> test with 'LR' LR tn, fp: 264, 26 LR fn, tp: 1, 6 LR f1 score: 0.308 LR cohens kappa score: 0.280 LR average precision score: 0.631 -> test with 'RF' RF tn, fp: 290, 0 RF fn, tp: 4, 3 RF f1 score: 0.600 RF cohens kappa score: 0.594 -> test with 'GB' GB tn, fp: 289, 1 GB fn, tp: 4, 3 GB f1 score: 0.545 GB cohens kappa score: 0.538 -> test with 'KNN' KNN tn, fp: 275, 15 KNN fn, tp: 2, 5 KNN f1 score: 0.370 KNN cohens kappa score: 0.348 ------ Step 4/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1132 synthetic samples -> retrain GAN for predict Epoch 1/10 1/116 [..............................] - ETA: 15s - loss: 0.2377 46/116 [==========>...................] - ETA: 0s - loss: 0.2619  90/116 [======================>.......] - ETA: 0s - loss: 0.2357 116/116 [==============================] - 0s 1ms/step - loss: 0.2252 Epoch 2/10 1/116 [..............................] - ETA: 0s - loss: 0.1586 44/116 [==========>...................] - ETA: 0s - loss: 0.1846 87/116 [=====================>........] - ETA: 0s - loss: 0.1749 116/116 [==============================] - 0s 1ms/step - loss: 0.1800 Epoch 3/10 1/116 [..............................] - ETA: 0s - loss: 0.4210 45/116 [==========>...................] - ETA: 0s - loss: 0.1667 90/116 [======================>.......] - ETA: 0s - loss: 0.1731 116/116 [==============================] - 0s 1ms/step - loss: 0.1672 Epoch 4/10 1/116 [..............................] - ETA: 0s - loss: 0.0965 45/116 [==========>...................] - ETA: 0s - loss: 0.1568 90/116 [======================>.......] - ETA: 0s - loss: 0.1632 116/116 [==============================] - 0s 1ms/step - loss: 0.1617 Epoch 5/10 1/116 [..............................] - ETA: 0s - loss: 0.1555 42/116 [=========>....................] - ETA: 0s - loss: 0.1766 84/116 [====================>.........] - ETA: 0s - loss: 0.1610 116/116 [==============================] - 0s 1ms/step - loss: 0.1624 Epoch 6/10 1/116 [..............................] - ETA: 0s - loss: 0.2747 43/116 [==========>...................] - ETA: 0s - loss: 0.1551 87/116 [=====================>........] - ETA: 0s - loss: 0.1567 116/116 [==============================] - 0s 1ms/step - loss: 0.1564 Epoch 7/10 1/116 [..............................] - ETA: 0s - loss: 0.0684 44/116 [==========>...................] - ETA: 0s - loss: 0.1572 89/116 [======================>.......] - ETA: 0s - loss: 0.1559 116/116 [==============================] - 0s 1ms/step - loss: 0.1544 Epoch 8/10 1/116 [..............................] - ETA: 0s - loss: 0.0839 43/116 [==========>...................] - ETA: 0s - loss: 0.1320 79/116 [===================>..........] - ETA: 0s - loss: 0.1501 116/116 [==============================] - ETA: 0s - loss: 0.1498 116/116 [==============================] - 0s 1ms/step - loss: 0.1498 Epoch 9/10 1/116 [..............................] - ETA: 0s - loss: 0.1928 39/116 [=========>....................] - ETA: 0s - loss: 0.1570 80/116 [===================>..........] - ETA: 0s - loss: 0.1571 116/116 [==============================] - 0s 1ms/step - loss: 0.1478 Epoch 10/10 1/116 [..............................] - ETA: 0s - loss: 0.3379 45/116 [==========>...................] - ETA: 0s - loss: 0.1338 89/116 [======================>.......] - ETA: 0s - loss: 0.1464 116/116 [==============================] - 0s 1ms/step - loss: 0.1463 -> test with GAN.predict GAN tn, fp: 275, 14 GAN fn, tp: 2, 5 GAN f1 score: 0.385 GAN cohens kappa score: 0.363 -> test with 'LR' LR tn, fp: 266, 23 LR fn, tp: 2, 5 LR f1 score: 0.286 LR cohens kappa score: 0.258 LR average precision score: 0.667 -> test with 'RF' RF tn, fp: 288, 1 RF fn, tp: 4, 3 RF f1 score: 0.545 RF cohens kappa score: 0.537 -> test with 'GB' GB tn, fp: 288, 1 GB fn, tp: 4, 3 GB f1 score: 0.545 GB cohens kappa score: 0.537 -> test with 'KNN' KNN tn, fp: 269, 20 KNN fn, tp: 2, 5 KNN f1 score: 0.312 KNN cohens kappa score: 0.286 ====== Step 5/5 ======= -> Shuffling data -> Spliting data to slices ------ Step 5/5: Slice 1/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1131 synthetic samples -> retrain GAN for predict Epoch 1/10 1/116 [..............................] - ETA: 18s - loss: 0.4932 38/116 [========>.....................] - ETA: 0s - loss: 0.4858  76/116 [==================>...........] - ETA: 0s - loss: 0.3946 116/116 [==============================] - 0s 1ms/step - loss: 0.3557 Epoch 2/10 1/116 [..............................] - ETA: 0s - loss: 0.3170 42/116 [=========>....................] - ETA: 0s - loss: 0.2680 83/116 [====================>.........] - ETA: 0s - loss: 0.2684 116/116 [==============================] - 0s 1ms/step - loss: 0.2584 Epoch 3/10 1/116 [..............................] - ETA: 0s - loss: 0.2132 42/116 [=========>....................] - ETA: 0s - loss: 0.2356 83/116 [====================>.........] - ETA: 0s - loss: 0.2512 116/116 [==============================] - 0s 1ms/step - loss: 0.2382 Epoch 4/10 1/116 [..............................] - ETA: 0s - loss: 0.2157 42/116 [=========>....................] - ETA: 0s - loss: 0.2454 84/116 [====================>.........] - ETA: 0s - loss: 0.2334 116/116 [==============================] - 0s 1ms/step - loss: 0.2277 Epoch 5/10 1/116 [..............................] - ETA: 0s - loss: 0.3089 41/116 [=========>....................] - ETA: 0s - loss: 0.1991 81/116 [===================>..........] - ETA: 0s - loss: 0.2037 116/116 [==============================] - 0s 1ms/step - loss: 0.2182 Epoch 6/10 1/116 [..............................] - ETA: 0s - loss: 0.1070 41/116 [=========>....................] - ETA: 0s - loss: 0.2029 83/116 [====================>.........] - ETA: 0s - loss: 0.2111 116/116 [==============================] - 0s 1ms/step - loss: 0.2113 Epoch 7/10 1/116 [..............................] - ETA: 0s - loss: 0.3694 42/116 [=========>....................] - ETA: 0s - loss: 0.2285 84/116 [====================>.........] - ETA: 0s - loss: 0.2034 116/116 [==============================] - 0s 1ms/step - loss: 0.2047 Epoch 8/10 1/116 [..............................] - ETA: 0s - loss: 0.1172 39/116 [=========>....................] - ETA: 0s - loss: 0.2343 78/116 [===================>..........] - ETA: 0s - loss: 0.2060 116/116 [==============================] - 0s 1ms/step - loss: 0.1955 Epoch 9/10 1/116 [..............................] - ETA: 0s - loss: 0.0865 41/116 [=========>....................] - ETA: 0s - loss: 0.1710 81/116 [===================>..........] - ETA: 0s - loss: 0.1829 116/116 [==============================] - 0s 1ms/step - loss: 0.1906 Epoch 10/10 1/116 [..............................] - ETA: 0s - loss: 0.0569 41/116 [=========>....................] - ETA: 0s - loss: 0.1816 82/116 [====================>.........] - ETA: 0s - loss: 0.1777 116/116 [==============================] - 0s 1ms/step - loss: 0.1846 -> test with GAN.predict GAN tn, fp: 263, 27 GAN fn, tp: 1, 6 GAN f1 score: 0.300 GAN cohens kappa score: 0.272 -> test with 'LR' LR tn, fp: 251, 39 LR fn, tp: 0, 7 LR f1 score: 0.264 LR cohens kappa score: 0.233 LR average precision score: 0.512 -> test with 'RF' RF tn, fp: 288, 2 RF fn, tp: 3, 4 RF f1 score: 0.615 RF cohens kappa score: 0.607 -> test with 'GB' GB tn, fp: 287, 3 GB fn, tp: 3, 4 GB f1 score: 0.571 GB cohens kappa score: 0.561 -> test with 'KNN' KNN tn, fp: 269, 21 KNN fn, tp: 1, 6 KNN f1 score: 0.353 KNN cohens kappa score: 0.328 ------ Step 5/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1131 synthetic samples -> retrain GAN for predict Epoch 1/10 1/116 [..............................] - ETA: 20s - loss: 0.5633 43/116 [==========>...................] - ETA: 0s - loss: 0.3516  84/116 [====================>.........] - ETA: 0s - loss: 0.3037 116/116 [==============================] - 0s 1ms/step - loss: 0.2778 Epoch 2/10 1/116 [..............................] - ETA: 0s - loss: 0.1925 41/116 [=========>....................] - ETA: 0s - loss: 0.1974 74/116 [==================>...........] - ETA: 0s - loss: 0.1848 107/116 [==========================>...] - ETA: 0s - loss: 0.1827 116/116 [==============================] - 0s 1ms/step - loss: 0.1831 Epoch 3/10 1/116 [..............................] - ETA: 0s - loss: 0.0685 41/116 [=========>....................] - ETA: 0s - loss: 0.1639 83/116 [====================>.........] - ETA: 0s - loss: 0.1620 116/116 [==============================] - 0s 1ms/step - loss: 0.1592 Epoch 4/10 1/116 [..............................] - ETA: 0s - loss: 0.0613 41/116 [=========>....................] - ETA: 0s - loss: 0.1546 83/116 [====================>.........] - ETA: 0s - loss: 0.1543 116/116 [==============================] - 0s 1ms/step - loss: 0.1435 Epoch 5/10 1/116 [..............................] - ETA: 0s - loss: 0.2099 40/116 [=========>....................] - ETA: 0s - loss: 0.1542 80/116 [===================>..........] - ETA: 0s - loss: 0.1416 116/116 [==============================] - 0s 1ms/step - loss: 0.1369 Epoch 6/10 1/116 [..............................] - ETA: 0s - loss: 0.0437 42/116 [=========>....................] - ETA: 0s - loss: 0.1537 84/116 [====================>.........] - ETA: 0s - loss: 0.1397 116/116 [==============================] - 0s 1ms/step - loss: 0.1301 Epoch 7/10 1/116 [..............................] - ETA: 0s - loss: 0.1070 42/116 [=========>....................] - ETA: 0s - loss: 0.1267 83/116 [====================>.........] - ETA: 0s - loss: 0.1251 116/116 [==============================] - 0s 1ms/step - loss: 0.1256 Epoch 8/10 1/116 [..............................] - ETA: 0s - loss: 0.2674 42/116 [=========>....................] - ETA: 0s - loss: 0.1069 82/116 [====================>.........] - ETA: 0s - loss: 0.1234 116/116 [==============================] - 0s 1ms/step - loss: 0.1227 Epoch 9/10 1/116 [..............................] - ETA: 0s - loss: 0.1343 39/116 [=========>....................] - ETA: 0s - loss: 0.1083 80/116 [===================>..........] - ETA: 0s - loss: 0.1103 116/116 [==============================] - 0s 1ms/step - loss: 0.1187 Epoch 10/10 1/116 [..............................] - ETA: 0s - loss: 0.1035 42/116 [=========>....................] - ETA: 0s - loss: 0.1079 82/116 [====================>.........] - ETA: 0s - loss: 0.1201 116/116 [==============================] - 0s 1ms/step - loss: 0.1162 -> test with GAN.predict GAN tn, fp: 277, 13 GAN fn, tp: 3, 4 GAN f1 score: 0.333 GAN cohens kappa score: 0.310 -> test with 'LR' LR tn, fp: 267, 23 LR fn, tp: 3, 4 LR f1 score: 0.235 LR cohens kappa score: 0.206 LR average precision score: 0.231 -> test with 'RF' RF tn, fp: 289, 1 RF fn, tp: 5, 2 RF f1 score: 0.400 RF cohens kappa score: 0.391 -> test with 'GB' GB tn, fp: 289, 1 GB fn, tp: 4, 3 GB f1 score: 0.545 GB cohens kappa score: 0.538 -> test with 'KNN' KNN tn, fp: 270, 20 KNN fn, tp: 3, 4 KNN f1 score: 0.258 KNN cohens kappa score: 0.230 ------ Step 5/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1131 synthetic samples -> retrain GAN for predict Epoch 1/10 1/116 [..............................] - ETA: 22s - loss: 0.3612 35/116 [========>.....................] - ETA: 0s - loss: 0.3627  70/116 [=================>............] - ETA: 0s - loss: 0.3377 105/116 [==========================>...] - ETA: 0s - loss: 0.3139 116/116 [==============================] - 0s 1ms/step - loss: 0.3122 Epoch 2/10 1/116 [..............................] - ETA: 0s - loss: 0.3226 37/116 [========>.....................] - ETA: 0s - loss: 0.2464 73/116 [=================>............] - ETA: 0s - loss: 0.2384 106/116 [==========================>...] - ETA: 0s - loss: 0.2429 116/116 [==============================] - 0s 1ms/step - loss: 0.2387 Epoch 3/10 1/116 [..............................] - ETA: 0s - loss: 0.1830 37/116 [========>.....................] - ETA: 0s - loss: 0.2185 71/116 [=================>............] - ETA: 0s - loss: 0.2151 106/116 [==========================>...] - ETA: 0s - loss: 0.2209 116/116 [==============================] - 0s 1ms/step - loss: 0.2218 Epoch 4/10 1/116 [..............................] - ETA: 0s - loss: 0.0519 37/116 [========>.....................] - ETA: 0s - loss: 0.2076 75/116 [==================>...........] - ETA: 0s - loss: 0.2130 110/116 [===========================>..] - ETA: 0s - loss: 0.2108 116/116 [==============================] - 0s 1ms/step - loss: 0.2079 Epoch 5/10 1/116 [..............................] - ETA: 0s - loss: 0.1905 35/116 [========>.....................] - ETA: 0s - loss: 0.2087 74/116 [==================>...........] - ETA: 0s - loss: 0.2053 110/116 [===========================>..] - ETA: 0s - loss: 0.2001 116/116 [==============================] - 0s 1ms/step - loss: 0.1984 Epoch 6/10 1/116 [..............................] - ETA: 0s - loss: 0.3701 35/116 [========>.....................] - ETA: 0s - loss: 0.1800 65/116 [===============>..............] - ETA: 0s - loss: 0.1821 105/116 [==========================>...] - ETA: 0s - loss: 0.1907 116/116 [==============================] - 0s 1ms/step - loss: 0.1934 Epoch 7/10 1/116 [..............................] - ETA: 0s - loss: 0.3446 41/116 [=========>....................] - ETA: 0s - loss: 0.1928 82/116 [====================>.........] - ETA: 0s - loss: 0.1877 116/116 [==============================] - 0s 1ms/step - loss: 0.1877 Epoch 8/10 1/116 [..............................] - ETA: 0s - loss: 0.0820 32/116 [=======>......................] - ETA: 0s - loss: 0.1826 63/116 [===============>..............] - ETA: 0s - loss: 0.1865 96/116 [=======================>......] - ETA: 0s - loss: 0.1912 116/116 [==============================] - 0s 2ms/step - loss: 0.1834 Epoch 9/10 1/116 [..............................] - ETA: 0s - loss: 0.1237 39/116 [=========>....................] - ETA: 0s - loss: 0.1751 74/116 [==================>...........] - ETA: 0s - loss: 0.1879 110/116 [===========================>..] - ETA: 0s - loss: 0.1782 116/116 [==============================] - 0s 1ms/step - loss: 0.1796 Epoch 10/10 1/116 [..............................] - ETA: 0s - loss: 0.1689 35/116 [========>.....................] - ETA: 0s - loss: 0.1600 71/116 [=================>............] - ETA: 0s - loss: 0.1763 106/116 [==========================>...] - ETA: 0s - loss: 0.1753 116/116 [==============================] - 0s 1ms/step - loss: 0.1767 -> test with GAN.predict GAN tn, fp: 258, 32 GAN fn, tp: 0, 7 GAN f1 score: 0.304 GAN cohens kappa score: 0.275 -> test with 'LR' LR tn, fp: 261, 29 LR fn, tp: 0, 7 LR f1 score: 0.326 LR cohens kappa score: 0.298 LR average precision score: 0.757 -> test with 'RF' RF tn, fp: 289, 1 RF fn, tp: 1, 6 RF f1 score: 0.857 RF cohens kappa score: 0.854 -> test with 'GB' GB tn, fp: 288, 2 GB fn, tp: 1, 6 GB f1 score: 0.800 GB cohens kappa score: 0.795 -> test with 'KNN' KNN tn, fp: 262, 28 KNN fn, tp: 0, 7 KNN f1 score: 0.333 KNN cohens kappa score: 0.306 ------ Step 5/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1131 synthetic samples -> retrain GAN for predict Epoch 1/10 1/116 [..............................] - ETA: 18s - loss: 0.3975 41/116 [=========>....................] - ETA: 0s - loss: 0.4836  81/116 [===================>..........] - ETA: 0s - loss: 0.4194 116/116 [==============================] - 0s 1ms/step - loss: 0.4042 Epoch 2/10 1/116 [..............................] - ETA: 0s - loss: 0.3056 39/116 [=========>....................] - ETA: 0s - loss: 0.2998 80/116 [===================>..........] - ETA: 0s - loss: 0.3155 116/116 [==============================] - 0s 1ms/step - loss: 0.3045 Epoch 3/10 1/116 [..............................] - ETA: 0s - loss: 0.2497 42/116 [=========>....................] - ETA: 0s - loss: 0.2795 83/116 [====================>.........] - ETA: 0s - loss: 0.2831 116/116 [==============================] - 0s 1ms/step - loss: 0.2771 Epoch 4/10 1/116 [..............................] - ETA: 0s - loss: 0.0912 42/116 [=========>....................] - ETA: 0s - loss: 0.2567 83/116 [====================>.........] - ETA: 0s - loss: 0.2542 116/116 [==============================] - 0s 1ms/step - loss: 0.2615 Epoch 5/10 1/116 [..............................] - ETA: 0s - loss: 0.5366 42/116 [=========>....................] - ETA: 0s - loss: 0.3016 84/116 [====================>.........] - ETA: 0s - loss: 0.2685 116/116 [==============================] - 0s 1ms/step - loss: 0.2555 Epoch 6/10 1/116 [..............................] - ETA: 0s - loss: 0.4602 36/116 [========>.....................] - ETA: 0s - loss: 0.2333 68/116 [================>.............] - ETA: 0s - loss: 0.2523 107/116 [==========================>...] - ETA: 0s - loss: 0.2500 116/116 [==============================] - 0s 1ms/step - loss: 0.2488 Epoch 7/10 1/116 [..............................] - ETA: 0s - loss: 0.2441 41/116 [=========>....................] - ETA: 0s - loss: 0.2608 81/116 [===================>..........] - ETA: 0s - loss: 0.2533 116/116 [==============================] - 0s 1ms/step - loss: 0.2430 Epoch 8/10 1/116 [..............................] - ETA: 0s - loss: 0.2281 40/116 [=========>....................] - ETA: 0s - loss: 0.2404 81/116 [===================>..........] - ETA: 0s - loss: 0.2365 116/116 [==============================] - 0s 1ms/step - loss: 0.2366 Epoch 9/10 1/116 [..............................] - ETA: 0s - loss: 0.3579 42/116 [=========>....................] - ETA: 0s - loss: 0.2169 84/116 [====================>.........] - ETA: 0s - loss: 0.2324 116/116 [==============================] - 0s 1ms/step - loss: 0.2310 Epoch 10/10 1/116 [..............................] - ETA: 0s - loss: 0.2189 42/116 [=========>....................] - ETA: 0s - loss: 0.2248 83/116 [====================>.........] - ETA: 0s - loss: 0.2263 116/116 [==============================] - 0s 1ms/step - loss: 0.2278 -> test with GAN.predict GAN tn, fp: 256, 34 GAN fn, tp: 1, 6 GAN f1 score: 0.255 GAN cohens kappa score: 0.224 -> test with 'LR' LR tn, fp: 250, 40 LR fn, tp: 0, 7 LR f1 score: 0.259 LR cohens kappa score: 0.228 LR average precision score: 0.271 -> test with 'RF' RF tn, fp: 290, 0 RF fn, tp: 5, 2 RF f1 score: 0.444 RF cohens kappa score: 0.439 -> test with 'GB' GB tn, fp: 289, 1 GB fn, tp: 6, 1 GB f1 score: 0.222 GB cohens kappa score: 0.214 -> test with 'KNN' KNN tn, fp: 272, 18 KNN fn, tp: 1, 6 KNN f1 score: 0.387 KNN cohens kappa score: 0.364 ------ Step 5/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1132 synthetic samples -> retrain GAN for predict Epoch 1/10 1/116 [..............................] - ETA: 42s - loss: 0.3781 43/116 [==========>...................] - ETA: 0s - loss: 0.3444  84/116 [====================>.........] - ETA: 0s - loss: 0.2964 116/116 [==============================] - 1s 1ms/step - loss: 0.2683 Epoch 2/10 1/116 [..............................] - ETA: 0s - loss: 0.1429 40/116 [=========>....................] - ETA: 0s - loss: 0.1911 74/116 [==================>...........] - ETA: 0s - loss: 0.1893 97/116 [========================>.....] - ETA: 0s - loss: 0.1819 116/116 [==============================] - 0s 2ms/step - loss: 0.1732 Epoch 3/10 1/116 [..............................] - ETA: 0s - loss: 0.2367 42/116 [=========>....................] - ETA: 0s - loss: 0.1520 86/116 [=====================>........] - ETA: 0s - loss: 0.1586 116/116 [==============================] - 0s 1ms/step - loss: 0.1508 Epoch 4/10 1/116 [..............................] - ETA: 0s - loss: 0.0961 43/116 [==========>...................] - ETA: 0s - loss: 0.1501 79/116 [===================>..........] - ETA: 0s - loss: 0.1408 116/116 [==============================] - ETA: 0s - loss: 0.1410 116/116 [==============================] - 0s 1ms/step - loss: 0.1410 Epoch 5/10 1/116 [..............................] - ETA: 0s - loss: 0.1626 37/116 [========>.....................] - ETA: 0s - loss: 0.1514 72/116 [=================>............] - ETA: 0s - loss: 0.1413 115/116 [============================>.] - ETA: 0s - loss: 0.1394 116/116 [==============================] - 0s 1ms/step - loss: 0.1405 Epoch 6/10 1/116 [..............................] - ETA: 0s - loss: 0.0555 45/116 [==========>...................] - ETA: 0s - loss: 0.1387 87/116 [=====================>........] - ETA: 0s - loss: 0.1340 116/116 [==============================] - 0s 1ms/step - loss: 0.1337 Epoch 7/10 1/116 [..............................] - ETA: 0s - loss: 0.1434 45/116 [==========>...................] - ETA: 0s - loss: 0.1189 89/116 [======================>.......] - ETA: 0s - loss: 0.1308 116/116 [==============================] - 0s 1ms/step - loss: 0.1336 Epoch 8/10 1/116 [..............................] - ETA: 0s - loss: 0.0397 44/116 [==========>...................] - ETA: 0s - loss: 0.1175 86/116 [=====================>........] - ETA: 0s - loss: 0.1293 116/116 [==============================] - 0s 1ms/step - loss: 0.1269 Epoch 9/10 1/116 [..............................] - ETA: 0s - loss: 0.1073 45/116 [==========>...................] - ETA: 0s - loss: 0.1141 89/116 [======================>.......] - ETA: 0s - loss: 0.1255 116/116 [==============================] - 0s 1ms/step - loss: 0.1249 Epoch 10/10 1/116 [..............................] - ETA: 0s - loss: 0.0296 44/116 [==========>...................] - ETA: 0s - loss: 0.1333 86/116 [=====================>........] - ETA: 0s - loss: 0.1289 116/116 [==============================] - 0s 1ms/step - loss: 0.1268 -> test with GAN.predict GAN tn, fp: 279, 10 GAN fn, tp: 2, 5 GAN f1 score: 0.455 GAN cohens kappa score: 0.436 -> test with 'LR' LR tn, fp: 269, 20 LR fn, tp: 2, 5 LR f1 score: 0.312 LR cohens kappa score: 0.286 LR average precision score: 0.419 -> test with 'RF' RF tn, fp: 288, 1 RF fn, tp: 5, 2 RF f1 score: 0.400 RF cohens kappa score: 0.391 -> test with 'GB' GB tn, fp: 288, 1 GB fn, tp: 5, 2 GB f1 score: 0.400 GB cohens kappa score: 0.391 -> test with 'KNN' KNN tn, fp: 267, 22 KNN fn, tp: 2, 5 KNN f1 score: 0.294 KNN cohens kappa score: 0.267 ### Exercise is done. -----[ LR ]----- maximum: LR tn, fp: 272, 45 LR fn, tp: 3, 7 LR f1 score: 0.387 LR cohens kappa score: 0.364 LR average precision score: 0.757 average: LR tn, fp: 260.24, 29.56 LR fn, tp: 0.96, 6.04 LR f1 score: 0.290 LR cohens kappa score: 0.261 LR average precision score: 0.501 minimum: LR tn, fp: 244, 18 LR fn, tp: 0, 4 LR f1 score: 0.217 LR cohens kappa score: 0.185 LR average precision score: 0.229 -----[ RF ]----- maximum: RF tn, fp: 290, 4 RF fn, tp: 7, 6 RF f1 score: 0.857 RF cohens kappa score: 0.854 average: RF tn, fp: 288.64, 1.16 RF fn, tp: 4.08, 2.92 RF f1 score: 0.510 RF cohens kappa score: 0.503 minimum: RF tn, fp: 286, 0 RF fn, tp: 1, 0 RF f1 score: 0.000 RF cohens kappa score: 0.000 -----[ GB ]----- maximum: GB tn, fp: 290, 5 GB fn, tp: 7, 6 GB f1 score: 0.800 GB cohens kappa score: 0.795 average: GB tn, fp: 287.84, 1.96 GB fn, tp: 4.0, 3.0 GB f1 score: 0.481 GB cohens kappa score: 0.472 minimum: GB tn, fp: 285, 0 GB fn, tp: 1, 0 GB f1 score: 0.000 GB cohens kappa score: -0.006 -----[ KNN ]----- maximum: KNN tn, fp: 278, 33 KNN fn, tp: 3, 7 KNN f1 score: 0.480 KNN cohens kappa score: 0.462 average: KNN tn, fp: 268.36, 21.44 KNN fn, tp: 1.4, 5.6 KNN f1 score: 0.334 KNN cohens kappa score: 0.308 minimum: KNN tn, fp: 257, 12 KNN fn, tp: 0, 4 KNN f1 score: 0.256 KNN cohens kappa score: 0.226 -----[ GAN ]----- maximum: GAN tn, fp: 280, 34 GAN fn, tp: 3, 7 GAN f1 score: 0.476 GAN cohens kappa score: 0.459 average: GAN tn, fp: 270.24, 19.56 GAN fn, tp: 1.4, 5.6 GAN f1 score: 0.358 GAN cohens kappa score: 0.334 minimum: GAN tn, fp: 256, 9 GAN fn, tp: 0, 4 GAN f1 score: 0.255 GAN cohens kappa score: 0.224