/////////////////////////////////////////// // Running convGAN-majority-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: 0.1779 42/116 [=========>....................] - ETA: 0s - loss: 0.2170  85/116 [====================>.........] - ETA: 0s - loss: 0.2147 116/116 [==============================] - 0s 1ms/step - loss: 0.2150 Epoch 2/10 1/116 [..............................] - ETA: 0s - loss: 0.0521 44/116 [==========>...................] - ETA: 0s - loss: 0.2073 86/116 [=====================>........] - ETA: 0s - loss: 0.2073 116/116 [==============================] - 0s 1ms/step - loss: 0.2072 Epoch 3/10 1/116 [..............................] - ETA: 0s - loss: 0.2420 43/116 [==========>...................] - ETA: 0s - loss: 0.2389 85/116 [====================>.........] - ETA: 0s - loss: 0.2110 116/116 [==============================] - 0s 1ms/step - loss: 0.2021 Epoch 4/10 1/116 [..............................] - ETA: 0s - loss: 0.2107 43/116 [==========>...................] - ETA: 0s - loss: 0.2137 82/116 [====================>.........] - ETA: 0s - loss: 0.2085 116/116 [==============================] - 0s 1ms/step - loss: 0.1967 Epoch 5/10 1/116 [..............................] - ETA: 0s - loss: 0.1610 41/116 [=========>....................] - ETA: 0s - loss: 0.1956 81/116 [===================>..........] - ETA: 0s - loss: 0.1997 116/116 [==============================] - 0s 1ms/step - loss: 0.1922 Epoch 6/10 1/116 [..............................] - ETA: 0s - loss: 0.1810 40/116 [=========>....................] - ETA: 0s - loss: 0.1834 73/116 [=================>............] - ETA: 0s - loss: 0.1819 110/116 [===========================>..] - ETA: 0s - loss: 0.1871 116/116 [==============================] - 0s 1ms/step - loss: 0.1864 Epoch 7/10 1/116 [..............................] - ETA: 0s - loss: 0.2663 39/116 [=========>....................] - ETA: 0s - loss: 0.1917 80/116 [===================>..........] - ETA: 0s - loss: 0.1935 116/116 [==============================] - 0s 1ms/step - loss: 0.1819 Epoch 8/10 1/116 [..............................] - ETA: 0s - loss: 0.1683 43/116 [==========>...................] - ETA: 0s - loss: 0.1812 85/116 [====================>.........] - ETA: 0s - loss: 0.1804 116/116 [==============================] - 0s 1ms/step - loss: 0.1763 Epoch 9/10 1/116 [..............................] - ETA: 0s - loss: 0.2777 42/116 [=========>....................] - ETA: 0s - loss: 0.1696 84/116 [====================>.........] - ETA: 0s - loss: 0.1651 116/116 [==============================] - 0s 1ms/step - loss: 0.1715 Epoch 10/10 1/116 [..............................] - ETA: 0s - loss: 0.2181 44/116 [==========>...................] - ETA: 0s - loss: 0.1634 85/116 [====================>.........] - ETA: 0s - loss: 0.1703 116/116 [==============================] - 0s 1ms/step - loss: 0.1694 -> test with GAN.predict GAN tn, fp: 274, 16 GAN fn, tp: 1, 6 GAN f1 score: 0.414 GAN cohens kappa score: 0.392 -> 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: 0.725 -> 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: 287, 3 GB fn, tp: 4, 3 GB f1 score: 0.462 GB cohens kappa score: 0.450 -> test with 'KNN' KNN tn, fp: 261, 29 KNN fn, tp: 2, 5 KNN f1 score: 0.244 KNN cohens kappa score: 0.213 ------ 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: 0.2448 42/116 [=========>....................] - ETA: 0s - loss: 0.2048  83/116 [====================>.........] - ETA: 0s - loss: 0.2006 116/116 [==============================] - 0s 1ms/step - loss: 0.1950 Epoch 2/10 1/116 [..............................] - ETA: 0s - loss: 0.3302 41/116 [=========>....................] - ETA: 0s - loss: 0.1823 83/116 [====================>.........] - ETA: 0s - loss: 0.1957 116/116 [==============================] - 0s 1ms/step - loss: 0.1909 Epoch 3/10 1/116 [..............................] - ETA: 0s - loss: 0.1534 43/116 [==========>...................] - ETA: 0s - loss: 0.1847 85/116 [====================>.........] - ETA: 0s - loss: 0.1839 116/116 [==============================] - 0s 1ms/step - loss: 0.1835 Epoch 4/10 1/116 [..............................] - ETA: 0s - loss: 0.0928 41/116 [=========>....................] - ETA: 0s - loss: 0.1539 83/116 [====================>.........] - ETA: 0s - loss: 0.1786 116/116 [==============================] - 0s 1ms/step - loss: 0.1796 Epoch 5/10 1/116 [..............................] - ETA: 0s - loss: 0.4071 44/116 [==========>...................] - ETA: 0s - loss: 0.1974 86/116 [=====================>........] - ETA: 0s - loss: 0.1801 116/116 [==============================] - 0s 1ms/step - loss: 0.1761 Epoch 6/10 1/116 [..............................] - ETA: 0s - loss: 0.1726 42/116 [=========>....................] - ETA: 0s - loss: 0.1738 84/116 [====================>.........] - ETA: 0s - loss: 0.1757 116/116 [==============================] - 0s 1ms/step - loss: 0.1717 Epoch 7/10 1/116 [..............................] - ETA: 0s - loss: 0.1046 41/116 [=========>....................] - ETA: 0s - loss: 0.1672 82/116 [====================>.........] - ETA: 0s - loss: 0.1692 116/116 [==============================] - 0s 1ms/step - loss: 0.1672 Epoch 8/10 1/116 [..............................] - ETA: 0s - loss: 0.1728 43/116 [==========>...................] - ETA: 0s - loss: 0.1732 84/116 [====================>.........] - ETA: 0s - loss: 0.1641 116/116 [==============================] - 0s 1ms/step - loss: 0.1659 Epoch 9/10 1/116 [..............................] - ETA: 0s - loss: 0.1503 41/116 [=========>....................] - ETA: 0s - loss: 0.1610 79/116 [===================>..........] - ETA: 0s - loss: 0.1663 116/116 [==============================] - 0s 1ms/step - loss: 0.1617 Epoch 10/10 1/116 [..............................] - ETA: 0s - loss: 0.1530 35/116 [========>.....................] - ETA: 0s - loss: 0.1795 68/116 [================>.............] - ETA: 0s - loss: 0.1623 105/116 [==========================>...] - ETA: 0s - loss: 0.1603 116/116 [==============================] - 0s 1ms/step - loss: 0.1582 -> test with GAN.predict GAN tn, fp: 257, 33 GAN fn, tp: 2, 5 GAN f1 score: 0.222 GAN cohens kappa score: 0.190 -> test with 'LR' LR tn, fp: 261, 29 LR fn, tp: 2, 5 LR f1 score: 0.244 LR cohens kappa score: 0.213 LR average precision score: 0.431 -> 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: 0.3114 42/116 [=========>....................] - ETA: 0s - loss: 0.1509  79/116 [===================>..........] - ETA: 0s - loss: 0.1521 116/116 [==============================] - 0s 1ms/step - loss: 0.1585 Epoch 2/10 1/116 [..............................] - ETA: 0s - loss: 0.0573 37/116 [========>.....................] - ETA: 0s - loss: 0.1627 74/116 [==================>...........] - ETA: 0s - loss: 0.1658 110/116 [===========================>..] - ETA: 0s - loss: 0.1573 116/116 [==============================] - 0s 1ms/step - loss: 0.1583 Epoch 3/10 1/116 [..............................] - ETA: 0s - loss: 0.1576 37/116 [========>.....................] - ETA: 0s - loss: 0.1483 74/116 [==================>...........] - ETA: 0s - loss: 0.1526 110/116 [===========================>..] - ETA: 0s - loss: 0.1517 116/116 [==============================] - 0s 1ms/step - loss: 0.1514 Epoch 4/10 1/116 [..............................] - ETA: 0s - loss: 0.1113 39/116 [=========>....................] - ETA: 0s - loss: 0.1234 75/116 [==================>...........] - ETA: 0s - loss: 0.1425 111/116 [===========================>..] - ETA: 0s - loss: 0.1481 116/116 [==============================] - 0s 1ms/step - loss: 0.1468 Epoch 5/10 1/116 [..............................] - ETA: 0s - loss: 0.0428 40/116 [=========>....................] - ETA: 0s - loss: 0.1407 77/116 [==================>...........] - ETA: 0s - loss: 0.1380 112/116 [===========================>..] - ETA: 0s - loss: 0.1426 116/116 [==============================] - 0s 1ms/step - loss: 0.1437 Epoch 6/10 1/116 [..............................] - ETA: 0s - loss: 0.1342 38/116 [========>.....................] - ETA: 0s - loss: 0.1580 74/116 [==================>...........] - ETA: 0s - loss: 0.1474 111/116 [===========================>..] - ETA: 0s - loss: 0.1436 116/116 [==============================] - 0s 1ms/step - loss: 0.1405 Epoch 7/10 1/116 [..............................] - ETA: 0s - loss: 0.0307 36/116 [========>.....................] - ETA: 0s - loss: 0.1506 74/116 [==================>...........] - ETA: 0s - loss: 0.1352 113/116 [============================>.] - ETA: 0s - loss: 0.1372 116/116 [==============================] - 0s 1ms/step - loss: 0.1370 Epoch 8/10 1/116 [..............................] - ETA: 0s - loss: 0.0667 43/116 [==========>...................] - ETA: 0s - loss: 0.1271 83/116 [====================>.........] - ETA: 0s - loss: 0.1297 116/116 [==============================] - ETA: 0s - loss: 0.1341 116/116 [==============================] - 0s 1ms/step - loss: 0.1341 Epoch 9/10 1/116 [..............................] - ETA: 0s - loss: 0.1069 35/116 [========>.....................] - ETA: 0s - loss: 0.1199 68/116 [================>.............] - ETA: 0s - loss: 0.1198 106/116 [==========================>...] - ETA: 0s - loss: 0.1293 116/116 [==============================] - 0s 1ms/step - loss: 0.1298 Epoch 10/10 1/116 [..............................] - ETA: 0s - loss: 0.0985 37/116 [========>.....................] - ETA: 0s - loss: 0.1365 73/116 [=================>............] - ETA: 0s - loss: 0.1300 111/116 [===========================>..] - ETA: 0s - loss: 0.1279 116/116 [==============================] - 0s 1ms/step - loss: 0.1269 -> test with GAN.predict GAN tn, fp: 280, 10 GAN fn, tp: 1, 6 GAN f1 score: 0.522 GAN cohens kappa score: 0.506 -> test with 'LR' LR tn, fp: 265, 25 LR fn, tp: 1, 6 LR f1 score: 0.316 LR cohens kappa score: 0.288 LR average precision score: 0.316 -> 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: 290, 0 GB fn, tp: 5, 2 GB f1 score: 0.444 GB cohens kappa score: 0.439 -> 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 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: 0.1796 44/116 [==========>...................] - ETA: 0s - loss: 0.1720  85/116 [====================>.........] - ETA: 0s - loss: 0.1796 116/116 [==============================] - 0s 1ms/step - loss: 0.1842 Epoch 2/10 1/116 [..............................] - ETA: 0s - loss: 0.0841 39/116 [=========>....................] - ETA: 0s - loss: 0.1824 76/116 [==================>...........] - ETA: 0s - loss: 0.1852 111/116 [===========================>..] - ETA: 0s - loss: 0.1793 116/116 [==============================] - 0s 1ms/step - loss: 0.1801 Epoch 3/10 1/116 [..............................] - ETA: 0s - loss: 0.0719 40/116 [=========>....................] - ETA: 0s - loss: 0.1887 80/116 [===================>..........] - ETA: 0s - loss: 0.1743 116/116 [==============================] - 0s 1ms/step - loss: 0.1760 Epoch 4/10 1/116 [..............................] - ETA: 0s - loss: 0.2275 42/116 [=========>....................] - ETA: 0s - loss: 0.1770 80/116 [===================>..........] - ETA: 0s - loss: 0.1726 116/116 [==============================] - 0s 1ms/step - loss: 0.1724 Epoch 5/10 1/116 [..............................] - ETA: 0s - loss: 0.3738 42/116 [=========>....................] - ETA: 0s - loss: 0.1689 83/116 [====================>.........] - ETA: 0s - loss: 0.1725 116/116 [==============================] - 0s 1ms/step - loss: 0.1659 Epoch 6/10 1/116 [..............................] - ETA: 0s - loss: 0.0816 41/116 [=========>....................] - ETA: 0s - loss: 0.1745 83/116 [====================>.........] - ETA: 0s - loss: 0.1582 116/116 [==============================] - 0s 1ms/step - loss: 0.1612 Epoch 7/10 1/116 [..............................] - ETA: 0s - loss: 0.1683 41/116 [=========>....................] - ETA: 0s - loss: 0.1425 81/116 [===================>..........] - ETA: 0s - loss: 0.1481 116/116 [==============================] - 0s 1ms/step - loss: 0.1574 Epoch 8/10 1/116 [..............................] - ETA: 0s - loss: 0.2456 37/116 [========>.....................] - ETA: 0s - loss: 0.1545 77/116 [==================>...........] - ETA: 0s - loss: 0.1549 116/116 [==============================] - 0s 1ms/step - loss: 0.1550 Epoch 9/10 1/116 [..............................] - ETA: 0s - loss: 0.0705 41/116 [=========>....................] - ETA: 0s - loss: 0.1244 79/116 [===================>..........] - ETA: 0s - loss: 0.1465 116/116 [==============================] - 0s 1ms/step - loss: 0.1547 Epoch 10/10 1/116 [..............................] - ETA: 0s - loss: 0.0301 43/116 [==========>...................] - ETA: 0s - loss: 0.1461 85/116 [====================>.........] - ETA: 0s - loss: 0.1505 116/116 [==============================] - 0s 1ms/step - loss: 0.1461 -> test with GAN.predict GAN tn, fp: 279, 11 GAN fn, tp: 2, 5 GAN f1 score: 0.435 GAN cohens kappa score: 0.416 -> 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: 0.533 -> 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: 288, 2 GB fn, tp: 4, 3 GB f1 score: 0.500 GB cohens kappa score: 0.490 -> 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 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: 0.1722 39/116 [=========>....................] - ETA: 0s - loss: 0.2097  80/116 [===================>..........] - ETA: 0s - loss: 0.2054 116/116 [==============================] - 0s 1ms/step - loss: 0.2085 Epoch 2/10 1/116 [..............................] - ETA: 0s - loss: 0.3050 42/116 [=========>....................] - ETA: 0s - loss: 0.2256 81/116 [===================>..........] - ETA: 0s - loss: 0.2012 116/116 [==============================] - ETA: 0s - loss: 0.2033 116/116 [==============================] - 0s 1ms/step - loss: 0.2033 Epoch 3/10 1/116 [..............................] - ETA: 0s - loss: 0.3344 39/116 [=========>....................] - ETA: 0s - loss: 0.2102 80/116 [===================>..........] - ETA: 0s - loss: 0.2033 116/116 [==============================] - 0s 1ms/step - loss: 0.1990 Epoch 4/10 1/116 [..............................] - ETA: 0s - loss: 0.2110 40/116 [=========>....................] - ETA: 0s - loss: 0.1991 79/116 [===================>..........] - ETA: 0s - loss: 0.1863 116/116 [==============================] - 0s 1ms/step - loss: 0.1937 Epoch 5/10 1/116 [..............................] - ETA: 0s - loss: 0.3503 43/116 [==========>...................] - ETA: 0s - loss: 0.1646 83/116 [====================>.........] - ETA: 0s - loss: 0.1768 116/116 [==============================] - 0s 1ms/step - loss: 0.1882 Epoch 6/10 1/116 [..............................] - ETA: 0s - loss: 0.2027 40/116 [=========>....................] - ETA: 0s - loss: 0.1803 79/116 [===================>..........] - ETA: 0s - loss: 0.1814 113/116 [============================>.] - ETA: 0s - loss: 0.1851 116/116 [==============================] - 0s 1ms/step - loss: 0.1833 Epoch 7/10 1/116 [..............................] - ETA: 0s - loss: 0.1334 38/116 [========>.....................] - ETA: 0s - loss: 0.1726 77/116 [==================>...........] - ETA: 0s - loss: 0.1777 116/116 [==============================] - 0s 1ms/step - loss: 0.1792 Epoch 8/10 1/116 [..............................] - ETA: 0s - loss: 0.0754 40/116 [=========>....................] - ETA: 0s - loss: 0.1823 79/116 [===================>..........] - ETA: 0s - loss: 0.1769 116/116 [==============================] - 0s 1ms/step - loss: 0.1763 Epoch 9/10 1/116 [..............................] - ETA: 0s - loss: 0.3084 40/116 [=========>....................] - ETA: 0s - loss: 0.1826 80/116 [===================>..........] - ETA: 0s - loss: 0.1751 116/116 [==============================] - ETA: 0s - loss: 0.1706 116/116 [==============================] - 0s 1ms/step - loss: 0.1706 Epoch 10/10 1/116 [..............................] - ETA: 0s - loss: 0.1892 41/116 [=========>....................] - ETA: 0s - loss: 0.1851 79/116 [===================>..........] - ETA: 0s - loss: 0.1800 114/116 [============================>.] - ETA: 0s - loss: 0.1714 116/116 [==============================] - 0s 1ms/step - loss: 0.1705 -> test with GAN.predict GAN tn, fp: 253, 36 GAN fn, tp: 1, 6 GAN f1 score: 0.245 GAN cohens kappa score: 0.213 -> test with 'LR' LR tn, fp: 245, 44 LR fn, tp: 0, 7 LR f1 score: 0.241 LR cohens kappa score: 0.208 LR average precision score: 0.565 -> 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: 289, 0 GB fn, tp: 3, 4 GB f1 score: 0.727 GB cohens kappa score: 0.722 -> test with 'KNN' KNN tn, fp: 258, 31 KNN fn, tp: 0, 7 KNN f1 score: 0.311 KNN cohens kappa score: 0.282 ====== 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: 0.0842 38/116 [========>.....................] - ETA: 0s - loss: 0.2208  80/116 [===================>..........] - ETA: 0s - loss: 0.2019 116/116 [==============================] - 0s 1ms/step - loss: 0.2074 Epoch 2/10 1/116 [..............................] - ETA: 0s - loss: 0.1327 43/116 [==========>...................] - ETA: 0s - loss: 0.1856 85/116 [====================>.........] - ETA: 0s - loss: 0.2065 116/116 [==============================] - 0s 1ms/step - loss: 0.2047 Epoch 3/10 1/116 [..............................] - ETA: 0s - loss: 0.1595 41/116 [=========>....................] - ETA: 0s - loss: 0.1768 82/116 [====================>.........] - ETA: 0s - loss: 0.1983 116/116 [==============================] - 0s 1ms/step - loss: 0.1991 Epoch 4/10 1/116 [..............................] - ETA: 0s - loss: 0.0389 42/116 [=========>....................] - ETA: 0s - loss: 0.1807 84/116 [====================>.........] - ETA: 0s - loss: 0.1909 116/116 [==============================] - 0s 1ms/step - loss: 0.1952 Epoch 5/10 1/116 [..............................] - ETA: 0s - loss: 0.2795 43/116 [==========>...................] - ETA: 0s - loss: 0.1936 85/116 [====================>.........] - ETA: 0s - loss: 0.1871 116/116 [==============================] - 0s 1ms/step - loss: 0.1918 Epoch 6/10 1/116 [..............................] - ETA: 0s - loss: 0.1901 41/116 [=========>....................] - ETA: 0s - loss: 0.1804 83/116 [====================>.........] - ETA: 0s - loss: 0.1958 116/116 [==============================] - 0s 1ms/step - loss: 0.1874 Epoch 7/10 1/116 [..............................] - ETA: 0s - loss: 0.3154 40/116 [=========>....................] - ETA: 0s - loss: 0.1887 82/116 [====================>.........] - ETA: 0s - loss: 0.1775 116/116 [==============================] - 0s 1ms/step - loss: 0.1822 Epoch 8/10 1/116 [..............................] - ETA: 0s - loss: 0.3151 42/116 [=========>....................] - ETA: 0s - loss: 0.1922 83/116 [====================>.........] - ETA: 0s - loss: 0.1832 116/116 [==============================] - 0s 1ms/step - loss: 0.1792 Epoch 9/10 1/116 [..............................] - ETA: 0s - loss: 0.2623 43/116 [==========>...................] - ETA: 0s - loss: 0.1721 85/116 [====================>.........] - ETA: 0s - loss: 0.1753 116/116 [==============================] - 0s 1ms/step - loss: 0.1753 Epoch 10/10 1/116 [..............................] - ETA: 0s - loss: 0.1918 36/116 [========>.....................] - ETA: 0s - loss: 0.1766 77/116 [==================>...........] - ETA: 0s - loss: 0.1661 115/116 [============================>.] - ETA: 0s - loss: 0.1729 116/116 [==============================] - 0s 1ms/step - loss: 0.1726 -> test with GAN.predict GAN tn, fp: 275, 15 GAN fn, tp: 1, 6 GAN f1 score: 0.429 GAN cohens kappa score: 0.408 -> test with 'LR' LR tn, fp: 264, 26 LR fn, tp: 0, 7 LR f1 score: 0.350 LR cohens kappa score: 0.324 LR average precision score: 0.678 -> 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: 287, 3 GB fn, tp: 4, 3 GB f1 score: 0.462 GB cohens kappa score: 0.450 -> test with 'KNN' KNN tn, fp: 265, 25 KNN fn, tp: 1, 6 KNN f1 score: 0.316 KNN cohens kappa score: 0.288 ------ 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: 0.3666 42/116 [=========>....................] - ETA: 0s - loss: 0.2240  84/116 [====================>.........] - ETA: 0s - loss: 0.2159 116/116 [==============================] - 0s 1ms/step - loss: 0.2244 Epoch 2/10 1/116 [..............................] - ETA: 0s - loss: 0.1075 41/116 [=========>....................] - ETA: 0s - loss: 0.2315 83/116 [====================>.........] - ETA: 0s - loss: 0.2253 116/116 [==============================] - 0s 1ms/step - loss: 0.2193 Epoch 3/10 1/116 [..............................] - ETA: 0s - loss: 0.2967 39/116 [=========>....................] - ETA: 0s - loss: 0.2155 78/116 [===================>..........] - ETA: 0s - loss: 0.2121 116/116 [==============================] - 0s 1ms/step - loss: 0.2160 Epoch 4/10 1/116 [..............................] - ETA: 0s - loss: 0.3273 39/116 [=========>....................] - ETA: 0s - loss: 0.2192 79/116 [===================>..........] - ETA: 0s - loss: 0.2014 116/116 [==============================] - 0s 1ms/step - loss: 0.2095 Epoch 5/10 1/116 [..............................] - ETA: 0s - loss: 0.2402 40/116 [=========>....................] - ETA: 0s - loss: 0.2044 80/116 [===================>..........] - ETA: 0s - loss: 0.2178 116/116 [==============================] - 0s 1ms/step - loss: 0.2054 Epoch 6/10 1/116 [..............................] - ETA: 0s - loss: 0.0546 43/116 [==========>...................] - ETA: 0s - loss: 0.1846 83/116 [====================>.........] - ETA: 0s - loss: 0.1943 116/116 [==============================] - 0s 1ms/step - loss: 0.2010 Epoch 7/10 1/116 [..............................] - ETA: 0s - loss: 0.1486 40/116 [=========>....................] - ETA: 0s - loss: 0.1814 82/116 [====================>.........] - ETA: 0s - loss: 0.1964 116/116 [==============================] - 0s 1ms/step - loss: 0.1974 Epoch 8/10 1/116 [..............................] - ETA: 0s - loss: 0.1360 43/116 [==========>...................] - ETA: 0s - loss: 0.2013 85/116 [====================>.........] - ETA: 0s - loss: 0.1991 116/116 [==============================] - 0s 1ms/step - loss: 0.1940 Epoch 9/10 1/116 [..............................] - ETA: 0s - loss: 0.0379 41/116 [=========>....................] - ETA: 0s - loss: 0.1898 82/116 [====================>.........] - ETA: 0s - loss: 0.1858 116/116 [==============================] - 0s 1ms/step - loss: 0.1902 Epoch 10/10 1/116 [..............................] - ETA: 0s - loss: 0.2177 43/116 [==========>...................] - ETA: 0s - loss: 0.1884 85/116 [====================>.........] - ETA: 0s - loss: 0.1846 116/116 [==============================] - 0s 1ms/step - loss: 0.1881 -> test with GAN.predict GAN tn, fp: 253, 37 GAN fn, tp: 0, 7 GAN f1 score: 0.275 GAN cohens kappa score: 0.244 -> test with 'LR' LR tn, fp: 249, 41 LR fn, tp: 0, 7 LR f1 score: 0.255 LR cohens kappa score: 0.223 LR average precision score: 0.222 -> 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: 288, 2 GB fn, tp: 4, 3 GB f1 score: 0.500 GB cohens kappa score: 0.490 -> 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 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: 17s - loss: 0.1277 43/116 [==========>...................] - ETA: 0s - loss: 0.2289  84/116 [====================>.........] - ETA: 0s - loss: 0.2133 116/116 [==============================] - 0s 1ms/step - loss: 0.2048 Epoch 2/10 1/116 [..............................] - ETA: 0s - loss: 0.3619 43/116 [==========>...................] - ETA: 0s - loss: 0.1959 85/116 [====================>.........] - ETA: 0s - loss: 0.2011 116/116 [==============================] - 0s 1ms/step - loss: 0.2009 Epoch 3/10 1/116 [..............................] - ETA: 0s - loss: 0.2499 42/116 [=========>....................] - ETA: 0s - loss: 0.1898 82/116 [====================>.........] - ETA: 0s - loss: 0.1937 116/116 [==============================] - 0s 1ms/step - loss: 0.1968 Epoch 4/10 1/116 [..............................] - ETA: 0s - loss: 0.1380 42/116 [=========>....................] - ETA: 0s - loss: 0.2024 83/116 [====================>.........] - ETA: 0s - loss: 0.1947 116/116 [==============================] - 0s 1ms/step - loss: 0.1937 Epoch 5/10 1/116 [..............................] - ETA: 0s - loss: 0.0862 42/116 [=========>....................] - ETA: 0s - loss: 0.1811 83/116 [====================>.........] - ETA: 0s - loss: 0.1974 116/116 [==============================] - 0s 1ms/step - loss: 0.1889 Epoch 6/10 1/116 [..............................] - ETA: 0s - loss: 0.2045 41/116 [=========>....................] - ETA: 0s - loss: 0.1904 82/116 [====================>.........] - ETA: 0s - loss: 0.1949 116/116 [==============================] - 0s 1ms/step - loss: 0.1861 Epoch 7/10 1/116 [..............................] - ETA: 0s - loss: 0.1855 41/116 [=========>....................] - ETA: 0s - loss: 0.1847 78/116 [===================>..........] - ETA: 0s - loss: 0.1871 116/116 [==============================] - 0s 1ms/step - loss: 0.1814 Epoch 8/10 1/116 [..............................] - ETA: 0s - loss: 0.1157 34/116 [=======>......................] - ETA: 0s - loss: 0.1910 71/116 [=================>............] - ETA: 0s - loss: 0.1821 105/116 [==========================>...] - ETA: 0s - loss: 0.1777 116/116 [==============================] - 0s 1ms/step - loss: 0.1774 Epoch 9/10 1/116 [..............................] - ETA: 0s - loss: 0.1415 41/116 [=========>....................] - ETA: 0s - loss: 0.1728 83/116 [====================>.........] - ETA: 0s - loss: 0.1847 116/116 [==============================] - 0s 1ms/step - loss: 0.1741 Epoch 10/10 1/116 [..............................] - ETA: 0s - loss: 0.1740 39/116 [=========>....................] - ETA: 0s - loss: 0.1692 80/116 [===================>..........] - ETA: 0s - loss: 0.1737 116/116 [==============================] - 0s 1ms/step - loss: 0.1738 -> test with GAN.predict GAN tn, fp: 266, 24 GAN fn, tp: 1, 6 GAN f1 score: 0.324 GAN cohens kappa score: 0.297 -> 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: 0.537 -> 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: 272, 18 KNN fn, tp: 2, 5 KNN f1 score: 0.333 KNN cohens kappa score: 0.308 ------ 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: 18s - loss: 0.0910 43/116 [==========>...................] - ETA: 0s - loss: 0.1749  82/116 [====================>.........] - ETA: 0s - loss: 0.1696 116/116 [==============================] - 0s 1ms/step - loss: 0.1696 Epoch 2/10 1/116 [..............................] - ETA: 0s - loss: 0.0837 41/116 [=========>....................] - ETA: 0s - loss: 0.1727 82/116 [====================>.........] - ETA: 0s - loss: 0.1697 116/116 [==============================] - 0s 1ms/step - loss: 0.1652 Epoch 3/10 1/116 [..............................] - ETA: 0s - loss: 0.3516 41/116 [=========>....................] - ETA: 0s - loss: 0.1632 83/116 [====================>.........] - ETA: 0s - loss: 0.1720 116/116 [==============================] - 0s 1ms/step - loss: 0.1623 Epoch 4/10 1/116 [..............................] - ETA: 0s - loss: 0.1419 43/116 [==========>...................] - ETA: 0s - loss: 0.1777 84/116 [====================>.........] - ETA: 0s - loss: 0.1633 116/116 [==============================] - 0s 1ms/step - loss: 0.1598 Epoch 5/10 1/116 [..............................] - ETA: 0s - loss: 0.1367 42/116 [=========>....................] - ETA: 0s - loss: 0.1678 82/116 [====================>.........] - ETA: 0s - loss: 0.1579 116/116 [==============================] - 0s 1ms/step - loss: 0.1559 Epoch 6/10 1/116 [..............................] - ETA: 0s - loss: 0.1227 42/116 [=========>....................] - ETA: 0s - loss: 0.1390 84/116 [====================>.........] - ETA: 0s - loss: 0.1442 116/116 [==============================] - 0s 1ms/step - loss: 0.1526 Epoch 7/10 1/116 [..............................] - ETA: 0s - loss: 0.0299 42/116 [=========>....................] - ETA: 0s - loss: 0.1441 84/116 [====================>.........] - ETA: 0s - loss: 0.1438 116/116 [==============================] - 0s 1ms/step - loss: 0.1499 Epoch 8/10 1/116 [..............................] - ETA: 0s - loss: 0.1819 43/116 [==========>...................] - ETA: 0s - loss: 0.1444 82/116 [====================>.........] - ETA: 0s - loss: 0.1429 116/116 [==============================] - 0s 1ms/step - loss: 0.1445 Epoch 9/10 1/116 [..............................] - ETA: 0s - loss: 0.2850 39/116 [=========>....................] - ETA: 0s - loss: 0.1711 79/116 [===================>..........] - ETA: 0s - loss: 0.1611 116/116 [==============================] - 0s 1ms/step - loss: 0.1447 Epoch 10/10 1/116 [..............................] - ETA: 0s - loss: 0.1922 43/116 [==========>...................] - ETA: 0s - loss: 0.1470 84/116 [====================>.........] - ETA: 0s - loss: 0.1367 116/116 [==============================] - 0s 1ms/step - loss: 0.1400 -> test with GAN.predict GAN tn, fp: 275, 15 GAN fn, tp: 2, 5 GAN f1 score: 0.370 GAN cohens kappa score: 0.348 -> test with 'LR' LR tn, fp: 258, 32 LR fn, tp: 2, 5 LR f1 score: 0.227 LR cohens kappa score: 0.195 LR average precision score: 0.561 -> 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: 287, 3 GB fn, tp: 5, 2 GB f1 score: 0.333 GB cohens kappa score: 0.320 -> test with 'KNN' KNN tn, fp: 269, 21 KNN fn, tp: 3, 4 KNN f1 score: 0.250 KNN cohens kappa score: 0.221 ------ 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: 0.2616 44/116 [==========>...................] - ETA: 0s - loss: 0.1641  88/116 [=====================>........] - ETA: 0s - loss: 0.1695 116/116 [==============================] - 0s 1ms/step - loss: 0.1772 Epoch 2/10 1/116 [..............................] - ETA: 0s - loss: 0.2631 45/116 [==========>...................] - ETA: 0s - loss: 0.1769 88/116 [=====================>........] - ETA: 0s - loss: 0.1749 116/116 [==============================] - 0s 1ms/step - loss: 0.1724 Epoch 3/10 1/116 [..............................] - ETA: 0s - loss: 0.1190 43/116 [==========>...................] - ETA: 0s - loss: 0.1612 84/116 [====================>.........] - ETA: 0s - loss: 0.1650 116/116 [==============================] - 0s 1ms/step - loss: 0.1682 Epoch 4/10 1/116 [..............................] - ETA: 0s - loss: 0.1557 44/116 [==========>...................] - ETA: 0s - loss: 0.1668 89/116 [======================>.......] - ETA: 0s - loss: 0.1724 116/116 [==============================] - 0s 1ms/step - loss: 0.1636 Epoch 5/10 1/116 [..............................] - ETA: 0s - loss: 0.1564 45/116 [==========>...................] - ETA: 0s - loss: 0.1606 88/116 [=====================>........] - ETA: 0s - loss: 0.1521 116/116 [==============================] - 0s 1ms/step - loss: 0.1594 Epoch 6/10 1/116 [..............................] - ETA: 0s - loss: 0.0733 46/116 [==========>...................] - ETA: 0s - loss: 0.1634 91/116 [======================>.......] - ETA: 0s - loss: 0.1546 116/116 [==============================] - 0s 1ms/step - loss: 0.1563 Epoch 7/10 1/116 [..............................] - ETA: 0s - loss: 0.1806 46/116 [==========>...................] - ETA: 0s - loss: 0.1532 91/116 [======================>.......] - ETA: 0s - loss: 0.1554 116/116 [==============================] - 0s 1ms/step - loss: 0.1522 Epoch 8/10 1/116 [..............................] - ETA: 0s - loss: 0.1756 43/116 [==========>...................] - ETA: 0s - loss: 0.1647 88/116 [=====================>........] - ETA: 0s - loss: 0.1467 116/116 [==============================] - 0s 1ms/step - loss: 0.1501 Epoch 9/10 1/116 [..............................] - ETA: 0s - loss: 0.1174 44/116 [==========>...................] - ETA: 0s - loss: 0.1296 89/116 [======================>.......] - ETA: 0s - loss: 0.1375 116/116 [==============================] - 0s 1ms/step - loss: 0.1453 Epoch 10/10 1/116 [..............................] - ETA: 0s - loss: 0.1220 37/116 [========>.....................] - ETA: 0s - loss: 0.1134 74/116 [==================>...........] - ETA: 0s - loss: 0.1374 114/116 [============================>.] - ETA: 0s - loss: 0.1433 116/116 [==============================] - 0s 1ms/step - loss: 0.1435 -> test with GAN.predict GAN tn, fp: 276, 13 GAN fn, tp: 1, 6 GAN f1 score: 0.462 GAN cohens kappa score: 0.442 -> test with 'LR' LR tn, fp: 266, 23 LR fn, tp: 1, 6 LR f1 score: 0.333 LR cohens kappa score: 0.307 LR average precision score: 0.504 -> 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: 271, 18 KNN fn, tp: 3, 4 KNN f1 score: 0.276 KNN cohens kappa score: 0.249 ====== 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: 20s - loss: 0.5496 43/116 [==========>...................] - ETA: 0s - loss: 0.1882  85/116 [====================>.........] - ETA: 0s - loss: 0.1688 116/116 [==============================] - 0s 1ms/step - loss: 0.1757 Epoch 2/10 1/116 [..............................] - ETA: 0s - loss: 0.0385 41/116 [=========>....................] - ETA: 0s - loss: 0.1520 83/116 [====================>.........] - ETA: 0s - loss: 0.1680 114/116 [============================>.] - ETA: 0s - loss: 0.1732 116/116 [==============================] - 0s 1ms/step - loss: 0.1724 Epoch 3/10 1/116 [..............................] - ETA: 0s - loss: 0.0817 31/116 [=======>......................] - ETA: 0s - loss: 0.1790 66/116 [================>.............] - ETA: 0s - loss: 0.1590 101/116 [=========================>....] - ETA: 0s - loss: 0.1683 116/116 [==============================] - 0s 2ms/step - loss: 0.1698 Epoch 4/10 1/116 [..............................] - ETA: 0s - loss: 0.0832 41/116 [=========>....................] - ETA: 0s - loss: 0.1641 81/116 [===================>..........] - ETA: 0s - loss: 0.1679 116/116 [==============================] - 0s 1ms/step - loss: 0.1648 Epoch 5/10 1/116 [..............................] - ETA: 0s - loss: 0.0674 41/116 [=========>....................] - ETA: 0s - loss: 0.1521 82/116 [====================>.........] - ETA: 0s - loss: 0.1556 116/116 [==============================] - 0s 1ms/step - loss: 0.1610 Epoch 6/10 1/116 [..............................] - ETA: 0s - loss: 0.0291 41/116 [=========>....................] - ETA: 0s - loss: 0.1952 83/116 [====================>.........] - ETA: 0s - loss: 0.1699 116/116 [==============================] - 0s 1ms/step - loss: 0.1587 Epoch 7/10 1/116 [..............................] - ETA: 0s - loss: 0.1767 42/116 [=========>....................] - ETA: 0s - loss: 0.1502 83/116 [====================>.........] - ETA: 0s - loss: 0.1590 116/116 [==============================] - 0s 1ms/step - loss: 0.1572 Epoch 8/10 1/116 [..............................] - ETA: 0s - loss: 0.3217 43/116 [==========>...................] - ETA: 0s - loss: 0.1575 85/116 [====================>.........] - ETA: 0s - loss: 0.1541 116/116 [==============================] - 0s 1ms/step - loss: 0.1507 Epoch 9/10 1/116 [..............................] - ETA: 0s - loss: 0.2682 43/116 [==========>...................] - ETA: 0s - loss: 0.1465 83/116 [====================>.........] - ETA: 0s - loss: 0.1471 116/116 [==============================] - 0s 1ms/step - loss: 0.1488 Epoch 10/10 1/116 [..............................] - ETA: 0s - loss: 0.1894 41/116 [=========>....................] - ETA: 0s - loss: 0.1568 82/116 [====================>.........] - ETA: 0s - loss: 0.1563 116/116 [==============================] - 0s 1ms/step - loss: 0.1458 -> 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: 264, 26 LR fn, tp: 1, 6 LR f1 score: 0.308 LR cohens kappa score: 0.280 LR average precision score: 0.636 -> 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: 3, 4 GB f1 score: 0.667 GB cohens kappa score: 0.660 -> 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 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: 0.1586 42/116 [=========>....................] - ETA: 0s - loss: 0.1826  83/116 [====================>.........] - ETA: 0s - loss: 0.1884 116/116 [==============================] - 0s 1ms/step - loss: 0.1911 Epoch 2/10 1/116 [..............................] - ETA: 0s - loss: 0.1461 39/116 [=========>....................] - ETA: 0s - loss: 0.1602 80/116 [===================>..........] - ETA: 0s - loss: 0.1793 116/116 [==============================] - 0s 1ms/step - loss: 0.1885 Epoch 3/10 1/116 [..............................] - ETA: 0s - loss: 0.2334 40/116 [=========>....................] - ETA: 0s - loss: 0.1882 81/116 [===================>..........] - ETA: 0s - loss: 0.1891 116/116 [==============================] - 0s 1ms/step - loss: 0.1873 Epoch 4/10 1/116 [..............................] - ETA: 0s - loss: 0.0404 41/116 [=========>....................] - ETA: 0s - loss: 0.1629 83/116 [====================>.........] - ETA: 0s - loss: 0.1817 116/116 [==============================] - 0s 1ms/step - loss: 0.1825 Epoch 5/10 1/116 [..............................] - ETA: 0s - loss: 0.2532 43/116 [==========>...................] - ETA: 0s - loss: 0.1702 84/116 [====================>.........] - ETA: 0s - loss: 0.1829 116/116 [==============================] - 0s 1ms/step - loss: 0.1784 Epoch 6/10 1/116 [..............................] - ETA: 0s - loss: 0.2009 43/116 [==========>...................] - ETA: 0s - loss: 0.1623 83/116 [====================>.........] - ETA: 0s - loss: 0.1780 116/116 [==============================] - 0s 1ms/step - loss: 0.1773 Epoch 7/10 1/116 [..............................] - ETA: 0s - loss: 0.1788 42/116 [=========>....................] - ETA: 0s - loss: 0.1814 84/116 [====================>.........] - ETA: 0s - loss: 0.1725 116/116 [==============================] - 0s 1ms/step - loss: 0.1753 Epoch 8/10 1/116 [..............................] - ETA: 0s - loss: 0.0947 31/116 [=======>......................] - ETA: 0s - loss: 0.1808 63/116 [===============>..............] - ETA: 0s - loss: 0.1781 98/116 [========================>.....] - ETA: 0s - loss: 0.1705 116/116 [==============================] - 0s 2ms/step - loss: 0.1704 Epoch 9/10 1/116 [..............................] - ETA: 0s - loss: 0.1378 43/116 [==========>...................] - ETA: 0s - loss: 0.1743 84/116 [====================>.........] - ETA: 0s - loss: 0.1697 116/116 [==============================] - 0s 1ms/step - loss: 0.1670 Epoch 10/10 1/116 [..............................] - ETA: 0s - loss: 0.1778 41/116 [=========>....................] - ETA: 0s - loss: 0.1600 83/116 [====================>.........] - ETA: 0s - loss: 0.1620 116/116 [==============================] - 0s 1ms/step - loss: 0.1628 -> test with GAN.predict GAN tn, fp: 271, 19 GAN fn, tp: 0, 7 GAN f1 score: 0.424 GAN cohens kappa score: 0.402 -> test with 'LR' LR tn, fp: 252, 38 LR fn, tp: 0, 7 LR f1 score: 0.269 LR cohens kappa score: 0.238 LR average precision score: 0.799 -> 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: 289, 1 GB fn, tp: 4, 3 GB f1 score: 0.545 GB cohens kappa score: 0.538 -> test with 'KNN' KNN tn, fp: 256, 34 KNN fn, tp: 0, 7 KNN f1 score: 0.292 KNN cohens kappa score: 0.262 ------ 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: 0.2425 43/116 [==========>...................] - ETA: 0s - loss: 0.1655  85/116 [====================>.........] - ETA: 0s - loss: 0.1793 116/116 [==============================] - 0s 1ms/step - loss: 0.1753 Epoch 2/10 1/116 [..............................] - ETA: 0s - loss: 0.1664 32/116 [=======>......................] - ETA: 0s - loss: 0.1565 72/116 [=================>............] - ETA: 0s - loss: 0.1678 111/116 [===========================>..] - ETA: 0s - loss: 0.1650 116/116 [==============================] - 0s 1ms/step - loss: 0.1688 Epoch 3/10 1/116 [..............................] - ETA: 0s - loss: 0.0629 43/116 [==========>...................] - ETA: 0s - loss: 0.1724 84/116 [====================>.........] - ETA: 0s - loss: 0.1695 116/116 [==============================] - 0s 1ms/step - loss: 0.1639 Epoch 4/10 1/116 [..............................] - ETA: 0s - loss: 0.1864 42/116 [=========>....................] - ETA: 0s - loss: 0.1540 82/116 [====================>.........] - ETA: 0s - loss: 0.1636 116/116 [==============================] - 0s 1ms/step - loss: 0.1590 Epoch 5/10 1/116 [..............................] - ETA: 0s - loss: 0.4631 42/116 [=========>....................] - ETA: 0s - loss: 0.1456 83/116 [====================>.........] - ETA: 0s - loss: 0.1572 116/116 [==============================] - 0s 1ms/step - loss: 0.1553 Epoch 6/10 1/116 [..............................] - ETA: 0s - loss: 0.0547 42/116 [=========>....................] - ETA: 0s - loss: 0.1304 83/116 [====================>.........] - ETA: 0s - loss: 0.1531 116/116 [==============================] - 0s 1ms/step - loss: 0.1510 Epoch 7/10 1/116 [..............................] - ETA: 0s - loss: 0.2165 43/116 [==========>...................] - ETA: 0s - loss: 0.1349 82/116 [====================>.........] - ETA: 0s - loss: 0.1422 116/116 [==============================] - 0s 1ms/step - loss: 0.1464 Epoch 8/10 1/116 [..............................] - ETA: 0s - loss: 0.1434 37/116 [========>.....................] - ETA: 0s - loss: 0.1372 79/116 [===================>..........] - ETA: 0s - loss: 0.1462 116/116 [==============================] - 0s 1ms/step - loss: 0.1483 Epoch 9/10 1/116 [..............................] - ETA: 0s - loss: 0.2286 42/116 [=========>....................] - ETA: 0s - loss: 0.1467 82/116 [====================>.........] - ETA: 0s - loss: 0.1442 116/116 [==============================] - 0s 1ms/step - loss: 0.1385 Epoch 10/10 1/116 [..............................] - ETA: 0s - loss: 0.1192 42/116 [=========>....................] - ETA: 0s - loss: 0.1291 80/116 [===================>..........] - ETA: 0s - loss: 0.1238 116/116 [==============================] - 0s 1ms/step - loss: 0.1339 -> test with GAN.predict GAN tn, fp: 277, 13 GAN fn, tp: 2, 5 GAN f1 score: 0.400 GAN cohens kappa score: 0.379 -> 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: 0.432 -> test with 'RF' RF tn, fp: 290, 0 RF fn, tp: 6, 1 RF f1 score: 0.250 RF cohens kappa score: 0.246 -> 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: 275, 15 KNN fn, tp: 2, 5 KNN f1 score: 0.370 KNN cohens kappa score: 0.348 ------ 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: 18s - loss: 0.1428 44/116 [==========>...................] - ETA: 0s - loss: 0.2396  86/116 [=====================>........] - ETA: 0s - loss: 0.2356 116/116 [==============================] - 0s 1ms/step - loss: 0.2261 Epoch 2/10 1/116 [..............................] - ETA: 0s - loss: 0.3539 43/116 [==========>...................] - ETA: 0s - loss: 0.2342 85/116 [====================>.........] - ETA: 0s - loss: 0.2221 116/116 [==============================] - 0s 1ms/step - loss: 0.2217 Epoch 3/10 1/116 [..............................] - ETA: 0s - loss: 0.1768 42/116 [=========>....................] - ETA: 0s - loss: 0.2371 82/116 [====================>.........] - ETA: 0s - loss: 0.2185 116/116 [==============================] - 0s 1ms/step - loss: 0.2175 Epoch 4/10 1/116 [..............................] - ETA: 0s - loss: 0.1149 34/116 [=======>......................] - ETA: 0s - loss: 0.1991 71/116 [=================>............] - ETA: 0s - loss: 0.2006 113/116 [============================>.] - ETA: 0s - loss: 0.2133 116/116 [==============================] - 0s 1ms/step - loss: 0.2137 Epoch 5/10 1/116 [..............................] - ETA: 0s - loss: 0.1866 41/116 [=========>....................] - ETA: 0s - loss: 0.2132 82/116 [====================>.........] - ETA: 0s - loss: 0.2087 116/116 [==============================] - 0s 1ms/step - loss: 0.2084 Epoch 6/10 1/116 [..............................] - ETA: 0s - loss: 0.0653 40/116 [=========>....................] - ETA: 0s - loss: 0.2173 82/116 [====================>.........] - ETA: 0s - loss: 0.2035 116/116 [==============================] - 0s 1ms/step - loss: 0.2039 Epoch 7/10 1/116 [..............................] - ETA: 0s - loss: 0.3060 42/116 [=========>....................] - ETA: 0s - loss: 0.1951 84/116 [====================>.........] - ETA: 0s - loss: 0.1968 116/116 [==============================] - 0s 1ms/step - loss: 0.2001 Epoch 8/10 1/116 [..............................] - ETA: 0s - loss: 0.3295 42/116 [=========>....................] - ETA: 0s - loss: 0.1934 84/116 [====================>.........] - ETA: 0s - loss: 0.1863 116/116 [==============================] - 0s 1ms/step - loss: 0.1966 Epoch 9/10 1/116 [..............................] - ETA: 0s - loss: 0.2142 42/116 [=========>....................] - ETA: 0s - loss: 0.2077 79/116 [===================>..........] - ETA: 0s - loss: 0.1984 116/116 [==============================] - 0s 1ms/step - loss: 0.1932 Epoch 10/10 1/116 [..............................] - ETA: 0s - loss: 0.1401 40/116 [=========>....................] - ETA: 0s - loss: 0.1794 79/116 [===================>..........] - ETA: 0s - loss: 0.1874 116/116 [==============================] - 0s 1ms/step - loss: 0.1873 -> test with GAN.predict GAN tn, fp: 269, 21 GAN fn, tp: 2, 5 GAN f1 score: 0.303 GAN cohens kappa score: 0.276 -> test with 'LR' LR tn, fp: 259, 31 LR fn, tp: 0, 7 LR f1 score: 0.311 LR cohens kappa score: 0.283 LR average precision score: 0.374 -> 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: 285, 5 GB fn, tp: 3, 4 GB f1 score: 0.500 GB cohens kappa score: 0.486 -> 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 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: 0.3311 45/116 [==========>...................] - ETA: 0s - loss: 0.1604  90/116 [======================>.......] - ETA: 0s - loss: 0.1600 116/116 [==============================] - 0s 1ms/step - loss: 0.1600 Epoch 2/10 1/116 [..............................] - ETA: 0s - loss: 0.1124 46/116 [==========>...................] - ETA: 0s - loss: 0.1621 90/116 [======================>.......] - ETA: 0s - loss: 0.1530 116/116 [==============================] - 0s 1ms/step - loss: 0.1551 Epoch 3/10 1/116 [..............................] - ETA: 0s - loss: 0.0688 45/116 [==========>...................] - ETA: 0s - loss: 0.1621 89/116 [======================>.......] - ETA: 0s - loss: 0.1533 116/116 [==============================] - 0s 1ms/step - loss: 0.1527 Epoch 4/10 1/116 [..............................] - ETA: 0s - loss: 0.0990 46/116 [==========>...................] - ETA: 0s - loss: 0.1554 91/116 [======================>.......] - ETA: 0s - loss: 0.1544 116/116 [==============================] - 0s 1ms/step - loss: 0.1486 Epoch 5/10 1/116 [..............................] - ETA: 0s - loss: 0.0410 46/116 [==========>...................] - ETA: 0s - loss: 0.1377 91/116 [======================>.......] - ETA: 0s - loss: 0.1453 116/116 [==============================] - 0s 1ms/step - loss: 0.1463 Epoch 6/10 1/116 [..............................] - ETA: 0s - loss: 0.2202 44/116 [==========>...................] - ETA: 0s - loss: 0.1260 86/116 [=====================>........] - ETA: 0s - loss: 0.1325 116/116 [==============================] - 0s 1ms/step - loss: 0.1427 Epoch 7/10 1/116 [..............................] - ETA: 0s - loss: 0.0582 44/116 [==========>...................] - ETA: 0s - loss: 0.1305 87/116 [=====================>........] - ETA: 0s - loss: 0.1326 116/116 [==============================] - 0s 1ms/step - loss: 0.1395 Epoch 8/10 1/116 [..............................] - ETA: 0s - loss: 0.0425 45/116 [==========>...................] - ETA: 0s - loss: 0.1432 86/116 [=====================>........] - ETA: 0s - loss: 0.1342 116/116 [==============================] - 0s 1ms/step - loss: 0.1359 Epoch 9/10 1/116 [..............................] - ETA: 0s - loss: 0.3542 39/116 [=========>....................] - ETA: 0s - loss: 0.1535 78/116 [===================>..........] - ETA: 0s - loss: 0.1397 116/116 [==============================] - 0s 1ms/step - loss: 0.1340 Epoch 10/10 1/116 [..............................] - ETA: 0s - loss: 0.0299 46/116 [==========>...................] - ETA: 0s - loss: 0.1339 90/116 [======================>.......] - ETA: 0s - loss: 0.1262 116/116 [==============================] - 0s 1ms/step - loss: 0.1311 -> 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: 1, 6 LR f1 score: 0.375 LR cohens kappa score: 0.351 LR average precision score: 0.370 -> test with 'RF' RF tn, fp: 289, 0 RF fn, tp: 6, 1 RF f1 score: 0.250 RF cohens kappa score: 0.246 -> 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: 279, 10 KNN fn, tp: 1, 6 KNN f1 score: 0.522 KNN cohens kappa score: 0.505 ====== 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: 0.1119 41/116 [=========>....................] - ETA: 0s - loss: 0.2045  81/116 [===================>..........] - ETA: 0s - loss: 0.1875 116/116 [==============================] - 0s 1ms/step - loss: 0.1849 Epoch 2/10 1/116 [..............................] - ETA: 0s - loss: 0.3346 41/116 [=========>....................] - ETA: 0s - loss: 0.1764 82/116 [====================>.........] - ETA: 0s - loss: 0.1694 116/116 [==============================] - 0s 1ms/step - loss: 0.1819 Epoch 3/10 1/116 [..............................] - ETA: 0s - loss: 0.2005 41/116 [=========>....................] - ETA: 0s - loss: 0.1865 82/116 [====================>.........] - ETA: 0s - loss: 0.1748 116/116 [==============================] - 0s 1ms/step - loss: 0.1761 Epoch 4/10 1/116 [..............................] - ETA: 0s - loss: 0.1868 40/116 [=========>....................] - ETA: 0s - loss: 0.1769 77/116 [==================>...........] - ETA: 0s - loss: 0.1759 116/116 [==============================] - 0s 1ms/step - loss: 0.1732 Epoch 5/10 1/116 [..............................] - ETA: 0s - loss: 0.2320 41/116 [=========>....................] - ETA: 0s - loss: 0.1801 81/116 [===================>..........] - ETA: 0s - loss: 0.1722 116/116 [==============================] - 0s 1ms/step - loss: 0.1685 Epoch 6/10 1/116 [..............................] - ETA: 0s - loss: 0.1886 40/116 [=========>....................] - ETA: 0s - loss: 0.1569 79/116 [===================>..........] - ETA: 0s - loss: 0.1677 116/116 [==============================] - 0s 1ms/step - loss: 0.1677 Epoch 7/10 1/116 [..............................] - ETA: 0s - loss: 0.0556 40/116 [=========>....................] - ETA: 0s - loss: 0.1580 80/116 [===================>..........] - ETA: 0s - loss: 0.1592 116/116 [==============================] - 0s 1ms/step - loss: 0.1630 Epoch 8/10 1/116 [..............................] - ETA: 0s - loss: 0.2029 43/116 [==========>...................] - ETA: 0s - loss: 0.1749 83/116 [====================>.........] - ETA: 0s - loss: 0.1619 116/116 [==============================] - 0s 1ms/step - loss: 0.1593 Epoch 9/10 1/116 [..............................] - ETA: 0s - loss: 0.0529 42/116 [=========>....................] - ETA: 0s - loss: 0.1741 85/116 [====================>.........] - ETA: 0s - loss: 0.1668 116/116 [==============================] - 0s 1ms/step - loss: 0.1597 Epoch 10/10 1/116 [..............................] - ETA: 0s - loss: 0.0821 34/116 [=======>......................] - ETA: 0s - loss: 0.1447 70/116 [=================>............] - ETA: 0s - loss: 0.1429 110/116 [===========================>..] - ETA: 0s - loss: 0.1518 116/116 [==============================] - 0s 1ms/step - loss: 0.1545 -> test with GAN.predict GAN tn, fp: 271, 19 GAN fn, tp: 1, 6 GAN f1 score: 0.375 GAN cohens kappa score: 0.351 -> test with 'LR' LR tn, fp: 275, 15 LR fn, tp: 1, 6 LR f1 score: 0.429 LR cohens kappa score: 0.408 LR average precision score: 0.732 -> 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: 3, 4 GB f1 score: 0.667 GB cohens kappa score: 0.660 -> test with 'KNN' KNN tn, fp: 274, 16 KNN fn, tp: 1, 6 KNN f1 score: 0.414 KNN cohens kappa score: 0.392 ------ 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: 18s - loss: 0.1402 43/116 [==========>...................] - ETA: 0s - loss: 0.2209  82/116 [====================>.........] - ETA: 0s - loss: 0.2274 116/116 [==============================] - 0s 1ms/step - loss: 0.2245 Epoch 2/10 1/116 [..............................] - ETA: 0s - loss: 0.4628 42/116 [=========>....................] - ETA: 0s - loss: 0.2173 83/116 [====================>.........] - ETA: 0s - loss: 0.2209 116/116 [==============================] - 0s 1ms/step - loss: 0.2201 Epoch 3/10 1/116 [..............................] - ETA: 0s - loss: 0.1563 41/116 [=========>....................] - ETA: 0s - loss: 0.2013 82/116 [====================>.........] - ETA: 0s - loss: 0.2133 116/116 [==============================] - 0s 1ms/step - loss: 0.2153 Epoch 4/10 1/116 [..............................] - ETA: 0s - loss: 0.1454 41/116 [=========>....................] - ETA: 0s - loss: 0.2010 81/116 [===================>..........] - ETA: 0s - loss: 0.2036 116/116 [==============================] - 0s 1ms/step - loss: 0.2114 Epoch 5/10 1/116 [..............................] - ETA: 0s - loss: 0.2141 42/116 [=========>....................] - ETA: 0s - loss: 0.2018 82/116 [====================>.........] - ETA: 0s - loss: 0.2062 116/116 [==============================] - 0s 1ms/step - loss: 0.2067 Epoch 6/10 1/116 [..............................] - ETA: 0s - loss: 0.0824 39/116 [=========>....................] - ETA: 0s - loss: 0.1973 79/116 [===================>..........] - ETA: 0s - loss: 0.1997 116/116 [==============================] - 0s 1ms/step - loss: 0.2013 Epoch 7/10 1/116 [..............................] - ETA: 0s - loss: 0.1398 40/116 [=========>....................] - ETA: 0s - loss: 0.2058 76/116 [==================>...........] - ETA: 0s - loss: 0.2032 111/116 [===========================>..] - ETA: 0s - loss: 0.1985 116/116 [==============================] - 0s 1ms/step - loss: 0.1959 Epoch 8/10 1/116 [..............................] - ETA: 0s - loss: 0.1443 38/116 [========>.....................] - ETA: 0s - loss: 0.2061 76/116 [==================>...........] - ETA: 0s - loss: 0.1963 115/116 [============================>.] - ETA: 0s - loss: 0.1934 116/116 [==============================] - 0s 1ms/step - loss: 0.1937 Epoch 9/10 1/116 [..............................] - ETA: 0s - loss: 0.2141 40/116 [=========>....................] - ETA: 0s - loss: 0.1866 81/116 [===================>..........] - ETA: 0s - loss: 0.1779 116/116 [==============================] - 0s 1ms/step - loss: 0.1889 Epoch 10/10 1/116 [..............................] - ETA: 0s - loss: 0.2499 41/116 [=========>....................] - ETA: 0s - loss: 0.1985 82/116 [====================>.........] - ETA: 0s - loss: 0.1824 116/116 [==============================] - 0s 1ms/step - loss: 0.1843 -> test with GAN.predict GAN tn, fp: 278, 12 GAN fn, tp: 1, 6 GAN f1 score: 0.480 GAN cohens kappa score: 0.462 -> test with 'LR' LR tn, fp: 257, 33 LR fn, tp: 0, 7 LR f1 score: 0.298 LR cohens kappa score: 0.269 LR average precision score: 0.242 -> 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: 268, 22 KNN fn, tp: 1, 6 KNN f1 score: 0.343 KNN cohens kappa score: 0.317 ------ 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: 18s - loss: 0.1182 41/116 [=========>....................] - ETA: 0s - loss: 0.1832  82/116 [====================>.........] - ETA: 0s - loss: 0.1804 116/116 [==============================] - 0s 1ms/step - loss: 0.1875 Epoch 2/10 1/116 [..............................] - ETA: 0s - loss: 0.1219 41/116 [=========>....................] - ETA: 0s - loss: 0.1782 81/116 [===================>..........] - ETA: 0s - loss: 0.1780 116/116 [==============================] - ETA: 0s - loss: 0.1821 116/116 [==============================] - 0s 1ms/step - loss: 0.1821 Epoch 3/10 1/116 [..............................] - ETA: 0s - loss: 0.2552 35/116 [========>.....................] - ETA: 0s - loss: 0.1934 70/116 [=================>............] - ETA: 0s - loss: 0.1887 110/116 [===========================>..] - ETA: 0s - loss: 0.1805 116/116 [==============================] - 0s 1ms/step - loss: 0.1794 Epoch 4/10 1/116 [..............................] - ETA: 0s - loss: 0.0954 41/116 [=========>....................] - ETA: 0s - loss: 0.1658 80/116 [===================>..........] - ETA: 0s - loss: 0.1730 116/116 [==============================] - 0s 1ms/step - loss: 0.1741 Epoch 5/10 1/116 [..............................] - ETA: 0s - loss: 0.1211 41/116 [=========>....................] - ETA: 0s - loss: 0.1646 82/116 [====================>.........] - ETA: 0s - loss: 0.1686 116/116 [==============================] - 0s 1ms/step - loss: 0.1686 Epoch 6/10 1/116 [..............................] - ETA: 0s - loss: 0.2210 39/116 [=========>....................] - ETA: 0s - loss: 0.1631 77/116 [==================>...........] - ETA: 0s - loss: 0.1694 116/116 [==============================] - 0s 1ms/step - loss: 0.1653 Epoch 7/10 1/116 [..............................] - ETA: 0s - loss: 0.2337 41/116 [=========>....................] - ETA: 0s - loss: 0.1818 80/116 [===================>..........] - ETA: 0s - loss: 0.1725 116/116 [==============================] - 0s 1ms/step - loss: 0.1612 Epoch 8/10 1/116 [..............................] - ETA: 0s - loss: 0.0528 41/116 [=========>....................] - ETA: 0s - loss: 0.1569 80/116 [===================>..........] - ETA: 0s - loss: 0.1500 113/116 [============================>.] - ETA: 0s - loss: 0.1579 116/116 [==============================] - 0s 1ms/step - loss: 0.1588 Epoch 9/10 1/116 [..............................] - ETA: 0s - loss: 0.0821 38/116 [========>.....................] - ETA: 0s - loss: 0.1517 79/116 [===================>..........] - ETA: 0s - loss: 0.1533 116/116 [==============================] - 0s 1ms/step - loss: 0.1569 Epoch 10/10 1/116 [..............................] - ETA: 0s - loss: 0.2246 41/116 [=========>....................] - ETA: 0s - loss: 0.1506 81/116 [===================>..........] - ETA: 0s - loss: 0.1586 116/116 [==============================] - 0s 1ms/step - loss: 0.1557 -> 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: 251, 39 LR fn, tp: 1, 6 LR f1 score: 0.231 LR cohens kappa score: 0.198 LR average precision score: 0.658 -> 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: 287, 3 GB fn, tp: 3, 4 GB f1 score: 0.571 GB cohens kappa score: 0.561 -> test with 'KNN' KNN tn, fp: 259, 31 KNN fn, tp: 0, 7 KNN f1 score: 0.311 KNN cohens kappa score: 0.283 ------ 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: 19s - loss: 0.2347 42/116 [=========>....................] - ETA: 0s - loss: 0.2313  83/116 [====================>.........] - ETA: 0s - loss: 0.2200 116/116 [==============================] - 0s 1ms/step - loss: 0.2124 Epoch 2/10 1/116 [..............................] - ETA: 0s - loss: 0.0930 42/116 [=========>....................] - ETA: 0s - loss: 0.2367 82/116 [====================>.........] - ETA: 0s - loss: 0.2094 116/116 [==============================] - 0s 1ms/step - loss: 0.2097 Epoch 3/10 1/116 [..............................] - ETA: 0s - loss: 0.1179 41/116 [=========>....................] - ETA: 0s - loss: 0.1965 80/116 [===================>..........] - ETA: 0s - loss: 0.2092 116/116 [==============================] - 0s 1ms/step - loss: 0.2061 Epoch 4/10 1/116 [..............................] - ETA: 0s - loss: 0.2551 41/116 [=========>....................] - ETA: 0s - loss: 0.2103 81/116 [===================>..........] - ETA: 0s - loss: 0.2084 116/116 [==============================] - 0s 1ms/step - loss: 0.2006 Epoch 5/10 1/116 [..............................] - ETA: 0s - loss: 0.0354 42/116 [=========>....................] - ETA: 0s - loss: 0.1730 83/116 [====================>.........] - ETA: 0s - loss: 0.1924 116/116 [==============================] - 0s 1ms/step - loss: 0.1974 Epoch 6/10 1/116 [..............................] - ETA: 0s - loss: 0.0883 40/116 [=========>....................] - ETA: 0s - loss: 0.1695 81/116 [===================>..........] - ETA: 0s - loss: 0.1880 116/116 [==============================] - 0s 1ms/step - loss: 0.1932 Epoch 7/10 1/116 [..............................] - ETA: 0s - loss: 0.2113 42/116 [=========>....................] - ETA: 0s - loss: 0.1934 79/116 [===================>..........] - ETA: 0s - loss: 0.1940 116/116 [==============================] - 0s 1ms/step - loss: 0.1895 Epoch 8/10 1/116 [..............................] - ETA: 0s - loss: 0.1474 36/116 [========>.....................] - ETA: 0s - loss: 0.1824 73/116 [=================>............] - ETA: 0s - loss: 0.1948 112/116 [===========================>..] - ETA: 0s - loss: 0.1876 116/116 [==============================] - 0s 1ms/step - loss: 0.1857 Epoch 9/10 1/116 [..............................] - ETA: 0s - loss: 0.0668 41/116 [=========>....................] - ETA: 0s - loss: 0.1636 77/116 [==================>...........] - ETA: 0s - loss: 0.1820 114/116 [============================>.] - ETA: 0s - loss: 0.1819 116/116 [==============================] - 0s 1ms/step - loss: 0.1807 Epoch 10/10 1/116 [..............................] - ETA: 0s - loss: 0.2148 41/116 [=========>....................] - ETA: 0s - loss: 0.1595 79/116 [===================>..........] - ETA: 0s - loss: 0.1725 116/116 [==============================] - 0s 1ms/step - loss: 0.1767 -> test with GAN.predict GAN tn, fp: 275, 15 GAN fn, tp: 1, 6 GAN f1 score: 0.429 GAN cohens kappa score: 0.408 -> 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: 0.637 -> 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: 263, 27 KNN fn, tp: 1, 6 KNN f1 score: 0.300 KNN cohens kappa score: 0.272 ------ 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: 0.2604 43/116 [==========>...................] - ETA: 0s - loss: 0.1759  87/116 [=====================>........] - ETA: 0s - loss: 0.1588 116/116 [==============================] - 0s 1ms/step - loss: 0.1659 Epoch 2/10 1/116 [..............................] - ETA: 0s - loss: 0.0420 44/116 [==========>...................] - ETA: 0s - loss: 0.1921 88/116 [=====================>........] - ETA: 0s - loss: 0.1733 116/116 [==============================] - 0s 1ms/step - loss: 0.1626 Epoch 3/10 1/116 [..............................] - ETA: 0s - loss: 0.3435 46/116 [==========>...................] - ETA: 0s - loss: 0.1570 91/116 [======================>.......] - ETA: 0s - loss: 0.1561 116/116 [==============================] - 0s 1ms/step - loss: 0.1609 Epoch 4/10 1/116 [..............................] - ETA: 0s - loss: 0.0908 46/116 [==========>...................] - ETA: 0s - loss: 0.1629 90/116 [======================>.......] - ETA: 0s - loss: 0.1583 116/116 [==============================] - 0s 1ms/step - loss: 0.1573 Epoch 5/10 1/116 [..............................] - ETA: 0s - loss: 0.1335 44/116 [==========>...................] - ETA: 0s - loss: 0.1726 89/116 [======================>.......] - ETA: 0s - loss: 0.1573 116/116 [==============================] - 0s 1ms/step - loss: 0.1541 Epoch 6/10 1/116 [..............................] - ETA: 0s - loss: 0.0518 43/116 [==========>...................] - ETA: 0s - loss: 0.1733 83/116 [====================>.........] - ETA: 0s - loss: 0.1562 116/116 [==============================] - 0s 1ms/step - loss: 0.1512 Epoch 7/10 1/116 [..............................] - ETA: 0s - loss: 0.0573 39/116 [=========>....................] - ETA: 0s - loss: 0.1384 82/116 [====================>.........] - ETA: 0s - loss: 0.1482 116/116 [==============================] - 0s 1ms/step - loss: 0.1480 Epoch 8/10 1/116 [..............................] - ETA: 0s - loss: 0.1357 43/116 [==========>...................] - ETA: 0s - loss: 0.1451 86/116 [=====================>........] - ETA: 0s - loss: 0.1485 116/116 [==============================] - 0s 1ms/step - loss: 0.1453 Epoch 9/10 1/116 [..............................] - ETA: 0s - loss: 0.1243 45/116 [==========>...................] - ETA: 0s - loss: 0.1374 90/116 [======================>.......] - ETA: 0s - loss: 0.1356 116/116 [==============================] - 0s 1ms/step - loss: 0.1410 Epoch 10/10 1/116 [..............................] - ETA: 0s - loss: 0.1079 45/116 [==========>...................] - ETA: 0s - loss: 0.1410 88/116 [=====================>........] - ETA: 0s - loss: 0.1359 116/116 [==============================] - 0s 1ms/step - loss: 0.1390 -> test with GAN.predict GAN tn, fp: 271, 18 GAN fn, tp: 2, 5 GAN f1 score: 0.333 GAN cohens kappa score: 0.308 -> 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: 0.678 -> 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: 5, 2 GB f1 score: 0.400 GB cohens kappa score: 0.391 -> test with 'KNN' KNN tn, fp: 275, 14 KNN fn, tp: 2, 5 KNN f1 score: 0.385 KNN cohens kappa score: 0.363 ====== 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: 17s - loss: 0.3105 43/116 [==========>...................] - ETA: 0s - loss: 0.2500  82/116 [====================>.........] - ETA: 0s - loss: 0.2452 116/116 [==============================] - 0s 1ms/step - loss: 0.2370 Epoch 2/10 1/116 [..............................] - ETA: 0s - loss: 0.0973 42/116 [=========>....................] - ETA: 0s - loss: 0.2376 83/116 [====================>.........] - ETA: 0s - loss: 0.2277 116/116 [==============================] - 0s 1ms/step - loss: 0.2329 Epoch 3/10 1/116 [..............................] - ETA: 0s - loss: 0.2272 42/116 [=========>....................] - ETA: 0s - loss: 0.2072 84/116 [====================>.........] - ETA: 0s - loss: 0.2164 116/116 [==============================] - 0s 1ms/step - loss: 0.2270 Epoch 4/10 1/116 [..............................] - ETA: 0s - loss: 0.0639 36/116 [========>.....................] - ETA: 0s - loss: 0.2200 69/116 [================>.............] - ETA: 0s - loss: 0.2112 111/116 [===========================>..] - ETA: 0s - loss: 0.2214 116/116 [==============================] - 0s 1ms/step - loss: 0.2206 Epoch 5/10 1/116 [..............................] - ETA: 0s - loss: 0.3521 45/116 [==========>...................] - ETA: 0s - loss: 0.2234 85/116 [====================>.........] - ETA: 0s - loss: 0.2119 116/116 [==============================] - 0s 1ms/step - loss: 0.2165 Epoch 6/10 1/116 [..............................] - ETA: 0s - loss: 0.2272 42/116 [=========>....................] - ETA: 0s - loss: 0.2191 82/116 [====================>.........] - ETA: 0s - loss: 0.2173 116/116 [==============================] - 0s 1ms/step - loss: 0.2129 Epoch 7/10 1/116 [..............................] - ETA: 0s - loss: 0.3796 42/116 [=========>....................] - ETA: 0s - loss: 0.2228 84/116 [====================>.........] - ETA: 0s - loss: 0.2085 116/116 [==============================] - 0s 1ms/step - loss: 0.2071 Epoch 8/10 1/116 [..............................] - ETA: 0s - loss: 0.0765 43/116 [==========>...................] - ETA: 0s - loss: 0.2023 85/116 [====================>.........] - ETA: 0s - loss: 0.1969 116/116 [==============================] - 0s 1ms/step - loss: 0.2025 Epoch 9/10 1/116 [..............................] - ETA: 0s - loss: 0.2647 43/116 [==========>...................] - ETA: 0s - loss: 0.1982 85/116 [====================>.........] - ETA: 0s - loss: 0.2019 116/116 [==============================] - 0s 1ms/step - loss: 0.2003 Epoch 10/10 1/116 [..............................] - ETA: 0s - loss: 0.3576 41/116 [=========>....................] - ETA: 0s - loss: 0.2025 83/116 [====================>.........] - ETA: 0s - loss: 0.1818 116/116 [==============================] - 0s 1ms/step - loss: 0.1961 -> test with GAN.predict GAN tn, fp: 257, 33 GAN fn, tp: 0, 7 GAN f1 score: 0.298 GAN cohens kappa score: 0.269 -> test with 'LR' LR tn, fp: 248, 42 LR fn, tp: 0, 7 LR f1 score: 0.250 LR cohens kappa score: 0.218 LR average precision score: 0.504 -> 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: 288, 2 GB fn, tp: 3, 4 GB f1 score: 0.615 GB cohens kappa score: 0.607 -> 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 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: 19s - loss: 0.0931 42/116 [=========>....................] - ETA: 0s - loss: 0.1601  84/116 [====================>.........] - ETA: 0s - loss: 0.1766 116/116 [==============================] - 0s 1ms/step - loss: 0.1795 Epoch 2/10 1/116 [..............................] - ETA: 0s - loss: 0.6478 39/116 [=========>....................] - ETA: 0s - loss: 0.1854 77/116 [==================>...........] - ETA: 0s - loss: 0.1747 116/116 [==============================] - 0s 1ms/step - loss: 0.1759 Epoch 3/10 1/116 [..............................] - ETA: 0s - loss: 0.2960 43/116 [==========>...................] - ETA: 0s - loss: 0.1977 83/116 [====================>.........] - ETA: 0s - loss: 0.1777 116/116 [==============================] - 0s 1ms/step - loss: 0.1721 Epoch 4/10 1/116 [..............................] - ETA: 0s - loss: 0.0351 35/116 [========>.....................] - ETA: 0s - loss: 0.1592 65/116 [===============>..............] - ETA: 0s - loss: 0.1492 95/116 [=======================>......] - ETA: 0s - loss: 0.1600 116/116 [==============================] - 0s 2ms/step - loss: 0.1694 Epoch 5/10 1/116 [..............................] - ETA: 0s - loss: 0.1173 42/116 [=========>....................] - ETA: 0s - loss: 0.1884 83/116 [====================>.........] - ETA: 0s - loss: 0.1703 116/116 [==============================] - 0s 1ms/step - loss: 0.1671 Epoch 6/10 1/116 [..............................] - ETA: 0s - loss: 0.1023 43/116 [==========>...................] - ETA: 0s - loss: 0.1707 85/116 [====================>.........] - ETA: 0s - loss: 0.1582 116/116 [==============================] - 0s 1ms/step - loss: 0.1624 Epoch 7/10 1/116 [..............................] - ETA: 0s - loss: 0.0948 42/116 [=========>....................] - ETA: 0s - loss: 0.1634 84/116 [====================>.........] - ETA: 0s - loss: 0.1496 116/116 [==============================] - 0s 1ms/step - loss: 0.1608 Epoch 8/10 1/116 [..............................] - ETA: 0s - loss: 0.1643 42/116 [=========>....................] - ETA: 0s - loss: 0.1647 84/116 [====================>.........] - ETA: 0s - loss: 0.1545 116/116 [==============================] - 0s 1ms/step - loss: 0.1585 Epoch 9/10 1/116 [..............................] - ETA: 0s - loss: 0.1377 42/116 [=========>....................] - ETA: 0s - loss: 0.1564 82/116 [====================>.........] - ETA: 0s - loss: 0.1503 116/116 [==============================] - 0s 1ms/step - loss: 0.1560 Epoch 10/10 1/116 [..............................] - ETA: 0s - loss: 0.1324 42/116 [=========>....................] - ETA: 0s - loss: 0.1397 83/116 [====================>.........] - ETA: 0s - loss: 0.1520 116/116 [==============================] - 0s 1ms/step - loss: 0.1528 -> test with GAN.predict GAN tn, fp: 272, 18 GAN fn, tp: 3, 4 GAN f1 score: 0.276 GAN cohens kappa score: 0.249 -> test with 'LR' LR tn, fp: 262, 28 LR fn, tp: 3, 4 LR f1 score: 0.205 LR cohens kappa score: 0.173 LR average precision score: 0.218 -> 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: 3, 4 KNN f1 score: 0.267 KNN cohens kappa score: 0.239 ------ 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: 18s - loss: 0.1187 42/116 [=========>....................] - ETA: 0s - loss: 0.1924  83/116 [====================>.........] - ETA: 0s - loss: 0.1974 116/116 [==============================] - 0s 1ms/step - loss: 0.1994 Epoch 2/10 1/116 [..............................] - ETA: 0s - loss: 0.2146 41/116 [=========>....................] - ETA: 0s - loss: 0.1753 81/116 [===================>..........] - ETA: 0s - loss: 0.1906 116/116 [==============================] - 0s 1ms/step - loss: 0.1913 Epoch 3/10 1/116 [..............................] - ETA: 0s - loss: 0.3842 41/116 [=========>....................] - ETA: 0s - loss: 0.2063 80/116 [===================>..........] - ETA: 0s - loss: 0.1887 116/116 [==============================] - 0s 1ms/step - loss: 0.1856 Epoch 4/10 1/116 [..............................] - ETA: 0s - loss: 0.1252 42/116 [=========>....................] - ETA: 0s - loss: 0.1844 83/116 [====================>.........] - ETA: 0s - loss: 0.1791 116/116 [==============================] - 0s 1ms/step - loss: 0.1796 Epoch 5/10 1/116 [..............................] - ETA: 0s - loss: 0.1397 42/116 [=========>....................] - ETA: 0s - loss: 0.1729 83/116 [====================>.........] - ETA: 0s - loss: 0.1838 116/116 [==============================] - 0s 1ms/step - loss: 0.1748 Epoch 6/10 1/116 [..............................] - ETA: 0s - loss: 0.4387 41/116 [=========>....................] - ETA: 0s - loss: 0.1677 81/116 [===================>..........] - ETA: 0s - loss: 0.1680 116/116 [==============================] - 0s 1ms/step - loss: 0.1695 Epoch 7/10 1/116 [..............................] - ETA: 0s - loss: 0.5847 41/116 [=========>....................] - ETA: 0s - loss: 0.1596 82/116 [====================>.........] - ETA: 0s - loss: 0.1650 116/116 [==============================] - 0s 1ms/step - loss: 0.1655 Epoch 8/10 1/116 [..............................] - ETA: 0s - loss: 0.3125 39/116 [=========>....................] - ETA: 0s - loss: 0.1409 72/116 [=================>............] - ETA: 0s - loss: 0.1620 104/116 [=========================>....] - ETA: 0s - loss: 0.1590 116/116 [==============================] - 0s 1ms/step - loss: 0.1594 Epoch 9/10 1/116 [..............................] - ETA: 0s - loss: 0.0585 40/116 [=========>....................] - ETA: 0s - loss: 0.1679 81/116 [===================>..........] - ETA: 0s - loss: 0.1526 116/116 [==============================] - 0s 1ms/step - loss: 0.1546 Epoch 10/10 1/116 [..............................] - ETA: 0s - loss: 0.1514 40/116 [=========>....................] - ETA: 0s - loss: 0.1544 81/116 [===================>..........] - ETA: 0s - loss: 0.1604 116/116 [==============================] - 0s 1ms/step - loss: 0.1512 -> test with GAN.predict GAN tn, fp: 267, 23 GAN fn, tp: 0, 7 GAN f1 score: 0.378 GAN cohens kappa score: 0.354 -> test with 'LR' LR tn, fp: 264, 26 LR fn, tp: 0, 7 LR f1 score: 0.350 LR cohens kappa score: 0.324 LR average precision score: 0.754 -> test with 'RF' RF tn, fp: 288, 2 RF fn, tp: 1, 6 RF f1 score: 0.800 RF cohens kappa score: 0.795 -> 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: 272, 18 KNN fn, tp: 0, 7 KNN f1 score: 0.438 KNN cohens kappa score: 0.416 ------ 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: 17s - loss: 0.2822 42/116 [=========>....................] - ETA: 0s - loss: 0.2313  83/116 [====================>.........] - ETA: 0s - loss: 0.2342 116/116 [==============================] - 0s 1ms/step - loss: 0.2258 Epoch 2/10 1/116 [..............................] - ETA: 0s - loss: 0.0878 42/116 [=========>....................] - ETA: 0s - loss: 0.2085 83/116 [====================>.........] - ETA: 0s - loss: 0.2124 116/116 [==============================] - 0s 1ms/step - loss: 0.2200 Epoch 3/10 1/116 [..............................] - ETA: 0s - loss: 0.2450 41/116 [=========>....................] - ETA: 0s - loss: 0.2163 80/116 [===================>..........] - ETA: 0s - loss: 0.2178 116/116 [==============================] - 0s 1ms/step - loss: 0.2169 Epoch 4/10 1/116 [..............................] - ETA: 0s - loss: 0.1901 41/116 [=========>....................] - ETA: 0s - loss: 0.2118 83/116 [====================>.........] - ETA: 0s - loss: 0.2169 116/116 [==============================] - 0s 1ms/step - loss: 0.2102 Epoch 5/10 1/116 [..............................] - ETA: 0s - loss: 0.2055 40/116 [=========>....................] - ETA: 0s - loss: 0.1968 81/116 [===================>..........] - ETA: 0s - loss: 0.2046 116/116 [==============================] - 0s 1ms/step - loss: 0.2074 Epoch 6/10 1/116 [..............................] - ETA: 0s - loss: 0.1206 43/116 [==========>...................] - ETA: 0s - loss: 0.2044 83/116 [====================>.........] - ETA: 0s - loss: 0.2107 116/116 [==============================] - 0s 1ms/step - loss: 0.2033 Epoch 7/10 1/116 [..............................] - ETA: 0s - loss: 0.1941 43/116 [==========>...................] - ETA: 0s - loss: 0.2134 84/116 [====================>.........] - ETA: 0s - loss: 0.1956 116/116 [==============================] - 0s 1ms/step - loss: 0.1994 Epoch 8/10 1/116 [..............................] - ETA: 0s - loss: 0.2524 38/116 [========>.....................] - ETA: 0s - loss: 0.2093 76/116 [==================>...........] - ETA: 0s - loss: 0.1931 110/116 [===========================>..] - ETA: 0s - loss: 0.1980 116/116 [==============================] - 0s 1ms/step - loss: 0.1980 Epoch 9/10 1/116 [..............................] - ETA: 0s - loss: 0.0505 40/116 [=========>....................] - ETA: 0s - loss: 0.1932 79/116 [===================>..........] - ETA: 0s - loss: 0.1991 116/116 [==============================] - 0s 1ms/step - loss: 0.1913 Epoch 10/10 1/116 [..............................] - ETA: 0s - loss: 0.2015 42/116 [=========>....................] - ETA: 0s - loss: 0.1893 83/116 [====================>.........] - ETA: 0s - loss: 0.1934 116/116 [==============================] - 0s 1ms/step - loss: 0.1897 -> test with GAN.predict GAN tn, fp: 269, 21 GAN fn, tp: 2, 5 GAN f1 score: 0.303 GAN cohens kappa score: 0.276 -> test with 'LR' LR tn, fp: 254, 36 LR fn, tp: 0, 7 LR f1 score: 0.280 LR cohens kappa score: 0.250 LR average precision score: 0.318 -> 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: 5, 2 GB f1 score: 0.400 GB cohens kappa score: 0.391 -> test with 'KNN' KNN tn, fp: 274, 16 KNN fn, tp: 1, 6 KNN f1 score: 0.414 KNN cohens kappa score: 0.392 ------ 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: 17s - loss: 0.2746 39/116 [=========>....................] - ETA: 0s - loss: 0.1733  77/116 [==================>...........] - ETA: 0s - loss: 0.1548 115/116 [============================>.] - ETA: 0s - loss: 0.1604 116/116 [==============================] - 0s 1ms/step - loss: 0.1613 Epoch 2/10 1/116 [..............................] - ETA: 0s - loss: 0.4020 39/116 [=========>....................] - ETA: 0s - loss: 0.1655 77/116 [==================>...........] - ETA: 0s - loss: 0.1596 115/116 [============================>.] - ETA: 0s - loss: 0.1573 116/116 [==============================] - 0s 1ms/step - loss: 0.1590 Epoch 3/10 1/116 [..............................] - ETA: 0s - loss: 0.1288 43/116 [==========>...................] - ETA: 0s - loss: 0.1458 85/116 [====================>.........] - ETA: 0s - loss: 0.1541 116/116 [==============================] - 0s 1ms/step - loss: 0.1548 Epoch 4/10 1/116 [..............................] - ETA: 0s - loss: 0.0328 37/116 [========>.....................] - ETA: 0s - loss: 0.1443 72/116 [=================>............] - ETA: 0s - loss: 0.1468 108/116 [==========================>...] - ETA: 0s - loss: 0.1515 116/116 [==============================] - 0s 1ms/step - loss: 0.1536 Epoch 5/10 1/116 [..............................] - ETA: 0s - loss: 0.2981 37/116 [========>.....................] - ETA: 0s - loss: 0.1344 72/116 [=================>............] - ETA: 0s - loss: 0.1520 108/116 [==========================>...] - ETA: 0s - loss: 0.1466 116/116 [==============================] - 0s 1ms/step - loss: 0.1518 Epoch 6/10 1/116 [..............................] - ETA: 0s - loss: 0.1074 33/116 [=======>......................] - ETA: 0s - loss: 0.1326 64/116 [===============>..............] - ETA: 0s - loss: 0.1270 100/116 [========================>.....] - ETA: 0s - loss: 0.1392 116/116 [==============================] - 0s 2ms/step - loss: 0.1481 Epoch 7/10 1/116 [..............................] - ETA: 0s - loss: 0.0791 35/116 [========>.....................] - ETA: 0s - loss: 0.1391 66/116 [================>.............] - ETA: 0s - loss: 0.1427 99/116 [========================>.....] - ETA: 0s - loss: 0.1469 116/116 [==============================] - 0s 2ms/step - loss: 0.1457 Epoch 8/10 1/116 [..............................] - ETA: 0s - loss: 0.0778 37/116 [========>.....................] - ETA: 0s - loss: 0.1322 73/116 [=================>............] - ETA: 0s - loss: 0.1440 115/116 [============================>.] - ETA: 0s - loss: 0.1471 116/116 [==============================] - 0s 1ms/step - loss: 0.1462 Epoch 9/10 1/116 [..............................] - ETA: 0s - loss: 0.0834 38/116 [========>.....................] - ETA: 0s - loss: 0.1181 76/116 [==================>...........] - ETA: 0s - loss: 0.1388 116/116 [==============================] - 0s 1ms/step - loss: 0.1411 Epoch 10/10 1/116 [..............................] - ETA: 0s - loss: 0.1192 39/116 [=========>....................] - ETA: 0s - loss: 0.1389 77/116 [==================>...........] - ETA: 0s - loss: 0.1440 116/116 [==============================] - ETA: 0s - loss: 0.1392 116/116 [==============================] - 0s 1ms/step - loss: 0.1392 -> test with GAN.predict GAN tn, fp: 273, 16 GAN fn, tp: 2, 5 GAN f1 score: 0.357 GAN cohens kappa score: 0.334 -> 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: 0.394 -> 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: 273, 16 KNN fn, tp: 2, 5 KNN f1 score: 0.357 KNN cohens kappa score: 0.334 ### Exercise is done. -----[ LR ]----- maximum: LR tn, fp: 275, 44 LR fn, tp: 3, 7 LR f1 score: 0.429 LR cohens kappa score: 0.408 LR average precision score: 0.799 average: LR tn, fp: 260.8, 29.0 LR fn, tp: 0.92, 6.08 LR f1 score: 0.295 LR cohens kappa score: 0.266 LR average precision score: 0.513 minimum: LR tn, fp: 245, 15 LR fn, tp: 0, 4 LR f1 score: 0.205 LR cohens kappa score: 0.173 LR average precision score: 0.218 -----[ RF ]----- maximum: RF tn, fp: 290, 4 RF fn, tp: 6, 6 RF f1 score: 0.800 RF cohens kappa score: 0.795 average: RF tn, fp: 288.76, 1.04 RF fn, tp: 4.24, 2.76 RF f1 score: 0.498 RF cohens kappa score: 0.490 minimum: RF tn, fp: 286, 0 RF fn, tp: 1, 1 RF f1 score: 0.250 RF cohens kappa score: 0.246 -----[ 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: 287.92, 1.88 GB fn, tp: 4.0, 3.0 GB f1 score: 0.490 GB cohens kappa score: 0.481 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: 279, 34 KNN fn, tp: 3, 7 KNN f1 score: 0.522 KNN cohens kappa score: 0.505 average: KNN tn, fp: 267.92, 21.88 KNN fn, tp: 1.32, 5.68 KNN f1 score: 0.337 KNN cohens kappa score: 0.311 minimum: KNN tn, fp: 256, 10 KNN fn, tp: 0, 4 KNN f1 score: 0.244 KNN cohens kappa score: 0.213 -----[ GAN ]----- maximum: GAN tn, fp: 280, 37 GAN fn, tp: 3, 7 GAN f1 score: 0.522 GAN cohens kappa score: 0.506 average: GAN tn, fp: 269.76, 20.04 GAN fn, tp: 1.28, 5.72 GAN f1 score: 0.366 GAN cohens kappa score: 0.342 minimum: GAN tn, fp: 253, 9 GAN fn, tp: 0, 4 GAN f1 score: 0.222 GAN cohens kappa score: 0.190