ConvGeNBenchmarkResults/data_result/ConvGeN-majority-5/folding_yeast6.log

///////////////////////////////////////////
// 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