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

///////////////////////////////////////////
// Running convGAN-proximary-5 on folding_yeast6
///////////////////////////////////////////

Load 'data_input/folding_yeast6'
from pickle file
Data loaded.
-> Shuffling data
### Start exercise for synthetic point generator

====== Step 1/5 =======
-> Shuffling data
-> Spliting data to slices

------ Step 1/5: Slice 1/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1131 synthetic samples
-> retrain GAN for predict
Epoch 1/10

  1/116 [..............................] - ETA: 17s - loss: 0.4931
 41/116 [=========>....................] - ETA: 0s - loss: 0.3604 
 81/116 [===================>..........] - ETA: 0s - loss: 0.3124
116/116 [==============================] - 0s 1ms/step - loss: 0.2884
Epoch 2/10

  1/116 [..............................] - ETA: 0s - loss: 0.3485
 42/116 [=========>....................] - ETA: 0s - loss: 0.2352
 81/116 [===================>..........] - ETA: 0s - loss: 0.2236
116/116 [==============================] - 0s 1ms/step - loss: 0.2290
Epoch 3/10

  1/116 [..............................] - ETA: 0s - loss: 0.1518
 39/116 [=========>....................] - ETA: 0s - loss: 0.1935
 74/116 [==================>...........] - ETA: 0s - loss: 0.2003
104/116 [=========================>....] - ETA: 0s - loss: 0.2110
116/116 [==============================] - 0s 1ms/step - loss: 0.2124
Epoch 4/10

  1/116 [..............................] - ETA: 0s - loss: 0.2238
 40/116 [=========>....................] - ETA: 0s - loss: 0.1764
 82/116 [====================>.........] - ETA: 0s - loss: 0.1970
116/116 [==============================] - 0s 1ms/step - loss: 0.2043
Epoch 5/10

  1/116 [..............................] - ETA: 0s - loss: 0.1379
 42/116 [=========>....................] - ETA: 0s - loss: 0.2055
 83/116 [====================>.........] - ETA: 0s - loss: 0.1929
116/116 [==============================] - 0s 1ms/step - loss: 0.1982
Epoch 6/10

  1/116 [..............................] - ETA: 0s - loss: 0.2845
 43/116 [==========>...................] - ETA: 0s - loss: 0.1932
 84/116 [====================>.........] - ETA: 0s - loss: 0.1879
116/116 [==============================] - 0s 1ms/step - loss: 0.1948
Epoch 7/10

  1/116 [..............................] - ETA: 0s - loss: 0.1497
 41/116 [=========>....................] - ETA: 0s - loss: 0.1760
 83/116 [====================>.........] - ETA: 0s - loss: 0.1932
116/116 [==============================] - 0s 1ms/step - loss: 0.1904
Epoch 8/10

  1/116 [..............................] - ETA: 0s - loss: 0.0777
 38/116 [========>.....................] - ETA: 0s - loss: 0.1707
 77/116 [==================>...........] - ETA: 0s - loss: 0.1766
116/116 [==============================] - 0s 1ms/step - loss: 0.1836
Epoch 9/10

  1/116 [..............................] - ETA: 0s - loss: 0.1008
 41/116 [=========>....................] - ETA: 0s - loss: 0.1854
 81/116 [===================>..........] - ETA: 0s - loss: 0.1826
116/116 [==============================] - 0s 1ms/step - loss: 0.1794
Epoch 10/10

  1/116 [..............................] - ETA: 0s - loss: 0.2045
 41/116 [=========>....................] - ETA: 0s - loss: 0.1573
 80/116 [===================>..........] - ETA: 0s - loss: 0.1751
116/116 [==============================] - 0s 1ms/step - loss: 0.1757
-> test with GAN.predict
GAN tn, fp: 272, 18
GAN fn, tp: 1, 6
GAN f1 score: 0.387
GAN cohens kappa score: 0.364

-> test with 'LR'
LR tn, fp: 262, 28
LR fn, tp: 0, 7
LR f1 score: 0.333
LR cohens kappa score: 0.306
LR average precision score: 0.684

-> test with 'RF'
RF tn, fp: 288, 2
RF fn, tp: 4, 3
RF f1 score: 0.500
RF cohens kappa score: 0.490

-> test with 'GB'
GB tn, fp: 287, 3
GB fn, tp: 4, 3
GB f1 score: 0.462
GB cohens kappa score: 0.450

-> test with 'KNN'
KNN tn, fp: 267, 23
KNN fn, tp: 1, 6
KNN f1 score: 0.333
KNN cohens kappa score: 0.307


------ Step 1/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1131 synthetic samples
-> retrain GAN for predict
Epoch 1/10

  1/116 [..............................] - ETA: 17s - loss: 0.4839
 41/116 [=========>....................] - ETA: 0s - loss: 0.3292 
 83/116 [====================>.........] - ETA: 0s - loss: 0.2970
116/116 [==============================] - 0s 1ms/step - loss: 0.2794
Epoch 2/10

  1/116 [..............................] - ETA: 0s - loss: 0.2256
 43/116 [==========>...................] - ETA: 0s - loss: 0.2304
 85/116 [====================>.........] - ETA: 0s - loss: 0.2312
116/116 [==============================] - 0s 1ms/step - loss: 0.2285
Epoch 3/10

  1/116 [..............................] - ETA: 0s - loss: 0.1380
 43/116 [==========>...................] - ETA: 0s - loss: 0.1986
 85/116 [====================>.........] - ETA: 0s - loss: 0.2139
116/116 [==============================] - 0s 1ms/step - loss: 0.2152
Epoch 4/10

  1/116 [..............................] - ETA: 0s - loss: 0.2217
 44/116 [==========>...................] - ETA: 0s - loss: 0.2120
 85/116 [====================>.........] - ETA: 0s - loss: 0.2114
116/116 [==============================] - 0s 1ms/step - loss: 0.2062
Epoch 5/10

  1/116 [..............................] - ETA: 0s - loss: 0.1556
 42/116 [=========>....................] - ETA: 0s - loss: 0.1676
 84/116 [====================>.........] - ETA: 0s - loss: 0.1927
116/116 [==============================] - 0s 1ms/step - loss: 0.1982
Epoch 6/10

  1/116 [..............................] - ETA: 0s - loss: 0.2457
 43/116 [==========>...................] - ETA: 0s - loss: 0.1706
 85/116 [====================>.........] - ETA: 0s - loss: 0.1841
116/116 [==============================] - 0s 1ms/step - loss: 0.1928
Epoch 7/10

  1/116 [..............................] - ETA: 0s - loss: 0.1352
 43/116 [==========>...................] - ETA: 0s - loss: 0.1600
 82/116 [====================>.........] - ETA: 0s - loss: 0.1843
116/116 [==============================] - 0s 1ms/step - loss: 0.1885
Epoch 8/10

  1/116 [..............................] - ETA: 0s - loss: 0.1100
 43/116 [==========>...................] - ETA: 0s - loss: 0.1878
 84/116 [====================>.........] - ETA: 0s - loss: 0.1818
116/116 [==============================] - 0s 1ms/step - loss: 0.1801
Epoch 9/10

  1/116 [..............................] - ETA: 0s - loss: 0.1294
 41/116 [=========>....................] - ETA: 0s - loss: 0.1664
 81/116 [===================>..........] - ETA: 0s - loss: 0.1826
116/116 [==============================] - 0s 1ms/step - loss: 0.1767
Epoch 10/10

  1/116 [..............................] - ETA: 0s - loss: 0.0551
 42/116 [=========>....................] - ETA: 0s - loss: 0.1691
 83/116 [====================>.........] - ETA: 0s - loss: 0.1743
116/116 [==============================] - 0s 1ms/step - loss: 0.1707
-> test with GAN.predict
GAN tn, fp: 261, 29
GAN fn, tp: 1, 6
GAN f1 score: 0.286
GAN cohens kappa score: 0.257

-> test with 'LR'
LR tn, fp: 256, 34
LR fn, tp: 2, 5
LR f1 score: 0.217
LR cohens kappa score: 0.185
LR average precision score: 0.427

-> test with 'RF'
RF tn, fp: 287, 3
RF fn, tp: 4, 3
RF f1 score: 0.462
RF cohens kappa score: 0.450

-> test with 'GB'
GB tn, fp: 286, 4
GB fn, tp: 3, 4
GB f1 score: 0.533
GB cohens kappa score: 0.521

-> test with 'KNN'
KNN tn, fp: 263, 27
KNN fn, tp: 2, 5
KNN f1 score: 0.256
KNN cohens kappa score: 0.226


------ Step 1/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1131 synthetic samples
-> retrain GAN for predict
Epoch 1/10

  1/116 [..............................] - ETA: 18s - loss: 0.2562
 43/116 [==========>...................] - ETA: 0s - loss: 0.3046 
 85/116 [====================>.........] - ETA: 0s - loss: 0.2806
116/116 [==============================] - 0s 1ms/step - loss: 0.2634
Epoch 2/10

  1/116 [..............................] - ETA: 0s - loss: 0.1331
 44/116 [==========>...................] - ETA: 0s - loss: 0.2020
 86/116 [=====================>........] - ETA: 0s - loss: 0.2060
116/116 [==============================] - 0s 1ms/step - loss: 0.2117
Epoch 3/10

  1/116 [..............................] - ETA: 0s - loss: 0.1020
 35/116 [========>.....................] - ETA: 0s - loss: 0.2115
 69/116 [================>.............] - ETA: 0s - loss: 0.2024
111/116 [===========================>..] - ETA: 0s - loss: 0.1979
116/116 [==============================] - 0s 1ms/step - loss: 0.1963
Epoch 4/10

  1/116 [..............................] - ETA: 0s - loss: 0.1439
 41/116 [=========>....................] - ETA: 0s - loss: 0.2142
 79/116 [===================>..........] - ETA: 0s - loss: 0.1930
116/116 [==============================] - 0s 1ms/step - loss: 0.1844
Epoch 5/10

  1/116 [..............................] - ETA: 0s - loss: 0.1417
 40/116 [=========>....................] - ETA: 0s - loss: 0.1835
 82/116 [====================>.........] - ETA: 0s - loss: 0.1760
116/116 [==============================] - 0s 1ms/step - loss: 0.1786
Epoch 6/10

  1/116 [..............................] - ETA: 0s - loss: 0.2404
 43/116 [==========>...................] - ETA: 0s - loss: 0.1779
 84/116 [====================>.........] - ETA: 0s - loss: 0.1686
116/116 [==============================] - 0s 1ms/step - loss: 0.1752
Epoch 7/10

  1/116 [..............................] - ETA: 0s - loss: 0.1578
 41/116 [=========>....................] - ETA: 0s - loss: 0.1788
 81/116 [===================>..........] - ETA: 0s - loss: 0.1769
116/116 [==============================] - 0s 1ms/step - loss: 0.1708
Epoch 8/10

  1/116 [..............................] - ETA: 0s - loss: 0.3369
 43/116 [==========>...................] - ETA: 0s - loss: 0.1750
 85/116 [====================>.........] - ETA: 0s - loss: 0.1697
116/116 [==============================] - 0s 1ms/step - loss: 0.1648
Epoch 9/10

  1/116 [..............................] - ETA: 0s - loss: 0.0738
 42/116 [=========>....................] - ETA: 0s - loss: 0.1565
 84/116 [====================>.........] - ETA: 0s - loss: 0.1549
116/116 [==============================] - 0s 1ms/step - loss: 0.1608
Epoch 10/10

  1/116 [..............................] - ETA: 0s - loss: 0.1021
 43/116 [==========>...................] - ETA: 0s - loss: 0.1657
 84/116 [====================>.........] - ETA: 0s - loss: 0.1603
116/116 [==============================] - 0s 1ms/step - loss: 0.1567
-> test with GAN.predict
GAN tn, fp: 270, 20
GAN fn, tp: 1, 6
GAN f1 score: 0.364
GAN cohens kappa score: 0.339

-> test with 'LR'
LR tn, fp: 252, 38
LR fn, tp: 1, 6
LR f1 score: 0.235
LR cohens kappa score: 0.203
LR average precision score: 0.229

-> test with 'RF'
RF tn, fp: 290, 0
RF fn, tp: 3, 4
RF f1 score: 0.727
RF cohens kappa score: 0.723

-> test with 'GB'
GB tn, fp: 290, 0
GB fn, tp: 5, 2
GB f1 score: 0.444
GB cohens kappa score: 0.439

-> test with 'KNN'
KNN tn, fp: 267, 23
KNN fn, tp: 1, 6
KNN f1 score: 0.333
KNN cohens kappa score: 0.307


------ Step 1/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1131 synthetic samples
-> retrain GAN for predict
Epoch 1/10

  1/116 [..............................] - ETA: 18s - loss: 0.3467
 41/116 [=========>....................] - ETA: 0s - loss: 0.3164 
 83/116 [====================>.........] - ETA: 0s - loss: 0.2711
116/116 [==============================] - 0s 1ms/step - loss: 0.2526
Epoch 2/10

  1/116 [..............................] - ETA: 0s - loss: 0.1345
 42/116 [=========>....................] - ETA: 0s - loss: 0.1990
 83/116 [====================>.........] - ETA: 0s - loss: 0.1980
116/116 [==============================] - 0s 1ms/step - loss: 0.1905
Epoch 3/10

  1/116 [..............................] - ETA: 0s - loss: 0.1291
 43/116 [==========>...................] - ETA: 0s - loss: 0.1638
 86/116 [=====================>........] - ETA: 0s - loss: 0.1762
116/116 [==============================] - 0s 1ms/step - loss: 0.1732
Epoch 4/10

  1/116 [..............................] - ETA: 0s - loss: 0.2314
 44/116 [==========>...................] - ETA: 0s - loss: 0.1752
 86/116 [=====================>........] - ETA: 0s - loss: 0.1641
116/116 [==============================] - 0s 1ms/step - loss: 0.1649
Epoch 5/10

  1/116 [..............................] - ETA: 0s - loss: 0.1365
 43/116 [==========>...................] - ETA: 0s - loss: 0.1670
 83/116 [====================>.........] - ETA: 0s - loss: 0.1623
116/116 [==============================] - 0s 1ms/step - loss: 0.1603
Epoch 6/10

  1/116 [..............................] - ETA: 0s - loss: 0.2004
 38/116 [========>.....................] - ETA: 0s - loss: 0.1608
 73/116 [=================>............] - ETA: 0s - loss: 0.1621
109/116 [===========================>..] - ETA: 0s - loss: 0.1538
116/116 [==============================] - 0s 1ms/step - loss: 0.1572
Epoch 7/10

  1/116 [..............................] - ETA: 0s - loss: 0.0592
 43/116 [==========>...................] - ETA: 0s - loss: 0.1541
 86/116 [=====================>........] - ETA: 0s - loss: 0.1516
116/116 [==============================] - 0s 1ms/step - loss: 0.1550
Epoch 8/10

  1/116 [..............................] - ETA: 0s - loss: 0.0915
 39/116 [=========>....................] - ETA: 0s - loss: 0.1744
 79/116 [===================>..........] - ETA: 0s - loss: 0.1457
116/116 [==============================] - 0s 1ms/step - loss: 0.1543
Epoch 9/10

  1/116 [..............................] - ETA: 0s - loss: 0.1507
 43/116 [==========>...................] - ETA: 0s - loss: 0.1809
 84/116 [====================>.........] - ETA: 0s - loss: 0.1472
116/116 [==============================] - 0s 1ms/step - loss: 0.1502
Epoch 10/10

  1/116 [..............................] - ETA: 0s - loss: 0.1411
 42/116 [=========>....................] - ETA: 0s - loss: 0.1618
 84/116 [====================>.........] - ETA: 0s - loss: 0.1586
116/116 [==============================] - 0s 1ms/step - loss: 0.1503
-> test with GAN.predict
GAN tn, fp: 270, 20
GAN fn, tp: 2, 5
GAN f1 score: 0.312
GAN cohens kappa score: 0.286

-> test with 'LR'
LR tn, fp: 270, 20
LR fn, tp: 1, 6
LR f1 score: 0.364
LR cohens kappa score: 0.339
LR average precision score: 0.588

-> test with 'RF'
RF tn, fp: 288, 2
RF fn, tp: 4, 3
RF f1 score: 0.500
RF cohens kappa score: 0.490

-> test with 'GB'
GB tn, fp: 288, 2
GB fn, tp: 5, 2
GB f1 score: 0.364
GB cohens kappa score: 0.353

-> test with 'KNN'
KNN tn, fp: 273, 17
KNN fn, tp: 1, 6
KNN f1 score: 0.400
KNN cohens kappa score: 0.378


------ Step 1/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1132 synthetic samples
-> retrain GAN for predict
Epoch 1/10

  1/116 [..............................] - ETA: 15s - loss: 0.3044
 37/116 [========>.....................] - ETA: 0s - loss: 0.3987 
 75/116 [==================>...........] - ETA: 0s - loss: 0.3815
107/116 [==========================>...] - ETA: 0s - loss: 0.3629
116/116 [==============================] - 0s 1ms/step - loss: 0.3569
Epoch 2/10

  1/116 [..............................] - ETA: 0s - loss: 0.2946
 32/116 [=======>......................] - ETA: 0s - loss: 0.2690
 69/116 [================>.............] - ETA: 0s - loss: 0.2624
109/116 [===========================>..] - ETA: 0s - loss: 0.2481
116/116 [==============================] - 0s 1ms/step - loss: 0.2502
Epoch 3/10

  1/116 [..............................] - ETA: 0s - loss: 0.1354
 42/116 [=========>....................] - ETA: 0s - loss: 0.2215
 83/116 [====================>.........] - ETA: 0s - loss: 0.2115
116/116 [==============================] - 0s 1ms/step - loss: 0.2169
Epoch 4/10

  1/116 [..............................] - ETA: 0s - loss: 0.0770
 44/116 [==========>...................] - ETA: 0s - loss: 0.1986
 83/116 [====================>.........] - ETA: 0s - loss: 0.2036
116/116 [==============================] - 0s 1ms/step - loss: 0.2030
Epoch 5/10

  1/116 [..............................] - ETA: 0s - loss: 0.1661
 44/116 [==========>...................] - ETA: 0s - loss: 0.2080
 82/116 [====================>.........] - ETA: 0s - loss: 0.2004
116/116 [==============================] - 0s 1ms/step - loss: 0.1957
Epoch 6/10

  1/116 [..............................] - ETA: 0s - loss: 0.2345
 43/116 [==========>...................] - ETA: 0s - loss: 0.2077
 88/116 [=====================>........] - ETA: 0s - loss: 0.1979
116/116 [==============================] - 0s 1ms/step - loss: 0.1907
Epoch 7/10

  1/116 [..............................] - ETA: 0s - loss: 0.1079
 41/116 [=========>....................] - ETA: 0s - loss: 0.1816
 82/116 [====================>.........] - ETA: 0s - loss: 0.1837
116/116 [==============================] - 0s 1ms/step - loss: 0.1865
Epoch 8/10

  1/116 [..............................] - ETA: 0s - loss: 0.0955
 43/116 [==========>...................] - ETA: 0s - loss: 0.1564
 86/116 [=====================>........] - ETA: 0s - loss: 0.1769
116/116 [==============================] - 0s 1ms/step - loss: 0.1823
Epoch 9/10

  1/116 [..............................] - ETA: 0s - loss: 0.0928
 46/116 [==========>...................] - ETA: 0s - loss: 0.1491
 89/116 [======================>.......] - ETA: 0s - loss: 0.1716
116/116 [==============================] - 0s 1ms/step - loss: 0.1776
Epoch 10/10

  1/116 [..............................] - ETA: 0s - loss: 0.2331
 45/116 [==========>...................] - ETA: 0s - loss: 0.1931
 87/116 [=====================>........] - ETA: 0s - loss: 0.1880
116/116 [==============================] - 0s 1ms/step - loss: 0.1754
-> test with GAN.predict
GAN tn, fp: 260, 29
GAN fn, tp: 1, 6
GAN f1 score: 0.286
GAN cohens kappa score: 0.256

-> test with 'LR'
LR tn, fp: 244, 45
LR fn, tp: 0, 7
LR f1 score: 0.237
LR cohens kappa score: 0.204
LR average precision score: 0.625

-> test with 'RF'
RF tn, fp: 289, 0
RF fn, tp: 4, 3
RF f1 score: 0.600
RF cohens kappa score: 0.594

-> test with 'GB'
GB tn, fp: 288, 1
GB fn, tp: 3, 4
GB f1 score: 0.667
GB cohens kappa score: 0.660

-> test with 'KNN'
KNN tn, fp: 262, 27
KNN fn, tp: 1, 6
KNN f1 score: 0.300
KNN cohens kappa score: 0.272


====== Step 2/5 =======
-> Shuffling data
-> Spliting data to slices

------ Step 2/5: Slice 1/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1131 synthetic samples
-> retrain GAN for predict
Epoch 1/10

  1/116 [..............................] - ETA: 19s - loss: 0.5174
 43/116 [==========>...................] - ETA: 0s - loss: 0.3912 
 84/116 [====================>.........] - ETA: 0s - loss: 0.3534
116/116 [==============================] - 0s 1ms/step - loss: 0.3276
Epoch 2/10

  1/116 [..............................] - ETA: 0s - loss: 0.1576
 43/116 [==========>...................] - ETA: 0s - loss: 0.2441
 84/116 [====================>.........] - ETA: 0s - loss: 0.2363
116/116 [==============================] - 0s 1ms/step - loss: 0.2305
Epoch 3/10

  1/116 [..............................] - ETA: 0s - loss: 0.1740
 43/116 [==========>...................] - ETA: 0s - loss: 0.1993
 83/116 [====================>.........] - ETA: 0s - loss: 0.1966
116/116 [==============================] - 0s 1ms/step - loss: 0.2043
Epoch 4/10

  1/116 [..............................] - ETA: 0s - loss: 0.1628
 40/116 [=========>....................] - ETA: 0s - loss: 0.1945
 81/116 [===================>..........] - ETA: 0s - loss: 0.1982
116/116 [==============================] - 0s 1ms/step - loss: 0.1926
Epoch 5/10

  1/116 [..............................] - ETA: 0s - loss: 0.0611
 43/116 [==========>...................] - ETA: 0s - loss: 0.1870
 83/116 [====================>.........] - ETA: 0s - loss: 0.1877
116/116 [==============================] - 0s 1ms/step - loss: 0.1836
Epoch 6/10

  1/116 [..............................] - ETA: 0s - loss: 0.0379
 43/116 [==========>...................] - ETA: 0s - loss: 0.1578
 84/116 [====================>.........] - ETA: 0s - loss: 0.1679
116/116 [==============================] - 0s 1ms/step - loss: 0.1771
Epoch 7/10

  1/116 [..............................] - ETA: 0s - loss: 0.1941
 43/116 [==========>...................] - ETA: 0s - loss: 0.1765
 83/116 [====================>.........] - ETA: 0s - loss: 0.1632
116/116 [==============================] - 0s 1ms/step - loss: 0.1726
Epoch 8/10

  1/116 [..............................] - ETA: 0s - loss: 0.2146
 41/116 [=========>....................] - ETA: 0s - loss: 0.1749
 81/116 [===================>..........] - ETA: 0s - loss: 0.1740
116/116 [==============================] - 0s 1ms/step - loss: 0.1682
Epoch 9/10

  1/116 [..............................] - ETA: 0s - loss: 0.0888
 42/116 [=========>....................] - ETA: 0s - loss: 0.1664
 83/116 [====================>.........] - ETA: 0s - loss: 0.1743
116/116 [==============================] - 0s 1ms/step - loss: 0.1601
Epoch 10/10

  1/116 [..............................] - ETA: 0s - loss: 0.0483
 42/116 [=========>....................] - ETA: 0s - loss: 0.1625
 84/116 [====================>.........] - ETA: 0s - loss: 0.1600
116/116 [==============================] - 0s 1ms/step - loss: 0.1580
-> test with GAN.predict
GAN tn, fp: 277, 13
GAN fn, tp: 1, 6
GAN f1 score: 0.462
GAN cohens kappa score: 0.442

-> test with 'LR'
LR tn, fp: 261, 29
LR fn, tp: 1, 6
LR f1 score: 0.286
LR cohens kappa score: 0.257
LR average precision score: 0.673

-> test with 'RF'
RF tn, fp: 288, 2
RF fn, tp: 3, 4
RF f1 score: 0.615
RF cohens kappa score: 0.607

-> test with 'GB'
GB tn, fp: 286, 4
GB fn, tp: 3, 4
GB f1 score: 0.533
GB cohens kappa score: 0.521

-> test with 'KNN'
KNN tn, fp: 269, 21
KNN fn, tp: 1, 6
KNN f1 score: 0.353
KNN cohens kappa score: 0.328


------ Step 2/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1131 synthetic samples
-> retrain GAN for predict
Epoch 1/10

  1/116 [..............................] - ETA: 17s - loss: 0.4289
 44/116 [==========>...................] - ETA: 0s - loss: 0.3494 
 81/116 [===================>..........] - ETA: 0s - loss: 0.3304
116/116 [==============================] - 0s 1ms/step - loss: 0.3216
Epoch 2/10

  1/116 [..............................] - ETA: 0s - loss: 0.2145
 43/116 [==========>...................] - ETA: 0s - loss: 0.2477
 84/116 [====================>.........] - ETA: 0s - loss: 0.2559
116/116 [==============================] - 0s 1ms/step - loss: 0.2559
Epoch 3/10

  1/116 [..............................] - ETA: 0s - loss: 0.3809
 43/116 [==========>...................] - ETA: 0s - loss: 0.2508
 84/116 [====================>.........] - ETA: 0s - loss: 0.2363
116/116 [==============================] - 0s 1ms/step - loss: 0.2338
Epoch 4/10

  1/116 [..............................] - ETA: 0s - loss: 0.2007
 42/116 [=========>....................] - ETA: 0s - loss: 0.2366
 83/116 [====================>.........] - ETA: 0s - loss: 0.2281
116/116 [==============================] - 0s 1ms/step - loss: 0.2188
Epoch 5/10

  1/116 [..............................] - ETA: 0s - loss: 0.2250
 43/116 [==========>...................] - ETA: 0s - loss: 0.2125
 85/116 [====================>.........] - ETA: 0s - loss: 0.2125
116/116 [==============================] - 0s 1ms/step - loss: 0.2086
Epoch 6/10

  1/116 [..............................] - ETA: 0s - loss: 0.1141
 41/116 [=========>....................] - ETA: 0s - loss: 0.1921
 82/116 [====================>.........] - ETA: 0s - loss: 0.2012
116/116 [==============================] - 0s 1ms/step - loss: 0.2033
Epoch 7/10

  1/116 [..............................] - ETA: 0s - loss: 0.2975
 43/116 [==========>...................] - ETA: 0s - loss: 0.1980
 85/116 [====================>.........] - ETA: 0s - loss: 0.1931
116/116 [==============================] - 0s 1ms/step - loss: 0.1999
Epoch 8/10

  1/116 [..............................] - ETA: 0s - loss: 0.3985
 40/116 [=========>....................] - ETA: 0s - loss: 0.1888
 75/116 [==================>...........] - ETA: 0s - loss: 0.1873
109/116 [===========================>..] - ETA: 0s - loss: 0.1938
116/116 [==============================] - 0s 1ms/step - loss: 0.1960
Epoch 9/10

  1/116 [..............................] - ETA: 0s - loss: 0.1619
 42/116 [=========>....................] - ETA: 0s - loss: 0.1836
 83/116 [====================>.........] - ETA: 0s - loss: 0.1830
116/116 [==============================] - 0s 1ms/step - loss: 0.1870
Epoch 10/10

  1/116 [..............................] - ETA: 0s - loss: 0.0898
 42/116 [=========>....................] - ETA: 0s - loss: 0.1819
 80/116 [===================>..........] - ETA: 0s - loss: 0.1719
114/116 [============================>.] - ETA: 0s - loss: 0.1812
116/116 [==============================] - 0s 1ms/step - loss: 0.1835
-> test with GAN.predict
GAN tn, fp: 263, 27
GAN fn, tp: 0, 7
GAN f1 score: 0.341
GAN cohens kappa score: 0.315

-> test with 'LR'
LR tn, fp: 251, 39
LR fn, tp: 0, 7
LR f1 score: 0.264
LR cohens kappa score: 0.233
LR average precision score: 0.229

-> test with 'RF'
RF tn, fp: 289, 1
RF fn, tp: 5, 2
RF f1 score: 0.400
RF cohens kappa score: 0.391

-> test with 'GB'
GB tn, fp: 288, 2
GB fn, tp: 4, 3
GB f1 score: 0.500
GB cohens kappa score: 0.490

-> test with 'KNN'
KNN tn, fp: 264, 26
KNN fn, tp: 0, 7
KNN f1 score: 0.350
KNN cohens kappa score: 0.324


------ Step 2/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1131 synthetic samples
-> retrain GAN for predict
Epoch 1/10

  1/116 [..............................] - ETA: 18s - loss: 0.4258
 36/116 [========>.....................] - ETA: 0s - loss: 0.3212 
 72/116 [=================>............] - ETA: 0s - loss: 0.2899
110/116 [===========================>..] - ETA: 0s - loss: 0.2672
116/116 [==============================] - 0s 1ms/step - loss: 0.2649
Epoch 2/10

  1/116 [..............................] - ETA: 0s - loss: 0.3471
 35/116 [========>.....................] - ETA: 0s - loss: 0.1975
 73/116 [=================>............] - ETA: 0s - loss: 0.1975
112/116 [===========================>..] - ETA: 0s - loss: 0.1960
116/116 [==============================] - 0s 1ms/step - loss: 0.1936
Epoch 3/10

  1/116 [..............................] - ETA: 0s - loss: 0.1180
 40/116 [=========>....................] - ETA: 0s - loss: 0.1755
 76/116 [==================>...........] - ETA: 0s - loss: 0.1712
113/116 [============================>.] - ETA: 0s - loss: 0.1691
116/116 [==============================] - 0s 1ms/step - loss: 0.1700
Epoch 4/10

  1/116 [..............................] - ETA: 0s - loss: 0.0547
 34/116 [=======>......................] - ETA: 0s - loss: 0.1644
 70/116 [=================>............] - ETA: 0s - loss: 0.1487
106/116 [==========================>...] - ETA: 0s - loss: 0.1523
116/116 [==============================] - 0s 1ms/step - loss: 0.1573
Epoch 5/10

  1/116 [..............................] - ETA: 0s - loss: 0.1459
 42/116 [=========>....................] - ETA: 0s - loss: 0.1623
 82/116 [====================>.........] - ETA: 0s - loss: 0.1538
116/116 [==============================] - 0s 1ms/step - loss: 0.1516
Epoch 6/10

  1/116 [..............................] - ETA: 0s - loss: 0.0911
 32/116 [=======>......................] - ETA: 0s - loss: 0.1568
 68/116 [================>.............] - ETA: 0s - loss: 0.1523
101/116 [=========================>....] - ETA: 0s - loss: 0.1456
116/116 [==============================] - 0s 2ms/step - loss: 0.1432
Epoch 7/10

  1/116 [..............................] - ETA: 0s - loss: 0.0718
 38/116 [========>.....................] - ETA: 0s - loss: 0.1556
 72/116 [=================>............] - ETA: 0s - loss: 0.1453
108/116 [==========================>...] - ETA: 0s - loss: 0.1409
116/116 [==============================] - 0s 1ms/step - loss: 0.1403
Epoch 8/10

  1/116 [..............................] - ETA: 0s - loss: 0.0489
 37/116 [========>.....................] - ETA: 0s - loss: 0.1245
 71/116 [=================>............] - ETA: 0s - loss: 0.1319
108/116 [==========================>...] - ETA: 0s - loss: 0.1338
116/116 [==============================] - 0s 1ms/step - loss: 0.1366
Epoch 9/10

  1/116 [..............................] - ETA: 0s - loss: 0.0365
 36/116 [========>.....................] - ETA: 0s - loss: 0.1381
 67/116 [================>.............] - ETA: 0s - loss: 0.1368
100/116 [========================>.....] - ETA: 0s - loss: 0.1313
116/116 [==============================] - 0s 2ms/step - loss: 0.1325
Epoch 10/10

  1/116 [..............................] - ETA: 0s - loss: 0.0745
 38/116 [========>.....................] - ETA: 0s - loss: 0.1685
 73/116 [=================>............] - ETA: 0s - loss: 0.1436
111/116 [===========================>..] - ETA: 0s - loss: 0.1287
116/116 [==============================] - 0s 1ms/step - loss: 0.1309
-> test with GAN.predict
GAN tn, fp: 272, 18
GAN fn, tp: 2, 5
GAN f1 score: 0.333
GAN cohens kappa score: 0.308

-> test with 'LR'
LR tn, fp: 262, 28
LR fn, tp: 1, 6
LR f1 score: 0.293
LR cohens kappa score: 0.264
LR average precision score: 0.420

-> test with 'RF'
RF tn, fp: 289, 1
RF fn, tp: 5, 2
RF f1 score: 0.400
RF cohens kappa score: 0.391

-> test with 'GB'
GB tn, fp: 288, 2
GB fn, tp: 4, 3
GB f1 score: 0.500
GB cohens kappa score: 0.490

-> test with 'KNN'
KNN tn, fp: 271, 19
KNN fn, tp: 2, 5
KNN f1 score: 0.323
KNN cohens kappa score: 0.297


------ Step 2/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1131 synthetic samples
-> retrain GAN for predict
Epoch 1/10

  1/116 [..............................] - ETA: 20s - loss: 0.3021
 42/116 [=========>....................] - ETA: 0s - loss: 0.2460 
 83/116 [====================>.........] - ETA: 0s - loss: 0.2300
116/116 [==============================] - 0s 1ms/step - loss: 0.2198
Epoch 2/10

  1/116 [..............................] - ETA: 0s - loss: 0.1576
 41/116 [=========>....................] - ETA: 0s - loss: 0.1599
 80/116 [===================>..........] - ETA: 0s - loss: 0.1717
116/116 [==============================] - 0s 1ms/step - loss: 0.1779
Epoch 3/10

  1/116 [..............................] - ETA: 0s - loss: 0.2547
 35/116 [========>.....................] - ETA: 0s - loss: 0.1852
 70/116 [=================>............] - ETA: 0s - loss: 0.1767
105/116 [==========================>...] - ETA: 0s - loss: 0.1645
116/116 [==============================] - 0s 1ms/step - loss: 0.1637
Epoch 4/10

  1/116 [..............................] - ETA: 0s - loss: 0.1413
 42/116 [=========>....................] - ETA: 0s - loss: 0.1790
 81/116 [===================>..........] - ETA: 0s - loss: 0.1639
116/116 [==============================] - 0s 1ms/step - loss: 0.1566
Epoch 5/10

  1/116 [..............................] - ETA: 0s - loss: 0.0388
 42/116 [=========>....................] - ETA: 0s - loss: 0.1369
 84/116 [====================>.........] - ETA: 0s - loss: 0.1502
116/116 [==============================] - 0s 1ms/step - loss: 0.1523
Epoch 6/10

  1/116 [..............................] - ETA: 0s - loss: 0.1055
 40/116 [=========>....................] - ETA: 0s - loss: 0.1593
 81/116 [===================>..........] - ETA: 0s - loss: 0.1515
116/116 [==============================] - 0s 1ms/step - loss: 0.1487
Epoch 7/10

  1/116 [..............................] - ETA: 0s - loss: 0.0492
 42/116 [=========>....................] - ETA: 0s - loss: 0.1538
 84/116 [====================>.........] - ETA: 0s - loss: 0.1474
116/116 [==============================] - 0s 1ms/step - loss: 0.1449
Epoch 8/10

  1/116 [..............................] - ETA: 0s - loss: 0.0883
 42/116 [=========>....................] - ETA: 0s - loss: 0.1641
 84/116 [====================>.........] - ETA: 0s - loss: 0.1498
116/116 [==============================] - 0s 1ms/step - loss: 0.1444
Epoch 9/10

  1/116 [..............................] - ETA: 0s - loss: 0.2343
 42/116 [=========>....................] - ETA: 0s - loss: 0.1347
 83/116 [====================>.........] - ETA: 0s - loss: 0.1456
116/116 [==============================] - 0s 1ms/step - loss: 0.1389
Epoch 10/10

  1/116 [..............................] - ETA: 0s - loss: 0.2105
 42/116 [=========>....................] - ETA: 0s - loss: 0.1576
 82/116 [====================>.........] - ETA: 0s - loss: 0.1441
116/116 [==============================] - 0s 1ms/step - loss: 0.1378
-> test with GAN.predict
GAN tn, fp: 268, 22
GAN fn, tp: 2, 5
GAN f1 score: 0.294
GAN cohens kappa score: 0.267

-> test with 'LR'
LR tn, fp: 262, 28
LR fn, tp: 2, 5
LR f1 score: 0.250
LR cohens kappa score: 0.220
LR average precision score: 0.558

-> test with 'RF'
RF tn, fp: 289, 1
RF fn, tp: 5, 2
RF f1 score: 0.400
RF cohens kappa score: 0.391

-> test with 'GB'
GB tn, fp: 286, 4
GB fn, tp: 5, 2
GB f1 score: 0.308
GB cohens kappa score: 0.292

-> test with 'KNN'
KNN tn, fp: 268, 22
KNN fn, tp: 2, 5
KNN f1 score: 0.294
KNN cohens kappa score: 0.267


------ Step 2/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1132 synthetic samples
-> retrain GAN for predict
Epoch 1/10

  1/116 [..............................] - ETA: 15s - loss: 0.4644
 45/116 [==========>...................] - ETA: 0s - loss: 0.3142 
 90/116 [======================>.......] - ETA: 0s - loss: 0.2834
116/116 [==============================] - 0s 1ms/step - loss: 0.2719
Epoch 2/10

  1/116 [..............................] - ETA: 0s - loss: 0.0738
 46/116 [==========>...................] - ETA: 0s - loss: 0.2189
 85/116 [====================>.........] - ETA: 0s - loss: 0.2156
116/116 [==============================] - 0s 1ms/step - loss: 0.2100
Epoch 3/10

  1/116 [..............................] - ETA: 0s - loss: 0.2406
 44/116 [==========>...................] - ETA: 0s - loss: 0.1804
 85/116 [====================>.........] - ETA: 0s - loss: 0.1825
116/116 [==============================] - 0s 1ms/step - loss: 0.1929
Epoch 4/10

  1/116 [..............................] - ETA: 0s - loss: 0.1650
 44/116 [==========>...................] - ETA: 0s - loss: 0.1890
 85/116 [====================>.........] - ETA: 0s - loss: 0.1963
116/116 [==============================] - 0s 1ms/step - loss: 0.1883
Epoch 5/10

  1/116 [..............................] - ETA: 0s - loss: 0.1306
 36/116 [========>.....................] - ETA: 0s - loss: 0.1782
 73/116 [=================>............] - ETA: 0s - loss: 0.1847
116/116 [==============================] - 0s 1ms/step - loss: 0.1796
Epoch 6/10

  1/116 [..............................] - ETA: 0s - loss: 0.0649
 45/116 [==========>...................] - ETA: 0s - loss: 0.1967
 88/116 [=====================>........] - ETA: 0s - loss: 0.1814
116/116 [==============================] - 0s 1ms/step - loss: 0.1775
Epoch 7/10

  1/116 [..............................] - ETA: 0s - loss: 0.0848
 46/116 [==========>...................] - ETA: 0s - loss: 0.1637
 89/116 [======================>.......] - ETA: 0s - loss: 0.1693
116/116 [==============================] - 0s 1ms/step - loss: 0.1741
Epoch 8/10

  1/116 [..............................] - ETA: 0s - loss: 0.3022
 46/116 [==========>...................] - ETA: 0s - loss: 0.1990
 92/116 [======================>.......] - ETA: 0s - loss: 0.1729
116/116 [==============================] - 0s 1ms/step - loss: 0.1698
Epoch 9/10

  1/116 [..............................] - ETA: 0s - loss: 0.0940
 45/116 [==========>...................] - ETA: 0s - loss: 0.1731
 90/116 [======================>.......] - ETA: 0s - loss: 0.1754
116/116 [==============================] - 0s 1ms/step - loss: 0.1674
Epoch 10/10

  1/116 [..............................] - ETA: 0s - loss: 0.1259
 46/116 [==========>...................] - ETA: 0s - loss: 0.1907
 92/116 [======================>.......] - ETA: 0s - loss: 0.1645
116/116 [==============================] - 0s 1ms/step - loss: 0.1657
-> test with GAN.predict
GAN tn, fp: 275, 14
GAN fn, tp: 1, 6
GAN f1 score: 0.444
GAN cohens kappa score: 0.424

-> test with 'LR'
LR tn, fp: 267, 22
LR fn, tp: 1, 6
LR f1 score: 0.343
LR cohens kappa score: 0.317
LR average precision score: 0.560

-> test with 'RF'
RF tn, fp: 289, 0
RF fn, tp: 5, 2
RF f1 score: 0.444
RF cohens kappa score: 0.439

-> test with 'GB'
GB tn, fp: 289, 0
GB fn, tp: 6, 1
GB f1 score: 0.250
GB cohens kappa score: 0.246

-> test with 'KNN'
KNN tn, fp: 274, 15
KNN fn, tp: 3, 4
KNN f1 score: 0.308
KNN cohens kappa score: 0.283


====== Step 3/5 =======
-> Shuffling data
-> Spliting data to slices

------ Step 3/5: Slice 1/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1131 synthetic samples
-> retrain GAN for predict
Epoch 1/10

  1/116 [..............................] - ETA: 17s - loss: 0.3072
 43/116 [==========>...................] - ETA: 0s - loss: 0.2552 
 84/116 [====================>.........] - ETA: 0s - loss: 0.2160
116/116 [==============================] - 0s 1ms/step - loss: 0.2005
Epoch 2/10

  1/116 [..............................] - ETA: 0s - loss: 0.1715
 42/116 [=========>....................] - ETA: 0s - loss: 0.1369
 84/116 [====================>.........] - ETA: 0s - loss: 0.1418
116/116 [==============================] - 0s 1ms/step - loss: 0.1377
Epoch 3/10

  1/116 [..............................] - ETA: 0s - loss: 0.1241
 43/116 [==========>...................] - ETA: 0s - loss: 0.1183
 84/116 [====================>.........] - ETA: 0s - loss: 0.1230
116/116 [==============================] - 0s 1ms/step - loss: 0.1198
Epoch 4/10

  1/116 [..............................] - ETA: 0s - loss: 0.2198
 41/116 [=========>....................] - ETA: 0s - loss: 0.1129
 78/116 [===================>..........] - ETA: 0s - loss: 0.1133
116/116 [==============================] - 0s 1ms/step - loss: 0.1151
Epoch 5/10

  1/116 [..............................] - ETA: 0s - loss: 0.0238
 41/116 [=========>....................] - ETA: 0s - loss: 0.0987
 82/116 [====================>.........] - ETA: 0s - loss: 0.1126
116/116 [==============================] - 0s 1ms/step - loss: 0.1087
Epoch 6/10

  1/116 [..............................] - ETA: 0s - loss: 0.0485
 43/116 [==========>...................] - ETA: 0s - loss: 0.1038
 83/116 [====================>.........] - ETA: 0s - loss: 0.0968
116/116 [==============================] - 0s 1ms/step - loss: 0.1052
Epoch 7/10

  1/116 [..............................] - ETA: 0s - loss: 0.1086
 42/116 [=========>....................] - ETA: 0s - loss: 0.0924
 83/116 [====================>.........] - ETA: 0s - loss: 0.1028
116/116 [==============================] - 0s 1ms/step - loss: 0.1045
Epoch 8/10

  1/116 [..............................] - ETA: 0s - loss: 0.0399
 42/116 [=========>....................] - ETA: 0s - loss: 0.1006
 84/116 [====================>.........] - ETA: 0s - loss: 0.1018
116/116 [==============================] - 0s 1ms/step - loss: 0.1030
Epoch 9/10

  1/116 [..............................] - ETA: 0s - loss: 0.0706
 43/116 [==========>...................] - ETA: 0s - loss: 0.1010
 84/116 [====================>.........] - ETA: 0s - loss: 0.1003
116/116 [==============================] - 0s 1ms/step - loss: 0.0980
Epoch 10/10

  1/116 [..............................] - ETA: 0s - loss: 0.3166
 33/116 [=======>......................] - ETA: 0s - loss: 0.0930
 66/116 [================>.............] - ETA: 0s - loss: 0.0983
103/116 [=========================>....] - ETA: 0s - loss: 0.0954
116/116 [==============================] - 0s 1ms/step - loss: 0.0973
-> test with GAN.predict
GAN tn, fp: 277, 13
GAN fn, tp: 3, 4
GAN f1 score: 0.333
GAN cohens kappa score: 0.310

-> test with 'LR'
LR tn, fp: 269, 21
LR fn, tp: 1, 6
LR f1 score: 0.353
LR cohens kappa score: 0.328
LR average precision score: 0.598

-> test with 'RF'
RF tn, fp: 289, 1
RF fn, tp: 4, 3
RF f1 score: 0.545
RF cohens kappa score: 0.538

-> test with 'GB'
GB tn, fp: 289, 1
GB fn, tp: 3, 4
GB f1 score: 0.667
GB cohens kappa score: 0.660

-> test with 'KNN'
KNN tn, fp: 272, 18
KNN fn, tp: 3, 4
KNN f1 score: 0.276
KNN cohens kappa score: 0.249


------ Step 3/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1131 synthetic samples
-> retrain GAN for predict
Epoch 1/10

  1/116 [..............................] - ETA: 17s - loss: 0.4285
 38/116 [========>.....................] - ETA: 0s - loss: 0.3682 
 80/116 [===================>..........] - ETA: 0s - loss: 0.3374
114/116 [============================>.] - ETA: 0s - loss: 0.3224
116/116 [==============================] - 0s 1ms/step - loss: 0.3221
Epoch 2/10

  1/116 [..............................] - ETA: 0s - loss: 0.3699
 36/116 [========>.....................] - ETA: 0s - loss: 0.2636
 70/116 [=================>............] - ETA: 0s - loss: 0.2676
110/116 [===========================>..] - ETA: 0s - loss: 0.2617
116/116 [==============================] - 0s 1ms/step - loss: 0.2565
Epoch 3/10

  1/116 [..............................] - ETA: 0s - loss: 0.2426
 40/116 [=========>....................] - ETA: 0s - loss: 0.2873
 80/116 [===================>..........] - ETA: 0s - loss: 0.2525
116/116 [==============================] - 0s 1ms/step - loss: 0.2426
Epoch 4/10

  1/116 [..............................] - ETA: 0s - loss: 0.1818
 42/116 [=========>....................] - ETA: 0s - loss: 0.2408
 83/116 [====================>.........] - ETA: 0s - loss: 0.2266
116/116 [==============================] - 0s 1ms/step - loss: 0.2304
Epoch 5/10

  1/116 [..............................] - ETA: 0s - loss: 0.2660
 42/116 [=========>....................] - ETA: 0s - loss: 0.2296
 83/116 [====================>.........] - ETA: 0s - loss: 0.2334
116/116 [==============================] - 0s 1ms/step - loss: 0.2303
Epoch 6/10

  1/116 [..............................] - ETA: 0s - loss: 0.1972
 42/116 [=========>....................] - ETA: 0s - loss: 0.2354
 84/116 [====================>.........] - ETA: 0s - loss: 0.2179
116/116 [==============================] - 0s 1ms/step - loss: 0.2175
Epoch 7/10

  1/116 [..............................] - ETA: 0s - loss: 0.1260
 43/116 [==========>...................] - ETA: 0s - loss: 0.1989
 84/116 [====================>.........] - ETA: 0s - loss: 0.2150
116/116 [==============================] - 0s 1ms/step - loss: 0.2124
Epoch 8/10

  1/116 [..............................] - ETA: 0s - loss: 0.0874
 42/116 [=========>....................] - ETA: 0s - loss: 0.2074
 82/116 [====================>.........] - ETA: 0s - loss: 0.2007
116/116 [==============================] - 0s 1ms/step - loss: 0.2106
Epoch 9/10

  1/116 [..............................] - ETA: 0s - loss: 0.2802
 43/116 [==========>...................] - ETA: 0s - loss: 0.2006
 84/116 [====================>.........] - ETA: 0s - loss: 0.2075
116/116 [==============================] - 0s 1ms/step - loss: 0.2034
Epoch 10/10

  1/116 [..............................] - ETA: 0s - loss: 0.1188
 43/116 [==========>...................] - ETA: 0s - loss: 0.2163
 83/116 [====================>.........] - ETA: 0s - loss: 0.2168
116/116 [==============================] - 0s 1ms/step - loss: 0.2003
-> test with GAN.predict
GAN tn, fp: 272, 18
GAN fn, tp: 0, 7
GAN f1 score: 0.438
GAN cohens kappa score: 0.416

-> test with 'LR'
LR tn, fp: 246, 44
LR fn, tp: 0, 7
LR f1 score: 0.241
LR cohens kappa score: 0.209
LR average precision score: 0.717

-> test with 'RF'
RF tn, fp: 290, 0
RF fn, tp: 3, 4
RF f1 score: 0.727
RF cohens kappa score: 0.723

-> test with 'GB'
GB tn, fp: 290, 0
GB fn, tp: 4, 3
GB f1 score: 0.600
GB cohens kappa score: 0.594

-> test with 'KNN'
KNN tn, fp: 258, 32
KNN fn, tp: 0, 7
KNN f1 score: 0.304
KNN cohens kappa score: 0.275


------ Step 3/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1131 synthetic samples
-> retrain GAN for predict
Epoch 1/10

  1/116 [..............................] - ETA: 17s - loss: 0.5050
 39/116 [=========>....................] - ETA: 0s - loss: 0.3237 
 74/116 [==================>...........] - ETA: 0s - loss: 0.2744
107/116 [==========================>...] - ETA: 0s - loss: 0.2556
116/116 [==============================] - 0s 1ms/step - loss: 0.2535
Epoch 2/10

  1/116 [..............................] - ETA: 0s - loss: 0.1513
 38/116 [========>.....................] - ETA: 0s - loss: 0.1950
 79/116 [===================>..........] - ETA: 0s - loss: 0.2031
116/116 [==============================] - 0s 1ms/step - loss: 0.1890
Epoch 3/10

  1/116 [..............................] - ETA: 0s - loss: 0.1316
 42/116 [=========>....................] - ETA: 0s - loss: 0.1893
 83/116 [====================>.........] - ETA: 0s - loss: 0.1769
116/116 [==============================] - 0s 1ms/step - loss: 0.1689
Epoch 4/10

  1/116 [..............................] - ETA: 0s - loss: 0.1087
 41/116 [=========>....................] - ETA: 0s - loss: 0.1413
 80/116 [===================>..........] - ETA: 0s - loss: 0.1666
116/116 [==============================] - 0s 1ms/step - loss: 0.1625
Epoch 5/10

  1/116 [..............................] - ETA: 0s - loss: 0.0904
 41/116 [=========>....................] - ETA: 0s - loss: 0.1666
 81/116 [===================>..........] - ETA: 0s - loss: 0.1535
116/116 [==============================] - 0s 1ms/step - loss: 0.1564
Epoch 6/10

  1/116 [..............................] - ETA: 0s - loss: 0.2277
 42/116 [=========>....................] - ETA: 0s - loss: 0.1420
 84/116 [====================>.........] - ETA: 0s - loss: 0.1675
116/116 [==============================] - 0s 1ms/step - loss: 0.1569
Epoch 7/10

  1/116 [..............................] - ETA: 0s - loss: 0.1459
 42/116 [=========>....................] - ETA: 0s - loss: 0.1537
 83/116 [====================>.........] - ETA: 0s - loss: 0.1549
116/116 [==============================] - 0s 1ms/step - loss: 0.1505
Epoch 8/10

  1/116 [..............................] - ETA: 0s - loss: 0.1845
 42/116 [=========>....................] - ETA: 0s - loss: 0.1662
 83/116 [====================>.........] - ETA: 0s - loss: 0.1479
116/116 [==============================] - 0s 1ms/step - loss: 0.1477
Epoch 9/10

  1/116 [..............................] - ETA: 0s - loss: 0.1360
 41/116 [=========>....................] - ETA: 0s - loss: 0.1468
 81/116 [===================>..........] - ETA: 0s - loss: 0.1415
116/116 [==============================] - 0s 1ms/step - loss: 0.1445
Epoch 10/10

  1/116 [..............................] - ETA: 0s - loss: 0.1706
 41/116 [=========>....................] - ETA: 0s - loss: 0.1591
 82/116 [====================>.........] - ETA: 0s - loss: 0.1503
116/116 [==============================] - 0s 1ms/step - loss: 0.1420
-> test with GAN.predict
GAN tn, fp: 277, 13
GAN fn, tp: 3, 4
GAN f1 score: 0.333
GAN cohens kappa score: 0.310

-> test with 'LR'
LR tn, fp: 269, 21
LR fn, tp: 2, 5
LR f1 score: 0.303
LR cohens kappa score: 0.276
LR average precision score: 0.395

-> test with 'RF'
RF tn, fp: 288, 2
RF fn, tp: 5, 2
RF f1 score: 0.364
RF cohens kappa score: 0.353

-> test with 'GB'
GB tn, fp: 288, 2
GB fn, tp: 6, 1
GB f1 score: 0.200
GB cohens kappa score: 0.189

-> test with 'KNN'
KNN tn, fp: 278, 12
KNN fn, tp: 2, 5
KNN f1 score: 0.417
KNN cohens kappa score: 0.397


------ Step 3/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1131 synthetic samples
-> retrain GAN for predict
Epoch 1/10

  1/116 [..............................] - ETA: 19s - loss: 0.5465
 37/116 [========>.....................] - ETA: 0s - loss: 0.4478 
 78/116 [===================>..........] - ETA: 0s - loss: 0.3983
116/116 [==============================] - 0s 1ms/step - loss: 0.3705
Epoch 2/10

  1/116 [..............................] - ETA: 0s - loss: 0.3108
 43/116 [==========>...................] - ETA: 0s - loss: 0.2587
 83/116 [====================>.........] - ETA: 0s - loss: 0.2665
116/116 [==============================] - 0s 1ms/step - loss: 0.2706
Epoch 3/10

  1/116 [..............................] - ETA: 0s - loss: 0.5236
 41/116 [=========>....................] - ETA: 0s - loss: 0.2625
 82/116 [====================>.........] - ETA: 0s - loss: 0.2504
116/116 [==============================] - 0s 1ms/step - loss: 0.2425
Epoch 4/10

  1/116 [..............................] - ETA: 0s - loss: 0.1895
 42/116 [=========>....................] - ETA: 0s - loss: 0.2356
 83/116 [====================>.........] - ETA: 0s - loss: 0.2312
116/116 [==============================] - 0s 1ms/step - loss: 0.2314
Epoch 5/10

  1/116 [..............................] - ETA: 0s - loss: 0.4162
 43/116 [==========>...................] - ETA: 0s - loss: 0.2170
 84/116 [====================>.........] - ETA: 0s - loss: 0.2165
116/116 [==============================] - 0s 1ms/step - loss: 0.2192
Epoch 6/10

  1/116 [..............................] - ETA: 0s - loss: 0.1860
 42/116 [=========>....................] - ETA: 0s - loss: 0.1913
 82/116 [====================>.........] - ETA: 0s - loss: 0.2023
116/116 [==============================] - 0s 1ms/step - loss: 0.2127
Epoch 7/10

  1/116 [..............................] - ETA: 0s - loss: 0.0556
 42/116 [=========>....................] - ETA: 0s - loss: 0.1880
 82/116 [====================>.........] - ETA: 0s - loss: 0.2112
116/116 [==============================] - 0s 1ms/step - loss: 0.2077
Epoch 8/10

  1/116 [..............................] - ETA: 0s - loss: 0.0349
 42/116 [=========>....................] - ETA: 0s - loss: 0.2202
 81/116 [===================>..........] - ETA: 0s - loss: 0.2106
116/116 [==============================] - 0s 1ms/step - loss: 0.1989
Epoch 9/10

  1/116 [..............................] - ETA: 0s - loss: 0.1459
 40/116 [=========>....................] - ETA: 0s - loss: 0.2066
 81/116 [===================>..........] - ETA: 0s - loss: 0.1953
116/116 [==============================] - 0s 1ms/step - loss: 0.1955
Epoch 10/10

  1/116 [..............................] - ETA: 0s - loss: 0.2971
 41/116 [=========>....................] - ETA: 0s - loss: 0.1949
 82/116 [====================>.........] - ETA: 0s - loss: 0.2029
116/116 [==============================] - 0s 1ms/step - loss: 0.1886
-> test with GAN.predict
GAN tn, fp: 271, 19
GAN fn, tp: 2, 5
GAN f1 score: 0.323
GAN cohens kappa score: 0.297

-> test with 'LR'
LR tn, fp: 255, 35
LR fn, tp: 0, 7
LR f1 score: 0.286
LR cohens kappa score: 0.256
LR average precision score: 0.400

-> test with 'RF'
RF tn, fp: 286, 4
RF fn, tp: 3, 4
RF f1 score: 0.533
RF cohens kappa score: 0.521

-> test with 'GB'
GB tn, fp: 285, 5
GB fn, tp: 3, 4
GB f1 score: 0.500
GB cohens kappa score: 0.486

-> test with 'KNN'
KNN tn, fp: 262, 28
KNN fn, tp: 1, 6
KNN f1 score: 0.293
KNN cohens kappa score: 0.264


------ Step 3/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1132 synthetic samples
-> retrain GAN for predict
Epoch 1/10

  1/116 [..............................] - ETA: 14s - loss: 0.3241
 45/116 [==========>...................] - ETA: 0s - loss: 0.3088 
 89/116 [======================>.......] - ETA: 0s - loss: 0.2846
116/116 [==============================] - 0s 1ms/step - loss: 0.2697
Epoch 2/10

  1/116 [..............................] - ETA: 0s - loss: 0.1568
 46/116 [==========>...................] - ETA: 0s - loss: 0.1828
 89/116 [======================>.......] - ETA: 0s - loss: 0.1740
116/116 [==============================] - 0s 1ms/step - loss: 0.1757
Epoch 3/10

  1/116 [..............................] - ETA: 0s - loss: 0.1616
 46/116 [==========>...................] - ETA: 0s - loss: 0.1761
 87/116 [=====================>........] - ETA: 0s - loss: 0.1549
116/116 [==============================] - 0s 1ms/step - loss: 0.1498
Epoch 4/10

  1/116 [..............................] - ETA: 0s - loss: 0.1576
 45/116 [==========>...................] - ETA: 0s - loss: 0.1215
 86/116 [=====================>........] - ETA: 0s - loss: 0.1379
116/116 [==============================] - 0s 1ms/step - loss: 0.1391
Epoch 5/10

  1/116 [..............................] - ETA: 0s - loss: 0.0541
 43/116 [==========>...................] - ETA: 0s - loss: 0.1081
 87/116 [=====================>........] - ETA: 0s - loss: 0.1313
116/116 [==============================] - 0s 1ms/step - loss: 0.1344
Epoch 6/10

  1/116 [..............................] - ETA: 0s - loss: 0.0852
 47/116 [===========>..................] - ETA: 0s - loss: 0.1361
 88/116 [=====================>........] - ETA: 0s - loss: 0.1357
116/116 [==============================] - 0s 1ms/step - loss: 0.1285
Epoch 7/10

  1/116 [..............................] - ETA: 0s - loss: 0.0900
 46/116 [==========>...................] - ETA: 0s - loss: 0.1225
 86/116 [=====================>........] - ETA: 0s - loss: 0.1262
116/116 [==============================] - 0s 1ms/step - loss: 0.1247
Epoch 8/10

  1/116 [..............................] - ETA: 0s - loss: 0.1444
 45/116 [==========>...................] - ETA: 0s - loss: 0.1364
 88/116 [=====================>........] - ETA: 0s - loss: 0.1177
116/116 [==============================] - 0s 1ms/step - loss: 0.1241
Epoch 9/10

  1/116 [..............................] - ETA: 0s - loss: 0.1417
 43/116 [==========>...................] - ETA: 0s - loss: 0.1271
 85/116 [====================>.........] - ETA: 0s - loss: 0.1233
116/116 [==============================] - 0s 1ms/step - loss: 0.1183
Epoch 10/10

  1/116 [..............................] - ETA: 0s - loss: 0.0577
 42/116 [=========>....................] - ETA: 0s - loss: 0.1023
 85/116 [====================>.........] - ETA: 0s - loss: 0.1173
116/116 [==============================] - 0s 1ms/step - loss: 0.1177
-> test with GAN.predict
GAN tn, fp: 280, 9
GAN fn, tp: 2, 5
GAN f1 score: 0.476
GAN cohens kappa score: 0.459

-> test with 'LR'
LR tn, fp: 270, 19
LR fn, tp: 2, 5
LR f1 score: 0.323
LR cohens kappa score: 0.297
LR average precision score: 0.362

-> test with 'RF'
RF tn, fp: 289, 0
RF fn, tp: 7, 0
RF f1 score: 0.000
RF cohens kappa score: 0.000

-> test with 'GB'
GB tn, fp: 288, 1
GB fn, tp: 7, 0
GB f1 score: 0.000
GB cohens kappa score: -0.006

-> test with 'KNN'
KNN tn, fp: 277, 12
KNN fn, tp: 1, 6
KNN f1 score: 0.480
KNN cohens kappa score: 0.462


====== Step 4/5 =======
-> Shuffling data
-> Spliting data to slices

------ Step 4/5: Slice 1/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1131 synthetic samples
-> retrain GAN for predict
Epoch 1/10

  1/116 [..............................] - ETA: 18s - loss: 0.4760
 42/116 [=========>....................] - ETA: 0s - loss: 0.4371 
 79/116 [===================>..........] - ETA: 0s - loss: 0.3877
114/116 [============================>.] - ETA: 0s - loss: 0.3562
116/116 [==============================] - 0s 1ms/step - loss: 0.3531
Epoch 2/10

  1/116 [..............................] - ETA: 0s - loss: 0.3167
 35/116 [========>.....................] - ETA: 0s - loss: 0.2476
 76/116 [==================>...........] - ETA: 0s - loss: 0.2444
116/116 [==============================] - 0s 1ms/step - loss: 0.2290
Epoch 3/10

  1/116 [..............................] - ETA: 0s - loss: 0.2655
 43/116 [==========>...................] - ETA: 0s - loss: 0.2059
 85/116 [====================>.........] - ETA: 0s - loss: 0.2094
116/116 [==============================] - 0s 1ms/step - loss: 0.1969
Epoch 4/10

  1/116 [..............................] - ETA: 0s - loss: 0.3847
 40/116 [=========>....................] - ETA: 0s - loss: 0.1777
 81/116 [===================>..........] - ETA: 0s - loss: 0.1854
116/116 [==============================] - 0s 1ms/step - loss: 0.1847
Epoch 5/10

  1/116 [..............................] - ETA: 0s - loss: 0.1577
 43/116 [==========>...................] - ETA: 0s - loss: 0.1579
 85/116 [====================>.........] - ETA: 0s - loss: 0.1667
116/116 [==============================] - 0s 1ms/step - loss: 0.1714
Epoch 6/10

  1/116 [..............................] - ETA: 0s - loss: 0.2027
 42/116 [=========>....................] - ETA: 0s - loss: 0.1570
 85/116 [====================>.........] - ETA: 0s - loss: 0.1557
116/116 [==============================] - 0s 1ms/step - loss: 0.1639
Epoch 7/10

  1/116 [..............................] - ETA: 0s - loss: 0.2187
 43/116 [==========>...................] - ETA: 0s - loss: 0.1557
 85/116 [====================>.........] - ETA: 0s - loss: 0.1597
116/116 [==============================] - 0s 1ms/step - loss: 0.1599
Epoch 8/10

  1/116 [..............................] - ETA: 0s - loss: 0.0884
 39/116 [=========>....................] - ETA: 0s - loss: 0.1700
 80/116 [===================>..........] - ETA: 0s - loss: 0.1521
116/116 [==============================] - 0s 1ms/step - loss: 0.1564
Epoch 9/10

  1/116 [..............................] - ETA: 0s - loss: 0.1745
 43/116 [==========>...................] - ETA: 0s - loss: 0.1557
 84/116 [====================>.........] - ETA: 0s - loss: 0.1521
116/116 [==============================] - 0s 1ms/step - loss: 0.1517
Epoch 10/10

  1/116 [..............................] - ETA: 0s - loss: 0.1025
 40/116 [=========>....................] - ETA: 0s - loss: 0.1596
 82/116 [====================>.........] - ETA: 0s - loss: 0.1536
116/116 [==============================] - 0s 1ms/step - loss: 0.1487
-> test with GAN.predict
GAN tn, fp: 276, 14
GAN fn, tp: 1, 6
GAN f1 score: 0.444
GAN cohens kappa score: 0.424

-> test with 'LR'
LR tn, fp: 272, 18
LR fn, tp: 1, 6
LR f1 score: 0.387
LR cohens kappa score: 0.364
LR average precision score: 0.721

-> test with 'RF'
RF tn, fp: 290, 0
RF fn, tp: 4, 3
RF f1 score: 0.600
RF cohens kappa score: 0.594

-> test with 'GB'
GB tn, fp: 289, 1
GB fn, tp: 2, 5
GB f1 score: 0.769
GB cohens kappa score: 0.764

-> test with 'KNN'
KNN tn, fp: 273, 17
KNN fn, tp: 1, 6
KNN f1 score: 0.400
KNN cohens kappa score: 0.378


------ Step 4/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1131 synthetic samples
-> retrain GAN for predict
Epoch 1/10

  1/116 [..............................] - ETA: 17s - loss: 0.5294
 40/116 [=========>....................] - ETA: 0s - loss: 0.2943 
 79/116 [===================>..........] - ETA: 0s - loss: 0.2743
116/116 [==============================] - 0s 1ms/step - loss: 0.2638
Epoch 2/10

  1/116 [..............................] - ETA: 0s - loss: 0.1092
 40/116 [=========>....................] - ETA: 0s - loss: 0.2321
 81/116 [===================>..........] - ETA: 0s - loss: 0.2164
116/116 [==============================] - 0s 1ms/step - loss: 0.2087
Epoch 3/10

  1/116 [..............................] - ETA: 0s - loss: 0.4276
 41/116 [=========>....................] - ETA: 0s - loss: 0.1918
 81/116 [===================>..........] - ETA: 0s - loss: 0.1946
116/116 [==============================] - 0s 1ms/step - loss: 0.1965
Epoch 4/10

  1/116 [..............................] - ETA: 0s - loss: 0.2578
 42/116 [=========>....................] - ETA: 0s - loss: 0.1715
 83/116 [====================>.........] - ETA: 0s - loss: 0.1733
116/116 [==============================] - 0s 1ms/step - loss: 0.1851
Epoch 5/10

  1/116 [..............................] - ETA: 0s - loss: 0.0406
 40/116 [=========>....................] - ETA: 0s - loss: 0.1733
 81/116 [===================>..........] - ETA: 0s - loss: 0.1649
116/116 [==============================] - 0s 1ms/step - loss: 0.1789
Epoch 6/10

  1/116 [..............................] - ETA: 0s - loss: 0.2648
 39/116 [=========>....................] - ETA: 0s - loss: 0.1916
 80/116 [===================>..........] - ETA: 0s - loss: 0.1747
116/116 [==============================] - 0s 1ms/step - loss: 0.1723
Epoch 7/10

  1/116 [..............................] - ETA: 0s - loss: 0.0461
 41/116 [=========>....................] - ETA: 0s - loss: 0.1706
 82/116 [====================>.........] - ETA: 0s - loss: 0.1727
116/116 [==============================] - 0s 1ms/step - loss: 0.1692
Epoch 8/10

  1/116 [..............................] - ETA: 0s - loss: 0.2510
 41/116 [=========>....................] - ETA: 0s - loss: 0.1513
 82/116 [====================>.........] - ETA: 0s - loss: 0.1543
116/116 [==============================] - 0s 1ms/step - loss: 0.1635
Epoch 9/10

  1/116 [..............................] - ETA: 0s - loss: 0.1281
 41/116 [=========>....................] - ETA: 0s - loss: 0.1571
 82/116 [====================>.........] - ETA: 0s - loss: 0.1589
116/116 [==============================] - 0s 1ms/step - loss: 0.1612
Epoch 10/10

  1/116 [..............................] - ETA: 0s - loss: 0.0560
 37/116 [========>.....................] - ETA: 0s - loss: 0.1242
 67/116 [================>.............] - ETA: 0s - loss: 0.1484
103/116 [=========================>....] - ETA: 0s - loss: 0.1470
116/116 [==============================] - 0s 1ms/step - loss: 0.1550
-> test with GAN.predict
GAN tn, fp: 270, 20
GAN fn, tp: 0, 7
GAN f1 score: 0.412
GAN cohens kappa score: 0.389

-> test with 'LR'
LR tn, fp: 260, 30
LR fn, tp: 0, 7
LR f1 score: 0.318
LR cohens kappa score: 0.290
LR average precision score: 0.294

-> test with 'RF'
RF tn, fp: 288, 2
RF fn, tp: 5, 2
RF f1 score: 0.364
RF cohens kappa score: 0.353

-> test with 'GB'
GB tn, fp: 286, 4
GB fn, tp: 4, 3
GB f1 score: 0.429
GB cohens kappa score: 0.415

-> test with 'KNN'
KNN tn, fp: 270, 20
KNN fn, tp: 2, 5
KNN f1 score: 0.312
KNN cohens kappa score: 0.286


------ Step 4/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1131 synthetic samples
-> retrain GAN for predict
Epoch 1/10

  1/116 [..............................] - ETA: 17s - loss: 0.4312
 41/116 [=========>....................] - ETA: 0s - loss: 0.3656 
 77/116 [==================>...........] - ETA: 0s - loss: 0.3230
112/116 [===========================>..] - ETA: 0s - loss: 0.2993
116/116 [==============================] - 0s 1ms/step - loss: 0.2977
Epoch 2/10

  1/116 [..............................] - ETA: 0s - loss: 0.2111
 39/116 [=========>....................] - ETA: 0s - loss: 0.2219
 80/116 [===================>..........] - ETA: 0s - loss: 0.2499
116/116 [==============================] - 0s 1ms/step - loss: 0.2295
Epoch 3/10

  1/116 [..............................] - ETA: 0s - loss: 0.1968
 40/116 [=========>....................] - ETA: 0s - loss: 0.2161
 79/116 [===================>..........] - ETA: 0s - loss: 0.2173
116/116 [==============================] - 0s 1ms/step - loss: 0.2112
Epoch 4/10

  1/116 [..............................] - ETA: 0s - loss: 0.1783
 42/116 [=========>....................] - ETA: 0s - loss: 0.2021
 81/116 [===================>..........] - ETA: 0s - loss: 0.1989
116/116 [==============================] - 0s 1ms/step - loss: 0.2023
Epoch 5/10

  1/116 [..............................] - ETA: 0s - loss: 0.1139
 40/116 [=========>....................] - ETA: 0s - loss: 0.2011
 82/116 [====================>.........] - ETA: 0s - loss: 0.2038
116/116 [==============================] - 0s 1ms/step - loss: 0.1953
Epoch 6/10

  1/116 [..............................] - ETA: 0s - loss: 0.1949
 42/116 [=========>....................] - ETA: 0s - loss: 0.1781
 81/116 [===================>..........] - ETA: 0s - loss: 0.1901
116/116 [==============================] - 0s 1ms/step - loss: 0.1939
Epoch 7/10

  1/116 [..............................] - ETA: 0s - loss: 0.1915
 42/116 [=========>....................] - ETA: 0s - loss: 0.2006
 83/116 [====================>.........] - ETA: 0s - loss: 0.1904
116/116 [==============================] - 0s 1ms/step - loss: 0.1930
Epoch 8/10

  1/116 [..............................] - ETA: 0s - loss: 0.2446
 41/116 [=========>....................] - ETA: 0s - loss: 0.2196
 80/116 [===================>..........] - ETA: 0s - loss: 0.1982
116/116 [==============================] - 0s 1ms/step - loss: 0.1879
Epoch 9/10

  1/116 [..............................] - ETA: 0s - loss: 0.3674
 41/116 [=========>....................] - ETA: 0s - loss: 0.1813
 81/116 [===================>..........] - ETA: 0s - loss: 0.1994
116/116 [==============================] - 0s 1ms/step - loss: 0.1843
Epoch 10/10

  1/116 [..............................] - ETA: 0s - loss: 0.1133
 42/116 [=========>....................] - ETA: 0s - loss: 0.1766
 81/116 [===================>..........] - ETA: 0s - loss: 0.1816
116/116 [==============================] - 0s 1ms/step - loss: 0.1799
-> test with GAN.predict
GAN tn, fp: 263, 27
GAN fn, tp: 1, 6
GAN f1 score: 0.300
GAN cohens kappa score: 0.272

-> test with 'LR'
LR tn, fp: 250, 40
LR fn, tp: 1, 6
LR f1 score: 0.226
LR cohens kappa score: 0.193
LR average precision score: 0.553

-> test with 'RF'
RF tn, fp: 288, 2
RF fn, tp: 2, 5
RF f1 score: 0.714
RF cohens kappa score: 0.707

-> test with 'GB'
GB tn, fp: 287, 3
GB fn, tp: 2, 5
GB f1 score: 0.667
GB cohens kappa score: 0.658

-> test with 'KNN'
KNN tn, fp: 257, 33
KNN fn, tp: 0, 7
KNN f1 score: 0.298
KNN cohens kappa score: 0.269


------ Step 4/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1131 synthetic samples
-> retrain GAN for predict
Epoch 1/10

  1/116 [..............................] - ETA: 17s - loss: 0.4122
 41/116 [=========>....................] - ETA: 0s - loss: 0.3662 
 81/116 [===================>..........] - ETA: 0s - loss: 0.3206
116/116 [==============================] - 0s 1ms/step - loss: 0.3041
Epoch 2/10

  1/116 [..............................] - ETA: 0s - loss: 0.2429
 41/116 [=========>....................] - ETA: 0s - loss: 0.2241
 79/116 [===================>..........] - ETA: 0s - loss: 0.2172
116/116 [==============================] - 0s 1ms/step - loss: 0.2125
Epoch 3/10

  1/116 [..............................] - ETA: 0s - loss: 0.1027
 41/116 [=========>....................] - ETA: 0s - loss: 0.1888
 83/116 [====================>.........] - ETA: 0s - loss: 0.1924
116/116 [==============================] - 0s 1ms/step - loss: 0.1827
Epoch 4/10

  1/116 [..............................] - ETA: 0s - loss: 0.1566
 39/116 [=========>....................] - ETA: 0s - loss: 0.1859
 79/116 [===================>..........] - ETA: 0s - loss: 0.1696
116/116 [==============================] - 0s 1ms/step - loss: 0.1702
Epoch 5/10

  1/116 [..............................] - ETA: 0s - loss: 0.1734
 41/116 [=========>....................] - ETA: 0s - loss: 0.1407
 81/116 [===================>..........] - ETA: 0s - loss: 0.1540
116/116 [==============================] - 0s 1ms/step - loss: 0.1623
Epoch 6/10

  1/116 [..............................] - ETA: 0s - loss: 0.0778
 41/116 [=========>....................] - ETA: 0s - loss: 0.1520
 83/116 [====================>.........] - ETA: 0s - loss: 0.1413
116/116 [==============================] - 0s 1ms/step - loss: 0.1533
Epoch 7/10

  1/116 [..............................] - ETA: 0s - loss: 0.0652
 40/116 [=========>....................] - ETA: 0s - loss: 0.1494
 77/116 [==================>...........] - ETA: 0s - loss: 0.1488
116/116 [==============================] - 0s 1ms/step - loss: 0.1494
Epoch 8/10

  1/116 [..............................] - ETA: 0s - loss: 0.0986
 42/116 [=========>....................] - ETA: 0s - loss: 0.1317
 82/116 [====================>.........] - ETA: 0s - loss: 0.1410
116/116 [==============================] - 0s 1ms/step - loss: 0.1451
Epoch 9/10

  1/116 [..............................] - ETA: 0s - loss: 0.2405
 43/116 [==========>...................] - ETA: 0s - loss: 0.1360
 82/116 [====================>.........] - ETA: 0s - loss: 0.1393
116/116 [==============================] - 0s 1ms/step - loss: 0.1413
Epoch 10/10

  1/116 [..............................] - ETA: 0s - loss: 0.0209
 42/116 [=========>....................] - ETA: 0s - loss: 0.1317
 84/116 [====================>.........] - ETA: 0s - loss: 0.1472
116/116 [==============================] - 0s 1ms/step - loss: 0.1449
-> test with GAN.predict
GAN tn, fp: 274, 16
GAN fn, tp: 2, 5
GAN f1 score: 0.357
GAN cohens kappa score: 0.334

-> test with 'LR'
LR tn, fp: 264, 26
LR fn, tp: 1, 6
LR f1 score: 0.308
LR cohens kappa score: 0.280
LR average precision score: 0.631

-> test with 'RF'
RF tn, fp: 290, 0
RF fn, tp: 4, 3
RF f1 score: 0.600
RF cohens kappa score: 0.594

-> test with 'GB'
GB tn, fp: 289, 1
GB fn, tp: 4, 3
GB f1 score: 0.545
GB cohens kappa score: 0.538

-> test with 'KNN'
KNN tn, fp: 275, 15
KNN fn, tp: 2, 5
KNN f1 score: 0.370
KNN cohens kappa score: 0.348


------ Step 4/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1132 synthetic samples
-> retrain GAN for predict
Epoch 1/10

  1/116 [..............................] - ETA: 15s - loss: 0.2377
 46/116 [==========>...................] - ETA: 0s - loss: 0.2619 
 90/116 [======================>.......] - ETA: 0s - loss: 0.2357
116/116 [==============================] - 0s 1ms/step - loss: 0.2252
Epoch 2/10

  1/116 [..............................] - ETA: 0s - loss: 0.1586
 44/116 [==========>...................] - ETA: 0s - loss: 0.1846
 87/116 [=====================>........] - ETA: 0s - loss: 0.1749
116/116 [==============================] - 0s 1ms/step - loss: 0.1800
Epoch 3/10

  1/116 [..............................] - ETA: 0s - loss: 0.4210
 45/116 [==========>...................] - ETA: 0s - loss: 0.1667
 90/116 [======================>.......] - ETA: 0s - loss: 0.1731
116/116 [==============================] - 0s 1ms/step - loss: 0.1672
Epoch 4/10

  1/116 [..............................] - ETA: 0s - loss: 0.0965
 45/116 [==========>...................] - ETA: 0s - loss: 0.1568
 90/116 [======================>.......] - ETA: 0s - loss: 0.1632
116/116 [==============================] - 0s 1ms/step - loss: 0.1617
Epoch 5/10

  1/116 [..............................] - ETA: 0s - loss: 0.1555
 42/116 [=========>....................] - ETA: 0s - loss: 0.1766
 84/116 [====================>.........] - ETA: 0s - loss: 0.1610
116/116 [==============================] - 0s 1ms/step - loss: 0.1624
Epoch 6/10

  1/116 [..............................] - ETA: 0s - loss: 0.2747
 43/116 [==========>...................] - ETA: 0s - loss: 0.1551
 87/116 [=====================>........] - ETA: 0s - loss: 0.1567
116/116 [==============================] - 0s 1ms/step - loss: 0.1564
Epoch 7/10

  1/116 [..............................] - ETA: 0s - loss: 0.0684
 44/116 [==========>...................] - ETA: 0s - loss: 0.1572
 89/116 [======================>.......] - ETA: 0s - loss: 0.1559
116/116 [==============================] - 0s 1ms/step - loss: 0.1544
Epoch 8/10

  1/116 [..............................] - ETA: 0s - loss: 0.0839
 43/116 [==========>...................] - ETA: 0s - loss: 0.1320
 79/116 [===================>..........] - ETA: 0s - loss: 0.1501
116/116 [==============================] - ETA: 0s - loss: 0.1498
116/116 [==============================] - 0s 1ms/step - loss: 0.1498
Epoch 9/10

  1/116 [..............................] - ETA: 0s - loss: 0.1928
 39/116 [=========>....................] - ETA: 0s - loss: 0.1570
 80/116 [===================>..........] - ETA: 0s - loss: 0.1571
116/116 [==============================] - 0s 1ms/step - loss: 0.1478
Epoch 10/10

  1/116 [..............................] - ETA: 0s - loss: 0.3379
 45/116 [==========>...................] - ETA: 0s - loss: 0.1338
 89/116 [======================>.......] - ETA: 0s - loss: 0.1464
116/116 [==============================] - 0s 1ms/step - loss: 0.1463
-> test with GAN.predict
GAN tn, fp: 275, 14
GAN fn, tp: 2, 5
GAN f1 score: 0.385
GAN cohens kappa score: 0.363

-> test with 'LR'
LR tn, fp: 266, 23
LR fn, tp: 2, 5
LR f1 score: 0.286
LR cohens kappa score: 0.258
LR average precision score: 0.667

-> test with 'RF'
RF tn, fp: 288, 1
RF fn, tp: 4, 3
RF f1 score: 0.545
RF cohens kappa score: 0.537

-> test with 'GB'
GB tn, fp: 288, 1
GB fn, tp: 4, 3
GB f1 score: 0.545
GB cohens kappa score: 0.537

-> test with 'KNN'
KNN tn, fp: 269, 20
KNN fn, tp: 2, 5
KNN f1 score: 0.312
KNN cohens kappa score: 0.286


====== Step 5/5 =======
-> Shuffling data
-> Spliting data to slices

------ Step 5/5: Slice 1/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1131 synthetic samples
-> retrain GAN for predict
Epoch 1/10

  1/116 [..............................] - ETA: 18s - loss: 0.4932
 38/116 [========>.....................] - ETA: 0s - loss: 0.4858 
 76/116 [==================>...........] - ETA: 0s - loss: 0.3946
116/116 [==============================] - 0s 1ms/step - loss: 0.3557
Epoch 2/10

  1/116 [..............................] - ETA: 0s - loss: 0.3170
 42/116 [=========>....................] - ETA: 0s - loss: 0.2680
 83/116 [====================>.........] - ETA: 0s - loss: 0.2684
116/116 [==============================] - 0s 1ms/step - loss: 0.2584
Epoch 3/10

  1/116 [..............................] - ETA: 0s - loss: 0.2132
 42/116 [=========>....................] - ETA: 0s - loss: 0.2356
 83/116 [====================>.........] - ETA: 0s - loss: 0.2512
116/116 [==============================] - 0s 1ms/step - loss: 0.2382
Epoch 4/10

  1/116 [..............................] - ETA: 0s - loss: 0.2157
 42/116 [=========>....................] - ETA: 0s - loss: 0.2454
 84/116 [====================>.........] - ETA: 0s - loss: 0.2334
116/116 [==============================] - 0s 1ms/step - loss: 0.2277
Epoch 5/10

  1/116 [..............................] - ETA: 0s - loss: 0.3089
 41/116 [=========>....................] - ETA: 0s - loss: 0.1991
 81/116 [===================>..........] - ETA: 0s - loss: 0.2037
116/116 [==============================] - 0s 1ms/step - loss: 0.2182
Epoch 6/10

  1/116 [..............................] - ETA: 0s - loss: 0.1070
 41/116 [=========>....................] - ETA: 0s - loss: 0.2029
 83/116 [====================>.........] - ETA: 0s - loss: 0.2111
116/116 [==============================] - 0s 1ms/step - loss: 0.2113
Epoch 7/10

  1/116 [..............................] - ETA: 0s - loss: 0.3694
 42/116 [=========>....................] - ETA: 0s - loss: 0.2285
 84/116 [====================>.........] - ETA: 0s - loss: 0.2034
116/116 [==============================] - 0s 1ms/step - loss: 0.2047
Epoch 8/10

  1/116 [..............................] - ETA: 0s - loss: 0.1172
 39/116 [=========>....................] - ETA: 0s - loss: 0.2343
 78/116 [===================>..........] - ETA: 0s - loss: 0.2060
116/116 [==============================] - 0s 1ms/step - loss: 0.1955
Epoch 9/10

  1/116 [..............................] - ETA: 0s - loss: 0.0865
 41/116 [=========>....................] - ETA: 0s - loss: 0.1710
 81/116 [===================>..........] - ETA: 0s - loss: 0.1829
116/116 [==============================] - 0s 1ms/step - loss: 0.1906
Epoch 10/10

  1/116 [..............................] - ETA: 0s - loss: 0.0569
 41/116 [=========>....................] - ETA: 0s - loss: 0.1816
 82/116 [====================>.........] - ETA: 0s - loss: 0.1777
116/116 [==============================] - 0s 1ms/step - loss: 0.1846
-> test with GAN.predict
GAN tn, fp: 263, 27
GAN fn, tp: 1, 6
GAN f1 score: 0.300
GAN cohens kappa score: 0.272

-> test with 'LR'
LR tn, fp: 251, 39
LR fn, tp: 0, 7
LR f1 score: 0.264
LR cohens kappa score: 0.233
LR average precision score: 0.512

-> test with 'RF'
RF tn, fp: 288, 2
RF fn, tp: 3, 4
RF f1 score: 0.615
RF cohens kappa score: 0.607

-> test with 'GB'
GB tn, fp: 287, 3
GB fn, tp: 3, 4
GB f1 score: 0.571
GB cohens kappa score: 0.561

-> test with 'KNN'
KNN tn, fp: 269, 21
KNN fn, tp: 1, 6
KNN f1 score: 0.353
KNN cohens kappa score: 0.328


------ Step 5/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1131 synthetic samples
-> retrain GAN for predict
Epoch 1/10

  1/116 [..............................] - ETA: 20s - loss: 0.5633
 43/116 [==========>...................] - ETA: 0s - loss: 0.3516 
 84/116 [====================>.........] - ETA: 0s - loss: 0.3037
116/116 [==============================] - 0s 1ms/step - loss: 0.2778
Epoch 2/10

  1/116 [..............................] - ETA: 0s - loss: 0.1925
 41/116 [=========>....................] - ETA: 0s - loss: 0.1974
 74/116 [==================>...........] - ETA: 0s - loss: 0.1848
107/116 [==========================>...] - ETA: 0s - loss: 0.1827
116/116 [==============================] - 0s 1ms/step - loss: 0.1831
Epoch 3/10

  1/116 [..............................] - ETA: 0s - loss: 0.0685
 41/116 [=========>....................] - ETA: 0s - loss: 0.1639
 83/116 [====================>.........] - ETA: 0s - loss: 0.1620
116/116 [==============================] - 0s 1ms/step - loss: 0.1592
Epoch 4/10

  1/116 [..............................] - ETA: 0s - loss: 0.0613
 41/116 [=========>....................] - ETA: 0s - loss: 0.1546
 83/116 [====================>.........] - ETA: 0s - loss: 0.1543
116/116 [==============================] - 0s 1ms/step - loss: 0.1435
Epoch 5/10

  1/116 [..............................] - ETA: 0s - loss: 0.2099
 40/116 [=========>....................] - ETA: 0s - loss: 0.1542
 80/116 [===================>..........] - ETA: 0s - loss: 0.1416
116/116 [==============================] - 0s 1ms/step - loss: 0.1369
Epoch 6/10

  1/116 [..............................] - ETA: 0s - loss: 0.0437
 42/116 [=========>....................] - ETA: 0s - loss: 0.1537
 84/116 [====================>.........] - ETA: 0s - loss: 0.1397
116/116 [==============================] - 0s 1ms/step - loss: 0.1301
Epoch 7/10

  1/116 [..............................] - ETA: 0s - loss: 0.1070
 42/116 [=========>....................] - ETA: 0s - loss: 0.1267
 83/116 [====================>.........] - ETA: 0s - loss: 0.1251
116/116 [==============================] - 0s 1ms/step - loss: 0.1256
Epoch 8/10

  1/116 [..............................] - ETA: 0s - loss: 0.2674
 42/116 [=========>....................] - ETA: 0s - loss: 0.1069
 82/116 [====================>.........] - ETA: 0s - loss: 0.1234
116/116 [==============================] - 0s 1ms/step - loss: 0.1227
Epoch 9/10

  1/116 [..............................] - ETA: 0s - loss: 0.1343
 39/116 [=========>....................] - ETA: 0s - loss: 0.1083
 80/116 [===================>..........] - ETA: 0s - loss: 0.1103
116/116 [==============================] - 0s 1ms/step - loss: 0.1187
Epoch 10/10

  1/116 [..............................] - ETA: 0s - loss: 0.1035
 42/116 [=========>....................] - ETA: 0s - loss: 0.1079
 82/116 [====================>.........] - ETA: 0s - loss: 0.1201
116/116 [==============================] - 0s 1ms/step - loss: 0.1162
-> test with GAN.predict
GAN tn, fp: 277, 13
GAN fn, tp: 3, 4
GAN f1 score: 0.333
GAN cohens kappa score: 0.310

-> test with 'LR'
LR tn, fp: 267, 23
LR fn, tp: 3, 4
LR f1 score: 0.235
LR cohens kappa score: 0.206
LR average precision score: 0.231

-> test with 'RF'
RF tn, fp: 289, 1
RF fn, tp: 5, 2
RF f1 score: 0.400
RF cohens kappa score: 0.391

-> test with 'GB'
GB tn, fp: 289, 1
GB fn, tp: 4, 3
GB f1 score: 0.545
GB cohens kappa score: 0.538

-> test with 'KNN'
KNN tn, fp: 270, 20
KNN fn, tp: 3, 4
KNN f1 score: 0.258
KNN cohens kappa score: 0.230


------ Step 5/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1131 synthetic samples
-> retrain GAN for predict
Epoch 1/10

  1/116 [..............................] - ETA: 22s - loss: 0.3612
 35/116 [========>.....................] - ETA: 0s - loss: 0.3627 
 70/116 [=================>............] - ETA: 0s - loss: 0.3377
105/116 [==========================>...] - ETA: 0s - loss: 0.3139
116/116 [==============================] - 0s 1ms/step - loss: 0.3122
Epoch 2/10

  1/116 [..............................] - ETA: 0s - loss: 0.3226
 37/116 [========>.....................] - ETA: 0s - loss: 0.2464
 73/116 [=================>............] - ETA: 0s - loss: 0.2384
106/116 [==========================>...] - ETA: 0s - loss: 0.2429
116/116 [==============================] - 0s 1ms/step - loss: 0.2387
Epoch 3/10

  1/116 [..............................] - ETA: 0s - loss: 0.1830
 37/116 [========>.....................] - ETA: 0s - loss: 0.2185
 71/116 [=================>............] - ETA: 0s - loss: 0.2151
106/116 [==========================>...] - ETA: 0s - loss: 0.2209
116/116 [==============================] - 0s 1ms/step - loss: 0.2218
Epoch 4/10

  1/116 [..............................] - ETA: 0s - loss: 0.0519
 37/116 [========>.....................] - ETA: 0s - loss: 0.2076
 75/116 [==================>...........] - ETA: 0s - loss: 0.2130
110/116 [===========================>..] - ETA: 0s - loss: 0.2108
116/116 [==============================] - 0s 1ms/step - loss: 0.2079
Epoch 5/10

  1/116 [..............................] - ETA: 0s - loss: 0.1905
 35/116 [========>.....................] - ETA: 0s - loss: 0.2087
 74/116 [==================>...........] - ETA: 0s - loss: 0.2053
110/116 [===========================>..] - ETA: 0s - loss: 0.2001
116/116 [==============================] - 0s 1ms/step - loss: 0.1984
Epoch 6/10

  1/116 [..............................] - ETA: 0s - loss: 0.3701
 35/116 [========>.....................] - ETA: 0s - loss: 0.1800
 65/116 [===============>..............] - ETA: 0s - loss: 0.1821
105/116 [==========================>...] - ETA: 0s - loss: 0.1907
116/116 [==============================] - 0s 1ms/step - loss: 0.1934
Epoch 7/10

  1/116 [..............................] - ETA: 0s - loss: 0.3446
 41/116 [=========>....................] - ETA: 0s - loss: 0.1928
 82/116 [====================>.........] - ETA: 0s - loss: 0.1877
116/116 [==============================] - 0s 1ms/step - loss: 0.1877
Epoch 8/10

  1/116 [..............................] - ETA: 0s - loss: 0.0820
 32/116 [=======>......................] - ETA: 0s - loss: 0.1826
 63/116 [===============>..............] - ETA: 0s - loss: 0.1865
 96/116 [=======================>......] - ETA: 0s - loss: 0.1912
116/116 [==============================] - 0s 2ms/step - loss: 0.1834
Epoch 9/10

  1/116 [..............................] - ETA: 0s - loss: 0.1237
 39/116 [=========>....................] - ETA: 0s - loss: 0.1751
 74/116 [==================>...........] - ETA: 0s - loss: 0.1879
110/116 [===========================>..] - ETA: 0s - loss: 0.1782
116/116 [==============================] - 0s 1ms/step - loss: 0.1796
Epoch 10/10

  1/116 [..............................] - ETA: 0s - loss: 0.1689
 35/116 [========>.....................] - ETA: 0s - loss: 0.1600
 71/116 [=================>............] - ETA: 0s - loss: 0.1763
106/116 [==========================>...] - ETA: 0s - loss: 0.1753
116/116 [==============================] - 0s 1ms/step - loss: 0.1767
-> test with GAN.predict
GAN tn, fp: 258, 32
GAN fn, tp: 0, 7
GAN f1 score: 0.304
GAN cohens kappa score: 0.275

-> test with 'LR'
LR tn, fp: 261, 29
LR fn, tp: 0, 7
LR f1 score: 0.326
LR cohens kappa score: 0.298
LR average precision score: 0.757

-> test with 'RF'
RF tn, fp: 289, 1
RF fn, tp: 1, 6
RF f1 score: 0.857
RF cohens kappa score: 0.854

-> test with 'GB'
GB tn, fp: 288, 2
GB fn, tp: 1, 6
GB f1 score: 0.800
GB cohens kappa score: 0.795

-> test with 'KNN'
KNN tn, fp: 262, 28
KNN fn, tp: 0, 7
KNN f1 score: 0.333
KNN cohens kappa score: 0.306


------ Step 5/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1131 synthetic samples
-> retrain GAN for predict
Epoch 1/10

  1/116 [..............................] - ETA: 18s - loss: 0.3975
 41/116 [=========>....................] - ETA: 0s - loss: 0.4836 
 81/116 [===================>..........] - ETA: 0s - loss: 0.4194
116/116 [==============================] - 0s 1ms/step - loss: 0.4042
Epoch 2/10

  1/116 [..............................] - ETA: 0s - loss: 0.3056
 39/116 [=========>....................] - ETA: 0s - loss: 0.2998
 80/116 [===================>..........] - ETA: 0s - loss: 0.3155
116/116 [==============================] - 0s 1ms/step - loss: 0.3045
Epoch 3/10

  1/116 [..............................] - ETA: 0s - loss: 0.2497
 42/116 [=========>....................] - ETA: 0s - loss: 0.2795
 83/116 [====================>.........] - ETA: 0s - loss: 0.2831
116/116 [==============================] - 0s 1ms/step - loss: 0.2771
Epoch 4/10

  1/116 [..............................] - ETA: 0s - loss: 0.0912
 42/116 [=========>....................] - ETA: 0s - loss: 0.2567
 83/116 [====================>.........] - ETA: 0s - loss: 0.2542
116/116 [==============================] - 0s 1ms/step - loss: 0.2615
Epoch 5/10

  1/116 [..............................] - ETA: 0s - loss: 0.5366
 42/116 [=========>....................] - ETA: 0s - loss: 0.3016
 84/116 [====================>.........] - ETA: 0s - loss: 0.2685
116/116 [==============================] - 0s 1ms/step - loss: 0.2555
Epoch 6/10

  1/116 [..............................] - ETA: 0s - loss: 0.4602
 36/116 [========>.....................] - ETA: 0s - loss: 0.2333
 68/116 [================>.............] - ETA: 0s - loss: 0.2523
107/116 [==========================>...] - ETA: 0s - loss: 0.2500
116/116 [==============================] - 0s 1ms/step - loss: 0.2488
Epoch 7/10

  1/116 [..............................] - ETA: 0s - loss: 0.2441
 41/116 [=========>....................] - ETA: 0s - loss: 0.2608
 81/116 [===================>..........] - ETA: 0s - loss: 0.2533
116/116 [==============================] - 0s 1ms/step - loss: 0.2430
Epoch 8/10

  1/116 [..............................] - ETA: 0s - loss: 0.2281
 40/116 [=========>....................] - ETA: 0s - loss: 0.2404
 81/116 [===================>..........] - ETA: 0s - loss: 0.2365
116/116 [==============================] - 0s 1ms/step - loss: 0.2366
Epoch 9/10

  1/116 [..............................] - ETA: 0s - loss: 0.3579
 42/116 [=========>....................] - ETA: 0s - loss: 0.2169
 84/116 [====================>.........] - ETA: 0s - loss: 0.2324
116/116 [==============================] - 0s 1ms/step - loss: 0.2310
Epoch 10/10

  1/116 [..............................] - ETA: 0s - loss: 0.2189
 42/116 [=========>....................] - ETA: 0s - loss: 0.2248
 83/116 [====================>.........] - ETA: 0s - loss: 0.2263
116/116 [==============================] - 0s 1ms/step - loss: 0.2278
-> test with GAN.predict
GAN tn, fp: 256, 34
GAN fn, tp: 1, 6
GAN f1 score: 0.255
GAN cohens kappa score: 0.224

-> test with 'LR'
LR tn, fp: 250, 40
LR fn, tp: 0, 7
LR f1 score: 0.259
LR cohens kappa score: 0.228
LR average precision score: 0.271

-> test with 'RF'
RF tn, fp: 290, 0
RF fn, tp: 5, 2
RF f1 score: 0.444
RF cohens kappa score: 0.439

-> test with 'GB'
GB tn, fp: 289, 1
GB fn, tp: 6, 1
GB f1 score: 0.222
GB cohens kappa score: 0.214

-> test with 'KNN'
KNN tn, fp: 272, 18
KNN fn, tp: 1, 6
KNN f1 score: 0.387
KNN cohens kappa score: 0.364


------ Step 5/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1132 synthetic samples
-> retrain GAN for predict
Epoch 1/10

  1/116 [..............................] - ETA: 42s - loss: 0.3781
 43/116 [==========>...................] - ETA: 0s - loss: 0.3444 
 84/116 [====================>.........] - ETA: 0s - loss: 0.2964
116/116 [==============================] - 1s 1ms/step - loss: 0.2683
Epoch 2/10

  1/116 [..............................] - ETA: 0s - loss: 0.1429
 40/116 [=========>....................] - ETA: 0s - loss: 0.1911
 74/116 [==================>...........] - ETA: 0s - loss: 0.1893
 97/116 [========================>.....] - ETA: 0s - loss: 0.1819
116/116 [==============================] - 0s 2ms/step - loss: 0.1732
Epoch 3/10

  1/116 [..............................] - ETA: 0s - loss: 0.2367
 42/116 [=========>....................] - ETA: 0s - loss: 0.1520
 86/116 [=====================>........] - ETA: 0s - loss: 0.1586
116/116 [==============================] - 0s 1ms/step - loss: 0.1508
Epoch 4/10

  1/116 [..............................] - ETA: 0s - loss: 0.0961
 43/116 [==========>...................] - ETA: 0s - loss: 0.1501
 79/116 [===================>..........] - ETA: 0s - loss: 0.1408
116/116 [==============================] - ETA: 0s - loss: 0.1410
116/116 [==============================] - 0s 1ms/step - loss: 0.1410
Epoch 5/10

  1/116 [..............................] - ETA: 0s - loss: 0.1626
 37/116 [========>.....................] - ETA: 0s - loss: 0.1514
 72/116 [=================>............] - ETA: 0s - loss: 0.1413
115/116 [============================>.] - ETA: 0s - loss: 0.1394
116/116 [==============================] - 0s 1ms/step - loss: 0.1405
Epoch 6/10

  1/116 [..............................] - ETA: 0s - loss: 0.0555
 45/116 [==========>...................] - ETA: 0s - loss: 0.1387
 87/116 [=====================>........] - ETA: 0s - loss: 0.1340
116/116 [==============================] - 0s 1ms/step - loss: 0.1337
Epoch 7/10

  1/116 [..............................] - ETA: 0s - loss: 0.1434
 45/116 [==========>...................] - ETA: 0s - loss: 0.1189
 89/116 [======================>.......] - ETA: 0s - loss: 0.1308
116/116 [==============================] - 0s 1ms/step - loss: 0.1336
Epoch 8/10

  1/116 [..............................] - ETA: 0s - loss: 0.0397
 44/116 [==========>...................] - ETA: 0s - loss: 0.1175
 86/116 [=====================>........] - ETA: 0s - loss: 0.1293
116/116 [==============================] - 0s 1ms/step - loss: 0.1269
Epoch 9/10

  1/116 [..............................] - ETA: 0s - loss: 0.1073
 45/116 [==========>...................] - ETA: 0s - loss: 0.1141
 89/116 [======================>.......] - ETA: 0s - loss: 0.1255
116/116 [==============================] - 0s 1ms/step - loss: 0.1249
Epoch 10/10

  1/116 [..............................] - ETA: 0s - loss: 0.0296
 44/116 [==========>...................] - ETA: 0s - loss: 0.1333
 86/116 [=====================>........] - ETA: 0s - loss: 0.1289
116/116 [==============================] - 0s 1ms/step - loss: 0.1268
-> test with GAN.predict
GAN tn, fp: 279, 10
GAN fn, tp: 2, 5
GAN f1 score: 0.455
GAN cohens kappa score: 0.436

-> test with 'LR'
LR tn, fp: 269, 20
LR fn, tp: 2, 5
LR f1 score: 0.312
LR cohens kappa score: 0.286
LR average precision score: 0.419

-> test with 'RF'
RF tn, fp: 288, 1
RF fn, tp: 5, 2
RF f1 score: 0.400
RF cohens kappa score: 0.391

-> test with 'GB'
GB tn, fp: 288, 1
GB fn, tp: 5, 2
GB f1 score: 0.400
GB cohens kappa score: 0.391

-> test with 'KNN'
KNN tn, fp: 267, 22
KNN fn, tp: 2, 5
KNN f1 score: 0.294
KNN cohens kappa score: 0.267

### Exercise is done.

-----[ LR ]-----
maximum:
LR tn, fp: 272, 45
LR fn, tp: 3, 7
LR f1 score: 0.387
LR cohens kappa score: 0.364
LR average precision score: 0.757


average:
LR tn, fp: 260.24, 29.56
LR fn, tp: 0.96, 6.04
LR f1 score: 0.290
LR cohens kappa score: 0.261
LR average precision score: 0.501


minimum:
LR tn, fp: 244, 18
LR fn, tp: 0, 4
LR f1 score: 0.217
LR cohens kappa score: 0.185
LR average precision score: 0.229


-----[ RF ]-----
maximum:
RF tn, fp: 290, 4
RF fn, tp: 7, 6
RF f1 score: 0.857
RF cohens kappa score: 0.854


average:
RF tn, fp: 288.64, 1.16
RF fn, tp: 4.08, 2.92
RF f1 score: 0.510
RF cohens kappa score: 0.503


minimum:
RF tn, fp: 286, 0
RF fn, tp: 1, 0
RF f1 score: 0.000
RF cohens kappa score: 0.000


-----[ GB ]-----
maximum:
GB tn, fp: 290, 5
GB fn, tp: 7, 6
GB f1 score: 0.800
GB cohens kappa score: 0.795


average:
GB tn, fp: 287.84, 1.96
GB fn, tp: 4.0, 3.0
GB f1 score: 0.481
GB cohens kappa score: 0.472


minimum:
GB tn, fp: 285, 0
GB fn, tp: 1, 0
GB f1 score: 0.000
GB cohens kappa score: -0.006


-----[ KNN ]-----
maximum:
KNN tn, fp: 278, 33
KNN fn, tp: 3, 7
KNN f1 score: 0.480
KNN cohens kappa score: 0.462


average:
KNN tn, fp: 268.36, 21.44
KNN fn, tp: 1.4, 5.6
KNN f1 score: 0.334
KNN cohens kappa score: 0.308


minimum:
KNN tn, fp: 257, 12
KNN fn, tp: 0, 4
KNN f1 score: 0.256
KNN cohens kappa score: 0.226


-----[ GAN ]-----
maximum:
GAN tn, fp: 280, 34
GAN fn, tp: 3, 7
GAN f1 score: 0.476
GAN cohens kappa score: 0.459


average:
GAN tn, fp: 270.24, 19.56
GAN fn, tp: 1.4, 5.6
GAN f1 score: 0.358
GAN cohens kappa score: 0.334


minimum:
GAN tn, fp: 256, 9
GAN fn, tp: 0, 4
GAN f1 score: 0.255
GAN cohens kappa score: 0.224