ConvGeNBenchmarkResults/data_result/convGAN-proximity-5/folding_abalone9-18.log

///////////////////////////////////////////
// Running convGAN-proximary-5 on folding_abalone9-18
///////////////////////////////////////////

Load 'data_input/folding_abalone9-18'
from pickle file
Data loaded.
-> Shuffling data
### Start exercise for synthetic point generator

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

------ Step 1/5: Slice 1/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 518 synthetic samples
-> retrain GAN for predict
Epoch 1/10

 1/56 [..............................] - ETA: 7s - loss: 0.3139
48/56 [========================>.....] - ETA: 0s - loss: 0.3170
56/56 [==============================] - 0s 1ms/step - loss: 0.3105
Epoch 2/10

 1/56 [..............................] - ETA: 0s - loss: 0.2118
49/56 [=========================>....] - ETA: 0s - loss: 0.2738
56/56 [==============================] - 0s 1ms/step - loss: 0.2798
Epoch 3/10

 1/56 [..............................] - ETA: 0s - loss: 0.3107
49/56 [=========================>....] - ETA: 0s - loss: 0.2656
56/56 [==============================] - 0s 1ms/step - loss: 0.2692
Epoch 4/10

 1/56 [..............................] - ETA: 0s - loss: 0.2456
50/56 [=========================>....] - ETA: 0s - loss: 0.2558
56/56 [==============================] - 0s 1ms/step - loss: 0.2661
Epoch 5/10

 1/56 [..............................] - ETA: 0s - loss: 0.2137
49/56 [=========================>....] - ETA: 0s - loss: 0.2639
56/56 [==============================] - 0s 1ms/step - loss: 0.2594
Epoch 6/10

 1/56 [..............................] - ETA: 0s - loss: 0.3034
48/56 [========================>.....] - ETA: 0s - loss: 0.2571
56/56 [==============================] - 0s 1ms/step - loss: 0.2606
Epoch 7/10

 1/56 [..............................] - ETA: 0s - loss: 0.2322
49/56 [=========================>....] - ETA: 0s - loss: 0.2535
56/56 [==============================] - 0s 1ms/step - loss: 0.2596
Epoch 8/10

 1/56 [..............................] - ETA: 0s - loss: 0.1629
49/56 [=========================>....] - ETA: 0s - loss: 0.2469
56/56 [==============================] - 0s 1ms/step - loss: 0.2525
Epoch 9/10

 1/56 [..............................] - ETA: 0s - loss: 0.2681
49/56 [=========================>....] - ETA: 0s - loss: 0.2412
56/56 [==============================] - 0s 1ms/step - loss: 0.2452
Epoch 10/10

 1/56 [..............................] - ETA: 0s - loss: 0.1973
46/56 [=======================>......] - ETA: 0s - loss: 0.2521
56/56 [==============================] - 0s 1ms/step - loss: 0.2437
-> test with GAN.predict
GAN tn, fp: 123, 15
GAN fn, tp: 0, 9
GAN f1 score: 0.545
GAN cohens kappa score: 0.501

-> test with 'LR'
LR tn, fp: 121, 17
LR fn, tp: 0, 9
LR f1 score: 0.514
LR cohens kappa score: 0.466
LR average precision score: 0.900

-> test with 'RF'
RF tn, fp: 135, 3
RF fn, tp: 8, 1
RF f1 score: 0.154
RF cohens kappa score: 0.121

-> test with 'GB'
GB tn, fp: 131, 7
GB fn, tp: 6, 3
GB f1 score: 0.316
GB cohens kappa score: 0.269

-> test with 'KNN'
KNN tn, fp: 127, 11
KNN fn, tp: 5, 4
KNN f1 score: 0.333
KNN cohens kappa score: 0.278


------ Step 1/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 518 synthetic samples
-> retrain GAN for predict
Epoch 1/10

 1/56 [..............................] - ETA: 7s - loss: 0.2407
48/56 [========================>.....] - ETA: 0s - loss: 0.2346
56/56 [==============================] - 0s 1ms/step - loss: 0.2280
Epoch 2/10

 1/56 [..............................] - ETA: 0s - loss: 0.3300
50/56 [=========================>....] - ETA: 0s - loss: 0.2117
56/56 [==============================] - 0s 1ms/step - loss: 0.2228
Epoch 3/10

 1/56 [..............................] - ETA: 0s - loss: 0.1163
49/56 [=========================>....] - ETA: 0s - loss: 0.1994
56/56 [==============================] - 0s 1ms/step - loss: 0.2135
Epoch 4/10

 1/56 [..............................] - ETA: 0s - loss: 0.0709
50/56 [=========================>....] - ETA: 0s - loss: 0.2164
56/56 [==============================] - 0s 1ms/step - loss: 0.2159
Epoch 5/10

 1/56 [..............................] - ETA: 0s - loss: 0.1597
49/56 [=========================>....] - ETA: 0s - loss: 0.2100
56/56 [==============================] - 0s 1ms/step - loss: 0.2090
Epoch 6/10

 1/56 [..............................] - ETA: 0s - loss: 0.1629
49/56 [=========================>....] - ETA: 0s - loss: 0.2090
56/56 [==============================] - 0s 1ms/step - loss: 0.2055
Epoch 7/10

 1/56 [..............................] - ETA: 0s - loss: 0.3150
49/56 [=========================>....] - ETA: 0s - loss: 0.1964
56/56 [==============================] - 0s 1ms/step - loss: 0.2037
Epoch 8/10

 1/56 [..............................] - ETA: 0s - loss: 0.1107
49/56 [=========================>....] - ETA: 0s - loss: 0.2042
56/56 [==============================] - 0s 1ms/step - loss: 0.2030
Epoch 9/10

 1/56 [..............................] - ETA: 0s - loss: 0.2163
50/56 [=========================>....] - ETA: 0s - loss: 0.1992
56/56 [==============================] - 0s 1ms/step - loss: 0.2018
Epoch 10/10

 1/56 [..............................] - ETA: 0s - loss: 0.1248
49/56 [=========================>....] - ETA: 0s - loss: 0.2044
56/56 [==============================] - 0s 1ms/step - loss: 0.2029
-> test with GAN.predict
GAN tn, fp: 122, 16
GAN fn, tp: 2, 7
GAN f1 score: 0.438
GAN cohens kappa score: 0.383

-> test with 'LR'
LR tn, fp: 128, 10
LR fn, tp: 3, 6
LR f1 score: 0.480
LR cohens kappa score: 0.436
LR average precision score: 0.573

-> test with 'RF'
RF tn, fp: 136, 2
RF fn, tp: 7, 2
RF f1 score: 0.308
RF cohens kappa score: 0.281

-> test with 'GB'
GB tn, fp: 132, 6
GB fn, tp: 5, 4
GB f1 score: 0.421
GB cohens kappa score: 0.381

-> test with 'KNN'
KNN tn, fp: 118, 20
KNN fn, tp: 3, 6
KNN f1 score: 0.343
KNN cohens kappa score: 0.277


------ Step 1/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 518 synthetic samples
-> retrain GAN for predict
Epoch 1/10

 1/56 [..............................] - ETA: 7s - loss: 0.2635
48/56 [========================>.....] - ETA: 0s - loss: 0.2359
56/56 [==============================] - 0s 1ms/step - loss: 0.2345
Epoch 2/10

 1/56 [..............................] - ETA: 0s - loss: 0.2846
49/56 [=========================>....] - ETA: 0s - loss: 0.2214
56/56 [==============================] - 0s 1ms/step - loss: 0.2227
Epoch 3/10

 1/56 [..............................] - ETA: 0s - loss: 0.1574
49/56 [=========================>....] - ETA: 0s - loss: 0.2240
56/56 [==============================] - 0s 1ms/step - loss: 0.2171
Epoch 4/10

 1/56 [..............................] - ETA: 0s - loss: 0.2210
48/56 [========================>.....] - ETA: 0s - loss: 0.2208
56/56 [==============================] - 0s 1ms/step - loss: 0.2166
Epoch 5/10

 1/56 [..............................] - ETA: 0s - loss: 0.2292
49/56 [=========================>....] - ETA: 0s - loss: 0.2114
56/56 [==============================] - 0s 1ms/step - loss: 0.2076
Epoch 6/10

 1/56 [..............................] - ETA: 0s - loss: 0.1754
49/56 [=========================>....] - ETA: 0s - loss: 0.2012
56/56 [==============================] - 0s 1ms/step - loss: 0.2072
Epoch 7/10

 1/56 [..............................] - ETA: 0s - loss: 0.1733
50/56 [=========================>....] - ETA: 0s - loss: 0.2011
56/56 [==============================] - 0s 1ms/step - loss: 0.2066
Epoch 8/10

 1/56 [..............................] - ETA: 0s - loss: 0.1175
50/56 [=========================>....] - ETA: 0s - loss: 0.2050
56/56 [==============================] - 0s 1ms/step - loss: 0.2051
Epoch 9/10

 1/56 [..............................] - ETA: 0s - loss: 0.2323
50/56 [=========================>....] - ETA: 0s - loss: 0.2060
56/56 [==============================] - 0s 1ms/step - loss: 0.2056
Epoch 10/10

 1/56 [..............................] - ETA: 0s - loss: 0.1022
49/56 [=========================>....] - ETA: 0s - loss: 0.2028
56/56 [==============================] - 0s 1ms/step - loss: 0.1999
-> test with GAN.predict
GAN tn, fp: 129, 9
GAN fn, tp: 2, 7
GAN f1 score: 0.560
GAN cohens kappa score: 0.523

-> test with 'LR'
LR tn, fp: 127, 11
LR fn, tp: 0, 9
LR f1 score: 0.621
LR cohens kappa score: 0.586
LR average precision score: 0.805

-> test with 'RF'
RF tn, fp: 133, 5
RF fn, tp: 6, 3
RF f1 score: 0.353
RF cohens kappa score: 0.313

-> test with 'GB'
GB tn, fp: 131, 7
GB fn, tp: 6, 3
GB f1 score: 0.316
GB cohens kappa score: 0.269

-> test with 'KNN'
KNN tn, fp: 124, 14
KNN fn, tp: 2, 7
KNN f1 score: 0.467
KNN cohens kappa score: 0.417


------ Step 1/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 518 synthetic samples
-> retrain GAN for predict
Epoch 1/10

 1/56 [..............................] - ETA: 8s - loss: 0.1883
48/56 [========================>.....] - ETA: 0s - loss: 0.2052
56/56 [==============================] - 0s 1ms/step - loss: 0.2019
Epoch 2/10

 1/56 [..............................] - ETA: 0s - loss: 0.1815
45/56 [=======================>......] - ETA: 0s - loss: 0.1912
56/56 [==============================] - 0s 1ms/step - loss: 0.1940
Epoch 3/10

 1/56 [..............................] - ETA: 0s - loss: 0.1067
49/56 [=========================>....] - ETA: 0s - loss: 0.1914
56/56 [==============================] - 0s 1ms/step - loss: 0.1935
Epoch 4/10

 1/56 [..............................] - ETA: 0s - loss: 0.1867
50/56 [=========================>....] - ETA: 0s - loss: 0.1895
56/56 [==============================] - 0s 1ms/step - loss: 0.1848
Epoch 5/10

 1/56 [..............................] - ETA: 0s - loss: 0.1981
50/56 [=========================>....] - ETA: 0s - loss: 0.1763
56/56 [==============================] - 0s 1ms/step - loss: 0.1816
Epoch 6/10

 1/56 [..............................] - ETA: 0s - loss: 0.0840
49/56 [=========================>....] - ETA: 0s - loss: 0.1715
56/56 [==============================] - 0s 1ms/step - loss: 0.1821
Epoch 7/10

 1/56 [..............................] - ETA: 0s - loss: 0.1028
43/56 [======================>.......] - ETA: 0s - loss: 0.1836
56/56 [==============================] - 0s 1ms/step - loss: 0.1797
Epoch 8/10

 1/56 [..............................] - ETA: 0s - loss: 0.1717
41/56 [====================>.........] - ETA: 0s - loss: 0.2001
56/56 [==============================] - 0s 1ms/step - loss: 0.1828
Epoch 9/10

 1/56 [..............................] - ETA: 0s - loss: 0.3013
49/56 [=========================>....] - ETA: 0s - loss: 0.1804
56/56 [==============================] - 0s 1ms/step - loss: 0.1774
Epoch 10/10

 1/56 [..............................] - ETA: 0s - loss: 0.1836
48/56 [========================>.....] - ETA: 0s - loss: 0.1884
56/56 [==============================] - 0s 1ms/step - loss: 0.1765
-> test with GAN.predict
GAN tn, fp: 127, 11
GAN fn, tp: 2, 7
GAN f1 score: 0.519
GAN cohens kappa score: 0.476

-> test with 'LR'
LR tn, fp: 131, 7
LR fn, tp: 1, 8
LR f1 score: 0.667
LR cohens kappa score: 0.639
LR average precision score: 0.669

-> test with 'RF'
RF tn, fp: 137, 1
RF fn, tp: 5, 4
RF f1 score: 0.571
RF cohens kappa score: 0.552

-> test with 'GB'
GB tn, fp: 136, 2
GB fn, tp: 5, 4
GB f1 score: 0.533
GB cohens kappa score: 0.509

-> test with 'KNN'
KNN tn, fp: 124, 14
KNN fn, tp: 2, 7
KNN f1 score: 0.467
KNN cohens kappa score: 0.417


------ Step 1/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 516 synthetic samples
-> retrain GAN for predict
Epoch 1/10

 1/56 [..............................] - ETA: 8s - loss: 0.3542
47/56 [========================>.....] - ETA: 0s - loss: 0.2176
56/56 [==============================] - 0s 1ms/step - loss: 0.2063
Epoch 2/10

 1/56 [..............................] - ETA: 0s - loss: 0.1315
48/56 [========================>.....] - ETA: 0s - loss: 0.1924
56/56 [==============================] - 0s 1ms/step - loss: 0.1923
Epoch 3/10

 1/56 [..............................] - ETA: 0s - loss: 0.1444
47/56 [========================>.....] - ETA: 0s - loss: 0.1957
56/56 [==============================] - 0s 1ms/step - loss: 0.1920
Epoch 4/10

 1/56 [..............................] - ETA: 0s - loss: 0.3794
48/56 [========================>.....] - ETA: 0s - loss: 0.1849
56/56 [==============================] - 0s 1ms/step - loss: 0.1904
Epoch 5/10

 1/56 [..............................] - ETA: 0s - loss: 0.1571
48/56 [========================>.....] - ETA: 0s - loss: 0.1909
56/56 [==============================] - 0s 1ms/step - loss: 0.1883
Epoch 6/10

 1/56 [..............................] - ETA: 0s - loss: 0.1487
49/56 [=========================>....] - ETA: 0s - loss: 0.1864
56/56 [==============================] - 0s 1ms/step - loss: 0.1890
Epoch 7/10

 1/56 [..............................] - ETA: 0s - loss: 0.1989
49/56 [=========================>....] - ETA: 0s - loss: 0.1829
56/56 [==============================] - 0s 1ms/step - loss: 0.1871
Epoch 8/10

 1/56 [..............................] - ETA: 0s - loss: 0.1690
49/56 [=========================>....] - ETA: 0s - loss: 0.1946
56/56 [==============================] - 0s 1ms/step - loss: 0.1880
Epoch 9/10

 1/56 [..............................] - ETA: 0s - loss: 0.2217
47/56 [========================>.....] - ETA: 0s - loss: 0.1890
56/56 [==============================] - 0s 1ms/step - loss: 0.1835
Epoch 10/10

 1/56 [..............................] - ETA: 0s - loss: 0.1385
49/56 [=========================>....] - ETA: 0s - loss: 0.1833
56/56 [==============================] - 0s 1ms/step - loss: 0.1823
-> test with GAN.predict
GAN tn, fp: 128, 9
GAN fn, tp: 2, 4
GAN f1 score: 0.421
GAN cohens kappa score: 0.386

-> test with 'LR'
LR tn, fp: 128, 9
LR fn, tp: 1, 5
LR f1 score: 0.500
LR cohens kappa score: 0.469
LR average precision score: 0.488

-> test with 'RF'
RF tn, fp: 134, 3
RF fn, tp: 4, 2
RF f1 score: 0.364
RF cohens kappa score: 0.338

-> test with 'GB'
GB tn, fp: 135, 2
GB fn, tp: 4, 2
GB f1 score: 0.400
GB cohens kappa score: 0.379

-> test with 'KNN'
KNN tn, fp: 127, 10
KNN fn, tp: 4, 2
KNN f1 score: 0.222
KNN cohens kappa score: 0.176


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

------ Step 2/5: Slice 1/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 518 synthetic samples
-> retrain GAN for predict
Epoch 1/10

 1/56 [..............................] - ETA: 9s - loss: 0.5198
46/56 [=======================>......] - ETA: 0s - loss: 0.3123
56/56 [==============================] - 0s 1ms/step - loss: 0.3063
Epoch 2/10

 1/56 [..............................] - ETA: 0s - loss: 0.1574
49/56 [=========================>....] - ETA: 0s - loss: 0.2654
56/56 [==============================] - 0s 1ms/step - loss: 0.2622
Epoch 3/10

 1/56 [..............................] - ETA: 0s - loss: 0.2367
48/56 [========================>.....] - ETA: 0s - loss: 0.2370
56/56 [==============================] - 0s 1ms/step - loss: 0.2432
Epoch 4/10

 1/56 [..............................] - ETA: 0s - loss: 0.1719
49/56 [=========================>....] - ETA: 0s - loss: 0.2384
56/56 [==============================] - 0s 1ms/step - loss: 0.2345
Epoch 5/10

 1/56 [..............................] - ETA: 0s - loss: 0.1483
44/56 [======================>.......] - ETA: 0s - loss: 0.2310
56/56 [==============================] - 0s 1ms/step - loss: 0.2326
Epoch 6/10

 1/56 [..............................] - ETA: 0s - loss: 0.1500
44/56 [======================>.......] - ETA: 0s - loss: 0.2235
56/56 [==============================] - 0s 1ms/step - loss: 0.2216
Epoch 7/10

 1/56 [..............................] - ETA: 0s - loss: 0.1761
49/56 [=========================>....] - ETA: 0s - loss: 0.2160
56/56 [==============================] - 0s 1ms/step - loss: 0.2173
Epoch 8/10

 1/56 [..............................] - ETA: 0s - loss: 0.2005
45/56 [=======================>......] - ETA: 0s - loss: 0.2181
56/56 [==============================] - 0s 1ms/step - loss: 0.2157
Epoch 9/10

 1/56 [..............................] - ETA: 0s - loss: 0.0900
49/56 [=========================>....] - ETA: 0s - loss: 0.2123
56/56 [==============================] - 0s 1ms/step - loss: 0.2136
Epoch 10/10

 1/56 [..............................] - ETA: 0s - loss: 0.1490
49/56 [=========================>....] - ETA: 0s - loss: 0.2099
56/56 [==============================] - 0s 1ms/step - loss: 0.2083
-> test with GAN.predict
GAN tn, fp: 130, 8
GAN fn, tp: 3, 6
GAN f1 score: 0.522
GAN cohens kappa score: 0.483

-> test with 'LR'
LR tn, fp: 121, 17
LR fn, tp: 1, 8
LR f1 score: 0.471
LR cohens kappa score: 0.418
LR average precision score: 0.615

-> test with 'RF'
RF tn, fp: 136, 2
RF fn, tp: 5, 4
RF f1 score: 0.533
RF cohens kappa score: 0.509

-> test with 'GB'
GB tn, fp: 136, 2
GB fn, tp: 5, 4
GB f1 score: 0.533
GB cohens kappa score: 0.509

-> test with 'KNN'
KNN tn, fp: 124, 14
KNN fn, tp: 3, 6
KNN f1 score: 0.414
KNN cohens kappa score: 0.360


------ Step 2/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 518 synthetic samples
-> retrain GAN for predict
Epoch 1/10

 1/56 [..............................] - ETA: 7s - loss: 0.2087
45/56 [=======================>......] - ETA: 0s - loss: 0.2563
56/56 [==============================] - 0s 1ms/step - loss: 0.2467
Epoch 2/10

 1/56 [..............................] - ETA: 0s - loss: 0.3892
49/56 [=========================>....] - ETA: 0s - loss: 0.2263
56/56 [==============================] - 0s 1ms/step - loss: 0.2234
Epoch 3/10

 1/56 [..............................] - ETA: 0s - loss: 0.2216
50/56 [=========================>....] - ETA: 0s - loss: 0.2153
56/56 [==============================] - 0s 1ms/step - loss: 0.2153
Epoch 4/10

 1/56 [..............................] - ETA: 0s - loss: 0.4768
49/56 [=========================>....] - ETA: 0s - loss: 0.2135
56/56 [==============================] - 0s 1ms/step - loss: 0.2094
Epoch 5/10

 1/56 [..............................] - ETA: 0s - loss: 0.0968
50/56 [=========================>....] - ETA: 0s - loss: 0.2049
56/56 [==============================] - 0s 1ms/step - loss: 0.2061
Epoch 6/10

 1/56 [..............................] - ETA: 0s - loss: 0.1665
48/56 [========================>.....] - ETA: 0s - loss: 0.2112
56/56 [==============================] - 0s 1ms/step - loss: 0.2057
Epoch 7/10

 1/56 [..............................] - ETA: 0s - loss: 0.1630
48/56 [========================>.....] - ETA: 0s - loss: 0.2047
56/56 [==============================] - 0s 1ms/step - loss: 0.2038
Epoch 8/10

 1/56 [..............................] - ETA: 0s - loss: 0.3013
50/56 [=========================>....] - ETA: 0s - loss: 0.1948
56/56 [==============================] - 0s 1ms/step - loss: 0.1963
Epoch 9/10

 1/56 [..............................] - ETA: 0s - loss: 0.3779
49/56 [=========================>....] - ETA: 0s - loss: 0.1951
56/56 [==============================] - 0s 1ms/step - loss: 0.1955
Epoch 10/10

 1/56 [..............................] - ETA: 0s - loss: 0.2761
50/56 [=========================>....] - ETA: 0s - loss: 0.1847
56/56 [==============================] - 0s 1ms/step - loss: 0.1937
-> test with GAN.predict
GAN tn, fp: 131, 7
GAN fn, tp: 2, 7
GAN f1 score: 0.609
GAN cohens kappa score: 0.577

-> test with 'LR'
LR tn, fp: 131, 7
LR fn, tp: 1, 8
LR f1 score: 0.667
LR cohens kappa score: 0.639
LR average precision score: 0.779

-> test with 'RF'
RF tn, fp: 137, 1
RF fn, tp: 5, 4
RF f1 score: 0.571
RF cohens kappa score: 0.552

-> test with 'GB'
GB tn, fp: 132, 6
GB fn, tp: 5, 4
GB f1 score: 0.421
GB cohens kappa score: 0.381

-> test with 'KNN'
KNN tn, fp: 128, 10
KNN fn, tp: 3, 6
KNN f1 score: 0.480
KNN cohens kappa score: 0.436


------ Step 2/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 518 synthetic samples
-> retrain GAN for predict
Epoch 1/10

 1/56 [..............................] - ETA: 7s - loss: 0.3832
49/56 [=========================>....] - ETA: 0s - loss: 0.2224
56/56 [==============================] - 0s 1ms/step - loss: 0.2263
Epoch 2/10

 1/56 [..............................] - ETA: 0s - loss: 0.1226
50/56 [=========================>....] - ETA: 0s - loss: 0.2063
56/56 [==============================] - 0s 1ms/step - loss: 0.2012
Epoch 3/10

 1/56 [..............................] - ETA: 0s - loss: 0.1903
49/56 [=========================>....] - ETA: 0s - loss: 0.1957
56/56 [==============================] - 0s 1ms/step - loss: 0.1927
Epoch 4/10

 1/56 [..............................] - ETA: 0s - loss: 0.1583
47/56 [========================>.....] - ETA: 0s - loss: 0.1902
56/56 [==============================] - 0s 1ms/step - loss: 0.1873
Epoch 5/10

 1/56 [..............................] - ETA: 0s - loss: 0.1104
48/56 [========================>.....] - ETA: 0s - loss: 0.1877
56/56 [==============================] - 0s 1ms/step - loss: 0.1853
Epoch 6/10

 1/56 [..............................] - ETA: 0s - loss: 0.3978
49/56 [=========================>....] - ETA: 0s - loss: 0.1900
56/56 [==============================] - 0s 1ms/step - loss: 0.1890
Epoch 7/10

 1/56 [..............................] - ETA: 0s - loss: 0.1967
49/56 [=========================>....] - ETA: 0s - loss: 0.1893
56/56 [==============================] - 0s 1ms/step - loss: 0.1847
Epoch 8/10

 1/56 [..............................] - ETA: 0s - loss: 0.0754
49/56 [=========================>....] - ETA: 0s - loss: 0.1870
56/56 [==============================] - 0s 1ms/step - loss: 0.1811
Epoch 9/10

 1/56 [..............................] - ETA: 0s - loss: 0.1500
46/56 [=======================>......] - ETA: 0s - loss: 0.1763
56/56 [==============================] - 0s 1ms/step - loss: 0.1799
Epoch 10/10

 1/56 [..............................] - ETA: 0s - loss: 0.1444
46/56 [=======================>......] - ETA: 0s - loss: 0.1732
56/56 [==============================] - 0s 1ms/step - loss: 0.1772
-> test with GAN.predict
GAN tn, fp: 131, 7
GAN fn, tp: 2, 7
GAN f1 score: 0.609
GAN cohens kappa score: 0.577

-> test with 'LR'
LR tn, fp: 133, 5
LR fn, tp: 2, 7
LR f1 score: 0.667
LR cohens kappa score: 0.642
LR average precision score: 0.692

-> test with 'RF'
RF tn, fp: 134, 4
RF fn, tp: 7, 2
RF f1 score: 0.267
RF cohens kappa score: 0.229

-> test with 'GB'
GB tn, fp: 130, 8
GB fn, tp: 6, 3
GB f1 score: 0.300
GB cohens kappa score: 0.249

-> test with 'KNN'
KNN tn, fp: 122, 16
KNN fn, tp: 1, 8
KNN f1 score: 0.485
KNN cohens kappa score: 0.434


------ Step 2/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 518 synthetic samples
-> retrain GAN for predict
Epoch 1/10

 1/56 [..............................] - ETA: 7s - loss: 0.1423
48/56 [========================>.....] - ETA: 0s - loss: 0.2661
56/56 [==============================] - 0s 1ms/step - loss: 0.2678
Epoch 2/10

 1/56 [..............................] - ETA: 0s - loss: 0.1593
50/56 [=========================>....] - ETA: 0s - loss: 0.2483
56/56 [==============================] - 0s 1ms/step - loss: 0.2468
Epoch 3/10

 1/56 [..............................] - ETA: 0s - loss: 0.1922
49/56 [=========================>....] - ETA: 0s - loss: 0.2419
56/56 [==============================] - 0s 1ms/step - loss: 0.2438
Epoch 4/10

 1/56 [..............................] - ETA: 0s - loss: 0.1893
50/56 [=========================>....] - ETA: 0s - loss: 0.2372
56/56 [==============================] - 0s 1ms/step - loss: 0.2374
Epoch 5/10

 1/56 [..............................] - ETA: 0s - loss: 0.3468
49/56 [=========================>....] - ETA: 0s - loss: 0.2322
56/56 [==============================] - 0s 1ms/step - loss: 0.2358
Epoch 6/10

 1/56 [..............................] - ETA: 0s - loss: 0.3377
50/56 [=========================>....] - ETA: 0s - loss: 0.2306
56/56 [==============================] - 0s 1ms/step - loss: 0.2333
Epoch 7/10

 1/56 [..............................] - ETA: 0s - loss: 0.1090
50/56 [=========================>....] - ETA: 0s - loss: 0.2296
56/56 [==============================] - 0s 1ms/step - loss: 0.2234
Epoch 8/10

 1/56 [..............................] - ETA: 0s - loss: 0.2045
50/56 [=========================>....] - ETA: 0s - loss: 0.2256
56/56 [==============================] - 0s 1ms/step - loss: 0.2252
Epoch 9/10

 1/56 [..............................] - ETA: 0s - loss: 0.3298
50/56 [=========================>....] - ETA: 0s - loss: 0.2233
56/56 [==============================] - 0s 1ms/step - loss: 0.2236
Epoch 10/10

 1/56 [..............................] - ETA: 0s - loss: 0.2426
50/56 [=========================>....] - ETA: 0s - loss: 0.2205
56/56 [==============================] - 0s 1ms/step - loss: 0.2218
-> test with GAN.predict
GAN tn, fp: 126, 12
GAN fn, tp: 2, 7
GAN f1 score: 0.500
GAN cohens kappa score: 0.455

-> test with 'LR'
LR tn, fp: 126, 12
LR fn, tp: 1, 8
LR f1 score: 0.552
LR cohens kappa score: 0.510
LR average precision score: 0.722

-> test with 'RF'
RF tn, fp: 132, 6
RF fn, tp: 6, 3
RF f1 score: 0.333
RF cohens kappa score: 0.290

-> test with 'GB'
GB tn, fp: 133, 5
GB fn, tp: 6, 3
GB f1 score: 0.353
GB cohens kappa score: 0.313

-> test with 'KNN'
KNN tn, fp: 117, 21
KNN fn, tp: 3, 6
KNN f1 score: 0.333
KNN cohens kappa score: 0.266


------ Step 2/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 516 synthetic samples
-> retrain GAN for predict
Epoch 1/10

 1/56 [..............................] - ETA: 7s - loss: 0.3023
46/56 [=======================>......] - ETA: 0s - loss: 0.3004
56/56 [==============================] - 0s 1ms/step - loss: 0.2928
Epoch 2/10

 1/56 [..............................] - ETA: 0s - loss: 0.2360
46/56 [=======================>......] - ETA: 0s - loss: 0.2585
56/56 [==============================] - 0s 1ms/step - loss: 0.2608
Epoch 3/10

 1/56 [..............................] - ETA: 0s - loss: 0.1936
45/56 [=======================>......] - ETA: 0s - loss: 0.2493
56/56 [==============================] - 0s 1ms/step - loss: 0.2479
Epoch 4/10

 1/56 [..............................] - ETA: 0s - loss: 0.3681
40/56 [====================>.........] - ETA: 0s - loss: 0.2417
56/56 [==============================] - 0s 1ms/step - loss: 0.2411
Epoch 5/10

 1/56 [..............................] - ETA: 0s - loss: 0.1630
46/56 [=======================>......] - ETA: 0s - loss: 0.2347
56/56 [==============================] - 0s 1ms/step - loss: 0.2363
Epoch 6/10

 1/56 [..............................] - ETA: 0s - loss: 0.3446
45/56 [=======================>......] - ETA: 0s - loss: 0.2383
56/56 [==============================] - 0s 1ms/step - loss: 0.2308
Epoch 7/10

 1/56 [..............................] - ETA: 0s - loss: 0.1040
43/56 [======================>.......] - ETA: 0s - loss: 0.2255
56/56 [==============================] - 0s 1ms/step - loss: 0.2330
Epoch 8/10

 1/56 [..............................] - ETA: 0s - loss: 0.3649
39/56 [===================>..........] - ETA: 0s - loss: 0.2345
56/56 [==============================] - 0s 1ms/step - loss: 0.2300
Epoch 9/10

 1/56 [..............................] - ETA: 0s - loss: 0.1428
38/56 [===================>..........] - ETA: 0s - loss: 0.2273
56/56 [==============================] - 0s 1ms/step - loss: 0.2277
Epoch 10/10

 1/56 [..............................] - ETA: 0s - loss: 0.3110
43/56 [======================>.......] - ETA: 0s - loss: 0.2220
56/56 [==============================] - 0s 1ms/step - loss: 0.2238
-> test with GAN.predict
GAN tn, fp: 125, 12
GAN fn, tp: 1, 5
GAN f1 score: 0.435
GAN cohens kappa score: 0.397

-> test with 'LR'
LR tn, fp: 128, 9
LR fn, tp: 1, 5
LR f1 score: 0.500
LR cohens kappa score: 0.469
LR average precision score: 0.660

-> test with 'RF'
RF tn, fp: 133, 4
RF fn, tp: 3, 3
RF f1 score: 0.462
RF cohens kappa score: 0.436

-> test with 'GB'
GB tn, fp: 128, 9
GB fn, tp: 3, 3
GB f1 score: 0.333
GB cohens kappa score: 0.294

-> test with 'KNN'
KNN tn, fp: 121, 16
KNN fn, tp: 2, 4
KNN f1 score: 0.308
KNN cohens kappa score: 0.260


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

------ Step 3/5: Slice 1/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 518 synthetic samples
-> retrain GAN for predict
Epoch 1/10

 1/56 [..............................] - ETA: 7s - loss: 0.1362
50/56 [=========================>....] - ETA: 0s - loss: 0.2042
56/56 [==============================] - 0s 1ms/step - loss: 0.2049
Epoch 2/10

 1/56 [..............................] - ETA: 0s - loss: 0.2579
49/56 [=========================>....] - ETA: 0s - loss: 0.1885
56/56 [==============================] - 0s 1ms/step - loss: 0.1844
Epoch 3/10

 1/56 [..............................] - ETA: 0s - loss: 0.2180
49/56 [=========================>....] - ETA: 0s - loss: 0.1706
56/56 [==============================] - 0s 1ms/step - loss: 0.1780
Epoch 4/10

 1/56 [..............................] - ETA: 0s - loss: 0.2120
49/56 [=========================>....] - ETA: 0s - loss: 0.1832
56/56 [==============================] - 0s 1ms/step - loss: 0.1756
Epoch 5/10

 1/56 [..............................] - ETA: 0s - loss: 0.0857
49/56 [=========================>....] - ETA: 0s - loss: 0.1624
56/56 [==============================] - 0s 1ms/step - loss: 0.1672
Epoch 6/10

 1/56 [..............................] - ETA: 0s - loss: 0.0752
50/56 [=========================>....] - ETA: 0s - loss: 0.1655
56/56 [==============================] - 0s 1ms/step - loss: 0.1613
Epoch 7/10

 1/56 [..............................] - ETA: 0s - loss: 0.1524
49/56 [=========================>....] - ETA: 0s - loss: 0.1665
56/56 [==============================] - 0s 1ms/step - loss: 0.1627
Epoch 8/10

 1/56 [..............................] - ETA: 0s - loss: 0.1300
50/56 [=========================>....] - ETA: 0s - loss: 0.1542
56/56 [==============================] - 0s 1ms/step - loss: 0.1599
Epoch 9/10

 1/56 [..............................] - ETA: 0s - loss: 0.0827
49/56 [=========================>....] - ETA: 0s - loss: 0.1559
56/56 [==============================] - 0s 1ms/step - loss: 0.1580
Epoch 10/10

 1/56 [..............................] - ETA: 0s - loss: 0.4976
44/56 [======================>.......] - ETA: 0s - loss: 0.1494
56/56 [==============================] - 0s 1ms/step - loss: 0.1564
-> test with GAN.predict
GAN tn, fp: 129, 9
GAN fn, tp: 4, 5
GAN f1 score: 0.435
GAN cohens kappa score: 0.389

-> test with 'LR'
LR tn, fp: 131, 7
LR fn, tp: 3, 6
LR f1 score: 0.545
LR cohens kappa score: 0.510
LR average precision score: 0.632

-> test with 'RF'
RF tn, fp: 135, 3
RF fn, tp: 8, 1
RF f1 score: 0.154
RF cohens kappa score: 0.121

-> test with 'GB'
GB tn, fp: 133, 5
GB fn, tp: 9, 0
GB f1 score: 0.000
GB cohens kappa score: -0.046

-> test with 'KNN'
KNN tn, fp: 127, 11
KNN fn, tp: 4, 5
KNN f1 score: 0.400
KNN cohens kappa score: 0.349


------ Step 3/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 518 synthetic samples
-> retrain GAN for predict
Epoch 1/10

 1/56 [..............................] - ETA: 7s - loss: 0.3375
49/56 [=========================>....] - ETA: 0s - loss: 0.2766
56/56 [==============================] - 0s 1ms/step - loss: 0.2821
Epoch 2/10

 1/56 [..............................] - ETA: 0s - loss: 0.2954
49/56 [=========================>....] - ETA: 0s - loss: 0.2556
56/56 [==============================] - 0s 1ms/step - loss: 0.2607
Epoch 3/10

 1/56 [..............................] - ETA: 0s - loss: 0.3850
49/56 [=========================>....] - ETA: 0s - loss: 0.2504
56/56 [==============================] - 0s 1ms/step - loss: 0.2561
Epoch 4/10

 1/56 [..............................] - ETA: 0s - loss: 0.3233
45/56 [=======================>......] - ETA: 0s - loss: 0.2509
56/56 [==============================] - 0s 1ms/step - loss: 0.2566
Epoch 5/10

 1/56 [..............................] - ETA: 0s - loss: 0.2869
43/56 [======================>.......] - ETA: 0s - loss: 0.2541
56/56 [==============================] - 0s 1ms/step - loss: 0.2549
Epoch 6/10

 1/56 [..............................] - ETA: 0s - loss: 0.3954
47/56 [========================>.....] - ETA: 0s - loss: 0.2613
56/56 [==============================] - 0s 1ms/step - loss: 0.2516
Epoch 7/10

 1/56 [..............................] - ETA: 0s - loss: 0.3010
49/56 [=========================>....] - ETA: 0s - loss: 0.2620
56/56 [==============================] - 0s 1ms/step - loss: 0.2558
Epoch 8/10

 1/56 [..............................] - ETA: 0s - loss: 0.1121
49/56 [=========================>....] - ETA: 0s - loss: 0.2553
56/56 [==============================] - 0s 1ms/step - loss: 0.2513
Epoch 9/10

 1/56 [..............................] - ETA: 0s - loss: 0.1591
48/56 [========================>.....] - ETA: 0s - loss: 0.2543
56/56 [==============================] - 0s 1ms/step - loss: 0.2544
Epoch 10/10

 1/56 [..............................] - ETA: 0s - loss: 0.5318
48/56 [========================>.....] - ETA: 0s - loss: 0.2418
56/56 [==============================] - 0s 1ms/step - loss: 0.2440
-> test with GAN.predict
GAN tn, fp: 130, 8
GAN fn, tp: 0, 9
GAN f1 score: 0.692
GAN cohens kappa score: 0.666

-> test with 'LR'
LR tn, fp: 130, 8
LR fn, tp: 0, 9
LR f1 score: 0.692
LR cohens kappa score: 0.666
LR average precision score: 0.897

-> test with 'RF'
RF tn, fp: 137, 1
RF fn, tp: 3, 6
RF f1 score: 0.750
RF cohens kappa score: 0.736

-> test with 'GB'
GB tn, fp: 133, 5
GB fn, tp: 3, 6
GB f1 score: 0.600
GB cohens kappa score: 0.571

-> test with 'KNN'
KNN tn, fp: 114, 24
KNN fn, tp: 2, 7
KNN f1 score: 0.350
KNN cohens kappa score: 0.282


------ Step 3/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 518 synthetic samples
-> retrain GAN for predict
Epoch 1/10

 1/56 [..............................] - ETA: 8s - loss: 0.2047
49/56 [=========================>....] - ETA: 0s - loss: 0.2176
56/56 [==============================] - 0s 1ms/step - loss: 0.2141
Epoch 2/10

 1/56 [..............................] - ETA: 0s - loss: 0.1747
49/56 [=========================>....] - ETA: 0s - loss: 0.1911
56/56 [==============================] - 0s 1ms/step - loss: 0.1851
Epoch 3/10

 1/56 [..............................] - ETA: 0s - loss: 0.2245
46/56 [=======================>......] - ETA: 0s - loss: 0.1803
56/56 [==============================] - 0s 1ms/step - loss: 0.1751
Epoch 4/10

 1/56 [..............................] - ETA: 0s - loss: 0.1362
49/56 [=========================>....] - ETA: 0s - loss: 0.1652
56/56 [==============================] - 0s 1ms/step - loss: 0.1707
Epoch 5/10

 1/56 [..............................] - ETA: 0s - loss: 0.1438
49/56 [=========================>....] - ETA: 0s - loss: 0.1729
56/56 [==============================] - 0s 1ms/step - loss: 0.1748
Epoch 6/10

 1/56 [..............................] - ETA: 0s - loss: 0.1629
49/56 [=========================>....] - ETA: 0s - loss: 0.1751
56/56 [==============================] - 0s 1ms/step - loss: 0.1654
Epoch 7/10

 1/56 [..............................] - ETA: 0s - loss: 0.1457
49/56 [=========================>....] - ETA: 0s - loss: 0.1679
56/56 [==============================] - 0s 1ms/step - loss: 0.1676
Epoch 8/10

 1/56 [..............................] - ETA: 0s - loss: 0.1484
50/56 [=========================>....] - ETA: 0s - loss: 0.1651
56/56 [==============================] - 0s 1ms/step - loss: 0.1669
Epoch 9/10

 1/56 [..............................] - ETA: 0s - loss: 0.0550
49/56 [=========================>....] - ETA: 0s - loss: 0.1583
56/56 [==============================] - 0s 1ms/step - loss: 0.1643
Epoch 10/10

 1/56 [..............................] - ETA: 0s - loss: 0.3054
49/56 [=========================>....] - ETA: 0s - loss: 0.1762
56/56 [==============================] - 0s 1ms/step - loss: 0.1649
-> test with GAN.predict
GAN tn, fp: 129, 9
GAN fn, tp: 4, 5
GAN f1 score: 0.435
GAN cohens kappa score: 0.389

-> test with 'LR'
LR tn, fp: 134, 4
LR fn, tp: 4, 5
LR f1 score: 0.556
LR cohens kappa score: 0.527
LR average precision score: 0.658

-> test with 'RF'
RF tn, fp: 133, 5
RF fn, tp: 8, 1
RF f1 score: 0.133
RF cohens kappa score: 0.089

-> test with 'GB'
GB tn, fp: 133, 5
GB fn, tp: 8, 1
GB f1 score: 0.133
GB cohens kappa score: 0.089

-> test with 'KNN'
KNN tn, fp: 127, 11
KNN fn, tp: 4, 5
KNN f1 score: 0.400
KNN cohens kappa score: 0.349


------ Step 3/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 518 synthetic samples
-> retrain GAN for predict
Epoch 1/10

 1/56 [..............................] - ETA: 7s - loss: 0.3215
47/56 [========================>.....] - ETA: 0s - loss: 0.2617
56/56 [==============================] - 0s 1ms/step - loss: 0.2603
Epoch 2/10

 1/56 [..............................] - ETA: 0s - loss: 0.1743
49/56 [=========================>....] - ETA: 0s - loss: 0.2110
56/56 [==============================] - 0s 1ms/step - loss: 0.2151
Epoch 3/10

 1/56 [..............................] - ETA: 0s - loss: 0.0760
49/56 [=========================>....] - ETA: 0s - loss: 0.2045
56/56 [==============================] - 0s 1ms/step - loss: 0.2023
Epoch 4/10

 1/56 [..............................] - ETA: 0s - loss: 0.2530
47/56 [========================>.....] - ETA: 0s - loss: 0.2080
56/56 [==============================] - 0s 1ms/step - loss: 0.2029
Epoch 5/10

 1/56 [..............................] - ETA: 0s - loss: 0.0733
44/56 [======================>.......] - ETA: 0s - loss: 0.1957
56/56 [==============================] - 0s 1ms/step - loss: 0.1923
Epoch 6/10

 1/56 [..............................] - ETA: 0s - loss: 0.1731
43/56 [======================>.......] - ETA: 0s - loss: 0.1919
56/56 [==============================] - 0s 1ms/step - loss: 0.1922
Epoch 7/10

 1/56 [..............................] - ETA: 0s - loss: 0.1584
48/56 [========================>.....] - ETA: 0s - loss: 0.1898
56/56 [==============================] - 0s 1ms/step - loss: 0.1963
Epoch 8/10

 1/56 [..............................] - ETA: 0s - loss: 0.3475
49/56 [=========================>....] - ETA: 0s - loss: 0.1952
56/56 [==============================] - 0s 1ms/step - loss: 0.1918
Epoch 9/10

 1/56 [..............................] - ETA: 0s - loss: 0.1989
49/56 [=========================>....] - ETA: 0s - loss: 0.1935
56/56 [==============================] - 0s 1ms/step - loss: 0.1929
Epoch 10/10

 1/56 [..............................] - ETA: 0s - loss: 0.0584
49/56 [=========================>....] - ETA: 0s - loss: 0.1887
56/56 [==============================] - 0s 1ms/step - loss: 0.1875
-> test with GAN.predict
GAN tn, fp: 128, 10
GAN fn, tp: 2, 7
GAN f1 score: 0.538
GAN cohens kappa score: 0.498

-> test with 'LR'
LR tn, fp: 117, 21
LR fn, tp: 1, 8
LR f1 score: 0.421
LR cohens kappa score: 0.361
LR average precision score: 0.669

-> test with 'RF'
RF tn, fp: 132, 6
RF fn, tp: 6, 3
RF f1 score: 0.333
RF cohens kappa score: 0.290

-> test with 'GB'
GB tn, fp: 131, 7
GB fn, tp: 7, 2
GB f1 score: 0.222
GB cohens kappa score: 0.171

-> test with 'KNN'
KNN tn, fp: 117, 21
KNN fn, tp: 4, 5
KNN f1 score: 0.286
KNN cohens kappa score: 0.214


------ Step 3/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 516 synthetic samples
-> retrain GAN for predict
Epoch 1/10

 1/56 [..............................] - ETA: 8s - loss: 0.3721
48/56 [========================>.....] - ETA: 0s - loss: 0.3107
56/56 [==============================] - 0s 1ms/step - loss: 0.3054
Epoch 2/10

 1/56 [..............................] - ETA: 0s - loss: 0.1666
48/56 [========================>.....] - ETA: 0s - loss: 0.2745
56/56 [==============================] - 0s 1ms/step - loss: 0.2676
Epoch 3/10

 1/56 [..............................] - ETA: 0s - loss: 0.3163
49/56 [=========================>....] - ETA: 0s - loss: 0.2637
56/56 [==============================] - 0s 1ms/step - loss: 0.2577
Epoch 4/10

 1/56 [..............................] - ETA: 0s - loss: 0.4875
49/56 [=========================>....] - ETA: 0s - loss: 0.2516
56/56 [==============================] - 0s 1ms/step - loss: 0.2516
Epoch 5/10

 1/56 [..............................] - ETA: 0s - loss: 0.1483
49/56 [=========================>....] - ETA: 0s - loss: 0.2565
56/56 [==============================] - 0s 1ms/step - loss: 0.2520
Epoch 6/10

 1/56 [..............................] - ETA: 0s - loss: 0.1717
47/56 [========================>.....] - ETA: 0s - loss: 0.2483
56/56 [==============================] - 0s 1ms/step - loss: 0.2476
Epoch 7/10

 1/56 [..............................] - ETA: 0s - loss: 0.1302
46/56 [=======================>......] - ETA: 0s - loss: 0.2507
56/56 [==============================] - 0s 1ms/step - loss: 0.2428
Epoch 8/10

 1/56 [..............................] - ETA: 0s - loss: 0.3707
46/56 [=======================>......] - ETA: 0s - loss: 0.2335
56/56 [==============================] - 0s 1ms/step - loss: 0.2393
Epoch 9/10

 1/56 [..............................] - ETA: 0s - loss: 0.2797
49/56 [=========================>....] - ETA: 0s - loss: 0.2346
56/56 [==============================] - 0s 1ms/step - loss: 0.2369
Epoch 10/10

 1/56 [..............................] - ETA: 0s - loss: 0.1580
48/56 [========================>.....] - ETA: 0s - loss: 0.2359
56/56 [==============================] - 0s 1ms/step - loss: 0.2334
-> test with GAN.predict
GAN tn, fp: 127, 10
GAN fn, tp: 1, 5
GAN f1 score: 0.476
GAN cohens kappa score: 0.443

-> test with 'LR'
LR tn, fp: 124, 13
LR fn, tp: 1, 5
LR f1 score: 0.417
LR cohens kappa score: 0.377
LR average precision score: 0.524

-> test with 'RF'
RF tn, fp: 134, 3
RF fn, tp: 5, 1
RF f1 score: 0.200
RF cohens kappa score: 0.172

-> test with 'GB'
GB tn, fp: 131, 6
GB fn, tp: 4, 2
GB f1 score: 0.286
GB cohens kappa score: 0.250

-> test with 'KNN'
KNN tn, fp: 123, 14
KNN fn, tp: 3, 3
KNN f1 score: 0.261
KNN cohens kappa score: 0.212


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

------ Step 4/5: Slice 1/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 518 synthetic samples
-> retrain GAN for predict
Epoch 1/10

 1/56 [..............................] - ETA: 8s - loss: 0.3891
48/56 [========================>.....] - ETA: 0s - loss: 0.2063
56/56 [==============================] - 0s 1ms/step - loss: 0.2079
Epoch 2/10

 1/56 [..............................] - ETA: 0s - loss: 0.5178
49/56 [=========================>....] - ETA: 0s - loss: 0.1920
56/56 [==============================] - 0s 1ms/step - loss: 0.1890
Epoch 3/10

 1/56 [..............................] - ETA: 0s - loss: 0.1397
49/56 [=========================>....] - ETA: 0s - loss: 0.1740
56/56 [==============================] - 0s 1ms/step - loss: 0.1793
Epoch 4/10

 1/56 [..............................] - ETA: 0s - loss: 0.1523
49/56 [=========================>....] - ETA: 0s - loss: 0.1816
56/56 [==============================] - 0s 1ms/step - loss: 0.1758
Epoch 5/10

 1/56 [..............................] - ETA: 0s - loss: 0.1697
49/56 [=========================>....] - ETA: 0s - loss: 0.1759
56/56 [==============================] - 0s 1ms/step - loss: 0.1718
Epoch 6/10

 1/56 [..............................] - ETA: 0s - loss: 0.0981
49/56 [=========================>....] - ETA: 0s - loss: 0.1583
56/56 [==============================] - 0s 1ms/step - loss: 0.1707
Epoch 7/10

 1/56 [..............................] - ETA: 0s - loss: 0.1355
49/56 [=========================>....] - ETA: 0s - loss: 0.1747
56/56 [==============================] - 0s 1ms/step - loss: 0.1722
Epoch 8/10

 1/56 [..............................] - ETA: 0s - loss: 0.0808
48/56 [========================>.....] - ETA: 0s - loss: 0.1573
56/56 [==============================] - 0s 1ms/step - loss: 0.1618
Epoch 9/10

 1/56 [..............................] - ETA: 0s - loss: 0.3679
49/56 [=========================>....] - ETA: 0s - loss: 0.1634
56/56 [==============================] - 0s 1ms/step - loss: 0.1622
Epoch 10/10

 1/56 [..............................] - ETA: 0s - loss: 0.0795
49/56 [=========================>....] - ETA: 0s - loss: 0.1594
56/56 [==============================] - 0s 1ms/step - loss: 0.1616
-> test with GAN.predict
GAN tn, fp: 130, 8
GAN fn, tp: 4, 5
GAN f1 score: 0.455
GAN cohens kappa score: 0.412

-> test with 'LR'
LR tn, fp: 129, 9
LR fn, tp: 4, 5
LR f1 score: 0.435
LR cohens kappa score: 0.389
LR average precision score: 0.528

-> test with 'RF'
RF tn, fp: 135, 3
RF fn, tp: 8, 1
RF f1 score: 0.154
RF cohens kappa score: 0.121

-> test with 'GB'
GB tn, fp: 134, 4
GB fn, tp: 6, 3
GB f1 score: 0.375
GB cohens kappa score: 0.340

-> test with 'KNN'
KNN tn, fp: 119, 19
KNN fn, tp: 5, 4
KNN f1 score: 0.250
KNN cohens kappa score: 0.178


------ Step 4/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 518 synthetic samples
-> retrain GAN for predict
Epoch 1/10

 1/56 [..............................] - ETA: 8s - loss: 0.2591
49/56 [=========================>....] - ETA: 0s - loss: 0.2513
56/56 [==============================] - 0s 1ms/step - loss: 0.2539
Epoch 2/10

 1/56 [..............................] - ETA: 0s - loss: 0.2648
50/56 [=========================>....] - ETA: 0s - loss: 0.2341
56/56 [==============================] - 0s 1ms/step - loss: 0.2342
Epoch 3/10

 1/56 [..............................] - ETA: 0s - loss: 0.1330
47/56 [========================>.....] - ETA: 0s - loss: 0.2301
56/56 [==============================] - 0s 1ms/step - loss: 0.2244
Epoch 4/10

 1/56 [..............................] - ETA: 0s - loss: 0.2741
49/56 [=========================>....] - ETA: 0s - loss: 0.2271
56/56 [==============================] - 0s 1ms/step - loss: 0.2198
Epoch 5/10

 1/56 [..............................] - ETA: 0s - loss: 0.2502
44/56 [======================>.......] - ETA: 0s - loss: 0.2170
56/56 [==============================] - 0s 1ms/step - loss: 0.2168
Epoch 6/10

 1/56 [..............................] - ETA: 0s - loss: 0.1363
42/56 [=====================>........] - ETA: 0s - loss: 0.2302
56/56 [==============================] - 0s 1ms/step - loss: 0.2196
Epoch 7/10

 1/56 [..............................] - ETA: 0s - loss: 0.1227
49/56 [=========================>....] - ETA: 0s - loss: 0.2077
56/56 [==============================] - 0s 1ms/step - loss: 0.2142
Epoch 8/10

 1/56 [..............................] - ETA: 0s - loss: 0.3130
49/56 [=========================>....] - ETA: 0s - loss: 0.2149
56/56 [==============================] - 0s 1ms/step - loss: 0.2103
Epoch 9/10

 1/56 [..............................] - ETA: 0s - loss: 0.1320
48/56 [========================>.....] - ETA: 0s - loss: 0.2109
56/56 [==============================] - 0s 1ms/step - loss: 0.2086
Epoch 10/10

 1/56 [..............................] - ETA: 0s - loss: 0.2576
49/56 [=========================>....] - ETA: 0s - loss: 0.2060
56/56 [==============================] - 0s 1ms/step - loss: 0.2074
-> test with GAN.predict
GAN tn, fp: 126, 12
GAN fn, tp: 2, 7
GAN f1 score: 0.500
GAN cohens kappa score: 0.455

-> test with 'LR'
LR tn, fp: 122, 16
LR fn, tp: 2, 7
LR f1 score: 0.438
LR cohens kappa score: 0.383
LR average precision score: 0.731

-> test with 'RF'
RF tn, fp: 133, 5
RF fn, tp: 6, 3
RF f1 score: 0.353
RF cohens kappa score: 0.313

-> test with 'GB'
GB tn, fp: 126, 12
GB fn, tp: 4, 5
GB f1 score: 0.385
GB cohens kappa score: 0.331

-> test with 'KNN'
KNN tn, fp: 119, 19
KNN fn, tp: 3, 6
KNN f1 score: 0.353
KNN cohens kappa score: 0.289


------ Step 4/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 518 synthetic samples
-> retrain GAN for predict
Epoch 1/10

 1/56 [..............................] - ETA: 8s - loss: 0.3396
49/56 [=========================>....] - ETA: 0s - loss: 0.3057
56/56 [==============================] - 0s 1ms/step - loss: 0.2962
Epoch 2/10

 1/56 [..............................] - ETA: 0s - loss: 0.2924
49/56 [=========================>....] - ETA: 0s - loss: 0.2696
56/56 [==============================] - 0s 1ms/step - loss: 0.2703
Epoch 3/10

 1/56 [..............................] - ETA: 0s - loss: 0.3754
49/56 [=========================>....] - ETA: 0s - loss: 0.2553
56/56 [==============================] - 0s 1ms/step - loss: 0.2541
Epoch 4/10

 1/56 [..............................] - ETA: 0s - loss: 0.1269
49/56 [=========================>....] - ETA: 0s - loss: 0.2523
56/56 [==============================] - 0s 1ms/step - loss: 0.2525
Epoch 5/10

 1/56 [..............................] - ETA: 0s - loss: 0.2213
49/56 [=========================>....] - ETA: 0s - loss: 0.2399
56/56 [==============================] - 0s 1ms/step - loss: 0.2493
Epoch 6/10

 1/56 [..............................] - ETA: 0s - loss: 0.5061
49/56 [=========================>....] - ETA: 0s - loss: 0.2336
56/56 [==============================] - 0s 1ms/step - loss: 0.2458
Epoch 7/10

 1/56 [..............................] - ETA: 0s - loss: 0.2685
49/56 [=========================>....] - ETA: 0s - loss: 0.2384
56/56 [==============================] - 0s 1ms/step - loss: 0.2432
Epoch 8/10

 1/56 [..............................] - ETA: 0s - loss: 0.1918
49/56 [=========================>....] - ETA: 0s - loss: 0.2403
56/56 [==============================] - 0s 1ms/step - loss: 0.2395
Epoch 9/10

 1/56 [..............................] - ETA: 0s - loss: 0.0840
49/56 [=========================>....] - ETA: 0s - loss: 0.2437
56/56 [==============================] - 0s 1ms/step - loss: 0.2374
Epoch 10/10

 1/56 [..............................] - ETA: 0s - loss: 0.3303
49/56 [=========================>....] - ETA: 0s - loss: 0.2342
56/56 [==============================] - 0s 1ms/step - loss: 0.2341
-> test with GAN.predict
GAN tn, fp: 125, 13
GAN fn, tp: 2, 7
GAN f1 score: 0.483
GAN cohens kappa score: 0.435

-> test with 'LR'
LR tn, fp: 125, 13
LR fn, tp: 1, 8
LR f1 score: 0.533
LR cohens kappa score: 0.490
LR average precision score: 0.727

-> test with 'RF'
RF tn, fp: 134, 4
RF fn, tp: 6, 3
RF f1 score: 0.375
RF cohens kappa score: 0.340

-> test with 'GB'
GB tn, fp: 134, 4
GB fn, tp: 7, 2
GB f1 score: 0.267
GB cohens kappa score: 0.229

-> test with 'KNN'
KNN tn, fp: 123, 15
KNN fn, tp: 4, 5
KNN f1 score: 0.345
KNN cohens kappa score: 0.284


------ Step 4/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 518 synthetic samples
-> retrain GAN for predict
Epoch 1/10

 1/56 [..............................] - ETA: 8s - loss: 0.2849
45/56 [=======================>......] - ETA: 0s - loss: 0.3005
56/56 [==============================] - 0s 1ms/step - loss: 0.2916
Epoch 2/10

 1/56 [..............................] - ETA: 0s - loss: 0.3584
50/56 [=========================>....] - ETA: 0s - loss: 0.2545
56/56 [==============================] - 0s 1ms/step - loss: 0.2578
Epoch 3/10

 1/56 [..............................] - ETA: 0s - loss: 0.2532
49/56 [=========================>....] - ETA: 0s - loss: 0.2435
56/56 [==============================] - 0s 1ms/step - loss: 0.2464
Epoch 4/10

 1/56 [..............................] - ETA: 0s - loss: 0.2418
50/56 [=========================>....] - ETA: 0s - loss: 0.2369
56/56 [==============================] - 0s 1ms/step - loss: 0.2395
Epoch 5/10

 1/56 [..............................] - ETA: 0s - loss: 0.4912
46/56 [=======================>......] - ETA: 0s - loss: 0.2460
56/56 [==============================] - 0s 1ms/step - loss: 0.2390
Epoch 6/10

 1/56 [..............................] - ETA: 0s - loss: 0.2449
49/56 [=========================>....] - ETA: 0s - loss: 0.2374
56/56 [==============================] - 0s 1ms/step - loss: 0.2394
Epoch 7/10

 1/56 [..............................] - ETA: 0s - loss: 0.2292
50/56 [=========================>....] - ETA: 0s - loss: 0.2512
56/56 [==============================] - 0s 1ms/step - loss: 0.2434
Epoch 8/10

 1/56 [..............................] - ETA: 0s - loss: 0.2250
49/56 [=========================>....] - ETA: 0s - loss: 0.2437
56/56 [==============================] - 0s 1ms/step - loss: 0.2381
Epoch 9/10

 1/56 [..............................] - ETA: 0s - loss: 0.2693
50/56 [=========================>....] - ETA: 0s - loss: 0.2318
56/56 [==============================] - 0s 1ms/step - loss: 0.2341
Epoch 10/10

 1/56 [..............................] - ETA: 0s - loss: 0.3104
50/56 [=========================>....] - ETA: 0s - loss: 0.2250
56/56 [==============================] - 0s 1ms/step - loss: 0.2284
-> test with GAN.predict
GAN tn, fp: 129, 9
GAN fn, tp: 1, 8
GAN f1 score: 0.615
GAN cohens kappa score: 0.582

-> test with 'LR'
LR tn, fp: 124, 14
LR fn, tp: 0, 9
LR f1 score: 0.562
LR cohens kappa score: 0.520
LR average precision score: 0.958

-> test with 'RF'
RF tn, fp: 133, 5
RF fn, tp: 7, 2
RF f1 score: 0.250
RF cohens kappa score: 0.208

-> test with 'GB'
GB tn, fp: 131, 7
GB fn, tp: 5, 4
GB f1 score: 0.400
GB cohens kappa score: 0.357

-> test with 'KNN'
KNN tn, fp: 120, 18
KNN fn, tp: 3, 6
KNN f1 score: 0.364
KNN cohens kappa score: 0.301


------ Step 4/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 516 synthetic samples
-> retrain GAN for predict
Epoch 1/10

 1/56 [..............................] - ETA: 7s - loss: 0.3238
50/56 [=========================>....] - ETA: 0s - loss: 0.2530
56/56 [==============================] - 0s 1ms/step - loss: 0.2508
Epoch 2/10

 1/56 [..............................] - ETA: 0s - loss: 0.1750
50/56 [=========================>....] - ETA: 0s - loss: 0.2356
56/56 [==============================] - 0s 1ms/step - loss: 0.2382
Epoch 3/10

 1/56 [..............................] - ETA: 0s - loss: 0.1995
50/56 [=========================>....] - ETA: 0s - loss: 0.2310
56/56 [==============================] - 0s 1ms/step - loss: 0.2334
Epoch 4/10

 1/56 [..............................] - ETA: 0s - loss: 0.2328
50/56 [=========================>....] - ETA: 0s - loss: 0.2345
56/56 [==============================] - 0s 1ms/step - loss: 0.2325
Epoch 5/10

 1/56 [..............................] - ETA: 0s - loss: 0.4335
50/56 [=========================>....] - ETA: 0s - loss: 0.2316
56/56 [==============================] - 0s 1ms/step - loss: 0.2312
Epoch 6/10

 1/56 [..............................] - ETA: 0s - loss: 0.1077
50/56 [=========================>....] - ETA: 0s - loss: 0.2239
56/56 [==============================] - 0s 1ms/step - loss: 0.2263
Epoch 7/10

 1/56 [..............................] - ETA: 0s - loss: 0.2216
50/56 [=========================>....] - ETA: 0s - loss: 0.2355
56/56 [==============================] - 0s 1ms/step - loss: 0.2265
Epoch 8/10

 1/56 [..............................] - ETA: 0s - loss: 0.2095
50/56 [=========================>....] - ETA: 0s - loss: 0.2196
56/56 [==============================] - 0s 1ms/step - loss: 0.2193
Epoch 9/10

 1/56 [..............................] - ETA: 0s - loss: 0.2192
45/56 [=======================>......] - ETA: 0s - loss: 0.2331
56/56 [==============================] - 0s 1ms/step - loss: 0.2233
Epoch 10/10

 1/56 [..............................] - ETA: 0s - loss: 0.1169
50/56 [=========================>....] - ETA: 0s - loss: 0.2106
56/56 [==============================] - 0s 1ms/step - loss: 0.2213
-> test with GAN.predict
GAN tn, fp: 130, 7
GAN fn, tp: 2, 4
GAN f1 score: 0.471
GAN cohens kappa score: 0.440

-> test with 'LR'
LR tn, fp: 129, 8
LR fn, tp: 1, 5
LR f1 score: 0.526
LR cohens kappa score: 0.497
LR average precision score: 0.518

-> test with 'RF'
RF tn, fp: 132, 5
RF fn, tp: 4, 2
RF f1 score: 0.308
RF cohens kappa score: 0.275

-> test with 'GB'
GB tn, fp: 132, 5
GB fn, tp: 4, 2
GB f1 score: 0.308
GB cohens kappa score: 0.275

-> test with 'KNN'
KNN tn, fp: 124, 13
KNN fn, tp: 3, 3
KNN f1 score: 0.273
KNN cohens kappa score: 0.225


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

------ Step 5/5: Slice 1/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 518 synthetic samples
-> retrain GAN for predict
Epoch 1/10

 1/56 [..............................] - ETA: 7s - loss: 0.1795
46/56 [=======================>......] - ETA: 0s - loss: 0.2000
56/56 [==============================] - 0s 1ms/step - loss: 0.2077
Epoch 2/10

 1/56 [..............................] - ETA: 0s - loss: 0.1479
42/56 [=====================>........] - ETA: 0s - loss: 0.2005
56/56 [==============================] - 0s 1ms/step - loss: 0.1997
Epoch 3/10

 1/56 [..............................] - ETA: 0s - loss: 0.3554
47/56 [========================>.....] - ETA: 0s - loss: 0.1998
56/56 [==============================] - 0s 1ms/step - loss: 0.1986
Epoch 4/10

 1/56 [..............................] - ETA: 0s - loss: 0.2524
49/56 [=========================>....] - ETA: 0s - loss: 0.1997
56/56 [==============================] - 0s 1ms/step - loss: 0.1954
Epoch 5/10

 1/56 [..............................] - ETA: 0s - loss: 0.1217
49/56 [=========================>....] - ETA: 0s - loss: 0.1945
56/56 [==============================] - 0s 1ms/step - loss: 0.1950
Epoch 6/10

 1/56 [..............................] - ETA: 0s - loss: 0.2385
50/56 [=========================>....] - ETA: 0s - loss: 0.1889
56/56 [==============================] - 0s 1ms/step - loss: 0.1909
Epoch 7/10

 1/56 [..............................] - ETA: 0s - loss: 0.0792
50/56 [=========================>....] - ETA: 0s - loss: 0.1936
56/56 [==============================] - 0s 1ms/step - loss: 0.1912
Epoch 8/10

 1/56 [..............................] - ETA: 0s - loss: 0.1513
49/56 [=========================>....] - ETA: 0s - loss: 0.1913
56/56 [==============================] - 0s 1ms/step - loss: 0.1924
Epoch 9/10

 1/56 [..............................] - ETA: 0s - loss: 0.0957
49/56 [=========================>....] - ETA: 0s - loss: 0.1816
56/56 [==============================] - 0s 1ms/step - loss: 0.1867
Epoch 10/10

 1/56 [..............................] - ETA: 0s - loss: 0.1030
50/56 [=========================>....] - ETA: 0s - loss: 0.1920
56/56 [==============================] - 0s 1ms/step - loss: 0.1940
-> test with GAN.predict
GAN tn, fp: 128, 10
GAN fn, tp: 4, 5
GAN f1 score: 0.417
GAN cohens kappa score: 0.368

-> test with 'LR'
LR tn, fp: 125, 13
LR fn, tp: 1, 8
LR f1 score: 0.533
LR cohens kappa score: 0.490
LR average precision score: 0.755

-> test with 'RF'
RF tn, fp: 132, 6
RF fn, tp: 8, 1
RF f1 score: 0.125
RF cohens kappa score: 0.075

-> test with 'GB'
GB tn, fp: 129, 9
GB fn, tp: 8, 1
GB f1 score: 0.105
GB cohens kappa score: 0.044

-> test with 'KNN'
KNN tn, fp: 117, 21
KNN fn, tp: 5, 4
KNN f1 score: 0.235
KNN cohens kappa score: 0.160


------ Step 5/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 518 synthetic samples
-> retrain GAN for predict
Epoch 1/10

 1/56 [..............................] - ETA: 7s - loss: 0.3113
48/56 [========================>.....] - ETA: 0s - loss: 0.3935
56/56 [==============================] - 0s 1ms/step - loss: 0.3818
Epoch 2/10

 1/56 [..............................] - ETA: 0s - loss: 0.3873
49/56 [=========================>....] - ETA: 0s - loss: 0.3027
56/56 [==============================] - 0s 1ms/step - loss: 0.3019
Epoch 3/10

 1/56 [..............................] - ETA: 0s - loss: 0.3392
49/56 [=========================>....] - ETA: 0s - loss: 0.2648
56/56 [==============================] - 0s 1ms/step - loss: 0.2744
Epoch 4/10

 1/56 [..............................] - ETA: 0s - loss: 0.2312
49/56 [=========================>....] - ETA: 0s - loss: 0.2624
56/56 [==============================] - 0s 1ms/step - loss: 0.2587
Epoch 5/10

 1/56 [..............................] - ETA: 0s - loss: 0.2391
49/56 [=========================>....] - ETA: 0s - loss: 0.2533
56/56 [==============================] - 0s 1ms/step - loss: 0.2509
Epoch 6/10

 1/56 [..............................] - ETA: 0s - loss: 0.2340
49/56 [=========================>....] - ETA: 0s - loss: 0.2398
56/56 [==============================] - 0s 1ms/step - loss: 0.2428
Epoch 7/10

 1/56 [..............................] - ETA: 0s - loss: 0.3075
49/56 [=========================>....] - ETA: 0s - loss: 0.2324
56/56 [==============================] - 0s 1ms/step - loss: 0.2345
Epoch 8/10

 1/56 [..............................] - ETA: 0s - loss: 0.2320
48/56 [========================>.....] - ETA: 0s - loss: 0.2364
56/56 [==============================] - 0s 1ms/step - loss: 0.2324
Epoch 9/10

 1/56 [..............................] - ETA: 0s - loss: 0.1967
49/56 [=========================>....] - ETA: 0s - loss: 0.2241
56/56 [==============================] - 0s 1ms/step - loss: 0.2231
Epoch 10/10

 1/56 [..............................] - ETA: 0s - loss: 0.2048
48/56 [========================>.....] - ETA: 0s - loss: 0.2157
56/56 [==============================] - 0s 1ms/step - loss: 0.2178
-> test with GAN.predict
GAN tn, fp: 128, 10
GAN fn, tp: 2, 7
GAN f1 score: 0.538
GAN cohens kappa score: 0.498

-> test with 'LR'
LR tn, fp: 128, 10
LR fn, tp: 1, 8
LR f1 score: 0.593
LR cohens kappa score: 0.556
LR average precision score: 0.671

-> test with 'RF'
RF tn, fp: 134, 4
RF fn, tp: 8, 1
RF f1 score: 0.143
RF cohens kappa score: 0.104

-> test with 'GB'
GB tn, fp: 132, 6
GB fn, tp: 7, 2
GB f1 score: 0.235
GB cohens kappa score: 0.189

-> test with 'KNN'
KNN tn, fp: 110, 28
KNN fn, tp: 4, 5
KNN f1 score: 0.238
KNN cohens kappa score: 0.157


------ Step 5/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 518 synthetic samples
-> retrain GAN for predict
Epoch 1/10

 1/56 [..............................] - ETA: 7s - loss: 0.5053
46/56 [=======================>......] - ETA: 0s - loss: 0.2127
56/56 [==============================] - 0s 1ms/step - loss: 0.2073
Epoch 2/10

 1/56 [..............................] - ETA: 0s - loss: 0.1119
49/56 [=========================>....] - ETA: 0s - loss: 0.1761
56/56 [==============================] - 0s 1ms/step - loss: 0.1802
Epoch 3/10

 1/56 [..............................] - ETA: 0s - loss: 0.2157
49/56 [=========================>....] - ETA: 0s - loss: 0.1799
56/56 [==============================] - 0s 1ms/step - loss: 0.1754
Epoch 4/10

 1/56 [..............................] - ETA: 0s - loss: 0.1917
49/56 [=========================>....] - ETA: 0s - loss: 0.1721
56/56 [==============================] - 0s 1ms/step - loss: 0.1762
Epoch 5/10

 1/56 [..............................] - ETA: 0s - loss: 0.0888
49/56 [=========================>....] - ETA: 0s - loss: 0.1752
56/56 [==============================] - 0s 1ms/step - loss: 0.1728
Epoch 6/10

 1/56 [..............................] - ETA: 0s - loss: 0.2025
49/56 [=========================>....] - ETA: 0s - loss: 0.1721
56/56 [==============================] - 0s 1ms/step - loss: 0.1735
Epoch 7/10

 1/56 [..............................] - ETA: 0s - loss: 0.1283
49/56 [=========================>....] - ETA: 0s - loss: 0.1649
56/56 [==============================] - 0s 1ms/step - loss: 0.1686
Epoch 8/10

 1/56 [..............................] - ETA: 0s - loss: 0.0902
45/56 [=======================>......] - ETA: 0s - loss: 0.1684
56/56 [==============================] - 0s 1ms/step - loss: 0.1649
Epoch 9/10

 1/56 [..............................] - ETA: 0s - loss: 0.1399
49/56 [=========================>....] - ETA: 0s - loss: 0.1644
56/56 [==============================] - 0s 1ms/step - loss: 0.1644
Epoch 10/10

 1/56 [..............................] - ETA: 0s - loss: 0.1484
49/56 [=========================>....] - ETA: 0s - loss: 0.1595
56/56 [==============================] - 0s 1ms/step - loss: 0.1648
-> test with GAN.predict
GAN tn, fp: 126, 12
GAN fn, tp: 4, 5
GAN f1 score: 0.385
GAN cohens kappa score: 0.331

-> test with 'LR'
LR tn, fp: 127, 11
LR fn, tp: 3, 6
LR f1 score: 0.462
LR cohens kappa score: 0.415
LR average precision score: 0.543

-> test with 'RF'
RF tn, fp: 134, 4
RF fn, tp: 6, 3
RF f1 score: 0.375
RF cohens kappa score: 0.340

-> test with 'GB'
GB tn, fp: 134, 4
GB fn, tp: 5, 4
GB f1 score: 0.471
GB cohens kappa score: 0.438

-> test with 'KNN'
KNN tn, fp: 125, 13
KNN fn, tp: 4, 5
KNN f1 score: 0.370
KNN cohens kappa score: 0.314


------ Step 5/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 518 synthetic samples
-> retrain GAN for predict
Epoch 1/10

 1/56 [..............................] - ETA: 8s - loss: 0.4582
40/56 [====================>.........] - ETA: 0s - loss: 0.2239
56/56 [==============================] - 0s 1ms/step - loss: 0.2205
Epoch 2/10

 1/56 [..............................] - ETA: 0s - loss: 0.1949
39/56 [===================>..........] - ETA: 0s - loss: 0.2035
56/56 [==============================] - 0s 1ms/step - loss: 0.1999
Epoch 3/10

 1/56 [..............................] - ETA: 0s - loss: 0.2725
42/56 [=====================>........] - ETA: 0s - loss: 0.2016
56/56 [==============================] - 0s 1ms/step - loss: 0.1910
Epoch 4/10

 1/56 [..............................] - ETA: 0s - loss: 0.0800
46/56 [=======================>......] - ETA: 0s - loss: 0.1895
56/56 [==============================] - 0s 1ms/step - loss: 0.1879
Epoch 5/10

 1/56 [..............................] - ETA: 0s - loss: 0.1216
46/56 [=======================>......] - ETA: 0s - loss: 0.1827
56/56 [==============================] - 0s 1ms/step - loss: 0.1869
Epoch 6/10

 1/56 [..............................] - ETA: 0s - loss: 0.0749
40/56 [====================>.........] - ETA: 0s - loss: 0.1728
56/56 [==============================] - 0s 1ms/step - loss: 0.1826
Epoch 7/10

 1/56 [..............................] - ETA: 0s - loss: 0.1862
46/56 [=======================>......] - ETA: 0s - loss: 0.1931
56/56 [==============================] - 0s 1ms/step - loss: 0.1911
Epoch 8/10

 1/56 [..............................] - ETA: 0s - loss: 0.2612
41/56 [====================>.........] - ETA: 0s - loss: 0.1756
56/56 [==============================] - 0s 1ms/step - loss: 0.1789
Epoch 9/10

 1/56 [..............................] - ETA: 0s - loss: 0.2274
44/56 [======================>.......] - ETA: 0s - loss: 0.1847
56/56 [==============================] - 0s 1ms/step - loss: 0.1772
Epoch 10/10

 1/56 [..............................] - ETA: 0s - loss: 0.1362
43/56 [======================>.......] - ETA: 0s - loss: 0.1771
56/56 [==============================] - 0s 1ms/step - loss: 0.1795
-> test with GAN.predict
GAN tn, fp: 131, 7
GAN fn, tp: 2, 7
GAN f1 score: 0.609
GAN cohens kappa score: 0.577

-> test with 'LR'
LR tn, fp: 130, 8
LR fn, tp: 1, 8
LR f1 score: 0.640
LR cohens kappa score: 0.609
LR average precision score: 0.894

-> test with 'RF'
RF tn, fp: 133, 5
RF fn, tp: 6, 3
RF f1 score: 0.353
RF cohens kappa score: 0.313

-> test with 'GB'
GB tn, fp: 133, 5
GB fn, tp: 4, 5
GB f1 score: 0.526
GB cohens kappa score: 0.494

-> test with 'KNN'
KNN tn, fp: 123, 15
KNN fn, tp: 4, 5
KNN f1 score: 0.345
KNN cohens kappa score: 0.284


------ Step 5/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 516 synthetic samples
-> retrain GAN for predict
Epoch 1/10

 1/56 [..............................] - ETA: 8s - loss: 0.2666
48/56 [========================>.....] - ETA: 0s - loss: 0.2991
56/56 [==============================] - 0s 1ms/step - loss: 0.2940
Epoch 2/10

 1/56 [..............................] - ETA: 0s - loss: 0.1052
50/56 [=========================>....] - ETA: 0s - loss: 0.2796
56/56 [==============================] - 0s 1ms/step - loss: 0.2734
Epoch 3/10

 1/56 [..............................] - ETA: 0s - loss: 0.1489
49/56 [=========================>....] - ETA: 0s - loss: 0.2568
56/56 [==============================] - 0s 1ms/step - loss: 0.2640
Epoch 4/10

 1/56 [..............................] - ETA: 0s - loss: 0.2715
50/56 [=========================>....] - ETA: 0s - loss: 0.2667
56/56 [==============================] - 0s 1ms/step - loss: 0.2649
Epoch 5/10

 1/56 [..............................] - ETA: 0s - loss: 0.1735
49/56 [=========================>....] - ETA: 0s - loss: 0.2606
56/56 [==============================] - 0s 1ms/step - loss: 0.2586
Epoch 6/10

 1/56 [..............................] - ETA: 0s - loss: 0.1865
49/56 [=========================>....] - ETA: 0s - loss: 0.2571
56/56 [==============================] - 0s 1ms/step - loss: 0.2552
Epoch 7/10

 1/56 [..............................] - ETA: 0s - loss: 0.2250
50/56 [=========================>....] - ETA: 0s - loss: 0.2519
56/56 [==============================] - 0s 1ms/step - loss: 0.2505
Epoch 8/10

 1/56 [..............................] - ETA: 0s - loss: 0.2226
46/56 [=======================>......] - ETA: 0s - loss: 0.2536
56/56 [==============================] - 0s 1ms/step - loss: 0.2494
Epoch 9/10

 1/56 [..............................] - ETA: 0s - loss: 0.3662
49/56 [=========================>....] - ETA: 0s - loss: 0.2405
56/56 [==============================] - 0s 1ms/step - loss: 0.2501
Epoch 10/10

 1/56 [..............................] - ETA: 0s - loss: 0.1495
50/56 [=========================>....] - ETA: 0s - loss: 0.2454
56/56 [==============================] - 0s 1ms/step - loss: 0.2503
-> test with GAN.predict
GAN tn, fp: 129, 8
GAN fn, tp: 0, 6
GAN f1 score: 0.600
GAN cohens kappa score: 0.575

-> test with 'LR'
LR tn, fp: 127, 10
LR fn, tp: 0, 6
LR f1 score: 0.545
LR cohens kappa score: 0.516
LR average precision score: 0.805

-> test with 'RF'
RF tn, fp: 133, 4
RF fn, tp: 3, 3
RF f1 score: 0.462
RF cohens kappa score: 0.436

-> test with 'GB'
GB tn, fp: 133, 4
GB fn, tp: 4, 2
GB f1 score: 0.333
GB cohens kappa score: 0.304

-> test with 'KNN'
KNN tn, fp: 125, 12
KNN fn, tp: 2, 4
KNN f1 score: 0.364
KNN cohens kappa score: 0.322

### Exercise is done.

-----[ LR ]-----
maximum:
LR tn, fp: 134, 21
LR fn, tp: 4, 9
LR f1 score: 0.692
LR cohens kappa score: 0.666
LR average precision score: 0.958


average:
LR tn, fp: 127.04, 10.76
LR fn, tp: 1.36, 7.04
LR f1 score: 0.541
LR cohens kappa score: 0.503
LR average precision score: 0.697


minimum:
LR tn, fp: 117, 4
LR fn, tp: 0, 5
LR f1 score: 0.417
LR cohens kappa score: 0.361
LR average precision score: 0.488


-----[ RF ]-----
maximum:
RF tn, fp: 137, 6
RF fn, tp: 8, 6
RF f1 score: 0.750
RF cohens kappa score: 0.736


average:
RF tn, fp: 134.04, 3.76
RF fn, tp: 5.92, 2.48
RF f1 score: 0.335
RF cohens kappa score: 0.302


minimum:
RF tn, fp: 132, 1
RF fn, tp: 3, 1
RF f1 score: 0.125
RF cohens kappa score: 0.075


-----[ GB ]-----
maximum:
GB tn, fp: 136, 12
GB fn, tp: 9, 6
GB f1 score: 0.600
GB cohens kappa score: 0.571


average:
GB tn, fp: 132.12, 5.68
GB fn, tp: 5.44, 2.96
GB f1 score: 0.343
GB cohens kappa score: 0.304


minimum:
GB tn, fp: 126, 2
GB fn, tp: 3, 0
GB f1 score: 0.000
GB cohens kappa score: -0.046


-----[ KNN ]-----
maximum:
KNN tn, fp: 128, 28
KNN fn, tp: 5, 8
KNN f1 score: 0.485
KNN cohens kappa score: 0.436


average:
KNN tn, fp: 121.8, 16.0
KNN fn, tp: 3.28, 5.12
KNN f1 score: 0.347
KNN cohens kappa score: 0.290


minimum:
KNN tn, fp: 110, 10
KNN fn, tp: 1, 2
KNN f1 score: 0.222
KNN cohens kappa score: 0.157


-----[ GAN ]-----
maximum:
GAN tn, fp: 131, 16
GAN fn, tp: 4, 9
GAN f1 score: 0.692
GAN cohens kappa score: 0.666


average:
GAN tn, fp: 127.88, 9.92
GAN fn, tp: 2.08, 6.32
GAN f1 score: 0.512
GAN cohens kappa score: 0.473


minimum:
GAN tn, fp: 122, 7
GAN fn, tp: 0, 4
GAN f1 score: 0.385
GAN cohens kappa score: 0.331