ConvGeNBenchmarkResults/data_result/convGAN-majority-full/folding_abalone9-18.log

///////////////////////////////////////////
// Running convGAN-majority-full 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: 0.0614
49/56 [=========================>....] - ETA: 0s - loss: 0.0987
56/56 [==============================] - 0s 1ms/step - loss: 0.1013
Epoch 2/10

 1/56 [..............................] - ETA: 0s - loss: 0.2406
49/56 [=========================>....] - ETA: 0s - loss: 0.0972
56/56 [==============================] - 0s 1ms/step - loss: 0.0940
Epoch 3/10

 1/56 [..............................] - ETA: 0s - loss: 0.2309
49/56 [=========================>....] - ETA: 0s - loss: 0.0894
56/56 [==============================] - 0s 1ms/step - loss: 0.0884
Epoch 4/10

 1/56 [..............................] - ETA: 0s - loss: 0.1345
49/56 [=========================>....] - ETA: 0s - loss: 0.0725
56/56 [==============================] - 0s 1ms/step - loss: 0.0902
Epoch 5/10

 1/56 [..............................] - ETA: 0s - loss: 0.0460
50/56 [=========================>....] - ETA: 0s - loss: 0.0806
56/56 [==============================] - 0s 1ms/step - loss: 0.0885
Epoch 6/10

 1/56 [..............................] - ETA: 0s - loss: 0.0116
50/56 [=========================>....] - ETA: 0s - loss: 0.0884
56/56 [==============================] - 0s 1ms/step - loss: 0.0869
Epoch 7/10

 1/56 [..............................] - ETA: 0s - loss: 0.0121
50/56 [=========================>....] - ETA: 0s - loss: 0.0811
56/56 [==============================] - 0s 1ms/step - loss: 0.0881
Epoch 8/10

 1/56 [..............................] - ETA: 0s - loss: 0.0323
50/56 [=========================>....] - ETA: 0s - loss: 0.0903
56/56 [==============================] - 0s 1ms/step - loss: 0.0884
Epoch 9/10

 1/56 [..............................] - ETA: 0s - loss: 0.0292
50/56 [=========================>....] - ETA: 0s - loss: 0.0871
56/56 [==============================] - 0s 1ms/step - loss: 0.0848
Epoch 10/10

 1/56 [..............................] - ETA: 0s - loss: 0.0436
49/56 [=========================>....] - ETA: 0s - loss: 0.0805
56/56 [==============================] - 0s 1ms/step - loss: 0.0837
-> test with GAN.predict
GAN tn, fp: 137, 1
GAN fn, tp: 3, 6
GAN f1 score: 0.750
GAN cohens kappa score: 0.736

-> test with 'LR'
LR tn, fp: 127, 11
LR fn, tp: 1, 8
LR f1 score: 0.571
LR cohens kappa score: 0.533
LR average precision score: 0.920

-> test with 'RF'
RF tn, fp: 138, 0
RF fn, tp: 6, 3
RF f1 score: 0.500
RF cohens kappa score: 0.484

-> test with 'GB'
GB tn, fp: 137, 1
GB fn, tp: 6, 3
GB f1 score: 0.462
GB cohens kappa score: 0.440

-> test with 'KNN'
KNN tn, fp: 137, 1
KNN fn, tp: 9, 0
KNN f1 score: 0.000
KNN cohens kappa score: -0.012


------ 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: 0.0052
48/56 [========================>.....] - ETA: 0s - loss: 0.1266
56/56 [==============================] - 0s 1ms/step - loss: 0.1203
Epoch 2/10

 1/56 [..............................] - ETA: 0s - loss: 0.2734
47/56 [========================>.....] - ETA: 0s - loss: 0.1029
56/56 [==============================] - 0s 1ms/step - loss: 0.1054
Epoch 3/10

 1/56 [..............................] - ETA: 0s - loss: 0.0378
31/56 [===============>..............] - ETA: 0s - loss: 0.1024
56/56 [==============================] - 0s 4ms/step - loss: 0.1057
Epoch 4/10

 1/56 [..............................] - ETA: 0s - loss: 0.0308
49/56 [=========================>....] - ETA: 0s - loss: 0.1023
56/56 [==============================] - 0s 1ms/step - loss: 0.0979
Epoch 5/10

 1/56 [..............................] - ETA: 0s - loss: 0.0124
49/56 [=========================>....] - ETA: 0s - loss: 0.1025
56/56 [==============================] - 0s 1ms/step - loss: 0.0968
Epoch 6/10

 1/56 [..............................] - ETA: 0s - loss: 0.0798
50/56 [=========================>....] - ETA: 0s - loss: 0.0999
56/56 [==============================] - 0s 1ms/step - loss: 0.0980
Epoch 7/10

 1/56 [..............................] - ETA: 0s - loss: 0.2325
49/56 [=========================>....] - ETA: 0s - loss: 0.0999
56/56 [==============================] - 0s 1ms/step - loss: 0.0937
Epoch 8/10

 1/56 [..............................] - ETA: 0s - loss: 0.0378
50/56 [=========================>....] - ETA: 0s - loss: 0.0907
56/56 [==============================] - 0s 1ms/step - loss: 0.0938
Epoch 9/10

 1/56 [..............................] - ETA: 0s - loss: 0.0535
49/56 [=========================>....] - ETA: 0s - loss: 0.0851
56/56 [==============================] - 0s 1ms/step - loss: 0.0921
Epoch 10/10

 1/56 [..............................] - ETA: 0s - loss: 0.3573
49/56 [=========================>....] - ETA: 0s - loss: 0.0914
56/56 [==============================] - 0s 1ms/step - loss: 0.0915
-> 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: 130, 8
LR fn, tp: 3, 6
LR f1 score: 0.522
LR cohens kappa score: 0.483
LR average precision score: 0.571

-> test with 'RF'
RF tn, fp: 137, 1
RF fn, tp: 8, 1
RF f1 score: 0.182
RF cohens kappa score: 0.163

-> test with 'GB'
GB tn, fp: 136, 2
GB fn, tp: 8, 1
GB f1 score: 0.167
GB cohens kappa score: 0.140

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


------ 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: 8s - loss: 0.0088
46/56 [=======================>......] - ETA: 0s - loss: 0.1133
56/56 [==============================] - 0s 1ms/step - loss: 0.1087
Epoch 2/10

 1/56 [..............................] - ETA: 0s - loss: 0.0119
46/56 [=======================>......] - ETA: 0s - loss: 0.0879
56/56 [==============================] - 0s 1ms/step - loss: 0.0925
Epoch 3/10

 1/56 [..............................] - ETA: 0s - loss: 0.0061
45/56 [=======================>......] - ETA: 0s - loss: 0.0930
56/56 [==============================] - 0s 1ms/step - loss: 0.0884
Epoch 4/10

 1/56 [..............................] - ETA: 0s - loss: 0.0375
46/56 [=======================>......] - ETA: 0s - loss: 0.0834
56/56 [==============================] - 0s 1ms/step - loss: 0.0893
Epoch 5/10

 1/56 [..............................] - ETA: 0s - loss: 0.0302
46/56 [=======================>......] - ETA: 0s - loss: 0.0829
56/56 [==============================] - 0s 1ms/step - loss: 0.0868
Epoch 6/10

 1/56 [..............................] - ETA: 0s - loss: 0.0561
44/56 [======================>.......] - ETA: 0s - loss: 0.0869
56/56 [==============================] - 0s 1ms/step - loss: 0.0857
Epoch 7/10

 1/56 [..............................] - ETA: 0s - loss: 0.0138
45/56 [=======================>......] - ETA: 0s - loss: 0.0824
56/56 [==============================] - 0s 1ms/step - loss: 0.0814
Epoch 8/10

 1/56 [..............................] - ETA: 0s - loss: 0.0120
46/56 [=======================>......] - ETA: 0s - loss: 0.0839
56/56 [==============================] - 0s 1ms/step - loss: 0.0805
Epoch 9/10

 1/56 [..............................] - ETA: 0s - loss: 0.0219
45/56 [=======================>......] - ETA: 0s - loss: 0.0755
56/56 [==============================] - 0s 1ms/step - loss: 0.0797
Epoch 10/10

 1/56 [..............................] - ETA: 0s - loss: 0.0243
46/56 [=======================>......] - ETA: 0s - loss: 0.0820
56/56 [==============================] - 0s 1ms/step - loss: 0.0860
-> test with GAN.predict
GAN tn, fp: 136, 2
GAN fn, tp: 4, 5
GAN f1 score: 0.625
GAN cohens kappa score: 0.604

-> test with 'LR'
LR tn, fp: 132, 6
LR fn, tp: 1, 8
LR f1 score: 0.696
LR cohens kappa score: 0.671
LR average precision score: 0.799

-> 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: 137, 1
GB fn, tp: 8, 1
GB f1 score: 0.182
GB cohens kappa score: 0.163

-> test with 'KNN'
KNN tn, fp: 138, 0
KNN fn, tp: 8, 1
KNN f1 score: 0.200
KNN cohens kappa score: 0.190


------ 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: 7s - loss: 0.4065
42/56 [=====================>........] - ETA: 0s - loss: 0.1108
56/56 [==============================] - 0s 1ms/step - loss: 0.1076
Epoch 2/10

 1/56 [..............................] - ETA: 0s - loss: 0.0198
50/56 [=========================>....] - ETA: 0s - loss: 0.0894
56/56 [==============================] - 0s 1ms/step - loss: 0.0919
Epoch 3/10

 1/56 [..............................] - ETA: 0s - loss: 0.1713
49/56 [=========================>....] - ETA: 0s - loss: 0.0890
56/56 [==============================] - 0s 1ms/step - loss: 0.0931
Epoch 4/10

 1/56 [..............................] - ETA: 0s - loss: 0.0488
50/56 [=========================>....] - ETA: 0s - loss: 0.0744
56/56 [==============================] - 0s 1ms/step - loss: 0.0844
Epoch 5/10

 1/56 [..............................] - ETA: 0s - loss: 0.0318
49/56 [=========================>....] - ETA: 0s - loss: 0.0923
56/56 [==============================] - 0s 1ms/step - loss: 0.0866
Epoch 6/10

 1/56 [..............................] - ETA: 0s - loss: 0.0301
49/56 [=========================>....] - ETA: 0s - loss: 0.0884
56/56 [==============================] - 0s 1ms/step - loss: 0.0823
Epoch 7/10

 1/56 [..............................] - ETA: 0s - loss: 0.0970
50/56 [=========================>....] - ETA: 0s - loss: 0.0877
56/56 [==============================] - 0s 1ms/step - loss: 0.0821
Epoch 8/10

 1/56 [..............................] - ETA: 0s - loss: 0.0395
50/56 [=========================>....] - ETA: 0s - loss: 0.0693
56/56 [==============================] - 0s 1ms/step - loss: 0.0796
Epoch 9/10

 1/56 [..............................] - ETA: 0s - loss: 0.0239
49/56 [=========================>....] - ETA: 0s - loss: 0.0828
56/56 [==============================] - 0s 1ms/step - loss: 0.0807
Epoch 10/10

 1/56 [..............................] - ETA: 0s - loss: 0.1084
49/56 [=========================>....] - ETA: 0s - loss: 0.0789
56/56 [==============================] - 0s 1ms/step - loss: 0.0819
-> test with GAN.predict
GAN tn, fp: 136, 2
GAN fn, tp: 6, 3
GAN f1 score: 0.429
GAN cohens kappa score: 0.402

-> 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: 0.634

-> test with 'RF'
RF tn, fp: 138, 0
RF fn, tp: 7, 2
RF f1 score: 0.364
RF cohens kappa score: 0.349

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

-> test with 'KNN'
KNN tn, fp: 138, 0
KNN fn, tp: 8, 1
KNN f1 score: 0.200
KNN cohens kappa score: 0.190


------ 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: 7s - loss: 0.0181
48/56 [========================>.....] - ETA: 0s - loss: 0.0913
56/56 [==============================] - 0s 1ms/step - loss: 0.0955
Epoch 2/10

 1/56 [..............................] - ETA: 0s - loss: 0.0252
33/56 [================>.............] - ETA: 0s - loss: 0.0665
56/56 [==============================] - 0s 1ms/step - loss: 0.0864
Epoch 3/10

 1/56 [..............................] - ETA: 0s - loss: 0.1864
38/56 [===================>..........] - ETA: 0s - loss: 0.0827
56/56 [==============================] - 0s 1ms/step - loss: 0.0965
Epoch 4/10

 1/56 [..............................] - ETA: 0s - loss: 0.0117
41/56 [====================>.........] - ETA: 0s - loss: 0.0815
56/56 [==============================] - 0s 1ms/step - loss: 0.0906
Epoch 5/10

 1/56 [..............................] - ETA: 0s - loss: 0.3460
46/56 [=======================>......] - ETA: 0s - loss: 0.1045
56/56 [==============================] - 0s 1ms/step - loss: 0.0970
Epoch 6/10

 1/56 [..............................] - ETA: 0s - loss: 0.0051
41/56 [====================>.........] - ETA: 0s - loss: 0.0709
56/56 [==============================] - 0s 1ms/step - loss: 0.0875
Epoch 7/10

 1/56 [..............................] - ETA: 0s - loss: 0.0269
45/56 [=======================>......] - ETA: 0s - loss: 0.0882
56/56 [==============================] - 0s 1ms/step - loss: 0.0876
Epoch 8/10

 1/56 [..............................] - ETA: 0s - loss: 0.0200
43/56 [======================>.......] - ETA: 0s - loss: 0.0735
56/56 [==============================] - 0s 1ms/step - loss: 0.0899
Epoch 9/10

 1/56 [..............................] - ETA: 0s - loss: 0.0074
44/56 [======================>.......] - ETA: 0s - loss: 0.0949
56/56 [==============================] - 0s 1ms/step - loss: 0.0870
Epoch 10/10

 1/56 [..............................] - ETA: 0s - loss: 0.3021
45/56 [=======================>......] - ETA: 0s - loss: 0.0956
56/56 [==============================] - 0s 1ms/step - loss: 0.0914
-> test with GAN.predict
GAN tn, fp: 135, 2
GAN fn, tp: 4, 2
GAN f1 score: 0.400
GAN cohens kappa score: 0.379

-> test with 'LR'
LR tn, fp: 133, 4
LR fn, tp: 2, 4
LR f1 score: 0.571
LR cohens kappa score: 0.550
LR average precision score: 0.484

-> test with 'RF'
RF tn, fp: 137, 0
RF fn, tp: 5, 1
RF f1 score: 0.286
RF cohens kappa score: 0.277

-> test with 'GB'
GB tn, fp: 136, 1
GB fn, tp: 5, 1
GB f1 score: 0.250
GB cohens kappa score: 0.234

-> test with 'KNN'
KNN tn, fp: 137, 0
KNN fn, tp: 5, 1
KNN f1 score: 0.286
KNN cohens kappa score: 0.277


====== 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: 8s - loss: 0.0605
49/56 [=========================>....] - ETA: 0s - loss: 0.1000
56/56 [==============================] - 0s 1ms/step - loss: 0.0955
Epoch 2/10

 1/56 [..............................] - ETA: 0s - loss: 0.0445
49/56 [=========================>....] - ETA: 0s - loss: 0.0916
56/56 [==============================] - 0s 1ms/step - loss: 0.0920
Epoch 3/10

 1/56 [..............................] - ETA: 0s - loss: 0.1234
49/56 [=========================>....] - ETA: 0s - loss: 0.0872
56/56 [==============================] - 0s 1ms/step - loss: 0.0878
Epoch 4/10

 1/56 [..............................] - ETA: 0s - loss: 0.4762
49/56 [=========================>....] - ETA: 0s - loss: 0.0875
56/56 [==============================] - 0s 1ms/step - loss: 0.0852
Epoch 5/10

 1/56 [..............................] - ETA: 0s - loss: 0.1695
49/56 [=========================>....] - ETA: 0s - loss: 0.0794
56/56 [==============================] - 0s 1ms/step - loss: 0.0823
Epoch 6/10

 1/56 [..............................] - ETA: 0s - loss: 0.2331
49/56 [=========================>....] - ETA: 0s - loss: 0.0908
56/56 [==============================] - 0s 1ms/step - loss: 0.0875
Epoch 7/10

 1/56 [..............................] - ETA: 0s - loss: 0.0096
49/56 [=========================>....] - ETA: 0s - loss: 0.0871
56/56 [==============================] - 0s 1ms/step - loss: 0.0887
Epoch 8/10

 1/56 [..............................] - ETA: 0s - loss: 0.1408
48/56 [========================>.....] - ETA: 0s - loss: 0.0887
56/56 [==============================] - 0s 1ms/step - loss: 0.0857
Epoch 9/10

 1/56 [..............................] - ETA: 0s - loss: 0.0821
49/56 [=========================>....] - ETA: 0s - loss: 0.0952
56/56 [==============================] - 0s 1ms/step - loss: 0.0903
Epoch 10/10

 1/56 [..............................] - ETA: 0s - loss: 0.0236
49/56 [=========================>....] - ETA: 0s - loss: 0.0810
56/56 [==============================] - 0s 1ms/step - loss: 0.0832
-> test with GAN.predict
GAN tn, fp: 135, 3
GAN fn, tp: 6, 3
GAN f1 score: 0.400
GAN cohens kappa score: 0.369

-> test with 'LR'
LR tn, fp: 131, 7
LR fn, tp: 2, 7
LR f1 score: 0.609
LR cohens kappa score: 0.577
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: 135, 3
GB fn, tp: 6, 3
GB f1 score: 0.400
GB cohens kappa score: 0.369

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


------ 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: 0.0036
50/56 [=========================>....] - ETA: 0s - loss: 0.1100
56/56 [==============================] - 0s 1ms/step - loss: 0.1072
Epoch 2/10

 1/56 [..............................] - ETA: 0s - loss: 0.1136
50/56 [=========================>....] - ETA: 0s - loss: 0.0969
56/56 [==============================] - 0s 1ms/step - loss: 0.0986
Epoch 3/10

 1/56 [..............................] - ETA: 0s - loss: 0.1500
50/56 [=========================>....] - ETA: 0s - loss: 0.0874
56/56 [==============================] - 0s 1ms/step - loss: 0.0952
Epoch 4/10

 1/56 [..............................] - ETA: 0s - loss: 0.0960
50/56 [=========================>....] - ETA: 0s - loss: 0.0974
56/56 [==============================] - 0s 1ms/step - loss: 0.0961
Epoch 5/10

 1/56 [..............................] - ETA: 0s - loss: 0.1029
48/56 [========================>.....] - ETA: 0s - loss: 0.0929
56/56 [==============================] - 0s 1ms/step - loss: 0.0925
Epoch 6/10

 1/56 [..............................] - ETA: 0s - loss: 0.1297
44/56 [======================>.......] - ETA: 0s - loss: 0.1033
56/56 [==============================] - 0s 1ms/step - loss: 0.0942
Epoch 7/10

 1/56 [..............................] - ETA: 0s - loss: 0.0412
45/56 [=======================>......] - ETA: 0s - loss: 0.0909
56/56 [==============================] - 0s 1ms/step - loss: 0.0941
Epoch 8/10

 1/56 [..............................] - ETA: 0s - loss: 0.0432
49/56 [=========================>....] - ETA: 0s - loss: 0.0952
56/56 [==============================] - 0s 1ms/step - loss: 0.0927
Epoch 9/10

 1/56 [..............................] - ETA: 0s - loss: 0.0618
49/56 [=========================>....] - ETA: 0s - loss: 0.0932
56/56 [==============================] - 0s 1ms/step - loss: 0.0917
Epoch 10/10

 1/56 [..............................] - ETA: 0s - loss: 0.0222
49/56 [=========================>....] - ETA: 0s - loss: 0.0830
56/56 [==============================] - 0s 1ms/step - loss: 0.0902
-> test with GAN.predict
GAN tn, fp: 133, 5
GAN fn, tp: 3, 6
GAN f1 score: 0.600
GAN cohens kappa score: 0.571

-> test with 'LR'
LR tn, fp: 132, 6
LR fn, tp: 2, 7
LR f1 score: 0.636
LR cohens kappa score: 0.608
LR average precision score: 0.779

-> 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: 137, 1
GB fn, tp: 8, 1
GB f1 score: 0.182
GB cohens kappa score: 0.163

-> test with 'KNN'
KNN tn, fp: 137, 1
KNN fn, tp: 8, 1
KNN f1 score: 0.182
KNN cohens kappa score: 0.163


------ 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: 0.0121
46/56 [=======================>......] - ETA: 0s - loss: 0.1086
56/56 [==============================] - 0s 1ms/step - loss: 0.0980
Epoch 2/10

 1/56 [..............................] - ETA: 0s - loss: 0.0175
50/56 [=========================>....] - ETA: 0s - loss: 0.0865
56/56 [==============================] - 0s 1ms/step - loss: 0.0849
Epoch 3/10

 1/56 [..............................] - ETA: 0s - loss: 0.0288
48/56 [========================>.....] - ETA: 0s - loss: 0.0824
56/56 [==============================] - 0s 1ms/step - loss: 0.0813
Epoch 4/10

 1/56 [..............................] - ETA: 0s - loss: 0.0158
50/56 [=========================>....] - ETA: 0s - loss: 0.0829
56/56 [==============================] - 0s 1ms/step - loss: 0.0799
Epoch 5/10

 1/56 [..............................] - ETA: 0s - loss: 0.1422
49/56 [=========================>....] - ETA: 0s - loss: 0.0730
56/56 [==============================] - 0s 1ms/step - loss: 0.0779
Epoch 6/10

 1/56 [..............................] - ETA: 0s - loss: 0.0437
50/56 [=========================>....] - ETA: 0s - loss: 0.0833
56/56 [==============================] - 0s 1ms/step - loss: 0.0808
Epoch 7/10

 1/56 [..............................] - ETA: 0s - loss: 0.0993
50/56 [=========================>....] - ETA: 0s - loss: 0.0790
56/56 [==============================] - 0s 1ms/step - loss: 0.0783
Epoch 8/10

 1/56 [..............................] - ETA: 0s - loss: 0.0115
50/56 [=========================>....] - ETA: 0s - loss: 0.0809
56/56 [==============================] - 0s 1ms/step - loss: 0.0764
Epoch 9/10

 1/56 [..............................] - ETA: 0s - loss: 0.0151
49/56 [=========================>....] - ETA: 0s - loss: 0.0797
56/56 [==============================] - 0s 1ms/step - loss: 0.0752
Epoch 10/10

 1/56 [..............................] - ETA: 0s - loss: 0.0830
49/56 [=========================>....] - ETA: 0s - loss: 0.0665
56/56 [==============================] - 0s 1ms/step - loss: 0.0733
-> test with GAN.predict
GAN tn, fp: 137, 1
GAN fn, tp: 4, 5
GAN f1 score: 0.667
GAN cohens kappa score: 0.649

-> test with 'LR'
LR tn, fp: 134, 4
LR fn, tp: 2, 7
LR f1 score: 0.700
LR cohens kappa score: 0.678
LR average precision score: 0.730

-> 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: 7, 2
GB f1 score: 0.267
GB cohens kappa score: 0.229

-> test with 'KNN'
KNN tn, fp: 138, 0
KNN fn, tp: 9, 0
KNN f1 score: 0.000
KNN cohens kappa score: 0.000


------ 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: 8s - loss: 0.7748
49/56 [=========================>....] - ETA: 0s - loss: 0.1241
56/56 [==============================] - 0s 1ms/step - loss: 0.1140
Epoch 2/10

 1/56 [..............................] - ETA: 0s - loss: 0.2186
50/56 [=========================>....] - ETA: 0s - loss: 0.0996
56/56 [==============================] - 0s 1ms/step - loss: 0.1016
Epoch 3/10

 1/56 [..............................] - ETA: 0s - loss: 0.3670
49/56 [=========================>....] - ETA: 0s - loss: 0.0849
56/56 [==============================] - 0s 1ms/step - loss: 0.0886
Epoch 4/10

 1/56 [..............................] - ETA: 0s - loss: 0.0177
49/56 [=========================>....] - ETA: 0s - loss: 0.0941
56/56 [==============================] - 0s 1ms/step - loss: 0.0886
Epoch 5/10

 1/56 [..............................] - ETA: 0s - loss: 0.0393
50/56 [=========================>....] - ETA: 0s - loss: 0.0822
56/56 [==============================] - 0s 1ms/step - loss: 0.0846
Epoch 6/10

 1/56 [..............................] - ETA: 0s - loss: 0.1120
50/56 [=========================>....] - ETA: 0s - loss: 0.0901
56/56 [==============================] - 0s 1ms/step - loss: 0.0842
Epoch 7/10

 1/56 [..............................] - ETA: 0s - loss: 0.0413
49/56 [=========================>....] - ETA: 0s - loss: 0.0812
56/56 [==============================] - 0s 1ms/step - loss: 0.0819
Epoch 8/10

 1/56 [..............................] - ETA: 0s - loss: 0.0328
50/56 [=========================>....] - ETA: 0s - loss: 0.0721
56/56 [==============================] - 0s 1ms/step - loss: 0.0794
Epoch 9/10

 1/56 [..............................] - ETA: 0s - loss: 0.0896
49/56 [=========================>....] - ETA: 0s - loss: 0.0794
56/56 [==============================] - 0s 1ms/step - loss: 0.0782
Epoch 10/10

 1/56 [..............................] - ETA: 0s - loss: 0.0424
50/56 [=========================>....] - ETA: 0s - loss: 0.0752
56/56 [==============================] - 0s 1ms/step - loss: 0.0785
-> test with GAN.predict
GAN tn, fp: 135, 3
GAN fn, tp: 6, 3
GAN f1 score: 0.400
GAN cohens kappa score: 0.369

-> test with 'LR'
LR tn, fp: 133, 5
LR fn, tp: 3, 6
LR f1 score: 0.600
LR cohens kappa score: 0.571
LR average precision score: 0.740

-> 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: 135, 3
GB fn, tp: 6, 3
GB f1 score: 0.400
GB cohens kappa score: 0.369

-> test with 'KNN'
KNN tn, fp: 137, 1
KNN fn, tp: 9, 0
KNN f1 score: 0.000
KNN cohens kappa score: -0.012


------ 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: 8s - loss: 0.0221
46/56 [=======================>......] - ETA: 0s - loss: 0.0921
56/56 [==============================] - 0s 1ms/step - loss: 0.0939
Epoch 2/10

 1/56 [..............................] - ETA: 0s - loss: 0.0277
46/56 [=======================>......] - ETA: 0s - loss: 0.0743
56/56 [==============================] - 0s 1ms/step - loss: 0.0918
Epoch 3/10

 1/56 [..............................] - ETA: 0s - loss: 0.0452
45/56 [=======================>......] - ETA: 0s - loss: 0.0821
56/56 [==============================] - 0s 1ms/step - loss: 0.0879
Epoch 4/10

 1/56 [..............................] - ETA: 0s - loss: 0.0281
45/56 [=======================>......] - ETA: 0s - loss: 0.0814
56/56 [==============================] - 0s 1ms/step - loss: 0.0933
Epoch 5/10

 1/56 [..............................] - ETA: 0s - loss: 0.0390
45/56 [=======================>......] - ETA: 0s - loss: 0.0909
56/56 [==============================] - 0s 1ms/step - loss: 0.0874
Epoch 6/10

 1/56 [..............................] - ETA: 0s - loss: 0.1012
45/56 [=======================>......] - ETA: 0s - loss: 0.0962
56/56 [==============================] - 0s 1ms/step - loss: 0.0895
Epoch 7/10

 1/56 [..............................] - ETA: 0s - loss: 0.0472
43/56 [======================>.......] - ETA: 0s - loss: 0.0849
56/56 [==============================] - 0s 1ms/step - loss: 0.0852
Epoch 8/10

 1/56 [..............................] - ETA: 0s - loss: 0.0049
42/56 [=====================>........] - ETA: 0s - loss: 0.0784
56/56 [==============================] - 0s 1ms/step - loss: 0.0865
Epoch 9/10

 1/56 [..............................] - ETA: 0s - loss: 0.0194
42/56 [=====================>........] - ETA: 0s - loss: 0.0870
56/56 [==============================] - 0s 1ms/step - loss: 0.0863
Epoch 10/10

 1/56 [..............................] - ETA: 0s - loss: 0.0064
46/56 [=======================>......] - ETA: 0s - loss: 0.0725
56/56 [==============================] - 0s 1ms/step - loss: 0.0838
-> test with GAN.predict
GAN tn, fp: 136, 1
GAN fn, tp: 3, 3
GAN f1 score: 0.600
GAN cohens kappa score: 0.586

-> 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: 0.645

-> test with 'RF'
RF tn, fp: 136, 1
RF fn, tp: 4, 2
RF f1 score: 0.444
RF cohens kappa score: 0.428

-> test with 'GB'
GB tn, fp: 136, 1
GB fn, tp: 5, 1
GB f1 score: 0.250
GB cohens kappa score: 0.234

-> test with 'KNN'
KNN tn, fp: 137, 0
KNN fn, tp: 5, 1
KNN f1 score: 0.286
KNN cohens kappa score: 0.277


====== 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: 9s - loss: 0.0056
49/56 [=========================>....] - ETA: 0s - loss: 0.0872
56/56 [==============================] - 0s 1ms/step - loss: 0.0860
Epoch 2/10

 1/56 [..............................] - ETA: 0s - loss: 0.0131
49/56 [=========================>....] - ETA: 0s - loss: 0.0879
56/56 [==============================] - 0s 1ms/step - loss: 0.0843
Epoch 3/10

 1/56 [..............................] - ETA: 0s - loss: 0.0308
49/56 [=========================>....] - ETA: 0s - loss: 0.0735
56/56 [==============================] - 0s 1ms/step - loss: 0.0800
Epoch 4/10

 1/56 [..............................] - ETA: 0s - loss: 0.5603
49/56 [=========================>....] - ETA: 0s - loss: 0.0840
56/56 [==============================] - 0s 1ms/step - loss: 0.0763
Epoch 5/10

 1/56 [..............................] - ETA: 0s - loss: 0.0168
49/56 [=========================>....] - ETA: 0s - loss: 0.0762
56/56 [==============================] - 0s 1ms/step - loss: 0.0783
Epoch 6/10

 1/56 [..............................] - ETA: 0s - loss: 0.0297
49/56 [=========================>....] - ETA: 0s - loss: 0.0749
56/56 [==============================] - 0s 1ms/step - loss: 0.0732
Epoch 7/10

 1/56 [..............................] - ETA: 0s - loss: 0.0157
49/56 [=========================>....] - ETA: 0s - loss: 0.0728
56/56 [==============================] - 0s 1ms/step - loss: 0.0711
Epoch 8/10

 1/56 [..............................] - ETA: 0s - loss: 0.0199
45/56 [=======================>......] - ETA: 0s - loss: 0.0786
56/56 [==============================] - 0s 1ms/step - loss: 0.0770
Epoch 9/10

 1/56 [..............................] - ETA: 0s - loss: 0.1037
44/56 [======================>.......] - ETA: 0s - loss: 0.0937
56/56 [==============================] - 0s 1ms/step - loss: 0.0830
Epoch 10/10

 1/56 [..............................] - ETA: 0s - loss: 0.0279
48/56 [========================>.....] - ETA: 0s - loss: 0.0676
56/56 [==============================] - 0s 1ms/step - loss: 0.0717
-> test with GAN.predict
GAN tn, fp: 135, 3
GAN fn, tp: 6, 3
GAN f1 score: 0.400
GAN cohens kappa score: 0.369

-> test with 'LR'
LR tn, fp: 131, 7
LR fn, tp: 5, 4
LR f1 score: 0.400
LR cohens kappa score: 0.357
LR average precision score: 0.526

-> test with 'RF'
RF tn, fp: 135, 3
RF fn, tp: 7, 2
RF f1 score: 0.286
RF cohens kappa score: 0.253

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

-> test with 'KNN'
KNN tn, fp: 136, 2
KNN fn, tp: 8, 1
KNN f1 score: 0.167
KNN cohens kappa score: 0.140


------ 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: 8s - loss: 0.0606
46/56 [=======================>......] - ETA: 0s - loss: 0.1043
56/56 [==============================] - 0s 1ms/step - loss: 0.1018
Epoch 2/10

 1/56 [..............................] - ETA: 0s - loss: 0.0732
46/56 [=======================>......] - ETA: 0s - loss: 0.1025
56/56 [==============================] - 0s 1ms/step - loss: 0.0947
Epoch 3/10

 1/56 [..............................] - ETA: 0s - loss: 0.0092
44/56 [======================>.......] - ETA: 0s - loss: 0.0896
56/56 [==============================] - 0s 1ms/step - loss: 0.0901
Epoch 4/10

 1/56 [..............................] - ETA: 0s - loss: 0.0104
43/56 [======================>.......] - ETA: 0s - loss: 0.0936
56/56 [==============================] - 0s 1ms/step - loss: 0.0881
Epoch 5/10

 1/56 [..............................] - ETA: 0s - loss: 0.0355
45/56 [=======================>......] - ETA: 0s - loss: 0.0861
56/56 [==============================] - 0s 1ms/step - loss: 0.0876
Epoch 6/10

 1/56 [..............................] - ETA: 0s - loss: 0.0366
45/56 [=======================>......] - ETA: 0s - loss: 0.0811
56/56 [==============================] - 0s 1ms/step - loss: 0.0851
Epoch 7/10

 1/56 [..............................] - ETA: 0s - loss: 0.0523
45/56 [=======================>......] - ETA: 0s - loss: 0.0792
56/56 [==============================] - 0s 1ms/step - loss: 0.0840
Epoch 8/10

 1/56 [..............................] - ETA: 0s - loss: 0.0292
44/56 [======================>.......] - ETA: 0s - loss: 0.0841
56/56 [==============================] - 0s 1ms/step - loss: 0.0808
Epoch 9/10

 1/56 [..............................] - ETA: 0s - loss: 0.0491
45/56 [=======================>......] - ETA: 0s - loss: 0.0911
56/56 [==============================] - 0s 1ms/step - loss: 0.0848
Epoch 10/10

 1/56 [..............................] - ETA: 0s - loss: 0.0152
45/56 [=======================>......] - ETA: 0s - loss: 0.0816
56/56 [==============================] - 0s 1ms/step - loss: 0.0844
-> test with GAN.predict
GAN tn, fp: 136, 2
GAN fn, tp: 4, 5
GAN f1 score: 0.625
GAN cohens kappa score: 0.604

-> test with 'LR'
LR tn, fp: 134, 4
LR fn, tp: 0, 9
LR f1 score: 0.818
LR cohens kappa score: 0.804
LR average precision score: 0.825

-> test with 'RF'
RF tn, fp: 136, 2
RF fn, tp: 8, 1
RF f1 score: 0.167
RF cohens kappa score: 0.140

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

-> test with 'KNN'
KNN tn, fp: 137, 1
KNN fn, tp: 9, 0
KNN f1 score: 0.000
KNN cohens kappa score: -0.012


------ 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: 0.0240
48/56 [========================>.....] - ETA: 0s - loss: 0.1141
56/56 [==============================] - 0s 1ms/step - loss: 0.1064
Epoch 2/10

 1/56 [..............................] - ETA: 0s - loss: 0.1729
48/56 [========================>.....] - ETA: 0s - loss: 0.1024
56/56 [==============================] - 0s 1ms/step - loss: 0.1030
Epoch 3/10

 1/56 [..............................] - ETA: 0s - loss: 0.0297
47/56 [========================>.....] - ETA: 0s - loss: 0.1201
56/56 [==============================] - 0s 1ms/step - loss: 0.1129
Epoch 4/10

 1/56 [..............................] - ETA: 0s - loss: 0.3140
49/56 [=========================>....] - ETA: 0s - loss: 0.1052
56/56 [==============================] - 0s 1ms/step - loss: 0.1011
Epoch 5/10

 1/56 [..............................] - ETA: 0s - loss: 0.0717
49/56 [=========================>....] - ETA: 0s - loss: 0.1084
56/56 [==============================] - 0s 1ms/step - loss: 0.1024
Epoch 6/10

 1/56 [..............................] - ETA: 0s - loss: 0.0318
50/56 [=========================>....] - ETA: 0s - loss: 0.1046
56/56 [==============================] - 0s 1ms/step - loss: 0.0998
Epoch 7/10

 1/56 [..............................] - ETA: 0s - loss: 0.2862
49/56 [=========================>....] - ETA: 0s - loss: 0.1066
56/56 [==============================] - 0s 1ms/step - loss: 0.1005
Epoch 8/10

 1/56 [..............................] - ETA: 0s - loss: 0.0723
50/56 [=========================>....] - ETA: 0s - loss: 0.0991
56/56 [==============================] - 0s 1ms/step - loss: 0.0966
Epoch 9/10

 1/56 [..............................] - ETA: 0s - loss: 0.0981
49/56 [=========================>....] - ETA: 0s - loss: 0.0911
56/56 [==============================] - 0s 1ms/step - loss: 0.0994
Epoch 10/10

 1/56 [..............................] - ETA: 0s - loss: 0.2255
50/56 [=========================>....] - ETA: 0s - loss: 0.1034
56/56 [==============================] - 0s 1ms/step - loss: 0.1040
-> test with GAN.predict
GAN tn, fp: 131, 7
GAN fn, tp: 6, 3
GAN f1 score: 0.316
GAN cohens kappa score: 0.269

-> test with 'LR'
LR tn, fp: 132, 6
LR fn, tp: 4, 5
LR f1 score: 0.500
LR cohens kappa score: 0.464
LR average precision score: 0.670

-> test with 'RF'
RF tn, fp: 135, 3
RF fn, tp: 7, 2
RF f1 score: 0.286
RF cohens kappa score: 0.253

-> test with 'GB'
GB tn, fp: 136, 2
GB fn, tp: 8, 1
GB f1 score: 0.167
GB cohens kappa score: 0.140

-> test with 'KNN'
KNN tn, fp: 136, 2
KNN fn, tp: 9, 0
KNN f1 score: 0.000
KNN cohens kappa score: -0.023


------ 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: 0.1700
48/56 [========================>.....] - ETA: 0s - loss: 0.0881
56/56 [==============================] - 0s 1ms/step - loss: 0.0949
Epoch 2/10

 1/56 [..............................] - ETA: 0s - loss: 0.2452
49/56 [=========================>....] - ETA: 0s - loss: 0.0910
56/56 [==============================] - 0s 1ms/step - loss: 0.0844
Epoch 3/10

 1/56 [..............................] - ETA: 0s - loss: 0.0429
49/56 [=========================>....] - ETA: 0s - loss: 0.0829
56/56 [==============================] - 0s 1ms/step - loss: 0.0846
Epoch 4/10

 1/56 [..............................] - ETA: 0s - loss: 0.0093
49/56 [=========================>....] - ETA: 0s - loss: 0.0783
56/56 [==============================] - 0s 1ms/step - loss: 0.0817
Epoch 5/10

 1/56 [..............................] - ETA: 0s - loss: 0.0663
49/56 [=========================>....] - ETA: 0s - loss: 0.0827
56/56 [==============================] - 0s 1ms/step - loss: 0.0802
Epoch 6/10

 1/56 [..............................] - ETA: 0s - loss: 0.0060
49/56 [=========================>....] - ETA: 0s - loss: 0.0699
56/56 [==============================] - 0s 1ms/step - loss: 0.0785
Epoch 7/10

 1/56 [..............................] - ETA: 0s - loss: 0.0152
50/56 [=========================>....] - ETA: 0s - loss: 0.0796
56/56 [==============================] - 0s 1ms/step - loss: 0.0790
Epoch 8/10

 1/56 [..............................] - ETA: 0s - loss: 0.0194
49/56 [=========================>....] - ETA: 0s - loss: 0.0776
56/56 [==============================] - 0s 1ms/step - loss: 0.0790
Epoch 9/10

 1/56 [..............................] - ETA: 0s - loss: 0.0087
49/56 [=========================>....] - ETA: 0s - loss: 0.0814
56/56 [==============================] - 0s 1ms/step - loss: 0.0790
Epoch 10/10

 1/56 [..............................] - ETA: 0s - loss: 0.1023
49/56 [=========================>....] - ETA: 0s - loss: 0.0733
56/56 [==============================] - 0s 1ms/step - loss: 0.0770
-> test with GAN.predict
GAN tn, fp: 136, 2
GAN fn, tp: 4, 5
GAN f1 score: 0.625
GAN cohens kappa score: 0.604

-> test with 'LR'
LR tn, fp: 128, 10
LR fn, tp: 2, 7
LR f1 score: 0.538
LR cohens kappa score: 0.498
LR average precision score: 0.668

-> test with 'RF'
RF tn, fp: 138, 0
RF fn, tp: 6, 3
RF f1 score: 0.500
RF cohens kappa score: 0.484

-> test with 'GB'
GB tn, fp: 138, 0
GB fn, tp: 5, 4
GB f1 score: 0.615
GB cohens kappa score: 0.600

-> test with 'KNN'
KNN tn, fp: 138, 0
KNN fn, tp: 8, 1
KNN f1 score: 0.200
KNN cohens kappa score: 0.190


------ 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: 0.2589
49/56 [=========================>....] - ETA: 0s - loss: 0.1165
56/56 [==============================] - 0s 1ms/step - loss: 0.1265
Epoch 2/10

 1/56 [..............................] - ETA: 0s - loss: 0.1902
47/56 [========================>.....] - ETA: 0s - loss: 0.1098
56/56 [==============================] - 0s 1ms/step - loss: 0.1055
Epoch 3/10

 1/56 [..............................] - ETA: 0s - loss: 0.1600
43/56 [======================>.......] - ETA: 0s - loss: 0.0996
56/56 [==============================] - 0s 1ms/step - loss: 0.0958
Epoch 4/10

 1/56 [..............................] - ETA: 0s - loss: 0.0085
46/56 [=======================>......] - ETA: 0s - loss: 0.0810
56/56 [==============================] - 0s 1ms/step - loss: 0.0850
Epoch 5/10

 1/56 [..............................] - ETA: 0s - loss: 0.0064
49/56 [=========================>....] - ETA: 0s - loss: 0.0928
56/56 [==============================] - 0s 1ms/step - loss: 0.0875
Epoch 6/10

 1/56 [..............................] - ETA: 0s - loss: 0.0136
50/56 [=========================>....] - ETA: 0s - loss: 0.0830
56/56 [==============================] - 0s 1ms/step - loss: 0.0860
Epoch 7/10

 1/56 [..............................] - ETA: 0s - loss: 0.0279
48/56 [========================>.....] - ETA: 0s - loss: 0.0871
56/56 [==============================] - 0s 1ms/step - loss: 0.0879
Epoch 8/10

 1/56 [..............................] - ETA: 0s - loss: 0.2052
47/56 [========================>.....] - ETA: 0s - loss: 0.0901
56/56 [==============================] - 0s 1ms/step - loss: 0.0849
Epoch 9/10

 1/56 [..............................] - ETA: 0s - loss: 0.4856
49/56 [=========================>....] - ETA: 0s - loss: 0.0873
56/56 [==============================] - 0s 1ms/step - loss: 0.0843
Epoch 10/10

 1/56 [..............................] - ETA: 0s - loss: 0.0050
50/56 [=========================>....] - ETA: 0s - loss: 0.0833
56/56 [==============================] - 0s 1ms/step - loss: 0.0863
-> test with GAN.predict
GAN tn, fp: 133, 4
GAN fn, tp: 2, 4
GAN f1 score: 0.571
GAN cohens kappa score: 0.550

-> test with 'LR'
LR tn, fp: 131, 6
LR fn, tp: 2, 4
LR f1 score: 0.500
LR cohens kappa score: 0.472
LR average precision score: 0.533

-> test with 'RF'
RF tn, fp: 136, 1
RF fn, tp: 5, 1
RF f1 score: 0.250
RF cohens kappa score: 0.234

-> 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: 136, 1
KNN fn, tp: 6, 0
KNN f1 score: 0.000
KNN cohens kappa score: -0.012


====== 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: 0.0188
48/56 [========================>.....] - ETA: 0s - loss: 0.1383
56/56 [==============================] - 0s 1ms/step - loss: 0.1387
Epoch 2/10

 1/56 [..............................] - ETA: 0s - loss: 0.0497
49/56 [=========================>....] - ETA: 0s - loss: 0.1001
56/56 [==============================] - 0s 1ms/step - loss: 0.1207
Epoch 3/10

 1/56 [..............................] - ETA: 0s - loss: 0.0496
49/56 [=========================>....] - ETA: 0s - loss: 0.1303
56/56 [==============================] - 0s 1ms/step - loss: 0.1214
Epoch 4/10

 1/56 [..............................] - ETA: 0s - loss: 0.0644
49/56 [=========================>....] - ETA: 0s - loss: 0.1128
56/56 [==============================] - 0s 1ms/step - loss: 0.1138
Epoch 5/10

 1/56 [..............................] - ETA: 0s - loss: 0.0493
49/56 [=========================>....] - ETA: 0s - loss: 0.1120
56/56 [==============================] - 0s 1ms/step - loss: 0.1108
Epoch 6/10

 1/56 [..............................] - ETA: 0s - loss: 0.0123
49/56 [=========================>....] - ETA: 0s - loss: 0.1081
56/56 [==============================] - 0s 1ms/step - loss: 0.1026
Epoch 7/10

 1/56 [..............................] - ETA: 0s - loss: 0.0280
50/56 [=========================>....] - ETA: 0s - loss: 0.1040
56/56 [==============================] - 0s 1ms/step - loss: 0.1019
Epoch 8/10

 1/56 [..............................] - ETA: 0s - loss: 0.0286
49/56 [=========================>....] - ETA: 0s - loss: 0.0913
56/56 [==============================] - 0s 1ms/step - loss: 0.0981
Epoch 9/10

 1/56 [..............................] - ETA: 0s - loss: 0.0505
49/56 [=========================>....] - ETA: 0s - loss: 0.1053
56/56 [==============================] - 0s 1ms/step - loss: 0.1007
Epoch 10/10

 1/56 [..............................] - ETA: 0s - loss: 0.0474
46/56 [=======================>......] - ETA: 0s - loss: 0.0832
56/56 [==============================] - 0s 1ms/step - loss: 0.0979
-> 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: 130, 8
LR fn, tp: 5, 4
LR f1 score: 0.381
LR cohens kappa score: 0.334
LR average precision score: 0.530

-> test with 'RF'
RF tn, fp: 136, 2
RF fn, tp: 6, 3
RF f1 score: 0.429
RF cohens kappa score: 0.402

-> test with 'GB'
GB tn, fp: 135, 3
GB fn, tp: 6, 3
GB f1 score: 0.400
GB cohens kappa score: 0.369

-> test with 'KNN'
KNN tn, fp: 136, 2
KNN fn, tp: 8, 1
KNN f1 score: 0.167
KNN cohens kappa score: 0.140


------ 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: 0.0070
49/56 [=========================>....] - ETA: 0s - loss: 0.0864
56/56 [==============================] - 0s 1ms/step - loss: 0.0904
Epoch 2/10

 1/56 [..............................] - ETA: 0s - loss: 0.2332
50/56 [=========================>....] - ETA: 0s - loss: 0.0809
56/56 [==============================] - 0s 1ms/step - loss: 0.0863
Epoch 3/10

 1/56 [..............................] - ETA: 0s - loss: 0.0186
49/56 [=========================>....] - ETA: 0s - loss: 0.0927
56/56 [==============================] - 0s 1ms/step - loss: 0.0937
Epoch 4/10

 1/56 [..............................] - ETA: 0s - loss: 0.1599
49/56 [=========================>....] - ETA: 0s - loss: 0.0676
56/56 [==============================] - 0s 1ms/step - loss: 0.0768
Epoch 5/10

 1/56 [..............................] - ETA: 0s - loss: 0.0636
48/56 [========================>.....] - ETA: 0s - loss: 0.0890
56/56 [==============================] - 0s 1ms/step - loss: 0.0808
Epoch 6/10

 1/56 [..............................] - ETA: 0s - loss: 0.1082
46/56 [=======================>......] - ETA: 0s - loss: 0.0741
56/56 [==============================] - 0s 1ms/step - loss: 0.0721
Epoch 7/10

 1/56 [..............................] - ETA: 0s - loss: 0.0681
46/56 [=======================>......] - ETA: 0s - loss: 0.0761
56/56 [==============================] - 0s 1ms/step - loss: 0.0727
Epoch 8/10

 1/56 [..............................] - ETA: 0s - loss: 0.0309
45/56 [=======================>......] - ETA: 0s - loss: 0.0817
56/56 [==============================] - 0s 1ms/step - loss: 0.0747
Epoch 9/10

 1/56 [..............................] - ETA: 0s - loss: 0.0105
48/56 [========================>.....] - ETA: 0s - loss: 0.0806
56/56 [==============================] - 0s 1ms/step - loss: 0.0718
Epoch 10/10

 1/56 [..............................] - ETA: 0s - loss: 0.1120
43/56 [======================>.......] - ETA: 0s - loss: 0.0843
56/56 [==============================] - 0s 1ms/step - loss: 0.0718
-> test with GAN.predict
GAN tn, fp: 136, 2
GAN fn, tp: 5, 4
GAN f1 score: 0.533
GAN cohens kappa score: 0.509

-> 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: 0.688

-> test with 'RF'
RF tn, fp: 135, 3
RF fn, tp: 6, 3
RF f1 score: 0.400
RF cohens kappa score: 0.369

-> test with 'GB'
GB tn, fp: 129, 9
GB fn, tp: 5, 4
GB f1 score: 0.364
GB cohens kappa score: 0.314

-> test with 'KNN'
KNN tn, fp: 138, 0
KNN fn, tp: 8, 1
KNN f1 score: 0.200
KNN cohens kappa score: 0.190


------ 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: 0.0670
49/56 [=========================>....] - ETA: 0s - loss: 0.1150
56/56 [==============================] - 0s 1ms/step - loss: 0.1145
Epoch 2/10

 1/56 [..............................] - ETA: 0s - loss: 0.0688
49/56 [=========================>....] - ETA: 0s - loss: 0.1134
56/56 [==============================] - 0s 1ms/step - loss: 0.1191
Epoch 3/10

 1/56 [..............................] - ETA: 0s - loss: 0.1365
48/56 [========================>.....] - ETA: 0s - loss: 0.1053
56/56 [==============================] - 0s 1ms/step - loss: 0.1059
Epoch 4/10

 1/56 [..............................] - ETA: 0s - loss: 0.0178
49/56 [=========================>....] - ETA: 0s - loss: 0.1094
56/56 [==============================] - 0s 1ms/step - loss: 0.1083
Epoch 5/10

 1/56 [..............................] - ETA: 0s - loss: 0.0226
49/56 [=========================>....] - ETA: 0s - loss: 0.1044
56/56 [==============================] - 0s 1ms/step - loss: 0.1046
Epoch 6/10

 1/56 [..............................] - ETA: 0s - loss: 0.0962
46/56 [=======================>......] - ETA: 0s - loss: 0.0966
56/56 [==============================] - 0s 1ms/step - loss: 0.1032
Epoch 7/10

 1/56 [..............................] - ETA: 0s - loss: 0.0266
41/56 [====================>.........] - ETA: 0s - loss: 0.0996
56/56 [==============================] - 0s 1ms/step - loss: 0.1050
Epoch 8/10

 1/56 [..............................] - ETA: 0s - loss: 0.3545
47/56 [========================>.....] - ETA: 0s - loss: 0.1105
56/56 [==============================] - 0s 1ms/step - loss: 0.1080
Epoch 9/10

 1/56 [..............................] - ETA: 0s - loss: 0.0386
49/56 [=========================>....] - ETA: 0s - loss: 0.1054
56/56 [==============================] - 0s 1ms/step - loss: 0.1051
Epoch 10/10

 1/56 [..............................] - ETA: 0s - loss: 0.0943
49/56 [=========================>....] - ETA: 0s - loss: 0.1072
56/56 [==============================] - 0s 1ms/step - loss: 0.1067
-> test with GAN.predict
GAN tn, fp: 137, 1
GAN fn, tp: 4, 5
GAN f1 score: 0.667
GAN cohens kappa score: 0.649

-> test with 'LR'
LR tn, fp: 129, 9
LR fn, tp: 1, 8
LR f1 score: 0.615
LR cohens kappa score: 0.582
LR average precision score: 0.671

-> test with 'RF'
RF tn, fp: 138, 0
RF fn, tp: 7, 2
RF f1 score: 0.364
RF cohens kappa score: 0.349

-> test with 'GB'
GB tn, fp: 137, 1
GB fn, tp: 7, 2
GB f1 score: 0.333
GB cohens kappa score: 0.312

-> test with 'KNN'
KNN tn, fp: 138, 0
KNN fn, tp: 8, 1
KNN f1 score: 0.200
KNN cohens kappa score: 0.190


------ 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: 0.5220
49/56 [=========================>....] - ETA: 0s - loss: 0.1154
56/56 [==============================] - 0s 1ms/step - loss: 0.1064
Epoch 2/10

 1/56 [..............................] - ETA: 0s - loss: 0.0190
50/56 [=========================>....] - ETA: 0s - loss: 0.0955
56/56 [==============================] - 0s 1ms/step - loss: 0.1002
Epoch 3/10

 1/56 [..............................] - ETA: 0s - loss: 0.0154
49/56 [=========================>....] - ETA: 0s - loss: 0.0739
56/56 [==============================] - 0s 1ms/step - loss: 0.0902
Epoch 4/10

 1/56 [..............................] - ETA: 0s - loss: 0.3329
49/56 [=========================>....] - ETA: 0s - loss: 0.0872
56/56 [==============================] - 0s 1ms/step - loss: 0.0886
Epoch 5/10

 1/56 [..............................] - ETA: 0s - loss: 0.1420
49/56 [=========================>....] - ETA: 0s - loss: 0.0828
56/56 [==============================] - 0s 1ms/step - loss: 0.0867
Epoch 6/10

 1/56 [..............................] - ETA: 0s - loss: 0.1011
49/56 [=========================>....] - ETA: 0s - loss: 0.0884
56/56 [==============================] - 0s 1ms/step - loss: 0.0869
Epoch 7/10

 1/56 [..............................] - ETA: 0s - loss: 0.0152
49/56 [=========================>....] - ETA: 0s - loss: 0.0685
56/56 [==============================] - 0s 1ms/step - loss: 0.0815
Epoch 8/10

 1/56 [..............................] - ETA: 0s - loss: 0.0325
49/56 [=========================>....] - ETA: 0s - loss: 0.0834
56/56 [==============================] - 0s 1ms/step - loss: 0.0841
Epoch 9/10

 1/56 [..............................] - ETA: 0s - loss: 0.1239
49/56 [=========================>....] - ETA: 0s - loss: 0.0843
56/56 [==============================] - 0s 1ms/step - loss: 0.0809
Epoch 10/10

 1/56 [..............................] - ETA: 0s - loss: 0.0548
49/56 [=========================>....] - ETA: 0s - loss: 0.0864
56/56 [==============================] - 0s 1ms/step - loss: 0.0796
-> test with GAN.predict
GAN tn, fp: 133, 5
GAN fn, tp: 6, 3
GAN f1 score: 0.353
GAN cohens kappa score: 0.313

-> test with 'LR'
LR tn, fp: 133, 5
LR fn, tp: 0, 9
LR f1 score: 0.783
LR cohens kappa score: 0.765
LR average precision score: 0.897

-> test with 'RF'
RF tn, fp: 138, 0
RF fn, tp: 7, 2
RF f1 score: 0.364
RF cohens kappa score: 0.349

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

-> test with 'KNN'
KNN tn, fp: 135, 3
KNN fn, tp: 9, 0
KNN f1 score: 0.000
KNN cohens kappa score: -0.032


------ 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: 8s - loss: 0.0097
49/56 [=========================>....] - ETA: 0s - loss: 0.0911
56/56 [==============================] - 0s 1ms/step - loss: 0.0873
Epoch 2/10

 1/56 [..............................] - ETA: 0s - loss: 0.0335
49/56 [=========================>....] - ETA: 0s - loss: 0.0907
56/56 [==============================] - 0s 1ms/step - loss: 0.0851
Epoch 3/10

 1/56 [..............................] - ETA: 0s - loss: 0.0028
49/56 [=========================>....] - ETA: 0s - loss: 0.0787
56/56 [==============================] - 0s 1ms/step - loss: 0.0784
Epoch 4/10

 1/56 [..............................] - ETA: 0s - loss: 0.1885
49/56 [=========================>....] - ETA: 0s - loss: 0.0723
56/56 [==============================] - 0s 1ms/step - loss: 0.0758
Epoch 5/10

 1/56 [..............................] - ETA: 0s - loss: 0.1350
49/56 [=========================>....] - ETA: 0s - loss: 0.0782
56/56 [==============================] - 0s 1ms/step - loss: 0.0782
Epoch 6/10

 1/56 [..............................] - ETA: 0s - loss: 0.0289
49/56 [=========================>....] - ETA: 0s - loss: 0.0721
56/56 [==============================] - 0s 1ms/step - loss: 0.0793
Epoch 7/10

 1/56 [..............................] - ETA: 0s - loss: 0.0465
44/56 [======================>.......] - ETA: 0s - loss: 0.0778
56/56 [==============================] - 0s 1ms/step - loss: 0.0737
Epoch 8/10

 1/56 [..............................] - ETA: 0s - loss: 0.2821
44/56 [======================>.......] - ETA: 0s - loss: 0.0813
56/56 [==============================] - 0s 1ms/step - loss: 0.0754
Epoch 9/10

 1/56 [..............................] - ETA: 0s - loss: 0.0175
47/56 [========================>.....] - ETA: 0s - loss: 0.0735
56/56 [==============================] - 0s 1ms/step - loss: 0.0747
Epoch 10/10

 1/56 [..............................] - ETA: 0s - loss: 0.0493
49/56 [=========================>....] - ETA: 0s - loss: 0.0779
56/56 [==============================] - 0s 1ms/step - loss: 0.0724
-> test with GAN.predict
GAN tn, fp: 136, 1
GAN fn, tp: 2, 4
GAN f1 score: 0.727
GAN cohens kappa score: 0.716

-> test with 'LR'
LR tn, fp: 132, 5
LR fn, tp: 2, 4
LR f1 score: 0.533
LR cohens kappa score: 0.509
LR average precision score: 0.626

-> test with 'RF'
RF tn, fp: 136, 1
RF fn, tp: 3, 3
RF f1 score: 0.600
RF cohens kappa score: 0.586

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

-> test with 'KNN'
KNN tn, fp: 136, 1
KNN fn, tp: 5, 1
KNN f1 score: 0.250
KNN cohens kappa score: 0.234


====== 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: 8s - loss: 0.1947
49/56 [=========================>....] - ETA: 0s - loss: 0.1141
56/56 [==============================] - 0s 1ms/step - loss: 0.1097
Epoch 2/10

 1/56 [..............................] - ETA: 0s - loss: 0.1009
50/56 [=========================>....] - ETA: 0s - loss: 0.1075
56/56 [==============================] - 0s 1ms/step - loss: 0.1085
Epoch 3/10

 1/56 [..............................] - ETA: 0s - loss: 0.0159
50/56 [=========================>....] - ETA: 0s - loss: 0.1181
56/56 [==============================] - 0s 1ms/step - loss: 0.1158
Epoch 4/10

 1/56 [..............................] - ETA: 0s - loss: 0.2182
49/56 [=========================>....] - ETA: 0s - loss: 0.1023
56/56 [==============================] - 0s 1ms/step - loss: 0.1067
Epoch 5/10

 1/56 [..............................] - ETA: 0s - loss: 0.0502
50/56 [=========================>....] - ETA: 0s - loss: 0.1023
56/56 [==============================] - 0s 1ms/step - loss: 0.1028
Epoch 6/10

 1/56 [..............................] - ETA: 0s - loss: 0.0281
49/56 [=========================>....] - ETA: 0s - loss: 0.1018
56/56 [==============================] - 0s 1ms/step - loss: 0.1044
Epoch 7/10

 1/56 [..............................] - ETA: 0s - loss: 0.1079
50/56 [=========================>....] - ETA: 0s - loss: 0.1018
56/56 [==============================] - 0s 1ms/step - loss: 0.0998
Epoch 8/10

 1/56 [..............................] - ETA: 0s - loss: 0.0598
50/56 [=========================>....] - ETA: 0s - loss: 0.1115
56/56 [==============================] - 0s 1ms/step - loss: 0.1048
Epoch 9/10

 1/56 [..............................] - ETA: 0s - loss: 0.0074
50/56 [=========================>....] - ETA: 0s - loss: 0.1038
56/56 [==============================] - 0s 1ms/step - loss: 0.1056
Epoch 10/10

 1/56 [..............................] - ETA: 0s - loss: 0.1623
50/56 [=========================>....] - ETA: 0s - loss: 0.1053
56/56 [==============================] - 0s 1ms/step - loss: 0.1046
-> test with GAN.predict
GAN tn, fp: 132, 6
GAN fn, tp: 4, 5
GAN f1 score: 0.500
GAN cohens kappa score: 0.464

-> test with 'LR'
LR tn, fp: 127, 11
LR fn, tp: 2, 7
LR f1 score: 0.519
LR cohens kappa score: 0.476
LR average precision score: 0.694

-> test with 'RF'
RF tn, fp: 138, 0
RF fn, tp: 9, 0
RF f1 score: 0.000
RF cohens kappa score: 0.000

-> test with 'GB'
GB tn, fp: 136, 2
GB fn, tp: 8, 1
GB f1 score: 0.167
GB cohens kappa score: 0.140

-> test with 'KNN'
KNN tn, fp: 136, 2
KNN fn, tp: 9, 0
KNN f1 score: 0.000
KNN cohens kappa score: -0.023


------ 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: 8s - loss: 0.0816
49/56 [=========================>....] - ETA: 0s - loss: 0.1233
56/56 [==============================] - 0s 1ms/step - loss: 0.1148
Epoch 2/10

 1/56 [..............................] - ETA: 0s - loss: 0.0130
49/56 [=========================>....] - ETA: 0s - loss: 0.1105
56/56 [==============================] - 0s 1ms/step - loss: 0.1111
Epoch 3/10

 1/56 [..............................] - ETA: 0s - loss: 0.0274
49/56 [=========================>....] - ETA: 0s - loss: 0.0999
56/56 [==============================] - 0s 1ms/step - loss: 0.1027
Epoch 4/10

 1/56 [..............................] - ETA: 0s - loss: 0.1439
49/56 [=========================>....] - ETA: 0s - loss: 0.0986
56/56 [==============================] - 0s 1ms/step - loss: 0.0986
Epoch 5/10

 1/56 [..............................] - ETA: 0s - loss: 0.0286
49/56 [=========================>....] - ETA: 0s - loss: 0.1038
56/56 [==============================] - 0s 1ms/step - loss: 0.0991
Epoch 6/10

 1/56 [..............................] - ETA: 0s - loss: 0.0447
49/56 [=========================>....] - ETA: 0s - loss: 0.0974
56/56 [==============================] - 0s 1ms/step - loss: 0.0954
Epoch 7/10

 1/56 [..............................] - ETA: 0s - loss: 0.1299
49/56 [=========================>....] - ETA: 0s - loss: 0.1020
56/56 [==============================] - 0s 1ms/step - loss: 0.0924
Epoch 8/10

 1/56 [..............................] - ETA: 0s - loss: 0.2587
49/56 [=========================>....] - ETA: 0s - loss: 0.0888
56/56 [==============================] - 0s 1ms/step - loss: 0.0920
Epoch 9/10

 1/56 [..............................] - ETA: 0s - loss: 0.0288
49/56 [=========================>....] - ETA: 0s - loss: 0.0879
56/56 [==============================] - 0s 1ms/step - loss: 0.0919
Epoch 10/10

 1/56 [..............................] - ETA: 0s - loss: 0.0543
47/56 [========================>.....] - ETA: 0s - loss: 0.0722
56/56 [==============================] - 0s 1ms/step - loss: 0.0889
-> test with GAN.predict
GAN tn, fp: 137, 1
GAN fn, tp: 5, 4
GAN f1 score: 0.571
GAN cohens kappa score: 0.552

-> test with 'LR'
LR tn, fp: 130, 8
LR fn, tp: 2, 7
LR f1 score: 0.583
LR cohens kappa score: 0.549
LR average precision score: 0.689

-> test with 'RF'
RF tn, fp: 138, 0
RF fn, tp: 7, 2
RF f1 score: 0.364
RF cohens kappa score: 0.349

-> test with 'GB'
GB tn, fp: 136, 2
GB fn, tp: 6, 3
GB f1 score: 0.429
GB cohens kappa score: 0.402

-> test with 'KNN'
KNN tn, fp: 138, 0
KNN fn, tp: 9, 0
KNN f1 score: 0.000
KNN cohens kappa score: 0.000


------ 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: 9s - loss: 0.0306
48/56 [========================>.....] - ETA: 0s - loss: 0.0972
56/56 [==============================] - 0s 1ms/step - loss: 0.0944
Epoch 2/10

 1/56 [..............................] - ETA: 0s - loss: 0.1812
49/56 [=========================>....] - ETA: 0s - loss: 0.0798
56/56 [==============================] - 0s 1ms/step - loss: 0.0749
Epoch 3/10

 1/56 [..............................] - ETA: 0s - loss: 0.0362
49/56 [=========================>....] - ETA: 0s - loss: 0.0784
56/56 [==============================] - 0s 1ms/step - loss: 0.0721
Epoch 4/10

 1/56 [..............................] - ETA: 0s - loss: 0.0121
47/56 [========================>.....] - ETA: 0s - loss: 0.0667
56/56 [==============================] - 0s 1ms/step - loss: 0.0671
Epoch 5/10

 1/56 [..............................] - ETA: 0s - loss: 0.0041
49/56 [=========================>....] - ETA: 0s - loss: 0.0723
56/56 [==============================] - 0s 1ms/step - loss: 0.0734
Epoch 6/10

 1/56 [..............................] - ETA: 0s - loss: 0.0187
49/56 [=========================>....] - ETA: 0s - loss: 0.0715
56/56 [==============================] - 0s 1ms/step - loss: 0.0691
Epoch 7/10

 1/56 [..............................] - ETA: 0s - loss: 0.0171
49/56 [=========================>....] - ETA: 0s - loss: 0.0684
56/56 [==============================] - 0s 1ms/step - loss: 0.0720
Epoch 8/10

 1/56 [..............................] - ETA: 0s - loss: 0.0200
49/56 [=========================>....] - ETA: 0s - loss: 0.0643
56/56 [==============================] - 0s 1ms/step - loss: 0.0658
Epoch 9/10

 1/56 [..............................] - ETA: 0s - loss: 0.0744
50/56 [=========================>....] - ETA: 0s - loss: 0.0660
56/56 [==============================] - 0s 1ms/step - loss: 0.0630
Epoch 10/10

 1/56 [..............................] - ETA: 0s - loss: 0.0060
50/56 [=========================>....] - ETA: 0s - loss: 0.0654
56/56 [==============================] - 0s 1ms/step - loss: 0.0631
-> test with GAN.predict
GAN tn, fp: 135, 3
GAN fn, tp: 6, 3
GAN f1 score: 0.400
GAN cohens kappa score: 0.369

-> test with 'LR'
LR tn, fp: 131, 7
LR fn, tp: 4, 5
LR f1 score: 0.476
LR cohens kappa score: 0.437
LR average precision score: 0.543

-> 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: 134, 4
GB fn, tp: 8, 1
GB f1 score: 0.143
GB cohens kappa score: 0.104

-> test with 'KNN'
KNN tn, fp: 136, 2
KNN fn, tp: 8, 1
KNN f1 score: 0.167
KNN cohens kappa score: 0.140


------ 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: 7s - loss: 0.0140
50/56 [=========================>....] - ETA: 0s - loss: 0.1184
56/56 [==============================] - 0s 1ms/step - loss: 0.1162
Epoch 2/10

 1/56 [..............................] - ETA: 0s - loss: 0.0198
49/56 [=========================>....] - ETA: 0s - loss: 0.1106
56/56 [==============================] - 0s 1ms/step - loss: 0.1100
Epoch 3/10

 1/56 [..............................] - ETA: 0s - loss: 0.0998
50/56 [=========================>....] - ETA: 0s - loss: 0.1170
56/56 [==============================] - 0s 1ms/step - loss: 0.1109
Epoch 4/10

 1/56 [..............................] - ETA: 0s - loss: 0.4057
50/56 [=========================>....] - ETA: 0s - loss: 0.1111
56/56 [==============================] - 0s 1ms/step - loss: 0.1068
Epoch 5/10

 1/56 [..............................] - ETA: 0s - loss: 0.1950
50/56 [=========================>....] - ETA: 0s - loss: 0.0987
56/56 [==============================] - 0s 1ms/step - loss: 0.1032
Epoch 6/10

 1/56 [..............................] - ETA: 0s - loss: 0.0264
49/56 [=========================>....] - ETA: 0s - loss: 0.1079
56/56 [==============================] - 0s 1ms/step - loss: 0.0996
Epoch 7/10

 1/56 [..............................] - ETA: 0s - loss: 0.0165
50/56 [=========================>....] - ETA: 0s - loss: 0.0982
56/56 [==============================] - 0s 1ms/step - loss: 0.1059
Epoch 8/10

 1/56 [..............................] - ETA: 0s - loss: 0.0154
50/56 [=========================>....] - ETA: 0s - loss: 0.0967
56/56 [==============================] - 0s 1ms/step - loss: 0.0986
Epoch 9/10

 1/56 [..............................] - ETA: 0s - loss: 0.0202
46/56 [=======================>......] - ETA: 0s - loss: 0.1043
56/56 [==============================] - 0s 1ms/step - loss: 0.0969
Epoch 10/10

 1/56 [..............................] - ETA: 0s - loss: 0.0110
50/56 [=========================>....] - ETA: 0s - loss: 0.1080
56/56 [==============================] - 0s 1ms/step - loss: 0.1028
-> test with GAN.predict
GAN tn, fp: 134, 4
GAN fn, tp: 1, 8
GAN f1 score: 0.762
GAN cohens kappa score: 0.744

-> 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: 0.882

-> test with 'RF'
RF tn, fp: 136, 2
RF fn, tp: 6, 3
RF f1 score: 0.429
RF cohens kappa score: 0.402

-> test with 'GB'
GB tn, fp: 137, 1
GB fn, tp: 7, 2
GB f1 score: 0.333
GB cohens kappa score: 0.312

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


------ 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: 7s - loss: 0.0751
50/56 [=========================>....] - ETA: 0s - loss: 0.1536
56/56 [==============================] - 0s 1ms/step - loss: 0.1428
Epoch 2/10

 1/56 [..............................] - ETA: 0s - loss: 0.0951
49/56 [=========================>....] - ETA: 0s - loss: 0.1105
56/56 [==============================] - 0s 1ms/step - loss: 0.1228
Epoch 3/10

 1/56 [..............................] - ETA: 0s - loss: 0.1758
50/56 [=========================>....] - ETA: 0s - loss: 0.1149
56/56 [==============================] - 0s 1ms/step - loss: 0.1131
Epoch 4/10

 1/56 [..............................] - ETA: 0s - loss: 0.0759
50/56 [=========================>....] - ETA: 0s - loss: 0.1129
56/56 [==============================] - 0s 1ms/step - loss: 0.1104
Epoch 5/10

 1/56 [..............................] - ETA: 0s - loss: 0.0277
50/56 [=========================>....] - ETA: 0s - loss: 0.1157
56/56 [==============================] - 0s 1ms/step - loss: 0.1117
Epoch 6/10

 1/56 [..............................] - ETA: 0s - loss: 1.0189
48/56 [========================>.....] - ETA: 0s - loss: 0.0889
56/56 [==============================] - 0s 1ms/step - loss: 0.0987
Epoch 7/10

 1/56 [..............................] - ETA: 0s - loss: 0.0293
50/56 [=========================>....] - ETA: 0s - loss: 0.0935
56/56 [==============================] - 0s 1ms/step - loss: 0.1014
Epoch 8/10

 1/56 [..............................] - ETA: 0s - loss: 0.0671
50/56 [=========================>....] - ETA: 0s - loss: 0.0915
56/56 [==============================] - 0s 1ms/step - loss: 0.0987
Epoch 9/10

 1/56 [..............................] - ETA: 0s - loss: 0.1939
48/56 [========================>.....] - ETA: 0s - loss: 0.1001
56/56 [==============================] - 0s 1ms/step - loss: 0.0957
Epoch 10/10

 1/56 [..............................] - ETA: 0s - loss: 0.0082
50/56 [=========================>....] - ETA: 0s - loss: 0.0888
56/56 [==============================] - 0s 1ms/step - loss: 0.0999
-> test with GAN.predict
GAN tn, fp: 137, 0
GAN fn, tp: 3, 3
GAN f1 score: 0.667
GAN cohens kappa score: 0.657

-> test with 'LR'
LR tn, fp: 132, 5
LR fn, tp: 2, 4
LR f1 score: 0.533
LR cohens kappa score: 0.509
LR average precision score: 0.827

-> test with 'RF'
RF tn, fp: 136, 1
RF fn, tp: 3, 3
RF f1 score: 0.600
RF cohens kappa score: 0.586

-> test with 'GB'
GB tn, fp: 137, 0
GB fn, tp: 4, 2
GB f1 score: 0.500
GB cohens kappa score: 0.489

-> test with 'KNN'
KNN tn, fp: 137, 0
KNN fn, tp: 5, 1
KNN f1 score: 0.286
KNN cohens kappa score: 0.277

### Exercise is done.

-----[ LR ]-----
maximum:
LR tn, fp: 134, 11
LR fn, tp: 5, 9
LR f1 score: 0.818
LR cohens kappa score: 0.804
LR average precision score: 0.920


average:
LR tn, fp: 131.08, 6.72
LR fn, tp: 2.24, 6.16
LR f1 score: 0.575
LR cohens kappa score: 0.544
LR average precision score: 0.687


minimum:
LR tn, fp: 127, 4
LR fn, tp: 0, 4
LR f1 score: 0.381
LR cohens kappa score: 0.334
LR average precision score: 0.484


-----[ RF ]-----
maximum:
RF tn, fp: 138, 4
RF fn, tp: 9, 4
RF f1 score: 0.600
RF cohens kappa score: 0.586


average:
RF tn, fp: 136.4, 1.4
RF fn, tp: 6.32, 2.08
RF f1 score: 0.348
RF cohens kappa score: 0.326


minimum:
RF tn, fp: 134, 0
RF fn, tp: 3, 0
RF f1 score: 0.000
RF cohens kappa score: 0.000


-----[ GB ]-----
maximum:
GB tn, fp: 138, 9
GB fn, tp: 8, 4
GB f1 score: 0.615
GB cohens kappa score: 0.600


average:
GB tn, fp: 135.56, 2.24
GB fn, tp: 6.36, 2.04
GB f1 score: 0.318
GB cohens kappa score: 0.292


minimum:
GB tn, fp: 129, 0
GB fn, tp: 4, 1
GB f1 score: 0.143
GB cohens kappa score: 0.104


-----[ KNN ]-----
maximum:
KNN tn, fp: 138, 3
KNN fn, tp: 9, 2
KNN f1 score: 0.308
KNN cohens kappa score: 0.281


average:
KNN tn, fp: 136.72, 1.08
KNN fn, tp: 7.72, 0.68
KNN f1 score: 0.136
KNN cohens kappa score: 0.120


minimum:
KNN tn, fp: 135, 0
KNN fn, tp: 5, 0
KNN f1 score: 0.000
KNN cohens kappa score: -0.032


-----[ GAN ]-----
maximum:
GAN tn, fp: 137, 10
GAN fn, tp: 6, 8
GAN f1 score: 0.762
GAN cohens kappa score: 0.744


average:
GAN tn, fp: 134.6, 3.2
GAN fn, tp: 4.2, 4.2
GAN f1 score: 0.538
GAN cohens kappa score: 0.512


minimum:
GAN tn, fp: 128, 0
GAN fn, tp: 1, 2
GAN f1 score: 0.316
GAN cohens kappa score: 0.269