ConvGeNBenchmarkResults/data_result/convGAN-proximity-5/folding_yeast5.log

///////////////////////////////////////////
// Running convGAN-proximary-5 on folding_yeast5
///////////////////////////////////////////

Load 'data_input/folding_yeast5'
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 1117 synthetic samples
-> retrain GAN for predict
Epoch 1/10

  1/116 [..............................] - ETA: 18s - loss: 0.4827
 41/116 [=========>....................] - ETA: 0s - loss: 0.5310 
 80/116 [===================>..........] - ETA: 0s - loss: 0.4731
113/116 [============================>.] - ETA: 0s - loss: 0.4178
116/116 [==============================] - 0s 1ms/step - loss: 0.4138
Epoch 2/10

  1/116 [..............................] - ETA: 0s - loss: 0.1428
 31/116 [=======>......................] - ETA: 0s - loss: 0.2228
 67/116 [================>.............] - ETA: 0s - loss: 0.2135
105/116 [==========================>...] - ETA: 0s - loss: 0.1905
116/116 [==============================] - 0s 1ms/step - loss: 0.1863
Epoch 3/10

  1/116 [..............................] - ETA: 0s - loss: 0.1076
 39/116 [=========>....................] - ETA: 0s - loss: 0.1550
 77/116 [==================>...........] - ETA: 0s - loss: 0.1423
116/116 [==============================] - 0s 1ms/step - loss: 0.1312
Epoch 4/10

  1/116 [..............................] - ETA: 0s - loss: 0.1288
 40/116 [=========>....................] - ETA: 0s - loss: 0.1307
 79/116 [===================>..........] - ETA: 0s - loss: 0.1155
116/116 [==============================] - 0s 1ms/step - loss: 0.1087
Epoch 5/10

  1/116 [..............................] - ETA: 0s - loss: 0.2291
 41/116 [=========>....................] - ETA: 0s - loss: 0.0948
 81/116 [===================>..........] - ETA: 0s - loss: 0.1009
116/116 [==============================] - 0s 1ms/step - loss: 0.0974
Epoch 6/10

  1/116 [..............................] - ETA: 0s - loss: 0.0528
 41/116 [=========>....................] - ETA: 0s - loss: 0.1063
 79/116 [===================>..........] - ETA: 0s - loss: 0.0899
116/116 [==============================] - 0s 1ms/step - loss: 0.0898
Epoch 7/10

  1/116 [..............................] - ETA: 0s - loss: 0.2327
 42/116 [=========>....................] - ETA: 0s - loss: 0.0840
 82/116 [====================>.........] - ETA: 0s - loss: 0.0889
116/116 [==============================] - 0s 1ms/step - loss: 0.0856
Epoch 8/10

  1/116 [..............................] - ETA: 0s - loss: 0.0081
 43/116 [==========>...................] - ETA: 0s - loss: 0.0838
 82/116 [====================>.........] - ETA: 0s - loss: 0.0828
116/116 [==============================] - 0s 1ms/step - loss: 0.0818
Epoch 9/10

  1/116 [..............................] - ETA: 0s - loss: 0.2570
 40/116 [=========>....................] - ETA: 0s - loss: 0.0931
 75/116 [==================>...........] - ETA: 0s - loss: 0.0730
107/116 [==========================>...] - ETA: 0s - loss: 0.0783
116/116 [==============================] - 0s 2ms/step - loss: 0.0775
Epoch 10/10

  1/116 [..............................] - ETA: 0s - loss: 0.0220
 31/116 [=======>......................] - ETA: 0s - loss: 0.0768
 65/116 [===============>..............] - ETA: 0s - loss: 0.0730
 96/116 [=======================>......] - ETA: 0s - loss: 0.0770
116/116 [==============================] - 0s 2ms/step - loss: 0.0756
-> test with GAN.predict
GAN tn, fp: 278, 10
GAN fn, tp: 3, 6
GAN f1 score: 0.480
GAN cohens kappa score: 0.459

-> test with 'LR'
LR tn, fp: 274, 14
LR fn, tp: 1, 8
LR f1 score: 0.516
LR cohens kappa score: 0.494
LR average precision score: 0.897

-> test with 'RF'
RF tn, fp: 288, 0
RF fn, tp: 5, 4
RF f1 score: 0.615
RF cohens kappa score: 0.608

-> test with 'GB'
GB tn, fp: 287, 1
GB fn, tp: 3, 6
GB f1 score: 0.750
GB cohens kappa score: 0.743

-> test with 'KNN'
KNN tn, fp: 280, 8
KNN fn, tp: 0, 9
KNN f1 score: 0.692
KNN cohens kappa score: 0.680


------ Step 1/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1117 synthetic samples
-> retrain GAN for predict
Epoch 1/10

  1/116 [..............................] - ETA: 18s - loss: 0.5337
 42/116 [=========>....................] - ETA: 0s - loss: 0.4231 
 79/116 [===================>..........] - ETA: 0s - loss: 0.3744
112/116 [===========================>..] - ETA: 0s - loss: 0.3421
116/116 [==============================] - 0s 1ms/step - loss: 0.3408
Epoch 2/10

  1/116 [..............................] - ETA: 0s - loss: 0.3199
 34/116 [=======>......................] - ETA: 0s - loss: 0.1996
 75/116 [==================>...........] - ETA: 0s - loss: 0.1782
115/116 [============================>.] - ETA: 0s - loss: 0.1629
116/116 [==============================] - 0s 1ms/step - loss: 0.1627
Epoch 3/10

  1/116 [..............................] - ETA: 0s - loss: 0.0323
 41/116 [=========>....................] - ETA: 0s - loss: 0.1232
 75/116 [==================>...........] - ETA: 0s - loss: 0.1159
111/116 [===========================>..] - ETA: 0s - loss: 0.1164
116/116 [==============================] - 0s 1ms/step - loss: 0.1147
Epoch 4/10

  1/116 [..............................] - ETA: 0s - loss: 0.1133
 39/116 [=========>....................] - ETA: 0s - loss: 0.1133
 78/116 [===================>..........] - ETA: 0s - loss: 0.1033
115/116 [============================>.] - ETA: 0s - loss: 0.0990
116/116 [==============================] - 0s 1ms/step - loss: 0.0988
Epoch 5/10

  1/116 [..............................] - ETA: 0s - loss: 0.0459
 39/116 [=========>....................] - ETA: 0s - loss: 0.0894
 76/116 [==================>...........] - ETA: 0s - loss: 0.0947
115/116 [============================>.] - ETA: 0s - loss: 0.0904
116/116 [==============================] - 0s 1ms/step - loss: 0.0904
Epoch 6/10

  1/116 [..............................] - ETA: 0s - loss: 0.0397
 40/116 [=========>....................] - ETA: 0s - loss: 0.0714
 79/116 [===================>..........] - ETA: 0s - loss: 0.0821
116/116 [==============================] - 0s 1ms/step - loss: 0.0830
Epoch 7/10

  1/116 [..............................] - ETA: 0s - loss: 0.0761
 40/116 [=========>....................] - ETA: 0s - loss: 0.0756
 77/116 [==================>...........] - ETA: 0s - loss: 0.0704
116/116 [==============================] - 0s 1ms/step - loss: 0.0783
Epoch 8/10

  1/116 [..............................] - ETA: 0s - loss: 0.0579
 41/116 [=========>....................] - ETA: 0s - loss: 0.0781
 81/116 [===================>..........] - ETA: 0s - loss: 0.0692
116/116 [==============================] - 0s 1ms/step - loss: 0.0768
Epoch 9/10

  1/116 [..............................] - ETA: 0s - loss: 0.0853
 39/116 [=========>....................] - ETA: 0s - loss: 0.0748
 78/116 [===================>..........] - ETA: 0s - loss: 0.0706
116/116 [==============================] - 0s 1ms/step - loss: 0.0733
Epoch 10/10

  1/116 [..............................] - ETA: 0s - loss: 0.0112
 42/116 [=========>....................] - ETA: 0s - loss: 0.0664
 80/116 [===================>..........] - ETA: 0s - loss: 0.0651
116/116 [==============================] - 0s 1ms/step - loss: 0.0695
-> test with GAN.predict
GAN tn, fp: 276, 12
GAN fn, tp: 0, 9
GAN f1 score: 0.600
GAN cohens kappa score: 0.582

-> test with 'LR'
LR tn, fp: 273, 15
LR fn, tp: 0, 9
LR f1 score: 0.545
LR cohens kappa score: 0.524
LR average precision score: 0.701

-> test with 'RF'
RF tn, fp: 285, 3
RF fn, tp: 2, 7
RF f1 score: 0.737
RF cohens kappa score: 0.728

-> test with 'GB'
GB tn, fp: 284, 4
GB fn, tp: 1, 8
GB f1 score: 0.762
GB cohens kappa score: 0.753

-> test with 'KNN'
KNN tn, fp: 273, 15
KNN fn, tp: 0, 9
KNN f1 score: 0.545
KNN cohens kappa score: 0.524


------ Step 1/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1117 synthetic samples
-> retrain GAN for predict
Epoch 1/10

  1/116 [..............................] - ETA: 18s - loss: 0.2982
 41/116 [=========>....................] - ETA: 0s - loss: 0.2365 
 79/116 [===================>..........] - ETA: 0s - loss: 0.1919
116/116 [==============================] - 0s 1ms/step - loss: 0.1754
Epoch 2/10

  1/116 [..............................] - ETA: 0s - loss: 0.1489
 41/116 [=========>....................] - ETA: 0s - loss: 0.0994
 80/116 [===================>..........] - ETA: 0s - loss: 0.1092
116/116 [==============================] - ETA: 0s - loss: 0.1108
116/116 [==============================] - 0s 1ms/step - loss: 0.1108
Epoch 3/10

  1/116 [..............................] - ETA: 0s - loss: 0.0582
 40/116 [=========>....................] - ETA: 0s - loss: 0.0842
 77/116 [==================>...........] - ETA: 0s - loss: 0.0924
116/116 [==============================] - 0s 1ms/step - loss: 0.0947
Epoch 4/10

  1/116 [..............................] - ETA: 0s - loss: 0.0547
 42/116 [=========>....................] - ETA: 0s - loss: 0.0942
 82/116 [====================>.........] - ETA: 0s - loss: 0.0892
116/116 [==============================] - 0s 1ms/step - loss: 0.0860
Epoch 5/10

  1/116 [..............................] - ETA: 0s - loss: 0.0285
 41/116 [=========>....................] - ETA: 0s - loss: 0.0859
 82/116 [====================>.........] - ETA: 0s - loss: 0.0865
116/116 [==============================] - 0s 1ms/step - loss: 0.0812
Epoch 6/10

  1/116 [..............................] - ETA: 0s - loss: 0.1254
 40/116 [=========>....................] - ETA: 0s - loss: 0.0886
 80/116 [===================>..........] - ETA: 0s - loss: 0.0750
116/116 [==============================] - 0s 1ms/step - loss: 0.0762
Epoch 7/10

  1/116 [..............................] - ETA: 0s - loss: 0.0259
 39/116 [=========>....................] - ETA: 0s - loss: 0.0554
 78/116 [===================>..........] - ETA: 0s - loss: 0.0661
116/116 [==============================] - 0s 1ms/step - loss: 0.0744
Epoch 8/10

  1/116 [..............................] - ETA: 0s - loss: 0.1373
 42/116 [=========>....................] - ETA: 0s - loss: 0.0852
 82/116 [====================>.........] - ETA: 0s - loss: 0.0775
116/116 [==============================] - 0s 1ms/step - loss: 0.0724
Epoch 9/10

  1/116 [..............................] - ETA: 0s - loss: 0.0924
 30/116 [======>.......................] - ETA: 0s - loss: 0.0631
 58/116 [==============>...............] - ETA: 0s - loss: 0.0599
 92/116 [======================>.......] - ETA: 0s - loss: 0.0668
116/116 [==============================] - 0s 2ms/step - loss: 0.0700
Epoch 10/10

  1/116 [..............................] - ETA: 0s - loss: 0.0334
 41/116 [=========>....................] - ETA: 0s - loss: 0.0541
 82/116 [====================>.........] - ETA: 0s - loss: 0.0647
116/116 [==============================] - 0s 1ms/step - loss: 0.0676
-> test with GAN.predict
GAN tn, fp: 280, 8
GAN fn, tp: 1, 8
GAN f1 score: 0.640
GAN cohens kappa score: 0.625

-> test with 'LR'
LR tn, fp: 277, 11
LR fn, tp: 0, 9
LR f1 score: 0.621
LR cohens kappa score: 0.604
LR average precision score: 0.603

-> test with 'RF'
RF tn, fp: 286, 2
RF fn, tp: 3, 6
RF f1 score: 0.706
RF cohens kappa score: 0.697

-> test with 'GB'
GB tn, fp: 284, 4
GB fn, tp: 3, 6
GB f1 score: 0.632
GB cohens kappa score: 0.619

-> test with 'KNN'
KNN tn, fp: 275, 13
KNN fn, tp: 0, 9
KNN f1 score: 0.581
KNN cohens kappa score: 0.562


------ Step 1/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1117 synthetic samples
-> retrain GAN for predict
Epoch 1/10

  1/116 [..............................] - ETA: 22s - loss: 0.6226
 44/116 [==========>...................] - ETA: 0s - loss: 0.6283 
 83/116 [====================>.........] - ETA: 0s - loss: 0.5243
116/116 [==============================] - 0s 1ms/step - loss: 0.4543
Epoch 2/10

  1/116 [..............................] - ETA: 0s - loss: 0.1891
 42/116 [=========>....................] - ETA: 0s - loss: 0.1770
 84/116 [====================>.........] - ETA: 0s - loss: 0.1730
116/116 [==============================] - 0s 1ms/step - loss: 0.1650
Epoch 3/10

  1/116 [..............................] - ETA: 0s - loss: 0.1014
 39/116 [=========>....................] - ETA: 0s - loss: 0.1252
 79/116 [===================>..........] - ETA: 0s - loss: 0.1230
116/116 [==============================] - 0s 1ms/step - loss: 0.1173
Epoch 4/10

  1/116 [..............................] - ETA: 0s - loss: 0.0670
 38/116 [========>.....................] - ETA: 0s - loss: 0.1031
 72/116 [=================>............] - ETA: 0s - loss: 0.1033
104/116 [=========================>....] - ETA: 0s - loss: 0.1006
116/116 [==============================] - 0s 1ms/step - loss: 0.1014
Epoch 5/10

  1/116 [..............................] - ETA: 0s - loss: 0.0901
 33/116 [=======>......................] - ETA: 0s - loss: 0.0718
 65/116 [===============>..............] - ETA: 0s - loss: 0.0816
 95/116 [=======================>......] - ETA: 0s - loss: 0.0899
116/116 [==============================] - 0s 2ms/step - loss: 0.0923
Epoch 6/10

  1/116 [..............................] - ETA: 0s - loss: 0.1378
 38/116 [========>.....................] - ETA: 0s - loss: 0.0712
 74/116 [==================>...........] - ETA: 0s - loss: 0.0772
110/116 [===========================>..] - ETA: 0s - loss: 0.0831
116/116 [==============================] - 0s 1ms/step - loss: 0.0860
Epoch 7/10

  1/116 [..............................] - ETA: 0s - loss: 0.0335
 37/116 [========>.....................] - ETA: 0s - loss: 0.0678
 72/116 [=================>............] - ETA: 0s - loss: 0.0864
109/116 [===========================>..] - ETA: 0s - loss: 0.0837
116/116 [==============================] - 0s 1ms/step - loss: 0.0845
Epoch 8/10

  1/116 [..............................] - ETA: 0s - loss: 0.1590
 33/116 [=======>......................] - ETA: 0s - loss: 0.0764
 66/116 [================>.............] - ETA: 0s - loss: 0.0795
105/116 [==========================>...] - ETA: 0s - loss: 0.0758
116/116 [==============================] - 0s 1ms/step - loss: 0.0815
Epoch 9/10

  1/116 [..............................] - ETA: 0s - loss: 0.0878
 39/116 [=========>....................] - ETA: 0s - loss: 0.0711
 77/116 [==================>...........] - ETA: 0s - loss: 0.0825
114/116 [============================>.] - ETA: 0s - loss: 0.0769
116/116 [==============================] - 0s 1ms/step - loss: 0.0784
Epoch 10/10

  1/116 [..............................] - ETA: 0s - loss: 0.0306
 36/116 [========>.....................] - ETA: 0s - loss: 0.0713
 71/116 [=================>............] - ETA: 0s - loss: 0.0777
107/116 [==========================>...] - ETA: 0s - loss: 0.0748
116/116 [==============================] - 0s 1ms/step - loss: 0.0762
-> test with GAN.predict
GAN tn, fp: 282, 6
GAN fn, tp: 3, 6
GAN f1 score: 0.571
GAN cohens kappa score: 0.556

-> test with 'LR'
LR tn, fp: 280, 8
LR fn, tp: 0, 9
LR f1 score: 0.692
LR cohens kappa score: 0.680
LR average precision score: 0.761

-> test with 'RF'
RF tn, fp: 288, 0
RF fn, tp: 6, 3
RF f1 score: 0.500
RF cohens kappa score: 0.492

-> test with 'GB'
GB tn, fp: 288, 0
GB fn, tp: 4, 5
GB f1 score: 0.714
GB cohens kappa score: 0.708

-> test with 'KNN'
KNN tn, fp: 284, 4
KNN fn, tp: 0, 9
KNN f1 score: 0.818
KNN cohens kappa score: 0.811


------ Step 1/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1116 synthetic samples
-> retrain GAN for predict
Epoch 1/10

  1/116 [..............................] - ETA: 20s - loss: 0.4361
 35/116 [========>.....................] - ETA: 0s - loss: 0.3313 
 75/116 [==================>...........] - ETA: 0s - loss: 0.2958
113/116 [============================>.] - ETA: 0s - loss: 0.2576
116/116 [==============================] - 0s 1ms/step - loss: 0.2551
Epoch 2/10

  1/116 [..............................] - ETA: 0s - loss: 0.2639
 41/116 [=========>....................] - ETA: 0s - loss: 0.1474
 80/116 [===================>..........] - ETA: 0s - loss: 0.1456
116/116 [==============================] - 0s 1ms/step - loss: 0.1367
Epoch 3/10

  1/116 [..............................] - ETA: 0s - loss: 0.2829
 41/116 [=========>....................] - ETA: 0s - loss: 0.1053
 82/116 [====================>.........] - ETA: 0s - loss: 0.1037
116/116 [==============================] - 0s 1ms/step - loss: 0.1061
Epoch 4/10

  1/116 [..............................] - ETA: 0s - loss: 0.1240
 42/116 [=========>....................] - ETA: 0s - loss: 0.0886
 82/116 [====================>.........] - ETA: 0s - loss: 0.1001
116/116 [==============================] - 0s 1ms/step - loss: 0.0950
Epoch 5/10

  1/116 [..............................] - ETA: 0s - loss: 0.1288
 38/116 [========>.....................] - ETA: 0s - loss: 0.0916
 76/116 [==================>...........] - ETA: 0s - loss: 0.0925
116/116 [==============================] - ETA: 0s - loss: 0.0883
116/116 [==============================] - 0s 1ms/step - loss: 0.0883
Epoch 6/10

  1/116 [..............................] - ETA: 0s - loss: 0.0439
 41/116 [=========>....................] - ETA: 0s - loss: 0.0982
 81/116 [===================>..........] - ETA: 0s - loss: 0.0840
116/116 [==============================] - 0s 1ms/step - loss: 0.0827
Epoch 7/10

  1/116 [..............................] - ETA: 0s - loss: 0.0761
 41/116 [=========>....................] - ETA: 0s - loss: 0.0721
 82/116 [====================>.........] - ETA: 0s - loss: 0.0779
116/116 [==============================] - 0s 1ms/step - loss: 0.0798
Epoch 8/10

  1/116 [..............................] - ETA: 0s - loss: 0.0387
 42/116 [=========>....................] - ETA: 0s - loss: 0.0952
 83/116 [====================>.........] - ETA: 0s - loss: 0.0837
116/116 [==============================] - 0s 1ms/step - loss: 0.0773
Epoch 9/10

  1/116 [..............................] - ETA: 0s - loss: 0.0154
 39/116 [=========>....................] - ETA: 0s - loss: 0.0663
 78/116 [===================>..........] - ETA: 0s - loss: 0.0735
116/116 [==============================] - 0s 1ms/step - loss: 0.0739
Epoch 10/10

  1/116 [..............................] - ETA: 0s - loss: 0.0863
 40/116 [=========>....................] - ETA: 0s - loss: 0.0761
 79/116 [===================>..........] - ETA: 0s - loss: 0.0806
116/116 [==============================] - 0s 1ms/step - loss: 0.0715
-> test with GAN.predict
GAN tn, fp: 272, 16
GAN fn, tp: 0, 8
GAN f1 score: 0.500
GAN cohens kappa score: 0.479

-> test with 'LR'
LR tn, fp: 275, 13
LR fn, tp: 0, 8
LR f1 score: 0.552
LR cohens kappa score: 0.533
LR average precision score: 0.703

-> test with 'RF'
RF tn, fp: 286, 2
RF fn, tp: 3, 5
RF f1 score: 0.667
RF cohens kappa score: 0.658

-> test with 'GB'
GB tn, fp: 286, 2
GB fn, tp: 2, 6
GB f1 score: 0.750
GB cohens kappa score: 0.743

-> test with 'KNN'
KNN tn, fp: 275, 13
KNN fn, tp: 0, 8
KNN f1 score: 0.552
KNN cohens kappa score: 0.533


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

------ Step 2/5: Slice 1/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1117 synthetic samples
-> retrain GAN for predict
Epoch 1/10

  1/116 [..............................] - ETA: 18s - loss: 0.3734
 42/116 [=========>....................] - ETA: 0s - loss: 0.2742 
 83/116 [====================>.........] - ETA: 0s - loss: 0.2499
116/116 [==============================] - 0s 1ms/step - loss: 0.2250
Epoch 2/10

  1/116 [..............................] - ETA: 0s - loss: 0.2634
 41/116 [=========>....................] - ETA: 0s - loss: 0.1525
 82/116 [====================>.........] - ETA: 0s - loss: 0.1464
116/116 [==============================] - 0s 1ms/step - loss: 0.1374
Epoch 3/10

  1/116 [..............................] - ETA: 0s - loss: 0.1667
 41/116 [=========>....................] - ETA: 0s - loss: 0.1102
 82/116 [====================>.........] - ETA: 0s - loss: 0.1132
116/116 [==============================] - 0s 1ms/step - loss: 0.1097
Epoch 4/10

  1/116 [..............................] - ETA: 0s - loss: 0.0358
 37/116 [========>.....................] - ETA: 0s - loss: 0.0930
 71/116 [=================>............] - ETA: 0s - loss: 0.0953
106/116 [==========================>...] - ETA: 0s - loss: 0.0918
116/116 [==============================] - 0s 1ms/step - loss: 0.0949
Epoch 5/10

  1/116 [..............................] - ETA: 0s - loss: 0.0369
 40/116 [=========>....................] - ETA: 0s - loss: 0.0815
 80/116 [===================>..........] - ETA: 0s - loss: 0.0916
116/116 [==============================] - 0s 1ms/step - loss: 0.0874
Epoch 6/10

  1/116 [..............................] - ETA: 0s - loss: 0.0190
 38/116 [========>.....................] - ETA: 0s - loss: 0.0939
 73/116 [=================>............] - ETA: 0s - loss: 0.0908
106/116 [==========================>...] - ETA: 0s - loss: 0.0853
116/116 [==============================] - 0s 1ms/step - loss: 0.0857
Epoch 7/10

  1/116 [..............................] - ETA: 0s - loss: 0.0438
 42/116 [=========>....................] - ETA: 0s - loss: 0.0856
 83/116 [====================>.........] - ETA: 0s - loss: 0.0811
116/116 [==============================] - 0s 1ms/step - loss: 0.0820
Epoch 8/10

  1/116 [..............................] - ETA: 0s - loss: 0.0284
 41/116 [=========>....................] - ETA: 0s - loss: 0.0805
 81/116 [===================>..........] - ETA: 0s - loss: 0.0811
116/116 [==============================] - 0s 1ms/step - loss: 0.0772
Epoch 9/10

  1/116 [..............................] - ETA: 0s - loss: 0.0908
 41/116 [=========>....................] - ETA: 0s - loss: 0.0736
 79/116 [===================>..........] - ETA: 0s - loss: 0.0754
116/116 [==============================] - 0s 1ms/step - loss: 0.0760
Epoch 10/10

  1/116 [..............................] - ETA: 0s - loss: 0.4501
 42/116 [=========>....................] - ETA: 0s - loss: 0.0790
 83/116 [====================>.........] - ETA: 0s - loss: 0.0724
116/116 [==============================] - 0s 1ms/step - loss: 0.0737
-> test with GAN.predict
GAN tn, fp: 277, 11
GAN fn, tp: 0, 9
GAN f1 score: 0.621
GAN cohens kappa score: 0.604

-> test with 'LR'
LR tn, fp: 275, 13
LR fn, tp: 0, 9
LR f1 score: 0.581
LR cohens kappa score: 0.562
LR average precision score: 0.703

-> test with 'RF'
RF tn, fp: 288, 0
RF fn, tp: 1, 8
RF f1 score: 0.941
RF cohens kappa score: 0.939

-> test with 'GB'
GB tn, fp: 287, 1
GB fn, tp: 1, 8
GB f1 score: 0.889
GB cohens kappa score: 0.885

-> test with 'KNN'
KNN tn, fp: 279, 9
KNN fn, tp: 0, 9
KNN f1 score: 0.667
KNN cohens kappa score: 0.653


------ Step 2/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1117 synthetic samples
-> retrain GAN for predict
Epoch 1/10

  1/116 [..............................] - ETA: 18s - loss: 0.3317
 42/116 [=========>....................] - ETA: 0s - loss: 0.2651 
 81/116 [===================>..........] - ETA: 0s - loss: 0.2289
116/116 [==============================] - 0s 1ms/step - loss: 0.2127
Epoch 2/10

  1/116 [..............................] - ETA: 0s - loss: 0.1379
 39/116 [=========>....................] - ETA: 0s - loss: 0.1582
 78/116 [===================>..........] - ETA: 0s - loss: 0.1408
116/116 [==============================] - 0s 1ms/step - loss: 0.1225
Epoch 3/10

  1/116 [..............................] - ETA: 0s - loss: 0.0662
 41/116 [=========>....................] - ETA: 0s - loss: 0.0821
 82/116 [====================>.........] - ETA: 0s - loss: 0.0907
116/116 [==============================] - 0s 1ms/step - loss: 0.0886
Epoch 4/10

  1/116 [..............................] - ETA: 0s - loss: 0.1906
 42/116 [=========>....................] - ETA: 0s - loss: 0.0778
 82/116 [====================>.........] - ETA: 0s - loss: 0.0675
116/116 [==============================] - 0s 1ms/step - loss: 0.0745
Epoch 5/10

  1/116 [..............................] - ETA: 0s - loss: 0.0107
 41/116 [=========>....................] - ETA: 0s - loss: 0.0741
 80/116 [===================>..........] - ETA: 0s - loss: 0.0629
116/116 [==============================] - 0s 1ms/step - loss: 0.0683
Epoch 6/10

  1/116 [..............................] - ETA: 0s - loss: 0.0688
 39/116 [=========>....................] - ETA: 0s - loss: 0.0663
 79/116 [===================>..........] - ETA: 0s - loss: 0.0621
116/116 [==============================] - 0s 1ms/step - loss: 0.0637
Epoch 7/10

  1/116 [..............................] - ETA: 0s - loss: 0.0446
 41/116 [=========>....................] - ETA: 0s - loss: 0.0659
 81/116 [===================>..........] - ETA: 0s - loss: 0.0598
116/116 [==============================] - 0s 1ms/step - loss: 0.0611
Epoch 8/10

  1/116 [..............................] - ETA: 0s - loss: 0.0115
 41/116 [=========>....................] - ETA: 0s - loss: 0.0609
 82/116 [====================>.........] - ETA: 0s - loss: 0.0609
116/116 [==============================] - 0s 1ms/step - loss: 0.0586
Epoch 9/10

  1/116 [..............................] - ETA: 0s - loss: 0.1719
 41/116 [=========>....................] - ETA: 0s - loss: 0.0599
 82/116 [====================>.........] - ETA: 0s - loss: 0.0577
116/116 [==============================] - 0s 1ms/step - loss: 0.0567
Epoch 10/10

  1/116 [..............................] - ETA: 0s - loss: 0.0913
 41/116 [=========>....................] - ETA: 0s - loss: 0.0565
 81/116 [===================>..........] - ETA: 0s - loss: 0.0558
116/116 [==============================] - 0s 1ms/step - loss: 0.0549
-> test with GAN.predict
GAN tn, fp: 273, 15
GAN fn, tp: 1, 8
GAN f1 score: 0.500
GAN cohens kappa score: 0.477

-> test with 'LR'
LR tn, fp: 270, 18
LR fn, tp: 1, 8
LR f1 score: 0.457
LR cohens kappa score: 0.432
LR average precision score: 0.320

-> test with 'RF'
RF tn, fp: 282, 6
RF fn, tp: 6, 3
RF f1 score: 0.333
RF cohens kappa score: 0.312

-> test with 'GB'
GB tn, fp: 282, 6
GB fn, tp: 6, 3
GB f1 score: 0.333
GB cohens kappa score: 0.312

-> test with 'KNN'
KNN tn, fp: 273, 15
KNN fn, tp: 1, 8
KNN f1 score: 0.500
KNN cohens kappa score: 0.477


------ Step 2/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1117 synthetic samples
-> retrain GAN for predict
Epoch 1/10

  1/116 [..............................] - ETA: 18s - loss: 0.4024
 39/116 [=========>....................] - ETA: 0s - loss: 0.2628 
 81/116 [===================>..........] - ETA: 0s - loss: 0.2131
116/116 [==============================] - 0s 1ms/step - loss: 0.1934
Epoch 2/10

  1/116 [..............................] - ETA: 0s - loss: 0.0799
 41/116 [=========>....................] - ETA: 0s - loss: 0.1359
 81/116 [===================>..........] - ETA: 0s - loss: 0.1322
116/116 [==============================] - 0s 1ms/step - loss: 0.1263
Epoch 3/10

  1/116 [..............................] - ETA: 0s - loss: 0.4314
 42/116 [=========>....................] - ETA: 0s - loss: 0.1319
 81/116 [===================>..........] - ETA: 0s - loss: 0.1158
116/116 [==============================] - 0s 1ms/step - loss: 0.1069
Epoch 4/10

  1/116 [..............................] - ETA: 0s - loss: 0.1604
 41/116 [=========>....................] - ETA: 0s - loss: 0.1041
 83/116 [====================>.........] - ETA: 0s - loss: 0.1013
116/116 [==============================] - 0s 1ms/step - loss: 0.0979
Epoch 5/10

  1/116 [..............................] - ETA: 0s - loss: 0.1867
 39/116 [=========>....................] - ETA: 0s - loss: 0.0867
 78/116 [===================>..........] - ETA: 0s - loss: 0.0834
113/116 [============================>.] - ETA: 0s - loss: 0.0933
116/116 [==============================] - 0s 1ms/step - loss: 0.0923
Epoch 6/10

  1/116 [..............................] - ETA: 0s - loss: 0.0541
 36/116 [========>.....................] - ETA: 0s - loss: 0.0857
 75/116 [==================>...........] - ETA: 0s - loss: 0.0876
114/116 [============================>.] - ETA: 0s - loss: 0.0883
116/116 [==============================] - 0s 1ms/step - loss: 0.0888
Epoch 7/10

  1/116 [..............................] - ETA: 0s - loss: 0.0213
 42/116 [=========>....................] - ETA: 0s - loss: 0.0674
 81/116 [===================>..........] - ETA: 0s - loss: 0.0785
116/116 [==============================] - 0s 1ms/step - loss: 0.0864
Epoch 8/10

  1/116 [..............................] - ETA: 0s - loss: 0.0652
 38/116 [========>.....................] - ETA: 0s - loss: 0.0831
 74/116 [==================>...........] - ETA: 0s - loss: 0.0814
114/116 [============================>.] - ETA: 0s - loss: 0.0844
116/116 [==============================] - 0s 1ms/step - loss: 0.0842
Epoch 9/10

  1/116 [..............................] - ETA: 0s - loss: 0.0657
 42/116 [=========>....................] - ETA: 0s - loss: 0.0672
 81/116 [===================>..........] - ETA: 0s - loss: 0.0810
116/116 [==============================] - 0s 1ms/step - loss: 0.0815
Epoch 10/10

  1/116 [..............................] - ETA: 0s - loss: 0.1210
 42/116 [=========>....................] - ETA: 0s - loss: 0.0859
 80/116 [===================>..........] - ETA: 0s - loss: 0.0810
116/116 [==============================] - 0s 1ms/step - loss: 0.0800
-> test with GAN.predict
GAN tn, fp: 281, 7
GAN fn, tp: 1, 8
GAN f1 score: 0.667
GAN cohens kappa score: 0.654

-> test with 'LR'
LR tn, fp: 281, 7
LR fn, tp: 0, 9
LR f1 score: 0.720
LR cohens kappa score: 0.709
LR average precision score: 0.762

-> test with 'RF'
RF tn, fp: 288, 0
RF fn, tp: 3, 6
RF f1 score: 0.800
RF cohens kappa score: 0.795

-> test with 'GB'
GB tn, fp: 286, 2
GB fn, tp: 1, 8
GB f1 score: 0.842
GB cohens kappa score: 0.837

-> test with 'KNN'
KNN tn, fp: 278, 10
KNN fn, tp: 0, 9
KNN f1 score: 0.643
KNN cohens kappa score: 0.628


------ Step 2/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1117 synthetic samples
-> retrain GAN for predict
Epoch 1/10

  1/116 [..............................] - ETA: 18s - loss: 0.3830
 37/116 [========>.....................] - ETA: 0s - loss: 0.1648 
 74/116 [==================>...........] - ETA: 0s - loss: 0.1554
115/116 [============================>.] - ETA: 0s - loss: 0.1416
116/116 [==============================] - 0s 1ms/step - loss: 0.1413
Epoch 2/10

  1/116 [..............................] - ETA: 0s - loss: 0.2550
 39/116 [=========>....................] - ETA: 0s - loss: 0.1083
 80/116 [===================>..........] - ETA: 0s - loss: 0.1077
116/116 [==============================] - 0s 1ms/step - loss: 0.1063
Epoch 3/10

  1/116 [..............................] - ETA: 0s - loss: 0.1065
 39/116 [=========>....................] - ETA: 0s - loss: 0.1050
 79/116 [===================>..........] - ETA: 0s - loss: 0.1046
116/116 [==============================] - 0s 1ms/step - loss: 0.0956
Epoch 4/10

  1/116 [..............................] - ETA: 0s - loss: 0.0350
 42/116 [=========>....................] - ETA: 0s - loss: 0.0916
 83/116 [====================>.........] - ETA: 0s - loss: 0.0882
116/116 [==============================] - 0s 1ms/step - loss: 0.0900
Epoch 5/10

  1/116 [..............................] - ETA: 0s - loss: 0.2414
 41/116 [=========>....................] - ETA: 0s - loss: 0.0706
 77/116 [==================>...........] - ETA: 0s - loss: 0.0798
116/116 [==============================] - 0s 1ms/step - loss: 0.0848
Epoch 6/10

  1/116 [..............................] - ETA: 0s - loss: 0.1867
 38/116 [========>.....................] - ETA: 0s - loss: 0.0734
 79/116 [===================>..........] - ETA: 0s - loss: 0.0820
116/116 [==============================] - 0s 1ms/step - loss: 0.0820
Epoch 7/10

  1/116 [..............................] - ETA: 0s - loss: 0.1595
 42/116 [=========>....................] - ETA: 0s - loss: 0.0691
 80/116 [===================>..........] - ETA: 0s - loss: 0.0703
116/116 [==============================] - 0s 1ms/step - loss: 0.0803
Epoch 8/10

  1/116 [..............................] - ETA: 0s - loss: 0.0348
 42/116 [=========>....................] - ETA: 0s - loss: 0.0916
 83/116 [====================>.........] - ETA: 0s - loss: 0.0837
116/116 [==============================] - 0s 1ms/step - loss: 0.0786
Epoch 9/10

  1/116 [..............................] - ETA: 0s - loss: 0.0319
 42/116 [=========>....................] - ETA: 0s - loss: 0.0731
 80/116 [===================>..........] - ETA: 0s - loss: 0.0713
116/116 [==============================] - 0s 1ms/step - loss: 0.0762
Epoch 10/10

  1/116 [..............................] - ETA: 0s - loss: 0.0447
 41/116 [=========>....................] - ETA: 0s - loss: 0.0699
 81/116 [===================>..........] - ETA: 0s - loss: 0.0655
116/116 [==============================] - 0s 1ms/step - loss: 0.0742
-> test with GAN.predict
GAN tn, fp: 277, 11
GAN fn, tp: 0, 9
GAN f1 score: 0.621
GAN cohens kappa score: 0.604

-> test with 'LR'
LR tn, fp: 273, 15
LR fn, tp: 0, 9
LR f1 score: 0.545
LR cohens kappa score: 0.524
LR average precision score: 0.878

-> test with 'RF'
RF tn, fp: 286, 2
RF fn, tp: 1, 8
RF f1 score: 0.842
RF cohens kappa score: 0.837

-> test with 'GB'
GB tn, fp: 286, 2
GB fn, tp: 1, 8
GB f1 score: 0.842
GB cohens kappa score: 0.837

-> test with 'KNN'
KNN tn, fp: 277, 11
KNN fn, tp: 0, 9
KNN f1 score: 0.621
KNN cohens kappa score: 0.604


------ Step 2/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1116 synthetic samples
-> retrain GAN for predict
Epoch 1/10

  1/116 [..............................] - ETA: 17s - loss: 0.2900
 41/116 [=========>....................] - ETA: 0s - loss: 0.2157 
 82/116 [====================>.........] - ETA: 0s - loss: 0.1839
116/116 [==============================] - 0s 1ms/step - loss: 0.1665
Epoch 2/10

  1/116 [..............................] - ETA: 0s - loss: 0.1874
 41/116 [=========>....................] - ETA: 0s - loss: 0.1226
 81/116 [===================>..........] - ETA: 0s - loss: 0.1120
116/116 [==============================] - 0s 1ms/step - loss: 0.1067
Epoch 3/10

  1/116 [..............................] - ETA: 0s - loss: 0.0910
 36/116 [========>.....................] - ETA: 0s - loss: 0.1158
 72/116 [=================>............] - ETA: 0s - loss: 0.0915
110/116 [===========================>..] - ETA: 0s - loss: 0.0896
116/116 [==============================] - 0s 1ms/step - loss: 0.0934
Epoch 4/10

  1/116 [..............................] - ETA: 0s - loss: 0.0112
 40/116 [=========>....................] - ETA: 0s - loss: 0.0905
 80/116 [===================>..........] - ETA: 0s - loss: 0.0979
116/116 [==============================] - 0s 1ms/step - loss: 0.0870
Epoch 5/10

  1/116 [..............................] - ETA: 0s - loss: 0.2915
 40/116 [=========>....................] - ETA: 0s - loss: 0.0835
 79/116 [===================>..........] - ETA: 0s - loss: 0.0789
116/116 [==============================] - 0s 1ms/step - loss: 0.0824
Epoch 6/10

  1/116 [..............................] - ETA: 0s - loss: 0.1384
 42/116 [=========>....................] - ETA: 0s - loss: 0.0707
 83/116 [====================>.........] - ETA: 0s - loss: 0.0801
116/116 [==============================] - 0s 1ms/step - loss: 0.0785
Epoch 7/10

  1/116 [..............................] - ETA: 0s - loss: 0.0203
 42/116 [=========>....................] - ETA: 0s - loss: 0.0632
 83/116 [====================>.........] - ETA: 0s - loss: 0.0689
116/116 [==============================] - 0s 1ms/step - loss: 0.0760
Epoch 8/10

  1/116 [..............................] - ETA: 0s - loss: 0.0165
 41/116 [=========>....................] - ETA: 0s - loss: 0.0672
 82/116 [====================>.........] - ETA: 0s - loss: 0.0760
116/116 [==============================] - 0s 1ms/step - loss: 0.0757
Epoch 9/10

  1/116 [..............................] - ETA: 0s - loss: 0.0110
 41/116 [=========>....................] - ETA: 0s - loss: 0.0841
 83/116 [====================>.........] - ETA: 0s - loss: 0.0844
116/116 [==============================] - 0s 1ms/step - loss: 0.0718
Epoch 10/10

  1/116 [..............................] - ETA: 0s - loss: 0.0710
 43/116 [==========>...................] - ETA: 0s - loss: 0.0794
 83/116 [====================>.........] - ETA: 0s - loss: 0.0727
116/116 [==============================] - 0s 1ms/step - loss: 0.0715
-> test with GAN.predict
GAN tn, fp: 279, 9
GAN fn, tp: 1, 7
GAN f1 score: 0.583
GAN cohens kappa score: 0.568

-> test with 'LR'
LR tn, fp: 277, 11
LR fn, tp: 0, 8
LR f1 score: 0.593
LR cohens kappa score: 0.576
LR average precision score: 0.607

-> test with 'RF'
RF tn, fp: 288, 0
RF fn, tp: 5, 3
RF f1 score: 0.545
RF cohens kappa score: 0.539

-> test with 'GB'
GB tn, fp: 286, 2
GB fn, tp: 4, 4
GB f1 score: 0.571
GB cohens kappa score: 0.561

-> test with 'KNN'
KNN tn, fp: 281, 7
KNN fn, tp: 0, 8
KNN f1 score: 0.696
KNN cohens kappa score: 0.685


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

------ Step 3/5: Slice 1/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1117 synthetic samples
-> retrain GAN for predict
Epoch 1/10

  1/116 [..............................] - ETA: 17s - loss: 0.2337
 41/116 [=========>....................] - ETA: 0s - loss: 0.1194 
 82/116 [====================>.........] - ETA: 0s - loss: 0.1019
116/116 [==============================] - 0s 1ms/step - loss: 0.1030
Epoch 2/10

  1/116 [..............................] - ETA: 0s - loss: 0.0292
 42/116 [=========>....................] - ETA: 0s - loss: 0.0881
 77/116 [==================>...........] - ETA: 0s - loss: 0.0843
116/116 [==============================] - ETA: 0s - loss: 0.0801
116/116 [==============================] - 0s 1ms/step - loss: 0.0801
Epoch 3/10

  1/116 [..............................] - ETA: 0s - loss: 0.1152
 41/116 [=========>....................] - ETA: 0s - loss: 0.0824
 82/116 [====================>.........] - ETA: 0s - loss: 0.0766
116/116 [==============================] - 0s 1ms/step - loss: 0.0730
Epoch 4/10

  1/116 [..............................] - ETA: 0s - loss: 0.0173
 43/116 [==========>...................] - ETA: 0s - loss: 0.0590
 85/116 [====================>.........] - ETA: 0s - loss: 0.0691
116/116 [==============================] - 0s 1ms/step - loss: 0.0689
Epoch 5/10

  1/116 [..............................] - ETA: 0s - loss: 0.0106
 43/116 [==========>...................] - ETA: 0s - loss: 0.0637
 85/116 [====================>.........] - ETA: 0s - loss: 0.0649
116/116 [==============================] - 0s 1ms/step - loss: 0.0661
Epoch 6/10

  1/116 [..............................] - ETA: 0s - loss: 0.0117
 43/116 [==========>...................] - ETA: 0s - loss: 0.0733
 83/116 [====================>.........] - ETA: 0s - loss: 0.0638
116/116 [==============================] - 0s 1ms/step - loss: 0.0641
Epoch 7/10

  1/116 [..............................] - ETA: 0s - loss: 0.0255
 41/116 [=========>....................] - ETA: 0s - loss: 0.0503
 81/116 [===================>..........] - ETA: 0s - loss: 0.0555
116/116 [==============================] - 0s 1ms/step - loss: 0.0614
Epoch 8/10

  1/116 [..............................] - ETA: 0s - loss: 0.0123
 39/116 [=========>....................] - ETA: 0s - loss: 0.0374
 77/116 [==================>...........] - ETA: 0s - loss: 0.0585
115/116 [============================>.] - ETA: 0s - loss: 0.0602
116/116 [==============================] - 0s 1ms/step - loss: 0.0601
Epoch 9/10

  1/116 [..............................] - ETA: 0s - loss: 0.0268
 43/116 [==========>...................] - ETA: 0s - loss: 0.0532
 83/116 [====================>.........] - ETA: 0s - loss: 0.0523
116/116 [==============================] - 0s 1ms/step - loss: 0.0599
Epoch 10/10

  1/116 [..............................] - ETA: 0s - loss: 0.1948
 42/116 [=========>....................] - ETA: 0s - loss: 0.0620
 83/116 [====================>.........] - ETA: 0s - loss: 0.0575
116/116 [==============================] - 0s 1ms/step - loss: 0.0590
-> test with GAN.predict
GAN tn, fp: 274, 14
GAN fn, tp: 1, 8
GAN f1 score: 0.516
GAN cohens kappa score: 0.494

-> test with 'LR'
LR tn, fp: 273, 15
LR fn, tp: 0, 9
LR f1 score: 0.545
LR cohens kappa score: 0.524
LR average precision score: 0.710

-> test with 'RF'
RF tn, fp: 287, 1
RF fn, tp: 3, 6
RF f1 score: 0.750
RF cohens kappa score: 0.743

-> test with 'GB'
GB tn, fp: 287, 1
GB fn, tp: 3, 6
GB f1 score: 0.750
GB cohens kappa score: 0.743

-> test with 'KNN'
KNN tn, fp: 276, 12
KNN fn, tp: 0, 9
KNN f1 score: 0.600
KNN cohens kappa score: 0.582


------ Step 3/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1117 synthetic samples
-> retrain GAN for predict
Epoch 1/10

  1/116 [..............................] - ETA: 17s - loss: 0.2515
 44/116 [==========>...................] - ETA: 0s - loss: 0.2723 
 85/116 [====================>.........] - ETA: 0s - loss: 0.2329
116/116 [==============================] - 0s 1ms/step - loss: 0.2160
Epoch 2/10

  1/116 [..............................] - ETA: 0s - loss: 0.3647
 43/116 [==========>...................] - ETA: 0s - loss: 0.1527
 85/116 [====================>.........] - ETA: 0s - loss: 0.1370
116/116 [==============================] - 0s 1ms/step - loss: 0.1291
Epoch 3/10

  1/116 [..............................] - ETA: 0s - loss: 0.1168
 42/116 [=========>....................] - ETA: 0s - loss: 0.1061
 84/116 [====================>.........] - ETA: 0s - loss: 0.0989
116/116 [==============================] - 0s 1ms/step - loss: 0.0985
Epoch 4/10

  1/116 [..............................] - ETA: 0s - loss: 0.0291
 36/116 [========>.....................] - ETA: 0s - loss: 0.0933
 71/116 [=================>............] - ETA: 0s - loss: 0.0853
108/116 [==========================>...] - ETA: 0s - loss: 0.0853
116/116 [==============================] - 0s 1ms/step - loss: 0.0837
Epoch 5/10

  1/116 [..............................] - ETA: 0s - loss: 0.0294
 41/116 [=========>....................] - ETA: 0s - loss: 0.0882
 81/116 [===================>..........] - ETA: 0s - loss: 0.0766
116/116 [==============================] - 0s 1ms/step - loss: 0.0757
Epoch 6/10

  1/116 [..............................] - ETA: 0s - loss: 0.1930
 41/116 [=========>....................] - ETA: 0s - loss: 0.0693
 82/116 [====================>.........] - ETA: 0s - loss: 0.0747
116/116 [==============================] - 0s 1ms/step - loss: 0.0702
Epoch 7/10

  1/116 [..............................] - ETA: 0s - loss: 0.0121
 38/116 [========>.....................] - ETA: 0s - loss: 0.0683
 78/116 [===================>..........] - ETA: 0s - loss: 0.0595
116/116 [==============================] - 0s 1ms/step - loss: 0.0661
Epoch 8/10

  1/116 [..............................] - ETA: 0s - loss: 0.2856
 40/116 [=========>....................] - ETA: 0s - loss: 0.0735
 80/116 [===================>..........] - ETA: 0s - loss: 0.0673
116/116 [==============================] - 0s 1ms/step - loss: 0.0625
Epoch 9/10

  1/116 [..............................] - ETA: 0s - loss: 0.0436
 38/116 [========>.....................] - ETA: 0s - loss: 0.0662
 78/116 [===================>..........] - ETA: 0s - loss: 0.0613
116/116 [==============================] - 0s 1ms/step - loss: 0.0612
Epoch 10/10

  1/116 [..............................] - ETA: 0s - loss: 0.0393
 43/116 [==========>...................] - ETA: 0s - loss: 0.0742
 84/116 [====================>.........] - ETA: 0s - loss: 0.0657
116/116 [==============================] - 0s 1ms/step - loss: 0.0593
-> test with GAN.predict
GAN tn, fp: 274, 14
GAN fn, tp: 1, 8
GAN f1 score: 0.516
GAN cohens kappa score: 0.494

-> test with 'LR'
LR tn, fp: 274, 14
LR fn, tp: 0, 9
LR f1 score: 0.562
LR cohens kappa score: 0.543
LR average precision score: 0.684

-> test with 'RF'
RF tn, fp: 287, 1
RF fn, tp: 3, 6
RF f1 score: 0.750
RF cohens kappa score: 0.743

-> test with 'GB'
GB tn, fp: 285, 3
GB fn, tp: 3, 6
GB f1 score: 0.667
GB cohens kappa score: 0.656

-> test with 'KNN'
KNN tn, fp: 276, 12
KNN fn, tp: 1, 8
KNN f1 score: 0.552
KNN cohens kappa score: 0.532


------ Step 3/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1117 synthetic samples
-> retrain GAN for predict
Epoch 1/10

  1/116 [..............................] - ETA: 17s - loss: 0.2704
 42/116 [=========>....................] - ETA: 0s - loss: 0.1515 
 83/116 [====================>.........] - ETA: 0s - loss: 0.1346
116/116 [==============================] - 0s 1ms/step - loss: 0.1284
Epoch 2/10

  1/116 [..............................] - ETA: 0s - loss: 0.1175
 43/116 [==========>...................] - ETA: 0s - loss: 0.1003
 84/116 [====================>.........] - ETA: 0s - loss: 0.0934
116/116 [==============================] - 0s 1ms/step - loss: 0.0958
Epoch 3/10

  1/116 [..............................] - ETA: 0s - loss: 0.0757
 41/116 [=========>....................] - ETA: 0s - loss: 0.0914
 81/116 [===================>..........] - ETA: 0s - loss: 0.0843
116/116 [==============================] - 0s 1ms/step - loss: 0.0859
Epoch 4/10

  1/116 [..............................] - ETA: 0s - loss: 0.1742
 42/116 [=========>....................] - ETA: 0s - loss: 0.0709
 83/116 [====================>.........] - ETA: 0s - loss: 0.0861
116/116 [==============================] - 0s 1ms/step - loss: 0.0812
Epoch 5/10

  1/116 [..............................] - ETA: 0s - loss: 0.0271
 42/116 [=========>....................] - ETA: 0s - loss: 0.0766
 81/116 [===================>..........] - ETA: 0s - loss: 0.0824
116/116 [==============================] - 0s 1ms/step - loss: 0.0773
Epoch 6/10

  1/116 [..............................] - ETA: 0s - loss: 0.1256
 40/116 [=========>....................] - ETA: 0s - loss: 0.0779
 75/116 [==================>...........] - ETA: 0s - loss: 0.0749
115/116 [============================>.] - ETA: 0s - loss: 0.0746
116/116 [==============================] - 0s 1ms/step - loss: 0.0746
Epoch 7/10

  1/116 [..............................] - ETA: 0s - loss: 0.0246
 40/116 [=========>....................] - ETA: 0s - loss: 0.0743
 80/116 [===================>..........] - ETA: 0s - loss: 0.0696
116/116 [==============================] - 0s 1ms/step - loss: 0.0730
Epoch 8/10

  1/116 [..............................] - ETA: 0s - loss: 0.1707
 39/116 [=========>....................] - ETA: 0s - loss: 0.0843
 78/116 [===================>..........] - ETA: 0s - loss: 0.0745
116/116 [==============================] - 0s 1ms/step - loss: 0.0707
Epoch 9/10

  1/116 [..............................] - ETA: 0s - loss: 0.2234
 41/116 [=========>....................] - ETA: 0s - loss: 0.0869
 81/116 [===================>..........] - ETA: 0s - loss: 0.0765
116/116 [==============================] - 0s 1ms/step - loss: 0.0691
Epoch 10/10

  1/116 [..............................] - ETA: 0s - loss: 0.0125
 42/116 [=========>....................] - ETA: 0s - loss: 0.0612
 83/116 [====================>.........] - ETA: 0s - loss: 0.0699
116/116 [==============================] - 0s 1ms/step - loss: 0.0686
-> test with GAN.predict
GAN tn, fp: 282, 6
GAN fn, tp: 1, 8
GAN f1 score: 0.696
GAN cohens kappa score: 0.684

-> test with 'LR'
LR tn, fp: 280, 8
LR fn, tp: 1, 8
LR f1 score: 0.640
LR cohens kappa score: 0.625
LR average precision score: 0.817

-> test with 'RF'
RF tn, fp: 288, 0
RF fn, tp: 5, 4
RF f1 score: 0.615
RF cohens kappa score: 0.608

-> test with 'GB'
GB tn, fp: 288, 0
GB fn, tp: 2, 7
GB f1 score: 0.875
GB cohens kappa score: 0.872

-> test with 'KNN'
KNN tn, fp: 285, 3
KNN fn, tp: 0, 9
KNN f1 score: 0.857
KNN cohens kappa score: 0.852


------ Step 3/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1117 synthetic samples
-> retrain GAN for predict
Epoch 1/10

  1/116 [..............................] - ETA: 18s - loss: 0.5103
 42/116 [=========>....................] - ETA: 0s - loss: 0.3051 
 82/116 [====================>.........] - ETA: 0s - loss: 0.2739
116/116 [==============================] - 0s 1ms/step - loss: 0.2486
Epoch 2/10

  1/116 [..............................] - ETA: 0s - loss: 0.2211
 41/116 [=========>....................] - ETA: 0s - loss: 0.1568
 80/116 [===================>..........] - ETA: 0s - loss: 0.1408
116/116 [==============================] - 0s 1ms/step - loss: 0.1348
Epoch 3/10

  1/116 [..............................] - ETA: 0s - loss: 0.2647
 42/116 [=========>....................] - ETA: 0s - loss: 0.1154
 83/116 [====================>.........] - ETA: 0s - loss: 0.1113
116/116 [==============================] - 0s 1ms/step - loss: 0.1064
Epoch 4/10

  1/116 [..............................] - ETA: 0s - loss: 0.0742
 42/116 [=========>....................] - ETA: 0s - loss: 0.0896
 83/116 [====================>.........] - ETA: 0s - loss: 0.0957
116/116 [==============================] - 0s 1ms/step - loss: 0.0922
Epoch 5/10

  1/116 [..............................] - ETA: 0s - loss: 0.0274
 41/116 [=========>....................] - ETA: 0s - loss: 0.0962
 81/116 [===================>..........] - ETA: 0s - loss: 0.0834
116/116 [==============================] - 0s 1ms/step - loss: 0.0846
Epoch 6/10

  1/116 [..............................] - ETA: 0s - loss: 0.0213
 41/116 [=========>....................] - ETA: 0s - loss: 0.0823
 83/116 [====================>.........] - ETA: 0s - loss: 0.0799
116/116 [==============================] - 0s 1ms/step - loss: 0.0795
Epoch 7/10

  1/116 [..............................] - ETA: 0s - loss: 0.1779
 40/116 [=========>....................] - ETA: 0s - loss: 0.0832
 82/116 [====================>.........] - ETA: 0s - loss: 0.0722
116/116 [==============================] - 0s 1ms/step - loss: 0.0759
Epoch 8/10

  1/116 [..............................] - ETA: 0s - loss: 0.0946
 42/116 [=========>....................] - ETA: 0s - loss: 0.0758
 83/116 [====================>.........] - ETA: 0s - loss: 0.0776
116/116 [==============================] - 0s 1ms/step - loss: 0.0725
Epoch 9/10

  1/116 [..............................] - ETA: 0s - loss: 0.0348
 37/116 [========>.....................] - ETA: 0s - loss: 0.0651
 78/116 [===================>..........] - ETA: 0s - loss: 0.0692
115/116 [============================>.] - ETA: 0s - loss: 0.0707
116/116 [==============================] - 0s 1ms/step - loss: 0.0706
Epoch 10/10

  1/116 [..............................] - ETA: 0s - loss: 0.0933
 41/116 [=========>....................] - ETA: 0s - loss: 0.0825
 81/116 [===================>..........] - ETA: 0s - loss: 0.0707
116/116 [==============================] - 0s 1ms/step - loss: 0.0688
-> test with GAN.predict
GAN tn, fp: 278, 10
GAN fn, tp: 1, 8
GAN f1 score: 0.593
GAN cohens kappa score: 0.575

-> test with 'LR'
LR tn, fp: 279, 9
LR fn, tp: 0, 9
LR f1 score: 0.667
LR cohens kappa score: 0.653
LR average precision score: 0.758

-> test with 'RF'
RF tn, fp: 288, 0
RF fn, tp: 5, 4
RF f1 score: 0.615
RF cohens kappa score: 0.608

-> test with 'GB'
GB tn, fp: 288, 0
GB fn, tp: 4, 5
GB f1 score: 0.714
GB cohens kappa score: 0.708

-> test with 'KNN'
KNN tn, fp: 281, 7
KNN fn, tp: 1, 8
KNN f1 score: 0.667
KNN cohens kappa score: 0.654


------ Step 3/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1116 synthetic samples
-> retrain GAN for predict
Epoch 1/10

  1/116 [..............................] - ETA: 20s - loss: 0.3570
 42/116 [=========>....................] - ETA: 0s - loss: 0.2799 
 84/116 [====================>.........] - ETA: 0s - loss: 0.2391
116/116 [==============================] - 0s 1ms/step - loss: 0.2123
Epoch 2/10

  1/116 [..............................] - ETA: 0s - loss: 0.1142
 42/116 [=========>....................] - ETA: 0s - loss: 0.1340
 84/116 [====================>.........] - ETA: 0s - loss: 0.1154
116/116 [==============================] - 0s 1ms/step - loss: 0.1225
Epoch 3/10

  1/116 [..............................] - ETA: 0s - loss: 0.0451
 41/116 [=========>....................] - ETA: 0s - loss: 0.1031
 76/116 [==================>...........] - ETA: 0s - loss: 0.0888
112/116 [===========================>..] - ETA: 0s - loss: 0.1010
116/116 [==============================] - 0s 1ms/step - loss: 0.0999
Epoch 4/10

  1/116 [..............................] - ETA: 0s - loss: 0.0434
 41/116 [=========>....................] - ETA: 0s - loss: 0.0969
 77/116 [==================>...........] - ETA: 0s - loss: 0.0793
116/116 [==============================] - 0s 1ms/step - loss: 0.0870
Epoch 5/10

  1/116 [..............................] - ETA: 0s - loss: 0.0200
 34/116 [=======>......................] - ETA: 0s - loss: 0.0987
 67/116 [================>.............] - ETA: 0s - loss: 0.0881
108/116 [==========================>...] - ETA: 0s - loss: 0.0811
116/116 [==============================] - 0s 1ms/step - loss: 0.0803
Epoch 6/10

  1/116 [..............................] - ETA: 0s - loss: 0.1271
 43/116 [==========>...................] - ETA: 0s - loss: 0.0734
 82/116 [====================>.........] - ETA: 0s - loss: 0.0749
116/116 [==============================] - 0s 1ms/step - loss: 0.0765
Epoch 7/10

  1/116 [..............................] - ETA: 0s - loss: 0.0992
 42/116 [=========>....................] - ETA: 0s - loss: 0.0744
 83/116 [====================>.........] - ETA: 0s - loss: 0.0714
116/116 [==============================] - 0s 1ms/step - loss: 0.0723
Epoch 8/10

  1/116 [..............................] - ETA: 0s - loss: 0.0078
 42/116 [=========>....................] - ETA: 0s - loss: 0.0737
 82/116 [====================>.........] - ETA: 0s - loss: 0.0708
116/116 [==============================] - 0s 1ms/step - loss: 0.0695
Epoch 9/10

  1/116 [..............................] - ETA: 0s - loss: 0.0213
 41/116 [=========>....................] - ETA: 0s - loss: 0.0688
 82/116 [====================>.........] - ETA: 0s - loss: 0.0690
116/116 [==============================] - 0s 1ms/step - loss: 0.0679
Epoch 10/10

  1/116 [..............................] - ETA: 0s - loss: 0.0517
 38/116 [========>.....................] - ETA: 0s - loss: 0.0651
 74/116 [==================>...........] - ETA: 0s - loss: 0.0709
108/116 [==========================>...] - ETA: 0s - loss: 0.0675
116/116 [==============================] - 0s 1ms/step - loss: 0.0659
-> test with GAN.predict
GAN tn, fp: 274, 14
GAN fn, tp: 0, 8
GAN f1 score: 0.533
GAN cohens kappa score: 0.514

-> test with 'LR'
LR tn, fp: 275, 13
LR fn, tp: 0, 8
LR f1 score: 0.552
LR cohens kappa score: 0.533
LR average precision score: 0.349

-> test with 'RF'
RF tn, fp: 283, 5
RF fn, tp: 1, 7
RF f1 score: 0.700
RF cohens kappa score: 0.690

-> test with 'GB'
GB tn, fp: 283, 5
GB fn, tp: 1, 7
GB f1 score: 0.700
GB cohens kappa score: 0.690

-> test with 'KNN'
KNN tn, fp: 275, 13
KNN fn, tp: 0, 8
KNN f1 score: 0.552
KNN cohens kappa score: 0.533


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

------ Step 4/5: Slice 1/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1117 synthetic samples
-> retrain GAN for predict
Epoch 1/10

  1/116 [..............................] - ETA: 18s - loss: 0.3488
 43/116 [==========>...................] - ETA: 0s - loss: 0.2442 
 84/116 [====================>.........] - ETA: 0s - loss: 0.2245
116/116 [==============================] - 0s 1ms/step - loss: 0.2118
Epoch 2/10

  1/116 [..............................] - ETA: 0s - loss: 0.1434
 42/116 [=========>....................] - ETA: 0s - loss: 0.1477
 83/116 [====================>.........] - ETA: 0s - loss: 0.1389
116/116 [==============================] - 0s 1ms/step - loss: 0.1317
Epoch 3/10

  1/116 [..............................] - ETA: 0s - loss: 0.0803
 39/116 [=========>....................] - ETA: 0s - loss: 0.0924
 81/116 [===================>..........] - ETA: 0s - loss: 0.1030
116/116 [==============================] - 0s 1ms/step - loss: 0.1066
Epoch 4/10

  1/116 [..............................] - ETA: 0s - loss: 0.0562
 39/116 [=========>....................] - ETA: 0s - loss: 0.1104
 80/116 [===================>..........] - ETA: 0s - loss: 0.0944
116/116 [==============================] - 0s 1ms/step - loss: 0.0937
Epoch 5/10

  1/116 [..............................] - ETA: 0s - loss: 0.0695
 42/116 [=========>....................] - ETA: 0s - loss: 0.0864
 81/116 [===================>..........] - ETA: 0s - loss: 0.0897
116/116 [==============================] - 0s 1ms/step - loss: 0.0874
Epoch 6/10

  1/116 [..............................] - ETA: 0s - loss: 0.0311
 39/116 [=========>....................] - ETA: 0s - loss: 0.0857
 80/116 [===================>..........] - ETA: 0s - loss: 0.0807
116/116 [==============================] - 0s 1ms/step - loss: 0.0844
Epoch 7/10

  1/116 [..............................] - ETA: 0s - loss: 0.0581
 38/116 [========>.....................] - ETA: 0s - loss: 0.0842
 79/116 [===================>..........] - ETA: 0s - loss: 0.0892
116/116 [==============================] - 0s 1ms/step - loss: 0.0795
Epoch 8/10

  1/116 [..............................] - ETA: 0s - loss: 0.0421
 40/116 [=========>....................] - ETA: 0s - loss: 0.0572
 77/116 [==================>...........] - ETA: 0s - loss: 0.0599
116/116 [==============================] - 0s 1ms/step - loss: 0.0765
Epoch 9/10

  1/116 [..............................] - ETA: 0s - loss: 0.0496
 43/116 [==========>...................] - ETA: 0s - loss: 0.0853
 84/116 [====================>.........] - ETA: 0s - loss: 0.0727
116/116 [==============================] - 0s 1ms/step - loss: 0.0758
Epoch 10/10

  1/116 [..............................] - ETA: 0s - loss: 0.0316
 41/116 [=========>....................] - ETA: 0s - loss: 0.0734
 79/116 [===================>..........] - ETA: 0s - loss: 0.0765
116/116 [==============================] - 0s 1ms/step - loss: 0.0730
-> test with GAN.predict
GAN tn, fp: 271, 17
GAN fn, tp: 0, 9
GAN f1 score: 0.514
GAN cohens kappa score: 0.491

-> test with 'LR'
LR tn, fp: 275, 13
LR fn, tp: 0, 9
LR f1 score: 0.581
LR cohens kappa score: 0.562
LR average precision score: 0.748

-> test with 'RF'
RF tn, fp: 286, 2
RF fn, tp: 1, 8
RF f1 score: 0.842
RF cohens kappa score: 0.837

-> test with 'GB'
GB tn, fp: 286, 2
GB fn, tp: 1, 8
GB f1 score: 0.842
GB cohens kappa score: 0.837

-> test with 'KNN'
KNN tn, fp: 277, 11
KNN fn, tp: 0, 9
KNN f1 score: 0.621
KNN cohens kappa score: 0.604


------ Step 4/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1117 synthetic samples
-> retrain GAN for predict
Epoch 1/10

  1/116 [..............................] - ETA: 18s - loss: 0.5631
 41/116 [=========>....................] - ETA: 0s - loss: 0.3563 
 83/116 [====================>.........] - ETA: 0s - loss: 0.2859
116/116 [==============================] - 0s 1ms/step - loss: 0.2502
Epoch 2/10

  1/116 [..............................] - ETA: 0s - loss: 0.0833
 43/116 [==========>...................] - ETA: 0s - loss: 0.1367
 85/116 [====================>.........] - ETA: 0s - loss: 0.1264
116/116 [==============================] - 0s 1ms/step - loss: 0.1199
Epoch 3/10

  1/116 [..............................] - ETA: 0s - loss: 0.0363
 41/116 [=========>....................] - ETA: 0s - loss: 0.0946
 81/116 [===================>..........] - ETA: 0s - loss: 0.0944
116/116 [==============================] - 0s 1ms/step - loss: 0.0899
Epoch 4/10

  1/116 [..............................] - ETA: 0s - loss: 0.1015
 41/116 [=========>....................] - ETA: 0s - loss: 0.0887
 82/116 [====================>.........] - ETA: 0s - loss: 0.0751
116/116 [==============================] - 0s 1ms/step - loss: 0.0777
Epoch 5/10

  1/116 [..............................] - ETA: 0s - loss: 0.0908
 41/116 [=========>....................] - ETA: 0s - loss: 0.0673
 82/116 [====================>.........] - ETA: 0s - loss: 0.0697
116/116 [==============================] - 0s 1ms/step - loss: 0.0719
Epoch 6/10

  1/116 [..............................] - ETA: 0s - loss: 0.0162
 42/116 [=========>....................] - ETA: 0s - loss: 0.0611
 84/116 [====================>.........] - ETA: 0s - loss: 0.0693
116/116 [==============================] - 0s 1ms/step - loss: 0.0693
Epoch 7/10

  1/116 [..............................] - ETA: 0s - loss: 0.0718
 40/116 [=========>....................] - ETA: 0s - loss: 0.0575
 79/116 [===================>..........] - ETA: 0s - loss: 0.0695
116/116 [==============================] - 0s 1ms/step - loss: 0.0657
Epoch 8/10

  1/116 [..............................] - ETA: 0s - loss: 0.0401
 43/116 [==========>...................] - ETA: 0s - loss: 0.0622
 84/116 [====================>.........] - ETA: 0s - loss: 0.0575
116/116 [==============================] - 0s 1ms/step - loss: 0.0622
Epoch 9/10

  1/116 [..............................] - ETA: 0s - loss: 0.0342
 39/116 [=========>....................] - ETA: 0s - loss: 0.0634
 73/116 [=================>............] - ETA: 0s - loss: 0.0627
107/116 [==========================>...] - ETA: 0s - loss: 0.0640
116/116 [==============================] - 0s 1ms/step - loss: 0.0639
Epoch 10/10

  1/116 [..............................] - ETA: 0s - loss: 0.0266
 43/116 [==========>...................] - ETA: 0s - loss: 0.0731
 83/116 [====================>.........] - ETA: 0s - loss: 0.0640
116/116 [==============================] - 0s 1ms/step - loss: 0.0596
-> test with GAN.predict
GAN tn, fp: 270, 18
GAN fn, tp: 1, 8
GAN f1 score: 0.457
GAN cohens kappa score: 0.432

-> test with 'LR'
LR tn, fp: 271, 17
LR fn, tp: 0, 9
LR f1 score: 0.514
LR cohens kappa score: 0.491
LR average precision score: 0.625

-> test with 'RF'
RF tn, fp: 287, 1
RF fn, tp: 2, 7
RF f1 score: 0.824
RF cohens kappa score: 0.818

-> test with 'GB'
GB tn, fp: 287, 1
GB fn, tp: 2, 7
GB f1 score: 0.824
GB cohens kappa score: 0.818

-> test with 'KNN'
KNN tn, fp: 278, 10
KNN fn, tp: 0, 9
KNN f1 score: 0.643
KNN cohens kappa score: 0.628


------ Step 4/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1117 synthetic samples
-> retrain GAN for predict
Epoch 1/10

  1/116 [..............................] - ETA: 18s - loss: 0.2390
 42/116 [=========>....................] - ETA: 0s - loss: 0.2663 
 80/116 [===================>..........] - ETA: 0s - loss: 0.2305
114/116 [============================>.] - ETA: 0s - loss: 0.2121
116/116 [==============================] - 0s 1ms/step - loss: 0.2114
Epoch 2/10

  1/116 [..............................] - ETA: 0s - loss: 0.2798
 34/116 [=======>......................] - ETA: 0s - loss: 0.1385
 73/116 [=================>............] - ETA: 0s - loss: 0.1432
114/116 [============================>.] - ETA: 0s - loss: 0.1385
116/116 [==============================] - 0s 1ms/step - loss: 0.1378
Epoch 3/10

  1/116 [..............................] - ETA: 0s - loss: 0.1008
 40/116 [=========>....................] - ETA: 0s - loss: 0.1217
 81/116 [===================>..........] - ETA: 0s - loss: 0.1147
116/116 [==============================] - 0s 1ms/step - loss: 0.1138
Epoch 4/10

  1/116 [..............................] - ETA: 0s - loss: 0.1171
 41/116 [=========>....................] - ETA: 0s - loss: 0.1055
 81/116 [===================>..........] - ETA: 0s - loss: 0.0969
116/116 [==============================] - 0s 1ms/step - loss: 0.1002
Epoch 5/10

  1/116 [..............................] - ETA: 0s - loss: 0.0253
 41/116 [=========>....................] - ETA: 0s - loss: 0.0801
 82/116 [====================>.........] - ETA: 0s - loss: 0.0902
116/116 [==============================] - 0s 1ms/step - loss: 0.0920
Epoch 6/10

  1/116 [..............................] - ETA: 0s - loss: 0.0386
 41/116 [=========>....................] - ETA: 0s - loss: 0.0803
 81/116 [===================>..........] - ETA: 0s - loss: 0.0752
116/116 [==============================] - 0s 1ms/step - loss: 0.0857
Epoch 7/10

  1/116 [..............................] - ETA: 0s - loss: 0.1226
 39/116 [=========>....................] - ETA: 0s - loss: 0.0993
 80/116 [===================>..........] - ETA: 0s - loss: 0.0849
116/116 [==============================] - 0s 1ms/step - loss: 0.0812
Epoch 8/10

  1/116 [..............................] - ETA: 0s - loss: 0.0484
 42/116 [=========>....................] - ETA: 0s - loss: 0.0760
 81/116 [===================>..........] - ETA: 0s - loss: 0.0742
116/116 [==============================] - 0s 1ms/step - loss: 0.0786
Epoch 9/10

  1/116 [..............................] - ETA: 0s - loss: 0.0312
 44/116 [==========>...................] - ETA: 0s - loss: 0.0812
 85/116 [====================>.........] - ETA: 0s - loss: 0.0811
116/116 [==============================] - 0s 1ms/step - loss: 0.0753
Epoch 10/10

  1/116 [..............................] - ETA: 0s - loss: 0.0421
 41/116 [=========>....................] - ETA: 0s - loss: 0.0791
 82/116 [====================>.........] - ETA: 0s - loss: 0.0757
116/116 [==============================] - 0s 1ms/step - loss: 0.0726
-> test with GAN.predict
GAN tn, fp: 279, 9
GAN fn, tp: 3, 6
GAN f1 score: 0.500
GAN cohens kappa score: 0.480

-> test with 'LR'
LR tn, fp: 278, 10
LR fn, tp: 2, 7
LR f1 score: 0.538
LR cohens kappa score: 0.519
LR average precision score: 0.650

-> test with 'RF'
RF tn, fp: 283, 5
RF fn, tp: 4, 5
RF f1 score: 0.526
RF cohens kappa score: 0.511

-> test with 'GB'
GB tn, fp: 283, 5
GB fn, tp: 4, 5
GB f1 score: 0.526
GB cohens kappa score: 0.511

-> test with 'KNN'
KNN tn, fp: 279, 9
KNN fn, tp: 0, 9
KNN f1 score: 0.667
KNN cohens kappa score: 0.653


------ Step 4/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1117 synthetic samples
-> retrain GAN for predict
Epoch 1/10

  1/116 [..............................] - ETA: 19s - loss: 0.3221
 41/116 [=========>....................] - ETA: 0s - loss: 0.3398 
 81/116 [===================>..........] - ETA: 0s - loss: 0.3006
116/116 [==============================] - 0s 1ms/step - loss: 0.2687
Epoch 2/10

  1/116 [..............................] - ETA: 0s - loss: 0.2994
 41/116 [=========>....................] - ETA: 0s - loss: 0.1628
 81/116 [===================>..........] - ETA: 0s - loss: 0.1627
116/116 [==============================] - 0s 1ms/step - loss: 0.1477
Epoch 3/10

  1/116 [..............................] - ETA: 0s - loss: 0.2950
 40/116 [=========>....................] - ETA: 0s - loss: 0.1158
 80/116 [===================>..........] - ETA: 0s - loss: 0.1128
116/116 [==============================] - ETA: 0s - loss: 0.1131
116/116 [==============================] - 0s 1ms/step - loss: 0.1131
Epoch 4/10

  1/116 [..............................] - ETA: 0s - loss: 0.2107
 39/116 [=========>....................] - ETA: 0s - loss: 0.1016
 79/116 [===================>..........] - ETA: 0s - loss: 0.0972
116/116 [==============================] - 0s 1ms/step - loss: 0.0970
Epoch 5/10

  1/116 [..............................] - ETA: 0s - loss: 0.0490
 41/116 [=========>....................] - ETA: 0s - loss: 0.1057
 81/116 [===================>..........] - ETA: 0s - loss: 0.0940
116/116 [==============================] - 0s 1ms/step - loss: 0.0895
Epoch 6/10

  1/116 [..............................] - ETA: 0s - loss: 0.0885
 42/116 [=========>....................] - ETA: 0s - loss: 0.0781
 82/116 [====================>.........] - ETA: 0s - loss: 0.0887
116/116 [==============================] - 0s 1ms/step - loss: 0.0836
Epoch 7/10

  1/116 [..............................] - ETA: 0s - loss: 0.1438
 37/116 [========>.....................] - ETA: 0s - loss: 0.0748
 74/116 [==================>...........] - ETA: 0s - loss: 0.0750
116/116 [==============================] - ETA: 0s - loss: 0.0794
116/116 [==============================] - 0s 1ms/step - loss: 0.0794
Epoch 8/10

  1/116 [..............................] - ETA: 0s - loss: 0.0344
 40/116 [=========>....................] - ETA: 0s - loss: 0.0759
 81/116 [===================>..........] - ETA: 0s - loss: 0.0721
116/116 [==============================] - 0s 1ms/step - loss: 0.0765
Epoch 9/10

  1/116 [..............................] - ETA: 0s - loss: 0.0282
 40/116 [=========>....................] - ETA: 0s - loss: 0.0774
 80/116 [===================>..........] - ETA: 0s - loss: 0.0736
116/116 [==============================] - 0s 1ms/step - loss: 0.0745
Epoch 10/10

  1/116 [..............................] - ETA: 0s - loss: 0.0369
 41/116 [=========>....................] - ETA: 0s - loss: 0.0697
 81/116 [===================>..........] - ETA: 0s - loss: 0.0756
116/116 [==============================] - 0s 1ms/step - loss: 0.0710
-> test with GAN.predict
GAN tn, fp: 280, 8
GAN fn, tp: 0, 9
GAN f1 score: 0.692
GAN cohens kappa score: 0.680

-> test with 'LR'
LR tn, fp: 280, 8
LR fn, tp: 0, 9
LR f1 score: 0.692
LR cohens kappa score: 0.680
LR average precision score: 0.696

-> test with 'RF'
RF tn, fp: 288, 0
RF fn, tp: 5, 4
RF f1 score: 0.615
RF cohens kappa score: 0.608

-> test with 'GB'
GB tn, fp: 287, 1
GB fn, tp: 3, 6
GB f1 score: 0.750
GB cohens kappa score: 0.743

-> test with 'KNN'
KNN tn, fp: 282, 6
KNN fn, tp: 1, 8
KNN f1 score: 0.696
KNN cohens kappa score: 0.684


------ Step 4/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1116 synthetic samples
-> retrain GAN for predict
Epoch 1/10

  1/116 [..............................] - ETA: 17s - loss: 0.2197
 42/116 [=========>....................] - ETA: 0s - loss: 0.2801 
 84/116 [====================>.........] - ETA: 0s - loss: 0.2500
116/116 [==============================] - 0s 1ms/step - loss: 0.2320
Epoch 2/10

  1/116 [..............................] - ETA: 0s - loss: 0.1336
 38/116 [========>.....................] - ETA: 0s - loss: 0.1643
 77/116 [==================>...........] - ETA: 0s - loss: 0.1587
116/116 [==============================] - 0s 1ms/step - loss: 0.1532
Epoch 3/10

  1/116 [..............................] - ETA: 0s - loss: 0.1881
 39/116 [=========>....................] - ETA: 0s - loss: 0.1241
 77/116 [==================>...........] - ETA: 0s - loss: 0.1196
116/116 [==============================] - 0s 1ms/step - loss: 0.1217
Epoch 4/10

  1/116 [..............................] - ETA: 0s - loss: 0.0878
 43/116 [==========>...................] - ETA: 0s - loss: 0.1005
 84/116 [====================>.........] - ETA: 0s - loss: 0.1084
116/116 [==============================] - 0s 1ms/step - loss: 0.1079
Epoch 5/10

  1/116 [..............................] - ETA: 0s - loss: 0.0660
 42/116 [=========>....................] - ETA: 0s - loss: 0.0884
 82/116 [====================>.........] - ETA: 0s - loss: 0.1013
116/116 [==============================] - 0s 1ms/step - loss: 0.1010
Epoch 6/10

  1/116 [..............................] - ETA: 0s - loss: 0.0700
 41/116 [=========>....................] - ETA: 0s - loss: 0.1162
 82/116 [====================>.........] - ETA: 0s - loss: 0.0938
116/116 [==============================] - 0s 1ms/step - loss: 0.0978
Epoch 7/10

  1/116 [..............................] - ETA: 0s - loss: 0.4742
 41/116 [=========>....................] - ETA: 0s - loss: 0.1292
 81/116 [===================>..........] - ETA: 0s - loss: 0.1014
116/116 [==============================] - 0s 1ms/step - loss: 0.0922
Epoch 8/10

  1/116 [..............................] - ETA: 0s - loss: 0.3748
 42/116 [=========>....................] - ETA: 0s - loss: 0.1025
 82/116 [====================>.........] - ETA: 0s - loss: 0.0896
116/116 [==============================] - 0s 1ms/step - loss: 0.0891
Epoch 9/10

  1/116 [..............................] - ETA: 0s - loss: 0.2381
 41/116 [=========>....................] - ETA: 0s - loss: 0.0924
 82/116 [====================>.........] - ETA: 0s - loss: 0.0893
116/116 [==============================] - 0s 1ms/step - loss: 0.0866
Epoch 10/10

  1/116 [..............................] - ETA: 0s - loss: 0.4065
 42/116 [=========>....................] - ETA: 0s - loss: 0.0833
 82/116 [====================>.........] - ETA: 0s - loss: 0.0799
116/116 [==============================] - 0s 1ms/step - loss: 0.0852
-> test with GAN.predict
GAN tn, fp: 276, 12
GAN fn, tp: 0, 8
GAN f1 score: 0.571
GAN cohens kappa score: 0.554

-> test with 'LR'
LR tn, fp: 272, 16
LR fn, tp: 0, 8
LR f1 score: 0.500
LR cohens kappa score: 0.479
LR average precision score: 0.640

-> test with 'RF'
RF tn, fp: 288, 0
RF fn, tp: 1, 7
RF f1 score: 0.933
RF cohens kappa score: 0.932

-> test with 'GB'
GB tn, fp: 286, 2
GB fn, tp: 1, 7
GB f1 score: 0.824
GB cohens kappa score: 0.818

-> test with 'KNN'
KNN tn, fp: 273, 15
KNN fn, tp: 0, 8
KNN f1 score: 0.516
KNN cohens kappa score: 0.496


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

------ Step 5/5: Slice 1/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1117 synthetic samples
-> retrain GAN for predict
Epoch 1/10

  1/116 [..............................] - ETA: 21s - loss: 0.3688
 42/116 [=========>....................] - ETA: 0s - loss: 0.3654 
 83/116 [====================>.........] - ETA: 0s - loss: 0.2998
116/116 [==============================] - 0s 1ms/step - loss: 0.2671
Epoch 2/10

  1/116 [..............................] - ETA: 0s - loss: 0.2327
 40/116 [=========>....................] - ETA: 0s - loss: 0.1493
 81/116 [===================>..........] - ETA: 0s - loss: 0.1420
116/116 [==============================] - 0s 1ms/step - loss: 0.1368
Epoch 3/10

  1/116 [..............................] - ETA: 0s - loss: 0.1644
 39/116 [=========>....................] - ETA: 0s - loss: 0.1137
 81/116 [===================>..........] - ETA: 0s - loss: 0.1129
116/116 [==============================] - 0s 1ms/step - loss: 0.1068
Epoch 4/10

  1/116 [..............................] - ETA: 0s - loss: 0.1838
 42/116 [=========>....................] - ETA: 0s - loss: 0.1018
 84/116 [====================>.........] - ETA: 0s - loss: 0.0982
116/116 [==============================] - 0s 1ms/step - loss: 0.0942
Epoch 5/10

  1/116 [..............................] - ETA: 0s - loss: 0.1060
 38/116 [========>.....................] - ETA: 0s - loss: 0.0885
 77/116 [==================>...........] - ETA: 0s - loss: 0.0870
116/116 [==============================] - ETA: 0s - loss: 0.0871
116/116 [==============================] - 0s 1ms/step - loss: 0.0871
Epoch 6/10

  1/116 [..............................] - ETA: 0s - loss: 0.0999
 39/116 [=========>....................] - ETA: 0s - loss: 0.0959
 75/116 [==================>...........] - ETA: 0s - loss: 0.0813
113/116 [============================>.] - ETA: 0s - loss: 0.0851
116/116 [==============================] - 0s 1ms/step - loss: 0.0840
Epoch 7/10

  1/116 [..............................] - ETA: 0s - loss: 0.0381
 37/116 [========>.....................] - ETA: 0s - loss: 0.0926
 76/116 [==================>...........] - ETA: 0s - loss: 0.0836
116/116 [==============================] - ETA: 0s - loss: 0.0790
116/116 [==============================] - 0s 1ms/step - loss: 0.0790
Epoch 8/10

  1/116 [..............................] - ETA: 0s - loss: 0.0287
 40/116 [=========>....................] - ETA: 0s - loss: 0.0844
 79/116 [===================>..........] - ETA: 0s - loss: 0.0823
116/116 [==============================] - 0s 1ms/step - loss: 0.0814
Epoch 9/10

  1/116 [..............................] - ETA: 0s - loss: 0.1329
 41/116 [=========>....................] - ETA: 0s - loss: 0.0848
 81/116 [===================>..........] - ETA: 0s - loss: 0.0774
116/116 [==============================] - 0s 1ms/step - loss: 0.0753
Epoch 10/10

  1/116 [..............................] - ETA: 0s - loss: 0.0183
 42/116 [=========>....................] - ETA: 0s - loss: 0.0694
 82/116 [====================>.........] - ETA: 0s - loss: 0.0707
116/116 [==============================] - 0s 1ms/step - loss: 0.0723
-> test with GAN.predict
GAN tn, fp: 272, 16
GAN fn, tp: 0, 9
GAN f1 score: 0.529
GAN cohens kappa score: 0.507

-> test with 'LR'
LR tn, fp: 272, 16
LR fn, tp: 0, 9
LR f1 score: 0.529
LR cohens kappa score: 0.507
LR average precision score: 0.710

-> test with 'RF'
RF tn, fp: 287, 1
RF fn, tp: 1, 8
RF f1 score: 0.889
RF cohens kappa score: 0.885

-> test with 'GB'
GB tn, fp: 284, 4
GB fn, tp: 0, 9
GB f1 score: 0.818
GB cohens kappa score: 0.811

-> test with 'KNN'
KNN tn, fp: 273, 15
KNN fn, tp: 0, 9
KNN f1 score: 0.545
KNN cohens kappa score: 0.524


------ Step 5/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1117 synthetic samples
-> retrain GAN for predict
Epoch 1/10

  1/116 [..............................] - ETA: 18s - loss: 0.5454
 41/116 [=========>....................] - ETA: 0s - loss: 0.3585 
 82/116 [====================>.........] - ETA: 0s - loss: 0.2861
116/116 [==============================] - 0s 1ms/step - loss: 0.2482
Epoch 2/10

  1/116 [..............................] - ETA: 0s - loss: 0.0838
 42/116 [=========>....................] - ETA: 0s - loss: 0.1260
 82/116 [====================>.........] - ETA: 0s - loss: 0.1199
116/116 [==============================] - 0s 1ms/step - loss: 0.1158
Epoch 3/10

  1/116 [..............................] - ETA: 0s - loss: 0.0416
 42/116 [=========>....................] - ETA: 0s - loss: 0.0817
 81/116 [===================>..........] - ETA: 0s - loss: 0.0857
116/116 [==============================] - 0s 1ms/step - loss: 0.0841
Epoch 4/10

  1/116 [..............................] - ETA: 0s - loss: 0.0437
 41/116 [=========>....................] - ETA: 0s - loss: 0.0762
 82/116 [====================>.........] - ETA: 0s - loss: 0.0727
116/116 [==============================] - 0s 1ms/step - loss: 0.0715
Epoch 5/10

  1/116 [..............................] - ETA: 0s - loss: 0.1181
 40/116 [=========>....................] - ETA: 0s - loss: 0.0555
 79/116 [===================>..........] - ETA: 0s - loss: 0.0628
116/116 [==============================] - 0s 1ms/step - loss: 0.0649
Epoch 6/10

  1/116 [..............................] - ETA: 0s - loss: 0.0290
 41/116 [=========>....................] - ETA: 0s - loss: 0.0651
 82/116 [====================>.........] - ETA: 0s - loss: 0.0591
116/116 [==============================] - ETA: 0s - loss: 0.0609
116/116 [==============================] - 0s 1ms/step - loss: 0.0609
Epoch 7/10

  1/116 [..............................] - ETA: 0s - loss: 0.0147
 35/116 [========>.....................] - ETA: 0s - loss: 0.0351
 66/116 [================>.............] - ETA: 0s - loss: 0.0505
105/116 [==========================>...] - ETA: 0s - loss: 0.0528
116/116 [==============================] - 0s 1ms/step - loss: 0.0562
Epoch 8/10

  1/116 [..............................] - ETA: 0s - loss: 0.0321
 41/116 [=========>....................] - ETA: 0s - loss: 0.0422
 80/116 [===================>..........] - ETA: 0s - loss: 0.0529
116/116 [==============================] - 0s 1ms/step - loss: 0.0541
Epoch 9/10

  1/116 [..............................] - ETA: 0s - loss: 0.0665
 40/116 [=========>....................] - ETA: 0s - loss: 0.0508
 81/116 [===================>..........] - ETA: 0s - loss: 0.0514
116/116 [==============================] - 0s 1ms/step - loss: 0.0519
Epoch 10/10

  1/116 [..............................] - ETA: 0s - loss: 0.0444
 41/116 [=========>....................] - ETA: 0s - loss: 0.0533
 80/116 [===================>..........] - ETA: 0s - loss: 0.0529
116/116 [==============================] - 0s 1ms/step - loss: 0.0506
-> test with GAN.predict
GAN tn, fp: 283, 5
GAN fn, tp: 3, 6
GAN f1 score: 0.600
GAN cohens kappa score: 0.586

-> test with 'LR'
LR tn, fp: 280, 8
LR fn, tp: 1, 8
LR f1 score: 0.640
LR cohens kappa score: 0.625
LR average precision score: 0.777

-> test with 'RF'
RF tn, fp: 288, 0
RF fn, tp: 3, 6
RF f1 score: 0.800
RF cohens kappa score: 0.795

-> test with 'GB'
GB tn, fp: 288, 0
GB fn, tp: 3, 6
GB f1 score: 0.800
GB cohens kappa score: 0.795

-> test with 'KNN'
KNN tn, fp: 283, 5
KNN fn, tp: 0, 9
KNN f1 score: 0.783
KNN cohens kappa score: 0.774


------ Step 5/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1117 synthetic samples
-> retrain GAN for predict
Epoch 1/10

  1/116 [..............................] - ETA: 18s - loss: 0.3089
 42/116 [=========>....................] - ETA: 0s - loss: 0.3385 
 83/116 [====================>.........] - ETA: 0s - loss: 0.2919
116/116 [==============================] - 0s 1ms/step - loss: 0.2608
Epoch 2/10

  1/116 [..............................] - ETA: 0s - loss: 0.0909
 42/116 [=========>....................] - ETA: 0s - loss: 0.1550
 83/116 [====================>.........] - ETA: 0s - loss: 0.1452
116/116 [==============================] - 0s 1ms/step - loss: 0.1439
Epoch 3/10

  1/116 [..............................] - ETA: 0s - loss: 0.0712
 41/116 [=========>....................] - ETA: 0s - loss: 0.1062
 81/116 [===================>..........] - ETA: 0s - loss: 0.1183
116/116 [==============================] - 0s 1ms/step - loss: 0.1147
Epoch 4/10

  1/116 [..............................] - ETA: 0s - loss: 0.0801
 42/116 [=========>....................] - ETA: 0s - loss: 0.0852
 83/116 [====================>.........] - ETA: 0s - loss: 0.0995
116/116 [==============================] - 0s 1ms/step - loss: 0.1042
Epoch 5/10

  1/116 [..............................] - ETA: 0s - loss: 0.0698
 42/116 [=========>....................] - ETA: 0s - loss: 0.0856
 81/116 [===================>..........] - ETA: 0s - loss: 0.0930
116/116 [==============================] - 0s 1ms/step - loss: 0.0978
Epoch 6/10

  1/116 [..............................] - ETA: 0s - loss: 0.0569
 38/116 [========>.....................] - ETA: 0s - loss: 0.1049
 79/116 [===================>..........] - ETA: 0s - loss: 0.0994
116/116 [==============================] - 0s 1ms/step - loss: 0.0923
Epoch 7/10

  1/116 [..............................] - ETA: 0s - loss: 0.0688
 42/116 [=========>....................] - ETA: 0s - loss: 0.1043
 83/116 [====================>.........] - ETA: 0s - loss: 0.0937
116/116 [==============================] - 0s 1ms/step - loss: 0.0885
Epoch 8/10

  1/116 [..............................] - ETA: 0s - loss: 0.0163
 41/116 [=========>....................] - ETA: 0s - loss: 0.0952
 79/116 [===================>..........] - ETA: 0s - loss: 0.0887
116/116 [==============================] - 0s 1ms/step - loss: 0.0859
Epoch 9/10

  1/116 [..............................] - ETA: 0s - loss: 0.0635
 36/116 [========>.....................] - ETA: 0s - loss: 0.0800
 71/116 [=================>............] - ETA: 0s - loss: 0.0898
109/116 [===========================>..] - ETA: 0s - loss: 0.0843
116/116 [==============================] - 0s 1ms/step - loss: 0.0828
Epoch 10/10

  1/116 [..............................] - ETA: 0s - loss: 0.0636
 41/116 [=========>....................] - ETA: 0s - loss: 0.0925
 81/116 [===================>..........] - ETA: 0s - loss: 0.0838
116/116 [==============================] - 0s 1ms/step - loss: 0.0790
-> test with GAN.predict
GAN tn, fp: 279, 9
GAN fn, tp: 0, 9
GAN f1 score: 0.667
GAN cohens kappa score: 0.653

-> test with 'LR'
LR tn, fp: 277, 11
LR fn, tp: 0, 9
LR f1 score: 0.621
LR cohens kappa score: 0.604
LR average precision score: 0.747

-> test with 'RF'
RF tn, fp: 287, 1
RF fn, tp: 2, 7
RF f1 score: 0.824
RF cohens kappa score: 0.818

-> test with 'GB'
GB tn, fp: 287, 1
GB fn, tp: 2, 7
GB f1 score: 0.824
GB cohens kappa score: 0.818

-> test with 'KNN'
KNN tn, fp: 282, 6
KNN fn, tp: 0, 9
KNN f1 score: 0.750
KNN cohens kappa score: 0.740


------ Step 5/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1117 synthetic samples
-> retrain GAN for predict
Epoch 1/10

  1/116 [..............................] - ETA: 21s - loss: 0.3188
 40/116 [=========>....................] - ETA: 0s - loss: 0.1700 
 80/116 [===================>..........] - ETA: 0s - loss: 0.1547
116/116 [==============================] - 0s 1ms/step - loss: 0.1421
Epoch 2/10

  1/116 [..............................] - ETA: 0s - loss: 0.1563
 42/116 [=========>....................] - ETA: 0s - loss: 0.1052
 81/116 [===================>..........] - ETA: 0s - loss: 0.0927
116/116 [==============================] - 0s 1ms/step - loss: 0.0892
Epoch 3/10

  1/116 [..............................] - ETA: 0s - loss: 0.0460
 38/116 [========>.....................] - ETA: 0s - loss: 0.0746
 71/116 [=================>............] - ETA: 0s - loss: 0.0733
102/116 [=========================>....] - ETA: 0s - loss: 0.0742
116/116 [==============================] - 0s 2ms/step - loss: 0.0748
Epoch 4/10

  1/116 [..............................] - ETA: 0s - loss: 0.0312
 40/116 [=========>....................] - ETA: 0s - loss: 0.0518
 80/116 [===================>..........] - ETA: 0s - loss: 0.0650
116/116 [==============================] - 0s 1ms/step - loss: 0.0664
Epoch 5/10

  1/116 [..............................] - ETA: 0s - loss: 0.1667
 41/116 [=========>....................] - ETA: 0s - loss: 0.0621
 83/116 [====================>.........] - ETA: 0s - loss: 0.0685
116/116 [==============================] - 0s 1ms/step - loss: 0.0624
Epoch 6/10

  1/116 [..............................] - ETA: 0s - loss: 0.0296
 42/116 [=========>....................] - ETA: 0s - loss: 0.0759
 82/116 [====================>.........] - ETA: 0s - loss: 0.0622
116/116 [==============================] - 0s 1ms/step - loss: 0.0580
Epoch 7/10

  1/116 [..............................] - ETA: 0s - loss: 0.0148
 39/116 [=========>....................] - ETA: 0s - loss: 0.0578
 80/116 [===================>..........] - ETA: 0s - loss: 0.0579
116/116 [==============================] - 0s 1ms/step - loss: 0.0556
Epoch 8/10

  1/116 [..............................] - ETA: 0s - loss: 0.0529
 40/116 [=========>....................] - ETA: 0s - loss: 0.0529
 82/116 [====================>.........] - ETA: 0s - loss: 0.0552
116/116 [==============================] - 0s 1ms/step - loss: 0.0550
Epoch 9/10

  1/116 [..............................] - ETA: 0s - loss: 0.0117
 43/116 [==========>...................] - ETA: 0s - loss: 0.0548
 83/116 [====================>.........] - ETA: 0s - loss: 0.0529
116/116 [==============================] - 0s 1ms/step - loss: 0.0514
Epoch 10/10

  1/116 [..............................] - ETA: 0s - loss: 0.0096
 40/116 [=========>....................] - ETA: 0s - loss: 0.0422
 80/116 [===================>..........] - ETA: 0s - loss: 0.0471
116/116 [==============================] - 0s 1ms/step - loss: 0.0505
-> test with GAN.predict
GAN tn, fp: 284, 4
GAN fn, tp: 3, 6
GAN f1 score: 0.632
GAN cohens kappa score: 0.619

-> test with 'LR'
LR tn, fp: 281, 7
LR fn, tp: 3, 6
LR f1 score: 0.545
LR cohens kappa score: 0.529
LR average precision score: 0.600

-> test with 'RF'
RF tn, fp: 287, 1
RF fn, tp: 4, 5
RF f1 score: 0.667
RF cohens kappa score: 0.658

-> test with 'GB'
GB tn, fp: 287, 1
GB fn, tp: 3, 6
GB f1 score: 0.750
GB cohens kappa score: 0.743

-> test with 'KNN'
KNN tn, fp: 283, 5
KNN fn, tp: 1, 8
KNN f1 score: 0.727
KNN cohens kappa score: 0.717


------ Step 5/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1116 synthetic samples
-> retrain GAN for predict
Epoch 1/10

  1/116 [..............................] - ETA: 18s - loss: 0.3437
 42/116 [=========>....................] - ETA: 0s - loss: 0.2813 
 83/116 [====================>.........] - ETA: 0s - loss: 0.2423
116/116 [==============================] - 0s 1ms/step - loss: 0.2221
Epoch 2/10

  1/116 [..............................] - ETA: 0s - loss: 0.2086
 41/116 [=========>....................] - ETA: 0s - loss: 0.1506
 81/116 [===================>..........] - ETA: 0s - loss: 0.1379
116/116 [==============================] - 0s 1ms/step - loss: 0.1328
Epoch 3/10

  1/116 [..............................] - ETA: 0s - loss: 0.1675
 42/116 [=========>....................] - ETA: 0s - loss: 0.1166
 82/116 [====================>.........] - ETA: 0s - loss: 0.1072
116/116 [==============================] - 0s 1ms/step - loss: 0.1051
Epoch 4/10

  1/116 [..............................] - ETA: 0s - loss: 0.0620
 43/116 [==========>...................] - ETA: 0s - loss: 0.0887
 83/116 [====================>.........] - ETA: 0s - loss: 0.0954
116/116 [==============================] - 0s 1ms/step - loss: 0.0939
Epoch 5/10

  1/116 [..............................] - ETA: 0s - loss: 0.1836
 43/116 [==========>...................] - ETA: 0s - loss: 0.0747
 80/116 [===================>..........] - ETA: 0s - loss: 0.0832
115/116 [============================>.] - ETA: 0s - loss: 0.0858
116/116 [==============================] - 0s 1ms/step - loss: 0.0861
Epoch 6/10

  1/116 [..............................] - ETA: 0s - loss: 0.0373
 38/116 [========>.....................] - ETA: 0s - loss: 0.0820
 74/116 [==================>...........] - ETA: 0s - loss: 0.0826
115/116 [============================>.] - ETA: 0s - loss: 0.0824
116/116 [==============================] - 0s 1ms/step - loss: 0.0825
Epoch 7/10

  1/116 [..............................] - ETA: 0s - loss: 0.1426
 41/116 [=========>....................] - ETA: 0s - loss: 0.0821
 81/116 [===================>..........] - ETA: 0s - loss: 0.0793
116/116 [==============================] - 0s 1ms/step - loss: 0.0776
Epoch 8/10

  1/116 [..............................] - ETA: 0s - loss: 0.2233
 42/116 [=========>....................] - ETA: 0s - loss: 0.0872
 83/116 [====================>.........] - ETA: 0s - loss: 0.0837
116/116 [==============================] - 0s 1ms/step - loss: 0.0753
Epoch 9/10

  1/116 [..............................] - ETA: 0s - loss: 0.0977
 42/116 [=========>....................] - ETA: 0s - loss: 0.0712
 83/116 [====================>.........] - ETA: 0s - loss: 0.0746
116/116 [==============================] - 0s 1ms/step - loss: 0.0718
Epoch 10/10

  1/116 [..............................] - ETA: 0s - loss: 0.0173
 42/116 [=========>....................] - ETA: 0s - loss: 0.0776
 83/116 [====================>.........] - ETA: 0s - loss: 0.0729
116/116 [==============================] - 0s 1ms/step - loss: 0.0722
-> test with GAN.predict
GAN tn, fp: 274, 14
GAN fn, tp: 1, 7
GAN f1 score: 0.483
GAN cohens kappa score: 0.462

-> test with 'LR'
LR tn, fp: 271, 17
LR fn, tp: 0, 8
LR f1 score: 0.485
LR cohens kappa score: 0.463
LR average precision score: 0.435

-> test with 'RF'
RF tn, fp: 284, 4
RF fn, tp: 3, 5
RF f1 score: 0.588
RF cohens kappa score: 0.576

-> test with 'GB'
GB tn, fp: 282, 6
GB fn, tp: 3, 5
GB f1 score: 0.526
GB cohens kappa score: 0.511

-> test with 'KNN'
KNN tn, fp: 275, 13
KNN fn, tp: 1, 7
KNN f1 score: 0.500
KNN cohens kappa score: 0.480

### Exercise is done.

-----[ LR ]-----
maximum:
LR tn, fp: 281, 18
LR fn, tp: 3, 9
LR f1 score: 0.720
LR cohens kappa score: 0.709
LR average precision score: 0.897


average:
LR tn, fp: 275.72, 12.28
LR fn, tp: 0.36, 8.44
LR f1 score: 0.577
LR cohens kappa score: 0.559
LR average precision score: 0.675


minimum:
LR tn, fp: 270, 7
LR fn, tp: 0, 6
LR f1 score: 0.457
LR cohens kappa score: 0.432
LR average precision score: 0.320


-----[ RF ]-----
maximum:
RF tn, fp: 288, 6
RF fn, tp: 6, 8
RF f1 score: 0.941
RF cohens kappa score: 0.939


average:
RF tn, fp: 286.52, 1.48
RF fn, tp: 3.12, 5.68
RF f1 score: 0.705
RF cohens kappa score: 0.698


minimum:
RF tn, fp: 282, 0
RF fn, tp: 1, 3
RF f1 score: 0.333
RF cohens kappa score: 0.312


-----[ GB ]-----
maximum:
GB tn, fp: 288, 6
GB fn, tp: 6, 9
GB f1 score: 0.889
GB cohens kappa score: 0.885


average:
GB tn, fp: 285.76, 2.24
GB fn, tp: 2.44, 6.36
GB f1 score: 0.731
GB cohens kappa score: 0.723


minimum:
GB tn, fp: 282, 0
GB fn, tp: 0, 3
GB f1 score: 0.333
GB cohens kappa score: 0.312


-----[ KNN ]-----
maximum:
KNN tn, fp: 285, 15
KNN fn, tp: 1, 9
KNN f1 score: 0.857
KNN cohens kappa score: 0.852


average:
KNN tn, fp: 278.12, 9.88
KNN fn, tp: 0.24, 8.56
KNN f1 score: 0.640
KNN cohens kappa score: 0.624


minimum:
KNN tn, fp: 273, 3
KNN fn, tp: 0, 7
KNN f1 score: 0.500
KNN cohens kappa score: 0.477


-----[ GAN ]-----
maximum:
GAN tn, fp: 284, 18
GAN fn, tp: 3, 9
GAN f1 score: 0.696
GAN cohens kappa score: 0.684


average:
GAN tn, fp: 277.0, 11.0
GAN fn, tp: 1.0, 7.8
GAN f1 score: 0.571
GAN cohens kappa score: 0.553


minimum:
GAN tn, fp: 270, 4
GAN fn, tp: 0, 6
GAN f1 score: 0.457
GAN cohens kappa score: 0.432