ConvGeNBenchmarkResults/data_result/convGAN-majority-full/folding_yeast5.log

///////////////////////////////////////////
// Running convGAN-majority-full 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: 19s - loss: 0.0022
 39/116 [=========>....................] - ETA: 0s - loss: 0.0377 
 72/116 [=================>............] - ETA: 0s - loss: 0.0378
105/116 [==========================>...] - ETA: 0s - loss: 0.0391
116/116 [==============================] - 0s 2ms/step - loss: 0.0422
Epoch 2/10

  1/116 [..............................] - ETA: 0s - loss: 0.0110
 39/116 [=========>....................] - ETA: 0s - loss: 0.0398
 71/116 [=================>............] - ETA: 0s - loss: 0.0411
103/116 [=========================>....] - ETA: 0s - loss: 0.0417
116/116 [==============================] - 0s 1ms/step - loss: 0.0417
Epoch 3/10

  1/116 [..............................] - ETA: 0s - loss: 0.0245
 31/116 [=======>......................] - ETA: 0s - loss: 0.0363
 70/116 [=================>............] - ETA: 0s - loss: 0.0428
109/116 [===========================>..] - ETA: 0s - loss: 0.0412
116/116 [==============================] - 0s 1ms/step - loss: 0.0413
Epoch 4/10

  1/116 [..............................] - ETA: 0s - loss: 0.0312
 38/116 [========>.....................] - ETA: 0s - loss: 0.0404
 76/116 [==================>...........] - ETA: 0s - loss: 0.0439
113/116 [============================>.] - ETA: 0s - loss: 0.0435
116/116 [==============================] - 0s 1ms/step - loss: 0.0429
Epoch 5/10

  1/116 [..............................] - ETA: 0s - loss: 0.0055
 38/116 [========>.....................] - ETA: 0s - loss: 0.0301
 73/116 [=================>............] - ETA: 0s - loss: 0.0382
111/116 [===========================>..] - ETA: 0s - loss: 0.0405
116/116 [==============================] - 0s 1ms/step - loss: 0.0404
Epoch 6/10

  1/116 [..............................] - ETA: 0s - loss: 0.0416
 39/116 [=========>....................] - ETA: 0s - loss: 0.0377
 79/116 [===================>..........] - ETA: 0s - loss: 0.0396
116/116 [==============================] - ETA: 0s - loss: 0.0398
116/116 [==============================] - 0s 1ms/step - loss: 0.0398
Epoch 7/10

  1/116 [..............................] - ETA: 0s - loss: 0.0223
 37/116 [========>.....................] - ETA: 0s - loss: 0.0346
 75/116 [==================>...........] - ETA: 0s - loss: 0.0341
114/116 [============================>.] - ETA: 0s - loss: 0.0408
116/116 [==============================] - 0s 1ms/step - loss: 0.0405
Epoch 8/10

  1/116 [..............................] - ETA: 0s - loss: 0.0026
 40/116 [=========>....................] - ETA: 0s - loss: 0.0231
 79/116 [===================>..........] - ETA: 0s - loss: 0.0347
113/116 [============================>.] - ETA: 0s - loss: 0.0378
116/116 [==============================] - 0s 1ms/step - loss: 0.0389
Epoch 9/10

  1/116 [..............................] - ETA: 0s - loss: 0.0050
 31/116 [=======>......................] - ETA: 0s - loss: 0.0284
 68/116 [================>.............] - ETA: 0s - loss: 0.0331
108/116 [==========================>...] - ETA: 0s - loss: 0.0397
116/116 [==============================] - 0s 1ms/step - loss: 0.0387
Epoch 10/10

  1/116 [..............................] - ETA: 0s - loss: 0.1205
 40/116 [=========>....................] - ETA: 0s - loss: 0.0450
 77/116 [==================>...........] - ETA: 0s - loss: 0.0381
114/116 [============================>.] - ETA: 0s - loss: 0.0387
116/116 [==============================] - 0s 1ms/step - loss: 0.0383
-> test with GAN.predict
GAN tn, fp: 285, 3
GAN fn, tp: 3, 6
GAN f1 score: 0.667
GAN cohens kappa score: 0.656

-> test with 'LR'
LR tn, fp: 282, 6
LR fn, tp: 0, 9
LR f1 score: 0.750
LR cohens kappa score: 0.740
LR average precision score: 0.895

-> 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: 4, 5
GB f1 score: 0.625
GB cohens kappa score: 0.615

-> 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: 0.0049
 37/116 [========>.....................] - ETA: 0s - loss: 0.0426 
 75/116 [==================>...........] - ETA: 0s - loss: 0.0503
112/116 [===========================>..] - ETA: 0s - loss: 0.0434
116/116 [==============================] - 0s 1ms/step - loss: 0.0425
Epoch 2/10

  1/116 [..............................] - ETA: 0s - loss: 0.1166
 39/116 [=========>....................] - ETA: 0s - loss: 0.0489
 76/116 [==================>...........] - ETA: 0s - loss: 0.0427
113/116 [============================>.] - ETA: 0s - loss: 0.0407
116/116 [==============================] - 0s 1ms/step - loss: 0.0416
Epoch 3/10

  1/116 [..............................] - ETA: 0s - loss: 0.0098
 39/116 [=========>....................] - ETA: 0s - loss: 0.0583
 77/116 [==================>...........] - ETA: 0s - loss: 0.0535
113/116 [============================>.] - ETA: 0s - loss: 0.0434
116/116 [==============================] - 0s 1ms/step - loss: 0.0427
Epoch 4/10

  1/116 [..............................] - ETA: 0s - loss: 0.0080
 40/116 [=========>....................] - ETA: 0s - loss: 0.0496
 78/116 [===================>..........] - ETA: 0s - loss: 0.0473
110/116 [===========================>..] - ETA: 0s - loss: 0.0460
116/116 [==============================] - 0s 1ms/step - loss: 0.0446
Epoch 5/10

  1/116 [..............................] - ETA: 0s - loss: 0.0121
 39/116 [=========>....................] - ETA: 0s - loss: 0.0555
 76/116 [==================>...........] - ETA: 0s - loss: 0.0493
113/116 [============================>.] - ETA: 0s - loss: 0.0429
116/116 [==============================] - 0s 1ms/step - loss: 0.0421
Epoch 6/10

  1/116 [..............................] - ETA: 0s - loss: 0.0055
 37/116 [========>.....................] - ETA: 0s - loss: 0.0335
 75/116 [==================>...........] - ETA: 0s - loss: 0.0328
111/116 [===========================>..] - ETA: 0s - loss: 0.0425
116/116 [==============================] - 0s 1ms/step - loss: 0.0415
Epoch 7/10

  1/116 [..............................] - ETA: 0s - loss: 0.0086
 40/116 [=========>....................] - ETA: 0s - loss: 0.0296
 79/116 [===================>..........] - ETA: 0s - loss: 0.0337
116/116 [==============================] - 0s 1ms/step - loss: 0.0404
Epoch 8/10

  1/116 [..............................] - ETA: 0s - loss: 0.0340
 38/116 [========>.....................] - ETA: 0s - loss: 0.0440
 77/116 [==================>...........] - ETA: 0s - loss: 0.0381
115/116 [============================>.] - ETA: 0s - loss: 0.0410
116/116 [==============================] - 0s 1ms/step - loss: 0.0410
Epoch 9/10

  1/116 [..............................] - ETA: 0s - loss: 0.0168
 40/116 [=========>....................] - ETA: 0s - loss: 0.0531
 73/116 [=================>............] - ETA: 0s - loss: 0.0518
111/116 [===========================>..] - ETA: 0s - loss: 0.0429
116/116 [==============================] - 0s 1ms/step - loss: 0.0419
Epoch 10/10

  1/116 [..............................] - ETA: 0s - loss: 0.1555
 37/116 [========>.....................] - ETA: 0s - loss: 0.0396
 72/116 [=================>............] - ETA: 0s - loss: 0.0462
105/116 [==========================>...] - ETA: 0s - loss: 0.0410
116/116 [==============================] - 0s 1ms/step - loss: 0.0405
-> test with GAN.predict
GAN tn, fp: 278, 10
GAN fn, tp: 0, 9
GAN f1 score: 0.643
GAN cohens kappa score: 0.628

-> 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: 0.696

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

-> test with 'GB'
GB tn, fp: 284, 4
GB fn, tp: 2, 7
GB f1 score: 0.700
GB cohens kappa score: 0.690

-> 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: 0.0015
 39/116 [=========>....................] - ETA: 0s - loss: 0.0499 
 78/116 [===================>..........] - ETA: 0s - loss: 0.0311
116/116 [==============================] - 0s 1ms/step - loss: 0.0344
Epoch 2/10

  1/116 [..............................] - ETA: 0s - loss: 0.0035
 41/116 [=========>....................] - ETA: 0s - loss: 0.0319
 81/116 [===================>..........] - ETA: 0s - loss: 0.0340
116/116 [==============================] - 0s 1ms/step - loss: 0.0366
Epoch 3/10

  1/116 [..............................] - ETA: 0s - loss: 0.0278
 40/116 [=========>....................] - ETA: 0s - loss: 0.0288
 79/116 [===================>..........] - ETA: 0s - loss: 0.0342
115/116 [============================>.] - ETA: 0s - loss: 0.0311
116/116 [==============================] - 0s 1ms/step - loss: 0.0311
Epoch 4/10

  1/116 [..............................] - ETA: 0s - loss: 0.0200
 42/116 [=========>....................] - ETA: 0s - loss: 0.0299
 82/116 [====================>.........] - ETA: 0s - loss: 0.0276
116/116 [==============================] - 0s 1ms/step - loss: 0.0324
Epoch 5/10

  1/116 [..............................] - ETA: 0s - loss: 0.0048
 40/116 [=========>....................] - ETA: 0s - loss: 0.0313
 78/116 [===================>..........] - ETA: 0s - loss: 0.0255
116/116 [==============================] - 0s 1ms/step - loss: 0.0322
Epoch 6/10

  1/116 [..............................] - ETA: 0s - loss: 0.1915
 41/116 [=========>....................] - ETA: 0s - loss: 0.0421
 78/116 [===================>..........] - ETA: 0s - loss: 0.0314
116/116 [==============================] - ETA: 0s - loss: 0.0305
116/116 [==============================] - 0s 1ms/step - loss: 0.0305
Epoch 7/10

  1/116 [..............................] - ETA: 0s - loss: 0.0057
 40/116 [=========>....................] - ETA: 0s - loss: 0.0271
 80/116 [===================>..........] - ETA: 0s - loss: 0.0283
114/116 [============================>.] - ETA: 0s - loss: 0.0324
116/116 [==============================] - 0s 1ms/step - loss: 0.0323
Epoch 8/10

  1/116 [..............................] - ETA: 0s - loss: 0.0181
 40/116 [=========>....................] - ETA: 0s - loss: 0.0167
 79/116 [===================>..........] - ETA: 0s - loss: 0.0266
116/116 [==============================] - 0s 1ms/step - loss: 0.0311
Epoch 9/10

  1/116 [..............................] - ETA: 0s - loss: 0.0375
 39/116 [=========>....................] - ETA: 0s - loss: 0.0200
 77/116 [==================>...........] - ETA: 0s - loss: 0.0343
116/116 [==============================] - 0s 1ms/step - loss: 0.0331
Epoch 10/10

  1/116 [..............................] - ETA: 0s - loss: 0.0461
 38/116 [========>.....................] - ETA: 0s - loss: 0.0414
 76/116 [==================>...........] - ETA: 0s - loss: 0.0308
110/116 [===========================>..] - ETA: 0s - loss: 0.0303
116/116 [==============================] - 0s 1ms/step - loss: 0.0291
-> 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: 0.621

-> 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: 285, 3
GB fn, tp: 3, 6
GB f1 score: 0.667
GB cohens kappa score: 0.656

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


------ 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: 18s - loss: 0.0255
 37/116 [========>.....................] - ETA: 0s - loss: 0.0352 
 71/116 [=================>............] - ETA: 0s - loss: 0.0338
104/116 [=========================>....] - ETA: 0s - loss: 0.0429
116/116 [==============================] - 0s 1ms/step - loss: 0.0440
Epoch 2/10

  1/116 [..............................] - ETA: 0s - loss: 0.0070
 33/116 [=======>......................] - ETA: 0s - loss: 0.0145
 36/116 [========>.....................] - ETA: 0s - loss: 0.0142
 71/116 [=================>............] - ETA: 0s - loss: 0.0240
103/116 [=========================>....] - ETA: 0s - loss: 0.0358
116/116 [==============================] - 0s 3ms/step - loss: 0.0406
Epoch 3/10

  1/116 [..............................] - ETA: 0s - loss: 0.0302
 40/116 [=========>....................] - ETA: 0s - loss: 0.0420
 79/116 [===================>..........] - ETA: 0s - loss: 0.0364
116/116 [==============================] - 0s 1ms/step - loss: 0.0428
Epoch 4/10

  1/116 [..............................] - ETA: 0s - loss: 0.1221
 41/116 [=========>....................] - ETA: 0s - loss: 0.0450
 79/116 [===================>..........] - ETA: 0s - loss: 0.0483
116/116 [==============================] - 0s 1ms/step - loss: 0.0400
Epoch 5/10

  1/116 [..............................] - ETA: 0s - loss: 0.0034
 40/116 [=========>....................] - ETA: 0s - loss: 0.0578
 76/116 [==================>...........] - ETA: 0s - loss: 0.0422
109/116 [===========================>..] - ETA: 0s - loss: 0.0431
116/116 [==============================] - 0s 1ms/step - loss: 0.0418
Epoch 6/10

  1/116 [..............................] - ETA: 0s - loss: 0.0769
 35/116 [========>.....................] - ETA: 0s - loss: 0.0381
 75/116 [==================>...........] - ETA: 0s - loss: 0.0447
112/116 [===========================>..] - ETA: 0s - loss: 0.0404
116/116 [==============================] - 0s 1ms/step - loss: 0.0413
Epoch 7/10

  1/116 [..............................] - ETA: 0s - loss: 0.0610
 40/116 [=========>....................] - ETA: 0s - loss: 0.0370
 78/116 [===================>..........] - ETA: 0s - loss: 0.0460
116/116 [==============================] - 0s 1ms/step - loss: 0.0396
Epoch 8/10

  1/116 [..............................] - ETA: 0s - loss: 0.0219
 39/116 [=========>....................] - ETA: 0s - loss: 0.0413
 76/116 [==================>...........] - ETA: 0s - loss: 0.0391
114/116 [============================>.] - ETA: 0s - loss: 0.0406
116/116 [==============================] - 0s 1ms/step - loss: 0.0403
Epoch 9/10

  1/116 [..............................] - ETA: 0s - loss: 0.1002
 39/116 [=========>....................] - ETA: 0s - loss: 0.0364
 75/116 [==================>...........] - ETA: 0s - loss: 0.0395
112/116 [===========================>..] - ETA: 0s - loss: 0.0393
116/116 [==============================] - 0s 1ms/step - loss: 0.0394
Epoch 10/10

  1/116 [..............................] - ETA: 0s - loss: 0.0116
 39/116 [=========>....................] - ETA: 0s - loss: 0.0386
 76/116 [==================>...........] - ETA: 0s - loss: 0.0483
114/116 [============================>.] - ETA: 0s - loss: 0.0391
116/116 [==============================] - 0s 1ms/step - loss: 0.0387
-> test with GAN.predict
GAN tn, fp: 285, 3
GAN fn, tp: 4, 5
GAN f1 score: 0.588
GAN cohens kappa score: 0.576

-> test with 'LR'
LR tn, fp: 281, 7
LR fn, tp: 2, 7
LR f1 score: 0.609
LR cohens kappa score: 0.594
LR average precision score: 0.738

-> 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: 286, 2
KNN fn, tp: 1, 8
KNN f1 score: 0.842
KNN cohens kappa score: 0.837


------ 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: 21s - loss: 0.0078
 39/116 [=========>....................] - ETA: 0s - loss: 0.0381 
 77/116 [==================>...........] - ETA: 0s - loss: 0.0387
114/116 [============================>.] - ETA: 0s - loss: 0.0476
116/116 [==============================] - 0s 1ms/step - loss: 0.0472
Epoch 2/10

  1/116 [..............................] - ETA: 0s - loss: 0.0054
 39/116 [=========>....................] - ETA: 0s - loss: 0.0377
 77/116 [==================>...........] - ETA: 0s - loss: 0.0482
116/116 [==============================] - ETA: 0s - loss: 0.0476
116/116 [==============================] - 0s 1ms/step - loss: 0.0476
Epoch 3/10

  1/116 [..............................] - ETA: 0s - loss: 0.0021
 39/116 [=========>....................] - ETA: 0s - loss: 0.0282
 71/116 [=================>............] - ETA: 0s - loss: 0.0454
109/116 [===========================>..] - ETA: 0s - loss: 0.0476
116/116 [==============================] - 0s 1ms/step - loss: 0.0470
Epoch 4/10

  1/116 [..............................] - ETA: 0s - loss: 0.0073
 40/116 [=========>....................] - ETA: 0s - loss: 0.0479
 78/116 [===================>..........] - ETA: 0s - loss: 0.0383
116/116 [==============================] - 0s 1ms/step - loss: 0.0455
Epoch 5/10

  1/116 [..............................] - ETA: 0s - loss: 0.0039
 38/116 [========>.....................] - ETA: 0s - loss: 0.0736
 80/116 [===================>..........] - ETA: 0s - loss: 0.0537
116/116 [==============================] - 0s 1ms/step - loss: 0.0470
Epoch 6/10

  1/116 [..............................] - ETA: 0s - loss: 0.0307
 40/116 [=========>....................] - ETA: 0s - loss: 0.0453
 80/116 [===================>..........] - ETA: 0s - loss: 0.0562
116/116 [==============================] - 0s 1ms/step - loss: 0.0477
Epoch 7/10

  1/116 [..............................] - ETA: 0s - loss: 0.0237
 38/116 [========>.....................] - ETA: 0s - loss: 0.0415
 76/116 [==================>...........] - ETA: 0s - loss: 0.0397
113/116 [============================>.] - ETA: 0s - loss: 0.0435
116/116 [==============================] - 0s 1ms/step - loss: 0.0442
Epoch 8/10

  1/116 [..............................] - ETA: 0s - loss: 0.0026
 38/116 [========>.....................] - ETA: 0s - loss: 0.0384
 75/116 [==================>...........] - ETA: 0s - loss: 0.0384
112/116 [===========================>..] - ETA: 0s - loss: 0.0448
116/116 [==============================] - 0s 1ms/step - loss: 0.0455
Epoch 9/10

  1/116 [..............................] - ETA: 0s - loss: 0.0026
 37/116 [========>.....................] - ETA: 0s - loss: 0.0444
 77/116 [==================>...........] - ETA: 0s - loss: 0.0422
115/116 [============================>.] - ETA: 0s - loss: 0.0458
116/116 [==============================] - 0s 1ms/step - loss: 0.0458
Epoch 10/10

  1/116 [..............................] - ETA: 0s - loss: 0.2057
 35/116 [========>.....................] - ETA: 0s - loss: 0.0616
 68/116 [================>.............] - ETA: 0s - loss: 0.0494
102/116 [=========================>....] - ETA: 0s - loss: 0.0493
116/116 [==============================] - 0s 2ms/step - loss: 0.0457
-> test with GAN.predict
GAN tn, fp: 279, 9
GAN fn, tp: 0, 8
GAN f1 score: 0.640
GAN cohens kappa score: 0.626

-> test with 'LR'
LR tn, fp: 273, 15
LR fn, tp: 0, 8
LR f1 score: 0.516
LR cohens kappa score: 0.496
LR average precision score: 0.703

-> test with 'RF'
RF tn, fp: 285, 3
RF fn, tp: 1, 7
RF f1 score: 0.778
RF cohens kappa score: 0.771

-> test with 'GB'
GB tn, fp: 285, 3
GB fn, tp: 1, 7
GB f1 score: 0.778
GB cohens kappa score: 0.771

-> test with 'KNN'
KNN tn, fp: 276, 12
KNN fn, tp: 0, 8
KNN f1 score: 0.571
KNN cohens kappa score: 0.554


====== 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: 23s - loss: 0.2017
 38/116 [========>.....................] - ETA: 0s - loss: 0.0630 
 75/116 [==================>...........] - ETA: 0s - loss: 0.0551
113/116 [============================>.] - ETA: 0s - loss: 0.0536
116/116 [==============================] - 0s 1ms/step - loss: 0.0530
Epoch 2/10

  1/116 [..............................] - ETA: 0s - loss: 0.0074
 39/116 [=========>....................] - ETA: 0s - loss: 0.0445
 76/116 [==================>...........] - ETA: 0s - loss: 0.0409
113/116 [============================>.] - ETA: 0s - loss: 0.0524
116/116 [==============================] - 0s 1ms/step - loss: 0.0517
Epoch 3/10

  1/116 [..............................] - ETA: 0s - loss: 0.0143
 39/116 [=========>....................] - ETA: 0s - loss: 0.0562
 78/116 [===================>..........] - ETA: 0s - loss: 0.0506
115/116 [============================>.] - ETA: 0s - loss: 0.0498
116/116 [==============================] - 0s 1ms/step - loss: 0.0513
Epoch 4/10

  1/116 [..............................] - ETA: 0s - loss: 0.0065
 38/116 [========>.....................] - ETA: 0s - loss: 0.0597
 76/116 [==================>...........] - ETA: 0s - loss: 0.0527
113/116 [============================>.] - ETA: 0s - loss: 0.0512
116/116 [==============================] - 0s 1ms/step - loss: 0.0509
Epoch 5/10

  1/116 [..............................] - ETA: 0s - loss: 0.0126
 36/116 [========>.....................] - ETA: 0s - loss: 0.0455
 72/116 [=================>............] - ETA: 0s - loss: 0.0529
108/116 [==========================>...] - ETA: 0s - loss: 0.0543
116/116 [==============================] - 0s 1ms/step - loss: 0.0519
Epoch 6/10

  1/116 [..............................] - ETA: 0s - loss: 0.0248
 37/116 [========>.....................] - ETA: 0s - loss: 0.0616
 74/116 [==================>...........] - ETA: 0s - loss: 0.0588
111/116 [===========================>..] - ETA: 0s - loss: 0.0533
116/116 [==============================] - 0s 1ms/step - loss: 0.0519
Epoch 7/10

  1/116 [..............................] - ETA: 0s - loss: 0.0112
 39/116 [=========>....................] - ETA: 0s - loss: 0.0503
 78/116 [===================>..........] - ETA: 0s - loss: 0.0438
116/116 [==============================] - 0s 1ms/step - loss: 0.0513
Epoch 8/10

  1/116 [..............................] - ETA: 0s - loss: 0.0210
 38/116 [========>.....................] - ETA: 0s - loss: 0.0660
 75/116 [==================>...........] - ETA: 0s - loss: 0.0583
113/116 [============================>.] - ETA: 0s - loss: 0.0502
116/116 [==============================] - 0s 1ms/step - loss: 0.0500
Epoch 9/10

  1/116 [..............................] - ETA: 0s - loss: 0.0198
 41/116 [=========>....................] - ETA: 0s - loss: 0.0466
 79/116 [===================>..........] - ETA: 0s - loss: 0.0499
116/116 [==============================] - 0s 1ms/step - loss: 0.0507
Epoch 10/10

  1/116 [..............................] - ETA: 0s - loss: 0.0071
 37/116 [========>.....................] - ETA: 0s - loss: 0.0611
 76/116 [==================>...........] - ETA: 0s - loss: 0.0476
114/116 [============================>.] - ETA: 0s - loss: 0.0491
116/116 [==============================] - 0s 1ms/step - loss: 0.0507
-> test with GAN.predict
GAN tn, fp: 279, 9
GAN fn, tp: 1, 8
GAN f1 score: 0.615
GAN cohens kappa score: 0.600

-> test with 'LR'
LR tn, fp: 276, 12
LR fn, tp: 0, 9
LR f1 score: 0.600
LR cohens kappa score: 0.582
LR average precision score: 0.703

-> 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: 278, 10
KNN fn, tp: 0, 9
KNN f1 score: 0.643
KNN cohens kappa score: 0.628


------ 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: 24s - loss: 0.0062
 40/116 [=========>....................] - ETA: 0s - loss: 0.0289 
 79/116 [===================>..........] - ETA: 0s - loss: 0.0342
116/116 [==============================] - 0s 1ms/step - loss: 0.0363
Epoch 2/10

  1/116 [..............................] - ETA: 0s - loss: 0.0218
 41/116 [=========>....................] - ETA: 0s - loss: 0.0302
 78/116 [===================>..........] - ETA: 0s - loss: 0.0279
116/116 [==============================] - ETA: 0s - loss: 0.0367
116/116 [==============================] - 0s 1ms/step - loss: 0.0367
Epoch 3/10

  1/116 [..............................] - ETA: 0s - loss: 0.0075
 38/116 [========>.....................] - ETA: 0s - loss: 0.0406
 77/116 [==================>...........] - ETA: 0s - loss: 0.0364
111/116 [===========================>..] - ETA: 0s - loss: 0.0370
116/116 [==============================] - 0s 1ms/step - loss: 0.0361
Epoch 4/10

  1/116 [..............................] - ETA: 0s - loss: 0.0121
 39/116 [=========>....................] - ETA: 0s - loss: 0.0411
 75/116 [==================>...........] - ETA: 0s - loss: 0.0345
112/116 [===========================>..] - ETA: 0s - loss: 0.0334
116/116 [==============================] - 0s 1ms/step - loss: 0.0352
Epoch 5/10

  1/116 [..............................] - ETA: 0s - loss: 0.0021
 38/116 [========>.....................] - ETA: 0s - loss: 0.0347
 75/116 [==================>...........] - ETA: 0s - loss: 0.0415
114/116 [============================>.] - ETA: 0s - loss: 0.0358
116/116 [==============================] - 0s 1ms/step - loss: 0.0357
Epoch 6/10

  1/116 [..............................] - ETA: 0s - loss: 0.0119
 38/116 [========>.....................] - ETA: 0s - loss: 0.0164
 76/116 [==================>...........] - ETA: 0s - loss: 0.0253
112/116 [===========================>..] - ETA: 0s - loss: 0.0352
116/116 [==============================] - 0s 1ms/step - loss: 0.0349
Epoch 7/10

  1/116 [..............................] - ETA: 0s - loss: 0.0046
 39/116 [=========>....................] - ETA: 0s - loss: 0.0399
 70/116 [=================>............] - ETA: 0s - loss: 0.0412
101/116 [=========================>....] - ETA: 0s - loss: 0.0359
116/116 [==============================] - 0s 2ms/step - loss: 0.0346
Epoch 8/10

  1/116 [..............................] - ETA: 0s - loss: 0.0128
 36/116 [========>.....................] - ETA: 0s - loss: 0.0353
 73/116 [=================>............] - ETA: 0s - loss: 0.0317
110/116 [===========================>..] - ETA: 0s - loss: 0.0357
116/116 [==============================] - 0s 1ms/step - loss: 0.0346
Epoch 9/10

  1/116 [..............................] - ETA: 0s - loss: 0.0029
 39/116 [=========>....................] - ETA: 0s - loss: 0.0375
 78/116 [===================>..........] - ETA: 0s - loss: 0.0384
115/116 [============================>.] - ETA: 0s - loss: 0.0351
116/116 [==============================] - 0s 1ms/step - loss: 0.0350
Epoch 10/10

  1/116 [..............................] - ETA: 0s - loss: 0.0555
 36/116 [========>.....................] - ETA: 0s - loss: 0.0293
 75/116 [==================>...........] - ETA: 0s - loss: 0.0364
111/116 [===========================>..] - ETA: 0s - loss: 0.0356
116/116 [==============================] - 0s 1ms/step - loss: 0.0346
-> test with GAN.predict
GAN tn, fp: 277, 11
GAN fn, tp: 3, 6
GAN f1 score: 0.462
GAN cohens kappa score: 0.439

-> test with 'LR'
LR tn, fp: 272, 16
LR fn, tp: 1, 8
LR f1 score: 0.485
LR cohens kappa score: 0.461
LR average precision score: 0.414

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

-> test with 'GB'
GB tn, fp: 280, 8
GB fn, tp: 4, 5
GB f1 score: 0.455
GB cohens kappa score: 0.434

-> 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 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: 19s - loss: 0.0031
 40/116 [=========>....................] - ETA: 0s - loss: 0.0551 
 73/116 [=================>............] - ETA: 0s - loss: 0.0545
111/116 [===========================>..] - ETA: 0s - loss: 0.0509
116/116 [==============================] - 0s 1ms/step - loss: 0.0505
Epoch 2/10

  1/116 [..............................] - ETA: 0s - loss: 0.0148
 39/116 [=========>....................] - ETA: 0s - loss: 0.0566
 75/116 [==================>...........] - ETA: 0s - loss: 0.0541
112/116 [===========================>..] - ETA: 0s - loss: 0.0504
116/116 [==============================] - 0s 1ms/step - loss: 0.0494
Epoch 3/10

  1/116 [..............................] - ETA: 0s - loss: 0.1451
 38/116 [========>.....................] - ETA: 0s - loss: 0.0487
 75/116 [==================>...........] - ETA: 0s - loss: 0.0490
113/116 [============================>.] - ETA: 0s - loss: 0.0505
116/116 [==============================] - 0s 1ms/step - loss: 0.0503
Epoch 4/10

  1/116 [..............................] - ETA: 0s - loss: 0.0817
 40/116 [=========>....................] - ETA: 0s - loss: 0.0585
 74/116 [==================>...........] - ETA: 0s - loss: 0.0538
108/116 [==========================>...] - ETA: 0s - loss: 0.0527
116/116 [==============================] - 0s 1ms/step - loss: 0.0512
Epoch 5/10

  1/116 [..............................] - ETA: 0s - loss: 0.1202
 36/116 [========>.....................] - ETA: 0s - loss: 0.0433
 74/116 [==================>...........] - ETA: 0s - loss: 0.0407
110/116 [===========================>..] - ETA: 0s - loss: 0.0492
116/116 [==============================] - 0s 1ms/step - loss: 0.0485
Epoch 6/10

  1/116 [..............................] - ETA: 0s - loss: 0.0125
 37/116 [========>.....................] - ETA: 0s - loss: 0.0562
 73/116 [=================>............] - ETA: 0s - loss: 0.0528
111/116 [===========================>..] - ETA: 0s - loss: 0.0506
116/116 [==============================] - 0s 1ms/step - loss: 0.0498
Epoch 7/10

  1/116 [..............................] - ETA: 0s - loss: 0.0454
 40/116 [=========>....................] - ETA: 0s - loss: 0.0562
 77/116 [==================>...........] - ETA: 0s - loss: 0.0496
112/116 [===========================>..] - ETA: 0s - loss: 0.0486
116/116 [==============================] - 0s 1ms/step - loss: 0.0484
Epoch 8/10

  1/116 [..............................] - ETA: 0s - loss: 0.0069
 37/116 [========>.....................] - ETA: 0s - loss: 0.0528
 75/116 [==================>...........] - ETA: 0s - loss: 0.0509
112/116 [===========================>..] - ETA: 0s - loss: 0.0482
116/116 [==============================] - 0s 1ms/step - loss: 0.0478
Epoch 9/10

  1/116 [..............................] - ETA: 0s - loss: 0.0379
 38/116 [========>.....................] - ETA: 0s - loss: 0.0555
 77/116 [==================>...........] - ETA: 0s - loss: 0.0468
115/116 [============================>.] - ETA: 0s - loss: 0.0474
116/116 [==============================] - 0s 1ms/step - loss: 0.0474
Epoch 10/10

  1/116 [..............................] - ETA: 0s - loss: 0.0570
 39/116 [=========>....................] - ETA: 0s - loss: 0.0501
 76/116 [==================>...........] - ETA: 0s - loss: 0.0476
108/116 [==========================>...] - ETA: 0s - loss: 0.0478
116/116 [==============================] - 0s 1ms/step - loss: 0.0474
-> test with GAN.predict
GAN tn, fp: 283, 5
GAN fn, tp: 1, 8
GAN f1 score: 0.727
GAN cohens kappa score: 0.717

-> test with 'LR'
LR tn, fp: 282, 6
LR fn, tp: 0, 9
LR f1 score: 0.750
LR cohens kappa score: 0.740
LR average precision score: 0.755

-> 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: 286, 2
GB fn, tp: 1, 8
GB f1 score: 0.842
GB cohens kappa score: 0.837

-> 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 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: 19s - loss: 0.0058
 40/116 [=========>....................] - ETA: 0s - loss: 0.0595 
 78/116 [===================>..........] - ETA: 0s - loss: 0.0489
116/116 [==============================] - ETA: 0s - loss: 0.0457
116/116 [==============================] - 0s 1ms/step - loss: 0.0457
Epoch 2/10

  1/116 [..............................] - ETA: 0s - loss: 0.0024
 40/116 [=========>....................] - ETA: 0s - loss: 0.0469
 79/116 [===================>..........] - ETA: 0s - loss: 0.0425
116/116 [==============================] - 0s 1ms/step - loss: 0.0427
Epoch 3/10

  1/116 [..............................] - ETA: 0s - loss: 0.0579
 40/116 [=========>....................] - ETA: 0s - loss: 0.0394
 76/116 [==================>...........] - ETA: 0s - loss: 0.0476
113/116 [============================>.] - ETA: 0s - loss: 0.0434
116/116 [==============================] - 0s 1ms/step - loss: 0.0443
Epoch 4/10

  1/116 [..............................] - ETA: 0s - loss: 0.1737
 37/116 [========>.....................] - ETA: 0s - loss: 0.0545
 73/116 [=================>............] - ETA: 0s - loss: 0.0494
112/116 [===========================>..] - ETA: 0s - loss: 0.0430
116/116 [==============================] - 0s 1ms/step - loss: 0.0438
Epoch 5/10

  1/116 [..............................] - ETA: 0s - loss: 0.3883
 31/116 [=======>......................] - ETA: 0s - loss: 0.0540
 64/116 [===============>..............] - ETA: 0s - loss: 0.0389
 97/116 [========================>.....] - ETA: 0s - loss: 0.0440
116/116 [==============================] - 0s 2ms/step - loss: 0.0432
Epoch 6/10

  1/116 [..............................] - ETA: 0s - loss: 0.0049
 38/116 [========>.....................] - ETA: 0s - loss: 0.0512
 75/116 [==================>...........] - ETA: 0s - loss: 0.0451
113/116 [============================>.] - ETA: 0s - loss: 0.0410
116/116 [==============================] - 0s 1ms/step - loss: 0.0405
Epoch 7/10

  1/116 [..............................] - ETA: 0s - loss: 0.0267
 38/116 [========>.....................] - ETA: 0s - loss: 0.0314
 73/116 [=================>............] - ETA: 0s - loss: 0.0371
110/116 [===========================>..] - ETA: 0s - loss: 0.0421
116/116 [==============================] - 0s 1ms/step - loss: 0.0430
Epoch 8/10

  1/116 [..............................] - ETA: 0s - loss: 0.0131
 40/116 [=========>....................] - ETA: 0s - loss: 0.0438
 80/116 [===================>..........] - ETA: 0s - loss: 0.0433
116/116 [==============================] - 0s 1ms/step - loss: 0.0401
Epoch 9/10

  1/116 [..............................] - ETA: 0s - loss: 0.0044
 40/116 [=========>....................] - ETA: 0s - loss: 0.0449
 79/116 [===================>..........] - ETA: 0s - loss: 0.0371
116/116 [==============================] - 0s 1ms/step - loss: 0.0400
Epoch 10/10

  1/116 [..............................] - ETA: 0s - loss: 0.0159
 39/116 [=========>....................] - ETA: 0s - loss: 0.0440
 73/116 [=================>............] - ETA: 0s - loss: 0.0425
111/116 [===========================>..] - ETA: 0s - loss: 0.0417
116/116 [==============================] - 0s 1ms/step - loss: 0.0411
-> 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: 279, 9
LR fn, tp: 0, 9
LR f1 score: 0.667
LR cohens kappa score: 0.653
LR average precision score: 0.897

-> 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: 276, 12
KNN fn, tp: 0, 9
KNN f1 score: 0.600
KNN cohens kappa score: 0.582


------ 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: 19s - loss: 0.0509
 39/116 [=========>....................] - ETA: 0s - loss: 0.0369 
 79/116 [===================>..........] - ETA: 0s - loss: 0.0362
115/116 [============================>.] - ETA: 0s - loss: 0.0403
116/116 [==============================] - 0s 1ms/step - loss: 0.0403
Epoch 2/10

  1/116 [..............................] - ETA: 0s - loss: 0.0105
 41/116 [=========>....................] - ETA: 0s - loss: 0.0542
 81/116 [===================>..........] - ETA: 0s - loss: 0.0409
116/116 [==============================] - 0s 1ms/step - loss: 0.0386
Epoch 3/10

  1/116 [..............................] - ETA: 0s - loss: 0.0121
 38/116 [========>.....................] - ETA: 0s - loss: 0.0516
 78/116 [===================>..........] - ETA: 0s - loss: 0.0464
113/116 [============================>.] - ETA: 0s - loss: 0.0394
116/116 [==============================] - 0s 1ms/step - loss: 0.0395
Epoch 4/10

  1/116 [..............................] - ETA: 0s - loss: 0.0052
 35/116 [========>.....................] - ETA: 0s - loss: 0.0263
 72/116 [=================>............] - ETA: 0s - loss: 0.0345
109/116 [===========================>..] - ETA: 0s - loss: 0.0406
116/116 [==============================] - 0s 1ms/step - loss: 0.0397
Epoch 5/10

  1/116 [..............................] - ETA: 0s - loss: 0.0041
 38/116 [========>.....................] - ETA: 0s - loss: 0.0369
 72/116 [=================>............] - ETA: 0s - loss: 0.0349
107/116 [==========================>...] - ETA: 0s - loss: 0.0390
116/116 [==============================] - 0s 1ms/step - loss: 0.0388
Epoch 6/10

  1/116 [..............................] - ETA: 0s - loss: 0.0033
 38/116 [========>.....................] - ETA: 0s - loss: 0.0395
 74/116 [==================>...........] - ETA: 0s - loss: 0.0343
113/116 [============================>.] - ETA: 0s - loss: 0.0385
116/116 [==============================] - 0s 1ms/step - loss: 0.0383
Epoch 7/10

  1/116 [..............................] - ETA: 0s - loss: 0.0542
 39/116 [=========>....................] - ETA: 0s - loss: 0.0444
 77/116 [==================>...........] - ETA: 0s - loss: 0.0365
114/116 [============================>.] - ETA: 0s - loss: 0.0383
116/116 [==============================] - 0s 1ms/step - loss: 0.0380
Epoch 8/10

  1/116 [..............................] - ETA: 0s - loss: 0.0508
 36/116 [========>.....................] - ETA: 0s - loss: 0.0329
 72/116 [=================>............] - ETA: 0s - loss: 0.0412
107/116 [==========================>...] - ETA: 0s - loss: 0.0412
116/116 [==============================] - 0s 1ms/step - loss: 0.0392
Epoch 9/10

  1/116 [..............................] - ETA: 0s - loss: 0.0114
 36/116 [========>.....................] - ETA: 0s - loss: 0.0418
 74/116 [==================>...........] - ETA: 0s - loss: 0.0307
111/116 [===========================>..] - ETA: 0s - loss: 0.0370
116/116 [==============================] - 0s 1ms/step - loss: 0.0382
Epoch 10/10

  1/116 [..............................] - ETA: 0s - loss: 0.2288
 39/116 [=========>....................] - ETA: 0s - loss: 0.0484
 74/116 [==================>...........] - ETA: 0s - loss: 0.0410
110/116 [===========================>..] - ETA: 0s - loss: 0.0388
116/116 [==============================] - 0s 1ms/step - loss: 0.0374
-> test with GAN.predict
GAN tn, fp: 282, 6
GAN fn, tp: 1, 7
GAN f1 score: 0.667
GAN cohens kappa score: 0.655

-> test with 'LR'
LR tn, fp: 279, 9
LR fn, tp: 0, 8
LR f1 score: 0.640
LR cohens kappa score: 0.626
LR average precision score: 0.664

-> test with 'RF'
RF tn, fp: 287, 1
RF fn, tp: 4, 4
RF f1 score: 0.615
RF cohens kappa score: 0.607

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

-> test with 'KNN'
KNN tn, fp: 282, 6
KNN fn, tp: 0, 8
KNN f1 score: 0.727
KNN cohens kappa score: 0.718


====== 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: 22s - loss: 0.0151
 41/116 [=========>....................] - ETA: 0s - loss: 0.0494 
 80/116 [===================>..........] - ETA: 0s - loss: 0.0434
116/116 [==============================] - 0s 1ms/step - loss: 0.0407
Epoch 2/10

  1/116 [..............................] - ETA: 0s - loss: 0.0210
 41/116 [=========>....................] - ETA: 0s - loss: 0.0285
 80/116 [===================>..........] - ETA: 0s - loss: 0.0377
116/116 [==============================] - 0s 1ms/step - loss: 0.0421
Epoch 3/10

  1/116 [..............................] - ETA: 0s - loss: 0.0130
 40/116 [=========>....................] - ETA: 0s - loss: 0.0391
 79/116 [===================>..........] - ETA: 0s - loss: 0.0385
116/116 [==============================] - 0s 1ms/step - loss: 0.0398
Epoch 4/10

  1/116 [..............................] - ETA: 0s - loss: 0.0060
 39/116 [=========>....................] - ETA: 0s - loss: 0.0580
 79/116 [===================>..........] - ETA: 0s - loss: 0.0480
116/116 [==============================] - 0s 1ms/step - loss: 0.0413
Epoch 5/10

  1/116 [..............................] - ETA: 0s - loss: 0.0242
 40/116 [=========>....................] - ETA: 0s - loss: 0.0221
 79/116 [===================>..........] - ETA: 0s - loss: 0.0372
116/116 [==============================] - 0s 1ms/step - loss: 0.0411
Epoch 6/10

  1/116 [..............................] - ETA: 0s - loss: 0.0110
 40/116 [=========>....................] - ETA: 0s - loss: 0.0365
 80/116 [===================>..........] - ETA: 0s - loss: 0.0403
116/116 [==============================] - 0s 1ms/step - loss: 0.0406
Epoch 7/10

  1/116 [..............................] - ETA: 0s - loss: 0.0067
 41/116 [=========>....................] - ETA: 0s - loss: 0.0468
 79/116 [===================>..........] - ETA: 0s - loss: 0.0388
116/116 [==============================] - 0s 1ms/step - loss: 0.0399
Epoch 8/10

  1/116 [..............................] - ETA: 0s - loss: 0.0066
 41/116 [=========>....................] - ETA: 0s - loss: 0.0330
 80/116 [===================>..........] - ETA: 0s - loss: 0.0316
116/116 [==============================] - 0s 1ms/step - loss: 0.0389
Epoch 9/10

  1/116 [..............................] - ETA: 0s - loss: 0.0223
 40/116 [=========>....................] - ETA: 0s - loss: 0.0523
 76/116 [==================>...........] - ETA: 0s - loss: 0.0423
116/116 [==============================] - 0s 1ms/step - loss: 0.0389
Epoch 10/10

  1/116 [..............................] - ETA: 0s - loss: 0.0049
 42/116 [=========>....................] - ETA: 0s - loss: 0.0321
 82/116 [====================>.........] - ETA: 0s - loss: 0.0449
116/116 [==============================] - 0s 1ms/step - loss: 0.0411
-> test with GAN.predict
GAN tn, fp: 279, 9
GAN fn, tp: 1, 8
GAN f1 score: 0.615
GAN cohens kappa score: 0.600

-> 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: 0.657

-> 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: 286, 2
GB fn, tp: 2, 7
GB f1 score: 0.778
GB cohens kappa score: 0.771

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


------ 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: 18s - loss: 0.0024
 41/116 [=========>....................] - ETA: 0s - loss: 0.0309 
 82/116 [====================>.........] - ETA: 0s - loss: 0.0309
116/116 [==============================] - 0s 1ms/step - loss: 0.0347
Epoch 2/10

  1/116 [..............................] - ETA: 0s - loss: 0.0067
 40/116 [=========>....................] - ETA: 0s - loss: 0.0402
 79/116 [===================>..........] - ETA: 0s - loss: 0.0393
116/116 [==============================] - 0s 1ms/step - loss: 0.0346
Epoch 3/10

  1/116 [..............................] - ETA: 0s - loss: 0.0050
 41/116 [=========>....................] - ETA: 0s - loss: 0.0377
 82/116 [====================>.........] - ETA: 0s - loss: 0.0397
116/116 [==============================] - 0s 1ms/step - loss: 0.0346
Epoch 4/10

  1/116 [..............................] - ETA: 0s - loss: 0.0023
 38/116 [========>.....................] - ETA: 0s - loss: 0.0281
 76/116 [==================>...........] - ETA: 0s - loss: 0.0379
116/116 [==============================] - ETA: 0s - loss: 0.0364
116/116 [==============================] - 0s 1ms/step - loss: 0.0364
Epoch 5/10

  1/116 [..............................] - ETA: 0s - loss: 0.0030
 39/116 [=========>....................] - ETA: 0s - loss: 0.0222
 79/116 [===================>..........] - ETA: 0s - loss: 0.0242
115/116 [============================>.] - ETA: 0s - loss: 0.0326
116/116 [==============================] - 0s 1ms/step - loss: 0.0326
Epoch 6/10

  1/116 [..............................] - ETA: 0s - loss: 0.0183
 42/116 [=========>....................] - ETA: 0s - loss: 0.0387
 82/116 [====================>.........] - ETA: 0s - loss: 0.0315
116/116 [==============================] - 0s 1ms/step - loss: 0.0339
Epoch 7/10

  1/116 [..............................] - ETA: 0s - loss: 0.2287
 40/116 [=========>....................] - ETA: 0s - loss: 0.0240
 78/116 [===================>..........] - ETA: 0s - loss: 0.0328
116/116 [==============================] - 0s 1ms/step - loss: 0.0308
Epoch 8/10

  1/116 [..............................] - ETA: 0s - loss: 0.2919
 35/116 [========>.....................] - ETA: 0s - loss: 0.0447
 71/116 [=================>............] - ETA: 0s - loss: 0.0370
111/116 [===========================>..] - ETA: 0s - loss: 0.0324
116/116 [==============================] - 0s 1ms/step - loss: 0.0318
Epoch 9/10

  1/116 [..............................] - ETA: 0s - loss: 0.0063
 40/116 [=========>....................] - ETA: 0s - loss: 0.0222
 80/116 [===================>..........] - ETA: 0s - loss: 0.0315
116/116 [==============================] - 0s 1ms/step - loss: 0.0316
Epoch 10/10

  1/116 [..............................] - ETA: 0s - loss: 0.0061
 40/116 [=========>....................] - ETA: 0s - loss: 0.0335
 79/116 [===================>..........] - ETA: 0s - loss: 0.0317
116/116 [==============================] - 0s 1ms/step - loss: 0.0307
-> test with GAN.predict
GAN tn, fp: 281, 7
GAN fn, tp: 2, 7
GAN f1 score: 0.609
GAN cohens kappa score: 0.594

-> test with 'LR'
LR tn, fp: 278, 10
LR fn, tp: 0, 9
LR f1 score: 0.643
LR cohens kappa score: 0.628
LR average precision score: 0.684

-> test with 'RF'
RF tn, fp: 286, 2
RF fn, tp: 2, 7
RF f1 score: 0.778
RF cohens kappa score: 0.771

-> test with 'GB'
GB tn, fp: 284, 4
GB fn, tp: 2, 7
GB f1 score: 0.700
GB cohens kappa score: 0.690

-> test with 'KNN'
KNN tn, fp: 278, 10
KNN fn, tp: 1, 8
KNN f1 score: 0.593
KNN cohens kappa score: 0.575


------ 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: 20s - loss: 0.0055
 34/116 [=======>......................] - ETA: 0s - loss: 0.0555 
 70/116 [=================>............] - ETA: 0s - loss: 0.0444
107/116 [==========================>...] - ETA: 0s - loss: 0.0519
116/116 [==============================] - 0s 1ms/step - loss: 0.0522
Epoch 2/10

  1/116 [..............................] - ETA: 0s - loss: 0.2861
 39/116 [=========>....................] - ETA: 0s - loss: 0.0396
 75/116 [==================>...........] - ETA: 0s - loss: 0.0445
103/116 [=========================>....] - ETA: 0s - loss: 0.0488
116/116 [==============================] - 0s 2ms/step - loss: 0.0502
Epoch 3/10

  1/116 [..............................] - ETA: 0s - loss: 0.0219
 32/116 [=======>......................] - ETA: 0s - loss: 0.0235
 68/116 [================>.............] - ETA: 0s - loss: 0.0500
103/116 [=========================>....] - ETA: 0s - loss: 0.0468
116/116 [==============================] - 0s 1ms/step - loss: 0.0489
Epoch 4/10

  1/116 [..............................] - ETA: 0s - loss: 0.2242
 32/116 [=======>......................] - ETA: 0s - loss: 0.0550
 69/116 [================>.............] - ETA: 0s - loss: 0.0542
102/116 [=========================>....] - ETA: 0s - loss: 0.0489
116/116 [==============================] - 0s 1ms/step - loss: 0.0489
Epoch 5/10

  1/116 [..............................] - ETA: 0s - loss: 0.1535
 35/116 [========>.....................] - ETA: 0s - loss: 0.0583
 68/116 [================>.............] - ETA: 0s - loss: 0.0531
103/116 [=========================>....] - ETA: 0s - loss: 0.0520
116/116 [==============================] - 0s 2ms/step - loss: 0.0499
Epoch 6/10

  1/116 [..............................] - ETA: 0s - loss: 0.0070
 39/116 [=========>....................] - ETA: 0s - loss: 0.0414
 77/116 [==================>...........] - ETA: 0s - loss: 0.0454
116/116 [==============================] - ETA: 0s - loss: 0.0474
116/116 [==============================] - 0s 1ms/step - loss: 0.0474
Epoch 7/10

  1/116 [..............................] - ETA: 0s - loss: 0.0036
 38/116 [========>.....................] - ETA: 0s - loss: 0.0595
 73/116 [=================>............] - ETA: 0s - loss: 0.0518
105/116 [==========================>...] - ETA: 0s - loss: 0.0495
116/116 [==============================] - 0s 1ms/step - loss: 0.0471
Epoch 8/10

  1/116 [..............................] - ETA: 0s - loss: 0.0622
 38/116 [========>.....................] - ETA: 0s - loss: 0.0439
 75/116 [==================>...........] - ETA: 0s - loss: 0.0555
110/116 [===========================>..] - ETA: 0s - loss: 0.0492
116/116 [==============================] - 0s 1ms/step - loss: 0.0483
Epoch 9/10

  1/116 [..............................] - ETA: 0s - loss: 0.0687
 36/116 [========>.....................] - ETA: 0s - loss: 0.0399
 71/116 [=================>............] - ETA: 0s - loss: 0.0502
110/116 [===========================>..] - ETA: 0s - loss: 0.0477
116/116 [==============================] - 0s 1ms/step - loss: 0.0466
Epoch 10/10

  1/116 [..............................] - ETA: 0s - loss: 0.0041
 36/116 [========>.....................] - ETA: 0s - loss: 0.0482
 70/116 [=================>............] - ETA: 0s - loss: 0.0525
107/116 [==========================>...] - ETA: 0s - loss: 0.0489
116/116 [==============================] - 0s 1ms/step - loss: 0.0471
-> test with GAN.predict
GAN tn, fp: 286, 2
GAN fn, tp: 3, 6
GAN f1 score: 0.706
GAN cohens kappa score: 0.697

-> test with 'LR'
LR tn, fp: 281, 7
LR fn, tp: 1, 8
LR f1 score: 0.667
LR cohens kappa score: 0.654
LR average precision score: 0.807

-> 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: 2, 7
GB f1 score: 0.824
GB cohens kappa score: 0.818

-> 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 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: 0.0368
 39/116 [=========>....................] - ETA: 0s - loss: 0.0361 
 79/116 [===================>..........] - ETA: 0s - loss: 0.0330
116/116 [==============================] - 0s 1ms/step - loss: 0.0355
Epoch 2/10

  1/116 [..............................] - ETA: 0s - loss: 0.0157
 41/116 [=========>....................] - ETA: 0s - loss: 0.0415
 79/116 [===================>..........] - ETA: 0s - loss: 0.0348
116/116 [==============================] - ETA: 0s - loss: 0.0348
116/116 [==============================] - 0s 1ms/step - loss: 0.0348
Epoch 3/10

  1/116 [..............................] - ETA: 0s - loss: 0.0029
 35/116 [========>.....................] - ETA: 0s - loss: 0.0429
 69/116 [================>.............] - ETA: 0s - loss: 0.0394
103/116 [=========================>....] - ETA: 0s - loss: 0.0398
116/116 [==============================] - 0s 1ms/step - loss: 0.0399
Epoch 4/10

  1/116 [..............................] - ETA: 0s - loss: 0.0061
 35/116 [========>.....................] - ETA: 0s - loss: 0.0463
 71/116 [=================>............] - ETA: 0s - loss: 0.0381
106/116 [==========================>...] - ETA: 0s - loss: 0.0346
116/116 [==============================] - 0s 1ms/step - loss: 0.0351
Epoch 5/10

  1/116 [..............................] - ETA: 0s - loss: 0.0329
 35/116 [========>.....................] - ETA: 0s - loss: 0.0274
 71/116 [=================>............] - ETA: 0s - loss: 0.0390
110/116 [===========================>..] - ETA: 0s - loss: 0.0347
116/116 [==============================] - 0s 1ms/step - loss: 0.0336
Epoch 6/10

  1/116 [..............................] - ETA: 0s - loss: 0.0194
 35/116 [========>.....................] - ETA: 0s - loss: 0.0231
 69/116 [================>.............] - ETA: 0s - loss: 0.0399
103/116 [=========================>....] - ETA: 0s - loss: 0.0336
116/116 [==============================] - 0s 1ms/step - loss: 0.0333
Epoch 7/10

  1/116 [..............................] - ETA: 0s - loss: 0.0019
 41/116 [=========>....................] - ETA: 0s - loss: 0.0400
 81/116 [===================>..........] - ETA: 0s - loss: 0.0357
116/116 [==============================] - ETA: 0s - loss: 0.0325
116/116 [==============================] - 0s 1ms/step - loss: 0.0325
Epoch 8/10

  1/116 [..............................] - ETA: 0s - loss: 0.0027
 40/116 [=========>....................] - ETA: 0s - loss: 0.0342
 77/116 [==================>...........] - ETA: 0s - loss: 0.0375
115/116 [============================>.] - ETA: 0s - loss: 0.0340
116/116 [==============================] - 0s 1ms/step - loss: 0.0342
Epoch 9/10

  1/116 [..............................] - ETA: 0s - loss: 0.0144
 38/116 [========>.....................] - ETA: 0s - loss: 0.0312
 77/116 [==================>...........] - ETA: 0s - loss: 0.0366
115/116 [============================>.] - ETA: 0s - loss: 0.0330
116/116 [==============================] - 0s 1ms/step - loss: 0.0330
Epoch 10/10

  1/116 [..............................] - ETA: 0s - loss: 0.0034
 39/116 [=========>....................] - ETA: 0s - loss: 0.0212
 77/116 [==================>...........] - ETA: 0s - loss: 0.0285
116/116 [==============================] - ETA: 0s - loss: 0.0326
116/116 [==============================] - 0s 1ms/step - loss: 0.0326
-> test with GAN.predict
GAN tn, fp: 286, 2
GAN fn, tp: 2, 7
GAN f1 score: 0.778
GAN cohens kappa score: 0.771

-> test with 'LR'
LR tn, fp: 282, 6
LR fn, tp: 0, 9
LR f1 score: 0.750
LR cohens kappa score: 0.740
LR average precision score: 0.738

-> 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: 5, 4
GB f1 score: 0.571
GB cohens kappa score: 0.562

-> 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 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: 0.1720
 33/116 [=======>......................] - ETA: 0s - loss: 0.0417 
 65/116 [===============>..............] - ETA: 0s - loss: 0.0414
 96/116 [=======================>......] - ETA: 0s - loss: 0.0410
116/116 [==============================] - 0s 2ms/step - loss: 0.0441
Epoch 2/10

  1/116 [..............................] - ETA: 0s - loss: 0.0113
 34/116 [=======>......................] - ETA: 0s - loss: 0.0237
 71/116 [=================>............] - ETA: 0s - loss: 0.0356
104/116 [=========================>....] - ETA: 0s - loss: 0.0417
116/116 [==============================] - 0s 2ms/step - loss: 0.0441
Epoch 3/10

  1/116 [..............................] - ETA: 0s - loss: 0.0023
 35/116 [========>.....................] - ETA: 0s - loss: 0.0413
 71/116 [=================>............] - ETA: 0s - loss: 0.0495
104/116 [=========================>....] - ETA: 0s - loss: 0.0460
116/116 [==============================] - 0s 1ms/step - loss: 0.0439
Epoch 4/10

  1/116 [..............................] - ETA: 0s - loss: 0.0057
 35/116 [========>.....................] - ETA: 0s - loss: 0.0302
 71/116 [=================>............] - ETA: 0s - loss: 0.0265
108/116 [==========================>...] - ETA: 0s - loss: 0.0405
116/116 [==============================] - 0s 1ms/step - loss: 0.0422
Epoch 5/10

  1/116 [..............................] - ETA: 0s - loss: 0.0649
 37/116 [========>.....................] - ETA: 0s - loss: 0.0347
 70/116 [=================>............] - ETA: 0s - loss: 0.0404
105/116 [==========================>...] - ETA: 0s - loss: 0.0455
116/116 [==============================] - 0s 1ms/step - loss: 0.0443
Epoch 6/10

  1/116 [..............................] - ETA: 0s - loss: 0.0365
 33/116 [=======>......................] - ETA: 0s - loss: 0.0430
 67/116 [================>.............] - ETA: 0s - loss: 0.0392
101/116 [=========================>....] - ETA: 0s - loss: 0.0455
116/116 [==============================] - 0s 2ms/step - loss: 0.0432
Epoch 7/10

  1/116 [..............................] - ETA: 0s - loss: 0.0177
 30/116 [======>.......................] - ETA: 0s - loss: 0.0359
 64/116 [===============>..............] - ETA: 0s - loss: 0.0354
103/116 [=========================>....] - ETA: 0s - loss: 0.0336
116/116 [==============================] - 0s 1ms/step - loss: 0.0423
Epoch 8/10

  1/116 [..............................] - ETA: 0s - loss: 0.0060
 37/116 [========>.....................] - ETA: 0s - loss: 0.0495
 73/116 [=================>............] - ETA: 0s - loss: 0.0451
110/116 [===========================>..] - ETA: 0s - loss: 0.0438
116/116 [==============================] - 0s 1ms/step - loss: 0.0423
Epoch 9/10

  1/116 [..............................] - ETA: 0s - loss: 0.0088
 36/116 [========>.....................] - ETA: 0s - loss: 0.0366
 72/116 [=================>............] - ETA: 0s - loss: 0.0417
109/116 [===========================>..] - ETA: 0s - loss: 0.0426
116/116 [==============================] - 0s 1ms/step - loss: 0.0434
Epoch 10/10

  1/116 [..............................] - ETA: 0s - loss: 0.0657
 37/116 [========>.....................] - ETA: 0s - loss: 0.0386
 72/116 [=================>............] - ETA: 0s - loss: 0.0334
108/116 [==========================>...] - ETA: 0s - loss: 0.0380
116/116 [==============================] - 0s 1ms/step - loss: 0.0421
-> 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: 275, 13
LR fn, tp: 0, 8
LR f1 score: 0.552
LR cohens kappa score: 0.533
LR average precision score: 0.397

-> 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: 277, 11
KNN fn, tp: 0, 8
KNN f1 score: 0.593
KNN cohens kappa score: 0.576


====== 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: 19s - loss: 0.0046
 36/116 [========>.....................] - ETA: 0s - loss: 0.0356 
 64/116 [===============>..............] - ETA: 0s - loss: 0.0424
104/116 [=========================>....] - ETA: 0s - loss: 0.0345
116/116 [==============================] - 0s 1ms/step - loss: 0.0386
Epoch 2/10

  1/116 [..............................] - ETA: 0s - loss: 0.1186
 41/116 [=========>....................] - ETA: 0s - loss: 0.0214
 81/116 [===================>..........] - ETA: 0s - loss: 0.0332
116/116 [==============================] - 0s 1ms/step - loss: 0.0379
Epoch 3/10

  1/116 [..............................] - ETA: 0s - loss: 0.1453
 42/116 [=========>....................] - ETA: 0s - loss: 0.0404
 82/116 [====================>.........] - ETA: 0s - loss: 0.0339
116/116 [==============================] - 0s 1ms/step - loss: 0.0376
Epoch 4/10

  1/116 [..............................] - ETA: 0s - loss: 0.0255
 40/116 [=========>....................] - ETA: 0s - loss: 0.0333
 81/116 [===================>..........] - ETA: 0s - loss: 0.0312
116/116 [==============================] - 0s 1ms/step - loss: 0.0356
Epoch 5/10

  1/116 [..............................] - ETA: 0s - loss: 0.0094
 41/116 [=========>....................] - ETA: 0s - loss: 0.0344
 78/116 [===================>..........] - ETA: 0s - loss: 0.0331
116/116 [==============================] - 0s 1ms/step - loss: 0.0364
Epoch 6/10

  1/116 [..............................] - ETA: 0s - loss: 0.0093
 41/116 [=========>....................] - ETA: 0s - loss: 0.0380
 81/116 [===================>..........] - ETA: 0s - loss: 0.0330
116/116 [==============================] - 0s 1ms/step - loss: 0.0367
Epoch 7/10

  1/116 [..............................] - ETA: 0s - loss: 0.0120
 41/116 [=========>....................] - ETA: 0s - loss: 0.0333
 79/116 [===================>..........] - ETA: 0s - loss: 0.0314
116/116 [==============================] - 0s 1ms/step - loss: 0.0341
Epoch 8/10

  1/116 [..............................] - ETA: 0s - loss: 0.0045
 40/116 [=========>....................] - ETA: 0s - loss: 0.0305
 80/116 [===================>..........] - ETA: 0s - loss: 0.0370
116/116 [==============================] - 0s 1ms/step - loss: 0.0340
Epoch 9/10

  1/116 [..............................] - ETA: 0s - loss: 0.0069
 42/116 [=========>....................] - ETA: 0s - loss: 0.0220
 83/116 [====================>.........] - ETA: 0s - loss: 0.0310
116/116 [==============================] - 0s 1ms/step - loss: 0.0342
Epoch 10/10

  1/116 [..............................] - ETA: 0s - loss: 0.0957
 41/116 [=========>....................] - ETA: 0s - loss: 0.0361
 82/116 [====================>.........] - ETA: 0s - loss: 0.0333
116/116 [==============================] - 0s 1ms/step - loss: 0.0345
-> test with GAN.predict
GAN tn, fp: 283, 5
GAN fn, tp: 1, 8
GAN f1 score: 0.727
GAN cohens kappa score: 0.717

-> test with 'LR'
LR tn, fp: 275, 13
LR fn, tp: 1, 8
LR f1 score: 0.533
LR cohens kappa score: 0.513
LR average precision score: 0.742

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

-> 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: 276, 12
KNN fn, tp: 0, 9
KNN f1 score: 0.600
KNN cohens kappa score: 0.582


------ 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: 20s - loss: 0.1755
 42/116 [=========>....................] - ETA: 0s - loss: 0.0498 
 80/116 [===================>..........] - ETA: 0s - loss: 0.0487
116/116 [==============================] - 0s 1ms/step - loss: 0.0448
Epoch 2/10

  1/116 [..............................] - ETA: 0s - loss: 0.0106
 41/116 [=========>....................] - ETA: 0s - loss: 0.0467
 81/116 [===================>..........] - ETA: 0s - loss: 0.0452
116/116 [==============================] - 0s 1ms/step - loss: 0.0437
Epoch 3/10

  1/116 [..............................] - ETA: 0s - loss: 0.2326
 40/116 [=========>....................] - ETA: 0s - loss: 0.0526
 77/116 [==================>...........] - ETA: 0s - loss: 0.0446
115/116 [============================>.] - ETA: 0s - loss: 0.0419
116/116 [==============================] - 0s 1ms/step - loss: 0.0418
Epoch 4/10

  1/116 [..............................] - ETA: 0s - loss: 0.0155
 39/116 [=========>....................] - ETA: 0s - loss: 0.0404
 78/116 [===================>..........] - ETA: 0s - loss: 0.0446
116/116 [==============================] - 0s 1ms/step - loss: 0.0415
Epoch 5/10

  1/116 [..............................] - ETA: 0s - loss: 0.0258
 39/116 [=========>....................] - ETA: 0s - loss: 0.0514
 77/116 [==================>...........] - ETA: 0s - loss: 0.0429
116/116 [==============================] - 0s 1ms/step - loss: 0.0422
Epoch 6/10

  1/116 [..............................] - ETA: 0s - loss: 0.0222
 39/116 [=========>....................] - ETA: 0s - loss: 0.0375
 78/116 [===================>..........] - ETA: 0s - loss: 0.0429
116/116 [==============================] - 0s 1ms/step - loss: 0.0403
Epoch 7/10

  1/116 [..............................] - ETA: 0s - loss: 0.0039
 38/116 [========>.....................] - ETA: 0s - loss: 0.0222
 74/116 [==================>...........] - ETA: 0s - loss: 0.0328
109/116 [===========================>..] - ETA: 0s - loss: 0.0415
116/116 [==============================] - 0s 1ms/step - loss: 0.0414
Epoch 8/10

  1/116 [..............................] - ETA: 0s - loss: 0.0068
 41/116 [=========>....................] - ETA: 0s - loss: 0.0576
 81/116 [===================>..........] - ETA: 0s - loss: 0.0484
116/116 [==============================] - 0s 1ms/step - loss: 0.0420
Epoch 9/10

  1/116 [..............................] - ETA: 0s - loss: 0.0095
 40/116 [=========>....................] - ETA: 0s - loss: 0.0406
 79/116 [===================>..........] - ETA: 0s - loss: 0.0469
116/116 [==============================] - 0s 1ms/step - loss: 0.0433
Epoch 10/10

  1/116 [..............................] - ETA: 0s - loss: 0.0218
 40/116 [=========>....................] - ETA: 0s - loss: 0.0272
 79/116 [===================>..........] - ETA: 0s - loss: 0.0419
113/116 [============================>.] - ETA: 0s - loss: 0.0411
116/116 [==============================] - 0s 1ms/step - loss: 0.0406
-> test with GAN.predict
GAN tn, fp: 283, 5
GAN fn, tp: 2, 7
GAN f1 score: 0.667
GAN cohens kappa score: 0.655

-> 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: 0.608

-> 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: 286, 2
GB fn, tp: 2, 7
GB f1 score: 0.778
GB cohens kappa score: 0.771

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


------ 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: 19s - loss: 0.0407
 42/116 [=========>....................] - ETA: 0s - loss: 0.0317 
 81/116 [===================>..........] - ETA: 0s - loss: 0.0250
116/116 [==============================] - 0s 1ms/step - loss: 0.0307
Epoch 2/10

  1/116 [..............................] - ETA: 0s - loss: 0.0885
 41/116 [=========>....................] - ETA: 0s - loss: 0.0269
 80/116 [===================>..........] - ETA: 0s - loss: 0.0285
116/116 [==============================] - 0s 1ms/step - loss: 0.0306
Epoch 3/10

  1/116 [..............................] - ETA: 0s - loss: 0.0384
 42/116 [=========>....................] - ETA: 0s - loss: 0.0257
 82/116 [====================>.........] - ETA: 0s - loss: 0.0311
116/116 [==============================] - 0s 1ms/step - loss: 0.0290
Epoch 4/10

  1/116 [..............................] - ETA: 0s - loss: 0.0136
 40/116 [=========>....................] - ETA: 0s - loss: 0.0292
 80/116 [===================>..........] - ETA: 0s - loss: 0.0278
116/116 [==============================] - 0s 1ms/step - loss: 0.0294
Epoch 5/10

  1/116 [..............................] - ETA: 0s - loss: 0.0094
 41/116 [=========>....................] - ETA: 0s - loss: 0.0298
 81/116 [===================>..........] - ETA: 0s - loss: 0.0263
116/116 [==============================] - 0s 1ms/step - loss: 0.0306
Epoch 6/10

  1/116 [..............................] - ETA: 0s - loss: 0.0419
 37/116 [========>.....................] - ETA: 0s - loss: 0.0325
 73/116 [=================>............] - ETA: 0s - loss: 0.0265
114/116 [============================>.] - ETA: 0s - loss: 0.0300
116/116 [==============================] - 0s 1ms/step - loss: 0.0298
Epoch 7/10

  1/116 [..............................] - ETA: 0s - loss: 0.0619
 41/116 [=========>....................] - ETA: 0s - loss: 0.0348
 80/116 [===================>..........] - ETA: 0s - loss: 0.0280
116/116 [==============================] - 0s 1ms/step - loss: 0.0286
Epoch 8/10

  1/116 [..............................] - ETA: 0s - loss: 0.0073
 38/116 [========>.....................] - ETA: 0s - loss: 0.0310
 79/116 [===================>..........] - ETA: 0s - loss: 0.0327
116/116 [==============================] - 0s 1ms/step - loss: 0.0278
Epoch 9/10

  1/116 [..............................] - ETA: 0s - loss: 0.0097
 41/116 [=========>....................] - ETA: 0s - loss: 0.0203
 81/116 [===================>..........] - ETA: 0s - loss: 0.0241
116/116 [==============================] - 0s 1ms/step - loss: 0.0306
Epoch 10/10

  1/116 [..............................] - ETA: 0s - loss: 0.0387
 40/116 [=========>....................] - ETA: 0s - loss: 0.0366
 79/116 [===================>..........] - ETA: 0s - loss: 0.0305
116/116 [==============================] - 0s 1ms/step - loss: 0.0286
-> test with GAN.predict
GAN tn, fp: 281, 7
GAN fn, tp: 4, 5
GAN f1 score: 0.476
GAN cohens kappa score: 0.457

-> test with 'LR'
LR tn, fp: 281, 7
LR fn, tp: 2, 7
LR f1 score: 0.609
LR cohens kappa score: 0.594
LR average precision score: 0.721

-> 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: 2, 7
KNN f1 score: 0.560
KNN cohens kappa score: 0.542


------ 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: 18s - loss: 0.0173
 37/116 [========>.....................] - ETA: 0s - loss: 0.0446 
 71/116 [=================>............] - ETA: 0s - loss: 0.0340
104/116 [=========================>....] - ETA: 0s - loss: 0.0330
116/116 [==============================] - 0s 1ms/step - loss: 0.0328
Epoch 2/10

  1/116 [..............................] - ETA: 0s - loss: 0.0326
 34/116 [=======>......................] - ETA: 0s - loss: 0.0175
 68/116 [================>.............] - ETA: 0s - loss: 0.0270
102/116 [=========================>....] - ETA: 0s - loss: 0.0374
116/116 [==============================] - 0s 2ms/step - loss: 0.0359
Epoch 3/10

  1/116 [..............................] - ETA: 0s - loss: 0.0042
 26/116 [=====>........................] - ETA: 0s - loss: 0.0427
 54/116 [============>.................] - ETA: 0s - loss: 0.0437
 89/116 [======================>.......] - ETA: 0s - loss: 0.0354
116/116 [==============================] - 0s 2ms/step - loss: 0.0341
Epoch 4/10

  1/116 [..............................] - ETA: 0s - loss: 0.0191
 35/116 [========>.....................] - ETA: 0s - loss: 0.0432
 68/116 [================>.............] - ETA: 0s - loss: 0.0331
103/116 [=========================>....] - ETA: 0s - loss: 0.0364
116/116 [==============================] - 0s 1ms/step - loss: 0.0347
Epoch 5/10

  1/116 [..............................] - ETA: 0s - loss: 0.0054
 38/116 [========>.....................] - ETA: 0s - loss: 0.0286
 73/116 [=================>............] - ETA: 0s - loss: 0.0287
104/116 [=========================>....] - ETA: 0s - loss: 0.0358
116/116 [==============================] - 0s 2ms/step - loss: 0.0346
Epoch 6/10

  1/116 [..............................] - ETA: 0s - loss: 8.6572e-04
 36/116 [========>.....................] - ETA: 0s - loss: 0.0414    
 70/116 [=================>............] - ETA: 0s - loss: 0.0379
105/116 [==========================>...] - ETA: 0s - loss: 0.0345
116/116 [==============================] - 0s 1ms/step - loss: 0.0340
Epoch 7/10

  1/116 [..............................] - ETA: 0s - loss: 0.0131
 34/116 [=======>......................] - ETA: 0s - loss: 0.0237
 68/116 [================>.............] - ETA: 0s - loss: 0.0313
102/116 [=========================>....] - ETA: 0s - loss: 0.0365
116/116 [==============================] - 0s 1ms/step - loss: 0.0340
Epoch 8/10

  1/116 [..............................] - ETA: 0s - loss: 0.0049
 37/116 [========>.....................] - ETA: 0s - loss: 0.0270
 73/116 [=================>............] - ETA: 0s - loss: 0.0345
110/116 [===========================>..] - ETA: 0s - loss: 0.0337
116/116 [==============================] - 0s 1ms/step - loss: 0.0347
Epoch 9/10

  1/116 [..............................] - ETA: 0s - loss: 0.0121
 37/116 [========>.....................] - ETA: 0s - loss: 0.0152
 72/116 [=================>............] - ETA: 0s - loss: 0.0291
111/116 [===========================>..] - ETA: 0s - loss: 0.0344
116/116 [==============================] - 0s 1ms/step - loss: 0.0341
Epoch 10/10

  1/116 [..............................] - ETA: 0s - loss: 0.0138
 31/116 [=======>......................] - ETA: 0s - loss: 0.0231
 66/116 [================>.............] - ETA: 0s - loss: 0.0392
102/116 [=========================>....] - ETA: 0s - loss: 0.0352
116/116 [==============================] - 0s 2ms/step - loss: 0.0328
-> test with GAN.predict
GAN tn, fp: 287, 1
GAN fn, tp: 3, 6
GAN f1 score: 0.750
GAN cohens kappa score: 0.743

-> test with 'LR'
LR tn, fp: 282, 6
LR fn, tp: 0, 9
LR f1 score: 0.750
LR cohens kappa score: 0.740
LR average precision score: 0.676

-> 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: 3, 6
GB f1 score: 0.800
GB cohens kappa score: 0.795

-> 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 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: 21s - loss: 0.0355
 42/116 [=========>....................] - ETA: 0s - loss: 0.0517 
 82/116 [====================>.........] - ETA: 0s - loss: 0.0520
116/116 [==============================] - 0s 1ms/step - loss: 0.0500
Epoch 2/10

  1/116 [..............................] - ETA: 0s - loss: 0.1493
 42/116 [=========>....................] - ETA: 0s - loss: 0.0454
 81/116 [===================>..........] - ETA: 0s - loss: 0.0556
116/116 [==============================] - 0s 1ms/step - loss: 0.0484
Epoch 3/10

  1/116 [..............................] - ETA: 0s - loss: 0.0029
 42/116 [=========>....................] - ETA: 0s - loss: 0.0530
 83/116 [====================>.........] - ETA: 0s - loss: 0.0443
116/116 [==============================] - 0s 1ms/step - loss: 0.0464
Epoch 4/10

  1/116 [..............................] - ETA: 0s - loss: 0.0187
 42/116 [=========>....................] - ETA: 0s - loss: 0.0398
 83/116 [====================>.........] - ETA: 0s - loss: 0.0458
116/116 [==============================] - 0s 1ms/step - loss: 0.0475
Epoch 5/10

  1/116 [..............................] - ETA: 0s - loss: 0.0196
 40/116 [=========>....................] - ETA: 0s - loss: 0.0412
 80/116 [===================>..........] - ETA: 0s - loss: 0.0477
116/116 [==============================] - 0s 1ms/step - loss: 0.0488
Epoch 6/10

  1/116 [..............................] - ETA: 0s - loss: 0.0100
 41/116 [=========>....................] - ETA: 0s - loss: 0.0580
 81/116 [===================>..........] - ETA: 0s - loss: 0.0488
116/116 [==============================] - 0s 1ms/step - loss: 0.0482
Epoch 7/10

  1/116 [..............................] - ETA: 0s - loss: 0.0084
 41/116 [=========>....................] - ETA: 0s - loss: 0.0634
 82/116 [====================>.........] - ETA: 0s - loss: 0.0511
116/116 [==============================] - 0s 1ms/step - loss: 0.0441
Epoch 8/10

  1/116 [..............................] - ETA: 0s - loss: 0.0071
 40/116 [=========>....................] - ETA: 0s - loss: 0.0559
 80/116 [===================>..........] - ETA: 0s - loss: 0.0528
116/116 [==============================] - 0s 1ms/step - loss: 0.0491
Epoch 9/10

  1/116 [..............................] - ETA: 0s - loss: 0.0028
 42/116 [=========>....................] - ETA: 0s - loss: 0.0495
 84/116 [====================>.........] - ETA: 0s - loss: 0.0482
116/116 [==============================] - 0s 1ms/step - loss: 0.0454
Epoch 10/10

  1/116 [..............................] - ETA: 0s - loss: 0.1459
 41/116 [=========>....................] - ETA: 0s - loss: 0.0456
 82/116 [====================>.........] - ETA: 0s - loss: 0.0484
116/116 [==============================] - 0s 1ms/step - loss: 0.0447
-> test with GAN.predict
GAN tn, fp: 283, 5
GAN fn, tp: 0, 8
GAN f1 score: 0.762
GAN cohens kappa score: 0.754

-> test with 'LR'
LR tn, fp: 278, 10
LR fn, tp: 0, 8
LR f1 score: 0.615
LR cohens kappa score: 0.600
LR average precision score: 0.773

-> test with 'RF'
RF tn, fp: 287, 1
RF fn, tp: 1, 7
RF f1 score: 0.875
RF cohens kappa score: 0.872

-> 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: 276, 12
KNN fn, tp: 0, 8
KNN f1 score: 0.571
KNN cohens kappa score: 0.554


====== 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: 20s - loss: 0.1756
 41/116 [=========>....................] - ETA: 0s - loss: 0.0599 
 81/116 [===================>..........] - ETA: 0s - loss: 0.0484
116/116 [==============================] - 0s 1ms/step - loss: 0.0406
Epoch 2/10

  1/116 [..............................] - ETA: 0s - loss: 0.2316
 42/116 [=========>....................] - ETA: 0s - loss: 0.0322
 80/116 [===================>..........] - ETA: 0s - loss: 0.0435
116/116 [==============================] - 0s 1ms/step - loss: 0.0412
Epoch 3/10

  1/116 [..............................] - ETA: 0s - loss: 0.0299
 42/116 [=========>....................] - ETA: 0s - loss: 0.0310
 82/116 [====================>.........] - ETA: 0s - loss: 0.0334
116/116 [==============================] - 0s 1ms/step - loss: 0.0410
Epoch 4/10

  1/116 [..............................] - ETA: 0s - loss: 0.0043
 41/116 [=========>....................] - ETA: 0s - loss: 0.0591
 81/116 [===================>..........] - ETA: 0s - loss: 0.0452
115/116 [============================>.] - ETA: 0s - loss: 0.0404
116/116 [==============================] - 0s 1ms/step - loss: 0.0403
Epoch 5/10

  1/116 [..............................] - ETA: 0s - loss: 0.0036
 40/116 [=========>....................] - ETA: 0s - loss: 0.0255
 79/116 [===================>..........] - ETA: 0s - loss: 0.0371
116/116 [==============================] - 0s 1ms/step - loss: 0.0404
Epoch 6/10

  1/116 [..............................] - ETA: 0s - loss: 0.0038
 40/116 [=========>....................] - ETA: 0s - loss: 0.0481
 80/116 [===================>..........] - ETA: 0s - loss: 0.0405
116/116 [==============================] - 0s 1ms/step - loss: 0.0412
Epoch 7/10

  1/116 [..............................] - ETA: 0s - loss: 0.0239
 42/116 [=========>....................] - ETA: 0s - loss: 0.0254
 79/116 [===================>..........] - ETA: 0s - loss: 0.0357
116/116 [==============================] - 0s 1ms/step - loss: 0.0388
Epoch 8/10

  1/116 [..............................] - ETA: 0s - loss: 0.0215
 38/116 [========>.....................] - ETA: 0s - loss: 0.0579
 76/116 [==================>...........] - ETA: 0s - loss: 0.0425
116/116 [==============================] - ETA: 0s - loss: 0.0420
116/116 [==============================] - 0s 1ms/step - loss: 0.0420
Epoch 9/10

  1/116 [..............................] - ETA: 0s - loss: 0.0122
 41/116 [=========>....................] - ETA: 0s - loss: 0.0195
 79/116 [===================>..........] - ETA: 0s - loss: 0.0399
111/116 [===========================>..] - ETA: 0s - loss: 0.0394
116/116 [==============================] - 0s 1ms/step - loss: 0.0401
Epoch 10/10

  1/116 [..............................] - ETA: 0s - loss: 0.0091
 35/116 [========>.....................] - ETA: 0s - loss: 0.0493
 72/116 [=================>............] - ETA: 0s - loss: 0.0386
111/116 [===========================>..] - ETA: 0s - loss: 0.0349
116/116 [==============================] - 0s 1ms/step - loss: 0.0380
-> test with GAN.predict
GAN tn, fp: 278, 10
GAN fn, tp: 0, 9
GAN f1 score: 0.643
GAN cohens kappa score: 0.628

-> 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: 0.724

-> test with 'RF'
RF tn, fp: 285, 3
RF fn, tp: 1, 8
RF f1 score: 0.800
RF cohens kappa score: 0.793

-> 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: 271, 17
KNN fn, tp: 0, 9
KNN f1 score: 0.514
KNN cohens kappa score: 0.491


------ 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: 24s - loss: 0.0087
 40/116 [=========>....................] - ETA: 0s - loss: 0.0304 
 78/116 [===================>..........] - ETA: 0s - loss: 0.0377
112/116 [===========================>..] - ETA: 0s - loss: 0.0370
116/116 [==============================] - 0s 1ms/step - loss: 0.0360
Epoch 2/10

  1/116 [..............................] - ETA: 0s - loss: 0.1759
 30/116 [======>.......................] - ETA: 0s - loss: 0.0270
 62/116 [===============>..............] - ETA: 0s - loss: 0.0320
 95/116 [=======================>......] - ETA: 0s - loss: 0.0315
116/116 [==============================] - 0s 2ms/step - loss: 0.0349
Epoch 3/10

  1/116 [..............................] - ETA: 0s - loss: 0.0032
 32/116 [=======>......................] - ETA: 0s - loss: 0.0175
 69/116 [================>.............] - ETA: 0s - loss: 0.0352
106/116 [==========================>...] - ETA: 0s - loss: 0.0367
116/116 [==============================] - 0s 1ms/step - loss: 0.0345
Epoch 4/10

  1/116 [..............................] - ETA: 0s - loss: 0.0741
 37/116 [========>.....................] - ETA: 0s - loss: 0.0409
 74/116 [==================>...........] - ETA: 0s - loss: 0.0344
108/116 [==========================>...] - ETA: 0s - loss: 0.0369
116/116 [==============================] - 0s 1ms/step - loss: 0.0358
Epoch 5/10

  1/116 [..............................] - ETA: 0s - loss: 0.0046
 38/116 [========>.....................] - ETA: 0s - loss: 0.0390
 70/116 [=================>............] - ETA: 0s - loss: 0.0395
101/116 [=========================>....] - ETA: 0s - loss: 0.0334
116/116 [==============================] - 0s 2ms/step - loss: 0.0353
Epoch 6/10

  1/116 [..............................] - ETA: 0s - loss: 0.0170
 40/116 [=========>....................] - ETA: 0s - loss: 0.0287
 78/116 [===================>..........] - ETA: 0s - loss: 0.0305
115/116 [============================>.] - ETA: 0s - loss: 0.0337
116/116 [==============================] - 0s 1ms/step - loss: 0.0337
Epoch 7/10

  1/116 [..............................] - ETA: 0s - loss: 0.0083
 38/116 [========>.....................] - ETA: 0s - loss: 0.0413
 74/116 [==================>...........] - ETA: 0s - loss: 0.0359
109/116 [===========================>..] - ETA: 0s - loss: 0.0336
116/116 [==============================] - 0s 1ms/step - loss: 0.0333
Epoch 8/10

  1/116 [..............................] - ETA: 0s - loss: 0.0061
 36/116 [========>.....................] - ETA: 0s - loss: 0.0332
 74/116 [==================>...........] - ETA: 0s - loss: 0.0351
116/116 [==============================] - 0s 1ms/step - loss: 0.0333
Epoch 9/10

  1/116 [..............................] - ETA: 0s - loss: 0.0074
 44/116 [==========>...................] - ETA: 0s - loss: 0.0448
 85/116 [====================>.........] - ETA: 0s - loss: 0.0348
116/116 [==============================] - 0s 1ms/step - loss: 0.0332
Epoch 10/10

  1/116 [..............................] - ETA: 0s - loss: 0.0829
 45/116 [==========>...................] - ETA: 0s - loss: 0.0315
 88/116 [=====================>........] - ETA: 0s - loss: 0.0291
116/116 [==============================] - 0s 1ms/step - loss: 0.0319
-> test with GAN.predict
GAN tn, fp: 285, 3
GAN fn, tp: 3, 6
GAN f1 score: 0.667
GAN cohens kappa score: 0.656

-> test with 'LR'
LR tn, fp: 282, 6
LR fn, tp: 0, 9
LR f1 score: 0.750
LR cohens kappa score: 0.740
LR average precision score: 0.812

-> 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: 284, 4
KNN fn, tp: 1, 8
KNN f1 score: 0.762
KNN cohens kappa score: 0.753


------ 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: 19s - loss: 0.0285
 42/116 [=========>....................] - ETA: 0s - loss: 0.0528 
 82/116 [====================>.........] - ETA: 0s - loss: 0.0518
116/116 [==============================] - 0s 1ms/step - loss: 0.0509
Epoch 2/10

  1/116 [..............................] - ETA: 0s - loss: 0.2353
 41/116 [=========>....................] - ETA: 0s - loss: 0.0898
 82/116 [====================>.........] - ETA: 0s - loss: 0.0582
116/116 [==============================] - 0s 1ms/step - loss: 0.0534
Epoch 3/10

  1/116 [..............................] - ETA: 0s - loss: 0.1484
 41/116 [=========>....................] - ETA: 0s - loss: 0.0352
 79/116 [===================>..........] - ETA: 0s - loss: 0.0457
116/116 [==============================] - 0s 1ms/step - loss: 0.0506
Epoch 4/10

  1/116 [..............................] - ETA: 0s - loss: 0.0129
 45/116 [==========>...................] - ETA: 0s - loss: 0.0564
 85/116 [====================>.........] - ETA: 0s - loss: 0.0495
116/116 [==============================] - 0s 1ms/step - loss: 0.0497
Epoch 5/10

  1/116 [..............................] - ETA: 0s - loss: 0.0481
 41/116 [=========>....................] - ETA: 0s - loss: 0.0506
 81/116 [===================>..........] - ETA: 0s - loss: 0.0518
116/116 [==============================] - 0s 1ms/step - loss: 0.0507
Epoch 6/10

  1/116 [..............................] - ETA: 0s - loss: 0.0449
 40/116 [=========>....................] - ETA: 0s - loss: 0.0458
 78/116 [===================>..........] - ETA: 0s - loss: 0.0400
116/116 [==============================] - 0s 1ms/step - loss: 0.0498
Epoch 7/10

  1/116 [..............................] - ETA: 0s - loss: 0.0065
 41/116 [=========>....................] - ETA: 0s - loss: 0.0440
 80/116 [===================>..........] - ETA: 0s - loss: 0.0492
116/116 [==============================] - 0s 1ms/step - loss: 0.0486
Epoch 8/10

  1/116 [..............................] - ETA: 0s - loss: 0.0066
 40/116 [=========>....................] - ETA: 0s - loss: 0.0461
 81/116 [===================>..........] - ETA: 0s - loss: 0.0455
116/116 [==============================] - 0s 1ms/step - loss: 0.0503
Epoch 9/10

  1/116 [..............................] - ETA: 0s - loss: 0.0357
 39/116 [=========>....................] - ETA: 0s - loss: 0.0582
 78/116 [===================>..........] - ETA: 0s - loss: 0.0488
116/116 [==============================] - 0s 1ms/step - loss: 0.0487
Epoch 10/10

  1/116 [..............................] - ETA: 0s - loss: 0.0085
 42/116 [=========>....................] - ETA: 0s - loss: 0.0496
 81/116 [===================>..........] - ETA: 0s - loss: 0.0537
116/116 [==============================] - 0s 1ms/step - loss: 0.0505
-> test with GAN.predict
GAN tn, fp: 283, 5
GAN fn, tp: 0, 9
GAN f1 score: 0.783
GAN cohens kappa score: 0.774

-> 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: 0.775

-> test with 'RF'
RF tn, fp: 286, 2
RF fn, tp: 2, 7
RF f1 score: 0.778
RF cohens kappa score: 0.771

-> 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: 281, 7
KNN fn, tp: 0, 9
KNN f1 score: 0.720
KNN cohens kappa score: 0.709


------ 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: 20s - loss: 0.0139
 42/116 [=========>....................] - ETA: 0s - loss: 0.0447 
 82/116 [====================>.........] - ETA: 0s - loss: 0.0336
116/116 [==============================] - 0s 1ms/step - loss: 0.0373
Epoch 2/10

  1/116 [..............................] - ETA: 0s - loss: 0.1829
 36/116 [========>.....................] - ETA: 0s - loss: 0.0371
 74/116 [==================>...........] - ETA: 0s - loss: 0.0348
112/116 [===========================>..] - ETA: 0s - loss: 0.0360
116/116 [==============================] - 0s 1ms/step - loss: 0.0365
Epoch 3/10

  1/116 [..............................] - ETA: 0s - loss: 0.0032
 40/116 [=========>....................] - ETA: 0s - loss: 0.0414
 78/116 [===================>..........] - ETA: 0s - loss: 0.0349
116/116 [==============================] - ETA: 0s - loss: 0.0357
116/116 [==============================] - 0s 1ms/step - loss: 0.0357
Epoch 4/10

  1/116 [..............................] - ETA: 0s - loss: 0.0338
 40/116 [=========>....................] - ETA: 0s - loss: 0.0404
 81/116 [===================>..........] - ETA: 0s - loss: 0.0388
116/116 [==============================] - 0s 1ms/step - loss: 0.0362
Epoch 5/10

  1/116 [..............................] - ETA: 0s - loss: 0.1273
 42/116 [=========>....................] - ETA: 0s - loss: 0.0288
 82/116 [====================>.........] - ETA: 0s - loss: 0.0384
116/116 [==============================] - 0s 1ms/step - loss: 0.0367
Epoch 6/10

  1/116 [..............................] - ETA: 0s - loss: 0.0127
 40/116 [=========>....................] - ETA: 0s - loss: 0.0362
 80/116 [===================>..........] - ETA: 0s - loss: 0.0330
116/116 [==============================] - 0s 1ms/step - loss: 0.0355
Epoch 7/10

  1/116 [..............................] - ETA: 0s - loss: 0.0043
 41/116 [=========>....................] - ETA: 0s - loss: 0.0377
 81/116 [===================>..........] - ETA: 0s - loss: 0.0327
116/116 [==============================] - 0s 1ms/step - loss: 0.0362
Epoch 8/10

  1/116 [..............................] - ETA: 0s - loss: 0.1054
 40/116 [=========>....................] - ETA: 0s - loss: 0.0442
 81/116 [===================>..........] - ETA: 0s - loss: 0.0347
116/116 [==============================] - 0s 1ms/step - loss: 0.0341
Epoch 9/10

  1/116 [..............................] - ETA: 0s - loss: 0.0413
 41/116 [=========>....................] - ETA: 0s - loss: 0.0354
 82/116 [====================>.........] - ETA: 0s - loss: 0.0406
116/116 [==============================] - 0s 1ms/step - loss: 0.0354
Epoch 10/10

  1/116 [..............................] - ETA: 0s - loss: 0.1409
 41/116 [=========>....................] - ETA: 0s - loss: 0.0501
 78/116 [===================>..........] - ETA: 0s - loss: 0.0375
116/116 [==============================] - 0s 1ms/step - loss: 0.0351
-> test with GAN.predict
GAN tn, fp: 285, 3
GAN fn, tp: 3, 6
GAN f1 score: 0.667
GAN cohens kappa score: 0.656

-> test with 'LR'
LR tn, fp: 281, 7
LR fn, tp: 2, 7
LR f1 score: 0.609
LR cohens kappa score: 0.594
LR average precision score: 0.577

-> 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: 284, 4
KNN fn, tp: 0, 9
KNN f1 score: 0.818
KNN cohens kappa score: 0.811


------ 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: 0.0050
 42/116 [=========>....................] - ETA: 0s - loss: 0.0371 
 83/116 [====================>.........] - ETA: 0s - loss: 0.0402
116/116 [==============================] - 0s 1ms/step - loss: 0.0335
Epoch 2/10

  1/116 [..............................] - ETA: 0s - loss: 0.3076
 42/116 [=========>....................] - ETA: 0s - loss: 0.0248
 78/116 [===================>..........] - ETA: 0s - loss: 0.0323
116/116 [==============================] - 0s 1ms/step - loss: 0.0308
Epoch 3/10

  1/116 [..............................] - ETA: 0s - loss: 0.0037
 42/116 [=========>....................] - ETA: 0s - loss: 0.0361
 82/116 [====================>.........] - ETA: 0s - loss: 0.0299
116/116 [==============================] - 0s 1ms/step - loss: 0.0289
Epoch 4/10

  1/116 [..............................] - ETA: 0s - loss: 0.0200
 41/116 [=========>....................] - ETA: 0s - loss: 0.0368
 77/116 [==================>...........] - ETA: 0s - loss: 0.0312
116/116 [==============================] - 0s 1ms/step - loss: 0.0302
Epoch 5/10

  1/116 [..............................] - ETA: 0s - loss: 0.0147
 42/116 [=========>....................] - ETA: 0s - loss: 0.0336
 83/116 [====================>.........] - ETA: 0s - loss: 0.0316
116/116 [==============================] - 0s 1ms/step - loss: 0.0289
Epoch 6/10

  1/116 [..............................] - ETA: 0s - loss: 0.0266
 41/116 [=========>....................] - ETA: 0s - loss: 0.0208
 82/116 [====================>.........] - ETA: 0s - loss: 0.0275
116/116 [==============================] - 0s 1ms/step - loss: 0.0302
Epoch 7/10

  1/116 [..............................] - ETA: 0s - loss: 0.0038
 40/116 [=========>....................] - ETA: 0s - loss: 0.0307
 78/116 [===================>..........] - ETA: 0s - loss: 0.0352
116/116 [==============================] - ETA: 0s - loss: 0.0338
116/116 [==============================] - 0s 1ms/step - loss: 0.0338
Epoch 8/10

  1/116 [..............................] - ETA: 0s - loss: 0.2976
 41/116 [=========>....................] - ETA: 0s - loss: 0.0474
 82/116 [====================>.........] - ETA: 0s - loss: 0.0324
116/116 [==============================] - 0s 1ms/step - loss: 0.0295
Epoch 9/10

  1/116 [..............................] - ETA: 0s - loss: 0.0110
 40/116 [=========>....................] - ETA: 0s - loss: 0.0377
 80/116 [===================>..........] - ETA: 0s - loss: 0.0320
116/116 [==============================] - 0s 1ms/step - loss: 0.0277
Epoch 10/10

  1/116 [..............................] - ETA: 0s - loss: 0.0021
 39/116 [=========>....................] - ETA: 0s - loss: 0.0279
 77/116 [==================>...........] - ETA: 0s - loss: 0.0234
116/116 [==============================] - 0s 1ms/step - loss: 0.0274
-> test with GAN.predict
GAN tn, fp: 278, 10
GAN fn, tp: 2, 6
GAN f1 score: 0.500
GAN cohens kappa score: 0.481

-> test with 'LR'
LR tn, fp: 277, 11
LR fn, tp: 1, 7
LR f1 score: 0.538
LR cohens kappa score: 0.521
LR average precision score: 0.507

-> 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: 2, 6
KNN f1 score: 0.444
KNN cohens kappa score: 0.422

### Exercise is done.

-----[ LR ]-----
maximum:
LR tn, fp: 282, 16
LR fn, tp: 2, 9
LR f1 score: 0.750
LR cohens kappa score: 0.740
LR average precision score: 0.897


average:
LR tn, fp: 277.92, 10.08
LR fn, tp: 0.48, 8.32
LR f1 score: 0.620
LR cohens kappa score: 0.604
LR average precision score: 0.691


minimum:
LR tn, fp: 272, 6
LR fn, tp: 0, 7
LR f1 score: 0.485
LR cohens kappa score: 0.461
LR average precision score: 0.397


-----[ RF ]-----
maximum:
RF tn, fp: 288, 7
RF fn, tp: 5, 9
RF f1 score: 0.941
RF cohens kappa score: 0.939


average:
RF tn, fp: 285.84, 2.16
RF fn, tp: 2.36, 6.44
RF f1 score: 0.738
RF cohens kappa score: 0.730


minimum:
RF tn, fp: 281, 0
RF fn, tp: 0, 4
RF f1 score: 0.526
RF cohens kappa score: 0.511


-----[ GB ]-----
maximum:
GB tn, fp: 288, 8
GB fn, tp: 5, 8
GB f1 score: 0.889
GB cohens kappa score: 0.885


average:
GB tn, fp: 285.28, 2.72
GB fn, tp: 2.28, 6.52
GB f1 score: 0.724
GB cohens kappa score: 0.716


minimum:
GB tn, fp: 280, 0
GB fn, tp: 1, 4
GB f1 score: 0.455
GB cohens kappa score: 0.434


-----[ KNN ]-----
maximum:
KNN tn, fp: 286, 17
KNN fn, tp: 2, 9
KNN f1 score: 0.842
KNN cohens kappa score: 0.837


average:
KNN tn, fp: 278.88, 9.12
KNN fn, tp: 0.48, 8.32
KNN f1 score: 0.646
KNN cohens kappa score: 0.631


minimum:
KNN tn, fp: 271, 2
KNN fn, tp: 0, 6
KNN f1 score: 0.444
KNN cohens kappa score: 0.422


-----[ GAN ]-----
maximum:
GAN tn, fp: 287, 11
GAN fn, tp: 4, 9
GAN f1 score: 0.783
GAN cohens kappa score: 0.774


average:
GAN tn, fp: 281.84, 6.16
GAN fn, tp: 1.64, 7.16
GAN f1 score: 0.652
GAN cohens kappa score: 0.639


minimum:
GAN tn, fp: 277, 1
GAN fn, tp: 0, 5
GAN f1 score: 0.462
GAN cohens kappa score: 0.439