ConvGeNBenchmarkResults/data_result/ConvGeN-majority-5/folding_car-vgood.log

///////////////////////////////////////////
// Running convGAN-majority-5 on folding_car-vgood
///////////////////////////////////////////

Load 'data_input/folding_car-vgood'
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 1278 synthetic samples
-> retrain GAN for predict
Epoch 1/10

  1/133 [..............................] - ETA: 15s - loss: 0.0268
 46/133 [=========>....................] - ETA: 0s - loss: 0.0356 
 91/133 [===================>..........] - ETA: 0s - loss: 0.0346
133/133 [==============================] - 0s 1ms/step - loss: 0.0364
Epoch 2/10

  1/133 [..............................] - ETA: 0s - loss: 0.0187
 52/133 [==========>...................] - ETA: 0s - loss: 0.0311
103/133 [======================>.......] - ETA: 0s - loss: 0.0294
133/133 [==============================] - 0s 991us/step - loss: 0.0343
Epoch 3/10

  1/133 [..............................] - ETA: 0s - loss: 0.0179
 52/133 [==========>...................] - ETA: 0s - loss: 0.0295
103/133 [======================>.......] - ETA: 0s - loss: 0.0328
133/133 [==============================] - 0s 993us/step - loss: 0.0335
Epoch 4/10

  1/133 [..............................] - ETA: 0s - loss: 0.0143
 52/133 [==========>...................] - ETA: 0s - loss: 0.0274
103/133 [======================>.......] - ETA: 0s - loss: 0.0332
133/133 [==============================] - 0s 987us/step - loss: 0.0314
Epoch 5/10

  1/133 [..............................] - ETA: 0s - loss: 0.0030
 49/133 [==========>...................] - ETA: 0s - loss: 0.0247
 97/133 [====================>.........] - ETA: 0s - loss: 0.0244
133/133 [==============================] - 0s 1ms/step - loss: 0.0293
Epoch 6/10

  1/133 [..............................] - ETA: 0s - loss: 0.0294
 49/133 [==========>...................] - ETA: 0s - loss: 0.0268
100/133 [=====================>........] - ETA: 0s - loss: 0.0285
133/133 [==============================] - 0s 1ms/step - loss: 0.0287
Epoch 7/10

  1/133 [..............................] - ETA: 0s - loss: 0.0146
 52/133 [==========>...................] - ETA: 0s - loss: 0.0214
103/133 [======================>.......] - ETA: 0s - loss: 0.0241
133/133 [==============================] - 0s 986us/step - loss: 0.0272
Epoch 8/10

  1/133 [..............................] - ETA: 0s - loss: 0.0327
 52/133 [==========>...................] - ETA: 0s - loss: 0.0311
103/133 [======================>.......] - ETA: 0s - loss: 0.0259
133/133 [==============================] - 0s 991us/step - loss: 0.0245
Epoch 9/10

  1/133 [..............................] - ETA: 0s - loss: 0.0076
 52/133 [==========>...................] - ETA: 0s - loss: 0.0305
103/133 [======================>.......] - ETA: 0s - loss: 0.0248
133/133 [==============================] - 0s 991us/step - loss: 0.0238
Epoch 10/10

  1/133 [..............................] - ETA: 0s - loss: 0.1428
 52/133 [==========>...................] - ETA: 0s - loss: 0.0211
103/133 [======================>.......] - ETA: 0s - loss: 0.0221
133/133 [==============================] - 0s 985us/step - loss: 0.0227
-> test with GAN.predict
GAN tn, fp: 328, 5
GAN fn, tp: 1, 12
GAN f1 score: 0.800
GAN cohens kappa score: 0.791

-> test with 'LR'
LR tn, fp: 285, 48
LR fn, tp: 0, 13
LR f1 score: 0.351
LR cohens kappa score: 0.309
LR average precision score: 0.356

-> test with 'RF'
RF tn, fp: 332, 1
RF fn, tp: 1, 12
RF f1 score: 0.923
RF cohens kappa score: 0.920

-> test with 'GB'
GB tn, fp: 331, 2
GB fn, tp: 0, 13
GB f1 score: 0.929
GB cohens kappa score: 0.926

-> test with 'KNN'
KNN tn, fp: 318, 15
KNN fn, tp: 0, 13
KNN f1 score: 0.634
KNN cohens kappa score: 0.614


------ Step 1/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1278 synthetic samples
-> retrain GAN for predict
Epoch 1/10

  1/133 [..............................] - ETA: 16s - loss: 0.0152
 50/133 [==========>...................] - ETA: 0s - loss: 0.0324 
100/133 [=====================>........] - ETA: 0s - loss: 0.0349
133/133 [==============================] - 0s 1ms/step - loss: 0.0374
Epoch 2/10

  1/133 [..............................] - ETA: 0s - loss: 0.0132
 52/133 [==========>...................] - ETA: 0s - loss: 0.0386
102/133 [======================>.......] - ETA: 0s - loss: 0.0389
133/133 [==============================] - 0s 1ms/step - loss: 0.0355
Epoch 3/10

  1/133 [..............................] - ETA: 0s - loss: 0.0095
 52/133 [==========>...................] - ETA: 0s - loss: 0.0287
103/133 [======================>.......] - ETA: 0s - loss: 0.0339
133/133 [==============================] - 0s 995us/step - loss: 0.0333
Epoch 4/10

  1/133 [..............................] - ETA: 0s - loss: 0.0117
 52/133 [==========>...................] - ETA: 0s - loss: 0.0292
103/133 [======================>.......] - ETA: 0s - loss: 0.0314
133/133 [==============================] - 0s 997us/step - loss: 0.0316
Epoch 5/10

  1/133 [..............................] - ETA: 0s - loss: 0.0252
 52/133 [==========>...................] - ETA: 0s - loss: 0.0263
103/133 [======================>.......] - ETA: 0s - loss: 0.0297
133/133 [==============================] - 0s 992us/step - loss: 0.0290
Epoch 6/10

  1/133 [..............................] - ETA: 0s - loss: 0.0155
 52/133 [==========>...................] - ETA: 0s - loss: 0.0309
103/133 [======================>.......] - ETA: 0s - loss: 0.0299
133/133 [==============================] - 0s 995us/step - loss: 0.0278
Epoch 7/10

  1/133 [..............................] - ETA: 0s - loss: 0.0017
 52/133 [==========>...................] - ETA: 0s - loss: 0.0182
103/133 [======================>.......] - ETA: 0s - loss: 0.0256
133/133 [==============================] - 0s 988us/step - loss: 0.0248
Epoch 8/10

  1/133 [..............................] - ETA: 0s - loss: 0.0040
 52/133 [==========>...................] - ETA: 0s - loss: 0.0240
103/133 [======================>.......] - ETA: 0s - loss: 0.0255
133/133 [==============================] - 0s 994us/step - loss: 0.0244
Epoch 9/10

  1/133 [..............................] - ETA: 0s - loss: 0.0314
 52/133 [==========>...................] - ETA: 0s - loss: 0.0205
103/133 [======================>.......] - ETA: 0s - loss: 0.0232
133/133 [==============================] - 0s 991us/step - loss: 0.0229
Epoch 10/10

  1/133 [..............................] - ETA: 0s - loss: 0.0137
 52/133 [==========>...................] - ETA: 0s - loss: 0.0206
103/133 [======================>.......] - ETA: 0s - loss: 0.0200
133/133 [==============================] - 0s 992us/step - loss: 0.0207
-> test with GAN.predict
GAN tn, fp: 331, 2
GAN fn, tp: 1, 12
GAN f1 score: 0.889
GAN cohens kappa score: 0.884

-> test with 'LR'
LR tn, fp: 295, 38
LR fn, tp: 1, 12
LR f1 score: 0.381
LR cohens kappa score: 0.342
LR average precision score: 0.301

-> test with 'RF'
RF tn, fp: 333, 0
RF fn, tp: 2, 11
RF f1 score: 0.917
RF cohens kappa score: 0.914

-> test with 'GB'
GB tn, fp: 333, 0
GB fn, tp: 0, 13
GB f1 score: 1.000
GB cohens kappa score: 1.000

-> test with 'KNN'
KNN tn, fp: 316, 17
KNN fn, tp: 2, 11
KNN f1 score: 0.537
KNN cohens kappa score: 0.512


------ Step 1/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1278 synthetic samples
-> retrain GAN for predict
Epoch 1/10

  1/133 [..............................] - ETA: 15s - loss: 0.0188
 51/133 [==========>...................] - ETA: 0s - loss: 0.0372 
102/133 [======================>.......] - ETA: 0s - loss: 0.0452
133/133 [==============================] - 0s 999us/step - loss: 0.0401
Epoch 2/10

  1/133 [..............................] - ETA: 0s - loss: 0.0094
 51/133 [==========>...................] - ETA: 0s - loss: 0.0412
101/133 [=====================>........] - ETA: 0s - loss: 0.0401
133/133 [==============================] - 0s 1ms/step - loss: 0.0381
Epoch 3/10

  1/133 [..............................] - ETA: 0s - loss: 0.0052
 52/133 [==========>...................] - ETA: 0s - loss: 0.0392
103/133 [======================>.......] - ETA: 0s - loss: 0.0363
133/133 [==============================] - 0s 990us/step - loss: 0.0373
Epoch 4/10

  1/133 [..............................] - ETA: 0s - loss: 0.0295
 44/133 [========>.....................] - ETA: 0s - loss: 0.0333
 95/133 [====================>.........] - ETA: 0s - loss: 0.0304
133/133 [==============================] - 0s 1ms/step - loss: 0.0331
Epoch 5/10

  1/133 [..............................] - ETA: 0s - loss: 0.0647
 44/133 [========>.....................] - ETA: 0s - loss: 0.0360
 93/133 [===================>..........] - ETA: 0s - loss: 0.0318
133/133 [==============================] - 0s 1ms/step - loss: 0.0329
Epoch 6/10

  1/133 [..............................] - ETA: 0s - loss: 0.0033
 50/133 [==========>...................] - ETA: 0s - loss: 0.0303
100/133 [=====================>........] - ETA: 0s - loss: 0.0294
133/133 [==============================] - 0s 1ms/step - loss: 0.0305
Epoch 7/10

  1/133 [..............................] - ETA: 0s - loss: 0.0187
 52/133 [==========>...................] - ETA: 0s - loss: 0.0222
103/133 [======================>.......] - ETA: 0s - loss: 0.0262
133/133 [==============================] - 0s 990us/step - loss: 0.0275
Epoch 8/10

  1/133 [..............................] - ETA: 0s - loss: 0.0173
 52/133 [==========>...................] - ETA: 0s - loss: 0.0192
103/133 [======================>.......] - ETA: 0s - loss: 0.0251
133/133 [==============================] - 0s 993us/step - loss: 0.0286
Epoch 9/10

  1/133 [..............................] - ETA: 0s - loss: 0.0264
 52/133 [==========>...................] - ETA: 0s - loss: 0.0254
103/133 [======================>.......] - ETA: 0s - loss: 0.0260
133/133 [==============================] - 0s 995us/step - loss: 0.0246
Epoch 10/10

  1/133 [..............................] - ETA: 0s - loss: 0.0054
 52/133 [==========>...................] - ETA: 0s - loss: 0.0284
103/133 [======================>.......] - ETA: 0s - loss: 0.0238
133/133 [==============================] - 0s 985us/step - loss: 0.0248
-> test with GAN.predict
GAN tn, fp: 326, 7
GAN fn, tp: 2, 11
GAN f1 score: 0.710
GAN cohens kappa score: 0.696

-> test with 'LR'
LR tn, fp: 283, 50
LR fn, tp: 0, 13
LR f1 score: 0.342
LR cohens kappa score: 0.298
LR average precision score: 0.410

-> test with 'RF'
RF tn, fp: 333, 0
RF fn, tp: 1, 12
RF f1 score: 0.960
RF cohens kappa score: 0.959

-> test with 'GB'
GB tn, fp: 332, 1
GB fn, tp: 1, 12
GB f1 score: 0.923
GB cohens kappa score: 0.920

-> test with 'KNN'
KNN tn, fp: 314, 19
KNN fn, tp: 0, 13
KNN f1 score: 0.578
KNN cohens kappa score: 0.554


------ Step 1/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1278 synthetic samples
-> retrain GAN for predict
Epoch 1/10

  1/133 [..............................] - ETA: 16s - loss: 0.2278
 52/133 [==========>...................] - ETA: 0s - loss: 0.0466 
103/133 [======================>.......] - ETA: 0s - loss: 0.0358
133/133 [==============================] - 0s 990us/step - loss: 0.0365
Epoch 2/10

  1/133 [..............................] - ETA: 0s - loss: 0.0211
 52/133 [==========>...................] - ETA: 0s - loss: 0.0359
103/133 [======================>.......] - ETA: 0s - loss: 0.0381
133/133 [==============================] - 0s 989us/step - loss: 0.0369
Epoch 3/10

  1/133 [..............................] - ETA: 0s - loss: 0.0125
 52/133 [==========>...................] - ETA: 0s - loss: 0.0445
103/133 [======================>.......] - ETA: 0s - loss: 0.0359
133/133 [==============================] - 0s 991us/step - loss: 0.0333
Epoch 4/10

  1/133 [..............................] - ETA: 0s - loss: 0.0052
 52/133 [==========>...................] - ETA: 0s - loss: 0.0276
 98/133 [=====================>........] - ETA: 0s - loss: 0.0318
133/133 [==============================] - 0s 1ms/step - loss: 0.0297
Epoch 5/10

  1/133 [..............................] - ETA: 0s - loss: 0.0272
 50/133 [==========>...................] - ETA: 0s - loss: 0.0264
100/133 [=====================>........] - ETA: 0s - loss: 0.0305
133/133 [==============================] - 0s 1ms/step - loss: 0.0294
Epoch 6/10

  1/133 [..............................] - ETA: 0s - loss: 0.0357
 52/133 [==========>...................] - ETA: 0s - loss: 0.0330
103/133 [======================>.......] - ETA: 0s - loss: 0.0296
133/133 [==============================] - 0s 988us/step - loss: 0.0278
Epoch 7/10

  1/133 [..............................] - ETA: 0s - loss: 0.0096
 52/133 [==========>...................] - ETA: 0s - loss: 0.0269
103/133 [======================>.......] - ETA: 0s - loss: 0.0265
133/133 [==============================] - 0s 985us/step - loss: 0.0262
Epoch 8/10

  1/133 [..............................] - ETA: 0s - loss: 0.0108
 52/133 [==========>...................] - ETA: 0s - loss: 0.0211
103/133 [======================>.......] - ETA: 0s - loss: 0.0207
133/133 [==============================] - 0s 986us/step - loss: 0.0244
Epoch 9/10

  1/133 [..............................] - ETA: 0s - loss: 0.0141
 52/133 [==========>...................] - ETA: 0s - loss: 0.0275
103/133 [======================>.......] - ETA: 0s - loss: 0.0243
133/133 [==============================] - 0s 988us/step - loss: 0.0244
Epoch 10/10

  1/133 [..............................] - ETA: 0s - loss: 0.0455
 51/133 [==========>...................] - ETA: 0s - loss: 0.0254
101/133 [=====================>........] - ETA: 0s - loss: 0.0220
133/133 [==============================] - 0s 1ms/step - loss: 0.0235
-> test with GAN.predict
GAN tn, fp: 330, 3
GAN fn, tp: 3, 10
GAN f1 score: 0.769
GAN cohens kappa score: 0.760

-> test with 'LR'
LR tn, fp: 293, 40
LR fn, tp: 0, 13
LR f1 score: 0.394
LR cohens kappa score: 0.355
LR average precision score: 0.359

-> test with 'RF'
RF tn, fp: 333, 0
RF fn, tp: 3, 10
RF f1 score: 0.870
RF cohens kappa score: 0.865

-> test with 'GB'
GB tn, fp: 333, 0
GB fn, tp: 0, 13
GB f1 score: 1.000
GB cohens kappa score: 1.000

-> test with 'KNN'
KNN tn, fp: 324, 9
KNN fn, tp: 0, 13
KNN f1 score: 0.743
KNN cohens kappa score: 0.730


------ Step 1/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1280 synthetic samples
-> retrain GAN for predict
Epoch 1/10

  1/134 [..............................] - ETA: 18s - loss: 0.0495
 50/134 [==========>...................] - ETA: 0s - loss: 0.0415 
 99/134 [=====================>........] - ETA: 0s - loss: 0.0391
134/134 [==============================] - 0s 1ms/step - loss: 0.0372
Epoch 2/10

  1/134 [..............................] - ETA: 0s - loss: 0.0060
 50/134 [==========>...................] - ETA: 0s - loss: 0.0300
 99/134 [=====================>........] - ETA: 0s - loss: 0.0276
134/134 [==============================] - 0s 1ms/step - loss: 0.0350
Epoch 3/10

  1/134 [..............................] - ETA: 0s - loss: 0.0172
 45/134 [=========>....................] - ETA: 0s - loss: 0.0201
 91/134 [===================>..........] - ETA: 0s - loss: 0.0236
134/134 [==============================] - 0s 1ms/step - loss: 0.0329
Epoch 4/10

  1/134 [..............................] - ETA: 0s - loss: 0.0343
 46/134 [=========>....................] - ETA: 0s - loss: 0.0291
 95/134 [====================>.........] - ETA: 0s - loss: 0.0258
134/134 [==============================] - 0s 1ms/step - loss: 0.0307
Epoch 5/10

  1/134 [..............................] - ETA: 0s - loss: 0.0343
 50/134 [==========>...................] - ETA: 0s - loss: 0.0296
 99/134 [=====================>........] - ETA: 0s - loss: 0.0315
134/134 [==============================] - 0s 1ms/step - loss: 0.0290
Epoch 6/10

  1/134 [..............................] - ETA: 0s - loss: 0.0112
 50/134 [==========>...................] - ETA: 0s - loss: 0.0356
 99/134 [=====================>........] - ETA: 0s - loss: 0.0275
134/134 [==============================] - 0s 1ms/step - loss: 0.0275
Epoch 7/10

  1/134 [..............................] - ETA: 0s - loss: 0.0619
 49/134 [=========>....................] - ETA: 0s - loss: 0.0206
 97/134 [====================>.........] - ETA: 0s - loss: 0.0268
134/134 [==============================] - 0s 1ms/step - loss: 0.0262
Epoch 8/10

  1/134 [..............................] - ETA: 0s - loss: 0.0037
 49/134 [=========>....................] - ETA: 0s - loss: 0.0339
 98/134 [====================>.........] - ETA: 0s - loss: 0.0284
134/134 [==============================] - 0s 1ms/step - loss: 0.0240
Epoch 9/10

  1/134 [..............................] - ETA: 0s - loss: 0.0018
 50/134 [==========>...................] - ETA: 0s - loss: 0.0183
 99/134 [=====================>........] - ETA: 0s - loss: 0.0238
134/134 [==============================] - 0s 1ms/step - loss: 0.0229
Epoch 10/10

  1/134 [..............................] - ETA: 0s - loss: 0.0091
 50/134 [==========>...................] - ETA: 0s - loss: 0.0228
 99/134 [=====================>........] - ETA: 0s - loss: 0.0229
134/134 [==============================] - 0s 1ms/step - loss: 0.0221
-> test with GAN.predict
GAN tn, fp: 329, 2
GAN fn, tp: 2, 11
GAN f1 score: 0.846
GAN cohens kappa score: 0.840

-> test with 'LR'
LR tn, fp: 300, 31
LR fn, tp: 2, 11
LR f1 score: 0.400
LR cohens kappa score: 0.363
LR average precision score: 0.437

-> test with 'RF'
RF tn, fp: 330, 1
RF fn, tp: 2, 11
RF f1 score: 0.880
RF cohens kappa score: 0.875

-> test with 'GB'
GB tn, fp: 328, 3
GB fn, tp: 1, 12
GB f1 score: 0.857
GB cohens kappa score: 0.851

-> test with 'KNN'
KNN tn, fp: 320, 11
KNN fn, tp: 0, 13
KNN f1 score: 0.703
KNN cohens kappa score: 0.687


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

------ Step 2/5: Slice 1/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1278 synthetic samples
-> retrain GAN for predict
Epoch 1/10

  1/133 [..............................] - ETA: 15s - loss: 0.0494
 46/133 [=========>....................] - ETA: 0s - loss: 0.0374 
 97/133 [====================>.........] - ETA: 0s - loss: 0.0382
133/133 [==============================] - 0s 1ms/step - loss: 0.0355
Epoch 2/10

  1/133 [..............................] - ETA: 0s - loss: 0.0126
 52/133 [==========>...................] - ETA: 0s - loss: 0.0286
103/133 [======================>.......] - ETA: 0s - loss: 0.0356
133/133 [==============================] - 0s 988us/step - loss: 0.0345
Epoch 3/10

  1/133 [..............................] - ETA: 0s - loss: 0.0130
 52/133 [==========>...................] - ETA: 0s - loss: 0.0344
103/133 [======================>.......] - ETA: 0s - loss: 0.0336
133/133 [==============================] - 0s 992us/step - loss: 0.0330
Epoch 4/10

  1/133 [..............................] - ETA: 0s - loss: 0.0095
 52/133 [==========>...................] - ETA: 0s - loss: 0.0360
103/133 [======================>.......] - ETA: 0s - loss: 0.0289
133/133 [==============================] - 0s 986us/step - loss: 0.0301
Epoch 5/10

  1/133 [..............................] - ETA: 0s - loss: 0.0108
 52/133 [==========>...................] - ETA: 0s - loss: 0.0375
103/133 [======================>.......] - ETA: 0s - loss: 0.0328
133/133 [==============================] - 0s 991us/step - loss: 0.0295
Epoch 6/10

  1/133 [..............................] - ETA: 0s - loss: 0.1089
 52/133 [==========>...................] - ETA: 0s - loss: 0.0316
103/133 [======================>.......] - ETA: 0s - loss: 0.0288
133/133 [==============================] - 0s 989us/step - loss: 0.0271
Epoch 7/10

  1/133 [..............................] - ETA: 0s - loss: 0.0162
 52/133 [==========>...................] - ETA: 0s - loss: 0.0173
103/133 [======================>.......] - ETA: 0s - loss: 0.0250
133/133 [==============================] - 0s 990us/step - loss: 0.0253
Epoch 8/10

  1/133 [..............................] - ETA: 0s - loss: 0.0094
 51/133 [==========>...................] - ETA: 0s - loss: 0.0170
102/133 [======================>.......] - ETA: 0s - loss: 0.0199
133/133 [==============================] - 0s 995us/step - loss: 0.0244
Epoch 9/10

  1/133 [..............................] - ETA: 0s - loss: 0.0016
 52/133 [==========>...................] - ETA: 0s - loss: 0.0234
 92/133 [===================>..........] - ETA: 0s - loss: 0.0242
133/133 [==============================] - 0s 1ms/step - loss: 0.0234
Epoch 10/10

  1/133 [..............................] - ETA: 0s - loss: 0.0233
 50/133 [==========>...................] - ETA: 0s - loss: 0.0249
100/133 [=====================>........] - ETA: 0s - loss: 0.0227
133/133 [==============================] - 0s 1ms/step - loss: 0.0221
-> test with GAN.predict
GAN tn, fp: 328, 5
GAN fn, tp: 3, 10
GAN f1 score: 0.714
GAN cohens kappa score: 0.702

-> test with 'LR'
LR tn, fp: 292, 41
LR fn, tp: 0, 13
LR f1 score: 0.388
LR cohens kappa score: 0.349
LR average precision score: 0.292

-> test with 'RF'
RF tn, fp: 333, 0
RF fn, tp: 2, 11
RF f1 score: 0.917
RF cohens kappa score: 0.914

-> test with 'GB'
GB tn, fp: 333, 0
GB fn, tp: 2, 11
GB f1 score: 0.917
GB cohens kappa score: 0.914

-> test with 'KNN'
KNN tn, fp: 315, 18
KNN fn, tp: 0, 13
KNN f1 score: 0.591
KNN cohens kappa score: 0.568


------ Step 2/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1278 synthetic samples
-> retrain GAN for predict
Epoch 1/10

  1/133 [..............................] - ETA: 15s - loss: 0.0733
 51/133 [==========>...................] - ETA: 0s - loss: 0.0466 
102/133 [======================>.......] - ETA: 0s - loss: 0.0403
133/133 [==============================] - 0s 1ms/step - loss: 0.0369
Epoch 2/10

  1/133 [..............................] - ETA: 0s - loss: 0.0247
 52/133 [==========>...................] - ETA: 0s - loss: 0.0333
103/133 [======================>.......] - ETA: 0s - loss: 0.0323
133/133 [==============================] - 0s 993us/step - loss: 0.0341
Epoch 3/10

  1/133 [..............................] - ETA: 0s - loss: 0.0229
 51/133 [==========>...................] - ETA: 0s - loss: 0.0360
102/133 [======================>.......] - ETA: 0s - loss: 0.0354
133/133 [==============================] - 0s 999us/step - loss: 0.0319
Epoch 4/10

  1/133 [..............................] - ETA: 0s - loss: 0.0683
 52/133 [==========>...................] - ETA: 0s - loss: 0.0310
103/133 [======================>.......] - ETA: 0s - loss: 0.0325
133/133 [==============================] - 0s 990us/step - loss: 0.0289
Epoch 5/10

  1/133 [..............................] - ETA: 0s - loss: 0.2729
 52/133 [==========>...................] - ETA: 0s - loss: 0.0301
103/133 [======================>.......] - ETA: 0s - loss: 0.0284
133/133 [==============================] - 0s 996us/step - loss: 0.0288
Epoch 6/10

  1/133 [..............................] - ETA: 0s - loss: 0.0027
 52/133 [==========>...................] - ETA: 0s - loss: 0.0279
103/133 [======================>.......] - ETA: 0s - loss: 0.0290
133/133 [==============================] - 0s 997us/step - loss: 0.0258
Epoch 7/10

  1/133 [..............................] - ETA: 0s - loss: 0.0111
 51/133 [==========>...................] - ETA: 0s - loss: 0.0244
102/133 [======================>.......] - ETA: 0s - loss: 0.0235
133/133 [==============================] - 0s 999us/step - loss: 0.0256
Epoch 8/10

  1/133 [..............................] - ETA: 0s - loss: 0.0151
 49/133 [==========>...................] - ETA: 0s - loss: 0.0196
 95/133 [====================>.........] - ETA: 0s - loss: 0.0246
133/133 [==============================] - 0s 1ms/step - loss: 0.0234
Epoch 9/10

  1/133 [..............................] - ETA: 0s - loss: 0.0078
 47/133 [=========>....................] - ETA: 0s - loss: 0.0173
 96/133 [====================>.........] - ETA: 0s - loss: 0.0235
133/133 [==============================] - 0s 1ms/step - loss: 0.0221
Epoch 10/10

  1/133 [..............................] - ETA: 0s - loss: 0.0967
 52/133 [==========>...................] - ETA: 0s - loss: 0.0192
103/133 [======================>.......] - ETA: 0s - loss: 0.0230
133/133 [==============================] - 0s 995us/step - loss: 0.0225
-> test with GAN.predict
GAN tn, fp: 325, 8
GAN fn, tp: 1, 12
GAN f1 score: 0.727
GAN cohens kappa score: 0.714

-> test with 'LR'
LR tn, fp: 277, 56
LR fn, tp: 0, 13
LR f1 score: 0.317
LR cohens kappa score: 0.271
LR average precision score: 0.265

-> test with 'RF'
RF tn, fp: 331, 2
RF fn, tp: 1, 12
RF f1 score: 0.889
RF cohens kappa score: 0.884

-> test with 'GB'
GB tn, fp: 327, 6
GB fn, tp: 0, 13
GB f1 score: 0.813
GB cohens kappa score: 0.804

-> test with 'KNN'
KNN tn, fp: 315, 18
KNN fn, tp: 0, 13
KNN f1 score: 0.591
KNN cohens kappa score: 0.568


------ Step 2/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1278 synthetic samples
-> retrain GAN for predict
Epoch 1/10

  1/133 [..............................] - ETA: 15s - loss: 0.0073
 50/133 [==========>...................] - ETA: 0s - loss: 0.0317 
100/133 [=====================>........] - ETA: 0s - loss: 0.0306
133/133 [==============================] - 0s 1ms/step - loss: 0.0340
Epoch 2/10

  1/133 [..............................] - ETA: 0s - loss: 0.0260
 52/133 [==========>...................] - ETA: 0s - loss: 0.0335
101/133 [=====================>........] - ETA: 0s - loss: 0.0302
133/133 [==============================] - 0s 1ms/step - loss: 0.0311
Epoch 3/10

  1/133 [..............................] - ETA: 0s - loss: 0.0165
 52/133 [==========>...................] - ETA: 0s - loss: 0.0337
103/133 [======================>.......] - ETA: 0s - loss: 0.0296
133/133 [==============================] - 0s 990us/step - loss: 0.0287
Epoch 4/10

  1/133 [..............................] - ETA: 0s - loss: 0.0105
 52/133 [==========>...................] - ETA: 0s - loss: 0.0297
103/133 [======================>.......] - ETA: 0s - loss: 0.0256
133/133 [==============================] - 0s 1ms/step - loss: 0.0273
Epoch 5/10

  1/133 [..............................] - ETA: 0s - loss: 0.0690
 50/133 [==========>...................] - ETA: 0s - loss: 0.0193
 99/133 [=====================>........] - ETA: 0s - loss: 0.0230
133/133 [==============================] - 0s 1ms/step - loss: 0.0247
Epoch 6/10

  1/133 [..............................] - ETA: 0s - loss: 0.0103
 52/133 [==========>...................] - ETA: 0s - loss: 0.0254
 99/133 [=====================>........] - ETA: 0s - loss: 0.0236
133/133 [==============================] - 0s 1ms/step - loss: 0.0254
Epoch 7/10

  1/133 [..............................] - ETA: 0s - loss: 0.0126
 45/133 [=========>....................] - ETA: 0s - loss: 0.0184
 94/133 [====================>.........] - ETA: 0s - loss: 0.0232
133/133 [==============================] - 0s 1ms/step - loss: 0.0217
Epoch 8/10

  1/133 [..............................] - ETA: 0s - loss: 0.0023
 52/133 [==========>...................] - ETA: 0s - loss: 0.0191
 98/133 [=====================>........] - ETA: 0s - loss: 0.0213
133/133 [==============================] - 0s 1ms/step - loss: 0.0209
Epoch 9/10

  1/133 [..............................] - ETA: 0s - loss: 0.0070
 52/133 [==========>...................] - ETA: 0s - loss: 0.0173
103/133 [======================>.......] - ETA: 0s - loss: 0.0202
133/133 [==============================] - 0s 990us/step - loss: 0.0194
Epoch 10/10

  1/133 [..............................] - ETA: 0s - loss: 0.0023
 52/133 [==========>...................] - ETA: 0s - loss: 0.0158
103/133 [======================>.......] - ETA: 0s - loss: 0.0180
133/133 [==============================] - 0s 988us/step - loss: 0.0185
-> test with GAN.predict
GAN tn, fp: 331, 2
GAN fn, tp: 1, 12
GAN f1 score: 0.889
GAN cohens kappa score: 0.884

-> test with 'LR'
LR tn, fp: 294, 39
LR fn, tp: 2, 11
LR f1 score: 0.349
LR cohens kappa score: 0.308
LR average precision score: 0.325

-> test with 'RF'
RF tn, fp: 332, 1
RF fn, tp: 2, 11
RF f1 score: 0.880
RF cohens kappa score: 0.876

-> test with 'GB'
GB tn, fp: 331, 2
GB fn, tp: 0, 13
GB f1 score: 0.929
GB cohens kappa score: 0.926

-> test with 'KNN'
KNN tn, fp: 322, 11
KNN fn, tp: 1, 12
KNN f1 score: 0.667
KNN cohens kappa score: 0.650


------ Step 2/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1278 synthetic samples
-> retrain GAN for predict
Epoch 1/10

  1/133 [..............................] - ETA: 17s - loss: 0.0307
 51/133 [==========>...................] - ETA: 0s - loss: 0.0342 
101/133 [=====================>........] - ETA: 0s - loss: 0.0359
133/133 [==============================] - 0s 1ms/step - loss: 0.0333
Epoch 2/10

  1/133 [..............................] - ETA: 0s - loss: 0.0072
 52/133 [==========>...................] - ETA: 0s - loss: 0.0346
102/133 [======================>.......] - ETA: 0s - loss: 0.0310
133/133 [==============================] - 0s 999us/step - loss: 0.0316
Epoch 3/10

  1/133 [..............................] - ETA: 0s - loss: 0.0070
 51/133 [==========>...................] - ETA: 0s - loss: 0.0185
102/133 [======================>.......] - ETA: 0s - loss: 0.0252
133/133 [==============================] - 0s 998us/step - loss: 0.0292
Epoch 4/10

  1/133 [..............................] - ETA: 0s - loss: 0.0172
 49/133 [==========>...................] - ETA: 0s - loss: 0.0210
 96/133 [====================>.........] - ETA: 0s - loss: 0.0264
133/133 [==============================] - 0s 1ms/step - loss: 0.0296
Epoch 5/10

  1/133 [..............................] - ETA: 0s - loss: 0.0157
 49/133 [==========>...................] - ETA: 0s - loss: 0.0298
 99/133 [=====================>........] - ETA: 0s - loss: 0.0289
133/133 [==============================] - 0s 1ms/step - loss: 0.0279
Epoch 6/10

  1/133 [..............................] - ETA: 0s - loss: 0.0183
 49/133 [==========>...................] - ETA: 0s - loss: 0.0288
100/133 [=====================>........] - ETA: 0s - loss: 0.0289
133/133 [==============================] - 0s 1ms/step - loss: 0.0250
Epoch 7/10

  1/133 [..............................] - ETA: 0s - loss: 0.0468
 52/133 [==========>...................] - ETA: 0s - loss: 0.0287
102/133 [======================>.......] - ETA: 0s - loss: 0.0244
133/133 [==============================] - 0s 996us/step - loss: 0.0240
Epoch 8/10

  1/133 [..............................] - ETA: 0s - loss: 0.0033
 52/133 [==========>...................] - ETA: 0s - loss: 0.0202
 98/133 [=====================>........] - ETA: 0s - loss: 0.0223
133/133 [==============================] - 0s 1ms/step - loss: 0.0237
Epoch 9/10

  1/133 [..............................] - ETA: 0s - loss: 0.0073
 47/133 [=========>....................] - ETA: 0s - loss: 0.0192
 97/133 [====================>.........] - ETA: 0s - loss: 0.0222
133/133 [==============================] - 0s 1ms/step - loss: 0.0222
Epoch 10/10

  1/133 [..............................] - ETA: 0s - loss: 0.0056
 52/133 [==========>...................] - ETA: 0s - loss: 0.0184
102/133 [======================>.......] - ETA: 0s - loss: 0.0196
133/133 [==============================] - 0s 996us/step - loss: 0.0209
-> test with GAN.predict
GAN tn, fp: 325, 8
GAN fn, tp: 2, 11
GAN f1 score: 0.688
GAN cohens kappa score: 0.673

-> test with 'LR'
LR tn, fp: 298, 35
LR fn, tp: 0, 13
LR f1 score: 0.426
LR cohens kappa score: 0.390
LR average precision score: 0.288

-> test with 'RF'
RF tn, fp: 333, 0
RF fn, tp: 3, 10
RF f1 score: 0.870
RF cohens kappa score: 0.865

-> test with 'GB'
GB tn, fp: 333, 0
GB fn, tp: 2, 11
GB f1 score: 0.917
GB cohens kappa score: 0.914

-> test with 'KNN'
KNN tn, fp: 324, 9
KNN fn, tp: 2, 11
KNN f1 score: 0.667
KNN cohens kappa score: 0.651


------ Step 2/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1280 synthetic samples
-> retrain GAN for predict
Epoch 1/10

  1/134 [..............................] - ETA: 18s - loss: 0.0232
 47/134 [=========>....................] - ETA: 0s - loss: 0.0327 
 95/134 [====================>.........] - ETA: 0s - loss: 0.0336
134/134 [==============================] - 0s 1ms/step - loss: 0.0350
Epoch 2/10

  1/134 [..............................] - ETA: 0s - loss: 0.0444
 49/134 [=========>....................] - ETA: 0s - loss: 0.0293
 98/134 [====================>.........] - ETA: 0s - loss: 0.0361
134/134 [==============================] - 0s 1ms/step - loss: 0.0335
Epoch 3/10

  1/134 [..............................] - ETA: 0s - loss: 0.0112
 46/134 [=========>....................] - ETA: 0s - loss: 0.0346
 91/134 [===================>..........] - ETA: 0s - loss: 0.0333
134/134 [==============================] - 0s 1ms/step - loss: 0.0319
Epoch 4/10

  1/134 [..............................] - ETA: 0s - loss: 0.0095
 49/134 [=========>....................] - ETA: 0s - loss: 0.0190
 97/134 [====================>.........] - ETA: 0s - loss: 0.0233
134/134 [==============================] - 0s 1ms/step - loss: 0.0300
Epoch 5/10

  1/134 [..............................] - ETA: 0s - loss: 0.0193
 50/134 [==========>...................] - ETA: 0s - loss: 0.0220
 99/134 [=====================>........] - ETA: 0s - loss: 0.0297
134/134 [==============================] - 0s 1ms/step - loss: 0.0294
Epoch 6/10

  1/134 [..............................] - ETA: 0s - loss: 0.0067
 49/134 [=========>....................] - ETA: 0s - loss: 0.0236
 97/134 [====================>.........] - ETA: 0s - loss: 0.0266
134/134 [==============================] - 0s 1ms/step - loss: 0.0271
Epoch 7/10

  1/134 [..............................] - ETA: 0s - loss: 0.0160
 44/134 [========>.....................] - ETA: 0s - loss: 0.0280
 86/134 [==================>...........] - ETA: 0s - loss: 0.0258
134/134 [==============================] - ETA: 0s - loss: 0.0271
134/134 [==============================] - 0s 1ms/step - loss: 0.0271
Epoch 8/10

  1/134 [..............................] - ETA: 0s - loss: 0.0163
 49/134 [=========>....................] - ETA: 0s - loss: 0.0187
 97/134 [====================>.........] - ETA: 0s - loss: 0.0236
134/134 [==============================] - 0s 1ms/step - loss: 0.0238
Epoch 9/10

  1/134 [..............................] - ETA: 0s - loss: 0.0108
 49/134 [=========>....................] - ETA: 0s - loss: 0.0244
 97/134 [====================>.........] - ETA: 0s - loss: 0.0245
134/134 [==============================] - 0s 1ms/step - loss: 0.0235
Epoch 10/10

  1/134 [..............................] - ETA: 0s - loss: 0.0166
 49/134 [=========>....................] - ETA: 0s - loss: 0.0280
 97/134 [====================>.........] - ETA: 0s - loss: 0.0238
134/134 [==============================] - 0s 1ms/step - loss: 0.0216
-> test with GAN.predict
GAN tn, fp: 325, 6
GAN fn, tp: 2, 11
GAN f1 score: 0.733
GAN cohens kappa score: 0.721

-> test with 'LR'
LR tn, fp: 288, 43
LR fn, tp: 0, 13
LR f1 score: 0.377
LR cohens kappa score: 0.336
LR average precision score: 0.524

-> test with 'RF'
RF tn, fp: 331, 0
RF fn, tp: 0, 13
RF f1 score: 1.000
RF cohens kappa score: 1.000

-> test with 'GB'
GB tn, fp: 331, 0
GB fn, tp: 0, 13
GB f1 score: 1.000
GB cohens kappa score: 1.000

-> test with 'KNN'
KNN tn, fp: 319, 12
KNN fn, tp: 0, 13
KNN f1 score: 0.684
KNN cohens kappa score: 0.668


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

------ Step 3/5: Slice 1/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1278 synthetic samples
-> retrain GAN for predict
Epoch 1/10

  1/133 [..............................] - ETA: 17s - loss: 0.0205
 42/133 [========>.....................] - ETA: 0s - loss: 0.0206 
 92/133 [===================>..........] - ETA: 0s - loss: 0.0276
133/133 [==============================] - 0s 1ms/step - loss: 0.0282
Epoch 2/10

  1/133 [..............................] - ETA: 0s - loss: 0.0083
 52/133 [==========>...................] - ETA: 0s - loss: 0.0231
103/133 [======================>.......] - ETA: 0s - loss: 0.0255
133/133 [==============================] - 0s 996us/step - loss: 0.0266
Epoch 3/10

  1/133 [..............................] - ETA: 0s - loss: 0.0359
 52/133 [==========>...................] - ETA: 0s - loss: 0.0214
103/133 [======================>.......] - ETA: 0s - loss: 0.0240
133/133 [==============================] - 0s 995us/step - loss: 0.0246
Epoch 4/10

  1/133 [..............................] - ETA: 0s - loss: 0.0192
 51/133 [==========>...................] - ETA: 0s - loss: 0.0265
102/133 [======================>.......] - ETA: 0s - loss: 0.0235
133/133 [==============================] - 0s 999us/step - loss: 0.0231
Epoch 5/10

  1/133 [..............................] - ETA: 0s - loss: 0.0018
 52/133 [==========>...................] - ETA: 0s - loss: 0.0206
103/133 [======================>.......] - ETA: 0s - loss: 0.0232
133/133 [==============================] - 0s 994us/step - loss: 0.0220
Epoch 6/10

  1/133 [..............................] - ETA: 0s - loss: 0.0120
 52/133 [==========>...................] - ETA: 0s - loss: 0.0167
103/133 [======================>.......] - ETA: 0s - loss: 0.0199
133/133 [==============================] - 0s 994us/step - loss: 0.0207
Epoch 7/10

  1/133 [..............................] - ETA: 0s - loss: 0.0116
 52/133 [==========>...................] - ETA: 0s - loss: 0.0172
103/133 [======================>.......] - ETA: 0s - loss: 0.0156
133/133 [==============================] - 0s 992us/step - loss: 0.0189
Epoch 8/10

  1/133 [..............................] - ETA: 0s - loss: 0.0347
 52/133 [==========>...................] - ETA: 0s - loss: 0.0224
103/133 [======================>.......] - ETA: 0s - loss: 0.0197
133/133 [==============================] - 0s 992us/step - loss: 0.0201
Epoch 9/10

  1/133 [..............................] - ETA: 0s - loss: 0.0093
 52/133 [==========>...................] - ETA: 0s - loss: 0.0169
103/133 [======================>.......] - ETA: 0s - loss: 0.0171
133/133 [==============================] - 0s 995us/step - loss: 0.0164
Epoch 10/10

  1/133 [..............................] - ETA: 0s - loss: 0.0292
 52/133 [==========>...................] - ETA: 0s - loss: 0.0145
103/133 [======================>.......] - ETA: 0s - loss: 0.0162
133/133 [==============================] - 0s 996us/step - loss: 0.0160
-> test with GAN.predict
GAN tn, fp: 329, 4
GAN fn, tp: 2, 11
GAN f1 score: 0.786
GAN cohens kappa score: 0.777

-> test with 'LR'
LR tn, fp: 294, 39
LR fn, tp: 1, 12
LR f1 score: 0.375
LR cohens kappa score: 0.335
LR average precision score: 0.266

-> test with 'RF'
RF tn, fp: 332, 1
RF fn, tp: 4, 9
RF f1 score: 0.783
RF cohens kappa score: 0.775

-> test with 'GB'
GB tn, fp: 331, 2
GB fn, tp: 3, 10
GB f1 score: 0.800
GB cohens kappa score: 0.793

-> test with 'KNN'
KNN tn, fp: 324, 9
KNN fn, tp: 3, 10
KNN f1 score: 0.625
KNN cohens kappa score: 0.607


------ Step 3/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1278 synthetic samples
-> retrain GAN for predict
Epoch 1/10

  1/133 [..............................] - ETA: 15s - loss: 0.0160
 51/133 [==========>...................] - ETA: 0s - loss: 0.0414 
102/133 [======================>.......] - ETA: 0s - loss: 0.0388
133/133 [==============================] - 0s 1ms/step - loss: 0.0373
Epoch 2/10

  1/133 [..............................] - ETA: 0s - loss: 0.0159
 52/133 [==========>...................] - ETA: 0s - loss: 0.0299
103/133 [======================>.......] - ETA: 0s - loss: 0.0319
133/133 [==============================] - 0s 995us/step - loss: 0.0346
Epoch 3/10

  1/133 [..............................] - ETA: 0s - loss: 0.0133
 52/133 [==========>...................] - ETA: 0s - loss: 0.0417
103/133 [======================>.......] - ETA: 0s - loss: 0.0317
133/133 [==============================] - 0s 992us/step - loss: 0.0319
Epoch 4/10

  1/133 [..............................] - ETA: 0s - loss: 0.0152
 52/133 [==========>...................] - ETA: 0s - loss: 0.0325
102/133 [======================>.......] - ETA: 0s - loss: 0.0294
133/133 [==============================] - 0s 995us/step - loss: 0.0318
Epoch 5/10

  1/133 [..............................] - ETA: 0s - loss: 0.0066
 52/133 [==========>...................] - ETA: 0s - loss: 0.0355
103/133 [======================>.......] - ETA: 0s - loss: 0.0365
133/133 [==============================] - 0s 992us/step - loss: 0.0320
Epoch 6/10

  1/133 [..............................] - ETA: 0s - loss: 0.0060
 52/133 [==========>...................] - ETA: 0s - loss: 0.0212
103/133 [======================>.......] - ETA: 0s - loss: 0.0236
133/133 [==============================] - 0s 991us/step - loss: 0.0278
Epoch 7/10

  1/133 [..............................] - ETA: 0s - loss: 0.0509
 52/133 [==========>...................] - ETA: 0s - loss: 0.0240
103/133 [======================>.......] - ETA: 0s - loss: 0.0259
133/133 [==============================] - 0s 989us/step - loss: 0.0278
Epoch 8/10

  1/133 [..............................] - ETA: 0s - loss: 0.0281
 52/133 [==========>...................] - ETA: 0s - loss: 0.0278
100/133 [=====================>........] - ETA: 0s - loss: 0.0278
133/133 [==============================] - 0s 1ms/step - loss: 0.0259
Epoch 9/10

  1/133 [..............................] - ETA: 0s - loss: 0.0106
 47/133 [=========>....................] - ETA: 0s - loss: 0.0227
 95/133 [====================>.........] - ETA: 0s - loss: 0.0261
133/133 [==============================] - 0s 1ms/step - loss: 0.0250
Epoch 10/10

  1/133 [..............................] - ETA: 0s - loss: 0.0156
 52/133 [==========>...................] - ETA: 0s - loss: 0.0317
103/133 [======================>.......] - ETA: 0s - loss: 0.0262
133/133 [==============================] - 0s 991us/step - loss: 0.0245
-> test with GAN.predict
GAN tn, fp: 328, 5
GAN fn, tp: 1, 12
GAN f1 score: 0.800
GAN cohens kappa score: 0.791

-> test with 'LR'
LR tn, fp: 296, 37
LR fn, tp: 0, 13
LR f1 score: 0.413
LR cohens kappa score: 0.375
LR average precision score: 0.396

-> test with 'RF'
RF tn, fp: 333, 0
RF fn, tp: 2, 11
RF f1 score: 0.917
RF cohens kappa score: 0.914

-> test with 'GB'
GB tn, fp: 331, 2
GB fn, tp: 0, 13
GB f1 score: 0.929
GB cohens kappa score: 0.926

-> test with 'KNN'
KNN tn, fp: 318, 15
KNN fn, tp: 0, 13
KNN f1 score: 0.634
KNN cohens kappa score: 0.614


------ Step 3/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1278 synthetic samples
-> retrain GAN for predict
Epoch 1/10

  1/133 [..............................] - ETA: 16s - loss: 0.0648
 51/133 [==========>...................] - ETA: 0s - loss: 0.0366 
101/133 [=====================>........] - ETA: 0s - loss: 0.0382
133/133 [==============================] - 0s 1ms/step - loss: 0.0373
Epoch 2/10

  1/133 [..............................] - ETA: 0s - loss: 0.0992
 52/133 [==========>...................] - ETA: 0s - loss: 0.0252
103/133 [======================>.......] - ETA: 0s - loss: 0.0316
133/133 [==============================] - 0s 996us/step - loss: 0.0340
Epoch 3/10

  1/133 [..............................] - ETA: 0s - loss: 0.0270
 52/133 [==========>...................] - ETA: 0s - loss: 0.0232
102/133 [======================>.......] - ETA: 0s - loss: 0.0335
133/133 [==============================] - 0s 997us/step - loss: 0.0321
Epoch 4/10

  1/133 [..............................] - ETA: 0s - loss: 0.0027
 52/133 [==========>...................] - ETA: 0s - loss: 0.0282
103/133 [======================>.......] - ETA: 0s - loss: 0.0334
133/133 [==============================] - 0s 997us/step - loss: 0.0308
Epoch 5/10

  1/133 [..............................] - ETA: 0s - loss: 0.0037
 51/133 [==========>...................] - ETA: 0s - loss: 0.0285
 94/133 [====================>.........] - ETA: 0s - loss: 0.0248
133/133 [==============================] - 0s 1ms/step - loss: 0.0288
Epoch 6/10

  1/133 [..............................] - ETA: 0s - loss: 0.1930
 48/133 [=========>....................] - ETA: 0s - loss: 0.0317
 99/133 [=====================>........] - ETA: 0s - loss: 0.0254
133/133 [==============================] - 0s 1ms/step - loss: 0.0273
Epoch 7/10

  1/133 [..............................] - ETA: 0s - loss: 0.0100
 51/133 [==========>...................] - ETA: 0s - loss: 0.0272
102/133 [======================>.......] - ETA: 0s - loss: 0.0236
133/133 [==============================] - 0s 1000us/step - loss: 0.0251
Epoch 8/10

  1/133 [..............................] - ETA: 0s - loss: 0.0122
 52/133 [==========>...................] - ETA: 0s - loss: 0.0278
101/133 [=====================>........] - ETA: 0s - loss: 0.0248
133/133 [==============================] - 0s 1ms/step - loss: 0.0238
Epoch 9/10

  1/133 [..............................] - ETA: 0s - loss: 0.0124
 52/133 [==========>...................] - ETA: 0s - loss: 0.0175
103/133 [======================>.......] - ETA: 0s - loss: 0.0180
133/133 [==============================] - 0s 1ms/step - loss: 0.0220
Epoch 10/10

  1/133 [..............................] - ETA: 0s - loss: 0.0070
 52/133 [==========>...................] - ETA: 0s - loss: 0.0241
103/133 [======================>.......] - ETA: 0s - loss: 0.0206
133/133 [==============================] - 0s 991us/step - loss: 0.0215
-> test with GAN.predict
GAN tn, fp: 327, 6
GAN fn, tp: 0, 13
GAN f1 score: 0.813
GAN cohens kappa score: 0.804

-> test with 'LR'
LR tn, fp: 279, 54
LR fn, tp: 0, 13
LR f1 score: 0.325
LR cohens kappa score: 0.280
LR average precision score: 0.333

-> test with 'RF'
RF tn, fp: 333, 0
RF fn, tp: 1, 12
RF f1 score: 0.960
RF cohens kappa score: 0.959

-> test with 'GB'
GB tn, fp: 332, 1
GB fn, tp: 0, 13
GB f1 score: 0.963
GB cohens kappa score: 0.961

-> test with 'KNN'
KNN tn, fp: 312, 21
KNN fn, tp: 0, 13
KNN f1 score: 0.553
KNN cohens kappa score: 0.528


------ Step 3/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1278 synthetic samples
-> retrain GAN for predict
Epoch 1/10

  1/133 [..............................] - ETA: 15s - loss: 0.0170
 49/133 [==========>...................] - ETA: 0s - loss: 0.0297 
 94/133 [====================>.........] - ETA: 0s - loss: 0.0290
133/133 [==============================] - 0s 1ms/step - loss: 0.0334
Epoch 2/10

  1/133 [..............................] - ETA: 0s - loss: 0.0048
 51/133 [==========>...................] - ETA: 0s - loss: 0.0173
102/133 [======================>.......] - ETA: 0s - loss: 0.0294
133/133 [==============================] - 0s 996us/step - loss: 0.0320
Epoch 3/10

  1/133 [..............................] - ETA: 0s - loss: 0.0085
 52/133 [==========>...................] - ETA: 0s - loss: 0.0280
103/133 [======================>.......] - ETA: 0s - loss: 0.0307
133/133 [==============================] - 0s 993us/step - loss: 0.0300
Epoch 4/10

  1/133 [..............................] - ETA: 0s - loss: 0.0102
 52/133 [==========>...................] - ETA: 0s - loss: 0.0335
103/133 [======================>.......] - ETA: 0s - loss: 0.0310
133/133 [==============================] - 0s 992us/step - loss: 0.0297
Epoch 5/10

  1/133 [..............................] - ETA: 0s - loss: 0.0054
 50/133 [==========>...................] - ETA: 0s - loss: 0.0269
 98/133 [=====================>........] - ETA: 0s - loss: 0.0271
133/133 [==============================] - 0s 1ms/step - loss: 0.0276
Epoch 6/10

  1/133 [..............................] - ETA: 0s - loss: 0.0197
 43/133 [========>.....................] - ETA: 0s - loss: 0.0305
 87/133 [==================>...........] - ETA: 0s - loss: 0.0261
133/133 [==============================] - 0s 1ms/step - loss: 0.0261
Epoch 7/10

  1/133 [..............................] - ETA: 0s - loss: 0.0125
 52/133 [==========>...................] - ETA: 0s - loss: 0.0275
103/133 [======================>.......] - ETA: 0s - loss: 0.0249
133/133 [==============================] - 0s 992us/step - loss: 0.0252
Epoch 8/10

  1/133 [..............................] - ETA: 0s - loss: 0.0400
 52/133 [==========>...................] - ETA: 0s - loss: 0.0221
103/133 [======================>.......] - ETA: 0s - loss: 0.0217
133/133 [==============================] - 0s 995us/step - loss: 0.0231
Epoch 9/10

  1/133 [..............................] - ETA: 0s - loss: 0.0032
 52/133 [==========>...................] - ETA: 0s - loss: 0.0214
103/133 [======================>.......] - ETA: 0s - loss: 0.0209
133/133 [==============================] - 0s 994us/step - loss: 0.0224
Epoch 10/10

  1/133 [..............................] - ETA: 0s - loss: 0.0268
 52/133 [==========>...................] - ETA: 0s - loss: 0.0234
103/133 [======================>.......] - ETA: 0s - loss: 0.0217
133/133 [==============================] - 0s 995us/step - loss: 0.0211
-> test with GAN.predict
GAN tn, fp: 328, 5
GAN fn, tp: 1, 12
GAN f1 score: 0.800
GAN cohens kappa score: 0.791

-> test with 'LR'
LR tn, fp: 297, 36
LR fn, tp: 0, 13
LR f1 score: 0.419
LR cohens kappa score: 0.383
LR average precision score: 0.384

-> test with 'RF'
RF tn, fp: 333, 0
RF fn, tp: 0, 13
RF f1 score: 1.000
RF cohens kappa score: 1.000

-> test with 'GB'
GB tn, fp: 333, 0
GB fn, tp: 0, 13
GB f1 score: 1.000
GB cohens kappa score: 1.000

-> test with 'KNN'
KNN tn, fp: 327, 6
KNN fn, tp: 0, 13
KNN f1 score: 0.813
KNN cohens kappa score: 0.804


------ Step 3/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1280 synthetic samples
-> retrain GAN for predict
Epoch 1/10

  1/134 [..............................] - ETA: 18s - loss: 0.0160
 49/134 [=========>....................] - ETA: 0s - loss: 0.0298 
 98/134 [====================>.........] - ETA: 0s - loss: 0.0344
134/134 [==============================] - 0s 1ms/step - loss: 0.0344
Epoch 2/10

  1/134 [..............................] - ETA: 0s - loss: 0.0185
 49/134 [=========>....................] - ETA: 0s - loss: 0.0367
 97/134 [====================>.........] - ETA: 0s - loss: 0.0354
134/134 [==============================] - 0s 1ms/step - loss: 0.0327
Epoch 3/10

  1/134 [..............................] - ETA: 0s - loss: 0.0441
 49/134 [=========>....................] - ETA: 0s - loss: 0.0272
 97/134 [====================>.........] - ETA: 0s - loss: 0.0342
134/134 [==============================] - 0s 1ms/step - loss: 0.0311
Epoch 4/10

  1/134 [..............................] - ETA: 0s - loss: 0.0054
 49/134 [=========>....................] - ETA: 0s - loss: 0.0298
 98/134 [====================>.........] - ETA: 0s - loss: 0.0321
134/134 [==============================] - 0s 1ms/step - loss: 0.0296
Epoch 5/10

  1/134 [..............................] - ETA: 0s - loss: 0.0303
 49/134 [=========>....................] - ETA: 0s - loss: 0.0332
 98/134 [====================>.........] - ETA: 0s - loss: 0.0314
134/134 [==============================] - 0s 1ms/step - loss: 0.0266
Epoch 6/10

  1/134 [..............................] - ETA: 0s - loss: 0.1425
 49/134 [=========>....................] - ETA: 0s - loss: 0.0236
 97/134 [====================>.........] - ETA: 0s - loss: 0.0277
134/134 [==============================] - 0s 1ms/step - loss: 0.0272
Epoch 7/10

  1/134 [..............................] - ETA: 0s - loss: 0.0118
 48/134 [=========>....................] - ETA: 0s - loss: 0.0280
 93/134 [===================>..........] - ETA: 0s - loss: 0.0249
134/134 [==============================] - 0s 1ms/step - loss: 0.0249
Epoch 8/10

  1/134 [..............................] - ETA: 0s - loss: 0.1274
 49/134 [=========>....................] - ETA: 0s - loss: 0.0293
 97/134 [====================>.........] - ETA: 0s - loss: 0.0257
134/134 [==============================] - 0s 1ms/step - loss: 0.0219
Epoch 9/10

  1/134 [..............................] - ETA: 0s - loss: 0.0275
 49/134 [=========>....................] - ETA: 0s - loss: 0.0272
 97/134 [====================>.........] - ETA: 0s - loss: 0.0231
134/134 [==============================] - 0s 1ms/step - loss: 0.0228
Epoch 10/10

  1/134 [..............................] - ETA: 0s - loss: 0.0030
 48/134 [=========>....................] - ETA: 0s - loss: 0.0241
 96/134 [====================>.........] - ETA: 0s - loss: 0.0209
134/134 [==============================] - 0s 1ms/step - loss: 0.0203
-> test with GAN.predict
GAN tn, fp: 327, 4
GAN fn, tp: 2, 11
GAN f1 score: 0.786
GAN cohens kappa score: 0.777

-> test with 'LR'
LR tn, fp: 295, 36
LR fn, tp: 2, 11
LR f1 score: 0.367
LR cohens kappa score: 0.327
LR average precision score: 0.382

-> test with 'RF'
RF tn, fp: 331, 0
RF fn, tp: 3, 10
RF f1 score: 0.870
RF cohens kappa score: 0.865

-> test with 'GB'
GB tn, fp: 331, 0
GB fn, tp: 2, 11
GB f1 score: 0.917
GB cohens kappa score: 0.914

-> test with 'KNN'
KNN tn, fp: 325, 6
KNN fn, tp: 1, 12
KNN f1 score: 0.774
KNN cohens kappa score: 0.764


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

------ Step 4/5: Slice 1/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1278 synthetic samples
-> retrain GAN for predict
Epoch 1/10

  1/133 [..............................] - ETA: 18s - loss: 0.0274
 50/133 [==========>...................] - ETA: 0s - loss: 0.0367 
100/133 [=====================>........] - ETA: 0s - loss: 0.0383
133/133 [==============================] - 0s 1ms/step - loss: 0.0373
Epoch 2/10

  1/133 [..............................] - ETA: 0s - loss: 0.0863
 52/133 [==========>...................] - ETA: 0s - loss: 0.0356
103/133 [======================>.......] - ETA: 0s - loss: 0.0356
133/133 [==============================] - 0s 998us/step - loss: 0.0352
Epoch 3/10

  1/133 [..............................] - ETA: 0s - loss: 0.0370
 51/133 [==========>...................] - ETA: 0s - loss: 0.0311
102/133 [======================>.......] - ETA: 0s - loss: 0.0288
133/133 [==============================] - 0s 999us/step - loss: 0.0331
Epoch 4/10

  1/133 [..............................] - ETA: 0s - loss: 0.1278
 51/133 [==========>...................] - ETA: 0s - loss: 0.0447
102/133 [======================>.......] - ETA: 0s - loss: 0.0343
133/133 [==============================] - 0s 1ms/step - loss: 0.0317
Epoch 5/10

  1/133 [..............................] - ETA: 0s - loss: 0.0497
 52/133 [==========>...................] - ETA: 0s - loss: 0.0392
103/133 [======================>.......] - ETA: 0s - loss: 0.0316
133/133 [==============================] - 0s 997us/step - loss: 0.0297
Epoch 6/10

  1/133 [..............................] - ETA: 0s - loss: 0.0134
 52/133 [==========>...................] - ETA: 0s - loss: 0.0259
102/133 [======================>.......] - ETA: 0s - loss: 0.0276
133/133 [==============================] - 0s 998us/step - loss: 0.0284
Epoch 7/10

  1/133 [..............................] - ETA: 0s - loss: 0.0329
 48/133 [=========>....................] - ETA: 0s - loss: 0.0290
 93/133 [===================>..........] - ETA: 0s - loss: 0.0284
133/133 [==============================] - 0s 1ms/step - loss: 0.0271
Epoch 8/10

  1/133 [..............................] - ETA: 0s - loss: 0.0867
 51/133 [==========>...................] - ETA: 0s - loss: 0.0258
101/133 [=====================>........] - ETA: 0s - loss: 0.0280
133/133 [==============================] - 0s 1ms/step - loss: 0.0246
Epoch 9/10

  1/133 [..............................] - ETA: 0s - loss: 0.0358
 48/133 [=========>....................] - ETA: 0s - loss: 0.0209
 98/133 [=====================>........] - ETA: 0s - loss: 0.0254
133/133 [==============================] - 0s 1ms/step - loss: 0.0241
Epoch 10/10

  1/133 [..............................] - ETA: 0s - loss: 0.0235
 52/133 [==========>...................] - ETA: 0s - loss: 0.0215
102/133 [======================>.......] - ETA: 0s - loss: 0.0219
133/133 [==============================] - 0s 1ms/step - loss: 0.0223
-> test with GAN.predict
GAN tn, fp: 332, 1
GAN fn, tp: 2, 11
GAN f1 score: 0.880
GAN cohens kappa score: 0.876

-> test with 'LR'
LR tn, fp: 295, 38
LR fn, tp: 1, 12
LR f1 score: 0.381
LR cohens kappa score: 0.342
LR average precision score: 0.333

-> test with 'RF'
RF tn, fp: 333, 0
RF fn, tp: 1, 12
RF f1 score: 0.960
RF cohens kappa score: 0.959

-> test with 'GB'
GB tn, fp: 333, 0
GB fn, tp: 1, 12
GB f1 score: 0.960
GB cohens kappa score: 0.959

-> test with 'KNN'
KNN tn, fp: 318, 15
KNN fn, tp: 0, 13
KNN f1 score: 0.634
KNN cohens kappa score: 0.614


------ Step 4/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1278 synthetic samples
-> retrain GAN for predict
Epoch 1/10

  1/133 [..............................] - ETA: 18s - loss: 0.0107
 51/133 [==========>...................] - ETA: 0s - loss: 0.0444 
101/133 [=====================>........] - ETA: 0s - loss: 0.0395
133/133 [==============================] - 0s 1ms/step - loss: 0.0374
Epoch 2/10

  1/133 [..............................] - ETA: 0s - loss: 0.0127
 51/133 [==========>...................] - ETA: 0s - loss: 0.0274
102/133 [======================>.......] - ETA: 0s - loss: 0.0364
133/133 [==============================] - 0s 1ms/step - loss: 0.0352
Epoch 3/10

  1/133 [..............................] - ETA: 0s - loss: 0.0412
 52/133 [==========>...................] - ETA: 0s - loss: 0.0288
102/133 [======================>.......] - ETA: 0s - loss: 0.0319
133/133 [==============================] - 0s 1ms/step - loss: 0.0328
Epoch 4/10

  1/133 [..............................] - ETA: 0s - loss: 0.0220
 52/133 [==========>...................] - ETA: 0s - loss: 0.0277
102/133 [======================>.......] - ETA: 0s - loss: 0.0303
133/133 [==============================] - 0s 1ms/step - loss: 0.0305
Epoch 5/10

  1/133 [..............................] - ETA: 0s - loss: 0.0339
 51/133 [==========>...................] - ETA: 0s - loss: 0.0323
101/133 [=====================>........] - ETA: 0s - loss: 0.0303
133/133 [==============================] - 0s 1ms/step - loss: 0.0285
Epoch 6/10

  1/133 [..............................] - ETA: 0s - loss: 0.0079
 51/133 [==========>...................] - ETA: 0s - loss: 0.0273
102/133 [======================>.......] - ETA: 0s - loss: 0.0280
133/133 [==============================] - 0s 1ms/step - loss: 0.0279
Epoch 7/10

  1/133 [..............................] - ETA: 0s - loss: 0.0072
 49/133 [==========>...................] - ETA: 0s - loss: 0.0197
 99/133 [=====================>........] - ETA: 0s - loss: 0.0292
133/133 [==============================] - 0s 1ms/step - loss: 0.0261
Epoch 8/10

  1/133 [..............................] - ETA: 0s - loss: 0.0119
 52/133 [==========>...................] - ETA: 0s - loss: 0.0223
103/133 [======================>.......] - ETA: 0s - loss: 0.0244
133/133 [==============================] - 0s 998us/step - loss: 0.0233
Epoch 9/10

  1/133 [..............................] - ETA: 0s - loss: 0.0305
 50/133 [==========>...................] - ETA: 0s - loss: 0.0204
 96/133 [====================>.........] - ETA: 0s - loss: 0.0187
133/133 [==============================] - 0s 1ms/step - loss: 0.0226
Epoch 10/10

  1/133 [..............................] - ETA: 0s - loss: 0.0181
 49/133 [==========>...................] - ETA: 0s - loss: 0.0224
100/133 [=====================>........] - ETA: 0s - loss: 0.0235
133/133 [==============================] - 0s 1ms/step - loss: 0.0222
-> test with GAN.predict
GAN tn, fp: 330, 3
GAN fn, tp: 1, 12
GAN f1 score: 0.857
GAN cohens kappa score: 0.851

-> test with 'LR'
LR tn, fp: 290, 43
LR fn, tp: 1, 12
LR f1 score: 0.353
LR cohens kappa score: 0.311
LR average precision score: 0.521

-> test with 'RF'
RF tn, fp: 333, 0
RF fn, tp: 1, 12
RF f1 score: 0.960
RF cohens kappa score: 0.959

-> test with 'GB'
GB tn, fp: 333, 0
GB fn, tp: 1, 12
GB f1 score: 0.960
GB cohens kappa score: 0.959

-> test with 'KNN'
KNN tn, fp: 325, 8
KNN fn, tp: 0, 13
KNN f1 score: 0.765
KNN cohens kappa score: 0.753


------ Step 4/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1278 synthetic samples
-> retrain GAN for predict
Epoch 1/10

  1/133 [..............................] - ETA: 18s - loss: 0.0276
 50/133 [==========>...................] - ETA: 0s - loss: 0.0530 
100/133 [=====================>........] - ETA: 0s - loss: 0.0441
133/133 [==============================] - 0s 1ms/step - loss: 0.0423
Epoch 2/10

  1/133 [..............................] - ETA: 0s - loss: 0.0102
 51/133 [==========>...................] - ETA: 0s - loss: 0.0385
100/133 [=====================>........] - ETA: 0s - loss: 0.0384
133/133 [==============================] - 0s 1ms/step - loss: 0.0393
Epoch 3/10

  1/133 [..............................] - ETA: 0s - loss: 0.1107
 51/133 [==========>...................] - ETA: 0s - loss: 0.0356
102/133 [======================>.......] - ETA: 0s - loss: 0.0372
133/133 [==============================] - 0s 1ms/step - loss: 0.0369
Epoch 4/10

  1/133 [..............................] - ETA: 0s - loss: 0.0050
 51/133 [==========>...................] - ETA: 0s - loss: 0.0333
101/133 [=====================>........] - ETA: 0s - loss: 0.0341
133/133 [==============================] - 0s 1ms/step - loss: 0.0350
Epoch 5/10

  1/133 [..............................] - ETA: 0s - loss: 0.0274
 51/133 [==========>...................] - ETA: 0s - loss: 0.0337
102/133 [======================>.......] - ETA: 0s - loss: 0.0346
133/133 [==============================] - 0s 1ms/step - loss: 0.0334
Epoch 6/10

  1/133 [..............................] - ETA: 0s - loss: 0.0335
 51/133 [==========>...................] - ETA: 0s - loss: 0.0265
101/133 [=====================>........] - ETA: 0s - loss: 0.0251
133/133 [==============================] - 0s 1ms/step - loss: 0.0300
Epoch 7/10

  1/133 [..............................] - ETA: 0s - loss: 0.0270
 51/133 [==========>...................] - ETA: 0s - loss: 0.0329
101/133 [=====================>........] - ETA: 0s - loss: 0.0277
133/133 [==============================] - 0s 1ms/step - loss: 0.0295
Epoch 8/10

  1/133 [..............................] - ETA: 0s - loss: 0.0127
 51/133 [==========>...................] - ETA: 0s - loss: 0.0191
102/133 [======================>.......] - ETA: 0s - loss: 0.0233
133/133 [==============================] - 0s 1ms/step - loss: 0.0265
Epoch 9/10

  1/133 [..............................] - ETA: 0s - loss: 0.0179
 49/133 [==========>...................] - ETA: 0s - loss: 0.0163
 99/133 [=====================>........] - ETA: 0s - loss: 0.0249
133/133 [==============================] - 0s 1ms/step - loss: 0.0255
Epoch 10/10

  1/133 [..............................] - ETA: 0s - loss: 0.0142
 52/133 [==========>...................] - ETA: 0s - loss: 0.0179
103/133 [======================>.......] - ETA: 0s - loss: 0.0178
133/133 [==============================] - 0s 997us/step - loss: 0.0223
-> test with GAN.predict
GAN tn, fp: 326, 7
GAN fn, tp: 1, 12
GAN f1 score: 0.750
GAN cohens kappa score: 0.738

-> test with 'LR'
LR tn, fp: 288, 45
LR fn, tp: 0, 13
LR f1 score: 0.366
LR cohens kappa score: 0.325
LR average precision score: 0.311

-> test with 'RF'
RF tn, fp: 332, 1
RF fn, tp: 0, 13
RF f1 score: 0.963
RF cohens kappa score: 0.961

-> test with 'GB'
GB tn, fp: 330, 3
GB fn, tp: 0, 13
GB f1 score: 0.897
GB cohens kappa score: 0.892

-> test with 'KNN'
KNN tn, fp: 318, 15
KNN fn, tp: 0, 13
KNN f1 score: 0.634
KNN cohens kappa score: 0.614


------ Step 4/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1278 synthetic samples
-> retrain GAN for predict
Epoch 1/10

  1/133 [..............................] - ETA: 18s - loss: 0.0858
 51/133 [==========>...................] - ETA: 0s - loss: 0.0278 
101/133 [=====================>........] - ETA: 0s - loss: 0.0291
133/133 [==============================] - 0s 1ms/step - loss: 0.0294
Epoch 2/10

  1/133 [..............................] - ETA: 0s - loss: 0.0123
 52/133 [==========>...................] - ETA: 0s - loss: 0.0234
103/133 [======================>.......] - ETA: 0s - loss: 0.0279
133/133 [==============================] - 0s 998us/step - loss: 0.0275
Epoch 3/10

  1/133 [..............................] - ETA: 0s - loss: 0.0077
 51/133 [==========>...................] - ETA: 0s - loss: 0.0314
101/133 [=====================>........] - ETA: 0s - loss: 0.0283
133/133 [==============================] - 0s 1ms/step - loss: 0.0265
Epoch 4/10

  1/133 [..............................] - ETA: 0s - loss: 0.0622
 52/133 [==========>...................] - ETA: 0s - loss: 0.0219
103/133 [======================>.......] - ETA: 0s - loss: 0.0273
133/133 [==============================] - 0s 996us/step - loss: 0.0254
Epoch 5/10

  1/133 [..............................] - ETA: 0s - loss: 0.0890
 52/133 [==========>...................] - ETA: 0s - loss: 0.0219
100/133 [=====================>........] - ETA: 0s - loss: 0.0220
133/133 [==============================] - 0s 1ms/step - loss: 0.0232
Epoch 6/10

  1/133 [..............................] - ETA: 0s - loss: 0.0396
 52/133 [==========>...................] - ETA: 0s - loss: 0.0305
102/133 [======================>.......] - ETA: 0s - loss: 0.0251
133/133 [==============================] - 0s 999us/step - loss: 0.0226
Epoch 7/10

  1/133 [..............................] - ETA: 0s - loss: 0.0179
 51/133 [==========>...................] - ETA: 0s - loss: 0.0205
100/133 [=====================>........] - ETA: 0s - loss: 0.0219
133/133 [==============================] - 0s 1ms/step - loss: 0.0211
Epoch 8/10

  1/133 [..............................] - ETA: 0s - loss: 0.0068
 52/133 [==========>...................] - ETA: 0s - loss: 0.0158
102/133 [======================>.......] - ETA: 0s - loss: 0.0185
133/133 [==============================] - 0s 1ms/step - loss: 0.0194
Epoch 9/10

  1/133 [..............................] - ETA: 0s - loss: 0.0047
 51/133 [==========>...................] - ETA: 0s - loss: 0.0152
102/133 [======================>.......] - ETA: 0s - loss: 0.0174
133/133 [==============================] - 0s 1ms/step - loss: 0.0185
Epoch 10/10

  1/133 [..............................] - ETA: 0s - loss: 0.0047
 52/133 [==========>...................] - ETA: 0s - loss: 0.0221
 99/133 [=====================>........] - ETA: 0s - loss: 0.0187
133/133 [==============================] - 0s 1ms/step - loss: 0.0173
-> test with GAN.predict
GAN tn, fp: 324, 9
GAN fn, tp: 6, 7
GAN f1 score: 0.483
GAN cohens kappa score: 0.460

-> test with 'LR'
LR tn, fp: 295, 38
LR fn, tp: 1, 12
LR f1 score: 0.381
LR cohens kappa score: 0.342
LR average precision score: 0.290

-> test with 'RF'
RF tn, fp: 332, 1
RF fn, tp: 6, 7
RF f1 score: 0.667
RF cohens kappa score: 0.657

-> test with 'GB'
GB tn, fp: 332, 1
GB fn, tp: 0, 13
GB f1 score: 0.963
GB cohens kappa score: 0.961

-> test with 'KNN'
KNN tn, fp: 317, 16
KNN fn, tp: 0, 13
KNN f1 score: 0.619
KNN cohens kappa score: 0.598


------ Step 4/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1280 synthetic samples
-> retrain GAN for predict
Epoch 1/10

  1/134 [..............................] - ETA: 18s - loss: 0.0120
 49/134 [=========>....................] - ETA: 0s - loss: 0.0448 
 98/134 [====================>.........] - ETA: 0s - loss: 0.0400
134/134 [==============================] - 0s 1ms/step - loss: 0.0392
Epoch 2/10

  1/134 [..............................] - ETA: 0s - loss: 0.0190
 44/134 [========>.....................] - ETA: 0s - loss: 0.0351
 87/134 [==================>...........] - ETA: 0s - loss: 0.0363
132/134 [============================>.] - ETA: 0s - loss: 0.0384
134/134 [==============================] - 0s 1ms/step - loss: 0.0385
Epoch 3/10

  1/134 [..............................] - ETA: 0s - loss: 0.0110
 49/134 [=========>....................] - ETA: 0s - loss: 0.0405
 97/134 [====================>.........] - ETA: 0s - loss: 0.0358
134/134 [==============================] - 0s 1ms/step - loss: 0.0338
Epoch 4/10

  1/134 [..............................] - ETA: 0s - loss: 0.0748
 50/134 [==========>...................] - ETA: 0s - loss: 0.0365
 98/134 [====================>.........] - ETA: 0s - loss: 0.0346
134/134 [==============================] - 0s 1ms/step - loss: 0.0334
Epoch 5/10

  1/134 [..............................] - ETA: 0s - loss: 0.0118
 50/134 [==========>...................] - ETA: 0s - loss: 0.0225
 99/134 [=====================>........] - ETA: 0s - loss: 0.0284
134/134 [==============================] - 0s 1ms/step - loss: 0.0315
Epoch 6/10

  1/134 [..............................] - ETA: 0s - loss: 0.0287
 50/134 [==========>...................] - ETA: 0s - loss: 0.0306
 99/134 [=====================>........] - ETA: 0s - loss: 0.0288
134/134 [==============================] - 0s 1ms/step - loss: 0.0292
Epoch 7/10

  1/134 [..............................] - ETA: 0s - loss: 0.0230
 49/134 [=========>....................] - ETA: 0s - loss: 0.0322
 95/134 [====================>.........] - ETA: 0s - loss: 0.0307
134/134 [==============================] - 0s 1ms/step - loss: 0.0287
Epoch 8/10

  1/134 [..............................] - ETA: 0s - loss: 0.0693
 50/134 [==========>...................] - ETA: 0s - loss: 0.0307
 99/134 [=====================>........] - ETA: 0s - loss: 0.0286
134/134 [==============================] - 0s 1ms/step - loss: 0.0247
Epoch 9/10

  1/134 [..............................] - ETA: 0s - loss: 0.0054
 50/134 [==========>...................] - ETA: 0s - loss: 0.0338
 99/134 [=====================>........] - ETA: 0s - loss: 0.0255
134/134 [==============================] - 0s 1ms/step - loss: 0.0246
Epoch 10/10

  1/134 [..............................] - ETA: 0s - loss: 0.0132
 49/134 [=========>....................] - ETA: 0s - loss: 0.0196
 97/134 [====================>.........] - ETA: 0s - loss: 0.0207
134/134 [==============================] - 0s 1ms/step - loss: 0.0227
-> test with GAN.predict
GAN tn, fp: 326, 5
GAN fn, tp: 1, 12
GAN f1 score: 0.800
GAN cohens kappa score: 0.791

-> test with 'LR'
LR tn, fp: 286, 45
LR fn, tp: 0, 13
LR f1 score: 0.366
LR cohens kappa score: 0.324
LR average precision score: 0.325

-> test with 'RF'
RF tn, fp: 331, 0
RF fn, tp: 2, 11
RF f1 score: 0.917
RF cohens kappa score: 0.914

-> test with 'GB'
GB tn, fp: 331, 0
GB fn, tp: 1, 12
GB f1 score: 0.960
GB cohens kappa score: 0.958

-> test with 'KNN'
KNN tn, fp: 315, 16
KNN fn, tp: 0, 13
KNN f1 score: 0.619
KNN cohens kappa score: 0.598


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

------ Step 5/5: Slice 1/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1278 synthetic samples
-> retrain GAN for predict
Epoch 1/10

  1/133 [..............................] - ETA: 15s - loss: 0.1865
 51/133 [==========>...................] - ETA: 0s - loss: 0.0481 
101/133 [=====================>........] - ETA: 0s - loss: 0.0434
133/133 [==============================] - 0s 1ms/step - loss: 0.0426
Epoch 2/10

  1/133 [..............................] - ETA: 0s - loss: 0.0322
 52/133 [==========>...................] - ETA: 0s - loss: 0.0361
102/133 [======================>.......] - ETA: 0s - loss: 0.0421
133/133 [==============================] - 0s 997us/step - loss: 0.0400
Epoch 3/10

  1/133 [..............................] - ETA: 0s - loss: 0.0076
 50/133 [==========>...................] - ETA: 0s - loss: 0.0323
 95/133 [====================>.........] - ETA: 0s - loss: 0.0363
133/133 [==============================] - 0s 1ms/step - loss: 0.0376
Epoch 4/10

  1/133 [..............................] - ETA: 0s - loss: 0.0176
 52/133 [==========>...................] - ETA: 0s - loss: 0.0250
103/133 [======================>.......] - ETA: 0s - loss: 0.0291
133/133 [==============================] - 0s 998us/step - loss: 0.0332
Epoch 5/10

  1/133 [..............................] - ETA: 0s - loss: 0.0387
 51/133 [==========>...................] - ETA: 0s - loss: 0.0303
102/133 [======================>.......] - ETA: 0s - loss: 0.0332
133/133 [==============================] - 0s 1ms/step - loss: 0.0324
Epoch 6/10

  1/133 [..............................] - ETA: 0s - loss: 0.1939
 50/133 [==========>...................] - ETA: 0s - loss: 0.0307
101/133 [=====================>........] - ETA: 0s - loss: 0.0276
133/133 [==============================] - 0s 1ms/step - loss: 0.0290
Epoch 7/10

  1/133 [..............................] - ETA: 0s - loss: 0.0143
 52/133 [==========>...................] - ETA: 0s - loss: 0.0163
102/133 [======================>.......] - ETA: 0s - loss: 0.0271
133/133 [==============================] - 0s 1ms/step - loss: 0.0291
Epoch 8/10

  1/133 [..............................] - ETA: 0s - loss: 0.0316
 52/133 [==========>...................] - ETA: 0s - loss: 0.0231
100/133 [=====================>........] - ETA: 0s - loss: 0.0263
133/133 [==============================] - 0s 1ms/step - loss: 0.0264
Epoch 9/10

  1/133 [..............................] - ETA: 0s - loss: 0.0513
 47/133 [=========>....................] - ETA: 0s - loss: 0.0277
 95/133 [====================>.........] - ETA: 0s - loss: 0.0259
133/133 [==============================] - 0s 1ms/step - loss: 0.0252
Epoch 10/10

  1/133 [..............................] - ETA: 0s - loss: 0.0134
 52/133 [==========>...................] - ETA: 0s - loss: 0.0252
103/133 [======================>.......] - ETA: 0s - loss: 0.0237
133/133 [==============================] - 0s 996us/step - loss: 0.0237
-> test with GAN.predict
GAN tn, fp: 327, 6
GAN fn, tp: 2, 11
GAN f1 score: 0.733
GAN cohens kappa score: 0.721

-> test with 'LR'
LR tn, fp: 272, 61
LR fn, tp: 0, 13
LR f1 score: 0.299
LR cohens kappa score: 0.251
LR average precision score: 0.333

-> test with 'RF'
RF tn, fp: 333, 0
RF fn, tp: 0, 13
RF f1 score: 1.000
RF cohens kappa score: 1.000

-> test with 'GB'
GB tn, fp: 333, 0
GB fn, tp: 2, 11
GB f1 score: 0.917
GB cohens kappa score: 0.914

-> test with 'KNN'
KNN tn, fp: 320, 13
KNN fn, tp: 0, 13
KNN f1 score: 0.667
KNN cohens kappa score: 0.649


------ Step 5/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1278 synthetic samples
-> retrain GAN for predict
Epoch 1/10

  1/133 [..............................] - ETA: 17s - loss: 0.0615
 42/133 [========>.....................] - ETA: 0s - loss: 0.0405 
 82/133 [=================>............] - ETA: 0s - loss: 0.0336
120/133 [==========================>...] - ETA: 0s - loss: 0.0305
133/133 [==============================] - 0s 1ms/step - loss: 0.0329
Epoch 2/10

  1/133 [..............................] - ETA: 0s - loss: 0.0134
 48/133 [=========>....................] - ETA: 0s - loss: 0.0244
 94/133 [====================>.........] - ETA: 0s - loss: 0.0301
133/133 [==============================] - 0s 1ms/step - loss: 0.0308
Epoch 3/10

  1/133 [..............................] - ETA: 0s - loss: 0.0225
 46/133 [=========>....................] - ETA: 0s - loss: 0.0287
 93/133 [===================>..........] - ETA: 0s - loss: 0.0253
133/133 [==============================] - 0s 1ms/step - loss: 0.0287
Epoch 4/10

  1/133 [..............................] - ETA: 0s - loss: 0.0117
 48/133 [=========>....................] - ETA: 0s - loss: 0.0264
 95/133 [====================>.........] - ETA: 0s - loss: 0.0256
133/133 [==============================] - 0s 1ms/step - loss: 0.0270
Epoch 5/10

  1/133 [..............................] - ETA: 0s - loss: 0.0594
 49/133 [==========>...................] - ETA: 0s - loss: 0.0261
 93/133 [===================>..........] - ETA: 0s - loss: 0.0252
133/133 [==============================] - 0s 1ms/step - loss: 0.0254
Epoch 6/10

  1/133 [..............................] - ETA: 0s - loss: 0.0115
 47/133 [=========>....................] - ETA: 0s - loss: 0.0208
 91/133 [===================>..........] - ETA: 0s - loss: 0.0248
133/133 [==============================] - 0s 1ms/step - loss: 0.0246
Epoch 7/10

  1/133 [..............................] - ETA: 0s - loss: 0.0192
 49/133 [==========>...................] - ETA: 0s - loss: 0.0155
 91/133 [===================>..........] - ETA: 0s - loss: 0.0240
133/133 [==============================] - 0s 1ms/step - loss: 0.0231
Epoch 8/10

  1/133 [..............................] - ETA: 0s - loss: 0.0117
 47/133 [=========>....................] - ETA: 0s - loss: 0.0234
 92/133 [===================>..........] - ETA: 0s - loss: 0.0224
133/133 [==============================] - 0s 1ms/step - loss: 0.0223
Epoch 9/10

  1/133 [..............................] - ETA: 0s - loss: 0.0076
 48/133 [=========>....................] - ETA: 0s - loss: 0.0222
 94/133 [====================>.........] - ETA: 0s - loss: 0.0211
133/133 [==============================] - 0s 1ms/step - loss: 0.0211
Epoch 10/10

  1/133 [..............................] - ETA: 0s - loss: 0.0093
 45/133 [=========>....................] - ETA: 0s - loss: 0.0207
 90/133 [===================>..........] - ETA: 0s - loss: 0.0214
133/133 [==============================] - 0s 1ms/step - loss: 0.0198
-> test with GAN.predict
GAN tn, fp: 325, 8
GAN fn, tp: 2, 11
GAN f1 score: 0.688
GAN cohens kappa score: 0.673

-> test with 'LR'
LR tn, fp: 299, 34
LR fn, tp: 3, 10
LR f1 score: 0.351
LR cohens kappa score: 0.311
LR average precision score: 0.361

-> test with 'RF'
RF tn, fp: 333, 0
RF fn, tp: 5, 8
RF f1 score: 0.762
RF cohens kappa score: 0.755

-> test with 'GB'
GB tn, fp: 332, 1
GB fn, tp: 0, 13
GB f1 score: 0.963
GB cohens kappa score: 0.961

-> test with 'KNN'
KNN tn, fp: 322, 11
KNN fn, tp: 1, 12
KNN f1 score: 0.667
KNN cohens kappa score: 0.650


------ Step 5/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1278 synthetic samples
-> retrain GAN for predict
Epoch 1/10

  1/133 [..............................] - ETA: 18s - loss: 0.0210
 50/133 [==========>...................] - ETA: 0s - loss: 0.0284 
100/133 [=====================>........] - ETA: 0s - loss: 0.0389
133/133 [==============================] - 0s 1ms/step - loss: 0.0380
Epoch 2/10

  1/133 [..............................] - ETA: 0s - loss: 0.0275
 51/133 [==========>...................] - ETA: 0s - loss: 0.0418
102/133 [======================>.......] - ETA: 0s - loss: 0.0383
133/133 [==============================] - 0s 997us/step - loss: 0.0370
Epoch 3/10

  1/133 [..............................] - ETA: 0s - loss: 0.0055
 50/133 [==========>...................] - ETA: 0s - loss: 0.0278
 97/133 [====================>.........] - ETA: 0s - loss: 0.0307
133/133 [==============================] - 0s 1ms/step - loss: 0.0327
Epoch 4/10

  1/133 [..............................] - ETA: 0s - loss: 0.0236
 51/133 [==========>...................] - ETA: 0s - loss: 0.0304
102/133 [======================>.......] - ETA: 0s - loss: 0.0318
133/133 [==============================] - 0s 1ms/step - loss: 0.0324
Epoch 5/10

  1/133 [..............................] - ETA: 0s - loss: 0.0083
 52/133 [==========>...................] - ETA: 0s - loss: 0.0280
103/133 [======================>.......] - ETA: 0s - loss: 0.0288
133/133 [==============================] - 0s 1ms/step - loss: 0.0308
Epoch 6/10

  1/133 [..............................] - ETA: 0s - loss: 0.0185
 48/133 [=========>....................] - ETA: 0s - loss: 0.0246
 98/133 [=====================>........] - ETA: 0s - loss: 0.0297
133/133 [==============================] - 0s 1ms/step - loss: 0.0295
Epoch 7/10

  1/133 [..............................] - ETA: 0s - loss: 0.0452
 47/133 [=========>....................] - ETA: 0s - loss: 0.0237
 94/133 [====================>.........] - ETA: 0s - loss: 0.0270
133/133 [==============================] - 0s 1ms/step - loss: 0.0284
Epoch 8/10

  1/133 [..............................] - ETA: 0s - loss: 0.0161
 51/133 [==========>...................] - ETA: 0s - loss: 0.0226
101/133 [=====================>........] - ETA: 0s - loss: 0.0278
133/133 [==============================] - 0s 1ms/step - loss: 0.0282
Epoch 9/10

  1/133 [..............................] - ETA: 0s - loss: 0.0053
 51/133 [==========>...................] - ETA: 0s - loss: 0.0267
102/133 [======================>.......] - ETA: 0s - loss: 0.0251
133/133 [==============================] - 0s 1ms/step - loss: 0.0247
Epoch 10/10

  1/133 [..............................] - ETA: 0s - loss: 0.0483
 48/133 [=========>....................] - ETA: 0s - loss: 0.0284
 93/133 [===================>..........] - ETA: 0s - loss: 0.0271
133/133 [==============================] - 0s 1ms/step - loss: 0.0233
-> test with GAN.predict
GAN tn, fp: 329, 4
GAN fn, tp: 2, 11
GAN f1 score: 0.786
GAN cohens kappa score: 0.777

-> test with 'LR'
LR tn, fp: 304, 29
LR fn, tp: 1, 12
LR f1 score: 0.444
LR cohens kappa score: 0.411
LR average precision score: 0.334

-> test with 'RF'
RF tn, fp: 333, 0
RF fn, tp: 0, 13
RF f1 score: 1.000
RF cohens kappa score: 1.000

-> test with 'GB'
GB tn, fp: 333, 0
GB fn, tp: 0, 13
GB f1 score: 1.000
GB cohens kappa score: 1.000

-> test with 'KNN'
KNN tn, fp: 318, 15
KNN fn, tp: 0, 13
KNN f1 score: 0.634
KNN cohens kappa score: 0.614


------ Step 5/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1278 synthetic samples
-> retrain GAN for predict
Epoch 1/10

  1/133 [..............................] - ETA: 16s - loss: 0.0084
 50/133 [==========>...................] - ETA: 0s - loss: 0.0390 
100/133 [=====================>........] - ETA: 0s - loss: 0.0377
133/133 [==============================] - 0s 1ms/step - loss: 0.0357
Epoch 2/10

  1/133 [..............................] - ETA: 0s - loss: 0.1887
 52/133 [==========>...................] - ETA: 0s - loss: 0.0392
102/133 [======================>.......] - ETA: 0s - loss: 0.0355
133/133 [==============================] - 0s 1ms/step - loss: 0.0338
Epoch 3/10

  1/133 [..............................] - ETA: 0s - loss: 0.0417
 52/133 [==========>...................] - ETA: 0s - loss: 0.0310
102/133 [======================>.......] - ETA: 0s - loss: 0.0331
133/133 [==============================] - 0s 1ms/step - loss: 0.0312
Epoch 4/10

  1/133 [..............................] - ETA: 0s - loss: 0.0075
 45/133 [=========>....................] - ETA: 0s - loss: 0.0275
 91/133 [===================>..........] - ETA: 0s - loss: 0.0320
133/133 [==============================] - 0s 1ms/step - loss: 0.0299
Epoch 5/10

  1/133 [..............................] - ETA: 0s - loss: 0.0130
 48/133 [=========>....................] - ETA: 0s - loss: 0.0381
 97/133 [====================>.........] - ETA: 0s - loss: 0.0284
133/133 [==============================] - 0s 1ms/step - loss: 0.0280
Epoch 6/10

  1/133 [..............................] - ETA: 0s - loss: 0.0120
 51/133 [==========>...................] - ETA: 0s - loss: 0.0245
101/133 [=====================>........] - ETA: 0s - loss: 0.0250
133/133 [==============================] - 0s 1ms/step - loss: 0.0261
Epoch 7/10

  1/133 [..............................] - ETA: 0s - loss: 0.0054
 52/133 [==========>...................] - ETA: 0s - loss: 0.0259
103/133 [======================>.......] - ETA: 0s - loss: 0.0266
133/133 [==============================] - 0s 996us/step - loss: 0.0253
Epoch 8/10

  1/133 [..............................] - ETA: 0s - loss: 0.0039
 52/133 [==========>...................] - ETA: 0s - loss: 0.0241
103/133 [======================>.......] - ETA: 0s - loss: 0.0237
133/133 [==============================] - 0s 999us/step - loss: 0.0248
Epoch 9/10

  1/133 [..............................] - ETA: 0s - loss: 0.0140
 51/133 [==========>...................] - ETA: 0s - loss: 0.0206
102/133 [======================>.......] - ETA: 0s - loss: 0.0223
133/133 [==============================] - 0s 1ms/step - loss: 0.0217
Epoch 10/10

  1/133 [..............................] - ETA: 0s - loss: 0.0164
 50/133 [==========>...................] - ETA: 0s - loss: 0.0222
100/133 [=====================>........] - ETA: 0s - loss: 0.0211
133/133 [==============================] - 0s 1ms/step - loss: 0.0209
-> test with GAN.predict
GAN tn, fp: 329, 4
GAN fn, tp: 2, 11
GAN f1 score: 0.786
GAN cohens kappa score: 0.777

-> test with 'LR'
LR tn, fp: 284, 49
LR fn, tp: 0, 13
LR f1 score: 0.347
LR cohens kappa score: 0.303
LR average precision score: 0.281

-> test with 'RF'
RF tn, fp: 332, 1
RF fn, tp: 1, 12
RF f1 score: 0.923
RF cohens kappa score: 0.920

-> test with 'GB'
GB tn, fp: 330, 3
GB fn, tp: 0, 13
GB f1 score: 0.897
GB cohens kappa score: 0.892

-> test with 'KNN'
KNN tn, fp: 323, 10
KNN fn, tp: 0, 13
KNN f1 score: 0.722
KNN cohens kappa score: 0.708


------ Step 5/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1280 synthetic samples
-> retrain GAN for predict
Epoch 1/10

  1/134 [..............................] - ETA: 23s - loss: 0.0069
 48/134 [=========>....................] - ETA: 0s - loss: 0.0326 
 96/134 [====================>.........] - ETA: 0s - loss: 0.0338
134/134 [==============================] - 0s 1ms/step - loss: 0.0338
Epoch 2/10

  1/134 [..............................] - ETA: 0s - loss: 0.0023
 49/134 [=========>....................] - ETA: 0s - loss: 0.0380
 97/134 [====================>.........] - ETA: 0s - loss: 0.0319
134/134 [==============================] - 0s 1ms/step - loss: 0.0316
Epoch 3/10

  1/134 [..............................] - ETA: 0s - loss: 0.0068
 49/134 [=========>....................] - ETA: 0s - loss: 0.0254
 92/134 [===================>..........] - ETA: 0s - loss: 0.0274
134/134 [==============================] - 0s 1ms/step - loss: 0.0296
Epoch 4/10

  1/134 [..............................] - ETA: 0s - loss: 0.0272
 49/134 [=========>....................] - ETA: 0s - loss: 0.0304
 97/134 [====================>.........] - ETA: 0s - loss: 0.0288
134/134 [==============================] - 0s 1ms/step - loss: 0.0274
Epoch 5/10

  1/134 [..............................] - ETA: 0s - loss: 0.0095
 49/134 [=========>....................] - ETA: 0s - loss: 0.0289
 97/134 [====================>.........] - ETA: 0s - loss: 0.0257
134/134 [==============================] - 0s 1ms/step - loss: 0.0257
Epoch 6/10

  1/134 [..............................] - ETA: 0s - loss: 0.0937
 49/134 [=========>....................] - ETA: 0s - loss: 0.0275
 97/134 [====================>.........] - ETA: 0s - loss: 0.0264
134/134 [==============================] - 0s 1ms/step - loss: 0.0247
Epoch 7/10

  1/134 [..............................] - ETA: 0s - loss: 0.0455
 49/134 [=========>....................] - ETA: 0s - loss: 0.0213
 97/134 [====================>.........] - ETA: 0s - loss: 0.0214
134/134 [==============================] - 0s 1ms/step - loss: 0.0226
Epoch 8/10

  1/134 [..............................] - ETA: 0s - loss: 0.0764
 49/134 [=========>....................] - ETA: 0s - loss: 0.0209
 97/134 [====================>.........] - ETA: 0s - loss: 0.0198
134/134 [==============================] - 0s 1ms/step - loss: 0.0214
Epoch 9/10

  1/134 [..............................] - ETA: 0s - loss: 0.0617
 47/134 [=========>....................] - ETA: 0s - loss: 0.0235
 96/134 [====================>.........] - ETA: 0s - loss: 0.0218
134/134 [==============================] - 0s 1ms/step - loss: 0.0198
Epoch 10/10

  1/134 [..............................] - ETA: 0s - loss: 0.0078
 50/134 [==========>...................] - ETA: 0s - loss: 0.0259
 97/134 [====================>.........] - ETA: 0s - loss: 0.0220
134/134 [==============================] - 0s 1ms/step - loss: 0.0194
-> test with GAN.predict
GAN tn, fp: 329, 2
GAN fn, tp: 3, 10
GAN f1 score: 0.800
GAN cohens kappa score: 0.792

-> test with 'LR'
LR tn, fp: 292, 39
LR fn, tp: 0, 13
LR f1 score: 0.400
LR cohens kappa score: 0.361
LR average precision score: 0.468

-> test with 'RF'
RF tn, fp: 331, 0
RF fn, tp: 1, 12
RF f1 score: 0.960
RF cohens kappa score: 0.958

-> test with 'GB'
GB tn, fp: 330, 1
GB fn, tp: 1, 12
GB f1 score: 0.923
GB cohens kappa score: 0.920

-> test with 'KNN'
KNN tn, fp: 321, 10
KNN fn, tp: 0, 13
KNN f1 score: 0.722
KNN cohens kappa score: 0.708

### Exercise is done.

-----[ LR ]-----
maximum:
LR tn, fp: 304, 61
LR fn, tp: 3, 13
LR f1 score: 0.444
LR cohens kappa score: 0.411
LR average precision score: 0.524


average:
LR tn, fp: 290.84, 41.76
LR fn, tp: 0.6, 12.4
LR f1 score: 0.373
LR cohens kappa score: 0.332
LR average precision score: 0.355


minimum:
LR tn, fp: 272, 29
LR fn, tp: 0, 10
LR f1 score: 0.299
LR cohens kappa score: 0.251
LR average precision score: 0.265


-----[ RF ]-----
maximum:
RF tn, fp: 333, 2
RF fn, tp: 6, 13
RF f1 score: 1.000
RF cohens kappa score: 1.000


average:
RF tn, fp: 332.24, 0.36
RF fn, tp: 1.76, 11.24
RF f1 score: 0.910
RF cohens kappa score: 0.907


minimum:
RF tn, fp: 330, 0
RF fn, tp: 0, 7
RF f1 score: 0.667
RF cohens kappa score: 0.657


-----[ GB ]-----
maximum:
GB tn, fp: 333, 6
GB fn, tp: 3, 13
GB f1 score: 1.000
GB cohens kappa score: 1.000


average:
GB tn, fp: 331.48, 1.12
GB fn, tp: 0.68, 12.32
GB f1 score: 0.933
GB cohens kappa score: 0.931


minimum:
GB tn, fp: 327, 0
GB fn, tp: 0, 10
GB f1 score: 0.800
GB cohens kappa score: 0.793


-----[ KNN ]-----
maximum:
KNN tn, fp: 327, 21
KNN fn, tp: 3, 13
KNN f1 score: 0.813
KNN cohens kappa score: 0.804


average:
KNN tn, fp: 319.6, 13.0
KNN fn, tp: 0.4, 12.6
KNN f1 score: 0.659
KNN cohens kappa score: 0.641


minimum:
KNN tn, fp: 312, 6
KNN fn, tp: 0, 10
KNN f1 score: 0.537
KNN cohens kappa score: 0.512


-----[ GAN ]-----
maximum:
GAN tn, fp: 332, 9
GAN fn, tp: 6, 13
GAN f1 score: 0.889
GAN cohens kappa score: 0.884


average:
GAN tn, fp: 327.76, 4.84
GAN fn, tp: 1.84, 11.16
GAN f1 score: 0.772
GAN cohens kappa score: 0.763


minimum:
GAN tn, fp: 324, 1
GAN fn, tp: 0, 7
GAN f1 score: 0.483
GAN cohens kappa score: 0.460