ConvGeNBenchmarkResults/data_result/convGAN-majority-full/folding_car-vgood.log

///////////////////////////////////////////
// Running convGAN-majority-full 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: 8.9905e-07
 51/133 [==========>...................] - ETA: 0s - loss: 0.0515     
102/133 [======================>.......] - ETA: 0s - loss: 0.0466
133/133 [==============================] - 0s 997us/step - loss: 0.0449
Epoch 2/10

  1/133 [..............................] - ETA: 0s - loss: 0.0020
 52/133 [==========>...................] - ETA: 0s - loss: 0.0252
103/133 [======================>.......] - ETA: 0s - loss: 0.0247
133/133 [==============================] - 0s 986us/step - loss: 0.0291
Epoch 3/10

  1/133 [..............................] - ETA: 0s - loss: 1.0109e-05
 52/133 [==========>...................] - ETA: 0s - loss: 0.0348    
103/133 [======================>.......] - ETA: 0s - loss: 0.0305
133/133 [==============================] - 0s 984us/step - loss: 0.0272
Epoch 4/10

  1/133 [..............................] - ETA: 0s - loss: 0.0432
 52/133 [==========>...................] - ETA: 0s - loss: 0.0235
103/133 [======================>.......] - ETA: 0s - loss: 0.0241
133/133 [==============================] - 0s 993us/step - loss: 0.0216
Epoch 5/10

  1/133 [..............................] - ETA: 0s - loss: 5.9335e-06
 53/133 [==========>...................] - ETA: 0s - loss: 0.0258    
104/133 [======================>.......] - ETA: 0s - loss: 0.0261
133/133 [==============================] - 0s 989us/step - loss: 0.0251
Epoch 6/10

  1/133 [..............................] - ETA: 0s - loss: 0.0144
 52/133 [==========>...................] - ETA: 0s - loss: 0.0304
104/133 [======================>.......] - ETA: 0s - loss: 0.0242
133/133 [==============================] - 0s 985us/step - loss: 0.0199
Epoch 7/10

  1/133 [..............................] - ETA: 0s - loss: 4.6013e-04
 52/133 [==========>...................] - ETA: 0s - loss: 0.0173    
103/133 [======================>.......] - ETA: 0s - loss: 0.0179
133/133 [==============================] - 0s 985us/step - loss: 0.0191
Epoch 8/10

  1/133 [..............................] - ETA: 0s - loss: 0.0011
 52/133 [==========>...................] - ETA: 0s - loss: 0.0203
103/133 [======================>.......] - ETA: 0s - loss: 0.0177
133/133 [==============================] - 0s 987us/step - loss: 0.0172
Epoch 9/10

  1/133 [..............................] - ETA: 0s - loss: 1.1574e-05
 52/133 [==========>...................] - ETA: 0s - loss: 0.0144    
103/133 [======================>.......] - ETA: 0s - loss: 0.0155
133/133 [==============================] - 0s 988us/step - loss: 0.0175
Epoch 10/10

  1/133 [..............................] - ETA: 0s - loss: 6.6941e-05
 53/133 [==========>...................] - ETA: 0s - loss: 0.0236    
105/133 [======================>.......] - ETA: 0s - loss: 0.0165
133/133 [==============================] - 0s 980us/step - loss: 0.0170
-> test with GAN.predict
GAN tn, fp: 331, 2
GAN fn, tp: 6, 7
GAN f1 score: 0.636
GAN cohens kappa score: 0.625

-> test with 'LR'
LR tn, fp: 303, 30
LR fn, tp: 1, 12
LR f1 score: 0.436
LR cohens kappa score: 0.402
LR average precision score: 0.365

-> 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: 333, 0
KNN fn, tp: 1, 12
KNN f1 score: 0.960
KNN cohens kappa score: 0.959


------ 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: 0.1735
 51/133 [==========>...................] - ETA: 0s - loss: 0.0577 
102/133 [======================>.......] - ETA: 0s - loss: 0.0487
133/133 [==============================] - 0s 997us/step - loss: 0.0407
Epoch 2/10

  1/133 [..............................] - ETA: 0s - loss: 1.3881e-04
 52/133 [==========>...................] - ETA: 0s - loss: 0.0258    
103/133 [======================>.......] - ETA: 0s - loss: 0.0274
133/133 [==============================] - 0s 985us/step - loss: 0.0281
Epoch 3/10

  1/133 [..............................] - ETA: 0s - loss: 3.9064e-06
 52/133 [==========>...................] - ETA: 0s - loss: 0.0281    
103/133 [======================>.......] - ETA: 0s - loss: 0.0320
133/133 [==============================] - 0s 986us/step - loss: 0.0272
Epoch 4/10

  1/133 [..............................] - ETA: 0s - loss: 0.0367
 52/133 [==========>...................] - ETA: 0s - loss: 0.0134
103/133 [======================>.......] - ETA: 0s - loss: 0.0190
133/133 [==============================] - 0s 986us/step - loss: 0.0218
Epoch 5/10

  1/133 [..............................] - ETA: 0s - loss: 0.0424
 52/133 [==========>...................] - ETA: 0s - loss: 0.0121
103/133 [======================>.......] - ETA: 0s - loss: 0.0167
133/133 [==============================] - 0s 984us/step - loss: 0.0163
Epoch 6/10

  1/133 [..............................] - ETA: 0s - loss: 3.6757e-05
 52/133 [==========>...................] - ETA: 0s - loss: 0.0136    
103/133 [======================>.......] - ETA: 0s - loss: 0.0178
133/133 [==============================] - 0s 994us/step - loss: 0.0172
Epoch 7/10

  1/133 [..............................] - ETA: 0s - loss: 6.9095e-04
 52/133 [==========>...................] - ETA: 0s - loss: 0.0136    
103/133 [======================>.......] - ETA: 0s - loss: 0.0169
133/133 [==============================] - 0s 993us/step - loss: 0.0167
Epoch 8/10

  1/133 [..............................] - ETA: 0s - loss: 8.6818e-04
 52/133 [==========>...................] - ETA: 0s - loss: 0.0044    
 96/133 [====================>.........] - ETA: 0s - loss: 0.0087
133/133 [==============================] - 0s 1ms/step - loss: 0.0148
Epoch 9/10

  1/133 [..............................] - ETA: 0s - loss: 0.0103
 46/133 [=========>....................] - ETA: 0s - loss: 0.0136
 97/133 [====================>.........] - ETA: 0s - loss: 0.0132
133/133 [==============================] - 0s 1ms/step - loss: 0.0135
Epoch 10/10

  1/133 [..............................] - ETA: 0s - loss: 6.2211e-06
 53/133 [==========>...................] - ETA: 0s - loss: 0.0139    
103/133 [======================>.......] - ETA: 0s - loss: 0.0148
133/133 [==============================] - 0s 994us/step - loss: 0.0139
-> test with GAN.predict
GAN tn, fp: 332, 1
GAN fn, tp: 4, 9
GAN f1 score: 0.783
GAN cohens kappa score: 0.775

-> test with 'LR'
LR tn, fp: 300, 33
LR fn, tp: 3, 10
LR f1 score: 0.357
LR cohens kappa score: 0.318
LR average precision score: 0.292

-> 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: 1, 12
GB f1 score: 0.960
GB cohens kappa score: 0.959

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


------ 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: 0.1562
 48/133 [=========>....................] - ETA: 0s - loss: 0.0614 
 98/133 [=====================>........] - ETA: 0s - loss: 0.0534
133/133 [==============================] - 0s 1ms/step - loss: 0.0441
Epoch 2/10

  1/133 [..............................] - ETA: 0s - loss: 3.4221e-04
 53/133 [==========>...................] - ETA: 0s - loss: 0.0134    
103/133 [======================>.......] - ETA: 0s - loss: 0.0244
133/133 [==============================] - 0s 1000us/step - loss: 0.0258
Epoch 3/10

  1/133 [..............................] - ETA: 0s - loss: 0.1459
 52/133 [==========>...................] - ETA: 0s - loss: 0.0250
103/133 [======================>.......] - ETA: 0s - loss: 0.0183
133/133 [==============================] - 0s 987us/step - loss: 0.0191
Epoch 4/10

  1/133 [..............................] - ETA: 0s - loss: 2.1129e-04
 52/133 [==========>...................] - ETA: 0s - loss: 0.0212    
103/133 [======================>.......] - ETA: 0s - loss: 0.0183
133/133 [==============================] - 0s 992us/step - loss: 0.0162
Epoch 5/10

  1/133 [..............................] - ETA: 0s - loss: 1.7554e-05
 52/133 [==========>...................] - ETA: 0s - loss: 0.0142    
 95/133 [====================>.........] - ETA: 0s - loss: 0.0131
133/133 [==============================] - 0s 1ms/step - loss: 0.0132
Epoch 6/10

  1/133 [..............................] - ETA: 0s - loss: 0.1782
 45/133 [=========>....................] - ETA: 0s - loss: 0.0169
 96/133 [====================>.........] - ETA: 0s - loss: 0.0116
133/133 [==============================] - 0s 1ms/step - loss: 0.0112
Epoch 7/10

  1/133 [..............................] - ETA: 0s - loss: 4.5335e-05
 52/133 [==========>...................] - ETA: 0s - loss: 0.0098    
103/133 [======================>.......] - ETA: 0s - loss: 0.0117
133/133 [==============================] - 0s 994us/step - loss: 0.0111
Epoch 8/10

  1/133 [..............................] - ETA: 0s - loss: 1.2575e-05
 52/133 [==========>...................] - ETA: 0s - loss: 0.0064    
103/133 [======================>.......] - ETA: 0s - loss: 0.0098
133/133 [==============================] - 0s 995us/step - loss: 0.0098
Epoch 9/10

  1/133 [..............................] - ETA: 0s - loss: 6.7928e-04
 52/133 [==========>...................] - ETA: 0s - loss: 0.0105    
101/133 [=====================>........] - ETA: 0s - loss: 0.0106
133/133 [==============================] - 0s 1ms/step - loss: 0.0093
Epoch 10/10

  1/133 [..............................] - ETA: 0s - loss: 1.6314e-04
 52/133 [==========>...................] - ETA: 0s - loss: 0.0059    
101/133 [=====================>........] - ETA: 0s - loss: 0.0077
133/133 [==============================] - 0s 1ms/step - loss: 0.0087
-> test with GAN.predict
GAN tn, fp: 327, 6
GAN fn, tp: 4, 9
GAN f1 score: 0.643
GAN cohens kappa score: 0.628

-> test with 'LR'
LR tn, fp: 295, 38
LR fn, tp: 0, 13
LR f1 score: 0.406
LR cohens kappa score: 0.368
LR average precision score: 0.400

-> 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: 1, 12
GB f1 score: 0.960
GB cohens kappa score: 0.959

-> test with 'KNN'
KNN tn, fp: 326, 7
KNN fn, tp: 2, 11
KNN f1 score: 0.710
KNN cohens kappa score: 0.696


------ 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: 3.5443e-06
 51/133 [==========>...................] - ETA: 0s - loss: 0.0829     
102/133 [======================>.......] - ETA: 0s - loss: 0.0644
133/133 [==============================] - 0s 993us/step - loss: 0.0578
Epoch 2/10

  1/133 [..............................] - ETA: 0s - loss: 0.0130
 53/133 [==========>...................] - ETA: 0s - loss: 0.0445
105/133 [======================>.......] - ETA: 0s - loss: 0.0411
133/133 [==============================] - 0s 982us/step - loss: 0.0357
Epoch 3/10

  1/133 [..............................] - ETA: 0s - loss: 1.9767e-04
 52/133 [==========>...................] - ETA: 0s - loss: 0.0410    
100/133 [=====================>........] - ETA: 0s - loss: 0.0296
133/133 [==============================] - 0s 1ms/step - loss: 0.0286
Epoch 4/10

  1/133 [..............................] - ETA: 0s - loss: 5.1779e-06
 47/133 [=========>....................] - ETA: 0s - loss: 0.0243    
 94/133 [====================>.........] - ETA: 0s - loss: 0.0249
133/133 [==============================] - 0s 1ms/step - loss: 0.0249
Epoch 5/10

  1/133 [..............................] - ETA: 0s - loss: 0.0018
 52/133 [==========>...................] - ETA: 0s - loss: 0.0295
103/133 [======================>.......] - ETA: 0s - loss: 0.0223
133/133 [==============================] - 0s 984us/step - loss: 0.0216
Epoch 6/10

  1/133 [..............................] - ETA: 0s - loss: 4.7801e-06
 52/133 [==========>...................] - ETA: 0s - loss: 0.0191    
103/133 [======================>.......] - ETA: 0s - loss: 0.0201
133/133 [==============================] - 0s 986us/step - loss: 0.0184
Epoch 7/10

  1/133 [..............................] - ETA: 0s - loss: 1.7417e-05
 52/133 [==========>...................] - ETA: 0s - loss: 0.0148    
103/133 [======================>.......] - ETA: 0s - loss: 0.0178
133/133 [==============================] - 0s 986us/step - loss: 0.0184
Epoch 8/10

  1/133 [..............................] - ETA: 0s - loss: 6.8766e-05
 52/133 [==========>...................] - ETA: 0s - loss: 0.0070    
103/133 [======================>.......] - ETA: 0s - loss: 0.0139
133/133 [==============================] - 0s 995us/step - loss: 0.0155
Epoch 9/10

  1/133 [..............................] - ETA: 0s - loss: 0.0030
 52/133 [==========>...................] - ETA: 0s - loss: 0.0171
103/133 [======================>.......] - ETA: 0s - loss: 0.0158
133/133 [==============================] - 0s 995us/step - loss: 0.0144
Epoch 10/10

  1/133 [..............................] - ETA: 0s - loss: 2.8375e-04
 53/133 [==========>...................] - ETA: 0s - loss: 0.0163    
104/133 [======================>.......] - ETA: 0s - loss: 0.0138
133/133 [==============================] - 0s 980us/step - loss: 0.0131
-> test with GAN.predict
GAN tn, fp: 331, 2
GAN fn, tp: 4, 9
GAN f1 score: 0.750
GAN cohens kappa score: 0.741

-> test with 'LR'
LR tn, fp: 305, 28
LR fn, tp: 1, 12
LR f1 score: 0.453
LR cohens kappa score: 0.420
LR average precision score: 0.377

-> 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: 0, 13
GB f1 score: 1.000
GB cohens kappa score: 1.000

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


------ 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: 19s - loss: 0.0061
 45/134 [=========>....................] - ETA: 0s - loss: 0.0656 
 88/134 [==================>...........] - ETA: 0s - loss: 0.0722
130/134 [============================>.] - ETA: 0s - loss: 0.0732
134/134 [==============================] - 0s 1ms/step - loss: 0.0735
Epoch 2/10

  1/134 [..............................] - ETA: 0s - loss: 2.3658e-06
 49/134 [=========>....................] - ETA: 0s - loss: 0.0267    
 97/134 [====================>.........] - ETA: 0s - loss: 0.0528
134/134 [==============================] - 0s 1ms/step - loss: 0.0462
Epoch 3/10

  1/134 [..............................] - ETA: 0s - loss: 5.4571e-06
 50/134 [==========>...................] - ETA: 0s - loss: 0.0464    
 98/134 [====================>.........] - ETA: 0s - loss: 0.0422
134/134 [==============================] - 0s 1ms/step - loss: 0.0368
Epoch 4/10

  1/134 [..............................] - ETA: 0s - loss: 0.0082
 50/134 [==========>...................] - ETA: 0s - loss: 0.0135
 99/134 [=====================>........] - ETA: 0s - loss: 0.0292
134/134 [==============================] - 0s 1ms/step - loss: 0.0342
Epoch 5/10

  1/134 [..............................] - ETA: 0s - loss: 7.3234e-06
 49/134 [=========>....................] - ETA: 0s - loss: 0.0457    
 98/134 [====================>.........] - ETA: 0s - loss: 0.0304
134/134 [==============================] - 0s 1ms/step - loss: 0.0249
Epoch 6/10

  1/134 [..............................] - ETA: 0s - loss: 2.3077e-05
 49/134 [=========>....................] - ETA: 0s - loss: 0.0181    
 97/134 [====================>.........] - ETA: 0s - loss: 0.0304
134/134 [==============================] - 0s 1ms/step - loss: 0.0278
Epoch 7/10

  1/134 [..............................] - ETA: 0s - loss: 0.0814
 49/134 [=========>....................] - ETA: 0s - loss: 0.0119
 98/134 [====================>.........] - ETA: 0s - loss: 0.0181
134/134 [==============================] - 0s 1ms/step - loss: 0.0228
Epoch 8/10

  1/134 [..............................] - ETA: 0s - loss: 0.0085
 48/134 [=========>....................] - ETA: 0s - loss: 0.0226
 96/134 [====================>.........] - ETA: 0s - loss: 0.0199
134/134 [==============================] - 0s 1ms/step - loss: 0.0217
Epoch 9/10

  1/134 [..............................] - ETA: 0s - loss: 1.3308e-05
 49/134 [=========>....................] - ETA: 0s - loss: 0.0249    
 97/134 [====================>.........] - ETA: 0s - loss: 0.0204
134/134 [==============================] - 0s 1ms/step - loss: 0.0210
Epoch 10/10

  1/134 [..............................] - ETA: 0s - loss: 5.5245e-04
 45/134 [=========>....................] - ETA: 0s - loss: 0.0106    
 93/134 [===================>..........] - ETA: 0s - loss: 0.0199
134/134 [==============================] - 0s 1ms/step - loss: 0.0192
-> test with GAN.predict
GAN tn, fp: 329, 2
GAN fn, tp: 4, 9
GAN f1 score: 0.750
GAN cohens kappa score: 0.741

-> test with 'LR'
LR tn, fp: 308, 23
LR fn, tp: 3, 10
LR f1 score: 0.435
LR cohens kappa score: 0.402
LR average precision score: 0.430

-> 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: 329, 2
GB fn, tp: 0, 13
GB f1 score: 0.929
GB cohens kappa score: 0.926

-> test with 'KNN'
KNN tn, fp: 329, 2
KNN fn, tp: 0, 13
KNN f1 score: 0.929
KNN cohens kappa score: 0.926


====== 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: 17s - loss: 1.4274e-06
 51/133 [==========>...................] - ETA: 0s - loss: 0.0463     
101/133 [=====================>........] - ETA: 0s - loss: 0.0618
133/133 [==============================] - 0s 1ms/step - loss: 0.0551
Epoch 2/10

  1/133 [..............................] - ETA: 0s - loss: 4.8779e-07
 52/133 [==========>...................] - ETA: 0s - loss: 0.0295    
103/133 [======================>.......] - ETA: 0s - loss: 0.0315
133/133 [==============================] - 0s 996us/step - loss: 0.0366
Epoch 3/10

  1/133 [..............................] - ETA: 0s - loss: 4.8495e-05
 52/133 [==========>...................] - ETA: 0s - loss: 0.0259    
 99/133 [=====================>........] - ETA: 0s - loss: 0.0176
133/133 [==============================] - 0s 1ms/step - loss: 0.0239
Epoch 4/10

  1/133 [..............................] - ETA: 0s - loss: 0.0023
 52/133 [==========>...................] - ETA: 0s - loss: 0.0326
103/133 [======================>.......] - ETA: 0s - loss: 0.0235
133/133 [==============================] - 0s 994us/step - loss: 0.0210
Epoch 5/10

  1/133 [..............................] - ETA: 0s - loss: 0.0201
 52/133 [==========>...................] - ETA: 0s - loss: 0.0225
103/133 [======================>.......] - ETA: 0s - loss: 0.0210
133/133 [==============================] - 0s 997us/step - loss: 0.0179
Epoch 6/10

  1/133 [..............................] - ETA: 0s - loss: 0.0183
 52/133 [==========>...................] - ETA: 0s - loss: 0.0083
101/133 [=====================>........] - ETA: 0s - loss: 0.0103
133/133 [==============================] - 0s 1ms/step - loss: 0.0155
Epoch 7/10

  1/133 [..............................] - ETA: 0s - loss: 0.0850
 47/133 [=========>....................] - ETA: 0s - loss: 0.0149
 96/133 [====================>.........] - ETA: 0s - loss: 0.0142
133/133 [==============================] - 0s 1ms/step - loss: 0.0155
Epoch 8/10

  1/133 [..............................] - ETA: 0s - loss: 3.1198e-06
 52/133 [==========>...................] - ETA: 0s - loss: 0.0069    
103/133 [======================>.......] - ETA: 0s - loss: 0.0166
133/133 [==============================] - 0s 990us/step - loss: 0.0144
Epoch 9/10

  1/133 [..............................] - ETA: 0s - loss: 0.0039
 43/133 [========>.....................] - ETA: 0s - loss: 0.0154
 85/133 [==================>...........] - ETA: 0s - loss: 0.0129
129/133 [============================>.] - ETA: 0s - loss: 0.0135
133/133 [==============================] - 0s 1ms/step - loss: 0.0131
Epoch 10/10

  1/133 [..............................] - ETA: 0s - loss: 1.1233e-05
 48/133 [=========>....................] - ETA: 0s - loss: 0.0102    
 98/133 [=====================>........] - ETA: 0s - loss: 0.0152
133/133 [==============================] - 0s 1ms/step - loss: 0.0138
-> test with GAN.predict
GAN tn, fp: 331, 2
GAN fn, tp: 5, 8
GAN f1 score: 0.696
GAN cohens kappa score: 0.685

-> test with 'LR'
LR tn, fp: 309, 24
LR fn, tp: 5, 8
LR f1 score: 0.356
LR cohens kappa score: 0.319
LR average precision score: 0.285

-> 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: 333, 0
GB fn, tp: 0, 13
GB f1 score: 1.000
GB cohens kappa score: 1.000

-> test with 'KNN'
KNN tn, fp: 330, 3
KNN fn, tp: 1, 12
KNN f1 score: 0.857
KNN cohens kappa score: 0.851


------ 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: 16s - loss: 5.5838e-06
 52/133 [==========>...................] - ETA: 0s - loss: 0.0745     
103/133 [======================>.......] - ETA: 0s - loss: 0.0502
133/133 [==============================] - 0s 994us/step - loss: 0.0534
Epoch 2/10

  1/133 [..............................] - ETA: 0s - loss: 1.6150e-06
 52/133 [==========>...................] - ETA: 0s - loss: 0.0313    
103/133 [======================>.......] - ETA: 0s - loss: 0.0376
133/133 [==============================] - 0s 985us/step - loss: 0.0390
Epoch 3/10

  1/133 [..............................] - ETA: 0s - loss: 6.0168e-06
 52/133 [==========>...................] - ETA: 0s - loss: 0.0274    
103/133 [======================>.......] - ETA: 0s - loss: 0.0354
133/133 [==============================] - 0s 996us/step - loss: 0.0297
Epoch 4/10

  1/133 [..............................] - ETA: 0s - loss: 0.0058
 51/133 [==========>...................] - ETA: 0s - loss: 0.0236
100/133 [=====================>........] - ETA: 0s - loss: 0.0247
133/133 [==============================] - 0s 1ms/step - loss: 0.0239
Epoch 5/10

  1/133 [..............................] - ETA: 0s - loss: 3.1223e-04
 52/133 [==========>...................] - ETA: 0s - loss: 0.0241    
100/133 [=====================>........] - ETA: 0s - loss: 0.0238
133/133 [==============================] - 0s 1ms/step - loss: 0.0221
Epoch 6/10

  1/133 [..............................] - ETA: 0s - loss: 0.0034
 51/133 [==========>...................] - ETA: 0s - loss: 0.0252
102/133 [======================>.......] - ETA: 0s - loss: 0.0181
133/133 [==============================] - 0s 991us/step - loss: 0.0184
Epoch 7/10

  1/133 [..............................] - ETA: 0s - loss: 0.0203
 52/133 [==========>...................] - ETA: 0s - loss: 0.0206
103/133 [======================>.......] - ETA: 0s - loss: 0.0219
133/133 [==============================] - 0s 990us/step - loss: 0.0171
Epoch 8/10

  1/133 [..............................] - ETA: 0s - loss: 2.0746e-06
 52/133 [==========>...................] - ETA: 0s - loss: 0.0199    
101/133 [=====================>........] - ETA: 0s - loss: 0.0137
133/133 [==============================] - 0s 1ms/step - loss: 0.0139
Epoch 9/10

  1/133 [..............................] - ETA: 0s - loss: 7.9716e-05
 49/133 [==========>...................] - ETA: 0s - loss: 0.0128    
100/133 [=====================>........] - ETA: 0s - loss: 0.0159
133/133 [==============================] - 0s 1ms/step - loss: 0.0138
Epoch 10/10

  1/133 [..............................] - ETA: 0s - loss: 0.0071
 52/133 [==========>...................] - ETA: 0s - loss: 0.0103
103/133 [======================>.......] - ETA: 0s - loss: 0.0105
133/133 [==============================] - 0s 991us/step - loss: 0.0141
-> test with GAN.predict
GAN tn, fp: 332, 1
GAN fn, tp: 4, 9
GAN f1 score: 0.783
GAN cohens kappa score: 0.775

-> test with 'LR'
LR tn, fp: 294, 39
LR fn, tp: 0, 13
LR f1 score: 0.400
LR cohens kappa score: 0.362
LR average precision score: 0.329

-> 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: 326, 7
KNN fn, tp: 1, 12
KNN f1 score: 0.750
KNN cohens kappa score: 0.738


------ 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: 17s - loss: 2.0750e-06
 51/133 [==========>...................] - ETA: 0s - loss: 0.0533     
102/133 [======================>.......] - ETA: 0s - loss: 0.0517
133/133 [==============================] - 0s 1ms/step - loss: 0.0514
Epoch 2/10

  1/133 [..............................] - ETA: 0s - loss: 1.5956e-05
 51/133 [==========>...................] - ETA: 0s - loss: 0.0254    
102/133 [======================>.......] - ETA: 0s - loss: 0.0309
133/133 [==============================] - 0s 1ms/step - loss: 0.0296
Epoch 3/10

  1/133 [..............................] - ETA: 0s - loss: 8.6009e-06
 52/133 [==========>...................] - ETA: 0s - loss: 0.0146    
103/133 [======================>.......] - ETA: 0s - loss: 0.0212
133/133 [==============================] - 0s 991us/step - loss: 0.0249
Epoch 4/10

  1/133 [..............................] - ETA: 0s - loss: 0.2891
 52/133 [==========>...................] - ETA: 0s - loss: 0.0345
103/133 [======================>.......] - ETA: 0s - loss: 0.0239
133/133 [==============================] - 0s 985us/step - loss: 0.0222
Epoch 5/10

  1/133 [..............................] - ETA: 0s - loss: 3.1527e-05
 52/133 [==========>...................] - ETA: 0s - loss: 0.0283    
 99/133 [=====================>........] - ETA: 0s - loss: 0.0206
133/133 [==============================] - 0s 1ms/step - loss: 0.0177
Epoch 6/10

  1/133 [..............................] - ETA: 0s - loss: 1.2337e-05
 48/133 [=========>....................] - ETA: 0s - loss: 0.0246    
 95/133 [====================>.........] - ETA: 0s - loss: 0.0204
133/133 [==============================] - 0s 1ms/step - loss: 0.0185
Epoch 7/10

  1/133 [..............................] - ETA: 0s - loss: 2.6076e-04
 52/133 [==========>...................] - ETA: 0s - loss: 0.0152    
103/133 [======================>.......] - ETA: 0s - loss: 0.0214
133/133 [==============================] - 0s 999us/step - loss: 0.0192
Epoch 8/10

  1/133 [..............................] - ETA: 0s - loss: 1.4218e-05
 52/133 [==========>...................] - ETA: 0s - loss: 0.0144    
100/133 [=====================>........] - ETA: 0s - loss: 0.0131
133/133 [==============================] - 0s 1ms/step - loss: 0.0156
Epoch 9/10

  1/133 [..............................] - ETA: 0s - loss: 2.4330e-05
 51/133 [==========>...................] - ETA: 0s - loss: 0.0139    
101/133 [=====================>........] - ETA: 0s - loss: 0.0154
133/133 [==============================] - 0s 1ms/step - loss: 0.0139
Epoch 10/10

  1/133 [..............................] - ETA: 0s - loss: 0.0176
 52/133 [==========>...................] - ETA: 0s - loss: 0.0117
103/133 [======================>.......] - ETA: 0s - loss: 0.0156
133/133 [==============================] - 0s 990us/step - loss: 0.0138
-> test with GAN.predict
GAN tn, fp: 331, 2
GAN fn, tp: 3, 10
GAN f1 score: 0.800
GAN cohens kappa score: 0.793

-> test with 'LR'
LR tn, fp: 303, 30
LR fn, tp: 2, 11
LR f1 score: 0.407
LR cohens kappa score: 0.372
LR average precision score: 0.334

-> 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: 332, 1
KNN fn, tp: 3, 10
KNN f1 score: 0.833
KNN cohens kappa score: 0.827


------ 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: 15s - loss: 0.2742
 49/133 [==========>...................] - ETA: 0s - loss: 0.0342 
 99/133 [=====================>........] - ETA: 0s - loss: 0.0404
133/133 [==============================] - 0s 1ms/step - loss: 0.0420
Epoch 2/10

  1/133 [..............................] - ETA: 0s - loss: 0.0149
 52/133 [==========>...................] - ETA: 0s - loss: 0.0233
103/133 [======================>.......] - ETA: 0s - loss: 0.0247
133/133 [==============================] - 0s 986us/step - loss: 0.0284
Epoch 3/10

  1/133 [..............................] - ETA: 0s - loss: 2.4979e-06
 52/133 [==========>...................] - ETA: 0s - loss: 0.0150    
103/133 [======================>.......] - ETA: 0s - loss: 0.0191
133/133 [==============================] - 0s 986us/step - loss: 0.0245
Epoch 4/10

  1/133 [..............................] - ETA: 0s - loss: 0.0661
 48/133 [=========>....................] - ETA: 0s - loss: 0.0109
 97/133 [====================>.........] - ETA: 0s - loss: 0.0238
133/133 [==============================] - 0s 1ms/step - loss: 0.0205
Epoch 5/10

  1/133 [..............................] - ETA: 0s - loss: 2.1955e-06
 53/133 [==========>...................] - ETA: 0s - loss: 0.0263    
104/133 [======================>.......] - ETA: 0s - loss: 0.0165
133/133 [==============================] - 0s 986us/step - loss: 0.0154
Epoch 6/10

  1/133 [..............................] - ETA: 0s - loss: 2.5915e-06
 52/133 [==========>...................] - ETA: 0s - loss: 0.0164    
104/133 [======================>.......] - ETA: 0s - loss: 0.0115
133/133 [==============================] - 0s 984us/step - loss: 0.0139
Epoch 7/10

  1/133 [..............................] - ETA: 0s - loss: 0.1567
 53/133 [==========>...................] - ETA: 0s - loss: 0.0130
104/133 [======================>.......] - ETA: 0s - loss: 0.0117
133/133 [==============================] - 0s 984us/step - loss: 0.0121
Epoch 8/10

  1/133 [..............................] - ETA: 0s - loss: 0.0024
 52/133 [==========>...................] - ETA: 0s - loss: 0.0051
103/133 [======================>.......] - ETA: 0s - loss: 0.0128
133/133 [==============================] - 0s 990us/step - loss: 0.0132
Epoch 9/10

  1/133 [..............................] - ETA: 0s - loss: 0.0018
 52/133 [==========>...................] - ETA: 0s - loss: 0.0087
103/133 [======================>.......] - ETA: 0s - loss: 0.0116
133/133 [==============================] - 0s 989us/step - loss: 0.0112
Epoch 10/10

  1/133 [..............................] - ETA: 0s - loss: 6.9869e-06
 52/133 [==========>...................] - ETA: 0s - loss: 0.0087    
104/133 [======================>.......] - ETA: 0s - loss: 0.0109
133/133 [==============================] - 0s 986us/step - loss: 0.0115
-> test with GAN.predict
GAN tn, fp: 327, 6
GAN fn, tp: 5, 8
GAN f1 score: 0.593
GAN cohens kappa score: 0.576

-> test with 'LR'
LR tn, fp: 306, 27
LR fn, tp: 0, 13
LR f1 score: 0.491
LR cohens kappa score: 0.460
LR average precision score: 0.298

-> 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: 3, 10
GB f1 score: 0.870
GB cohens kappa score: 0.865

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


------ 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: 23s - loss: 1.3652e-06
 49/134 [=========>....................] - ETA: 0s - loss: 0.0573     
 98/134 [====================>.........] - ETA: 0s - loss: 0.0702
134/134 [==============================] - 0s 1ms/step - loss: 0.0616
Epoch 2/10

  1/134 [..............................] - ETA: 0s - loss: 6.8564e-07
 49/134 [=========>....................] - ETA: 0s - loss: 0.0188    
 98/134 [====================>.........] - ETA: 0s - loss: 0.0322
134/134 [==============================] - 0s 1ms/step - loss: 0.0285
Epoch 3/10

  1/134 [..............................] - ETA: 0s - loss: 0.4708
 48/134 [=========>....................] - ETA: 0s - loss: 0.0342
 90/134 [===================>..........] - ETA: 0s - loss: 0.0247
133/134 [============================>.] - ETA: 0s - loss: 0.0239
134/134 [==============================] - 0s 1ms/step - loss: 0.0238
Epoch 4/10

  1/134 [..............................] - ETA: 0s - loss: 6.4692e-07
 46/134 [=========>....................] - ETA: 0s - loss: 0.0067    
 95/134 [====================>.........] - ETA: 0s - loss: 0.0190
134/134 [==============================] - 0s 1ms/step - loss: 0.0243
Epoch 5/10

  1/134 [..............................] - ETA: 0s - loss: 8.2855e-07
 49/134 [=========>....................] - ETA: 0s - loss: 0.0179    
 97/134 [====================>.........] - ETA: 0s - loss: 0.0214
134/134 [==============================] - 0s 1ms/step - loss: 0.0214
Epoch 6/10

  1/134 [..............................] - ETA: 0s - loss: 0.0018
 49/134 [=========>....................] - ETA: 0s - loss: 0.0116
 97/134 [====================>.........] - ETA: 0s - loss: 0.0208
134/134 [==============================] - 0s 1ms/step - loss: 0.0192
Epoch 7/10

  1/134 [..............................] - ETA: 0s - loss: 1.1005e-06
 49/134 [=========>....................] - ETA: 0s - loss: 0.0152    
 97/134 [====================>.........] - ETA: 0s - loss: 0.0162
134/134 [==============================] - 0s 1ms/step - loss: 0.0193
Epoch 8/10

  1/134 [..............................] - ETA: 0s - loss: 4.1779e-06
 50/134 [==========>...................] - ETA: 0s - loss: 0.0110    
 99/134 [=====================>........] - ETA: 0s - loss: 0.0132
134/134 [==============================] - 0s 1ms/step - loss: 0.0156
Epoch 9/10

  1/134 [..............................] - ETA: 0s - loss: 0.0207
 44/134 [========>.....................] - ETA: 0s - loss: 0.0176
 92/134 [===================>..........] - ETA: 0s - loss: 0.0120
134/134 [==============================] - 0s 1ms/step - loss: 0.0175
Epoch 10/10

  1/134 [..............................] - ETA: 0s - loss: 0.0089
 47/134 [=========>....................] - ETA: 0s - loss: 0.0110
 95/134 [====================>.........] - ETA: 0s - loss: 0.0109
134/134 [==============================] - 0s 1ms/step - loss: 0.0149
-> test with GAN.predict
GAN tn, fp: 326, 5
GAN fn, tp: 3, 10
GAN f1 score: 0.714
GAN cohens kappa score: 0.702

-> test with 'LR'
LR tn, fp: 303, 28
LR fn, tp: 1, 12
LR f1 score: 0.453
LR cohens kappa score: 0.420
LR average precision score: 0.559

-> 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: 331, 0
KNN fn, tp: 0, 13
KNN f1 score: 1.000
KNN cohens kappa score: 1.000


====== 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: 16s - loss: 3.9267e-04
 51/133 [==========>...................] - ETA: 0s - loss: 0.0692     
102/133 [======================>.......] - ETA: 0s - loss: 0.0777
133/133 [==============================] - 0s 998us/step - loss: 0.0747
Epoch 2/10

  1/133 [..............................] - ETA: 0s - loss: 2.7408e-06
 52/133 [==========>...................] - ETA: 0s - loss: 0.0526    
103/133 [======================>.......] - ETA: 0s - loss: 0.0411
133/133 [==============================] - 0s 990us/step - loss: 0.0345
Epoch 3/10

  1/133 [..............................] - ETA: 0s - loss: 6.2981e-06
 50/133 [==========>...................] - ETA: 0s - loss: 0.0368    
101/133 [=====================>........] - ETA: 0s - loss: 0.0362
133/133 [==============================] - 0s 1ms/step - loss: 0.0313
Epoch 4/10

  1/133 [..............................] - ETA: 0s - loss: 5.9492e-06
 50/133 [==========>...................] - ETA: 0s - loss: 0.0211    
100/133 [=====================>........] - ETA: 0s - loss: 0.0240
133/133 [==============================] - 0s 1ms/step - loss: 0.0277
Epoch 5/10

  1/133 [..............................] - ETA: 0s - loss: 7.7788e-06
 53/133 [==========>...................] - ETA: 0s - loss: 0.0360    
104/133 [======================>.......] - ETA: 0s - loss: 0.0261
133/133 [==============================] - 0s 985us/step - loss: 0.0239
Epoch 6/10

  1/133 [..............................] - ETA: 0s - loss: 2.3003e-05
 51/133 [==========>...................] - ETA: 0s - loss: 0.0237    
102/133 [======================>.......] - ETA: 0s - loss: 0.0222
133/133 [==============================] - 0s 996us/step - loss: 0.0208
Epoch 7/10

  1/133 [..............................] - ETA: 0s - loss: 0.2385
 52/133 [==========>...................] - ETA: 0s - loss: 0.0234
104/133 [======================>.......] - ETA: 0s - loss: 0.0211
133/133 [==============================] - 0s 1ms/step - loss: 0.0193
Epoch 8/10

  1/133 [..............................] - ETA: 0s - loss: 0.0162
 51/133 [==========>...................] - ETA: 0s - loss: 0.0081
 97/133 [====================>.........] - ETA: 0s - loss: 0.0183
133/133 [==============================] - 0s 1ms/step - loss: 0.0200
Epoch 9/10

  1/133 [..............................] - ETA: 0s - loss: 5.6497e-06
 49/133 [==========>...................] - ETA: 0s - loss: 0.0117    
100/133 [=====================>........] - ETA: 0s - loss: 0.0166
133/133 [==============================] - 0s 1ms/step - loss: 0.0152
Epoch 10/10

  1/133 [..............................] - ETA: 0s - loss: 1.6416e-05
 52/133 [==========>...................] - ETA: 0s - loss: 0.0109    
103/133 [======================>.......] - ETA: 0s - loss: 0.0146
133/133 [==============================] - 0s 984us/step - loss: 0.0169
-> test with GAN.predict
GAN tn, fp: 331, 2
GAN fn, tp: 7, 6
GAN f1 score: 0.571
GAN cohens kappa score: 0.559

-> test with 'LR'
LR tn, fp: 304, 29
LR fn, tp: 2, 11
LR f1 score: 0.415
LR cohens kappa score: 0.380
LR average precision score: 0.310

-> 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: 333, 0
KNN fn, tp: 3, 10
KNN f1 score: 0.870
KNN cohens kappa score: 0.865


------ 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: 16s - loss: 1.2150e-06
 51/133 [==========>...................] - ETA: 0s - loss: 0.0443     
101/133 [=====================>........] - ETA: 0s - loss: 0.0616
133/133 [==============================] - 0s 1ms/step - loss: 0.0574
Epoch 2/10

  1/133 [..............................] - ETA: 0s - loss: 0.6370
 52/133 [==========>...................] - ETA: 0s - loss: 0.0375
103/133 [======================>.......] - ETA: 0s - loss: 0.0379
133/133 [==============================] - 0s 1ms/step - loss: 0.0362
Epoch 3/10

  1/133 [..............................] - ETA: 0s - loss: 1.3386e-06
 51/133 [==========>...................] - ETA: 0s - loss: 0.0349    
102/133 [======================>.......] - ETA: 0s - loss: 0.0303
133/133 [==============================] - 0s 1ms/step - loss: 0.0274
Epoch 4/10

  1/133 [..............................] - ETA: 0s - loss: 6.9959e-05
 52/133 [==========>...................] - ETA: 0s - loss: 0.0304    
103/133 [======================>.......] - ETA: 0s - loss: 0.0218
133/133 [==============================] - 0s 1ms/step - loss: 0.0225
Epoch 5/10

  1/133 [..............................] - ETA: 0s - loss: 1.6452e-05
 46/133 [=========>....................] - ETA: 0s - loss: 0.0155    
 93/133 [===================>..........] - ETA: 0s - loss: 0.0216
133/133 [==============================] - 0s 1ms/step - loss: 0.0214
Epoch 6/10

  1/133 [..............................] - ETA: 0s - loss: 2.8900e-04
 52/133 [==========>...................] - ETA: 0s - loss: 0.0167    
103/133 [======================>.......] - ETA: 0s - loss: 0.0188
133/133 [==============================] - 0s 994us/step - loss: 0.0193
Epoch 7/10

  1/133 [..............................] - ETA: 0s - loss: 4.3592e-05
 52/133 [==========>...................] - ETA: 0s - loss: 0.0155    
102/133 [======================>.......] - ETA: 0s - loss: 0.0147
133/133 [==============================] - 0s 999us/step - loss: 0.0169
Epoch 8/10

  1/133 [..............................] - ETA: 0s - loss: 1.0018e-05
 50/133 [==========>...................] - ETA: 0s - loss: 0.0107    
101/133 [=====================>........] - ETA: 0s - loss: 0.0148
133/133 [==============================] - 0s 1ms/step - loss: 0.0155
Epoch 9/10

  1/133 [..............................] - ETA: 0s - loss: 0.0062
 52/133 [==========>...................] - ETA: 0s - loss: 0.0158
103/133 [======================>.......] - ETA: 0s - loss: 0.0147
133/133 [==============================] - 0s 991us/step - loss: 0.0140
Epoch 10/10

  1/133 [..............................] - ETA: 0s - loss: 5.8354e-05
 50/133 [==========>...................] - ETA: 0s - loss: 0.0168    
100/133 [=====================>........] - ETA: 0s - loss: 0.0137
133/133 [==============================] - 0s 1ms/step - loss: 0.0143
-> test with GAN.predict
GAN tn, fp: 331, 2
GAN fn, tp: 2, 11
GAN f1 score: 0.846
GAN cohens kappa score: 0.840

-> test with 'LR'
LR tn, fp: 309, 24
LR fn, tp: 0, 13
LR f1 score: 0.520
LR cohens kappa score: 0.492
LR average precision score: 0.433

-> 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: 332, 1
GB fn, tp: 0, 13
GB f1 score: 0.963
GB cohens kappa score: 0.961

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


------ 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: 1.7397e-05
 51/133 [==========>...................] - ETA: 0s - loss: 0.0428     
102/133 [======================>.......] - ETA: 0s - loss: 0.0471
133/133 [==============================] - 0s 993us/step - loss: 0.0499
Epoch 2/10

  1/133 [..............................] - ETA: 0s - loss: 6.2705e-06
 52/133 [==========>...................] - ETA: 0s - loss: 0.0304    
 99/133 [=====================>........] - ETA: 0s - loss: 0.0288
133/133 [==============================] - 0s 1ms/step - loss: 0.0305
Epoch 3/10

  1/133 [..............................] - ETA: 0s - loss: 0.2084
 48/133 [=========>....................] - ETA: 0s - loss: 0.0315
 98/133 [=====================>........] - ETA: 0s - loss: 0.0237
133/133 [==============================] - 0s 1ms/step - loss: 0.0224
Epoch 4/10

  1/133 [..............................] - ETA: 0s - loss: 9.6316e-04
 52/133 [==========>...................] - ETA: 0s - loss: 0.0258    
103/133 [======================>.......] - ETA: 0s - loss: 0.0213
133/133 [==============================] - 0s 990us/step - loss: 0.0207
Epoch 5/10

  1/133 [..............................] - ETA: 0s - loss: 4.0876e-04
 47/133 [=========>....................] - ETA: 0s - loss: 0.0266    
 98/133 [=====================>........] - ETA: 0s - loss: 0.0211
133/133 [==============================] - 0s 1ms/step - loss: 0.0182
Epoch 6/10

  1/133 [..............................] - ETA: 0s - loss: 7.3352e-06
 52/133 [==========>...................] - ETA: 0s - loss: 0.0218    
104/133 [======================>.......] - ETA: 0s - loss: 0.0150
133/133 [==============================] - 0s 983us/step - loss: 0.0136
Epoch 7/10

  1/133 [..............................] - ETA: 0s - loss: 9.2029e-05
 52/133 [==========>...................] - ETA: 0s - loss: 0.0067    
104/133 [======================>.......] - ETA: 0s - loss: 0.0116
133/133 [==============================] - 0s 984us/step - loss: 0.0144
Epoch 8/10

  1/133 [..............................] - ETA: 0s - loss: 0.0323
 52/133 [==========>...................] - ETA: 0s - loss: 0.0173
102/133 [======================>.......] - ETA: 0s - loss: 0.0161
133/133 [==============================] - 0s 995us/step - loss: 0.0135
Epoch 9/10

  1/133 [..............................] - ETA: 0s - loss: 9.3994e-06
 52/133 [==========>...................] - ETA: 0s - loss: 0.0065    
103/133 [======================>.......] - ETA: 0s - loss: 0.0114
133/133 [==============================] - 0s 1ms/step - loss: 0.0103
Epoch 10/10

  1/133 [..............................] - ETA: 0s - loss: 0.0012
 39/133 [=======>......................] - ETA: 0s - loss: 0.0105
 84/133 [=================>............] - ETA: 0s - loss: 0.0107
129/133 [============================>.] - ETA: 0s - loss: 0.0116
133/133 [==============================] - 0s 1ms/step - loss: 0.0113
-> test with GAN.predict
GAN tn, fp: 332, 1
GAN fn, tp: 3, 10
GAN f1 score: 0.833
GAN cohens kappa score: 0.827

-> test with 'LR'
LR tn, fp: 298, 35
LR fn, tp: 1, 12
LR f1 score: 0.400
LR cohens kappa score: 0.362
LR average precision score: 0.326

-> 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: 332, 1
KNN fn, tp: 1, 12
KNN f1 score: 0.923
KNN cohens kappa score: 0.920


------ 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: 16s - loss: 1.6681e-06
 46/133 [=========>....................] - ETA: 0s - loss: 0.0302     
 93/133 [===================>..........] - ETA: 0s - loss: 0.0376
133/133 [==============================] - 0s 1ms/step - loss: 0.0421
Epoch 2/10

  1/133 [..............................] - ETA: 0s - loss: 5.9094e-06
 48/133 [=========>....................] - ETA: 0s - loss: 0.0210    
 95/133 [====================>.........] - ETA: 0s - loss: 0.0288
133/133 [==============================] - 0s 1ms/step - loss: 0.0285
Epoch 3/10

  1/133 [..............................] - ETA: 0s - loss: 5.3610e-04
 49/133 [==========>...................] - ETA: 0s - loss: 0.0271    
 95/133 [====================>.........] - ETA: 0s - loss: 0.0327
133/133 [==============================] - 0s 1ms/step - loss: 0.0258
Epoch 4/10

  1/133 [..............................] - ETA: 0s - loss: 4.6395e-06
 47/133 [=========>....................] - ETA: 0s - loss: 0.0190    
 95/133 [====================>.........] - ETA: 0s - loss: 0.0182
133/133 [==============================] - 0s 1ms/step - loss: 0.0233
Epoch 5/10

  1/133 [..............................] - ETA: 0s - loss: 3.3125e-06
 49/133 [==========>...................] - ETA: 0s - loss: 0.0142    
 96/133 [====================>.........] - ETA: 0s - loss: 0.0154
133/133 [==============================] - 0s 1ms/step - loss: 0.0170
Epoch 6/10

  1/133 [..............................] - ETA: 0s - loss: 4.8561e-06
 46/133 [=========>....................] - ETA: 0s - loss: 0.0258    
 94/133 [====================>.........] - ETA: 0s - loss: 0.0159
133/133 [==============================] - 0s 1ms/step - loss: 0.0172
Epoch 7/10

  1/133 [..............................] - ETA: 0s - loss: 8.4834e-04
 48/133 [=========>....................] - ETA: 0s - loss: 0.0141    
 96/133 [====================>.........] - ETA: 0s - loss: 0.0158
133/133 [==============================] - 0s 1ms/step - loss: 0.0169
Epoch 8/10

  1/133 [..............................] - ETA: 0s - loss: 6.8423e-04
 47/133 [=========>....................] - ETA: 0s - loss: 0.0199    
 87/133 [==================>...........] - ETA: 0s - loss: 0.0190
117/133 [=========================>....] - ETA: 0s - loss: 0.0174
133/133 [==============================] - 0s 2ms/step - loss: 0.0159
Epoch 9/10

  1/133 [..............................] - ETA: 0s - loss: 0.0015
 23/133 [====>.........................] - ETA: 0s - loss: 0.0206
 49/133 [==========>...................] - ETA: 0s - loss: 0.0163
 83/133 [=================>............] - ETA: 0s - loss: 0.0155
117/133 [=========================>....] - ETA: 0s - loss: 0.0125
133/133 [==============================] - 0s 2ms/step - loss: 0.0110
Epoch 10/10

  1/133 [..............................] - ETA: 0s - loss: 1.2995e-05
 38/133 [=======>......................] - ETA: 0s - loss: 0.0124    
 70/133 [==============>...............] - ETA: 0s - loss: 0.0110
115/133 [========================>.....] - ETA: 0s - loss: 0.0141
133/133 [==============================] - 0s 1ms/step - loss: 0.0134
-> test with GAN.predict
GAN tn, fp: 331, 2
GAN fn, tp: 3, 10
GAN f1 score: 0.800
GAN cohens kappa score: 0.793

-> test with 'LR'
LR tn, fp: 298, 35
LR fn, tp: 1, 12
LR f1 score: 0.400
LR cohens kappa score: 0.362
LR average precision score: 0.386

-> 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: 333, 0
KNN fn, tp: 0, 13
KNN f1 score: 1.000
KNN cohens kappa score: 1.000


------ 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: 19s - loss: 3.9676e-05
 49/134 [=========>....................] - ETA: 0s - loss: 0.0878     
 97/134 [====================>.........] - ETA: 0s - loss: 0.0639
134/134 [==============================] - 0s 1ms/step - loss: 0.0590
Epoch 2/10

  1/134 [..............................] - ETA: 0s - loss: 0.0020
 44/134 [========>.....................] - ETA: 0s - loss: 0.0227
 91/134 [===================>..........] - ETA: 0s - loss: 0.0457
134/134 [==============================] - 0s 1ms/step - loss: 0.0405
Epoch 3/10

  1/134 [..............................] - ETA: 0s - loss: 3.7881e-06
 44/134 [========>.....................] - ETA: 0s - loss: 0.0373    
 92/134 [===================>..........] - ETA: 0s - loss: 0.0345
134/134 [==============================] - 0s 1ms/step - loss: 0.0348
Epoch 4/10

  1/134 [..............................] - ETA: 0s - loss: 0.0013
 50/134 [==========>...................] - ETA: 0s - loss: 0.0377
 98/134 [====================>.........] - ETA: 0s - loss: 0.0269
134/134 [==============================] - 0s 1ms/step - loss: 0.0269
Epoch 5/10

  1/134 [..............................] - ETA: 0s - loss: 6.5640e-06
 48/134 [=========>....................] - ETA: 0s - loss: 0.0252    
 96/134 [====================>.........] - ETA: 0s - loss: 0.0228
134/134 [==============================] - 0s 1ms/step - loss: 0.0230
Epoch 6/10

  1/134 [..............................] - ETA: 0s - loss: 7.2914e-04
 49/134 [=========>....................] - ETA: 0s - loss: 0.0172    
 97/134 [====================>.........] - ETA: 0s - loss: 0.0165
134/134 [==============================] - 0s 1ms/step - loss: 0.0203
Epoch 7/10

  1/134 [..............................] - ETA: 0s - loss: 0.0051
 49/134 [=========>....................] - ETA: 0s - loss: 0.0199
 98/134 [====================>.........] - ETA: 0s - loss: 0.0159
134/134 [==============================] - 0s 1ms/step - loss: 0.0185
Epoch 8/10

  1/134 [..............................] - ETA: 0s - loss: 5.1791e-06
 50/134 [==========>...................] - ETA: 0s - loss: 0.0180    
 98/134 [====================>.........] - ETA: 0s - loss: 0.0151
134/134 [==============================] - 0s 1ms/step - loss: 0.0155
Epoch 9/10

  1/134 [..............................] - ETA: 0s - loss: 1.5844e-05
 48/134 [=========>....................] - ETA: 0s - loss: 0.0216    
 95/134 [====================>.........] - ETA: 0s - loss: 0.0194
134/134 [==============================] - 0s 1ms/step - loss: 0.0176
Epoch 10/10

  1/134 [..............................] - ETA: 0s - loss: 6.2495e-06
 47/134 [=========>....................] - ETA: 0s - loss: 0.0201    
 90/134 [===================>..........] - ETA: 0s - loss: 0.0174
132/134 [============================>.] - ETA: 0s - loss: 0.0150
134/134 [==============================] - 0s 1ms/step - loss: 0.0148
-> test with GAN.predict
GAN tn, fp: 327, 4
GAN fn, tp: 3, 10
GAN f1 score: 0.741
GAN cohens kappa score: 0.730

-> test with 'LR'
LR tn, fp: 306, 25
LR fn, tp: 3, 10
LR f1 score: 0.417
LR cohens kappa score: 0.383
LR average precision score: 0.386

-> test with 'RF'
RF tn, fp: 331, 0
RF fn, tp: 5, 8
RF f1 score: 0.762
RF cohens kappa score: 0.755

-> 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: 331, 0
KNN fn, tp: 2, 11
KNN f1 score: 0.917
KNN cohens kappa score: 0.914


====== 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: 16s - loss: 8.6887e-04
 51/133 [==========>...................] - ETA: 0s - loss: 0.0969     
102/133 [======================>.......] - ETA: 0s - loss: 0.0706
133/133 [==============================] - 0s 997us/step - loss: 0.0686
Epoch 2/10

  1/133 [..............................] - ETA: 0s - loss: 0.0444
 52/133 [==========>...................] - ETA: 0s - loss: 0.0473
103/133 [======================>.......] - ETA: 0s - loss: 0.0454
133/133 [==============================] - 0s 986us/step - loss: 0.0516
Epoch 3/10

  1/133 [..............................] - ETA: 0s - loss: 0.0157
 52/133 [==========>...................] - ETA: 0s - loss: 0.0478
103/133 [======================>.......] - ETA: 0s - loss: 0.0370
133/133 [==============================] - 0s 988us/step - loss: 0.0358
Epoch 4/10

  1/133 [..............................] - ETA: 0s - loss: 0.0148
 53/133 [==========>...................] - ETA: 0s - loss: 0.0397
104/133 [======================>.......] - ETA: 0s - loss: 0.0440
133/133 [==============================] - 0s 984us/step - loss: 0.0381
Epoch 5/10

  1/133 [..............................] - ETA: 0s - loss: 0.0085
 51/133 [==========>...................] - ETA: 0s - loss: 0.0276
 98/133 [=====================>........] - ETA: 0s - loss: 0.0282
133/133 [==============================] - 0s 1ms/step - loss: 0.0277
Epoch 6/10

  1/133 [..............................] - ETA: 0s - loss: 0.0111
 50/133 [==========>...................] - ETA: 0s - loss: 0.0188
101/133 [=====================>........] - ETA: 0s - loss: 0.0254
133/133 [==============================] - 0s 1ms/step - loss: 0.0249
Epoch 7/10

  1/133 [..............................] - ETA: 0s - loss: 9.3741e-06
 46/133 [=========>....................] - ETA: 0s - loss: 0.0354    
 92/133 [===================>..........] - ETA: 0s - loss: 0.0229
133/133 [==============================] - 0s 1ms/step - loss: 0.0231
Epoch 8/10

  1/133 [..............................] - ETA: 0s - loss: 8.7900e-06
 52/133 [==========>...................] - ETA: 0s - loss: 0.0198    
103/133 [======================>.......] - ETA: 0s - loss: 0.0245
133/133 [==============================] - 0s 986us/step - loss: 0.0202
Epoch 9/10

  1/133 [..............................] - ETA: 0s - loss: 1.2751e-05
 52/133 [==========>...................] - ETA: 0s - loss: 0.0162    
104/133 [======================>.......] - ETA: 0s - loss: 0.0209
133/133 [==============================] - 0s 987us/step - loss: 0.0196
Epoch 10/10

  1/133 [..............................] - ETA: 0s - loss: 1.3965e-05
 53/133 [==========>...................] - ETA: 0s - loss: 0.0256    
104/133 [======================>.......] - ETA: 0s - loss: 0.0208
133/133 [==============================] - 0s 1ms/step - loss: 0.0187
-> test with GAN.predict
GAN tn, fp: 333, 0
GAN fn, tp: 6, 7
GAN f1 score: 0.700
GAN cohens kappa score: 0.692

-> test with 'LR'
LR tn, fp: 305, 28
LR fn, tp: 1, 12
LR f1 score: 0.453
LR cohens kappa score: 0.420
LR average precision score: 0.420

-> 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: 1, 12
GB f1 score: 0.960
GB cohens kappa score: 0.959

-> test with 'KNN'
KNN tn, fp: 331, 2
KNN fn, tp: 2, 11
KNN f1 score: 0.846
KNN cohens kappa score: 0.840


------ 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: 17s - loss: 6.2274e-07
 51/133 [==========>...................] - ETA: 0s - loss: 0.0951     
102/133 [======================>.......] - ETA: 0s - loss: 0.0847
133/133 [==============================] - 0s 1ms/step - loss: 0.0752
Epoch 2/10

  1/133 [..............................] - ETA: 0s - loss: 6.0864e-06
 47/133 [=========>....................] - ETA: 0s - loss: 0.0420    
 94/133 [====================>.........] - ETA: 0s - loss: 0.0580
133/133 [==============================] - 0s 1ms/step - loss: 0.0500
Epoch 3/10

  1/133 [..............................] - ETA: 0s - loss: 1.7489e-06
 52/133 [==========>...................] - ETA: 0s - loss: 0.0217    
101/133 [=====================>........] - ETA: 0s - loss: 0.0261
133/133 [==============================] - 0s 1ms/step - loss: 0.0364
Epoch 4/10

  1/133 [..............................] - ETA: 0s - loss: 4.9584e-05
 52/133 [==========>...................] - ETA: 0s - loss: 0.0174    
103/133 [======================>.......] - ETA: 0s - loss: 0.0194
133/133 [==============================] - 0s 993us/step - loss: 0.0291
Epoch 5/10

  1/133 [..............................] - ETA: 0s - loss: 2.9646e-06
 52/133 [==========>...................] - ETA: 0s - loss: 0.0150    
102/133 [======================>.......] - ETA: 0s - loss: 0.0145
133/133 [==============================] - 0s 991us/step - loss: 0.0246
Epoch 6/10

  1/133 [..............................] - ETA: 0s - loss: 0.0010
 52/133 [==========>...................] - ETA: 0s - loss: 0.0185
 99/133 [=====================>........] - ETA: 0s - loss: 0.0227
133/133 [==============================] - 0s 1ms/step - loss: 0.0249
Epoch 7/10

  1/133 [..............................] - ETA: 0s - loss: 2.9751e-06
 52/133 [==========>...................] - ETA: 0s - loss: 0.0256    
103/133 [======================>.......] - ETA: 0s - loss: 0.0223
133/133 [==============================] - 0s 998us/step - loss: 0.0216
Epoch 8/10

  1/133 [..............................] - ETA: 0s - loss: 6.2179e-06
 52/133 [==========>...................] - ETA: 0s - loss: 0.0226    
103/133 [======================>.......] - ETA: 0s - loss: 0.0215
133/133 [==============================] - 0s 1000us/step - loss: 0.0188
Epoch 9/10

  1/133 [..............................] - ETA: 0s - loss: 5.5780e-06
 52/133 [==========>...................] - ETA: 0s - loss: 0.0249    
102/133 [======================>.......] - ETA: 0s - loss: 0.0202
133/133 [==============================] - 0s 999us/step - loss: 0.0165
Epoch 10/10

  1/133 [..............................] - ETA: 0s - loss: 4.1703e-06
 51/133 [==========>...................] - ETA: 0s - loss: 0.0101    
102/133 [======================>.......] - ETA: 0s - loss: 0.0129
133/133 [==============================] - 0s 1ms/step - loss: 0.0150
-> test with GAN.predict
GAN tn, fp: 332, 1
GAN fn, tp: 4, 9
GAN f1 score: 0.783
GAN cohens kappa score: 0.775

-> test with 'LR'
LR tn, fp: 297, 36
LR fn, tp: 1, 12
LR f1 score: 0.393
LR cohens kappa score: 0.355
LR average precision score: 0.514

-> 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: 333, 0
GB fn, tp: 0, 13
GB f1 score: 1.000
GB cohens kappa score: 1.000

-> test with 'KNN'
KNN tn, fp: 333, 0
KNN fn, tp: 4, 9
KNN f1 score: 0.818
KNN cohens kappa score: 0.812


------ 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: 0.7179
 49/133 [==========>...................] - ETA: 0s - loss: 0.0372 
 97/133 [====================>.........] - ETA: 0s - loss: 0.0347
133/133 [==============================] - 0s 1ms/step - loss: 0.0419
Epoch 2/10

  1/133 [..............................] - ETA: 0s - loss: 0.0012
 52/133 [==========>...................] - ETA: 0s - loss: 0.0307
102/133 [======================>.......] - ETA: 0s - loss: 0.0233
133/133 [==============================] - 0s 999us/step - loss: 0.0277
Epoch 3/10

  1/133 [..............................] - ETA: 0s - loss: 6.2871e-06
 52/133 [==========>...................] - ETA: 0s - loss: 0.0212    
103/133 [======================>.......] - ETA: 0s - loss: 0.0194
133/133 [==============================] - 0s 995us/step - loss: 0.0188
Epoch 4/10

  1/133 [..............................] - ETA: 0s - loss: 1.4383e-05
 52/133 [==========>...................] - ETA: 0s - loss: 0.0129    
103/133 [======================>.......] - ETA: 0s - loss: 0.0163
133/133 [==============================] - 0s 995us/step - loss: 0.0146
Epoch 5/10

  1/133 [..............................] - ETA: 0s - loss: 2.9647e-05
 52/133 [==========>...................] - ETA: 0s - loss: 0.0105    
103/133 [======================>.......] - ETA: 0s - loss: 0.0139
133/133 [==============================] - 0s 998us/step - loss: 0.0131
Epoch 6/10

  1/133 [..............................] - ETA: 0s - loss: 7.4600e-06
 52/133 [==========>...................] - ETA: 0s - loss: 0.0175    
101/133 [=====================>........] - ETA: 0s - loss: 0.0122
133/133 [==============================] - 0s 1ms/step - loss: 0.0131
Epoch 7/10

  1/133 [..............................] - ETA: 0s - loss: 0.0252
 51/133 [==========>...................] - ETA: 0s - loss: 0.0093
100/133 [=====================>........] - ETA: 0s - loss: 0.0099
133/133 [==============================] - 0s 1ms/step - loss: 0.0123
Epoch 8/10

  1/133 [..............................] - ETA: 0s - loss: 0.0281
 47/133 [=========>....................] - ETA: 0s - loss: 0.0070
 94/133 [====================>.........] - ETA: 0s - loss: 0.0107
133/133 [==============================] - 0s 1ms/step - loss: 0.0099
Epoch 9/10

  1/133 [..............................] - ETA: 0s - loss: 0.0014
 52/133 [==========>...................] - ETA: 0s - loss: 0.0056
103/133 [======================>.......] - ETA: 0s - loss: 0.0070
133/133 [==============================] - 0s 990us/step - loss: 0.0092
Epoch 10/10

  1/133 [..............................] - ETA: 0s - loss: 1.0563e-04
 50/133 [==========>...................] - ETA: 0s - loss: 0.0065    
100/133 [=====================>........] - ETA: 0s - loss: 0.0080
133/133 [==============================] - 0s 1ms/step - loss: 0.0088
-> 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: 298, 35
LR fn, tp: 1, 12
LR f1 score: 0.400
LR cohens kappa score: 0.362
LR average precision score: 0.312

-> 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: 330, 3
KNN fn, tp: 0, 13
KNN f1 score: 0.897
KNN cohens kappa score: 0.892


------ 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: 15s - loss: 3.8479e-06
 51/133 [==========>...................] - ETA: 0s - loss: 0.0587     
102/133 [======================>.......] - ETA: 0s - loss: 0.0568
133/133 [==============================] - 0s 1ms/step - loss: 0.0525
Epoch 2/10

  1/133 [..............................] - ETA: 0s - loss: 4.6599e-04
 52/133 [==========>...................] - ETA: 0s - loss: 0.0579    
103/133 [======================>.......] - ETA: 0s - loss: 0.0407
133/133 [==============================] - 0s 991us/step - loss: 0.0402
Epoch 3/10

  1/133 [..............................] - ETA: 0s - loss: 0.1806
 52/133 [==========>...................] - ETA: 0s - loss: 0.0302
103/133 [======================>.......] - ETA: 0s - loss: 0.0300
133/133 [==============================] - 0s 989us/step - loss: 0.0259
Epoch 4/10

  1/133 [..............................] - ETA: 0s - loss: 2.6903e-05
 49/133 [==========>...................] - ETA: 0s - loss: 0.0185    
100/133 [=====================>........] - ETA: 0s - loss: 0.0198
133/133 [==============================] - 0s 1ms/step - loss: 0.0217
Epoch 5/10

  1/133 [..............................] - ETA: 0s - loss: 0.0024
 52/133 [==========>...................] - ETA: 0s - loss: 0.0085
103/133 [======================>.......] - ETA: 0s - loss: 0.0199
133/133 [==============================] - 0s 996us/step - loss: 0.0200
Epoch 6/10

  1/133 [..............................] - ETA: 0s - loss: 4.2247e-05
 52/133 [==========>...................] - ETA: 0s - loss: 0.0151    
102/133 [======================>.......] - ETA: 0s - loss: 0.0169
133/133 [==============================] - 0s 1ms/step - loss: 0.0170
Epoch 7/10

  1/133 [..............................] - ETA: 0s - loss: 0.0317
 47/133 [=========>....................] - ETA: 0s - loss: 0.0129
 94/133 [====================>.........] - ETA: 0s - loss: 0.0154
133/133 [==============================] - 0s 1ms/step - loss: 0.0153
Epoch 8/10

  1/133 [..............................] - ETA: 0s - loss: 4.6428e-05
 52/133 [==========>...................] - ETA: 0s - loss: 0.0176    
103/133 [======================>.......] - ETA: 0s - loss: 0.0178
133/133 [==============================] - 0s 991us/step - loss: 0.0152
Epoch 9/10

  1/133 [..............................] - ETA: 0s - loss: 4.0178e-05
 52/133 [==========>...................] - ETA: 0s - loss: 0.0132    
103/133 [======================>.......] - ETA: 0s - loss: 0.0122
133/133 [==============================] - 0s 989us/step - loss: 0.0135
Epoch 10/10

  1/133 [..............................] - ETA: 0s - loss: 7.0648e-05
 52/133 [==========>...................] - ETA: 0s - loss: 0.0068    
103/133 [======================>.......] - ETA: 0s - loss: 0.0124
133/133 [==============================] - 0s 999us/step - loss: 0.0128
-> test with GAN.predict
GAN tn, fp: 329, 4
GAN fn, tp: 7, 6
GAN f1 score: 0.522
GAN cohens kappa score: 0.506

-> test with 'LR'
LR tn, fp: 304, 29
LR fn, tp: 3, 10
LR f1 score: 0.385
LR cohens kappa score: 0.348
LR average precision score: 0.277

-> 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: 332, 1
KNN fn, tp: 1, 12
KNN f1 score: 0.923
KNN cohens kappa score: 0.920


------ 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: 22s - loss: 5.3228e-04
 43/134 [========>.....................] - ETA: 0s - loss: 0.0616     
 85/134 [==================>...........] - ETA: 0s - loss: 0.0466
133/134 [============================>.] - ETA: 0s - loss: 0.0532
134/134 [==============================] - 0s 1ms/step - loss: 0.0532
Epoch 2/10

  1/134 [..............................] - ETA: 0s - loss: 0.2456
 48/134 [=========>....................] - ETA: 0s - loss: 0.0424
 96/134 [====================>.........] - ETA: 0s - loss: 0.0287
134/134 [==============================] - 0s 1ms/step - loss: 0.0278
Epoch 3/10

  1/134 [..............................] - ETA: 0s - loss: 1.2164e-05
 49/134 [=========>....................] - ETA: 0s - loss: 0.0231    
 97/134 [====================>.........] - ETA: 0s - loss: 0.0234
134/134 [==============================] - 0s 1ms/step - loss: 0.0206
Epoch 4/10

  1/134 [..............................] - ETA: 0s - loss: 5.0380e-06
 49/134 [=========>....................] - ETA: 0s - loss: 0.0253    
 97/134 [====================>.........] - ETA: 0s - loss: 0.0212
134/134 [==============================] - 0s 1ms/step - loss: 0.0164
Epoch 5/10

  1/134 [..............................] - ETA: 0s - loss: 0.1761
 49/134 [=========>....................] - ETA: 0s - loss: 0.0153
 97/134 [====================>.........] - ETA: 0s - loss: 0.0183
134/134 [==============================] - 0s 1ms/step - loss: 0.0157
Epoch 6/10

  1/134 [..............................] - ETA: 0s - loss: 6.7345e-06
 49/134 [=========>....................] - ETA: 0s - loss: 0.0115    
 92/134 [===================>..........] - ETA: 0s - loss: 0.0150
134/134 [==============================] - 0s 1ms/step - loss: 0.0150
Epoch 7/10

  1/134 [..............................] - ETA: 0s - loss: 4.0590e-04
 49/134 [=========>....................] - ETA: 0s - loss: 0.0180    
 97/134 [====================>.........] - ETA: 0s - loss: 0.0127
134/134 [==============================] - 0s 1ms/step - loss: 0.0130
Epoch 8/10

  1/134 [..............................] - ETA: 0s - loss: 1.9236e-05
 49/134 [=========>....................] - ETA: 0s - loss: 0.0100    
 97/134 [====================>.........] - ETA: 0s - loss: 0.0112
134/134 [==============================] - 0s 1ms/step - loss: 0.0110
Epoch 9/10

  1/134 [..............................] - ETA: 0s - loss: 0.0053
 49/134 [=========>....................] - ETA: 0s - loss: 0.0091
 97/134 [====================>.........] - ETA: 0s - loss: 0.0118
134/134 [==============================] - 0s 1ms/step - loss: 0.0110
Epoch 10/10

  1/134 [..............................] - ETA: 0s - loss: 0.0178
 49/134 [=========>....................] - ETA: 0s - loss: 0.0064
 97/134 [====================>.........] - ETA: 0s - loss: 0.0087
134/134 [==============================] - 0s 1ms/step - loss: 0.0099
-> test with GAN.predict
GAN tn, fp: 328, 3
GAN fn, tp: 4, 9
GAN f1 score: 0.720
GAN cohens kappa score: 0.709

-> test with 'LR'
LR tn, fp: 302, 29
LR fn, tp: 2, 11
LR f1 score: 0.415
LR cohens kappa score: 0.380
LR average precision score: 0.333

-> 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: 331, 0
GB fn, tp: 0, 13
GB f1 score: 1.000
GB cohens kappa score: 1.000

-> test with 'KNN'
KNN tn, fp: 324, 7
KNN fn, tp: 1, 12
KNN f1 score: 0.750
KNN cohens kappa score: 0.738


====== 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: 19s - loss: 0.0024
 51/133 [==========>...................] - ETA: 0s - loss: 0.0520 
101/133 [=====================>........] - ETA: 0s - loss: 0.0424
133/133 [==============================] - 0s 1ms/step - loss: 0.0353
Epoch 2/10

  1/133 [..............................] - ETA: 0s - loss: 9.7177e-06
 52/133 [==========>...................] - ETA: 0s - loss: 0.0153    
103/133 [======================>.......] - ETA: 0s - loss: 0.0208
133/133 [==============================] - 0s 995us/step - loss: 0.0199
Epoch 3/10

  1/133 [..............................] - ETA: 0s - loss: 0.0024
 52/133 [==========>...................] - ETA: 0s - loss: 0.0282
103/133 [======================>.......] - ETA: 0s - loss: 0.0227
133/133 [==============================] - 0s 994us/step - loss: 0.0213
Epoch 4/10

  1/133 [..............................] - ETA: 0s - loss: 2.2763e-04
 52/133 [==========>...................] - ETA: 0s - loss: 0.0108    
103/133 [======================>.......] - ETA: 0s - loss: 0.0123
133/133 [==============================] - 0s 995us/step - loss: 0.0147
Epoch 5/10

  1/133 [..............................] - ETA: 0s - loss: 3.4967e-05
 52/133 [==========>...................] - ETA: 0s - loss: 0.0191    
103/133 [======================>.......] - ETA: 0s - loss: 0.0154
133/133 [==============================] - 0s 992us/step - loss: 0.0167
Epoch 6/10

  1/133 [..............................] - ETA: 0s - loss: 0.0073
 51/133 [==========>...................] - ETA: 0s - loss: 0.0054
101/133 [=====================>........] - ETA: 0s - loss: 0.0136
133/133 [==============================] - 0s 1ms/step - loss: 0.0143
Epoch 7/10

  1/133 [..............................] - ETA: 0s - loss: 0.0170
 50/133 [==========>...................] - ETA: 0s - loss: 0.0099
101/133 [=====================>........] - ETA: 0s - loss: 0.0151
133/133 [==============================] - 0s 1ms/step - loss: 0.0132
Epoch 8/10

  1/133 [..............................] - ETA: 0s - loss: 5.7424e-04
 52/133 [==========>...................] - ETA: 0s - loss: 0.0108    
102/133 [======================>.......] - ETA: 0s - loss: 0.0122
133/133 [==============================] - 0s 1ms/step - loss: 0.0120
Epoch 9/10

  1/133 [..............................] - ETA: 0s - loss: 1.2325e-05
 52/133 [==========>...................] - ETA: 0s - loss: 0.0118    
103/133 [======================>.......] - ETA: 0s - loss: 0.0086
133/133 [==============================] - 0s 991us/step - loss: 0.0092
Epoch 10/10

  1/133 [..............................] - ETA: 0s - loss: 1.4192e-04
 52/133 [==========>...................] - ETA: 0s - loss: 0.0122    
103/133 [======================>.......] - ETA: 0s - loss: 0.0097
133/133 [==============================] - 0s 991us/step - loss: 0.0090
-> test with GAN.predict
GAN tn, fp: 332, 1
GAN fn, tp: 3, 10
GAN f1 score: 0.833
GAN cohens kappa score: 0.827

-> 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: 0.291

-> 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: 330, 3
KNN fn, tp: 0, 13
KNN f1 score: 0.897
KNN cohens kappa score: 0.892


------ 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: 5.3369e-06
 46/133 [=========>....................] - ETA: 0s - loss: 0.0310     
 87/133 [==================>...........] - ETA: 0s - loss: 0.0549
130/133 [============================>.] - ETA: 0s - loss: 0.0459
133/133 [==============================] - 0s 1ms/step - loss: 0.0449
Epoch 2/10

  1/133 [..............................] - ETA: 0s - loss: 1.2061e-05
 51/133 [==========>...................] - ETA: 0s - loss: 0.0168    
102/133 [======================>.......] - ETA: 0s - loss: 0.0195
133/133 [==============================] - 0s 1ms/step - loss: 0.0265
Epoch 3/10

  1/133 [..............................] - ETA: 0s - loss: 6.8805e-06
 44/133 [========>.....................] - ETA: 0s - loss: 0.0234    
 95/133 [====================>.........] - ETA: 0s - loss: 0.0237
133/133 [==============================] - 0s 1ms/step - loss: 0.0236
Epoch 4/10

  1/133 [..............................] - ETA: 0s - loss: 2.9472e-05
 48/133 [=========>....................] - ETA: 0s - loss: 0.0092    
 99/133 [=====================>........] - ETA: 0s - loss: 0.0126
133/133 [==============================] - 0s 1ms/step - loss: 0.0160
Epoch 5/10

  1/133 [..............................] - ETA: 0s - loss: 0.0069
 50/133 [==========>...................] - ETA: 0s - loss: 0.0079
 98/133 [=====================>........] - ETA: 0s - loss: 0.0109
133/133 [==============================] - 0s 1ms/step - loss: 0.0168
Epoch 6/10

  1/133 [..............................] - ETA: 0s - loss: 9.7259e-06
 52/133 [==========>...................] - ETA: 0s - loss: 0.0156    
102/133 [======================>.......] - ETA: 0s - loss: 0.0140
133/133 [==============================] - 0s 1000us/step - loss: 0.0158
Epoch 7/10

  1/133 [..............................] - ETA: 0s - loss: 3.7540e-05
 51/133 [==========>...................] - ETA: 0s - loss: 0.0093    
 96/133 [====================>.........] - ETA: 0s - loss: 0.0138
133/133 [==============================] - 0s 1ms/step - loss: 0.0140
Epoch 8/10

  1/133 [..............................] - ETA: 0s - loss: 0.0029
 51/133 [==========>...................] - ETA: 0s - loss: 0.0192
 98/133 [=====================>........] - ETA: 0s - loss: 0.0132
133/133 [==============================] - 0s 1ms/step - loss: 0.0134
Epoch 9/10

  1/133 [..............................] - ETA: 0s - loss: 2.1097e-04
 52/133 [==========>...................] - ETA: 0s - loss: 0.0129    
102/133 [======================>.......] - ETA: 0s - loss: 0.0107
133/133 [==============================] - 0s 1ms/step - loss: 0.0096
Epoch 10/10

  1/133 [..............................] - ETA: 0s - loss: 0.0786
 51/133 [==========>...................] - ETA: 0s - loss: 0.0148
101/133 [=====================>........] - ETA: 0s - loss: 0.0107
133/133 [==============================] - 0s 1ms/step - loss: 0.0090
-> test with GAN.predict
GAN tn, fp: 331, 2
GAN fn, tp: 6, 7
GAN f1 score: 0.636
GAN cohens kappa score: 0.625

-> test with 'LR'
LR tn, fp: 312, 21
LR fn, tp: 3, 10
LR f1 score: 0.455
LR cohens kappa score: 0.424
LR average precision score: 0.338

-> 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: 330, 3
KNN fn, tp: 1, 12
KNN f1 score: 0.857
KNN cohens kappa score: 0.851


------ 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: 19s - loss: 0.4206
 51/133 [==========>...................] - ETA: 0s - loss: 0.0931 
102/133 [======================>.......] - ETA: 0s - loss: 0.0750
133/133 [==============================] - 0s 1ms/step - loss: 0.0770
Epoch 2/10

  1/133 [..............................] - ETA: 0s - loss: 3.2657e-07
 51/133 [==========>...................] - ETA: 0s - loss: 0.0565    
102/133 [======================>.......] - ETA: 0s - loss: 0.0594
133/133 [==============================] - 0s 1ms/step - loss: 0.0504
Epoch 3/10

  1/133 [..............................] - ETA: 0s - loss: 0.0537
 52/133 [==========>...................] - ETA: 0s - loss: 0.0415
103/133 [======================>.......] - ETA: 0s - loss: 0.0377
133/133 [==============================] - 0s 991us/step - loss: 0.0325
Epoch 4/10

  1/133 [..............................] - ETA: 0s - loss: 3.9033e-06
 52/133 [==========>...................] - ETA: 0s - loss: 0.0183    
102/133 [======================>.......] - ETA: 0s - loss: 0.0340
133/133 [==============================] - 0s 997us/step - loss: 0.0308
Epoch 5/10

  1/133 [..............................] - ETA: 0s - loss: 1.1025e-06
 52/133 [==========>...................] - ETA: 0s - loss: 0.0313    
103/133 [======================>.......] - ETA: 0s - loss: 0.0252
133/133 [==============================] - 0s 990us/step - loss: 0.0287
Epoch 6/10

  1/133 [..............................] - ETA: 0s - loss: 2.0820e-06
 52/133 [==========>...................] - ETA: 0s - loss: 0.0212    
103/133 [======================>.......] - ETA: 0s - loss: 0.0229
133/133 [==============================] - 0s 994us/step - loss: 0.0251
Epoch 7/10

  1/133 [..............................] - ETA: 0s - loss: 0.0087
 52/133 [==========>...................] - ETA: 0s - loss: 0.0193
103/133 [======================>.......] - ETA: 0s - loss: 0.0185
133/133 [==============================] - 0s 992us/step - loss: 0.0202
Epoch 8/10

  1/133 [..............................] - ETA: 0s - loss: 1.5027e-05
 52/133 [==========>...................] - ETA: 0s - loss: 0.0229    
100/133 [=====================>........] - ETA: 0s - loss: 0.0175
133/133 [==============================] - 0s 1ms/step - loss: 0.0184
Epoch 9/10

  1/133 [..............................] - ETA: 0s - loss: 0.0139
 46/133 [=========>....................] - ETA: 0s - loss: 0.0174
 94/133 [====================>.........] - ETA: 0s - loss: 0.0177
133/133 [==============================] - 0s 1ms/step - loss: 0.0172
Epoch 10/10

  1/133 [..............................] - ETA: 0s - loss: 0.0013
 52/133 [==========>...................] - ETA: 0s - loss: 0.0076
 99/133 [=====================>........] - ETA: 0s - loss: 0.0150
133/133 [==============================] - 0s 1ms/step - loss: 0.0156
-> test with GAN.predict
GAN tn, fp: 330, 3
GAN fn, tp: 5, 8
GAN f1 score: 0.667
GAN cohens kappa score: 0.655

-> test with 'LR'
LR tn, fp: 313, 20
LR fn, tp: 3, 10
LR f1 score: 0.465
LR cohens kappa score: 0.436
LR average precision score: 0.338

-> 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: 333, 0
KNN fn, tp: 3, 10
KNN f1 score: 0.870
KNN cohens kappa score: 0.865


------ 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: 18s - loss: 1.5161e-05
 51/133 [==========>...................] - ETA: 0s - loss: 0.0823     
102/133 [======================>.......] - ETA: 0s - loss: 0.0771
133/133 [==============================] - 0s 1ms/step - loss: 0.0632
Epoch 2/10

  1/133 [..............................] - ETA: 0s - loss: 9.7206e-05
 52/133 [==========>...................] - ETA: 0s - loss: 0.0475    
103/133 [======================>.......] - ETA: 0s - loss: 0.0369
133/133 [==============================] - 0s 999us/step - loss: 0.0298
Epoch 3/10

  1/133 [..............................] - ETA: 0s - loss: 1.1666e-05
 52/133 [==========>...................] - ETA: 0s - loss: 0.0328    
103/133 [======================>.......] - ETA: 0s - loss: 0.0269
133/133 [==============================] - 0s 996us/step - loss: 0.0244
Epoch 4/10

  1/133 [..............................] - ETA: 0s - loss: 7.7657e-04
 52/133 [==========>...................] - ETA: 0s - loss: 0.0194    
103/133 [======================>.......] - ETA: 0s - loss: 0.0194
133/133 [==============================] - 0s 994us/step - loss: 0.0210
Epoch 5/10

  1/133 [..............................] - ETA: 0s - loss: 1.3079e-04
 52/133 [==========>...................] - ETA: 0s - loss: 0.0087    
103/133 [======================>.......] - ETA: 0s - loss: 0.0164
133/133 [==============================] - 0s 996us/step - loss: 0.0190
Epoch 6/10

  1/133 [..............................] - ETA: 0s - loss: 0.0046
 52/133 [==========>...................] - ETA: 0s - loss: 0.0243
103/133 [======================>.......] - ETA: 0s - loss: 0.0178
133/133 [==============================] - 0s 994us/step - loss: 0.0167
Epoch 7/10

  1/133 [..............................] - ETA: 0s - loss: 6.5036e-06
 52/133 [==========>...................] - ETA: 0s - loss: 0.0156    
102/133 [======================>.......] - ETA: 0s - loss: 0.0190
133/133 [==============================] - 0s 1ms/step - loss: 0.0166
Epoch 8/10

  1/133 [..............................] - ETA: 0s - loss: 0.0012
 52/133 [==========>...................] - ETA: 0s - loss: 0.0125
103/133 [======================>.......] - ETA: 0s - loss: 0.0114
133/133 [==============================] - 0s 995us/step - loss: 0.0148
Epoch 9/10

  1/133 [..............................] - ETA: 0s - loss: 4.4639e-05
 52/133 [==========>...................] - ETA: 0s - loss: 0.0173    
103/133 [======================>.......] - ETA: 0s - loss: 0.0134
133/133 [==============================] - 0s 998us/step - loss: 0.0151
Epoch 10/10

  1/133 [..............................] - ETA: 0s - loss: 0.0083
 51/133 [==========>...................] - ETA: 0s - loss: 0.0090
 99/133 [=====================>........] - ETA: 0s - loss: 0.0113
133/133 [==============================] - 0s 1ms/step - loss: 0.0126
-> 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: 296, 37
LR fn, tp: 1, 12
LR f1 score: 0.387
LR cohens kappa score: 0.348
LR average precision score: 0.295

-> 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: 0, 13
GB f1 score: 1.000
GB cohens kappa score: 1.000

-> test with 'KNN'
KNN tn, fp: 329, 4
KNN fn, tp: 0, 13
KNN f1 score: 0.867
KNN cohens kappa score: 0.861


------ 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: 20s - loss: 0.2510
 49/134 [=========>....................] - ETA: 0s - loss: 0.0667 
 97/134 [====================>.........] - ETA: 0s - loss: 0.0732
134/134 [==============================] - 0s 1ms/step - loss: 0.0791
Epoch 2/10

  1/134 [..............................] - ETA: 0s - loss: 3.2878e-04
 49/134 [=========>....................] - ETA: 0s - loss: 0.0284    
 97/134 [====================>.........] - ETA: 0s - loss: 0.0351
134/134 [==============================] - 0s 1ms/step - loss: 0.0424
Epoch 3/10

  1/134 [..............................] - ETA: 0s - loss: 1.7080e-05
 49/134 [=========>....................] - ETA: 0s - loss: 0.0291    
 95/134 [====================>.........] - ETA: 0s - loss: 0.0280
134/134 [==============================] - 0s 1ms/step - loss: 0.0334
Epoch 4/10

  1/134 [..............................] - ETA: 0s - loss: 0.0491
 48/134 [=========>....................] - ETA: 0s - loss: 0.0285
 96/134 [====================>.........] - ETA: 0s - loss: 0.0260
134/134 [==============================] - 0s 1ms/step - loss: 0.0266
Epoch 5/10

  1/134 [..............................] - ETA: 0s - loss: 8.3756e-05
 49/134 [=========>....................] - ETA: 0s - loss: 0.0207    
 97/134 [====================>.........] - ETA: 0s - loss: 0.0236
134/134 [==============================] - 0s 1ms/step - loss: 0.0221
Epoch 6/10

  1/134 [..............................] - ETA: 0s - loss: 0.0665
 49/134 [=========>....................] - ETA: 0s - loss: 0.0301
 97/134 [====================>.........] - ETA: 0s - loss: 0.0276
134/134 [==============================] - 0s 1ms/step - loss: 0.0250
Epoch 7/10

  1/134 [..............................] - ETA: 0s - loss: 8.4235e-04
 49/134 [=========>....................] - ETA: 0s - loss: 0.0212    
 97/134 [====================>.........] - ETA: 0s - loss: 0.0216
134/134 [==============================] - 0s 1ms/step - loss: 0.0191
Epoch 8/10

  1/134 [..............................] - ETA: 0s - loss: 6.2428e-06
 50/134 [==========>...................] - ETA: 0s - loss: 0.0233    
 98/134 [====================>.........] - ETA: 0s - loss: 0.0231
134/134 [==============================] - 0s 1ms/step - loss: 0.0189
Epoch 9/10

  1/134 [..............................] - ETA: 0s - loss: 1.7209e-04
 49/134 [=========>....................] - ETA: 0s - loss: 0.0095    
 97/134 [====================>.........] - ETA: 0s - loss: 0.0200
134/134 [==============================] - 0s 1ms/step - loss: 0.0177
Epoch 10/10

  1/134 [..............................] - ETA: 0s - loss: 0.0251
 49/134 [=========>....................] - ETA: 0s - loss: 0.0133
 97/134 [====================>.........] - ETA: 0s - loss: 0.0118
134/134 [==============================] - 0s 1ms/step - loss: 0.0167
-> test with GAN.predict
GAN tn, fp: 328, 3
GAN fn, tp: 5, 8
GAN f1 score: 0.667
GAN cohens kappa score: 0.655

-> test with 'LR'
LR tn, fp: 301, 30
LR fn, tp: 0, 13
LR f1 score: 0.464
LR cohens kappa score: 0.431
LR average precision score: 0.479

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

-> 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: 330, 1
KNN fn, tp: 2, 11
KNN f1 score: 0.880
KNN cohens kappa score: 0.875

### Exercise is done.

-----[ LR ]-----
maximum:
LR tn, fp: 313, 41
LR fn, tp: 5, 13
LR f1 score: 0.520
LR cohens kappa score: 0.492
LR average precision score: 0.559


average:
LR tn, fp: 302.44, 30.16
LR fn, tp: 1.52, 11.48
LR f1 score: 0.422
LR cohens kappa score: 0.387
LR average precision score: 0.364


minimum:
LR tn, fp: 292, 20
LR fn, tp: 0, 8
LR f1 score: 0.356
LR cohens kappa score: 0.318
LR average precision score: 0.277


-----[ RF ]-----
maximum:
RF tn, fp: 333, 1
RF fn, tp: 5, 13
RF f1 score: 1.000
RF cohens kappa score: 1.000


average:
RF tn, fp: 332.4, 0.2
RF fn, tp: 1.48, 11.52
RF f1 score: 0.929
RF cohens kappa score: 0.927


minimum:
RF tn, fp: 330, 0
RF fn, tp: 0, 8
RF f1 score: 0.762
RF cohens kappa score: 0.755


-----[ GB ]-----
maximum:
GB tn, fp: 333, 2
GB fn, tp: 3, 13
GB f1 score: 1.000
GB cohens kappa score: 1.000


average:
GB tn, fp: 332.48, 0.12
GB fn, tp: 0.32, 12.68
GB f1 score: 0.982
GB cohens kappa score: 0.982


minimum:
GB tn, fp: 329, 0
GB fn, tp: 0, 10
GB f1 score: 0.870
GB cohens kappa score: 0.865


-----[ KNN ]-----
maximum:
KNN tn, fp: 333, 7
KNN fn, tp: 4, 13
KNN f1 score: 1.000
KNN cohens kappa score: 1.000


average:
KNN tn, fp: 330.56, 2.04
KNN fn, tp: 1.28, 11.72
KNN f1 score: 0.879
KNN cohens kappa score: 0.874


minimum:
KNN tn, fp: 324, 0
KNN fn, tp: 0, 9
KNN f1 score: 0.710
KNN cohens kappa score: 0.696


-----[ GAN ]-----
maximum:
GAN tn, fp: 333, 6
GAN fn, tp: 7, 11
GAN f1 score: 0.846
GAN cohens kappa score: 0.840


average:
GAN tn, fp: 330.0, 2.6
GAN fn, tp: 4.16, 8.84
GAN f1 score: 0.721
GAN cohens kappa score: 0.712


minimum:
GAN tn, fp: 326, 0
GAN fn, tp: 2, 6
GAN f1 score: 0.522
GAN cohens kappa score: 0.506