ConvGeNBenchmarkResults/data_result/SimpleGAN/kaggle_creditcard.log

///////////////////////////////////////////
// Running SimpleGAN on kaggle_creditcard
///////////////////////////////////////////

Load 'data_input/kaggle_creditcard'
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
Epoch 1/3
Epoch 2/3
Epoch 3/3
-> create 227059 synthetic samples
-> test with 'LR'
LR tn, fp: 56821, 42
LR fn, tp: 35, 64
LR f1 score: 0.624
LR cohens kappa score: 0.624
LR average precision score: 0.489

-> test with 'GB'
GB tn, fp: 56848, 15
GB fn, tp: 32, 67
GB f1 score: 0.740
GB cohens kappa score: 0.740

-> test with 'KNN'
KNN tn, fp: 56863, 0
KNN fn, tp: 96, 3
KNN f1 score: 0.059
KNN cohens kappa score: 0.059


------ Step 1/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Epoch 1/3
Epoch 2/3
Epoch 3/3
-> create 227059 synthetic samples
-> test with 'LR'
LR tn, fp: 56853, 10
LR fn, tp: 30, 69
LR f1 score: 0.775
LR cohens kappa score: 0.775
LR average precision score: 0.707

-> test with 'GB'
GB tn, fp: 56856, 7
GB fn, tp: 23, 76
GB f1 score: 0.835
GB cohens kappa score: 0.835

-> test with 'KNN'
KNN tn, fp: 56863, 0
KNN fn, tp: 97, 2
KNN f1 score: 0.040
KNN cohens kappa score: 0.040


------ Step 1/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Epoch 1/3
Epoch 2/3
Epoch 3/3
-> create 227059 synthetic samples
-> test with 'LR'
LR tn, fp: 56852, 11
LR fn, tp: 36, 63
LR f1 score: 0.728
LR cohens kappa score: 0.728
LR average precision score: 0.640

-> test with 'GB'
GB tn, fp: 56848, 15
GB fn, tp: 24, 75
GB f1 score: 0.794
GB cohens kappa score: 0.793

-> test with 'KNN'
KNN tn, fp: 56862, 1
KNN fn, tp: 98, 1
KNN f1 score: 0.020
KNN cohens kappa score: 0.020


------ Step 1/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Epoch 1/3
Epoch 2/3
Epoch 3/3
-> create 227059 synthetic samples
-> test with 'LR'
LR tn, fp: 56847, 16
LR fn, tp: 30, 69
LR f1 score: 0.750
LR cohens kappa score: 0.750
LR average precision score: 0.743

-> test with 'GB'
GB tn, fp: 56851, 12
GB fn, tp: 24, 75
GB f1 score: 0.806
GB cohens kappa score: 0.806

-> test with 'KNN'
KNN tn, fp: 56863, 0
KNN fn, tp: 98, 1
KNN f1 score: 0.020
KNN cohens kappa score: 0.020


------ Step 1/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Epoch 1/3
Epoch 2/3
Epoch 3/3
-> create 227056 synthetic samples
-> test with 'LR'
LR tn, fp: 56854, 9
LR fn, tp: 37, 59
LR f1 score: 0.720
LR cohens kappa score: 0.719
LR average precision score: 0.738

-> test with 'GB'
GB tn, fp: 56845, 18
GB fn, tp: 25, 71
GB f1 score: 0.768
GB cohens kappa score: 0.767

-> test with 'KNN'
KNN tn, fp: 56863, 0
KNN fn, tp: 96, 0
KNN f1 score: 0.000
KNN cohens kappa score: 0.000


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

------ Step 2/5: Slice 1/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Epoch 1/3
Epoch 2/3
Epoch 3/3
-> create 227059 synthetic samples
-> test with 'LR'
LR tn, fp: 56835, 28
LR fn, tp: 40, 59
LR f1 score: 0.634
LR cohens kappa score: 0.634
LR average precision score: 0.612

-> test with 'GB'
GB tn, fp: 56848, 15
GB fn, tp: 20, 79
GB f1 score: 0.819
GB cohens kappa score: 0.818

-> test with 'KNN'
KNN tn, fp: 56863, 0
KNN fn, tp: 97, 2
KNN f1 score: 0.040
KNN cohens kappa score: 0.040


------ Step 2/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Epoch 1/3
Epoch 2/3
Epoch 3/3
-> create 227059 synthetic samples
-> test with 'LR'
LR tn, fp: 56842, 21
LR fn, tp: 34, 65
LR f1 score: 0.703
LR cohens kappa score: 0.702
LR average precision score: 0.606

-> test with 'GB'
GB tn, fp: 56850, 13
GB fn, tp: 24, 75
GB f1 score: 0.802
GB cohens kappa score: 0.802

-> test with 'KNN'
KNN tn, fp: 56862, 1
KNN fn, tp: 98, 1
KNN f1 score: 0.020
KNN cohens kappa score: 0.020


------ Step 2/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Epoch 1/3
Epoch 2/3
Epoch 3/3
-> create 227059 synthetic samples
-> test with 'LR'
LR tn, fp: 56847, 16
LR fn, tp: 40, 59
LR f1 score: 0.678
LR cohens kappa score: 0.678
LR average precision score: 0.610

-> test with 'GB'
GB tn, fp: 56854, 9
GB fn, tp: 27, 72
GB f1 score: 0.800
GB cohens kappa score: 0.800

-> test with 'KNN'
KNN tn, fp: 56863, 0
KNN fn, tp: 97, 2
KNN f1 score: 0.040
KNN cohens kappa score: 0.040


------ Step 2/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Epoch 1/3
Epoch 2/3
Epoch 3/3
-> create 227059 synthetic samples
-> test with 'LR'
LR tn, fp: 56850, 13
LR fn, tp: 43, 56
LR f1 score: 0.667
LR cohens kappa score: 0.666
LR average precision score: 0.630

-> test with 'GB'
GB tn, fp: 56859, 4
GB fn, tp: 25, 74
GB f1 score: 0.836
GB cohens kappa score: 0.836

-> test with 'KNN'
KNN tn, fp: 56863, 0
KNN fn, tp: 99, 0
KNN f1 score: 0.000
KNN cohens kappa score: 0.000


------ Step 2/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Epoch 1/3
Epoch 2/3
Epoch 3/3
-> create 227056 synthetic samples
-> test with 'LR'
LR tn, fp: 56849, 14
LR fn, tp: 31, 65
LR f1 score: 0.743
LR cohens kappa score: 0.742
LR average precision score: 0.683

-> test with 'GB'
GB tn, fp: 56851, 12
GB fn, tp: 22, 74
GB f1 score: 0.813
GB cohens kappa score: 0.813

-> test with 'KNN'
KNN tn, fp: 56863, 0
KNN fn, tp: 95, 1
KNN f1 score: 0.021
KNN cohens kappa score: 0.021


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

------ Step 3/5: Slice 1/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Epoch 1/3
Epoch 2/3
Epoch 3/3
-> create 227059 synthetic samples
-> test with 'LR'
LR tn, fp: 56844, 19
LR fn, tp: 42, 57
LR f1 score: 0.651
LR cohens kappa score: 0.651
LR average precision score: 0.595

-> test with 'GB'
GB tn, fp: 56838, 25
GB fn, tp: 27, 72
GB f1 score: 0.735
GB cohens kappa score: 0.734

-> test with 'KNN'
KNN tn, fp: 56863, 0
KNN fn, tp: 97, 2
KNN f1 score: 0.040
KNN cohens kappa score: 0.040


------ Step 3/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Epoch 1/3
Epoch 2/3
Epoch 3/3
-> create 227059 synthetic samples
-> test with 'LR'
LR tn, fp: 56846, 17
LR fn, tp: 30, 69
LR f1 score: 0.746
LR cohens kappa score: 0.746
LR average precision score: 0.671

-> test with 'GB'
GB tn, fp: 56850, 13
GB fn, tp: 24, 75
GB f1 score: 0.802
GB cohens kappa score: 0.802

-> test with 'KNN'
KNN tn, fp: 56863, 0
KNN fn, tp: 99, 0
KNN f1 score: 0.000
KNN cohens kappa score: 0.000


------ Step 3/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Epoch 1/3
Epoch 2/3
Epoch 3/3
-> create 227059 synthetic samples
-> test with 'LR'
LR tn, fp: 56850, 13
LR fn, tp: 33, 66
LR f1 score: 0.742
LR cohens kappa score: 0.741
LR average precision score: 0.680

-> test with 'GB'
GB tn, fp: 56861, 2
GB fn, tp: 22, 77
GB f1 score: 0.865
GB cohens kappa score: 0.865

-> test with 'KNN'
KNN tn, fp: 56863, 0
KNN fn, tp: 98, 1
KNN f1 score: 0.020
KNN cohens kappa score: 0.020


------ Step 3/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Epoch 1/3
Epoch 2/3
Epoch 3/3
-> create 227059 synthetic samples
-> test with 'LR'
LR tn, fp: 56852, 11
LR fn, tp: 43, 56
LR f1 score: 0.675
LR cohens kappa score: 0.674
LR average precision score: 0.674

-> test with 'GB'
GB tn, fp: 56857, 6
GB fn, tp: 27, 72
GB f1 score: 0.814
GB cohens kappa score: 0.813

-> test with 'KNN'
KNN tn, fp: 56863, 0
KNN fn, tp: 97, 2
KNN f1 score: 0.040
KNN cohens kappa score: 0.040


------ Step 3/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Epoch 1/3
Epoch 2/3
Epoch 3/3
-> create 227056 synthetic samples
-> test with 'LR'
LR tn, fp: 56858, 5
LR fn, tp: 35, 61
LR f1 score: 0.753
LR cohens kappa score: 0.753
LR average precision score: 0.702

-> test with 'GB'
GB tn, fp: 56855, 8
GB fn, tp: 26, 70
GB f1 score: 0.805
GB cohens kappa score: 0.804

-> test with 'KNN'
KNN tn, fp: 56863, 0
KNN fn, tp: 95, 1
KNN f1 score: 0.021
KNN cohens kappa score: 0.021


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

------ Step 4/5: Slice 1/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Epoch 1/3
Epoch 2/3
Epoch 3/3
-> create 227059 synthetic samples
-> test with 'LR'
LR tn, fp: 56847, 16
LR fn, tp: 33, 66
LR f1 score: 0.729
LR cohens kappa score: 0.729
LR average precision score: 0.676

-> test with 'GB'
GB tn, fp: 56850, 13
GB fn, tp: 23, 76
GB f1 score: 0.809
GB cohens kappa score: 0.808

-> test with 'KNN'
KNN tn, fp: 56863, 0
KNN fn, tp: 99, 0
KNN f1 score: 0.000
KNN cohens kappa score: 0.000


------ Step 4/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Epoch 1/3
Epoch 2/3
Epoch 3/3
-> create 227059 synthetic samples
-> test with 'LR'
LR tn, fp: 56843, 20
LR fn, tp: 35, 64
LR f1 score: 0.699
LR cohens kappa score: 0.699
LR average precision score: 0.585

-> test with 'GB'
GB tn, fp: 56848, 15
GB fn, tp: 23, 76
GB f1 score: 0.800
GB cohens kappa score: 0.800

-> test with 'KNN'
KNN tn, fp: 56863, 0
KNN fn, tp: 98, 1
KNN f1 score: 0.020
KNN cohens kappa score: 0.020


------ Step 4/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Epoch 1/3
Epoch 2/3
Epoch 3/3
-> create 227059 synthetic samples
-> test with 'LR'
LR tn, fp: 56853, 10
LR fn, tp: 32, 67
LR f1 score: 0.761
LR cohens kappa score: 0.761
LR average precision score: 0.700

-> test with 'GB'
GB tn, fp: 56853, 10
GB fn, tp: 25, 74
GB f1 score: 0.809
GB cohens kappa score: 0.808

-> test with 'KNN'
KNN tn, fp: 56863, 0
KNN fn, tp: 99, 0
KNN f1 score: 0.000
KNN cohens kappa score: 0.000


------ Step 4/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Epoch 1/3
Epoch 2/3
Epoch 3/3
-> create 227059 synthetic samples
-> test with 'LR'
LR tn, fp: 56856, 7
LR fn, tp: 32, 67
LR f1 score: 0.775
LR cohens kappa score: 0.774
LR average precision score: 0.721

-> test with 'GB'
GB tn, fp: 56861, 2
GB fn, tp: 18, 81
GB f1 score: 0.890
GB cohens kappa score: 0.890

-> test with 'KNN'
KNN tn, fp: 56863, 0
KNN fn, tp: 99, 0
KNN f1 score: 0.000
KNN cohens kappa score: 0.000


------ Step 4/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Epoch 1/3
Epoch 2/3
Epoch 3/3
-> create 227056 synthetic samples
-> test with 'LR'
LR tn, fp: 56855, 8
LR fn, tp: 45, 51
LR f1 score: 0.658
LR cohens kappa score: 0.658
LR average precision score: 0.653

-> test with 'GB'
GB tn, fp: 56850, 13
GB fn, tp: 26, 70
GB f1 score: 0.782
GB cohens kappa score: 0.782

-> test with 'KNN'
KNN tn, fp: 56863, 0
KNN fn, tp: 93, 3
KNN f1 score: 0.061
KNN cohens kappa score: 0.061


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

------ Step 5/5: Slice 1/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Epoch 1/3
Epoch 2/3
Epoch 3/3
-> create 227059 synthetic samples
-> test with 'LR'
LR tn, fp: 56849, 14
LR fn, tp: 38, 61
LR f1 score: 0.701
LR cohens kappa score: 0.701
LR average precision score: 0.660

-> test with 'GB'
GB tn, fp: 56853, 10
GB fn, tp: 30, 69
GB f1 score: 0.775
GB cohens kappa score: 0.775

-> test with 'KNN'
KNN tn, fp: 56863, 0
KNN fn, tp: 97, 2
KNN f1 score: 0.040
KNN cohens kappa score: 0.040


------ Step 5/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Epoch 1/3
Epoch 2/3
Epoch 3/3
-> create 227059 synthetic samples
-> test with 'LR'
LR tn, fp: 56856, 7
LR fn, tp: 36, 63
LR f1 score: 0.746
LR cohens kappa score: 0.745
LR average precision score: 0.778

-> test with 'GB'
GB tn, fp: 56859, 4
GB fn, tp: 18, 81
GB f1 score: 0.880
GB cohens kappa score: 0.880

-> test with 'KNN'
KNN tn, fp: 56863, 0
KNN fn, tp: 98, 1
KNN f1 score: 0.020
KNN cohens kappa score: 0.020


------ Step 5/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Epoch 1/3
Epoch 2/3
Epoch 3/3
-> create 227059 synthetic samples
-> test with 'LR'
LR tn, fp: 56853, 10
LR fn, tp: 33, 66
LR f1 score: 0.754
LR cohens kappa score: 0.754
LR average precision score: 0.652

-> test with 'GB'
GB tn, fp: 56848, 15
GB fn, tp: 24, 75
GB f1 score: 0.794
GB cohens kappa score: 0.793

-> test with 'KNN'
KNN tn, fp: 56863, 0
KNN fn, tp: 96, 3
KNN f1 score: 0.059
KNN cohens kappa score: 0.059


------ Step 5/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Epoch 1/3
Epoch 2/3
Epoch 3/3
-> create 227059 synthetic samples
-> test with 'LR'
LR tn, fp: 56856, 7
LR fn, tp: 40, 59
LR f1 score: 0.715
LR cohens kappa score: 0.715
LR average precision score: 0.729

-> test with 'GB'
GB tn, fp: 56859, 4
GB fn, tp: 21, 78
GB f1 score: 0.862
GB cohens kappa score: 0.862

-> test with 'KNN'
KNN tn, fp: 56863, 0
KNN fn, tp: 99, 0
KNN f1 score: 0.000
KNN cohens kappa score: 0.000


------ Step 5/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Epoch 1/3
Epoch 2/3
Epoch 3/3
-> create 227056 synthetic samples
-> test with 'LR'
LR tn, fp: 56850, 13
LR fn, tp: 37, 59
LR f1 score: 0.702
LR cohens kappa score: 0.702
LR average precision score: 0.628

-> test with 'GB'
GB tn, fp: 56844, 19
GB fn, tp: 27, 69
GB f1 score: 0.750
GB cohens kappa score: 0.750

-> test with 'KNN'
KNN tn, fp: 56863, 0
KNN fn, tp: 96, 0
KNN f1 score: 0.000
KNN cohens kappa score: 0.000

### Exercise is done.

-----[ LR ]-----
maximum:
LR tn, fp: 56858, 42
LR fn, tp: 45, 69
LR f1 score: 0.775
LR cohens kappa score: 0.775
LR average precision score: 0.778


average:
LR tn, fp: 56848.72, 14.28
LR fn, tp: 36.0, 62.4
LR f1 score: 0.713
LR cohens kappa score: 0.713
LR average precision score: 0.662


minimum:
LR tn, fp: 56821, 5
LR fn, tp: 30, 51
LR f1 score: 0.624
LR cohens kappa score: 0.624
LR average precision score: 0.489


-----[ GB ]-----
maximum:
GB tn, fp: 56861, 25
GB fn, tp: 32, 81
GB f1 score: 0.890
GB cohens kappa score: 0.890


average:
GB tn, fp: 56851.84, 11.16
GB fn, tp: 24.28, 74.12
GB f1 score: 0.807
GB cohens kappa score: 0.807


minimum:
GB tn, fp: 56838, 2
GB fn, tp: 18, 67
GB f1 score: 0.735
GB cohens kappa score: 0.734


-----[ KNN ]-----
maximum:
KNN tn, fp: 56863, 1
KNN fn, tp: 99, 3
KNN f1 score: 0.061
KNN cohens kappa score: 0.061


average:
KNN tn, fp: 56862.92, 0.08
KNN fn, tp: 97.24, 1.16
KNN f1 score: 0.023
KNN cohens kappa score: 0.023


minimum:
KNN tn, fp: 56862, 0
KNN fn, tp: 93, 0
KNN f1 score: 0.000
KNN cohens kappa score: 0.000