ConvGeNBenchmarkResults/data_result/SimpleGAN/folding_car_good.log

///////////////////////////////////////////
// Running SimpleGAN on folding_car_good
///////////////////////////////////////////

Load 'data_input/folding_car_good'
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
Epoch 1/3
Epoch 2/3
Epoch 3/3
-> create 1272 synthetic samples
-> test with 'LR'
LR tn, fp: 211, 121
LR fn, tp: 9, 5
LR f1 score: 0.071
LR cohens kappa score: -0.002
LR average precision score: 0.045

-> test with 'GB'
GB tn, fp: 328, 4
GB fn, tp: 7, 7
GB f1 score: 0.560
GB cohens kappa score: 0.544

-> test with 'KNN'
KNN tn, fp: 332, 0
KNN fn, tp: 12, 2
KNN f1 score: 0.250
KNN cohens kappa score: 0.242


------ Step 1/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Epoch 1/3
Epoch 2/3
Epoch 3/3
-> create 1272 synthetic samples
-> test with 'LR'
LR tn, fp: 325, 7
LR fn, tp: 14, 0
LR f1 score: 0.000
LR cohens kappa score: -0.028
LR average precision score: 0.051

-> test with 'GB'
GB tn, fp: 332, 0
GB fn, tp: 0, 14
GB f1 score: 1.000
GB cohens kappa score: 1.000

-> test with 'KNN'
KNN tn, fp: 332, 0
KNN fn, tp: 12, 2
KNN f1 score: 0.250
KNN cohens kappa score: 0.242


------ Step 1/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Epoch 1/3
Epoch 2/3
Epoch 3/3
-> create 1272 synthetic samples
-> test with 'LR'
LR tn, fp: 202, 130
LR fn, tp: 7, 7
LR f1 score: 0.093
LR cohens kappa score: 0.021
LR average precision score: 0.045

-> test with 'GB'
GB tn, fp: 329, 3
GB fn, tp: 6, 8
GB f1 score: 0.640
GB cohens kappa score: 0.627

-> test with 'KNN'
KNN tn, fp: 329, 3
KNN fn, tp: 9, 5
KNN f1 score: 0.455
KNN cohens kappa score: 0.438


------ Step 1/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Epoch 1/3
Epoch 2/3
Epoch 3/3
-> create 1272 synthetic samples
-> test with 'LR'
LR tn, fp: 219, 113
LR fn, tp: 8, 6
LR f1 score: 0.090
LR cohens kappa score: 0.019
LR average precision score: 0.053

-> test with 'GB'
GB tn, fp: 325, 7
GB fn, tp: 8, 6
GB f1 score: 0.444
GB cohens kappa score: 0.422

-> test with 'KNN'
KNN tn, fp: 328, 4
KNN fn, tp: 13, 1
KNN f1 score: 0.105
KNN cohens kappa score: 0.086


------ Step 1/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Epoch 1/3
Epoch 2/3
Epoch 3/3
-> create 1272 synthetic samples
-> test with 'LR'
LR tn, fp: 200, 131
LR fn, tp: 5, 8
LR f1 score: 0.105
LR cohens kappa score: 0.039
LR average precision score: 0.061

-> test with 'GB'
GB tn, fp: 323, 8
GB fn, tp: 2, 11
GB f1 score: 0.688
GB cohens kappa score: 0.673

-> test with 'KNN'
KNN tn, fp: 329, 2
KNN fn, tp: 10, 3
KNN f1 score: 0.333
KNN cohens kappa score: 0.319


====== 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 1272 synthetic samples
-> test with 'LR'
LR tn, fp: 205, 127
LR fn, tp: 7, 7
LR f1 score: 0.095
LR cohens kappa score: 0.023
LR average precision score: 0.047

-> test with 'GB'
GB tn, fp: 329, 3
GB fn, tp: 6, 8
GB f1 score: 0.640
GB cohens kappa score: 0.627

-> test with 'KNN'
KNN tn, fp: 329, 3
KNN fn, tp: 12, 2
KNN f1 score: 0.211
KNN cohens kappa score: 0.193


------ Step 2/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Epoch 1/3
Epoch 2/3
Epoch 3/3
-> create 1272 synthetic samples
-> test with 'LR'
LR tn, fp: 196, 136
LR fn, tp: 8, 6
LR f1 score: 0.077
LR cohens kappa score: 0.004
LR average precision score: 0.041

-> test with 'GB'
GB tn, fp: 324, 8
GB fn, tp: 5, 9
GB f1 score: 0.581
GB cohens kappa score: 0.561

-> test with 'KNN'
KNN tn, fp: 328, 4
KNN fn, tp: 11, 3
KNN f1 score: 0.286
KNN cohens kappa score: 0.266


------ Step 2/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Epoch 1/3
Epoch 2/3
Epoch 3/3
-> create 1272 synthetic samples
-> test with 'LR'
LR tn, fp: 212, 120
LR fn, tp: 5, 9
LR f1 score: 0.126
LR cohens kappa score: 0.057
LR average precision score: 0.057

-> test with 'GB'
GB tn, fp: 331, 1
GB fn, tp: 9, 5
GB f1 score: 0.500
GB cohens kappa score: 0.488

-> test with 'KNN'
KNN tn, fp: 331, 1
KNN fn, tp: 10, 4
KNN f1 score: 0.421
KNN cohens kappa score: 0.408


------ Step 2/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Epoch 1/3
Epoch 2/3
Epoch 3/3
-> create 1272 synthetic samples
-> test with 'LR'
LR tn, fp: 223, 109
LR fn, tp: 11, 3
LR f1 score: 0.048
LR cohens kappa score: -0.026
LR average precision score: 0.048

-> test with 'GB'
GB tn, fp: 326, 6
GB fn, tp: 5, 9
GB f1 score: 0.621
GB cohens kappa score: 0.604

-> test with 'KNN'
KNN tn, fp: 329, 3
KNN fn, tp: 14, 0
KNN f1 score: 0.000
KNN cohens kappa score: -0.014


------ Step 2/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Epoch 1/3
Epoch 2/3
Epoch 3/3
-> create 1272 synthetic samples
-> test with 'LR'
LR tn, fp: 211, 120
LR fn, tp: 7, 6
LR f1 score: 0.086
LR cohens kappa score: 0.019
LR average precision score: 0.047

-> test with 'GB'
GB tn, fp: 325, 6
GB fn, tp: 4, 9
GB f1 score: 0.643
GB cohens kappa score: 0.628

-> test with 'KNN'
KNN tn, fp: 329, 2
KNN fn, tp: 10, 3
KNN f1 score: 0.333
KNN cohens kappa score: 0.319


====== 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 1272 synthetic samples
-> test with 'LR'
LR tn, fp: 303, 29
LR fn, tp: 11, 3
LR f1 score: 0.130
LR cohens kappa score: 0.079
LR average precision score: 0.062

-> test with 'GB'
GB tn, fp: 332, 0
GB fn, tp: 3, 11
GB f1 score: 0.880
GB cohens kappa score: 0.876

-> test with 'KNN'
KNN tn, fp: 331, 1
KNN fn, tp: 13, 1
KNN f1 score: 0.125
KNN cohens kappa score: 0.116


------ Step 3/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Epoch 1/3
Epoch 2/3
Epoch 3/3
-> create 1272 synthetic samples
-> test with 'LR'
LR tn, fp: 323, 9
LR fn, tp: 13, 1
LR f1 score: 0.083
LR cohens kappa score: 0.051
LR average precision score: 0.071

-> test with 'GB'
GB tn, fp: 329, 3
GB fn, tp: 1, 13
GB f1 score: 0.867
GB cohens kappa score: 0.861

-> test with 'KNN'
KNN tn, fp: 332, 0
KNN fn, tp: 13, 1
KNN f1 score: 0.133
KNN cohens kappa score: 0.129


------ Step 3/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Epoch 1/3
Epoch 2/3
Epoch 3/3
-> create 1272 synthetic samples
-> test with 'LR'
LR tn, fp: 308, 24
LR fn, tp: 14, 0
LR f1 score: 0.000
LR cohens kappa score: -0.054
LR average precision score: 0.043

-> test with 'GB'
GB tn, fp: 331, 1
GB fn, tp: 9, 5
GB f1 score: 0.500
GB cohens kappa score: 0.488

-> test with 'KNN'
KNN tn, fp: 332, 0
KNN fn, tp: 11, 3
KNN f1 score: 0.353
KNN cohens kappa score: 0.344


------ Step 3/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Epoch 1/3
Epoch 2/3
Epoch 3/3
-> create 1272 synthetic samples
-> test with 'LR'
LR tn, fp: 224, 108
LR fn, tp: 7, 7
LR f1 score: 0.109
LR cohens kappa score: 0.039
LR average precision score: 0.050

-> test with 'GB'
GB tn, fp: 330, 2
GB fn, tp: 6, 8
GB f1 score: 0.667
GB cohens kappa score: 0.655

-> test with 'KNN'
KNN tn, fp: 332, 0
KNN fn, tp: 13, 1
KNN f1 score: 0.133
KNN cohens kappa score: 0.129


------ Step 3/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Epoch 1/3
Epoch 2/3
Epoch 3/3
-> create 1272 synthetic samples
-> test with 'LR'
LR tn, fp: 313, 18
LR fn, tp: 11, 2
LR f1 score: 0.121
LR cohens kappa score: 0.079
LR average precision score: 0.063

-> test with 'GB'
GB tn, fp: 328, 3
GB fn, tp: 2, 11
GB f1 score: 0.815
GB cohens kappa score: 0.807

-> test with 'KNN'
KNN tn, fp: 330, 1
KNN fn, tp: 9, 4
KNN f1 score: 0.444
KNN cohens kappa score: 0.433


====== 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 1272 synthetic samples
-> test with 'LR'
LR tn, fp: 307, 25
LR fn, tp: 11, 3
LR f1 score: 0.143
LR cohens kappa score: 0.094
LR average precision score: 0.094

-> test with 'GB'
GB tn, fp: 332, 0
GB fn, tp: 0, 14
GB f1 score: 1.000
GB cohens kappa score: 1.000

-> test with 'KNN'
KNN tn, fp: 332, 0
KNN fn, tp: 12, 2
KNN f1 score: 0.250
KNN cohens kappa score: 0.242


------ Step 4/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Epoch 1/3
Epoch 2/3
Epoch 3/3
-> create 1272 synthetic samples
-> test with 'LR'
LR tn, fp: 201, 131
LR fn, tp: 8, 6
LR f1 score: 0.079
LR cohens kappa score: 0.007
LR average precision score: 0.041

-> test with 'GB'
GB tn, fp: 327, 5
GB fn, tp: 9, 5
GB f1 score: 0.417
GB cohens kappa score: 0.396

-> test with 'KNN'
KNN tn, fp: 332, 0
KNN fn, tp: 11, 3
KNN f1 score: 0.353
KNN cohens kappa score: 0.344


------ Step 4/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Epoch 1/3
Epoch 2/3
Epoch 3/3
-> create 1272 synthetic samples
-> test with 'LR'
LR tn, fp: 318, 14
LR fn, tp: 14, 0
LR f1 score: 0.000
LR cohens kappa score: -0.042
LR average precision score: 0.047

-> test with 'GB'
GB tn, fp: 331, 1
GB fn, tp: 5, 9
GB f1 score: 0.750
GB cohens kappa score: 0.741

-> test with 'KNN'
KNN tn, fp: 332, 0
KNN fn, tp: 11, 3
KNN f1 score: 0.353
KNN cohens kappa score: 0.344


------ Step 4/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Epoch 1/3
Epoch 2/3
Epoch 3/3
-> create 1272 synthetic samples
-> test with 'LR'
LR tn, fp: 202, 130
LR fn, tp: 11, 3
LR f1 score: 0.041
LR cohens kappa score: -0.035
LR average precision score: 0.040

-> test with 'GB'
GB tn, fp: 328, 4
GB fn, tp: 3, 11
GB f1 score: 0.759
GB cohens kappa score: 0.748

-> test with 'KNN'
KNN tn, fp: 327, 5
KNN fn, tp: 10, 4
KNN f1 score: 0.348
KNN cohens kappa score: 0.326


------ Step 4/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Epoch 1/3
Epoch 2/3
Epoch 3/3
-> create 1272 synthetic samples
-> test with 'LR'
LR tn, fp: 215, 116
LR fn, tp: 5, 8
LR f1 score: 0.117
LR cohens kappa score: 0.052
LR average precision score: 0.060

-> test with 'GB'
GB tn, fp: 327, 4
GB fn, tp: 7, 6
GB f1 score: 0.522
GB cohens kappa score: 0.505

-> test with 'KNN'
KNN tn, fp: 330, 1
KNN fn, tp: 11, 2
KNN f1 score: 0.250
KNN cohens kappa score: 0.239


====== 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 1272 synthetic samples
-> test with 'LR'
LR tn, fp: 209, 123
LR fn, tp: 10, 4
LR f1 score: 0.057
LR cohens kappa score: -0.017
LR average precision score: 0.038

-> test with 'GB'
GB tn, fp: 329, 3
GB fn, tp: 10, 4
GB f1 score: 0.381
GB cohens kappa score: 0.364

-> test with 'KNN'
KNN tn, fp: 330, 2
KNN fn, tp: 12, 2
KNN f1 score: 0.222
KNN cohens kappa score: 0.208


------ Step 5/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Epoch 1/3
Epoch 2/3
Epoch 3/3
-> create 1272 synthetic samples
-> test with 'LR'
LR tn, fp: 323, 9
LR fn, tp: 14, 0
LR f1 score: 0.000
LR cohens kappa score: -0.033
LR average precision score: 0.094

-> test with 'GB'
GB tn, fp: 331, 1
GB fn, tp: 4, 10
GB f1 score: 0.800
GB cohens kappa score: 0.793

-> test with 'KNN'
KNN tn, fp: 332, 0
KNN fn, tp: 9, 5
KNN f1 score: 0.526
KNN cohens kappa score: 0.516


------ Step 5/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Epoch 1/3
Epoch 2/3
Epoch 3/3
-> create 1272 synthetic samples
-> test with 'LR'
LR tn, fp: 312, 20
LR fn, tp: 14, 0
LR f1 score: 0.000
LR cohens kappa score: -0.050
LR average precision score: 0.052

-> test with 'GB'
GB tn, fp: 326, 6
GB fn, tp: 2, 12
GB f1 score: 0.750
GB cohens kappa score: 0.738

-> test with 'KNN'
KNN tn, fp: 332, 0
KNN fn, tp: 10, 4
KNN f1 score: 0.444
KNN cohens kappa score: 0.434


------ Step 5/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Epoch 1/3
Epoch 2/3
Epoch 3/3
-> create 1272 synthetic samples
-> test with 'LR'
LR tn, fp: 216, 116
LR fn, tp: 7, 7
LR f1 score: 0.102
LR cohens kappa score: 0.032
LR average precision score: 0.062

-> test with 'GB'
GB tn, fp: 328, 4
GB fn, tp: 7, 7
GB f1 score: 0.560
GB cohens kappa score: 0.544

-> test with 'KNN'
KNN tn, fp: 330, 2
KNN fn, tp: 13, 1
KNN f1 score: 0.118
KNN cohens kappa score: 0.105


------ Step 5/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Epoch 1/3
Epoch 2/3
Epoch 3/3
-> create 1272 synthetic samples
-> test with 'LR'
LR tn, fp: 203, 128
LR fn, tp: 9, 4
LR f1 score: 0.055
LR cohens kappa score: -0.015
LR average precision score: 0.043

-> test with 'GB'
GB tn, fp: 322, 9
GB fn, tp: 6, 7
GB f1 score: 0.483
GB cohens kappa score: 0.460

-> test with 'KNN'
KNN tn, fp: 326, 5
KNN fn, tp: 9, 4
KNN f1 score: 0.364
KNN cohens kappa score: 0.343

### Exercise is done.

-----[ LR ]-----
maximum:
LR tn, fp: 325, 136
LR fn, tp: 14, 9
LR f1 score: 0.143
LR cohens kappa score: 0.094
LR average precision score: 0.094


average:
LR tn, fp: 247.24, 84.56
LR fn, tp: 9.6, 4.2
LR f1 score: 0.073
LR cohens kappa score: 0.013
LR average precision score: 0.054


minimum:
LR tn, fp: 196, 7
LR fn, tp: 5, 0
LR f1 score: 0.000
LR cohens kappa score: -0.054
LR average precision score: 0.038


-----[ GB ]-----
maximum:
GB tn, fp: 332, 9
GB fn, tp: 10, 14
GB f1 score: 1.000
GB cohens kappa score: 1.000


average:
GB tn, fp: 328.12, 3.68
GB fn, tp: 5.04, 8.76
GB f1 score: 0.659
GB cohens kappa score: 0.646


minimum:
GB tn, fp: 322, 0
GB fn, tp: 0, 4
GB f1 score: 0.381
GB cohens kappa score: 0.364


-----[ KNN ]-----
maximum:
KNN tn, fp: 332, 5
KNN fn, tp: 14, 5
KNN f1 score: 0.526
KNN cohens kappa score: 0.516


average:
KNN tn, fp: 330.24, 1.56
KNN fn, tp: 11.2, 2.6
KNN f1 score: 0.282
KNN cohens kappa score: 0.270


minimum:
KNN tn, fp: 326, 0
KNN fn, tp: 9, 0
KNN f1 score: 0.000
KNN cohens kappa score: -0.014