ConvGeNBenchmarkResults/data_result/ctGAN/folding_car_good.log

///////////////////////////////////////////
// Running ctGAN 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
-> create 1272 synthetic samples
-> test with 'LR'
LR tn, fp: 163, 169
LR fn, tp: 5, 9
LR f1 score: 0.094
LR cohens kappa score: 0.020
LR average precision score: 0.059

-> test with 'GB'
GB tn, fp: 304, 28
GB fn, tp: 0, 14
GB f1 score: 0.500
GB cohens kappa score: 0.468

-> test with 'KNN'
KNN tn, fp: 244, 88
KNN fn, tp: 0, 14
KNN f1 score: 0.241
KNN cohens kappa score: 0.183


------ Step 1/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1272 synthetic samples
-> test with 'LR'
LR tn, fp: 191, 141
LR fn, tp: 3, 11
LR f1 score: 0.133
LR cohens kappa score: 0.063
LR average precision score: 0.108

-> test with 'GB'
GB tn, fp: 300, 32
GB fn, tp: 0, 14
GB f1 score: 0.467
GB cohens kappa score: 0.431

-> test with 'KNN'
KNN tn, fp: 236, 96
KNN fn, tp: 0, 14
KNN f1 score: 0.226
KNN cohens kappa score: 0.166


------ Step 1/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1272 synthetic samples
-> test with 'LR'
LR tn, fp: 182, 150
LR fn, tp: 7, 7
LR f1 score: 0.082
LR cohens kappa score: 0.008
LR average precision score: 0.061

-> test with 'GB'
GB tn, fp: 311, 21
GB fn, tp: 0, 14
GB f1 score: 0.571
GB cohens kappa score: 0.545

-> test with 'KNN'
KNN tn, fp: 259, 73
KNN fn, tp: 0, 14
KNN f1 score: 0.277
KNN cohens kappa score: 0.223


------ Step 1/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1272 synthetic samples
-> test with 'LR'
LR tn, fp: 180, 152
LR fn, tp: 5, 9
LR f1 score: 0.103
LR cohens kappa score: 0.031
LR average precision score: 0.098

-> test with 'GB'
GB tn, fp: 311, 21
GB fn, tp: 0, 14
GB f1 score: 0.571
GB cohens kappa score: 0.545

-> test with 'KNN'
KNN tn, fp: 254, 78
KNN fn, tp: 0, 14
KNN f1 score: 0.264
KNN cohens kappa score: 0.209


------ Step 1/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1272 synthetic samples
-> test with 'LR'
LR tn, fp: 177, 154
LR fn, tp: 4, 9
LR f1 score: 0.102
LR cohens kappa score: 0.035
LR average precision score: 0.046

-> test with 'GB'
GB tn, fp: 310, 21
GB fn, tp: 0, 13
GB f1 score: 0.553
GB cohens kappa score: 0.527

-> test with 'KNN'
KNN tn, fp: 263, 68
KNN fn, tp: 0, 13
KNN f1 score: 0.277
KNN cohens kappa score: 0.226


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

------ Step 2/5: Slice 1/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1272 synthetic samples
-> test with 'LR'
LR tn, fp: 162, 170
LR fn, tp: 5, 9
LR f1 score: 0.093
LR cohens kappa score: 0.020
LR average precision score: 0.076

-> test with 'GB'
GB tn, fp: 312, 20
GB fn, tp: 0, 14
GB f1 score: 0.583
GB cohens kappa score: 0.558

-> test with 'KNN'
KNN tn, fp: 247, 85
KNN fn, tp: 0, 14
KNN f1 score: 0.248
KNN cohens kappa score: 0.190


------ Step 2/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1272 synthetic samples
-> test with 'LR'
LR tn, fp: 164, 168
LR fn, tp: 5, 9
LR f1 score: 0.094
LR cohens kappa score: 0.021
LR average precision score: 0.064

-> test with 'GB'
GB tn, fp: 316, 16
GB fn, tp: 0, 14
GB f1 score: 0.636
GB cohens kappa score: 0.615

-> test with 'KNN'
KNN tn, fp: 261, 71
KNN fn, tp: 0, 14
KNN f1 score: 0.283
KNN cohens kappa score: 0.229


------ Step 2/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1272 synthetic samples
-> test with 'LR'
LR tn, fp: 200, 132
LR fn, tp: 3, 11
LR f1 score: 0.140
LR cohens kappa score: 0.072
LR average precision score: 0.075

-> test with 'GB'
GB tn, fp: 306, 26
GB fn, tp: 0, 14
GB f1 score: 0.519
GB cohens kappa score: 0.488

-> test with 'KNN'
KNN tn, fp: 261, 71
KNN fn, tp: 0, 14
KNN f1 score: 0.283
KNN cohens kappa score: 0.229


------ Step 2/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1272 synthetic samples
-> test with 'LR'
LR tn, fp: 173, 159
LR fn, tp: 6, 8
LR f1 score: 0.088
LR cohens kappa score: 0.015
LR average precision score: 0.046

-> test with 'GB'
GB tn, fp: 296, 36
GB fn, tp: 0, 14
GB f1 score: 0.438
GB cohens kappa score: 0.400

-> test with 'KNN'
KNN tn, fp: 217, 115
KNN fn, tp: 0, 14
KNN f1 score: 0.196
KNN cohens kappa score: 0.132


------ Step 2/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1272 synthetic samples
-> test with 'LR'
LR tn, fp: 207, 124
LR fn, tp: 3, 10
LR f1 score: 0.136
LR cohens kappa score: 0.072
LR average precision score: 0.066

-> test with 'GB'
GB tn, fp: 306, 25
GB fn, tp: 0, 13
GB f1 score: 0.510
GB cohens kappa score: 0.481

-> test with 'KNN'
KNN tn, fp: 250, 81
KNN fn, tp: 0, 13
KNN f1 score: 0.243
KNN cohens kappa score: 0.189


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

------ Step 3/5: Slice 1/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1272 synthetic samples
-> test with 'LR'
LR tn, fp: 165, 167
LR fn, tp: 4, 10
LR f1 score: 0.105
LR cohens kappa score: 0.032
LR average precision score: 0.065

-> test with 'GB'
GB tn, fp: 306, 26
GB fn, tp: 0, 14
GB f1 score: 0.519
GB cohens kappa score: 0.488

-> test with 'KNN'
KNN tn, fp: 232, 100
KNN fn, tp: 0, 14
KNN f1 score: 0.219
KNN cohens kappa score: 0.158


------ Step 3/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1272 synthetic samples
-> test with 'LR'
LR tn, fp: 170, 162
LR fn, tp: 5, 9
LR f1 score: 0.097
LR cohens kappa score: 0.024
LR average precision score: 0.057

-> test with 'GB'
GB tn, fp: 312, 20
GB fn, tp: 0, 14
GB f1 score: 0.583
GB cohens kappa score: 0.558

-> test with 'KNN'
KNN tn, fp: 261, 71
KNN fn, tp: 0, 14
KNN f1 score: 0.283
KNN cohens kappa score: 0.229


------ Step 3/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1272 synthetic samples
-> test with 'LR'
LR tn, fp: 177, 155
LR fn, tp: 6, 8
LR f1 score: 0.090
LR cohens kappa score: 0.017
LR average precision score: 0.063

-> test with 'GB'
GB tn, fp: 308, 24
GB fn, tp: 0, 14
GB f1 score: 0.538
GB cohens kappa score: 0.509

-> test with 'KNN'
KNN tn, fp: 248, 84
KNN fn, tp: 0, 14
KNN f1 score: 0.250
KNN cohens kappa score: 0.193


------ Step 3/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1272 synthetic samples
-> test with 'LR'
LR tn, fp: 172, 160
LR fn, tp: 3, 11
LR f1 score: 0.119
LR cohens kappa score: 0.048
LR average precision score: 0.073

-> test with 'GB'
GB tn, fp: 305, 27
GB fn, tp: 0, 14
GB f1 score: 0.509
GB cohens kappa score: 0.478

-> test with 'KNN'
KNN tn, fp: 251, 81
KNN fn, tp: 0, 14
KNN f1 score: 0.257
KNN cohens kappa score: 0.200


------ Step 3/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1272 synthetic samples
-> test with 'LR'
LR tn, fp: 174, 157
LR fn, tp: 6, 7
LR f1 score: 0.079
LR cohens kappa score: 0.010
LR average precision score: 0.057

-> test with 'GB'
GB tn, fp: 305, 26
GB fn, tp: 0, 13
GB f1 score: 0.500
GB cohens kappa score: 0.470

-> test with 'KNN'
KNN tn, fp: 230, 101
KNN fn, tp: 0, 13
KNN f1 score: 0.205
KNN cohens kappa score: 0.147


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

------ Step 4/5: Slice 1/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1272 synthetic samples
-> test with 'LR'
LR tn, fp: 178, 154
LR fn, tp: 3, 11
LR f1 score: 0.123
LR cohens kappa score: 0.052
LR average precision score: 0.084

-> test with 'GB'
GB tn, fp: 315, 17
GB fn, tp: 0, 14
GB f1 score: 0.622
GB cohens kappa score: 0.600

-> test with 'KNN'
KNN tn, fp: 256, 76
KNN fn, tp: 0, 14
KNN f1 score: 0.269
KNN cohens kappa score: 0.214


------ Step 4/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1272 synthetic samples
-> test with 'LR'
LR tn, fp: 187, 145
LR fn, tp: 9, 5
LR f1 score: 0.061
LR cohens kappa score: -0.014
LR average precision score: 0.061

-> test with 'GB'
GB tn, fp: 304, 28
GB fn, tp: 0, 14
GB f1 score: 0.500
GB cohens kappa score: 0.468

-> test with 'KNN'
KNN tn, fp: 233, 99
KNN fn, tp: 0, 14
KNN f1 score: 0.220
KNN cohens kappa score: 0.160


------ Step 4/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1272 synthetic samples
-> test with 'LR'
LR tn, fp: 164, 168
LR fn, tp: 1, 13
LR f1 score: 0.133
LR cohens kappa score: 0.063
LR average precision score: 0.068

-> test with 'GB'
GB tn, fp: 304, 28
GB fn, tp: 0, 14
GB f1 score: 0.500
GB cohens kappa score: 0.468

-> test with 'KNN'
KNN tn, fp: 242, 90
KNN fn, tp: 0, 14
KNN f1 score: 0.237
KNN cohens kappa score: 0.179


------ Step 4/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1272 synthetic samples
-> test with 'LR'
LR tn, fp: 202, 130
LR fn, tp: 6, 8
LR f1 score: 0.105
LR cohens kappa score: 0.034
LR average precision score: 0.057

-> test with 'GB'
GB tn, fp: 308, 24
GB fn, tp: 0, 14
GB f1 score: 0.538
GB cohens kappa score: 0.509

-> test with 'KNN'
KNN tn, fp: 257, 75
KNN fn, tp: 0, 14
KNN f1 score: 0.272
KNN cohens kappa score: 0.217


------ Step 4/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1272 synthetic samples
-> test with 'LR'
LR tn, fp: 168, 163
LR fn, tp: 3, 10
LR f1 score: 0.108
LR cohens kappa score: 0.040
LR average precision score: 0.081

-> test with 'GB'
GB tn, fp: 305, 26
GB fn, tp: 0, 13
GB f1 score: 0.500
GB cohens kappa score: 0.470

-> test with 'KNN'
KNN tn, fp: 237, 94
KNN fn, tp: 0, 13
KNN f1 score: 0.217
KNN cohens kappa score: 0.160


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

------ Step 5/5: Slice 1/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1272 synthetic samples
-> test with 'LR'
LR tn, fp: 164, 168
LR fn, tp: 3, 11
LR f1 score: 0.114
LR cohens kappa score: 0.042
LR average precision score: 0.058

-> test with 'GB'
GB tn, fp: 303, 29
GB fn, tp: 0, 14
GB f1 score: 0.491
GB cohens kappa score: 0.458

-> test with 'KNN'
KNN tn, fp: 246, 86
KNN fn, tp: 0, 14
KNN f1 score: 0.246
KNN cohens kappa score: 0.188


------ Step 5/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1272 synthetic samples
-> test with 'LR'
LR tn, fp: 170, 162
LR fn, tp: 2, 12
LR f1 score: 0.128
LR cohens kappa score: 0.057
LR average precision score: 0.062

-> test with 'GB'
GB tn, fp: 314, 18
GB fn, tp: 0, 14
GB f1 score: 0.609
GB cohens kappa score: 0.585

-> test with 'KNN'
KNN tn, fp: 241, 91
KNN fn, tp: 0, 14
KNN f1 score: 0.235
KNN cohens kappa score: 0.176


------ Step 5/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1272 synthetic samples
-> test with 'LR'
LR tn, fp: 148, 184
LR fn, tp: 3, 11
LR f1 score: 0.105
LR cohens kappa score: 0.032
LR average precision score: 0.147

-> test with 'GB'
GB tn, fp: 301, 31
GB fn, tp: 0, 14
GB f1 score: 0.475
GB cohens kappa score: 0.440

-> test with 'KNN'
KNN tn, fp: 244, 88
KNN fn, tp: 0, 14
KNN f1 score: 0.241
KNN cohens kappa score: 0.183


------ Step 5/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1272 synthetic samples
-> test with 'LR'
LR tn, fp: 180, 152
LR fn, tp: 3, 11
LR f1 score: 0.124
LR cohens kappa score: 0.054
LR average precision score: 0.102

-> test with 'GB'
GB tn, fp: 309, 23
GB fn, tp: 0, 14
GB f1 score: 0.549
GB cohens kappa score: 0.521

-> test with 'KNN'
KNN tn, fp: 245, 87
KNN fn, tp: 0, 14
KNN f1 score: 0.243
KNN cohens kappa score: 0.186


------ Step 5/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1272 synthetic samples
-> test with 'LR'
LR tn, fp: 191, 140
LR fn, tp: 4, 9
LR f1 score: 0.111
LR cohens kappa score: 0.045
LR average precision score: 0.064

-> test with 'GB'
GB tn, fp: 309, 22
GB fn, tp: 0, 13
GB f1 score: 0.542
GB cohens kappa score: 0.515

-> test with 'KNN'
KNN tn, fp: 250, 81
KNN fn, tp: 0, 13
KNN f1 score: 0.243
KNN cohens kappa score: 0.189

### Exercise is done.

-----[ LR ]-----
maximum:
LR tn, fp: 207, 184
LR fn, tp: 9, 13
LR f1 score: 0.140
LR cohens kappa score: 0.072
LR average precision score: 0.147


average:
LR tn, fp: 176.36, 155.44
LR fn, tp: 4.28, 9.52
LR f1 score: 0.107
LR cohens kappa score: 0.036
LR average precision score: 0.072


minimum:
LR tn, fp: 148, 124
LR fn, tp: 1, 5
LR f1 score: 0.061
LR cohens kappa score: -0.014
LR average precision score: 0.046


-----[ GB ]-----
maximum:
GB tn, fp: 316, 36
GB fn, tp: 0, 14
GB f1 score: 0.636
GB cohens kappa score: 0.615


average:
GB tn, fp: 307.2, 24.6
GB fn, tp: 0.0, 13.8
GB f1 score: 0.533
GB cohens kappa score: 0.504


minimum:
GB tn, fp: 296, 16
GB fn, tp: 0, 13
GB f1 score: 0.438
GB cohens kappa score: 0.400


-----[ KNN ]-----
maximum:
KNN tn, fp: 263, 115
KNN fn, tp: 0, 14
KNN f1 score: 0.283
KNN cohens kappa score: 0.229


average:
KNN tn, fp: 246.6, 85.2
KNN fn, tp: 0.0, 13.8
KNN f1 score: 0.247
KNN cohens kappa score: 0.190


minimum:
KNN tn, fp: 217, 68
KNN fn, tp: 0, 13
KNN f1 score: 0.196
KNN cohens kappa score: 0.132