ConvGeNBenchmarkResults/data_result/ctGAN/folding_abalone9-18.log

///////////////////////////////////////////
// Running ctGAN on folding_abalone9-18
///////////////////////////////////////////

Load 'data_input/folding_abalone9-18'
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 518 synthetic samples
-> test with 'LR'
LR tn, fp: 132, 6
LR fn, tp: 4, 5
LR f1 score: 0.500
LR cohens kappa score: 0.464
LR average precision score: 0.554

-> test with 'GB'
GB tn, fp: 134, 4
GB fn, tp: 4, 5
GB f1 score: 0.556
GB cohens kappa score: 0.527

-> test with 'KNN'
KNN tn, fp: 138, 0
KNN fn, tp: 9, 0
KNN f1 score: 0.000
KNN cohens kappa score: 0.000


------ Step 1/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 518 synthetic samples
-> test with 'LR'
LR tn, fp: 136, 2
LR fn, tp: 7, 2
LR f1 score: 0.308
LR cohens kappa score: 0.281
LR average precision score: 0.363

-> test with 'GB'
GB tn, fp: 135, 3
GB fn, tp: 6, 3
GB f1 score: 0.400
GB cohens kappa score: 0.369

-> test with 'KNN'
KNN tn, fp: 135, 3
KNN fn, tp: 8, 1
KNN f1 score: 0.154
KNN cohens kappa score: 0.121


------ Step 1/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 518 synthetic samples
-> test with 'LR'
LR tn, fp: 130, 8
LR fn, tp: 8, 1
LR f1 score: 0.111
LR cohens kappa score: 0.053
LR average precision score: 0.072

-> test with 'GB'
GB tn, fp: 135, 3
GB fn, tp: 7, 2
GB f1 score: 0.286
GB cohens kappa score: 0.253

-> test with 'KNN'
KNN tn, fp: 138, 0
KNN fn, tp: 8, 1
KNN f1 score: 0.200
KNN cohens kappa score: 0.190


------ Step 1/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 518 synthetic samples
-> test with 'LR'
LR tn, fp: 133, 5
LR fn, tp: 5, 4
LR f1 score: 0.444
LR cohens kappa score: 0.408
LR average precision score: 0.390

-> test with 'GB'
GB tn, fp: 137, 1
GB fn, tp: 7, 2
GB f1 score: 0.333
GB cohens kappa score: 0.312

-> test with 'KNN'
KNN tn, fp: 137, 1
KNN fn, tp: 7, 2
KNN f1 score: 0.333
KNN cohens kappa score: 0.312


------ Step 1/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 516 synthetic samples
-> test with 'LR'
LR tn, fp: 134, 3
LR fn, tp: 5, 1
LR f1 score: 0.200
LR cohens kappa score: 0.172
LR average precision score: 0.159

-> test with 'GB'
GB tn, fp: 137, 0
GB fn, tp: 6, 0
GB f1 score: 0.000
GB cohens kappa score: 0.000

-> test with 'KNN'
KNN tn, fp: 137, 0
KNN fn, tp: 5, 1
KNN f1 score: 0.286
KNN cohens kappa score: 0.277


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

------ Step 2/5: Slice 1/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 518 synthetic samples
-> test with 'LR'
LR tn, fp: 136, 2
LR fn, tp: 6, 3
LR f1 score: 0.429
LR cohens kappa score: 0.402
LR average precision score: 0.470

-> test with 'GB'
GB tn, fp: 137, 1
GB fn, tp: 7, 2
GB f1 score: 0.333
GB cohens kappa score: 0.312

-> test with 'KNN'
KNN tn, fp: 137, 1
KNN fn, tp: 8, 1
KNN f1 score: 0.182
KNN cohens kappa score: 0.163


------ Step 2/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 518 synthetic samples
-> test with 'LR'
LR tn, fp: 125, 13
LR fn, tp: 7, 2
LR f1 score: 0.167
LR cohens kappa score: 0.098
LR average precision score: 0.234

-> test with 'GB'
GB tn, fp: 133, 5
GB fn, tp: 6, 3
GB f1 score: 0.353
GB cohens kappa score: 0.313

-> test with 'KNN'
KNN tn, fp: 138, 0
KNN fn, tp: 8, 1
KNN f1 score: 0.200
KNN cohens kappa score: 0.190


------ Step 2/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 518 synthetic samples
-> test with 'LR'
LR tn, fp: 131, 7
LR fn, tp: 8, 1
LR f1 score: 0.118
LR cohens kappa score: 0.064
LR average precision score: 0.089

-> test with 'GB'
GB tn, fp: 134, 4
GB fn, tp: 7, 2
GB f1 score: 0.267
GB cohens kappa score: 0.229

-> test with 'KNN'
KNN tn, fp: 135, 3
KNN fn, tp: 7, 2
KNN f1 score: 0.286
KNN cohens kappa score: 0.253


------ Step 2/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 518 synthetic samples
-> test with 'LR'
LR tn, fp: 128, 10
LR fn, tp: 3, 6
LR f1 score: 0.480
LR cohens kappa score: 0.436
LR average precision score: 0.651

-> test with 'GB'
GB tn, fp: 135, 3
GB fn, tp: 5, 4
GB f1 score: 0.500
GB cohens kappa score: 0.472

-> test with 'KNN'
KNN tn, fp: 137, 1
KNN fn, tp: 7, 2
KNN f1 score: 0.333
KNN cohens kappa score: 0.312


------ Step 2/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 516 synthetic samples
-> test with 'LR'
LR tn, fp: 126, 11
LR fn, tp: 3, 3
LR f1 score: 0.300
LR cohens kappa score: 0.256
LR average precision score: 0.205

-> test with 'GB'
GB tn, fp: 129, 8
GB fn, tp: 4, 2
GB f1 score: 0.250
GB cohens kappa score: 0.208

-> test with 'KNN'
KNN tn, fp: 137, 0
KNN fn, tp: 5, 1
KNN f1 score: 0.286
KNN cohens kappa score: 0.277


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

------ Step 3/5: Slice 1/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 518 synthetic samples
-> test with 'LR'
LR tn, fp: 132, 6
LR fn, tp: 6, 3
LR f1 score: 0.333
LR cohens kappa score: 0.290
LR average precision score: 0.398

-> test with 'GB'
GB tn, fp: 135, 3
GB fn, tp: 8, 1
GB f1 score: 0.154
GB cohens kappa score: 0.121

-> test with 'KNN'
KNN tn, fp: 138, 0
KNN fn, tp: 8, 1
KNN f1 score: 0.200
KNN cohens kappa score: 0.190


------ Step 3/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 518 synthetic samples
-> test with 'LR'
LR tn, fp: 123, 15
LR fn, tp: 7, 2
LR f1 score: 0.154
LR cohens kappa score: 0.080
LR average precision score: 0.103

-> test with 'GB'
GB tn, fp: 133, 5
GB fn, tp: 6, 3
GB f1 score: 0.353
GB cohens kappa score: 0.313

-> test with 'KNN'
KNN tn, fp: 136, 2
KNN fn, tp: 9, 0
KNN f1 score: 0.000
KNN cohens kappa score: -0.023


------ Step 3/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 518 synthetic samples
-> test with 'LR'
LR tn, fp: 137, 1
LR fn, tp: 5, 4
LR f1 score: 0.571
LR cohens kappa score: 0.552
LR average precision score: 0.617

-> test with 'GB'
GB tn, fp: 136, 2
GB fn, tp: 7, 2
GB f1 score: 0.308
GB cohens kappa score: 0.281

-> test with 'KNN'
KNN tn, fp: 137, 1
KNN fn, tp: 9, 0
KNN f1 score: 0.000
KNN cohens kappa score: -0.012


------ Step 3/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 518 synthetic samples
-> test with 'LR'
LR tn, fp: 131, 7
LR fn, tp: 4, 5
LR f1 score: 0.476
LR cohens kappa score: 0.437
LR average precision score: 0.584

-> test with 'GB'
GB tn, fp: 133, 5
GB fn, tp: 4, 5
GB f1 score: 0.526
GB cohens kappa score: 0.494

-> test with 'KNN'
KNN tn, fp: 138, 0
KNN fn, tp: 6, 3
KNN f1 score: 0.500
KNN cohens kappa score: 0.484


------ Step 3/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 516 synthetic samples
-> test with 'LR'
LR tn, fp: 120, 17
LR fn, tp: 5, 1
LR f1 score: 0.083
LR cohens kappa score: 0.022
LR average precision score: 0.224

-> test with 'GB'
GB tn, fp: 133, 4
GB fn, tp: 5, 1
GB f1 score: 0.182
GB cohens kappa score: 0.149

-> test with 'KNN'
KNN tn, fp: 133, 4
KNN fn, tp: 5, 1
KNN f1 score: 0.182
KNN cohens kappa score: 0.149


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

------ Step 4/5: Slice 1/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 518 synthetic samples
-> test with 'LR'
LR tn, fp: 131, 7
LR fn, tp: 5, 4
LR f1 score: 0.400
LR cohens kappa score: 0.357
LR average precision score: 0.514

-> test with 'GB'
GB tn, fp: 135, 3
GB fn, tp: 6, 3
GB f1 score: 0.400
GB cohens kappa score: 0.369

-> test with 'KNN'
KNN tn, fp: 138, 0
KNN fn, tp: 7, 2
KNN f1 score: 0.364
KNN cohens kappa score: 0.349


------ Step 4/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 518 synthetic samples
-> test with 'LR'
LR tn, fp: 119, 19
LR fn, tp: 4, 5
LR f1 score: 0.303
LR cohens kappa score: 0.235
LR average precision score: 0.394

-> test with 'GB'
GB tn, fp: 133, 5
GB fn, tp: 6, 3
GB f1 score: 0.353
GB cohens kappa score: 0.313

-> test with 'KNN'
KNN tn, fp: 137, 1
KNN fn, tp: 7, 2
KNN f1 score: 0.333
KNN cohens kappa score: 0.312


------ Step 4/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 518 synthetic samples
-> test with 'LR'
LR tn, fp: 137, 1
LR fn, tp: 5, 4
LR f1 score: 0.571
LR cohens kappa score: 0.552
LR average precision score: 0.580

-> test with 'GB'
GB tn, fp: 133, 5
GB fn, tp: 6, 3
GB f1 score: 0.353
GB cohens kappa score: 0.313

-> test with 'KNN'
KNN tn, fp: 138, 0
KNN fn, tp: 9, 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
-> create 518 synthetic samples
-> test with 'LR'
LR tn, fp: 111, 27
LR fn, tp: 5, 4
LR f1 score: 0.200
LR cohens kappa score: 0.116
LR average precision score: 0.204

-> test with 'GB'
GB tn, fp: 136, 2
GB fn, tp: 7, 2
GB f1 score: 0.308
GB cohens kappa score: 0.281

-> test with 'KNN'
KNN tn, fp: 136, 2
KNN fn, tp: 8, 1
KNN f1 score: 0.167
KNN cohens kappa score: 0.140


------ Step 4/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 516 synthetic samples
-> test with 'LR'
LR tn, fp: 131, 6
LR fn, tp: 3, 3
LR f1 score: 0.400
LR cohens kappa score: 0.368
LR average precision score: 0.361

-> test with 'GB'
GB tn, fp: 130, 7
GB fn, tp: 5, 1
GB f1 score: 0.143
GB cohens kappa score: 0.100

-> test with 'KNN'
KNN tn, fp: 136, 1
KNN fn, tp: 5, 1
KNN f1 score: 0.250
KNN cohens kappa score: 0.234


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

------ Step 5/5: Slice 1/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 518 synthetic samples
-> test with 'LR'
LR tn, fp: 124, 14
LR fn, tp: 9, 0
LR f1 score: 0.000
LR cohens kappa score: -0.081
LR average precision score: 0.071

-> test with 'GB'
GB tn, fp: 129, 9
GB fn, tp: 8, 1
GB f1 score: 0.105
GB cohens kappa score: 0.044

-> test with 'KNN'
KNN tn, fp: 134, 4
KNN fn, tp: 8, 1
KNN f1 score: 0.143
KNN cohens kappa score: 0.104


------ Step 5/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 518 synthetic samples
-> test with 'LR'
LR tn, fp: 136, 2
LR fn, tp: 5, 4
LR f1 score: 0.533
LR cohens kappa score: 0.509
LR average precision score: 0.588

-> test with 'GB'
GB tn, fp: 135, 3
GB fn, tp: 7, 2
GB f1 score: 0.286
GB cohens kappa score: 0.253

-> test with 'KNN'
KNN tn, fp: 138, 0
KNN fn, tp: 9, 0
KNN f1 score: 0.000
KNN cohens kappa score: 0.000


------ Step 5/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 518 synthetic samples
-> test with 'LR'
LR tn, fp: 96, 42
LR fn, tp: 2, 7
LR f1 score: 0.241
LR cohens kappa score: 0.154
LR average precision score: 0.306

-> test with 'GB'
GB tn, fp: 133, 5
GB fn, tp: 7, 2
GB f1 score: 0.250
GB cohens kappa score: 0.208

-> test with 'KNN'
KNN tn, fp: 135, 3
KNN fn, tp: 7, 2
KNN f1 score: 0.286
KNN cohens kappa score: 0.253


------ Step 5/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 518 synthetic samples
-> test with 'LR'
LR tn, fp: 127, 11
LR fn, tp: 5, 4
LR f1 score: 0.333
LR cohens kappa score: 0.278
LR average precision score: 0.408

-> test with 'GB'
GB tn, fp: 136, 2
GB fn, tp: 6, 3
GB f1 score: 0.429
GB cohens kappa score: 0.402

-> test with 'KNN'
KNN tn, fp: 138, 0
KNN fn, tp: 7, 2
KNN f1 score: 0.364
KNN cohens kappa score: 0.349


------ Step 5/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 516 synthetic samples
-> test with 'LR'
LR tn, fp: 126, 11
LR fn, tp: 4, 2
LR f1 score: 0.211
LR cohens kappa score: 0.162
LR average precision score: 0.304

-> test with 'GB'
GB tn, fp: 137, 0
GB fn, tp: 3, 3
GB f1 score: 0.667
GB cohens kappa score: 0.657

-> test with 'KNN'
KNN tn, fp: 137, 0
KNN fn, tp: 5, 1
KNN f1 score: 0.286
KNN cohens kappa score: 0.277

### Exercise is done.

-----[ LR ]-----
maximum:
LR tn, fp: 137, 42
LR fn, tp: 9, 7
LR f1 score: 0.571
LR cohens kappa score: 0.552
LR average precision score: 0.651


average:
LR tn, fp: 127.68, 10.12
LR fn, tp: 5.2, 3.2
LR f1 score: 0.315
LR cohens kappa score: 0.267
LR average precision score: 0.354


minimum:
LR tn, fp: 96, 1
LR fn, tp: 2, 0
LR f1 score: 0.000
LR cohens kappa score: -0.081
LR average precision score: 0.071


-----[ GB ]-----
maximum:
GB tn, fp: 137, 9
GB fn, tp: 8, 5
GB f1 score: 0.667
GB cohens kappa score: 0.657


average:
GB tn, fp: 134.12, 3.68
GB fn, tp: 6.0, 2.4
GB f1 score: 0.324
GB cohens kappa score: 0.292


minimum:
GB tn, fp: 129, 0
GB fn, tp: 3, 0
GB f1 score: 0.000
GB cohens kappa score: 0.000


-----[ KNN ]-----
maximum:
KNN tn, fp: 138, 4
KNN fn, tp: 9, 3
KNN f1 score: 0.500
KNN cohens kappa score: 0.484


average:
KNN tn, fp: 136.72, 1.08
KNN fn, tp: 7.24, 1.16
KNN f1 score: 0.213
KNN cohens kappa score: 0.196


minimum:
KNN tn, fp: 133, 0
KNN fn, tp: 5, 0
KNN f1 score: 0.000
KNN cohens kappa score: -0.023