ConvGeNBenchmarkResults/data_result/ctGAN/folding_yeast6.log

///////////////////////////////////////////
// Running ctGAN on folding_yeast6
///////////////////////////////////////////

Load 'data_input/folding_yeast6'
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 1131 synthetic samples
-> test with 'LR'
LR tn, fp: 265, 25
LR fn, tp: 1, 6
LR f1 score: 0.316
LR cohens kappa score: 0.288
LR average precision score: 0.445

-> test with 'GB'
GB tn, fp: 285, 5
GB fn, tp: 3, 4
GB f1 score: 0.500
GB cohens kappa score: 0.486

-> test with 'KNN'
KNN tn, fp: 273, 17
KNN fn, tp: 1, 6
KNN f1 score: 0.400
KNN cohens kappa score: 0.378


------ Step 1/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1131 synthetic samples
-> test with 'LR'
LR tn, fp: 248, 42
LR fn, tp: 3, 4
LR f1 score: 0.151
LR cohens kappa score: 0.115
LR average precision score: 0.286

-> test with 'GB'
GB tn, fp: 282, 8
GB fn, tp: 4, 3
GB f1 score: 0.333
GB cohens kappa score: 0.314

-> test with 'KNN'
KNN tn, fp: 272, 18
KNN fn, tp: 2, 5
KNN f1 score: 0.333
KNN cohens kappa score: 0.308


------ Step 1/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1131 synthetic samples
-> test with 'LR'
LR tn, fp: 250, 40
LR fn, tp: 5, 2
LR f1 score: 0.082
LR cohens kappa score: 0.043
LR average precision score: 0.083

-> test with 'GB'
GB tn, fp: 285, 5
GB fn, tp: 4, 3
GB f1 score: 0.400
GB cohens kappa score: 0.385

-> test with 'KNN'
KNN tn, fp: 277, 13
KNN fn, tp: 5, 2
KNN f1 score: 0.182
KNN cohens kappa score: 0.155


------ Step 1/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1131 synthetic samples
-> test with 'LR'
LR tn, fp: 244, 46
LR fn, tp: 1, 6
LR f1 score: 0.203
LR cohens kappa score: 0.169
LR average precision score: 0.369

-> test with 'GB'
GB tn, fp: 283, 7
GB fn, tp: 5, 2
GB f1 score: 0.250
GB cohens kappa score: 0.230

-> test with 'KNN'
KNN tn, fp: 259, 31
KNN fn, tp: 1, 6
KNN f1 score: 0.273
KNN cohens kappa score: 0.243


------ Step 1/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1132 synthetic samples
-> test with 'LR'
LR tn, fp: 206, 83
LR fn, tp: 0, 7
LR f1 score: 0.144
LR cohens kappa score: 0.105
LR average precision score: 0.195

-> test with 'GB'
GB tn, fp: 285, 4
GB fn, tp: 4, 3
GB f1 score: 0.429
GB cohens kappa score: 0.415

-> test with 'KNN'
KNN tn, fp: 259, 30
KNN fn, tp: 0, 7
KNN f1 score: 0.318
KNN cohens kappa score: 0.290


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

------ Step 2/5: Slice 1/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1131 synthetic samples
-> test with 'LR'
LR tn, fp: 256, 34
LR fn, tp: 0, 7
LR f1 score: 0.292
LR cohens kappa score: 0.262
LR average precision score: 0.685

-> test with 'GB'
GB tn, fp: 283, 7
GB fn, tp: 3, 4
GB f1 score: 0.444
GB cohens kappa score: 0.428

-> test with 'KNN'
KNN tn, fp: 274, 16
KNN fn, tp: 1, 6
KNN f1 score: 0.414
KNN cohens kappa score: 0.392


------ Step 2/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1131 synthetic samples
-> test with 'LR'
LR tn, fp: 260, 30
LR fn, tp: 7, 0
LR f1 score: 0.000
LR cohens kappa score: -0.040
LR average precision score: 0.045

-> test with 'GB'
GB tn, fp: 286, 4
GB fn, tp: 6, 1
GB f1 score: 0.167
GB cohens kappa score: 0.150

-> test with 'KNN'
KNN tn, fp: 275, 15
KNN fn, tp: 3, 4
KNN f1 score: 0.308
KNN cohens kappa score: 0.283


------ Step 2/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1131 synthetic samples
-> test with 'LR'
LR tn, fp: 255, 35
LR fn, tp: 1, 6
LR f1 score: 0.250
LR cohens kappa score: 0.219
LR average precision score: 0.265

-> test with 'GB'
GB tn, fp: 279, 11
GB fn, tp: 5, 2
GB f1 score: 0.200
GB cohens kappa score: 0.175

-> test with 'KNN'
KNN tn, fp: 268, 22
KNN fn, tp: 1, 6
KNN f1 score: 0.343
KNN cohens kappa score: 0.317


------ Step 2/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1131 synthetic samples
-> test with 'LR'
LR tn, fp: 258, 32
LR fn, tp: 2, 5
LR f1 score: 0.227
LR cohens kappa score: 0.195
LR average precision score: 0.140

-> test with 'GB'
GB tn, fp: 286, 4
GB fn, tp: 6, 1
GB f1 score: 0.167
GB cohens kappa score: 0.150

-> test with 'KNN'
KNN tn, fp: 269, 21
KNN fn, tp: 2, 5
KNN f1 score: 0.303
KNN cohens kappa score: 0.276


------ Step 2/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1132 synthetic samples
-> test with 'LR'
LR tn, fp: 269, 20
LR fn, tp: 2, 5
LR f1 score: 0.312
LR cohens kappa score: 0.286
LR average precision score: 0.295

-> test with 'GB'
GB tn, fp: 283, 6
GB fn, tp: 5, 2
GB f1 score: 0.267
GB cohens kappa score: 0.248

-> test with 'KNN'
KNN tn, fp: 277, 12
KNN fn, tp: 3, 4
KNN f1 score: 0.348
KNN cohens kappa score: 0.326


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

------ Step 3/5: Slice 1/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1131 synthetic samples
-> test with 'LR'
LR tn, fp: 274, 16
LR fn, tp: 2, 5
LR f1 score: 0.357
LR cohens kappa score: 0.334
LR average precision score: 0.411

-> test with 'GB'
GB tn, fp: 287, 3
GB fn, tp: 3, 4
GB f1 score: 0.571
GB cohens kappa score: 0.561

-> test with 'KNN'
KNN tn, fp: 285, 5
KNN fn, tp: 3, 4
KNN f1 score: 0.500
KNN cohens kappa score: 0.486


------ Step 3/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1131 synthetic samples
-> test with 'LR'
LR tn, fp: 225, 65
LR fn, tp: 0, 7
LR f1 score: 0.177
LR cohens kappa score: 0.140
LR average precision score: 0.218

-> test with 'GB'
GB tn, fp: 285, 5
GB fn, tp: 5, 2
GB f1 score: 0.286
GB cohens kappa score: 0.268

-> test with 'KNN'
KNN tn, fp: 259, 31
KNN fn, tp: 0, 7
KNN f1 score: 0.311
KNN cohens kappa score: 0.283


------ Step 3/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1131 synthetic samples
-> test with 'LR'
LR tn, fp: 278, 12
LR fn, tp: 4, 3
LR f1 score: 0.273
LR cohens kappa score: 0.249
LR average precision score: 0.284

-> test with 'GB'
GB tn, fp: 284, 6
GB fn, tp: 6, 1
GB f1 score: 0.143
GB cohens kappa score: 0.122

-> test with 'KNN'
KNN tn, fp: 281, 9
KNN fn, tp: 3, 4
KNN f1 score: 0.400
KNN cohens kappa score: 0.381


------ Step 3/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1131 synthetic samples
-> test with 'LR'
LR tn, fp: 234, 56
LR fn, tp: 1, 6
LR f1 score: 0.174
LR cohens kappa score: 0.137
LR average precision score: 0.355

-> test with 'GB'
GB tn, fp: 279, 11
GB fn, tp: 3, 4
GB f1 score: 0.364
GB cohens kappa score: 0.343

-> test with 'KNN'
KNN tn, fp: 260, 30
KNN fn, tp: 1, 6
KNN f1 score: 0.279
KNN cohens kappa score: 0.249


------ Step 3/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1132 synthetic samples
-> test with 'LR'
LR tn, fp: 272, 17
LR fn, tp: 5, 2
LR f1 score: 0.154
LR cohens kappa score: 0.124
LR average precision score: 0.238

-> test with 'GB'
GB tn, fp: 286, 3
GB fn, tp: 4, 3
GB f1 score: 0.462
GB cohens kappa score: 0.450

-> test with 'KNN'
KNN tn, fp: 279, 10
KNN fn, tp: 5, 2
KNN f1 score: 0.211
KNN cohens kappa score: 0.186


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

------ Step 4/5: Slice 1/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1131 synthetic samples
-> test with 'LR'
LR tn, fp: 282, 8
LR fn, tp: 1, 6
LR f1 score: 0.571
LR cohens kappa score: 0.558
LR average precision score: 0.769

-> test with 'GB'
GB tn, fp: 285, 5
GB fn, tp: 1, 6
GB f1 score: 0.667
GB cohens kappa score: 0.657

-> test with 'KNN'
KNN tn, fp: 282, 8
KNN fn, tp: 1, 6
KNN f1 score: 0.571
KNN cohens kappa score: 0.558


------ Step 4/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1131 synthetic samples
-> test with 'LR'
LR tn, fp: 264, 26
LR fn, tp: 2, 5
LR f1 score: 0.263
LR cohens kappa score: 0.234
LR average precision score: 0.173

-> test with 'GB'
GB tn, fp: 282, 8
GB fn, tp: 7, 0
GB f1 score: 0.000
GB cohens kappa score: -0.026

-> test with 'KNN'
KNN tn, fp: 275, 15
KNN fn, tp: 2, 5
KNN f1 score: 0.370
KNN cohens kappa score: 0.348


------ Step 4/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1131 synthetic samples
-> test with 'LR'
LR tn, fp: 241, 49
LR fn, tp: 1, 6
LR f1 score: 0.194
LR cohens kappa score: 0.158
LR average precision score: 0.518

-> test with 'GB'
GB tn, fp: 282, 8
GB fn, tp: 3, 4
GB f1 score: 0.421
GB cohens kappa score: 0.403

-> test with 'KNN'
KNN tn, fp: 265, 25
KNN fn, tp: 1, 6
KNN f1 score: 0.316
KNN cohens kappa score: 0.288


------ Step 4/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1131 synthetic samples
-> test with 'LR'
LR tn, fp: 266, 24
LR fn, tp: 2, 5
LR f1 score: 0.278
LR cohens kappa score: 0.249
LR average precision score: 0.317

-> test with 'GB'
GB tn, fp: 286, 4
GB fn, tp: 4, 3
GB f1 score: 0.429
GB cohens kappa score: 0.415

-> test with 'KNN'
KNN tn, fp: 281, 9
KNN fn, tp: 3, 4
KNN f1 score: 0.400
KNN cohens kappa score: 0.381


------ Step 4/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1132 synthetic samples
-> test with 'LR'
LR tn, fp: 248, 41
LR fn, tp: 3, 4
LR f1 score: 0.154
LR cohens kappa score: 0.118
LR average precision score: 0.409

-> test with 'GB'
GB tn, fp: 284, 5
GB fn, tp: 4, 3
GB f1 score: 0.400
GB cohens kappa score: 0.384

-> test with 'KNN'
KNN tn, fp: 275, 14
KNN fn, tp: 2, 5
KNN f1 score: 0.385
KNN cohens kappa score: 0.363


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

------ Step 5/5: Slice 1/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1131 synthetic samples
-> test with 'LR'
LR tn, fp: 274, 16
LR fn, tp: 3, 4
LR f1 score: 0.296
LR cohens kappa score: 0.271
LR average precision score: 0.331

-> test with 'GB'
GB tn, fp: 282, 8
GB fn, tp: 2, 5
GB f1 score: 0.500
GB cohens kappa score: 0.484

-> test with 'KNN'
KNN tn, fp: 279, 11
KNN fn, tp: 3, 4
KNN f1 score: 0.364
KNN cohens kappa score: 0.343


------ Step 5/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1131 synthetic samples
-> test with 'LR'
LR tn, fp: 257, 33
LR fn, tp: 3, 4
LR f1 score: 0.182
LR cohens kappa score: 0.148
LR average precision score: 0.151

-> test with 'GB'
GB tn, fp: 287, 3
GB fn, tp: 6, 1
GB f1 score: 0.182
GB cohens kappa score: 0.168

-> test with 'KNN'
KNN tn, fp: 274, 16
KNN fn, tp: 3, 4
KNN f1 score: 0.296
KNN cohens kappa score: 0.271


------ Step 5/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1131 synthetic samples
-> test with 'LR'
LR tn, fp: 219, 71
LR fn, tp: 6, 1
LR f1 score: 0.025
LR cohens kappa score: -0.018
LR average precision score: 0.026

-> test with 'GB'
GB tn, fp: 279, 11
GB fn, tp: 3, 4
GB f1 score: 0.364
GB cohens kappa score: 0.343

-> test with 'KNN'
KNN tn, fp: 268, 22
KNN fn, tp: 3, 4
KNN f1 score: 0.242
KNN cohens kappa score: 0.213


------ Step 5/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1131 synthetic samples
-> test with 'LR'
LR tn, fp: 276, 14
LR fn, tp: 4, 3
LR f1 score: 0.250
LR cohens kappa score: 0.224
LR average precision score: 0.279

-> test with 'GB'
GB tn, fp: 285, 5
GB fn, tp: 5, 2
GB f1 score: 0.286
GB cohens kappa score: 0.268

-> test with 'KNN'
KNN tn, fp: 278, 12
KNN fn, tp: 3, 4
KNN f1 score: 0.348
KNN cohens kappa score: 0.326


------ Step 5/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1132 synthetic samples
-> test with 'LR'
LR tn, fp: 270, 19
LR fn, tp: 2, 5
LR f1 score: 0.323
LR cohens kappa score: 0.297
LR average precision score: 0.301

-> test with 'GB'
GB tn, fp: 285, 4
GB fn, tp: 4, 3
GB f1 score: 0.429
GB cohens kappa score: 0.415

-> test with 'KNN'
KNN tn, fp: 273, 16
KNN fn, tp: 2, 5
KNN f1 score: 0.357
KNN cohens kappa score: 0.334

### Exercise is done.

-----[ LR ]-----
maximum:
LR tn, fp: 282, 83
LR fn, tp: 7, 7
LR f1 score: 0.571
LR cohens kappa score: 0.558
LR average precision score: 0.769


average:
LR tn, fp: 255.64, 34.16
LR fn, tp: 2.44, 4.56
LR f1 score: 0.226
LR cohens kappa score: 0.195
LR average precision score: 0.303


minimum:
LR tn, fp: 206, 8
LR fn, tp: 0, 0
LR f1 score: 0.000
LR cohens kappa score: -0.040
LR average precision score: 0.026


-----[ GB ]-----
maximum:
GB tn, fp: 287, 11
GB fn, tp: 7, 6
GB f1 score: 0.667
GB cohens kappa score: 0.657


average:
GB tn, fp: 283.8, 6.0
GB fn, tp: 4.2, 2.8
GB f1 score: 0.346
GB cohens kappa score: 0.329


minimum:
GB tn, fp: 279, 3
GB fn, tp: 1, 0
GB f1 score: 0.000
GB cohens kappa score: -0.026


-----[ KNN ]-----
maximum:
KNN tn, fp: 285, 31
KNN fn, tp: 5, 7
KNN f1 score: 0.571
KNN cohens kappa score: 0.558


average:
KNN tn, fp: 272.68, 17.12
KNN fn, tp: 2.16, 4.84
KNN f1 score: 0.343
KNN cohens kappa score: 0.319


minimum:
KNN tn, fp: 259, 5
KNN fn, tp: 0, 2
KNN f1 score: 0.182
KNN cohens kappa score: 0.155