ConvGeNBenchmarkResults/data_result/ctGAN/folding_yeast4.log

///////////////////////////////////////////
// Running ctGAN on folding_yeast4
///////////////////////////////////////////

Load 'data_input/folding_yeast4'
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 1106 synthetic samples
-> test with 'LR'
LR tn, fp: 260, 27
LR fn, tp: 2, 9
LR f1 score: 0.383
LR cohens kappa score: 0.346
LR average precision score: 0.233

-> test with 'GB'
GB tn, fp: 279, 8
GB fn, tp: 8, 3
GB f1 score: 0.273
GB cohens kappa score: 0.245

-> test with 'KNN'
KNN tn, fp: 280, 7
KNN fn, tp: 5, 6
KNN f1 score: 0.500
KNN cohens kappa score: 0.479


------ Step 1/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1106 synthetic samples
-> test with 'LR'
LR tn, fp: 247, 40
LR fn, tp: 7, 4
LR f1 score: 0.145
LR cohens kappa score: 0.092
LR average precision score: 0.071

-> test with 'GB'
GB tn, fp: 276, 11
GB fn, tp: 7, 4
GB f1 score: 0.308
GB cohens kappa score: 0.277

-> test with 'KNN'
KNN tn, fp: 274, 13
KNN fn, tp: 6, 5
KNN f1 score: 0.345
KNN cohens kappa score: 0.313


------ Step 1/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1106 synthetic samples
-> test with 'LR'
LR tn, fp: 257, 30
LR fn, tp: 4, 7
LR f1 score: 0.292
LR cohens kappa score: 0.249
LR average precision score: 0.178

-> test with 'GB'
GB tn, fp: 281, 6
GB fn, tp: 8, 3
GB f1 score: 0.300
GB cohens kappa score: 0.276

-> test with 'KNN'
KNN tn, fp: 271, 16
KNN fn, tp: 6, 5
KNN f1 score: 0.312
KNN cohens kappa score: 0.277


------ Step 1/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1106 synthetic samples
-> test with 'LR'
LR tn, fp: 269, 18
LR fn, tp: 7, 4
LR f1 score: 0.242
LR cohens kappa score: 0.203
LR average precision score: 0.173

-> test with 'GB'
GB tn, fp: 276, 11
GB fn, tp: 8, 3
GB f1 score: 0.240
GB cohens kappa score: 0.207

-> test with 'KNN'
KNN tn, fp: 274, 13
KNN fn, tp: 8, 3
KNN f1 score: 0.222
KNN cohens kappa score: 0.187


------ Step 1/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1104 synthetic samples
-> test with 'LR'
LR tn, fp: 260, 25
LR fn, tp: 4, 3
LR f1 score: 0.171
LR cohens kappa score: 0.138
LR average precision score: 0.147

-> test with 'GB'
GB tn, fp: 280, 5
GB fn, tp: 4, 3
GB f1 score: 0.400
GB cohens kappa score: 0.384

-> test with 'KNN'
KNN tn, fp: 279, 6
KNN fn, tp: 5, 2
KNN f1 score: 0.267
KNN cohens kappa score: 0.247


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

------ Step 2/5: Slice 1/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1106 synthetic samples
-> test with 'LR'
LR tn, fp: 266, 21
LR fn, tp: 6, 5
LR f1 score: 0.270
LR cohens kappa score: 0.230
LR average precision score: 0.165

-> test with 'GB'
GB tn, fp: 278, 9
GB fn, tp: 6, 5
GB f1 score: 0.400
GB cohens kappa score: 0.374

-> test with 'KNN'
KNN tn, fp: 274, 13
KNN fn, tp: 9, 2
KNN f1 score: 0.154
KNN cohens kappa score: 0.116


------ Step 2/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1106 synthetic samples
-> test with 'LR'
LR tn, fp: 233, 54
LR fn, tp: 2, 9
LR f1 score: 0.243
LR cohens kappa score: 0.192
LR average precision score: 0.288

-> test with 'GB'
GB tn, fp: 273, 14
GB fn, tp: 8, 3
GB f1 score: 0.214
GB cohens kappa score: 0.177

-> test with 'KNN'
KNN tn, fp: 260, 27
KNN fn, tp: 3, 8
KNN f1 score: 0.348
KNN cohens kappa score: 0.309


------ Step 2/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1106 synthetic samples
-> test with 'LR'
LR tn, fp: 266, 21
LR fn, tp: 6, 5
LR f1 score: 0.270
LR cohens kappa score: 0.230
LR average precision score: 0.337

-> test with 'GB'
GB tn, fp: 281, 6
GB fn, tp: 7, 4
GB f1 score: 0.381
GB cohens kappa score: 0.358

-> test with 'KNN'
KNN tn, fp: 280, 7
KNN fn, tp: 6, 5
KNN f1 score: 0.435
KNN cohens kappa score: 0.412


------ Step 2/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1106 synthetic samples
-> test with 'LR'
LR tn, fp: 268, 19
LR fn, tp: 5, 6
LR f1 score: 0.333
LR cohens kappa score: 0.297
LR average precision score: 0.241

-> test with 'GB'
GB tn, fp: 278, 9
GB fn, tp: 8, 3
GB f1 score: 0.261
GB cohens kappa score: 0.231

-> test with 'KNN'
KNN tn, fp: 282, 5
KNN fn, tp: 8, 3
KNN f1 score: 0.316
KNN cohens kappa score: 0.294


------ Step 2/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1104 synthetic samples
-> test with 'LR'
LR tn, fp: 229, 56
LR fn, tp: 2, 5
LR f1 score: 0.147
LR cohens kappa score: 0.109
LR average precision score: 0.247

-> test with 'GB'
GB tn, fp: 273, 12
GB fn, tp: 6, 1
GB f1 score: 0.100
GB cohens kappa score: 0.071

-> test with 'KNN'
KNN tn, fp: 265, 20
KNN fn, tp: 4, 3
KNN f1 score: 0.200
KNN cohens kappa score: 0.169


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

------ Step 3/5: Slice 1/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1106 synthetic samples
-> test with 'LR'
LR tn, fp: 247, 40
LR fn, tp: 8, 3
LR f1 score: 0.111
LR cohens kappa score: 0.056
LR average precision score: 0.167

-> test with 'GB'
GB tn, fp: 277, 10
GB fn, tp: 9, 2
GB f1 score: 0.174
GB cohens kappa score: 0.141

-> test with 'KNN'
KNN tn, fp: 276, 11
KNN fn, tp: 7, 4
KNN f1 score: 0.308
KNN cohens kappa score: 0.277


------ Step 3/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1106 synthetic samples
-> test with 'LR'
LR tn, fp: 266, 21
LR fn, tp: 3, 8
LR f1 score: 0.400
LR cohens kappa score: 0.366
LR average precision score: 0.245

-> test with 'GB'
GB tn, fp: 274, 13
GB fn, tp: 9, 2
GB f1 score: 0.154
GB cohens kappa score: 0.116

-> test with 'KNN'
KNN tn, fp: 275, 12
KNN fn, tp: 10, 1
KNN f1 score: 0.083
KNN cohens kappa score: 0.045


------ Step 3/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1106 synthetic samples
-> test with 'LR'
LR tn, fp: 241, 46
LR fn, tp: 4, 7
LR f1 score: 0.219
LR cohens kappa score: 0.168
LR average precision score: 0.197

-> test with 'GB'
GB tn, fp: 282, 5
GB fn, tp: 8, 3
GB f1 score: 0.316
GB cohens kappa score: 0.294

-> test with 'KNN'
KNN tn, fp: 276, 11
KNN fn, tp: 7, 4
KNN f1 score: 0.308
KNN cohens kappa score: 0.277


------ Step 3/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1106 synthetic samples
-> test with 'LR'
LR tn, fp: 239, 48
LR fn, tp: 3, 8
LR f1 score: 0.239
LR cohens kappa score: 0.189
LR average precision score: 0.239

-> test with 'GB'
GB tn, fp: 277, 10
GB fn, tp: 8, 3
GB f1 score: 0.250
GB cohens kappa score: 0.219

-> test with 'KNN'
KNN tn, fp: 277, 10
KNN fn, tp: 7, 4
KNN f1 score: 0.320
KNN cohens kappa score: 0.291


------ Step 3/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1104 synthetic samples
-> test with 'LR'
LR tn, fp: 242, 43
LR fn, tp: 5, 2
LR f1 score: 0.077
LR cohens kappa score: 0.037
LR average precision score: 0.052

-> test with 'GB'
GB tn, fp: 276, 9
GB fn, tp: 4, 3
GB f1 score: 0.316
GB cohens kappa score: 0.294

-> test with 'KNN'
KNN tn, fp: 268, 17
KNN fn, tp: 3, 4
KNN f1 score: 0.286
KNN cohens kappa score: 0.259


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

------ Step 4/5: Slice 1/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1106 synthetic samples
-> test with 'LR'
LR tn, fp: 253, 34
LR fn, tp: 5, 6
LR f1 score: 0.235
LR cohens kappa score: 0.188
LR average precision score: 0.236

-> test with 'GB'
GB tn, fp: 280, 7
GB fn, tp: 9, 2
GB f1 score: 0.200
GB cohens kappa score: 0.173

-> test with 'KNN'
KNN tn, fp: 278, 9
KNN fn, tp: 8, 3
KNN f1 score: 0.261
KNN cohens kappa score: 0.231


------ Step 4/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1106 synthetic samples
-> test with 'LR'
LR tn, fp: 256, 31
LR fn, tp: 6, 5
LR f1 score: 0.213
LR cohens kappa score: 0.166
LR average precision score: 0.161

-> test with 'GB'
GB tn, fp: 283, 4
GB fn, tp: 10, 1
GB f1 score: 0.125
GB cohens kappa score: 0.104

-> test with 'KNN'
KNN tn, fp: 271, 16
KNN fn, tp: 5, 6
KNN f1 score: 0.364
KNN cohens kappa score: 0.331


------ Step 4/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1106 synthetic samples
-> test with 'LR'
LR tn, fp: 265, 22
LR fn, tp: 6, 5
LR f1 score: 0.263
LR cohens kappa score: 0.222
LR average precision score: 0.134

-> test with 'GB'
GB tn, fp: 276, 11
GB fn, tp: 8, 3
GB f1 score: 0.240
GB cohens kappa score: 0.207

-> test with 'KNN'
KNN tn, fp: 271, 16
KNN fn, tp: 9, 2
KNN f1 score: 0.138
KNN cohens kappa score: 0.097


------ Step 4/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1106 synthetic samples
-> test with 'LR'
LR tn, fp: 209, 78
LR fn, tp: 2, 9
LR f1 score: 0.184
LR cohens kappa score: 0.126
LR average precision score: 0.166

-> test with 'GB'
GB tn, fp: 265, 22
GB fn, tp: 8, 3
GB f1 score: 0.167
GB cohens kappa score: 0.122

-> test with 'KNN'
KNN tn, fp: 255, 32
KNN fn, tp: 4, 7
KNN f1 score: 0.280
KNN cohens kappa score: 0.236


------ Step 4/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1104 synthetic samples
-> test with 'LR'
LR tn, fp: 246, 39
LR fn, tp: 3, 4
LR f1 score: 0.160
LR cohens kappa score: 0.124
LR average precision score: 0.081

-> test with 'GB'
GB tn, fp: 273, 12
GB fn, tp: 3, 4
GB f1 score: 0.348
GB cohens kappa score: 0.325

-> test with 'KNN'
KNN tn, fp: 273, 12
KNN fn, tp: 3, 4
KNN f1 score: 0.348
KNN cohens kappa score: 0.325


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

------ Step 5/5: Slice 1/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1106 synthetic samples
-> test with 'LR'
LR tn, fp: 251, 36
LR fn, tp: 6, 5
LR f1 score: 0.192
LR cohens kappa score: 0.142
LR average precision score: 0.116

-> test with 'GB'
GB tn, fp: 275, 12
GB fn, tp: 11, 0
GB f1 score: 0.000
GB cohens kappa score: -0.040

-> test with 'KNN'
KNN tn, fp: 278, 9
KNN fn, tp: 7, 4
KNN f1 score: 0.333
KNN cohens kappa score: 0.306


------ Step 5/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1106 synthetic samples
-> test with 'LR'
LR tn, fp: 264, 23
LR fn, tp: 4, 7
LR f1 score: 0.341
LR cohens kappa score: 0.304
LR average precision score: 0.502

-> test with 'GB'
GB tn, fp: 276, 11
GB fn, tp: 5, 6
GB f1 score: 0.429
GB cohens kappa score: 0.402

-> test with 'KNN'
KNN tn, fp: 275, 12
KNN fn, tp: 4, 7
KNN f1 score: 0.467
KNN cohens kappa score: 0.441


------ Step 5/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1106 synthetic samples
-> test with 'LR'
LR tn, fp: 212, 75
LR fn, tp: 5, 6
LR f1 score: 0.130
LR cohens kappa score: 0.070
LR average precision score: 0.190

-> test with 'GB'
GB tn, fp: 274, 13
GB fn, tp: 9, 2
GB f1 score: 0.154
GB cohens kappa score: 0.116

-> test with 'KNN'
KNN tn, fp: 260, 27
KNN fn, tp: 6, 5
KNN f1 score: 0.233
KNN cohens kappa score: 0.188


------ Step 5/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1106 synthetic samples
-> test with 'LR'
LR tn, fp: 252, 35
LR fn, tp: 1, 10
LR f1 score: 0.357
LR cohens kappa score: 0.317
LR average precision score: 0.288

-> test with 'GB'
GB tn, fp: 272, 15
GB fn, tp: 6, 5
GB f1 score: 0.323
GB cohens kappa score: 0.289

-> test with 'KNN'
KNN tn, fp: 273, 14
KNN fn, tp: 6, 5
KNN f1 score: 0.333
KNN cohens kappa score: 0.301


------ Step 5/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1104 synthetic samples
-> test with 'LR'
LR tn, fp: 270, 15
LR fn, tp: 5, 2
LR f1 score: 0.167
LR cohens kappa score: 0.137
LR average precision score: 0.112

-> test with 'GB'
GB tn, fp: 280, 5
GB fn, tp: 5, 2
GB f1 score: 0.286
GB cohens kappa score: 0.268

-> test with 'KNN'
KNN tn, fp: 280, 5
KNN fn, tp: 5, 2
KNN f1 score: 0.286
KNN cohens kappa score: 0.268

### Exercise is done.

-----[ LR ]-----
maximum:
LR tn, fp: 270, 78
LR fn, tp: 8, 10
LR f1 score: 0.400
LR cohens kappa score: 0.366
LR average precision score: 0.502


average:
LR tn, fp: 250.72, 35.88
LR fn, tp: 4.44, 5.76
LR f1 score: 0.231
LR cohens kappa score: 0.188
LR average precision score: 0.199


minimum:
LR tn, fp: 209, 15
LR fn, tp: 1, 2
LR f1 score: 0.077
LR cohens kappa score: 0.037
LR average precision score: 0.052


-----[ GB ]-----
maximum:
GB tn, fp: 283, 22
GB fn, tp: 11, 6
GB f1 score: 0.429
GB cohens kappa score: 0.402


average:
GB tn, fp: 276.6, 10.0
GB fn, tp: 7.28, 2.92
GB f1 score: 0.254
GB cohens kappa score: 0.225


minimum:
GB tn, fp: 265, 4
GB fn, tp: 3, 0
GB f1 score: 0.000
GB cohens kappa score: -0.040


-----[ KNN ]-----
maximum:
KNN tn, fp: 282, 32
KNN fn, tp: 10, 8
KNN f1 score: 0.500
KNN cohens kappa score: 0.479


average:
KNN tn, fp: 273.0, 13.6
KNN fn, tp: 6.04, 4.16
KNN f1 score: 0.298
KNN cohens kappa score: 0.267


minimum:
KNN tn, fp: 255, 5
KNN fn, tp: 3, 1
KNN f1 score: 0.083
KNN cohens kappa score: 0.045