ConvGeNBenchmarkResults/data_result/convGAN-proximary-5/folding_yeast6.log

///////////////////////////////////////////
// Running convGAN-proximary-5 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 GAN.predict
GAN tn, fp: 274, 16
GAN fn, tp: 1, 6
GAN f1 score: 0.414
GAN cohens kappa score: 0.392

-> test with 'LR'
LR tn, fp: 266, 24
LR fn, tp: 1, 6
LR f1 score: 0.324
LR cohens kappa score: 0.297
LR average precision score: 0.661

-> test with 'GB'
GB tn, fp: 287, 3
GB fn, tp: 4, 3
GB f1 score: 0.462
GB cohens kappa score: 0.450

-> test with 'KNN'
KNN tn, fp: 272, 18
KNN fn, tp: 1, 6
KNN f1 score: 0.387
KNN cohens kappa score: 0.364


------ Step 1/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1131 synthetic samples
-> test with GAN.predict
GAN tn, fp: 262, 28
GAN fn, tp: 3, 4
GAN f1 score: 0.205
GAN cohens kappa score: 0.173

-> test with 'LR'
LR tn, fp: 261, 29
LR fn, tp: 2, 5
LR f1 score: 0.244
LR cohens kappa score: 0.213
LR average precision score: 0.428

-> test with 'GB'
GB tn, fp: 286, 4
GB fn, tp: 3, 4
GB f1 score: 0.533
GB cohens kappa score: 0.521

-> test with 'KNN'
KNN tn, fp: 265, 25
KNN fn, tp: 2, 5
KNN f1 score: 0.270
KNN cohens kappa score: 0.241


------ Step 1/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1131 synthetic samples
-> test with GAN.predict
GAN tn, fp: 245, 45
GAN fn, tp: 2, 5
GAN f1 score: 0.175
GAN cohens kappa score: 0.140

-> test with 'LR'
LR tn, fp: 260, 30
LR fn, tp: 1, 6
LR f1 score: 0.279
LR cohens kappa score: 0.249
LR average precision score: 0.265

-> test with 'GB'
GB tn, fp: 290, 0
GB fn, tp: 5, 2
GB f1 score: 0.444
GB cohens kappa score: 0.439

-> test with 'KNN'
KNN tn, fp: 267, 23
KNN fn, tp: 1, 6
KNN f1 score: 0.333
KNN cohens kappa score: 0.307


------ Step 1/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1131 synthetic samples
-> test with GAN.predict
GAN tn, fp: 251, 39
GAN fn, tp: 3, 4
GAN f1 score: 0.160
GAN cohens kappa score: 0.125

-> test with 'LR'
LR tn, fp: 267, 23
LR fn, tp: 1, 6
LR f1 score: 0.333
LR cohens kappa score: 0.307
LR average precision score: 0.583

-> test with 'GB'
GB tn, fp: 287, 3
GB fn, tp: 4, 3
GB f1 score: 0.462
GB cohens kappa score: 0.450

-> test with 'KNN'
KNN tn, fp: 271, 19
KNN fn, tp: 1, 6
KNN f1 score: 0.375
KNN cohens kappa score: 0.351


------ Step 1/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1132 synthetic samples
-> test with GAN.predict
GAN tn, fp: 218, 71
GAN fn, tp: 2, 5
GAN f1 score: 0.120
GAN cohens kappa score: 0.081

-> test with 'LR'
LR tn, fp: 245, 44
LR fn, tp: 0, 7
LR f1 score: 0.241
LR cohens kappa score: 0.208
LR average precision score: 0.547

-> test with 'GB'
GB tn, fp: 288, 1
GB fn, tp: 3, 4
GB f1 score: 0.667
GB cohens kappa score: 0.660

-> test with 'KNN'
KNN tn, fp: 264, 25
KNN fn, tp: 0, 7
KNN f1 score: 0.359
KNN cohens kappa score: 0.333


====== 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 GAN.predict
GAN tn, fp: 215, 75
GAN fn, tp: 2, 5
GAN f1 score: 0.115
GAN cohens kappa score: 0.075

-> test with 'LR'
LR tn, fp: 260, 30
LR fn, tp: 0, 7
LR f1 score: 0.318
LR cohens kappa score: 0.290
LR average precision score: 0.610

-> test with 'GB'
GB tn, fp: 286, 4
GB fn, tp: 3, 4
GB f1 score: 0.533
GB cohens kappa score: 0.521

-> test with 'KNN'
KNN tn, fp: 266, 24
KNN fn, tp: 1, 6
KNN f1 score: 0.324
KNN cohens kappa score: 0.297


------ Step 2/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1131 synthetic samples
-> test with GAN.predict
GAN tn, fp: 254, 36
GAN fn, tp: 1, 6
GAN f1 score: 0.245
GAN cohens kappa score: 0.213

-> test with 'LR'
LR tn, fp: 252, 38
LR fn, tp: 0, 7
LR f1 score: 0.269
LR cohens kappa score: 0.238
LR average precision score: 0.246

-> test with 'GB'
GB tn, fp: 288, 2
GB fn, tp: 4, 3
GB f1 score: 0.500
GB cohens kappa score: 0.490

-> test with 'KNN'
KNN tn, fp: 257, 33
KNN fn, tp: 0, 7
KNN f1 score: 0.298
KNN cohens kappa score: 0.269


------ Step 2/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1131 synthetic samples
-> test with GAN.predict
GAN tn, fp: 264, 26
GAN fn, tp: 4, 3
GAN f1 score: 0.167
GAN cohens kappa score: 0.134

-> test with 'LR'
LR tn, fp: 259, 31
LR fn, tp: 1, 6
LR f1 score: 0.273
LR cohens kappa score: 0.243
LR average precision score: 0.528

-> test with 'GB'
GB tn, fp: 288, 2
GB fn, tp: 4, 3
GB f1 score: 0.500
GB cohens kappa score: 0.490

-> test with 'KNN'
KNN tn, fp: 266, 24
KNN fn, tp: 2, 5
KNN f1 score: 0.278
KNN cohens kappa score: 0.249


------ Step 2/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1131 synthetic samples
-> test with GAN.predict
GAN tn, fp: 282, 8
GAN fn, tp: 3, 4
GAN f1 score: 0.421
GAN cohens kappa score: 0.403

-> 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: 0.575

-> test with 'GB'
GB tn, fp: 287, 3
GB fn, tp: 5, 2
GB f1 score: 0.333
GB cohens kappa score: 0.320

-> 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 GAN.predict
GAN tn, fp: 279, 10
GAN fn, tp: 4, 3
GAN f1 score: 0.300
GAN cohens kappa score: 0.278

-> test with 'LR'
LR tn, fp: 272, 17
LR fn, tp: 1, 6
LR f1 score: 0.400
LR cohens kappa score: 0.377
LR average precision score: 0.493

-> test with 'GB'
GB tn, fp: 289, 0
GB fn, tp: 6, 1
GB f1 score: 0.250
GB cohens kappa score: 0.246

-> test with 'KNN'
KNN tn, fp: 273, 16
KNN fn, tp: 3, 4
KNN f1 score: 0.296
KNN cohens kappa score: 0.271


====== 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 GAN.predict
GAN tn, fp: 249, 41
GAN fn, tp: 3, 4
GAN f1 score: 0.154
GAN cohens kappa score: 0.118

-> test with 'LR'
LR tn, fp: 266, 24
LR fn, tp: 1, 6
LR f1 score: 0.324
LR cohens kappa score: 0.297
LR average precision score: 0.623

-> test with 'GB'
GB tn, fp: 289, 1
GB fn, tp: 4, 3
GB f1 score: 0.545
GB cohens kappa score: 0.538

-> test with 'KNN'
KNN tn, fp: 271, 19
KNN fn, tp: 1, 6
KNN f1 score: 0.375
KNN cohens kappa score: 0.351


------ Step 3/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1131 synthetic samples
-> test with GAN.predict
GAN tn, fp: 278, 12
GAN fn, tp: 2, 5
GAN f1 score: 0.417
GAN cohens kappa score: 0.397

-> test with 'LR'
LR tn, fp: 250, 40
LR fn, tp: 0, 7
LR f1 score: 0.259
LR cohens kappa score: 0.228
LR average precision score: 0.800

-> test with 'GB'
GB tn, fp: 289, 1
GB fn, tp: 4, 3
GB f1 score: 0.545
GB cohens kappa score: 0.538

-> test with 'KNN'
KNN tn, fp: 257, 33
KNN fn, tp: 0, 7
KNN f1 score: 0.298
KNN cohens kappa score: 0.269


------ Step 3/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1131 synthetic samples
-> test with GAN.predict
GAN tn, fp: 266, 24
GAN fn, tp: 2, 5
GAN f1 score: 0.278
GAN cohens kappa score: 0.249

-> test with 'LR'
LR tn, fp: 268, 22
LR fn, tp: 2, 5
LR f1 score: 0.294
LR cohens kappa score: 0.267
LR average precision score: 0.413

-> test with 'GB'
GB tn, fp: 288, 2
GB fn, tp: 5, 2
GB f1 score: 0.364
GB cohens kappa score: 0.353

-> test with 'KNN'
KNN tn, fp: 271, 19
KNN fn, tp: 2, 5
KNN f1 score: 0.323
KNN cohens kappa score: 0.297


------ Step 3/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1131 synthetic samples
-> test with GAN.predict
GAN tn, fp: 236, 54
GAN fn, tp: 2, 5
GAN f1 score: 0.152
GAN cohens kappa score: 0.114

-> test with 'LR'
LR tn, fp: 254, 36
LR fn, tp: 1, 6
LR f1 score: 0.245
LR cohens kappa score: 0.213
LR average precision score: 0.381

-> test with 'GB'
GB tn, fp: 286, 4
GB fn, tp: 3, 4
GB f1 score: 0.533
GB cohens kappa score: 0.521

-> test with 'KNN'
KNN tn, fp: 264, 26
KNN fn, tp: 1, 6
KNN f1 score: 0.308
KNN cohens kappa score: 0.280


------ Step 3/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1132 synthetic samples
-> test with GAN.predict
GAN tn, fp: 249, 40
GAN fn, tp: 3, 4
GAN f1 score: 0.157
GAN cohens kappa score: 0.121

-> test with 'LR'
LR tn, fp: 268, 21
LR fn, tp: 1, 6
LR f1 score: 0.353
LR cohens kappa score: 0.328
LR average precision score: 0.411

-> test with 'GB'
GB tn, fp: 288, 1
GB fn, tp: 7, 0
GB f1 score: 0.000
GB cohens kappa score: -0.006

-> test with 'KNN'
KNN tn, fp: 273, 16
KNN fn, tp: 1, 6
KNN f1 score: 0.414
KNN cohens kappa score: 0.392


====== 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 GAN.predict
GAN tn, fp: 268, 22
GAN fn, tp: 1, 6
GAN f1 score: 0.343
GAN cohens kappa score: 0.317

-> test with 'LR'
LR tn, fp: 271, 19
LR fn, tp: 1, 6
LR f1 score: 0.375
LR cohens kappa score: 0.351
LR average precision score: 0.731

-> test with 'GB'
GB tn, fp: 289, 1
GB fn, tp: 3, 4
GB f1 score: 0.667
GB cohens kappa score: 0.660

-> 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 4/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1131 synthetic samples
-> test with GAN.predict
GAN tn, fp: 260, 30
GAN fn, tp: 5, 2
GAN f1 score: 0.103
GAN cohens kappa score: 0.066

-> test with 'LR'
LR tn, fp: 257, 33
LR fn, tp: 0, 7
LR f1 score: 0.298
LR cohens kappa score: 0.269
LR average precision score: 0.252

-> test with 'GB'
GB tn, fp: 287, 3
GB fn, tp: 4, 3
GB f1 score: 0.462
GB cohens kappa score: 0.450

-> test with 'KNN'
KNN tn, fp: 271, 19
KNN fn, tp: 1, 6
KNN f1 score: 0.375
KNN cohens kappa score: 0.351


------ Step 4/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1131 synthetic samples
-> test with GAN.predict
GAN tn, fp: 272, 18
GAN fn, tp: 3, 4
GAN f1 score: 0.276
GAN cohens kappa score: 0.249

-> test with 'LR'
LR tn, fp: 257, 33
LR fn, tp: 1, 6
LR f1 score: 0.261
LR cohens kappa score: 0.230
LR average precision score: 0.548

-> test with 'GB'
GB tn, fp: 288, 2
GB fn, tp: 2, 5
GB f1 score: 0.714
GB cohens kappa score: 0.707

-> test with 'KNN'
KNN tn, fp: 265, 25
KNN fn, tp: 2, 5
KNN f1 score: 0.270
KNN cohens kappa score: 0.241


------ Step 4/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1131 synthetic samples
-> test with GAN.predict
GAN tn, fp: 247, 43
GAN fn, tp: 2, 5
GAN f1 score: 0.182
GAN cohens kappa score: 0.147

-> test with 'LR'
LR tn, fp: 262, 28
LR fn, tp: 1, 6
LR f1 score: 0.293
LR cohens kappa score: 0.264
LR average precision score: 0.639

-> test with 'GB'
GB tn, fp: 287, 3
GB fn, tp: 4, 3
GB f1 score: 0.462
GB cohens kappa score: 0.450

-> test with 'KNN'
KNN tn, fp: 270, 20
KNN fn, tp: 1, 6
KNN f1 score: 0.364
KNN cohens kappa score: 0.339


------ Step 4/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1132 synthetic samples
-> test with GAN.predict
GAN tn, fp: 268, 21
GAN fn, tp: 5, 2
GAN f1 score: 0.133
GAN cohens kappa score: 0.101

-> test with 'LR'
LR tn, fp: 268, 21
LR fn, tp: 2, 5
LR f1 score: 0.303
LR cohens kappa score: 0.276
LR average precision score: 0.661

-> test with 'GB'
GB tn, fp: 288, 1
GB fn, tp: 4, 3
GB f1 score: 0.545
GB cohens kappa score: 0.537

-> test with 'KNN'
KNN tn, fp: 276, 13
KNN fn, tp: 2, 5
KNN f1 score: 0.400
KNN cohens kappa score: 0.379


====== 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 GAN.predict
GAN tn, fp: 233, 57
GAN fn, tp: 3, 4
GAN f1 score: 0.118
GAN cohens kappa score: 0.079

-> test with 'LR'
LR tn, fp: 250, 40
LR fn, tp: 0, 7
LR f1 score: 0.259
LR cohens kappa score: 0.228
LR average precision score: 0.514

-> test with 'GB'
GB tn, fp: 288, 2
GB fn, tp: 3, 4
GB f1 score: 0.615
GB cohens kappa score: 0.607

-> 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 5/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1131 synthetic samples
-> test with GAN.predict
GAN tn, fp: 274, 16
GAN fn, tp: 3, 4
GAN f1 score: 0.296
GAN cohens kappa score: 0.271

-> test with 'LR'
LR tn, fp: 268, 22
LR fn, tp: 3, 4
LR f1 score: 0.242
LR cohens kappa score: 0.213
LR average precision score: 0.230

-> test with 'GB'
GB tn, fp: 289, 1
GB fn, tp: 4, 3
GB f1 score: 0.545
GB cohens kappa score: 0.538

-> test with 'KNN'
KNN tn, fp: 273, 17
KNN fn, tp: 3, 4
KNN f1 score: 0.286
KNN cohens kappa score: 0.260


------ Step 5/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1131 synthetic samples
-> test with GAN.predict
GAN tn, fp: 217, 73
GAN fn, tp: 0, 7
GAN f1 score: 0.161
GAN cohens kappa score: 0.123

-> 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: 0.689

-> test with 'GB'
GB tn, fp: 289, 1
GB fn, tp: 1, 6
GB f1 score: 0.857
GB cohens kappa score: 0.854

-> test with 'KNN'
KNN tn, fp: 265, 25
KNN fn, tp: 0, 7
KNN f1 score: 0.359
KNN cohens kappa score: 0.333


------ Step 5/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1131 synthetic samples
-> test with GAN.predict
GAN tn, fp: 234, 56
GAN fn, tp: 2, 5
GAN f1 score: 0.147
GAN cohens kappa score: 0.109

-> test with 'LR'
LR tn, fp: 257, 33
LR fn, tp: 0, 7
LR f1 score: 0.298
LR cohens kappa score: 0.269
LR average precision score: 0.342

-> test with 'GB'
GB tn, fp: 289, 1
GB fn, tp: 6, 1
GB f1 score: 0.222
GB cohens kappa score: 0.214

-> 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 5/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1132 synthetic samples
-> test with GAN.predict
GAN tn, fp: 279, 10
GAN fn, tp: 4, 3
GAN f1 score: 0.300
GAN cohens kappa score: 0.278

-> 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: 0.439

-> test with 'GB'
GB tn, fp: 287, 2
GB fn, tp: 5, 2
GB f1 score: 0.364
GB cohens kappa score: 0.353

-> test with 'KNN'
KNN tn, fp: 274, 15
KNN fn, tp: 2, 5
KNN f1 score: 0.370
KNN cohens kappa score: 0.348

### Exercise is done.

-----[ LR ]-----
maximum:
LR tn, fp: 272, 44
LR fn, tp: 3, 7
LR f1 score: 0.400
LR cohens kappa score: 0.377
LR average precision score: 0.800


average:
LR tn, fp: 260.88, 28.92
LR fn, tp: 0.96, 6.04
LR f1 score: 0.293
LR cohens kappa score: 0.264
LR average precision score: 0.505


minimum:
LR tn, fp: 245, 17
LR fn, tp: 0, 4
LR f1 score: 0.227
LR cohens kappa score: 0.195
LR average precision score: 0.230


-----[ GB ]-----
maximum:
GB tn, fp: 290, 4
GB fn, tp: 7, 6
GB f1 score: 0.857
GB cohens kappa score: 0.854


average:
GB tn, fp: 287.88, 1.92
GB fn, tp: 4.0, 3.0
GB f1 score: 0.485
GB cohens kappa score: 0.476


minimum:
GB tn, fp: 286, 0
GB fn, tp: 1, 0
GB f1 score: 0.000
GB cohens kappa score: -0.006


-----[ KNN ]-----
maximum:
KNN tn, fp: 276, 33
KNN fn, tp: 3, 7
KNN f1 score: 0.414
KNN cohens kappa score: 0.392


average:
KNN tn, fp: 268.28, 21.52
KNN fn, tp: 1.28, 5.72
KNN f1 score: 0.339
KNN cohens kappa score: 0.313


minimum:
KNN tn, fp: 257, 13
KNN fn, tp: 0, 4
KNN f1 score: 0.270
KNN cohens kappa score: 0.241


-----[ GAN ]-----
maximum:
GAN tn, fp: 282, 75
GAN fn, tp: 5, 7
GAN f1 score: 0.421
GAN cohens kappa score: 0.403


average:
GAN tn, fp: 254.96, 34.84
GAN fn, tp: 2.6, 4.4
GAN f1 score: 0.221
GAN cohens kappa score: 0.190


minimum:
GAN tn, fp: 215, 8
GAN fn, tp: 0, 2
GAN f1 score: 0.103
GAN cohens kappa score: 0.066