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

///////////////////////////////////////////
// Running convGAN-majority-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: 269, 21
GAN fn, tp: 1, 6
GAN f1 score: 0.353
GAN cohens kappa score: 0.328

-> test with 'LR'
LR tn, fp: 262, 28
LR fn, tp: 0, 7
LR f1 score: 0.333
LR cohens kappa score: 0.306
LR average precision score: 0.691

-> 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: 265, 25
KNN fn, tp: 1, 6
KNN f1 score: 0.316
KNN cohens kappa score: 0.288


------ 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: 265, 25
GAN fn, tp: 2, 5
GAN f1 score: 0.270
GAN cohens kappa score: 0.241

-> 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: 0.432

-> 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: 266, 24
KNN fn, tp: 3, 4
KNN f1 score: 0.229
KNN cohens kappa score: 0.198


------ 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: 272, 18
GAN fn, tp: 1, 6
GAN f1 score: 0.387
GAN cohens kappa score: 0.364

-> test with 'LR'
LR tn, fp: 261, 29
LR fn, tp: 1, 6
LR f1 score: 0.286
LR cohens kappa score: 0.257
LR average precision score: 0.290

-> 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: 271, 19
KNN fn, tp: 1, 6
KNN f1 score: 0.375
KNN cohens kappa score: 0.351


------ 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: 277, 13
GAN fn, tp: 2, 5
GAN f1 score: 0.400
GAN cohens kappa score: 0.379

-> test with 'LR'
LR tn, fp: 269, 21
LR fn, tp: 2, 5
LR f1 score: 0.303
LR cohens kappa score: 0.276
LR average precision score: 0.552

-> 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: 274, 16
KNN fn, tp: 1, 6
KNN f1 score: 0.414
KNN cohens kappa score: 0.392


------ 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: 250, 39
GAN fn, tp: 0, 7
GAN f1 score: 0.264
GAN cohens kappa score: 0.233

-> 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: 0.554

-> test with 'GB'
GB tn, fp: 289, 0
GB fn, tp: 3, 4
GB f1 score: 0.727
GB cohens kappa score: 0.722

-> test with 'KNN'
KNN tn, fp: 261, 28
KNN fn, tp: 1, 6
KNN f1 score: 0.293
KNN cohens kappa score: 0.264


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

-> test with 'LR'
LR tn, fp: 264, 26
LR fn, tp: 0, 7
LR f1 score: 0.350
LR cohens kappa score: 0.324
LR average precision score: 0.678

-> 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: 273, 17
KNN fn, tp: 1, 6
KNN f1 score: 0.400
KNN cohens kappa score: 0.378


------ 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: 257, 33
GAN fn, tp: 0, 7
GAN f1 score: 0.298
GAN cohens kappa score: 0.269

-> test with 'LR'
LR tn, fp: 251, 39
LR fn, tp: 0, 7
LR f1 score: 0.264
LR cohens kappa score: 0.233
LR average precision score: 0.245

-> 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: 262, 28
KNN fn, tp: 0, 7
KNN f1 score: 0.333
KNN cohens kappa score: 0.306


------ 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: 265, 25
GAN fn, tp: 1, 6
GAN f1 score: 0.316
GAN cohens kappa score: 0.288

-> test with 'LR'
LR tn, fp: 261, 29
LR fn, tp: 1, 6
LR f1 score: 0.286
LR cohens kappa score: 0.257
LR average precision score: 0.511

-> 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: 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: 269, 21
GAN fn, tp: 2, 5
GAN f1 score: 0.303
GAN cohens kappa score: 0.276

-> test with 'LR'
LR tn, fp: 260, 30
LR fn, tp: 2, 5
LR f1 score: 0.238
LR cohens kappa score: 0.207
LR average precision score: 0.557

-> 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: 1, 6
KNN f1 score: 0.353
KNN cohens kappa score: 0.328


------ 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: 272, 17
GAN fn, tp: 1, 6
GAN f1 score: 0.400
GAN cohens kappa score: 0.377

-> test with 'LR'
LR tn, fp: 269, 20
LR fn, tp: 1, 6
LR f1 score: 0.364
LR cohens kappa score: 0.339
LR average precision score: 0.527

-> 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: 274, 15
KNN fn, tp: 2, 5
KNN f1 score: 0.370
KNN cohens kappa score: 0.348


====== 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: 265, 25
GAN fn, tp: 1, 6
GAN f1 score: 0.316
GAN cohens kappa score: 0.288

-> test with 'LR'
LR tn, fp: 261, 29
LR fn, tp: 1, 6
LR f1 score: 0.286
LR cohens kappa score: 0.257
LR average precision score: 0.636

-> 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: 267, 23
KNN fn, tp: 1, 6
KNN f1 score: 0.333
KNN cohens kappa score: 0.307


------ 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: 262, 28
GAN fn, tp: 0, 7
GAN f1 score: 0.333
GAN cohens kappa score: 0.306

-> test with 'LR'
LR tn, fp: 247, 43
LR fn, tp: 0, 7
LR f1 score: 0.246
LR cohens kappa score: 0.213
LR average precision score: 0.813

-> 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: 260, 30
KNN fn, tp: 0, 7
KNN f1 score: 0.318
KNN cohens kappa score: 0.290


------ 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: 275, 15
GAN fn, tp: 2, 5
GAN f1 score: 0.370
GAN cohens kappa score: 0.348

-> test with 'LR'
LR tn, fp: 267, 23
LR fn, tp: 2, 5
LR f1 score: 0.286
LR cohens kappa score: 0.258
LR average precision score: 0.437

-> 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: 275, 15
KNN fn, tp: 3, 4
KNN f1 score: 0.308
KNN cohens kappa score: 0.283


------ 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: 262, 28
GAN fn, tp: 1, 6
GAN f1 score: 0.293
GAN cohens kappa score: 0.264

-> 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: 0.383

-> 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: 262, 28
KNN fn, tp: 1, 6
KNN f1 score: 0.293
KNN cohens kappa score: 0.264


------ 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: 272, 17
GAN fn, tp: 1, 6
GAN f1 score: 0.400
GAN cohens kappa score: 0.377

-> 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: 0.405

-> 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: 270, 19
KNN fn, tp: 1, 6
KNN f1 score: 0.375
KNN cohens kappa score: 0.351


====== 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: 277, 13
GAN fn, tp: 1, 6
GAN f1 score: 0.462
GAN cohens kappa score: 0.442

-> test with 'LR'
LR tn, fp: 272, 18
LR fn, tp: 1, 6
LR f1 score: 0.387
LR cohens kappa score: 0.364
LR average precision score: 0.704

-> 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: 270, 20
KNN fn, tp: 1, 6
KNN f1 score: 0.364
KNN cohens kappa score: 0.339


------ 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: 270, 20
GAN fn, tp: 0, 7
GAN f1 score: 0.412
GAN cohens kappa score: 0.389

-> test with 'LR'
LR tn, fp: 259, 31
LR fn, tp: 0, 7
LR f1 score: 0.311
LR cohens kappa score: 0.283
LR average precision score: 0.246

-> 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: 275, 15
KNN fn, tp: 0, 7
KNN f1 score: 0.483
KNN cohens kappa score: 0.464


------ 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: 254, 36
GAN fn, tp: 1, 6
GAN f1 score: 0.245
GAN cohens kappa score: 0.213

-> test with 'LR'
LR tn, fp: 250, 40
LR fn, tp: 1, 6
LR f1 score: 0.226
LR cohens kappa score: 0.193
LR average precision score: 0.550

-> test with 'GB'
GB tn, fp: 287, 3
GB fn, tp: 2, 5
GB f1 score: 0.667
GB cohens kappa score: 0.658

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


------ 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: 272, 18
GAN fn, tp: 1, 6
GAN f1 score: 0.387
GAN cohens kappa score: 0.364

-> test with 'LR'
LR tn, fp: 268, 22
LR fn, tp: 1, 6
LR f1 score: 0.343
LR cohens kappa score: 0.317
LR average precision score: 0.652

-> 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: 277, 13
KNN fn, tp: 2, 5
KNN f1 score: 0.400
KNN cohens kappa score: 0.379


------ 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: 274, 15
GAN fn, tp: 2, 5
GAN f1 score: 0.370
GAN cohens kappa score: 0.348

-> test with 'LR'
LR tn, fp: 267, 22
LR fn, tp: 2, 5
LR f1 score: 0.294
LR cohens kappa score: 0.267
LR average precision score: 0.679

-> 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: 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 GAN.predict
GAN tn, fp: 263, 27
GAN fn, tp: 0, 7
GAN f1 score: 0.341
GAN cohens kappa score: 0.315

-> test with 'LR'
LR tn, fp: 249, 41
LR fn, tp: 0, 7
LR f1 score: 0.255
LR cohens kappa score: 0.223
LR average precision score: 0.500

-> 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: 2, 5
KNN f1 score: 0.233
KNN cohens kappa score: 0.201


------ 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: 275, 15
GAN fn, tp: 3, 4
GAN f1 score: 0.308
GAN cohens kappa score: 0.283

-> test with 'LR'
LR tn, fp: 266, 24
LR fn, tp: 3, 4
LR f1 score: 0.229
LR cohens kappa score: 0.198
LR average precision score: 0.217

-> 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: 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 GAN.predict
GAN tn, fp: 261, 29
GAN fn, tp: 0, 7
GAN f1 score: 0.326
GAN cohens kappa score: 0.298

-> test with 'LR'
LR tn, fp: 262, 28
LR fn, tp: 0, 7
LR f1 score: 0.333
LR cohens kappa score: 0.306
LR average precision score: 0.713

-> test with 'GB'
GB tn, fp: 288, 2
GB fn, tp: 1, 6
GB f1 score: 0.800
GB cohens kappa score: 0.795

-> test with 'KNN'
KNN tn, fp: 267, 23
KNN fn, tp: 0, 7
KNN f1 score: 0.378
KNN cohens kappa score: 0.354


------ 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: 269, 21
GAN fn, tp: 1, 6
GAN f1 score: 0.353
GAN cohens kappa score: 0.328

-> 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: 0.285

-> test with 'GB'
GB tn, fp: 289, 1
GB fn, tp: 5, 2
GB f1 score: 0.400
GB cohens kappa score: 0.391

-> 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 5/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1132 synthetic samples
-> test with GAN.predict
GAN tn, fp: 272, 17
GAN fn, tp: 2, 5
GAN f1 score: 0.345
GAN cohens kappa score: 0.320

-> test with 'LR'
LR tn, fp: 272, 17
LR fn, tp: 2, 5
LR f1 score: 0.345
LR cohens kappa score: 0.320
LR average precision score: 0.418

-> test with 'GB'
GB tn, fp: 286, 3
GB fn, tp: 5, 2
GB f1 score: 0.333
GB cohens kappa score: 0.320

-> test with 'KNN'
KNN tn, fp: 280, 9
KNN fn, tp: 2, 5
KNN f1 score: 0.476
KNN cohens kappa score: 0.459

### Exercise is done.

-----[ LR ]-----
maximum:
LR tn, fp: 272, 44
LR fn, tp: 3, 7
LR f1 score: 0.387
LR cohens kappa score: 0.364
LR average precision score: 0.813


average:
LR tn, fp: 261.04, 28.76
LR fn, tp: 0.92, 6.08
LR f1 score: 0.296
LR cohens kappa score: 0.268
LR average precision score: 0.507


minimum:
LR tn, fp: 245, 17
LR fn, tp: 0, 4
LR f1 score: 0.226
LR cohens kappa score: 0.193
LR average precision score: 0.217


-----[ GB ]-----
maximum:
GB tn, fp: 290, 5
GB fn, tp: 7, 6
GB f1 score: 0.800
GB cohens kappa score: 0.795


average:
GB tn, fp: 287.72, 2.08
GB fn, tp: 3.92, 3.08
GB f1 score: 0.493
GB cohens kappa score: 0.483


minimum:
GB tn, fp: 285, 0
GB fn, tp: 1, 0
GB f1 score: 0.000
GB cohens kappa score: -0.006


-----[ KNN ]-----
maximum:
KNN tn, fp: 280, 38
KNN fn, tp: 3, 7
KNN f1 score: 0.483
KNN cohens kappa score: 0.464


average:
KNN tn, fp: 268.6, 21.2
KNN fn, tp: 1.32, 5.68
KNN f1 score: 0.344
KNN cohens kappa score: 0.318


minimum:
KNN tn, fp: 252, 9
KNN fn, tp: 0, 4
KNN f1 score: 0.229
KNN cohens kappa score: 0.198


-----[ GAN ]-----
maximum:
GAN tn, fp: 277, 39
GAN fn, tp: 3, 7
GAN f1 score: 0.462
GAN cohens kappa score: 0.442


average:
GAN tn, fp: 267.48, 22.32
GAN fn, tp: 1.08, 5.92
GAN f1 score: 0.344
GAN cohens kappa score: 0.318


minimum:
GAN tn, fp: 250, 13
GAN fn, tp: 0, 4
GAN f1 score: 0.245
GAN cohens kappa score: 0.213