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

///////////////////////////////////////////
// Running convGAN-proxymary-full 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: 284, 6
GAN fn, tp: 2, 5
GAN f1 score: 0.556
GAN cohens kappa score: 0.542

-> 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: 0.689

-> 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: 274, 16
GAN fn, tp: 2, 5
GAN f1 score: 0.357
GAN cohens kappa score: 0.334

-> 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: 0.428

-> 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: 273, 17
KNN fn, tp: 3, 4
KNN f1 score: 0.286
KNN cohens kappa score: 0.260


------ 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: 279, 11
GAN fn, tp: 6, 1
GAN f1 score: 0.105
GAN cohens kappa score: 0.078

-> test with 'LR'
LR tn, fp: 263, 27
LR fn, tp: 1, 6
LR f1 score: 0.300
LR cohens kappa score: 0.272
LR average precision score: 0.305

-> 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: 277, 13
KNN fn, tp: 1, 6
KNN f1 score: 0.462
KNN cohens kappa score: 0.442


------ 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: 268, 22
GAN fn, tp: 3, 4
GAN f1 score: 0.242
GAN cohens kappa score: 0.213

-> test with 'LR'
LR tn, fp: 271, 19
LR fn, tp: 2, 5
LR f1 score: 0.323
LR cohens kappa score: 0.297
LR average precision score: 0.614

-> 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: 281, 9
KNN fn, tp: 1, 6
KNN f1 score: 0.545
KNN cohens kappa score: 0.530


------ 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: 275, 14
GAN fn, tp: 2, 5
GAN f1 score: 0.385
GAN cohens kappa score: 0.363

-> test with 'LR'
LR tn, fp: 254, 35
LR fn, tp: 0, 7
LR f1 score: 0.286
LR cohens kappa score: 0.256
LR average precision score: 0.704

-> 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: 265, 24
KNN fn, tp: 0, 7
KNN f1 score: 0.368
KNN cohens kappa score: 0.343


====== 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: 264, 26
GAN fn, tp: 2, 5
GAN f1 score: 0.263
GAN cohens kappa score: 0.234

-> 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: 0.669

-> 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: 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 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: 262, 28
LR fn, tp: 0, 7
LR f1 score: 0.333
LR cohens kappa score: 0.306
LR average precision score: 0.346

-> 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: 268, 22
KNN fn, tp: 0, 7
KNN f1 score: 0.389
KNN cohens kappa score: 0.365


------ 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: 281, 9
GAN fn, tp: 2, 5
GAN f1 score: 0.476
GAN cohens kappa score: 0.459

-> 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: 0.525

-> 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: 273, 17
KNN fn, tp: 2, 5
KNN f1 score: 0.345
KNN cohens kappa score: 0.321


------ 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: 279, 11
GAN fn, tp: 3, 4
GAN f1 score: 0.364
GAN cohens kappa score: 0.343

-> 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: 0.594

-> 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: 272, 18
KNN fn, tp: 2, 5
KNN f1 score: 0.333
KNN cohens kappa score: 0.308


------ 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: 280, 9
GAN fn, tp: 4, 3
GAN f1 score: 0.316
GAN cohens kappa score: 0.295

-> 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: 0.524

-> 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: 279, 10
KNN fn, tp: 3, 4
KNN f1 score: 0.381
KNN cohens kappa score: 0.361


====== 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: 274, 16
GAN fn, tp: 5, 2
GAN f1 score: 0.160
GAN cohens kappa score: 0.130

-> 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: 0.648

-> 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: 276, 14
KNN fn, tp: 1, 6
KNN f1 score: 0.444
KNN cohens kappa score: 0.424


------ 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: 283, 7
GAN fn, tp: 4, 3
GAN f1 score: 0.353
GAN cohens kappa score: 0.334

-> 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: 0.782

-> 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: 264, 26
KNN fn, tp: 0, 7
KNN f1 score: 0.350
KNN cohens kappa score: 0.324


------ 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: 272, 18
GAN fn, tp: 4, 3
GAN f1 score: 0.214
GAN cohens kappa score: 0.185

-> test with 'LR'
LR tn, fp: 273, 17
LR fn, tp: 2, 5
LR f1 score: 0.345
LR cohens kappa score: 0.321
LR average precision score: 0.448

-> 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: 277, 13
KNN fn, tp: 3, 4
KNN f1 score: 0.333
KNN cohens kappa score: 0.310


------ 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: 266, 24
GAN fn, tp: 2, 5
GAN f1 score: 0.278
GAN cohens kappa score: 0.249

-> 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: 0.420

-> 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: 268, 22
KNN fn, tp: 1, 6
KNN f1 score: 0.343
KNN cohens kappa score: 0.317


------ 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: 281, 8
GAN fn, tp: 3, 4
GAN f1 score: 0.421
GAN cohens kappa score: 0.403

-> test with 'LR'
LR tn, fp: 274, 15
LR fn, tp: 2, 5
LR f1 score: 0.370
LR cohens kappa score: 0.348
LR average precision score: 0.456

-> 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: 281, 8
KNN fn, tp: 1, 6
KNN f1 score: 0.571
KNN cohens kappa score: 0.557


====== 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: 280, 10
GAN fn, tp: 2, 5
GAN f1 score: 0.455
GAN cohens kappa score: 0.436

-> test with 'LR'
LR tn, fp: 275, 15
LR fn, tp: 1, 6
LR f1 score: 0.429
LR cohens kappa score: 0.408
LR average precision score: 0.662

-> test with 'GB'
GB tn, fp: 289, 1
GB fn, tp: 2, 5
GB f1 score: 0.769
GB cohens kappa score: 0.764

-> 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 4/5: 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: 264, 26
LR fn, tp: 0, 7
LR f1 score: 0.350
LR cohens kappa score: 0.324
LR average precision score: 0.248

-> test with 'GB'
GB tn, fp: 286, 4
GB fn, tp: 5, 2
GB f1 score: 0.308
GB cohens kappa score: 0.292

-> test with 'KNN'
KNN tn, fp: 275, 15
KNN fn, tp: 1, 6
KNN f1 score: 0.429
KNN cohens kappa score: 0.408


------ 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: 279, 11
GAN fn, tp: 2, 5
GAN f1 score: 0.435
GAN cohens kappa score: 0.416

-> 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: 0.549

-> test with 'GB'
GB tn, fp: 286, 4
GB fn, tp: 2, 5
GB f1 score: 0.625
GB cohens kappa score: 0.615

-> 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 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1131 synthetic samples
-> test with GAN.predict
GAN tn, fp: 280, 10
GAN fn, tp: 3, 4
GAN f1 score: 0.381
GAN cohens kappa score: 0.361

-> 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: 0.640

-> 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: 279, 11
KNN fn, tp: 2, 5
KNN f1 score: 0.435
KNN cohens kappa score: 0.416


------ 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: 280, 9
GAN fn, tp: 4, 3
GAN f1 score: 0.316
GAN cohens kappa score: 0.295

-> test with 'LR'
LR tn, fp: 274, 15
LR fn, tp: 2, 5
LR f1 score: 0.370
LR cohens kappa score: 0.348
LR average precision score: 0.675

-> test with 'GB'
GB tn, fp: 287, 2
GB fn, tp: 4, 3
GB f1 score: 0.500
GB cohens kappa score: 0.490

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


====== 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: 280, 10
GAN fn, tp: 2, 5
GAN f1 score: 0.455
GAN cohens kappa score: 0.436

-> test with 'LR'
LR tn, fp: 268, 22
LR fn, tp: 0, 7
LR f1 score: 0.389
LR cohens kappa score: 0.365
LR average precision score: 0.504

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


------ 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: 278, 12
GAN fn, tp: 3, 4
GAN f1 score: 0.348
GAN cohens kappa score: 0.326

-> test with 'LR'
LR tn, fp: 270, 20
LR fn, tp: 3, 4
LR f1 score: 0.258
LR cohens kappa score: 0.230
LR average precision score: 0.223

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


------ 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: 279, 11
GAN fn, tp: 0, 7
GAN f1 score: 0.560
GAN cohens kappa score: 0.545

-> test with 'LR'
LR tn, fp: 268, 22
LR fn, tp: 0, 7
LR f1 score: 0.389
LR cohens kappa score: 0.365
LR average precision score: 0.754

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


------ 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: 247, 43
GAN fn, tp: 3, 4
GAN f1 score: 0.148
GAN cohens kappa score: 0.112

-> 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: 0.543

-> 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: 280, 10
KNN fn, tp: 1, 6
KNN f1 score: 0.522
KNN cohens kappa score: 0.506


------ 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: 278, 11
GAN fn, tp: 4, 3
GAN f1 score: 0.286
GAN cohens kappa score: 0.262

-> test with 'LR'
LR tn, fp: 273, 16
LR fn, tp: 2, 5
LR f1 score: 0.357
LR cohens kappa score: 0.334
LR average precision score: 0.441

-> test with 'GB'
GB tn, fp: 288, 1
GB fn, tp: 5, 2
GB f1 score: 0.400
GB cohens kappa score: 0.391

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

### Exercise is done.

-----[ LR ]-----
maximum:
LR tn, fp: 275, 35
LR fn, tp: 3, 7
LR f1 score: 0.429
LR cohens kappa score: 0.408
LR average precision score: 0.782


average:
LR tn, fp: 267.04, 22.76
LR fn, tp: 1.12, 5.88
LR f1 score: 0.334
LR cohens kappa score: 0.308
LR average precision score: 0.536


minimum:
LR tn, fp: 254, 15
LR fn, tp: 0, 4
LR f1 score: 0.244
LR cohens kappa score: 0.213
LR average precision score: 0.223


-----[ 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: 287.48, 2.32
GB fn, tp: 3.84, 3.16
GB f1 score: 0.484
GB cohens kappa score: 0.475


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: 281, 26
KNN fn, tp: 3, 7
KNN f1 score: 0.571
KNN cohens kappa score: 0.557


average:
KNN tn, fp: 274.16, 15.64
KNN fn, tp: 1.36, 5.64
KNN f1 score: 0.406
KNN cohens kappa score: 0.384


minimum:
KNN tn, fp: 264, 8
KNN fn, tp: 0, 4
KNN f1 score: 0.286
KNN cohens kappa score: 0.260


-----[ GAN ]-----
maximum:
GAN tn, fp: 284, 43
GAN fn, tp: 6, 7
GAN f1 score: 0.560
GAN cohens kappa score: 0.545


average:
GAN tn, fp: 275.64, 14.16
GAN fn, tp: 2.92, 4.08
GAN f1 score: 0.340
GAN cohens kappa score: 0.317


minimum:
GAN tn, fp: 247, 6
GAN fn, tp: 0, 1
GAN f1 score: 0.105
GAN cohens kappa score: 0.078