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

///////////////////////////////////////////
// Running convGAN-proximary-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: 280, 10
GAN fn, tp: 1, 6
GAN f1 score: 0.522
GAN cohens kappa score: 0.506

-> 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: 0.692

-> 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 1/5: Slice 2/5 -------
-> 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: 269, 21
LR fn, tp: 2, 5
LR f1 score: 0.303
LR cohens kappa score: 0.276
LR average precision score: 0.428

-> 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: 2, 5
KNN f1 score: 0.345
KNN cohens kappa score: 0.321


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

-> 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: 0.336

-> 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: 278, 12
KNN fn, tp: 1, 6
KNN f1 score: 0.480
KNN cohens kappa score: 0.462


------ 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: 278, 12
GAN fn, tp: 2, 5
GAN f1 score: 0.417
GAN cohens kappa score: 0.397

-> test with 'LR'
LR tn, fp: 273, 17
LR fn, tp: 1, 6
LR f1 score: 0.400
LR cohens kappa score: 0.378
LR average precision score: 0.613

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


------ 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: 273, 16
GAN fn, tp: 1, 6
GAN f1 score: 0.414
GAN cohens kappa score: 0.392

-> test with 'LR'
LR tn, fp: 253, 36
LR fn, tp: 0, 7
LR f1 score: 0.280
LR cohens kappa score: 0.249
LR average precision score: 0.634

-> test with 'GB'
GB tn, fp: 285, 4
GB fn, tp: 2, 5
GB f1 score: 0.625
GB cohens kappa score: 0.615

-> test with 'KNN'
KNN tn, fp: 263, 26
KNN fn, tp: 0, 7
KNN f1 score: 0.350
KNN cohens kappa score: 0.324


====== 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: 274, 16
GAN fn, tp: 1, 6
GAN f1 score: 0.414
GAN cohens kappa score: 0.392

-> test with 'LR'
LR tn, fp: 273, 17
LR fn, tp: 1, 6
LR f1 score: 0.400
LR cohens kappa score: 0.378
LR average precision score: 0.666

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

-> 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: 0.250

-> 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: 271, 19
KNN fn, tp: 0, 7
KNN f1 score: 0.424
KNN cohens kappa score: 0.402


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

-> 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: 0.529

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


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

-> test with 'LR'
LR tn, fp: 263, 27
LR fn, tp: 2, 5
LR f1 score: 0.256
LR cohens kappa score: 0.226
LR average precision score: 0.529

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


------ 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: 3, 4
GAN f1 score: 0.381
GAN cohens kappa score: 0.361

-> 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: 0.507

-> 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: 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: 267, 23
LR fn, tp: 1, 6
LR f1 score: 0.333
LR cohens kappa score: 0.307
LR average precision score: 0.636

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

-> 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: 0.743

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


------ 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: 274, 16
GAN fn, tp: 3, 4
GAN f1 score: 0.296
GAN cohens kappa score: 0.271

-> test with 'LR'
LR tn, fp: 270, 20
LR fn, tp: 2, 5
LR f1 score: 0.312
LR cohens kappa score: 0.286
LR average precision score: 0.424

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


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

-> test with 'LR'
LR tn, fp: 264, 26
LR fn, tp: 1, 6
LR f1 score: 0.308
LR cohens kappa score: 0.280
LR average precision score: 0.374

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

-> 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 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: 276, 13
LR fn, tp: 2, 5
LR f1 score: 0.400
LR cohens kappa score: 0.379
LR average precision score: 0.514

-> 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: 282, 7
KNN fn, tp: 1, 6
KNN f1 score: 0.600
KNN cohens kappa score: 0.587


====== 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: 282, 8
GAN fn, tp: 3, 4
GAN f1 score: 0.421
GAN cohens kappa score: 0.403

-> test with 'LR'
LR tn, fp: 274, 16
LR fn, tp: 1, 6
LR f1 score: 0.414
LR cohens kappa score: 0.392
LR average precision score: 0.682

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


------ 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: 283, 7
GAN fn, tp: 1, 6
GAN f1 score: 0.600
GAN cohens kappa score: 0.587

-> 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: 0.288

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


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

-> 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: 0.551

-> test with 'GB'
GB tn, fp: 286, 4
GB fn, tp: 1, 6
GB f1 score: 0.706
GB cohens kappa score: 0.697

-> 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 4/5: Slice 4/5 -------
-> 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: 271, 19
LR fn, tp: 1, 6
LR f1 score: 0.375
LR cohens kappa score: 0.351
LR average precision score: 0.649

-> 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: 278, 12
KNN fn, tp: 2, 5
KNN f1 score: 0.417
KNN cohens kappa score: 0.397


------ 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: 282, 7
GAN fn, tp: 2, 5
GAN f1 score: 0.526
GAN cohens kappa score: 0.512

-> 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: 0.648

-> 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: 279, 10
KNN fn, tp: 2, 5
KNN f1 score: 0.455
KNN cohens kappa score: 0.436


====== 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: 274, 16
GAN fn, tp: 1, 6
GAN f1 score: 0.414
GAN cohens kappa score: 0.392

-> test with 'LR'
LR tn, fp: 269, 21
LR fn, tp: 0, 7
LR f1 score: 0.400
LR cohens kappa score: 0.376
LR average precision score: 0.514

-> 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 5/5: Slice 2/5 -------
-> 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: 263, 27
LR fn, tp: 3, 4
LR f1 score: 0.211
LR cohens kappa score: 0.179
LR average precision score: 0.228

-> 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: 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: 269, 21
GAN fn, tp: 0, 7
GAN f1 score: 0.400
GAN cohens kappa score: 0.376

-> 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: 0.698

-> test with 'GB'
GB tn, fp: 286, 4
GB fn, tp: 0, 7
GB f1 score: 0.778
GB cohens kappa score: 0.771

-> test with 'KNN'
KNN tn, fp: 269, 21
KNN fn, tp: 0, 7
KNN f1 score: 0.400
KNN cohens kappa score: 0.376


------ 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: 277, 13
GAN fn, tp: 4, 3
GAN f1 score: 0.261
GAN cohens kappa score: 0.236

-> 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: 0.325

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


------ 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: 270, 19
GAN fn, tp: 5, 2
GAN f1 score: 0.143
GAN cohens kappa score: 0.111

-> test with 'LR'
LR tn, fp: 271, 18
LR fn, tp: 2, 5
LR f1 score: 0.333
LR cohens kappa score: 0.308
LR average precision score: 0.429

-> 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: 269, 20
KNN fn, tp: 2, 5
KNN f1 score: 0.312
KNN cohens kappa score: 0.286

### Exercise is done.

-----[ LR ]-----
maximum:
LR tn, fp: 276, 39
LR fn, tp: 3, 7
LR f1 score: 0.414
LR cohens kappa score: 0.392
LR average precision score: 0.743


average:
LR tn, fp: 265.68, 24.12
LR fn, tp: 1.08, 5.92
LR f1 score: 0.326
LR cohens kappa score: 0.300
LR average precision score: 0.515


minimum:
LR tn, fp: 251, 13
LR fn, tp: 0, 4
LR f1 score: 0.211
LR cohens kappa score: 0.179
LR average precision score: 0.228


-----[ GB ]-----
maximum:
GB tn, fp: 289, 9
GB fn, tp: 5, 7
GB f1 score: 0.778
GB cohens kappa score: 0.771


average:
GB tn, fp: 286.48, 3.32
GB fn, tp: 3.2, 3.8
GB f1 score: 0.535
GB cohens kappa score: 0.524


minimum:
GB tn, fp: 281, 1
GB fn, tp: 0, 2
GB f1 score: 0.333
GB cohens kappa score: 0.320


-----[ KNN ]-----
maximum:
KNN tn, fp: 282, 26
KNN fn, tp: 3, 7
KNN f1 score: 0.600
KNN cohens kappa score: 0.587


average:
KNN tn, fp: 272.88, 16.92
KNN fn, tp: 1.24, 5.76
KNN f1 score: 0.394
KNN cohens kappa score: 0.371


minimum:
KNN tn, fp: 263, 7
KNN fn, tp: 0, 4
KNN f1 score: 0.296
KNN cohens kappa score: 0.271


-----[ GAN ]-----
maximum:
GAN tn, fp: 283, 25
GAN fn, tp: 5, 7
GAN f1 score: 0.600
GAN cohens kappa score: 0.587


average:
GAN tn, fp: 275.12, 14.68
GAN fn, tp: 1.96, 5.04
GAN f1 score: 0.388
GAN cohens kappa score: 0.366


minimum:
GAN tn, fp: 265, 7
GAN fn, tp: 0, 2
GAN f1 score: 0.143
GAN cohens kappa score: 0.111