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

///////////////////////////////////////////
// Running convGAN-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 'LR'
LR tn, fp: 270, 20
LR fn, tp: 1, 6
LR f1 score: 0.364
LR cohens kappa score: 0.339
LR average precision score: 0.664

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


------ Step 1/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1131 synthetic samples
-> 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: 0.425

-> 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: 2, 5
KNN f1 score: 0.323
KNN cohens kappa score: 0.297


------ Step 1/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1131 synthetic samples
-> 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: 0.319

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


------ Step 1/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1131 synthetic samples
-> 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: 0.615

-> 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: 275, 15
KNN fn, tp: 1, 6
KNN f1 score: 0.429
KNN cohens kappa score: 0.408


------ Step 1/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1132 synthetic samples
-> test with 'LR'
LR tn, fp: 250, 39
LR fn, tp: 0, 7
LR f1 score: 0.264
LR cohens kappa score: 0.233
LR average precision score: 0.655

-> 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: 268, 21
KNN fn, tp: 1, 6
KNN f1 score: 0.353
KNN cohens kappa score: 0.328


====== 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 '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: 0.677

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


------ Step 2/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1131 synthetic samples
-> 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: 0.261

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


------ Step 2/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1131 synthetic samples
-> 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: 0.513

-> 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: 1, 6
KNN f1 score: 0.400
KNN cohens kappa score: 0.378


------ Step 2/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1131 synthetic samples
-> test with 'LR'
LR tn, fp: 266, 24
LR fn, tp: 2, 5
LR f1 score: 0.278
LR cohens kappa score: 0.249
LR average precision score: 0.625

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


------ Step 2/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1132 synthetic samples
-> test with 'LR'
LR tn, fp: 270, 19
LR fn, tp: 1, 6
LR f1 score: 0.375
LR cohens kappa score: 0.351
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: 2, 5
KNN f1 score: 0.455
KNN cohens kappa score: 0.436


====== 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 '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: 0.655

-> 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: 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 '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: 0.827

-> test with 'GB'
GB tn, fp: 290, 0
GB fn, tp: 4, 3
GB f1 score: 0.600
GB cohens kappa score: 0.594

-> 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 '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: 0.418

-> 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: 281, 9
KNN fn, tp: 3, 4
KNN f1 score: 0.400
KNN cohens kappa score: 0.381


------ Step 3/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1131 synthetic samples
-> 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: 0.394

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


------ Step 3/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1132 synthetic samples
-> 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: 0.381

-> 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: 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 'LR'
LR tn, fp: 276, 14
LR fn, tp: 1, 6
LR f1 score: 0.444
LR cohens kappa score: 0.424
LR average precision score: 0.741

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


------ Step 4/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1131 synthetic samples
-> 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: 0.256

-> test with 'GB'
GB tn, fp: 284, 6
GB fn, tp: 3, 4
GB f1 score: 0.471
GB cohens kappa score: 0.455

-> test with 'KNN'
KNN tn, fp: 276, 14
KNN fn, tp: 0, 7
KNN f1 score: 0.500
KNN cohens kappa score: 0.482


------ Step 4/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1131 synthetic samples
-> test with 'LR'
LR tn, fp: 256, 34
LR fn, tp: 1, 6
LR f1 score: 0.255
LR cohens kappa score: 0.224
LR average precision score: 0.630

-> 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: 263, 27
KNN fn, tp: 0, 7
KNN f1 score: 0.341
KNN cohens kappa score: 0.315


------ Step 4/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1131 synthetic samples
-> 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: 0.649

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

-> test with 'KNN'
KNN tn, fp: 278, 11
KNN fn, tp: 2, 5
KNN f1 score: 0.435
KNN cohens kappa score: 0.416


====== 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 'LR'
LR tn, fp: 261, 29
LR fn, tp: 0, 7
LR f1 score: 0.326
LR cohens kappa score: 0.298
LR average precision score: 0.510

-> 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: 264, 26
KNN fn, tp: 1, 6
KNN f1 score: 0.308
KNN cohens kappa score: 0.280


------ Step 5/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1131 synthetic samples
-> 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: 0.214

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


------ Step 5/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1131 synthetic samples
-> test with 'LR'
LR tn, fp: 266, 24
LR fn, tp: 0, 7
LR f1 score: 0.368
LR cohens kappa score: 0.343
LR average precision score: 0.759

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


------ Step 5/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1131 synthetic samples
-> 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: 0.522

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


------ Step 5/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1132 synthetic samples
-> 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: 0.440

-> 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: 278, 11
KNN fn, tp: 2, 5
KNN f1 score: 0.435
KNN cohens kappa score: 0.416

### Exercise is done.

-----[ LR ]-----
maximum:
LR tn, fp: 276, 39
LR fn, tp: 3, 7
LR f1 score: 0.444
LR cohens kappa score: 0.424
LR average precision score: 0.827


average:
LR tn, fp: 265.56, 24.24
LR fn, tp: 1.16, 5.84
LR f1 score: 0.321
LR cohens kappa score: 0.294
LR average precision score: 0.533


minimum:
LR tn, fp: 250, 14
LR fn, tp: 0, 4
LR f1 score: 0.255
LR cohens kappa score: 0.224
LR average precision score: 0.214


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


average:
GB tn, fp: 287.84, 1.96
GB fn, tp: 4.0, 3.0
GB f1 score: 0.487
GB cohens kappa score: 0.478


minimum:
GB tn, fp: 284, 0
GB fn, tp: 1, 0
GB f1 score: 0.000
GB cohens kappa score: -0.006


-----[ KNN ]-----
maximum:
KNN tn, fp: 282, 30
KNN fn, tp: 3, 7
KNN f1 score: 0.600
KNN cohens kappa score: 0.587


average:
KNN tn, fp: 273.28, 16.52
KNN fn, tp: 1.24, 5.76
KNN f1 score: 0.404
KNN cohens kappa score: 0.382


minimum:
KNN tn, fp: 260, 7
KNN fn, tp: 0, 4
KNN f1 score: 0.308
KNN cohens kappa score: 0.280