ConvGeNBenchmarkResults/data_result/ctGAN/folding_yeast5.log

///////////////////////////////////////////
// Running ctGAN on folding_yeast5
///////////////////////////////////////////

Load 'data_input/folding_yeast5'
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 1117 synthetic samples
-> test with 'LR'
LR tn, fp: 269, 19
LR fn, tp: 1, 8
LR f1 score: 0.444
LR cohens kappa score: 0.418
LR average precision score: 0.906

-> test with 'GB'
GB tn, fp: 281, 7
GB fn, tp: 3, 6
GB f1 score: 0.545
GB cohens kappa score: 0.529

-> test with 'KNN'
KNN tn, fp: 276, 12
KNN fn, tp: 1, 8
KNN f1 score: 0.552
KNN cohens kappa score: 0.532


------ Step 1/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1117 synthetic samples
-> test with 'LR'
LR tn, fp: 281, 7
LR fn, tp: 2, 7
LR f1 score: 0.609
LR cohens kappa score: 0.594
LR average precision score: 0.709

-> test with 'GB'
GB tn, fp: 283, 5
GB fn, tp: 1, 8
GB f1 score: 0.727
GB cohens kappa score: 0.717

-> test with 'KNN'
KNN tn, fp: 283, 5
KNN fn, tp: 1, 8
KNN f1 score: 0.727
KNN cohens kappa score: 0.717


------ Step 1/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1117 synthetic samples
-> test with 'LR'
LR tn, fp: 270, 18
LR fn, tp: 0, 9
LR f1 score: 0.500
LR cohens kappa score: 0.476
LR average precision score: 0.576

-> test with 'GB'
GB tn, fp: 278, 10
GB fn, tp: 2, 7
GB f1 score: 0.538
GB cohens kappa score: 0.519

-> test with 'KNN'
KNN tn, fp: 275, 13
KNN fn, tp: 0, 9
KNN f1 score: 0.581
KNN cohens kappa score: 0.562


------ Step 1/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1117 synthetic samples
-> test with 'LR'
LR tn, fp: 268, 20
LR fn, tp: 0, 9
LR f1 score: 0.474
LR cohens kappa score: 0.448
LR average precision score: 0.604

-> test with 'GB'
GB tn, fp: 280, 8
GB fn, tp: 3, 6
GB f1 score: 0.522
GB cohens kappa score: 0.503

-> test with 'KNN'
KNN tn, fp: 275, 13
KNN fn, tp: 1, 8
KNN f1 score: 0.533
KNN cohens kappa score: 0.513


------ Step 1/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1116 synthetic samples
-> test with 'LR'
LR tn, fp: 273, 15
LR fn, tp: 0, 8
LR f1 score: 0.516
LR cohens kappa score: 0.496
LR average precision score: 0.500

-> test with 'GB'
GB tn, fp: 282, 6
GB fn, tp: 1, 7
GB f1 score: 0.667
GB cohens kappa score: 0.655

-> test with 'KNN'
KNN tn, fp: 277, 11
KNN fn, tp: 1, 7
KNN f1 score: 0.538
KNN cohens kappa score: 0.521


====== Step 2/5 =======
-> Shuffling data
-> Spliting data to slices

------ Step 2/5: Slice 1/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1117 synthetic samples
-> test with 'LR'
LR tn, fp: 271, 17
LR fn, tp: 0, 9
LR f1 score: 0.514
LR cohens kappa score: 0.491
LR average precision score: 0.748

-> test with 'GB'
GB tn, fp: 280, 8
GB fn, tp: 0, 9
GB f1 score: 0.692
GB cohens kappa score: 0.680

-> test with 'KNN'
KNN tn, fp: 273, 15
KNN fn, tp: 0, 9
KNN f1 score: 0.545
KNN cohens kappa score: 0.524


------ Step 2/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1117 synthetic samples
-> test with 'LR'
LR tn, fp: 275, 13
LR fn, tp: 4, 5
LR f1 score: 0.370
LR cohens kappa score: 0.344
LR average precision score: 0.409

-> test with 'GB'
GB tn, fp: 276, 12
GB fn, tp: 3, 6
GB f1 score: 0.444
GB cohens kappa score: 0.421

-> test with 'KNN'
KNN tn, fp: 276, 12
KNN fn, tp: 0, 9
KNN f1 score: 0.600
KNN cohens kappa score: 0.582


------ Step 2/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1117 synthetic samples
-> test with 'LR'
LR tn, fp: 282, 6
LR fn, tp: 0, 9
LR f1 score: 0.750
LR cohens kappa score: 0.740
LR average precision score: 0.895

-> test with 'GB'
GB tn, fp: 287, 1
GB fn, tp: 2, 7
GB f1 score: 0.824
GB cohens kappa score: 0.818

-> test with 'KNN'
KNN tn, fp: 284, 4
KNN fn, tp: 1, 8
KNN f1 score: 0.762
KNN cohens kappa score: 0.753


------ Step 2/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1117 synthetic samples
-> test with 'LR'
LR tn, fp: 275, 13
LR fn, tp: 0, 9
LR f1 score: 0.581
LR cohens kappa score: 0.562
LR average precision score: 0.647

-> test with 'GB'
GB tn, fp: 280, 8
GB fn, tp: 1, 8
GB f1 score: 0.640
GB cohens kappa score: 0.625

-> test with 'KNN'
KNN tn, fp: 278, 10
KNN fn, tp: 1, 8
KNN f1 score: 0.593
KNN cohens kappa score: 0.575


------ Step 2/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1116 synthetic samples
-> test with 'LR'
LR tn, fp: 280, 8
LR fn, tp: 3, 5
LR f1 score: 0.476
LR cohens kappa score: 0.458
LR average precision score: 0.563

-> test with 'GB'
GB tn, fp: 285, 3
GB fn, tp: 3, 5
GB f1 score: 0.625
GB cohens kappa score: 0.615

-> test with 'KNN'
KNN tn, fp: 280, 8
KNN fn, tp: 2, 6
KNN f1 score: 0.545
KNN cohens kappa score: 0.529


====== Step 3/5 =======
-> Shuffling data
-> Spliting data to slices

------ Step 3/5: Slice 1/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1117 synthetic samples
-> test with 'LR'
LR tn, fp: 272, 16
LR fn, tp: 1, 8
LR f1 score: 0.485
LR cohens kappa score: 0.461
LR average precision score: 0.721

-> test with 'GB'
GB tn, fp: 283, 5
GB fn, tp: 3, 6
GB f1 score: 0.600
GB cohens kappa score: 0.586

-> test with 'KNN'
KNN tn, fp: 276, 12
KNN fn, tp: 1, 8
KNN f1 score: 0.552
KNN cohens kappa score: 0.532


------ Step 3/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1117 synthetic samples
-> test with 'LR'
LR tn, fp: 278, 10
LR fn, tp: 2, 7
LR f1 score: 0.538
LR cohens kappa score: 0.519
LR average precision score: 0.673

-> test with 'GB'
GB tn, fp: 283, 5
GB fn, tp: 2, 7
GB f1 score: 0.667
GB cohens kappa score: 0.655

-> test with 'KNN'
KNN tn, fp: 280, 8
KNN fn, tp: 2, 7
KNN f1 score: 0.583
KNN cohens kappa score: 0.567


------ Step 3/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1117 synthetic samples
-> test with 'LR'
LR tn, fp: 284, 4
LR fn, tp: 2, 7
LR f1 score: 0.700
LR cohens kappa score: 0.690
LR average precision score: 0.751

-> test with 'GB'
GB tn, fp: 287, 1
GB fn, tp: 1, 8
GB f1 score: 0.889
GB cohens kappa score: 0.885

-> test with 'KNN'
KNN tn, fp: 287, 1
KNN fn, tp: 2, 7
KNN f1 score: 0.824
KNN cohens kappa score: 0.818


------ Step 3/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1117 synthetic samples
-> test with 'LR'
LR tn, fp: 281, 7
LR fn, tp: 0, 9
LR f1 score: 0.720
LR cohens kappa score: 0.709
LR average precision score: 0.717

-> test with 'GB'
GB tn, fp: 281, 7
GB fn, tp: 3, 6
GB f1 score: 0.545
GB cohens kappa score: 0.529

-> test with 'KNN'
KNN tn, fp: 280, 8
KNN fn, tp: 1, 8
KNN f1 score: 0.640
KNN cohens kappa score: 0.625


------ Step 3/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1116 synthetic samples
-> test with 'LR'
LR tn, fp: 279, 9
LR fn, tp: 3, 5
LR f1 score: 0.455
LR cohens kappa score: 0.435
LR average precision score: 0.520

-> test with 'GB'
GB tn, fp: 281, 7
GB fn, tp: 1, 7
GB f1 score: 0.636
GB cohens kappa score: 0.623

-> test with 'KNN'
KNN tn, fp: 280, 8
KNN fn, tp: 1, 7
KNN f1 score: 0.609
KNN cohens kappa score: 0.594


====== Step 4/5 =======
-> Shuffling data
-> Spliting data to slices

------ Step 4/5: Slice 1/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1117 synthetic samples
-> test with 'LR'
LR tn, fp: 270, 18
LR fn, tp: 0, 9
LR f1 score: 0.500
LR cohens kappa score: 0.476
LR average precision score: 0.619

-> test with 'GB'
GB tn, fp: 283, 5
GB fn, tp: 2, 7
GB f1 score: 0.667
GB cohens kappa score: 0.655

-> test with 'KNN'
KNN tn, fp: 276, 12
KNN fn, tp: 0, 9
KNN f1 score: 0.600
KNN cohens kappa score: 0.582


------ Step 4/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1117 synthetic samples
-> test with 'LR'
LR tn, fp: 275, 13
LR fn, tp: 1, 8
LR f1 score: 0.533
LR cohens kappa score: 0.513
LR average precision score: 0.556

-> test with 'GB'
GB tn, fp: 283, 5
GB fn, tp: 2, 7
GB f1 score: 0.667
GB cohens kappa score: 0.655

-> test with 'KNN'
KNN tn, fp: 278, 10
KNN fn, tp: 0, 9
KNN f1 score: 0.643
KNN cohens kappa score: 0.628


------ Step 4/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1117 synthetic samples
-> test with 'LR'
LR tn, fp: 269, 19
LR fn, tp: 0, 9
LR f1 score: 0.486
LR cohens kappa score: 0.462
LR average precision score: 0.816

-> test with 'GB'
GB tn, fp: 280, 8
GB fn, tp: 3, 6
GB f1 score: 0.522
GB cohens kappa score: 0.503

-> test with 'KNN'
KNN tn, fp: 271, 17
KNN fn, tp: 0, 9
KNN f1 score: 0.514
KNN cohens kappa score: 0.491


------ Step 4/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1117 synthetic samples
-> test with 'LR'
LR tn, fp: 265, 23
LR fn, tp: 0, 9
LR f1 score: 0.439
LR cohens kappa score: 0.411
LR average precision score: 0.689

-> test with 'GB'
GB tn, fp: 282, 6
GB fn, tp: 5, 4
GB f1 score: 0.421
GB cohens kappa score: 0.402

-> test with 'KNN'
KNN tn, fp: 269, 19
KNN fn, tp: 0, 9
KNN f1 score: 0.486
KNN cohens kappa score: 0.462


------ Step 4/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1116 synthetic samples
-> test with 'LR'
LR tn, fp: 268, 20
LR fn, tp: 1, 7
LR f1 score: 0.400
LR cohens kappa score: 0.374
LR average precision score: 0.312

-> test with 'GB'
GB tn, fp: 284, 4
GB fn, tp: 2, 6
GB f1 score: 0.667
GB cohens kappa score: 0.656

-> test with 'KNN'
KNN tn, fp: 279, 9
KNN fn, tp: 2, 6
KNN f1 score: 0.522
KNN cohens kappa score: 0.504


====== Step 5/5 =======
-> Shuffling data
-> Spliting data to slices

------ Step 5/5: Slice 1/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1117 synthetic samples
-> test with 'LR'
LR tn, fp: 271, 17
LR fn, tp: 0, 9
LR f1 score: 0.514
LR cohens kappa score: 0.491
LR average precision score: 0.459

-> test with 'GB'
GB tn, fp: 278, 10
GB fn, tp: 2, 7
GB f1 score: 0.538
GB cohens kappa score: 0.519

-> test with 'KNN'
KNN tn, fp: 271, 17
KNN fn, tp: 0, 9
KNN f1 score: 0.514
KNN cohens kappa score: 0.491


------ Step 5/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1117 synthetic samples
-> test with 'LR'
LR tn, fp: 285, 3
LR fn, tp: 2, 7
LR f1 score: 0.737
LR cohens kappa score: 0.728
LR average precision score: 0.828

-> test with 'GB'
GB tn, fp: 287, 1
GB fn, tp: 2, 7
GB f1 score: 0.824
GB cohens kappa score: 0.818

-> test with 'KNN'
KNN tn, fp: 287, 1
KNN fn, tp: 2, 7
KNN f1 score: 0.824
KNN cohens kappa score: 0.818


------ Step 5/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1117 synthetic samples
-> test with 'LR'
LR tn, fp: 276, 12
LR fn, tp: 0, 9
LR f1 score: 0.600
LR cohens kappa score: 0.582
LR average precision score: 0.700

-> test with 'GB'
GB tn, fp: 286, 2
GB fn, tp: 1, 8
GB f1 score: 0.842
GB cohens kappa score: 0.837

-> test with 'KNN'
KNN tn, fp: 277, 11
KNN fn, tp: 0, 9
KNN f1 score: 0.621
KNN cohens kappa score: 0.604


------ Step 5/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1117 synthetic samples
-> test with 'LR'
LR tn, fp: 280, 8
LR fn, tp: 3, 6
LR f1 score: 0.522
LR cohens kappa score: 0.503
LR average precision score: 0.517

-> test with 'GB'
GB tn, fp: 281, 7
GB fn, tp: 3, 6
GB f1 score: 0.545
GB cohens kappa score: 0.529

-> test with 'KNN'
KNN tn, fp: 283, 5
KNN fn, tp: 2, 7
KNN f1 score: 0.667
KNN cohens kappa score: 0.655


------ Step 5/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1116 synthetic samples
-> test with 'LR'
LR tn, fp: 267, 21
LR fn, tp: 1, 7
LR f1 score: 0.389
LR cohens kappa score: 0.362
LR average precision score: 0.288

-> test with 'GB'
GB tn, fp: 278, 10
GB fn, tp: 2, 6
GB f1 score: 0.500
GB cohens kappa score: 0.481

-> test with 'KNN'
KNN tn, fp: 268, 20
KNN fn, tp: 1, 7
KNN f1 score: 0.400
KNN cohens kappa score: 0.374

### Exercise is done.

-----[ LR ]-----
maximum:
LR tn, fp: 285, 23
LR fn, tp: 4, 9
LR f1 score: 0.750
LR cohens kappa score: 0.740
LR average precision score: 0.906


average:
LR tn, fp: 274.56, 13.44
LR fn, tp: 1.04, 7.76
LR f1 score: 0.530
LR cohens kappa score: 0.510
LR average precision score: 0.629


minimum:
LR tn, fp: 265, 3
LR fn, tp: 0, 5
LR f1 score: 0.370
LR cohens kappa score: 0.344
LR average precision score: 0.288


-----[ GB ]-----
maximum:
GB tn, fp: 287, 12
GB fn, tp: 5, 9
GB f1 score: 0.889
GB cohens kappa score: 0.885


average:
GB tn, fp: 281.96, 6.04
GB fn, tp: 2.12, 6.68
GB f1 score: 0.630
GB cohens kappa score: 0.617


minimum:
GB tn, fp: 276, 1
GB fn, tp: 0, 4
GB f1 score: 0.421
GB cohens kappa score: 0.402


-----[ KNN ]-----
maximum:
KNN tn, fp: 287, 20
KNN fn, tp: 2, 9
KNN f1 score: 0.824
KNN cohens kappa score: 0.818


average:
KNN tn, fp: 277.56, 10.44
KNN fn, tp: 0.88, 7.92
KNN f1 score: 0.599
KNN cohens kappa score: 0.582


minimum:
KNN tn, fp: 268, 1
KNN fn, tp: 0, 6
KNN f1 score: 0.400
KNN cohens kappa score: 0.374