ConvGeNBenchmarkResults/data_result/ProWRAS/folding_yeast5.log

///////////////////////////////////////////
// Running ProWRAS 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: 279, 9
LR fn, tp: 1, 8
LR f1 score: 0.615
LR cohens kappa score: 0.600
LR average precision score: 0.870

-> test with 'RF'
RF tn, fp: 288, 0
RF fn, tp: 5, 4
RF f1 score: 0.615
RF cohens kappa score: 0.608

-> test with 'GB'
GB tn, fp: 287, 1
GB fn, tp: 5, 4
GB f1 score: 0.571
GB cohens kappa score: 0.562

-> 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 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1117 synthetic samples
-> test with 'LR'
LR tn, fp: 273, 15
LR fn, tp: 0, 9
LR f1 score: 0.545
LR cohens kappa score: 0.524
LR average precision score: 0.706

-> test with 'RF'
RF tn, fp: 284, 4
RF fn, tp: 0, 9
RF f1 score: 0.818
RF cohens kappa score: 0.811

-> test with 'GB'
GB tn, fp: 285, 3
GB fn, tp: 1, 8
GB f1 score: 0.800
GB cohens kappa score: 0.793

-> 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 1/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1117 synthetic samples
-> test with 'LR'
LR tn, fp: 278, 10
LR fn, tp: 1, 8
LR f1 score: 0.593
LR cohens kappa score: 0.575
LR average precision score: 0.600

-> test with 'RF'
RF tn, fp: 284, 4
RF fn, tp: 3, 6
RF f1 score: 0.632
RF cohens kappa score: 0.619

-> test with 'GB'
GB tn, fp: 284, 4
GB fn, tp: 2, 7
GB f1 score: 0.700
GB cohens kappa score: 0.690

-> 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 1/5: 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: 2, 7
LR f1 score: 0.609
LR cohens kappa score: 0.594
LR average precision score: 0.746

-> test with 'RF'
RF tn, fp: 288, 0
RF fn, tp: 4, 5
RF f1 score: 0.714
RF cohens kappa score: 0.708

-> test with 'GB'
GB tn, fp: 288, 0
GB fn, tp: 4, 5
GB f1 score: 0.714
GB cohens kappa score: 0.708

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


------ Step 1/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1116 synthetic samples
-> test with 'LR'
LR tn, fp: 275, 13
LR fn, tp: 0, 8
LR f1 score: 0.552
LR cohens kappa score: 0.533
LR average precision score: 0.697

-> test with 'RF'
RF tn, fp: 283, 5
RF fn, tp: 1, 7
RF f1 score: 0.700
RF cohens kappa score: 0.690

-> test with 'GB'
GB tn, fp: 284, 4
GB fn, tp: 1, 7
GB f1 score: 0.737
GB cohens kappa score: 0.728

-> test with 'KNN'
KNN tn, fp: 280, 8
KNN fn, tp: 0, 8
KNN f1 score: 0.667
KNN cohens kappa score: 0.654


====== 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: 276, 12
LR fn, tp: 0, 9
LR f1 score: 0.600
LR cohens kappa score: 0.582
LR average precision score: 0.637

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

-> 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: 283, 5
KNN fn, tp: 1, 8
KNN f1 score: 0.727
KNN cohens kappa score: 0.717


------ Step 2/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1117 synthetic samples
-> test with 'LR'
LR tn, fp: 273, 15
LR fn, tp: 1, 8
LR f1 score: 0.500
LR cohens kappa score: 0.477
LR average precision score: 0.349

-> test with 'RF'
RF tn, fp: 284, 4
RF fn, tp: 6, 3
RF f1 score: 0.375
RF cohens kappa score: 0.358

-> test with 'GB'
GB tn, fp: 280, 8
GB fn, tp: 6, 3
GB f1 score: 0.300
GB cohens kappa score: 0.276

-> test with 'KNN'
KNN tn, fp: 277, 11
KNN fn, tp: 2, 7
KNN f1 score: 0.519
KNN cohens kappa score: 0.498


------ Step 2/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1117 synthetic samples
-> test with 'LR'
LR tn, fp: 281, 7
LR fn, tp: 1, 8
LR f1 score: 0.667
LR cohens kappa score: 0.654
LR average precision score: 0.713

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

-> 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: 281, 7
KNN fn, tp: 0, 9
KNN f1 score: 0.720
KNN cohens kappa score: 0.709


------ Step 2/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1117 synthetic samples
-> test with 'LR'
LR tn, fp: 274, 14
LR fn, tp: 0, 9
LR f1 score: 0.562
LR cohens kappa score: 0.543
LR average precision score: 0.878

-> test with 'RF'
RF tn, fp: 286, 2
RF fn, tp: 3, 6
RF f1 score: 0.706
RF cohens kappa score: 0.697

-> test with 'GB'
GB tn, fp: 285, 3
GB fn, tp: 1, 8
GB f1 score: 0.800
GB cohens kappa score: 0.793

-> test with 'KNN'
KNN tn, fp: 283, 5
KNN fn, tp: 0, 9
KNN f1 score: 0.783
KNN cohens kappa score: 0.774


------ Step 2/5: 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: 1, 7
LR f1 score: 0.583
LR cohens kappa score: 0.568
LR average precision score: 0.589

-> test with 'RF'
RF tn, fp: 286, 2
RF fn, tp: 4, 4
RF f1 score: 0.571
RF cohens kappa score: 0.561

-> test with 'GB'
GB tn, fp: 286, 2
GB fn, tp: 5, 3
GB f1 score: 0.462
GB cohens kappa score: 0.450

-> test with 'KNN'
KNN tn, fp: 284, 4
KNN fn, tp: 1, 7
KNN f1 score: 0.737
KNN cohens kappa score: 0.728


====== 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: 273, 15
LR fn, tp: 0, 9
LR f1 score: 0.545
LR cohens kappa score: 0.524
LR average precision score: 0.674

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

-> test with 'GB'
GB tn, fp: 287, 1
GB fn, tp: 3, 6
GB f1 score: 0.750
GB cohens kappa score: 0.743

-> test with 'KNN'
KNN tn, fp: 279, 9
KNN fn, tp: 1, 8
KNN f1 score: 0.615
KNN cohens kappa score: 0.600


------ Step 3/5: 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: 0, 9
LR f1 score: 0.581
LR cohens kappa score: 0.562
LR average precision score: 0.685

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

-> test with 'GB'
GB tn, fp: 285, 3
GB fn, tp: 2, 7
GB f1 score: 0.737
GB cohens kappa score: 0.728

-> test with 'KNN'
KNN tn, fp: 279, 9
KNN fn, tp: 1, 8
KNN f1 score: 0.615
KNN cohens kappa score: 0.600


------ Step 3/5: 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: 2, 7
LR f1 score: 0.636
LR cohens kappa score: 0.623
LR average precision score: 0.808

-> test with 'RF'
RF tn, fp: 288, 0
RF fn, tp: 4, 5
RF f1 score: 0.714
RF cohens kappa score: 0.708

-> test with 'GB'
GB tn, fp: 288, 0
GB fn, tp: 3, 6
GB f1 score: 0.800
GB cohens kappa score: 0.795

-> test with 'KNN'
KNN tn, fp: 285, 3
KNN fn, tp: 0, 9
KNN f1 score: 0.857
KNN cohens kappa score: 0.852


------ 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: 0.738

-> test with 'RF'
RF tn, fp: 286, 2
RF fn, tp: 3, 6
RF f1 score: 0.706
RF cohens kappa score: 0.697

-> test with 'GB'
GB tn, fp: 287, 1
GB fn, tp: 4, 5
GB f1 score: 0.667
GB cohens kappa score: 0.658

-> test with 'KNN'
KNN tn, fp: 282, 6
KNN fn, tp: 1, 8
KNN f1 score: 0.696
KNN cohens kappa score: 0.684


------ Step 3/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1116 synthetic samples
-> test with 'LR'
LR tn, fp: 275, 13
LR fn, tp: 0, 8
LR f1 score: 0.552
LR cohens kappa score: 0.533
LR average precision score: 0.327

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

-> test with 'GB'
GB tn, fp: 283, 5
GB fn, tp: 1, 7
GB f1 score: 0.700
GB cohens kappa score: 0.690

-> test with 'KNN'
KNN tn, fp: 282, 6
KNN fn, tp: 0, 8
KNN f1 score: 0.727
KNN cohens kappa score: 0.718


====== 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: 277, 11
LR fn, tp: 1, 8
LR f1 score: 0.571
LR cohens kappa score: 0.553
LR average precision score: 0.718

-> test with 'RF'
RF tn, fp: 286, 2
RF fn, tp: 2, 7
RF f1 score: 0.778
RF cohens kappa score: 0.771

-> test with 'GB'
GB tn, fp: 286, 2
GB fn, tp: 3, 6
GB f1 score: 0.706
GB cohens kappa score: 0.697

-> test with 'KNN'
KNN tn, fp: 282, 6
KNN fn, tp: 0, 9
KNN f1 score: 0.750
KNN cohens kappa score: 0.740


------ Step 4/5: Slice 2/5 -------
-> 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: 0.655

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

-> 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: 282, 6
KNN fn, tp: 0, 9
KNN f1 score: 0.750
KNN cohens kappa score: 0.740


------ Step 4/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1117 synthetic samples
-> test with 'LR'
LR tn, fp: 280, 8
LR fn, tp: 2, 7
LR f1 score: 0.583
LR cohens kappa score: 0.567
LR average precision score: 0.647

-> test with 'RF'
RF tn, fp: 283, 5
RF fn, tp: 4, 5
RF f1 score: 0.526
RF cohens kappa score: 0.511

-> test with 'GB'
GB tn, fp: 283, 5
GB fn, tp: 4, 5
GB f1 score: 0.526
GB cohens kappa score: 0.511

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


------ Step 4/5: 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: 0.696

-> test with 'RF'
RF tn, fp: 288, 0
RF fn, tp: 3, 6
RF f1 score: 0.800
RF cohens kappa score: 0.795

-> test with 'GB'
GB tn, fp: 287, 1
GB fn, tp: 3, 6
GB f1 score: 0.750
GB cohens kappa score: 0.743

-> 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 4/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1116 synthetic samples
-> test with 'LR'
LR tn, fp: 276, 12
LR fn, tp: 0, 8
LR f1 score: 0.571
LR cohens kappa score: 0.554
LR average precision score: 0.745

-> test with 'RF'
RF tn, fp: 285, 3
RF fn, tp: 1, 7
RF f1 score: 0.778
RF cohens kappa score: 0.771

-> test with 'GB'
GB tn, fp: 285, 3
GB fn, tp: 1, 7
GB f1 score: 0.778
GB cohens kappa score: 0.771

-> test with 'KNN'
KNN tn, fp: 277, 11
KNN fn, tp: 0, 8
KNN f1 score: 0.593
KNN cohens kappa score: 0.576


====== 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: 273, 15
LR fn, tp: 0, 9
LR f1 score: 0.545
LR cohens kappa score: 0.524
LR average precision score: 0.724

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

-> 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: 274, 14
KNN fn, tp: 0, 9
KNN f1 score: 0.562
KNN cohens kappa score: 0.543


------ Step 5/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1117 synthetic samples
-> test with 'LR'
LR tn, fp: 280, 8
LR fn, tp: 1, 8
LR f1 score: 0.640
LR cohens kappa score: 0.625
LR average precision score: 0.781

-> test with 'RF'
RF tn, fp: 287, 1
RF fn, tp: 4, 5
RF f1 score: 0.667
RF cohens kappa score: 0.658

-> test with 'GB'
GB tn, fp: 287, 1
GB fn, tp: 3, 6
GB f1 score: 0.750
GB cohens kappa score: 0.743

-> 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 5/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1117 synthetic samples
-> test with 'LR'
LR tn, fp: 278, 10
LR fn, tp: 0, 9
LR f1 score: 0.643
LR cohens kappa score: 0.628
LR average precision score: 0.747

-> test with 'RF'
RF tn, fp: 286, 2
RF fn, tp: 0, 9
RF f1 score: 0.900
RF cohens kappa score: 0.897

-> 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: 284, 4
KNN fn, tp: 0, 9
KNN f1 score: 0.818
KNN cohens kappa score: 0.811


------ Step 5/5: 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: 1, 8
LR f1 score: 0.667
LR cohens kappa score: 0.654
LR average precision score: 0.587

-> test with 'RF'
RF tn, fp: 287, 1
RF fn, tp: 6, 3
RF f1 score: 0.462
RF cohens kappa score: 0.451

-> test with 'GB'
GB tn, fp: 287, 1
GB fn, tp: 4, 5
GB f1 score: 0.667
GB cohens kappa score: 0.658

-> test with 'KNN'
KNN tn, fp: 284, 4
KNN fn, tp: 0, 9
KNN f1 score: 0.818
KNN cohens kappa score: 0.811


------ Step 5/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1116 synthetic samples
-> test with 'LR'
LR tn, fp: 276, 12
LR fn, tp: 1, 7
LR f1 score: 0.519
LR cohens kappa score: 0.499
LR average precision score: 0.396

-> test with 'RF'
RF tn, fp: 284, 4
RF fn, tp: 3, 5
RF f1 score: 0.588
RF cohens kappa score: 0.576

-> test with 'GB'
GB tn, fp: 283, 5
GB fn, tp: 2, 6
GB f1 score: 0.632
GB cohens kappa score: 0.620

-> test with 'KNN'
KNN tn, fp: 276, 12
KNN fn, tp: 2, 6
KNN f1 score: 0.462
KNN cohens kappa score: 0.441

### Exercise is done.

-----[ LR ]-----
maximum:
LR tn, fp: 282, 16
LR fn, tp: 2, 9
LR f1 score: 0.720
LR cohens kappa score: 0.709
LR average precision score: 0.878


average:
LR tn, fp: 277.16, 10.84
LR fn, tp: 0.64, 8.16
LR f1 score: 0.592
LR cohens kappa score: 0.575
LR average precision score: 0.668


minimum:
LR tn, fp: 272, 6
LR fn, tp: 0, 7
LR f1 score: 0.485
LR cohens kappa score: 0.461
LR average precision score: 0.327


-----[ RF ]-----
maximum:
RF tn, fp: 288, 5
RF fn, tp: 6, 9
RF f1 score: 0.900
RF cohens kappa score: 0.897


average:
RF tn, fp: 285.8, 2.2
RF fn, tp: 2.72, 6.08
RF f1 score: 0.705
RF cohens kappa score: 0.697


minimum:
RF tn, fp: 283, 0
RF fn, tp: 0, 3
RF f1 score: 0.375
RF cohens kappa score: 0.358


-----[ GB ]-----
maximum:
GB tn, fp: 288, 8
GB fn, tp: 6, 8
GB f1 score: 0.889
GB cohens kappa score: 0.885


average:
GB tn, fp: 285.44, 2.56
GB fn, tp: 2.56, 6.24
GB f1 score: 0.707
GB cohens kappa score: 0.698


minimum:
GB tn, fp: 280, 0
GB fn, tp: 1, 3
GB f1 score: 0.300
GB cohens kappa score: 0.276


-----[ KNN ]-----
maximum:
KNN tn, fp: 287, 14
KNN fn, tp: 3, 9
KNN f1 score: 0.889
KNN cohens kappa score: 0.885


average:
KNN tn, fp: 281.04, 6.96
KNN fn, tp: 0.68, 8.12
KNN f1 score: 0.691
KNN cohens kappa score: 0.679


minimum:
KNN tn, fp: 274, 1
KNN fn, tp: 0, 6
KNN f1 score: 0.462
KNN cohens kappa score: 0.441