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: 0.871

-> 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: 0.678

-> 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: 0.587

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

-> 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 '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: 286, 2
KNN fn, tp: 1, 8
KNN f1 score: 0.842
KNN cohens kappa score: 0.837


------ 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 '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: 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: 277, 11
LR fn, tp: 0, 9
LR f1 score: 0.621
LR cohens kappa score: 0.604
LR average precision score: 0.637

-> 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: 0.344

-> test with 'GB'
GB tn, fp: 280, 8
GB fn, tp: 5, 4
GB f1 score: 0.381
GB cohens kappa score: 0.359

-> 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: 0.744

-> 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: 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 '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: 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: 0.603

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

-> 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: 0.683

-> 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 '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 '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: 0.748

-> 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: 0.332

-> 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: 276, 12
LR fn, tp: 1, 8
LR f1 score: 0.552
LR cohens kappa score: 0.532
LR average precision score: 0.718

-> 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: 271, 17
LR fn, tp: 1, 8
LR f1 score: 0.471
LR cohens kappa score: 0.446
LR average precision score: 0.650

-> 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: 0.602

-> 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: 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 '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: 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: 0.733

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

-> 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: 0.779

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

-> 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: 0.584

-> 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: 0.391

-> test with 'GB'
GB tn, fp: 282, 6
GB fn, tp: 2, 6
GB f1 score: 0.600
GB cohens kappa score: 0.587

-> 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, 17
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: 277.12, 10.88
LR fn, tp: 0.64, 8.16
LR f1 score: 0.592
LR cohens kappa score: 0.574
LR average precision score: 0.667


minimum:
LR tn, fp: 271, 6
LR fn, tp: 0, 7
LR f1 score: 0.471
LR cohens kappa score: 0.446
LR average precision score: 0.332


-----[ GB ]-----
maximum:
GB tn, fp: 288, 8
GB fn, tp: 5, 8
GB f1 score: 0.889
GB cohens kappa score: 0.885


average:
GB tn, fp: 285.4, 2.6
GB fn, tp: 2.56, 6.24
GB f1 score: 0.708
GB cohens kappa score: 0.699


minimum:
GB tn, fp: 280, 0
GB fn, tp: 1, 4
GB f1 score: 0.381
GB cohens kappa score: 0.359


-----[ KNN ]-----
maximum:
KNN tn, fp: 286, 14
KNN fn, tp: 3, 9
KNN f1 score: 0.857
KNN cohens kappa score: 0.852


average:
KNN tn, fp: 281.0, 7.0
KNN fn, tp: 0.68, 8.12
KNN f1 score: 0.689
KNN cohens kappa score: 0.677


minimum:
KNN tn, fp: 274, 2
KNN fn, tp: 0, 6
KNN f1 score: 0.462
KNN cohens kappa score: 0.441