ConvGeNBenchmarkResults/data_result/Repeater/folding_yeast5.log

///////////////////////////////////////////
// Running Repeater 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: 274, 14
LR fn, tp: 0, 9
LR f1 score: 0.562
LR cohens kappa score: 0.543
LR average precision score: 0.902

-> test with 'RF'
RF tn, fp: 286, 2
RF fn, tp: 4, 5
RF f1 score: 0.625
RF cohens kappa score: 0.615

-> test with 'GB'
GB tn, fp: 285, 3
GB fn, tp: 5, 4
GB f1 score: 0.500
GB cohens kappa score: 0.486

-> 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 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: 0.698

-> 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: 0, 9
GB f1 score: 0.857
GB cohens kappa score: 0.852

-> 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 1/5: Slice 3/5 -------
-> 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: 0.602

-> 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: 3, 6
GB f1 score: 0.632
GB cohens kappa score: 0.619

-> 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 1/5: 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: 0, 9
LR f1 score: 0.692
LR cohens kappa score: 0.680
LR average precision score: 0.776

-> 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: 288, 0
GB fn, tp: 2, 7
GB f1 score: 0.875
GB cohens kappa score: 0.872

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


------ Step 1/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1116 synthetic samples
-> test with 'LR'
LR tn, fp: 271, 17
LR fn, tp: 0, 8
LR f1 score: 0.485
LR cohens kappa score: 0.463
LR average precision score: 0.688

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

-> 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: 276, 12
KNN fn, tp: 0, 8
KNN f1 score: 0.571
KNN cohens kappa score: 0.554


====== 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: 275, 13
LR fn, tp: 0, 9
LR f1 score: 0.581
LR cohens kappa score: 0.562
LR average precision score: 0.710

-> 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: 280, 8
KNN fn, tp: 0, 9
KNN f1 score: 0.692
KNN cohens kappa score: 0.680


------ Step 2/5: Slice 2/5 -------
-> 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: 0.416

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

-> test with 'GB'
GB tn, fp: 280, 8
GB fn, tp: 4, 5
GB f1 score: 0.455
GB cohens kappa score: 0.434

-> 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 2/5: 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: 0.773

-> test with 'RF'
RF tn, fp: 287, 1
RF fn, tp: 0, 9
RF f1 score: 0.947
RF cohens kappa score: 0.946

-> 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 4/5 -------
-> 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: 0.894

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

-> 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: 278, 10
KNN fn, tp: 0, 9
KNN f1 score: 0.643
KNN cohens kappa score: 0.628


------ Step 2/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1116 synthetic samples
-> test with 'LR'
LR tn, fp: 277, 11
LR fn, tp: 0, 8
LR f1 score: 0.593
LR cohens kappa score: 0.576
LR average precision score: 0.639

-> 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: 6, 2
GB f1 score: 0.333
GB cohens kappa score: 0.321

-> 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 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: 271, 17
LR fn, tp: 0, 9
LR f1 score: 0.514
LR cohens kappa score: 0.491
LR average precision score: 0.671

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

-> 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: 275, 13
KNN fn, tp: 0, 9
KNN f1 score: 0.581
KNN cohens kappa score: 0.562


------ Step 3/5: Slice 2/5 -------
-> 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: 0.707

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

-> 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: 277, 11
KNN fn, tp: 0, 9
KNN f1 score: 0.621
KNN cohens kappa score: 0.604


------ Step 3/5: 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: 0, 9
LR f1 score: 0.692
LR cohens kappa score: 0.680
LR average precision score: 0.838

-> 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: 2, 7
GB f1 score: 0.778
GB cohens kappa score: 0.771

-> 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 3/5: Slice 4/5 -------
-> 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: 0.758

-> 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: 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: 0.387

-> 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: 283, 5
GB fn, tp: 1, 7
GB f1 score: 0.700
GB cohens kappa score: 0.690

-> 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 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: 273, 15
LR fn, tp: 0, 9
LR f1 score: 0.545
LR cohens kappa score: 0.524
LR average precision score: 0.744

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

-> 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: 278, 10
KNN fn, tp: 0, 9
KNN f1 score: 0.643
KNN cohens kappa score: 0.628


------ 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: 0, 9
LR f1 score: 0.529
LR cohens kappa score: 0.507
LR average precision score: 0.598

-> 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: 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 4/5: Slice 3/5 -------
-> 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: 0.688

-> 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: 279, 9
KNN fn, tp: 0, 9
KNN f1 score: 0.667
KNN cohens kappa score: 0.653


------ Step 4/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1117 synthetic samples
-> test with 'LR'
LR tn, fp: 279, 9
LR fn, tp: 0, 9
LR f1 score: 0.667
LR cohens kappa score: 0.653
LR average precision score: 0.681

-> 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: 280, 8
KNN fn, tp: 1, 8
KNN f1 score: 0.640
KNN cohens kappa score: 0.625


------ Step 4/5: 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: 0.640

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

-> 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: 275, 13
KNN fn, tp: 0, 8
KNN f1 score: 0.552
KNN cohens kappa score: 0.533


====== 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: 0.720

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

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

-> 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 5/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1117 synthetic samples
-> test with 'LR'
LR tn, fp: 279, 9
LR fn, tp: 0, 9
LR f1 score: 0.667
LR cohens kappa score: 0.653
LR average precision score: 0.799

-> 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: 288, 0
GB fn, tp: 3, 6
GB f1 score: 0.800
GB cohens kappa score: 0.795

-> 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 5/5: Slice 3/5 -------
-> 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: 0.775

-> 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: 2, 7
GB f1 score: 0.778
GB cohens kappa score: 0.771

-> 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 5/5: Slice 4/5 -------
-> 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: 0.582

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

-> 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: 283, 5
KNN fn, tp: 0, 9
KNN f1 score: 0.783
KNN cohens kappa score: 0.774


------ Step 5/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1116 synthetic samples
-> test with 'LR'
LR tn, fp: 271, 17
LR fn, tp: 0, 8
LR f1 score: 0.485
LR cohens kappa score: 0.463
LR average precision score: 0.469

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

-> test with 'GB'
GB tn, fp: 281, 7
GB fn, tp: 2, 6
GB f1 score: 0.571
GB cohens kappa score: 0.557

-> test with 'KNN'
KNN tn, fp: 275, 13
KNN fn, tp: 1, 7
KNN f1 score: 0.500
KNN cohens kappa score: 0.480

### Exercise is done.

-----[ LR ]-----
maximum:
LR tn, fp: 280, 19
LR fn, tp: 1, 9
LR f1 score: 0.692
LR cohens kappa score: 0.680
LR average precision score: 0.902


average:
LR tn, fp: 274.68, 13.32
LR fn, tp: 0.04, 8.76
LR f1 score: 0.574
LR cohens kappa score: 0.555
LR average precision score: 0.686


minimum:
LR tn, fp: 269, 8
LR fn, tp: 0, 8
LR f1 score: 0.485
LR cohens kappa score: 0.462
LR average precision score: 0.387


-----[ RF ]-----
maximum:
RF tn, fp: 288, 7
RF fn, tp: 4, 9
RF f1 score: 0.947
RF cohens kappa score: 0.946


average:
RF tn, fp: 285.4, 2.6
RF fn, tp: 2.16, 6.64
RF f1 score: 0.736
RF cohens kappa score: 0.728


minimum:
RF tn, fp: 281, 0
RF fn, tp: 0, 4
RF f1 score: 0.526
RF cohens kappa score: 0.511


-----[ GB ]-----
maximum:
GB tn, fp: 288, 8
GB fn, tp: 6, 9
GB f1 score: 0.889
GB cohens kappa score: 0.885


average:
GB tn, fp: 285.04, 2.96
GB fn, tp: 2.24, 6.56
GB f1 score: 0.712
GB cohens kappa score: 0.703


minimum:
GB tn, fp: 280, 0
GB fn, tp: 0, 2
GB f1 score: 0.333
GB cohens kappa score: 0.321


-----[ KNN ]-----
maximum:
KNN tn, fp: 286, 14
KNN fn, tp: 1, 9
KNN f1 score: 0.900
KNN cohens kappa score: 0.897


average:
KNN tn, fp: 279.28, 8.72
KNN fn, tp: 0.16, 8.64
KNN f1 score: 0.670
KNN cohens kappa score: 0.657


minimum:
KNN tn, fp: 274, 2
KNN fn, tp: 0, 7
KNN f1 score: 0.500
KNN cohens kappa score: 0.480