ConvGeNBenchmarkResults/data_result/convGAN-proximary-full/folding_yeast5.log

///////////////////////////////////////////
// Running convGAN-proximary-full 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 GAN.predict
GAN tn, fp: 283, 5
GAN fn, tp: 5, 4
GAN f1 score: 0.444
GAN cohens kappa score: 0.427

-> 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: 0.888

-> test with 'GB'
GB tn, fp: 286, 2
GB fn, tp: 5, 4
GB f1 score: 0.533
GB cohens kappa score: 0.522

-> 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 1/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1117 synthetic samples
-> test with GAN.predict
GAN tn, fp: 261, 27
GAN fn, tp: 1, 8
GAN f1 score: 0.364
GAN cohens kappa score: 0.331

-> 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: 0.694

-> 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 1/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1117 synthetic samples
-> test with GAN.predict
GAN tn, fp: 281, 7
GAN fn, tp: 1, 8
GAN f1 score: 0.667
GAN cohens kappa score: 0.654

-> 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: 0.595

-> 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: 0, 9
KNN f1 score: 0.643
KNN cohens kappa score: 0.628


------ Step 1/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1117 synthetic samples
-> test with GAN.predict
GAN tn, fp: 285, 3
GAN fn, tp: 2, 7
GAN f1 score: 0.737
GAN cohens kappa score: 0.728

-> 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: 0.776

-> 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: 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 GAN.predict
GAN tn, fp: 279, 9
GAN fn, tp: 1, 7
GAN f1 score: 0.583
GAN cohens kappa score: 0.568

-> 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: 0.704

-> 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: 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 GAN.predict
GAN tn, fp: 279, 9
GAN fn, tp: 0, 9
GAN f1 score: 0.667
GAN cohens kappa score: 0.653

-> 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: 0.686

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


------ Step 2/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1117 synthetic samples
-> test with GAN.predict
GAN tn, fp: 274, 14
GAN fn, tp: 5, 4
GAN f1 score: 0.296
GAN cohens kappa score: 0.267

-> 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: 0.414

-> 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: 276, 12
KNN fn, tp: 1, 8
KNN f1 score: 0.552
KNN cohens kappa score: 0.532


------ Step 2/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1117 synthetic samples
-> test with GAN.predict
GAN tn, fp: 282, 6
GAN fn, tp: 1, 8
GAN f1 score: 0.696
GAN cohens kappa score: 0.684

-> 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: 0.773

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


------ Step 2/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1117 synthetic samples
-> test with GAN.predict
GAN tn, fp: 281, 7
GAN fn, tp: 0, 9
GAN f1 score: 0.720
GAN cohens kappa score: 0.709

-> 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: 0.891

-> 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 GAN.predict
GAN tn, fp: 273, 15
GAN fn, tp: 2, 6
GAN f1 score: 0.414
GAN cohens kappa score: 0.390

-> test with 'LR'
LR tn, fp: 280, 8
LR fn, tp: 0, 8
LR f1 score: 0.667
LR cohens kappa score: 0.654
LR average precision score: 0.639

-> 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: 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 GAN.predict
GAN tn, fp: 281, 7
GAN fn, tp: 2, 7
GAN f1 score: 0.609
GAN cohens kappa score: 0.594

-> 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: 0.694

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


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

-> 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: 0.701

-> 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: 276, 12
KNN fn, tp: 0, 9
KNN f1 score: 0.600
KNN cohens kappa score: 0.582


------ Step 3/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1117 synthetic samples
-> test with GAN.predict
GAN tn, fp: 283, 5
GAN fn, tp: 3, 6
GAN f1 score: 0.600
GAN cohens kappa score: 0.586

-> 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: 0.828

-> 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: 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 GAN.predict
GAN tn, fp: 263, 25
GAN fn, tp: 3, 6
GAN f1 score: 0.300
GAN cohens kappa score: 0.266

-> 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: 0.738

-> test with 'GB'
GB tn, fp: 286, 2
GB fn, tp: 5, 4
GB f1 score: 0.533
GB cohens kappa score: 0.522

-> 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 GAN.predict
GAN tn, fp: 277, 11
GAN fn, tp: 3, 5
GAN f1 score: 0.417
GAN cohens kappa score: 0.395

-> 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: 0.394

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


====== 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 GAN.predict
GAN tn, fp: 279, 9
GAN fn, tp: 1, 8
GAN f1 score: 0.615
GAN cohens kappa score: 0.600

-> 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: 0.744

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


------ Step 4/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1117 synthetic samples
-> test with GAN.predict
GAN tn, fp: 278, 10
GAN fn, tp: 4, 5
GAN f1 score: 0.417
GAN cohens kappa score: 0.394

-> test with 'LR'
LR tn, fp: 274, 14
LR fn, tp: 1, 8
LR f1 score: 0.516
LR cohens kappa score: 0.494
LR average precision score: 0.598

-> 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: 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 GAN.predict
GAN tn, fp: 278, 10
GAN fn, tp: 3, 6
GAN f1 score: 0.480
GAN cohens kappa score: 0.459

-> 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: 0.678

-> 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: 2, 7
KNN f1 score: 0.560
KNN cohens kappa score: 0.542


------ Step 4/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1117 synthetic samples
-> test with GAN.predict
GAN tn, fp: 282, 6
GAN fn, tp: 1, 8
GAN f1 score: 0.696
GAN cohens kappa score: 0.684

-> 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: 0.668

-> 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 GAN.predict
GAN tn, fp: 283, 5
GAN fn, tp: 1, 7
GAN f1 score: 0.700
GAN cohens kappa score: 0.690

-> 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: 0.754

-> 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: 274, 14
KNN fn, tp: 0, 8
KNN f1 score: 0.533
KNN cohens kappa score: 0.514


====== 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 GAN.predict
GAN tn, fp: 276, 12
GAN fn, tp: 0, 9
GAN f1 score: 0.600
GAN cohens kappa score: 0.582

-> 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: 0.724

-> 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: 273, 15
KNN fn, tp: 0, 9
KNN f1 score: 0.545
KNN cohens kappa score: 0.524


------ Step 5/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1117 synthetic samples
-> test with GAN.predict
GAN tn, fp: 285, 3
GAN fn, tp: 4, 5
GAN f1 score: 0.588
GAN cohens kappa score: 0.576

-> 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: 0.834

-> 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: 1, 8
KNN f1 score: 0.727
KNN cohens kappa score: 0.717


------ Step 5/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1117 synthetic samples
-> test with GAN.predict
GAN tn, fp: 284, 4
GAN fn, tp: 1, 8
GAN f1 score: 0.762
GAN cohens kappa score: 0.753

-> 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: 0.764

-> 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: 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 GAN.predict
GAN tn, fp: 277, 11
GAN fn, tp: 4, 5
GAN f1 score: 0.400
GAN cohens kappa score: 0.376

-> 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: 0.591

-> 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 GAN.predict
GAN tn, fp: 283, 5
GAN fn, tp: 3, 5
GAN f1 score: 0.556
GAN cohens kappa score: 0.542

-> test with 'LR'
LR tn, fp: 275, 13
LR fn, tp: 1, 7
LR f1 score: 0.500
LR cohens kappa score: 0.480
LR average precision score: 0.506

-> test with 'GB'
GB tn, fp: 282, 6
GB fn, tp: 3, 5
GB f1 score: 0.526
GB cohens kappa score: 0.511

-> test with 'KNN'
KNN tn, fp: 274, 14
KNN fn, tp: 1, 7
KNN f1 score: 0.483
KNN cohens kappa score: 0.462

### Exercise is done.

-----[ LR ]-----
maximum:
LR tn, fp: 281, 17
LR fn, tp: 1, 9
LR f1 score: 0.720
LR cohens kappa score: 0.709
LR average precision score: 0.891


average:
LR tn, fp: 277.0, 11.0
LR fn, tp: 0.2, 8.6
LR f1 score: 0.612
LR cohens kappa score: 0.596
LR average precision score: 0.691


minimum:
LR tn, fp: 271, 7
LR fn, tp: 0, 7
LR f1 score: 0.485
LR cohens kappa score: 0.461
LR average precision score: 0.394


-----[ GB ]-----
maximum:
GB tn, fp: 288, 8
GB fn, tp: 5, 8
GB f1 score: 0.842
GB cohens kappa score: 0.837


average:
GB tn, fp: 285.2, 2.8
GB fn, tp: 2.36, 6.44
GB f1 score: 0.713
GB cohens kappa score: 0.704


minimum:
GB tn, fp: 280, 0
GB fn, tp: 1, 4
GB f1 score: 0.455
GB cohens kappa score: 0.434


-----[ KNN ]-----
maximum:
KNN tn, fp: 286, 15
KNN fn, tp: 2, 9
KNN f1 score: 0.900
KNN cohens kappa score: 0.897


average:
KNN tn, fp: 278.72, 9.28
KNN fn, tp: 0.28, 8.52
KNN f1 score: 0.651
KNN cohens kappa score: 0.637


minimum:
KNN tn, fp: 273, 2
KNN fn, tp: 0, 7
KNN f1 score: 0.483
KNN cohens kappa score: 0.462


-----[ GAN ]-----
maximum:
GAN tn, fp: 285, 27
GAN fn, tp: 5, 9
GAN f1 score: 0.762
GAN cohens kappa score: 0.753


average:
GAN tn, fp: 278.68, 9.32
GAN fn, tp: 2.12, 6.68
GAN f1 score: 0.556
GAN cohens kappa score: 0.539


minimum:
GAN tn, fp: 261, 3
GAN fn, tp: 0, 4
GAN f1 score: 0.296
GAN cohens kappa score: 0.266