ConvGeNBenchmarkResults/data_result/SpheredNoise/folding_hypothyroid.log

///////////////////////////////////////////
// Running SpheredNoise on folding_hypothyroid
///////////////////////////////////////////

Load 'data_input/folding_hypothyroid'
from pickle file
non empty cut in data_input/folding_hypothyroid! (1 points)
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
Train 2409/120 points
-> new disc
-> calc distances
-> statistics
trained 120 points min:1.0 max:91.88035698668132
-> create 2289 synthetic samples
-> test with 'LR'
LR tn, fp: 597, 6
LR fn, tp: 20, 11
LR f1 score: 0.458
LR cohens kappa score: 0.439
LR average precision score: 0.527

-> test with 'GB'
GB tn, fp: 601, 2
GB fn, tp: 7, 24
GB f1 score: 0.842
GB cohens kappa score: 0.835

-> test with 'KNN'
KNN tn, fp: 600, 3
KNN fn, tp: 15, 16
KNN f1 score: 0.640
KNN cohens kappa score: 0.626


------ Step 1/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Train 2409/120 points
-> new disc
-> calc distances
-> statistics
trained 120 points min:7.416198487095663 max:91.88035698668132
-> create 2289 synthetic samples
-> test with 'LR'
LR tn, fp: 592, 11
LR fn, tp: 19, 12
LR f1 score: 0.444
LR cohens kappa score: 0.420
LR average precision score: 0.503

-> test with 'GB'
GB tn, fp: 594, 9
GB fn, tp: 9, 22
GB f1 score: 0.710
GB cohens kappa score: 0.695

-> test with 'KNN'
KNN tn, fp: 596, 7
KNN fn, tp: 15, 16
KNN f1 score: 0.593
KNN cohens kappa score: 0.575


------ Step 1/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Train 2409/120 points
-> new disc
-> calc distances
-> statistics
trained 120 points min:1.0 max:91.97282207261013
-> create 2289 synthetic samples
-> test with 'LR'
LR tn, fp: 594, 9
LR fn, tp: 24, 7
LR f1 score: 0.298
LR cohens kappa score: 0.274
LR average precision score: 0.420

-> test with 'GB'
GB tn, fp: 600, 3
GB fn, tp: 7, 24
GB f1 score: 0.828
GB cohens kappa score: 0.819

-> test with 'KNN'
KNN tn, fp: 599, 4
KNN fn, tp: 16, 15
KNN f1 score: 0.600
KNN cohens kappa score: 0.585


------ Step 1/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Train 2409/120 points
-> new disc
-> calc distances
-> statistics
trained 120 points min:1.0 max:91.88035698668132
-> create 2289 synthetic samples
-> test with 'LR'
LR tn, fp: 594, 9
LR fn, tp: 21, 10
LR f1 score: 0.400
LR cohens kappa score: 0.377
LR average precision score: 0.412

-> test with 'GB'
GB tn, fp: 600, 3
GB fn, tp: 11, 20
GB f1 score: 0.741
GB cohens kappa score: 0.729

-> test with 'KNN'
KNN tn, fp: 601, 2
KNN fn, tp: 16, 15
KNN f1 score: 0.625
KNN cohens kappa score: 0.612


------ Step 1/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Train 2412/124 points
-> new disc
-> calc distances
-> statistics
trained 124 points min:1.0 max:90.58145505565695
-> create 2288 synthetic samples
-> test with 'LR'
LR tn, fp: 596, 4
LR fn, tp: 17, 10
LR f1 score: 0.488
LR cohens kappa score: 0.472
LR average precision score: 0.566

-> test with 'GB'
GB tn, fp: 598, 2
GB fn, tp: 5, 22
GB f1 score: 0.863
GB cohens kappa score: 0.857

-> test with 'KNN'
KNN tn, fp: 597, 3
KNN fn, tp: 16, 11
KNN f1 score: 0.537
KNN cohens kappa score: 0.523


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

------ Step 2/5: Slice 1/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Train 2409/120 points
-> new disc
-> calc distances
-> statistics
trained 120 points min:1.0 max:91.97282207261013
-> create 2289 synthetic samples
-> test with 'LR'
LR tn, fp: 596, 7
LR fn, tp: 20, 11
LR f1 score: 0.449
LR cohens kappa score: 0.428
LR average precision score: 0.484

-> test with 'GB'
GB tn, fp: 596, 7
GB fn, tp: 12, 19
GB f1 score: 0.667
GB cohens kappa score: 0.651

-> test with 'KNN'
KNN tn, fp: 598, 5
KNN fn, tp: 22, 9
KNN f1 score: 0.400
KNN cohens kappa score: 0.381


------ Step 2/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Train 2409/120 points
-> new disc
-> calc distances
-> statistics
trained 120 points min:7.416198487095663 max:91.88035698668132
-> create 2289 synthetic samples
-> test with 'LR'
LR tn, fp: 593, 10
LR fn, tp: 24, 7
LR f1 score: 0.292
LR cohens kappa score: 0.266
LR average precision score: 0.492

-> test with 'GB'
GB tn, fp: 597, 6
GB fn, tp: 9, 22
GB f1 score: 0.746
GB cohens kappa score: 0.733

-> test with 'KNN'
KNN tn, fp: 600, 3
KNN fn, tp: 15, 16
KNN f1 score: 0.640
KNN cohens kappa score: 0.626


------ Step 2/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Train 2409/120 points
-> new disc
-> calc distances
-> statistics
trained 120 points min:1.0 max:91.88035698668132
-> create 2289 synthetic samples
-> test with 'LR'
LR tn, fp: 595, 8
LR fn, tp: 17, 14
LR f1 score: 0.528
LR cohens kappa score: 0.508
LR average precision score: 0.555

-> test with 'GB'
GB tn, fp: 600, 3
GB fn, tp: 12, 19
GB f1 score: 0.717
GB cohens kappa score: 0.705

-> test with 'KNN'
KNN tn, fp: 601, 2
KNN fn, tp: 16, 15
KNN f1 score: 0.625
KNN cohens kappa score: 0.612


------ Step 2/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Train 2409/120 points
-> new disc
-> calc distances
-> statistics
trained 120 points min:9.219544457292887 max:91.88035698668132
-> create 2289 synthetic samples
-> test with 'LR'
LR tn, fp: 596, 7
LR fn, tp: 26, 5
LR f1 score: 0.233
LR cohens kappa score: 0.211
LR average precision score: 0.409

-> test with 'GB'
GB tn, fp: 599, 4
GB fn, tp: 6, 25
GB f1 score: 0.833
GB cohens kappa score: 0.825

-> test with 'KNN'
KNN tn, fp: 596, 7
KNN fn, tp: 16, 15
KNN f1 score: 0.566
KNN cohens kappa score: 0.548


------ Step 2/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Train 2412/124 points
-> new disc
-> calc distances
-> statistics
trained 124 points min:1.0 max:75.78918128598566
-> create 2288 synthetic samples
-> test with 'LR'
LR tn, fp: 592, 8
LR fn, tp: 16, 11
LR f1 score: 0.478
LR cohens kappa score: 0.459
LR average precision score: 0.573

-> test with 'GB'
GB tn, fp: 597, 3
GB fn, tp: 5, 22
GB f1 score: 0.846
GB cohens kappa score: 0.840

-> test with 'KNN'
KNN tn, fp: 595, 5
KNN fn, tp: 12, 15
KNN f1 score: 0.638
KNN cohens kappa score: 0.625


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

------ Step 3/5: Slice 1/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Train 2409/120 points
-> new disc
-> calc distances
-> statistics
trained 120 points min:7.416198487095663 max:91.88035698668132
-> create 2289 synthetic samples
-> test with 'LR'
LR tn, fp: 599, 4
LR fn, tp: 22, 9
LR f1 score: 0.409
LR cohens kappa score: 0.392
LR average precision score: 0.559

-> test with 'GB'
GB tn, fp: 603, 0
GB fn, tp: 12, 19
GB f1 score: 0.760
GB cohens kappa score: 0.751

-> test with 'KNN'
KNN tn, fp: 602, 1
KNN fn, tp: 19, 12
KNN f1 score: 0.545
KNN cohens kappa score: 0.532


------ Step 3/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Train 2409/120 points
-> new disc
-> calc distances
-> statistics
trained 120 points min:1.0 max:76.87652437513027
-> create 2289 synthetic samples
-> test with 'LR'
LR tn, fp: 593, 10
LR fn, tp: 22, 9
LR f1 score: 0.360
LR cohens kappa score: 0.335
LR average precision score: 0.348

-> test with 'GB'
GB tn, fp: 595, 8
GB fn, tp: 6, 25
GB f1 score: 0.781
GB cohens kappa score: 0.770

-> test with 'KNN'
KNN tn, fp: 598, 5
KNN fn, tp: 16, 15
KNN f1 score: 0.588
KNN cohens kappa score: 0.572


------ Step 3/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Train 2409/120 points
-> new disc
-> calc distances
-> statistics
trained 120 points min:1.0 max:91.88035698668132
-> create 2289 synthetic samples
-> test with 'LR'
LR tn, fp: 594, 9
LR fn, tp: 16, 15
LR f1 score: 0.545
LR cohens kappa score: 0.525
LR average precision score: 0.665

-> test with 'GB'
GB tn, fp: 598, 5
GB fn, tp: 5, 26
GB f1 score: 0.839
GB cohens kappa score: 0.830

-> test with 'KNN'
KNN tn, fp: 596, 7
KNN fn, tp: 15, 16
KNN f1 score: 0.593
KNN cohens kappa score: 0.575


------ Step 3/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Train 2409/120 points
-> new disc
-> calc distances
-> statistics
trained 120 points min:9.0 max:91.97282207261013
-> create 2289 synthetic samples
-> test with 'LR'
LR tn, fp: 597, 6
LR fn, tp: 19, 12
LR f1 score: 0.490
LR cohens kappa score: 0.471
LR average precision score: 0.512

-> test with 'GB'
GB tn, fp: 597, 6
GB fn, tp: 10, 21
GB f1 score: 0.724
GB cohens kappa score: 0.711

-> test with 'KNN'
KNN tn, fp: 599, 4
KNN fn, tp: 17, 14
KNN f1 score: 0.571
KNN cohens kappa score: 0.555


------ Step 3/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Train 2412/124 points
-> new disc
-> calc distances
-> statistics
trained 124 points min:1.0 max:91.88035698668132
-> create 2288 synthetic samples
-> test with 'LR'
LR tn, fp: 595, 5
LR fn, tp: 22, 5
LR f1 score: 0.270
LR cohens kappa score: 0.253
LR average precision score: 0.356

-> test with 'GB'
GB tn, fp: 597, 3
GB fn, tp: 8, 19
GB f1 score: 0.776
GB cohens kappa score: 0.766

-> test with 'KNN'
KNN tn, fp: 599, 1
KNN fn, tp: 14, 13
KNN f1 score: 0.634
KNN cohens kappa score: 0.623


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

------ Step 4/5: Slice 1/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Train 2409/120 points
-> new disc
-> calc distances
-> statistics
trained 120 points min:7.416198487095663 max:91.97282207261013
-> create 2289 synthetic samples
-> test with 'LR'
LR tn, fp: 592, 11
LR fn, tp: 21, 10
LR f1 score: 0.385
LR cohens kappa score: 0.359
LR average precision score: 0.393

-> test with 'GB'
GB tn, fp: 598, 5
GB fn, tp: 4, 27
GB f1 score: 0.857
GB cohens kappa score: 0.850

-> test with 'KNN'
KNN tn, fp: 600, 3
KNN fn, tp: 21, 10
KNN f1 score: 0.455
KNN cohens kappa score: 0.438


------ Step 4/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Train 2409/120 points
-> new disc
-> calc distances
-> statistics
trained 120 points min:1.0 max:91.88035698668132
-> create 2289 synthetic samples
-> test with 'LR'
LR tn, fp: 595, 8
LR fn, tp: 21, 10
LR f1 score: 0.408
LR cohens kappa score: 0.386
LR average precision score: 0.495

-> test with 'GB'
GB tn, fp: 599, 4
GB fn, tp: 8, 23
GB f1 score: 0.793
GB cohens kappa score: 0.783

-> test with 'KNN'
KNN tn, fp: 599, 4
KNN fn, tp: 18, 13
KNN f1 score: 0.542
KNN cohens kappa score: 0.525


------ Step 4/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Train 2409/120 points
-> new disc
-> calc distances
-> statistics
trained 120 points min:1.0 max:91.88035698668132
-> create 2289 synthetic samples
-> test with 'LR'
LR tn, fp: 599, 4
LR fn, tp: 22, 9
LR f1 score: 0.409
LR cohens kappa score: 0.392
LR average precision score: 0.632

-> test with 'GB'
GB tn, fp: 599, 4
GB fn, tp: 7, 24
GB f1 score: 0.814
GB cohens kappa score: 0.804

-> test with 'KNN'
KNN tn, fp: 600, 3
KNN fn, tp: 17, 14
KNN f1 score: 0.583
KNN cohens kappa score: 0.568


------ Step 4/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Train 2409/120 points
-> new disc
-> calc distances
-> statistics
trained 120 points min:1.0 max:75.78918128598566
-> create 2289 synthetic samples
-> test with 'LR'
LR tn, fp: 594, 9
LR fn, tp: 18, 13
LR f1 score: 0.491
LR cohens kappa score: 0.469
LR average precision score: 0.501

-> test with 'GB'
GB tn, fp: 600, 3
GB fn, tp: 8, 23
GB f1 score: 0.807
GB cohens kappa score: 0.798

-> test with 'KNN'
KNN tn, fp: 597, 6
KNN fn, tp: 17, 14
KNN f1 score: 0.549
KNN cohens kappa score: 0.531


------ Step 4/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Train 2412/124 points
-> new disc
-> calc distances
-> statistics
trained 124 points min:9.0 max:91.88035698668132
-> create 2288 synthetic samples
-> test with 'LR'
LR tn, fp: 593, 7
LR fn, tp: 19, 8
LR f1 score: 0.381
LR cohens kappa score: 0.361
LR average precision score: 0.436

-> test with 'GB'
GB tn, fp: 595, 5
GB fn, tp: 9, 18
GB f1 score: 0.720
GB cohens kappa score: 0.708

-> test with 'KNN'
KNN tn, fp: 593, 7
KNN fn, tp: 12, 15
KNN f1 score: 0.612
KNN cohens kappa score: 0.597


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

------ Step 5/5: Slice 1/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Train 2409/120 points
-> new disc
-> calc distances
-> statistics
trained 120 points min:1.0 max:91.88035698668132
-> create 2289 synthetic samples
-> test with 'LR'
LR tn, fp: 589, 14
LR fn, tp: 19, 12
LR f1 score: 0.421
LR cohens kappa score: 0.394
LR average precision score: 0.480

-> test with 'GB'
GB tn, fp: 599, 4
GB fn, tp: 9, 22
GB f1 score: 0.772
GB cohens kappa score: 0.761

-> test with 'KNN'
KNN tn, fp: 599, 4
KNN fn, tp: 17, 14
KNN f1 score: 0.571
KNN cohens kappa score: 0.555


------ Step 5/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Train 2409/120 points
-> new disc
-> calc distances
-> statistics
trained 120 points min:1.0 max:91.88035698668132
-> create 2289 synthetic samples
-> test with 'LR'
LR tn, fp: 598, 5
LR fn, tp: 20, 11
LR f1 score: 0.468
LR cohens kappa score: 0.450
LR average precision score: 0.480

-> test with 'GB'
GB tn, fp: 600, 3
GB fn, tp: 8, 23
GB f1 score: 0.807
GB cohens kappa score: 0.798

-> test with 'KNN'
KNN tn, fp: 599, 4
KNN fn, tp: 18, 13
KNN f1 score: 0.542
KNN cohens kappa score: 0.525


------ Step 5/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Train 2409/120 points
-> new disc
-> calc distances
-> statistics
trained 120 points min:7.416198487095663 max:91.88035698668132
-> create 2289 synthetic samples
-> test with 'LR'
LR tn, fp: 593, 10
LR fn, tp: 18, 13
LR f1 score: 0.481
LR cohens kappa score: 0.459
LR average precision score: 0.553

-> test with 'GB'
GB tn, fp: 598, 5
GB fn, tp: 14, 17
GB f1 score: 0.642
GB cohens kappa score: 0.626

-> test with 'KNN'
KNN tn, fp: 598, 5
KNN fn, tp: 18, 13
KNN f1 score: 0.531
KNN cohens kappa score: 0.513


------ Step 5/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Train 2409/120 points
-> new disc
-> calc distances
-> statistics
trained 120 points min:1.0 max:91.88035698668132
-> create 2289 synthetic samples
-> test with 'LR'
LR tn, fp: 601, 2
LR fn, tp: 22, 9
LR f1 score: 0.429
LR cohens kappa score: 0.414
LR average precision score: 0.552

-> test with 'GB'
GB tn, fp: 600, 3
GB fn, tp: 6, 25
GB f1 score: 0.847
GB cohens kappa score: 0.840

-> test with 'KNN'
KNN tn, fp: 601, 2
KNN fn, tp: 14, 17
KNN f1 score: 0.680
KNN cohens kappa score: 0.668


------ Step 5/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Train 2412/124 points
-> new disc
-> calc distances
-> statistics
trained 124 points min:1.0 max:75.78918128598566
-> create 2288 synthetic samples
-> test with 'LR'
LR tn, fp: 590, 10
LR fn, tp: 21, 6
LR f1 score: 0.279
LR cohens kappa score: 0.255
LR average precision score: 0.399

-> test with 'GB'
GB tn, fp: 596, 4
GB fn, tp: 8, 19
GB f1 score: 0.760
GB cohens kappa score: 0.750

-> test with 'KNN'
KNN tn, fp: 597, 3
KNN fn, tp: 17, 10
KNN f1 score: 0.500
KNN cohens kappa score: 0.486

### Exercise is done.

-----[ LR ]-----
maximum:
LR tn, fp: 601, 14
LR fn, tp: 26, 15
LR f1 score: 0.545
LR cohens kappa score: 0.525
LR average precision score: 0.665


average:
LR tn, fp: 594.68, 7.72
LR fn, tp: 20.24, 9.96
LR f1 score: 0.412
LR cohens kappa score: 0.391
LR average precision score: 0.492


minimum:
LR tn, fp: 589, 2
LR fn, tp: 16, 5
LR f1 score: 0.233
LR cohens kappa score: 0.211
LR average precision score: 0.348


-----[ GB ]-----
maximum:
GB tn, fp: 603, 9
GB fn, tp: 14, 27
GB f1 score: 0.863
GB cohens kappa score: 0.857


average:
GB tn, fp: 598.24, 4.16
GB fn, tp: 8.2, 22.0
GB f1 score: 0.780
GB cohens kappa score: 0.769


minimum:
GB tn, fp: 594, 0
GB fn, tp: 4, 17
GB f1 score: 0.642
GB cohens kappa score: 0.626


-----[ KNN ]-----
maximum:
KNN tn, fp: 602, 7
KNN fn, tp: 22, 17
KNN f1 score: 0.680
KNN cohens kappa score: 0.668


average:
KNN tn, fp: 598.4, 4.0
KNN fn, tp: 16.36, 13.84
KNN f1 score: 0.574
KNN cohens kappa score: 0.559


minimum:
KNN tn, fp: 593, 1
KNN fn, tp: 12, 9
KNN f1 score: 0.400
KNN cohens kappa score: 0.381