ConvGeNBenchmarkResults/data_result/SpheredNoise/folding_flare-F.log

///////////////////////////////////////////
// Running SpheredNoise on folding_flare-F
///////////////////////////////////////////

Load 'data_input/folding_flare-F'
from pickle file
non empty cut in data_input/folding_flare-F! (23 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 818/34 points
-> new disc
-> calc distances
-> statistics
trained 34 points min:1.0 max:1.4142135623730951
-> create 784 synthetic samples
-> test with 'LR'
LR tn, fp: 202, 3
LR fn, tp: 9, 0
LR f1 score: 0.000
LR cohens kappa score: -0.021
LR average precision score: 0.109

-> test with 'GB'
GB tn, fp: 204, 1
GB fn, tp: 9, 0
GB f1 score: 0.000
GB cohens kappa score: -0.008

-> test with 'KNN'
KNN tn, fp: 200, 5
KNN fn, tp: 9, 0
KNN f1 score: 0.000
KNN cohens kappa score: -0.031


------ Step 1/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Train 818/34 points
-> new disc
-> calc distances
-> statistics
trained 34 points min:1.0 max:1.7320508075688772
-> create 784 synthetic samples
-> test with 'LR'
LR tn, fp: 202, 3
LR fn, tp: 6, 3
LR f1 score: 0.400
LR cohens kappa score: 0.379
LR average precision score: 0.373

-> test with 'GB'
GB tn, fp: 203, 2
GB fn, tp: 8, 1
GB f1 score: 0.167
GB cohens kappa score: 0.149

-> test with 'KNN'
KNN tn, fp: 204, 1
KNN fn, tp: 9, 0
KNN f1 score: 0.000
KNN cohens kappa score: -0.008


------ Step 1/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Train 818/34 points
-> new disc
-> calc distances
-> statistics
trained 34 points min:1.0 max:1.7320508075688772
-> create 784 synthetic samples
-> test with 'LR'
LR tn, fp: 204, 1
LR fn, tp: 8, 1
LR f1 score: 0.182
LR cohens kappa score: 0.169
LR average precision score: 0.309

-> test with 'GB'
GB tn, fp: 205, 0
GB fn, tp: 9, 0
GB f1 score: 0.000
GB cohens kappa score: 0.000

-> test with 'KNN'
KNN tn, fp: 205, 0
KNN fn, tp: 9, 0
KNN f1 score: 0.000
KNN cohens kappa score: 0.000


------ Step 1/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Train 818/34 points
-> new disc
-> calc distances
-> statistics
trained 34 points min:1.0 max:2.0
-> create 784 synthetic samples
-> test with 'LR'
LR tn, fp: 203, 2
LR fn, tp: 5, 4
LR f1 score: 0.533
LR cohens kappa score: 0.517
LR average precision score: 0.726

-> test with 'GB'
GB tn, fp: 203, 2
GB fn, tp: 8, 1
GB f1 score: 0.167
GB cohens kappa score: 0.149

-> test with 'KNN'
KNN tn, fp: 205, 0
KNN fn, tp: 9, 0
KNN f1 score: 0.000
KNN cohens kappa score: 0.000


------ Step 1/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Train 820/36 points
-> new disc
-> calc distances
-> statistics
trained 36 points min:1.0 max:1.7320508075688772
-> create 784 synthetic samples
-> test with 'LR'
LR tn, fp: 196, 7
LR fn, tp: 5, 2
LR f1 score: 0.250
LR cohens kappa score: 0.221
LR average precision score: 0.199

-> test with 'GB'
GB tn, fp: 200, 3
GB fn, tp: 6, 1
GB f1 score: 0.182
GB cohens kappa score: 0.161

-> test with 'KNN'
KNN tn, fp: 203, 0
KNN fn, tp: 7, 0
KNN f1 score: 0.000
KNN cohens kappa score: 0.000


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

------ Step 2/5: Slice 1/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Train 818/34 points
-> new disc
-> calc distances
-> statistics
trained 34 points min:1.0 max:1.4142135623730951
-> create 784 synthetic samples
-> test with 'LR'
LR tn, fp: 202, 3
LR fn, tp: 6, 3
LR f1 score: 0.400
LR cohens kappa score: 0.379
LR average precision score: 0.399

-> test with 'GB'
GB tn, fp: 203, 2
GB fn, tp: 7, 2
GB f1 score: 0.308
GB cohens kappa score: 0.289

-> test with 'KNN'
KNN tn, fp: 205, 0
KNN fn, tp: 9, 0
KNN f1 score: 0.000
KNN cohens kappa score: 0.000


------ Step 2/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Train 818/34 points
-> new disc
-> calc distances
-> statistics
trained 34 points min:1.0 max:1.7320508075688772
-> create 784 synthetic samples
-> test with 'LR'
LR tn, fp: 203, 2
LR fn, tp: 7, 2
LR f1 score: 0.308
LR cohens kappa score: 0.289
LR average precision score: 0.316

-> test with 'GB'
GB tn, fp: 203, 2
GB fn, tp: 9, 0
GB f1 score: 0.000
GB cohens kappa score: -0.016

-> test with 'KNN'
KNN tn, fp: 205, 0
KNN fn, tp: 9, 0
KNN f1 score: 0.000
KNN cohens kappa score: 0.000


------ Step 2/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Train 818/34 points
-> new disc
-> calc distances
-> statistics
trained 34 points min:1.0 max:2.0
-> create 784 synthetic samples
-> test with 'LR'
LR tn, fp: 202, 3
LR fn, tp: 7, 2
LR f1 score: 0.286
LR cohens kappa score: 0.264
LR average precision score: 0.281

-> test with 'GB'
GB tn, fp: 205, 0
GB fn, tp: 8, 1
GB f1 score: 0.200
GB cohens kappa score: 0.193

-> test with 'KNN'
KNN tn, fp: 205, 0
KNN fn, tp: 9, 0
KNN f1 score: 0.000
KNN cohens kappa score: 0.000


------ Step 2/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Train 818/34 points
-> new disc
-> calc distances
-> statistics
trained 34 points min:1.0 max:1.7320508075688772
-> create 784 synthetic samples
-> test with 'LR'
LR tn, fp: 202, 3
LR fn, tp: 8, 1
LR f1 score: 0.154
LR cohens kappa score: 0.131
LR average precision score: 0.278

-> test with 'GB'
GB tn, fp: 204, 1
GB fn, tp: 8, 1
GB f1 score: 0.182
GB cohens kappa score: 0.169

-> test with 'KNN'
KNN tn, fp: 205, 0
KNN fn, tp: 9, 0
KNN f1 score: 0.000
KNN cohens kappa score: 0.000


------ Step 2/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Train 820/36 points
-> new disc
-> calc distances
-> statistics
trained 36 points min:1.0 max:1.7320508075688772
-> create 784 synthetic samples
-> test with 'LR'
LR tn, fp: 201, 2
LR fn, tp: 4, 3
LR f1 score: 0.500
LR cohens kappa score: 0.486
LR average precision score: 0.473

-> test with 'GB'
GB tn, fp: 201, 2
GB fn, tp: 6, 1
GB f1 score: 0.200
GB cohens kappa score: 0.184

-> test with 'KNN'
KNN tn, fp: 203, 0
KNN fn, tp: 7, 0
KNN f1 score: 0.000
KNN cohens kappa score: 0.000


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

------ Step 3/5: Slice 1/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Train 818/34 points
-> new disc
-> calc distances
-> statistics
trained 34 points min:1.0 max:1.7320508075688772
-> create 784 synthetic samples
-> test with 'LR'
LR tn, fp: 204, 1
LR fn, tp: 3, 6
LR f1 score: 0.750
LR cohens kappa score: 0.740
LR average precision score: 0.750

-> test with 'GB'
GB tn, fp: 205, 0
GB fn, tp: 9, 0
GB f1 score: 0.000
GB cohens kappa score: 0.000

-> test with 'KNN'
KNN tn, fp: 205, 0
KNN fn, tp: 9, 0
KNN f1 score: 0.000
KNN cohens kappa score: 0.000


------ Step 3/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Train 818/34 points
-> new disc
-> calc distances
-> statistics
trained 34 points min:1.0 max:2.23606797749979
-> create 784 synthetic samples
-> test with 'LR'
LR tn, fp: 198, 7
LR fn, tp: 6, 3
LR f1 score: 0.316
LR cohens kappa score: 0.284
LR average precision score: 0.210

-> test with 'GB'
GB tn, fp: 200, 5
GB fn, tp: 6, 3
GB f1 score: 0.353
GB cohens kappa score: 0.326

-> test with 'KNN'
KNN tn, fp: 204, 1
KNN fn, tp: 9, 0
KNN f1 score: 0.000
KNN cohens kappa score: -0.008


------ Step 3/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Train 818/34 points
-> new disc
-> calc distances
-> statistics
trained 34 points min:1.0 max:1.7320508075688772
-> create 784 synthetic samples
-> test with 'LR'
LR tn, fp: 204, 1
LR fn, tp: 8, 1
LR f1 score: 0.182
LR cohens kappa score: 0.169
LR average precision score: 0.373

-> test with 'GB'
GB tn, fp: 204, 1
GB fn, tp: 9, 0
GB f1 score: 0.000
GB cohens kappa score: -0.008

-> test with 'KNN'
KNN tn, fp: 205, 0
KNN fn, tp: 9, 0
KNN f1 score: 0.000
KNN cohens kappa score: 0.000


------ Step 3/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Train 818/34 points
-> new disc
-> calc distances
-> statistics
trained 34 points min:1.0 max:1.7320508075688772
-> create 784 synthetic samples
-> test with 'LR'
LR tn, fp: 204, 1
LR fn, tp: 9, 0
LR f1 score: 0.000
LR cohens kappa score: -0.008
LR average precision score: 0.273

-> test with 'GB'
GB tn, fp: 204, 1
GB fn, tp: 9, 0
GB f1 score: 0.000
GB cohens kappa score: -0.008

-> test with 'KNN'
KNN tn, fp: 205, 0
KNN fn, tp: 9, 0
KNN f1 score: 0.000
KNN cohens kappa score: 0.000


------ Step 3/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Train 820/36 points
-> new disc
-> calc distances
-> statistics
trained 36 points min:1.0 max:2.0
-> create 784 synthetic samples
-> test with 'LR'
LR tn, fp: 199, 4
LR fn, tp: 6, 1
LR f1 score: 0.167
LR cohens kappa score: 0.143
LR average precision score: 0.326

-> test with 'GB'
GB tn, fp: 198, 5
GB fn, tp: 5, 2
GB f1 score: 0.286
GB cohens kappa score: 0.261

-> test with 'KNN'
KNN tn, fp: 200, 3
KNN fn, tp: 7, 0
KNN f1 score: 0.000
KNN cohens kappa score: -0.020


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

------ Step 4/5: Slice 1/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Train 818/34 points
-> new disc
-> calc distances
-> statistics
trained 34 points min:1.0 max:1.7320508075688772
-> create 784 synthetic samples
-> test with 'LR'
LR tn, fp: 200, 5
LR fn, tp: 9, 0
LR f1 score: 0.000
LR cohens kappa score: -0.031
LR average precision score: 0.170

-> test with 'GB'
GB tn, fp: 199, 6
GB fn, tp: 9, 0
GB f1 score: 0.000
GB cohens kappa score: -0.035

-> test with 'KNN'
KNN tn, fp: 205, 0
KNN fn, tp: 9, 0
KNN f1 score: 0.000
KNN cohens kappa score: 0.000


------ Step 4/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Train 818/34 points
-> new disc
-> calc distances
-> statistics
trained 34 points min:1.0 max:1.7320508075688772
-> create 784 synthetic samples
-> test with 'LR'
LR tn, fp: 205, 0
LR fn, tp: 6, 3
LR f1 score: 0.500
LR cohens kappa score: 0.489
LR average precision score: 0.558

-> test with 'GB'
GB tn, fp: 204, 1
GB fn, tp: 8, 1
GB f1 score: 0.182
GB cohens kappa score: 0.169

-> test with 'KNN'
KNN tn, fp: 205, 0
KNN fn, tp: 9, 0
KNN f1 score: 0.000
KNN cohens kappa score: 0.000


------ Step 4/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Train 818/34 points
-> new disc
-> calc distances
-> statistics
trained 34 points min:1.0 max:2.0
-> create 784 synthetic samples
-> test with 'LR'
LR tn, fp: 200, 5
LR fn, tp: 7, 2
LR f1 score: 0.250
LR cohens kappa score: 0.221
LR average precision score: 0.209

-> test with 'GB'
GB tn, fp: 205, 0
GB fn, tp: 8, 1
GB f1 score: 0.200
GB cohens kappa score: 0.193

-> test with 'KNN'
KNN tn, fp: 204, 1
KNN fn, tp: 9, 0
KNN f1 score: 0.000
KNN cohens kappa score: -0.008


------ Step 4/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Train 818/34 points
-> new disc
-> calc distances
-> statistics
trained 34 points min:1.0 max:1.4142135623730951
-> create 784 synthetic samples
-> test with 'LR'
LR tn, fp: 201, 4
LR fn, tp: 6, 3
LR f1 score: 0.375
LR cohens kappa score: 0.351
LR average precision score: 0.385

-> test with 'GB'
GB tn, fp: 203, 2
GB fn, tp: 7, 2
GB f1 score: 0.308
GB cohens kappa score: 0.289

-> test with 'KNN'
KNN tn, fp: 205, 0
KNN fn, tp: 9, 0
KNN f1 score: 0.000
KNN cohens kappa score: 0.000


------ Step 4/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Train 820/36 points
-> new disc
-> calc distances
-> statistics
trained 36 points min:1.0 max:1.7320508075688772
-> create 784 synthetic samples
-> test with 'LR'
LR tn, fp: 203, 0
LR fn, tp: 4, 3
LR f1 score: 0.600
LR cohens kappa score: 0.592
LR average precision score: 0.607

-> test with 'GB'
GB tn, fp: 201, 2
GB fn, tp: 6, 1
GB f1 score: 0.200
GB cohens kappa score: 0.184

-> test with 'KNN'
KNN tn, fp: 203, 0
KNN fn, tp: 7, 0
KNN f1 score: 0.000
KNN cohens kappa score: 0.000


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

------ Step 5/5: Slice 1/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Train 818/34 points
-> new disc
-> calc distances
-> statistics
trained 34 points min:1.0 max:1.7320508075688772
-> create 784 synthetic samples
-> test with 'LR'
LR tn, fp: 204, 1
LR fn, tp: 8, 1
LR f1 score: 0.182
LR cohens kappa score: 0.169
LR average precision score: 0.217

-> test with 'GB'
GB tn, fp: 204, 1
GB fn, tp: 8, 1
GB f1 score: 0.182
GB cohens kappa score: 0.169

-> test with 'KNN'
KNN tn, fp: 205, 0
KNN fn, tp: 9, 0
KNN f1 score: 0.000
KNN cohens kappa score: 0.000


------ Step 5/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Train 818/34 points
-> new disc
-> calc distances
-> statistics
trained 34 points min:1.0 max:1.7320508075688772
-> create 784 synthetic samples
-> test with 'LR'
LR tn, fp: 203, 2
LR fn, tp: 6, 3
LR f1 score: 0.429
LR cohens kappa score: 0.411
LR average precision score: 0.418

-> test with 'GB'
GB tn, fp: 205, 0
GB fn, tp: 9, 0
GB f1 score: 0.000
GB cohens kappa score: 0.000

-> test with 'KNN'
KNN tn, fp: 205, 0
KNN fn, tp: 9, 0
KNN f1 score: 0.000
KNN cohens kappa score: 0.000


------ Step 5/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Train 818/34 points
-> new disc
-> calc distances
-> statistics
trained 34 points min:1.0 max:1.4142135623730951
-> create 784 synthetic samples
-> test with 'LR'
LR tn, fp: 199, 6
LR fn, tp: 4, 5
LR f1 score: 0.500
LR cohens kappa score: 0.476
LR average precision score: 0.411

-> test with 'GB'
GB tn, fp: 205, 0
GB fn, tp: 8, 1
GB f1 score: 0.200
GB cohens kappa score: 0.193

-> test with 'KNN'
KNN tn, fp: 205, 0
KNN fn, tp: 9, 0
KNN f1 score: 0.000
KNN cohens kappa score: 0.000


------ Step 5/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Train 818/34 points
-> new disc
-> calc distances
-> statistics
trained 34 points min:1.0 max:1.7320508075688772
-> create 784 synthetic samples
-> test with 'LR'
LR tn, fp: 203, 2
LR fn, tp: 9, 0
LR f1 score: 0.000
LR cohens kappa score: -0.016
LR average precision score: 0.186

-> test with 'GB'
GB tn, fp: 203, 2
GB fn, tp: 9, 0
GB f1 score: 0.000
GB cohens kappa score: -0.016

-> test with 'KNN'
KNN tn, fp: 205, 0
KNN fn, tp: 9, 0
KNN f1 score: 0.000
KNN cohens kappa score: 0.000


------ Step 5/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Train 820/36 points
-> new disc
-> calc distances
-> statistics
trained 36 points min:1.0 max:1.7320508075688772
-> create 784 synthetic samples
-> test with 'LR'
LR tn, fp: 203, 0
LR fn, tp: 5, 2
LR f1 score: 0.444
LR cohens kappa score: 0.436
LR average precision score: 0.438

-> test with 'GB'
GB tn, fp: 199, 4
GB fn, tp: 5, 2
GB f1 score: 0.308
GB cohens kappa score: 0.286

-> test with 'KNN'
KNN tn, fp: 202, 1
KNN fn, tp: 7, 0
KNN f1 score: 0.000
KNN cohens kappa score: -0.008

### Exercise is done.

-----[ LR ]-----
maximum:
LR tn, fp: 205, 7
LR fn, tp: 9, 6
LR f1 score: 0.750
LR cohens kappa score: 0.740
LR average precision score: 0.750


average:
LR tn, fp: 201.88, 2.72
LR fn, tp: 6.44, 2.16
LR f1 score: 0.308
LR cohens kappa score: 0.290
LR average precision score: 0.360


minimum:
LR tn, fp: 196, 0
LR fn, tp: 3, 0
LR f1 score: 0.000
LR cohens kappa score: -0.031
LR average precision score: 0.109


-----[ GB ]-----
maximum:
GB tn, fp: 205, 6
GB fn, tp: 9, 3
GB f1 score: 0.353
GB cohens kappa score: 0.326


average:
GB tn, fp: 202.8, 1.8
GB fn, tp: 7.72, 0.88
GB f1 score: 0.145
GB cohens kappa score: 0.131


minimum:
GB tn, fp: 198, 0
GB fn, tp: 5, 0
GB f1 score: 0.000
GB cohens kappa score: -0.035


-----[ KNN ]-----
maximum:
KNN tn, fp: 205, 5
KNN fn, tp: 9, 0
KNN f1 score: 0.000
KNN cohens kappa score: 0.000


average:
KNN tn, fp: 204.12, 0.48
KNN fn, tp: 8.6, 0.0
KNN f1 score: 0.000
KNN cohens kappa score: -0.003


minimum:
KNN tn, fp: 200, 0
KNN fn, tp: 7, 0
KNN f1 score: 0.000
KNN cohens kappa score: -0.031