ConvGeNBenchmarkResults/data_result/SpheredNoise/folding_car-vgood.log

///////////////////////////////////////////
// Running SpheredNoise on folding_car-vgood
///////////////////////////////////////////

Load 'data_input/folding_car-vgood'
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
Train 1330/52 points
-> new disc
-> calc distances
-> statistics
trained 52 points min:1.0 max:1.0
-> create 1278 synthetic samples
-> test with 'LR'
LR tn, fp: 315, 18
LR fn, tp: 12, 1
LR f1 score: 0.062
LR cohens kappa score: 0.019
LR average precision score: 0.184

-> test with 'GB'
GB tn, fp: 333, 0
GB fn, tp: 0, 13
GB f1 score: 1.000
GB cohens kappa score: 1.000

-> test with 'KNN'
KNN tn, fp: 328, 5
KNN fn, tp: 10, 3
KNN f1 score: 0.286
KNN cohens kappa score: 0.265


------ Step 1/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Train 1330/52 points
-> new disc
-> calc distances
-> statistics
trained 52 points min:1.0 max:1.0
-> create 1278 synthetic samples
-> test with 'LR'
LR tn, fp: 314, 19
LR fn, tp: 9, 4
LR f1 score: 0.222
LR cohens kappa score: 0.183
LR average precision score: 0.182

-> test with 'GB'
GB tn, fp: 333, 0
GB fn, tp: 0, 13
GB f1 score: 1.000
GB cohens kappa score: 1.000

-> test with 'KNN'
KNN tn, fp: 332, 1
KNN fn, tp: 8, 5
KNN f1 score: 0.526
KNN cohens kappa score: 0.515


------ Step 1/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Train 1330/52 points
-> new disc
-> calc distances
-> statistics
trained 52 points min:1.0 max:1.0
-> create 1278 synthetic samples
-> test with 'LR'
LR tn, fp: 314, 19
LR fn, tp: 7, 6
LR f1 score: 0.316
LR cohens kappa score: 0.280
LR average precision score: 0.272

-> test with 'GB'
GB tn, fp: 333, 0
GB fn, tp: 1, 12
GB f1 score: 0.960
GB cohens kappa score: 0.959

-> test with 'KNN'
KNN tn, fp: 330, 3
KNN fn, tp: 6, 7
KNN f1 score: 0.609
KNN cohens kappa score: 0.595


------ Step 1/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Train 1330/52 points
-> new disc
-> calc distances
-> statistics
trained 52 points min:1.0 max:1.0
-> create 1278 synthetic samples
-> test with 'LR'
LR tn, fp: 321, 12
LR fn, tp: 9, 4
LR f1 score: 0.276
LR cohens kappa score: 0.245
LR average precision score: 0.248

-> test with 'GB'
GB tn, fp: 333, 0
GB fn, tp: 0, 13
GB f1 score: 1.000
GB cohens kappa score: 1.000

-> test with 'KNN'
KNN tn, fp: 333, 0
KNN fn, tp: 8, 5
KNN f1 score: 0.556
KNN cohens kappa score: 0.546


------ Step 1/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Train 1332/52 points
-> new disc
-> calc distances
-> statistics
trained 52 points min:1.0 max:1.4142135623730951
-> create 1280 synthetic samples
-> test with 'LR'
LR tn, fp: 320, 11
LR fn, tp: 8, 5
LR f1 score: 0.345
LR cohens kappa score: 0.316
LR average precision score: 0.222

-> test with 'GB'
GB tn, fp: 329, 2
GB fn, tp: 0, 13
GB f1 score: 0.929
GB cohens kappa score: 0.926

-> test with 'KNN'
KNN tn, fp: 329, 2
KNN fn, tp: 7, 6
KNN f1 score: 0.571
KNN cohens kappa score: 0.559


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

------ Step 2/5: Slice 1/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Train 1330/52 points
-> new disc
-> calc distances
-> statistics
trained 52 points min:1.0 max:1.0
-> create 1278 synthetic samples
-> test with 'LR'
LR tn, fp: 316, 17
LR fn, tp: 9, 4
LR f1 score: 0.235
LR cohens kappa score: 0.198
LR average precision score: 0.331

-> test with 'GB'
GB tn, fp: 332, 1
GB fn, tp: 0, 13
GB f1 score: 0.963
GB cohens kappa score: 0.961

-> test with 'KNN'
KNN tn, fp: 332, 1
KNN fn, tp: 8, 5
KNN f1 score: 0.526
KNN cohens kappa score: 0.515


------ Step 2/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Train 1330/52 points
-> new disc
-> calc distances
-> statistics
trained 52 points min:1.0 max:1.4142135623730951
-> create 1278 synthetic samples
-> test with 'LR'
LR tn, fp: 307, 26
LR fn, tp: 8, 5
LR f1 score: 0.227
LR cohens kappa score: 0.184
LR average precision score: 0.167

-> test with 'GB'
GB tn, fp: 328, 5
GB fn, tp: 0, 13
GB f1 score: 0.839
GB cohens kappa score: 0.831

-> test with 'KNN'
KNN tn, fp: 326, 7
KNN fn, tp: 6, 7
KNN f1 score: 0.519
KNN cohens kappa score: 0.499


------ Step 2/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Train 1330/52 points
-> new disc
-> calc distances
-> statistics
trained 52 points min:1.0 max:1.0
-> create 1278 synthetic samples
-> test with 'LR'
LR tn, fp: 320, 13
LR fn, tp: 10, 3
LR f1 score: 0.207
LR cohens kappa score: 0.173
LR average precision score: 0.208

-> test with 'GB'
GB tn, fp: 333, 0
GB fn, tp: 0, 13
GB f1 score: 1.000
GB cohens kappa score: 1.000

-> test with 'KNN'
KNN tn, fp: 330, 3
KNN fn, tp: 8, 5
KNN f1 score: 0.476
KNN cohens kappa score: 0.461


------ Step 2/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Train 1330/52 points
-> new disc
-> calc distances
-> statistics
trained 52 points min:1.0 max:1.0
-> create 1278 synthetic samples
-> test with 'LR'
LR tn, fp: 322, 11
LR fn, tp: 9, 4
LR f1 score: 0.286
LR cohens kappa score: 0.256
LR average precision score: 0.307

-> test with 'GB'
GB tn, fp: 333, 0
GB fn, tp: 3, 10
GB f1 score: 0.870
GB cohens kappa score: 0.865

-> test with 'KNN'
KNN tn, fp: 333, 0
KNN fn, tp: 9, 4
KNN f1 score: 0.471
KNN cohens kappa score: 0.461


------ Step 2/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Train 1332/52 points
-> new disc
-> calc distances
-> statistics
trained 52 points min:1.0 max:1.0
-> create 1280 synthetic samples
-> test with 'LR'
LR tn, fp: 317, 14
LR fn, tp: 10, 3
LR f1 score: 0.200
LR cohens kappa score: 0.164
LR average precision score: 0.182

-> test with 'GB'
GB tn, fp: 331, 0
GB fn, tp: 0, 13
GB f1 score: 1.000
GB cohens kappa score: 1.000

-> test with 'KNN'
KNN tn, fp: 330, 1
KNN fn, tp: 8, 5
KNN f1 score: 0.526
KNN cohens kappa score: 0.515


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

------ Step 3/5: Slice 1/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Train 1330/52 points
-> new disc
-> calc distances
-> statistics
trained 52 points min:1.0 max:1.0
-> create 1278 synthetic samples
-> test with 'LR'
LR tn, fp: 318, 15
LR fn, tp: 11, 2
LR f1 score: 0.133
LR cohens kappa score: 0.095
LR average precision score: 0.192

-> test with 'GB'
GB tn, fp: 333, 0
GB fn, tp: 3, 10
GB f1 score: 0.870
GB cohens kappa score: 0.865

-> test with 'KNN'
KNN tn, fp: 330, 3
KNN fn, tp: 7, 6
KNN f1 score: 0.545
KNN cohens kappa score: 0.531


------ Step 3/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Train 1330/52 points
-> new disc
-> calc distances
-> statistics
trained 52 points min:1.0 max:1.0
-> create 1278 synthetic samples
-> test with 'LR'
LR tn, fp: 314, 19
LR fn, tp: 9, 4
LR f1 score: 0.222
LR cohens kappa score: 0.183
LR average precision score: 0.184

-> test with 'GB'
GB tn, fp: 332, 1
GB fn, tp: 0, 13
GB f1 score: 0.963
GB cohens kappa score: 0.961

-> test with 'KNN'
KNN tn, fp: 330, 3
KNN fn, tp: 8, 5
KNN f1 score: 0.476
KNN cohens kappa score: 0.461


------ Step 3/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Train 1330/52 points
-> new disc
-> calc distances
-> statistics
trained 52 points min:1.0 max:1.0
-> create 1278 synthetic samples
-> test with 'LR'
LR tn, fp: 309, 24
LR fn, tp: 6, 7
LR f1 score: 0.318
LR cohens kappa score: 0.280
LR average precision score: 0.246

-> test with 'GB'
GB tn, fp: 331, 2
GB fn, tp: 0, 13
GB f1 score: 0.929
GB cohens kappa score: 0.926

-> test with 'KNN'
KNN tn, fp: 330, 3
KNN fn, tp: 7, 6
KNN f1 score: 0.545
KNN cohens kappa score: 0.531


------ Step 3/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Train 1330/52 points
-> new disc
-> calc distances
-> statistics
trained 52 points min:1.0 max:1.0
-> create 1278 synthetic samples
-> test with 'LR'
LR tn, fp: 320, 13
LR fn, tp: 9, 4
LR f1 score: 0.267
LR cohens kappa score: 0.234
LR average precision score: 0.234

-> test with 'GB'
GB tn, fp: 333, 0
GB fn, tp: 0, 13
GB f1 score: 1.000
GB cohens kappa score: 1.000

-> test with 'KNN'
KNN tn, fp: 329, 4
KNN fn, tp: 8, 5
KNN f1 score: 0.455
KNN cohens kappa score: 0.437


------ Step 3/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Train 1332/52 points
-> new disc
-> calc distances
-> statistics
trained 52 points min:1.0 max:1.0
-> create 1280 synthetic samples
-> test with 'LR'
LR tn, fp: 322, 9
LR fn, tp: 10, 3
LR f1 score: 0.240
LR cohens kappa score: 0.211
LR average precision score: 0.286

-> test with 'GB'
GB tn, fp: 331, 0
GB fn, tp: 4, 9
GB f1 score: 0.818
GB cohens kappa score: 0.812

-> test with 'KNN'
KNN tn, fp: 331, 0
KNN fn, tp: 8, 5
KNN f1 score: 0.556
KNN cohens kappa score: 0.546


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

------ Step 4/5: Slice 1/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Train 1330/52 points
-> new disc
-> calc distances
-> statistics
trained 52 points min:1.0 max:1.0
-> create 1278 synthetic samples
-> test with 'LR'
LR tn, fp: 319, 14
LR fn, tp: 10, 3
LR f1 score: 0.200
LR cohens kappa score: 0.164
LR average precision score: 0.215

-> test with 'GB'
GB tn, fp: 333, 0
GB fn, tp: 0, 13
GB f1 score: 1.000
GB cohens kappa score: 1.000

-> test with 'KNN'
KNN tn, fp: 333, 0
KNN fn, tp: 11, 2
KNN f1 score: 0.267
KNN cohens kappa score: 0.259


------ Step 4/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Train 1330/52 points
-> new disc
-> calc distances
-> statistics
trained 52 points min:1.0 max:1.0
-> create 1278 synthetic samples
-> test with 'LR'
LR tn, fp: 314, 19
LR fn, tp: 7, 6
LR f1 score: 0.316
LR cohens kappa score: 0.280
LR average precision score: 0.243

-> test with 'GB'
GB tn, fp: 332, 1
GB fn, tp: 1, 12
GB f1 score: 0.923
GB cohens kappa score: 0.920

-> test with 'KNN'
KNN tn, fp: 328, 5
KNN fn, tp: 9, 4
KNN f1 score: 0.364
KNN cohens kappa score: 0.343


------ Step 4/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Train 1330/52 points
-> new disc
-> calc distances
-> statistics
trained 52 points min:1.0 max:1.0
-> create 1278 synthetic samples
-> test with 'LR'
LR tn, fp: 315, 18
LR fn, tp: 10, 3
LR f1 score: 0.176
LR cohens kappa score: 0.136
LR average precision score: 0.179

-> test with 'GB'
GB tn, fp: 332, 1
GB fn, tp: 0, 13
GB f1 score: 0.963
GB cohens kappa score: 0.961

-> test with 'KNN'
KNN tn, fp: 330, 3
KNN fn, tp: 6, 7
KNN f1 score: 0.609
KNN cohens kappa score: 0.595


------ Step 4/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Train 1330/52 points
-> new disc
-> calc distances
-> statistics
trained 52 points min:1.0 max:1.0
-> create 1278 synthetic samples
-> test with 'LR'
LR tn, fp: 320, 13
LR fn, tp: 9, 4
LR f1 score: 0.267
LR cohens kappa score: 0.234
LR average precision score: 0.243

-> test with 'GB'
GB tn, fp: 333, 0
GB fn, tp: 0, 13
GB f1 score: 1.000
GB cohens kappa score: 1.000

-> test with 'KNN'
KNN tn, fp: 330, 3
KNN fn, tp: 9, 4
KNN f1 score: 0.400
KNN cohens kappa score: 0.384


------ Step 4/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Train 1332/52 points
-> new disc
-> calc distances
-> statistics
trained 52 points min:1.0 max:1.0
-> create 1280 synthetic samples
-> test with 'LR'
LR tn, fp: 317, 14
LR fn, tp: 9, 4
LR f1 score: 0.258
LR cohens kappa score: 0.224
LR average precision score: 0.223

-> test with 'GB'
GB tn, fp: 331, 0
GB fn, tp: 1, 12
GB f1 score: 0.960
GB cohens kappa score: 0.958

-> test with 'KNN'
KNN tn, fp: 329, 2
KNN fn, tp: 7, 6
KNN f1 score: 0.571
KNN cohens kappa score: 0.559


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

------ Step 5/5: Slice 1/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Train 1330/52 points
-> new disc
-> calc distances
-> statistics
trained 52 points min:1.0 max:1.0
-> create 1278 synthetic samples
-> test with 'LR'
LR tn, fp: 310, 23
LR fn, tp: 7, 6
LR f1 score: 0.286
LR cohens kappa score: 0.247
LR average precision score: 0.230

-> test with 'GB'
GB tn, fp: 333, 0
GB fn, tp: 0, 13
GB f1 score: 1.000
GB cohens kappa score: 1.000

-> test with 'KNN'
KNN tn, fp: 325, 8
KNN fn, tp: 7, 6
KNN f1 score: 0.444
KNN cohens kappa score: 0.422


------ Step 5/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Train 1330/52 points
-> new disc
-> calc distances
-> statistics
trained 52 points min:1.0 max:1.0
-> create 1278 synthetic samples
-> test with 'LR'
LR tn, fp: 320, 13
LR fn, tp: 11, 2
LR f1 score: 0.143
LR cohens kappa score: 0.107
LR average precision score: 0.180

-> test with 'GB'
GB tn, fp: 333, 0
GB fn, tp: 3, 10
GB f1 score: 0.870
GB cohens kappa score: 0.865

-> test with 'KNN'
KNN tn, fp: 332, 1
KNN fn, tp: 9, 4
KNN f1 score: 0.444
KNN cohens kappa score: 0.433


------ Step 5/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Train 1330/52 points
-> new disc
-> calc distances
-> statistics
trained 52 points min:1.0 max:1.4142135623730951
-> create 1278 synthetic samples
-> test with 'LR'
LR tn, fp: 321, 12
LR fn, tp: 7, 6
LR f1 score: 0.387
LR cohens kappa score: 0.359
LR average precision score: 0.284

-> test with 'GB'
GB tn, fp: 333, 0
GB fn, tp: 0, 13
GB f1 score: 1.000
GB cohens kappa score: 1.000

-> test with 'KNN'
KNN tn, fp: 332, 1
KNN fn, tp: 8, 5
KNN f1 score: 0.526
KNN cohens kappa score: 0.515


------ Step 5/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Train 1330/52 points
-> new disc
-> calc distances
-> statistics
trained 52 points min:1.0 max:1.0
-> create 1278 synthetic samples
-> test with 'LR'
LR tn, fp: 314, 19
LR fn, tp: 9, 4
LR f1 score: 0.222
LR cohens kappa score: 0.183
LR average precision score: 0.197

-> test with 'GB'
GB tn, fp: 333, 0
GB fn, tp: 2, 11
GB f1 score: 0.917
GB cohens kappa score: 0.914

-> test with 'KNN'
KNN tn, fp: 330, 3
KNN fn, tp: 4, 9
KNN f1 score: 0.720
KNN cohens kappa score: 0.710


------ Step 5/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Train 1332/52 points
-> new disc
-> calc distances
-> statistics
trained 52 points min:1.0 max:1.0
-> create 1280 synthetic samples
-> test with 'LR'
LR tn, fp: 317, 14
LR fn, tp: 9, 4
LR f1 score: 0.258
LR cohens kappa score: 0.224
LR average precision score: 0.205

-> test with 'GB'
GB tn, fp: 329, 2
GB fn, tp: 1, 12
GB f1 score: 0.889
GB cohens kappa score: 0.884

-> test with 'KNN'
KNN tn, fp: 328, 3
KNN fn, tp: 6, 7
KNN f1 score: 0.609
KNN cohens kappa score: 0.595

### Exercise is done.

-----[ LR ]-----
maximum:
LR tn, fp: 322, 26
LR fn, tp: 12, 7
LR f1 score: 0.387
LR cohens kappa score: 0.359
LR average precision score: 0.331


average:
LR tn, fp: 316.64, 15.96
LR fn, tp: 8.96, 4.04
LR f1 score: 0.243
LR cohens kappa score: 0.207
LR average precision score: 0.226


minimum:
LR tn, fp: 307, 9
LR fn, tp: 6, 1
LR f1 score: 0.062
LR cohens kappa score: 0.019
LR average precision score: 0.167


-----[ GB ]-----
maximum:
GB tn, fp: 333, 5
GB fn, tp: 4, 13
GB f1 score: 1.000
GB cohens kappa score: 1.000


average:
GB tn, fp: 332.0, 0.6
GB fn, tp: 0.76, 12.24
GB f1 score: 0.946
GB cohens kappa score: 0.944


minimum:
GB tn, fp: 328, 0
GB fn, tp: 0, 9
GB f1 score: 0.818
GB cohens kappa score: 0.812


-----[ KNN ]-----
maximum:
KNN tn, fp: 333, 8
KNN fn, tp: 11, 9
KNN f1 score: 0.720
KNN cohens kappa score: 0.710


average:
KNN tn, fp: 330.0, 2.6
KNN fn, tp: 7.68, 5.32
KNN f1 score: 0.504
KNN cohens kappa score: 0.490


minimum:
KNN tn, fp: 325, 0
KNN fn, tp: 4, 2
KNN f1 score: 0.267
KNN cohens kappa score: 0.259