ConvGeNBenchmarkResults/data_result/SpheredNoise/folding_winequality-red-4.log

///////////////////////////////////////////
// Running SpheredNoise on folding_winequality-red-4
///////////////////////////////////////////

Load 'data_input/folding_winequality-red-4'
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 1236/42 points
-> new disc
-> calc distances
-> statistics
trained 42 points min:0.542887025540305 max:13.164740804983591
-> create 1194 synthetic samples
-> test with 'LR'
LR tn, fp: 309, 1
LR fn, tp: 11, 0
LR f1 score: 0.000
LR cohens kappa score: -0.006
LR average precision score: 0.160

-> test with 'GB'
GB tn, fp: 308, 2
GB fn, tp: 10, 1
GB f1 score: 0.143
GB cohens kappa score: 0.130

-> test with 'KNN'
KNN tn, fp: 310, 0
KNN fn, tp: 11, 0
KNN f1 score: 0.000
KNN cohens kappa score: 0.000


------ Step 1/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Train 1236/42 points
-> new disc
-> calc distances
-> statistics
trained 42 points min:0.542887025540305 max:13.364807372932841
-> create 1194 synthetic samples
-> test with 'LR'
LR tn, fp: 309, 1
LR fn, tp: 11, 0
LR f1 score: 0.000
LR cohens kappa score: -0.006
LR average precision score: 0.134

-> test with 'GB'
GB tn, fp: 308, 2
GB fn, tp: 11, 0
GB f1 score: 0.000
GB cohens kappa score: -0.011

-> test with 'KNN'
KNN tn, fp: 310, 0
KNN fn, tp: 11, 0
KNN f1 score: 0.000
KNN cohens kappa score: 0.000


------ Step 1/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Train 1236/42 points
-> new disc
-> calc distances
-> statistics
trained 42 points min:0.7107056774783781 max:13.164740804983591
-> create 1194 synthetic samples
-> test with 'LR'
LR tn, fp: 310, 0
LR fn, tp: 11, 0
LR f1 score: 0.000
LR cohens kappa score: 0.000
LR average precision score: 0.183

-> test with 'GB'
GB tn, fp: 307, 3
GB fn, tp: 11, 0
GB f1 score: 0.000
GB cohens kappa score: -0.015

-> test with 'KNN'
KNN tn, fp: 310, 0
KNN fn, tp: 11, 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 1236/42 points
-> new disc
-> calc distances
-> statistics
trained 42 points min:0.542887025540305 max:3.9618467065751037
-> create 1194 synthetic samples
-> test with 'LR'
LR tn, fp: 308, 2
LR fn, tp: 10, 1
LR f1 score: 0.143
LR cohens kappa score: 0.130
LR average precision score: 0.113

-> test with 'GB'
GB tn, fp: 307, 3
GB fn, tp: 10, 1
GB f1 score: 0.133
GB cohens kappa score: 0.117

-> test with 'KNN'
KNN tn, fp: 310, 0
KNN fn, tp: 11, 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 1240/44 points
-> new disc
-> calc distances
-> statistics
trained 44 points min:0.7107056774783781 max:13.164740804983591
-> create 1196 synthetic samples
-> test with 'LR'
LR tn, fp: 306, 0
LR fn, tp: 8, 1
LR f1 score: 0.200
LR cohens kappa score: 0.195
LR average precision score: 0.218

-> test with 'GB'
GB tn, fp: 306, 0
GB fn, tp: 8, 1
GB f1 score: 0.200
GB cohens kappa score: 0.195

-> test with 'KNN'
KNN tn, fp: 306, 0
KNN fn, tp: 9, 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 1236/42 points
-> new disc
-> calc distances
-> statistics
trained 42 points min:0.542887025540305 max:13.364807372932841
-> create 1194 synthetic samples
-> test with 'LR'
LR tn, fp: 309, 1
LR fn, tp: 10, 1
LR f1 score: 0.154
LR cohens kappa score: 0.145
LR average precision score: 0.145

-> test with 'GB'
GB tn, fp: 307, 3
GB fn, tp: 10, 1
GB f1 score: 0.133
GB cohens kappa score: 0.117

-> test with 'KNN'
KNN tn, fp: 310, 0
KNN fn, tp: 11, 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 1236/42 points
-> new disc
-> calc distances
-> statistics
trained 42 points min:0.9743119738564242 max:13.164740804983591
-> create 1194 synthetic samples
-> test with 'LR'
LR tn, fp: 310, 0
LR fn, tp: 11, 0
LR f1 score: 0.000
LR cohens kappa score: 0.000
LR average precision score: 0.135

-> test with 'GB'
GB tn, fp: 307, 3
GB fn, tp: 11, 0
GB f1 score: 0.000
GB cohens kappa score: -0.015

-> test with 'KNN'
KNN tn, fp: 310, 0
KNN fn, tp: 11, 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 1236/42 points
-> new disc
-> calc distances
-> statistics
trained 42 points min:0.6050000330578494 max:5.15715300079414
-> create 1194 synthetic samples
-> test with 'LR'
LR tn, fp: 308, 2
LR fn, tp: 11, 0
LR f1 score: 0.000
LR cohens kappa score: -0.011
LR average precision score: 0.126

-> test with 'GB'
GB tn, fp: 307, 3
GB fn, tp: 11, 0
GB f1 score: 0.000
GB cohens kappa score: -0.015

-> test with 'KNN'
KNN tn, fp: 310, 0
KNN fn, tp: 11, 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 1236/42 points
-> new disc
-> calc distances
-> statistics
trained 42 points min:0.542887025540305 max:13.164740804983591
-> create 1194 synthetic samples
-> test with 'LR'
LR tn, fp: 310, 0
LR fn, tp: 11, 0
LR f1 score: 0.000
LR cohens kappa score: 0.000
LR average precision score: 0.259

-> test with 'GB'
GB tn, fp: 310, 0
GB fn, tp: 11, 0
GB f1 score: 0.000
GB cohens kappa score: 0.000

-> test with 'KNN'
KNN tn, fp: 310, 0
KNN fn, tp: 11, 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 1240/44 points
-> new disc
-> calc distances
-> statistics
trained 44 points min:0.542887025540305 max:13.164740804983591
-> create 1196 synthetic samples
-> test with 'LR'
LR tn, fp: 305, 1
LR fn, tp: 9, 0
LR f1 score: 0.000
LR cohens kappa score: -0.006
LR average precision score: 0.091

-> test with 'GB'
GB tn, fp: 301, 5
GB fn, tp: 8, 1
GB f1 score: 0.133
GB cohens kappa score: 0.113

-> test with 'KNN'
KNN tn, fp: 306, 0
KNN fn, tp: 9, 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 1236/42 points
-> new disc
-> calc distances
-> statistics
trained 42 points min:0.542887025540305 max:13.164740804983591
-> create 1194 synthetic samples
-> test with 'LR'
LR tn, fp: 309, 1
LR fn, tp: 11, 0
LR f1 score: 0.000
LR cohens kappa score: -0.006
LR average precision score: 0.168

-> test with 'GB'
GB tn, fp: 309, 1
GB fn, tp: 10, 1
GB f1 score: 0.154
GB cohens kappa score: 0.145

-> test with 'KNN'
KNN tn, fp: 310, 0
KNN fn, tp: 11, 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 1236/42 points
-> new disc
-> calc distances
-> statistics
trained 42 points min:0.6050000330578494 max:13.164740804983591
-> create 1194 synthetic samples
-> test with 'LR'
LR tn, fp: 310, 0
LR fn, tp: 11, 0
LR f1 score: 0.000
LR cohens kappa score: 0.000
LR average precision score: 0.208

-> test with 'GB'
GB tn, fp: 308, 2
GB fn, tp: 11, 0
GB f1 score: 0.000
GB cohens kappa score: -0.011

-> test with 'KNN'
KNN tn, fp: 310, 0
KNN fn, tp: 11, 0
KNN f1 score: 0.000
KNN cohens kappa score: 0.000


------ Step 3/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Train 1236/42 points
-> new disc
-> calc distances
-> statistics
trained 42 points min:0.542887025540305 max:8.006020815261474
-> create 1194 synthetic samples
-> test with 'LR'
LR tn, fp: 308, 2
LR fn, tp: 11, 0
LR f1 score: 0.000
LR cohens kappa score: -0.011
LR average precision score: 0.079

-> test with 'GB'
GB tn, fp: 305, 5
GB fn, tp: 11, 0
GB f1 score: 0.000
GB cohens kappa score: -0.022

-> test with 'KNN'
KNN tn, fp: 310, 0
KNN fn, tp: 11, 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 1236/42 points
-> new disc
-> calc distances
-> statistics
trained 42 points min:0.7107056774783781 max:13.164740804983591
-> create 1194 synthetic samples
-> test with 'LR'
LR tn, fp: 309, 1
LR fn, tp: 11, 0
LR f1 score: 0.000
LR cohens kappa score: -0.006
LR average precision score: 0.169

-> test with 'GB'
GB tn, fp: 309, 1
GB fn, tp: 11, 0
GB f1 score: 0.000
GB cohens kappa score: -0.006

-> test with 'KNN'
KNN tn, fp: 310, 0
KNN fn, tp: 11, 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 1240/44 points
-> new disc
-> calc distances
-> statistics
trained 44 points min:0.542887025540305 max:13.364807372932841
-> create 1196 synthetic samples
-> test with 'LR'
LR tn, fp: 306, 0
LR fn, tp: 9, 0
LR f1 score: 0.000
LR cohens kappa score: 0.000
LR average precision score: 0.104

-> test with 'GB'
GB tn, fp: 305, 1
GB fn, tp: 8, 1
GB f1 score: 0.182
GB cohens kappa score: 0.173

-> test with 'KNN'
KNN tn, fp: 306, 0
KNN fn, tp: 9, 0
KNN f1 score: 0.000
KNN cohens kappa score: 0.000


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

------ Step 4/5: Slice 1/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Train 1236/42 points
-> new disc
-> calc distances
-> statistics
trained 42 points min:0.8812840168753769 max:13.164740804983591
-> create 1194 synthetic samples
-> test with 'LR'
LR tn, fp: 310, 0
LR fn, tp: 11, 0
LR f1 score: 0.000
LR cohens kappa score: 0.000
LR average precision score: 0.344

-> test with 'GB'
GB tn, fp: 309, 1
GB fn, tp: 10, 1
GB f1 score: 0.154
GB cohens kappa score: 0.145

-> test with 'KNN'
KNN tn, fp: 310, 0
KNN fn, tp: 11, 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 1236/42 points
-> new disc
-> calc distances
-> statistics
trained 42 points min:0.542887025540305 max:5.15715300079414
-> create 1194 synthetic samples
-> test with 'LR'
LR tn, fp: 309, 1
LR fn, tp: 11, 0
LR f1 score: 0.000
LR cohens kappa score: -0.006
LR average precision score: 0.184

-> test with 'GB'
GB tn, fp: 309, 1
GB fn, tp: 10, 1
GB f1 score: 0.154
GB cohens kappa score: 0.145

-> test with 'KNN'
KNN tn, fp: 310, 0
KNN fn, tp: 11, 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 1236/42 points
-> new disc
-> calc distances
-> statistics
trained 42 points min:0.542887025540305 max:13.164740804983591
-> create 1194 synthetic samples
-> test with 'LR'
LR tn, fp: 310, 0
LR fn, tp: 11, 0
LR f1 score: 0.000
LR cohens kappa score: 0.000
LR average precision score: 0.102

-> test with 'GB'
GB tn, fp: 308, 2
GB fn, tp: 11, 0
GB f1 score: 0.000
GB cohens kappa score: -0.011

-> test with 'KNN'
KNN tn, fp: 310, 0
KNN fn, tp: 11, 0
KNN f1 score: 0.000
KNN cohens kappa score: 0.000


------ Step 4/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Train 1236/42 points
-> new disc
-> calc distances
-> statistics
trained 42 points min:0.542887025540305 max:13.164740804983591
-> create 1194 synthetic samples
-> test with 'LR'
LR tn, fp: 306, 4
LR fn, tp: 10, 1
LR f1 score: 0.125
LR cohens kappa score: 0.106
LR average precision score: 0.143

-> test with 'GB'
GB tn, fp: 308, 2
GB fn, tp: 11, 0
GB f1 score: 0.000
GB cohens kappa score: -0.011

-> test with 'KNN'
KNN tn, fp: 310, 0
KNN fn, tp: 11, 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 1240/44 points
-> new disc
-> calc distances
-> statistics
trained 44 points min:0.542887025540305 max:13.364807372932841
-> create 1196 synthetic samples
-> test with 'LR'
LR tn, fp: 306, 0
LR fn, tp: 9, 0
LR f1 score: 0.000
LR cohens kappa score: 0.000
LR average precision score: 0.048

-> test with 'GB'
GB tn, fp: 303, 3
GB fn, tp: 9, 0
GB f1 score: 0.000
GB cohens kappa score: -0.014

-> test with 'KNN'
KNN tn, fp: 306, 0
KNN fn, tp: 9, 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 1236/42 points
-> new disc
-> calc distances
-> statistics
trained 42 points min:0.542887025540305 max:5.15715300079414
-> create 1194 synthetic samples
-> test with 'LR'
LR tn, fp: 309, 1
LR fn, tp: 11, 0
LR f1 score: 0.000
LR cohens kappa score: -0.006
LR average precision score: 0.084

-> test with 'GB'
GB tn, fp: 308, 2
GB fn, tp: 11, 0
GB f1 score: 0.000
GB cohens kappa score: -0.011

-> test with 'KNN'
KNN tn, fp: 310, 0
KNN fn, tp: 11, 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 1236/42 points
-> new disc
-> calc distances
-> statistics
trained 42 points min:0.6050000330578494 max:13.164740804983591
-> create 1194 synthetic samples
-> test with 'LR'
LR tn, fp: 310, 0
LR fn, tp: 11, 0
LR f1 score: 0.000
LR cohens kappa score: 0.000
LR average precision score: 0.096

-> test with 'GB'
GB tn, fp: 308, 2
GB fn, tp: 10, 1
GB f1 score: 0.143
GB cohens kappa score: 0.130

-> test with 'KNN'
KNN tn, fp: 310, 0
KNN fn, tp: 11, 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 1236/42 points
-> new disc
-> calc distances
-> statistics
trained 42 points min:0.542887025540305 max:13.164740804983591
-> create 1194 synthetic samples
-> test with 'LR'
LR tn, fp: 309, 1
LR fn, tp: 11, 0
LR f1 score: 0.000
LR cohens kappa score: -0.006
LR average precision score: 0.260

-> test with 'GB'
GB tn, fp: 307, 3
GB fn, tp: 10, 1
GB f1 score: 0.133
GB cohens kappa score: 0.117

-> test with 'KNN'
KNN tn, fp: 310, 0
KNN fn, tp: 11, 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 1236/42 points
-> new disc
-> calc distances
-> statistics
trained 42 points min:0.542887025540305 max:13.164740804983591
-> create 1194 synthetic samples
-> test with 'LR'
LR tn, fp: 309, 1
LR fn, tp: 10, 1
LR f1 score: 0.154
LR cohens kappa score: 0.145
LR average precision score: 0.192

-> test with 'GB'
GB tn, fp: 309, 1
GB fn, tp: 10, 1
GB f1 score: 0.154
GB cohens kappa score: 0.145

-> test with 'KNN'
KNN tn, fp: 310, 0
KNN fn, tp: 11, 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 1240/44 points
-> new disc
-> calc distances
-> statistics
trained 44 points min:0.6050000330578494 max:13.164740804983591
-> create 1196 synthetic samples
-> test with 'LR'
LR tn, fp: 305, 1
LR fn, tp: 9, 0
LR f1 score: 0.000
LR cohens kappa score: -0.006
LR average precision score: 0.167

-> test with 'GB'
GB tn, fp: 301, 5
GB fn, tp: 9, 0
GB f1 score: 0.000
GB cohens kappa score: -0.021

-> test with 'KNN'
KNN tn, fp: 306, 0
KNN fn, tp: 9, 0
KNN f1 score: 0.000
KNN cohens kappa score: 0.000

### Exercise is done.

-----[ LR ]-----
maximum:
LR tn, fp: 310, 4
LR fn, tp: 11, 1
LR f1 score: 0.200
LR cohens kappa score: 0.195
LR average precision score: 0.344


average:
LR tn, fp: 308.36, 0.84
LR fn, tp: 10.4, 0.2
LR f1 score: 0.031
LR cohens kappa score: 0.026
LR average precision score: 0.156


minimum:
LR tn, fp: 305, 0
LR fn, tp: 8, 0
LR f1 score: 0.000
LR cohens kappa score: -0.011
LR average precision score: 0.048


-----[ GB ]-----
maximum:
GB tn, fp: 310, 5
GB fn, tp: 11, 1
GB f1 score: 0.200
GB cohens kappa score: 0.195


average:
GB tn, fp: 306.96, 2.24
GB fn, tp: 10.12, 0.48
GB f1 score: 0.073
GB cohens kappa score: 0.060


minimum:
GB tn, fp: 301, 0
GB fn, tp: 8, 0
GB f1 score: 0.000
GB cohens kappa score: -0.022


-----[ KNN ]-----
maximum:
KNN tn, fp: 310, 0
KNN fn, tp: 11, 0
KNN f1 score: 0.000
KNN cohens kappa score: 0.000


average:
KNN tn, fp: 309.2, 0.0
KNN fn, tp: 10.6, 0.0
KNN f1 score: 0.000
KNN cohens kappa score: 0.000


minimum:
KNN tn, fp: 306, 0
KNN fn, tp: 9, 0
KNN f1 score: 0.000
KNN cohens kappa score: 0.000