ConvGeNBenchmarkResults/data_result/SpheredNoise/folding_yeast4.log

///////////////////////////////////////////
// Running SpheredNoise on folding_yeast4
///////////////////////////////////////////

Load 'data_input/folding_yeast4'
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 1146/40 points
-> new disc
-> calc distances
-> statistics
trained 40 points min:0.026457513110645887 max:0.21118712081942884
-> create 1106 synthetic samples
-> test with 'LR'
LR tn, fp: 285, 2
LR fn, tp: 8, 3
LR f1 score: 0.375
LR cohens kappa score: 0.360
LR average precision score: 0.428

-> test with 'GB'
GB tn, fp: 284, 3
GB fn, tp: 10, 1
GB f1 score: 0.133
GB cohens kappa score: 0.116

-> test with 'KNN'
KNN tn, fp: 287, 0
KNN fn, tp: 10, 1
KNN f1 score: 0.167
KNN cohens kappa score: 0.162


------ Step 1/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Train 1146/40 points
-> new disc
-> calc distances
-> statistics
trained 40 points min:0.026457513110645887 max:0.20808652046684803
-> create 1106 synthetic samples
-> test with 'LR'
LR tn, fp: 286, 1
LR fn, tp: 7, 4
LR f1 score: 0.500
LR cohens kappa score: 0.488
LR average precision score: 0.656

-> test with 'GB'
GB tn, fp: 283, 4
GB fn, tp: 6, 5
GB f1 score: 0.500
GB cohens kappa score: 0.483

-> test with 'KNN'
KNN tn, fp: 287, 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 1146/40 points
-> new disc
-> calc distances
-> statistics
trained 40 points min:0.03872983346207415 max:0.25000000000000006
-> create 1106 synthetic samples
-> test with 'LR'
LR tn, fp: 285, 2
LR fn, tp: 10, 1
LR f1 score: 0.143
LR cohens kappa score: 0.129
LR average precision score: 0.390

-> test with 'GB'
GB tn, fp: 286, 1
GB fn, tp: 10, 1
GB f1 score: 0.154
GB cohens kappa score: 0.144

-> test with 'KNN'
KNN tn, fp: 287, 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 1146/40 points
-> new disc
-> calc distances
-> statistics
trained 40 points min:0.03872983346207415 max:0.20808652046684803
-> create 1106 synthetic samples
-> test with 'LR'
LR tn, fp: 284, 3
LR fn, tp: 9, 2
LR f1 score: 0.250
LR cohens kappa score: 0.232
LR average precision score: 0.213

-> test with 'GB'
GB tn, fp: 282, 5
GB fn, tp: 9, 2
GB f1 score: 0.222
GB cohens kappa score: 0.199

-> test with 'KNN'
KNN tn, fp: 285, 2
KNN fn, tp: 11, 0
KNN f1 score: 0.000
KNN cohens kappa score: -0.011


------ Step 1/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Train 1148/44 points
-> new disc
-> calc distances
-> statistics
trained 44 points min:0.026457513110645887 max:0.23043437243605827
-> create 1104 synthetic samples
-> test with 'LR'
LR tn, fp: 284, 1
LR fn, tp: 6, 1
LR f1 score: 0.222
LR cohens kappa score: 0.214
LR average precision score: 0.493

-> test with 'GB'
GB tn, fp: 284, 1
GB fn, tp: 5, 2
GB f1 score: 0.400
GB cohens kappa score: 0.391

-> test with 'KNN'
KNN tn, fp: 284, 1
KNN fn, tp: 7, 0
KNN f1 score: 0.000
KNN cohens kappa score: -0.006


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

------ Step 2/5: Slice 1/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Train 1146/40 points
-> new disc
-> calc distances
-> statistics
trained 40 points min:0.026457513110645887 max:0.20808652046684803
-> create 1106 synthetic samples
-> test with 'LR'
LR tn, fp: 286, 1
LR fn, tp: 10, 1
LR f1 score: 0.154
LR cohens kappa score: 0.144
LR average precision score: 0.286

-> test with 'GB'
GB tn, fp: 281, 6
GB fn, tp: 10, 1
GB f1 score: 0.111
GB cohens kappa score: 0.085

-> test with 'KNN'
KNN tn, fp: 287, 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 1146/40 points
-> new disc
-> calc distances
-> statistics
trained 40 points min:0.026457513110645887 max:0.22181073012818836
-> create 1106 synthetic samples
-> test with 'LR'
LR tn, fp: 284, 3
LR fn, tp: 5, 6
LR f1 score: 0.600
LR cohens kappa score: 0.586
LR average precision score: 0.473

-> test with 'GB'
GB tn, fp: 285, 2
GB fn, tp: 7, 4
GB f1 score: 0.471
GB cohens kappa score: 0.456

-> test with 'KNN'
KNN tn, fp: 286, 1
KNN fn, tp: 9, 2
KNN f1 score: 0.286
KNN cohens kappa score: 0.274


------ Step 2/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Train 1146/40 points
-> new disc
-> calc distances
-> statistics
trained 40 points min:0.026457513110645887 max:0.2366431913239846
-> create 1106 synthetic samples
-> test with 'LR'
LR tn, fp: 285, 2
LR fn, tp: 9, 2
LR f1 score: 0.267
LR cohens kappa score: 0.252
LR average precision score: 0.402

-> test with 'GB'
GB tn, fp: 285, 2
GB fn, tp: 8, 3
GB f1 score: 0.375
GB cohens kappa score: 0.360

-> test with 'KNN'
KNN tn, fp: 286, 1
KNN fn, tp: 11, 0
KNN f1 score: 0.000
KNN cohens kappa score: -0.006


------ Step 2/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Train 1146/40 points
-> new disc
-> calc distances
-> statistics
trained 40 points min:0.052915026221291794 max:0.20049937655763428
-> create 1106 synthetic samples
-> test with 'LR'
LR tn, fp: 286, 1
LR fn, tp: 10, 1
LR f1 score: 0.154
LR cohens kappa score: 0.144
LR average precision score: 0.388

-> test with 'GB'
GB tn, fp: 285, 2
GB fn, tp: 8, 3
GB f1 score: 0.375
GB cohens kappa score: 0.360

-> test with 'KNN'
KNN tn, fp: 286, 1
KNN fn, tp: 11, 0
KNN f1 score: 0.000
KNN cohens kappa score: -0.006


------ Step 2/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Train 1148/44 points
-> new disc
-> calc distances
-> statistics
trained 44 points min:0.026457513110645887 max:0.20808652046684803
-> create 1104 synthetic samples
-> test with 'LR'
LR tn, fp: 284, 1
LR fn, tp: 6, 1
LR f1 score: 0.222
LR cohens kappa score: 0.214
LR average precision score: 0.505

-> test with 'GB'
GB tn, fp: 283, 2
GB fn, tp: 6, 1
GB f1 score: 0.200
GB cohens kappa score: 0.188

-> test with 'KNN'
KNN tn, fp: 285, 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 1146/40 points
-> new disc
-> calc distances
-> statistics
trained 40 points min:0.026457513110645887 max:0.20808652046684803
-> create 1106 synthetic samples
-> test with 'LR'
LR tn, fp: 285, 2
LR fn, tp: 8, 3
LR f1 score: 0.375
LR cohens kappa score: 0.360
LR average precision score: 0.408

-> test with 'GB'
GB tn, fp: 285, 2
GB fn, tp: 9, 2
GB f1 score: 0.267
GB cohens kappa score: 0.252

-> test with 'KNN'
KNN tn, fp: 286, 1
KNN fn, tp: 11, 0
KNN f1 score: 0.000
KNN cohens kappa score: -0.006


------ Step 3/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Train 1146/40 points
-> new disc
-> calc distances
-> statistics
trained 40 points min:0.026457513110645887 max:0.2366431913239846
-> create 1106 synthetic samples
-> test with 'LR'
LR tn, fp: 284, 3
LR fn, tp: 11, 0
LR f1 score: 0.000
LR cohens kappa score: -0.016
LR average precision score: 0.382

-> test with 'GB'
GB tn, fp: 284, 3
GB fn, tp: 10, 1
GB f1 score: 0.133
GB cohens kappa score: 0.116

-> test with 'KNN'
KNN tn, fp: 287, 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 1146/40 points
-> new disc
-> calc distances
-> statistics
trained 40 points min:0.04000000000000002 max:0.20808652046684803
-> create 1106 synthetic samples
-> test with 'LR'
LR tn, fp: 283, 4
LR fn, tp: 10, 1
LR f1 score: 0.125
LR cohens kappa score: 0.104
LR average precision score: 0.247

-> test with 'GB'
GB tn, fp: 283, 4
GB fn, tp: 9, 2
GB f1 score: 0.235
GB cohens kappa score: 0.215

-> test with 'KNN'
KNN tn, fp: 287, 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 1146/40 points
-> new disc
-> calc distances
-> statistics
trained 40 points min:0.03872983346207415 max:0.20808652046684803
-> create 1106 synthetic samples
-> test with 'LR'
LR tn, fp: 286, 1
LR fn, tp: 8, 3
LR f1 score: 0.400
LR cohens kappa score: 0.388
LR average precision score: 0.502

-> test with 'GB'
GB tn, fp: 283, 4
GB fn, tp: 6, 5
GB f1 score: 0.500
GB cohens kappa score: 0.483

-> test with 'KNN'
KNN tn, fp: 287, 0
KNN fn, tp: 10, 1
KNN f1 score: 0.167
KNN cohens kappa score: 0.162


------ Step 3/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Train 1148/44 points
-> new disc
-> calc distances
-> statistics
trained 44 points min:0.026457513110645887 max:0.19672315572906002
-> create 1104 synthetic samples
-> test with 'LR'
LR tn, fp: 285, 0
LR fn, tp: 5, 2
LR f1 score: 0.444
LR cohens kappa score: 0.438
LR average precision score: 0.444

-> test with 'GB'
GB tn, fp: 281, 4
GB fn, tp: 4, 3
GB f1 score: 0.429
GB cohens kappa score: 0.415

-> test with 'KNN'
KNN tn, fp: 285, 0
KNN fn, tp: 5, 2
KNN f1 score: 0.444
KNN cohens kappa score: 0.438


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

------ Step 4/5: Slice 1/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Train 1146/40 points
-> new disc
-> calc distances
-> statistics
trained 40 points min:0.03872983346207415 max:0.21000000000000002
-> create 1106 synthetic samples
-> test with 'LR'
LR tn, fp: 287, 0
LR fn, tp: 8, 3
LR f1 score: 0.429
LR cohens kappa score: 0.419
LR average precision score: 0.471

-> test with 'GB'
GB tn, fp: 284, 3
GB fn, tp: 11, 0
GB f1 score: 0.000
GB cohens kappa score: -0.016

-> test with 'KNN'
KNN tn, fp: 287, 0
KNN fn, tp: 10, 1
KNN f1 score: 0.167
KNN cohens kappa score: 0.162


------ Step 4/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Train 1146/40 points
-> new disc
-> calc distances
-> statistics
trained 40 points min:0.026457513110645887 max:0.21725560982400433
-> create 1106 synthetic samples
-> test with 'LR'
LR tn, fp: 287, 0
LR fn, tp: 11, 0
LR f1 score: 0.000
LR cohens kappa score: 0.000
LR average precision score: 0.436

-> test with 'GB'
GB tn, fp: 286, 1
GB fn, tp: 7, 4
GB f1 score: 0.500
GB cohens kappa score: 0.488

-> test with 'KNN'
KNN tn, fp: 286, 1
KNN fn, tp: 11, 0
KNN f1 score: 0.000
KNN cohens kappa score: -0.006


------ Step 4/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Train 1146/40 points
-> new disc
-> calc distances
-> statistics
trained 40 points min:0.04000000000000002 max:0.2366431913239847
-> create 1106 synthetic samples
-> test with 'LR'
LR tn, fp: 285, 2
LR fn, tp: 10, 1
LR f1 score: 0.143
LR cohens kappa score: 0.129
LR average precision score: 0.244

-> test with 'GB'
GB tn, fp: 282, 5
GB fn, tp: 10, 1
GB f1 score: 0.118
GB cohens kappa score: 0.094

-> test with 'KNN'
KNN tn, fp: 287, 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 1146/40 points
-> new disc
-> calc distances
-> statistics
trained 40 points min:0.026457513110645887 max:0.21118712081942884
-> create 1106 synthetic samples
-> test with 'LR'
LR tn, fp: 283, 4
LR fn, tp: 8, 3
LR f1 score: 0.333
LR cohens kappa score: 0.314
LR average precision score: 0.306

-> test with 'GB'
GB tn, fp: 282, 5
GB fn, tp: 10, 1
GB f1 score: 0.118
GB cohens kappa score: 0.094

-> test with 'KNN'
KNN tn, fp: 286, 1
KNN fn, tp: 10, 1
KNN f1 score: 0.154
KNN cohens kappa score: 0.144


------ Step 4/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Train 1148/44 points
-> new disc
-> calc distances
-> statistics
trained 44 points min:0.026457513110645887 max:0.2366431913239846
-> create 1104 synthetic samples
-> test with 'LR'
LR tn, fp: 283, 2
LR fn, tp: 4, 3
LR f1 score: 0.500
LR cohens kappa score: 0.490
LR average precision score: 0.586

-> test with 'GB'
GB tn, fp: 283, 2
GB fn, tp: 5, 2
GB f1 score: 0.364
GB cohens kappa score: 0.352

-> test with 'KNN'
KNN tn, fp: 285, 0
KNN fn, tp: 5, 2
KNN f1 score: 0.444
KNN cohens kappa score: 0.438


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

------ Step 5/5: Slice 1/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Train 1146/40 points
-> new disc
-> calc distances
-> statistics
trained 40 points min:0.03872983346207415 max:0.21118712081942884
-> create 1106 synthetic samples
-> test with 'LR'
LR tn, fp: 286, 1
LR fn, tp: 10, 1
LR f1 score: 0.154
LR cohens kappa score: 0.144
LR average precision score: 0.270

-> test with 'GB'
GB tn, fp: 285, 2
GB fn, tp: 11, 0
GB f1 score: 0.000
GB cohens kappa score: -0.011

-> test with 'KNN'
KNN tn, fp: 287, 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 1146/40 points
-> new disc
-> calc distances
-> statistics
trained 40 points min:0.026457513110645887 max:0.22561028345356957
-> create 1106 synthetic samples
-> test with 'LR'
LR tn, fp: 285, 2
LR fn, tp: 8, 3
LR f1 score: 0.375
LR cohens kappa score: 0.360
LR average precision score: 0.544

-> test with 'GB'
GB tn, fp: 285, 2
GB fn, tp: 9, 2
GB f1 score: 0.267
GB cohens kappa score: 0.252

-> test with 'KNN'
KNN tn, fp: 287, 0
KNN fn, tp: 9, 2
KNN f1 score: 0.308
KNN cohens kappa score: 0.300


------ Step 5/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Train 1146/40 points
-> new disc
-> calc distances
-> statistics
trained 40 points min:0.026457513110645887 max:0.23043437243605827
-> create 1106 synthetic samples
-> test with 'LR'
LR tn, fp: 287, 0
LR fn, tp: 10, 1
LR f1 score: 0.167
LR cohens kappa score: 0.162
LR average precision score: 0.548

-> test with 'GB'
GB tn, fp: 286, 1
GB fn, tp: 8, 3
GB f1 score: 0.400
GB cohens kappa score: 0.388

-> test with 'KNN'
KNN tn, fp: 287, 0
KNN fn, tp: 10, 1
KNN f1 score: 0.167
KNN cohens kappa score: 0.162


------ Step 5/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Train 1146/40 points
-> new disc
-> calc distances
-> statistics
trained 40 points min:0.026457513110645887 max:0.25000000000000006
-> create 1106 synthetic samples
-> test with 'LR'
LR tn, fp: 284, 3
LR fn, tp: 7, 4
LR f1 score: 0.444
LR cohens kappa score: 0.428
LR average precision score: 0.503

-> test with 'GB'
GB tn, fp: 281, 6
GB fn, tp: 9, 2
GB f1 score: 0.211
GB cohens kappa score: 0.185

-> test with 'KNN'
KNN tn, fp: 287, 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 1148/44 points
-> new disc
-> calc distances
-> statistics
trained 44 points min:0.026457513110645887 max:0.20049937655763428
-> create 1104 synthetic samples
-> test with 'LR'
LR tn, fp: 283, 2
LR fn, tp: 6, 1
LR f1 score: 0.200
LR cohens kappa score: 0.188
LR average precision score: 0.180

-> test with 'GB'
GB tn, fp: 283, 2
GB fn, tp: 5, 2
GB f1 score: 0.364
GB cohens kappa score: 0.352

-> test with 'KNN'
KNN tn, fp: 285, 0
KNN fn, tp: 7, 0
KNN f1 score: 0.000
KNN cohens kappa score: 0.000

### Exercise is done.

-----[ LR ]-----
maximum:
LR tn, fp: 287, 4
LR fn, tp: 11, 6
LR f1 score: 0.600
LR cohens kappa score: 0.586
LR average precision score: 0.656


average:
LR tn, fp: 284.88, 1.72
LR fn, tp: 8.16, 2.04
LR f1 score: 0.279
LR cohens kappa score: 0.267
LR average precision score: 0.412


minimum:
LR tn, fp: 283, 0
LR fn, tp: 4, 0
LR f1 score: 0.000
LR cohens kappa score: -0.016
LR average precision score: 0.180


-----[ GB ]-----
maximum:
GB tn, fp: 286, 6
GB fn, tp: 11, 5
GB f1 score: 0.500
GB cohens kappa score: 0.488


average:
GB tn, fp: 283.64, 2.96
GB fn, tp: 8.08, 2.12
GB f1 score: 0.274
GB cohens kappa score: 0.258


minimum:
GB tn, fp: 281, 1
GB fn, tp: 4, 0
GB f1 score: 0.000
GB cohens kappa score: -0.016


-----[ KNN ]-----
maximum:
KNN tn, fp: 287, 2
KNN fn, tp: 11, 2
KNN f1 score: 0.444
KNN cohens kappa score: 0.438


average:
KNN tn, fp: 286.24, 0.36
KNN fn, tp: 9.68, 0.52
KNN f1 score: 0.092
KNN cohens kappa score: 0.088


minimum:
KNN tn, fp: 284, 0
KNN fn, tp: 5, 0
KNN f1 score: 0.000
KNN cohens kappa score: -0.011