ConvGeNBenchmarkResults/data_result/convGAN/folding_flare-F.log

///////////////////////////////////////////
// Running convGAN 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
-> create 784 synthetic samples
-> test with 'LR'
LR tn, fp: 174, 31
LR fn, tp: 6, 3
LR f1 score: 0.140
LR cohens kappa score: 0.078
LR average precision score: 0.088

-> test with 'GB'
GB tn, fp: 198, 7
GB fn, tp: 8, 1
GB f1 score: 0.118
GB cohens kappa score: 0.081

-> test with 'KNN'
KNN tn, fp: 179, 26
KNN fn, tp: 4, 5
KNN f1 score: 0.250
KNN cohens kappa score: 0.198


------ Step 1/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 784 synthetic samples
-> test with 'LR'
LR tn, fp: 155, 50
LR fn, tp: 0, 9
LR f1 score: 0.265
LR cohens kappa score: 0.207
LR average precision score: 0.406

-> 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: 171, 34
KNN fn, tp: 3, 6
KNN f1 score: 0.245
KNN cohens kappa score: 0.189


------ Step 1/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 784 synthetic samples
-> test with 'LR'
LR tn, fp: 176, 29
LR fn, tp: 4, 5
LR f1 score: 0.233
LR cohens kappa score: 0.178
LR average precision score: 0.275

-> 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: 177, 28
KNN fn, tp: 4, 5
KNN f1 score: 0.238
KNN cohens kappa score: 0.184


------ Step 1/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 784 synthetic samples
-> test with 'LR'
LR tn, fp: 181, 24
LR fn, tp: 0, 9
LR f1 score: 0.429
LR cohens kappa score: 0.388
LR average precision score: 0.680

-> test with 'GB'
GB tn, fp: 204, 1
GB fn, tp: 7, 2
GB f1 score: 0.333
GB cohens kappa score: 0.319

-> test with 'KNN'
KNN tn, fp: 183, 22
KNN fn, tp: 3, 6
KNN f1 score: 0.324
KNN cohens kappa score: 0.278


------ Step 1/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 784 synthetic samples
-> test with 'LR'
LR tn, fp: 170, 33
LR fn, tp: 2, 5
LR f1 score: 0.222
LR cohens kappa score: 0.176
LR average precision score: 0.205

-> 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: 173, 30
KNN fn, tp: 2, 5
KNN f1 score: 0.238
KNN cohens kappa score: 0.193


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

------ Step 2/5: Slice 1/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 784 synthetic samples
-> test with 'LR'
LR tn, fp: 168, 37
LR fn, tp: 2, 7
LR f1 score: 0.264
LR cohens kappa score: 0.209
LR average precision score: 0.410

-> 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: 177, 28
KNN fn, tp: 2, 7
KNN f1 score: 0.318
KNN cohens kappa score: 0.269


------ Step 2/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 784 synthetic samples
-> test with 'LR'
LR tn, fp: 169, 36
LR fn, tp: 3, 6
LR f1 score: 0.235
LR cohens kappa score: 0.178
LR average precision score: 0.365

-> 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: 174, 31
KNN fn, tp: 2, 7
KNN f1 score: 0.298
KNN cohens kappa score: 0.247


------ Step 2/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 784 synthetic samples
-> test with 'LR'
LR tn, fp: 175, 30
LR fn, tp: 3, 6
LR f1 score: 0.267
LR cohens kappa score: 0.214
LR average precision score: 0.265

-> 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: 181, 24
KNN fn, tp: 4, 5
KNN f1 score: 0.263
KNN cohens kappa score: 0.213


------ Step 2/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 784 synthetic samples
-> test with 'LR'
LR tn, fp: 184, 21
LR fn, tp: 4, 5
LR f1 score: 0.286
LR cohens kappa score: 0.238
LR average precision score: 0.308

-> 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: 179, 26
KNN fn, tp: 3, 6
KNN f1 score: 0.293
KNN cohens kappa score: 0.243


------ Step 2/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 784 synthetic samples
-> test with 'LR'
LR tn, fp: 173, 30
LR fn, tp: 0, 7
LR f1 score: 0.318
LR cohens kappa score: 0.278
LR average precision score: 0.371

-> 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: 172, 31
KNN fn, tp: 1, 6
KNN f1 score: 0.273
KNN cohens kappa score: 0.230


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

------ Step 3/5: Slice 1/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 784 synthetic samples
-> test with 'LR'
LR tn, fp: 185, 20
LR fn, tp: 2, 7
LR f1 score: 0.389
LR cohens kappa score: 0.348
LR average precision score: 0.734

-> 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: 193, 12
KNN fn, tp: 3, 6
KNN f1 score: 0.444
KNN cohens kappa score: 0.411


------ Step 3/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 784 synthetic samples
-> test with 'LR'
LR tn, fp: 164, 41
LR fn, tp: 2, 7
LR f1 score: 0.246
LR cohens kappa score: 0.188
LR average precision score: 0.223

-> test with 'GB'
GB tn, fp: 194, 11
GB fn, tp: 5, 4
GB f1 score: 0.333
GB cohens kappa score: 0.296

-> test with 'KNN'
KNN tn, fp: 166, 39
KNN fn, tp: 4, 5
KNN f1 score: 0.189
KNN cohens kappa score: 0.128


------ Step 3/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 784 synthetic samples
-> test with 'LR'
LR tn, fp: 169, 36
LR fn, tp: 2, 7
LR f1 score: 0.269
LR cohens kappa score: 0.215
LR average precision score: 0.447

-> 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: 177, 28
KNN fn, tp: 2, 7
KNN f1 score: 0.318
KNN cohens kappa score: 0.269


------ Step 3/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 784 synthetic samples
-> test with 'LR'
LR tn, fp: 179, 26
LR fn, tp: 4, 5
LR f1 score: 0.250
LR cohens kappa score: 0.198
LR average precision score: 0.327

-> 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: 176, 29
KNN fn, tp: 3, 6
KNN f1 score: 0.273
KNN cohens kappa score: 0.221


------ Step 3/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 784 synthetic samples
-> test with 'LR'
LR tn, fp: 159, 44
LR fn, tp: 1, 6
LR f1 score: 0.211
LR cohens kappa score: 0.161
LR average precision score: 0.239

-> test with 'GB'
GB tn, fp: 197, 6
GB fn, tp: 6, 1
GB f1 score: 0.143
GB cohens kappa score: 0.113

-> test with 'KNN'
KNN tn, fp: 179, 24
KNN fn, tp: 4, 3
KNN f1 score: 0.176
KNN cohens kappa score: 0.130


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

------ Step 4/5: Slice 1/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 784 synthetic samples
-> test with 'LR'
LR tn, fp: 174, 31
LR fn, tp: 1, 8
LR f1 score: 0.333
LR cohens kappa score: 0.284
LR average precision score: 0.205

-> test with 'GB'
GB tn, fp: 202, 3
GB fn, tp: 9, 0
GB f1 score: 0.000
GB cohens kappa score: -0.021

-> test with 'KNN'
KNN tn, fp: 173, 32
KNN fn, tp: 4, 5
KNN f1 score: 0.217
KNN cohens kappa score: 0.161


------ Step 4/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 784 synthetic samples
-> test with 'LR'
LR tn, fp: 178, 27
LR fn, tp: 2, 7
LR f1 score: 0.326
LR cohens kappa score: 0.278
LR average precision score: 0.534

-> 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: 180, 25
KNN fn, tp: 4, 5
KNN f1 score: 0.256
KNN cohens kappa score: 0.205


------ Step 4/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 784 synthetic samples
-> test with 'LR'
LR tn, fp: 169, 36
LR fn, tp: 4, 5
LR f1 score: 0.200
LR cohens kappa score: 0.141
LR average precision score: 0.228

-> 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: 178, 27
KNN fn, tp: 4, 5
KNN f1 score: 0.244
KNN cohens kappa score: 0.191


------ Step 4/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 784 synthetic samples
-> test with 'LR'
LR tn, fp: 170, 35
LR fn, tp: 0, 9
LR f1 score: 0.340
LR cohens kappa score: 0.290
LR average precision score: 0.359

-> test with 'GB'
GB tn, fp: 202, 3
GB fn, tp: 7, 2
GB f1 score: 0.286
GB cohens kappa score: 0.264

-> test with 'KNN'
KNN tn, fp: 177, 28
KNN fn, tp: 2, 7
KNN f1 score: 0.318
KNN cohens kappa score: 0.269


------ Step 4/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 784 synthetic samples
-> test with 'LR'
LR tn, fp: 170, 33
LR fn, tp: 1, 6
LR f1 score: 0.261
LR cohens kappa score: 0.217
LR average precision score: 0.549

-> test with 'GB'
GB tn, fp: 202, 1
GB fn, tp: 6, 1
GB f1 score: 0.222
GB cohens kappa score: 0.211

-> test with 'KNN'
KNN tn, fp: 168, 35
KNN fn, tp: 3, 4
KNN f1 score: 0.174
KNN cohens kappa score: 0.124


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

------ Step 5/5: Slice 1/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 784 synthetic samples
-> test with 'LR'
LR tn, fp: 180, 25
LR fn, tp: 4, 5
LR f1 score: 0.256
LR cohens kappa score: 0.205
LR average precision score: 0.205

-> test with 'GB'
GB tn, fp: 202, 3
GB fn, tp: 8, 1
GB f1 score: 0.154
GB cohens kappa score: 0.131

-> test with 'KNN'
KNN tn, fp: 179, 26
KNN fn, tp: 3, 6
KNN f1 score: 0.293
KNN cohens kappa score: 0.243


------ Step 5/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 784 synthetic samples
-> test with 'LR'
LR tn, fp: 173, 32
LR fn, tp: 2, 7
LR f1 score: 0.292
LR cohens kappa score: 0.240
LR average precision score: 0.485

-> 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: 176, 29
KNN fn, tp: 3, 6
KNN f1 score: 0.273
KNN cohens kappa score: 0.221


------ Step 5/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 784 synthetic samples
-> test with 'LR'
LR tn, fp: 178, 27
LR fn, tp: 0, 9
LR f1 score: 0.400
LR cohens kappa score: 0.357
LR average precision score: 0.433

-> test with 'GB'
GB tn, fp: 205, 0
GB fn, tp: 7, 2
GB f1 score: 0.364
GB cohens kappa score: 0.354

-> test with 'KNN'
KNN tn, fp: 173, 32
KNN fn, tp: 3, 6
KNN f1 score: 0.255
KNN cohens kappa score: 0.201


------ Step 5/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 784 synthetic samples
-> test with 'LR'
LR tn, fp: 178, 27
LR fn, tp: 4, 5
LR f1 score: 0.244
LR cohens kappa score: 0.191
LR average precision score: 0.205

-> 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: 188, 17
KNN fn, tp: 4, 5
KNN f1 score: 0.323
KNN cohens kappa score: 0.280


------ Step 5/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 784 synthetic samples
-> test with 'LR'
LR tn, fp: 163, 40
LR fn, tp: 2, 5
LR f1 score: 0.192
LR cohens kappa score: 0.143
LR average precision score: 0.343

-> test with 'GB'
GB tn, fp: 202, 1
GB fn, tp: 5, 2
GB f1 score: 0.400
GB cohens kappa score: 0.388

-> test with 'KNN'
KNN tn, fp: 172, 31
KNN fn, tp: 3, 4
KNN f1 score: 0.190
KNN cohens kappa score: 0.143

### Exercise is done.

-----[ LR ]-----
maximum:
LR tn, fp: 185, 50
LR fn, tp: 6, 9
LR f1 score: 0.429
LR cohens kappa score: 0.388
LR average precision score: 0.734


average:
LR tn, fp: 172.56, 32.04
LR fn, tp: 2.2, 6.4
LR f1 score: 0.275
LR cohens kappa score: 0.224
LR average precision score: 0.356


minimum:
LR tn, fp: 155, 20
LR fn, tp: 0, 3
LR f1 score: 0.140
LR cohens kappa score: 0.078
LR average precision score: 0.088


-----[ GB ]-----
maximum:
GB tn, fp: 205, 11
GB fn, tp: 9, 4
GB f1 score: 0.400
GB cohens kappa score: 0.388


average:
GB tn, fp: 202.32, 2.28
GB fn, tp: 7.56, 1.04
GB f1 score: 0.164
GB cohens kappa score: 0.148


minimum:
GB tn, fp: 194, 0
GB fn, tp: 5, 0
GB f1 score: 0.000
GB cohens kappa score: -0.021


-----[ KNN ]-----
maximum:
KNN tn, fp: 193, 39
KNN fn, tp: 4, 7
KNN f1 score: 0.444
KNN cohens kappa score: 0.411


average:
KNN tn, fp: 176.84, 27.76
KNN fn, tp: 3.08, 5.52
KNN f1 score: 0.267
KNN cohens kappa score: 0.218


minimum:
KNN tn, fp: 166, 12
KNN fn, tp: 1, 3
KNN f1 score: 0.174
KNN cohens kappa score: 0.124