ConvGeNBenchmarkResults/data_result/convGAN/folding_yeast4.log

///////////////////////////////////////////
// Running convGAN 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
-> create 1106 synthetic samples
-> test with 'LR'
LR tn, fp: 241, 46
LR fn, tp: 2, 9
LR f1 score: 0.273
LR cohens kappa score: 0.225
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: 257, 30
KNN fn, tp: 3, 8
KNN f1 score: 0.327
KNN cohens kappa score: 0.286


------ Step 1/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1106 synthetic samples
-> test with 'LR'
LR tn, fp: 233, 54
LR fn, tp: 1, 10
LR f1 score: 0.267
LR cohens kappa score: 0.217
LR average precision score: 0.615

-> test with 'GB'
GB tn, fp: 284, 3
GB fn, tp: 6, 5
GB f1 score: 0.526
GB cohens kappa score: 0.511

-> test with 'KNN'
KNN tn, fp: 260, 27
KNN fn, tp: 3, 8
KNN f1 score: 0.348
KNN cohens kappa score: 0.309


------ Step 1/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1106 synthetic samples
-> test with 'LR'
LR tn, fp: 242, 45
LR fn, tp: 1, 10
LR f1 score: 0.303
LR cohens kappa score: 0.257
LR average precision score: 0.269

-> 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: 258, 29
KNN fn, tp: 5, 6
KNN f1 score: 0.261
KNN cohens kappa score: 0.217


------ Step 1/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1106 synthetic samples
-> test with 'LR'
LR tn, fp: 248, 39
LR fn, tp: 6, 5
LR f1 score: 0.182
LR cohens kappa score: 0.130
LR average precision score: 0.168

-> test with 'GB'
GB tn, fp: 281, 6
GB fn, tp: 8, 3
GB f1 score: 0.300
GB cohens kappa score: 0.276

-> test with 'KNN'
KNN tn, fp: 262, 25
KNN fn, tp: 4, 7
KNN f1 score: 0.326
KNN cohens kappa score: 0.286


------ Step 1/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1104 synthetic samples
-> test with 'LR'
LR tn, fp: 242, 43
LR fn, tp: 1, 6
LR f1 score: 0.214
LR cohens kappa score: 0.180
LR average precision score: 0.400

-> test with 'GB'
GB tn, fp: 284, 1
GB fn, tp: 6, 1
GB f1 score: 0.222
GB cohens kappa score: 0.214

-> test with 'KNN'
KNN tn, fp: 261, 24
KNN fn, tp: 1, 6
KNN f1 score: 0.324
KNN cohens kappa score: 0.297


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

------ Step 2/5: Slice 1/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1106 synthetic samples
-> test with 'LR'
LR tn, fp: 245, 42
LR fn, tp: 2, 9
LR f1 score: 0.290
LR cohens kappa score: 0.244
LR average precision score: 0.320

-> 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: 265, 22
KNN fn, tp: 3, 8
KNN f1 score: 0.390
KNN cohens kappa score: 0.355


------ Step 2/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1106 synthetic samples
-> test with 'LR'
LR tn, fp: 243, 44
LR fn, tp: 3, 8
LR f1 score: 0.254
LR cohens kappa score: 0.206
LR average precision score: 0.457

-> test with 'GB'
GB tn, fp: 284, 3
GB fn, tp: 7, 4
GB f1 score: 0.444
GB cohens kappa score: 0.428

-> test with 'KNN'
KNN tn, fp: 239, 48
KNN fn, tp: 3, 8
KNN f1 score: 0.239
KNN cohens kappa score: 0.189


------ Step 2/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1106 synthetic samples
-> test with 'LR'
LR tn, fp: 240, 47
LR fn, tp: 4, 7
LR f1 score: 0.215
LR cohens kappa score: 0.164
LR average precision score: 0.375

-> 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: 252, 35
KNN fn, tp: 3, 8
KNN f1 score: 0.296
KNN cohens kappa score: 0.252


------ Step 2/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1106 synthetic samples
-> test with 'LR'
LR tn, fp: 250, 37
LR fn, tp: 3, 8
LR f1 score: 0.286
LR cohens kappa score: 0.241
LR average precision score: 0.298

-> 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: 267, 20
KNN fn, tp: 3, 8
KNN f1 score: 0.410
KNN cohens kappa score: 0.377


------ Step 2/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1104 synthetic samples
-> test with 'LR'
LR tn, fp: 224, 61
LR fn, tp: 1, 6
LR f1 score: 0.162
LR cohens kappa score: 0.124
LR average precision score: 0.401

-> test with 'GB'
GB tn, fp: 284, 1
GB fn, tp: 6, 1
GB f1 score: 0.222
GB cohens kappa score: 0.214

-> test with 'KNN'
KNN tn, fp: 262, 23
KNN fn, tp: 1, 6
KNN f1 score: 0.333
KNN cohens kappa score: 0.307


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

------ Step 3/5: Slice 1/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1106 synthetic samples
-> test with 'LR'
LR tn, fp: 236, 51
LR fn, tp: 1, 10
LR f1 score: 0.278
LR cohens kappa score: 0.230
LR average precision score: 0.403

-> 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: 263, 24
KNN fn, tp: 2, 9
KNN f1 score: 0.409
KNN cohens kappa score: 0.374


------ Step 3/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1106 synthetic samples
-> test with 'LR'
LR tn, fp: 238, 49
LR fn, tp: 2, 9
LR f1 score: 0.261
LR cohens kappa score: 0.212
LR average precision score: 0.400

-> 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: 258, 29
KNN fn, tp: 1, 10
KNN f1 score: 0.400
KNN cohens kappa score: 0.363


------ Step 3/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1106 synthetic samples
-> test with 'LR'
LR tn, fp: 245, 42
LR fn, tp: 3, 8
LR f1 score: 0.262
LR cohens kappa score: 0.215
LR average precision score: 0.228

-> test with 'GB'
GB tn, fp: 283, 4
GB fn, tp: 10, 1
GB f1 score: 0.125
GB cohens kappa score: 0.104

-> test with 'KNN'
KNN tn, fp: 257, 30
KNN fn, tp: 2, 9
KNN f1 score: 0.360
KNN cohens kappa score: 0.321


------ Step 3/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1106 synthetic samples
-> test with 'LR'
LR tn, fp: 243, 44
LR fn, tp: 3, 8
LR f1 score: 0.254
LR cohens kappa score: 0.206
LR average precision score: 0.532

-> test with 'GB'
GB tn, fp: 284, 3
GB fn, tp: 9, 2
GB f1 score: 0.250
GB cohens kappa score: 0.232

-> test with 'KNN'
KNN tn, fp: 259, 28
KNN fn, tp: 5, 6
KNN f1 score: 0.267
KNN cohens kappa score: 0.223


------ Step 3/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1104 synthetic samples
-> test with 'LR'
LR tn, fp: 242, 43
LR fn, tp: 2, 5
LR f1 score: 0.182
LR cohens kappa score: 0.146
LR average precision score: 0.413

-> test with 'GB'
GB tn, fp: 284, 1
GB fn, tp: 4, 3
GB f1 score: 0.545
GB cohens kappa score: 0.537

-> test with 'KNN'
KNN tn, fp: 255, 30
KNN fn, tp: 1, 6
KNN f1 score: 0.279
KNN cohens kappa score: 0.249


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

------ Step 4/5: Slice 1/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1106 synthetic samples
-> test with 'LR'
LR tn, fp: 255, 32
LR fn, tp: 4, 7
LR f1 score: 0.280
LR cohens kappa score: 0.236
LR average precision score: 0.474

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

-> test with 'KNN'
KNN tn, fp: 267, 20
KNN fn, tp: 6, 5
KNN f1 score: 0.278
KNN cohens kappa score: 0.239


------ Step 4/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1106 synthetic samples
-> test with 'LR'
LR tn, fp: 243, 44
LR fn, tp: 2, 9
LR f1 score: 0.281
LR cohens kappa score: 0.234
LR average precision score: 0.350

-> 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: 256, 31
KNN fn, tp: 2, 9
KNN f1 score: 0.353
KNN cohens kappa score: 0.313


------ Step 4/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1106 synthetic samples
-> test with 'LR'
LR tn, fp: 230, 57
LR fn, tp: 2, 9
LR f1 score: 0.234
LR cohens kappa score: 0.182
LR average precision score: 0.257

-> 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: 249, 38
KNN fn, tp: 1, 10
KNN f1 score: 0.339
KNN cohens kappa score: 0.297


------ Step 4/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1106 synthetic samples
-> test with 'LR'
LR tn, fp: 243, 44
LR fn, tp: 3, 8
LR f1 score: 0.254
LR cohens kappa score: 0.206
LR average precision score: 0.295

-> 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: 254, 33
KNN fn, tp: 4, 7
KNN f1 score: 0.275
KNN cohens kappa score: 0.230


------ Step 4/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1104 synthetic samples
-> test with 'LR'
LR tn, fp: 243, 42
LR fn, tp: 2, 5
LR f1 score: 0.185
LR cohens kappa score: 0.150
LR average precision score: 0.511

-> test with 'GB'
GB tn, fp: 282, 3
GB fn, tp: 5, 2
GB f1 score: 0.333
GB cohens kappa score: 0.320

-> test with 'KNN'
KNN tn, fp: 255, 30
KNN fn, tp: 2, 5
KNN f1 score: 0.238
KNN cohens kappa score: 0.206


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

------ Step 5/5: Slice 1/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1106 synthetic samples
-> test with 'LR'
LR tn, fp: 253, 34
LR fn, tp: 3, 8
LR f1 score: 0.302
LR cohens kappa score: 0.259
LR average precision score: 0.241

-> 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: 268, 19
KNN fn, tp: 1, 10
KNN f1 score: 0.500
KNN cohens kappa score: 0.472


------ Step 5/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1106 synthetic samples
-> test with 'LR'
LR tn, fp: 230, 57
LR fn, tp: 2, 9
LR f1 score: 0.234
LR cohens kappa score: 0.182
LR average precision score: 0.486

-> 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: 257, 30
KNN fn, tp: 1, 10
KNN f1 score: 0.392
KNN cohens kappa score: 0.355


------ Step 5/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1106 synthetic samples
-> test with 'LR'
LR tn, fp: 240, 47
LR fn, tp: 3, 8
LR f1 score: 0.242
LR cohens kappa score: 0.193
LR average precision score: 0.531

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

-> test with 'KNN'
KNN tn, fp: 260, 27
KNN fn, tp: 5, 6
KNN f1 score: 0.273
KNN cohens kappa score: 0.230


------ Step 5/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1106 synthetic samples
-> test with 'LR'
LR tn, fp: 242, 45
LR fn, tp: 1, 10
LR f1 score: 0.303
LR cohens kappa score: 0.257
LR average precision score: 0.515

-> 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: 256, 31
KNN fn, tp: 2, 9
KNN f1 score: 0.353
KNN cohens kappa score: 0.313


------ Step 5/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1104 synthetic samples
-> test with 'LR'
LR tn, fp: 231, 54
LR fn, tp: 2, 5
LR f1 score: 0.152
LR cohens kappa score: 0.114
LR average precision score: 0.130

-> test with 'GB'
GB tn, fp: 281, 4
GB fn, tp: 5, 2
GB f1 score: 0.308
GB cohens kappa score: 0.292

-> test with 'KNN'
KNN tn, fp: 266, 19
KNN fn, tp: 2, 5
KNN f1 score: 0.323
KNN cohens kappa score: 0.296

### Exercise is done.

-----[ LR ]-----
maximum:
LR tn, fp: 255, 61
LR fn, tp: 6, 10
LR f1 score: 0.303
LR cohens kappa score: 0.259
LR average precision score: 0.615


average:
LR tn, fp: 240.88, 45.72
LR fn, tp: 2.36, 7.84
LR f1 score: 0.246
LR cohens kappa score: 0.200
LR average precision score: 0.378


minimum:
LR tn, fp: 224, 32
LR fn, tp: 1, 5
LR f1 score: 0.152
LR cohens kappa score: 0.114
LR average precision score: 0.130


-----[ GB ]-----
maximum:
GB tn, fp: 287, 6
GB fn, tp: 11, 5
GB f1 score: 0.545
GB cohens kappa score: 0.537


average:
GB tn, fp: 284.0, 2.6
GB fn, tp: 8.28, 1.92
GB f1 score: 0.257
GB cohens kappa score: 0.242


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


-----[ KNN ]-----
maximum:
KNN tn, fp: 268, 48
KNN fn, tp: 6, 10
KNN f1 score: 0.500
KNN cohens kappa score: 0.472


average:
KNN tn, fp: 258.52, 28.08
KNN fn, tp: 2.64, 7.56
KNN f1 score: 0.332
KNN cohens kappa score: 0.294


minimum:
KNN tn, fp: 239, 19
KNN fn, tp: 1, 5
KNN f1 score: 0.238
KNN cohens kappa score: 0.189