ConvGeNBenchmarkResults/data_result/convGAN-old-full/folding_yeast4.log

///////////////////////////////////////////
// Running convGAN-full 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: 255, 32
LR fn, tp: 2, 9
LR f1 score: 0.346
LR cohens kappa score: 0.306
LR average precision score: 0.383

-> 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: 261, 26
KNN fn, tp: 2, 9
KNN f1 score: 0.391
KNN cohens kappa score: 0.355


------ Step 1/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1106 synthetic samples
-> test with 'LR'
LR tn, fp: 239, 48
LR fn, tp: 1, 10
LR f1 score: 0.290
LR cohens kappa score: 0.243
LR average precision score: 0.615

-> test with 'GB'
GB tn, fp: 282, 5
GB fn, tp: 6, 5
GB f1 score: 0.476
GB cohens kappa score: 0.457

-> test with 'KNN'
KNN tn, fp: 254, 33
KNN fn, tp: 1, 10
KNN f1 score: 0.370
KNN cohens kappa score: 0.331


------ Step 1/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1106 synthetic samples
-> test with 'LR'
LR tn, fp: 249, 38
LR fn, tp: 1, 10
LR f1 score: 0.339
LR cohens kappa score: 0.297
LR average precision score: 0.243

-> test with 'GB'
GB tn, fp: 285, 2
GB fn, tp: 10, 1
GB f1 score: 0.143
GB cohens kappa score: 0.129

-> test with 'KNN'
KNN tn, fp: 257, 30
KNN fn, tp: 4, 7
KNN f1 score: 0.292
KNN cohens kappa score: 0.249


------ Step 1/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1106 synthetic samples
-> test with 'LR'
LR tn, fp: 256, 31
LR fn, tp: 6, 5
LR f1 score: 0.213
LR cohens kappa score: 0.166
LR average precision score: 0.243

-> 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: 264, 23
KNN fn, tp: 4, 7
KNN f1 score: 0.341
KNN cohens kappa score: 0.304


------ Step 1/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1104 synthetic samples
-> test with 'LR'
LR tn, fp: 240, 45
LR fn, tp: 1, 6
LR f1 score: 0.207
LR cohens kappa score: 0.172
LR average precision score: 0.396

-> 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: 250, 37
LR fn, tp: 2, 9
LR f1 score: 0.316
LR cohens kappa score: 0.272
LR average precision score: 0.344

-> test with 'GB'
GB tn, fp: 282, 5
GB fn, tp: 7, 4
GB f1 score: 0.400
GB cohens kappa score: 0.379

-> test with 'KNN'
KNN tn, fp: 262, 25
KNN fn, tp: 3, 8
KNN f1 score: 0.364
KNN cohens kappa score: 0.326


------ Step 2/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1106 synthetic samples
-> test with 'LR'
LR tn, fp: 242, 45
LR fn, tp: 3, 8
LR f1 score: 0.250
LR cohens kappa score: 0.201
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: 236, 51
KNN fn, tp: 2, 9
KNN f1 score: 0.254
KNN cohens kappa score: 0.204


------ Step 2/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1106 synthetic samples
-> test with 'LR'
LR tn, fp: 252, 35
LR fn, tp: 4, 7
LR f1 score: 0.264
LR cohens kappa score: 0.218
LR average precision score: 0.387

-> test with 'GB'
GB tn, fp: 284, 3
GB fn, tp: 8, 3
GB f1 score: 0.353
GB cohens kappa score: 0.336

-> test with 'KNN'
KNN tn, fp: 251, 36
KNN fn, tp: 2, 9
KNN f1 score: 0.321
KNN cohens kappa score: 0.279


------ 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: 2, 9
LR f1 score: 0.316
LR cohens kappa score: 0.272
LR average precision score: 0.299

-> 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: 2, 9
KNN f1 score: 0.450
KNN cohens kappa score: 0.419


------ Step 2/5: 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: 0.348

-> 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: 248, 39
LR fn, tp: 3, 8
LR f1 score: 0.276
LR cohens kappa score: 0.230
LR average precision score: 0.415

-> 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: 262, 25
KNN fn, tp: 3, 8
KNN f1 score: 0.364
KNN cohens kappa score: 0.326


------ Step 3/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1106 synthetic samples
-> test with 'LR'
LR tn, fp: 251, 36
LR fn, tp: 2, 9
LR f1 score: 0.321
LR cohens kappa score: 0.279
LR average precision score: 0.384

-> test with 'GB'
GB tn, fp: 285, 2
GB fn, tp: 10, 1
GB f1 score: 0.143
GB cohens kappa score: 0.129

-> test with 'KNN'
KNN tn, fp: 260, 27
KNN fn, tp: 1, 10
KNN f1 score: 0.417
KNN cohens kappa score: 0.381


------ Step 3/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1106 synthetic samples
-> test with 'LR'
LR tn, fp: 246, 41
LR fn, tp: 3, 8
LR f1 score: 0.267
LR cohens kappa score: 0.220
LR average precision score: 0.240

-> 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: 258, 29
KNN fn, tp: 3, 8
KNN f1 score: 0.333
KNN cohens kappa score: 0.293


------ Step 3/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1106 synthetic samples
-> test with 'LR'
LR tn, fp: 246, 41
LR fn, tp: 2, 9
LR f1 score: 0.295
LR cohens kappa score: 0.250
LR average precision score: 0.522

-> 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: 256, 31
KNN fn, tp: 4, 7
KNN f1 score: 0.286
KNN cohens kappa score: 0.242


------ Step 3/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1104 synthetic samples
-> test with 'LR'
LR tn, fp: 246, 39
LR fn, tp: 2, 5
LR f1 score: 0.196
LR cohens kappa score: 0.161
LR average precision score: 0.395

-> 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: 254, 31
KNN fn, tp: 1, 6
KNN f1 score: 0.273
KNN cohens kappa score: 0.242


====== 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: 263, 24
LR fn, tp: 4, 7
LR f1 score: 0.333
LR cohens kappa score: 0.295
LR average precision score: 0.479

-> 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: 269, 18
KNN fn, tp: 5, 6
KNN f1 score: 0.343
KNN cohens kappa score: 0.308


------ Step 4/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1106 synthetic samples
-> test with 'LR'
LR tn, fp: 247, 40
LR fn, tp: 2, 9
LR f1 score: 0.300
LR cohens kappa score: 0.255
LR average precision score: 0.306

-> test with 'GB'
GB tn, fp: 283, 4
GB fn, tp: 8, 3
GB f1 score: 0.333
GB cohens kappa score: 0.314

-> 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: 238, 49
LR fn, tp: 2, 9
LR f1 score: 0.261
LR cohens kappa score: 0.212
LR average precision score: 0.248

-> 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: 254, 33
KNN fn, tp: 1, 10
KNN f1 score: 0.370
KNN cohens kappa score: 0.331


------ 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: 0.286

-> 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: 259, 28
KNN fn, tp: 3, 8
KNN f1 score: 0.340
KNN cohens kappa score: 0.301


------ Step 4/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1104 synthetic samples
-> test with 'LR'
LR tn, fp: 248, 37
LR fn, tp: 2, 5
LR f1 score: 0.204
LR cohens kappa score: 0.170
LR average precision score: 0.501

-> test with 'GB'
GB tn, fp: 278, 7
GB fn, tp: 4, 3
GB f1 score: 0.353
GB cohens kappa score: 0.334

-> test with 'KNN'
KNN tn, fp: 260, 25
KNN fn, tp: 2, 5
KNN f1 score: 0.270
KNN cohens kappa score: 0.241


====== 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: 252, 35
LR fn, tp: 3, 8
LR f1 score: 0.296
LR cohens kappa score: 0.252
LR average precision score: 0.226

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


------ Step 5/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1106 synthetic samples
-> test with 'LR'
LR tn, fp: 237, 50
LR fn, tp: 2, 9
LR f1 score: 0.257
LR cohens kappa score: 0.208
LR average precision score: 0.495

-> 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: 1, 10
KNN f1 score: 0.385
KNN cohens kappa score: 0.347


------ Step 5/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1106 synthetic samples
-> test with 'LR'
LR tn, fp: 255, 32
LR fn, tp: 3, 8
LR f1 score: 0.314
LR cohens kappa score: 0.272
LR average precision score: 0.530

-> test with 'GB'
GB tn, fp: 287, 0
GB fn, tp: 8, 3
GB f1 score: 0.429
GB cohens kappa score: 0.419

-> test with 'KNN'
KNN tn, fp: 255, 32
KNN fn, tp: 4, 7
KNN f1 score: 0.280
KNN cohens kappa score: 0.236


------ Step 5/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1106 synthetic samples
-> test with 'LR'
LR tn, fp: 246, 41
LR fn, tp: 1, 10
LR f1 score: 0.323
LR cohens kappa score: 0.279
LR average precision score: 0.534

-> 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: 3, 8
KNN f1 score: 0.320
KNN cohens kappa score: 0.278


------ Step 5/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1104 synthetic samples
-> test with 'LR'
LR tn, fp: 240, 45
LR fn, tp: 3, 4
LR f1 score: 0.143
LR cohens kappa score: 0.105
LR average precision score: 0.124

-> 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: 264, 21
KNN fn, tp: 1, 6
KNN f1 score: 0.353
KNN cohens kappa score: 0.327

### Exercise is done.

-----[ LR ]-----
maximum:
LR tn, fp: 263, 50
LR fn, tp: 6, 10
LR f1 score: 0.346
LR cohens kappa score: 0.306
LR average precision score: 0.615


average:
LR tn, fp: 247.24, 39.36
LR fn, tp: 2.4, 7.8
LR f1 score: 0.272
LR cohens kappa score: 0.229
LR average precision score: 0.376


minimum:
LR tn, fp: 237, 24
LR fn, tp: 1, 4
LR f1 score: 0.143
LR cohens kappa score: 0.105
LR average precision score: 0.124


-----[ GB ]-----
maximum:
GB tn, fp: 287, 7
GB fn, tp: 11, 5
GB f1 score: 0.545
GB cohens kappa score: 0.537


average:
GB tn, fp: 283.56, 3.04
GB fn, tp: 8.04, 2.16
GB f1 score: 0.273
GB cohens kappa score: 0.257


minimum:
GB tn, fp: 278, 0
GB fn, tp: 4, 0
GB f1 score: 0.000
GB cohens kappa score: -0.016


-----[ KNN ]-----
maximum:
KNN tn, fp: 269, 51
KNN fn, tp: 5, 10
KNN f1 score: 0.450
KNN cohens kappa score: 0.419


average:
KNN tn, fp: 258.28, 28.32
KNN fn, tp: 2.32, 7.88
KNN f1 score: 0.341
KNN cohens kappa score: 0.304


minimum:
KNN tn, fp: 236, 18
KNN fn, tp: 1, 5
KNN f1 score: 0.254
KNN cohens kappa score: 0.204