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

///////////////////////////////////////////
// Running convGAN-proximary-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 GAN.predict
GAN tn, fp: 264, 23
GAN fn, tp: 3, 8
GAN f1 score: 0.381
GAN cohens kappa score: 0.345

-> test with 'LR'
LR tn, fp: 256, 31
LR fn, tp: 3, 8
LR f1 score: 0.320
LR cohens kappa score: 0.278
LR average precision score: 0.343

-> test with 'GB'
GB tn, fp: 279, 8
GB fn, tp: 7, 4
GB f1 score: 0.348
GB cohens kappa score: 0.322

-> test with 'KNN'
KNN tn, fp: 261, 26
KNN fn, tp: 3, 8
KNN f1 score: 0.356
KNN cohens kappa score: 0.317


------ Step 1/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1106 synthetic samples
-> test with GAN.predict
GAN tn, fp: 241, 46
GAN fn, tp: 3, 8
GAN f1 score: 0.246
GAN cohens kappa score: 0.197

-> 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: 0.581

-> 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: 255, 32
KNN fn, tp: 2, 9
KNN f1 score: 0.346
KNN cohens kappa score: 0.306


------ Step 1/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1106 synthetic samples
-> test with GAN.predict
GAN tn, fp: 247, 40
GAN fn, tp: 4, 7
GAN f1 score: 0.241
GAN cohens kappa score: 0.193

-> test with 'LR'
LR tn, fp: 249, 38
LR fn, tp: 2, 9
LR f1 score: 0.310
LR cohens kappa score: 0.266
LR average precision score: 0.299

-> 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: 264, 23
KNN fn, tp: 3, 8
KNN f1 score: 0.381
KNN cohens kappa score: 0.345


------ Step 1/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1106 synthetic samples
-> test with GAN.predict
GAN tn, fp: 262, 25
GAN fn, tp: 7, 4
GAN f1 score: 0.200
GAN cohens kappa score: 0.155

-> 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: 0.221

-> test with 'GB'
GB tn, fp: 279, 8
GB fn, tp: 8, 3
GB f1 score: 0.273
GB cohens kappa score: 0.245

-> test with 'KNN'
KNN tn, fp: 265, 22
KNN fn, tp: 5, 6
KNN f1 score: 0.308
KNN cohens kappa score: 0.269


------ Step 1/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1104 synthetic samples
-> test with GAN.predict
GAN tn, fp: 246, 39
GAN fn, tp: 2, 5
GAN f1 score: 0.196
GAN cohens kappa score: 0.161

-> test with 'LR'
LR tn, fp: 248, 37
LR fn, tp: 1, 6
LR f1 score: 0.240
LR cohens kappa score: 0.207
LR average precision score: 0.466

-> 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: 255, 30
KNN fn, tp: 1, 6
KNN f1 score: 0.279
KNN cohens kappa score: 0.249


====== 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 GAN.predict
GAN tn, fp: 249, 38
GAN fn, tp: 4, 7
GAN f1 score: 0.250
GAN cohens kappa score: 0.203

-> test with 'LR'
LR tn, fp: 254, 33
LR fn, tp: 3, 8
LR f1 score: 0.308
LR cohens kappa score: 0.265
LR average precision score: 0.345

-> test with 'GB'
GB tn, fp: 280, 7
GB fn, tp: 6, 5
GB f1 score: 0.435
GB cohens kappa score: 0.412

-> test with 'KNN'
KNN tn, fp: 264, 23
KNN fn, tp: 3, 8
KNN f1 score: 0.381
KNN cohens kappa score: 0.345


------ Step 2/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1106 synthetic samples
-> test with GAN.predict
GAN tn, fp: 249, 38
GAN fn, tp: 4, 7
GAN f1 score: 0.250
GAN cohens kappa score: 0.203

-> test with 'LR'
LR tn, fp: 249, 38
LR fn, tp: 2, 9
LR f1 score: 0.310
LR cohens kappa score: 0.266
LR average precision score: 0.438

-> test with 'GB'
GB tn, fp: 270, 17
GB fn, tp: 5, 6
GB f1 score: 0.353
GB cohens kappa score: 0.319

-> test with 'KNN'
KNN tn, fp: 246, 41
KNN fn, tp: 2, 9
KNN f1 score: 0.295
KNN cohens kappa score: 0.250


------ Step 2/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1106 synthetic samples
-> test with GAN.predict
GAN tn, fp: 248, 39
GAN fn, tp: 5, 6
GAN f1 score: 0.214
GAN cohens kappa score: 0.165

-> test with 'LR'
LR tn, fp: 249, 38
LR fn, tp: 4, 7
LR f1 score: 0.250
LR cohens kappa score: 0.203
LR average precision score: 0.339

-> 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: 256, 31
KNN fn, tp: 3, 8
KNN f1 score: 0.320
KNN cohens kappa score: 0.278


------ Step 2/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1106 synthetic samples
-> test with GAN.predict
GAN tn, fp: 253, 34
GAN fn, tp: 3, 8
GAN f1 score: 0.302
GAN cohens kappa score: 0.259

-> test with 'LR'
LR tn, fp: 249, 38
LR fn, tp: 3, 8
LR f1 score: 0.281
LR cohens kappa score: 0.235
LR average precision score: 0.351

-> test with 'GB'
GB tn, fp: 274, 13
GB fn, tp: 5, 6
GB f1 score: 0.400
GB cohens kappa score: 0.371

-> test with 'KNN'
KNN tn, fp: 268, 19
KNN fn, tp: 2, 9
KNN f1 score: 0.462
KNN cohens kappa score: 0.431


------ Step 2/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1104 synthetic samples
-> test with GAN.predict
GAN tn, fp: 257, 28
GAN fn, tp: 2, 5
GAN f1 score: 0.250
GAN cohens kappa score: 0.219

-> test with 'LR'
LR tn, fp: 248, 37
LR fn, tp: 1, 6
LR f1 score: 0.240
LR cohens kappa score: 0.207
LR average precision score: 0.419

-> test with 'GB'
GB tn, fp: 274, 11
GB fn, tp: 4, 3
GB f1 score: 0.286
GB cohens kappa score: 0.262

-> test with 'KNN'
KNN tn, fp: 265, 20
KNN fn, tp: 2, 5
KNN f1 score: 0.312
KNN cohens kappa score: 0.286


====== 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 GAN.predict
GAN tn, fp: 254, 33
GAN fn, tp: 3, 8
GAN f1 score: 0.308
GAN cohens kappa score: 0.265

-> 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: 0.370

-> test with 'GB'
GB tn, fp: 281, 6
GB fn, tp: 7, 4
GB f1 score: 0.381
GB cohens kappa score: 0.358

-> test with 'KNN'
KNN tn, fp: 268, 19
KNN fn, tp: 4, 7
KNN f1 score: 0.378
KNN cohens kappa score: 0.344


------ Step 3/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1106 synthetic samples
-> test with GAN.predict
GAN tn, fp: 273, 14
GAN fn, tp: 4, 7
GAN f1 score: 0.437
GAN cohens kappa score: 0.409

-> 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: 0.395

-> test with 'GB'
GB tn, fp: 281, 6
GB fn, tp: 6, 5
GB f1 score: 0.455
GB cohens kappa score: 0.434

-> test with 'KNN'
KNN tn, fp: 259, 28
KNN fn, tp: 2, 9
KNN f1 score: 0.375
KNN cohens kappa score: 0.337


------ Step 3/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1106 synthetic samples
-> test with GAN.predict
GAN tn, fp: 269, 18
GAN fn, tp: 6, 5
GAN f1 score: 0.294
GAN cohens kappa score: 0.257

-> test with 'LR'
LR tn, fp: 249, 38
LR fn, tp: 4, 7
LR f1 score: 0.250
LR cohens kappa score: 0.203
LR average precision score: 0.223

-> test with 'GB'
GB tn, fp: 279, 8
GB fn, tp: 9, 2
GB f1 score: 0.190
GB cohens kappa score: 0.161

-> test with 'KNN'
KNN tn, fp: 259, 28
KNN fn, tp: 4, 7
KNN f1 score: 0.304
KNN cohens kappa score: 0.263


------ Step 3/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1106 synthetic samples
-> test with GAN.predict
GAN tn, fp: 269, 18
GAN fn, tp: 3, 8
GAN f1 score: 0.432
GAN cohens kappa score: 0.401

-> test with 'LR'
LR tn, fp: 249, 38
LR fn, tp: 3, 8
LR f1 score: 0.281
LR cohens kappa score: 0.235
LR average precision score: 0.431

-> test with 'GB'
GB tn, fp: 274, 13
GB fn, tp: 5, 6
GB f1 score: 0.400
GB cohens kappa score: 0.371

-> test with 'KNN'
KNN tn, fp: 261, 26
KNN fn, tp: 5, 6
KNN f1 score: 0.279
KNN cohens kappa score: 0.237


------ Step 3/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1104 synthetic samples
-> test with GAN.predict
GAN tn, fp: 259, 26
GAN fn, tp: 2, 5
GAN f1 score: 0.263
GAN cohens kappa score: 0.233

-> test with 'LR'
LR tn, fp: 251, 34
LR fn, tp: 2, 5
LR f1 score: 0.217
LR cohens kappa score: 0.184
LR average precision score: 0.422

-> 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: 261, 24
KNN fn, tp: 2, 5
KNN f1 score: 0.278
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 GAN.predict
GAN tn, fp: 277, 10
GAN fn, tp: 5, 6
GAN f1 score: 0.444
GAN cohens kappa score: 0.419

-> test with 'LR'
LR tn, fp: 260, 27
LR fn, tp: 5, 6
LR f1 score: 0.273
LR cohens kappa score: 0.230
LR average precision score: 0.438

-> test with 'GB'
GB tn, fp: 280, 7
GB fn, tp: 6, 5
GB f1 score: 0.435
GB cohens kappa score: 0.412

-> test with 'KNN'
KNN tn, fp: 272, 15
KNN fn, tp: 7, 4
KNN f1 score: 0.267
KNN cohens kappa score: 0.231


------ Step 4/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1106 synthetic samples
-> test with GAN.predict
GAN tn, fp: 247, 40
GAN fn, tp: 3, 8
GAN f1 score: 0.271
GAN cohens kappa score: 0.225

-> test with 'LR'
LR tn, fp: 252, 35
LR fn, tp: 2, 9
LR f1 score: 0.327
LR cohens kappa score: 0.285
LR average precision score: 0.375

-> test with 'GB'
GB tn, fp: 276, 11
GB fn, tp: 4, 7
GB f1 score: 0.483
GB cohens kappa score: 0.458

-> test with 'KNN'
KNN tn, fp: 260, 27
KNN fn, tp: 2, 9
KNN f1 score: 0.383
KNN cohens kappa score: 0.346


------ Step 4/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1106 synthetic samples
-> test with GAN.predict
GAN tn, fp: 254, 33
GAN fn, tp: 5, 6
GAN f1 score: 0.240
GAN cohens kappa score: 0.194

-> test with 'LR'
LR tn, fp: 244, 43
LR fn, tp: 2, 9
LR f1 score: 0.286
LR cohens kappa score: 0.239
LR average precision score: 0.239

-> 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: 257, 30
KNN fn, tp: 2, 9
KNN f1 score: 0.360
KNN cohens kappa score: 0.321


------ Step 4/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1106 synthetic samples
-> test with GAN.predict
GAN tn, fp: 263, 24
GAN fn, tp: 4, 7
GAN f1 score: 0.333
GAN cohens kappa score: 0.295

-> 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: 0.291

-> test with 'GB'
GB tn, fp: 274, 13
GB fn, tp: 6, 5
GB f1 score: 0.345
GB cohens kappa score: 0.313

-> 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 4/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1104 synthetic samples
-> test with GAN.predict
GAN tn, fp: 261, 24
GAN fn, tp: 3, 4
GAN f1 score: 0.229
GAN cohens kappa score: 0.198

-> 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: 0.447

-> test with 'GB'
GB tn, fp: 276, 9
GB fn, tp: 3, 4
GB f1 score: 0.400
GB cohens kappa score: 0.381

-> test with 'KNN'
KNN tn, fp: 261, 24
KNN fn, tp: 2, 5
KNN f1 score: 0.278
KNN cohens kappa score: 0.249


====== 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 GAN.predict
GAN tn, fp: 266, 21
GAN fn, tp: 4, 7
GAN f1 score: 0.359
GAN cohens kappa score: 0.323

-> test with 'LR'
LR tn, fp: 256, 31
LR fn, tp: 5, 6
LR f1 score: 0.250
LR cohens kappa score: 0.205
LR average precision score: 0.242

-> 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: 267, 20
KNN fn, tp: 4, 7
KNN f1 score: 0.368
KNN cohens kappa score: 0.333


------ Step 5/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1106 synthetic samples
-> test with GAN.predict
GAN tn, fp: 246, 41
GAN fn, tp: 5, 6
GAN f1 score: 0.207
GAN cohens kappa score: 0.156

-> test with 'LR'
LR tn, fp: 242, 45
LR fn, tp: 2, 9
LR f1 score: 0.277
LR cohens kappa score: 0.230
LR average precision score: 0.513

-> test with 'GB'
GB tn, fp: 280, 7
GB fn, tp: 5, 6
GB f1 score: 0.500
GB cohens kappa score: 0.479

-> 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 5/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1106 synthetic samples
-> test with GAN.predict
GAN tn, fp: 260, 27
GAN fn, tp: 5, 6
GAN f1 score: 0.273
GAN cohens kappa score: 0.230

-> 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: 0.446

-> test with 'GB'
GB tn, fp: 283, 4
GB fn, tp: 7, 4
GB f1 score: 0.421
GB cohens kappa score: 0.402

-> test with 'KNN'
KNN tn, fp: 257, 30
KNN fn, tp: 5, 6
KNN f1 score: 0.255
KNN cohens kappa score: 0.211


------ Step 5/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1106 synthetic samples
-> test with GAN.predict
GAN tn, fp: 267, 20
GAN fn, tp: 5, 6
GAN f1 score: 0.324
GAN cohens kappa score: 0.287

-> test with 'LR'
LR tn, fp: 248, 39
LR fn, tp: 1, 10
LR f1 score: 0.333
LR cohens kappa score: 0.291
LR average precision score: 0.495

-> test with 'GB'
GB tn, fp: 282, 5
GB fn, tp: 5, 6
GB f1 score: 0.545
GB cohens kappa score: 0.528

-> 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 5/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1104 synthetic samples
-> test with GAN.predict
GAN tn, fp: 269, 16
GAN fn, tp: 3, 4
GAN f1 score: 0.296
GAN cohens kappa score: 0.270

-> test with 'LR'
LR tn, fp: 249, 36
LR fn, tp: 3, 4
LR f1 score: 0.170
LR cohens kappa score: 0.135
LR average precision score: 0.167

-> test with 'GB'
GB tn, fp: 279, 6
GB fn, tp: 5, 2
GB f1 score: 0.267
GB cohens kappa score: 0.247

-> test with 'KNN'
KNN tn, fp: 269, 16
KNN fn, tp: 2, 5
KNN f1 score: 0.357
KNN cohens kappa score: 0.333

### Exercise is done.

-----[ LR ]-----
maximum:
LR tn, fp: 260, 45
LR fn, tp: 6, 10
LR f1 score: 0.333
LR cohens kappa score: 0.291
LR average precision score: 0.581


average:
LR tn, fp: 250.16, 36.44
LR fn, tp: 2.72, 7.48
LR f1 score: 0.274
LR cohens kappa score: 0.232
LR average precision score: 0.372


minimum:
LR tn, fp: 242, 27
LR fn, tp: 1, 4
LR f1 score: 0.170
LR cohens kappa score: 0.135
LR average precision score: 0.167


-----[ GB ]-----
maximum:
GB tn, fp: 283, 17
GB fn, tp: 9, 7
GB f1 score: 0.545
GB cohens kappa score: 0.528


average:
GB tn, fp: 278.88, 7.72
GB fn, tp: 6.08, 4.12
GB f1 score: 0.365
GB cohens kappa score: 0.342


minimum:
GB tn, fp: 270, 2
GB fn, tp: 3, 1
GB f1 score: 0.190
GB cohens kappa score: 0.161


-----[ KNN ]-----
maximum:
KNN tn, fp: 272, 41
KNN fn, tp: 7, 10
KNN f1 score: 0.462
KNN cohens kappa score: 0.431


average:
KNN tn, fp: 260.96, 25.64
KNN fn, tp: 3.0, 7.2
KNN f1 score: 0.334
KNN cohens kappa score: 0.298


minimum:
KNN tn, fp: 246, 15
KNN fn, tp: 1, 4
KNN f1 score: 0.255
KNN cohens kappa score: 0.211


-----[ GAN ]-----
maximum:
GAN tn, fp: 277, 46
GAN fn, tp: 7, 8
GAN f1 score: 0.444
GAN cohens kappa score: 0.419


average:
GAN tn, fp: 258.0, 28.6
GAN fn, tp: 3.88, 6.32
GAN f1 score: 0.290
GAN cohens kappa score: 0.250


minimum:
GAN tn, fp: 241, 10
GAN fn, tp: 2, 4
GAN f1 score: 0.196
GAN cohens kappa score: 0.155