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

///////////////////////////////////////////
// Running convGAN-majority-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: 255, 32
GAN fn, tp: 2, 9
GAN f1 score: 0.346
GAN cohens kappa score: 0.306

-> 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: 0.403

-> 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: 264, 23
KNN fn, tp: 2, 9
KNN f1 score: 0.419
KNN cohens kappa score: 0.385


------ 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: 253, 34
GAN fn, tp: 2, 9
GAN f1 score: 0.333
GAN cohens kappa score: 0.292

-> test with 'LR'
LR tn, fp: 238, 49
LR fn, tp: 1, 10
LR f1 score: 0.286
LR cohens kappa score: 0.238
LR average precision score: 0.637

-> 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: 259, 28
KNN fn, tp: 2, 9
KNN f1 score: 0.375
KNN cohens kappa score: 0.337


------ 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: 250, 37
GAN fn, tp: 2, 9
GAN f1 score: 0.316
GAN cohens kappa score: 0.272

-> 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: 0.267

-> 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 GAN.predict
GAN tn, fp: 263, 24
GAN fn, tp: 6, 5
GAN f1 score: 0.250
GAN cohens kappa score: 0.208

-> 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: 0.188

-> 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: 5, 6
KNN f1 score: 0.300
KNN cohens kappa score: 0.260


------ 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: 234, 51
GAN fn, tp: 1, 6
GAN f1 score: 0.188
GAN cohens kappa score: 0.151

-> test with 'LR'
LR tn, fp: 246, 39
LR fn, tp: 1, 6
LR f1 score: 0.231
LR cohens kappa score: 0.197
LR average precision score: 0.381

-> 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: 263, 22
KNN fn, tp: 1, 6
KNN f1 score: 0.343
KNN cohens kappa score: 0.317


====== 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: 262, 25
GAN fn, tp: 2, 9
GAN f1 score: 0.400
GAN cohens kappa score: 0.365

-> 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: 0.319

-> 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: 269, 18
KNN fn, tp: 3, 8
KNN f1 score: 0.432
KNN cohens kappa score: 0.401


------ 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: 254, 33
GAN fn, tp: 3, 8
GAN f1 score: 0.308
GAN cohens kappa score: 0.265

-> 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: 0.453

-> 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: 236, 51
KNN fn, tp: 3, 8
KNN f1 score: 0.229
KNN cohens kappa score: 0.177


------ 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: 261, 26
GAN fn, tp: 4, 7
GAN f1 score: 0.318
GAN cohens kappa score: 0.278

-> test with 'LR'
LR tn, fp: 257, 30
LR fn, tp: 4, 7
LR f1 score: 0.292
LR cohens kappa score: 0.249
LR average precision score: 0.405

-> 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: 258, 29
KNN fn, tp: 4, 7
KNN f1 score: 0.298
KNN cohens kappa score: 0.256


------ 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: 247, 40
GAN fn, tp: 2, 9
GAN f1 score: 0.300
GAN cohens kappa score: 0.255

-> 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: 0.300

-> 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: 264, 23
KNN fn, tp: 2, 9
KNN f1 score: 0.419
KNN cohens kappa score: 0.385


------ 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: 245, 40
GAN fn, tp: 2, 5
GAN f1 score: 0.192
GAN cohens kappa score: 0.157

-> test with 'LR'
LR tn, fp: 244, 41
LR fn, tp: 1, 6
LR f1 score: 0.222
LR cohens kappa score: 0.188
LR average precision score: 0.405

-> 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: 266, 19
KNN fn, tp: 1, 6
KNN f1 score: 0.375
KNN cohens kappa score: 0.351


====== 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: 272, 15
GAN fn, tp: 5, 6
GAN f1 score: 0.375
GAN cohens kappa score: 0.343

-> 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: 0.374

-> 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: 265, 22
KNN fn, tp: 2, 9
KNN f1 score: 0.429
KNN cohens kappa score: 0.396


------ 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: 265, 22
GAN fn, tp: 4, 7
GAN f1 score: 0.350
GAN cohens kappa score: 0.313

-> 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: 0.391

-> 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: 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: 259, 28
GAN fn, tp: 5, 6
GAN f1 score: 0.267
GAN cohens kappa score: 0.223

-> 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: 0.250

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


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

-> 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: 0.527

-> test with 'GB'
GB tn, fp: 278, 9
GB fn, tp: 5, 6
GB f1 score: 0.462
GB cohens kappa score: 0.438

-> 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 3/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1104 synthetic samples
-> test with GAN.predict
GAN tn, fp: 260, 25
GAN fn, tp: 2, 5
GAN f1 score: 0.270
GAN cohens kappa score: 0.241

-> test with 'LR'
LR tn, fp: 251, 34
LR fn, tp: 3, 4
LR f1 score: 0.178
LR cohens kappa score: 0.143
LR average precision score: 0.397

-> 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: 257, 28
KNN fn, tp: 1, 6
KNN f1 score: 0.293
KNN cohens kappa score: 0.263


====== 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: 270, 17
GAN fn, tp: 5, 6
GAN f1 score: 0.353
GAN cohens kappa score: 0.319

-> test with 'LR'
LR tn, fp: 260, 27
LR fn, tp: 4, 7
LR f1 score: 0.311
LR cohens kappa score: 0.270
LR average precision score: 0.476

-> 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: 270, 17
KNN fn, tp: 5, 6
KNN f1 score: 0.353
KNN cohens kappa score: 0.319


------ 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: 264, 23
GAN fn, tp: 5, 6
GAN f1 score: 0.300
GAN cohens kappa score: 0.260

-> 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: 0.320

-> 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: 253, 34
KNN fn, tp: 2, 9
KNN f1 score: 0.333
KNN cohens kappa score: 0.292


------ 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: 260, 27
GAN fn, tp: 3, 8
GAN f1 score: 0.348
GAN cohens kappa score: 0.309

-> 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: 0.265

-> 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: 259, 28
KNN fn, tp: 1, 10
KNN f1 score: 0.408
KNN cohens kappa score: 0.372


------ 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: 254, 33
GAN fn, tp: 3, 8
GAN f1 score: 0.308
GAN cohens kappa score: 0.265

-> test with 'LR'
LR tn, fp: 246, 41
LR fn, tp: 4, 7
LR f1 score: 0.237
LR cohens kappa score: 0.189
LR average precision score: 0.295

-> 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: 257, 30
KNN fn, tp: 3, 8
KNN f1 score: 0.327
KNN cohens kappa score: 0.286


------ 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: 257, 28
GAN fn, tp: 2, 5
GAN f1 score: 0.250
GAN cohens kappa score: 0.219

-> test with 'LR'
LR tn, fp: 250, 35
LR fn, tp: 2, 5
LR f1 score: 0.213
LR cohens kappa score: 0.179
LR average precision score: 0.522

-> 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: 264, 21
KNN fn, tp: 2, 5
KNN f1 score: 0.303
KNN cohens kappa score: 0.276


====== 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: 268, 19
GAN fn, tp: 4, 7
GAN f1 score: 0.378
GAN cohens kappa score: 0.344

-> test with 'LR'
LR tn, fp: 257, 30
LR fn, tp: 4, 7
LR f1 score: 0.292
LR cohens kappa score: 0.249
LR average precision score: 0.229

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


------ 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: 251, 36
GAN fn, tp: 2, 9
GAN f1 score: 0.321
GAN cohens kappa score: 0.279

-> test with 'LR'
LR tn, fp: 235, 52
LR fn, tp: 2, 9
LR f1 score: 0.250
LR cohens kappa score: 0.200
LR average precision score: 0.503

-> 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: 259, 28
KNN fn, tp: 1, 10
KNN f1 score: 0.408
KNN cohens kappa score: 0.372


------ 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: 269, 18
GAN fn, tp: 4, 7
GAN f1 score: 0.389
GAN cohens kappa score: 0.356

-> test with 'LR'
LR tn, fp: 258, 29
LR fn, tp: 3, 8
LR f1 score: 0.333
LR cohens kappa score: 0.293
LR average precision score: 0.558

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


------ 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: 256, 31
GAN fn, tp: 1, 10
GAN f1 score: 0.385
GAN cohens kappa score: 0.347

-> test with 'LR'
LR tn, fp: 250, 37
LR fn, tp: 1, 10
LR f1 score: 0.345
LR cohens kappa score: 0.303
LR average precision score: 0.523

-> test with 'GB'
GB tn, fp: 280, 7
GB fn, tp: 9, 2
GB f1 score: 0.200
GB cohens kappa score: 0.173

-> test with 'KNN'
KNN tn, fp: 250, 37
KNN fn, tp: 3, 8
KNN f1 score: 0.286
KNN cohens kappa score: 0.241


------ 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: 262, 23
GAN fn, tp: 4, 3
GAN f1 score: 0.182
GAN cohens kappa score: 0.150

-> test with 'LR'
LR tn, fp: 245, 40
LR fn, tp: 3, 4
LR f1 score: 0.157
LR cohens kappa score: 0.120
LR average precision score: 0.119

-> 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: 1, 6
KNN f1 score: 0.375
KNN cohens kappa score: 0.351

### Exercise is done.

-----[ LR ]-----
maximum:
LR tn, fp: 260, 52
LR fn, tp: 6, 10
LR f1 score: 0.345
LR cohens kappa score: 0.303
LR average precision score: 0.637


average:
LR tn, fp: 248.4, 38.2
LR fn, tp: 2.72, 7.48
LR f1 score: 0.267
LR cohens kappa score: 0.224
LR average precision score: 0.380


minimum:
LR tn, fp: 235, 27
LR fn, tp: 1, 4
LR f1 score: 0.157
LR cohens kappa score: 0.120
LR average precision score: 0.119


-----[ GB ]-----
maximum:
GB tn, fp: 287, 9
GB fn, tp: 11, 6
GB f1 score: 0.471
GB cohens kappa score: 0.456


average:
GB tn, fp: 283.76, 2.84
GB fn, tp: 7.96, 2.24
GB f1 score: 0.279
GB cohens kappa score: 0.264


minimum:
GB tn, fp: 278, 0
GB fn, tp: 4, 0
GB f1 score: 0.000
GB cohens kappa score: -0.011


-----[ KNN ]-----
maximum:
KNN tn, fp: 270, 51
KNN fn, tp: 5, 10
KNN f1 score: 0.432
KNN cohens kappa score: 0.401


average:
KNN tn, fp: 260.32, 26.28
KNN fn, tp: 2.44, 7.76
KNN f1 score: 0.355
KNN cohens kappa score: 0.319


minimum:
KNN tn, fp: 236, 17
KNN fn, tp: 1, 5
KNN f1 score: 0.229
KNN cohens kappa score: 0.177


-----[ GAN ]-----
maximum:
GAN tn, fp: 272, 51
GAN fn, tp: 6, 10
GAN f1 score: 0.400
GAN cohens kappa score: 0.365


average:
GAN tn, fp: 257.96, 28.64
GAN fn, tp: 3.12, 7.08
GAN f1 score: 0.310
GAN cohens kappa score: 0.272


minimum:
GAN tn, fp: 234, 15
GAN fn, tp: 1, 3
GAN f1 score: 0.182
GAN cohens kappa score: 0.150