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

-> 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: 0.339

-> 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: 258, 29
KNN fn, tp: 2, 9
KNN f1 score: 0.367
KNN cohens kappa score: 0.329


------ 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: 244, 43
GAN fn, tp: 1, 10
GAN f1 score: 0.312
GAN cohens kappa score: 0.268

-> 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: 0.610

-> test with 'GB'
GB tn, fp: 280, 7
GB fn, tp: 7, 4
GB f1 score: 0.364
GB cohens kappa score: 0.339

-> 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 1/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1106 synthetic samples
-> test with GAN.predict
GAN tn, fp: 257, 30
GAN fn, tp: 4, 7
GAN f1 score: 0.292
GAN cohens kappa score: 0.249

-> 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: 0.309

-> 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: 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: 255, 32
GAN fn, tp: 7, 4
GAN f1 score: 0.170
GAN cohens kappa score: 0.120

-> 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: 0.220

-> 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: 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: 244, 41
GAN fn, tp: 1, 6
GAN f1 score: 0.222
GAN cohens kappa score: 0.188

-> 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: 0.475

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


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

-> 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: 0.343

-> 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: 270, 17
KNN fn, tp: 4, 7
KNN f1 score: 0.400
KNN cohens kappa score: 0.368


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

-> 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: 0.438

-> test with 'GB'
GB tn, fp: 275, 12
GB fn, tp: 6, 5
GB f1 score: 0.357
GB cohens kappa score: 0.327

-> test with 'KNN'
KNN tn, fp: 246, 41
KNN fn, tp: 3, 8
KNN f1 score: 0.267
KNN cohens kappa score: 0.220


------ 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: 255, 32
GAN fn, tp: 5, 6
GAN f1 score: 0.245
GAN cohens kappa score: 0.199

-> test with 'LR'
LR tn, fp: 251, 36
LR fn, tp: 4, 7
LR f1 score: 0.259
LR cohens kappa score: 0.213
LR average precision score: 0.342

-> test with 'GB'
GB tn, fp: 280, 7
GB fn, tp: 7, 4
GB f1 score: 0.364
GB cohens kappa score: 0.339

-> 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 2/5: 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: 4, 7
GAN f1 score: 0.298
GAN cohens kappa score: 0.256

-> 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: 0.319

-> test with 'GB'
GB tn, fp: 281, 6
GB fn, tp: 4, 7
GB f1 score: 0.583
GB cohens kappa score: 0.566

-> 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: 1, 6
GAN f1 score: 0.293
GAN cohens kappa score: 0.263

-> test with 'LR'
LR tn, fp: 251, 34
LR fn, tp: 1, 6
LR f1 score: 0.255
LR cohens kappa score: 0.224
LR average precision score: 0.427

-> test with 'GB'
GB tn, fp: 278, 7
GB fn, tp: 5, 2
GB f1 score: 0.250
GB cohens kappa score: 0.229

-> test with 'KNN'
KNN tn, fp: 267, 18
KNN fn, tp: 2, 5
KNN f1 score: 0.333
KNN cohens kappa score: 0.308


====== 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: 251, 36
GAN fn, tp: 3, 8
GAN f1 score: 0.291
GAN cohens kappa score: 0.246

-> 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: 0.422

-> test with 'GB'
GB tn, fp: 276, 11
GB fn, tp: 6, 5
GB f1 score: 0.370
GB cohens kappa score: 0.342

-> test with 'KNN'
KNN tn, fp: 266, 21
KNN fn, tp: 3, 8
KNN f1 score: 0.400
KNN cohens kappa score: 0.366


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

-> 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: 0.388

-> test with 'GB'
GB tn, fp: 277, 10
GB fn, tp: 6, 5
GB f1 score: 0.385
GB cohens kappa score: 0.357

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

-> test with 'LR'
LR tn, fp: 250, 37
LR fn, tp: 4, 7
LR f1 score: 0.255
LR cohens kappa score: 0.208
LR average precision score: 0.235

-> test with 'GB'
GB tn, fp: 278, 9
GB fn, tp: 8, 3
GB f1 score: 0.261
GB cohens kappa score: 0.231

-> 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 3/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1106 synthetic samples
-> test with GAN.predict
GAN tn, fp: 244, 43
GAN fn, tp: 2, 9
GAN f1 score: 0.286
GAN cohens kappa score: 0.239

-> test with 'LR'
LR tn, fp: 251, 36
LR fn, tp: 3, 8
LR f1 score: 0.291
LR cohens kappa score: 0.246
LR average precision score: 0.444

-> test with 'GB'
GB tn, fp: 276, 11
GB fn, tp: 5, 6
GB f1 score: 0.429
GB cohens kappa score: 0.402

-> 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 3/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1104 synthetic samples
-> test with GAN.predict
GAN tn, fp: 251, 34
GAN fn, tp: 2, 5
GAN f1 score: 0.217
GAN cohens kappa score: 0.184

-> test with 'LR'
LR tn, fp: 252, 33
LR fn, tp: 2, 5
LR f1 score: 0.222
LR cohens kappa score: 0.189
LR average precision score: 0.407

-> 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: 259, 26
KNN fn, tp: 2, 5
KNN f1 score: 0.263
KNN cohens kappa score: 0.233


====== 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: 261, 26
LR fn, tp: 5, 6
LR f1 score: 0.279
LR cohens kappa score: 0.237
LR average precision score: 0.435

-> 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: 238, 49
GAN fn, tp: 3, 8
GAN f1 score: 0.235
GAN cohens kappa score: 0.185

-> 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: 0.374

-> test with 'GB'
GB tn, fp: 280, 7
GB fn, tp: 7, 4
GB f1 score: 0.364
GB cohens kappa score: 0.339

-> 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 4/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1106 synthetic samples
-> test with GAN.predict
GAN tn, fp: 249, 38
GAN fn, tp: 5, 6
GAN f1 score: 0.218
GAN cohens kappa score: 0.169

-> test with 'LR'
LR tn, fp: 248, 39
LR fn, tp: 2, 9
LR f1 score: 0.305
LR cohens kappa score: 0.261
LR average precision score: 0.245

-> test with 'GB'
GB tn, fp: 282, 5
GB fn, tp: 8, 3
GB f1 score: 0.316
GB cohens kappa score: 0.294

-> 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 4/5: 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: 3, 8
GAN f1 score: 0.320
GAN cohens kappa score: 0.278

-> test with 'LR'
LR tn, fp: 244, 43
LR fn, tp: 3, 8
LR f1 score: 0.258
LR cohens kappa score: 0.210
LR average precision score: 0.290

-> test with 'GB'
GB tn, fp: 275, 12
GB fn, tp: 6, 5
GB f1 score: 0.357
GB cohens kappa score: 0.327

-> 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 4/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1104 synthetic samples
-> test with GAN.predict
GAN tn, fp: 250, 35
GAN fn, tp: 1, 6
GAN f1 score: 0.250
GAN cohens kappa score: 0.218

-> 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: 0.443

-> test with 'GB'
GB tn, fp: 274, 11
GB fn, tp: 3, 4
GB f1 score: 0.364
GB cohens kappa score: 0.342

-> 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 GAN.predict
GAN tn, fp: 251, 36
GAN fn, tp: 3, 8
GAN f1 score: 0.291
GAN cohens kappa score: 0.246

-> test with 'LR'
LR tn, fp: 258, 29
LR fn, tp: 4, 7
LR f1 score: 0.298
LR cohens kappa score: 0.256
LR average precision score: 0.250

-> 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: 239, 48
GAN fn, tp: 3, 8
GAN f1 score: 0.239
GAN cohens kappa score: 0.189

-> test with 'LR'
LR tn, fp: 240, 47
LR fn, tp: 2, 9
LR f1 score: 0.269
LR cohens kappa score: 0.221
LR average precision score: 0.511

-> 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: 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: 261, 26
GAN fn, tp: 3, 8
GAN f1 score: 0.356
GAN cohens kappa score: 0.317

-> 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: 0.455

-> 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: 259, 28
KNN fn, tp: 4, 7
KNN f1 score: 0.304
KNN cohens kappa score: 0.263


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

-> 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: 0.495

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


------ 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: 256, 29
GAN fn, tp: 3, 4
GAN f1 score: 0.200
GAN cohens kappa score: 0.167

-> test with 'LR'
LR tn, fp: 252, 33
LR fn, tp: 3, 4
LR f1 score: 0.182
LR cohens kappa score: 0.147
LR average precision score: 0.172

-> test with 'GB'
GB tn, fp: 277, 8
GB fn, tp: 5, 2
GB f1 score: 0.235
GB cohens kappa score: 0.213

-> test with 'KNN'
KNN tn, fp: 268, 17
KNN fn, tp: 2, 5
KNN f1 score: 0.345
KNN cohens kappa score: 0.320

### Exercise is done.

-----[ LR ]-----
maximum:
LR tn, fp: 261, 47
LR fn, tp: 6, 10
LR f1 score: 0.345
LR cohens kappa score: 0.303
LR average precision score: 0.610


average:
LR tn, fp: 250.8, 35.8
LR fn, tp: 2.72, 7.48
LR f1 score: 0.277
LR cohens kappa score: 0.235
LR average precision score: 0.376


minimum:
LR tn, fp: 240, 26
LR fn, tp: 1, 4
LR f1 score: 0.182
LR cohens kappa score: 0.147
LR average precision score: 0.172


-----[ GB ]-----
maximum:
GB tn, fp: 284, 12
GB fn, tp: 10, 7
GB f1 score: 0.583
GB cohens kappa score: 0.566


average:
GB tn, fp: 278.92, 7.68
GB fn, tp: 6.32, 3.88
GB f1 score: 0.347
GB cohens kappa score: 0.323


minimum:
GB tn, fp: 274, 3
GB fn, tp: 3, 1
GB f1 score: 0.133
GB cohens kappa score: 0.116


-----[ 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: 261.72, 24.88
KNN fn, tp: 3.04, 7.16
KNN f1 score: 0.339
KNN cohens kappa score: 0.303


minimum:
KNN tn, fp: 246, 15
KNN fn, tp: 1, 4
KNN f1 score: 0.250
KNN cohens kappa score: 0.219


-----[ GAN ]-----
maximum:
GAN tn, fp: 270, 49
GAN fn, tp: 7, 10
GAN f1 score: 0.356
GAN cohens kappa score: 0.319


average:
GAN tn, fp: 252.56, 34.04
GAN fn, tp: 3.16, 7.04
GAN f1 score: 0.276
GAN cohens kappa score: 0.234


minimum:
GAN tn, fp: 238, 17
GAN fn, tp: 1, 4
GAN f1 score: 0.170
GAN cohens kappa score: 0.120