ConvGeNBenchmarkResults/data_result/convGAN-majority-5/folding_yeast4.log

///////////////////////////////////////////
// Running convGAN-majority-5 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: 259, 28
GAN fn, tp: 2, 9
GAN f1 score: 0.375
GAN cohens kappa score: 0.337

-> 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: 0.379

-> 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: 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: 1, 10
GAN f1 score: 0.299
GAN cohens kappa score: 0.252

-> test with 'LR'
LR tn, fp: 229, 58
LR fn, tp: 1, 10
LR f1 score: 0.253
LR cohens kappa score: 0.202
LR average precision score: 0.620

-> 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: 251, 36
KNN fn, tp: 0, 11
KNN f1 score: 0.379
KNN cohens kappa score: 0.340


------ 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: 240, 47
GAN fn, tp: 1, 10
GAN f1 score: 0.294
GAN cohens kappa score: 0.248

-> test with 'LR'
LR tn, fp: 243, 44
LR fn, tp: 1, 10
LR f1 score: 0.308
LR cohens kappa score: 0.262
LR average precision score: 0.257

-> 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: 250, 37
KNN fn, tp: 3, 8
KNN f1 score: 0.286
KNN cohens kappa score: 0.241


------ 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: 258, 29
GAN fn, tp: 6, 5
GAN f1 score: 0.222
GAN cohens kappa score: 0.176

-> test with 'LR'
LR tn, fp: 250, 37
LR fn, tp: 6, 5
LR f1 score: 0.189
LR cohens kappa score: 0.138
LR average precision score: 0.183

-> 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: 5, 6
KNN f1 score: 0.286
KNN cohens kappa score: 0.245


------ 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: 240, 45
GAN fn, tp: 1, 6
GAN f1 score: 0.207
GAN cohens kappa score: 0.172

-> test with 'LR'
LR tn, fp: 234, 51
LR fn, tp: 1, 6
LR f1 score: 0.188
LR cohens kappa score: 0.151
LR average precision score: 0.398

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


====== 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: 261, 26
GAN fn, tp: 2, 9
GAN f1 score: 0.391
GAN cohens kappa score: 0.355

-> 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: 0.317

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


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

-> 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: 0.459

-> 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: 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 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: 244, 43
LR fn, tp: 4, 7
LR f1 score: 0.230
LR cohens kappa score: 0.180
LR average precision score: 0.376

-> 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: 252, 35
KNN fn, tp: 2, 9
KNN f1 score: 0.327
KNN cohens kappa score: 0.285


------ 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: 248, 39
GAN fn, tp: 2, 9
GAN f1 score: 0.305
GAN cohens kappa score: 0.261

-> 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: 0.313

-> 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: 263, 24
KNN fn, tp: 3, 8
KNN f1 score: 0.372
KNN cohens kappa score: 0.336


------ 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: 239, 46
GAN fn, tp: 1, 6
GAN f1 score: 0.203
GAN cohens kappa score: 0.168

-> test with 'LR'
LR tn, fp: 229, 56
LR fn, tp: 1, 6
LR f1 score: 0.174
LR cohens kappa score: 0.137
LR average precision score: 0.392

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

-> 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: 0.456

-> 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: 263, 24
KNN fn, tp: 3, 8
KNN f1 score: 0.372
KNN cohens kappa score: 0.336


------ 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: 260, 27
GAN fn, tp: 2, 9
GAN f1 score: 0.383
GAN cohens kappa score: 0.346

-> 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: 0.406

-> 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 GAN.predict
GAN tn, fp: 248, 39
GAN fn, tp: 4, 7
GAN f1 score: 0.246
GAN cohens kappa score: 0.198

-> 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: 0.240

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


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

-> test with 'LR'
LR tn, fp: 241, 46
LR fn, tp: 3, 8
LR f1 score: 0.246
LR cohens kappa score: 0.197
LR average precision score: 0.513

-> 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: 260, 27
KNN fn, tp: 5, 6
KNN f1 score: 0.273
KNN cohens kappa score: 0.230


------ 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: 254, 31
GAN fn, tp: 3, 4
GAN f1 score: 0.190
GAN cohens kappa score: 0.157

-> test with 'LR'
LR tn, fp: 245, 40
LR fn, tp: 2, 5
LR f1 score: 0.192
LR cohens kappa score: 0.157
LR average precision score: 0.392

-> 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: 253, 32
KNN fn, tp: 1, 6
KNN f1 score: 0.267
KNN cohens kappa score: 0.236


====== 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: 263, 24
GAN fn, tp: 5, 6
GAN f1 score: 0.293
GAN cohens kappa score: 0.252

-> 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: 0.456

-> 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: 263, 24
KNN fn, tp: 6, 5
KNN f1 score: 0.250
KNN cohens kappa score: 0.208


------ 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: 246, 41
GAN fn, tp: 2, 9
GAN f1 score: 0.295
GAN cohens kappa score: 0.250

-> test with 'LR'
LR tn, fp: 239, 48
LR fn, tp: 2, 9
LR f1 score: 0.265
LR cohens kappa score: 0.216
LR average precision score: 0.289

-> test with 'GB'
GB tn, fp: 283, 4
GB fn, tp: 9, 2
GB f1 score: 0.235
GB cohens kappa score: 0.215

-> test with 'KNN'
KNN tn, fp: 255, 32
KNN fn, tp: 3, 8
KNN f1 score: 0.314
KNN cohens kappa score: 0.272


------ 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: 245, 42
GAN fn, tp: 2, 9
GAN f1 score: 0.290
GAN cohens kappa score: 0.244

-> 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: 0.237

-> 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: 250, 37
KNN fn, tp: 1, 10
KNN f1 score: 0.345
KNN cohens kappa score: 0.303


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

-> 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: 0.294

-> test with 'GB'
GB tn, fp: 283, 4
GB fn, tp: 9, 2
GB f1 score: 0.235
GB cohens kappa score: 0.215

-> 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 4/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1104 synthetic samples
-> test with GAN.predict
GAN tn, fp: 255, 30
GAN fn, tp: 2, 5
GAN f1 score: 0.238
GAN cohens kappa score: 0.206

-> test with 'LR'
LR tn, fp: 249, 36
LR fn, tp: 2, 5
LR f1 score: 0.208
LR cohens kappa score: 0.175
LR average precision score: 0.540

-> test with 'GB'
GB tn, fp: 282, 3
GB fn, tp: 4, 3
GB f1 score: 0.462
GB cohens kappa score: 0.449

-> 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 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: 248, 39
LR fn, tp: 3, 8
LR f1 score: 0.276
LR cohens kappa score: 0.230
LR average precision score: 0.205

-> 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: 260, 27
KNN fn, tp: 1, 10
KNN f1 score: 0.417
KNN cohens kappa score: 0.381


------ 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: 252, 35
GAN fn, tp: 2, 9
GAN f1 score: 0.327
GAN cohens kappa score: 0.285

-> test with 'LR'
LR tn, fp: 229, 58
LR fn, tp: 2, 9
LR f1 score: 0.231
LR cohens kappa score: 0.179
LR average precision score: 0.519

-> 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: 251, 36
KNN fn, tp: 1, 10
KNN f1 score: 0.351
KNN cohens kappa score: 0.310


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

-> test with 'LR'
LR tn, fp: 237, 50
LR fn, tp: 3, 8
LR f1 score: 0.232
LR cohens kappa score: 0.181
LR average precision score: 0.539

-> test with 'GB'
GB tn, fp: 287, 0
GB fn, tp: 9, 2
GB f1 score: 0.308
GB cohens kappa score: 0.300

-> 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 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: 1, 10
GAN f1 score: 0.370
GAN cohens kappa score: 0.331

-> test with 'LR'
LR tn, fp: 244, 43
LR fn, tp: 1, 10
LR f1 score: 0.312
LR cohens kappa score: 0.268
LR average precision score: 0.525

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


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

-> test with 'LR'
LR tn, fp: 233, 52
LR fn, tp: 2, 5
LR f1 score: 0.156
LR cohens kappa score: 0.119
LR average precision score: 0.133

-> 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: 252, 58
LR fn, tp: 6, 10
LR f1 score: 0.312
LR cohens kappa score: 0.268
LR average precision score: 0.620


average:
LR tn, fp: 240.64, 45.96
LR fn, tp: 2.36, 7.84
LR f1 score: 0.244
LR cohens kappa score: 0.199
LR average precision score: 0.378


minimum:
LR tn, fp: 229, 35
LR fn, tp: 1, 5
LR f1 score: 0.156
LR cohens kappa score: 0.119
LR average precision score: 0.133


-----[ GB ]-----
maximum:
GB tn, fp: 287, 6
GB fn, tp: 11, 4
GB f1 score: 0.545
GB cohens kappa score: 0.537


average:
GB tn, fp: 283.8, 2.8
GB fn, tp: 8.32, 1.88
GB f1 score: 0.253
GB cohens kappa score: 0.238


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: 264, 48
KNN fn, tp: 6, 11
KNN f1 score: 0.417
KNN cohens kappa score: 0.381


average:
KNN tn, fp: 256.72, 29.88
KNN fn, tp: 2.56, 7.64
KNN f1 score: 0.320
KNN cohens kappa score: 0.282


minimum:
KNN tn, fp: 239, 21
KNN fn, tp: 0, 5
KNN f1 score: 0.238
KNN cohens kappa score: 0.189


-----[ GAN ]-----
maximum:
GAN tn, fp: 263, 47
GAN fn, tp: 6, 10
GAN f1 score: 0.391
GAN cohens kappa score: 0.355


average:
GAN tn, fp: 251.88, 34.72
GAN fn, tp: 2.52, 7.68
GAN f1 score: 0.292
GAN cohens kappa score: 0.252


minimum:
GAN tn, fp: 239, 24
GAN fn, tp: 1, 4
GAN f1 score: 0.190
GAN cohens kappa score: 0.157