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

///////////////////////////////////////////
// Running convGAN-proxymary-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: 257, 30
GAN fn, tp: 5, 6
GAN f1 score: 0.255
GAN cohens kappa score: 0.211

-> 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: 0.397

-> 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: 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 GAN.predict
GAN tn, fp: 237, 50
GAN fn, tp: 3, 8
GAN f1 score: 0.232
GAN cohens kappa score: 0.181

-> 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: 0.633

-> 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: 1, 10
KNN f1 score: 0.392
KNN cohens kappa score: 0.355


------ 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: 273, 14
GAN fn, tp: 5, 6
GAN f1 score: 0.387
GAN cohens kappa score: 0.356

-> 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: 0.274

-> 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: 253, 34
KNN fn, tp: 3, 8
KNN f1 score: 0.302
KNN cohens kappa score: 0.259


------ 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: 277, 10
GAN fn, tp: 6, 5
GAN f1 score: 0.385
GAN cohens kappa score: 0.357

-> test with 'LR'
LR tn, fp: 257, 30
LR fn, tp: 6, 5
LR f1 score: 0.217
LR cohens kappa score: 0.171
LR average precision score: 0.206

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


------ 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: 266, 19
GAN fn, tp: 2, 5
GAN f1 score: 0.323
GAN cohens kappa score: 0.296

-> 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: 0.410

-> 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: 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 GAN.predict
GAN tn, fp: 266, 21
GAN fn, tp: 6, 5
GAN f1 score: 0.270
GAN cohens kappa score: 0.230

-> 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: 0.382

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


------ 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: 279, 8
GAN fn, tp: 4, 7
GAN f1 score: 0.538
GAN cohens kappa score: 0.518

-> 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: 0.452

-> test with 'GB'
GB tn, fp: 284, 3
GB fn, tp: 6, 5
GB f1 score: 0.526
GB cohens kappa score: 0.511

-> 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 GAN.predict
GAN tn, fp: 271, 16
GAN fn, tp: 6, 5
GAN f1 score: 0.312
GAN cohens kappa score: 0.277

-> 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: 0.389

-> 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: 3, 8
KNN f1 score: 0.333
KNN cohens kappa score: 0.293


------ 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: 277, 10
GAN fn, tp: 4, 7
GAN f1 score: 0.500
GAN cohens kappa score: 0.477

-> 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: 0.295

-> 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: 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: 268, 17
GAN fn, tp: 2, 5
GAN f1 score: 0.345
GAN cohens kappa score: 0.320

-> test with 'LR'
LR tn, fp: 243, 42
LR fn, tp: 1, 6
LR f1 score: 0.218
LR cohens kappa score: 0.184
LR average precision score: 0.392

-> test with 'GB'
GB tn, fp: 279, 6
GB fn, tp: 6, 1
GB f1 score: 0.143
GB cohens kappa score: 0.122

-> test with 'KNN'
KNN tn, fp: 267, 18
KNN fn, tp: 1, 6
KNN f1 score: 0.387
KNN cohens kappa score: 0.363


====== 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: 269, 18
GAN fn, tp: 6, 5
GAN f1 score: 0.294
GAN cohens kappa score: 0.257

-> test with 'LR'
LR tn, fp: 247, 40
LR fn, tp: 3, 8
LR f1 score: 0.271
LR cohens kappa score: 0.225
LR average precision score: 0.360

-> 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: 262, 25
KNN fn, tp: 2, 9
KNN f1 score: 0.400
KNN cohens kappa score: 0.365


------ 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: 268, 19
GAN fn, tp: 7, 4
GAN f1 score: 0.235
GAN cohens kappa score: 0.195

-> 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: 0.401

-> 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: 262, 25
KNN fn, tp: 2, 9
KNN f1 score: 0.400
KNN cohens kappa score: 0.365


------ 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: 276, 11
GAN fn, tp: 7, 4
GAN f1 score: 0.308
GAN cohens kappa score: 0.277

-> 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: 0.249

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


------ 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: 261, 26
GAN fn, tp: 5, 6
GAN f1 score: 0.279
GAN cohens kappa score: 0.237

-> 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: 0.528

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


------ 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: 274, 11
GAN fn, tp: 3, 4
GAN f1 score: 0.364
GAN cohens kappa score: 0.342

-> 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: 0.398

-> 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: 256, 29
KNN fn, tp: 1, 6
KNN f1 score: 0.286
KNN cohens kappa score: 0.256


====== 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: 274, 13
GAN fn, tp: 7, 4
GAN f1 score: 0.286
GAN cohens kappa score: 0.252

-> test with 'LR'
LR tn, fp: 265, 22
LR fn, tp: 5, 6
LR f1 score: 0.308
LR cohens kappa score: 0.269
LR average precision score: 0.461

-> 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: 273, 14
KNN fn, tp: 6, 5
KNN f1 score: 0.333
KNN cohens kappa score: 0.301


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

-> 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: 0.302

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


------ 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: 275, 12
GAN fn, tp: 6, 5
GAN f1 score: 0.357
GAN cohens kappa score: 0.327

-> test with 'LR'
LR tn, fp: 238, 49
LR fn, tp: 3, 8
LR f1 score: 0.235
LR cohens kappa score: 0.185
LR average precision score: 0.292

-> test with 'GB'
GB tn, fp: 282, 5
GB fn, tp: 11, 0
GB f1 score: 0.000
GB cohens kappa score: -0.024

-> 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 4/5: Slice 4/5 -------
-> 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: 248, 39
LR fn, tp: 4, 7
LR f1 score: 0.246
LR cohens kappa score: 0.198
LR average precision score: 0.276

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


------ 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: 270, 15
GAN fn, tp: 4, 3
GAN f1 score: 0.240
GAN cohens kappa score: 0.213

-> 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: 0.500

-> test with 'GB'
GB tn, fp: 280, 5
GB fn, tp: 4, 3
GB f1 score: 0.400
GB cohens kappa score: 0.384

-> 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 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: 280, 7
GAN fn, tp: 8, 3
GAN f1 score: 0.286
GAN cohens kappa score: 0.260

-> test with 'LR'
LR tn, fp: 255, 32
LR fn, tp: 4, 7
LR f1 score: 0.280
LR cohens kappa score: 0.236
LR average precision score: 0.223

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


------ 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: 275, 12
GAN fn, tp: 5, 6
GAN f1 score: 0.414
GAN cohens kappa score: 0.386

-> test with 'LR'
LR tn, fp: 233, 54
LR fn, tp: 2, 9
LR f1 score: 0.243
LR cohens kappa score: 0.192
LR average precision score: 0.492

-> 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 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: 260, 27
LR fn, tp: 3, 8
LR f1 score: 0.348
LR cohens kappa score: 0.309
LR average precision score: 0.544

-> 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: 268, 19
KNN fn, tp: 3, 8
KNN f1 score: 0.421
KNN cohens kappa score: 0.389


------ 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: 261, 26
GAN fn, tp: 6, 5
GAN f1 score: 0.238
GAN cohens kappa score: 0.194

-> 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: 0.540

-> test with 'GB'
GB tn, fp: 281, 6
GB fn, tp: 10, 1
GB f1 score: 0.111
GB cohens kappa score: 0.085

-> 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 5/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1104 synthetic samples
-> test with GAN.predict
GAN tn, fp: 266, 19
GAN fn, tp: 5, 2
GAN f1 score: 0.143
GAN cohens kappa score: 0.111

-> test with 'LR'
LR tn, fp: 241, 44
LR fn, tp: 3, 4
LR f1 score: 0.145
LR cohens kappa score: 0.108
LR average precision score: 0.122

-> test with 'GB'
GB tn, fp: 280, 5
GB fn, tp: 6, 1
GB f1 score: 0.154
GB cohens kappa score: 0.135

-> test with 'KNN'
KNN tn, fp: 262, 23
KNN fn, tp: 1, 6
KNN f1 score: 0.333
KNN cohens kappa score: 0.307

### Exercise is done.

-----[ LR ]-----
maximum:
LR tn, fp: 265, 54
LR fn, tp: 6, 10
LR f1 score: 0.348
LR cohens kappa score: 0.309
LR average precision score: 0.633


average:
LR tn, fp: 248.48, 38.12
LR fn, tp: 2.72, 7.48
LR f1 score: 0.268
LR cohens kappa score: 0.225
LR average precision score: 0.381


minimum:
LR tn, fp: 233, 22
LR fn, tp: 1, 4
LR f1 score: 0.145
LR cohens kappa score: 0.108
LR average precision score: 0.122


-----[ GB ]-----
maximum:
GB tn, fp: 287, 8
GB fn, tp: 11, 5
GB f1 score: 0.545
GB cohens kappa score: 0.537


average:
GB tn, fp: 283.2, 3.4
GB fn, tp: 8.2, 2.0
GB f1 score: 0.249
GB cohens kappa score: 0.232


minimum:
GB tn, fp: 279, 0
GB fn, tp: 4, 0
GB f1 score: 0.000
GB cohens kappa score: -0.024


-----[ KNN ]-----
maximum:
KNN tn, fp: 273, 51
KNN fn, tp: 6, 10
KNN f1 score: 0.421
KNN cohens kappa score: 0.389


average:
KNN tn, fp: 258.84, 27.76
KNN fn, tp: 2.52, 7.68
KNN f1 score: 0.340
KNN cohens kappa score: 0.303


minimum:
KNN tn, fp: 236, 14
KNN fn, tp: 1, 5
KNN f1 score: 0.250
KNN cohens kappa score: 0.204


-----[ GAN ]-----
maximum:
GAN tn, fp: 280, 50
GAN fn, tp: 8, 8
GAN f1 score: 0.538
GAN cohens kappa score: 0.518


average:
GAN tn, fp: 267.92, 18.68
GAN fn, tp: 5.0, 5.2
GAN f1 score: 0.317
GAN cohens kappa score: 0.284


minimum:
GAN tn, fp: 237, 7
GAN fn, tp: 2, 2
GAN f1 score: 0.143
GAN cohens kappa score: 0.111