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

///////////////////////////////////////////
// Running convGAN-proximary-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: 195, 92
GAN fn, tp: 6, 5
GAN f1 score: 0.093
GAN cohens kappa score: 0.028

-> test with 'LR'
LR tn, fp: 238, 49
LR fn, tp: 2, 9
LR f1 score: 0.261
LR cohens kappa score: 0.212
LR average precision score: 0.398

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


------ 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: 143, 144
GAN fn, tp: 3, 8
GAN f1 score: 0.098
GAN cohens kappa score: 0.031

-> 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: 0.678

-> test with 'GB'
GB tn, fp: 284, 3
GB fn, tp: 7, 4
GB f1 score: 0.444
GB cohens kappa score: 0.428

-> test with 'KNN'
KNN tn, fp: 265, 22
KNN fn, tp: 0, 11
KNN f1 score: 0.500
KNN cohens kappa score: 0.471


------ 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: 152, 135
GAN fn, tp: 5, 6
GAN f1 score: 0.079
GAN cohens kappa score: 0.011

-> test with 'LR'
LR tn, fp: 234, 53
LR fn, tp: 1, 10
LR f1 score: 0.270
LR cohens kappa score: 0.221
LR average precision score: 0.260

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


------ 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: 245, 42
GAN fn, tp: 7, 4
GAN f1 score: 0.140
GAN cohens kappa score: 0.086

-> 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: 0.177

-> 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: 260, 27
KNN fn, tp: 4, 7
KNN f1 score: 0.311
KNN cohens kappa score: 0.270


------ 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: 229, 56
GAN fn, tp: 3, 4
GAN f1 score: 0.119
GAN cohens kappa score: 0.080

-> test with 'LR'
LR tn, fp: 237, 48
LR fn, tp: 1, 6
LR f1 score: 0.197
LR cohens kappa score: 0.161
LR average precision score: 0.406

-> test with 'GB'
GB tn, fp: 284, 1
GB fn, tp: 5, 2
GB f1 score: 0.400
GB cohens kappa score: 0.391

-> test with 'KNN'
KNN tn, fp: 258, 27
KNN fn, tp: 1, 6
KNN f1 score: 0.300
KNN cohens kappa score: 0.271


====== 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: 177, 110
GAN fn, tp: 3, 8
GAN f1 score: 0.124
GAN cohens kappa score: 0.061

-> test with 'LR'
LR tn, fp: 242, 45
LR fn, tp: 1, 10
LR f1 score: 0.303
LR cohens kappa score: 0.257
LR average precision score: 0.242

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


------ 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: 270, 17
GAN fn, tp: 3, 8
GAN f1 score: 0.444
GAN cohens kappa score: 0.414

-> test with 'LR'
LR tn, fp: 247, 40
LR fn, tp: 2, 9
LR f1 score: 0.300
LR cohens kappa score: 0.255
LR average precision score: 0.444

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


------ 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: 249, 38
GAN fn, tp: 5, 6
GAN f1 score: 0.218
GAN cohens kappa score: 0.169

-> 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: 0.386

-> 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: 3, 8
KNN f1 score: 0.296
KNN cohens kappa score: 0.252


------ 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: 272, 15
GAN fn, tp: 6, 5
GAN f1 score: 0.323
GAN cohens kappa score: 0.289

-> 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: 0.332

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


------ 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: 190, 95
GAN fn, tp: 2, 5
GAN f1 score: 0.093
GAN cohens kappa score: 0.051

-> test with 'LR'
LR tn, fp: 227, 58
LR fn, tp: 1, 6
LR f1 score: 0.169
LR cohens kappa score: 0.131
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: 264, 21
KNN fn, tp: 2, 5
KNN f1 score: 0.303
KNN cohens kappa score: 0.276


====== 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: 229, 58
GAN fn, tp: 6, 5
GAN f1 score: 0.135
GAN cohens kappa score: 0.077

-> 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: 0.429

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


------ 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: 239, 48
GAN fn, tp: 7, 4
GAN f1 score: 0.127
GAN cohens kappa score: 0.070

-> 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: 0.384

-> 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 3/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1106 synthetic samples
-> test with GAN.predict
GAN tn, fp: 280, 7
GAN fn, tp: 7, 4
GAN f1 score: 0.364
GAN cohens kappa score: 0.339

-> 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: 0.232

-> test with 'GB'
GB tn, fp: 282, 5
GB fn, tp: 9, 2
GB f1 score: 0.222
GB cohens kappa score: 0.199

-> 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 3/5: 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: 4, 7
GAN f1 score: 0.241
GAN cohens kappa score: 0.193

-> 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: 0.527

-> 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: 262, 25
KNN fn, tp: 5, 6
KNN f1 score: 0.286
KNN cohens kappa score: 0.245


------ 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: 234, 51
GAN fn, tp: 5, 2
GAN f1 score: 0.067
GAN cohens kappa score: 0.025

-> test with 'LR'
LR tn, fp: 246, 39
LR fn, tp: 2, 5
LR f1 score: 0.196
LR cohens kappa score: 0.161
LR average precision score: 0.394

-> test with 'GB'
GB tn, fp: 284, 1
GB fn, tp: 5, 2
GB f1 score: 0.400
GB cohens kappa score: 0.391

-> test with 'KNN'
KNN tn, fp: 259, 26
KNN fn, tp: 1, 6
KNN f1 score: 0.308
KNN cohens kappa score: 0.279


====== 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: 206, 81
GAN fn, tp: 5, 6
GAN f1 score: 0.122
GAN cohens kappa score: 0.061

-> 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: 0.472

-> 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: 4, 7
KNN f1 score: 0.333
KNN cohens kappa score: 0.295


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

-> 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: 0.347

-> 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: 260, 27
KNN fn, tp: 4, 7
KNN f1 score: 0.311
KNN cohens kappa score: 0.270


------ 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: 242, 45
GAN fn, tp: 5, 6
GAN f1 score: 0.194
GAN cohens kappa score: 0.141

-> 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: 0.261

-> 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: 253, 34
KNN fn, tp: 1, 10
KNN f1 score: 0.364
KNN cohens kappa score: 0.324


------ 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: 269, 18
GAN fn, tp: 5, 6
GAN f1 score: 0.343
GAN cohens kappa score: 0.308

-> test with 'LR'
LR tn, fp: 243, 44
LR fn, tp: 3, 8
LR f1 score: 0.254
LR cohens kappa score: 0.206
LR average precision score: 0.288

-> test with 'GB'
GB tn, fp: 283, 4
GB fn, tp: 11, 0
GB f1 score: 0.000
GB cohens kappa score: -0.020

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


------ 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: 226, 59
GAN fn, tp: 2, 5
GAN f1 score: 0.141
GAN cohens kappa score: 0.102

-> test with 'LR'
LR tn, fp: 238, 47
LR fn, tp: 2, 5
LR f1 score: 0.169
LR cohens kappa score: 0.133
LR average precision score: 0.500

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


====== 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: 232, 55
GAN fn, tp: 7, 4
GAN f1 score: 0.114
GAN cohens kappa score: 0.056

-> 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: 0.205

-> 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: 261, 26
KNN fn, tp: 1, 10
KNN f1 score: 0.426
KNN cohens kappa score: 0.391


------ 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: 267, 20
GAN fn, tp: 6, 5
GAN f1 score: 0.278
GAN cohens kappa score: 0.239

-> 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: 0.486

-> test with 'GB'
GB tn, fp: 286, 1
GB fn, tp: 8, 3
GB f1 score: 0.400
GB cohens kappa score: 0.388

-> 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 5/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1106 synthetic samples
-> test with GAN.predict
GAN tn, fp: 278, 9
GAN fn, tp: 8, 3
GAN f1 score: 0.261
GAN cohens kappa score: 0.231

-> 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: 0.542

-> 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: 262, 25
KNN fn, tp: 6, 5
KNN f1 score: 0.244
KNN cohens kappa score: 0.201


------ 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: 266, 21
GAN fn, tp: 6, 5
GAN f1 score: 0.270
GAN cohens kappa score: 0.230

-> test with 'LR'
LR tn, fp: 240, 47
LR fn, tp: 1, 10
LR f1 score: 0.294
LR cohens kappa score: 0.248
LR average precision score: 0.546

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


------ 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: 246, 39
GAN fn, tp: 5, 2
GAN f1 score: 0.083
GAN cohens kappa score: 0.044

-> test with 'LR'
LR tn, fp: 234, 51
LR fn, tp: 3, 4
LR f1 score: 0.129
LR cohens kappa score: 0.090
LR average precision score: 0.144

-> 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: 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: 253, 58
LR fn, tp: 6, 10
LR f1 score: 0.303
LR cohens kappa score: 0.259
LR average precision score: 0.678


average:
LR tn, fp: 240.72, 45.88
LR fn, tp: 2.32, 7.88
LR f1 score: 0.246
LR cohens kappa score: 0.200
LR average precision score: 0.379


minimum:
LR tn, fp: 227, 34
LR fn, tp: 1, 4
LR f1 score: 0.129
LR cohens kappa score: 0.090
LR average precision score: 0.144


-----[ GB ]-----
maximum:
GB tn, fp: 287, 6
GB fn, tp: 11, 5
GB f1 score: 0.526
GB cohens kappa score: 0.511


average:
GB tn, fp: 283.96, 2.64
GB fn, tp: 8.28, 1.92
GB f1 score: 0.258
GB cohens kappa score: 0.243


minimum:
GB tn, fp: 281, 0
GB fn, tp: 4, 0
GB f1 score: 0.000
GB cohens kappa score: -0.020


-----[ KNN ]-----
maximum:
KNN tn, fp: 268, 49
KNN fn, tp: 6, 11
KNN f1 score: 0.500
KNN cohens kappa score: 0.471


average:
KNN tn, fp: 258.68, 27.92
KNN fn, tp: 2.68, 7.52
KNN f1 score: 0.331
KNN cohens kappa score: 0.293


minimum:
KNN tn, fp: 238, 17
KNN fn, tp: 0, 5
KNN f1 score: 0.235
KNN cohens kappa score: 0.185


-----[ GAN ]-----
maximum:
GAN tn, fp: 280, 144
GAN fn, tp: 8, 8
GAN f1 score: 0.444
GAN cohens kappa score: 0.414


average:
GAN tn, fp: 233.8, 52.8
GAN fn, tp: 4.96, 5.24
GAN f1 score: 0.193
GAN cohens kappa score: 0.147


minimum:
GAN tn, fp: 143, 7
GAN fn, tp: 2, 2
GAN f1 score: 0.067
GAN cohens kappa score: 0.011