ConvGeNBenchmarkResults/data_result/convGAN-majority-full/folding_flare-F.log

///////////////////////////////////////////
// Running convGAN-majority-full on folding_flare-F
///////////////////////////////////////////

Load 'data_input/folding_flare-F'
from pickle file
non empty cut in data_input/folding_flare-F! (23 points)
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 784 synthetic samples
-> test with GAN.predict
GAN tn, fp: 194, 11
GAN fn, tp: 8, 1
GAN f1 score: 0.095
GAN cohens kappa score: 0.050

-> test with 'LR'
LR tn, fp: 181, 24
LR fn, tp: 8, 1
LR f1 score: 0.059
LR cohens kappa score: -0.003
LR average precision score: 0.084

-> test with 'GB'
GB tn, fp: 200, 5
GB fn, tp: 8, 1
GB f1 score: 0.133
GB cohens kappa score: 0.103

-> test with 'KNN'
KNN tn, fp: 180, 25
KNN fn, tp: 5, 4
KNN f1 score: 0.211
KNN cohens kappa score: 0.156


------ Step 1/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 784 synthetic samples
-> test with GAN.predict
GAN tn, fp: 193, 12
GAN fn, tp: 4, 5
GAN f1 score: 0.385
GAN cohens kappa score: 0.349

-> test with 'LR'
LR tn, fp: 162, 43
LR fn, tp: 1, 8
LR f1 score: 0.267
LR cohens kappa score: 0.210
LR average precision score: 0.348

-> test with 'GB'
GB tn, fp: 201, 4
GB fn, tp: 8, 1
GB f1 score: 0.143
GB cohens kappa score: 0.116

-> test with 'KNN'
KNN tn, fp: 165, 40
KNN fn, tp: 2, 7
KNN f1 score: 0.250
KNN cohens kappa score: 0.193


------ Step 1/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 784 synthetic samples
-> test with GAN.predict
GAN tn, fp: 197, 8
GAN fn, tp: 4, 5
GAN f1 score: 0.455
GAN cohens kappa score: 0.426

-> test with 'LR'
LR tn, fp: 181, 24
LR fn, tp: 4, 5
LR f1 score: 0.263
LR cohens kappa score: 0.213
LR average precision score: 0.432

-> test with 'GB'
GB tn, fp: 204, 1
GB fn, tp: 8, 1
GB f1 score: 0.182
GB cohens kappa score: 0.169

-> test with 'KNN'
KNN tn, fp: 173, 32
KNN fn, tp: 4, 5
KNN f1 score: 0.217
KNN cohens kappa score: 0.161


------ Step 1/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 784 synthetic samples
-> test with GAN.predict
GAN tn, fp: 201, 4
GAN fn, tp: 8, 1
GAN f1 score: 0.143
GAN cohens kappa score: 0.116

-> test with 'LR'
LR tn, fp: 187, 18
LR fn, tp: 0, 9
LR f1 score: 0.500
LR cohens kappa score: 0.466
LR average precision score: 0.740

-> test with 'GB'
GB tn, fp: 204, 1
GB fn, tp: 7, 2
GB f1 score: 0.333
GB cohens kappa score: 0.319

-> test with 'KNN'
KNN tn, fp: 189, 16
KNN fn, tp: 5, 4
KNN f1 score: 0.276
KNN cohens kappa score: 0.231


------ Step 1/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 784 synthetic samples
-> test with GAN.predict
GAN tn, fp: 189, 14
GAN fn, tp: 2, 5
GAN f1 score: 0.385
GAN cohens kappa score: 0.353

-> test with 'LR'
LR tn, fp: 178, 25
LR fn, tp: 3, 4
LR f1 score: 0.222
LR cohens kappa score: 0.178
LR average precision score: 0.294

-> test with 'GB'
GB tn, fp: 201, 2
GB fn, tp: 7, 0
GB f1 score: 0.000
GB cohens kappa score: -0.015

-> test with 'KNN'
KNN tn, fp: 175, 28
KNN fn, tp: 2, 5
KNN f1 score: 0.250
KNN cohens kappa score: 0.206


====== Step 2/5 =======
-> Shuffling data
-> Spliting data to slices

------ Step 2/5: Slice 1/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 784 synthetic samples
-> test with GAN.predict
GAN tn, fp: 194, 11
GAN fn, tp: 2, 7
GAN f1 score: 0.519
GAN cohens kappa score: 0.490

-> test with 'LR'
LR tn, fp: 177, 28
LR fn, tp: 2, 7
LR f1 score: 0.318
LR cohens kappa score: 0.269
LR average precision score: 0.482

-> test with 'GB'
GB tn, fp: 201, 4
GB fn, tp: 8, 1
GB f1 score: 0.143
GB cohens kappa score: 0.116

-> test with 'KNN'
KNN tn, fp: 177, 28
KNN fn, tp: 2, 7
KNN f1 score: 0.318
KNN cohens kappa score: 0.269


------ Step 2/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 784 synthetic samples
-> test with GAN.predict
GAN tn, fp: 191, 14
GAN fn, tp: 4, 5
GAN f1 score: 0.357
GAN cohens kappa score: 0.318

-> test with 'LR'
LR tn, fp: 175, 30
LR fn, tp: 4, 5
LR f1 score: 0.227
LR cohens kappa score: 0.172
LR average precision score: 0.487

-> test with 'GB'
GB tn, fp: 203, 2
GB fn, tp: 9, 0
GB f1 score: 0.000
GB cohens kappa score: -0.016

-> test with 'KNN'
KNN tn, fp: 180, 25
KNN fn, tp: 2, 7
KNN f1 score: 0.341
KNN cohens kappa score: 0.295


------ Step 2/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 784 synthetic samples
-> test with GAN.predict
GAN tn, fp: 199, 6
GAN fn, tp: 7, 2
GAN f1 score: 0.235
GAN cohens kappa score: 0.204

-> test with 'LR'
LR tn, fp: 176, 29
LR fn, tp: 3, 6
LR f1 score: 0.273
LR cohens kappa score: 0.221
LR average precision score: 0.387

-> test with 'GB'
GB tn, fp: 202, 3
GB fn, tp: 8, 1
GB f1 score: 0.154
GB cohens kappa score: 0.131

-> test with 'KNN'
KNN tn, fp: 182, 23
KNN fn, tp: 5, 4
KNN f1 score: 0.222
KNN cohens kappa score: 0.170


------ Step 2/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 784 synthetic samples
-> test with GAN.predict
GAN tn, fp: 197, 8
GAN fn, tp: 6, 3
GAN f1 score: 0.300
GAN cohens kappa score: 0.266

-> test with 'LR'
LR tn, fp: 187, 18
LR fn, tp: 4, 5
LR f1 score: 0.312
LR cohens kappa score: 0.268
LR average precision score: 0.295

-> test with 'GB'
GB tn, fp: 202, 3
GB fn, tp: 8, 1
GB f1 score: 0.154
GB cohens kappa score: 0.131

-> test with 'KNN'
KNN tn, fp: 178, 27
KNN fn, tp: 3, 6
KNN f1 score: 0.286
KNN cohens kappa score: 0.235


------ Step 2/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 784 synthetic samples
-> test with GAN.predict
GAN tn, fp: 189, 14
GAN fn, tp: 3, 4
GAN f1 score: 0.320
GAN cohens kappa score: 0.286

-> test with 'LR'
LR tn, fp: 175, 28
LR fn, tp: 0, 7
LR f1 score: 0.333
LR cohens kappa score: 0.294
LR average precision score: 0.398

-> test with 'GB'
GB tn, fp: 201, 2
GB fn, tp: 6, 1
GB f1 score: 0.200
GB cohens kappa score: 0.184

-> test with 'KNN'
KNN tn, fp: 174, 29
KNN fn, tp: 1, 6
KNN f1 score: 0.286
KNN cohens kappa score: 0.244


====== Step 3/5 =======
-> Shuffling data
-> Spliting data to slices

------ Step 3/5: Slice 1/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 784 synthetic samples
-> test with GAN.predict
GAN tn, fp: 203, 2
GAN fn, tp: 5, 4
GAN f1 score: 0.533
GAN cohens kappa score: 0.517

-> test with 'LR'
LR tn, fp: 188, 17
LR fn, tp: 2, 7
LR f1 score: 0.424
LR cohens kappa score: 0.387
LR average precision score: 0.770

-> test with 'GB'
GB tn, fp: 205, 0
GB fn, tp: 8, 1
GB f1 score: 0.200
GB cohens kappa score: 0.193

-> test with 'KNN'
KNN tn, fp: 190, 15
KNN fn, tp: 4, 5
KNN f1 score: 0.345
KNN cohens kappa score: 0.304


------ Step 3/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 784 synthetic samples
-> test with GAN.predict
GAN tn, fp: 187, 18
GAN fn, tp: 5, 4
GAN f1 score: 0.258
GAN cohens kappa score: 0.211

-> test with 'LR'
LR tn, fp: 172, 33
LR fn, tp: 2, 7
LR f1 score: 0.286
LR cohens kappa score: 0.233
LR average precision score: 0.271

-> test with 'GB'
GB tn, fp: 195, 10
GB fn, tp: 5, 4
GB f1 score: 0.348
GB cohens kappa score: 0.313

-> test with 'KNN'
KNN tn, fp: 175, 30
KNN fn, tp: 3, 6
KNN f1 score: 0.267
KNN cohens kappa score: 0.214


------ Step 3/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 784 synthetic samples
-> test with GAN.predict
GAN tn, fp: 202, 3
GAN fn, tp: 5, 4
GAN f1 score: 0.500
GAN cohens kappa score: 0.481

-> test with 'LR'
LR tn, fp: 181, 24
LR fn, tp: 3, 6
LR f1 score: 0.308
LR cohens kappa score: 0.260
LR average precision score: 0.491

-> test with 'GB'
GB tn, fp: 205, 0
GB fn, tp: 9, 0
GB f1 score: 0.000
GB cohens kappa score: 0.000

-> test with 'KNN'
KNN tn, fp: 172, 33
KNN fn, tp: 2, 7
KNN f1 score: 0.286
KNN cohens kappa score: 0.233


------ Step 3/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 784 synthetic samples
-> test with GAN.predict
GAN tn, fp: 193, 12
GAN fn, tp: 5, 4
GAN f1 score: 0.320
GAN cohens kappa score: 0.281

-> test with 'LR'
LR tn, fp: 189, 16
LR fn, tp: 5, 4
LR f1 score: 0.276
LR cohens kappa score: 0.231
LR average precision score: 0.315

-> test with 'GB'
GB tn, fp: 204, 1
GB fn, tp: 9, 0
GB f1 score: 0.000
GB cohens kappa score: -0.008

-> test with 'KNN'
KNN tn, fp: 185, 20
KNN fn, tp: 4, 5
KNN f1 score: 0.294
KNN cohens kappa score: 0.248


------ Step 3/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 784 synthetic samples
-> test with GAN.predict
GAN tn, fp: 196, 7
GAN fn, tp: 6, 1
GAN f1 score: 0.133
GAN cohens kappa score: 0.101

-> test with 'LR'
LR tn, fp: 168, 35
LR fn, tp: 1, 6
LR f1 score: 0.250
LR cohens kappa score: 0.205
LR average precision score: 0.261

-> test with 'GB'
GB tn, fp: 199, 4
GB fn, tp: 6, 1
GB f1 score: 0.167
GB cohens kappa score: 0.143

-> test with 'KNN'
KNN tn, fp: 188, 15
KNN fn, tp: 6, 1
KNN f1 score: 0.087
KNN cohens kappa score: 0.043


====== Step 4/5 =======
-> Shuffling data
-> Spliting data to slices

------ Step 4/5: Slice 1/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 784 synthetic samples
-> test with GAN.predict
GAN tn, fp: 195, 10
GAN fn, tp: 6, 3
GAN f1 score: 0.273
GAN cohens kappa score: 0.235

-> test with 'LR'
LR tn, fp: 180, 25
LR fn, tp: 2, 7
LR f1 score: 0.341
LR cohens kappa score: 0.295
LR average precision score: 0.192

-> test with 'GB'
GB tn, fp: 202, 3
GB fn, tp: 9, 0
GB f1 score: 0.000
GB cohens kappa score: -0.021

-> test with 'KNN'
KNN tn, fp: 182, 23
KNN fn, tp: 4, 5
KNN f1 score: 0.270
KNN cohens kappa score: 0.221


------ Step 4/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 784 synthetic samples
-> test with GAN.predict
GAN tn, fp: 203, 2
GAN fn, tp: 8, 1
GAN f1 score: 0.167
GAN cohens kappa score: 0.149

-> test with 'LR'
LR tn, fp: 186, 19
LR fn, tp: 4, 5
LR f1 score: 0.303
LR cohens kappa score: 0.258
LR average precision score: 0.579

-> test with 'GB'
GB tn, fp: 201, 4
GB fn, tp: 8, 1
GB f1 score: 0.143
GB cohens kappa score: 0.116

-> test with 'KNN'
KNN tn, fp: 184, 21
KNN fn, tp: 4, 5
KNN f1 score: 0.286
KNN cohens kappa score: 0.238


------ Step 4/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 784 synthetic samples
-> test with GAN.predict
GAN tn, fp: 191, 14
GAN fn, tp: 4, 5
GAN f1 score: 0.357
GAN cohens kappa score: 0.318

-> test with 'LR'
LR tn, fp: 173, 32
LR fn, tp: 4, 5
LR f1 score: 0.217
LR cohens kappa score: 0.161
LR average precision score: 0.311

-> test with 'GB'
GB tn, fp: 204, 1
GB fn, tp: 8, 1
GB f1 score: 0.182
GB cohens kappa score: 0.169

-> test with 'KNN'
KNN tn, fp: 181, 24
KNN fn, tp: 3, 6
KNN f1 score: 0.308
KNN cohens kappa score: 0.260


------ Step 4/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 784 synthetic samples
-> test with GAN.predict
GAN tn, fp: 195, 10
GAN fn, tp: 7, 2
GAN f1 score: 0.190
GAN cohens kappa score: 0.150

-> test with 'LR'
LR tn, fp: 179, 26
LR fn, tp: 2, 7
LR f1 score: 0.333
LR cohens kappa score: 0.286
LR average precision score: 0.384

-> test with 'GB'
GB tn, fp: 201, 4
GB fn, tp: 7, 2
GB f1 score: 0.267
GB cohens kappa score: 0.241

-> test with 'KNN'
KNN tn, fp: 184, 21
KNN fn, tp: 3, 6
KNN f1 score: 0.333
KNN cohens kappa score: 0.288


------ Step 4/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 784 synthetic samples
-> test with GAN.predict
GAN tn, fp: 197, 6
GAN fn, tp: 6, 1
GAN f1 score: 0.143
GAN cohens kappa score: 0.113

-> test with 'LR'
LR tn, fp: 181, 22
LR fn, tp: 2, 5
LR f1 score: 0.294
LR cohens kappa score: 0.255
LR average precision score: 0.361

-> test with 'GB'
GB tn, fp: 202, 1
GB fn, tp: 7, 0
GB f1 score: 0.000
GB cohens kappa score: -0.008

-> test with 'KNN'
KNN tn, fp: 177, 26
KNN fn, tp: 3, 4
KNN f1 score: 0.216
KNN cohens kappa score: 0.171


====== Step 5/5 =======
-> Shuffling data
-> Spliting data to slices

------ Step 5/5: Slice 1/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 784 synthetic samples
-> test with GAN.predict
GAN tn, fp: 193, 12
GAN fn, tp: 4, 5
GAN f1 score: 0.385
GAN cohens kappa score: 0.349

-> test with 'LR'
LR tn, fp: 180, 25
LR fn, tp: 4, 5
LR f1 score: 0.256
LR cohens kappa score: 0.205
LR average precision score: 0.258

-> test with 'GB'
GB tn, fp: 202, 3
GB fn, tp: 8, 1
GB f1 score: 0.154
GB cohens kappa score: 0.131

-> test with 'KNN'
KNN tn, fp: 179, 26
KNN fn, tp: 3, 6
KNN f1 score: 0.293
KNN cohens kappa score: 0.243


------ Step 5/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 784 synthetic samples
-> test with GAN.predict
GAN tn, fp: 196, 9
GAN fn, tp: 6, 3
GAN f1 score: 0.286
GAN cohens kappa score: 0.250

-> test with 'LR'
LR tn, fp: 177, 28
LR fn, tp: 2, 7
LR f1 score: 0.318
LR cohens kappa score: 0.269
LR average precision score: 0.421

-> test with 'GB'
GB tn, fp: 204, 1
GB fn, tp: 9, 0
GB f1 score: 0.000
GB cohens kappa score: -0.008

-> test with 'KNN'
KNN tn, fp: 170, 35
KNN fn, tp: 3, 6
KNN f1 score: 0.240
KNN cohens kappa score: 0.184


------ Step 5/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 784 synthetic samples
-> test with GAN.predict
GAN tn, fp: 191, 14
GAN fn, tp: 5, 4
GAN f1 score: 0.296
GAN cohens kappa score: 0.254

-> test with 'LR'
LR tn, fp: 178, 27
LR fn, tp: 0, 9
LR f1 score: 0.400
LR cohens kappa score: 0.357
LR average precision score: 0.490

-> test with 'GB'
GB tn, fp: 204, 1
GB fn, tp: 8, 1
GB f1 score: 0.182
GB cohens kappa score: 0.169

-> test with 'KNN'
KNN tn, fp: 177, 28
KNN fn, tp: 4, 5
KNN f1 score: 0.238
KNN cohens kappa score: 0.184


------ Step 5/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 784 synthetic samples
-> test with GAN.predict
GAN tn, fp: 202, 3
GAN fn, tp: 6, 3
GAN f1 score: 0.400
GAN cohens kappa score: 0.379

-> test with 'LR'
LR tn, fp: 181, 24
LR fn, tp: 5, 4
LR f1 score: 0.216
LR cohens kappa score: 0.163
LR average precision score: 0.198

-> test with 'GB'
GB tn, fp: 203, 2
GB fn, tp: 9, 0
GB f1 score: 0.000
GB cohens kappa score: -0.016

-> test with 'KNN'
KNN tn, fp: 188, 17
KNN fn, tp: 6, 3
KNN f1 score: 0.207
KNN cohens kappa score: 0.158


------ Step 5/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 784 synthetic samples
-> test with GAN.predict
GAN tn, fp: 192, 11
GAN fn, tp: 5, 2
GAN f1 score: 0.200
GAN cohens kappa score: 0.164

-> test with 'LR'
LR tn, fp: 175, 28
LR fn, tp: 2, 5
LR f1 score: 0.250
LR cohens kappa score: 0.206
LR average precision score: 0.439

-> test with 'GB'
GB tn, fp: 197, 6
GB fn, tp: 5, 2
GB f1 score: 0.267
GB cohens kappa score: 0.240

-> test with 'KNN'
KNN tn, fp: 178, 25
KNN fn, tp: 3, 4
KNN f1 score: 0.222
KNN cohens kappa score: 0.178

### Exercise is done.

-----[ LR ]-----
maximum:
LR tn, fp: 189, 43
LR fn, tp: 8, 9
LR f1 score: 0.500
LR cohens kappa score: 0.466
LR average precision score: 0.770


average:
LR tn, fp: 178.68, 25.92
LR fn, tp: 2.76, 5.84
LR f1 score: 0.290
LR cohens kappa score: 0.242
LR average precision score: 0.388


minimum:
LR tn, fp: 162, 16
LR fn, tp: 0, 1
LR f1 score: 0.059
LR cohens kappa score: -0.003
LR average precision score: 0.084


-----[ GB ]-----
maximum:
GB tn, fp: 205, 10
GB fn, tp: 9, 4
GB f1 score: 0.348
GB cohens kappa score: 0.319


average:
GB tn, fp: 201.88, 2.72
GB fn, tp: 7.68, 0.92
GB f1 score: 0.134
GB cohens kappa score: 0.116


minimum:
GB tn, fp: 195, 0
GB fn, tp: 5, 0
GB f1 score: 0.000
GB cohens kappa score: -0.021


-----[ KNN ]-----
maximum:
KNN tn, fp: 190, 40
KNN fn, tp: 6, 7
KNN f1 score: 0.345
KNN cohens kappa score: 0.304


average:
KNN tn, fp: 179.32, 25.28
KNN fn, tp: 3.44, 5.16
KNN f1 score: 0.262
KNN cohens kappa score: 0.213


minimum:
KNN tn, fp: 165, 15
KNN fn, tp: 1, 1
KNN f1 score: 0.087
KNN cohens kappa score: 0.043


-----[ GAN ]-----
maximum:
GAN tn, fp: 203, 18
GAN fn, tp: 8, 7
GAN f1 score: 0.533
GAN cohens kappa score: 0.517


average:
GAN tn, fp: 195.2, 9.4
GAN fn, tp: 5.24, 3.36
GAN f1 score: 0.305
GAN cohens kappa score: 0.272


minimum:
GAN tn, fp: 187, 2
GAN fn, tp: 2, 1
GAN f1 score: 0.095
GAN cohens kappa score: 0.050