ConvGeNBenchmarkResults/data_result/convGAN-majority-5/folding_flare-F.log

///////////////////////////////////////////
// Running convGAN-majority-5 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: 187, 18
GAN fn, tp: 7, 2
GAN f1 score: 0.138
GAN cohens kappa score: 0.085

-> test with 'LR'
LR tn, fp: 176, 29
LR fn, tp: 6, 3
LR f1 score: 0.146
LR cohens kappa score: 0.086
LR average precision score: 0.096

-> 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: 182, 23
KNN fn, tp: 4, 5
KNN f1 score: 0.270
KNN cohens kappa score: 0.221


------ 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: 178, 27
GAN fn, tp: 2, 7
GAN f1 score: 0.326
GAN cohens kappa score: 0.278

-> test with 'LR'
LR tn, fp: 158, 47
LR fn, tp: 1, 8
LR f1 score: 0.250
LR cohens kappa score: 0.192
LR average precision score: 0.347

-> 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: 172, 33
KNN fn, tp: 3, 6
KNN f1 score: 0.250
KNN cohens kappa score: 0.195


------ 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: 188, 17
GAN fn, tp: 4, 5
GAN f1 score: 0.323
GAN cohens kappa score: 0.280

-> test with 'LR'
LR tn, fp: 175, 30
LR fn, tp: 3, 6
LR f1 score: 0.267
LR cohens kappa score: 0.214
LR average precision score: 0.311

-> 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: 178, 27
KNN fn, tp: 4, 5
KNN f1 score: 0.244
KNN cohens kappa score: 0.191


------ 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: 193, 12
GAN fn, tp: 3, 6
GAN f1 score: 0.444
GAN cohens kappa score: 0.411

-> test with 'LR'
LR tn, fp: 182, 23
LR fn, tp: 0, 9
LR f1 score: 0.439
LR cohens kappa score: 0.400
LR average precision score: 0.726

-> 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: 186, 19
KNN fn, tp: 3, 6
KNN f1 score: 0.353
KNN cohens kappa score: 0.310


------ 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: 175, 28
GAN fn, tp: 2, 5
GAN f1 score: 0.250
GAN cohens kappa score: 0.206

-> test with 'LR'
LR tn, fp: 172, 31
LR fn, tp: 3, 4
LR f1 score: 0.190
LR cohens kappa score: 0.143
LR average precision score: 0.204

-> test with 'GB'
GB tn, fp: 200, 3
GB fn, tp: 6, 1
GB f1 score: 0.182
GB cohens kappa score: 0.161

-> test with 'KNN'
KNN tn, fp: 173, 30
KNN fn, tp: 1, 6
KNN f1 score: 0.279
KNN cohens kappa score: 0.236


====== 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: 175, 30
GAN fn, tp: 2, 7
GAN f1 score: 0.304
GAN cohens kappa score: 0.254

-> test with 'LR'
LR tn, fp: 174, 31
LR fn, tp: 1, 8
LR f1 score: 0.333
LR cohens kappa score: 0.284
LR average precision score: 0.321

-> test with 'GB'
GB tn, fp: 203, 2
GB fn, tp: 8, 1
GB f1 score: 0.167
GB cohens kappa score: 0.149

-> test with 'KNN'
KNN tn, fp: 181, 24
KNN fn, tp: 2, 7
KNN f1 score: 0.350
KNN cohens kappa score: 0.305


------ 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: 175, 30
GAN fn, tp: 3, 6
GAN f1 score: 0.267
GAN cohens kappa score: 0.214

-> test with 'LR'
LR tn, fp: 170, 35
LR fn, tp: 3, 6
LR f1 score: 0.240
LR cohens kappa score: 0.184
LR average precision score: 0.408

-> 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: 178, 27
KNN fn, tp: 4, 5
KNN f1 score: 0.244
KNN cohens kappa score: 0.191


------ 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: 181, 24
GAN fn, tp: 4, 5
GAN f1 score: 0.263
GAN cohens kappa score: 0.213

-> test with 'LR'
LR tn, fp: 170, 35
LR fn, tp: 2, 7
LR f1 score: 0.275
LR cohens kappa score: 0.221
LR average precision score: 0.269

-> 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: 178, 27
KNN fn, tp: 4, 5
KNN f1 score: 0.244
KNN cohens kappa score: 0.191


------ 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: 192, 13
GAN fn, tp: 5, 4
GAN f1 score: 0.308
GAN cohens kappa score: 0.267

-> 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: 0.306

-> 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: 180, 25
KNN fn, tp: 5, 4
KNN f1 score: 0.211
KNN cohens kappa score: 0.156


------ 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: 175, 28
GAN fn, tp: 1, 6
GAN f1 score: 0.293
GAN cohens kappa score: 0.251

-> test with 'LR'
LR tn, fp: 164, 39
LR fn, tp: 0, 7
LR f1 score: 0.264
LR cohens kappa score: 0.219
LR average precision score: 0.403

-> test with 'GB'
GB tn, fp: 202, 1
GB fn, tp: 5, 2
GB f1 score: 0.400
GB cohens kappa score: 0.388

-> test with 'KNN'
KNN tn, fp: 172, 31
KNN fn, tp: 1, 6
KNN f1 score: 0.273
KNN cohens kappa score: 0.230


====== 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: 195, 10
GAN fn, tp: 1, 8
GAN f1 score: 0.593
GAN cohens kappa score: 0.568

-> test with 'LR'
LR tn, fp: 185, 20
LR fn, tp: 2, 7
LR f1 score: 0.389
LR cohens kappa score: 0.348
LR average precision score: 0.759

-> 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: 192, 13
KNN fn, tp: 3, 6
KNN f1 score: 0.429
KNN cohens kappa score: 0.394


------ 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: 176, 29
GAN fn, tp: 3, 6
GAN f1 score: 0.273
GAN cohens kappa score: 0.221

-> test with 'LR'
LR tn, fp: 163, 42
LR fn, tp: 2, 7
LR f1 score: 0.241
LR cohens kappa score: 0.183
LR average precision score: 0.222

-> test with 'GB'
GB tn, fp: 201, 4
GB fn, tp: 6, 3
GB f1 score: 0.375
GB cohens kappa score: 0.351

-> test with 'KNN'
KNN tn, fp: 164, 41
KNN fn, tp: 4, 5
KNN f1 score: 0.182
KNN cohens kappa score: 0.120


------ 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: 188, 17
GAN fn, tp: 3, 6
GAN f1 score: 0.375
GAN cohens kappa score: 0.335

-> test with 'LR'
LR tn, fp: 173, 32
LR fn, tp: 2, 7
LR f1 score: 0.292
LR cohens kappa score: 0.240
LR average precision score: 0.448

-> test with 'GB'
GB tn, fp: 203, 2
GB fn, tp: 8, 1
GB f1 score: 0.167
GB cohens kappa score: 0.149

-> test with 'KNN'
KNN tn, fp: 170, 35
KNN fn, tp: 2, 7
KNN f1 score: 0.275
KNN cohens kappa score: 0.221


------ 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: 186, 19
GAN fn, tp: 4, 5
GAN f1 score: 0.303
GAN cohens kappa score: 0.258

-> 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: 0.244

-> 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: 178, 27
KNN fn, tp: 4, 5
KNN f1 score: 0.244
KNN cohens kappa score: 0.191


------ 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: 180, 23
GAN fn, tp: 4, 3
GAN f1 score: 0.182
GAN cohens kappa score: 0.136

-> test with 'LR'
LR tn, fp: 159, 44
LR fn, tp: 1, 6
LR f1 score: 0.211
LR cohens kappa score: 0.161
LR average precision score: 0.194

-> test with 'GB'
GB tn, fp: 199, 4
GB fn, tp: 5, 2
GB f1 score: 0.308
GB cohens kappa score: 0.286

-> test with 'KNN'
KNN tn, fp: 181, 22
KNN fn, tp: 3, 4
KNN f1 score: 0.242
KNN cohens kappa score: 0.200


====== 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: 183, 22
GAN fn, tp: 2, 7
GAN f1 score: 0.368
GAN cohens kappa score: 0.325

-> test with 'LR'
LR tn, fp: 169, 36
LR fn, tp: 1, 8
LR f1 score: 0.302
LR cohens kappa score: 0.249
LR average precision score: 0.222

-> test with 'GB'
GB tn, fp: 198, 7
GB fn, tp: 9, 0
GB f1 score: 0.000
GB cohens kappa score: -0.038

-> test with 'KNN'
KNN tn, fp: 170, 35
KNN fn, tp: 2, 7
KNN f1 score: 0.275
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: 198, 7
GAN fn, tp: 5, 4
GAN f1 score: 0.400
GAN cohens kappa score: 0.371

-> test with 'LR'
LR tn, fp: 178, 27
LR fn, tp: 3, 6
LR f1 score: 0.286
LR cohens kappa score: 0.235
LR average precision score: 0.522

-> test with 'GB'
GB tn, fp: 205, 0
GB fn, tp: 6, 3
GB f1 score: 0.500
GB cohens kappa score: 0.489

-> test with 'KNN'
KNN tn, fp: 178, 27
KNN fn, tp: 5, 4
KNN f1 score: 0.200
KNN cohens kappa score: 0.144


------ 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: 181, 24
GAN fn, tp: 3, 6
GAN f1 score: 0.308
GAN cohens kappa score: 0.260

-> test with 'LR'
LR tn, fp: 168, 37
LR fn, tp: 4, 5
LR f1 score: 0.196
LR cohens kappa score: 0.136
LR average precision score: 0.241

-> test with 'GB'
GB tn, fp: 205, 0
GB fn, tp: 7, 2
GB f1 score: 0.364
GB cohens kappa score: 0.354

-> test with 'KNN'
KNN tn, fp: 176, 29
KNN fn, tp: 4, 5
KNN f1 score: 0.233
KNN cohens kappa score: 0.178


------ 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: 183, 22
GAN fn, tp: 3, 6
GAN f1 score: 0.324
GAN cohens kappa score: 0.278

-> test with 'LR'
LR tn, fp: 171, 34
LR fn, tp: 1, 8
LR f1 score: 0.314
LR cohens kappa score: 0.263
LR average precision score: 0.409

-> test with 'GB'
GB tn, fp: 203, 2
GB fn, tp: 7, 2
GB f1 score: 0.308
GB cohens kappa score: 0.289

-> 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 4/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 784 synthetic samples
-> test with GAN.predict
GAN tn, fp: 178, 25
GAN fn, tp: 2, 5
GAN f1 score: 0.270
GAN cohens kappa score: 0.229

-> test with 'LR'
LR tn, fp: 169, 34
LR fn, tp: 2, 5
LR f1 score: 0.217
LR cohens kappa score: 0.171
LR average precision score: 0.496

-> test with 'GB'
GB tn, fp: 202, 1
GB fn, tp: 6, 1
GB f1 score: 0.222
GB cohens kappa score: 0.211

-> test with 'KNN'
KNN tn, fp: 169, 34
KNN fn, tp: 3, 4
KNN f1 score: 0.178
KNN cohens kappa score: 0.129


====== 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: 186, 19
GAN fn, tp: 3, 6
GAN f1 score: 0.353
GAN cohens kappa score: 0.310

-> 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: 0.196

-> 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: 183, 22
KNN fn, tp: 4, 5
KNN f1 score: 0.278
KNN cohens kappa score: 0.229


------ 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: 179, 26
GAN fn, tp: 3, 6
GAN f1 score: 0.293
GAN cohens kappa score: 0.243

-> test with 'LR'
LR tn, fp: 174, 31
LR fn, tp: 2, 7
LR f1 score: 0.298
LR cohens kappa score: 0.247
LR average precision score: 0.465

-> 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: 3, 6
KNN f1 score: 0.255
KNN cohens kappa score: 0.201


------ 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: 184, 21
GAN fn, tp: 1, 8
GAN f1 score: 0.421
GAN cohens kappa score: 0.381

-> test with 'LR'
LR tn, fp: 175, 30
LR fn, tp: 1, 8
LR f1 score: 0.340
LR cohens kappa score: 0.292
LR average precision score: 0.447

-> 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: 174, 31
KNN fn, tp: 2, 7
KNN f1 score: 0.298
KNN cohens kappa score: 0.247


------ 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: 196, 9
GAN fn, tp: 6, 3
GAN f1 score: 0.286
GAN cohens kappa score: 0.250

-> test with 'LR'
LR tn, fp: 179, 26
LR fn, tp: 4, 5
LR f1 score: 0.250
LR cohens kappa score: 0.198
LR average precision score: 0.177

-> 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: 185, 20
KNN fn, tp: 5, 4
KNN f1 score: 0.242
KNN cohens kappa score: 0.193


------ 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: 176, 27
GAN fn, tp: 4, 3
GAN f1 score: 0.162
GAN cohens kappa score: 0.114

-> test with 'LR'
LR tn, fp: 162, 41
LR fn, tp: 2, 5
LR f1 score: 0.189
LR cohens kappa score: 0.139
LR average precision score: 0.387

-> test with 'GB'
GB tn, fp: 198, 5
GB fn, tp: 4, 3
GB f1 score: 0.400
GB cohens kappa score: 0.378

-> test with 'KNN'
KNN tn, fp: 171, 32
KNN fn, tp: 3, 4
KNN f1 score: 0.186
KNN cohens kappa score: 0.138

### Exercise is done.

-----[ LR ]-----
maximum:
LR tn, fp: 185, 47
LR fn, tp: 6, 9
LR f1 score: 0.439
LR cohens kappa score: 0.400
LR average precision score: 0.759


average:
LR tn, fp: 172.12, 32.48
LR fn, tp: 2.32, 6.28
LR f1 score: 0.267
LR cohens kappa score: 0.216
LR average precision score: 0.353


minimum:
LR tn, fp: 158, 20
LR fn, tp: 0, 3
LR f1 score: 0.146
LR cohens kappa score: 0.086
LR average precision score: 0.096


-----[ GB ]-----
maximum:
GB tn, fp: 205, 7
GB fn, tp: 9, 3
GB f1 score: 0.500
GB cohens kappa score: 0.489


average:
GB tn, fp: 202.68, 1.92
GB fn, tp: 7.36, 1.24
GB f1 score: 0.205
GB cohens kappa score: 0.190


minimum:
GB tn, fp: 198, 0
GB fn, tp: 4, 0
GB f1 score: 0.000
GB cohens kappa score: -0.038


-----[ KNN ]-----
maximum:
KNN tn, fp: 192, 41
KNN fn, tp: 5, 7
KNN f1 score: 0.429
KNN cohens kappa score: 0.394


average:
KNN tn, fp: 176.96, 27.64
KNN fn, tp: 3.2, 5.4
KNN f1 score: 0.263
KNN cohens kappa score: 0.213


minimum:
KNN tn, fp: 164, 13
KNN fn, tp: 1, 4
KNN f1 score: 0.178
KNN cohens kappa score: 0.120


-----[ GAN ]-----
maximum:
GAN tn, fp: 198, 30
GAN fn, tp: 7, 8
GAN f1 score: 0.593
GAN cohens kappa score: 0.568


average:
GAN tn, fp: 183.52, 21.08
GAN fn, tp: 3.2, 5.4
GAN f1 score: 0.313
GAN cohens kappa score: 0.270


minimum:
GAN tn, fp: 175, 7
GAN fn, tp: 1, 2
GAN f1 score: 0.138
GAN cohens kappa score: 0.085