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

///////////////////////////////////////////
// Running convGAN-proximary-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: 177, 28
GAN fn, tp: 4, 5
GAN f1 score: 0.238
GAN cohens kappa score: 0.184

-> test with 'LR'
LR tn, fp: 174, 31
LR fn, tp: 7, 2
LR f1 score: 0.095
LR cohens kappa score: 0.031
LR average precision score: 0.076

-> 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: 4, 5
KNN f1 score: 0.256
KNN cohens kappa score: 0.205


------ 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: 184, 21
GAN fn, tp: 4, 5
GAN f1 score: 0.286
GAN cohens kappa score: 0.238

-> 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: 0.367

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


------ 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: 191, 14
GAN fn, tp: 6, 3
GAN f1 score: 0.231
GAN cohens kappa score: 0.186

-> 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: 0.524

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


------ 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: 195, 10
GAN fn, tp: 5, 4
GAN f1 score: 0.348
GAN cohens kappa score: 0.313

-> 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: 0.793

-> 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: 196, 9
KNN fn, tp: 5, 4
KNN f1 score: 0.364
KNN cohens kappa score: 0.330


------ 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: 183, 20
GAN fn, tp: 3, 4
GAN f1 score: 0.258
GAN cohens kappa score: 0.218

-> 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: 0.226

-> 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: 184, 19
KNN fn, tp: 3, 4
KNN f1 score: 0.267
KNN cohens kappa score: 0.227


====== 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: 184, 21
GAN fn, tp: 5, 4
GAN f1 score: 0.235
GAN cohens kappa score: 0.185

-> 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: 0.410

-> 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: 190, 15
KNN fn, tp: 4, 5
KNN f1 score: 0.345
KNN cohens kappa score: 0.304


------ 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: 182, 23
GAN fn, tp: 3, 6
GAN f1 score: 0.316
GAN cohens kappa score: 0.269

-> test with 'LR'
LR tn, fp: 171, 34
LR fn, tp: 4, 5
LR f1 score: 0.208
LR cohens kappa score: 0.150
LR average precision score: 0.367

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


------ 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: 200, 5
GAN fn, tp: 7, 2
GAN f1 score: 0.250
GAN cohens kappa score: 0.221

-> 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: 0.396

-> 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: 191, 14
KNN fn, tp: 5, 4
KNN f1 score: 0.296
KNN cohens kappa score: 0.254


------ 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: 194, 11
GAN fn, tp: 6, 3
GAN f1 score: 0.261
GAN cohens kappa score: 0.221

-> test with 'LR'
LR tn, fp: 190, 15
LR fn, tp: 4, 5
LR f1 score: 0.345
LR cohens kappa score: 0.304
LR average precision score: 0.295

-> 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: 191, 14
KNN fn, tp: 3, 6
KNN f1 score: 0.414
KNN cohens kappa score: 0.378


------ 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: 180, 23
GAN fn, tp: 1, 6
GAN f1 score: 0.333
GAN cohens kappa score: 0.295

-> test with 'LR'
LR tn, fp: 173, 30
LR fn, tp: 0, 7
LR f1 score: 0.318
LR cohens kappa score: 0.278
LR average precision score: 0.440

-> 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: 173, 30
KNN fn, tp: 1, 6
KNN f1 score: 0.279
KNN cohens kappa score: 0.236


====== 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: 184, 21
GAN fn, tp: 4, 5
GAN f1 score: 0.286
GAN cohens kappa score: 0.238

-> test with 'LR'
LR tn, fp: 187, 18
LR fn, tp: 1, 8
LR f1 score: 0.457
LR cohens kappa score: 0.421
LR average precision score: 0.816

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


------ 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: 172, 33
GAN fn, tp: 5, 4
GAN f1 score: 0.174
GAN cohens kappa score: 0.114

-> 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: 0.247

-> test with 'GB'
GB tn, fp: 198, 7
GB fn, tp: 5, 4
GB f1 score: 0.400
GB cohens kappa score: 0.371

-> test with 'KNN'
KNN tn, fp: 177, 28
KNN fn, tp: 3, 6
KNN f1 score: 0.279
KNN cohens kappa score: 0.228


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

-> test with 'LR'
LR tn, fp: 180, 25
LR fn, tp: 3, 6
LR f1 score: 0.300
LR cohens kappa score: 0.251
LR average precision score: 0.376

-> 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: 173, 32
KNN fn, tp: 2, 7
KNN f1 score: 0.292
KNN cohens kappa score: 0.240


------ 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: 190, 15
GAN fn, tp: 3, 6
GAN f1 score: 0.400
GAN cohens kappa score: 0.362

-> test with 'LR'
LR tn, fp: 190, 15
LR fn, tp: 5, 4
LR f1 score: 0.286
LR cohens kappa score: 0.242
LR average precision score: 0.202

-> 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: 193, 12
KNN fn, tp: 4, 5
KNN f1 score: 0.385
KNN cohens kappa score: 0.349


------ 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: 177, 26
GAN fn, tp: 5, 2
GAN f1 score: 0.114
GAN cohens kappa score: 0.064

-> test with 'LR'
LR tn, fp: 169, 34
LR fn, tp: 1, 6
LR f1 score: 0.255
LR cohens kappa score: 0.211
LR average precision score: 0.272

-> 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: 186, 17
KNN fn, tp: 6, 1
KNN f1 score: 0.080
KNN cohens kappa score: 0.034


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

-> test with 'LR'
LR tn, fp: 183, 22
LR fn, tp: 3, 6
LR f1 score: 0.324
LR cohens kappa score: 0.278
LR average precision score: 0.181

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


------ 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: 187, 18
GAN fn, tp: 6, 3
GAN f1 score: 0.200
GAN cohens kappa score: 0.150

-> test with 'LR'
LR tn, fp: 188, 17
LR fn, tp: 4, 5
LR f1 score: 0.323
LR cohens kappa score: 0.280
LR average precision score: 0.606

-> test with 'GB'
GB tn, fp: 202, 3
GB fn, tp: 7, 2
GB f1 score: 0.286
GB cohens kappa score: 0.264

-> test with 'KNN'
KNN tn, fp: 189, 16
KNN fn, tp: 6, 3
KNN f1 score: 0.214
KNN cohens kappa score: 0.167


------ 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: 180, 25
GAN fn, tp: 3, 6
GAN f1 score: 0.300
GAN cohens kappa score: 0.251

-> test with 'LR'
LR tn, fp: 176, 29
LR fn, tp: 4, 5
LR f1 score: 0.233
LR cohens kappa score: 0.178
LR average precision score: 0.268

-> 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: 4, 5
KNN f1 score: 0.270
KNN cohens kappa score: 0.221


------ 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: 191, 14
GAN fn, tp: 4, 5
GAN f1 score: 0.357
GAN cohens kappa score: 0.318

-> 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: 0.397

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


------ 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: 182, 21
GAN fn, tp: 3, 4
GAN f1 score: 0.250
GAN cohens kappa score: 0.209

-> test with 'LR'
LR tn, fp: 177, 26
LR fn, tp: 1, 6
LR f1 score: 0.308
LR cohens kappa score: 0.268
LR average precision score: 0.536

-> 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: 178, 25
KNN fn, tp: 3, 4
KNN f1 score: 0.222
KNN cohens kappa score: 0.178


====== 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: 191, 14
GAN fn, tp: 4, 5
GAN f1 score: 0.357
GAN cohens kappa score: 0.318

-> test with 'LR'
LR tn, fp: 189, 16
LR fn, tp: 6, 3
LR f1 score: 0.214
LR cohens kappa score: 0.167
LR average precision score: 0.253

-> 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: 192, 13
KNN fn, tp: 2, 7
KNN f1 score: 0.483
KNN cohens kappa score: 0.451


------ 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: 192, 13
GAN fn, tp: 7, 2
GAN f1 score: 0.167
GAN cohens kappa score: 0.120

-> test with 'LR'
LR tn, fp: 187, 18
LR fn, tp: 3, 6
LR f1 score: 0.364
LR cohens kappa score: 0.322
LR average precision score: 0.368

-> 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: 4, 5
KNN f1 score: 0.370
KNN cohens kappa score: 0.333


------ 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: 166, 39
GAN fn, tp: 4, 5
GAN f1 score: 0.189
GAN cohens kappa score: 0.128

-> test with 'LR'
LR tn, fp: 168, 37
LR fn, tp: 0, 9
LR f1 score: 0.327
LR cohens kappa score: 0.276
LR average precision score: 0.502

-> 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: 2, 7
KNN f1 score: 0.292
KNN cohens kappa score: 0.240


------ 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: 3, 6
GAN f1 score: 0.500
GAN cohens kappa score: 0.472

-> test with 'LR'
LR tn, fp: 196, 9
LR fn, tp: 5, 4
LR f1 score: 0.364
LR cohens kappa score: 0.330
LR average precision score: 0.212

-> 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: 198, 7
KNN fn, tp: 6, 3
KNN f1 score: 0.316
KNN cohens kappa score: 0.284


------ 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: 181, 22
GAN fn, tp: 5, 2
GAN f1 score: 0.129
GAN cohens kappa score: 0.082

-> test with 'LR'
LR tn, fp: 179, 24
LR fn, tp: 2, 5
LR f1 score: 0.278
LR cohens kappa score: 0.237
LR average precision score: 0.426

-> 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: 190, 13
KNN fn, tp: 4, 3
KNN f1 score: 0.261
KNN cohens kappa score: 0.225

### Exercise is done.

-----[ LR ]-----
maximum:
LR tn, fp: 196, 37
LR fn, tp: 7, 9
LR f1 score: 0.500
LR cohens kappa score: 0.466
LR average precision score: 0.816


average:
LR tn, fp: 180.08, 24.52
LR fn, tp: 2.68, 5.92
LR f1 score: 0.306
LR cohens kappa score: 0.261
LR average precision score: 0.382


minimum:
LR tn, fp: 168, 9
LR fn, tp: 0, 2
LR f1 score: 0.095
LR cohens kappa score: 0.031
LR average precision score: 0.076


-----[ GB ]-----
maximum:
GB tn, fp: 205, 7
GB fn, tp: 9, 4
GB f1 score: 0.400
GB cohens kappa score: 0.371


average:
GB tn, fp: 202.24, 2.36
GB fn, tp: 7.6, 1.0
GB f1 score: 0.149
GB cohens kappa score: 0.133


minimum:
GB tn, fp: 197, 0
GB fn, tp: 5, 0
GB f1 score: 0.000
GB cohens kappa score: -0.021


-----[ KNN ]-----
maximum:
KNN tn, fp: 198, 32
KNN fn, tp: 6, 7
KNN f1 score: 0.483
KNN cohens kappa score: 0.451


average:
KNN tn, fp: 186.0, 18.6
KNN fn, tp: 3.64, 4.96
KNN f1 score: 0.312
KNN cohens kappa score: 0.270


minimum:
KNN tn, fp: 173, 7
KNN fn, tp: 1, 1
KNN f1 score: 0.080
KNN cohens kappa score: 0.034


-----[ GAN ]-----
maximum:
GAN tn, fp: 200, 39
GAN fn, tp: 7, 6
GAN f1 score: 0.500
GAN cohens kappa score: 0.472


average:
GAN tn, fp: 184.6, 20.0
GAN fn, tp: 4.28, 4.32
GAN f1 score: 0.268
GAN cohens kappa score: 0.223


minimum:
GAN tn, fp: 166, 5
GAN fn, tp: 1, 2
GAN f1 score: 0.114
GAN cohens kappa score: 0.064