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: 204, 1
GAN fn, tp: 9, 0
GAN f1 score: 0.000
GAN cohens kappa score: -0.008

-> test with 'LR'
LR tn, fp: 180, 25
LR fn, tp: 8, 1
LR f1 score: 0.057
LR cohens kappa score: -0.006
LR average precision score: 0.081

-> 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: 185, 20
KNN fn, tp: 6, 3
KNN f1 score: 0.188
KNN cohens kappa score: 0.135


------ 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: 190, 15
GAN fn, tp: 6, 3
GAN f1 score: 0.222
GAN cohens kappa score: 0.176

-> test with 'LR'
LR tn, fp: 165, 40
LR fn, tp: 1, 8
LR f1 score: 0.281
LR cohens kappa score: 0.226
LR average precision score: 0.398

-> 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: 169, 36
KNN fn, tp: 2, 7
KNN f1 score: 0.269
KNN cohens kappa score: 0.215


------ 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: 201, 4
GAN fn, tp: 7, 2
GAN f1 score: 0.267
GAN cohens kappa score: 0.241

-> 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: 0.506

-> 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: 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: 202, 3
GAN fn, tp: 8, 1
GAN f1 score: 0.154
GAN cohens kappa score: 0.131

-> test with 'LR'
LR tn, fp: 190, 15
LR fn, tp: 0, 9
LR f1 score: 0.545
LR cohens kappa score: 0.516
LR average precision score: 0.796

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


------ 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: 192, 11
GAN fn, tp: 5, 2
GAN f1 score: 0.200
GAN cohens kappa score: 0.164

-> test with 'LR'
LR tn, fp: 181, 22
LR fn, tp: 3, 4
LR f1 score: 0.242
LR cohens kappa score: 0.200
LR average precision score: 0.236

-> test with 'GB'
GB tn, fp: 199, 4
GB fn, tp: 4, 3
GB f1 score: 0.429
GB cohens kappa score: 0.409

-> test with 'KNN'
KNN tn, fp: 182, 21
KNN fn, tp: 2, 5
KNN f1 score: 0.303
KNN cohens kappa score: 0.264


====== 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: 203, 2
GAN fn, tp: 6, 3
GAN f1 score: 0.429
GAN cohens kappa score: 0.411

-> 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: 0.405

-> 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: 189, 16
KNN fn, tp: 3, 6
KNN f1 score: 0.387
KNN cohens kappa score: 0.348


------ 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: 200, 5
GAN fn, tp: 8, 1
GAN f1 score: 0.133
GAN cohens kappa score: 0.103

-> 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: 0.372

-> 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: 4, 5
KNN f1 score: 0.294
KNN cohens kappa score: 0.248


------ 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: 179, 26
LR fn, tp: 3, 6
LR f1 score: 0.293
LR cohens kappa score: 0.243
LR average precision score: 0.389

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


------ 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: 198, 7
GAN fn, tp: 7, 2
GAN f1 score: 0.222
GAN cohens kappa score: 0.188

-> 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: 0.296

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


------ 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: 186, 17
GAN fn, tp: 4, 3
GAN f1 score: 0.222
GAN cohens kappa score: 0.182

-> test with 'LR'
LR tn, fp: 167, 36
LR fn, tp: 0, 7
LR f1 score: 0.280
LR cohens kappa score: 0.236
LR average precision score: 0.428

-> 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 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: 201, 4
GAN fn, tp: 6, 3
GAN f1 score: 0.375
GAN cohens kappa score: 0.351

-> test with 'LR'
LR tn, fp: 185, 20
LR fn, tp: 1, 8
LR f1 score: 0.432
LR cohens kappa score: 0.394
LR average precision score: 0.829

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


------ 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: 190, 15
GAN fn, tp: 6, 3
GAN f1 score: 0.222
GAN cohens kappa score: 0.176

-> test with 'LR'
LR tn, fp: 175, 30
LR fn, tp: 2, 7
LR f1 score: 0.304
LR cohens kappa score: 0.254
LR average precision score: 0.249

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

-> 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 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 784 synthetic samples
-> test with GAN.predict
GAN tn, fp: 196, 9
GAN fn, tp: 8, 1
GAN f1 score: 0.105
GAN cohens kappa score: 0.064

-> test with 'LR'
LR tn, fp: 179, 26
LR fn, tp: 3, 6
LR f1 score: 0.293
LR cohens kappa score: 0.243
LR average precision score: 0.367

-> 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: 175, 30
KNN fn, tp: 2, 7
KNN f1 score: 0.304
KNN cohens kappa score: 0.254


------ 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: 205, 0
GAN fn, tp: 8, 1
GAN f1 score: 0.200
GAN cohens kappa score: 0.193

-> 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: 0.231

-> 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: 192, 13
KNN fn, tp: 4, 5
KNN f1 score: 0.370
KNN cohens kappa score: 0.333


------ 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: 199, 4
GAN fn, tp: 6, 1
GAN f1 score: 0.167
GAN cohens kappa score: 0.143

-> test with 'LR'
LR tn, fp: 183, 20
LR fn, tp: 2, 5
LR f1 score: 0.312
LR cohens kappa score: 0.275
LR average precision score: 0.274

-> test with 'GB'
GB tn, fp: 198, 5
GB fn, tp: 6, 1
GB f1 score: 0.154
GB cohens kappa score: 0.127

-> test with 'KNN'
KNN tn, fp: 191, 12
KNN fn, tp: 6, 1
KNN f1 score: 0.100
KNN cohens kappa score: 0.059


====== 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: 7, 2
GAN f1 score: 0.190
GAN cohens kappa score: 0.150

-> test with 'LR'
LR tn, fp: 184, 21
LR fn, tp: 3, 6
LR f1 score: 0.333
LR cohens kappa score: 0.288
LR average precision score: 0.174

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


------ 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: 204, 1
GAN fn, tp: 8, 1
GAN f1 score: 0.182
GAN cohens kappa score: 0.169

-> 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: 0.592

-> 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: 189, 16
KNN fn, tp: 5, 4
KNN f1 score: 0.276
KNN cohens kappa score: 0.231


------ 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: 202, 3
GAN fn, tp: 6, 3
GAN f1 score: 0.400
GAN cohens kappa score: 0.379

-> test with 'LR'
LR tn, fp: 183, 22
LR fn, tp: 4, 5
LR f1 score: 0.278
LR cohens kappa score: 0.229
LR average precision score: 0.266

-> 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: 188, 17
KNN fn, tp: 5, 4
KNN f1 score: 0.267
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: 202, 3
GAN fn, tp: 7, 2
GAN f1 score: 0.286
GAN cohens kappa score: 0.264

-> test with 'LR'
LR tn, fp: 187, 18
LR fn, tp: 2, 7
LR f1 score: 0.412
LR cohens kappa score: 0.373
LR average precision score: 0.402

-> 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: 193, 12
KNN fn, tp: 6, 3
KNN f1 score: 0.250
KNN cohens kappa score: 0.208


------ 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: 199, 4
GAN fn, tp: 6, 1
GAN f1 score: 0.167
GAN cohens kappa score: 0.143

-> test with 'LR'
LR tn, fp: 183, 20
LR fn, tp: 1, 6
LR f1 score: 0.364
LR cohens kappa score: 0.328
LR average precision score: 0.490

-> 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: 187, 16
KNN fn, tp: 4, 3
KNN f1 score: 0.231
KNN cohens kappa score: 0.191


====== 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: 178, 27
LR fn, tp: 4, 5
LR f1 score: 0.244
LR cohens kappa score: 0.191
LR average precision score: 0.254

-> 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: 2, 7
KNN f1 score: 0.378
KNN cohens kappa score: 0.336


------ 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: 201, 4
GAN fn, tp: 9, 0
GAN f1 score: 0.000
GAN cohens kappa score: -0.027

-> test with 'LR'
LR tn, fp: 189, 16
LR fn, tp: 3, 6
LR f1 score: 0.387
LR cohens kappa score: 0.348
LR average precision score: 0.352

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


------ 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: 195, 10
GAN fn, tp: 6, 3
GAN f1 score: 0.273
GAN cohens kappa score: 0.235

-> test with 'LR'
LR tn, fp: 170, 35
LR fn, tp: 0, 9
LR f1 score: 0.340
LR cohens kappa score: 0.290
LR average precision score: 0.503

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


------ 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: 197, 8
GAN fn, tp: 6, 3
GAN f1 score: 0.300
GAN cohens kappa score: 0.266

-> test with 'LR'
LR tn, fp: 191, 14
LR fn, tp: 5, 4
LR f1 score: 0.296
LR cohens kappa score: 0.254
LR average precision score: 0.207

-> 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: 195, 10
KNN fn, tp: 6, 3
KNN f1 score: 0.273
KNN cohens kappa score: 0.235


------ 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: 190, 13
GAN fn, tp: 5, 2
GAN f1 score: 0.182
GAN cohens kappa score: 0.143

-> test with 'LR'
LR tn, fp: 168, 35
LR fn, tp: 2, 5
LR f1 score: 0.213
LR cohens kappa score: 0.165
LR average precision score: 0.440

-> 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: 173, 30
KNN fn, tp: 3, 4
KNN f1 score: 0.195
KNN cohens kappa score: 0.148

### Exercise is done.

-----[ LR ]-----
maximum:
LR tn, fp: 191, 40
LR fn, tp: 8, 9
LR f1 score: 0.545
LR cohens kappa score: 0.516
LR average precision score: 0.829


average:
LR tn, fp: 181.08, 23.52
LR fn, tp: 2.8, 5.8
LR f1 score: 0.309
LR cohens kappa score: 0.264
LR average precision score: 0.381


minimum:
LR tn, fp: 165, 14
LR fn, tp: 0, 1
LR f1 score: 0.057
LR cohens kappa score: -0.006
LR average precision score: 0.081


-----[ GB ]-----
maximum:
GB tn, fp: 205, 9
GB fn, tp: 9, 4
GB f1 score: 0.429
GB cohens kappa score: 0.409


average:
GB tn, fp: 202.08, 2.52
GB fn, tp: 7.56, 1.04
GB f1 score: 0.157
GB cohens kappa score: 0.140


minimum:
GB tn, fp: 196, 0
GB fn, tp: 4, 0
GB f1 score: 0.000
GB cohens kappa score: -0.021


-----[ KNN ]-----
maximum:
KNN tn, fp: 198, 36
KNN fn, tp: 6, 7
KNN f1 score: 0.429
KNN cohens kappa score: 0.394


average:
KNN tn, fp: 185.4, 19.2
KNN fn, tp: 3.76, 4.84
KNN f1 score: 0.296
KNN cohens kappa score: 0.253


minimum:
KNN tn, fp: 169, 7
KNN fn, tp: 1, 1
KNN f1 score: 0.100
KNN cohens kappa score: 0.059


-----[ GAN ]-----
maximum:
GAN tn, fp: 205, 17
GAN fn, tp: 9, 5
GAN f1 score: 0.429
GAN cohens kappa score: 0.411


average:
GAN tn, fp: 197.8, 6.8
GAN fn, tp: 6.6, 2.0
GAN f1 score: 0.221
GAN cohens kappa score: 0.192


minimum:
GAN tn, fp: 186, 0
GAN fn, tp: 4, 0
GAN f1 score: 0.000
GAN cohens kappa score: -0.027