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

///////////////////////////////////////////
// Running convGAN-proximary-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: 4, 5
GAN f1 score: 0.312
GAN cohens kappa score: 0.268

-> test with 'LR'
LR tn, fp: 172, 33
LR fn, tp: 6, 3
LR f1 score: 0.133
LR cohens kappa score: 0.071
LR average precision score: 0.088

-> 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: 176, 29
KNN fn, tp: 4, 5
KNN f1 score: 0.233
KNN cohens kappa score: 0.178


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

-> test with 'LR'
LR tn, fp: 157, 48
LR fn, tp: 1, 8
LR f1 score: 0.246
LR cohens kappa score: 0.187
LR average precision score: 0.405

-> 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: 169, 36
KNN fn, tp: 3, 6
KNN f1 score: 0.235
KNN cohens kappa score: 0.178


------ 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: 179, 26
GAN fn, tp: 5, 4
GAN f1 score: 0.205
GAN cohens kappa score: 0.150

-> 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: 0.277

-> 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: 5, 4
KNN f1 score: 0.195
KNN cohens kappa score: 0.139


------ 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: 180, 25
GAN fn, tp: 6, 3
GAN f1 score: 0.162
GAN cohens kappa score: 0.105

-> test with 'LR'
LR tn, fp: 181, 24
LR fn, tp: 0, 9
LR f1 score: 0.429
LR cohens kappa score: 0.388
LR average precision score: 0.680

-> 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: 180, 23
GAN fn, tp: 5, 2
GAN f1 score: 0.125
GAN cohens kappa score: 0.077

-> 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: 0.267

-> 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: 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: 141, 64
GAN fn, tp: 5, 4
GAN f1 score: 0.104
GAN cohens kappa score: 0.032

-> test with 'LR'
LR tn, fp: 167, 38
LR fn, tp: 1, 8
LR f1 score: 0.291
LR cohens kappa score: 0.237
LR average precision score: 0.385

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


------ 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: 146, 59
GAN fn, tp: 6, 3
GAN f1 score: 0.085
GAN cohens kappa score: 0.012

-> test with 'LR'
LR tn, fp: 169, 36
LR fn, tp: 3, 6
LR f1 score: 0.235
LR cohens kappa score: 0.178
LR average precision score: 0.374

-> 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: 176, 29
KNN fn, tp: 2, 7
KNN f1 score: 0.311
KNN cohens kappa score: 0.261


------ 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: 179, 26
GAN fn, tp: 8, 1
GAN f1 score: 0.056
GAN cohens kappa score: -0.008

-> 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: 0.261

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


------ 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: 150, 55
GAN fn, tp: 5, 4
GAN f1 score: 0.118
GAN cohens kappa score: 0.048

-> 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: 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 2/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 784 synthetic samples
-> test with GAN.predict
GAN tn, fp: 96, 107
GAN fn, tp: 3, 4
GAN f1 score: 0.068
GAN cohens kappa score: 0.005

-> 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: 0.380

-> 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: 112, 93
GAN fn, tp: 4, 5
GAN f1 score: 0.093
GAN cohens kappa score: 0.018

-> test with 'LR'
LR tn, fp: 186, 19
LR fn, tp: 2, 7
LR f1 score: 0.400
LR cohens kappa score: 0.360
LR average precision score: 0.737

-> 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: 196, 9
KNN fn, tp: 3, 6
KNN f1 score: 0.500
KNN cohens kappa score: 0.472


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

-> test with 'LR'
LR tn, fp: 167, 38
LR fn, tp: 2, 7
LR f1 score: 0.259
LR cohens kappa score: 0.203
LR average precision score: 0.189

-> test with 'GB'
GB tn, fp: 203, 2
GB fn, tp: 6, 3
GB f1 score: 0.429
GB cohens kappa score: 0.411

-> test with 'KNN'
KNN tn, fp: 164, 41
KNN fn, tp: 3, 6
KNN f1 score: 0.214
KNN cohens kappa score: 0.155


------ 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: 153, 52
GAN fn, tp: 6, 3
GAN f1 score: 0.094
GAN cohens kappa score: 0.023

-> test with 'LR'
LR tn, fp: 171, 34
LR fn, tp: 3, 6
LR f1 score: 0.245
LR cohens kappa score: 0.189
LR average precision score: 0.442

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


------ 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: 187, 18
GAN fn, tp: 7, 2
GAN f1 score: 0.138
GAN cohens kappa score: 0.085

-> 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: 0.370

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


------ 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: 183, 20
GAN fn, tp: 4, 3
GAN f1 score: 0.200
GAN cohens kappa score: 0.157

-> test with 'LR'
LR tn, fp: 164, 39
LR fn, tp: 1, 6
LR f1 score: 0.231
LR cohens kappa score: 0.184
LR average precision score: 0.257

-> test with 'GB'
GB tn, fp: 198, 5
GB fn, tp: 5, 2
GB f1 score: 0.286
GB cohens kappa score: 0.261

-> 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: 66, 139
GAN fn, tp: 4, 5
GAN f1 score: 0.065
GAN cohens kappa score: -0.015

-> test with 'LR'
LR tn, fp: 170, 35
LR fn, tp: 1, 8
LR f1 score: 0.308
LR cohens kappa score: 0.256
LR average precision score: 0.206

-> 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: 156, 49
GAN fn, tp: 7, 2
GAN f1 score: 0.067
GAN cohens kappa score: -0.005

-> 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: 0.545

-> 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: 183, 22
KNN fn, tp: 5, 4
KNN f1 score: 0.229
KNN cohens kappa score: 0.177


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

-> test with 'LR'
LR tn, fp: 173, 32
LR fn, tp: 3, 6
LR f1 score: 0.255
LR cohens kappa score: 0.201
LR average precision score: 0.243

-> 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: 171, 34
KNN fn, tp: 4, 5
KNN f1 score: 0.208
KNN cohens kappa score: 0.150


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

-> 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: 0.360

-> test with 'GB'
GB tn, fp: 203, 2
GB fn, tp: 6, 3
GB f1 score: 0.429
GB cohens kappa score: 0.411

-> test with 'KNN'
KNN tn, fp: 182, 23
KNN fn, tp: 2, 7
KNN f1 score: 0.359
KNN cohens kappa score: 0.315


------ 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: 1, 6
GAN f1 score: 0.316
GAN cohens kappa score: 0.276

-> test with 'LR'
LR tn, fp: 176, 27
LR fn, tp: 2, 5
LR f1 score: 0.256
LR cohens kappa score: 0.213
LR average precision score: 0.687

-> 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: 175, 28
KNN fn, tp: 3, 4
KNN f1 score: 0.205
KNN cohens kappa score: 0.159


====== 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: 112, 93
GAN fn, tp: 3, 6
GAN f1 score: 0.111
GAN cohens kappa score: 0.037

-> 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: 0.199

-> 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: 147, 58
GAN fn, tp: 3, 6
GAN f1 score: 0.164
GAN cohens kappa score: 0.098

-> 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: 0.485

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


------ 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: 130, 75
GAN fn, tp: 4, 5
GAN f1 score: 0.112
GAN cohens kappa score: 0.040

-> test with 'LR'
LR tn, fp: 174, 31
LR fn, tp: 0, 9
LR f1 score: 0.367
LR cohens kappa score: 0.321
LR average precision score: 0.489

-> 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: 176, 29
KNN fn, tp: 2, 7
KNN f1 score: 0.311
KNN cohens kappa score: 0.261


------ 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: 153, 52
GAN fn, tp: 5, 4
GAN f1 score: 0.123
GAN cohens kappa score: 0.055

-> 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: 0.236

-> 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 5/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 784 synthetic samples
-> test with GAN.predict
GAN tn, fp: 159, 44
GAN fn, tp: 5, 2
GAN f1 score: 0.075
GAN cohens kappa score: 0.019

-> test with 'LR'
LR tn, fp: 163, 40
LR fn, tp: 2, 5
LR f1 score: 0.192
LR cohens kappa score: 0.143
LR average precision score: 0.449

-> 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: 174, 29
KNN fn, tp: 4, 3
KNN f1 score: 0.154
KNN cohens kappa score: 0.105

### Exercise is done.

-----[ LR ]-----
maximum:
LR tn, fp: 186, 48
LR fn, tp: 6, 9
LR f1 score: 0.429
LR cohens kappa score: 0.388
LR average precision score: 0.737


average:
LR tn, fp: 172.8, 31.8
LR fn, tp: 2.28, 6.32
LR f1 score: 0.272
LR cohens kappa score: 0.221
LR average precision score: 0.373


minimum:
LR tn, fp: 157, 19
LR fn, tp: 0, 3
LR f1 score: 0.133
LR cohens kappa score: 0.071
LR average precision score: 0.088


-----[ GB ]-----
maximum:
GB tn, fp: 205, 5
GB fn, tp: 9, 3
GB f1 score: 0.429
GB cohens kappa score: 0.411


average:
GB tn, fp: 202.96, 1.64
GB fn, tp: 7.52, 1.08
GB f1 score: 0.179
GB cohens kappa score: 0.165


minimum:
GB tn, fp: 198, 0
GB fn, tp: 5, 0
GB f1 score: 0.000
GB cohens kappa score: -0.021


-----[ KNN ]-----
maximum:
KNN tn, fp: 196, 41
KNN fn, tp: 5, 7
KNN f1 score: 0.500
KNN cohens kappa score: 0.472


average:
KNN tn, fp: 177.84, 26.76
KNN fn, tp: 3.08, 5.52
KNN f1 score: 0.275
KNN cohens kappa score: 0.226


minimum:
KNN tn, fp: 164, 9
KNN fn, tp: 1, 3
KNN f1 score: 0.154
KNN cohens kappa score: 0.105


-----[ GAN ]-----
maximum:
GAN tn, fp: 190, 139
GAN fn, tp: 8, 6
GAN f1 score: 0.353
GAN cohens kappa score: 0.310


average:
GAN tn, fp: 157.04, 47.56
GAN fn, tp: 4.6, 4.0
GAN f1 score: 0.165
GAN cohens kappa score: 0.106


minimum:
GAN tn, fp: 66, 15
GAN fn, tp: 1, 1
GAN f1 score: 0.056
GAN cohens kappa score: -0.015