ConvGeNBenchmarkResults/data_result/convGAN-proximary-full/folding_car_good.log

///////////////////////////////////////////
// Running convGAN-proximary-full on folding_car_good
///////////////////////////////////////////

Load 'data_input/folding_car_good'
from pickle file
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 1272 synthetic samples
-> test with GAN.predict
GAN tn, fp: 291, 41
GAN fn, tp: 1, 13
GAN f1 score: 0.382
GAN cohens kappa score: 0.340

-> test with 'LR'
LR tn, fp: 177, 155
LR fn, tp: 5, 9
LR f1 score: 0.101
LR cohens kappa score: 0.029
LR average precision score: 0.067

-> test with 'GB'
GB tn, fp: 329, 3
GB fn, tp: 2, 12
GB f1 score: 0.828
GB cohens kappa score: 0.820

-> test with 'KNN'
KNN tn, fp: 292, 40
KNN fn, tp: 0, 14
KNN f1 score: 0.412
KNN cohens kappa score: 0.371


------ Step 1/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1272 synthetic samples
-> test with GAN.predict
GAN tn, fp: 294, 38
GAN fn, tp: 4, 10
GAN f1 score: 0.323
GAN cohens kappa score: 0.277

-> test with 'LR'
LR tn, fp: 194, 138
LR fn, tp: 3, 11
LR f1 score: 0.135
LR cohens kappa score: 0.066
LR average precision score: 0.088

-> test with 'GB'
GB tn, fp: 331, 1
GB fn, tp: 1, 13
GB f1 score: 0.929
GB cohens kappa score: 0.926

-> test with 'KNN'
KNN tn, fp: 315, 17
KNN fn, tp: 2, 12
KNN f1 score: 0.558
KNN cohens kappa score: 0.533


------ Step 1/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1272 synthetic samples
-> test with GAN.predict
GAN tn, fp: 306, 26
GAN fn, tp: 0, 14
GAN f1 score: 0.519
GAN cohens kappa score: 0.488

-> test with 'LR'
LR tn, fp: 181, 151
LR fn, tp: 5, 9
LR f1 score: 0.103
LR cohens kappa score: 0.031
LR average precision score: 0.059

-> test with 'GB'
GB tn, fp: 331, 1
GB fn, tp: 4, 10
GB f1 score: 0.800
GB cohens kappa score: 0.793

-> test with 'KNN'
KNN tn, fp: 300, 32
KNN fn, tp: 1, 13
KNN f1 score: 0.441
KNN cohens kappa score: 0.404


------ Step 1/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1272 synthetic samples
-> test with GAN.predict
GAN tn, fp: 302, 30
GAN fn, tp: 2, 12
GAN f1 score: 0.429
GAN cohens kappa score: 0.392

-> test with 'LR'
LR tn, fp: 198, 134
LR fn, tp: 5, 9
LR f1 score: 0.115
LR cohens kappa score: 0.044
LR average precision score: 0.080

-> test with 'GB'
GB tn, fp: 328, 4
GB fn, tp: 3, 11
GB f1 score: 0.759
GB cohens kappa score: 0.748

-> test with 'KNN'
KNN tn, fp: 304, 28
KNN fn, tp: 0, 14
KNN f1 score: 0.500
KNN cohens kappa score: 0.468


------ Step 1/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1272 synthetic samples
-> test with GAN.predict
GAN tn, fp: 298, 33
GAN fn, tp: 4, 9
GAN f1 score: 0.327
GAN cohens kappa score: 0.286

-> test with 'LR'
LR tn, fp: 184, 147
LR fn, tp: 5, 8
LR f1 score: 0.095
LR cohens kappa score: 0.027
LR average precision score: 0.053

-> test with 'GB'
GB tn, fp: 328, 3
GB fn, tp: 2, 11
GB f1 score: 0.815
GB cohens kappa score: 0.807

-> test with 'KNN'
KNN tn, fp: 313, 18
KNN fn, tp: 1, 12
KNN f1 score: 0.558
KNN cohens kappa score: 0.534


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

------ Step 2/5: Slice 1/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1272 synthetic samples
-> test with GAN.predict
GAN tn, fp: 303, 29
GAN fn, tp: 6, 8
GAN f1 score: 0.314
GAN cohens kappa score: 0.271

-> test with 'LR'
LR tn, fp: 166, 166
LR fn, tp: 4, 10
LR f1 score: 0.105
LR cohens kappa score: 0.033
LR average precision score: 0.069

-> test with 'GB'
GB tn, fp: 330, 2
GB fn, tp: 3, 11
GB f1 score: 0.815
GB cohens kappa score: 0.807

-> test with 'KNN'
KNN tn, fp: 312, 20
KNN fn, tp: 1, 13
KNN f1 score: 0.553
KNN cohens kappa score: 0.526


------ Step 2/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1272 synthetic samples
-> test with GAN.predict
GAN tn, fp: 294, 38
GAN fn, tp: 0, 14
GAN f1 score: 0.424
GAN cohens kappa score: 0.385

-> test with 'LR'
LR tn, fp: 178, 154
LR fn, tp: 4, 10
LR f1 score: 0.112
LR cohens kappa score: 0.041
LR average precision score: 0.072

-> test with 'GB'
GB tn, fp: 330, 2
GB fn, tp: 1, 13
GB f1 score: 0.897
GB cohens kappa score: 0.892

-> test with 'KNN'
KNN tn, fp: 308, 24
KNN fn, tp: 1, 13
KNN f1 score: 0.510
KNN cohens kappa score: 0.479


------ Step 2/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1272 synthetic samples
-> test with GAN.predict
GAN tn, fp: 298, 34
GAN fn, tp: 2, 12
GAN f1 score: 0.400
GAN cohens kappa score: 0.360

-> test with 'LR'
LR tn, fp: 198, 134
LR fn, tp: 5, 9
LR f1 score: 0.115
LR cohens kappa score: 0.044
LR average precision score: 0.074

-> test with 'GB'
GB tn, fp: 332, 0
GB fn, tp: 4, 10
GB f1 score: 0.833
GB cohens kappa score: 0.828

-> test with 'KNN'
KNN tn, fp: 316, 16
KNN fn, tp: 3, 11
KNN f1 score: 0.537
KNN cohens kappa score: 0.510


------ Step 2/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1272 synthetic samples
-> test with GAN.predict
GAN tn, fp: 287, 45
GAN fn, tp: 2, 12
GAN f1 score: 0.338
GAN cohens kappa score: 0.292

-> test with 'LR'
LR tn, fp: 189, 143
LR fn, tp: 8, 6
LR f1 score: 0.074
LR cohens kappa score: -0.000
LR average precision score: 0.049

-> test with 'GB'
GB tn, fp: 331, 1
GB fn, tp: 4, 10
GB f1 score: 0.800
GB cohens kappa score: 0.793

-> test with 'KNN'
KNN tn, fp: 289, 43
KNN fn, tp: 1, 13
KNN f1 score: 0.371
KNN cohens kappa score: 0.328


------ Step 2/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1272 synthetic samples
-> test with GAN.predict
GAN tn, fp: 311, 20
GAN fn, tp: 3, 10
GAN f1 score: 0.465
GAN cohens kappa score: 0.435

-> test with 'LR'
LR tn, fp: 193, 138
LR fn, tp: 6, 7
LR f1 score: 0.089
LR cohens kappa score: 0.021
LR average precision score: 0.077

-> test with 'GB'
GB tn, fp: 328, 3
GB fn, tp: 1, 12
GB f1 score: 0.857
GB cohens kappa score: 0.851

-> test with 'KNN'
KNN tn, fp: 310, 21
KNN fn, tp: 0, 13
KNN f1 score: 0.553
KNN cohens kappa score: 0.527


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

------ Step 3/5: Slice 1/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1272 synthetic samples
-> test with GAN.predict
GAN tn, fp: 304, 28
GAN fn, tp: 3, 11
GAN f1 score: 0.415
GAN cohens kappa score: 0.378

-> test with 'LR'
LR tn, fp: 174, 158
LR fn, tp: 2, 12
LR f1 score: 0.130
LR cohens kappa score: 0.060
LR average precision score: 0.078

-> test with 'GB'
GB tn, fp: 331, 1
GB fn, tp: 3, 11
GB f1 score: 0.846
GB cohens kappa score: 0.840

-> test with 'KNN'
KNN tn, fp: 313, 19
KNN fn, tp: 1, 13
KNN f1 score: 0.565
KNN cohens kappa score: 0.539


------ Step 3/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1272 synthetic samples
-> test with GAN.predict
GAN tn, fp: 306, 26
GAN fn, tp: 0, 14
GAN f1 score: 0.519
GAN cohens kappa score: 0.488

-> test with 'LR'
LR tn, fp: 198, 134
LR fn, tp: 5, 9
LR f1 score: 0.115
LR cohens kappa score: 0.044
LR average precision score: 0.069

-> test with 'GB'
GB tn, fp: 329, 3
GB fn, tp: 0, 14
GB f1 score: 0.903
GB cohens kappa score: 0.899

-> test with 'KNN'
KNN tn, fp: 308, 24
KNN fn, tp: 2, 12
KNN f1 score: 0.480
KNN cohens kappa score: 0.448


------ Step 3/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1272 synthetic samples
-> test with GAN.predict
GAN tn, fp: 309, 23
GAN fn, tp: 3, 11
GAN f1 score: 0.458
GAN cohens kappa score: 0.425

-> test with 'LR'
LR tn, fp: 188, 144
LR fn, tp: 6, 8
LR f1 score: 0.096
LR cohens kappa score: 0.024
LR average precision score: 0.058

-> test with 'GB'
GB tn, fp: 330, 2
GB fn, tp: 7, 7
GB f1 score: 0.609
GB cohens kappa score: 0.596

-> test with 'KNN'
KNN tn, fp: 310, 22
KNN fn, tp: 3, 11
KNN f1 score: 0.468
KNN cohens kappa score: 0.436


------ Step 3/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1272 synthetic samples
-> test with GAN.predict
GAN tn, fp: 301, 31
GAN fn, tp: 2, 12
GAN f1 score: 0.421
GAN cohens kappa score: 0.383

-> test with 'LR'
LR tn, fp: 181, 151
LR fn, tp: 2, 12
LR f1 score: 0.136
LR cohens kappa score: 0.066
LR average precision score: 0.083

-> test with 'GB'
GB tn, fp: 332, 0
GB fn, tp: 2, 12
GB f1 score: 0.923
GB cohens kappa score: 0.920

-> test with 'KNN'
KNN tn, fp: 301, 31
KNN fn, tp: 1, 13
KNN f1 score: 0.448
KNN cohens kappa score: 0.412


------ Step 3/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1272 synthetic samples
-> test with GAN.predict
GAN tn, fp: 301, 30
GAN fn, tp: 1, 12
GAN f1 score: 0.436
GAN cohens kappa score: 0.402

-> test with 'LR'
LR tn, fp: 180, 151
LR fn, tp: 5, 8
LR f1 score: 0.093
LR cohens kappa score: 0.025
LR average precision score: 0.059

-> test with 'GB'
GB tn, fp: 329, 2
GB fn, tp: 1, 12
GB f1 score: 0.889
GB cohens kappa score: 0.884

-> test with 'KNN'
KNN tn, fp: 294, 37
KNN fn, tp: 0, 13
KNN f1 score: 0.413
KNN cohens kappa score: 0.375


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

------ Step 4/5: Slice 1/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1272 synthetic samples
-> test with GAN.predict
GAN tn, fp: 305, 27
GAN fn, tp: 2, 12
GAN f1 score: 0.453
GAN cohens kappa score: 0.418

-> test with 'LR'
LR tn, fp: 183, 149
LR fn, tp: 4, 10
LR f1 score: 0.116
LR cohens kappa score: 0.045
LR average precision score: 0.069

-> test with 'GB'
GB tn, fp: 332, 0
GB fn, tp: 0, 14
GB f1 score: 1.000
GB cohens kappa score: 1.000

-> test with 'KNN'
KNN tn, fp: 326, 6
KNN fn, tp: 2, 12
KNN f1 score: 0.750
KNN cohens kappa score: 0.738


------ Step 4/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1272 synthetic samples
-> test with GAN.predict
GAN tn, fp: 303, 29
GAN fn, tp: 3, 11
GAN f1 score: 0.407
GAN cohens kappa score: 0.370

-> test with 'LR'
LR tn, fp: 182, 150
LR fn, tp: 5, 9
LR f1 score: 0.104
LR cohens kappa score: 0.032
LR average precision score: 0.067

-> test with 'GB'
GB tn, fp: 332, 0
GB fn, tp: 2, 12
GB f1 score: 0.923
GB cohens kappa score: 0.920

-> test with 'KNN'
KNN tn, fp: 306, 26
KNN fn, tp: 1, 13
KNN f1 score: 0.491
KNN cohens kappa score: 0.458


------ Step 4/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1272 synthetic samples
-> test with GAN.predict
GAN tn, fp: 294, 38
GAN fn, tp: 1, 13
GAN f1 score: 0.400
GAN cohens kappa score: 0.359

-> test with 'LR'
LR tn, fp: 176, 156
LR fn, tp: 4, 10
LR f1 score: 0.111
LR cohens kappa score: 0.039
LR average precision score: 0.067

-> test with 'GB'
GB tn, fp: 331, 1
GB fn, tp: 2, 12
GB f1 score: 0.889
GB cohens kappa score: 0.884

-> test with 'KNN'
KNN tn, fp: 300, 32
KNN fn, tp: 0, 14
KNN f1 score: 0.467
KNN cohens kappa score: 0.431


------ Step 4/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1272 synthetic samples
-> test with GAN.predict
GAN tn, fp: 297, 35
GAN fn, tp: 1, 13
GAN f1 score: 0.419
GAN cohens kappa score: 0.381

-> test with 'LR'
LR tn, fp: 200, 132
LR fn, tp: 6, 8
LR f1 score: 0.104
LR cohens kappa score: 0.033
LR average precision score: 0.057

-> test with 'GB'
GB tn, fp: 331, 1
GB fn, tp: 2, 12
GB f1 score: 0.889
GB cohens kappa score: 0.884

-> test with 'KNN'
KNN tn, fp: 308, 24
KNN fn, tp: 1, 13
KNN f1 score: 0.510
KNN cohens kappa score: 0.479


------ Step 4/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1272 synthetic samples
-> test with GAN.predict
GAN tn, fp: 293, 38
GAN fn, tp: 2, 11
GAN f1 score: 0.355
GAN cohens kappa score: 0.314

-> test with 'LR'
LR tn, fp: 176, 155
LR fn, tp: 1, 12
LR f1 score: 0.133
LR cohens kappa score: 0.068
LR average precision score: 0.077

-> test with 'GB'
GB tn, fp: 328, 3
GB fn, tp: 6, 7
GB f1 score: 0.609
GB cohens kappa score: 0.595

-> test with 'KNN'
KNN tn, fp: 301, 30
KNN fn, tp: 1, 12
KNN f1 score: 0.436
KNN cohens kappa score: 0.402


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

------ Step 5/5: Slice 1/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1272 synthetic samples
-> test with GAN.predict
GAN tn, fp: 293, 39
GAN fn, tp: 2, 12
GAN f1 score: 0.369
GAN cohens kappa score: 0.326

-> test with 'LR'
LR tn, fp: 182, 150
LR fn, tp: 8, 6
LR f1 score: 0.071
LR cohens kappa score: -0.004
LR average precision score: 0.055

-> test with 'GB'
GB tn, fp: 331, 1
GB fn, tp: 6, 8
GB f1 score: 0.696
GB cohens kappa score: 0.686

-> test with 'KNN'
KNN tn, fp: 305, 27
KNN fn, tp: 1, 13
KNN f1 score: 0.481
KNN cohens kappa score: 0.448


------ Step 5/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1272 synthetic samples
-> test with GAN.predict
GAN tn, fp: 313, 19
GAN fn, tp: 3, 11
GAN f1 score: 0.500
GAN cohens kappa score: 0.471

-> test with 'LR'
LR tn, fp: 196, 136
LR fn, tp: 6, 8
LR f1 score: 0.101
LR cohens kappa score: 0.030
LR average precision score: 0.081

-> test with 'GB'
GB tn, fp: 331, 1
GB fn, tp: 1, 13
GB f1 score: 0.929
GB cohens kappa score: 0.926

-> test with 'KNN'
KNN tn, fp: 311, 21
KNN fn, tp: 0, 14
KNN f1 score: 0.571
KNN cohens kappa score: 0.545


------ Step 5/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1272 synthetic samples
-> test with GAN.predict
GAN tn, fp: 306, 26
GAN fn, tp: 2, 12
GAN f1 score: 0.462
GAN cohens kappa score: 0.428

-> test with 'LR'
LR tn, fp: 168, 164
LR fn, tp: 4, 10
LR f1 score: 0.106
LR cohens kappa score: 0.034
LR average precision score: 0.085

-> test with 'GB'
GB tn, fp: 329, 3
GB fn, tp: 1, 13
GB f1 score: 0.867
GB cohens kappa score: 0.861

-> test with 'KNN'
KNN tn, fp: 300, 32
KNN fn, tp: 0, 14
KNN f1 score: 0.467
KNN cohens kappa score: 0.431


------ Step 5/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1272 synthetic samples
-> test with GAN.predict
GAN tn, fp: 309, 23
GAN fn, tp: 3, 11
GAN f1 score: 0.458
GAN cohens kappa score: 0.425

-> test with 'LR'
LR tn, fp: 180, 152
LR fn, tp: 4, 10
LR f1 score: 0.114
LR cohens kappa score: 0.042
LR average precision score: 0.081

-> test with 'GB'
GB tn, fp: 330, 2
GB fn, tp: 2, 12
GB f1 score: 0.857
GB cohens kappa score: 0.851

-> test with 'KNN'
KNN tn, fp: 301, 31
KNN fn, tp: 0, 14
KNN f1 score: 0.475
KNN cohens kappa score: 0.440


------ Step 5/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1272 synthetic samples
-> test with GAN.predict
GAN tn, fp: 301, 30
GAN fn, tp: 3, 10
GAN f1 score: 0.377
GAN cohens kappa score: 0.340

-> test with 'LR'
LR tn, fp: 184, 147
LR fn, tp: 4, 9
LR f1 score: 0.107
LR cohens kappa score: 0.039
LR average precision score: 0.067

-> test with 'GB'
GB tn, fp: 330, 1
GB fn, tp: 0, 13
GB f1 score: 0.963
GB cohens kappa score: 0.961

-> test with 'KNN'
KNN tn, fp: 287, 44
KNN fn, tp: 0, 13
KNN f1 score: 0.371
KNN cohens kappa score: 0.330

### Exercise is done.

-----[ LR ]-----
maximum:
LR tn, fp: 200, 166
LR fn, tp: 8, 12
LR f1 score: 0.136
LR cohens kappa score: 0.068
LR average precision score: 0.088


average:
LR tn, fp: 184.24, 147.56
LR fn, tp: 4.64, 9.16
LR f1 score: 0.107
LR cohens kappa score: 0.037
LR average precision score: 0.070


minimum:
LR tn, fp: 166, 132
LR fn, tp: 1, 6
LR f1 score: 0.071
LR cohens kappa score: -0.004
LR average precision score: 0.049


-----[ GB ]-----
maximum:
GB tn, fp: 332, 4
GB fn, tp: 7, 14
GB f1 score: 1.000
GB cohens kappa score: 1.000


average:
GB tn, fp: 330.16, 1.64
GB fn, tp: 2.4, 11.4
GB f1 score: 0.845
GB cohens kappa score: 0.839


minimum:
GB tn, fp: 328, 0
GB fn, tp: 0, 7
GB f1 score: 0.609
GB cohens kappa score: 0.595


-----[ KNN ]-----
maximum:
KNN tn, fp: 326, 44
KNN fn, tp: 3, 14
KNN f1 score: 0.750
KNN cohens kappa score: 0.738


average:
KNN tn, fp: 305.2, 26.6
KNN fn, tp: 0.92, 12.88
KNN f1 score: 0.495
KNN cohens kappa score: 0.464


minimum:
KNN tn, fp: 287, 6
KNN fn, tp: 0, 11
KNN f1 score: 0.371
KNN cohens kappa score: 0.328


-----[ GAN ]-----
maximum:
GAN tn, fp: 313, 45
GAN fn, tp: 6, 14
GAN f1 score: 0.519
GAN cohens kappa score: 0.488


average:
GAN tn, fp: 300.76, 31.04
GAN fn, tp: 2.2, 11.6
GAN f1 score: 0.415
GAN cohens kappa score: 0.377


minimum:
GAN tn, fp: 287, 19
GAN fn, tp: 0, 8
GAN f1 score: 0.314
GAN cohens kappa score: 0.271