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

///////////////////////////////////////////
// Running convGAN-majority-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: 328, 4
GAN fn, tp: 2, 12
GAN f1 score: 0.800
GAN cohens kappa score: 0.791

-> test with 'LR'
LR tn, fp: 179, 153
LR fn, tp: 5, 9
LR f1 score: 0.102
LR cohens kappa score: 0.030
LR average precision score: 0.070

-> 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: 297, 35
KNN fn, tp: 0, 14
KNN f1 score: 0.444
KNN cohens kappa score: 0.407


------ 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: 327, 5
GAN fn, tp: 4, 10
GAN f1 score: 0.690
GAN cohens kappa score: 0.676

-> test with 'LR'
LR tn, fp: 190, 142
LR fn, tp: 1, 13
LR f1 score: 0.154
LR cohens kappa score: 0.086
LR average precision score: 0.090

-> test with 'GB'
GB tn, fp: 330, 2
GB fn, tp: 5, 9
GB f1 score: 0.720
GB cohens kappa score: 0.710

-> test with 'KNN'
KNN tn, fp: 291, 41
KNN fn, tp: 0, 14
KNN f1 score: 0.406
KNN cohens kappa score: 0.365


------ 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: 325, 7
GAN fn, tp: 3, 11
GAN f1 score: 0.688
GAN cohens kappa score: 0.673

-> test with 'LR'
LR tn, fp: 186, 146
LR fn, tp: 5, 9
LR f1 score: 0.107
LR cohens kappa score: 0.035
LR average precision score: 0.061

-> 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: 307, 25
KNN fn, tp: 0, 14
KNN f1 score: 0.528
KNN cohens kappa score: 0.498


------ 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: 324, 8
GAN fn, tp: 5, 9
GAN f1 score: 0.581
GAN cohens kappa score: 0.561

-> test with 'LR'
LR tn, fp: 188, 144
LR fn, tp: 5, 9
LR f1 score: 0.108
LR cohens kappa score: 0.036
LR average precision score: 0.077

-> 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: 294, 38
KNN fn, tp: 0, 14
KNN f1 score: 0.424
KNN cohens kappa score: 0.385


------ 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: 326, 5
GAN fn, tp: 4, 9
GAN f1 score: 0.667
GAN cohens kappa score: 0.653

-> test with 'LR'
LR tn, fp: 179, 152
LR fn, tp: 4, 9
LR f1 score: 0.103
LR cohens kappa score: 0.036
LR average precision score: 0.055

-> test with 'GB'
GB tn, fp: 326, 5
GB fn, tp: 1, 12
GB f1 score: 0.800
GB cohens kappa score: 0.791

-> test with 'KNN'
KNN tn, fp: 298, 33
KNN fn, tp: 1, 12
KNN f1 score: 0.414
KNN cohens kappa score: 0.377


====== 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: 325, 7
GAN fn, tp: 3, 11
GAN f1 score: 0.688
GAN cohens kappa score: 0.673

-> 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: 0.072

-> 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: 310, 22
KNN fn, tp: 1, 13
KNN f1 score: 0.531
KNN cohens kappa score: 0.502


------ 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: 319, 13
GAN fn, tp: 1, 13
GAN f1 score: 0.650
GAN cohens kappa score: 0.631

-> test with 'LR'
LR tn, fp: 177, 155
LR fn, tp: 4, 10
LR f1 score: 0.112
LR cohens kappa score: 0.040
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: 300, 32
KNN fn, tp: 0, 14
KNN f1 score: 0.467
KNN cohens kappa score: 0.431


------ 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: 322, 10
GAN fn, tp: 5, 9
GAN f1 score: 0.545
GAN cohens kappa score: 0.523

-> test with 'LR'
LR tn, fp: 190, 142
LR fn, tp: 5, 9
LR f1 score: 0.109
LR cohens kappa score: 0.038
LR average precision score: 0.070

-> test with 'GB'
GB tn, fp: 332, 0
GB fn, tp: 7, 7
GB f1 score: 0.667
GB cohens kappa score: 0.657

-> test with 'KNN'
KNN tn, fp: 294, 38
KNN fn, tp: 1, 13
KNN f1 score: 0.400
KNN cohens kappa score: 0.359


------ 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: 323, 9
GAN fn, tp: 3, 11
GAN f1 score: 0.647
GAN cohens kappa score: 0.629

-> test with 'LR'
LR tn, fp: 188, 144
LR fn, tp: 8, 6
LR f1 score: 0.073
LR cohens kappa score: -0.001
LR average precision score: 0.049

-> 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: 280, 52
KNN fn, tp: 0, 14
KNN f1 score: 0.350
KNN cohens kappa score: 0.303


------ 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: 326, 5
GAN fn, tp: 7, 6
GAN f1 score: 0.500
GAN cohens kappa score: 0.482

-> test with 'LR'
LR tn, fp: 187, 144
LR fn, tp: 5, 8
LR f1 score: 0.097
LR cohens kappa score: 0.029
LR average precision score: 0.079

-> 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: 301, 30
KNN fn, tp: 0, 13
KNN f1 score: 0.464
KNN cohens kappa score: 0.431


====== 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: 326, 6
GAN fn, tp: 5, 9
GAN f1 score: 0.621
GAN cohens kappa score: 0.604

-> 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: 0.079

-> 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: 298, 34
KNN fn, tp: 0, 14
KNN f1 score: 0.452
KNN cohens kappa score: 0.415


------ 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: 323, 9
GAN fn, tp: 2, 12
GAN f1 score: 0.686
GAN cohens kappa score: 0.670

-> test with 'LR'
LR tn, fp: 197, 135
LR fn, tp: 4, 10
LR f1 score: 0.126
LR cohens kappa score: 0.056
LR average precision score: 0.070

-> test with 'GB'
GB tn, fp: 328, 4
GB fn, tp: 1, 13
GB f1 score: 0.839
GB cohens kappa score: 0.831

-> test with 'KNN'
KNN tn, fp: 297, 35
KNN fn, tp: 0, 14
KNN f1 score: 0.444
KNN cohens kappa score: 0.407


------ 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: 328, 4
GAN fn, tp: 8, 6
GAN f1 score: 0.500
GAN cohens kappa score: 0.483

-> test with 'LR'
LR tn, fp: 192, 140
LR fn, tp: 6, 8
LR f1 score: 0.099
LR cohens kappa score: 0.027
LR average precision score: 0.058

-> 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: 313, 19
KNN fn, tp: 2, 12
KNN f1 score: 0.533
KNN cohens kappa score: 0.506


------ 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: 326, 6
GAN fn, tp: 5, 9
GAN f1 score: 0.621
GAN cohens kappa score: 0.604

-> test with 'LR'
LR tn, fp: 179, 153
LR fn, tp: 2, 12
LR f1 score: 0.134
LR cohens kappa score: 0.064
LR average precision score: 0.085

-> 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: 304, 28
KNN fn, tp: 0, 14
KNN f1 score: 0.500
KNN cohens kappa score: 0.468


------ 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: 317, 14
GAN fn, tp: 3, 10
GAN f1 score: 0.541
GAN cohens kappa score: 0.517

-> test with 'LR'
LR tn, fp: 177, 154
LR fn, tp: 5, 8
LR f1 score: 0.091
LR cohens kappa score: 0.023
LR average precision score: 0.057

-> 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: 292, 39
KNN fn, tp: 0, 13
KNN f1 score: 0.400
KNN cohens kappa score: 0.361


====== 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: 324, 8
GAN fn, tp: 5, 9
GAN f1 score: 0.581
GAN cohens kappa score: 0.561

-> test with 'LR'
LR tn, fp: 181, 151
LR fn, tp: 4, 10
LR f1 score: 0.114
LR cohens kappa score: 0.043
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: 309, 23
KNN fn, tp: 0, 14
KNN f1 score: 0.549
KNN cohens kappa score: 0.521


------ 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: 323, 9
GAN fn, tp: 1, 13
GAN f1 score: 0.722
GAN cohens kappa score: 0.708

-> 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: 0.067

-> 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: 290, 42
KNN fn, tp: 0, 14
KNN f1 score: 0.400
KNN cohens kappa score: 0.358


------ 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: 321, 11
GAN fn, tp: 4, 10
GAN f1 score: 0.571
GAN cohens kappa score: 0.550

-> test with 'LR'
LR tn, fp: 172, 160
LR fn, tp: 3, 11
LR f1 score: 0.119
LR cohens kappa score: 0.048
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: 292, 40
KNN fn, tp: 0, 14
KNN f1 score: 0.412
KNN cohens kappa score: 0.371


------ 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: 324, 8
GAN fn, tp: 5, 9
GAN f1 score: 0.581
GAN cohens kappa score: 0.561

-> test with 'LR'
LR tn, fp: 201, 131
LR fn, tp: 6, 8
LR f1 score: 0.105
LR cohens kappa score: 0.034
LR average precision score: 0.057

-> 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: 1, 13
KNN f1 score: 0.542
KNN cohens kappa score: 0.514


------ 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: 326, 5
GAN fn, tp: 2, 11
GAN f1 score: 0.759
GAN cohens kappa score: 0.748

-> test with 'LR'
LR tn, fp: 181, 150
LR fn, tp: 1, 12
LR f1 score: 0.137
LR cohens kappa score: 0.072
LR average precision score: 0.077

-> test with 'GB'
GB tn, fp: 327, 4
GB fn, tp: 5, 8
GB f1 score: 0.640
GB cohens kappa score: 0.626

-> test with 'KNN'
KNN tn, fp: 300, 31
KNN fn, tp: 1, 12
KNN f1 score: 0.429
KNN cohens kappa score: 0.393


====== 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: 324, 8
GAN fn, tp: 2, 12
GAN f1 score: 0.706
GAN cohens kappa score: 0.691

-> test with 'LR'
LR tn, fp: 179, 153
LR fn, tp: 8, 6
LR f1 score: 0.069
LR cohens kappa score: -0.005
LR average precision score: 0.054

-> 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: 294, 38
KNN fn, tp: 0, 14
KNN f1 score: 0.424
KNN cohens kappa score: 0.385


------ 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: 328, 4
GAN fn, tp: 4, 10
GAN f1 score: 0.714
GAN cohens kappa score: 0.702

-> test with 'LR'
LR tn, fp: 192, 140
LR fn, tp: 6, 8
LR f1 score: 0.099
LR cohens kappa score: 0.027
LR average precision score: 0.076

-> 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: 300, 32
KNN fn, tp: 0, 14
KNN f1 score: 0.467
KNN cohens kappa score: 0.431


------ 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: 323, 9
GAN fn, tp: 2, 12
GAN f1 score: 0.686
GAN cohens kappa score: 0.670

-> test with 'LR'
LR tn, fp: 167, 165
LR fn, tp: 4, 10
LR f1 score: 0.106
LR cohens kappa score: 0.033
LR average precision score: 0.083

-> test with 'GB'
GB tn, fp: 326, 6
GB fn, tp: 1, 13
GB f1 score: 0.788
GB cohens kappa score: 0.778

-> test with 'KNN'
KNN tn, fp: 290, 42
KNN fn, tp: 0, 14
KNN f1 score: 0.400
KNN cohens kappa score: 0.358


------ 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: 325, 7
GAN fn, tp: 6, 8
GAN f1 score: 0.552
GAN cohens kappa score: 0.532

-> test with 'LR'
LR tn, fp: 184, 148
LR fn, tp: 4, 10
LR f1 score: 0.116
LR cohens kappa score: 0.045
LR average precision score: 0.083

-> test with 'GB'
GB tn, fp: 332, 0
GB fn, tp: 5, 9
GB f1 score: 0.783
GB cohens kappa score: 0.775

-> test with 'KNN'
KNN tn, fp: 316, 16
KNN fn, tp: 1, 13
KNN f1 score: 0.605
KNN cohens kappa score: 0.582


------ 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: 324, 7
GAN fn, tp: 1, 12
GAN f1 score: 0.750
GAN cohens kappa score: 0.738

-> test with 'LR'
LR tn, fp: 185, 146
LR fn, tp: 4, 9
LR f1 score: 0.107
LR cohens kappa score: 0.040
LR average precision score: 0.066

-> test with 'GB'
GB tn, fp: 331, 0
GB fn, tp: 3, 10
GB f1 score: 0.870
GB cohens kappa score: 0.865

-> test with 'KNN'
KNN tn, fp: 305, 26
KNN fn, tp: 0, 13
KNN f1 score: 0.500
KNN cohens kappa score: 0.470

### Exercise is done.

-----[ LR ]-----
maximum:
LR tn, fp: 201, 166
LR fn, tp: 8, 13
LR f1 score: 0.154
LR cohens kappa score: 0.086
LR average precision score: 0.090


average:
LR tn, fp: 182.88, 148.92
LR fn, tp: 4.4, 9.4
LR f1 score: 0.109
LR cohens kappa score: 0.038
LR average precision score: 0.070


minimum:
LR tn, fp: 166, 131
LR fn, tp: 1, 6
LR f1 score: 0.069
LR cohens kappa score: -0.005
LR average precision score: 0.049


-----[ GB ]-----
maximum:
GB tn, fp: 332, 6
GB fn, tp: 7, 14
GB f1 score: 1.000
GB cohens kappa score: 1.000


average:
GB tn, fp: 329.96, 1.84
GB fn, tp: 2.44, 11.36
GB f1 score: 0.839
GB cohens kappa score: 0.832


minimum:
GB tn, fp: 326, 0
GB fn, tp: 0, 7
GB f1 score: 0.640
GB cohens kappa score: 0.626


-----[ KNN ]-----
maximum:
KNN tn, fp: 316, 52
KNN fn, tp: 2, 14
KNN f1 score: 0.605
KNN cohens kappa score: 0.582


average:
KNN tn, fp: 299.32, 32.48
KNN fn, tp: 0.32, 13.48
KNN f1 score: 0.459
KNN cohens kappa score: 0.424


minimum:
KNN tn, fp: 280, 16
KNN fn, tp: 0, 12
KNN f1 score: 0.350
KNN cohens kappa score: 0.303


-----[ GAN ]-----
maximum:
GAN tn, fp: 328, 14
GAN fn, tp: 8, 13
GAN f1 score: 0.800
GAN cohens kappa score: 0.791


average:
GAN tn, fp: 324.28, 7.52
GAN fn, tp: 3.68, 10.12
GAN f1 score: 0.642
GAN cohens kappa score: 0.625


minimum:
GAN tn, fp: 317, 4
GAN fn, tp: 1, 6
GAN f1 score: 0.500
GAN cohens kappa score: 0.482