ConvGeNBenchmarkResults/data_result/convGAN-majority-5/folding_car_good.log

///////////////////////////////////////////
// Running convGAN-majority-5 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: 3, 11
GAN f1 score: 0.759
GAN cohens kappa score: 0.748

-> test with 'LR'
LR tn, fp: 179, 153
LR fn, tp: 6, 8
LR f1 score: 0.091
LR cohens kappa score: 0.018
LR average precision score: 0.060

-> 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: 327, 5
KNN fn, tp: 0, 14
KNN f1 score: 0.848
KNN cohens kappa score: 0.841


------ 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: 2, 12
GAN f1 score: 0.774
GAN cohens kappa score: 0.764

-> 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: 0.083

-> 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: 313, 19
KNN fn, tp: 0, 14
KNN f1 score: 0.596
KNN cohens kappa score: 0.571


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

-> test with 'LR'
LR tn, fp: 179, 153
LR fn, tp: 4, 10
LR f1 score: 0.113
LR cohens kappa score: 0.042
LR average precision score: 0.056

-> 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: 308, 24
KNN fn, tp: 0, 14
KNN f1 score: 0.538
KNN cohens kappa score: 0.509


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

-> test with 'LR'
LR tn, fp: 178, 154
LR fn, tp: 3, 11
LR f1 score: 0.123
LR cohens kappa score: 0.052
LR average precision score: 0.076

-> 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: 315, 17
KNN fn, tp: 2, 12
KNN f1 score: 0.558
KNN cohens kappa score: 0.533


------ 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: 322, 9
GAN fn, tp: 3, 10
GAN f1 score: 0.625
GAN cohens kappa score: 0.607

-> test with 'LR'
LR tn, fp: 182, 149
LR fn, tp: 2, 11
LR f1 score: 0.127
LR cohens kappa score: 0.062
LR average precision score: 0.060

-> test with 'GB'
GB tn, fp: 326, 5
GB fn, tp: 2, 11
GB f1 score: 0.759
GB cohens kappa score: 0.748

-> test with 'KNN'
KNN tn, fp: 308, 23
KNN fn, tp: 1, 12
KNN f1 score: 0.500
KNN cohens kappa score: 0.471


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

-> test with 'LR'
LR tn, fp: 150, 182
LR fn, tp: 5, 9
LR f1 score: 0.088
LR cohens kappa score: 0.013
LR average precision score: 0.062

-> test with 'GB'
GB tn, fp: 331, 1
GB fn, tp: 7, 7
GB f1 score: 0.636
GB cohens kappa score: 0.625

-> 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: 328, 4
GAN fn, tp: 2, 12
GAN f1 score: 0.800
GAN cohens kappa score: 0.791

-> test with 'LR'
LR tn, fp: 173, 159
LR fn, tp: 3, 11
LR f1 score: 0.120
LR cohens kappa score: 0.048
LR average precision score: 0.071

-> 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: 322, 10
KNN fn, tp: 0, 14
KNN f1 score: 0.737
KNN cohens kappa score: 0.723


------ 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: 4, 10
GAN f1 score: 0.588
GAN cohens kappa score: 0.568

-> test with 'LR'
LR tn, fp: 199, 133
LR fn, tp: 3, 11
LR f1 score: 0.139
LR cohens kappa score: 0.071
LR average precision score: 0.072

-> test with 'GB'
GB tn, fp: 332, 0
GB fn, tp: 8, 6
GB f1 score: 0.600
GB cohens kappa score: 0.590

-> test with 'KNN'
KNN tn, fp: 317, 15
KNN fn, tp: 3, 11
KNN f1 score: 0.550
KNN cohens kappa score: 0.525


------ 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: 185, 147
LR fn, tp: 9, 5
LR f1 score: 0.060
LR cohens kappa score: -0.015
LR average precision score: 0.050

-> test with 'GB'
GB tn, fp: 330, 2
GB fn, tp: 4, 10
GB f1 score: 0.769
GB cohens kappa score: 0.760

-> test with 'KNN'
KNN tn, fp: 295, 37
KNN fn, tp: 1, 13
KNN f1 score: 0.406
KNN cohens kappa score: 0.366


------ 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: 324, 7
GAN fn, tp: 2, 11
GAN f1 score: 0.710
GAN cohens kappa score: 0.696

-> test with 'LR'
LR tn, fp: 195, 136
LR fn, tp: 5, 8
LR f1 score: 0.102
LR cohens kappa score: 0.035
LR average precision score: 0.072

-> 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: 294, 37
KNN fn, tp: 0, 13
KNN f1 score: 0.413
KNN cohens kappa score: 0.375


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

-> test with 'LR'
LR tn, fp: 174, 158
LR fn, tp: 3, 11
LR f1 score: 0.120
LR cohens kappa score: 0.049
LR average precision score: 0.077

-> 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: 314, 18
KNN fn, tp: 1, 13
KNN f1 score: 0.578
KNN cohens kappa score: 0.553


------ 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: 321, 11
GAN fn, tp: 0, 14
GAN f1 score: 0.718
GAN cohens kappa score: 0.703

-> 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: 330, 2
GB fn, tp: 3, 11
GB f1 score: 0.815
GB cohens kappa score: 0.807

-> 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 3/5: 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: 0, 14
GAN f1 score: 0.757
GAN cohens kappa score: 0.744

-> test with 'LR'
LR tn, fp: 180, 152
LR fn, tp: 6, 8
LR f1 score: 0.092
LR cohens kappa score: 0.019
LR average precision score: 0.056

-> test with 'GB'
GB tn, fp: 330, 2
GB fn, tp: 9, 5
GB f1 score: 0.476
GB cohens kappa score: 0.462

-> test with 'KNN'
KNN tn, fp: 314, 18
KNN fn, tp: 1, 13
KNN f1 score: 0.578
KNN cohens kappa score: 0.553


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

-> test with 'LR'
LR tn, fp: 185, 147
LR fn, tp: 1, 13
LR f1 score: 0.149
LR cohens kappa score: 0.081
LR average precision score: 0.070

-> 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: 303, 29
KNN fn, tp: 0, 14
KNN f1 score: 0.491
KNN cohens kappa score: 0.458


------ 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: 323, 8
GAN fn, tp: 1, 12
GAN f1 score: 0.727
GAN cohens kappa score: 0.714

-> test with 'LR'
LR tn, fp: 165, 166
LR fn, tp: 5, 8
LR f1 score: 0.086
LR cohens kappa score: 0.016
LR average precision score: 0.049

-> test with 'GB'
GB tn, fp: 327, 4
GB fn, tp: 6, 7
GB f1 score: 0.583
GB cohens kappa score: 0.568

-> test with 'KNN'
KNN tn, fp: 290, 41
KNN fn, tp: 0, 13
KNN f1 score: 0.388
KNN cohens kappa score: 0.348


====== 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: 322, 10
GAN fn, tp: 2, 12
GAN f1 score: 0.667
GAN cohens kappa score: 0.649

-> test with 'LR'
LR tn, fp: 176, 156
LR fn, tp: 3, 11
LR f1 score: 0.122
LR cohens kappa score: 0.051
LR average precision score: 0.065

-> 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: 313, 19
KNN fn, tp: 0, 14
KNN f1 score: 0.596
KNN cohens kappa score: 0.571


------ 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: 326, 6
GAN fn, tp: 7, 7
GAN f1 score: 0.519
GAN cohens kappa score: 0.499

-> test with 'LR'
LR tn, fp: 179, 153
LR fn, tp: 6, 8
LR f1 score: 0.091
LR cohens kappa score: 0.018
LR average precision score: 0.062

-> test with 'GB'
GB tn, fp: 330, 2
GB fn, tp: 9, 5
GB f1 score: 0.476
GB cohens kappa score: 0.462

-> test with 'KNN'
KNN tn, fp: 307, 25
KNN fn, tp: 1, 13
KNN f1 score: 0.500
KNN cohens kappa score: 0.469


------ 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: 326, 6
GAN fn, tp: 3, 11
GAN f1 score: 0.710
GAN cohens kappa score: 0.696

-> 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: 0.061

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


------ 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: 327, 5
GAN fn, tp: 1, 13
GAN f1 score: 0.813
GAN cohens kappa score: 0.804

-> test with 'LR'
LR tn, fp: 198, 134
LR fn, tp: 7, 7
LR f1 score: 0.090
LR cohens kappa score: 0.018
LR average precision score: 0.056

-> test with 'GB'
GB tn, fp: 330, 2
GB fn, tp: 6, 8
GB f1 score: 0.667
GB cohens kappa score: 0.655

-> test with 'KNN'
KNN tn, fp: 318, 14
KNN fn, tp: 0, 14
KNN f1 score: 0.667
KNN cohens kappa score: 0.648


------ 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: 320, 11
GAN fn, tp: 3, 10
GAN f1 score: 0.588
GAN cohens kappa score: 0.568

-> test with 'LR'
LR tn, fp: 168, 163
LR fn, tp: 2, 11
LR f1 score: 0.118
LR cohens kappa score: 0.051
LR average precision score: 0.079

-> test with 'GB'
GB tn, fp: 327, 4
GB fn, tp: 7, 6
GB f1 score: 0.522
GB cohens kappa score: 0.505

-> test with 'KNN'
KNN tn, fp: 317, 14
KNN fn, tp: 1, 12
KNN f1 score: 0.615
KNN cohens kappa score: 0.595


====== 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: 318, 14
GAN fn, tp: 3, 11
GAN f1 score: 0.564
GAN cohens kappa score: 0.540

-> test with 'LR'
LR tn, fp: 185, 147
LR fn, tp: 8, 6
LR f1 score: 0.072
LR cohens kappa score: -0.002
LR average precision score: 0.051

-> 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: 311, 21
KNN fn, tp: 4, 10
KNN f1 score: 0.444
KNN cohens kappa score: 0.412


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

-> 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: 0.067

-> test with 'GB'
GB tn, fp: 330, 2
GB fn, tp: 6, 8
GB f1 score: 0.667
GB cohens kappa score: 0.655

-> 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 5/5: 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: 164, 168
LR fn, tp: 4, 10
LR f1 score: 0.104
LR cohens kappa score: 0.032
LR average precision score: 0.075

-> test with 'GB'
GB tn, fp: 327, 5
GB fn, tp: 2, 12
GB f1 score: 0.774
GB cohens kappa score: 0.764

-> 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: 330, 2
GAN fn, tp: 3, 11
GAN f1 score: 0.815
GAN cohens kappa score: 0.807

-> 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: 0.073

-> test with 'GB'
GB tn, fp: 331, 1
GB fn, tp: 7, 7
GB f1 score: 0.636
GB cohens kappa score: 0.625

-> 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 5/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1272 synthetic samples
-> test with GAN.predict
GAN tn, fp: 321, 10
GAN fn, tp: 0, 13
GAN f1 score: 0.722
GAN cohens kappa score: 0.708

-> test with 'LR'
LR tn, fp: 183, 148
LR fn, tp: 4, 9
LR f1 score: 0.106
LR cohens kappa score: 0.039
LR average precision score: 0.062

-> 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: 304, 27
KNN fn, tp: 0, 13
KNN f1 score: 0.491
KNN cohens kappa score: 0.460

### Exercise is done.

-----[ LR ]-----
maximum:
LR tn, fp: 199, 182
LR fn, tp: 9, 13
LR f1 score: 0.149
LR cohens kappa score: 0.081
LR average precision score: 0.083


average:
LR tn, fp: 179.36, 152.44
LR fn, tp: 4.4, 9.4
LR f1 score: 0.107
LR cohens kappa score: 0.036
LR average precision score: 0.065


minimum:
LR tn, fp: 150, 133
LR fn, tp: 1, 5
LR f1 score: 0.060
LR cohens kappa score: -0.015
LR average precision score: 0.049


-----[ GB ]-----
maximum:
GB tn, fp: 332, 5
GB fn, tp: 9, 13
GB f1 score: 0.897
GB cohens kappa score: 0.892


average:
GB tn, fp: 329.88, 1.92
GB fn, tp: 4.8, 9.0
GB f1 score: 0.718
GB cohens kappa score: 0.709


minimum:
GB tn, fp: 326, 0
GB fn, tp: 1, 5
GB f1 score: 0.476
GB cohens kappa score: 0.462


-----[ KNN ]-----
maximum:
KNN tn, fp: 327, 41
KNN fn, tp: 4, 14
KNN f1 score: 0.848
KNN cohens kappa score: 0.841


average:
KNN tn, fp: 309.32, 22.48
KNN fn, tp: 0.68, 13.12
KNN f1 score: 0.545
KNN cohens kappa score: 0.517


minimum:
KNN tn, fp: 290, 5
KNN fn, tp: 0, 10
KNN f1 score: 0.388
KNN cohens kappa score: 0.348


-----[ GAN ]-----
maximum:
GAN tn, fp: 330, 14
GAN fn, tp: 7, 14
GAN f1 score: 0.815
GAN cohens kappa score: 0.807


average:
GAN tn, fp: 324.04, 7.76
GAN fn, tp: 2.4, 11.4
GAN f1 score: 0.693
GAN cohens kappa score: 0.678


minimum:
GAN tn, fp: 318, 2
GAN fn, tp: 0, 7
GAN f1 score: 0.519
GAN cohens kappa score: 0.499