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

///////////////////////////////////////////
// Running convGAN-proximary-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: 327, 5
GAN fn, tp: 2, 12
GAN f1 score: 0.774
GAN cohens kappa score: 0.764

-> 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: 331, 1
GB fn, tp: 1, 13
GB f1 score: 0.929
GB cohens kappa score: 0.926

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


------ 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: 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: 2, 12
LR f1 score: 0.129
LR cohens kappa score: 0.059
LR average precision score: 0.069

-> 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: 300, 32
KNN fn, tp: 1, 13
KNN f1 score: 0.441
KNN cohens kappa score: 0.404


------ 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: 324, 8
GAN fn, tp: 1, 13
GAN f1 score: 0.743
GAN cohens kappa score: 0.730

-> 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: 0.059

-> 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: 311, 21
KNN fn, tp: 0, 14
KNN f1 score: 0.571
KNN cohens kappa score: 0.545


------ 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: 319, 13
GAN fn, tp: 0, 14
GAN f1 score: 0.683
GAN cohens kappa score: 0.665

-> 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: 0.076

-> 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: 308, 24
KNN fn, tp: 1, 13
KNN f1 score: 0.510
KNN cohens kappa score: 0.479


------ 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: 320, 11
GAN fn, tp: 2, 11
GAN f1 score: 0.629
GAN cohens kappa score: 0.610

-> 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: 0.055

-> 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: 314, 17
KNN fn, tp: 1, 12
KNN f1 score: 0.571
KNN cohens kappa score: 0.548


====== 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: 322, 10
GAN fn, tp: 0, 14
GAN f1 score: 0.737
GAN cohens kappa score: 0.723

-> test with 'LR'
LR tn, fp: 160, 172
LR fn, tp: 3, 11
LR f1 score: 0.112
LR cohens kappa score: 0.039
LR average precision score: 0.061

-> 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: 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: 315, 17
GAN fn, tp: 0, 14
GAN f1 score: 0.622
GAN cohens kappa score: 0.600

-> 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: 0.061

-> test with 'GB'
GB tn, fp: 327, 5
GB fn, tp: 4, 10
GB f1 score: 0.690
GB cohens kappa score: 0.676

-> test with 'KNN'
KNN tn, fp: 312, 20
KNN fn, tp: 2, 12
KNN f1 score: 0.522
KNN cohens kappa score: 0.493


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

-> test with 'LR'
LR tn, fp: 192, 140
LR fn, tp: 4, 10
LR f1 score: 0.122
LR cohens kappa score: 0.052
LR average precision score: 0.072

-> test with 'GB'
GB tn, fp: 330, 2
GB fn, tp: 8, 6
GB f1 score: 0.545
GB cohens kappa score: 0.532

-> 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 2/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1272 synthetic samples
-> test with GAN.predict
GAN tn, fp: 316, 16
GAN fn, tp: 1, 13
GAN f1 score: 0.605
GAN cohens kappa score: 0.582

-> test with 'LR'
LR tn, fp: 190, 142
LR fn, tp: 9, 5
LR f1 score: 0.062
LR cohens kappa score: -0.013
LR average precision score: 0.051

-> 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: 315, 17
KNN fn, tp: 3, 11
KNN f1 score: 0.524
KNN cohens kappa score: 0.497


------ 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: 325, 6
GAN fn, tp: 3, 10
GAN f1 score: 0.690
GAN cohens kappa score: 0.676

-> test with 'LR'
LR tn, fp: 185, 146
LR fn, tp: 5, 8
LR f1 score: 0.096
LR cohens kappa score: 0.028
LR average precision score: 0.074

-> test with 'GB'
GB tn, fp: 327, 4
GB fn, tp: 8, 5
GB f1 score: 0.455
GB cohens kappa score: 0.437

-> test with 'KNN'
KNN tn, fp: 316, 15
KNN fn, tp: 1, 12
KNN f1 score: 0.600
KNN cohens kappa score: 0.578


====== 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: 316, 16
GAN fn, tp: 1, 13
GAN f1 score: 0.605
GAN cohens kappa score: 0.582

-> test with 'LR'
LR tn, fp: 170, 162
LR fn, tp: 4, 10
LR f1 score: 0.108
LR cohens kappa score: 0.035
LR average precision score: 0.077

-> 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: 312, 20
KNN fn, tp: 2, 12
KNN f1 score: 0.522
KNN cohens kappa score: 0.493


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

-> test with 'LR'
LR tn, fp: 189, 143
LR fn, tp: 3, 11
LR f1 score: 0.131
LR cohens kappa score: 0.061
LR average precision score: 0.066

-> 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: 301, 31
KNN fn, tp: 0, 14
KNN f1 score: 0.475
KNN cohens kappa score: 0.440


------ 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: 311, 21
GAN fn, tp: 1, 13
GAN f1 score: 0.542
GAN cohens kappa score: 0.514

-> 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: 0.056

-> test with 'GB'
GB tn, fp: 329, 3
GB fn, tp: 8, 6
GB f1 score: 0.522
GB cohens kappa score: 0.506

-> 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 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1272 synthetic samples
-> test with GAN.predict
GAN tn, fp: 317, 15
GAN fn, tp: 2, 12
GAN f1 score: 0.585
GAN cohens kappa score: 0.562

-> test with 'LR'
LR tn, fp: 173, 159
LR fn, tp: 1, 13
LR f1 score: 0.140
LR cohens kappa score: 0.070
LR average precision score: 0.077

-> 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: 315, 17
KNN fn, tp: 0, 14
KNN f1 score: 0.622
KNN cohens kappa score: 0.600


------ 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: 322, 9
GAN fn, tp: 1, 12
GAN f1 score: 0.706
GAN cohens kappa score: 0.691

-> test with 'LR'
LR tn, fp: 169, 162
LR fn, tp: 5, 8
LR f1 score: 0.087
LR cohens kappa score: 0.019
LR average precision score: 0.052

-> 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: 308, 23
KNN fn, tp: 0, 13
KNN f1 score: 0.531
KNN cohens kappa score: 0.503


====== 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: 319, 13
GAN fn, tp: 4, 10
GAN f1 score: 0.541
GAN cohens kappa score: 0.516

-> test with 'LR'
LR tn, fp: 181, 151
LR fn, tp: 3, 11
LR f1 score: 0.125
LR cohens kappa score: 0.055
LR average precision score: 0.066

-> test with 'GB'
GB tn, fp: 332, 0
GB fn, tp: 1, 13
GB f1 score: 0.963
GB cohens kappa score: 0.961

-> 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 4/5: 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: 5, 9
GAN f1 score: 0.500
GAN cohens kappa score: 0.474

-> 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: 0.053

-> 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: 309, 23
KNN fn, tp: 1, 13
KNN f1 score: 0.520
KNN cohens kappa score: 0.490


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

-> 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: 0.064

-> test with 'GB'
GB tn, fp: 331, 1
GB fn, tp: 5, 9
GB f1 score: 0.750
GB cohens kappa score: 0.741

-> test with 'KNN'
KNN tn, fp: 302, 30
KNN fn, tp: 0, 14
KNN f1 score: 0.483
KNN cohens kappa score: 0.449


------ 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: 328, 4
GAN fn, tp: 3, 11
GAN f1 score: 0.759
GAN cohens kappa score: 0.748

-> test with 'LR'
LR tn, fp: 196, 136
LR fn, tp: 5, 9
LR f1 score: 0.113
LR cohens kappa score: 0.043
LR average precision score: 0.054

-> test with 'GB'
GB tn, fp: 329, 3
GB fn, tp: 4, 10
GB f1 score: 0.741
GB cohens kappa score: 0.730

-> test with 'KNN'
KNN tn, fp: 315, 17
KNN fn, tp: 0, 14
KNN f1 score: 0.622
KNN cohens kappa score: 0.600


------ 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: 321, 10
GAN fn, tp: 2, 11
GAN f1 score: 0.647
GAN cohens kappa score: 0.630

-> test with 'LR'
LR tn, fp: 177, 154
LR fn, tp: 2, 11
LR f1 score: 0.124
LR cohens kappa score: 0.058
LR average precision score: 0.077

-> 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: 310, 21
KNN fn, tp: 1, 12
KNN f1 score: 0.522
KNN cohens kappa score: 0.494


====== 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: 187, 145
LR fn, tp: 8, 6
LR f1 score: 0.073
LR cohens kappa score: -0.001
LR average precision score: 0.052

-> 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: 302, 30
KNN fn, tp: 2, 12
KNN f1 score: 0.429
KNN cohens kappa score: 0.392


------ 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: 188, 144
LR fn, tp: 5, 9
LR f1 score: 0.108
LR cohens kappa score: 0.036
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: 329, 3
KNN fn, tp: 0, 14
KNN f1 score: 0.903
KNN cohens kappa score: 0.899


------ 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: 314, 18
GAN fn, tp: 4, 10
GAN f1 score: 0.476
GAN cohens kappa score: 0.446

-> test with 'LR'
LR tn, fp: 162, 170
LR fn, tp: 3, 11
LR f1 score: 0.113
LR cohens kappa score: 0.041
LR average precision score: 0.078

-> test with 'GB'
GB tn, fp: 329, 3
GB fn, tp: 3, 11
GB f1 score: 0.786
GB cohens kappa score: 0.777

-> 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 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: 5, 9
GAN f1 score: 0.643
GAN cohens kappa score: 0.628

-> 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: 0.075

-> 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 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1272 synthetic samples
-> test with GAN.predict
GAN tn, fp: 314, 17
GAN fn, tp: 2, 11
GAN f1 score: 0.537
GAN cohens kappa score: 0.511

-> test with 'LR'
LR tn, fp: 178, 153
LR fn, tp: 4, 9
LR f1 score: 0.103
LR cohens kappa score: 0.035
LR average precision score: 0.065

-> 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: 299, 32
KNN fn, tp: 0, 13
KNN f1 score: 0.448
KNN cohens kappa score: 0.414

### Exercise is done.

-----[ LR ]-----
maximum:
LR tn, fp: 196, 172
LR fn, tp: 9, 13
LR f1 score: 0.140
LR cohens kappa score: 0.070
LR average precision score: 0.078


average:
LR tn, fp: 179.36, 152.44
LR fn, tp: 4.32, 9.48
LR f1 score: 0.108
LR cohens kappa score: 0.037
LR average precision score: 0.065


minimum:
LR tn, fp: 160, 136
LR fn, tp: 1, 5
LR f1 score: 0.062
LR cohens kappa score: -0.013
LR average precision score: 0.051


-----[ GB ]-----
maximum:
GB tn, fp: 332, 5
GB fn, tp: 9, 13
GB f1 score: 0.963
GB cohens kappa score: 0.961


average:
GB tn, fp: 329.84, 1.96
GB fn, tp: 4.88, 8.92
GB f1 score: 0.714
GB cohens kappa score: 0.704


minimum:
GB tn, fp: 327, 0
GB fn, tp: 1, 5
GB f1 score: 0.455
GB cohens kappa score: 0.437


-----[ KNN ]-----
maximum:
KNN tn, fp: 329, 32
KNN fn, tp: 3, 14
KNN f1 score: 0.903
KNN cohens kappa score: 0.899


average:
KNN tn, fp: 311.16, 20.64
KNN fn, tp: 0.76, 13.04
KNN f1 score: 0.568
KNN cohens kappa score: 0.542


minimum:
KNN tn, fp: 299, 3
KNN fn, tp: 0, 11
KNN f1 score: 0.429
KNN cohens kappa score: 0.392


-----[ GAN ]-----
maximum:
GAN tn, fp: 328, 22
GAN fn, tp: 5, 14
GAN f1 score: 0.774
GAN cohens kappa score: 0.764


average:
GAN tn, fp: 319.56, 12.24
GAN fn, tp: 2.16, 11.64
GAN f1 score: 0.626
GAN cohens kappa score: 0.606


minimum:
GAN tn, fp: 310, 4
GAN fn, tp: 0, 9
GAN f1 score: 0.476
GAN cohens kappa score: 0.446