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

///////////////////////////////////////////
// Running convGAN-proxymary-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: 326, 6
GAN fn, tp: 2, 12
GAN f1 score: 0.750
GAN cohens kappa score: 0.738

-> 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: 0.058

-> test with 'GB'
GB tn, fp: 328, 4
GB fn, tp: 4, 10
GB f1 score: 0.714
GB cohens kappa score: 0.702

-> test with 'KNN'
KNN tn, fp: 325, 7
KNN fn, tp: 1, 13
KNN f1 score: 0.765
KNN cohens kappa score: 0.753


------ 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: 322, 10
GAN fn, tp: 7, 7
GAN f1 score: 0.452
GAN cohens kappa score: 0.426

-> test with 'LR'
LR tn, fp: 182, 150
LR fn, tp: 4, 10
LR f1 score: 0.115
LR cohens kappa score: 0.044
LR average precision score: 0.085

-> 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: 323, 9
KNN fn, tp: 0, 14
KNN f1 score: 0.757
KNN cohens kappa score: 0.744


------ 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: 321, 11
GAN fn, tp: 1, 13
GAN f1 score: 0.684
GAN cohens kappa score: 0.667

-> test with 'LR'
LR tn, fp: 174, 158
LR fn, tp: 7, 7
LR f1 score: 0.078
LR cohens kappa score: 0.004
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: 319, 13
KNN fn, tp: 1, 13
KNN f1 score: 0.650
KNN cohens kappa score: 0.631


------ 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: 317, 15
GAN fn, tp: 2, 12
GAN f1 score: 0.585
GAN cohens kappa score: 0.562

-> test with 'LR'
LR tn, fp: 185, 147
LR fn, tp: 4, 10
LR f1 score: 0.117
LR cohens kappa score: 0.046
LR average precision score: 0.077

-> 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: 318, 14
KNN fn, tp: 2, 12
KNN f1 score: 0.600
KNN cohens kappa score: 0.578


------ 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: 323, 8
GAN fn, tp: 3, 10
GAN f1 score: 0.645
GAN cohens kappa score: 0.629

-> test with 'LR'
LR tn, fp: 179, 152
LR fn, tp: 5, 8
LR f1 score: 0.092
LR cohens kappa score: 0.024
LR average precision score: 0.056

-> test with 'GB'
GB tn, fp: 329, 2
GB fn, tp: 3, 10
GB f1 score: 0.800
GB cohens kappa score: 0.792

-> test with 'KNN'
KNN tn, fp: 316, 15
KNN fn, tp: 0, 13
KNN f1 score: 0.634
KNN cohens kappa score: 0.614


====== 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: 168, 164
LR fn, tp: 5, 9
LR f1 score: 0.096
LR cohens kappa score: 0.023
LR average precision score: 0.064

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


------ 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: 317, 15
GAN fn, tp: 1, 13
GAN f1 score: 0.619
GAN cohens kappa score: 0.597

-> 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: 0.069

-> 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: 324, 8
KNN fn, tp: 0, 14
KNN f1 score: 0.778
KNN cohens kappa score: 0.766


------ 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: 318, 14
GAN fn, tp: 2, 12
GAN f1 score: 0.600
GAN cohens kappa score: 0.578

-> test with 'LR'
LR tn, fp: 190, 142
LR fn, tp: 4, 10
LR f1 score: 0.120
LR cohens kappa score: 0.050
LR average precision score: 0.072

-> test with 'GB'
GB tn, fp: 332, 0
GB fn, tp: 6, 8
GB f1 score: 0.727
GB cohens kappa score: 0.719

-> test with 'KNN'
KNN tn, fp: 321, 11
KNN fn, tp: 2, 12
KNN f1 score: 0.649
KNN cohens kappa score: 0.630


------ 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: 317, 15
GAN fn, tp: 3, 11
GAN f1 score: 0.550
GAN cohens kappa score: 0.525

-> test with 'LR'
LR tn, fp: 190, 142
LR fn, tp: 7, 7
LR f1 score: 0.086
LR cohens kappa score: 0.013
LR average precision score: 0.051

-> 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: 2, 12
KNN f1 score: 0.511
KNN cohens kappa score: 0.481


------ 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: 5, 8
GAN f1 score: 0.593
GAN cohens kappa score: 0.576

-> test with 'LR'
LR tn, fp: 190, 141
LR fn, tp: 5, 8
LR f1 score: 0.099
LR cohens kappa score: 0.031
LR average precision score: 0.073

-> 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: 312, 19
KNN fn, tp: 0, 13
KNN f1 score: 0.578
KNN cohens kappa score: 0.554


====== 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: 319, 13
GAN fn, tp: 3, 11
GAN f1 score: 0.579
GAN cohens kappa score: 0.556

-> test with 'LR'
LR tn, fp: 174, 158
LR fn, tp: 4, 10
LR f1 score: 0.110
LR cohens kappa score: 0.038
LR average precision score: 0.073

-> 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: 318, 14
KNN fn, tp: 1, 13
KNN f1 score: 0.634
KNN cohens kappa score: 0.614


------ 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: 318, 14
GAN fn, tp: 2, 12
GAN f1 score: 0.600
GAN cohens kappa score: 0.578

-> 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: 317, 15
KNN fn, tp: 0, 14
KNN f1 score: 0.651
KNN cohens kappa score: 0.631


------ 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: 322, 10
GAN fn, tp: 3, 11
GAN f1 score: 0.629
GAN cohens kappa score: 0.610

-> test with 'LR'
LR tn, fp: 189, 143
LR fn, tp: 5, 9
LR f1 score: 0.108
LR cohens kappa score: 0.037
LR average precision score: 0.055

-> test with 'GB'
GB tn, fp: 329, 3
GB fn, tp: 6, 8
GB f1 score: 0.640
GB cohens kappa score: 0.627

-> test with 'KNN'
KNN tn, fp: 322, 10
KNN fn, tp: 2, 12
KNN f1 score: 0.667
KNN cohens kappa score: 0.649


------ 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: 325, 7
GAN fn, tp: 4, 10
GAN f1 score: 0.645
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: 0.086

-> test with 'GB'
GB tn, fp: 332, 0
GB fn, tp: 6, 8
GB f1 score: 0.727
GB cohens kappa score: 0.719

-> test with 'KNN'
KNN tn, fp: 321, 11
KNN fn, tp: 0, 14
KNN f1 score: 0.718
KNN cohens kappa score: 0.703


------ 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: 2, 11
GAN f1 score: 0.667
GAN cohens kappa score: 0.651

-> test with 'LR'
LR tn, fp: 167, 164
LR fn, tp: 5, 8
LR f1 score: 0.086
LR cohens kappa score: 0.017
LR average precision score: 0.052

-> test with 'GB'
GB tn, fp: 328, 3
GB fn, tp: 5, 8
GB f1 score: 0.667
GB cohens kappa score: 0.655

-> test with 'KNN'
KNN tn, fp: 312, 19
KNN fn, tp: 0, 13
KNN f1 score: 0.578
KNN cohens kappa score: 0.554


====== 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: 321, 11
GAN fn, tp: 3, 11
GAN f1 score: 0.611
GAN cohens kappa score: 0.591

-> 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: 332, 0
GB fn, tp: 0, 14
GB f1 score: 1.000
GB cohens kappa score: 1.000

-> test with 'KNN'
KNN tn, fp: 325, 7
KNN fn, tp: 0, 14
KNN f1 score: 0.800
KNN cohens kappa score: 0.790


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

-> test with 'LR'
LR tn, fp: 186, 146
LR fn, tp: 6, 8
LR f1 score: 0.095
LR cohens kappa score: 0.023
LR average precision score: 0.061

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


------ 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: 318, 14
GAN fn, tp: 5, 9
GAN f1 score: 0.486
GAN cohens kappa score: 0.459

-> test with 'LR'
LR tn, fp: 174, 158
LR fn, tp: 5, 9
LR f1 score: 0.099
LR cohens kappa score: 0.027
LR average precision score: 0.070

-> 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: 314, 18
KNN fn, tp: 0, 14
KNN f1 score: 0.609
KNN cohens kappa score: 0.585


------ 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: 326, 6
GAN fn, tp: 2, 12
GAN f1 score: 0.750
GAN cohens kappa score: 0.738

-> test with 'LR'
LR tn, fp: 191, 141
LR fn, tp: 5, 9
LR f1 score: 0.110
LR cohens kappa score: 0.039
LR average precision score: 0.055

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


------ 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: 318, 13
GAN fn, tp: 1, 12
GAN f1 score: 0.632
GAN cohens kappa score: 0.612

-> test with 'LR'
LR tn, fp: 170, 161
LR fn, tp: 2, 11
LR f1 score: 0.119
LR cohens kappa score: 0.052
LR average precision score: 0.082

-> 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: 319, 12
KNN fn, tp: 0, 13
KNN f1 score: 0.684
KNN cohens kappa score: 0.668


====== 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: 308, 24
GAN fn, tp: 2, 12
GAN f1 score: 0.480
GAN cohens kappa score: 0.448

-> 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: 8, 6
GB f1 score: 0.571
GB cohens kappa score: 0.560

-> test with 'KNN'
KNN tn, fp: 320, 12
KNN fn, tp: 1, 13
KNN f1 score: 0.667
KNN cohens kappa score: 0.648


------ 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: 322, 10
GAN fn, tp: 3, 11
GAN f1 score: 0.629
GAN cohens kappa score: 0.610

-> test with 'LR'
LR tn, fp: 185, 147
LR fn, tp: 5, 9
LR f1 score: 0.106
LR cohens kappa score: 0.034
LR average precision score: 0.069

-> 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 5/5: Slice 3/5 -------
-> 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: 164, 168
LR fn, tp: 3, 11
LR f1 score: 0.114
LR cohens kappa score: 0.042
LR average precision score: 0.074

-> test with 'GB'
GB tn, fp: 328, 4
GB fn, tp: 2, 12
GB f1 score: 0.800
GB cohens kappa score: 0.791

-> test with 'KNN'
KNN tn, fp: 320, 12
KNN fn, tp: 1, 13
KNN f1 score: 0.667
KNN cohens kappa score: 0.648


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

-> test with 'LR'
LR tn, fp: 170, 162
LR fn, tp: 3, 11
LR f1 score: 0.118
LR cohens kappa score: 0.046
LR average precision score: 0.065

-> 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: 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: 312, 19
GAN fn, tp: 2, 11
GAN f1 score: 0.512
GAN cohens kappa score: 0.484

-> test with 'LR'
LR tn, fp: 180, 151
LR fn, tp: 4, 9
LR f1 score: 0.104
LR cohens kappa score: 0.037
LR average precision score: 0.065

-> test with 'GB'
GB tn, fp: 331, 0
GB fn, tp: 2, 11
GB f1 score: 0.917
GB cohens kappa score: 0.914

-> test with 'KNN'
KNN tn, fp: 308, 23
KNN fn, tp: 0, 13
KNN f1 score: 0.531
KNN cohens kappa score: 0.503

### Exercise is done.

-----[ LR ]-----
maximum:
LR tn, fp: 191, 168
LR fn, tp: 8, 11
LR f1 score: 0.123
LR cohens kappa score: 0.052
LR average precision score: 0.086


average:
LR tn, fp: 179.32, 152.48
LR fn, tp: 4.64, 9.16
LR f1 score: 0.104
LR cohens kappa score: 0.033
LR average precision score: 0.066


minimum:
LR tn, fp: 164, 141
LR fn, tp: 2, 6
LR f1 score: 0.073
LR cohens kappa score: -0.001
LR average precision score: 0.051


-----[ GB ]-----
maximum:
GB tn, fp: 332, 4
GB fn, tp: 9, 14
GB f1 score: 1.000
GB cohens kappa score: 1.000


average:
GB tn, fp: 330.08, 1.72
GB fn, tp: 4.72, 9.08
GB f1 score: 0.730
GB cohens kappa score: 0.721


minimum:
GB tn, fp: 327, 0
GB fn, tp: 0, 5
GB f1 score: 0.476
GB cohens kappa score: 0.462


-----[ KNN ]-----
maximum:
KNN tn, fp: 325, 23
KNN fn, tp: 2, 14
KNN f1 score: 0.800
KNN cohens kappa score: 0.790


average:
KNN tn, fp: 317.68, 14.12
KNN fn, tp: 0.6, 13.2
KNN f1 score: 0.649
KNN cohens kappa score: 0.629


minimum:
KNN tn, fp: 308, 7
KNN fn, tp: 0, 12
KNN f1 score: 0.511
KNN cohens kappa score: 0.481


-----[ GAN ]-----
maximum:
GAN tn, fp: 326, 24
GAN fn, tp: 7, 14
GAN f1 score: 0.750
GAN cohens kappa score: 0.738


average:
GAN tn, fp: 319.72, 12.08
GAN fn, tp: 2.76, 11.04
GAN f1 score: 0.603
GAN cohens kappa score: 0.582


minimum:
GAN tn, fp: 308, 6
GAN fn, tp: 0, 7
GAN f1 score: 0.452
GAN cohens kappa score: 0.426