ConvGeNBenchmarkResults/data_result/Repeater/folding_car_good.log

///////////////////////////////////////////
// Running Repeater 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 '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: 0.061

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

-> test with 'KNN'
KNN tn, fp: 274, 58
KNN fn, tp: 0, 14
KNN f1 score: 0.326
KNN cohens kappa score: 0.277


------ Step 1/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1272 synthetic samples
-> test with 'LR'
LR tn, fp: 185, 147
LR fn, tp: 3, 11
LR f1 score: 0.128
LR cohens kappa score: 0.058
LR average precision score: 0.087

-> test with 'GB'
GB tn, fp: 327, 5
GB fn, tp: 0, 14
GB f1 score: 0.848
GB cohens kappa score: 0.841

-> test with 'KNN'
KNN tn, fp: 278, 54
KNN fn, tp: 0, 14
KNN f1 score: 0.341
KNN cohens kappa score: 0.294


------ Step 1/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1272 synthetic samples
-> test with 'LR'
LR tn, fp: 172, 160
LR fn, tp: 4, 10
LR f1 score: 0.109
LR cohens kappa score: 0.037
LR average precision score: 0.057

-> test with 'GB'
GB tn, fp: 328, 4
GB fn, tp: 0, 14
GB f1 score: 0.875
GB cohens kappa score: 0.869

-> test with 'KNN'
KNN tn, fp: 284, 48
KNN fn, tp: 0, 14
KNN f1 score: 0.368
KNN cohens kappa score: 0.324


------ Step 1/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1272 synthetic samples
-> 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: 0.077

-> test with 'GB'
GB tn, fp: 327, 5
GB fn, tp: 0, 14
GB f1 score: 0.848
GB cohens kappa score: 0.841

-> test with 'KNN'
KNN tn, fp: 283, 49
KNN fn, tp: 0, 14
KNN f1 score: 0.364
KNN cohens kappa score: 0.319


------ Step 1/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1272 synthetic samples
-> test with 'LR'
LR tn, fp: 175, 156
LR fn, tp: 4, 9
LR f1 score: 0.101
LR cohens kappa score: 0.033
LR average precision score: 0.056

-> test with 'GB'
GB tn, fp: 325, 6
GB fn, tp: 0, 13
GB f1 score: 0.813
GB cohens kappa score: 0.804

-> 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 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 'LR'
LR tn, fp: 157, 175
LR fn, tp: 4, 10
LR f1 score: 0.101
LR cohens kappa score: 0.027
LR average precision score: 0.068

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

-> test with 'KNN'
KNN tn, fp: 286, 46
KNN fn, tp: 1, 13
KNN f1 score: 0.356
KNN cohens kappa score: 0.311


------ Step 2/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1272 synthetic samples
-> test with 'LR'
LR tn, fp: 169, 163
LR fn, tp: 3, 11
LR f1 score: 0.117
LR cohens kappa score: 0.046
LR average precision score: 0.070

-> 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: 285, 47
KNN fn, tp: 0, 14
KNN f1 score: 0.373
KNN cohens kappa score: 0.329


------ Step 2/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1272 synthetic samples
-> test with 'LR'
LR tn, fp: 186, 146
LR fn, tp: 4, 10
LR f1 score: 0.118
LR cohens kappa score: 0.047
LR average precision score: 0.072

-> test with 'GB'
GB tn, fp: 330, 2
GB fn, tp: 0, 14
GB f1 score: 0.933
GB cohens kappa score: 0.930

-> 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 2/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1272 synthetic samples
-> test with 'LR'
LR tn, fp: 183, 149
LR fn, tp: 7, 7
LR f1 score: 0.082
LR cohens kappa score: 0.009
LR average precision score: 0.050

-> test with 'GB'
GB tn, fp: 323, 9
GB fn, tp: 0, 14
GB f1 score: 0.757
GB cohens kappa score: 0.744

-> test with 'KNN'
KNN tn, fp: 269, 63
KNN fn, tp: 0, 14
KNN f1 score: 0.308
KNN cohens kappa score: 0.257


------ Step 2/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1272 synthetic samples
-> 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: 0.077

-> test with 'GB'
GB tn, fp: 325, 6
GB fn, tp: 0, 13
GB f1 score: 0.813
GB cohens kappa score: 0.804

-> test with 'KNN'
KNN tn, fp: 271, 60
KNN fn, tp: 0, 13
KNN f1 score: 0.302
KNN cohens kappa score: 0.254


====== 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 'LR'
LR tn, fp: 168, 164
LR fn, tp: 3, 11
LR f1 score: 0.116
LR cohens kappa score: 0.045
LR average precision score: 0.077

-> test with 'GB'
GB tn, fp: 328, 4
GB fn, tp: 0, 14
GB f1 score: 0.875
GB cohens kappa score: 0.869

-> test with 'KNN'
KNN tn, fp: 285, 47
KNN fn, tp: 0, 14
KNN f1 score: 0.373
KNN cohens kappa score: 0.329


------ Step 3/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1272 synthetic samples
-> test with 'LR'
LR tn, fp: 190, 142
LR fn, tp: 3, 11
LR f1 score: 0.132
LR cohens kappa score: 0.062
LR average precision score: 0.067

-> test with 'GB'
GB tn, fp: 324, 8
GB fn, tp: 0, 14
GB f1 score: 0.778
GB cohens kappa score: 0.766

-> test with 'KNN'
KNN tn, fp: 284, 48
KNN fn, tp: 0, 14
KNN f1 score: 0.368
KNN cohens kappa score: 0.324


------ Step 3/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1272 synthetic samples
-> 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: 326, 6
GB fn, tp: 0, 14
GB f1 score: 0.824
GB cohens kappa score: 0.815

-> test with 'KNN'
KNN tn, fp: 285, 47
KNN fn, tp: 1, 13
KNN f1 score: 0.351
KNN cohens kappa score: 0.306


------ Step 3/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1272 synthetic samples
-> 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: 0.083

-> 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: 291, 41
KNN fn, tp: 0, 14
KNN f1 score: 0.406
KNN cohens kappa score: 0.365


------ Step 3/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1272 synthetic samples
-> test with 'LR'
LR tn, fp: 168, 163
LR fn, tp: 4, 9
LR f1 score: 0.097
LR cohens kappa score: 0.029
LR average precision score: 0.055

-> test with 'GB'
GB tn, fp: 325, 6
GB fn, tp: 0, 13
GB f1 score: 0.813
GB cohens kappa score: 0.804

-> test with 'KNN'
KNN tn, fp: 268, 63
KNN fn, tp: 0, 13
KNN f1 score: 0.292
KNN cohens kappa score: 0.243


====== 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 'LR'
LR tn, fp: 177, 155
LR fn, tp: 3, 11
LR f1 score: 0.122
LR cohens kappa score: 0.051
LR average precision score: 0.067

-> test with 'GB'
GB tn, fp: 327, 5
GB fn, tp: 0, 14
GB f1 score: 0.848
GB cohens kappa score: 0.841

-> test with 'KNN'
KNN tn, fp: 286, 46
KNN fn, tp: 0, 14
KNN f1 score: 0.378
KNN cohens kappa score: 0.335


------ Step 4/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1272 synthetic samples
-> test with 'LR'
LR tn, fp: 173, 159
LR fn, tp: 6, 8
LR f1 score: 0.088
LR cohens kappa score: 0.015
LR average precision score: 0.063

-> 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: 284, 48
KNN fn, tp: 0, 14
KNN f1 score: 0.368
KNN cohens kappa score: 0.324


------ Step 4/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1272 synthetic samples
-> 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: 0.066

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

-> test with 'KNN'
KNN tn, fp: 284, 48
KNN fn, tp: 0, 14
KNN f1 score: 0.368
KNN cohens kappa score: 0.324


------ Step 4/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1272 synthetic samples
-> test with 'LR'
LR tn, fp: 189, 143
LR fn, tp: 6, 8
LR f1 score: 0.097
LR cohens kappa score: 0.025
LR average precision score: 0.056

-> test with 'GB'
GB tn, fp: 326, 6
GB fn, tp: 0, 14
GB f1 score: 0.824
GB cohens kappa score: 0.815

-> test with 'KNN'
KNN tn, fp: 288, 44
KNN fn, tp: 0, 14
KNN f1 score: 0.389
KNN cohens kappa score: 0.346


------ Step 4/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1272 synthetic samples
-> test with 'LR'
LR tn, fp: 170, 161
LR fn, tp: 1, 12
LR f1 score: 0.129
LR cohens kappa score: 0.063
LR average precision score: 0.081

-> test with 'GB'
GB tn, fp: 325, 6
GB fn, tp: 0, 13
GB f1 score: 0.813
GB cohens kappa score: 0.804

-> test with 'KNN'
KNN tn, fp: 279, 52
KNN fn, tp: 0, 13
KNN f1 score: 0.333
KNN cohens kappa score: 0.289


====== 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 'LR'
LR tn, fp: 175, 157
LR fn, tp: 8, 6
LR f1 score: 0.068
LR cohens kappa score: -0.007
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: 288, 44
KNN fn, tp: 0, 14
KNN f1 score: 0.389
KNN cohens kappa score: 0.346


------ Step 5/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1272 synthetic samples
-> test with 'LR'
LR tn, fp: 187, 145
LR fn, tp: 6, 8
LR f1 score: 0.096
LR cohens kappa score: 0.023
LR average precision score: 0.070

-> test with 'GB'
GB tn, fp: 327, 5
GB fn, tp: 0, 14
GB f1 score: 0.848
GB cohens kappa score: 0.841

-> test with 'KNN'
KNN tn, fp: 279, 53
KNN fn, tp: 0, 14
KNN f1 score: 0.346
KNN cohens kappa score: 0.299


------ Step 5/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1272 synthetic samples
-> 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: 0.079

-> test with 'GB'
GB tn, fp: 323, 9
GB fn, tp: 0, 14
GB f1 score: 0.757
GB cohens kappa score: 0.744

-> test with 'KNN'
KNN tn, fp: 276, 56
KNN fn, tp: 0, 14
KNN f1 score: 0.333
KNN cohens kappa score: 0.285


------ Step 5/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1272 synthetic samples
-> test with 'LR'
LR tn, fp: 173, 159
LR fn, tp: 4, 10
LR f1 score: 0.109
LR cohens kappa score: 0.037
LR average precision score: 0.078

-> test with 'GB'
GB tn, fp: 326, 6
GB fn, tp: 0, 14
GB f1 score: 0.824
GB cohens kappa score: 0.815

-> test with 'KNN'
KNN tn, fp: 282, 50
KNN fn, tp: 0, 14
KNN f1 score: 0.359
KNN cohens kappa score: 0.313


------ Step 5/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1272 synthetic samples
-> test with 'LR'
LR tn, fp: 176, 155
LR fn, tp: 4, 9
LR f1 score: 0.102
LR cohens kappa score: 0.034
LR average precision score: 0.065

-> test with 'GB'
GB tn, fp: 327, 4
GB fn, tp: 0, 13
GB f1 score: 0.867
GB cohens kappa score: 0.861

-> test with 'KNN'
KNN tn, fp: 279, 52
KNN fn, tp: 0, 13
KNN f1 score: 0.333
KNN cohens kappa score: 0.289

### Exercise is done.

-----[ LR ]-----
maximum:
LR tn, fp: 190, 175
LR fn, tp: 8, 12
LR f1 score: 0.132
LR cohens kappa score: 0.063
LR average precision score: 0.087


average:
LR tn, fp: 175.32, 156.48
LR fn, tp: 4.24, 9.56
LR f1 score: 0.106
LR cohens kappa score: 0.035
LR average precision score: 0.068


minimum:
LR tn, fp: 157, 142
LR fn, tp: 1, 6
LR f1 score: 0.068
LR cohens kappa score: -0.007
LR average precision score: 0.050


-----[ GB ]-----
maximum:
GB tn, fp: 330, 9
GB fn, tp: 0, 14
GB f1 score: 0.933
GB cohens kappa score: 0.930


average:
GB tn, fp: 326.4, 5.4
GB fn, tp: 0.0, 13.8
GB f1 score: 0.839
GB cohens kappa score: 0.831


minimum:
GB tn, fp: 323, 2
GB fn, tp: 0, 13
GB f1 score: 0.757
GB cohens kappa score: 0.744


-----[ KNN ]-----
maximum:
KNN tn, fp: 292, 63
KNN fn, tp: 1, 14
KNN f1 score: 0.412
KNN cohens kappa score: 0.371


average:
KNN tn, fp: 282.0, 49.8
KNN fn, tp: 0.08, 13.72
KNN f1 score: 0.357
KNN cohens kappa score: 0.312


minimum:
KNN tn, fp: 268, 40
KNN fn, tp: 0, 13
KNN f1 score: 0.292
KNN cohens kappa score: 0.243