ConvGeNBenchmarkResults/data_result/ctGAN/folding_car_good.log

///////////////////////////////////////////
// Running ctGAN 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: 169, 163
LR fn, tp: 4, 10
LR f1 score: 0.107
LR cohens kappa score: 0.035
LR average precision score: 0.056

-> test with 'RF'
RF tn, fp: 304, 28
RF fn, tp: 0, 14
RF f1 score: 0.500
RF cohens kappa score: 0.468

-> test with 'GB'
GB tn, fp: 304, 28
GB fn, tp: 0, 14
GB f1 score: 0.500
GB cohens kappa score: 0.468

-> test with 'KNN'
KNN tn, fp: 221, 111
KNN fn, tp: 0, 14
KNN f1 score: 0.201
KNN cohens kappa score: 0.139


------ Step 1/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1272 synthetic samples
-> test with 'LR'
LR tn, fp: 191, 141
LR fn, tp: 3, 11
LR f1 score: 0.133
LR cohens kappa score: 0.063
LR average precision score: 0.079

-> test with 'RF'
RF tn, fp: 300, 32
RF fn, tp: 0, 14
RF f1 score: 0.467
RF cohens kappa score: 0.431

-> test with 'GB'
GB tn, fp: 300, 32
GB fn, tp: 0, 14
GB f1 score: 0.467
GB cohens kappa score: 0.431

-> test with 'KNN'
KNN tn, fp: 229, 103
KNN fn, tp: 0, 14
KNN f1 score: 0.214
KNN cohens kappa score: 0.152


------ Step 1/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1272 synthetic samples
-> test with 'LR'
LR tn, fp: 179, 153
LR fn, tp: 7, 7
LR f1 score: 0.080
LR cohens kappa score: 0.007
LR average precision score: 0.055

-> test with 'RF'
RF tn, fp: 311, 21
RF fn, tp: 0, 14
RF f1 score: 0.571
RF cohens kappa score: 0.545

-> test with 'GB'
GB tn, fp: 311, 21
GB fn, tp: 0, 14
GB f1 score: 0.571
GB cohens kappa score: 0.545

-> test with 'KNN'
KNN tn, fp: 240, 92
KNN fn, tp: 0, 14
KNN f1 score: 0.233
KNN cohens kappa score: 0.174


------ Step 1/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1272 synthetic samples
-> test with 'LR'
LR tn, fp: 149, 183
LR fn, tp: 1, 13
LR f1 score: 0.124
LR cohens kappa score: 0.052
LR average precision score: 0.086

-> test with 'RF'
RF tn, fp: 311, 21
RF fn, tp: 0, 14
RF f1 score: 0.571
RF cohens kappa score: 0.545

-> test with 'GB'
GB tn, fp: 311, 21
GB fn, tp: 0, 14
GB f1 score: 0.571
GB cohens kappa score: 0.545

-> test with 'KNN'
KNN tn, fp: 230, 102
KNN fn, tp: 0, 14
KNN f1 score: 0.215
KNN cohens kappa score: 0.154


------ Step 1/5: 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: 2, 11
LR f1 score: 0.119
LR cohens kappa score: 0.052
LR average precision score: 0.063

-> test with 'RF'
RF tn, fp: 310, 21
RF fn, tp: 0, 13
RF f1 score: 0.553
RF cohens kappa score: 0.527

-> test with 'GB'
GB tn, fp: 310, 21
GB fn, tp: 0, 13
GB f1 score: 0.553
GB cohens kappa score: 0.527

-> test with 'KNN'
KNN tn, fp: 244, 87
KNN fn, tp: 0, 13
KNN f1 score: 0.230
KNN cohens kappa score: 0.175


====== 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: 156, 176
LR fn, tp: 4, 10
LR f1 score: 0.100
LR cohens kappa score: 0.027
LR average precision score: 0.058

-> test with 'RF'
RF tn, fp: 312, 20
RF fn, tp: 0, 14
RF f1 score: 0.583
RF cohens kappa score: 0.558

-> test with 'GB'
GB tn, fp: 312, 20
GB fn, tp: 0, 14
GB f1 score: 0.583
GB cohens kappa score: 0.558

-> test with 'KNN'
KNN tn, fp: 235, 97
KNN fn, tp: 0, 14
KNN f1 score: 0.224
KNN cohens kappa score: 0.164


------ Step 2/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1272 synthetic samples
-> test with 'LR'
LR tn, fp: 161, 171
LR fn, tp: 3, 11
LR f1 score: 0.112
LR cohens kappa score: 0.040
LR average precision score: 0.067

-> test with 'RF'
RF tn, fp: 316, 16
RF fn, tp: 0, 14
RF f1 score: 0.636
RF cohens kappa score: 0.615

-> test with 'GB'
GB tn, fp: 316, 16
GB fn, tp: 0, 14
GB f1 score: 0.636
GB cohens kappa score: 0.615

-> test with 'KNN'
KNN tn, fp: 246, 86
KNN fn, tp: 0, 14
KNN f1 score: 0.246
KNN cohens kappa score: 0.188


------ 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: 0.067

-> test with 'RF'
RF tn, fp: 306, 26
RF fn, tp: 0, 14
RF f1 score: 0.519
RF cohens kappa score: 0.488

-> test with 'GB'
GB tn, fp: 306, 26
GB fn, tp: 0, 14
GB f1 score: 0.519
GB cohens kappa score: 0.488

-> test with 'KNN'
KNN tn, fp: 243, 89
KNN fn, tp: 0, 14
KNN f1 score: 0.239
KNN cohens kappa score: 0.181


------ Step 2/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1272 synthetic samples
-> test with 'LR'
LR tn, fp: 172, 160
LR fn, tp: 5, 9
LR f1 score: 0.098
LR cohens kappa score: 0.026
LR average precision score: 0.050

-> test with 'RF'
RF tn, fp: 296, 36
RF fn, tp: 0, 14
RF f1 score: 0.438
RF cohens kappa score: 0.400

-> test with 'GB'
GB tn, fp: 296, 36
GB fn, tp: 0, 14
GB f1 score: 0.438
GB cohens kappa score: 0.400

-> test with 'KNN'
KNN tn, fp: 212, 120
KNN fn, tp: 0, 14
KNN f1 score: 0.189
KNN cohens kappa score: 0.125


------ Step 2/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1272 synthetic samples
-> test with 'LR'
LR tn, fp: 189, 142
LR fn, tp: 3, 10
LR f1 score: 0.121
LR cohens kappa score: 0.055
LR average precision score: 0.069

-> test with 'RF'
RF tn, fp: 306, 25
RF fn, tp: 0, 13
RF f1 score: 0.510
RF cohens kappa score: 0.481

-> test with 'GB'
GB tn, fp: 306, 25
GB fn, tp: 0, 13
GB f1 score: 0.510
GB cohens kappa score: 0.481

-> test with 'KNN'
KNN tn, fp: 239, 92
KNN fn, tp: 0, 13
KNN f1 score: 0.220
KNN cohens kappa score: 0.164


====== 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: 171, 161
LR fn, tp: 3, 11
LR f1 score: 0.118
LR cohens kappa score: 0.047
LR average precision score: 0.063

-> test with 'RF'
RF tn, fp: 306, 26
RF fn, tp: 0, 14
RF f1 score: 0.519
RF cohens kappa score: 0.488

-> test with 'GB'
GB tn, fp: 306, 26
GB fn, tp: 0, 14
GB f1 score: 0.519
GB cohens kappa score: 0.488

-> test with 'KNN'
KNN tn, fp: 226, 106
KNN fn, tp: 0, 14
KNN f1 score: 0.209
KNN cohens kappa score: 0.147


------ Step 3/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1272 synthetic samples
-> 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: 0.067

-> test with 'RF'
RF tn, fp: 312, 20
RF fn, tp: 0, 14
RF f1 score: 0.583
RF cohens kappa score: 0.558

-> test with 'GB'
GB tn, fp: 312, 20
GB fn, tp: 0, 14
GB f1 score: 0.583
GB cohens kappa score: 0.558

-> test with 'KNN'
KNN tn, fp: 240, 92
KNN fn, tp: 0, 14
KNN f1 score: 0.233
KNN cohens kappa score: 0.174


------ Step 3/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1272 synthetic samples
-> test with 'LR'
LR tn, fp: 164, 168
LR fn, tp: 5, 9
LR f1 score: 0.094
LR cohens kappa score: 0.021
LR average precision score: 0.057

-> test with 'RF'
RF tn, fp: 308, 24
RF fn, tp: 0, 14
RF f1 score: 0.538
RF cohens kappa score: 0.509

-> test with 'GB'
GB tn, fp: 308, 24
GB fn, tp: 0, 14
GB f1 score: 0.538
GB cohens kappa score: 0.509

-> test with 'KNN'
KNN tn, fp: 243, 89
KNN fn, tp: 0, 14
KNN f1 score: 0.239
KNN cohens kappa score: 0.181


------ Step 3/5: Slice 4/5 -------
-> 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: 0.081

-> test with 'RF'
RF tn, fp: 305, 27
RF fn, tp: 0, 14
RF f1 score: 0.509
RF cohens kappa score: 0.478

-> test with 'GB'
GB tn, fp: 305, 27
GB fn, tp: 0, 14
GB f1 score: 0.509
GB cohens kappa score: 0.478

-> test with 'KNN'
KNN tn, fp: 244, 88
KNN fn, tp: 0, 14
KNN f1 score: 0.241
KNN cohens kappa score: 0.183


------ Step 3/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1272 synthetic samples
-> test with 'LR'
LR tn, fp: 172, 159
LR fn, tp: 5, 8
LR f1 score: 0.089
LR cohens kappa score: 0.020
LR average precision score: 0.058

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

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

-> test with 'KNN'
KNN tn, fp: 212, 119
KNN fn, tp: 0, 13
KNN f1 score: 0.179
KNN cohens kappa score: 0.119


====== 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: 195, 137
LR fn, tp: 6, 8
LR f1 score: 0.101
LR cohens kappa score: 0.029
LR average precision score: 0.079

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

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

-> test with 'KNN'
KNN tn, fp: 241, 91
KNN fn, tp: 0, 14
KNN f1 score: 0.235
KNN cohens kappa score: 0.176


------ Step 4/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1272 synthetic samples
-> test with 'LR'
LR tn, fp: 171, 161
LR fn, tp: 4, 10
LR f1 score: 0.108
LR cohens kappa score: 0.036
LR average precision score: 0.063

-> test with 'RF'
RF tn, fp: 304, 28
RF fn, tp: 0, 14
RF f1 score: 0.500
RF cohens kappa score: 0.468

-> test with 'GB'
GB tn, fp: 304, 28
GB fn, tp: 0, 14
GB f1 score: 0.500
GB cohens kappa score: 0.468

-> test with 'KNN'
KNN tn, fp: 224, 108
KNN fn, tp: 0, 14
KNN f1 score: 0.206
KNN cohens kappa score: 0.144


------ Step 4/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1272 synthetic samples
-> 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: 0.082

-> test with 'RF'
RF tn, fp: 304, 28
RF fn, tp: 0, 14
RF f1 score: 0.500
RF cohens kappa score: 0.468

-> test with 'GB'
GB tn, fp: 304, 28
GB fn, tp: 0, 14
GB f1 score: 0.500
GB cohens kappa score: 0.468

-> test with 'KNN'
KNN tn, fp: 233, 99
KNN fn, tp: 0, 14
KNN f1 score: 0.220
KNN cohens kappa score: 0.160


------ Step 4/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1272 synthetic samples
-> 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: 0.053

-> test with 'RF'
RF tn, fp: 308, 24
RF fn, tp: 0, 14
RF f1 score: 0.538
RF cohens kappa score: 0.509

-> test with 'GB'
GB tn, fp: 308, 24
GB fn, tp: 0, 14
GB f1 score: 0.538
GB cohens kappa score: 0.509

-> test with 'KNN'
KNN tn, fp: 241, 91
KNN fn, tp: 0, 14
KNN f1 score: 0.235
KNN cohens kappa score: 0.176


------ Step 4/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1272 synthetic samples
-> test with 'LR'
LR tn, fp: 178, 153
LR fn, tp: 2, 11
LR f1 score: 0.124
LR cohens kappa score: 0.058
LR average precision score: 0.094

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

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

-> test with 'KNN'
KNN tn, fp: 220, 111
KNN fn, tp: 0, 13
KNN f1 score: 0.190
KNN cohens kappa score: 0.130


====== 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: 174, 158
LR fn, tp: 4, 10
LR f1 score: 0.110
LR cohens kappa score: 0.038
LR average precision score: 0.056

-> test with 'RF'
RF tn, fp: 303, 29
RF fn, tp: 0, 14
RF f1 score: 0.491
RF cohens kappa score: 0.458

-> test with 'GB'
GB tn, fp: 303, 29
GB fn, tp: 0, 14
GB f1 score: 0.491
GB cohens kappa score: 0.458

-> test with 'KNN'
KNN tn, fp: 236, 96
KNN fn, tp: 0, 14
KNN f1 score: 0.226
KNN cohens kappa score: 0.166


------ Step 5/5: 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: 4, 10
LR f1 score: 0.117
LR cohens kappa score: 0.046
LR average precision score: 0.068

-> test with 'RF'
RF tn, fp: 314, 18
RF fn, tp: 0, 14
RF f1 score: 0.609
RF cohens kappa score: 0.585

-> test with 'GB'
GB tn, fp: 314, 18
GB fn, tp: 0, 14
GB f1 score: 0.609
GB cohens kappa score: 0.585

-> test with 'KNN'
KNN tn, fp: 238, 94
KNN fn, tp: 0, 14
KNN f1 score: 0.230
KNN cohens kappa score: 0.170


------ Step 5/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1272 synthetic samples
-> 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: 0.091

-> test with 'RF'
RF tn, fp: 301, 31
RF fn, tp: 0, 14
RF f1 score: 0.475
RF cohens kappa score: 0.440

-> test with 'GB'
GB tn, fp: 301, 31
GB fn, tp: 0, 14
GB f1 score: 0.475
GB cohens kappa score: 0.440

-> test with 'KNN'
KNN tn, fp: 223, 109
KNN fn, tp: 0, 14
KNN f1 score: 0.204
KNN cohens kappa score: 0.142


------ Step 5/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1272 synthetic samples
-> 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: 0.063

-> test with 'RF'
RF tn, fp: 309, 23
RF fn, tp: 0, 14
RF f1 score: 0.549
RF cohens kappa score: 0.521

-> test with 'GB'
GB tn, fp: 309, 23
GB fn, tp: 0, 14
GB f1 score: 0.549
GB cohens kappa score: 0.521

-> test with 'KNN'
KNN tn, fp: 235, 97
KNN fn, tp: 0, 14
KNN f1 score: 0.224
KNN cohens kappa score: 0.164


------ Step 5/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1272 synthetic samples
-> test with 'LR'
LR tn, fp: 169, 162
LR fn, tp: 4, 9
LR f1 score: 0.098
LR cohens kappa score: 0.030
LR average precision score: 0.104

-> test with 'RF'
RF tn, fp: 309, 22
RF fn, tp: 0, 13
RF f1 score: 0.542
RF cohens kappa score: 0.515

-> test with 'GB'
GB tn, fp: 309, 22
GB fn, tp: 0, 13
GB f1 score: 0.542
GB cohens kappa score: 0.515

-> test with 'KNN'
KNN tn, fp: 235, 96
KNN fn, tp: 0, 13
KNN f1 score: 0.213
KNN cohens kappa score: 0.156

### Exercise is done.

-----[ LR ]-----
maximum:
LR tn, fp: 195, 183
LR fn, tp: 7, 13
LR f1 score: 0.133
LR cohens kappa score: 0.063
LR average precision score: 0.104


average:
LR tn, fp: 175.32, 156.48
LR fn, tp: 3.96, 9.84
LR f1 score: 0.109
LR cohens kappa score: 0.038
LR average precision score: 0.069


minimum:
LR tn, fp: 149, 137
LR fn, tp: 1, 7
LR f1 score: 0.080
LR cohens kappa score: 0.007
LR average precision score: 0.050


-----[ RF ]-----
maximum:
RF tn, fp: 316, 36
RF fn, tp: 0, 14
RF f1 score: 0.636
RF cohens kappa score: 0.615


average:
RF tn, fp: 307.2, 24.6
RF fn, tp: 0.0, 13.8
RF f1 score: 0.533
RF cohens kappa score: 0.504


minimum:
RF tn, fp: 296, 16
RF fn, tp: 0, 13
RF f1 score: 0.438
RF cohens kappa score: 0.400


-----[ GB ]-----
maximum:
GB tn, fp: 316, 36
GB fn, tp: 0, 14
GB f1 score: 0.636
GB cohens kappa score: 0.615


average:
GB tn, fp: 307.2, 24.6
GB fn, tp: 0.0, 13.8
GB f1 score: 0.533
GB cohens kappa score: 0.504


minimum:
GB tn, fp: 296, 16
GB fn, tp: 0, 13
GB f1 score: 0.438
GB cohens kappa score: 0.400


-----[ KNN ]-----
maximum:
KNN tn, fp: 246, 120
KNN fn, tp: 0, 14
KNN f1 score: 0.246
KNN cohens kappa score: 0.188


average:
KNN tn, fp: 233.2, 98.6
KNN fn, tp: 0.0, 13.8
KNN f1 score: 0.220
KNN cohens kappa score: 0.160


minimum:
KNN tn, fp: 212, 86
KNN fn, tp: 0, 13
KNN f1 score: 0.179
KNN cohens kappa score: 0.119