Added benchmark results for ctGAN.

data_result/ctGAN/folding_abalone9-18.csv

@@ -0,0 +1,92 @@
+LR
+Nr.;TN;TP;FN;FP;f1 score;cohens kappa score;average precision score
+1;132.000;5.000;4.000;6.000;0.500;0.464;0.554
+2;136.000;2.000;7.000;2.000;0.308;0.281;0.363
+3;130.000;1.000;8.000;8.000;0.111;0.053;0.072
+4;133.000;4.000;5.000;5.000;0.444;0.408;0.390
+5;134.000;1.000;5.000;3.000;0.200;0.172;0.159
+6;136.000;3.000;6.000;2.000;0.429;0.402;0.470
+7;125.000;2.000;7.000;13.000;0.167;0.098;0.234
+8;131.000;1.000;8.000;7.000;0.118;0.064;0.089
+9;128.000;6.000;3.000;10.000;0.480;0.436;0.651
+10;126.000;3.000;3.000;11.000;0.300;0.256;0.205
+11;132.000;3.000;6.000;6.000;0.333;0.290;0.398
+12;123.000;2.000;7.000;15.000;0.154;0.080;0.103
+13;137.000;4.000;5.000;1.000;0.571;0.552;0.617
+14;131.000;5.000;4.000;7.000;0.476;0.437;0.584
+15;120.000;1.000;5.000;17.000;0.083;0.022;0.224
+16;131.000;4.000;5.000;7.000;0.400;0.357;0.514
+17;119.000;5.000;4.000;19.000;0.303;0.235;0.394
+18;137.000;4.000;5.000;1.000;0.571;0.552;0.580
+19;111.000;4.000;5.000;27.000;0.200;0.116;0.204
+20;131.000;3.000;3.000;6.000;0.400;0.368;0.361
+21;124.000;0.000;9.000;14.000;0.000;-0.081;0.071
+22;136.000;4.000;5.000;2.000;0.533;0.509;0.588
+23;96.000;7.000;2.000;42.000;0.241;0.154;0.306
+24;127.000;4.000;5.000;11.000;0.333;0.278;0.408
+25;126.000;2.000;4.000;11.000;0.211;0.162;0.304
+max;137.000;7.000;9.000;42.000;0.571;0.552;0.651
+avg;127.680;3.200;5.200;10.120;0.315;0.267;0.354
+min;96.000;0.000;2.000;1.000;0.000;-0.081;0.071
+---
+GB
+Nr.;TN;TP;FN;FP;f1 score;cohens kappa score
+1;134.000;5.000;4.000;4.000;0.556;0.527
+2;135.000;3.000;6.000;3.000;0.400;0.369
+3;135.000;2.000;7.000;3.000;0.286;0.253
+4;137.000;2.000;7.000;1.000;0.333;0.312
+5;137.000;0.000;6.000;0.000;0.000;0.000
+6;137.000;2.000;7.000;1.000;0.333;0.312
+7;133.000;3.000;6.000;5.000;0.353;0.313
+8;134.000;2.000;7.000;4.000;0.267;0.229
+9;135.000;4.000;5.000;3.000;0.500;0.472
+10;129.000;2.000;4.000;8.000;0.250;0.208
+11;135.000;1.000;8.000;3.000;0.154;0.121
+12;133.000;3.000;6.000;5.000;0.353;0.313
+13;136.000;2.000;7.000;2.000;0.308;0.281
+14;133.000;5.000;4.000;5.000;0.526;0.494
+15;133.000;1.000;5.000;4.000;0.182;0.149
+16;135.000;3.000;6.000;3.000;0.400;0.369
+17;133.000;3.000;6.000;5.000;0.353;0.313
+18;133.000;3.000;6.000;5.000;0.353;0.313
+19;136.000;2.000;7.000;2.000;0.308;0.281
+20;130.000;1.000;5.000;7.000;0.143;0.100
+21;129.000;1.000;8.000;9.000;0.105;0.044
+22;135.000;2.000;7.000;3.000;0.286;0.253
+23;133.000;2.000;7.000;5.000;0.250;0.208
+24;136.000;3.000;6.000;2.000;0.429;0.402
+25;137.000;3.000;3.000;0.000;0.667;0.657
+max;137.000;5.000;8.000;9.000;0.667;0.657
+avg;134.120;2.400;6.000;3.680;0.324;0.292
+min;129.000;0.000;3.000;0.000;0.000;0.000
+---
+KNN
+Nr.;TN;TP;FN;FP;f1 score;cohens kappa score
+1;138.000;0.000;9.000;0.000;0.000;0.000
+2;135.000;1.000;8.000;3.000;0.154;0.121
+3;138.000;1.000;8.000;0.000;0.200;0.190
+4;137.000;2.000;7.000;1.000;0.333;0.312
+5;137.000;1.000;5.000;0.000;0.286;0.277
+6;137.000;1.000;8.000;1.000;0.182;0.163
+7;138.000;1.000;8.000;0.000;0.200;0.190
+8;135.000;2.000;7.000;3.000;0.286;0.253
+9;137.000;2.000;7.000;1.000;0.333;0.312
+10;137.000;1.000;5.000;0.000;0.286;0.277
+11;138.000;1.000;8.000;0.000;0.200;0.190
+12;136.000;0.000;9.000;2.000;0.000;-0.023
+13;137.000;0.000;9.000;1.000;0.000;-0.012
+14;138.000;3.000;6.000;0.000;0.500;0.484
+15;133.000;1.000;5.000;4.000;0.182;0.149
+16;138.000;2.000;7.000;0.000;0.364;0.349
+17;137.000;2.000;7.000;1.000;0.333;0.312
+18;138.000;0.000;9.000;0.000;0.000;0.000
+19;136.000;1.000;8.000;2.000;0.167;0.140
+20;136.000;1.000;5.000;1.000;0.250;0.234
+21;134.000;1.000;8.000;4.000;0.143;0.104
+22;138.000;0.000;9.000;0.000;0.000;0.000
+23;135.000;2.000;7.000;3.000;0.286;0.253
+24;138.000;2.000;7.000;0.000;0.364;0.349
+25;137.000;1.000;5.000;0.000;0.286;0.277
+max;138.000;3.000;9.000;4.000;0.500;0.484
+avg;136.720;1.160;7.240;1.080;0.213;0.196
+min;133.000;0.000;5.000;0.000;0.000;-0.023

data_result/ctGAN/folding_abalone9-18.log

@@ -0,0 +1,701 @@
+
+
+///////////////////////////////////////////
+// Running ctGAN on folding_abalone9-18
+///////////////////////////////////////////
+
+Load 'data_input/folding_abalone9-18'
+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 518 synthetic samples
+-> test with 'LR'
+LR tn, fp: 132, 6
+LR fn, tp: 4, 5
+LR f1 score: 0.500
+LR cohens kappa score: 0.464
+LR average precision score: 0.554
+
+-> test with 'GB'
+GB tn, fp: 134, 4
+GB fn, tp: 4, 5
+GB f1 score: 0.556
+GB cohens kappa score: 0.527
+
+-> test with 'KNN'
+KNN tn, fp: 138, 0
+KNN fn, tp: 9, 0
+KNN f1 score: 0.000
+KNN cohens kappa score: 0.000
+
+
+------ Step 1/5: Slice 2/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 518 synthetic samples
+-> test with 'LR'
+LR tn, fp: 136, 2
+LR fn, tp: 7, 2
+LR f1 score: 0.308
+LR cohens kappa score: 0.281
+LR average precision score: 0.363
+
+-> test with 'GB'
+GB tn, fp: 135, 3
+GB fn, tp: 6, 3
+GB f1 score: 0.400
+GB cohens kappa score: 0.369
+
+-> test with 'KNN'
+KNN tn, fp: 135, 3
+KNN fn, tp: 8, 1
+KNN f1 score: 0.154
+KNN cohens kappa score: 0.121
+
+
+------ Step 1/5: Slice 3/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 518 synthetic samples
+-> test with 'LR'
+LR tn, fp: 130, 8
+LR fn, tp: 8, 1
+LR f1 score: 0.111
+LR cohens kappa score: 0.053
+LR average precision score: 0.072
+
+-> test with 'GB'
+GB tn, fp: 135, 3
+GB fn, tp: 7, 2
+GB f1 score: 0.286
+GB cohens kappa score: 0.253
+
+-> test with 'KNN'
+KNN tn, fp: 138, 0
+KNN fn, tp: 8, 1
+KNN f1 score: 0.200
+KNN cohens kappa score: 0.190
+
+
+------ Step 1/5: Slice 4/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 518 synthetic samples
+-> test with 'LR'
+LR tn, fp: 133, 5
+LR fn, tp: 5, 4
+LR f1 score: 0.444
+LR cohens kappa score: 0.408
+LR average precision score: 0.390
+
+-> test with 'GB'
+GB tn, fp: 137, 1
+GB fn, tp: 7, 2
+GB f1 score: 0.333
+GB cohens kappa score: 0.312
+
+-> test with 'KNN'
+KNN tn, fp: 137, 1
+KNN fn, tp: 7, 2
+KNN f1 score: 0.333
+KNN cohens kappa score: 0.312
+
+
+------ Step 1/5: Slice 5/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 516 synthetic samples
+-> test with 'LR'
+LR tn, fp: 134, 3
+LR fn, tp: 5, 1
+LR f1 score: 0.200
+LR cohens kappa score: 0.172
+LR average precision score: 0.159
+
+-> test with 'GB'
+GB tn, fp: 137, 0
+GB fn, tp: 6, 0
+GB f1 score: 0.000
+GB cohens kappa score: 0.000
+
+-> test with 'KNN'
+KNN tn, fp: 137, 0
+KNN fn, tp: 5, 1
+KNN f1 score: 0.286
+KNN cohens kappa score: 0.277
+
+
+====== Step 2/5 =======
+-> Shuffling data
+-> Spliting data to slices
+
+------ Step 2/5: Slice 1/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 518 synthetic samples
+-> test with 'LR'
+LR tn, fp: 136, 2
+LR fn, tp: 6, 3
+LR f1 score: 0.429
+LR cohens kappa score: 0.402
+LR average precision score: 0.470
+
+-> test with 'GB'
+GB tn, fp: 137, 1
+GB fn, tp: 7, 2
+GB f1 score: 0.333
+GB cohens kappa score: 0.312
+
+-> test with 'KNN'
+KNN tn, fp: 137, 1
+KNN fn, tp: 8, 1
+KNN f1 score: 0.182
+KNN cohens kappa score: 0.163
+
+
+------ Step 2/5: Slice 2/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 518 synthetic samples
+-> test with 'LR'
+LR tn, fp: 125, 13
+LR fn, tp: 7, 2
+LR f1 score: 0.167
+LR cohens kappa score: 0.098
+LR average precision score: 0.234
+
+-> test with 'GB'
+GB tn, fp: 133, 5
+GB fn, tp: 6, 3
+GB f1 score: 0.353
+GB cohens kappa score: 0.313
+
+-> test with 'KNN'
+KNN tn, fp: 138, 0
+KNN fn, tp: 8, 1
+KNN f1 score: 0.200
+KNN cohens kappa score: 0.190
+
+
+------ Step 2/5: Slice 3/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 518 synthetic samples
+-> test with 'LR'
+LR tn, fp: 131, 7
+LR fn, tp: 8, 1
+LR f1 score: 0.118
+LR cohens kappa score: 0.064
+LR average precision score: 0.089
+
+-> test with 'GB'
+GB tn, fp: 134, 4
+GB fn, tp: 7, 2
+GB f1 score: 0.267
+GB cohens kappa score: 0.229
+
+-> test with 'KNN'
+KNN tn, fp: 135, 3
+KNN fn, tp: 7, 2
+KNN f1 score: 0.286
+KNN cohens kappa score: 0.253
+
+
+------ Step 2/5: Slice 4/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 518 synthetic samples
+-> test with 'LR'
+LR tn, fp: 128, 10
+LR fn, tp: 3, 6
+LR f1 score: 0.480
+LR cohens kappa score: 0.436
+LR average precision score: 0.651
+
+-> test with 'GB'
+GB tn, fp: 135, 3
+GB fn, tp: 5, 4
+GB f1 score: 0.500
+GB cohens kappa score: 0.472
+
+-> test with 'KNN'
+KNN tn, fp: 137, 1
+KNN fn, tp: 7, 2
+KNN f1 score: 0.333
+KNN cohens kappa score: 0.312
+
+
+------ Step 2/5: Slice 5/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 516 synthetic samples
+-> test with 'LR'
+LR tn, fp: 126, 11
+LR fn, tp: 3, 3
+LR f1 score: 0.300
+LR cohens kappa score: 0.256
+LR average precision score: 0.205
+
+-> test with 'GB'
+GB tn, fp: 129, 8
+GB fn, tp: 4, 2
+GB f1 score: 0.250
+GB cohens kappa score: 0.208
+
+-> test with 'KNN'
+KNN tn, fp: 137, 0
+KNN fn, tp: 5, 1
+KNN f1 score: 0.286
+KNN cohens kappa score: 0.277
+
+
+====== Step 3/5 =======
+-> Shuffling data
+-> Spliting data to slices
+
+------ Step 3/5: Slice 1/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 518 synthetic samples
+-> test with 'LR'
+LR tn, fp: 132, 6
+LR fn, tp: 6, 3
+LR f1 score: 0.333
+LR cohens kappa score: 0.290
+LR average precision score: 0.398
+
+-> test with 'GB'
+GB tn, fp: 135, 3
+GB fn, tp: 8, 1
+GB f1 score: 0.154
+GB cohens kappa score: 0.121
+
+-> test with 'KNN'
+KNN tn, fp: 138, 0
+KNN fn, tp: 8, 1
+KNN f1 score: 0.200
+KNN cohens kappa score: 0.190
+
+
+------ Step 3/5: Slice 2/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 518 synthetic samples
+-> test with 'LR'
+LR tn, fp: 123, 15
+LR fn, tp: 7, 2
+LR f1 score: 0.154
+LR cohens kappa score: 0.080
+LR average precision score: 0.103
+
+-> test with 'GB'
+GB tn, fp: 133, 5
+GB fn, tp: 6, 3
+GB f1 score: 0.353
+GB cohens kappa score: 0.313
+
+-> test with 'KNN'
+KNN tn, fp: 136, 2
+KNN fn, tp: 9, 0
+KNN f1 score: 0.000
+KNN cohens kappa score: -0.023
+
+
+------ Step 3/5: Slice 3/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 518 synthetic samples
+-> test with 'LR'
+LR tn, fp: 137, 1
+LR fn, tp: 5, 4
+LR f1 score: 0.571
+LR cohens kappa score: 0.552
+LR average precision score: 0.617
+
+-> test with 'GB'
+GB tn, fp: 136, 2
+GB fn, tp: 7, 2
+GB f1 score: 0.308
+GB cohens kappa score: 0.281
+
+-> test with 'KNN'
+KNN tn, fp: 137, 1
+KNN fn, tp: 9, 0
+KNN f1 score: 0.000
+KNN cohens kappa score: -0.012
+
+
+------ Step 3/5: Slice 4/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 518 synthetic samples
+-> test with 'LR'
+LR tn, fp: 131, 7
+LR fn, tp: 4, 5
+LR f1 score: 0.476
+LR cohens kappa score: 0.437
+LR average precision score: 0.584
+
+-> test with 'GB'
+GB tn, fp: 133, 5
+GB fn, tp: 4, 5
+GB f1 score: 0.526
+GB cohens kappa score: 0.494
+
+-> test with 'KNN'
+KNN tn, fp: 138, 0
+KNN fn, tp: 6, 3
+KNN f1 score: 0.500
+KNN cohens kappa score: 0.484
+
+
+------ Step 3/5: Slice 5/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 516 synthetic samples
+-> test with 'LR'
+LR tn, fp: 120, 17
+LR fn, tp: 5, 1
+LR f1 score: 0.083
+LR cohens kappa score: 0.022
+LR average precision score: 0.224
+
+-> test with 'GB'
+GB tn, fp: 133, 4
+GB fn, tp: 5, 1
+GB f1 score: 0.182
+GB cohens kappa score: 0.149
+
+-> test with 'KNN'
+KNN tn, fp: 133, 4
+KNN fn, tp: 5, 1
+KNN f1 score: 0.182
+KNN cohens kappa score: 0.149
+
+
+====== Step 4/5 =======
+-> Shuffling data
+-> Spliting data to slices
+
+------ Step 4/5: Slice 1/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 518 synthetic samples
+-> test with 'LR'
+LR tn, fp: 131, 7
+LR fn, tp: 5, 4
+LR f1 score: 0.400
+LR cohens kappa score: 0.357
+LR average precision score: 0.514
+
+-> test with 'GB'
+GB tn, fp: 135, 3
+GB fn, tp: 6, 3
+GB f1 score: 0.400
+GB cohens kappa score: 0.369
+
+-> test with 'KNN'
+KNN tn, fp: 138, 0
+KNN fn, tp: 7, 2
+KNN f1 score: 0.364
+KNN cohens kappa score: 0.349
+
+
+------ Step 4/5: Slice 2/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 518 synthetic samples
+-> test with 'LR'
+LR tn, fp: 119, 19
+LR fn, tp: 4, 5
+LR f1 score: 0.303
+LR cohens kappa score: 0.235
+LR average precision score: 0.394
+
+-> test with 'GB'
+GB tn, fp: 133, 5
+GB fn, tp: 6, 3
+GB f1 score: 0.353
+GB cohens kappa score: 0.313
+
+-> test with 'KNN'
+KNN tn, fp: 137, 1
+KNN fn, tp: 7, 2
+KNN f1 score: 0.333
+KNN cohens kappa score: 0.312
+
+
+------ Step 4/5: Slice 3/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 518 synthetic samples
+-> test with 'LR'
+LR tn, fp: 137, 1
+LR fn, tp: 5, 4
+LR f1 score: 0.571
+LR cohens kappa score: 0.552
+LR average precision score: 0.580
+
+-> test with 'GB'
+GB tn, fp: 133, 5
+GB fn, tp: 6, 3
+GB f1 score: 0.353
+GB cohens kappa score: 0.313
+
+-> test with 'KNN'
+KNN tn, fp: 138, 0
+KNN fn, tp: 9, 0
+KNN f1 score: 0.000
+KNN cohens kappa score: 0.000
+
+
+------ Step 4/5: Slice 4/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 518 synthetic samples
+-> test with 'LR'
+LR tn, fp: 111, 27
+LR fn, tp: 5, 4
+LR f1 score: 0.200
+LR cohens kappa score: 0.116
+LR average precision score: 0.204
+
+-> test with 'GB'
+GB tn, fp: 136, 2
+GB fn, tp: 7, 2
+GB f1 score: 0.308
+GB cohens kappa score: 0.281
+
+-> test with 'KNN'
+KNN tn, fp: 136, 2
+KNN fn, tp: 8, 1
+KNN f1 score: 0.167
+KNN cohens kappa score: 0.140
+
+
+------ Step 4/5: Slice 5/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 516 synthetic samples
+-> test with 'LR'
+LR tn, fp: 131, 6
+LR fn, tp: 3, 3
+LR f1 score: 0.400
+LR cohens kappa score: 0.368
+LR average precision score: 0.361
+
+-> test with 'GB'
+GB tn, fp: 130, 7
+GB fn, tp: 5, 1
+GB f1 score: 0.143
+GB cohens kappa score: 0.100
+
+-> test with 'KNN'
+KNN tn, fp: 136, 1
+KNN fn, tp: 5, 1
+KNN f1 score: 0.250
+KNN cohens kappa score: 0.234
+
+
+====== Step 5/5 =======
+-> Shuffling data
+-> Spliting data to slices
+
+------ Step 5/5: Slice 1/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 518 synthetic samples
+-> test with 'LR'
+LR tn, fp: 124, 14
+LR fn, tp: 9, 0
+LR f1 score: 0.000
+LR cohens kappa score: -0.081
+LR average precision score: 0.071
+
+-> test with 'GB'
+GB tn, fp: 129, 9
+GB fn, tp: 8, 1
+GB f1 score: 0.105
+GB cohens kappa score: 0.044
+
+-> test with 'KNN'
+KNN tn, fp: 134, 4
+KNN fn, tp: 8, 1
+KNN f1 score: 0.143
+KNN cohens kappa score: 0.104
+
+
+------ Step 5/5: Slice 2/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 518 synthetic samples
+-> test with 'LR'
+LR tn, fp: 136, 2
+LR fn, tp: 5, 4
+LR f1 score: 0.533
+LR cohens kappa score: 0.509
+LR average precision score: 0.588
+
+-> test with 'GB'
+GB tn, fp: 135, 3
+GB fn, tp: 7, 2
+GB f1 score: 0.286
+GB cohens kappa score: 0.253
+
+-> test with 'KNN'
+KNN tn, fp: 138, 0
+KNN fn, tp: 9, 0
+KNN f1 score: 0.000
+KNN cohens kappa score: 0.000
+
+
+------ Step 5/5: Slice 3/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 518 synthetic samples
+-> test with 'LR'
+LR tn, fp: 96, 42
+LR fn, tp: 2, 7
+LR f1 score: 0.241
+LR cohens kappa score: 0.154
+LR average precision score: 0.306
+
+-> test with 'GB'
+GB tn, fp: 133, 5
+GB fn, tp: 7, 2
+GB f1 score: 0.250
+GB cohens kappa score: 0.208
+
+-> test with 'KNN'
+KNN tn, fp: 135, 3
+KNN fn, tp: 7, 2
+KNN f1 score: 0.286
+KNN cohens kappa score: 0.253
+
+
+------ Step 5/5: Slice 4/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 518 synthetic samples
+-> test with 'LR'
+LR tn, fp: 127, 11
+LR fn, tp: 5, 4
+LR f1 score: 0.333
+LR cohens kappa score: 0.278
+LR average precision score: 0.408
+
+-> test with 'GB'
+GB tn, fp: 136, 2
+GB fn, tp: 6, 3
+GB f1 score: 0.429
+GB cohens kappa score: 0.402
+
+-> test with 'KNN'
+KNN tn, fp: 138, 0
+KNN fn, tp: 7, 2
+KNN f1 score: 0.364
+KNN cohens kappa score: 0.349
+
+
+------ Step 5/5: Slice 5/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 516 synthetic samples
+-> test with 'LR'
+LR tn, fp: 126, 11
+LR fn, tp: 4, 2
+LR f1 score: 0.211
+LR cohens kappa score: 0.162
+LR average precision score: 0.304
+
+-> test with 'GB'
+GB tn, fp: 137, 0
+GB fn, tp: 3, 3
+GB f1 score: 0.667
+GB cohens kappa score: 0.657
+
+-> test with 'KNN'
+KNN tn, fp: 137, 0
+KNN fn, tp: 5, 1
+KNN f1 score: 0.286
+KNN cohens kappa score: 0.277
+
+### Exercise is done.
+
+-----[ LR ]-----
+maximum:
+LR tn, fp: 137, 42
+LR fn, tp: 9, 7
+LR f1 score: 0.571
+LR cohens kappa score: 0.552
+LR average precision score: 0.651
+
+
+average:
+LR tn, fp: 127.68, 10.12
+LR fn, tp: 5.2, 3.2
+LR f1 score: 0.315
+LR cohens kappa score: 0.267
+LR average precision score: 0.354
+
+
+minimum:
+LR tn, fp: 96, 1
+LR fn, tp: 2, 0
+LR f1 score: 0.000
+LR cohens kappa score: -0.081
+LR average precision score: 0.071
+
+
+-----[ GB ]-----
+maximum:
+GB tn, fp: 137, 9
+GB fn, tp: 8, 5
+GB f1 score: 0.667
+GB cohens kappa score: 0.657
+
+
+average:
+GB tn, fp: 134.12, 3.68
+GB fn, tp: 6.0, 2.4
+GB f1 score: 0.324
+GB cohens kappa score: 0.292
+
+
+minimum:
+GB tn, fp: 129, 0
+GB fn, tp: 3, 0
+GB f1 score: 0.000
+GB cohens kappa score: 0.000
+
+
+-----[ KNN ]-----
+maximum:
+KNN tn, fp: 138, 4
+KNN fn, tp: 9, 3
+KNN f1 score: 0.500
+KNN cohens kappa score: 0.484
+
+
+average:
+KNN tn, fp: 136.72, 1.08
+KNN fn, tp: 7.24, 1.16
+KNN f1 score: 0.213
+KNN cohens kappa score: 0.196
+
+
+minimum:
+KNN tn, fp: 133, 0
+KNN fn, tp: 5, 0
+KNN f1 score: 0.000
+KNN cohens kappa score: -0.023
+

data_result/ctGAN/folding_abalone_17_vs_7_8_9_10.csv

@@ -0,0 +1,92 @@
+LR
+Nr.;TN;TP;FN;FP;f1 score;cohens kappa score;average precision score
+1;420.000;8.000;4.000;36.000;0.286;0.256;0.403
+2;434.000;5.000;7.000;22.000;0.256;0.229;0.275
+3;392.000;2.000;10.000;64.000;0.051;0.008;0.056
+4;343.000;4.000;8.000;113.000;0.062;0.016;0.170
+5;361.000;7.000;3.000;95.000;0.125;0.089;0.164
+6;440.000;0.000;12.000;16.000;0.000;-0.030;0.033
+7;426.000;7.000;5.000;30.000;0.286;0.257;0.286
+8;452.000;2.000;10.000;4.000;0.222;0.209;0.175
+9;376.000;6.000;6.000;80.000;0.122;0.081;0.100
+10;420.000;3.000;7.000;36.000;0.122;0.091;0.265
+11;439.000;4.000;8.000;17.000;0.242;0.217;0.247
+12;433.000;3.000;9.000;23.000;0.158;0.127;0.203
+13;379.000;5.000;7.000;77.000;0.106;0.065;0.072
+14;418.000;6.000;6.000;38.000;0.214;0.181;0.174
+15;433.000;4.000;6.000;23.000;0.216;0.191;0.141
+16;443.000;6.000;6.000;13.000;0.387;0.367;0.335
+17;447.000;6.000;6.000;9.000;0.444;0.428;0.451
+18;410.000;5.000;7.000;46.000;0.159;0.122;0.192
+19;444.000;2.000;10.000;12.000;0.154;0.130;0.223
+20;407.000;3.000;7.000;49.000;0.097;0.063;0.095
+21;374.000;5.000;7.000;82.000;0.101;0.059;0.057
+22;423.000;0.000;12.000;33.000;0.000;-0.039;0.038
+23;430.000;3.000;9.000;26.000;0.146;0.114;0.119
+24;419.000;6.000;6.000;37.000;0.218;0.186;0.199
+25;434.000;2.000;8.000;22.000;0.118;0.090;0.049
+max;452.000;8.000;12.000;113.000;0.444;0.428;0.451
+avg;415.880;4.160;7.440;40.120;0.172;0.140;0.181
+min;343.000;0.000;3.000;4.000;0.000;-0.039;0.033
+---
+GB
+Nr.;TN;TP;FN;FP;f1 score;cohens kappa score
+1;450.000;2.000;10.000;6.000;0.200;0.183
+2;452.000;4.000;8.000;4.000;0.400;0.387
+3;447.000;1.000;11.000;9.000;0.091;0.069
+4;442.000;3.000;9.000;14.000;0.207;0.182
+5;451.000;1.000;9.000;5.000;0.125;0.111
+6;453.000;3.000;9.000;3.000;0.333;0.322
+7;445.000;2.000;10.000;11.000;0.160;0.137
+8;452.000;2.000;10.000;4.000;0.222;0.209
+9;454.000;3.000;9.000;2.000;0.353;0.343
+10;446.000;1.000;9.000;10.000;0.095;0.074
+11;451.000;2.000;10.000;5.000;0.211;0.195
+12;453.000;2.000;10.000;3.000;0.235;0.224
+13;449.000;3.000;9.000;7.000;0.273;0.255
+14;449.000;0.000;12.000;7.000;0.000;-0.019
+15;451.000;3.000;7.000;5.000;0.333;0.320
+16;452.000;2.000;10.000;4.000;0.222;0.209
+17;451.000;4.000;8.000;5.000;0.381;0.367
+18;452.000;0.000;12.000;4.000;0.000;-0.013
+19;451.000;2.000;10.000;5.000;0.211;0.195
+20;452.000;0.000;10.000;4.000;0.000;-0.012
+21;449.000;2.000;10.000;7.000;0.190;0.172
+22;446.000;1.000;11.000;10.000;0.087;0.064
+23;447.000;1.000;11.000;9.000;0.091;0.069
+24;453.000;2.000;10.000;3.000;0.235;0.224
+25;449.000;2.000;8.000;7.000;0.211;0.194
+max;454.000;4.000;12.000;14.000;0.400;0.387
+avg;449.880;1.920;9.680;6.120;0.195;0.178
+min;442.000;0.000;7.000;2.000;0.000;-0.019
+---
+KNN
+Nr.;TN;TP;FN;FP;f1 score;cohens kappa score
+1;455.000;3.000;9.000;1.000;0.375;0.367
+2;456.000;2.000;10.000;0.000;0.286;0.280
+3;452.000;0.000;12.000;4.000;0.000;-0.013
+4;456.000;1.000;11.000;0.000;0.154;0.150
+5;455.000;1.000;9.000;1.000;0.167;0.161
+6;454.000;2.000;10.000;2.000;0.250;0.240
+7;455.000;1.000;11.000;1.000;0.143;0.137
+8;455.000;0.000;12.000;1.000;0.000;-0.004
+9;456.000;0.000;12.000;0.000;0.000;0.000
+10;456.000;1.000;9.000;0.000;0.182;0.179
+11;456.000;1.000;11.000;0.000;0.154;0.150
+12;455.000;0.000;12.000;1.000;0.000;-0.004
+13;454.000;3.000;9.000;2.000;0.353;0.343
+14;454.000;0.000;12.000;2.000;0.000;-0.007
+15;453.000;1.000;9.000;3.000;0.143;0.132
+16;456.000;0.000;12.000;0.000;0.000;0.000
+17;455.000;0.000;12.000;1.000;0.000;-0.004
+18;456.000;0.000;12.000;0.000;0.000;0.000
+19;455.000;2.000;10.000;1.000;0.267;0.259
+20;455.000;1.000;9.000;1.000;0.167;0.161
+21;456.000;1.000;11.000;0.000;0.154;0.150
+22;454.000;0.000;12.000;2.000;0.000;-0.007
+23;455.000;2.000;10.000;1.000;0.267;0.259
+24;455.000;0.000;12.000;1.000;0.000;-0.004
+25;454.000;0.000;10.000;2.000;0.000;-0.007
+max;456.000;3.000;12.000;4.000;0.375;0.367
+avg;454.920;0.880;10.720;1.080;0.122;0.117
+min;452.000;0.000;9.000;0.000;0.000;-0.013

data_result/ctGAN/folding_abalone_17_vs_7_8_9_10.log

@@ -0,0 +1,701 @@
+
+
+///////////////////////////////////////////
+// Running ctGAN on folding_abalone_17_vs_7_8_9_10
+///////////////////////////////////////////
+
+Load 'data_input/folding_abalone_17_vs_7_8_9_10'
+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 1778 synthetic samples
+-> test with 'LR'
+LR tn, fp: 420, 36
+LR fn, tp: 4, 8
+LR f1 score: 0.286
+LR cohens kappa score: 0.256
+LR average precision score: 0.403
+
+-> test with 'GB'
+GB tn, fp: 450, 6
+GB fn, tp: 10, 2
+GB f1 score: 0.200
+GB cohens kappa score: 0.183
+
+-> test with 'KNN'
+KNN tn, fp: 455, 1
+KNN fn, tp: 9, 3
+KNN f1 score: 0.375
+KNN cohens kappa score: 0.367
+
+
+------ Step 1/5: Slice 2/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 1778 synthetic samples
+-> test with 'LR'
+LR tn, fp: 434, 22
+LR fn, tp: 7, 5
+LR f1 score: 0.256
+LR cohens kappa score: 0.229
+LR average precision score: 0.275
+
+-> test with 'GB'
+GB tn, fp: 452, 4
+GB fn, tp: 8, 4
+GB f1 score: 0.400
+GB cohens kappa score: 0.387
+
+-> test with 'KNN'
+KNN tn, fp: 456, 0
+KNN fn, tp: 10, 2
+KNN f1 score: 0.286
+KNN cohens kappa score: 0.280
+
+
+------ Step 1/5: Slice 3/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 1778 synthetic samples
+-> test with 'LR'
+LR tn, fp: 392, 64
+LR fn, tp: 10, 2
+LR f1 score: 0.051
+LR cohens kappa score: 0.008
+LR average precision score: 0.056
+
+-> test with 'GB'
+GB tn, fp: 447, 9
+GB fn, tp: 11, 1
+GB f1 score: 0.091
+GB cohens kappa score: 0.069
+
+-> test with 'KNN'
+KNN tn, fp: 452, 4
+KNN fn, tp: 12, 0
+KNN f1 score: 0.000
+KNN cohens kappa score: -0.013
+
+
+------ Step 1/5: Slice 4/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 1778 synthetic samples
+-> test with 'LR'
+LR tn, fp: 343, 113
+LR fn, tp: 8, 4
+LR f1 score: 0.062
+LR cohens kappa score: 0.016
+LR average precision score: 0.170
+
+-> test with 'GB'
+GB tn, fp: 442, 14
+GB fn, tp: 9, 3
+GB f1 score: 0.207
+GB cohens kappa score: 0.182
+
+-> test with 'KNN'
+KNN tn, fp: 456, 0
+KNN fn, tp: 11, 1
+KNN f1 score: 0.154
+KNN cohens kappa score: 0.150
+
+
+------ Step 1/5: Slice 5/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 1776 synthetic samples
+-> test with 'LR'
+LR tn, fp: 361, 95
+LR fn, tp: 3, 7
+LR f1 score: 0.125
+LR cohens kappa score: 0.089
+LR average precision score: 0.164
+
+-> test with 'GB'
+GB tn, fp: 451, 5
+GB fn, tp: 9, 1
+GB f1 score: 0.125
+GB cohens kappa score: 0.111
+
+-> test with 'KNN'
+KNN tn, fp: 455, 1
+KNN fn, tp: 9, 1
+KNN f1 score: 0.167
+KNN cohens kappa score: 0.161
+
+
+====== Step 2/5 =======
+-> Shuffling data
+-> Spliting data to slices
+
+------ Step 2/5: Slice 1/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 1778 synthetic samples
+-> test with 'LR'
+LR tn, fp: 440, 16
+LR fn, tp: 12, 0
+LR f1 score: 0.000
+LR cohens kappa score: -0.030
+LR average precision score: 0.033
+
+-> test with 'GB'
+GB tn, fp: 453, 3
+GB fn, tp: 9, 3
+GB f1 score: 0.333
+GB cohens kappa score: 0.322
+
+-> test with 'KNN'
+KNN tn, fp: 454, 2
+KNN fn, tp: 10, 2
+KNN f1 score: 0.250
+KNN cohens kappa score: 0.240
+
+
+------ Step 2/5: Slice 2/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 1778 synthetic samples
+-> test with 'LR'
+LR tn, fp: 426, 30
+LR fn, tp: 5, 7
+LR f1 score: 0.286
+LR cohens kappa score: 0.257
+LR average precision score: 0.286
+
+-> test with 'GB'
+GB tn, fp: 445, 11
+GB fn, tp: 10, 2
+GB f1 score: 0.160
+GB cohens kappa score: 0.137
+
+-> test with 'KNN'
+KNN tn, fp: 455, 1
+KNN fn, tp: 11, 1
+KNN f1 score: 0.143
+KNN cohens kappa score: 0.137
+
+
+------ Step 2/5: Slice 3/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 1778 synthetic samples
+-> test with 'LR'
+LR tn, fp: 452, 4
+LR fn, tp: 10, 2
+LR f1 score: 0.222
+LR cohens kappa score: 0.209
+LR average precision score: 0.175
+
+-> test with 'GB'
+GB tn, fp: 452, 4
+GB fn, tp: 10, 2
+GB f1 score: 0.222
+GB cohens kappa score: 0.209
+
+-> test with 'KNN'
+KNN tn, fp: 455, 1
+KNN fn, tp: 12, 0
+KNN f1 score: 0.000
+KNN cohens kappa score: -0.004
+
+
+------ Step 2/5: Slice 4/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 1778 synthetic samples
+-> test with 'LR'
+LR tn, fp: 376, 80
+LR fn, tp: 6, 6
+LR f1 score: 0.122
+LR cohens kappa score: 0.081
+LR average precision score: 0.100
+
+-> test with 'GB'
+GB tn, fp: 454, 2
+GB fn, tp: 9, 3
+GB f1 score: 0.353
+GB cohens kappa score: 0.343
+
+-> test with 'KNN'
+KNN tn, fp: 456, 0
+KNN fn, tp: 12, 0
+KNN f1 score: 0.000
+KNN cohens kappa score: 0.000
+
+
+------ Step 2/5: Slice 5/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 1776 synthetic samples
+-> test with 'LR'
+LR tn, fp: 420, 36
+LR fn, tp: 7, 3
+LR f1 score: 0.122
+LR cohens kappa score: 0.091
+LR average precision score: 0.265
+
+-> test with 'GB'
+GB tn, fp: 446, 10
+GB fn, tp: 9, 1
+GB f1 score: 0.095
+GB cohens kappa score: 0.074
+
+-> test with 'KNN'
+KNN tn, fp: 456, 0
+KNN fn, tp: 9, 1
+KNN f1 score: 0.182
+KNN cohens kappa score: 0.179
+
+
+====== Step 3/5 =======
+-> Shuffling data
+-> Spliting data to slices
+
+------ Step 3/5: Slice 1/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 1778 synthetic samples
+-> test with 'LR'
+LR tn, fp: 439, 17
+LR fn, tp: 8, 4
+LR f1 score: 0.242
+LR cohens kappa score: 0.217
+LR average precision score: 0.247
+
+-> test with 'GB'
+GB tn, fp: 451, 5
+GB fn, tp: 10, 2
+GB f1 score: 0.211
+GB cohens kappa score: 0.195
+
+-> test with 'KNN'
+KNN tn, fp: 456, 0
+KNN fn, tp: 11, 1
+KNN f1 score: 0.154
+KNN cohens kappa score: 0.150
+
+
+------ Step 3/5: Slice 2/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 1778 synthetic samples
+-> test with 'LR'
+LR tn, fp: 433, 23
+LR fn, tp: 9, 3
+LR f1 score: 0.158
+LR cohens kappa score: 0.127
+LR average precision score: 0.203
+
+-> test with 'GB'
+GB tn, fp: 453, 3
+GB fn, tp: 10, 2
+GB f1 score: 0.235
+GB cohens kappa score: 0.224
+
+-> test with 'KNN'
+KNN tn, fp: 455, 1
+KNN fn, tp: 12, 0
+KNN f1 score: 0.000
+KNN cohens kappa score: -0.004
+
+
+------ Step 3/5: Slice 3/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 1778 synthetic samples
+-> test with 'LR'
+LR tn, fp: 379, 77
+LR fn, tp: 7, 5
+LR f1 score: 0.106
+LR cohens kappa score: 0.065
+LR average precision score: 0.072
+
+-> test with 'GB'
+GB tn, fp: 449, 7
+GB fn, tp: 9, 3
+GB f1 score: 0.273
+GB cohens kappa score: 0.255
+
+-> test with 'KNN'
+KNN tn, fp: 454, 2
+KNN fn, tp: 9, 3
+KNN f1 score: 0.353
+KNN cohens kappa score: 0.343
+
+
+------ Step 3/5: Slice 4/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 1778 synthetic samples
+-> test with 'LR'
+LR tn, fp: 418, 38
+LR fn, tp: 6, 6
+LR f1 score: 0.214
+LR cohens kappa score: 0.181
+LR average precision score: 0.174
+
+-> test with 'GB'
+GB tn, fp: 449, 7
+GB fn, tp: 12, 0
+GB f1 score: 0.000
+GB cohens kappa score: -0.019
+
+-> test with 'KNN'
+KNN tn, fp: 454, 2
+KNN fn, tp: 12, 0
+KNN f1 score: 0.000
+KNN cohens kappa score: -0.007
+
+
+------ Step 3/5: Slice 5/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 1776 synthetic samples
+-> test with 'LR'
+LR tn, fp: 433, 23
+LR fn, tp: 6, 4
+LR f1 score: 0.216
+LR cohens kappa score: 0.191
+LR average precision score: 0.141
+
+-> test with 'GB'
+GB tn, fp: 451, 5
+GB fn, tp: 7, 3
+GB f1 score: 0.333
+GB cohens kappa score: 0.320
+
+-> test with 'KNN'
+KNN tn, fp: 453, 3
+KNN fn, tp: 9, 1
+KNN f1 score: 0.143
+KNN cohens kappa score: 0.132
+
+
+====== Step 4/5 =======
+-> Shuffling data
+-> Spliting data to slices
+
+------ Step 4/5: Slice 1/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 1778 synthetic samples
+-> test with 'LR'
+LR tn, fp: 443, 13
+LR fn, tp: 6, 6
+LR f1 score: 0.387
+LR cohens kappa score: 0.367
+LR average precision score: 0.335
+
+-> test with 'GB'
+GB tn, fp: 452, 4
+GB fn, tp: 10, 2
+GB f1 score: 0.222
+GB cohens kappa score: 0.209
+
+-> test with 'KNN'
+KNN tn, fp: 456, 0
+KNN fn, tp: 12, 0
+KNN f1 score: 0.000
+KNN cohens kappa score: 0.000
+
+
+------ Step 4/5: Slice 2/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 1778 synthetic samples
+-> test with 'LR'
+LR tn, fp: 447, 9
+LR fn, tp: 6, 6
+LR f1 score: 0.444
+LR cohens kappa score: 0.428
+LR average precision score: 0.451
+
+-> test with 'GB'
+GB tn, fp: 451, 5
+GB fn, tp: 8, 4
+GB f1 score: 0.381
+GB cohens kappa score: 0.367
+
+-> test with 'KNN'
+KNN tn, fp: 455, 1
+KNN fn, tp: 12, 0
+KNN f1 score: 0.000
+KNN cohens kappa score: -0.004
+
+
+------ Step 4/5: Slice 3/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 1778 synthetic samples
+-> test with 'LR'
+LR tn, fp: 410, 46
+LR fn, tp: 7, 5
+LR f1 score: 0.159
+LR cohens kappa score: 0.122
+LR average precision score: 0.192
+
+-> test with 'GB'
+GB tn, fp: 452, 4
+GB fn, tp: 12, 0
+GB f1 score: 0.000
+GB cohens kappa score: -0.013
+
+-> test with 'KNN'
+KNN tn, fp: 456, 0
+KNN fn, tp: 12, 0
+KNN f1 score: 0.000
+KNN cohens kappa score: 0.000
+
+
+------ Step 4/5: Slice 4/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 1778 synthetic samples
+-> test with 'LR'
+LR tn, fp: 444, 12
+LR fn, tp: 10, 2
+LR f1 score: 0.154
+LR cohens kappa score: 0.130
+LR average precision score: 0.223
+
+-> test with 'GB'
+GB tn, fp: 451, 5
+GB fn, tp: 10, 2
+GB f1 score: 0.211
+GB cohens kappa score: 0.195
+
+-> test with 'KNN'
+KNN tn, fp: 455, 1
+KNN fn, tp: 10, 2
+KNN f1 score: 0.267
+KNN cohens kappa score: 0.259
+
+
+------ Step 4/5: Slice 5/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 1776 synthetic samples
+-> test with 'LR'
+LR tn, fp: 407, 49
+LR fn, tp: 7, 3
+LR f1 score: 0.097
+LR cohens kappa score: 0.063
+LR average precision score: 0.095
+
+-> test with 'GB'
+GB tn, fp: 452, 4
+GB fn, tp: 10, 0
+GB f1 score: 0.000
+GB cohens kappa score: -0.012
+
+-> test with 'KNN'
+KNN tn, fp: 455, 1
+KNN fn, tp: 9, 1
+KNN f1 score: 0.167
+KNN cohens kappa score: 0.161
+
+
+====== Step 5/5 =======
+-> Shuffling data
+-> Spliting data to slices
+
+------ Step 5/5: Slice 1/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 1778 synthetic samples
+-> test with 'LR'
+LR tn, fp: 374, 82
+LR fn, tp: 7, 5
+LR f1 score: 0.101
+LR cohens kappa score: 0.059
+LR average precision score: 0.057
+
+-> test with 'GB'
+GB tn, fp: 449, 7
+GB fn, tp: 10, 2
+GB f1 score: 0.190
+GB cohens kappa score: 0.172
+
+-> test with 'KNN'
+KNN tn, fp: 456, 0
+KNN fn, tp: 11, 1
+KNN f1 score: 0.154
+KNN cohens kappa score: 0.150
+
+
+------ Step 5/5: Slice 2/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 1778 synthetic samples
+-> test with 'LR'
+LR tn, fp: 423, 33
+LR fn, tp: 12, 0
+LR f1 score: 0.000
+LR cohens kappa score: -0.039
+LR average precision score: 0.038
+
+-> test with 'GB'
+GB tn, fp: 446, 10
+GB fn, tp: 11, 1
+GB f1 score: 0.087
+GB cohens kappa score: 0.064
+
+-> test with 'KNN'
+KNN tn, fp: 454, 2
+KNN fn, tp: 12, 0
+KNN f1 score: 0.000
+KNN cohens kappa score: -0.007
+
+
+------ Step 5/5: Slice 3/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 1778 synthetic samples
+-> test with 'LR'
+LR tn, fp: 430, 26
+LR fn, tp: 9, 3
+LR f1 score: 0.146
+LR cohens kappa score: 0.114
+LR average precision score: 0.119
+
+-> test with 'GB'
+GB tn, fp: 447, 9
+GB fn, tp: 11, 1
+GB f1 score: 0.091
+GB cohens kappa score: 0.069
+
+-> test with 'KNN'
+KNN tn, fp: 455, 1
+KNN fn, tp: 10, 2
+KNN f1 score: 0.267
+KNN cohens kappa score: 0.259
+
+
+------ Step 5/5: Slice 4/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 1778 synthetic samples
+-> test with 'LR'
+LR tn, fp: 419, 37
+LR fn, tp: 6, 6
+LR f1 score: 0.218
+LR cohens kappa score: 0.186
+LR average precision score: 0.199
+
+-> test with 'GB'
+GB tn, fp: 453, 3
+GB fn, tp: 10, 2
+GB f1 score: 0.235
+GB cohens kappa score: 0.224
+
+-> test with 'KNN'
+KNN tn, fp: 455, 1
+KNN fn, tp: 12, 0
+KNN f1 score: 0.000
+KNN cohens kappa score: -0.004
+
+
+------ Step 5/5: Slice 5/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 1776 synthetic samples
+-> test with 'LR'
+LR tn, fp: 434, 22
+LR fn, tp: 8, 2
+LR f1 score: 0.118
+LR cohens kappa score: 0.090
+LR average precision score: 0.049
+
+-> test with 'GB'
+GB tn, fp: 449, 7
+GB fn, tp: 8, 2
+GB f1 score: 0.211
+GB cohens kappa score: 0.194
+
+-> test with 'KNN'
+KNN tn, fp: 454, 2
+KNN fn, tp: 10, 0
+KNN f1 score: 0.000
+KNN cohens kappa score: -0.007
+
+### Exercise is done.
+
+-----[ LR ]-----
+maximum:
+LR tn, fp: 452, 113
+LR fn, tp: 12, 8
+LR f1 score: 0.444
+LR cohens kappa score: 0.428
+LR average precision score: 0.451
+
+
+average:
+LR tn, fp: 415.88, 40.12
+LR fn, tp: 7.44, 4.16
+LR f1 score: 0.172
+LR cohens kappa score: 0.140
+LR average precision score: 0.181
+
+
+minimum:
+LR tn, fp: 343, 4
+LR fn, tp: 3, 0
+LR f1 score: 0.000
+LR cohens kappa score: -0.039
+LR average precision score: 0.033
+
+
+-----[ GB ]-----
+maximum:
+GB tn, fp: 454, 14
+GB fn, tp: 12, 4
+GB f1 score: 0.400
+GB cohens kappa score: 0.387
+
+
+average:
+GB tn, fp: 449.88, 6.12
+GB fn, tp: 9.68, 1.92
+GB f1 score: 0.195
+GB cohens kappa score: 0.178
+
+
+minimum:
+GB tn, fp: 442, 2
+GB fn, tp: 7, 0
+GB f1 score: 0.000
+GB cohens kappa score: -0.019
+
+
+-----[ KNN ]-----
+maximum:
+KNN tn, fp: 456, 4
+KNN fn, tp: 12, 3
+KNN f1 score: 0.375
+KNN cohens kappa score: 0.367
+
+
+average:
+KNN tn, fp: 454.92, 1.08
+KNN fn, tp: 10.72, 0.88
+KNN f1 score: 0.122
+KNN cohens kappa score: 0.117
+
+
+minimum:
+KNN tn, fp: 452, 0
+KNN fn, tp: 9, 0
+KNN f1 score: 0.000
+KNN cohens kappa score: -0.013
+

data_result/ctGAN/folding_car-vgood.csv

@@ -0,0 +1,92 @@
+LR
+Nr.;TN;TP;FN;FP;f1 score;cohens kappa score;average precision score
+1;284.000;13.000;0.000;49.000;0.347;0.303;0.335
+2;272.000;12.000;1.000;61.000;0.279;0.230;0.281
+3;263.000;13.000;0.000;70.000;0.271;0.220;0.359
+4;280.000;13.000;0.000;53.000;0.329;0.284;0.412
+5;281.000;13.000;0.000;50.000;0.342;0.298;0.410
+6;273.000;13.000;0.000;60.000;0.302;0.255;0.274
+7;264.000;13.000;0.000;69.000;0.274;0.223;0.309
+8;285.000;13.000;0.000;48.000;0.351;0.309;0.352
+9;286.000;13.000;0.000;47.000;0.356;0.314;0.345
+10;274.000;13.000;0.000;57.000;0.313;0.266;0.552
+11;285.000;13.000;0.000;48.000;0.351;0.309;0.285
+12;285.000;13.000;0.000;48.000;0.351;0.309;0.426
+13;271.000;13.000;0.000;62.000;0.295;0.247;0.330
+14;280.000;13.000;0.000;53.000;0.329;0.284;0.450
+15;274.000;13.000;0.000;57.000;0.313;0.266;0.384
+16;282.000;13.000;0.000;51.000;0.338;0.294;0.431
+17;275.000;13.000;0.000;58.000;0.310;0.263;0.477
+18;275.000;13.000;0.000;58.000;0.310;0.263;0.287
+19;285.000;13.000;0.000;48.000;0.351;0.309;0.276
+20;274.000;13.000;0.000;57.000;0.313;0.266;0.348
+21;265.000;13.000;0.000;68.000;0.277;0.227;0.337
+22;284.000;13.000;0.000;49.000;0.347;0.303;0.364
+23;284.000;13.000;0.000;49.000;0.347;0.303;0.361
+24;275.000;13.000;0.000;58.000;0.310;0.263;0.308
+25;279.000;13.000;0.000;52.000;0.333;0.289;0.498
+max;286.000;13.000;1.000;70.000;0.356;0.314;0.552
+avg;277.400;12.960;0.040;55.200;0.322;0.276;0.368
+min;263.000;12.000;0.000;47.000;0.271;0.220;0.274
+---
+GB
+Nr.;TN;TP;FN;FP;f1 score;cohens kappa score
+1;330.000;13.000;0.000;3.000;0.897;0.892
+2;326.000;13.000;0.000;7.000;0.788;0.778
+3;325.000;13.000;0.000;8.000;0.765;0.753
+4;327.000;13.000;0.000;6.000;0.813;0.804
+5;324.000;13.000;0.000;7.000;0.788;0.778
+6;325.000;13.000;0.000;8.000;0.765;0.753
+7;322.000;13.000;0.000;11.000;0.703;0.687
+8;328.000;13.000;0.000;5.000;0.839;0.831
+9;328.000;13.000;0.000;5.000;0.839;0.831
+10;329.000;13.000;0.000;2.000;0.929;0.926
+11;328.000;13.000;0.000;5.000;0.839;0.831
+12;328.000;13.000;0.000;5.000;0.839;0.831
+13;321.000;13.000;0.000;12.000;0.684;0.668
+14;328.000;13.000;0.000;5.000;0.839;0.831
+15;327.000;13.000;0.000;4.000;0.867;0.861
+16;327.000;13.000;0.000;6.000;0.813;0.804
+17;328.000;13.000;0.000;5.000;0.839;0.831
+18;327.000;13.000;0.000;6.000;0.813;0.804
+19;327.000;13.000;0.000;6.000;0.813;0.804
+20;323.000;13.000;0.000;8.000;0.765;0.753
+21;324.000;13.000;0.000;9.000;0.743;0.730
+22;330.000;13.000;0.000;3.000;0.897;0.892
+23;329.000;13.000;0.000;4.000;0.867;0.861
+24;324.000;13.000;0.000;9.000;0.743;0.730
+25;325.000;13.000;0.000;6.000;0.813;0.804
+max;330.000;13.000;0.000;12.000;0.929;0.926
+avg;326.400;13.000;0.000;6.200;0.812;0.803
+min;321.000;13.000;0.000;2.000;0.684;0.668
+---
+KNN
+Nr.;TN;TP;FN;FP;f1 score;cohens kappa score
+1;278.000;13.000;0.000;55.000;0.321;0.275
+2;275.000;13.000;0.000;58.000;0.310;0.263
+3;263.000;13.000;0.000;70.000;0.271;0.220
+4;279.000;13.000;0.000;54.000;0.325;0.280
+5;277.000;13.000;0.000;54.000;0.325;0.279
+6;272.000;13.000;0.000;61.000;0.299;0.251
+7;266.000;13.000;0.000;67.000;0.280;0.230
+8;271.000;13.000;0.000;62.000;0.295;0.247
+9;275.000;13.000;0.000;58.000;0.310;0.263
+10;281.000;13.000;0.000;50.000;0.342;0.298
+11;270.000;13.000;0.000;63.000;0.292;0.244
+12;270.000;13.000;0.000;63.000;0.292;0.244
+13;273.000;13.000;0.000;60.000;0.302;0.255
+14;271.000;13.000;0.000;62.000;0.295;0.247
+15;282.000;13.000;0.000;49.000;0.347;0.303
+16;277.000;13.000;0.000;56.000;0.317;0.271
+17;274.000;13.000;0.000;59.000;0.306;0.259
+18;276.000;13.000;0.000;57.000;0.313;0.267
+19;275.000;13.000;0.000;58.000;0.310;0.263
+20;265.000;13.000;0.000;66.000;0.283;0.233
+21;268.000;13.000;0.000;65.000;0.286;0.237
+22;283.000;13.000;0.000;50.000;0.342;0.298
+23;270.000;13.000;0.000;63.000;0.292;0.244
+24;275.000;13.000;0.000;58.000;0.310;0.263
+25;277.000;13.000;0.000;54.000;0.325;0.279
+max;283.000;13.000;0.000;70.000;0.347;0.303
+avg;273.720;13.000;0.000;58.880;0.308;0.260
+min;263.000;13.000;0.000;49.000;0.271;0.220

data_result/ctGAN/folding_car-vgood.log

@@ -0,0 +1,701 @@
+
+
+///////////////////////////////////////////
+// Running ctGAN on folding_car-vgood
+///////////////////////////////////////////
+
+Load 'data_input/folding_car-vgood'
+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 1278 synthetic samples
+-> test with 'LR'
+LR tn, fp: 284, 49
+LR fn, tp: 0, 13
+LR f1 score: 0.347
+LR cohens kappa score: 0.303
+LR average precision score: 0.335
+
+-> test with 'GB'
+GB tn, fp: 330, 3
+GB fn, tp: 0, 13
+GB f1 score: 0.897
+GB cohens kappa score: 0.892
+
+-> test with 'KNN'
+KNN tn, fp: 278, 55
+KNN fn, tp: 0, 13
+KNN f1 score: 0.321
+KNN cohens kappa score: 0.275
+
+
+------ Step 1/5: Slice 2/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 1278 synthetic samples
+-> test with 'LR'
+LR tn, fp: 272, 61
+LR fn, tp: 1, 12
+LR f1 score: 0.279
+LR cohens kappa score: 0.230
+LR average precision score: 0.281
+
+-> test with 'GB'
+GB tn, fp: 326, 7
+GB fn, tp: 0, 13
+GB f1 score: 0.788
+GB cohens kappa score: 0.778
+
+-> test with 'KNN'
+KNN tn, fp: 275, 58
+KNN fn, tp: 0, 13
+KNN f1 score: 0.310
+KNN cohens kappa score: 0.263
+
+
+------ Step 1/5: Slice 3/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 1278 synthetic samples
+-> test with 'LR'
+LR tn, fp: 263, 70
+LR fn, tp: 0, 13
+LR f1 score: 0.271
+LR cohens kappa score: 0.220
+LR average precision score: 0.359
+
+-> test with 'GB'
+GB tn, fp: 325, 8
+GB fn, tp: 0, 13
+GB f1 score: 0.765
+GB cohens kappa score: 0.753
+
+-> test with 'KNN'
+KNN tn, fp: 263, 70
+KNN fn, tp: 0, 13
+KNN f1 score: 0.271
+KNN cohens kappa score: 0.220
+
+
+------ Step 1/5: Slice 4/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 1278 synthetic samples
+-> test with 'LR'
+LR tn, fp: 280, 53
+LR fn, tp: 0, 13
+LR f1 score: 0.329
+LR cohens kappa score: 0.284
+LR average precision score: 0.412
+
+-> test with 'GB'
+GB tn, fp: 327, 6
+GB fn, tp: 0, 13
+GB f1 score: 0.813
+GB cohens kappa score: 0.804
+
+-> test with 'KNN'
+KNN tn, fp: 279, 54
+KNN fn, tp: 0, 13
+KNN f1 score: 0.325
+KNN cohens kappa score: 0.280
+
+
+------ Step 1/5: Slice 5/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 1280 synthetic samples
+-> test with 'LR'
+LR tn, fp: 281, 50
+LR fn, tp: 0, 13
+LR f1 score: 0.342
+LR cohens kappa score: 0.298
+LR average precision score: 0.410
+
+-> test with 'GB'
+GB tn, fp: 324, 7
+GB fn, tp: 0, 13
+GB f1 score: 0.788
+GB cohens kappa score: 0.778
+
+-> test with 'KNN'
+KNN tn, fp: 277, 54
+KNN fn, tp: 0, 13
+KNN f1 score: 0.325
+KNN cohens kappa score: 0.279
+
+
+====== Step 2/5 =======
+-> Shuffling data
+-> Spliting data to slices
+
+------ Step 2/5: Slice 1/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 1278 synthetic samples
+-> test with 'LR'
+LR tn, fp: 273, 60
+LR fn, tp: 0, 13
+LR f1 score: 0.302
+LR cohens kappa score: 0.255
+LR average precision score: 0.274
+
+-> test with 'GB'
+GB tn, fp: 325, 8
+GB fn, tp: 0, 13
+GB f1 score: 0.765
+GB cohens kappa score: 0.753
+
+-> test with 'KNN'
+KNN tn, fp: 272, 61
+KNN fn, tp: 0, 13
+KNN f1 score: 0.299
+KNN cohens kappa score: 0.251
+
+
+------ Step 2/5: Slice 2/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 1278 synthetic samples
+-> test with 'LR'
+LR tn, fp: 264, 69
+LR fn, tp: 0, 13
+LR f1 score: 0.274
+LR cohens kappa score: 0.223
+LR average precision score: 0.309
+
+-> test with 'GB'
+GB tn, fp: 322, 11
+GB fn, tp: 0, 13
+GB f1 score: 0.703
+GB cohens kappa score: 0.687
+
+-> test with 'KNN'
+KNN tn, fp: 266, 67
+KNN fn, tp: 0, 13
+KNN f1 score: 0.280
+KNN cohens kappa score: 0.230
+
+
+------ Step 2/5: Slice 3/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 1278 synthetic samples
+-> test with 'LR'
+LR tn, fp: 285, 48
+LR fn, tp: 0, 13
+LR f1 score: 0.351
+LR cohens kappa score: 0.309
+LR average precision score: 0.352
+
+-> test with 'GB'
+GB tn, fp: 328, 5
+GB fn, tp: 0, 13
+GB f1 score: 0.839
+GB cohens kappa score: 0.831
+
+-> test with 'KNN'
+KNN tn, fp: 271, 62
+KNN fn, tp: 0, 13
+KNN f1 score: 0.295
+KNN cohens kappa score: 0.247
+
+
+------ Step 2/5: Slice 4/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 1278 synthetic samples
+-> test with 'LR'
+LR tn, fp: 286, 47
+LR fn, tp: 0, 13
+LR f1 score: 0.356
+LR cohens kappa score: 0.314
+LR average precision score: 0.345
+
+-> test with 'GB'
+GB tn, fp: 328, 5
+GB fn, tp: 0, 13
+GB f1 score: 0.839
+GB cohens kappa score: 0.831
+
+-> test with 'KNN'
+KNN tn, fp: 275, 58
+KNN fn, tp: 0, 13
+KNN f1 score: 0.310
+KNN cohens kappa score: 0.263
+
+
+------ Step 2/5: Slice 5/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 1280 synthetic samples
+-> test with 'LR'
+LR tn, fp: 274, 57
+LR fn, tp: 0, 13
+LR f1 score: 0.313
+LR cohens kappa score: 0.266
+LR average precision score: 0.552
+
+-> test with 'GB'
+GB tn, fp: 329, 2
+GB fn, tp: 0, 13
+GB f1 score: 0.929
+GB cohens kappa score: 0.926
+
+-> test with 'KNN'
+KNN tn, fp: 281, 50
+KNN fn, tp: 0, 13
+KNN f1 score: 0.342
+KNN cohens kappa score: 0.298
+
+
+====== Step 3/5 =======
+-> Shuffling data
+-> Spliting data to slices
+
+------ Step 3/5: Slice 1/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 1278 synthetic samples
+-> test with 'LR'
+LR tn, fp: 285, 48
+LR fn, tp: 0, 13
+LR f1 score: 0.351
+LR cohens kappa score: 0.309
+LR average precision score: 0.285
+
+-> test with 'GB'
+GB tn, fp: 328, 5
+GB fn, tp: 0, 13
+GB f1 score: 0.839
+GB cohens kappa score: 0.831
+
+-> test with 'KNN'
+KNN tn, fp: 270, 63
+KNN fn, tp: 0, 13
+KNN f1 score: 0.292
+KNN cohens kappa score: 0.244
+
+
+------ Step 3/5: Slice 2/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 1278 synthetic samples
+-> test with 'LR'
+LR tn, fp: 285, 48
+LR fn, tp: 0, 13
+LR f1 score: 0.351
+LR cohens kappa score: 0.309
+LR average precision score: 0.426
+
+-> test with 'GB'
+GB tn, fp: 328, 5
+GB fn, tp: 0, 13
+GB f1 score: 0.839
+GB cohens kappa score: 0.831
+
+-> test with 'KNN'
+KNN tn, fp: 270, 63
+KNN fn, tp: 0, 13
+KNN f1 score: 0.292
+KNN cohens kappa score: 0.244
+
+
+------ Step 3/5: Slice 3/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 1278 synthetic samples
+-> test with 'LR'
+LR tn, fp: 271, 62
+LR fn, tp: 0, 13
+LR f1 score: 0.295
+LR cohens kappa score: 0.247
+LR average precision score: 0.330
+
+-> test with 'GB'
+GB tn, fp: 321, 12
+GB fn, tp: 0, 13
+GB f1 score: 0.684
+GB cohens kappa score: 0.668
+
+-> test with 'KNN'
+KNN tn, fp: 273, 60
+KNN fn, tp: 0, 13
+KNN f1 score: 0.302
+KNN cohens kappa score: 0.255
+
+
+------ Step 3/5: Slice 4/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 1278 synthetic samples
+-> test with 'LR'
+LR tn, fp: 280, 53
+LR fn, tp: 0, 13
+LR f1 score: 0.329
+LR cohens kappa score: 0.284
+LR average precision score: 0.450
+
+-> test with 'GB'
+GB tn, fp: 328, 5
+GB fn, tp: 0, 13
+GB f1 score: 0.839
+GB cohens kappa score: 0.831
+
+-> test with 'KNN'
+KNN tn, fp: 271, 62
+KNN fn, tp: 0, 13
+KNN f1 score: 0.295
+KNN cohens kappa score: 0.247
+
+
+------ Step 3/5: Slice 5/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 1280 synthetic samples
+-> test with 'LR'
+LR tn, fp: 274, 57
+LR fn, tp: 0, 13
+LR f1 score: 0.313
+LR cohens kappa score: 0.266
+LR average precision score: 0.384
+
+-> 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: 282, 49
+KNN fn, tp: 0, 13
+KNN f1 score: 0.347
+KNN cohens kappa score: 0.303
+
+
+====== Step 4/5 =======
+-> Shuffling data
+-> Spliting data to slices
+
+------ Step 4/5: Slice 1/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 1278 synthetic samples
+-> test with 'LR'
+LR tn, fp: 282, 51
+LR fn, tp: 0, 13
+LR f1 score: 0.338
+LR cohens kappa score: 0.294
+LR average precision score: 0.431
+
+-> test with 'GB'
+GB tn, fp: 327, 6
+GB fn, tp: 0, 13
+GB f1 score: 0.813
+GB cohens kappa score: 0.804
+
+-> test with 'KNN'
+KNN tn, fp: 277, 56
+KNN fn, tp: 0, 13
+KNN f1 score: 0.317
+KNN cohens kappa score: 0.271
+
+
+------ Step 4/5: Slice 2/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 1278 synthetic samples
+-> test with 'LR'
+LR tn, fp: 275, 58
+LR fn, tp: 0, 13
+LR f1 score: 0.310
+LR cohens kappa score: 0.263
+LR average precision score: 0.477
+
+-> test with 'GB'
+GB tn, fp: 328, 5
+GB fn, tp: 0, 13
+GB f1 score: 0.839
+GB cohens kappa score: 0.831
+
+-> test with 'KNN'
+KNN tn, fp: 274, 59
+KNN fn, tp: 0, 13
+KNN f1 score: 0.306
+KNN cohens kappa score: 0.259
+
+
+------ Step 4/5: Slice 3/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 1278 synthetic samples
+-> test with 'LR'
+LR tn, fp: 275, 58
+LR fn, tp: 0, 13
+LR f1 score: 0.310
+LR cohens kappa score: 0.263
+LR average precision score: 0.287
+
+-> test with 'GB'
+GB tn, fp: 327, 6
+GB fn, tp: 0, 13
+GB f1 score: 0.813
+GB cohens kappa score: 0.804
+
+-> test with 'KNN'
+KNN tn, fp: 276, 57
+KNN fn, tp: 0, 13
+KNN f1 score: 0.313
+KNN cohens kappa score: 0.267
+
+
+------ Step 4/5: Slice 4/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 1278 synthetic samples
+-> test with 'LR'
+LR tn, fp: 285, 48
+LR fn, tp: 0, 13
+LR f1 score: 0.351
+LR cohens kappa score: 0.309
+LR average precision score: 0.276
+
+-> test with 'GB'
+GB tn, fp: 327, 6
+GB fn, tp: 0, 13
+GB f1 score: 0.813
+GB cohens kappa score: 0.804
+
+-> test with 'KNN'
+KNN tn, fp: 275, 58
+KNN fn, tp: 0, 13
+KNN f1 score: 0.310
+KNN cohens kappa score: 0.263
+
+
+------ Step 4/5: Slice 5/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 1280 synthetic samples
+-> test with 'LR'
+LR tn, fp: 274, 57
+LR fn, tp: 0, 13
+LR f1 score: 0.313
+LR cohens kappa score: 0.266
+LR average precision score: 0.348
+
+-> test with 'GB'
+GB tn, fp: 323, 8
+GB fn, tp: 0, 13
+GB f1 score: 0.765
+GB cohens kappa score: 0.753
+
+-> test with 'KNN'
+KNN tn, fp: 265, 66
+KNN fn, tp: 0, 13
+KNN f1 score: 0.283
+KNN cohens kappa score: 0.233
+
+
+====== Step 5/5 =======
+-> Shuffling data
+-> Spliting data to slices
+
+------ Step 5/5: Slice 1/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 1278 synthetic samples
+-> test with 'LR'
+LR tn, fp: 265, 68
+LR fn, tp: 0, 13
+LR f1 score: 0.277
+LR cohens kappa score: 0.227
+LR average precision score: 0.337
+
+-> test with 'GB'
+GB tn, fp: 324, 9
+GB fn, tp: 0, 13
+GB f1 score: 0.743
+GB cohens kappa score: 0.730
+
+-> test with 'KNN'
+KNN tn, fp: 268, 65
+KNN fn, tp: 0, 13
+KNN f1 score: 0.286
+KNN cohens kappa score: 0.237
+
+
+------ Step 5/5: Slice 2/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 1278 synthetic samples
+-> test with 'LR'
+LR tn, fp: 284, 49
+LR fn, tp: 0, 13
+LR f1 score: 0.347
+LR cohens kappa score: 0.303
+LR average precision score: 0.364
+
+-> test with 'GB'
+GB tn, fp: 330, 3
+GB fn, tp: 0, 13
+GB f1 score: 0.897
+GB cohens kappa score: 0.892
+
+-> test with 'KNN'
+KNN tn, fp: 283, 50
+KNN fn, tp: 0, 13
+KNN f1 score: 0.342
+KNN cohens kappa score: 0.298
+
+
+------ Step 5/5: Slice 3/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 1278 synthetic samples
+-> test with 'LR'
+LR tn, fp: 284, 49
+LR fn, tp: 0, 13
+LR f1 score: 0.347
+LR cohens kappa score: 0.303
+LR average precision score: 0.361
+
+-> test with 'GB'
+GB tn, fp: 329, 4
+GB fn, tp: 0, 13
+GB f1 score: 0.867
+GB cohens kappa score: 0.861
+
+-> test with 'KNN'
+KNN tn, fp: 270, 63
+KNN fn, tp: 0, 13
+KNN f1 score: 0.292
+KNN cohens kappa score: 0.244
+
+
+------ Step 5/5: Slice 4/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 1278 synthetic samples
+-> test with 'LR'
+LR tn, fp: 275, 58
+LR fn, tp: 0, 13
+LR f1 score: 0.310
+LR cohens kappa score: 0.263
+LR average precision score: 0.308
+
+-> test with 'GB'
+GB tn, fp: 324, 9
+GB fn, tp: 0, 13
+GB f1 score: 0.743
+GB cohens kappa score: 0.730
+
+-> test with 'KNN'
+KNN tn, fp: 275, 58
+KNN fn, tp: 0, 13
+KNN f1 score: 0.310
+KNN cohens kappa score: 0.263
+
+
+------ Step 5/5: Slice 5/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 1280 synthetic samples
+-> test with 'LR'
+LR tn, fp: 279, 52
+LR fn, tp: 0, 13
+LR f1 score: 0.333
+LR cohens kappa score: 0.289
+LR average precision score: 0.498
+
+-> 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: 277, 54
+KNN fn, tp: 0, 13
+KNN f1 score: 0.325
+KNN cohens kappa score: 0.279
+
+### Exercise is done.
+
+-----[ LR ]-----
+maximum:
+LR tn, fp: 286, 70
+LR fn, tp: 1, 13
+LR f1 score: 0.356
+LR cohens kappa score: 0.314
+LR average precision score: 0.552
+
+
+average:
+LR tn, fp: 277.4, 55.2
+LR fn, tp: 0.04, 12.96
+LR f1 score: 0.322
+LR cohens kappa score: 0.276
+LR average precision score: 0.368
+
+
+minimum:
+LR tn, fp: 263, 47
+LR fn, tp: 0, 12
+LR f1 score: 0.271
+LR cohens kappa score: 0.220
+LR average precision score: 0.274
+
+
+-----[ GB ]-----
+maximum:
+GB tn, fp: 330, 12
+GB fn, tp: 0, 13
+GB f1 score: 0.929
+GB cohens kappa score: 0.926
+
+
+average:
+GB tn, fp: 326.4, 6.2
+GB fn, tp: 0.0, 13.0
+GB f1 score: 0.812
+GB cohens kappa score: 0.803
+
+
+minimum:
+GB tn, fp: 321, 2
+GB fn, tp: 0, 13
+GB f1 score: 0.684
+GB cohens kappa score: 0.668
+
+
+-----[ KNN ]-----
+maximum:
+KNN tn, fp: 283, 70
+KNN fn, tp: 0, 13
+KNN f1 score: 0.347
+KNN cohens kappa score: 0.303
+
+
+average:
+KNN tn, fp: 273.72, 58.88
+KNN fn, tp: 0.0, 13.0
+KNN f1 score: 0.308
+KNN cohens kappa score: 0.260
+
+
+minimum:
+KNN tn, fp: 263, 49
+KNN fn, tp: 0, 13
+KNN f1 score: 0.271
+KNN cohens kappa score: 0.220
+

data_result/ctGAN/folding_car_good.csv

@@ -0,0 +1,92 @@
+LR
+Nr.;TN;TP;FN;FP;f1 score;cohens kappa score;average precision score
+1;163.000;9.000;5.000;169.000;0.094;0.020;0.059
+2;191.000;11.000;3.000;141.000;0.133;0.063;0.108
+3;182.000;7.000;7.000;150.000;0.082;0.008;0.061
+4;180.000;9.000;5.000;152.000;0.103;0.031;0.098
+5;177.000;9.000;4.000;154.000;0.102;0.035;0.046
+6;162.000;9.000;5.000;170.000;0.093;0.020;0.076
+7;164.000;9.000;5.000;168.000;0.094;0.021;0.064
+8;200.000;11.000;3.000;132.000;0.140;0.072;0.075
+9;173.000;8.000;6.000;159.000;0.088;0.015;0.046
+10;207.000;10.000;3.000;124.000;0.136;0.072;0.066
+11;165.000;10.000;4.000;167.000;0.105;0.032;0.065
+12;170.000;9.000;5.000;162.000;0.097;0.024;0.057
+13;177.000;8.000;6.000;155.000;0.090;0.017;0.063
+14;172.000;11.000;3.000;160.000;0.119;0.048;0.073
+15;174.000;7.000;6.000;157.000;0.079;0.010;0.057
+16;178.000;11.000;3.000;154.000;0.123;0.052;0.084
+17;187.000;5.000;9.000;145.000;0.061;-0.014;0.061
+18;164.000;13.000;1.000;168.000;0.133;0.063;0.068
+19;202.000;8.000;6.000;130.000;0.105;0.034;0.057
+20;168.000;10.000;3.000;163.000;0.108;0.040;0.081
+21;164.000;11.000;3.000;168.000;0.114;0.042;0.058
+22;170.000;12.000;2.000;162.000;0.128;0.057;0.062
+23;148.000;11.000;3.000;184.000;0.105;0.032;0.147
+24;180.000;11.000;3.000;152.000;0.124;0.054;0.102
+25;191.000;9.000;4.000;140.000;0.111;0.045;0.064
+max;207.000;13.000;9.000;184.000;0.140;0.072;0.147
+avg;176.360;9.520;4.280;155.440;0.107;0.036;0.072
+min;148.000;5.000;1.000;124.000;0.061;-0.014;0.046
+---
+GB
+Nr.;TN;TP;FN;FP;f1 score;cohens kappa score
+1;304.000;14.000;0.000;28.000;0.500;0.468
+2;300.000;14.000;0.000;32.000;0.467;0.431
+3;311.000;14.000;0.000;21.000;0.571;0.545
+4;311.000;14.000;0.000;21.000;0.571;0.545
+5;310.000;13.000;0.000;21.000;0.553;0.527
+6;312.000;14.000;0.000;20.000;0.583;0.558
+7;316.000;14.000;0.000;16.000;0.636;0.615
+8;306.000;14.000;0.000;26.000;0.519;0.488
+9;296.000;14.000;0.000;36.000;0.438;0.400
+10;306.000;13.000;0.000;25.000;0.510;0.481
+11;306.000;14.000;0.000;26.000;0.519;0.488
+12;312.000;14.000;0.000;20.000;0.583;0.558
+13;308.000;14.000;0.000;24.000;0.538;0.509
+14;305.000;14.000;0.000;27.000;0.509;0.478
+15;305.000;13.000;0.000;26.000;0.500;0.470
+16;315.000;14.000;0.000;17.000;0.622;0.600
+17;304.000;14.000;0.000;28.000;0.500;0.468
+18;304.000;14.000;0.000;28.000;0.500;0.468
+19;308.000;14.000;0.000;24.000;0.538;0.509
+20;305.000;13.000;0.000;26.000;0.500;0.470
+21;303.000;14.000;0.000;29.000;0.491;0.458
+22;314.000;14.000;0.000;18.000;0.609;0.585
+23;301.000;14.000;0.000;31.000;0.475;0.440
+24;309.000;14.000;0.000;23.000;0.549;0.521
+25;309.000;13.000;0.000;22.000;0.542;0.515
+max;316.000;14.000;0.000;36.000;0.636;0.615
+avg;307.200;13.800;0.000;24.600;0.533;0.504
+min;296.000;13.000;0.000;16.000;0.438;0.400
+---
+KNN
+Nr.;TN;TP;FN;FP;f1 score;cohens kappa score
+1;244.000;14.000;0.000;88.000;0.241;0.183
+2;236.000;14.000;0.000;96.000;0.226;0.166
+3;259.000;14.000;0.000;73.000;0.277;0.223
+4;254.000;14.000;0.000;78.000;0.264;0.209
+5;263.000;13.000;0.000;68.000;0.277;0.226
+6;247.000;14.000;0.000;85.000;0.248;0.190
+7;261.000;14.000;0.000;71.000;0.283;0.229
+8;261.000;14.000;0.000;71.000;0.283;0.229
+9;217.000;14.000;0.000;115.000;0.196;0.132
+10;250.000;13.000;0.000;81.000;0.243;0.189
+11;232.000;14.000;0.000;100.000;0.219;0.158
+12;261.000;14.000;0.000;71.000;0.283;0.229
+13;248.000;14.000;0.000;84.000;0.250;0.193
+14;251.000;14.000;0.000;81.000;0.257;0.200
+15;230.000;13.000;0.000;101.000;0.205;0.147
+16;256.000;14.000;0.000;76.000;0.269;0.214
+17;233.000;14.000;0.000;99.000;0.220;0.160
+18;242.000;14.000;0.000;90.000;0.237;0.179
+19;257.000;14.000;0.000;75.000;0.272;0.217
+20;237.000;13.000;0.000;94.000;0.217;0.160
+21;246.000;14.000;0.000;86.000;0.246;0.188
+22;241.000;14.000;0.000;91.000;0.235;0.176
+23;244.000;14.000;0.000;88.000;0.241;0.183
+24;245.000;14.000;0.000;87.000;0.243;0.186
+25;250.000;13.000;0.000;81.000;0.243;0.189
+max;263.000;14.000;0.000;115.000;0.283;0.229
+avg;246.600;13.800;0.000;85.200;0.247;0.190
+min;217.000;13.000;0.000;68.000;0.196;0.132

data_result/ctGAN/folding_car_good.log

@@ -0,0 +1,701 @@
+
+
+///////////////////////////////////////////
+// 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: 163, 169
+LR fn, tp: 5, 9
+LR f1 score: 0.094
+LR cohens kappa score: 0.020
+LR average precision score: 0.059
+
+-> 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: 244, 88
+KNN fn, tp: 0, 14
+KNN f1 score: 0.241
+KNN cohens kappa score: 0.183
+
+
+------ 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: 0.108
+
+-> 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: 236, 96
+KNN fn, tp: 0, 14
+KNN f1 score: 0.226
+KNN cohens kappa score: 0.166
+
+
+------ Step 1/5: Slice 3/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 1272 synthetic samples
+-> test with 'LR'
+LR tn, fp: 182, 150
+LR fn, tp: 7, 7
+LR f1 score: 0.082
+LR cohens kappa score: 0.008
+LR average precision score: 0.061
+
+-> 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: 259, 73
+KNN fn, tp: 0, 14
+KNN f1 score: 0.277
+KNN cohens kappa score: 0.223
+
+
+------ Step 1/5: Slice 4/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 1272 synthetic samples
+-> test with 'LR'
+LR tn, fp: 180, 152
+LR fn, tp: 5, 9
+LR f1 score: 0.103
+LR cohens kappa score: 0.031
+LR average precision score: 0.098
+
+-> 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: 254, 78
+KNN fn, tp: 0, 14
+KNN f1 score: 0.264
+KNN cohens kappa score: 0.209
+
+
+------ Step 1/5: Slice 5/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 1272 synthetic samples
+-> test with 'LR'
+LR tn, fp: 177, 154
+LR fn, tp: 4, 9
+LR f1 score: 0.102
+LR cohens kappa score: 0.035
+LR average precision score: 0.046
+
+-> 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: 263, 68
+KNN fn, tp: 0, 13
+KNN f1 score: 0.277
+KNN cohens kappa score: 0.226
+
+
+====== 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: 162, 170
+LR fn, tp: 5, 9
+LR f1 score: 0.093
+LR cohens kappa score: 0.020
+LR average precision score: 0.076
+
+-> 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: 247, 85
+KNN fn, tp: 0, 14
+KNN f1 score: 0.248
+KNN cohens kappa score: 0.190
+
+
+------ Step 2/5: Slice 2/5 -------
+-> 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: 0.064
+
+-> 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: 261, 71
+KNN fn, tp: 0, 14
+KNN f1 score: 0.283
+KNN cohens kappa score: 0.229
+
+
+------ Step 2/5: Slice 3/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 1272 synthetic samples
+-> test with 'LR'
+LR tn, fp: 200, 132
+LR fn, tp: 3, 11
+LR f1 score: 0.140
+LR cohens kappa score: 0.072
+LR average precision score: 0.075
+
+-> 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: 261, 71
+KNN fn, tp: 0, 14
+KNN f1 score: 0.283
+KNN cohens kappa score: 0.229
+
+
+------ Step 2/5: 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: 6, 8
+LR f1 score: 0.088
+LR cohens kappa score: 0.015
+LR average precision score: 0.046
+
+-> 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: 217, 115
+KNN fn, tp: 0, 14
+KNN f1 score: 0.196
+KNN cohens kappa score: 0.132
+
+
+------ Step 2/5: Slice 5/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 1272 synthetic samples
+-> test with 'LR'
+LR tn, fp: 207, 124
+LR fn, tp: 3, 10
+LR f1 score: 0.136
+LR cohens kappa score: 0.072
+LR average precision score: 0.066
+
+-> 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: 250, 81
+KNN fn, tp: 0, 13
+KNN f1 score: 0.243
+KNN cohens kappa score: 0.189
+
+
+====== 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: 165, 167
+LR fn, tp: 4, 10
+LR f1 score: 0.105
+LR cohens kappa score: 0.032
+LR average precision score: 0.065
+
+-> 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: 232, 100
+KNN fn, tp: 0, 14
+KNN f1 score: 0.219
+KNN cohens kappa score: 0.158
+
+
+------ Step 3/5: Slice 2/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 1272 synthetic samples
+-> test with 'LR'
+LR tn, fp: 170, 162
+LR fn, tp: 5, 9
+LR f1 score: 0.097
+LR cohens kappa score: 0.024
+LR average precision score: 0.057
+
+-> 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: 261, 71
+KNN fn, tp: 0, 14
+KNN f1 score: 0.283
+KNN cohens kappa score: 0.229
+
+
+------ Step 3/5: Slice 3/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 1272 synthetic samples
+-> test with 'LR'
+LR tn, fp: 177, 155
+LR fn, tp: 6, 8
+LR f1 score: 0.090
+LR cohens kappa score: 0.017
+LR average precision score: 0.063
+
+-> 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: 248, 84
+KNN fn, tp: 0, 14
+KNN f1 score: 0.250
+KNN cohens kappa score: 0.193
+
+
+------ Step 3/5: 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: 3, 11
+LR f1 score: 0.119
+LR cohens kappa score: 0.048
+LR average precision score: 0.073
+
+-> 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: 251, 81
+KNN fn, tp: 0, 14
+KNN f1 score: 0.257
+KNN cohens kappa score: 0.200
+
+
+------ Step 3/5: Slice 5/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 1272 synthetic samples
+-> test with 'LR'
+LR tn, fp: 174, 157
+LR fn, tp: 6, 7
+LR f1 score: 0.079
+LR cohens kappa score: 0.010
+LR average precision score: 0.057
+
+-> 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: 230, 101
+KNN fn, tp: 0, 13
+KNN f1 score: 0.205
+KNN cohens kappa score: 0.147
+
+
+====== 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: 178, 154
+LR fn, tp: 3, 11
+LR f1 score: 0.123
+LR cohens kappa score: 0.052
+LR average precision score: 0.084
+
+-> 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: 256, 76
+KNN fn, tp: 0, 14
+KNN f1 score: 0.269
+KNN cohens kappa score: 0.214
+
+
+------ Step 4/5: 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: 9, 5
+LR f1 score: 0.061
+LR cohens kappa score: -0.014
+LR average precision score: 0.061
+
+-> 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 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: 1, 13
+LR f1 score: 0.133
+LR cohens kappa score: 0.063
+LR average precision score: 0.068
+
+-> 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: 242, 90
+KNN fn, tp: 0, 14
+KNN f1 score: 0.237
+KNN cohens kappa score: 0.179
+
+
+------ Step 4/5: Slice 4/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 1272 synthetic samples
+-> test with 'LR'
+LR tn, fp: 202, 130
+LR fn, tp: 6, 8
+LR f1 score: 0.105
+LR cohens kappa score: 0.034
+LR average precision score: 0.057
+
+-> 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: 257, 75
+KNN fn, tp: 0, 14
+KNN f1 score: 0.272
+KNN cohens kappa score: 0.217
+
+
+------ Step 4/5: 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: 3, 10
+LR f1 score: 0.108
+LR cohens kappa score: 0.040
+LR average precision score: 0.081
+
+-> 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: 237, 94
+KNN fn, tp: 0, 13
+KNN f1 score: 0.217
+KNN cohens kappa score: 0.160
+
+
+====== 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: 164, 168
+LR fn, tp: 3, 11
+LR f1 score: 0.114
+LR cohens kappa score: 0.042
+LR average precision score: 0.058
+
+-> 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: 246, 86
+KNN fn, tp: 0, 14
+KNN f1 score: 0.246
+KNN cohens kappa score: 0.188
+
+
+------ Step 5/5: Slice 2/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 1272 synthetic samples
+-> test with 'LR'
+LR tn, fp: 170, 162
+LR fn, tp: 2, 12
+LR f1 score: 0.128
+LR cohens kappa score: 0.057
+LR average precision score: 0.062
+
+-> 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: 241, 91
+KNN fn, tp: 0, 14
+KNN f1 score: 0.235
+KNN cohens kappa score: 0.176
+
+
+------ Step 5/5: Slice 3/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 1272 synthetic samples
+-> test with 'LR'
+LR tn, fp: 148, 184
+LR fn, tp: 3, 11
+LR f1 score: 0.105
+LR cohens kappa score: 0.032
+LR average precision score: 0.147
+
+-> 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: 244, 88
+KNN fn, tp: 0, 14
+KNN f1 score: 0.241
+KNN cohens kappa score: 0.183
+
+
+------ Step 5/5: Slice 4/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 1272 synthetic samples
+-> test with 'LR'
+LR tn, fp: 180, 152
+LR fn, tp: 3, 11
+LR f1 score: 0.124
+LR cohens kappa score: 0.054
+LR average precision score: 0.102
+
+-> 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: 245, 87
+KNN fn, tp: 0, 14
+KNN f1 score: 0.243
+KNN cohens kappa score: 0.186
+
+
+------ Step 5/5: Slice 5/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 1272 synthetic samples
+-> test with 'LR'
+LR tn, fp: 191, 140
+LR fn, tp: 4, 9
+LR f1 score: 0.111
+LR cohens kappa score: 0.045
+LR average precision score: 0.064
+
+-> 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: 250, 81
+KNN fn, tp: 0, 13
+KNN f1 score: 0.243
+KNN cohens kappa score: 0.189
+
+### Exercise is done.
+
+-----[ LR ]-----
+maximum:
+LR tn, fp: 207, 184
+LR fn, tp: 9, 13
+LR f1 score: 0.140
+LR cohens kappa score: 0.072
+LR average precision score: 0.147
+
+
+average:
+LR tn, fp: 176.36, 155.44
+LR fn, tp: 4.28, 9.52
+LR f1 score: 0.107
+LR cohens kappa score: 0.036
+LR average precision score: 0.072
+
+
+minimum:
+LR tn, fp: 148, 124
+LR fn, tp: 1, 5
+LR f1 score: 0.061
+LR cohens kappa score: -0.014
+LR average precision score: 0.046
+
+
+-----[ 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: 263, 115
+KNN fn, tp: 0, 14
+KNN f1 score: 0.283
+KNN cohens kappa score: 0.229
+
+
+average:
+KNN tn, fp: 246.6, 85.2
+KNN fn, tp: 0.0, 13.8
+KNN f1 score: 0.247
+KNN cohens kappa score: 0.190
+
+
+minimum:
+KNN tn, fp: 217, 68
+KNN fn, tp: 0, 13
+KNN f1 score: 0.196
+KNN cohens kappa score: 0.132
+