Added benchmark for convGAN with full neighbourhood.

data_result/convGAN-full/folding_abalone9-18.csv

@@ -0,0 +1,92 @@
+LR
+Nr.;TN;TP;FN;FP;f1 score;cohens kappa score;average precision score
+1;119.000;9.000;0.000;19.000;0.486;0.434;0.879
+2;130.000;6.000;3.000;8.000;0.522;0.483;0.581
+3;129.000;8.000;1.000;9.000;0.615;0.582;0.809
+4;132.000;5.000;4.000;6.000;0.500;0.464;0.582
+5;127.000;4.000;2.000;10.000;0.400;0.363;0.452
+6;122.000;8.000;1.000;16.000;0.485;0.434;0.651
+7;133.000;8.000;1.000;5.000;0.727;0.706;0.783
+8;128.000;7.000;2.000;10.000;0.538;0.498;0.653
+9;128.000;9.000;0.000;10.000;0.643;0.610;0.719
+10;128.000;5.000;1.000;9.000;0.500;0.469;0.660
+11;128.000;5.000;4.000;10.000;0.417;0.368;0.556
+12;133.000;9.000;0.000;5.000;0.783;0.765;0.906
+13;133.000;6.000;3.000;5.000;0.600;0.571;0.693
+14;120.000;7.000;2.000;18.000;0.412;0.354;0.630
+15;128.000;5.000;1.000;9.000;0.500;0.469;0.564
+16;129.000;6.000;3.000;9.000;0.500;0.459;0.547
+17;126.000;7.000;2.000;12.000;0.500;0.455;0.732
+18;125.000;8.000;1.000;13.000;0.533;0.490;0.664
+19;123.000;9.000;0.000;15.000;0.545;0.501;0.906
+20;131.000;5.000;1.000;6.000;0.588;0.565;0.510
+21;125.000;7.000;2.000;13.000;0.483;0.435;0.693
+22;127.000;8.000;1.000;11.000;0.571;0.533;0.677
+23;129.000;5.000;4.000;9.000;0.435;0.389;0.532
+24;131.000;8.000;1.000;7.000;0.667;0.639;0.841
+25;129.000;5.000;1.000;8.000;0.526;0.497;0.809
+max;133.000;9.000;4.000;19.000;0.783;0.765;0.906
+avg;127.720;6.760;1.640;10.080;0.539;0.501;0.681
+min;119.000;4.000;0.000;5.000;0.400;0.354;0.452
+---
+GB
+Nr.;TN;TP;FN;FP;f1 score;cohens kappa score
+1;132.000;3.000;6.000;6.000;0.333;0.290
+2;131.000;4.000;5.000;7.000;0.400;0.357
+3;132.000;5.000;4.000;6.000;0.500;0.464
+4;136.000;3.000;6.000;2.000;0.429;0.402
+5;134.000;2.000;4.000;3.000;0.364;0.338
+6;136.000;4.000;5.000;2.000;0.533;0.509
+7;131.000;4.000;5.000;7.000;0.400;0.357
+8;130.000;4.000;5.000;8.000;0.381;0.334
+9;131.000;3.000;6.000;7.000;0.316;0.269
+10;128.000;3.000;3.000;9.000;0.333;0.294
+11;131.000;2.000;7.000;7.000;0.222;0.171
+12;133.000;7.000;2.000;5.000;0.667;0.642
+13;131.000;2.000;7.000;7.000;0.222;0.171
+14;130.000;3.000;6.000;8.000;0.300;0.249
+15;131.000;3.000;3.000;6.000;0.400;0.368
+16;135.000;3.000;6.000;3.000;0.400;0.369
+17;128.000;5.000;4.000;10.000;0.417;0.368
+18;132.000;5.000;4.000;6.000;0.500;0.464
+19;130.000;3.000;6.000;8.000;0.300;0.249
+20;130.000;2.000;4.000;7.000;0.267;0.228
+21;131.000;2.000;7.000;7.000;0.222;0.171
+22;132.000;3.000;6.000;6.000;0.333;0.290
+23;131.000;2.000;7.000;7.000;0.222;0.171
+24;132.000;4.000;5.000;6.000;0.421;0.381
+25;133.000;3.000;3.000;4.000;0.462;0.436
+max;136.000;7.000;7.000;10.000;0.667;0.642
+avg;131.640;3.360;5.040;6.160;0.374;0.334
+min;128.000;2.000;2.000;2.000;0.222;0.171
+---
+KNN
+Nr.;TN;TP;FN;FP;f1 score;cohens kappa score
+1;120.000;6.000;3.000;18.000;0.364;0.301
+2;120.000;8.000;1.000;18.000;0.457;0.403
+3;128.000;6.000;3.000;10.000;0.480;0.436
+4;132.000;6.000;3.000;6.000;0.571;0.539
+5;127.000;4.000;2.000;10.000;0.400;0.363
+6;129.000;4.000;5.000;9.000;0.364;0.314
+7;127.000;6.000;3.000;11.000;0.462;0.415
+8;123.000;7.000;2.000;15.000;0.452;0.399
+9;123.000;4.000;5.000;15.000;0.286;0.221
+10;120.000;4.000;2.000;17.000;0.296;0.247
+11;121.000;3.000;6.000;17.000;0.207;0.134
+12;124.000;8.000;1.000;14.000;0.516;0.470
+13;127.000;4.000;5.000;11.000;0.333;0.278
+14;123.000;5.000;4.000;15.000;0.345;0.284
+15;119.000;4.000;2.000;18.000;0.286;0.235
+16;128.000;3.000;6.000;10.000;0.273;0.216
+17;120.000;6.000;3.000;18.000;0.364;0.301
+18;121.000;6.000;3.000;17.000;0.375;0.315
+19;116.000;7.000;2.000;22.000;0.368;0.303
+20;125.000;5.000;1.000;12.000;0.435;0.397
+21;125.000;2.000;7.000;13.000;0.167;0.098
+22;114.000;4.000;5.000;24.000;0.216;0.136
+23;122.000;4.000;5.000;16.000;0.276;0.209
+24;128.000;7.000;2.000;10.000;0.538;0.498
+25;128.000;3.000;3.000;9.000;0.333;0.294
+max;132.000;8.000;7.000;24.000;0.571;0.539
+avg;123.600;5.040;3.360;14.200;0.367;0.312
+min;114.000;2.000;1.000;6.000;0.167;0.098

data_result/convGAN-full/folding_abalone9-18.log

@@ -0,0 +1,701 @@
+
+
+///////////////////////////////////////////
+// Running convGAN-full 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: 119, 19
+LR fn, tp: 0, 9
+LR f1 score: 0.486
+LR cohens kappa score: 0.434
+LR average precision score: 0.879
+
+-> test with 'GB'
+GB tn, fp: 132, 6
+GB fn, tp: 6, 3
+GB f1 score: 0.333
+GB cohens kappa score: 0.290
+
+-> test with 'KNN'
+KNN tn, fp: 120, 18
+KNN fn, tp: 3, 6
+KNN f1 score: 0.364
+KNN cohens kappa score: 0.301
+
+
+------ Step 1/5: Slice 2/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 518 synthetic samples
+-> test with 'LR'
+LR tn, fp: 130, 8
+LR fn, tp: 3, 6
+LR f1 score: 0.522
+LR cohens kappa score: 0.483
+LR average precision score: 0.581
+
+-> test with 'GB'
+GB tn, fp: 131, 7
+GB fn, tp: 5, 4
+GB f1 score: 0.400
+GB cohens kappa score: 0.357
+
+-> test with 'KNN'
+KNN tn, fp: 120, 18
+KNN fn, tp: 1, 8
+KNN f1 score: 0.457
+KNN cohens kappa score: 0.403
+
+
+------ Step 1/5: Slice 3/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 518 synthetic samples
+-> test with 'LR'
+LR tn, fp: 129, 9
+LR fn, tp: 1, 8
+LR f1 score: 0.615
+LR cohens kappa score: 0.582
+LR average precision score: 0.809
+
+-> test with 'GB'
+GB tn, fp: 132, 6
+GB fn, tp: 4, 5
+GB f1 score: 0.500
+GB cohens kappa score: 0.464
+
+-> test with 'KNN'
+KNN tn, fp: 128, 10
+KNN fn, tp: 3, 6
+KNN f1 score: 0.480
+KNN cohens kappa score: 0.436
+
+
+------ Step 1/5: Slice 4/5 -------
+-> 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: 0.582
+
+-> 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: 132, 6
+KNN fn, tp: 3, 6
+KNN f1 score: 0.571
+KNN cohens kappa score: 0.539
+
+
+------ Step 1/5: Slice 5/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 516 synthetic samples
+-> test with 'LR'
+LR tn, fp: 127, 10
+LR fn, tp: 2, 4
+LR f1 score: 0.400
+LR cohens kappa score: 0.363
+LR average precision score: 0.452
+
+-> test with 'GB'
+GB tn, fp: 134, 3
+GB fn, tp: 4, 2
+GB f1 score: 0.364
+GB cohens kappa score: 0.338
+
+-> test with 'KNN'
+KNN tn, fp: 127, 10
+KNN fn, tp: 2, 4
+KNN f1 score: 0.400
+KNN cohens kappa score: 0.363
+
+
+====== 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: 122, 16
+LR fn, tp: 1, 8
+LR f1 score: 0.485
+LR cohens kappa score: 0.434
+LR average precision score: 0.651
+
+-> test with 'GB'
+GB tn, fp: 136, 2
+GB fn, tp: 5, 4
+GB f1 score: 0.533
+GB cohens kappa score: 0.509
+
+-> test with 'KNN'
+KNN tn, fp: 129, 9
+KNN fn, tp: 5, 4
+KNN f1 score: 0.364
+KNN cohens kappa score: 0.314
+
+
+------ Step 2/5: Slice 2/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 518 synthetic samples
+-> test with 'LR'
+LR tn, fp: 133, 5
+LR fn, tp: 1, 8
+LR f1 score: 0.727
+LR cohens kappa score: 0.706
+LR average precision score: 0.783
+
+-> test with 'GB'
+GB tn, fp: 131, 7
+GB fn, tp: 5, 4
+GB f1 score: 0.400
+GB cohens kappa score: 0.357
+
+-> test with 'KNN'
+KNN tn, fp: 127, 11
+KNN fn, tp: 3, 6
+KNN f1 score: 0.462
+KNN cohens kappa score: 0.415
+
+
+------ Step 2/5: Slice 3/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 518 synthetic samples
+-> test with 'LR'
+LR tn, fp: 128, 10
+LR fn, tp: 2, 7
+LR f1 score: 0.538
+LR cohens kappa score: 0.498
+LR average precision score: 0.653
+
+-> test with 'GB'
+GB tn, fp: 130, 8
+GB fn, tp: 5, 4
+GB f1 score: 0.381
+GB cohens kappa score: 0.334
+
+-> test with 'KNN'
+KNN tn, fp: 123, 15
+KNN fn, tp: 2, 7
+KNN f1 score: 0.452
+KNN cohens kappa score: 0.399
+
+
+------ 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: 0, 9
+LR f1 score: 0.643
+LR cohens kappa score: 0.610
+LR average precision score: 0.719
+
+-> test with 'GB'
+GB tn, fp: 131, 7
+GB fn, tp: 6, 3
+GB f1 score: 0.316
+GB cohens kappa score: 0.269
+
+-> test with 'KNN'
+KNN tn, fp: 123, 15
+KNN fn, tp: 5, 4
+KNN f1 score: 0.286
+KNN cohens kappa score: 0.221
+
+
+------ Step 2/5: Slice 5/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 516 synthetic samples
+-> test with 'LR'
+LR tn, fp: 128, 9
+LR fn, tp: 1, 5
+LR f1 score: 0.500
+LR cohens kappa score: 0.469
+LR average precision score: 0.660
+
+-> test with 'GB'
+GB tn, fp: 128, 9
+GB fn, tp: 3, 3
+GB f1 score: 0.333
+GB cohens kappa score: 0.294
+
+-> test with 'KNN'
+KNN tn, fp: 120, 17
+KNN fn, tp: 2, 4
+KNN f1 score: 0.296
+KNN cohens kappa score: 0.247
+
+
+====== 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: 128, 10
+LR fn, tp: 4, 5
+LR f1 score: 0.417
+LR cohens kappa score: 0.368
+LR average precision score: 0.556
+
+-> test with 'GB'
+GB tn, fp: 131, 7
+GB fn, tp: 7, 2
+GB f1 score: 0.222
+GB cohens kappa score: 0.171
+
+-> test with 'KNN'
+KNN tn, fp: 121, 17
+KNN fn, tp: 6, 3
+KNN f1 score: 0.207
+KNN cohens kappa score: 0.134
+
+
+------ Step 3/5: Slice 2/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 518 synthetic samples
+-> test with 'LR'
+LR tn, fp: 133, 5
+LR fn, tp: 0, 9
+LR f1 score: 0.783
+LR cohens kappa score: 0.765
+LR average precision score: 0.906
+
+-> test with 'GB'
+GB tn, fp: 133, 5
+GB fn, tp: 2, 7
+GB f1 score: 0.667
+GB cohens kappa score: 0.642
+
+-> test with 'KNN'
+KNN tn, fp: 124, 14
+KNN fn, tp: 1, 8
+KNN f1 score: 0.516
+KNN cohens kappa score: 0.470
+
+
+------ Step 3/5: Slice 3/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 518 synthetic samples
+-> test with 'LR'
+LR tn, fp: 133, 5
+LR fn, tp: 3, 6
+LR f1 score: 0.600
+LR cohens kappa score: 0.571
+LR average precision score: 0.693
+
+-> test with 'GB'
+GB tn, fp: 131, 7
+GB fn, tp: 7, 2
+GB f1 score: 0.222
+GB cohens kappa score: 0.171
+
+-> test with 'KNN'
+KNN tn, fp: 127, 11
+KNN fn, tp: 5, 4
+KNN f1 score: 0.333
+KNN cohens kappa score: 0.278
+
+
+------ Step 3/5: Slice 4/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 518 synthetic samples
+-> test with 'LR'
+LR tn, fp: 120, 18
+LR fn, tp: 2, 7
+LR f1 score: 0.412
+LR cohens kappa score: 0.354
+LR average precision score: 0.630
+
+-> test with 'GB'
+GB tn, fp: 130, 8
+GB fn, tp: 6, 3
+GB f1 score: 0.300
+GB cohens kappa score: 0.249
+
+-> test with 'KNN'
+KNN tn, fp: 123, 15
+KNN fn, tp: 4, 5
+KNN f1 score: 0.345
+KNN cohens kappa score: 0.284
+
+
+------ Step 3/5: Slice 5/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 516 synthetic samples
+-> test with 'LR'
+LR tn, fp: 128, 9
+LR fn, tp: 1, 5
+LR f1 score: 0.500
+LR cohens kappa score: 0.469
+LR average precision score: 0.564
+
+-> test with 'GB'
+GB tn, fp: 131, 6
+GB fn, tp: 3, 3
+GB f1 score: 0.400
+GB cohens kappa score: 0.368
+
+-> test with 'KNN'
+KNN tn, fp: 119, 18
+KNN fn, tp: 2, 4
+KNN f1 score: 0.286
+KNN cohens kappa score: 0.235
+
+
+====== 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: 129, 9
+LR fn, tp: 3, 6
+LR f1 score: 0.500
+LR cohens kappa score: 0.459
+LR average precision score: 0.547
+
+-> 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: 128, 10
+KNN fn, tp: 6, 3
+KNN f1 score: 0.273
+KNN cohens kappa score: 0.216
+
+
+------ Step 4/5: Slice 2/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 518 synthetic samples
+-> test with 'LR'
+LR tn, fp: 126, 12
+LR fn, tp: 2, 7
+LR f1 score: 0.500
+LR cohens kappa score: 0.455
+LR average precision score: 0.732
+
+-> test with 'GB'
+GB tn, fp: 128, 10
+GB fn, tp: 4, 5
+GB f1 score: 0.417
+GB cohens kappa score: 0.368
+
+-> test with 'KNN'
+KNN tn, fp: 120, 18
+KNN fn, tp: 3, 6
+KNN f1 score: 0.364
+KNN cohens kappa score: 0.301
+
+
+------ Step 4/5: Slice 3/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 518 synthetic samples
+-> test with 'LR'
+LR tn, fp: 125, 13
+LR fn, tp: 1, 8
+LR f1 score: 0.533
+LR cohens kappa score: 0.490
+LR average precision score: 0.664
+
+-> test with 'GB'
+GB tn, fp: 132, 6
+GB fn, tp: 4, 5
+GB f1 score: 0.500
+GB cohens kappa score: 0.464
+
+-> test with 'KNN'
+KNN tn, fp: 121, 17
+KNN fn, tp: 3, 6
+KNN f1 score: 0.375
+KNN cohens kappa score: 0.315
+
+
+------ Step 4/5: Slice 4/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 518 synthetic samples
+-> test with 'LR'
+LR tn, fp: 123, 15
+LR fn, tp: 0, 9
+LR f1 score: 0.545
+LR cohens kappa score: 0.501
+LR average precision score: 0.906
+
+-> test with 'GB'
+GB tn, fp: 130, 8
+GB fn, tp: 6, 3
+GB f1 score: 0.300
+GB cohens kappa score: 0.249
+
+-> test with 'KNN'
+KNN tn, fp: 116, 22
+KNN fn, tp: 2, 7
+KNN f1 score: 0.368
+KNN cohens kappa score: 0.303
+
+
+------ 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: 1, 5
+LR f1 score: 0.588
+LR cohens kappa score: 0.565
+LR average precision score: 0.510
+
+-> test with 'GB'
+GB tn, fp: 130, 7
+GB fn, tp: 4, 2
+GB f1 score: 0.267
+GB cohens kappa score: 0.228
+
+-> test with 'KNN'
+KNN tn, fp: 125, 12
+KNN fn, tp: 1, 5
+KNN f1 score: 0.435
+KNN cohens kappa score: 0.397
+
+
+====== 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: 125, 13
+LR fn, tp: 2, 7
+LR f1 score: 0.483
+LR cohens kappa score: 0.435
+LR average precision score: 0.693
+
+-> test with 'GB'
+GB tn, fp: 131, 7
+GB fn, tp: 7, 2
+GB f1 score: 0.222
+GB cohens kappa score: 0.171
+
+-> test with 'KNN'
+KNN tn, fp: 125, 13
+KNN fn, tp: 7, 2
+KNN f1 score: 0.167
+KNN cohens kappa score: 0.098
+
+
+------ Step 5/5: Slice 2/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 518 synthetic samples
+-> test with 'LR'
+LR tn, fp: 127, 11
+LR fn, tp: 1, 8
+LR f1 score: 0.571
+LR cohens kappa score: 0.533
+LR average precision score: 0.677
+
+-> test with 'GB'
+GB tn, fp: 132, 6
+GB fn, tp: 6, 3
+GB f1 score: 0.333
+GB cohens kappa score: 0.290
+
+-> test with 'KNN'
+KNN tn, fp: 114, 24
+KNN fn, tp: 5, 4
+KNN f1 score: 0.216
+KNN cohens kappa score: 0.136
+
+
+------ Step 5/5: Slice 3/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 518 synthetic samples
+-> test with 'LR'
+LR tn, fp: 129, 9
+LR fn, tp: 4, 5
+LR f1 score: 0.435
+LR cohens kappa score: 0.389
+LR average precision score: 0.532
+
+-> test with 'GB'
+GB tn, fp: 131, 7
+GB fn, tp: 7, 2
+GB f1 score: 0.222
+GB cohens kappa score: 0.171
+
+-> test with 'KNN'
+KNN tn, fp: 122, 16
+KNN fn, tp: 5, 4
+KNN f1 score: 0.276
+KNN cohens kappa score: 0.209
+
+
+------ Step 5/5: 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: 1, 8
+LR f1 score: 0.667
+LR cohens kappa score: 0.639
+LR average precision score: 0.841
+
+-> test with 'GB'
+GB tn, fp: 132, 6
+GB fn, tp: 5, 4
+GB f1 score: 0.421
+GB cohens kappa score: 0.381
+
+-> test with 'KNN'
+KNN tn, fp: 128, 10
+KNN fn, tp: 2, 7
+KNN f1 score: 0.538
+KNN cohens kappa score: 0.498
+
+
+------ Step 5/5: Slice 5/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 516 synthetic samples
+-> test with 'LR'
+LR tn, fp: 129, 8
+LR fn, tp: 1, 5
+LR f1 score: 0.526
+LR cohens kappa score: 0.497
+LR average precision score: 0.809
+
+-> test with 'GB'
+GB tn, fp: 133, 4
+GB fn, tp: 3, 3
+GB f1 score: 0.462
+GB cohens kappa score: 0.436
+
+-> test with 'KNN'
+KNN tn, fp: 128, 9
+KNN fn, tp: 3, 3
+KNN f1 score: 0.333
+KNN cohens kappa score: 0.294
+
+### Exercise is done.
+
+-----[ LR ]-----
+maximum:
+LR tn, fp: 133, 19
+LR fn, tp: 4, 9
+LR f1 score: 0.783
+LR cohens kappa score: 0.765
+LR average precision score: 0.906
+
+
+average:
+LR tn, fp: 127.72, 10.08
+LR fn, tp: 1.64, 6.76
+LR f1 score: 0.539
+LR cohens kappa score: 0.501
+LR average precision score: 0.681
+
+
+minimum:
+LR tn, fp: 119, 5
+LR fn, tp: 0, 4
+LR f1 score: 0.400
+LR cohens kappa score: 0.354
+LR average precision score: 0.452
+
+
+-----[ GB ]-----
+maximum:
+GB tn, fp: 136, 10
+GB fn, tp: 7, 7
+GB f1 score: 0.667
+GB cohens kappa score: 0.642
+
+
+average:
+GB tn, fp: 131.64, 6.16
+GB fn, tp: 5.04, 3.36
+GB f1 score: 0.374
+GB cohens kappa score: 0.334
+
+
+minimum:
+GB tn, fp: 128, 2
+GB fn, tp: 2, 2
+GB f1 score: 0.222
+GB cohens kappa score: 0.171
+
+
+-----[ KNN ]-----
+maximum:
+KNN tn, fp: 132, 24
+KNN fn, tp: 7, 8
+KNN f1 score: 0.571
+KNN cohens kappa score: 0.539
+
+
+average:
+KNN tn, fp: 123.6, 14.2
+KNN fn, tp: 3.36, 5.04
+KNN f1 score: 0.367
+KNN cohens kappa score: 0.312
+
+
+minimum:
+KNN tn, fp: 114, 6
+KNN fn, tp: 1, 2
+KNN f1 score: 0.167
+KNN cohens kappa score: 0.098
+

data_result/convGAN-full/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;11.000;1.000;36.000;0.373;0.346;0.441
+2;416.000;10.000;2.000;40.000;0.323;0.293;0.609
+3;425.000;7.000;5.000;31.000;0.280;0.251;0.340
+4;416.000;9.000;3.000;40.000;0.295;0.265;0.535
+5;418.000;7.000;3.000;38.000;0.255;0.227;0.306
+6;422.000;11.000;1.000;34.000;0.386;0.360;0.655
+7;409.000;12.000;0.000;47.000;0.338;0.309;0.524
+8;421.000;7.000;5.000;35.000;0.259;0.228;0.265
+9;428.000;9.000;3.000;28.000;0.367;0.342;0.529
+10;420.000;7.000;3.000;36.000;0.264;0.238;0.430
+11;415.000;9.000;3.000;41.000;0.290;0.260;0.513
+12;410.000;7.000;5.000;46.000;0.215;0.181;0.310
+13;422.000;11.000;1.000;34.000;0.386;0.360;0.488
+14;417.000;9.000;3.000;39.000;0.300;0.270;0.309
+15;412.000;9.000;1.000;44.000;0.286;0.259;0.561
+16;416.000;11.000;1.000;40.000;0.349;0.321;0.581
+17;417.000;11.000;1.000;39.000;0.355;0.327;0.585
+18;418.000;10.000;2.000;38.000;0.333;0.305;0.487
+19;430.000;10.000;2.000;26.000;0.417;0.393;0.494
+20;422.000;8.000;2.000;34.000;0.308;0.283;0.337
+21;412.000;10.000;2.000;44.000;0.303;0.273;0.386
+22;422.000;10.000;2.000;34.000;0.357;0.330;0.364
+23;423.000;9.000;3.000;33.000;0.333;0.306;0.347
+24;419.000;11.000;1.000;37.000;0.367;0.340;0.699
+25;416.000;9.000;1.000;40.000;0.305;0.279;0.419
+max;430.000;12.000;5.000;47.000;0.417;0.393;0.699
+avg;418.640;9.360;2.240;37.360;0.322;0.294;0.461
+min;409.000;7.000;0.000;26.000;0.215;0.181;0.265
+---
+GB
+Nr.;TN;TP;FN;FP;f1 score;cohens kappa score
+1;439.000;6.000;6.000;17.000;0.343;0.320
+2;433.000;7.000;5.000;23.000;0.333;0.308
+3;439.000;4.000;8.000;17.000;0.242;0.217
+4;440.000;6.000;6.000;16.000;0.353;0.331
+5;446.000;3.000;7.000;10.000;0.261;0.242
+6;441.000;4.000;8.000;15.000;0.258;0.234
+7;440.000;8.000;4.000;16.000;0.444;0.425
+8;440.000;3.000;9.000;16.000;0.194;0.167
+9;448.000;5.000;7.000;8.000;0.400;0.384
+10;438.000;5.000;5.000;18.000;0.303;0.282
+11;437.000;5.000;7.000;19.000;0.278;0.252
+12;433.000;4.000;8.000;23.000;0.205;0.176
+13;440.000;7.000;5.000;16.000;0.400;0.379
+14;444.000;6.000;6.000;12.000;0.400;0.381
+15;439.000;7.000;3.000;17.000;0.412;0.393
+16;439.000;7.000;5.000;17.000;0.389;0.367
+17;440.000;6.000;6.000;16.000;0.353;0.331
+18;439.000;4.000;8.000;17.000;0.242;0.217
+19;449.000;2.000;10.000;7.000;0.190;0.172
+20;445.000;4.000;6.000;11.000;0.320;0.302
+21;439.000;5.000;7.000;17.000;0.294;0.270
+22;442.000;4.000;8.000;14.000;0.267;0.243
+23;437.000;6.000;6.000;19.000;0.324;0.300
+24;436.000;3.000;9.000;20.000;0.171;0.143
+25;443.000;8.000;2.000;13.000;0.516;0.502
+max;449.000;8.000;10.000;23.000;0.516;0.502
+avg;440.240;5.160;6.440;15.760;0.316;0.293
+min;433.000;2.000;2.000;7.000;0.171;0.143
+---
+KNN
+Nr.;TN;TP;FN;FP;f1 score;cohens kappa score
+1;426.000;7.000;5.000;30.000;0.286;0.257
+2;417.000;10.000;2.000;39.000;0.328;0.299
+3;429.000;7.000;5.000;27.000;0.304;0.277
+4;419.000;10.000;2.000;37.000;0.339;0.311
+5;426.000;4.000;6.000;30.000;0.182;0.154
+6;420.000;8.000;4.000;36.000;0.286;0.256
+7;409.000;10.000;2.000;47.000;0.290;0.258
+8;417.000;7.000;5.000;39.000;0.241;0.209
+9;428.000;8.000;4.000;28.000;0.333;0.307
+10;413.000;7.000;3.000;43.000;0.233;0.205
+11;425.000;8.000;4.000;31.000;0.314;0.286
+12;421.000;9.000;3.000;35.000;0.321;0.293
+13;416.000;7.000;5.000;40.000;0.237;0.205
+14;423.000;10.000;2.000;33.000;0.364;0.337
+15;421.000;8.000;2.000;35.000;0.302;0.277
+16;418.000;9.000;3.000;38.000;0.305;0.275
+17;412.000;10.000;2.000;44.000;0.303;0.273
+18;416.000;8.000;4.000;40.000;0.267;0.235
+19;429.000;7.000;5.000;27.000;0.304;0.277
+20;427.000;5.000;5.000;29.000;0.227;0.201
+21;421.000;7.000;5.000;35.000;0.259;0.228
+22;424.000;8.000;4.000;32.000;0.308;0.279
+23;420.000;11.000;1.000;36.000;0.373;0.346
+24;427.000;7.000;5.000;29.000;0.292;0.263
+25;423.000;8.000;2.000;33.000;0.314;0.289
+max;429.000;11.000;6.000;47.000;0.373;0.346
+avg;421.080;8.000;3.600;34.920;0.292;0.264
+min;409.000;4.000;1.000;27.000;0.182;0.154

data_result/convGAN-full/folding_abalone_17_vs_7_8_9_10.log

@@ -0,0 +1,701 @@
+
+
+///////////////////////////////////////////
+// Running convGAN-full 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: 1, 11
+LR f1 score: 0.373
+LR cohens kappa score: 0.346
+LR average precision score: 0.441
+
+-> test with 'GB'
+GB tn, fp: 439, 17
+GB fn, tp: 6, 6
+GB f1 score: 0.343
+GB cohens kappa score: 0.320
+
+-> test with 'KNN'
+KNN tn, fp: 426, 30
+KNN fn, tp: 5, 7
+KNN f1 score: 0.286
+KNN cohens kappa score: 0.257
+
+
+------ Step 1/5: Slice 2/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 1778 synthetic samples
+-> test with 'LR'
+LR tn, fp: 416, 40
+LR fn, tp: 2, 10
+LR f1 score: 0.323
+LR cohens kappa score: 0.293
+LR average precision score: 0.609
+
+-> test with 'GB'
+GB tn, fp: 433, 23
+GB fn, tp: 5, 7
+GB f1 score: 0.333
+GB cohens kappa score: 0.308
+
+-> test with 'KNN'
+KNN tn, fp: 417, 39
+KNN fn, tp: 2, 10
+KNN f1 score: 0.328
+KNN cohens kappa score: 0.299
+
+
+------ Step 1/5: Slice 3/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 1778 synthetic samples
+-> test with 'LR'
+LR tn, fp: 425, 31
+LR fn, tp: 5, 7
+LR f1 score: 0.280
+LR cohens kappa score: 0.251
+LR average precision score: 0.340
+
+-> test with 'GB'
+GB tn, fp: 439, 17
+GB fn, tp: 8, 4
+GB f1 score: 0.242
+GB cohens kappa score: 0.217
+
+-> test with 'KNN'
+KNN tn, fp: 429, 27
+KNN fn, tp: 5, 7
+KNN f1 score: 0.304
+KNN cohens kappa score: 0.277
+
+
+------ Step 1/5: Slice 4/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 1778 synthetic samples
+-> test with 'LR'
+LR tn, fp: 416, 40
+LR fn, tp: 3, 9
+LR f1 score: 0.295
+LR cohens kappa score: 0.265
+LR average precision score: 0.535
+
+-> test with 'GB'
+GB tn, fp: 440, 16
+GB fn, tp: 6, 6
+GB f1 score: 0.353
+GB cohens kappa score: 0.331
+
+-> test with 'KNN'
+KNN tn, fp: 419, 37
+KNN fn, tp: 2, 10
+KNN f1 score: 0.339
+KNN cohens kappa score: 0.311
+
+
+------ Step 1/5: Slice 5/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 1776 synthetic samples
+-> test with 'LR'
+LR tn, fp: 418, 38
+LR fn, tp: 3, 7
+LR f1 score: 0.255
+LR cohens kappa score: 0.227
+LR average precision score: 0.306
+
+-> test with 'GB'
+GB tn, fp: 446, 10
+GB fn, tp: 7, 3
+GB f1 score: 0.261
+GB cohens kappa score: 0.242
+
+-> test with 'KNN'
+KNN tn, fp: 426, 30
+KNN fn, tp: 6, 4
+KNN f1 score: 0.182
+KNN cohens kappa score: 0.154
+
+
+====== 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: 422, 34
+LR fn, tp: 1, 11
+LR f1 score: 0.386
+LR cohens kappa score: 0.360
+LR average precision score: 0.655
+
+-> test with 'GB'
+GB tn, fp: 441, 15
+GB fn, tp: 8, 4
+GB f1 score: 0.258
+GB cohens kappa score: 0.234
+
+-> test with 'KNN'
+KNN tn, fp: 420, 36
+KNN fn, tp: 4, 8
+KNN f1 score: 0.286
+KNN cohens kappa score: 0.256
+
+
+------ Step 2/5: Slice 2/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 1778 synthetic samples
+-> test with 'LR'
+LR tn, fp: 409, 47
+LR fn, tp: 0, 12
+LR f1 score: 0.338
+LR cohens kappa score: 0.309
+LR average precision score: 0.524
+
+-> test with 'GB'
+GB tn, fp: 440, 16
+GB fn, tp: 4, 8
+GB f1 score: 0.444
+GB cohens kappa score: 0.425
+
+-> test with 'KNN'
+KNN tn, fp: 409, 47
+KNN fn, tp: 2, 10
+KNN f1 score: 0.290
+KNN cohens kappa score: 0.258
+
+
+------ Step 2/5: Slice 3/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 1778 synthetic samples
+-> test with 'LR'
+LR tn, fp: 421, 35
+LR fn, tp: 5, 7
+LR f1 score: 0.259
+LR cohens kappa score: 0.228
+LR average precision score: 0.265
+
+-> test with 'GB'
+GB tn, fp: 440, 16
+GB fn, tp: 9, 3
+GB f1 score: 0.194
+GB cohens kappa score: 0.167
+
+-> test with 'KNN'
+KNN tn, fp: 417, 39
+KNN fn, tp: 5, 7
+KNN f1 score: 0.241
+KNN cohens kappa score: 0.209
+
+
+------ Step 2/5: Slice 4/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 1778 synthetic samples
+-> test with 'LR'
+LR tn, fp: 428, 28
+LR fn, tp: 3, 9
+LR f1 score: 0.367
+LR cohens kappa score: 0.342
+LR average precision score: 0.529
+
+-> test with 'GB'
+GB tn, fp: 448, 8
+GB fn, tp: 7, 5
+GB f1 score: 0.400
+GB cohens kappa score: 0.384
+
+-> test with 'KNN'
+KNN tn, fp: 428, 28
+KNN fn, tp: 4, 8
+KNN f1 score: 0.333
+KNN cohens kappa score: 0.307
+
+
+------ 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: 3, 7
+LR f1 score: 0.264
+LR cohens kappa score: 0.238
+LR average precision score: 0.430
+
+-> test with 'GB'
+GB tn, fp: 438, 18
+GB fn, tp: 5, 5
+GB f1 score: 0.303
+GB cohens kappa score: 0.282
+
+-> test with 'KNN'
+KNN tn, fp: 413, 43
+KNN fn, tp: 3, 7
+KNN f1 score: 0.233
+KNN cohens kappa score: 0.205
+
+
+====== 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: 415, 41
+LR fn, tp: 3, 9
+LR f1 score: 0.290
+LR cohens kappa score: 0.260
+LR average precision score: 0.513
+
+-> test with 'GB'
+GB tn, fp: 437, 19
+GB fn, tp: 7, 5
+GB f1 score: 0.278
+GB cohens kappa score: 0.252
+
+-> test with 'KNN'
+KNN tn, fp: 425, 31
+KNN fn, tp: 4, 8
+KNN f1 score: 0.314
+KNN cohens kappa score: 0.286
+
+
+------ Step 3/5: Slice 2/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 1778 synthetic samples
+-> test with 'LR'
+LR tn, fp: 410, 46
+LR fn, tp: 5, 7
+LR f1 score: 0.215
+LR cohens kappa score: 0.181
+LR average precision score: 0.310
+
+-> test with 'GB'
+GB tn, fp: 433, 23
+GB fn, tp: 8, 4
+GB f1 score: 0.205
+GB cohens kappa score: 0.176
+
+-> test with 'KNN'
+KNN tn, fp: 421, 35
+KNN fn, tp: 3, 9
+KNN f1 score: 0.321
+KNN cohens kappa score: 0.293
+
+
+------ Step 3/5: Slice 3/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 1778 synthetic samples
+-> test with 'LR'
+LR tn, fp: 422, 34
+LR fn, tp: 1, 11
+LR f1 score: 0.386
+LR cohens kappa score: 0.360
+LR average precision score: 0.488
+
+-> test with 'GB'
+GB tn, fp: 440, 16
+GB fn, tp: 5, 7
+GB f1 score: 0.400
+GB cohens kappa score: 0.379
+
+-> test with 'KNN'
+KNN tn, fp: 416, 40
+KNN fn, tp: 5, 7
+KNN f1 score: 0.237
+KNN cohens kappa score: 0.205
+
+
+------ Step 3/5: Slice 4/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 1778 synthetic samples
+-> test with 'LR'
+LR tn, fp: 417, 39
+LR fn, tp: 3, 9
+LR f1 score: 0.300
+LR cohens kappa score: 0.270
+LR average precision score: 0.309
+
+-> test with 'GB'
+GB tn, fp: 444, 12
+GB fn, tp: 6, 6
+GB f1 score: 0.400
+GB cohens kappa score: 0.381
+
+-> test with 'KNN'
+KNN tn, fp: 423, 33
+KNN fn, tp: 2, 10
+KNN f1 score: 0.364
+KNN cohens kappa score: 0.337
+
+
+------ Step 3/5: Slice 5/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 1776 synthetic samples
+-> test with 'LR'
+LR tn, fp: 412, 44
+LR fn, tp: 1, 9
+LR f1 score: 0.286
+LR cohens kappa score: 0.259
+LR average precision score: 0.561
+
+-> test with 'GB'
+GB tn, fp: 439, 17
+GB fn, tp: 3, 7
+GB f1 score: 0.412
+GB cohens kappa score: 0.393
+
+-> test with 'KNN'
+KNN tn, fp: 421, 35
+KNN fn, tp: 2, 8
+KNN f1 score: 0.302
+KNN cohens kappa score: 0.277
+
+
+====== 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: 416, 40
+LR fn, tp: 1, 11
+LR f1 score: 0.349
+LR cohens kappa score: 0.321
+LR average precision score: 0.581
+
+-> test with 'GB'
+GB tn, fp: 439, 17
+GB fn, tp: 5, 7
+GB f1 score: 0.389
+GB cohens kappa score: 0.367
+
+-> test with 'KNN'
+KNN tn, fp: 418, 38
+KNN fn, tp: 3, 9
+KNN f1 score: 0.305
+KNN cohens kappa score: 0.275
+
+
+------ Step 4/5: Slice 2/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 1778 synthetic samples
+-> test with 'LR'
+LR tn, fp: 417, 39
+LR fn, tp: 1, 11
+LR f1 score: 0.355
+LR cohens kappa score: 0.327
+LR average precision score: 0.585
+
+-> test with 'GB'
+GB tn, fp: 440, 16
+GB fn, tp: 6, 6
+GB f1 score: 0.353
+GB cohens kappa score: 0.331
+
+-> test with 'KNN'
+KNN tn, fp: 412, 44
+KNN fn, tp: 2, 10
+KNN f1 score: 0.303
+KNN cohens kappa score: 0.273
+
+
+------ Step 4/5: Slice 3/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 1778 synthetic samples
+-> test with 'LR'
+LR tn, fp: 418, 38
+LR fn, tp: 2, 10
+LR f1 score: 0.333
+LR cohens kappa score: 0.305
+LR average precision score: 0.487
+
+-> test with 'GB'
+GB tn, fp: 439, 17
+GB fn, tp: 8, 4
+GB f1 score: 0.242
+GB cohens kappa score: 0.217
+
+-> test with 'KNN'
+KNN tn, fp: 416, 40
+KNN fn, tp: 4, 8
+KNN f1 score: 0.267
+KNN cohens kappa score: 0.235
+
+
+------ Step 4/5: Slice 4/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 1778 synthetic samples
+-> test with 'LR'
+LR tn, fp: 430, 26
+LR fn, tp: 2, 10
+LR f1 score: 0.417
+LR cohens kappa score: 0.393
+LR average precision score: 0.494
+
+-> 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: 429, 27
+KNN fn, tp: 5, 7
+KNN f1 score: 0.304
+KNN cohens kappa score: 0.277
+
+
+------ Step 4/5: Slice 5/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 1776 synthetic samples
+-> test with 'LR'
+LR tn, fp: 422, 34
+LR fn, tp: 2, 8
+LR f1 score: 0.308
+LR cohens kappa score: 0.283
+LR average precision score: 0.337
+
+-> test with 'GB'
+GB tn, fp: 445, 11
+GB fn, tp: 6, 4
+GB f1 score: 0.320
+GB cohens kappa score: 0.302
+
+-> test with 'KNN'
+KNN tn, fp: 427, 29
+KNN fn, tp: 5, 5
+KNN f1 score: 0.227
+KNN cohens kappa score: 0.201
+
+
+====== 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: 412, 44
+LR fn, tp: 2, 10
+LR f1 score: 0.303
+LR cohens kappa score: 0.273
+LR average precision score: 0.386
+
+-> test with 'GB'
+GB tn, fp: 439, 17
+GB fn, tp: 7, 5
+GB f1 score: 0.294
+GB cohens kappa score: 0.270
+
+-> test with 'KNN'
+KNN tn, fp: 421, 35
+KNN fn, tp: 5, 7
+KNN f1 score: 0.259
+KNN cohens kappa score: 0.228
+
+
+------ Step 5/5: Slice 2/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 1778 synthetic samples
+-> test with 'LR'
+LR tn, fp: 422, 34
+LR fn, tp: 2, 10
+LR f1 score: 0.357
+LR cohens kappa score: 0.330
+LR average precision score: 0.364
+
+-> test with 'GB'
+GB tn, fp: 442, 14
+GB fn, tp: 8, 4
+GB f1 score: 0.267
+GB cohens kappa score: 0.243
+
+-> test with 'KNN'
+KNN tn, fp: 424, 32
+KNN fn, tp: 4, 8
+KNN f1 score: 0.308
+KNN cohens kappa score: 0.279
+
+
+------ Step 5/5: Slice 3/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 1778 synthetic samples
+-> test with 'LR'
+LR tn, fp: 423, 33
+LR fn, tp: 3, 9
+LR f1 score: 0.333
+LR cohens kappa score: 0.306
+LR average precision score: 0.347
+
+-> test with 'GB'
+GB tn, fp: 437, 19
+GB fn, tp: 6, 6
+GB f1 score: 0.324
+GB cohens kappa score: 0.300
+
+-> test with 'KNN'
+KNN tn, fp: 420, 36
+KNN fn, tp: 1, 11
+KNN f1 score: 0.373
+KNN cohens kappa score: 0.346
+
+
+------ 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: 1, 11
+LR f1 score: 0.367
+LR cohens kappa score: 0.340
+LR average precision score: 0.699
+
+-> test with 'GB'
+GB tn, fp: 436, 20
+GB fn, tp: 9, 3
+GB f1 score: 0.171
+GB cohens kappa score: 0.143
+
+-> test with 'KNN'
+KNN tn, fp: 427, 29
+KNN fn, tp: 5, 7
+KNN f1 score: 0.292
+KNN cohens kappa score: 0.263
+
+
+------ Step 5/5: Slice 5/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 1776 synthetic samples
+-> test with 'LR'
+LR tn, fp: 416, 40
+LR fn, tp: 1, 9
+LR f1 score: 0.305
+LR cohens kappa score: 0.279
+LR average precision score: 0.419
+
+-> test with 'GB'
+GB tn, fp: 443, 13
+GB fn, tp: 2, 8
+GB f1 score: 0.516
+GB cohens kappa score: 0.502
+
+-> test with 'KNN'
+KNN tn, fp: 423, 33
+KNN fn, tp: 2, 8
+KNN f1 score: 0.314
+KNN cohens kappa score: 0.289
+
+### Exercise is done.
+
+-----[ LR ]-----
+maximum:
+LR tn, fp: 430, 47
+LR fn, tp: 5, 12
+LR f1 score: 0.417
+LR cohens kappa score: 0.393
+LR average precision score: 0.699
+
+
+average:
+LR tn, fp: 418.64, 37.36
+LR fn, tp: 2.24, 9.36
+LR f1 score: 0.322
+LR cohens kappa score: 0.294
+LR average precision score: 0.461
+
+
+minimum:
+LR tn, fp: 409, 26
+LR fn, tp: 0, 7
+LR f1 score: 0.215
+LR cohens kappa score: 0.181
+LR average precision score: 0.265
+
+
+-----[ GB ]-----
+maximum:
+GB tn, fp: 449, 23
+GB fn, tp: 10, 8
+GB f1 score: 0.516
+GB cohens kappa score: 0.502
+
+
+average:
+GB tn, fp: 440.24, 15.76
+GB fn, tp: 6.44, 5.16
+GB f1 score: 0.316
+GB cohens kappa score: 0.293
+
+
+minimum:
+GB tn, fp: 433, 7
+GB fn, tp: 2, 2
+GB f1 score: 0.171
+GB cohens kappa score: 0.143
+
+
+-----[ KNN ]-----
+maximum:
+KNN tn, fp: 429, 47
+KNN fn, tp: 6, 11
+KNN f1 score: 0.373
+KNN cohens kappa score: 0.346
+
+
+average:
+KNN tn, fp: 421.08, 34.92
+KNN fn, tp: 3.6, 8.0
+KNN f1 score: 0.292
+KNN cohens kappa score: 0.264
+
+
+minimum:
+KNN tn, fp: 409, 27
+KNN fn, tp: 1, 4
+KNN f1 score: 0.182
+KNN cohens kappa score: 0.154
+

data_result/convGAN-full/folding_car-vgood.csv

@@ -0,0 +1,92 @@
+LR
+Nr.;TN;TP;FN;FP;f1 score;cohens kappa score;average precision score
+1;288.000;13.000;0.000;45.000;0.366;0.325;0.313
+2;297.000;10.000;3.000;36.000;0.339;0.298;0.302
+3;288.000;13.000;0.000;45.000;0.366;0.325;0.389
+4;296.000;13.000;0.000;37.000;0.413;0.375;0.357
+5;301.000;11.000;2.000;30.000;0.407;0.371;0.443
+6;297.000;13.000;0.000;36.000;0.419;0.383;0.288
+7;279.000;13.000;0.000;54.000;0.325;0.280;0.373
+8;294.000;12.000;1.000;39.000;0.375;0.335;0.336
+9;296.000;13.000;0.000;37.000;0.413;0.375;0.285
+10;292.000;12.000;1.000;39.000;0.375;0.335;0.551
+11;296.000;12.000;1.000;37.000;0.387;0.348;0.311
+12;299.000;13.000;0.000;34.000;0.433;0.398;0.438
+13;284.000;13.000;0.000;49.000;0.347;0.303;0.316
+14;293.000;13.000;0.000;40.000;0.394;0.355;0.407
+15;298.000;11.000;2.000;33.000;0.386;0.348;0.371
+16;298.000;13.000;0.000;35.000;0.426;0.390;0.360
+17;293.000;12.000;1.000;40.000;0.369;0.329;0.496
+18;289.000;13.000;0.000;44.000;0.371;0.330;0.317
+19;299.000;11.000;2.000;34.000;0.379;0.341;0.270
+20;298.000;12.000;1.000;33.000;0.414;0.377;0.323
+21;284.000;13.000;0.000;49.000;0.347;0.303;0.292
+22;299.000;10.000;3.000;34.000;0.351;0.311;0.360
+23;306.000;11.000;2.000;27.000;0.431;0.398;0.332
+24;285.000;13.000;0.000;48.000;0.351;0.309;0.284
+25;292.000;13.000;0.000;39.000;0.400;0.361;0.466
+max;306.000;13.000;3.000;54.000;0.433;0.398;0.551
+avg;293.640;12.240;0.760;38.960;0.383;0.344;0.359
+min;279.000;10.000;0.000;27.000;0.325;0.280;0.270
+---
+GB
+Nr.;TN;TP;FN;FP;f1 score;cohens kappa score
+1;331.000;13.000;0.000;2.000;0.929;0.926
+2;332.000;13.000;0.000;1.000;0.963;0.961
+3;333.000;12.000;1.000;0.000;0.960;0.959
+4;333.000;13.000;0.000;0.000;1.000;1.000
+5;328.000;13.000;0.000;3.000;0.897;0.892
+6;333.000;11.000;2.000;0.000;0.917;0.914
+7;331.000;13.000;0.000;2.000;0.929;0.926
+8;332.000;13.000;0.000;1.000;0.963;0.961
+9;333.000;11.000;2.000;0.000;0.917;0.914
+10;331.000;13.000;0.000;0.000;1.000;1.000
+11;333.000;13.000;0.000;0.000;1.000;1.000
+12;333.000;13.000;0.000;0.000;1.000;1.000
+13;332.000;13.000;0.000;1.000;0.963;0.961
+14;332.000;13.000;0.000;1.000;0.963;0.961
+15;331.000;12.000;1.000;0.000;0.960;0.958
+16;333.000;12.000;1.000;0.000;0.960;0.959
+17;332.000;12.000;1.000;1.000;0.923;0.920
+18;332.000;13.000;0.000;1.000;0.963;0.961
+19;333.000;13.000;0.000;0.000;1.000;1.000
+20;331.000;13.000;0.000;0.000;1.000;1.000
+21;332.000;13.000;0.000;1.000;0.963;0.961
+22;333.000;13.000;0.000;0.000;1.000;1.000
+23;333.000;13.000;0.000;0.000;1.000;1.000
+24;332.000;13.000;0.000;1.000;0.963;0.961
+25;329.000;12.000;1.000;2.000;0.889;0.884
+max;333.000;13.000;2.000;3.000;1.000;1.000
+avg;331.920;12.640;0.360;0.680;0.961;0.959
+min;328.000;11.000;0.000;0.000;0.889;0.884
+---
+KNN
+Nr.;TN;TP;FN;FP;f1 score;cohens kappa score
+1;323.000;13.000;0.000;10.000;0.722;0.708
+2;321.000;13.000;0.000;12.000;0.684;0.668
+3;323.000;13.000;0.000;10.000;0.722;0.708
+4;324.000;13.000;0.000;9.000;0.743;0.730
+5;322.000;13.000;0.000;9.000;0.743;0.730
+6;317.000;13.000;0.000;16.000;0.619;0.598
+7;316.000;13.000;0.000;17.000;0.605;0.583
+8;320.000;13.000;0.000;13.000;0.667;0.649
+9;324.000;13.000;0.000;9.000;0.743;0.730
+10;329.000;13.000;0.000;2.000;0.929;0.926
+11;323.000;13.000;0.000;10.000;0.722;0.708
+12;327.000;13.000;0.000;6.000;0.813;0.804
+13;320.000;13.000;0.000;13.000;0.667;0.649
+14;319.000;13.000;0.000;14.000;0.650;0.631
+15;326.000;13.000;0.000;5.000;0.839;0.831
+16;328.000;13.000;0.000;5.000;0.839;0.831
+17;329.000;13.000;0.000;4.000;0.867;0.861
+18;319.000;13.000;0.000;14.000;0.650;0.631
+19;320.000;13.000;0.000;13.000;0.667;0.649
+20;321.000;13.000;0.000;10.000;0.722;0.708
+21;326.000;13.000;0.000;7.000;0.788;0.778
+22;329.000;12.000;1.000;4.000;0.828;0.820
+23;327.000;13.000;0.000;6.000;0.813;0.804
+24;320.000;13.000;0.000;13.000;0.667;0.649
+25;323.000;11.000;2.000;8.000;0.688;0.673
+max;329.000;13.000;2.000;17.000;0.929;0.926
+avg;323.040;12.880;0.120;9.560;0.736;0.722
+min;316.000;11.000;0.000;2.000;0.605;0.583

data_result/convGAN-full/folding_car-vgood.log

@@ -0,0 +1,701 @@
+
+
+///////////////////////////////////////////
+// Running convGAN-full 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: 288, 45
+LR fn, tp: 0, 13
+LR f1 score: 0.366
+LR cohens kappa score: 0.325
+LR average precision score: 0.313
+
+-> test with 'GB'
+GB tn, fp: 331, 2
+GB fn, tp: 0, 13
+GB f1 score: 0.929
+GB cohens kappa score: 0.926
+
+-> test with 'KNN'
+KNN tn, fp: 323, 10
+KNN fn, tp: 0, 13
+KNN f1 score: 0.722
+KNN cohens kappa score: 0.708
+
+
+------ Step 1/5: Slice 2/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 1278 synthetic samples
+-> test with 'LR'
+LR tn, fp: 297, 36
+LR fn, tp: 3, 10
+LR f1 score: 0.339
+LR cohens kappa score: 0.298
+LR average precision score: 0.302
+
+-> test with 'GB'
+GB tn, fp: 332, 1
+GB fn, tp: 0, 13
+GB f1 score: 0.963
+GB cohens kappa score: 0.961
+
+-> test with 'KNN'
+KNN tn, fp: 321, 12
+KNN fn, tp: 0, 13
+KNN f1 score: 0.684
+KNN cohens kappa score: 0.668
+
+
+------ Step 1/5: Slice 3/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 1278 synthetic samples
+-> test with 'LR'
+LR tn, fp: 288, 45
+LR fn, tp: 0, 13
+LR f1 score: 0.366
+LR cohens kappa score: 0.325
+LR average precision score: 0.389
+
+-> test with 'GB'
+GB tn, fp: 333, 0
+GB fn, tp: 1, 12
+GB f1 score: 0.960
+GB cohens kappa score: 0.959
+
+-> test with 'KNN'
+KNN tn, fp: 323, 10
+KNN fn, tp: 0, 13
+KNN f1 score: 0.722
+KNN cohens kappa score: 0.708
+
+
+------ Step 1/5: Slice 4/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 1278 synthetic samples
+-> test with 'LR'
+LR tn, fp: 296, 37
+LR fn, tp: 0, 13
+LR f1 score: 0.413
+LR cohens kappa score: 0.375
+LR average precision score: 0.357
+
+-> test with 'GB'
+GB tn, fp: 333, 0
+GB fn, tp: 0, 13
+GB f1 score: 1.000
+GB cohens kappa score: 1.000
+
+-> test with 'KNN'
+KNN tn, fp: 324, 9
+KNN fn, tp: 0, 13
+KNN f1 score: 0.743
+KNN cohens kappa score: 0.730
+
+
+------ Step 1/5: Slice 5/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 1280 synthetic samples
+-> test with 'LR'
+LR tn, fp: 301, 30
+LR fn, tp: 2, 11
+LR f1 score: 0.407
+LR cohens kappa score: 0.371
+LR average precision score: 0.443
+
+-> test with 'GB'
+GB tn, fp: 328, 3
+GB fn, tp: 0, 13
+GB f1 score: 0.897
+GB cohens kappa score: 0.892
+
+-> test with 'KNN'
+KNN tn, fp: 322, 9
+KNN fn, tp: 0, 13
+KNN f1 score: 0.743
+KNN cohens kappa score: 0.730
+
+
+====== 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: 297, 36
+LR fn, tp: 0, 13
+LR f1 score: 0.419
+LR cohens kappa score: 0.383
+LR average precision score: 0.288
+
+-> test with 'GB'
+GB tn, fp: 333, 0
+GB fn, tp: 2, 11
+GB f1 score: 0.917
+GB cohens kappa score: 0.914
+
+-> test with 'KNN'
+KNN tn, fp: 317, 16
+KNN fn, tp: 0, 13
+KNN f1 score: 0.619
+KNN cohens kappa score: 0.598
+
+
+------ Step 2/5: Slice 2/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 1278 synthetic samples
+-> test with 'LR'
+LR tn, fp: 279, 54
+LR fn, tp: 0, 13
+LR f1 score: 0.325
+LR cohens kappa score: 0.280
+LR average precision score: 0.373
+
+-> test with 'GB'
+GB tn, fp: 331, 2
+GB fn, tp: 0, 13
+GB f1 score: 0.929
+GB cohens kappa score: 0.926
+
+-> test with 'KNN'
+KNN tn, fp: 316, 17
+KNN fn, tp: 0, 13
+KNN f1 score: 0.605
+KNN cohens kappa score: 0.583
+
+
+------ Step 2/5: Slice 3/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 1278 synthetic samples
+-> test with 'LR'
+LR tn, fp: 294, 39
+LR fn, tp: 1, 12
+LR f1 score: 0.375
+LR cohens kappa score: 0.335
+LR average precision score: 0.336
+
+-> test with 'GB'
+GB tn, fp: 332, 1
+GB fn, tp: 0, 13
+GB f1 score: 0.963
+GB cohens kappa score: 0.961
+
+-> test with 'KNN'
+KNN tn, fp: 320, 13
+KNN fn, tp: 0, 13
+KNN f1 score: 0.667
+KNN cohens kappa score: 0.649
+
+
+------ Step 2/5: Slice 4/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 1278 synthetic samples
+-> test with 'LR'
+LR tn, fp: 296, 37
+LR fn, tp: 0, 13
+LR f1 score: 0.413
+LR cohens kappa score: 0.375
+LR average precision score: 0.285
+
+-> test with 'GB'
+GB tn, fp: 333, 0
+GB fn, tp: 2, 11
+GB f1 score: 0.917
+GB cohens kappa score: 0.914
+
+-> test with 'KNN'
+KNN tn, fp: 324, 9
+KNN fn, tp: 0, 13
+KNN f1 score: 0.743
+KNN cohens kappa score: 0.730
+
+
+------ Step 2/5: Slice 5/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 1280 synthetic samples
+-> test with 'LR'
+LR tn, fp: 292, 39
+LR fn, tp: 1, 12
+LR f1 score: 0.375
+LR cohens kappa score: 0.335
+LR average precision score: 0.551
+
+-> test with 'GB'
+GB tn, fp: 331, 0
+GB fn, tp: 0, 13
+GB f1 score: 1.000
+GB cohens kappa score: 1.000
+
+-> test with 'KNN'
+KNN tn, fp: 329, 2
+KNN fn, tp: 0, 13
+KNN f1 score: 0.929
+KNN cohens kappa score: 0.926
+
+
+====== 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: 296, 37
+LR fn, tp: 1, 12
+LR f1 score: 0.387
+LR cohens kappa score: 0.348
+LR average precision score: 0.311
+
+-> test with 'GB'
+GB tn, fp: 333, 0
+GB fn, tp: 0, 13
+GB f1 score: 1.000
+GB cohens kappa score: 1.000
+
+-> test with 'KNN'
+KNN tn, fp: 323, 10
+KNN fn, tp: 0, 13
+KNN f1 score: 0.722
+KNN cohens kappa score: 0.708
+
+
+------ Step 3/5: Slice 2/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 1278 synthetic samples
+-> test with 'LR'
+LR tn, fp: 299, 34
+LR fn, tp: 0, 13
+LR f1 score: 0.433
+LR cohens kappa score: 0.398
+LR average precision score: 0.438
+
+-> test with 'GB'
+GB tn, fp: 333, 0
+GB fn, tp: 0, 13
+GB f1 score: 1.000
+GB cohens kappa score: 1.000
+
+-> test with 'KNN'
+KNN tn, fp: 327, 6
+KNN fn, tp: 0, 13
+KNN f1 score: 0.813
+KNN cohens kappa score: 0.804
+
+
+------ Step 3/5: 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: 0.316
+
+-> test with 'GB'
+GB tn, fp: 332, 1
+GB fn, tp: 0, 13
+GB f1 score: 0.963
+GB cohens kappa score: 0.961
+
+-> test with 'KNN'
+KNN tn, fp: 320, 13
+KNN fn, tp: 0, 13
+KNN f1 score: 0.667
+KNN cohens kappa score: 0.649
+
+
+------ Step 3/5: Slice 4/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 1278 synthetic samples
+-> test with 'LR'
+LR tn, fp: 293, 40
+LR fn, tp: 0, 13
+LR f1 score: 0.394
+LR cohens kappa score: 0.355
+LR average precision score: 0.407
+
+-> test with 'GB'
+GB tn, fp: 332, 1
+GB fn, tp: 0, 13
+GB f1 score: 0.963
+GB cohens kappa score: 0.961
+
+-> test with 'KNN'
+KNN tn, fp: 319, 14
+KNN fn, tp: 0, 13
+KNN f1 score: 0.650
+KNN cohens kappa score: 0.631
+
+
+------ Step 3/5: Slice 5/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 1280 synthetic samples
+-> test with 'LR'
+LR tn, fp: 298, 33
+LR fn, tp: 2, 11
+LR f1 score: 0.386
+LR cohens kappa score: 0.348
+LR average precision score: 0.371
+
+-> test with 'GB'
+GB tn, fp: 331, 0
+GB fn, tp: 1, 12
+GB f1 score: 0.960
+GB cohens kappa score: 0.958
+
+-> test with 'KNN'
+KNN tn, fp: 326, 5
+KNN fn, tp: 0, 13
+KNN f1 score: 0.839
+KNN cohens kappa score: 0.831
+
+
+====== 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: 298, 35
+LR fn, tp: 0, 13
+LR f1 score: 0.426
+LR cohens kappa score: 0.390
+LR average precision score: 0.360
+
+-> test with 'GB'
+GB tn, fp: 333, 0
+GB fn, tp: 1, 12
+GB f1 score: 0.960
+GB cohens kappa score: 0.959
+
+-> test with 'KNN'
+KNN tn, fp: 328, 5
+KNN fn, tp: 0, 13
+KNN f1 score: 0.839
+KNN cohens kappa score: 0.831
+
+
+------ Step 4/5: Slice 2/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 1278 synthetic samples
+-> test with 'LR'
+LR tn, fp: 293, 40
+LR fn, tp: 1, 12
+LR f1 score: 0.369
+LR cohens kappa score: 0.329
+LR average precision score: 0.496
+
+-> test with 'GB'
+GB tn, fp: 332, 1
+GB fn, tp: 1, 12
+GB f1 score: 0.923
+GB cohens kappa score: 0.920
+
+-> test with 'KNN'
+KNN tn, fp: 329, 4
+KNN fn, tp: 0, 13
+KNN f1 score: 0.867
+KNN cohens kappa score: 0.861
+
+
+------ Step 4/5: Slice 3/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 1278 synthetic samples
+-> test with 'LR'
+LR tn, fp: 289, 44
+LR fn, tp: 0, 13
+LR f1 score: 0.371
+LR cohens kappa score: 0.330
+LR average precision score: 0.317
+
+-> test with 'GB'
+GB tn, fp: 332, 1
+GB fn, tp: 0, 13
+GB f1 score: 0.963
+GB cohens kappa score: 0.961
+
+-> test with 'KNN'
+KNN tn, fp: 319, 14
+KNN fn, tp: 0, 13
+KNN f1 score: 0.650
+KNN cohens kappa score: 0.631
+
+
+------ Step 4/5: Slice 4/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 1278 synthetic samples
+-> test with 'LR'
+LR tn, fp: 299, 34
+LR fn, tp: 2, 11
+LR f1 score: 0.379
+LR cohens kappa score: 0.341
+LR average precision score: 0.270
+
+-> test with 'GB'
+GB tn, fp: 333, 0
+GB fn, tp: 0, 13
+GB f1 score: 1.000
+GB cohens kappa score: 1.000
+
+-> test with 'KNN'
+KNN tn, fp: 320, 13
+KNN fn, tp: 0, 13
+KNN f1 score: 0.667
+KNN cohens kappa score: 0.649
+
+
+------ Step 4/5: Slice 5/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 1280 synthetic samples
+-> test with 'LR'
+LR tn, fp: 298, 33
+LR fn, tp: 1, 12
+LR f1 score: 0.414
+LR cohens kappa score: 0.377
+LR average precision score: 0.323
+
+-> test with 'GB'
+GB tn, fp: 331, 0
+GB fn, tp: 0, 13
+GB f1 score: 1.000
+GB cohens kappa score: 1.000
+
+-> test with 'KNN'
+KNN tn, fp: 321, 10
+KNN fn, tp: 0, 13
+KNN f1 score: 0.722
+KNN cohens kappa score: 0.708
+
+
+====== 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: 284, 49
+LR fn, tp: 0, 13
+LR f1 score: 0.347
+LR cohens kappa score: 0.303
+LR average precision score: 0.292
+
+-> test with 'GB'
+GB tn, fp: 332, 1
+GB fn, tp: 0, 13
+GB f1 score: 0.963
+GB cohens kappa score: 0.961
+
+-> test with 'KNN'
+KNN tn, fp: 326, 7
+KNN fn, tp: 0, 13
+KNN f1 score: 0.788
+KNN cohens kappa score: 0.778
+
+
+------ Step 5/5: Slice 2/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 1278 synthetic samples
+-> test with 'LR'
+LR tn, fp: 299, 34
+LR fn, tp: 3, 10
+LR f1 score: 0.351
+LR cohens kappa score: 0.311
+LR average precision score: 0.360
+
+-> test with 'GB'
+GB tn, fp: 333, 0
+GB fn, tp: 0, 13
+GB f1 score: 1.000
+GB cohens kappa score: 1.000
+
+-> test with 'KNN'
+KNN tn, fp: 329, 4
+KNN fn, tp: 1, 12
+KNN f1 score: 0.828
+KNN cohens kappa score: 0.820
+
+
+------ Step 5/5: Slice 3/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 1278 synthetic samples
+-> test with 'LR'
+LR tn, fp: 306, 27
+LR fn, tp: 2, 11
+LR f1 score: 0.431
+LR cohens kappa score: 0.398
+LR average precision score: 0.332
+
+-> test with 'GB'
+GB tn, fp: 333, 0
+GB fn, tp: 0, 13
+GB f1 score: 1.000
+GB cohens kappa score: 1.000
+
+-> test with 'KNN'
+KNN tn, fp: 327, 6
+KNN fn, tp: 0, 13
+KNN f1 score: 0.813
+KNN cohens kappa score: 0.804
+
+
+------ Step 5/5: 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: 0.284
+
+-> test with 'GB'
+GB tn, fp: 332, 1
+GB fn, tp: 0, 13
+GB f1 score: 0.963
+GB cohens kappa score: 0.961
+
+-> test with 'KNN'
+KNN tn, fp: 320, 13
+KNN fn, tp: 0, 13
+KNN f1 score: 0.667
+KNN cohens kappa score: 0.649
+
+
+------ Step 5/5: Slice 5/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 1280 synthetic samples
+-> test with 'LR'
+LR tn, fp: 292, 39
+LR fn, tp: 0, 13
+LR f1 score: 0.400
+LR cohens kappa score: 0.361
+LR average precision score: 0.466
+
+-> test with 'GB'
+GB tn, fp: 329, 2
+GB fn, tp: 1, 12
+GB f1 score: 0.889
+GB cohens kappa score: 0.884
+
+-> test with 'KNN'
+KNN tn, fp: 323, 8
+KNN fn, tp: 2, 11
+KNN f1 score: 0.688
+KNN cohens kappa score: 0.673
+
+### Exercise is done.
+
+-----[ LR ]-----
+maximum:
+LR tn, fp: 306, 54
+LR fn, tp: 3, 13
+LR f1 score: 0.433
+LR cohens kappa score: 0.398
+LR average precision score: 0.551
+
+
+average:
+LR tn, fp: 293.64, 38.96
+LR fn, tp: 0.76, 12.24
+LR f1 score: 0.383
+LR cohens kappa score: 0.344
+LR average precision score: 0.359
+
+
+minimum:
+LR tn, fp: 279, 27
+LR fn, tp: 0, 10
+LR f1 score: 0.325
+LR cohens kappa score: 0.280
+LR average precision score: 0.270
+
+
+-----[ GB ]-----
+maximum:
+GB tn, fp: 333, 3
+GB fn, tp: 2, 13
+GB f1 score: 1.000
+GB cohens kappa score: 1.000
+
+
+average:
+GB tn, fp: 331.92, 0.68
+GB fn, tp: 0.36, 12.64
+GB f1 score: 0.961
+GB cohens kappa score: 0.959
+
+
+minimum:
+GB tn, fp: 328, 0
+GB fn, tp: 0, 11
+GB f1 score: 0.889
+GB cohens kappa score: 0.884
+
+
+-----[ KNN ]-----
+maximum:
+KNN tn, fp: 329, 17
+KNN fn, tp: 2, 13
+KNN f1 score: 0.929
+KNN cohens kappa score: 0.926
+
+
+average:
+KNN tn, fp: 323.04, 9.56
+KNN fn, tp: 0.12, 12.88
+KNN f1 score: 0.736
+KNN cohens kappa score: 0.722
+
+
+minimum:
+KNN tn, fp: 316, 2
+KNN fn, tp: 0, 11
+KNN f1 score: 0.605
+KNN cohens kappa score: 0.583
+

data_result/convGAN-full/folding_car_good.csv

@@ -0,0 +1,92 @@
+LR
+Nr.;TN;TP;FN;FP;f1 score;cohens kappa score;average precision score
+1;179.000;8.000;6.000;153.000;0.091;0.018;0.057
+2;179.000;10.000;4.000;153.000;0.113;0.042;0.064
+3;178.000;8.000;6.000;154.000;0.091;0.018;0.056
+4;182.000;11.000;3.000;150.000;0.126;0.055;0.078
+5;184.000;11.000;2.000;147.000;0.129;0.063;0.058
+6;164.000;9.000;5.000;168.000;0.094;0.021;0.066
+7;174.000;11.000;3.000;158.000;0.120;0.049;0.070
+8;194.000;10.000;4.000;138.000;0.123;0.053;0.071
+9;190.000;5.000;9.000;142.000;0.062;-0.013;0.051
+10;187.000;8.000;5.000;144.000;0.097;0.029;0.073
+11;177.000;11.000;3.000;155.000;0.122;0.051;0.068
+12;193.000;9.000;5.000;139.000;0.111;0.040;0.067
+13;181.000;8.000;6.000;151.000;0.092;0.020;0.056
+14;179.000;11.000;3.000;153.000;0.124;0.053;0.083
+15;173.000;8.000;5.000;158.000;0.089;0.021;0.052
+16;166.000;10.000;4.000;166.000;0.105;0.033;0.061
+17;184.000;8.000;6.000;148.000;0.094;0.021;0.055
+18;173.000;10.000;4.000;159.000;0.109;0.037;0.069
+19;185.000;8.000;6.000;147.000;0.095;0.022;0.053
+20;172.000;11.000;2.000;159.000;0.120;0.054;0.082
+21;187.000;6.000;8.000;145.000;0.073;-0.001;0.051
+22;183.000;10.000;4.000;149.000;0.116;0.045;0.069
+23;166.000;10.000;4.000;166.000;0.105;0.033;0.075
+24;172.000;10.000;4.000;160.000;0.109;0.037;0.072
+25;176.000;10.000;3.000;155.000;0.112;0.045;0.057
+max;194.000;11.000;9.000;168.000;0.129;0.063;0.083
+avg;179.120;9.240;4.560;152.680;0.105;0.034;0.064
+min;164.000;5.000;2.000;138.000;0.062;-0.013;0.051
+---
+GB
+Nr.;TN;TP;FN;FP;f1 score;cohens kappa score
+1;330.000;10.000;4.000;2.000;0.769;0.760
+2;330.000;8.000;6.000;2.000;0.667;0.655
+3;331.000;11.000;3.000;1.000;0.846;0.840
+4;331.000;8.000;6.000;1.000;0.696;0.686
+5;330.000;9.000;4.000;1.000;0.783;0.775
+6;330.000;8.000;6.000;2.000;0.667;0.655
+7;332.000;13.000;1.000;0.000;0.963;0.961
+8;331.000;7.000;7.000;1.000;0.636;0.625
+9;331.000;11.000;3.000;1.000;0.846;0.840
+10;328.000;12.000;1.000;3.000;0.857;0.851
+11;330.000;9.000;5.000;2.000;0.720;0.710
+12;330.000;14.000;0.000;2.000;0.933;0.930
+13;330.000;6.000;8.000;2.000;0.545;0.532
+14;332.000;6.000;8.000;0.000;0.600;0.590
+15;328.000;10.000;3.000;3.000;0.769;0.760
+16;330.000;8.000;6.000;2.000;0.667;0.655
+17;330.000;7.000;7.000;2.000;0.609;0.596
+18;331.000;10.000;4.000;1.000;0.800;0.793
+19;327.000;8.000;6.000;5.000;0.593;0.576
+20;328.000;5.000;8.000;3.000;0.476;0.461
+21;330.000;8.000;6.000;2.000;0.667;0.655
+22;330.000;9.000;5.000;2.000;0.720;0.710
+23;327.000;12.000;2.000;5.000;0.774;0.764
+24;332.000;8.000;6.000;0.000;0.727;0.719
+25;331.000;11.000;2.000;0.000;0.917;0.914
+max;332.000;14.000;8.000;5.000;0.963;0.961
+avg;330.000;9.120;4.680;1.800;0.730;0.721
+min;327.000;5.000;0.000;0.000;0.476;0.461
+---
+KNN
+Nr.;TN;TP;FN;FP;f1 score;cohens kappa score
+1;308.000;14.000;0.000;24.000;0.538;0.509
+2;321.000;14.000;0.000;11.000;0.718;0.703
+3;320.000;14.000;0.000;12.000;0.700;0.683
+4;308.000;13.000;1.000;24.000;0.510;0.479
+5;318.000;13.000;0.000;13.000;0.667;0.649
+6;323.000;13.000;1.000;9.000;0.722;0.708
+7;323.000;13.000;1.000;9.000;0.722;0.708
+8;327.000;13.000;1.000;5.000;0.813;0.804
+9;322.000;11.000;3.000;10.000;0.629;0.610
+10;314.000;13.000;0.000;17.000;0.605;0.583
+11;310.000;13.000;1.000;22.000;0.531;0.502
+12;311.000;14.000;0.000;21.000;0.571;0.545
+13;319.000;12.000;2.000;13.000;0.615;0.594
+14;314.000;14.000;0.000;18.000;0.609;0.585
+15;305.000;12.000;1.000;26.000;0.471;0.439
+16;321.000;14.000;0.000;11.000;0.718;0.703
+17;300.000;14.000;0.000;32.000;0.467;0.431
+18;313.000;13.000;1.000;19.000;0.565;0.539
+19;321.000;14.000;0.000;11.000;0.718;0.703
+20;313.000;12.000;1.000;18.000;0.558;0.534
+21;313.000;11.000;3.000;19.000;0.500;0.471
+22;313.000;14.000;0.000;19.000;0.596;0.571
+23;319.000;14.000;0.000;13.000;0.683;0.665
+24;322.000;14.000;0.000;10.000;0.737;0.723
+25;315.000;13.000;0.000;16.000;0.619;0.598
+max;327.000;14.000;3.000;32.000;0.813;0.804
+avg;315.720;13.160;0.640;16.080;0.623;0.602
+min;300.000;11.000;0.000;5.000;0.467;0.431

data_result/convGAN-full/folding_car_good.log

@@ -0,0 +1,701 @@
+
+
+///////////////////////////////////////////
+// Running convGAN-full on folding_car_good
+///////////////////////////////////////////
+
+Load 'data_input/folding_car_good'
+from pickle file
+Data loaded.
+-> Shuffling data
+### Start exercise for synthetic point generator
+
+====== Step 1/5 =======
+-> Shuffling data
+-> Spliting data to slices
+
+------ Step 1/5: Slice 1/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 1272 synthetic samples
+-> test with 'LR'
+LR tn, fp: 179, 153
+LR fn, tp: 6, 8
+LR f1 score: 0.091
+LR cohens kappa score: 0.018
+LR average precision score: 0.057
+
+-> test with 'GB'
+GB tn, fp: 330, 2
+GB fn, tp: 4, 10
+GB f1 score: 0.769
+GB cohens kappa score: 0.760
+
+-> test with 'KNN'
+KNN tn, fp: 308, 24
+KNN fn, tp: 0, 14
+KNN f1 score: 0.538
+KNN cohens kappa score: 0.509
+
+
+------ Step 1/5: Slice 2/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 1272 synthetic samples
+-> test with 'LR'
+LR tn, fp: 179, 153
+LR fn, tp: 4, 10
+LR f1 score: 0.113
+LR cohens kappa score: 0.042
+LR average precision score: 0.064
+
+-> test with 'GB'
+GB tn, fp: 330, 2
+GB fn, tp: 6, 8
+GB f1 score: 0.667
+GB cohens kappa score: 0.655
+
+-> test with 'KNN'
+KNN tn, fp: 321, 11
+KNN fn, tp: 0, 14
+KNN f1 score: 0.718
+KNN cohens kappa score: 0.703
+
+
+------ Step 1/5: Slice 3/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 1272 synthetic samples
+-> test with 'LR'
+LR tn, fp: 178, 154
+LR fn, tp: 6, 8
+LR f1 score: 0.091
+LR cohens kappa score: 0.018
+LR average precision score: 0.056
+
+-> test with 'GB'
+GB tn, fp: 331, 1
+GB fn, tp: 3, 11
+GB f1 score: 0.846
+GB cohens kappa score: 0.840
+
+-> test with 'KNN'
+KNN tn, fp: 320, 12
+KNN fn, tp: 0, 14
+KNN f1 score: 0.700
+KNN cohens kappa score: 0.683
+
+
+------ Step 1/5: Slice 4/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 1272 synthetic samples
+-> test with 'LR'
+LR tn, fp: 182, 150
+LR fn, tp: 3, 11
+LR f1 score: 0.126
+LR cohens kappa score: 0.055
+LR average precision score: 0.078
+
+-> test with 'GB'
+GB tn, fp: 331, 1
+GB fn, tp: 6, 8
+GB f1 score: 0.696
+GB cohens kappa score: 0.686
+
+-> test with 'KNN'
+KNN tn, fp: 308, 24
+KNN fn, tp: 1, 13
+KNN f1 score: 0.510
+KNN cohens kappa score: 0.479
+
+
+------ Step 1/5: Slice 5/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 1272 synthetic samples
+-> test with 'LR'
+LR tn, fp: 184, 147
+LR fn, tp: 2, 11
+LR f1 score: 0.129
+LR cohens kappa score: 0.063
+LR average precision score: 0.058
+
+-> test with 'GB'
+GB tn, fp: 330, 1
+GB fn, tp: 4, 9
+GB f1 score: 0.783
+GB cohens kappa score: 0.775
+
+-> test with 'KNN'
+KNN tn, fp: 318, 13
+KNN fn, tp: 0, 13
+KNN f1 score: 0.667
+KNN cohens kappa score: 0.649
+
+
+====== 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: 164, 168
+LR fn, tp: 5, 9
+LR f1 score: 0.094
+LR cohens kappa score: 0.021
+LR average precision score: 0.066
+
+-> test with 'GB'
+GB tn, fp: 330, 2
+GB fn, tp: 6, 8
+GB f1 score: 0.667
+GB cohens kappa score: 0.655
+
+-> test with 'KNN'
+KNN tn, fp: 323, 9
+KNN fn, tp: 1, 13
+KNN f1 score: 0.722
+KNN cohens kappa score: 0.708
+
+
+------ Step 2/5: Slice 2/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 1272 synthetic samples
+-> test with 'LR'
+LR tn, fp: 174, 158
+LR fn, tp: 3, 11
+LR f1 score: 0.120
+LR cohens kappa score: 0.049
+LR average precision score: 0.070
+
+-> test with 'GB'
+GB tn, fp: 332, 0
+GB fn, tp: 1, 13
+GB f1 score: 0.963
+GB cohens kappa score: 0.961
+
+-> test with 'KNN'
+KNN tn, fp: 323, 9
+KNN fn, tp: 1, 13
+KNN f1 score: 0.722
+KNN cohens kappa score: 0.708
+
+
+------ Step 2/5: Slice 3/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 1272 synthetic samples
+-> test with 'LR'
+LR tn, fp: 194, 138
+LR fn, tp: 4, 10
+LR f1 score: 0.123
+LR cohens kappa score: 0.053
+LR average precision score: 0.071
+
+-> test with 'GB'
+GB tn, fp: 331, 1
+GB fn, tp: 7, 7
+GB f1 score: 0.636
+GB cohens kappa score: 0.625
+
+-> test with 'KNN'
+KNN tn, fp: 327, 5
+KNN fn, tp: 1, 13
+KNN f1 score: 0.813
+KNN cohens kappa score: 0.804
+
+
+------ Step 2/5: Slice 4/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 1272 synthetic samples
+-> test with 'LR'
+LR tn, fp: 190, 142
+LR fn, tp: 9, 5
+LR f1 score: 0.062
+LR cohens kappa score: -0.013
+LR average precision score: 0.051
+
+-> test with 'GB'
+GB tn, fp: 331, 1
+GB fn, tp: 3, 11
+GB f1 score: 0.846
+GB cohens kappa score: 0.840
+
+-> test with 'KNN'
+KNN tn, fp: 322, 10
+KNN fn, tp: 3, 11
+KNN f1 score: 0.629
+KNN cohens kappa score: 0.610
+
+
+------ Step 2/5: Slice 5/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 1272 synthetic samples
+-> test with 'LR'
+LR tn, fp: 187, 144
+LR fn, tp: 5, 8
+LR f1 score: 0.097
+LR cohens kappa score: 0.029
+LR average precision score: 0.073
+
+-> test with 'GB'
+GB tn, fp: 328, 3
+GB fn, tp: 1, 12
+GB f1 score: 0.857
+GB cohens kappa score: 0.851
+
+-> test with 'KNN'
+KNN tn, fp: 314, 17
+KNN fn, tp: 0, 13
+KNN f1 score: 0.605
+KNN cohens kappa score: 0.583
+
+
+====== 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: 177, 155
+LR fn, tp: 3, 11
+LR f1 score: 0.122
+LR cohens kappa score: 0.051
+LR average precision score: 0.068
+
+-> test with 'GB'
+GB tn, fp: 330, 2
+GB fn, tp: 5, 9
+GB f1 score: 0.720
+GB cohens kappa score: 0.710
+
+-> test with 'KNN'
+KNN tn, fp: 310, 22
+KNN fn, tp: 1, 13
+KNN f1 score: 0.531
+KNN cohens kappa score: 0.502
+
+
+------ Step 3/5: Slice 2/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 1272 synthetic samples
+-> test with 'LR'
+LR tn, fp: 193, 139
+LR fn, tp: 5, 9
+LR f1 score: 0.111
+LR cohens kappa score: 0.040
+LR average precision score: 0.067
+
+-> 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: 311, 21
+KNN fn, tp: 0, 14
+KNN f1 score: 0.571
+KNN cohens kappa score: 0.545
+
+
+------ Step 3/5: 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: 6, 8
+LR f1 score: 0.092
+LR cohens kappa score: 0.020
+LR average precision score: 0.056
+
+-> test with 'GB'
+GB tn, fp: 330, 2
+GB fn, tp: 8, 6
+GB f1 score: 0.545
+GB cohens kappa score: 0.532
+
+-> test with 'KNN'
+KNN tn, fp: 319, 13
+KNN fn, tp: 2, 12
+KNN f1 score: 0.615
+KNN cohens kappa score: 0.594
+
+
+------ Step 3/5: 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: 3, 11
+LR f1 score: 0.124
+LR cohens kappa score: 0.053
+LR average precision score: 0.083
+
+-> test with 'GB'
+GB tn, fp: 332, 0
+GB fn, tp: 8, 6
+GB f1 score: 0.600
+GB cohens kappa score: 0.590
+
+-> test with 'KNN'
+KNN tn, fp: 314, 18
+KNN fn, tp: 0, 14
+KNN f1 score: 0.609
+KNN cohens kappa score: 0.585
+
+
+------ Step 3/5: Slice 5/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 1272 synthetic samples
+-> test with 'LR'
+LR tn, fp: 173, 158
+LR fn, tp: 5, 8
+LR f1 score: 0.089
+LR cohens kappa score: 0.021
+LR average precision score: 0.052
+
+-> test with 'GB'
+GB tn, fp: 328, 3
+GB fn, tp: 3, 10
+GB f1 score: 0.769
+GB cohens kappa score: 0.760
+
+-> test with 'KNN'
+KNN tn, fp: 305, 26
+KNN fn, tp: 1, 12
+KNN f1 score: 0.471
+KNN cohens kappa score: 0.439
+
+
+====== 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: 166, 166
+LR fn, tp: 4, 10
+LR f1 score: 0.105
+LR cohens kappa score: 0.033
+LR average precision score: 0.061
+
+-> test with 'GB'
+GB tn, fp: 330, 2
+GB fn, tp: 6, 8
+GB f1 score: 0.667
+GB cohens kappa score: 0.655
+
+-> test with 'KNN'
+KNN tn, fp: 321, 11
+KNN fn, tp: 0, 14
+KNN f1 score: 0.718
+KNN cohens kappa score: 0.703
+
+
+------ Step 4/5: Slice 2/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 1272 synthetic samples
+-> test with 'LR'
+LR tn, fp: 184, 148
+LR fn, tp: 6, 8
+LR f1 score: 0.094
+LR cohens kappa score: 0.021
+LR average precision score: 0.055
+
+-> test with 'GB'
+GB tn, fp: 330, 2
+GB fn, tp: 7, 7
+GB f1 score: 0.609
+GB cohens kappa score: 0.596
+
+-> test with 'KNN'
+KNN tn, fp: 300, 32
+KNN fn, tp: 0, 14
+KNN f1 score: 0.467
+KNN cohens kappa score: 0.431
+
+
+------ Step 4/5: Slice 3/5 -------
+-> 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: 0.069
+
+-> test with 'GB'
+GB tn, fp: 331, 1
+GB fn, tp: 4, 10
+GB f1 score: 0.800
+GB cohens kappa score: 0.793
+
+-> test with 'KNN'
+KNN tn, fp: 313, 19
+KNN fn, tp: 1, 13
+KNN f1 score: 0.565
+KNN cohens kappa score: 0.539
+
+
+------ Step 4/5: 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: 6, 8
+LR f1 score: 0.095
+LR cohens kappa score: 0.022
+LR average precision score: 0.053
+
+-> test with 'GB'
+GB tn, fp: 327, 5
+GB fn, tp: 6, 8
+GB f1 score: 0.593
+GB cohens kappa score: 0.576
+
+-> test with 'KNN'
+KNN tn, fp: 321, 11
+KNN fn, tp: 0, 14
+KNN f1 score: 0.718
+KNN cohens kappa score: 0.703
+
+
+------ Step 4/5: 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: 2, 11
+LR f1 score: 0.120
+LR cohens kappa score: 0.054
+LR average precision score: 0.082
+
+-> test with 'GB'
+GB tn, fp: 328, 3
+GB fn, tp: 8, 5
+GB f1 score: 0.476
+GB cohens kappa score: 0.461
+
+-> test with 'KNN'
+KNN tn, fp: 313, 18
+KNN fn, tp: 1, 12
+KNN f1 score: 0.558
+KNN cohens kappa score: 0.534
+
+
+====== 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: 187, 145
+LR fn, tp: 8, 6
+LR f1 score: 0.073
+LR cohens kappa score: -0.001
+LR average precision score: 0.051
+
+-> test with 'GB'
+GB tn, fp: 330, 2
+GB fn, tp: 6, 8
+GB f1 score: 0.667
+GB cohens kappa score: 0.655
+
+-> test with 'KNN'
+KNN tn, fp: 313, 19
+KNN fn, tp: 3, 11
+KNN f1 score: 0.500
+KNN cohens kappa score: 0.471
+
+
+------ Step 5/5: Slice 2/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 1272 synthetic samples
+-> test with 'LR'
+LR tn, fp: 183, 149
+LR fn, tp: 4, 10
+LR f1 score: 0.116
+LR cohens kappa score: 0.045
+LR average precision score: 0.069
+
+-> test with 'GB'
+GB tn, fp: 330, 2
+GB fn, tp: 5, 9
+GB f1 score: 0.720
+GB cohens kappa score: 0.710
+
+-> test with 'KNN'
+KNN tn, fp: 313, 19
+KNN fn, tp: 0, 14
+KNN f1 score: 0.596
+KNN cohens kappa score: 0.571
+
+
+------ 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: 0.075
+
+-> test with 'GB'
+GB tn, fp: 327, 5
+GB fn, tp: 2, 12
+GB f1 score: 0.774
+GB cohens kappa score: 0.764
+
+-> test with 'KNN'
+KNN tn, fp: 319, 13
+KNN fn, tp: 0, 14
+KNN f1 score: 0.683
+KNN cohens kappa score: 0.665
+
+
+------ Step 5/5: 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: 4, 10
+LR f1 score: 0.109
+LR cohens kappa score: 0.037
+LR average precision score: 0.072
+
+-> test with 'GB'
+GB tn, fp: 332, 0
+GB fn, tp: 6, 8
+GB f1 score: 0.727
+GB cohens kappa score: 0.719
+
+-> test with 'KNN'
+KNN tn, fp: 322, 10
+KNN fn, tp: 0, 14
+KNN f1 score: 0.737
+KNN cohens kappa score: 0.723
+
+
+------ 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: 3, 10
+LR f1 score: 0.112
+LR cohens kappa score: 0.045
+LR average precision score: 0.057
+
+-> test with 'GB'
+GB tn, fp: 331, 0
+GB fn, tp: 2, 11
+GB f1 score: 0.917
+GB cohens kappa score: 0.914
+
+-> test with 'KNN'
+KNN tn, fp: 315, 16
+KNN fn, tp: 0, 13
+KNN f1 score: 0.619
+KNN cohens kappa score: 0.598
+
+### Exercise is done.
+
+-----[ LR ]-----
+maximum:
+LR tn, fp: 194, 168
+LR fn, tp: 9, 11
+LR f1 score: 0.129
+LR cohens kappa score: 0.063
+LR average precision score: 0.083
+
+
+average:
+LR tn, fp: 179.12, 152.68
+LR fn, tp: 4.56, 9.24
+LR f1 score: 0.105
+LR cohens kappa score: 0.034
+LR average precision score: 0.064
+
+
+minimum:
+LR tn, fp: 164, 138
+LR fn, tp: 2, 5
+LR f1 score: 0.062
+LR cohens kappa score: -0.013
+LR average precision score: 0.051
+
+
+-----[ GB ]-----
+maximum:
+GB tn, fp: 332, 5
+GB fn, tp: 8, 14
+GB f1 score: 0.963
+GB cohens kappa score: 0.961
+
+
+average:
+GB tn, fp: 330.0, 1.8
+GB fn, tp: 4.68, 9.12
+GB f1 score: 0.730
+GB cohens kappa score: 0.721
+
+
+minimum:
+GB tn, fp: 327, 0
+GB fn, tp: 0, 5
+GB f1 score: 0.476
+GB cohens kappa score: 0.461
+
+
+-----[ KNN ]-----
+maximum:
+KNN tn, fp: 327, 32
+KNN fn, tp: 3, 14
+KNN f1 score: 0.813
+KNN cohens kappa score: 0.804
+
+
+average:
+KNN tn, fp: 315.72, 16.08
+KNN fn, tp: 0.64, 13.16
+KNN f1 score: 0.623
+KNN cohens kappa score: 0.602
+
+
+minimum:
+KNN tn, fp: 300, 5
+KNN fn, tp: 0, 11
+KNN f1 score: 0.467
+KNN cohens kappa score: 0.431
+