Calculated algorithm benchmarks.

data_result/Repeater/folding_abalone9-18.csv

@@ -0,0 +1,92 @@
+LR
+Nr.;TN;TP;FN;FP;f1 score;cohens kappa score;average precision score
+1;115.000;9.000;0.000;23.000;0.439;0.380;0.927
+2;126.000;6.000;3.000;12.000;0.444;0.395;0.587
+3;126.000;8.000;1.000;12.000;0.552;0.510;0.805
+4;120.000;7.000;2.000;18.000;0.412;0.354;0.526
+5;124.000;5.000;1.000;13.000;0.417;0.377;0.482
+6;119.000;8.000;1.000;19.000;0.444;0.388;0.651
+7;127.000;8.000;1.000;11.000;0.571;0.533;0.787
+8;127.000;7.000;2.000;11.000;0.519;0.476;0.726
+9;120.000;9.000;0.000;18.000;0.500;0.449;0.710
+10;124.000;5.000;1.000;13.000;0.417;0.377;0.661
+11;124.000;7.000;2.000;14.000;0.467;0.417;0.548
+12;127.000;9.000;0.000;11.000;0.621;0.586;0.906
+13;128.000;5.000;4.000;10.000;0.417;0.368;0.653
+14;114.000;7.000;2.000;24.000;0.350;0.282;0.620
+15;122.000;5.000;1.000;15.000;0.385;0.342;0.528
+16;125.000;5.000;4.000;13.000;0.370;0.314;0.529
+17;120.000;7.000;2.000;18.000;0.412;0.354;0.650
+18;123.000;8.000;1.000;15.000;0.500;0.452;0.743
+19;120.000;9.000;0.000;18.000;0.500;0.449;0.947
+20;124.000;5.000;1.000;13.000;0.417;0.377;0.589
+21;116.000;7.000;2.000;22.000;0.368;0.303;0.690
+22;122.000;8.000;1.000;16.000;0.485;0.434;0.680
+23;123.000;6.000;3.000;15.000;0.400;0.344;0.521
+24;123.000;8.000;1.000;15.000;0.500;0.452;0.878
+25;127.000;6.000;0.000;10.000;0.545;0.516;0.819
+max;128.000;9.000;4.000;24.000;0.621;0.586;0.947
+avg;122.640;6.960;1.440;15.160;0.458;0.409;0.687
+min;114.000;5.000;0.000;10.000;0.350;0.282;0.482
+---
+GB
+Nr.;TN;TP;FN;FP;f1 score;cohens kappa score
+1;134.000;6.000;3.000;4.000;0.632;0.606
+2;131.000;3.000;6.000;7.000;0.316;0.269
+3;135.000;2.000;7.000;3.000;0.286;0.253
+4;137.000;3.000;6.000;1.000;0.462;0.440
+5;132.000;1.000;5.000;5.000;0.167;0.130
+6;133.000;3.000;6.000;5.000;0.353;0.313
+7;135.000;4.000;5.000;3.000;0.500;0.472
+8;131.000;4.000;5.000;7.000;0.400;0.357
+9;130.000;4.000;5.000;8.000;0.381;0.334
+10;129.000;4.000;2.000;8.000;0.444;0.412
+11;133.000;2.000;7.000;5.000;0.250;0.208
+12;134.000;3.000;6.000;4.000;0.375;0.340
+13;133.000;2.000;7.000;5.000;0.250;0.208
+14;132.000;5.000;4.000;6.000;0.500;0.464
+15;132.000;3.000;3.000;5.000;0.429;0.400
+16;131.000;3.000;6.000;7.000;0.316;0.269
+17;126.000;6.000;3.000;12.000;0.444;0.395
+18;131.000;4.000;5.000;7.000;0.400;0.357
+19;132.000;5.000;4.000;6.000;0.500;0.464
+20;133.000;2.000;4.000;4.000;0.333;0.304
+21;133.000;2.000;7.000;5.000;0.250;0.208
+22;133.000;5.000;4.000;5.000;0.526;0.494
+23;132.000;2.000;7.000;6.000;0.235;0.189
+24;133.000;6.000;3.000;5.000;0.600;0.571
+25;134.000;2.000;4.000;3.000;0.364;0.338
+max;137.000;6.000;7.000;12.000;0.632;0.606
+avg;132.360;3.440;4.960;5.440;0.388;0.352
+min;126.000;1.000;2.000;1.000;0.167;0.130
+---
+KNN
+Nr.;TN;TP;FN;FP;f1 score;cohens kappa score
+1;124.000;4.000;5.000;14.000;0.296;0.234
+2;122.000;4.000;5.000;16.000;0.276;0.209
+3;126.000;5.000;4.000;12.000;0.385;0.331
+4;124.000;7.000;2.000;14.000;0.467;0.417
+5;127.000;3.000;3.000;10.000;0.316;0.274
+6;130.000;4.000;5.000;8.000;0.381;0.334
+7;125.000;5.000;4.000;13.000;0.370;0.314
+8;119.000;6.000;3.000;19.000;0.353;0.289
+9;124.000;5.000;4.000;14.000;0.357;0.299
+10;124.000;4.000;2.000;13.000;0.348;0.305
+11;121.000;6.000;3.000;17.000;0.375;0.315
+12;119.000;6.000;3.000;19.000;0.353;0.289
+13;126.000;3.000;6.000;12.000;0.250;0.188
+14;122.000;5.000;4.000;16.000;0.333;0.271
+15;126.000;3.000;3.000;11.000;0.300;0.256
+16;124.000;4.000;5.000;14.000;0.296;0.234
+17;124.000;7.000;2.000;14.000;0.467;0.417
+18;128.000;4.000;5.000;10.000;0.348;0.295
+19;122.000;6.000;3.000;16.000;0.387;0.329
+20;121.000;4.000;2.000;16.000;0.308;0.260
+21;123.000;4.000;5.000;15.000;0.286;0.221
+22;122.000;5.000;4.000;16.000;0.333;0.271
+23;124.000;4.000;5.000;14.000;0.296;0.234
+24;126.000;5.000;4.000;12.000;0.385;0.331
+25;124.000;3.000;3.000;13.000;0.273;0.225
+max;130.000;7.000;6.000;19.000;0.467;0.417
+avg;123.880;4.640;3.760;13.920;0.342;0.286
+min;119.000;3.000;2.000;8.000;0.250;0.188

data_result/Repeater/folding_abalone9-18.log

@@ -0,0 +1,701 @@
+
+
+///////////////////////////////////////////
+// Running Repeater 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: 115, 23
+LR fn, tp: 0, 9
+LR f1 score: 0.439
+LR cohens kappa score: 0.380
+LR average precision score: 0.927
+
+-> test with 'GB'
+GB tn, fp: 134, 4
+GB fn, tp: 3, 6
+GB f1 score: 0.632
+GB cohens kappa score: 0.606
+
+-> test with 'KNN'
+KNN tn, fp: 124, 14
+KNN fn, tp: 5, 4
+KNN f1 score: 0.296
+KNN cohens kappa score: 0.234
+
+
+------ Step 1/5: 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: 3, 6
+LR f1 score: 0.444
+LR cohens kappa score: 0.395
+LR average precision score: 0.587
+
+-> 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: 122, 16
+KNN fn, tp: 5, 4
+KNN f1 score: 0.276
+KNN cohens kappa score: 0.209
+
+
+------ Step 1/5: Slice 3/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 518 synthetic samples
+-> test with 'LR'
+LR tn, fp: 126, 12
+LR fn, tp: 1, 8
+LR f1 score: 0.552
+LR cohens kappa score: 0.510
+LR average precision score: 0.805
+
+-> 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: 126, 12
+KNN fn, tp: 4, 5
+KNN f1 score: 0.385
+KNN cohens kappa score: 0.331
+
+
+------ Step 1/5: 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: 0.526
+
+-> test with 'GB'
+GB tn, fp: 137, 1
+GB fn, tp: 6, 3
+GB f1 score: 0.462
+GB cohens kappa score: 0.440
+
+-> test with 'KNN'
+KNN tn, fp: 124, 14
+KNN fn, tp: 2, 7
+KNN f1 score: 0.467
+KNN cohens kappa score: 0.417
+
+
+------ Step 1/5: Slice 5/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 516 synthetic samples
+-> test with 'LR'
+LR tn, fp: 124, 13
+LR fn, tp: 1, 5
+LR f1 score: 0.417
+LR cohens kappa score: 0.377
+LR average precision score: 0.482
+
+-> test with 'GB'
+GB tn, fp: 132, 5
+GB fn, tp: 5, 1
+GB f1 score: 0.167
+GB cohens kappa score: 0.130
+
+-> test with 'KNN'
+KNN tn, fp: 127, 10
+KNN fn, tp: 3, 3
+KNN f1 score: 0.316
+KNN cohens kappa score: 0.274
+
+
+====== 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: 119, 19
+LR fn, tp: 1, 8
+LR f1 score: 0.444
+LR cohens kappa score: 0.388
+LR average precision score: 0.651
+
+-> 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: 130, 8
+KNN fn, tp: 5, 4
+KNN f1 score: 0.381
+KNN cohens kappa score: 0.334
+
+
+------ Step 2/5: 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: 0.787
+
+-> 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: 125, 13
+KNN fn, tp: 4, 5
+KNN f1 score: 0.370
+KNN cohens kappa score: 0.314
+
+
+------ Step 2/5: Slice 3/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 518 synthetic samples
+-> test with 'LR'
+LR tn, fp: 127, 11
+LR fn, tp: 2, 7
+LR f1 score: 0.519
+LR cohens kappa score: 0.476
+LR average precision score: 0.726
+
+-> 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: 119, 19
+KNN fn, tp: 3, 6
+KNN f1 score: 0.353
+KNN cohens kappa score: 0.289
+
+
+------ Step 2/5: 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: 0, 9
+LR f1 score: 0.500
+LR cohens kappa score: 0.449
+LR average precision score: 0.710
+
+-> 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: 124, 14
+KNN fn, tp: 4, 5
+KNN f1 score: 0.357
+KNN cohens kappa score: 0.299
+
+
+------ Step 2/5: Slice 5/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 516 synthetic samples
+-> test with 'LR'
+LR tn, fp: 124, 13
+LR fn, tp: 1, 5
+LR f1 score: 0.417
+LR cohens kappa score: 0.377
+LR average precision score: 0.661
+
+-> test with 'GB'
+GB tn, fp: 129, 8
+GB fn, tp: 2, 4
+GB f1 score: 0.444
+GB cohens kappa score: 0.412
+
+-> test with 'KNN'
+KNN tn, fp: 124, 13
+KNN fn, tp: 2, 4
+KNN f1 score: 0.348
+KNN cohens kappa score: 0.305
+
+
+====== 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: 124, 14
+LR fn, tp: 2, 7
+LR f1 score: 0.467
+LR cohens kappa score: 0.417
+LR average precision score: 0.548
+
+-> 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: 121, 17
+KNN fn, tp: 3, 6
+KNN f1 score: 0.375
+KNN cohens kappa score: 0.315
+
+
+------ Step 3/5: 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: 0, 9
+LR f1 score: 0.621
+LR cohens kappa score: 0.586
+LR average precision score: 0.906
+
+-> test with 'GB'
+GB tn, fp: 134, 4
+GB fn, tp: 6, 3
+GB f1 score: 0.375
+GB cohens kappa score: 0.340
+
+-> test with 'KNN'
+KNN tn, fp: 119, 19
+KNN fn, tp: 3, 6
+KNN f1 score: 0.353
+KNN cohens kappa score: 0.289
+
+
+------ Step 3/5: 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: 4, 5
+LR f1 score: 0.417
+LR cohens kappa score: 0.368
+LR average precision score: 0.653
+
+-> 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: 126, 12
+KNN fn, tp: 6, 3
+KNN f1 score: 0.250
+KNN cohens kappa score: 0.188
+
+
+------ Step 3/5: Slice 4/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 518 synthetic samples
+-> test with 'LR'
+LR tn, fp: 114, 24
+LR fn, tp: 2, 7
+LR f1 score: 0.350
+LR cohens kappa score: 0.282
+LR average precision score: 0.620
+
+-> 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: 122, 16
+KNN fn, tp: 4, 5
+KNN f1 score: 0.333
+KNN cohens kappa score: 0.271
+
+
+------ Step 3/5: Slice 5/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 516 synthetic samples
+-> test with 'LR'
+LR tn, fp: 122, 15
+LR fn, tp: 1, 5
+LR f1 score: 0.385
+LR cohens kappa score: 0.342
+LR average precision score: 0.528
+
+-> test with 'GB'
+GB tn, fp: 132, 5
+GB fn, tp: 3, 3
+GB f1 score: 0.429
+GB cohens kappa score: 0.400
+
+-> test with 'KNN'
+KNN tn, fp: 126, 11
+KNN fn, tp: 3, 3
+KNN f1 score: 0.300
+KNN cohens kappa score: 0.256
+
+
+====== 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: 125, 13
+LR fn, tp: 4, 5
+LR f1 score: 0.370
+LR cohens kappa score: 0.314
+LR average precision score: 0.529
+
+-> 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: 124, 14
+KNN fn, tp: 5, 4
+KNN f1 score: 0.296
+KNN cohens kappa score: 0.234
+
+
+------ Step 4/5: Slice 2/5 -------
+-> 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: 0.650
+
+-> test with 'GB'
+GB tn, fp: 126, 12
+GB fn, tp: 3, 6
+GB f1 score: 0.444
+GB cohens kappa score: 0.395
+
+-> test with 'KNN'
+KNN tn, fp: 124, 14
+KNN fn, tp: 2, 7
+KNN f1 score: 0.467
+KNN cohens kappa score: 0.417
+
+
+------ Step 4/5: Slice 3/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 518 synthetic samples
+-> test with 'LR'
+LR tn, fp: 123, 15
+LR fn, tp: 1, 8
+LR f1 score: 0.500
+LR cohens kappa score: 0.452
+LR average precision score: 0.743
+
+-> 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: 128, 10
+KNN fn, tp: 5, 4
+KNN f1 score: 0.348
+KNN cohens kappa score: 0.295
+
+
+------ Step 4/5: 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: 0, 9
+LR f1 score: 0.500
+LR cohens kappa score: 0.449
+LR average precision score: 0.947
+
+-> 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: 122, 16
+KNN fn, tp: 3, 6
+KNN f1 score: 0.387
+KNN cohens kappa score: 0.329
+
+
+------ Step 4/5: Slice 5/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 516 synthetic samples
+-> test with 'LR'
+LR tn, fp: 124, 13
+LR fn, tp: 1, 5
+LR f1 score: 0.417
+LR cohens kappa score: 0.377
+LR average precision score: 0.589
+
+-> test with 'GB'
+GB tn, fp: 133, 4
+GB fn, tp: 4, 2
+GB f1 score: 0.333
+GB cohens kappa score: 0.304
+
+-> test with 'KNN'
+KNN tn, fp: 121, 16
+KNN fn, tp: 2, 4
+KNN f1 score: 0.308
+KNN cohens kappa score: 0.260
+
+
+====== 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: 116, 22
+LR fn, tp: 2, 7
+LR f1 score: 0.368
+LR cohens kappa score: 0.303
+LR average precision score: 0.690
+
+-> 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: 123, 15
+KNN fn, tp: 5, 4
+KNN f1 score: 0.286
+KNN cohens kappa score: 0.221
+
+
+------ Step 5/5: Slice 2/5 -------
+-> 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: 0.680
+
+-> 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: 122, 16
+KNN fn, tp: 4, 5
+KNN f1 score: 0.333
+KNN cohens kappa score: 0.271
+
+
+------ Step 5/5: Slice 3/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 518 synthetic samples
+-> test with 'LR'
+LR tn, fp: 123, 15
+LR fn, tp: 3, 6
+LR f1 score: 0.400
+LR cohens kappa score: 0.344
+LR average precision score: 0.521
+
+-> test with 'GB'
+GB tn, fp: 132, 6
+GB fn, tp: 7, 2
+GB f1 score: 0.235
+GB cohens kappa score: 0.189
+
+-> test with 'KNN'
+KNN tn, fp: 124, 14
+KNN fn, tp: 5, 4
+KNN f1 score: 0.296
+KNN cohens kappa score: 0.234
+
+
+------ Step 5/5: 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: 1, 8
+LR f1 score: 0.500
+LR cohens kappa score: 0.452
+LR average precision score: 0.878
+
+-> test with 'GB'
+GB tn, fp: 133, 5
+GB fn, tp: 3, 6
+GB f1 score: 0.600
+GB cohens kappa score: 0.571
+
+-> test with 'KNN'
+KNN tn, fp: 126, 12
+KNN fn, tp: 4, 5
+KNN f1 score: 0.385
+KNN cohens kappa score: 0.331
+
+
+------ Step 5/5: 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: 0, 6
+LR f1 score: 0.545
+LR cohens kappa score: 0.516
+LR average precision score: 0.819
+
+-> 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: 124, 13
+KNN fn, tp: 3, 3
+KNN f1 score: 0.273
+KNN cohens kappa score: 0.225
+
+### Exercise is done.
+
+-----[ LR ]-----
+maximum:
+LR tn, fp: 128, 24
+LR fn, tp: 4, 9
+LR f1 score: 0.621
+LR cohens kappa score: 0.586
+LR average precision score: 0.947
+
+
+average:
+LR tn, fp: 122.64, 15.16
+LR fn, tp: 1.44, 6.96
+LR f1 score: 0.458
+LR cohens kappa score: 0.409
+LR average precision score: 0.687
+
+
+minimum:
+LR tn, fp: 114, 10
+LR fn, tp: 0, 5
+LR f1 score: 0.350
+LR cohens kappa score: 0.282
+LR average precision score: 0.482
+
+
+-----[ GB ]-----
+maximum:
+GB tn, fp: 137, 12
+GB fn, tp: 7, 6
+GB f1 score: 0.632
+GB cohens kappa score: 0.606
+
+
+average:
+GB tn, fp: 132.36, 5.44
+GB fn, tp: 4.96, 3.44
+GB f1 score: 0.388
+GB cohens kappa score: 0.352
+
+
+minimum:
+GB tn, fp: 126, 1
+GB fn, tp: 2, 1
+GB f1 score: 0.167
+GB cohens kappa score: 0.130
+
+
+-----[ KNN ]-----
+maximum:
+KNN tn, fp: 130, 19
+KNN fn, tp: 6, 7
+KNN f1 score: 0.467
+KNN cohens kappa score: 0.417
+
+
+average:
+KNN tn, fp: 123.88, 13.92
+KNN fn, tp: 3.76, 4.64
+KNN f1 score: 0.342
+KNN cohens kappa score: 0.286
+
+
+minimum:
+KNN tn, fp: 119, 8
+KNN fn, tp: 2, 3
+KNN f1 score: 0.250
+KNN cohens kappa score: 0.188
+

data_result/Repeater/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;401.000;12.000;0.000;55.000;0.304;0.272;0.466
+2;402.000;10.000;2.000;54.000;0.263;0.230;0.617
+3;410.000;10.000;2.000;46.000;0.294;0.263;0.355
+4;407.000;11.000;1.000;49.000;0.306;0.275;0.562
+5;400.000;9.000;1.000;56.000;0.240;0.211;0.312
+6;407.000;11.000;1.000;49.000;0.306;0.275;0.628
+7;393.000;12.000;0.000;63.000;0.276;0.242;0.550
+8;400.000;10.000;2.000;56.000;0.256;0.223;0.257
+9;418.000;9.000;3.000;38.000;0.305;0.275;0.520
+10;404.000;8.000;2.000;52.000;0.229;0.199;0.426
+11;405.000;9.000;3.000;51.000;0.250;0.217;0.522
+12;399.000;10.000;2.000;57.000;0.253;0.219;0.354
+13;411.000;11.000;1.000;45.000;0.324;0.294;0.493
+14;407.000;10.000;2.000;49.000;0.282;0.250;0.332
+15;396.000;10.000;0.000;60.000;0.250;0.221;0.593
+16;402.000;11.000;1.000;54.000;0.286;0.253;0.640
+17;383.000;11.000;1.000;73.000;0.229;0.193;0.622
+18;406.000;10.000;2.000;50.000;0.278;0.246;0.433
+19;413.000;11.000;1.000;43.000;0.333;0.304;0.480
+20;413.000;8.000;2.000;43.000;0.262;0.235;0.343
+21;394.000;10.000;2.000;62.000;0.238;0.203;0.373
+22;403.000;10.000;2.000;53.000;0.267;0.234;0.354
+23;407.000;10.000;2.000;49.000;0.282;0.250;0.365
+24;399.000;11.000;1.000;57.000;0.275;0.242;0.696
+25;409.000;10.000;0.000;47.000;0.299;0.272;0.490
+max;418.000;12.000;3.000;73.000;0.333;0.304;0.696
+avg;403.560;10.160;1.440;52.440;0.275;0.244;0.471
+min;383.000;8.000;0.000;38.000;0.229;0.193;0.257
+---
+GB
+Nr.;TN;TP;FN;FP;f1 score;cohens kappa score
+1;435.000;8.000;4.000;21.000;0.390;0.367
+2;439.000;7.000;5.000;17.000;0.389;0.367
+3;442.000;6.000;6.000;14.000;0.375;0.354
+4;439.000;5.000;7.000;17.000;0.294;0.270
+5;445.000;4.000;6.000;11.000;0.320;0.302
+6;443.000;8.000;4.000;13.000;0.485;0.467
+7;436.000;9.000;3.000;20.000;0.439;0.418
+8;439.000;5.000;7.000;17.000;0.294;0.270
+9;446.000;3.000;9.000;10.000;0.240;0.219
+10;436.000;4.000;6.000;20.000;0.235;0.211
+11;445.000;5.000;7.000;11.000;0.357;0.338
+12;431.000;5.000;7.000;25.000;0.238;0.209
+13;442.000;9.000;3.000;14.000;0.514;0.497
+14;443.000;3.000;9.000;13.000;0.214;0.191
+15;441.000;6.000;4.000;15.000;0.387;0.369
+16;438.000;7.000;5.000;18.000;0.378;0.356
+17;438.000;6.000;6.000;18.000;0.333;0.310
+18;447.000;3.000;9.000;9.000;0.250;0.230
+19;442.000;4.000;8.000;14.000;0.267;0.243
+20;444.000;6.000;4.000;12.000;0.429;0.412
+21;440.000;6.000;6.000;16.000;0.353;0.331
+22;440.000;1.000;11.000;16.000;0.069;0.040
+23;446.000;7.000;5.000;10.000;0.483;0.467
+24;436.000;5.000;7.000;20.000;0.270;0.244
+25;440.000;5.000;5.000;16.000;0.323;0.302
+max;447.000;9.000;11.000;25.000;0.514;0.497
+avg;440.520;5.480;6.120;15.480;0.333;0.311
+min;431.000;1.000;3.000;9.000;0.069;0.040
+---
+KNN
+Nr.;TN;TP;FN;FP;f1 score;cohens kappa score
+1;437.000;6.000;6.000;19.000;0.324;0.300
+2;426.000;8.000;4.000;30.000;0.320;0.292
+3;438.000;7.000;5.000;18.000;0.378;0.356
+4;442.000;7.000;5.000;14.000;0.424;0.405
+5;436.000;4.000;6.000;20.000;0.235;0.211
+6;436.000;4.000;8.000;20.000;0.222;0.195
+7;432.000;8.000;4.000;24.000;0.364;0.339
+8;432.000;6.000;6.000;24.000;0.286;0.259
+9;437.000;6.000;6.000;19.000;0.324;0.300
+10;432.000;7.000;3.000;24.000;0.341;0.319
+11;437.000;8.000;4.000;19.000;0.410;0.389
+12;436.000;7.000;5.000;20.000;0.359;0.335
+13;430.000;7.000;5.000;26.000;0.311;0.284
+14;438.000;2.000;10.000;18.000;0.125;0.096
+15;434.000;6.000;4.000;22.000;0.316;0.293
+16;429.000;8.000;4.000;27.000;0.340;0.314
+17;441.000;7.000;5.000;15.000;0.412;0.392
+18;429.000;5.000;7.000;27.000;0.227;0.197
+19;438.000;4.000;8.000;18.000;0.235;0.209
+20;438.000;6.000;4.000;18.000;0.353;0.333
+21;433.000;4.000;8.000;23.000;0.205;0.176
+22;431.000;6.000;6.000;25.000;0.279;0.251
+23;433.000;6.000;6.000;23.000;0.293;0.266
+24;441.000;8.000;4.000;15.000;0.457;0.438
+25;437.000;6.000;4.000;19.000;0.343;0.322
+max;442.000;8.000;10.000;30.000;0.457;0.438
+avg;434.920;6.120;5.480;21.080;0.315;0.291
+min;426.000;2.000;3.000;14.000;0.125;0.096

data_result/Repeater/folding_abalone_17_vs_7_8_9_10.log

@@ -0,0 +1,701 @@
+
+
+///////////////////////////////////////////
+// Running Repeater 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: 401, 55
+LR fn, tp: 0, 12
+LR f1 score: 0.304
+LR cohens kappa score: 0.272
+LR average precision score: 0.466
+
+-> test with 'GB'
+GB tn, fp: 435, 21
+GB fn, tp: 4, 8
+GB f1 score: 0.390
+GB cohens kappa score: 0.367
+
+-> test with 'KNN'
+KNN tn, fp: 437, 19
+KNN fn, tp: 6, 6
+KNN f1 score: 0.324
+KNN cohens kappa score: 0.300
+
+
+------ Step 1/5: Slice 2/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 1778 synthetic samples
+-> test with 'LR'
+LR tn, fp: 402, 54
+LR fn, tp: 2, 10
+LR f1 score: 0.263
+LR cohens kappa score: 0.230
+LR average precision score: 0.617
+
+-> 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: 426, 30
+KNN fn, tp: 4, 8
+KNN f1 score: 0.320
+KNN cohens kappa score: 0.292
+
+
+------ Step 1/5: 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: 2, 10
+LR f1 score: 0.294
+LR cohens kappa score: 0.263
+LR average precision score: 0.355
+
+-> test with 'GB'
+GB tn, fp: 442, 14
+GB fn, tp: 6, 6
+GB f1 score: 0.375
+GB cohens kappa score: 0.354
+
+-> test with 'KNN'
+KNN tn, fp: 438, 18
+KNN fn, tp: 5, 7
+KNN f1 score: 0.378
+KNN cohens kappa score: 0.356
+
+
+------ Step 1/5: Slice 4/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 1778 synthetic samples
+-> test with 'LR'
+LR tn, fp: 407, 49
+LR fn, tp: 1, 11
+LR f1 score: 0.306
+LR cohens kappa score: 0.275
+LR average precision score: 0.562
+
+-> 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: 442, 14
+KNN fn, tp: 5, 7
+KNN f1 score: 0.424
+KNN cohens kappa score: 0.405
+
+
+------ Step 1/5: Slice 5/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 1776 synthetic samples
+-> test with 'LR'
+LR tn, fp: 400, 56
+LR fn, tp: 1, 9
+LR f1 score: 0.240
+LR cohens kappa score: 0.211
+LR average precision score: 0.312
+
+-> 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: 436, 20
+KNN fn, tp: 6, 4
+KNN f1 score: 0.235
+KNN cohens kappa score: 0.211
+
+
+====== 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: 407, 49
+LR fn, tp: 1, 11
+LR f1 score: 0.306
+LR cohens kappa score: 0.275
+LR average precision score: 0.628
+
+-> test with 'GB'
+GB tn, fp: 443, 13
+GB fn, tp: 4, 8
+GB f1 score: 0.485
+GB cohens kappa score: 0.467
+
+-> test with 'KNN'
+KNN tn, fp: 436, 20
+KNN fn, tp: 8, 4
+KNN f1 score: 0.222
+KNN cohens kappa score: 0.195
+
+
+------ Step 2/5: Slice 2/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 1778 synthetic samples
+-> test with 'LR'
+LR tn, fp: 393, 63
+LR fn, tp: 0, 12
+LR f1 score: 0.276
+LR cohens kappa score: 0.242
+LR average precision score: 0.550
+
+-> test with 'GB'
+GB tn, fp: 436, 20
+GB fn, tp: 3, 9
+GB f1 score: 0.439
+GB cohens kappa score: 0.418
+
+-> test with 'KNN'
+KNN tn, fp: 432, 24
+KNN fn, tp: 4, 8
+KNN f1 score: 0.364
+KNN cohens kappa score: 0.339
+
+
+------ Step 2/5: Slice 3/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 1778 synthetic samples
+-> test with 'LR'
+LR tn, fp: 400, 56
+LR fn, tp: 2, 10
+LR f1 score: 0.256
+LR cohens kappa score: 0.223
+LR average precision score: 0.257
+
+-> 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: 432, 24
+KNN fn, tp: 6, 6
+KNN f1 score: 0.286
+KNN cohens kappa score: 0.259
+
+
+------ Step 2/5: 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: 3, 9
+LR f1 score: 0.305
+LR cohens kappa score: 0.275
+LR average precision score: 0.520
+
+-> test with 'GB'
+GB tn, fp: 446, 10
+GB fn, tp: 9, 3
+GB f1 score: 0.240
+GB cohens kappa score: 0.219
+
+-> test with 'KNN'
+KNN tn, fp: 437, 19
+KNN fn, tp: 6, 6
+KNN f1 score: 0.324
+KNN cohens kappa score: 0.300
+
+
+------ Step 2/5: Slice 5/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 1776 synthetic samples
+-> test with 'LR'
+LR tn, fp: 404, 52
+LR fn, tp: 2, 8
+LR f1 score: 0.229
+LR cohens kappa score: 0.199
+LR average precision score: 0.426
+
+-> test with 'GB'
+GB tn, fp: 436, 20
+GB fn, tp: 6, 4
+GB f1 score: 0.235
+GB cohens kappa score: 0.211
+
+-> test with 'KNN'
+KNN tn, fp: 432, 24
+KNN fn, tp: 3, 7
+KNN f1 score: 0.341
+KNN cohens kappa score: 0.319
+
+
+====== 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: 405, 51
+LR fn, tp: 3, 9
+LR f1 score: 0.250
+LR cohens kappa score: 0.217
+LR average precision score: 0.522
+
+-> test with 'GB'
+GB tn, fp: 445, 11
+GB fn, tp: 7, 5
+GB f1 score: 0.357
+GB cohens kappa score: 0.338
+
+-> test with 'KNN'
+KNN tn, fp: 437, 19
+KNN fn, tp: 4, 8
+KNN f1 score: 0.410
+KNN cohens kappa score: 0.389
+
+
+------ Step 3/5: Slice 2/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 1778 synthetic samples
+-> test with 'LR'
+LR tn, fp: 399, 57
+LR fn, tp: 2, 10
+LR f1 score: 0.253
+LR cohens kappa score: 0.219
+LR average precision score: 0.354
+
+-> test with 'GB'
+GB tn, fp: 431, 25
+GB fn, tp: 7, 5
+GB f1 score: 0.238
+GB cohens kappa score: 0.209
+
+-> test with 'KNN'
+KNN tn, fp: 436, 20
+KNN fn, tp: 5, 7
+KNN f1 score: 0.359
+KNN cohens kappa score: 0.335
+
+
+------ Step 3/5: Slice 3/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 1778 synthetic samples
+-> test with 'LR'
+LR tn, fp: 411, 45
+LR fn, tp: 1, 11
+LR f1 score: 0.324
+LR cohens kappa score: 0.294
+LR average precision score: 0.493
+
+-> test with 'GB'
+GB tn, fp: 442, 14
+GB fn, tp: 3, 9
+GB f1 score: 0.514
+GB cohens kappa score: 0.497
+
+-> test with 'KNN'
+KNN tn, fp: 430, 26
+KNN fn, tp: 5, 7
+KNN f1 score: 0.311
+KNN cohens kappa score: 0.284
+
+
+------ Step 3/5: Slice 4/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 1778 synthetic samples
+-> test with 'LR'
+LR tn, fp: 407, 49
+LR fn, tp: 2, 10
+LR f1 score: 0.282
+LR cohens kappa score: 0.250
+LR average precision score: 0.332
+
+-> test with 'GB'
+GB tn, fp: 443, 13
+GB fn, tp: 9, 3
+GB f1 score: 0.214
+GB cohens kappa score: 0.191
+
+-> test with 'KNN'
+KNN tn, fp: 438, 18
+KNN fn, tp: 10, 2
+KNN f1 score: 0.125
+KNN cohens kappa score: 0.096
+
+
+------ Step 3/5: Slice 5/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 1776 synthetic samples
+-> test with 'LR'
+LR tn, fp: 396, 60
+LR fn, tp: 0, 10
+LR f1 score: 0.250
+LR cohens kappa score: 0.221
+LR average precision score: 0.593
+
+-> test with 'GB'
+GB tn, fp: 441, 15
+GB fn, tp: 4, 6
+GB f1 score: 0.387
+GB cohens kappa score: 0.369
+
+-> test with 'KNN'
+KNN tn, fp: 434, 22
+KNN fn, tp: 4, 6
+KNN f1 score: 0.316
+KNN cohens kappa score: 0.293
+
+
+====== 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: 402, 54
+LR fn, tp: 1, 11
+LR f1 score: 0.286
+LR cohens kappa score: 0.253
+LR average precision score: 0.640
+
+-> test with 'GB'
+GB tn, fp: 438, 18
+GB fn, tp: 5, 7
+GB f1 score: 0.378
+GB cohens kappa score: 0.356
+
+-> test with 'KNN'
+KNN tn, fp: 429, 27
+KNN fn, tp: 4, 8
+KNN f1 score: 0.340
+KNN cohens kappa score: 0.314
+
+
+------ Step 4/5: Slice 2/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 1778 synthetic samples
+-> test with 'LR'
+LR tn, fp: 383, 73
+LR fn, tp: 1, 11
+LR f1 score: 0.229
+LR cohens kappa score: 0.193
+LR average precision score: 0.622
+
+-> test with 'GB'
+GB tn, fp: 438, 18
+GB fn, tp: 6, 6
+GB f1 score: 0.333
+GB cohens kappa score: 0.310
+
+-> test with 'KNN'
+KNN tn, fp: 441, 15
+KNN fn, tp: 5, 7
+KNN f1 score: 0.412
+KNN cohens kappa score: 0.392
+
+
+------ Step 4/5: Slice 3/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 1778 synthetic samples
+-> test with 'LR'
+LR tn, fp: 406, 50
+LR fn, tp: 2, 10
+LR f1 score: 0.278
+LR cohens kappa score: 0.246
+LR average precision score: 0.433
+
+-> test with 'GB'
+GB tn, fp: 447, 9
+GB fn, tp: 9, 3
+GB f1 score: 0.250
+GB cohens kappa score: 0.230
+
+-> test with 'KNN'
+KNN tn, fp: 429, 27
+KNN fn, tp: 7, 5
+KNN f1 score: 0.227
+KNN cohens kappa score: 0.197
+
+
+------ Step 4/5: Slice 4/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 1778 synthetic samples
+-> test with 'LR'
+LR tn, fp: 413, 43
+LR fn, tp: 1, 11
+LR f1 score: 0.333
+LR cohens kappa score: 0.304
+LR average precision score: 0.480
+
+-> 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: 438, 18
+KNN fn, tp: 8, 4
+KNN f1 score: 0.235
+KNN cohens kappa score: 0.209
+
+
+------ Step 4/5: Slice 5/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 1776 synthetic samples
+-> test with 'LR'
+LR tn, fp: 413, 43
+LR fn, tp: 2, 8
+LR f1 score: 0.262
+LR cohens kappa score: 0.235
+LR average precision score: 0.343
+
+-> test with 'GB'
+GB tn, fp: 444, 12
+GB fn, tp: 4, 6
+GB f1 score: 0.429
+GB cohens kappa score: 0.412
+
+-> test with 'KNN'
+KNN tn, fp: 438, 18
+KNN fn, tp: 4, 6
+KNN f1 score: 0.353
+KNN cohens kappa score: 0.333
+
+
+====== 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: 394, 62
+LR fn, tp: 2, 10
+LR f1 score: 0.238
+LR cohens kappa score: 0.203
+LR average precision score: 0.373
+
+-> 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: 433, 23
+KNN fn, tp: 8, 4
+KNN f1 score: 0.205
+KNN cohens kappa score: 0.176
+
+
+------ Step 5/5: Slice 2/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 1778 synthetic samples
+-> test with 'LR'
+LR tn, fp: 403, 53
+LR fn, tp: 2, 10
+LR f1 score: 0.267
+LR cohens kappa score: 0.234
+LR average precision score: 0.354
+
+-> test with 'GB'
+GB tn, fp: 440, 16
+GB fn, tp: 11, 1
+GB f1 score: 0.069
+GB cohens kappa score: 0.040
+
+-> test with 'KNN'
+KNN tn, fp: 431, 25
+KNN fn, tp: 6, 6
+KNN f1 score: 0.279
+KNN cohens kappa score: 0.251
+
+
+------ Step 5/5: Slice 3/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 1778 synthetic samples
+-> test with 'LR'
+LR tn, fp: 407, 49
+LR fn, tp: 2, 10
+LR f1 score: 0.282
+LR cohens kappa score: 0.250
+LR average precision score: 0.365
+
+-> test with 'GB'
+GB tn, fp: 446, 10
+GB fn, tp: 5, 7
+GB f1 score: 0.483
+GB cohens kappa score: 0.467
+
+-> test with 'KNN'
+KNN tn, fp: 433, 23
+KNN fn, tp: 6, 6
+KNN f1 score: 0.293
+KNN cohens kappa score: 0.266
+
+
+------ Step 5/5: Slice 4/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 1778 synthetic samples
+-> test with 'LR'
+LR tn, fp: 399, 57
+LR fn, tp: 1, 11
+LR f1 score: 0.275
+LR cohens kappa score: 0.242
+LR average precision score: 0.696
+
+-> test with 'GB'
+GB tn, fp: 436, 20
+GB fn, tp: 7, 5
+GB f1 score: 0.270
+GB cohens kappa score: 0.244
+
+-> test with 'KNN'
+KNN tn, fp: 441, 15
+KNN fn, tp: 4, 8
+KNN f1 score: 0.457
+KNN cohens kappa score: 0.438
+
+
+------ Step 5/5: Slice 5/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 1776 synthetic samples
+-> test with 'LR'
+LR tn, fp: 409, 47
+LR fn, tp: 0, 10
+LR f1 score: 0.299
+LR cohens kappa score: 0.272
+LR average precision score: 0.490
+
+-> test with 'GB'
+GB tn, fp: 440, 16
+GB fn, tp: 5, 5
+GB f1 score: 0.323
+GB cohens kappa score: 0.302
+
+-> test with 'KNN'
+KNN tn, fp: 437, 19
+KNN fn, tp: 4, 6
+KNN f1 score: 0.343
+KNN cohens kappa score: 0.322
+
+### Exercise is done.
+
+-----[ LR ]-----
+maximum:
+LR tn, fp: 418, 73
+LR fn, tp: 3, 12
+LR f1 score: 0.333
+LR cohens kappa score: 0.304
+LR average precision score: 0.696
+
+
+average:
+LR tn, fp: 403.56, 52.44
+LR fn, tp: 1.44, 10.16
+LR f1 score: 0.275
+LR cohens kappa score: 0.244
+LR average precision score: 0.471
+
+
+minimum:
+LR tn, fp: 383, 38
+LR fn, tp: 0, 8
+LR f1 score: 0.229
+LR cohens kappa score: 0.193
+LR average precision score: 0.257
+
+
+-----[ GB ]-----
+maximum:
+GB tn, fp: 447, 25
+GB fn, tp: 11, 9
+GB f1 score: 0.514
+GB cohens kappa score: 0.497
+
+
+average:
+GB tn, fp: 440.52, 15.48
+GB fn, tp: 6.12, 5.48
+GB f1 score: 0.333
+GB cohens kappa score: 0.311
+
+
+minimum:
+GB tn, fp: 431, 9
+GB fn, tp: 3, 1
+GB f1 score: 0.069
+GB cohens kappa score: 0.040
+
+
+-----[ KNN ]-----
+maximum:
+KNN tn, fp: 442, 30
+KNN fn, tp: 10, 8
+KNN f1 score: 0.457
+KNN cohens kappa score: 0.438
+
+
+average:
+KNN tn, fp: 434.92, 21.08
+KNN fn, tp: 5.48, 6.12
+KNN f1 score: 0.315
+KNN cohens kappa score: 0.291
+
+
+minimum:
+KNN tn, fp: 426, 14
+KNN fn, tp: 3, 2
+KNN f1 score: 0.125
+KNN cohens kappa score: 0.096
+

data_result/Repeater/folding_car-vgood.csv

@@ -0,0 +1,92 @@
+LR
+Nr.;TN;TP;FN;FP;f1 score;cohens kappa score;average precision score
+1;286.000;13.000;0.000;47.000;0.356;0.314;0.363
+2;286.000;13.000;0.000;47.000;0.356;0.314;0.304
+3;278.000;13.000;0.000;55.000;0.321;0.275;0.392
+4;290.000;13.000;0.000;43.000;0.377;0.336;0.367
+5;294.000;12.000;1.000;37.000;0.387;0.348;0.447
+6;290.000;13.000;0.000;43.000;0.377;0.336;0.289
+7;274.000;13.000;0.000;59.000;0.306;0.259;0.366
+8;293.000;12.000;1.000;40.000;0.369;0.329;0.345
+9;295.000;13.000;0.000;38.000;0.406;0.368;0.286
+10;284.000;13.000;0.000;47.000;0.356;0.314;0.555
+11;288.000;13.000;0.000;45.000;0.366;0.325;0.308
+12;294.000;13.000;0.000;39.000;0.400;0.362;0.436
+13;280.000;13.000;0.000;53.000;0.329;0.284;0.314
+14;291.000;13.000;0.000;42.000;0.382;0.342;0.385
+15;286.000;12.000;1.000;45.000;0.343;0.300;0.376
+16;291.000;13.000;0.000;42.000;0.382;0.342;0.419
+17;284.000;13.000;0.000;49.000;0.347;0.303;0.511
+18;280.000;13.000;0.000;53.000;0.329;0.284;0.320
+19;290.000;12.000;1.000;43.000;0.353;0.311;0.275
+20;289.000;13.000;0.000;42.000;0.382;0.342;0.321
+21;279.000;13.000;0.000;54.000;0.325;0.280;0.292
+22;293.000;12.000;1.000;40.000;0.369;0.329;0.359
+23;298.000;13.000;0.000;35.000;0.426;0.390;0.336
+24;282.000;13.000;0.000;51.000;0.338;0.294;0.295
+25;287.000;13.000;0.000;44.000;0.371;0.330;0.488
+max;298.000;13.000;1.000;59.000;0.426;0.390;0.555
+avg;287.280;12.800;0.200;45.320;0.362;0.320;0.366
+min;274.000;12.000;0.000;35.000;0.306;0.259;0.275
+---
+GB
+Nr.;TN;TP;FN;FP;f1 score;cohens kappa score
+1;332.000;13.000;0.000;1.000;0.963;0.961
+2;332.000;13.000;0.000;1.000;0.963;0.961
+3;331.000;13.000;0.000;2.000;0.929;0.926
+4;332.000;13.000;0.000;1.000;0.963;0.961
+5;326.000;13.000;0.000;5.000;0.839;0.831
+6;330.000;13.000;0.000;3.000;0.897;0.892
+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;332.000;13.000;0.000;1.000;0.963;0.961
+10;331.000;13.000;0.000;0.000;1.000;1.000
+11;332.000;13.000;0.000;1.000;0.963;0.961
+12;331.000;13.000;0.000;2.000;0.929;0.926
+13;328.000;13.000;0.000;5.000;0.839;0.831
+14;333.000;13.000;0.000;0.000;1.000;1.000
+15;330.000;13.000;0.000;1.000;0.963;0.961
+16;331.000;13.000;0.000;2.000;0.929;0.926
+17;332.000;13.000;0.000;1.000;0.963;0.961
+18;332.000;13.000;0.000;1.000;0.963;0.961
+19;332.000;13.000;0.000;1.000;0.963;0.961
+20;329.000;13.000;0.000;2.000;0.929;0.926
+21;331.000;13.000;0.000;2.000;0.929;0.926
+22;332.000;13.000;0.000;1.000;0.963;0.961
+23;332.000;13.000;0.000;1.000;0.963;0.961
+24;327.000;13.000;0.000;6.000;0.813;0.804
+25;329.000;13.000;0.000;2.000;0.929;0.926
+max;333.000;13.000;0.000;6.000;1.000;1.000
+avg;330.800;13.000;0.000;1.800;0.938;0.935
+min;326.000;13.000;0.000;0.000;0.813;0.804
+---
+KNN
+Nr.;TN;TP;FN;FP;f1 score;cohens kappa score
+1;303.000;13.000;0.000;30.000;0.464;0.431
+2;294.000;13.000;0.000;39.000;0.400;0.362
+3;286.000;13.000;0.000;47.000;0.356;0.314
+4;294.000;13.000;0.000;39.000;0.400;0.362
+5;300.000;13.000;0.000;31.000;0.456;0.422
+6;297.000;13.000;0.000;36.000;0.419;0.383
+7;287.000;13.000;0.000;46.000;0.361;0.319
+8;298.000;13.000;0.000;35.000;0.426;0.390
+9;293.000;13.000;0.000;40.000;0.394;0.355
+10;303.000;13.000;0.000;28.000;0.481;0.450
+11;293.000;13.000;0.000;40.000;0.394;0.355
+12;298.000;13.000;0.000;35.000;0.426;0.390
+13;295.000;13.000;0.000;38.000;0.406;0.368
+14;288.000;13.000;0.000;45.000;0.366;0.325
+15;297.000;13.000;0.000;34.000;0.433;0.398
+16;296.000;13.000;0.000;37.000;0.413;0.375
+17;299.000;13.000;0.000;34.000;0.433;0.398
+18;291.000;13.000;0.000;42.000;0.382;0.342
+19;293.000;13.000;0.000;40.000;0.394;0.355
+20;294.000;13.000;0.000;37.000;0.413;0.375
+21;293.000;13.000;0.000;40.000;0.394;0.355
+22;303.000;13.000;0.000;30.000;0.464;0.431
+23;298.000;13.000;0.000;35.000;0.426;0.390
+24;288.000;13.000;0.000;45.000;0.366;0.325
+25;299.000;13.000;0.000;32.000;0.448;0.414
+max;303.000;13.000;0.000;47.000;0.481;0.450
+avg;295.200;13.000;0.000;37.400;0.413;0.375
+min;286.000;13.000;0.000;28.000;0.356;0.314

data_result/Repeater/folding_car-vgood.log

@@ -0,0 +1,701 @@
+
+
+///////////////////////////////////////////
+// Running Repeater 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: 286, 47
+LR fn, tp: 0, 13
+LR f1 score: 0.356
+LR cohens kappa score: 0.314
+LR average precision score: 0.363
+
+-> 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: 303, 30
+KNN fn, tp: 0, 13
+KNN f1 score: 0.464
+KNN cohens kappa score: 0.431
+
+
+------ Step 1/5: Slice 2/5 -------
+-> 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: 0.304
+
+-> 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: 294, 39
+KNN fn, tp: 0, 13
+KNN f1 score: 0.400
+KNN cohens kappa score: 0.362
+
+
+------ Step 1/5: Slice 3/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 1278 synthetic samples
+-> test with 'LR'
+LR tn, fp: 278, 55
+LR fn, tp: 0, 13
+LR f1 score: 0.321
+LR cohens kappa score: 0.275
+LR average precision score: 0.392
+
+-> 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: 286, 47
+KNN fn, tp: 0, 13
+KNN f1 score: 0.356
+KNN cohens kappa score: 0.314
+
+
+------ Step 1/5: Slice 4/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 1278 synthetic samples
+-> test with 'LR'
+LR tn, fp: 290, 43
+LR fn, tp: 0, 13
+LR f1 score: 0.377
+LR cohens kappa score: 0.336
+LR average precision score: 0.367
+
+-> 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: 294, 39
+KNN fn, tp: 0, 13
+KNN f1 score: 0.400
+KNN cohens kappa score: 0.362
+
+
+------ Step 1/5: Slice 5/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 1280 synthetic samples
+-> test with 'LR'
+LR tn, fp: 294, 37
+LR fn, tp: 1, 12
+LR f1 score: 0.387
+LR cohens kappa score: 0.348
+LR average precision score: 0.447
+
+-> test with 'GB'
+GB tn, fp: 326, 5
+GB fn, tp: 0, 13
+GB f1 score: 0.839
+GB cohens kappa score: 0.831
+
+-> test with 'KNN'
+KNN tn, fp: 300, 31
+KNN fn, tp: 0, 13
+KNN f1 score: 0.456
+KNN cohens kappa score: 0.422
+
+
+====== 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: 290, 43
+LR fn, tp: 0, 13
+LR f1 score: 0.377
+LR cohens kappa score: 0.336
+LR average precision score: 0.289
+
+-> 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: 297, 36
+KNN fn, tp: 0, 13
+KNN f1 score: 0.419
+KNN cohens kappa score: 0.383
+
+
+------ Step 2/5: Slice 2/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 1278 synthetic samples
+-> test with 'LR'
+LR tn, fp: 274, 59
+LR fn, tp: 0, 13
+LR f1 score: 0.306
+LR cohens kappa score: 0.259
+LR average precision score: 0.366
+
+-> 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: 287, 46
+KNN fn, tp: 0, 13
+KNN f1 score: 0.361
+KNN cohens kappa score: 0.319
+
+
+------ Step 2/5: Slice 3/5 -------
+-> 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: 0.345
+
+-> 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: 298, 35
+KNN fn, tp: 0, 13
+KNN f1 score: 0.426
+KNN cohens kappa score: 0.390
+
+
+------ Step 2/5: Slice 4/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 1278 synthetic samples
+-> test with 'LR'
+LR tn, fp: 295, 38
+LR fn, tp: 0, 13
+LR f1 score: 0.406
+LR cohens kappa score: 0.368
+LR average precision score: 0.286
+
+-> 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: 293, 40
+KNN fn, tp: 0, 13
+KNN f1 score: 0.394
+KNN cohens kappa score: 0.355
+
+
+------ Step 2/5: Slice 5/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 1280 synthetic samples
+-> test with 'LR'
+LR tn, fp: 284, 47
+LR fn, tp: 0, 13
+LR f1 score: 0.356
+LR cohens kappa score: 0.314
+LR average precision score: 0.555
+
+-> 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: 303, 28
+KNN fn, tp: 0, 13
+KNN f1 score: 0.481
+KNN cohens kappa score: 0.450
+
+
+====== 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: 288, 45
+LR fn, tp: 0, 13
+LR f1 score: 0.366
+LR cohens kappa score: 0.325
+LR average precision score: 0.308
+
+-> 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: 293, 40
+KNN fn, tp: 0, 13
+KNN f1 score: 0.394
+KNN cohens kappa score: 0.355
+
+
+------ Step 3/5: Slice 2/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 1278 synthetic samples
+-> test with 'LR'
+LR tn, fp: 294, 39
+LR fn, tp: 0, 13
+LR f1 score: 0.400
+LR cohens kappa score: 0.362
+LR average precision score: 0.436
+
+-> 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: 298, 35
+KNN fn, tp: 0, 13
+KNN f1 score: 0.426
+KNN cohens kappa score: 0.390
+
+
+------ Step 3/5: Slice 3/5 -------
+-> 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: 0.314
+
+-> 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: 295, 38
+KNN fn, tp: 0, 13
+KNN f1 score: 0.406
+KNN cohens kappa score: 0.368
+
+
+------ Step 3/5: Slice 4/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 1278 synthetic samples
+-> test with 'LR'
+LR tn, fp: 291, 42
+LR fn, tp: 0, 13
+LR f1 score: 0.382
+LR cohens kappa score: 0.342
+LR average precision score: 0.385
+
+-> 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: 288, 45
+KNN fn, tp: 0, 13
+KNN f1 score: 0.366
+KNN cohens kappa score: 0.325
+
+
+------ Step 3/5: Slice 5/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 1280 synthetic samples
+-> test with 'LR'
+LR tn, fp: 286, 45
+LR fn, tp: 1, 12
+LR f1 score: 0.343
+LR cohens kappa score: 0.300
+LR average precision score: 0.376
+
+-> test with 'GB'
+GB tn, fp: 330, 1
+GB fn, tp: 0, 13
+GB f1 score: 0.963
+GB cohens kappa score: 0.961
+
+-> test with 'KNN'
+KNN tn, fp: 297, 34
+KNN fn, tp: 0, 13
+KNN f1 score: 0.433
+KNN cohens kappa score: 0.398
+
+
+====== 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: 291, 42
+LR fn, tp: 0, 13
+LR f1 score: 0.382
+LR cohens kappa score: 0.342
+LR average precision score: 0.419
+
+-> 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: 296, 37
+KNN fn, tp: 0, 13
+KNN f1 score: 0.413
+KNN cohens kappa score: 0.375
+
+
+------ Step 4/5: 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: 0.511
+
+-> 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: 299, 34
+KNN fn, tp: 0, 13
+KNN f1 score: 0.433
+KNN cohens kappa score: 0.398
+
+
+------ Step 4/5: Slice 3/5 -------
+-> 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: 0.320
+
+-> 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: 291, 42
+KNN fn, tp: 0, 13
+KNN f1 score: 0.382
+KNN cohens kappa score: 0.342
+
+
+------ Step 4/5: Slice 4/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 1278 synthetic samples
+-> test with 'LR'
+LR tn, fp: 290, 43
+LR fn, tp: 1, 12
+LR f1 score: 0.353
+LR cohens kappa score: 0.311
+LR average precision score: 0.275
+
+-> 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: 293, 40
+KNN fn, tp: 0, 13
+KNN f1 score: 0.394
+KNN cohens kappa score: 0.355
+
+
+------ Step 4/5: Slice 5/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 1280 synthetic samples
+-> test with 'LR'
+LR tn, fp: 289, 42
+LR fn, tp: 0, 13
+LR f1 score: 0.382
+LR cohens kappa score: 0.342
+LR average precision score: 0.321
+
+-> 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: 294, 37
+KNN fn, tp: 0, 13
+KNN f1 score: 0.413
+KNN cohens kappa score: 0.375
+
+
+====== 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: 279, 54
+LR fn, tp: 0, 13
+LR f1 score: 0.325
+LR cohens kappa score: 0.280
+LR average precision score: 0.292
+
+-> 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: 293, 40
+KNN fn, tp: 0, 13
+KNN f1 score: 0.394
+KNN cohens kappa score: 0.355
+
+
+------ Step 5/5: 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: 0.359
+
+-> 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: 303, 30
+KNN fn, tp: 0, 13
+KNN f1 score: 0.464
+KNN cohens kappa score: 0.431
+
+
+------ Step 5/5: Slice 3/5 -------
+-> 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: 0.336
+
+-> 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: 298, 35
+KNN fn, tp: 0, 13
+KNN f1 score: 0.426
+KNN cohens kappa score: 0.390
+
+
+------ Step 5/5: Slice 4/5 -------
+-> 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: 0.295
+
+-> 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: 288, 45
+KNN fn, tp: 0, 13
+KNN f1 score: 0.366
+KNN cohens kappa score: 0.325
+
+
+------ Step 5/5: Slice 5/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 1280 synthetic samples
+-> test with 'LR'
+LR tn, fp: 287, 44
+LR fn, tp: 0, 13
+LR f1 score: 0.371
+LR cohens kappa score: 0.330
+LR average precision score: 0.488
+
+-> 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: 299, 32
+KNN fn, tp: 0, 13
+KNN f1 score: 0.448
+KNN cohens kappa score: 0.414
+
+### Exercise is done.
+
+-----[ LR ]-----
+maximum:
+LR tn, fp: 298, 59
+LR fn, tp: 1, 13
+LR f1 score: 0.426
+LR cohens kappa score: 0.390
+LR average precision score: 0.555
+
+
+average:
+LR tn, fp: 287.28, 45.32
+LR fn, tp: 0.2, 12.8
+LR f1 score: 0.362
+LR cohens kappa score: 0.320
+LR average precision score: 0.366
+
+
+minimum:
+LR tn, fp: 274, 35
+LR fn, tp: 0, 12
+LR f1 score: 0.306
+LR cohens kappa score: 0.259
+LR average precision score: 0.275
+
+
+-----[ GB ]-----
+maximum:
+GB tn, fp: 333, 6
+GB fn, tp: 0, 13
+GB f1 score: 1.000
+GB cohens kappa score: 1.000
+
+
+average:
+GB tn, fp: 330.8, 1.8
+GB fn, tp: 0.0, 13.0
+GB f1 score: 0.938
+GB cohens kappa score: 0.935
+
+
+minimum:
+GB tn, fp: 326, 0
+GB fn, tp: 0, 13
+GB f1 score: 0.813
+GB cohens kappa score: 0.804
+
+
+-----[ KNN ]-----
+maximum:
+KNN tn, fp: 303, 47
+KNN fn, tp: 0, 13
+KNN f1 score: 0.481
+KNN cohens kappa score: 0.450
+
+
+average:
+KNN tn, fp: 295.2, 37.4
+KNN fn, tp: 0.0, 13.0
+KNN f1 score: 0.413
+KNN cohens kappa score: 0.375
+
+
+minimum:
+KNN tn, fp: 286, 28
+KNN fn, tp: 0, 13
+KNN f1 score: 0.356
+KNN cohens kappa score: 0.314
+

data_result/Repeater/folding_car_good.csv

@@ -0,0 +1,92 @@
+LR
+Nr.;TN;TP;FN;FP;f1 score;cohens kappa score;average precision score
+1;170.000;10.000;4.000;162.000;0.108;0.035;0.061
+2;185.000;11.000;3.000;147.000;0.128;0.058;0.087
+3;172.000;10.000;4.000;160.000;0.109;0.037;0.057
+4;178.000;10.000;4.000;154.000;0.112;0.041;0.077
+5;175.000;9.000;4.000;156.000;0.101;0.033;0.056
+6;157.000;10.000;4.000;175.000;0.101;0.027;0.068
+7;169.000;11.000;3.000;163.000;0.117;0.046;0.070
+8;186.000;10.000;4.000;146.000;0.118;0.047;0.072
+9;183.000;7.000;7.000;149.000;0.082;0.009;0.050
+10;180.000;8.000;5.000;151.000;0.093;0.025;0.077
+11;168.000;11.000;3.000;164.000;0.116;0.045;0.077
+12;190.000;11.000;3.000;142.000;0.132;0.062;0.067
+13;180.000;8.000;6.000;152.000;0.092;0.019;0.056
+14;170.000;11.000;3.000;162.000;0.118;0.046;0.083
+15;168.000;9.000;4.000;163.000;0.097;0.029;0.055
+16;177.000;11.000;3.000;155.000;0.122;0.051;0.067
+17;173.000;8.000;6.000;159.000;0.088;0.015;0.063
+18;170.000;10.000;4.000;162.000;0.108;0.035;0.066
+19;189.000;8.000;6.000;143.000;0.097;0.025;0.056
+20;170.000;12.000;1.000;161.000;0.129;0.063;0.081
+21;175.000;6.000;8.000;157.000;0.068;-0.007;0.054
+22;187.000;8.000;6.000;145.000;0.096;0.023;0.070
+23;162.000;11.000;3.000;170.000;0.113;0.041;0.079
+24;173.000;10.000;4.000;159.000;0.109;0.037;0.078
+25;176.000;9.000;4.000;155.000;0.102;0.034;0.065
+max;190.000;12.000;8.000;175.000;0.132;0.063;0.087
+avg;175.320;9.560;4.240;156.480;0.106;0.035;0.068
+min;157.000;6.000;1.000;142.000;0.068;-0.007;0.050
+---
+GB
+Nr.;TN;TP;FN;FP;f1 score;cohens kappa score
+1;325.000;14.000;0.000;7.000;0.800;0.790
+2;327.000;14.000;0.000;5.000;0.848;0.841
+3;328.000;14.000;0.000;4.000;0.875;0.869
+4;327.000;14.000;0.000;5.000;0.848;0.841
+5;325.000;13.000;0.000;6.000;0.813;0.804
+6;325.000;14.000;0.000;7.000;0.800;0.790
+7;329.000;14.000;0.000;3.000;0.903;0.899
+8;330.000;14.000;0.000;2.000;0.933;0.930
+9;323.000;14.000;0.000;9.000;0.757;0.744
+10;325.000;13.000;0.000;6.000;0.813;0.804
+11;328.000;14.000;0.000;4.000;0.875;0.869
+12;324.000;14.000;0.000;8.000;0.778;0.766
+13;326.000;14.000;0.000;6.000;0.824;0.815
+14;329.000;14.000;0.000;3.000;0.903;0.899
+15;325.000;13.000;0.000;6.000;0.813;0.804
+16;327.000;14.000;0.000;5.000;0.848;0.841
+17;329.000;14.000;0.000;3.000;0.903;0.899
+18;325.000;14.000;0.000;7.000;0.800;0.790
+19;326.000;14.000;0.000;6.000;0.824;0.815
+20;325.000;13.000;0.000;6.000;0.813;0.804
+21;329.000;14.000;0.000;3.000;0.903;0.899
+22;327.000;14.000;0.000;5.000;0.848;0.841
+23;323.000;14.000;0.000;9.000;0.757;0.744
+24;326.000;14.000;0.000;6.000;0.824;0.815
+25;327.000;13.000;0.000;4.000;0.867;0.861
+max;330.000;14.000;0.000;9.000;0.933;0.930
+avg;326.400;13.800;0.000;5.400;0.839;0.831
+min;323.000;13.000;0.000;2.000;0.757;0.744
+---
+KNN
+Nr.;TN;TP;FN;FP;f1 score;cohens kappa score
+1;274.000;14.000;0.000;58.000;0.326;0.277
+2;278.000;14.000;0.000;54.000;0.341;0.294
+3;284.000;14.000;0.000;48.000;0.368;0.324
+4;283.000;14.000;0.000;49.000;0.364;0.319
+5;290.000;13.000;0.000;41.000;0.388;0.348
+6;286.000;13.000;1.000;46.000;0.356;0.311
+7;285.000;14.000;0.000;47.000;0.373;0.329
+8;292.000;14.000;0.000;40.000;0.412;0.371
+9;269.000;14.000;0.000;63.000;0.308;0.257
+10;271.000;13.000;0.000;60.000;0.302;0.254
+11;285.000;14.000;0.000;47.000;0.373;0.329
+12;284.000;14.000;0.000;48.000;0.368;0.324
+13;285.000;13.000;1.000;47.000;0.351;0.306
+14;291.000;14.000;0.000;41.000;0.406;0.365
+15;268.000;13.000;0.000;63.000;0.292;0.243
+16;286.000;14.000;0.000;46.000;0.378;0.335
+17;284.000;14.000;0.000;48.000;0.368;0.324
+18;284.000;14.000;0.000;48.000;0.368;0.324
+19;288.000;14.000;0.000;44.000;0.389;0.346
+20;279.000;13.000;0.000;52.000;0.333;0.289
+21;288.000;14.000;0.000;44.000;0.389;0.346
+22;279.000;14.000;0.000;53.000;0.346;0.299
+23;276.000;14.000;0.000;56.000;0.333;0.285
+24;282.000;14.000;0.000;50.000;0.359;0.313
+25;279.000;13.000;0.000;52.000;0.333;0.289
+max;292.000;14.000;1.000;63.000;0.412;0.371
+avg;282.000;13.720;0.080;49.800;0.357;0.312
+min;268.000;13.000;0.000;40.000;0.292;0.243

data_result/Repeater/folding_car_good.log

@@ -0,0 +1,701 @@
+
+
+///////////////////////////////////////////
+// Running Repeater on folding_car_good
+///////////////////////////////////////////
+
+Load 'data_input/folding_car_good'
+from pickle file
+Data loaded.
+-> Shuffling data
+### Start exercise for synthetic point generator
+
+====== Step 1/5 =======
+-> Shuffling data
+-> Spliting data to slices
+
+------ Step 1/5: Slice 1/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 1272 synthetic samples
+-> test with 'LR'
+LR tn, fp: 170, 162
+LR fn, tp: 4, 10
+LR f1 score: 0.108
+LR cohens kappa score: 0.035
+LR average precision score: 0.061
+
+-> test with 'GB'
+GB tn, fp: 325, 7
+GB fn, tp: 0, 14
+GB f1 score: 0.800
+GB cohens kappa score: 0.790
+
+-> test with 'KNN'
+KNN tn, fp: 274, 58
+KNN fn, tp: 0, 14
+KNN f1 score: 0.326
+KNN cohens kappa score: 0.277
+
+
+------ Step 1/5: Slice 2/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 1272 synthetic samples
+-> test with 'LR'
+LR tn, fp: 185, 147
+LR fn, tp: 3, 11
+LR f1 score: 0.128
+LR cohens kappa score: 0.058
+LR average precision score: 0.087
+
+-> test with 'GB'
+GB tn, fp: 327, 5
+GB fn, tp: 0, 14
+GB f1 score: 0.848
+GB cohens kappa score: 0.841
+
+-> test with 'KNN'
+KNN tn, fp: 278, 54
+KNN fn, tp: 0, 14
+KNN f1 score: 0.341
+KNN cohens kappa score: 0.294
+
+
+------ Step 1/5: Slice 3/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 1272 synthetic samples
+-> test with 'LR'
+LR tn, fp: 172, 160
+LR fn, tp: 4, 10
+LR f1 score: 0.109
+LR cohens kappa score: 0.037
+LR average precision score: 0.057
+
+-> test with 'GB'
+GB tn, fp: 328, 4
+GB fn, tp: 0, 14
+GB f1 score: 0.875
+GB cohens kappa score: 0.869
+
+-> test with 'KNN'
+KNN tn, fp: 284, 48
+KNN fn, tp: 0, 14
+KNN f1 score: 0.368
+KNN cohens kappa score: 0.324
+
+
+------ Step 1/5: Slice 4/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 1272 synthetic samples
+-> test with 'LR'
+LR tn, fp: 178, 154
+LR fn, tp: 4, 10
+LR f1 score: 0.112
+LR cohens kappa score: 0.041
+LR average precision score: 0.077
+
+-> test with 'GB'
+GB tn, fp: 327, 5
+GB fn, tp: 0, 14
+GB f1 score: 0.848
+GB cohens kappa score: 0.841
+
+-> test with 'KNN'
+KNN tn, fp: 283, 49
+KNN fn, tp: 0, 14
+KNN f1 score: 0.364
+KNN cohens kappa score: 0.319
+
+
+------ Step 1/5: Slice 5/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 1272 synthetic samples
+-> test with 'LR'
+LR tn, fp: 175, 156
+LR fn, tp: 4, 9
+LR f1 score: 0.101
+LR cohens kappa score: 0.033
+LR average precision score: 0.056
+
+-> test with 'GB'
+GB tn, fp: 325, 6
+GB fn, tp: 0, 13
+GB f1 score: 0.813
+GB cohens kappa score: 0.804
+
+-> test with 'KNN'
+KNN tn, fp: 290, 41
+KNN fn, tp: 0, 13
+KNN f1 score: 0.388
+KNN cohens kappa score: 0.348
+
+
+====== Step 2/5 =======
+-> Shuffling data
+-> Spliting data to slices
+
+------ Step 2/5: Slice 1/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 1272 synthetic samples
+-> test with 'LR'
+LR tn, fp: 157, 175
+LR fn, tp: 4, 10
+LR f1 score: 0.101
+LR cohens kappa score: 0.027
+LR average precision score: 0.068
+
+-> test with 'GB'
+GB tn, fp: 325, 7
+GB fn, tp: 0, 14
+GB f1 score: 0.800
+GB cohens kappa score: 0.790
+
+-> test with 'KNN'
+KNN tn, fp: 286, 46
+KNN fn, tp: 1, 13
+KNN f1 score: 0.356
+KNN cohens kappa score: 0.311
+
+
+------ Step 2/5: Slice 2/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 1272 synthetic samples
+-> test with 'LR'
+LR tn, fp: 169, 163
+LR fn, tp: 3, 11
+LR f1 score: 0.117
+LR cohens kappa score: 0.046
+LR average precision score: 0.070
+
+-> test with 'GB'
+GB tn, fp: 329, 3
+GB fn, tp: 0, 14
+GB f1 score: 0.903
+GB cohens kappa score: 0.899
+
+-> test with 'KNN'
+KNN tn, fp: 285, 47
+KNN fn, tp: 0, 14
+KNN f1 score: 0.373
+KNN cohens kappa score: 0.329
+
+
+------ Step 2/5: Slice 3/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 1272 synthetic samples
+-> test with 'LR'
+LR tn, fp: 186, 146
+LR fn, tp: 4, 10
+LR f1 score: 0.118
+LR cohens kappa score: 0.047
+LR average precision score: 0.072
+
+-> test with 'GB'
+GB tn, fp: 330, 2
+GB fn, tp: 0, 14
+GB f1 score: 0.933
+GB cohens kappa score: 0.930
+
+-> test with 'KNN'
+KNN tn, fp: 292, 40
+KNN fn, tp: 0, 14
+KNN f1 score: 0.412
+KNN cohens kappa score: 0.371
+
+
+------ Step 2/5: Slice 4/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 1272 synthetic samples
+-> test with 'LR'
+LR tn, fp: 183, 149
+LR fn, tp: 7, 7
+LR f1 score: 0.082
+LR cohens kappa score: 0.009
+LR average precision score: 0.050
+
+-> test with 'GB'
+GB tn, fp: 323, 9
+GB fn, tp: 0, 14
+GB f1 score: 0.757
+GB cohens kappa score: 0.744
+
+-> test with 'KNN'
+KNN tn, fp: 269, 63
+KNN fn, tp: 0, 14
+KNN f1 score: 0.308
+KNN cohens kappa score: 0.257
+
+
+------ Step 2/5: Slice 5/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 1272 synthetic samples
+-> test with 'LR'
+LR tn, fp: 180, 151
+LR fn, tp: 5, 8
+LR f1 score: 0.093
+LR cohens kappa score: 0.025
+LR average precision score: 0.077
+
+-> test with 'GB'
+GB tn, fp: 325, 6
+GB fn, tp: 0, 13
+GB f1 score: 0.813
+GB cohens kappa score: 0.804
+
+-> test with 'KNN'
+KNN tn, fp: 271, 60
+KNN fn, tp: 0, 13
+KNN f1 score: 0.302
+KNN cohens kappa score: 0.254
+
+
+====== Step 3/5 =======
+-> Shuffling data
+-> Spliting data to slices
+
+------ Step 3/5: Slice 1/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 1272 synthetic samples
+-> test with 'LR'
+LR tn, fp: 168, 164
+LR fn, tp: 3, 11
+LR f1 score: 0.116
+LR cohens kappa score: 0.045
+LR average precision score: 0.077
+
+-> test with 'GB'
+GB tn, fp: 328, 4
+GB fn, tp: 0, 14
+GB f1 score: 0.875
+GB cohens kappa score: 0.869
+
+-> test with 'KNN'
+KNN tn, fp: 285, 47
+KNN fn, tp: 0, 14
+KNN f1 score: 0.373
+KNN cohens kappa score: 0.329
+
+
+------ Step 3/5: Slice 2/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 1272 synthetic samples
+-> test with 'LR'
+LR tn, fp: 190, 142
+LR fn, tp: 3, 11
+LR f1 score: 0.132
+LR cohens kappa score: 0.062
+LR average precision score: 0.067
+
+-> test with 'GB'
+GB tn, fp: 324, 8
+GB fn, tp: 0, 14
+GB f1 score: 0.778
+GB cohens kappa score: 0.766
+
+-> test with 'KNN'
+KNN tn, fp: 284, 48
+KNN fn, tp: 0, 14
+KNN f1 score: 0.368
+KNN cohens kappa score: 0.324
+
+
+------ Step 3/5: Slice 3/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 1272 synthetic samples
+-> test with 'LR'
+LR tn, fp: 180, 152
+LR fn, tp: 6, 8
+LR f1 score: 0.092
+LR cohens kappa score: 0.019
+LR average precision score: 0.056
+
+-> test with 'GB'
+GB tn, fp: 326, 6
+GB fn, tp: 0, 14
+GB f1 score: 0.824
+GB cohens kappa score: 0.815
+
+-> test with 'KNN'
+KNN tn, fp: 285, 47
+KNN fn, tp: 1, 13
+KNN f1 score: 0.351
+KNN cohens kappa score: 0.306
+
+
+------ Step 3/5: Slice 4/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 1272 synthetic samples
+-> test with 'LR'
+LR tn, fp: 170, 162
+LR fn, tp: 3, 11
+LR f1 score: 0.118
+LR cohens kappa score: 0.046
+LR average precision score: 0.083
+
+-> test with 'GB'
+GB tn, fp: 329, 3
+GB fn, tp: 0, 14
+GB f1 score: 0.903
+GB cohens kappa score: 0.899
+
+-> test with 'KNN'
+KNN tn, fp: 291, 41
+KNN fn, tp: 0, 14
+KNN f1 score: 0.406
+KNN cohens kappa score: 0.365
+
+
+------ Step 3/5: Slice 5/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 1272 synthetic samples
+-> test with 'LR'
+LR tn, fp: 168, 163
+LR fn, tp: 4, 9
+LR f1 score: 0.097
+LR cohens kappa score: 0.029
+LR average precision score: 0.055
+
+-> test with 'GB'
+GB tn, fp: 325, 6
+GB fn, tp: 0, 13
+GB f1 score: 0.813
+GB cohens kappa score: 0.804
+
+-> test with 'KNN'
+KNN tn, fp: 268, 63
+KNN fn, tp: 0, 13
+KNN f1 score: 0.292
+KNN cohens kappa score: 0.243
+
+
+====== Step 4/5 =======
+-> Shuffling data
+-> Spliting data to slices
+
+------ Step 4/5: Slice 1/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 1272 synthetic samples
+-> test with 'LR'
+LR tn, fp: 177, 155
+LR fn, tp: 3, 11
+LR f1 score: 0.122
+LR cohens kappa score: 0.051
+LR average precision score: 0.067
+
+-> test with 'GB'
+GB tn, fp: 327, 5
+GB fn, tp: 0, 14
+GB f1 score: 0.848
+GB cohens kappa score: 0.841
+
+-> test with 'KNN'
+KNN tn, fp: 286, 46
+KNN fn, tp: 0, 14
+KNN f1 score: 0.378
+KNN cohens kappa score: 0.335
+
+
+------ Step 4/5: Slice 2/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 1272 synthetic samples
+-> test with 'LR'
+LR tn, fp: 173, 159
+LR fn, tp: 6, 8
+LR f1 score: 0.088
+LR cohens kappa score: 0.015
+LR average precision score: 0.063
+
+-> test with 'GB'
+GB tn, fp: 329, 3
+GB fn, tp: 0, 14
+GB f1 score: 0.903
+GB cohens kappa score: 0.899
+
+-> test with 'KNN'
+KNN tn, fp: 284, 48
+KNN fn, tp: 0, 14
+KNN f1 score: 0.368
+KNN cohens kappa score: 0.324
+
+
+------ Step 4/5: Slice 3/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 1272 synthetic samples
+-> test with 'LR'
+LR tn, fp: 170, 162
+LR fn, tp: 4, 10
+LR f1 score: 0.108
+LR cohens kappa score: 0.035
+LR average precision score: 0.066
+
+-> test with 'GB'
+GB tn, fp: 325, 7
+GB fn, tp: 0, 14
+GB f1 score: 0.800
+GB cohens kappa score: 0.790
+
+-> test with 'KNN'
+KNN tn, fp: 284, 48
+KNN fn, tp: 0, 14
+KNN f1 score: 0.368
+KNN cohens kappa score: 0.324
+
+
+------ Step 4/5: Slice 4/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 1272 synthetic samples
+-> test with 'LR'
+LR tn, fp: 189, 143
+LR fn, tp: 6, 8
+LR f1 score: 0.097
+LR cohens kappa score: 0.025
+LR average precision score: 0.056
+
+-> test with 'GB'
+GB tn, fp: 326, 6
+GB fn, tp: 0, 14
+GB f1 score: 0.824
+GB cohens kappa score: 0.815
+
+-> test with 'KNN'
+KNN tn, fp: 288, 44
+KNN fn, tp: 0, 14
+KNN f1 score: 0.389
+KNN cohens kappa score: 0.346
+
+
+------ Step 4/5: Slice 5/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 1272 synthetic samples
+-> test with 'LR'
+LR tn, fp: 170, 161
+LR fn, tp: 1, 12
+LR f1 score: 0.129
+LR cohens kappa score: 0.063
+LR average precision score: 0.081
+
+-> test with 'GB'
+GB tn, fp: 325, 6
+GB fn, tp: 0, 13
+GB f1 score: 0.813
+GB cohens kappa score: 0.804
+
+-> test with 'KNN'
+KNN tn, fp: 279, 52
+KNN fn, tp: 0, 13
+KNN f1 score: 0.333
+KNN cohens kappa score: 0.289
+
+
+====== Step 5/5 =======
+-> Shuffling data
+-> Spliting data to slices
+
+------ Step 5/5: Slice 1/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 1272 synthetic samples
+-> test with 'LR'
+LR tn, fp: 175, 157
+LR fn, tp: 8, 6
+LR f1 score: 0.068
+LR cohens kappa score: -0.007
+LR average precision score: 0.054
+
+-> test with 'GB'
+GB tn, fp: 329, 3
+GB fn, tp: 0, 14
+GB f1 score: 0.903
+GB cohens kappa score: 0.899
+
+-> test with 'KNN'
+KNN tn, fp: 288, 44
+KNN fn, tp: 0, 14
+KNN f1 score: 0.389
+KNN cohens kappa score: 0.346
+
+
+------ Step 5/5: Slice 2/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 1272 synthetic samples
+-> test with 'LR'
+LR tn, fp: 187, 145
+LR fn, tp: 6, 8
+LR f1 score: 0.096
+LR cohens kappa score: 0.023
+LR average precision score: 0.070
+
+-> test with 'GB'
+GB tn, fp: 327, 5
+GB fn, tp: 0, 14
+GB f1 score: 0.848
+GB cohens kappa score: 0.841
+
+-> test with 'KNN'
+KNN tn, fp: 279, 53
+KNN fn, tp: 0, 14
+KNN f1 score: 0.346
+KNN cohens kappa score: 0.299
+
+
+------ Step 5/5: Slice 3/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 1272 synthetic samples
+-> test with 'LR'
+LR tn, fp: 162, 170
+LR fn, tp: 3, 11
+LR f1 score: 0.113
+LR cohens kappa score: 0.041
+LR average precision score: 0.079
+
+-> test with 'GB'
+GB tn, fp: 323, 9
+GB fn, tp: 0, 14
+GB f1 score: 0.757
+GB cohens kappa score: 0.744
+
+-> test with 'KNN'
+KNN tn, fp: 276, 56
+KNN fn, tp: 0, 14
+KNN f1 score: 0.333
+KNN cohens kappa score: 0.285
+
+
+------ Step 5/5: Slice 4/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 1272 synthetic samples
+-> test with 'LR'
+LR tn, fp: 173, 159
+LR fn, tp: 4, 10
+LR f1 score: 0.109
+LR cohens kappa score: 0.037
+LR average precision score: 0.078
+
+-> test with 'GB'
+GB tn, fp: 326, 6
+GB fn, tp: 0, 14
+GB f1 score: 0.824
+GB cohens kappa score: 0.815
+
+-> test with 'KNN'
+KNN tn, fp: 282, 50
+KNN fn, tp: 0, 14
+KNN f1 score: 0.359
+KNN cohens kappa score: 0.313
+
+
+------ Step 5/5: Slice 5/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 1272 synthetic samples
+-> test with 'LR'
+LR tn, fp: 176, 155
+LR fn, tp: 4, 9
+LR f1 score: 0.102
+LR cohens kappa score: 0.034
+LR average precision score: 0.065
+
+-> test with 'GB'
+GB tn, fp: 327, 4
+GB fn, tp: 0, 13
+GB f1 score: 0.867
+GB cohens kappa score: 0.861
+
+-> test with 'KNN'
+KNN tn, fp: 279, 52
+KNN fn, tp: 0, 13
+KNN f1 score: 0.333
+KNN cohens kappa score: 0.289
+
+### Exercise is done.
+
+-----[ LR ]-----
+maximum:
+LR tn, fp: 190, 175
+LR fn, tp: 8, 12
+LR f1 score: 0.132
+LR cohens kappa score: 0.063
+LR average precision score: 0.087
+
+
+average:
+LR tn, fp: 175.32, 156.48
+LR fn, tp: 4.24, 9.56
+LR f1 score: 0.106
+LR cohens kappa score: 0.035
+LR average precision score: 0.068
+
+
+minimum:
+LR tn, fp: 157, 142
+LR fn, tp: 1, 6
+LR f1 score: 0.068
+LR cohens kappa score: -0.007
+LR average precision score: 0.050
+
+
+-----[ GB ]-----
+maximum:
+GB tn, fp: 330, 9
+GB fn, tp: 0, 14
+GB f1 score: 0.933
+GB cohens kappa score: 0.930
+
+
+average:
+GB tn, fp: 326.4, 5.4
+GB fn, tp: 0.0, 13.8
+GB f1 score: 0.839
+GB cohens kappa score: 0.831
+
+
+minimum:
+GB tn, fp: 323, 2
+GB fn, tp: 0, 13
+GB f1 score: 0.757
+GB cohens kappa score: 0.744
+
+
+-----[ KNN ]-----
+maximum:
+KNN tn, fp: 292, 63
+KNN fn, tp: 1, 14
+KNN f1 score: 0.412
+KNN cohens kappa score: 0.371
+
+
+average:
+KNN tn, fp: 282.0, 49.8
+KNN fn, tp: 0.08, 13.72
+KNN f1 score: 0.357
+KNN cohens kappa score: 0.312
+
+
+minimum:
+KNN tn, fp: 268, 40
+KNN fn, tp: 0, 13
+KNN f1 score: 0.292
+KNN cohens kappa score: 0.243
+