Updated convGAN benchmark.

data_result/convGAN-majority-5/folding_abalone9-18.csv

@@ -0,0 +1,123 @@
+LR
+Nr.;TN;TP;FN;FP;f1 score;cohens kappa score;average precision score
+1;121.000;9.000;0.000;17.000;0.514;0.466;0.888
+2;132.000;6.000;3.000;6.000;0.571;0.539;0.581
+3;126.000;8.000;1.000;12.000;0.552;0.510;0.815
+4;129.000;7.000;2.000;9.000;0.560;0.523;0.600
+5;128.000;4.000;2.000;9.000;0.421;0.386;0.477
+6;119.000;7.000;2.000;19.000;0.400;0.340;0.625
+7;130.000;8.000;1.000;8.000;0.640;0.609;0.784
+8;131.000;7.000;2.000;7.000;0.609;0.577;0.731
+9;122.000;8.000;1.000;16.000;0.485;0.434;0.715
+10;125.000;5.000;1.000;12.000;0.435;0.397;0.579
+11;131.000;7.000;2.000;7.000;0.609;0.577;0.631
+12;131.000;9.000;0.000;7.000;0.720;0.696;0.869
+13;133.000;5.000;4.000;5.000;0.526;0.494;0.695
+14;119.000;7.000;2.000;19.000;0.400;0.340;0.624
+15;126.000;5.000;1.000;11.000;0.455;0.419;0.549
+16;128.000;5.000;4.000;10.000;0.417;0.368;0.557
+17;126.000;6.000;3.000;12.000;0.444;0.395;0.702
+18;124.000;8.000;1.000;14.000;0.516;0.470;0.728
+19;122.000;9.000;0.000;16.000;0.529;0.483;0.967
+20;130.000;5.000;1.000;7.000;0.556;0.529;0.518
+21;123.000;7.000;2.000;15.000;0.452;0.399;0.698
+22;126.000;9.000;0.000;12.000;0.600;0.562;0.731
+23;128.000;5.000;4.000;10.000;0.417;0.368;0.542
+24;131.000;8.000;1.000;7.000;0.667;0.639;0.932
+25;128.000;6.000;0.000;9.000;0.571;0.544;0.802
+max;133.000;9.000;4.000;19.000;0.720;0.696;0.967
+avg;126.760;6.800;1.600;11.040;0.523;0.483;0.694
+min;119.000;4.000;0.000;5.000;0.400;0.340;0.477
+---
+GB
+Nr.;TN;TP;FN;FP;f1 score;cohens kappa score
+1;132.000;4.000;5.000;6.000;0.421;0.381
+2;133.000;3.000;6.000;5.000;0.353;0.313
+3;131.000;2.000;7.000;7.000;0.222;0.171
+4;132.000;4.000;5.000;6.000;0.421;0.381
+5;133.000;2.000;4.000;4.000;0.333;0.304
+6;135.000;4.000;5.000;3.000;0.500;0.472
+7;132.000;3.000;6.000;6.000;0.333;0.290
+8;131.000;4.000;5.000;7.000;0.400;0.357
+9;132.000;3.000;6.000;6.000;0.333;0.290
+10;128.000;3.000;3.000;9.000;0.333;0.294
+11;131.000;1.000;8.000;7.000;0.118;0.064
+12;132.000;6.000;3.000;6.000;0.571;0.539
+13;132.000;2.000;7.000;6.000;0.235;0.189
+14;129.000;2.000;7.000;9.000;0.200;0.142
+15;131.000;2.000;4.000;6.000;0.286;0.250
+16;133.000;3.000;6.000;5.000;0.353;0.313
+17;127.000;5.000;4.000;11.000;0.400;0.349
+18;129.000;3.000;6.000;9.000;0.286;0.232
+19;130.000;4.000;5.000;8.000;0.381;0.334
+20;130.000;2.000;4.000;7.000;0.267;0.228
+21;132.000;2.000;7.000;6.000;0.235;0.189
+22;134.000;3.000;6.000;4.000;0.375;0.340
+23;130.000;3.000;6.000;8.000;0.300;0.249
+24;135.000;5.000;4.000;3.000;0.588;0.563
+25;133.000;3.000;3.000;4.000;0.462;0.436
+max;135.000;6.000;8.000;11.000;0.588;0.563
+avg;131.480;3.120;5.280;6.320;0.348;0.307
+min;127.000;1.000;3.000;3.000;0.118;0.064
+---
+KNN
+Nr.;TN;TP;FN;FP;f1 score;cohens kappa score
+1;119.000;8.000;1.000;19.000;0.444;0.388
+2;117.000;7.000;2.000;21.000;0.378;0.315
+3;129.000;6.000;3.000;9.000;0.500;0.459
+4;120.000;6.000;3.000;18.000;0.364;0.301
+5;127.000;3.000;3.000;10.000;0.316;0.274
+6;127.000;4.000;5.000;11.000;0.333;0.278
+7;127.000;6.000;3.000;11.000;0.462;0.415
+8;125.000;7.000;2.000;13.000;0.483;0.435
+9;121.000;6.000;3.000;17.000;0.375;0.315
+10;124.000;4.000;2.000;13.000;0.348;0.305
+11;128.000;4.000;5.000;10.000;0.348;0.295
+12;117.000;8.000;1.000;21.000;0.421;0.361
+13;127.000;5.000;4.000;11.000;0.400;0.349
+14;119.000;4.000;5.000;19.000;0.250;0.178
+15;117.000;4.000;2.000;20.000;0.267;0.214
+16;127.000;5.000;4.000;11.000;0.400;0.349
+17;119.000;6.000;3.000;19.000;0.353;0.289
+18;123.000;5.000;4.000;15.000;0.345;0.284
+19;120.000;7.000;2.000;18.000;0.412;0.354
+20;122.000;4.000;2.000;15.000;0.320;0.274
+21;117.000;4.000;5.000;21.000;0.235;0.160
+22;119.000;5.000;4.000;19.000;0.303;0.235
+23;123.000;3.000;6.000;15.000;0.222;0.153
+24;128.000;6.000;3.000;10.000;0.480;0.436
+25;126.000;4.000;2.000;11.000;0.381;0.341
+max;129.000;8.000;6.000;21.000;0.500;0.459
+avg;122.720;5.240;3.160;15.080;0.366;0.310
+min;117.000;3.000;1.000;9.000;0.222;0.153
+---
+GAN
+Nr.;TN;TP;FN;FP;f1 score;cohens kappa score
+1;121.000;9.000;0.000;17.000;0.514;0.466
+2;128.000;6.000;3.000;10.000;0.480;0.436
+3;129.000;6.000;3.000;9.000;0.500;0.459
+4;124.000;7.000;2.000;14.000;0.467;0.417
+5;126.000;4.000;2.000;11.000;0.381;0.341
+6;129.000;6.000;3.000;9.000;0.500;0.459
+7;123.000;6.000;3.000;15.000;0.400;0.344
+8;126.000;7.000;2.000;12.000;0.500;0.455
+9;124.000;8.000;1.000;14.000;0.516;0.470
+10;122.000;4.000;2.000;15.000;0.320;0.274
+11;121.000;6.000;3.000;17.000;0.375;0.315
+12;125.000;9.000;0.000;13.000;0.581;0.541
+13;127.000;5.000;4.000;11.000;0.400;0.349
+14;122.000;6.000;3.000;16.000;0.387;0.329
+15;118.000;5.000;1.000;19.000;0.333;0.285
+16;126.000;6.000;3.000;12.000;0.444;0.395
+17;120.000;7.000;2.000;18.000;0.412;0.354
+18;120.000;7.000;2.000;18.000;0.412;0.354
+19;118.000;9.000;0.000;20.000;0.474;0.419
+20;131.000;4.000;2.000;6.000;0.500;0.472
+21;123.000;6.000;3.000;15.000;0.400;0.344
+22;121.000;5.000;4.000;17.000;0.323;0.258
+23;126.000;5.000;4.000;12.000;0.385;0.331
+24;126.000;8.000;1.000;12.000;0.552;0.510
+25;128.000;5.000;1.000;9.000;0.500;0.469
+max;131.000;9.000;4.000;20.000;0.581;0.541
+avg;124.160;6.240;2.160;13.640;0.442;0.394
+min;118.000;4.000;0.000;6.000;0.320;0.258

data_result/convGAN-majority-5/folding_abalone9-18.log

@@ -0,0 +1,873 @@
+
+
+///////////////////////////////////////////
+// Running convGAN-majority-5 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 GAN.predict
+GAN tn, fp: 121, 17
+GAN fn, tp: 0, 9
+GAN f1 score: 0.514
+GAN cohens kappa score: 0.466
+
+-> test with 'LR'
+LR tn, fp: 121, 17
+LR fn, tp: 0, 9
+LR f1 score: 0.514
+LR cohens kappa score: 0.466
+LR average precision score: 0.888
+
+-> 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: 119, 19
+KNN fn, tp: 1, 8
+KNN f1 score: 0.444
+KNN cohens kappa score: 0.388
+
+
+------ Step 1/5: Slice 2/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 518 synthetic samples
+-> test with GAN.predict
+GAN tn, fp: 128, 10
+GAN fn, tp: 3, 6
+GAN f1 score: 0.480
+GAN cohens kappa score: 0.436
+
+-> test with 'LR'
+LR tn, fp: 132, 6
+LR fn, tp: 3, 6
+LR f1 score: 0.571
+LR cohens kappa score: 0.539
+LR average precision score: 0.581
+
+-> 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: 117, 21
+KNN fn, tp: 2, 7
+KNN f1 score: 0.378
+KNN cohens kappa score: 0.315
+
+
+------ Step 1/5: Slice 3/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 518 synthetic samples
+-> test with GAN.predict
+GAN tn, fp: 129, 9
+GAN fn, tp: 3, 6
+GAN f1 score: 0.500
+GAN cohens kappa score: 0.459
+
+-> 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: 0.815
+
+-> 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: 129, 9
+KNN fn, tp: 3, 6
+KNN f1 score: 0.500
+KNN cohens kappa score: 0.459
+
+
+------ Step 1/5: Slice 4/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 518 synthetic samples
+-> test with GAN.predict
+GAN tn, fp: 124, 14
+GAN fn, tp: 2, 7
+GAN f1 score: 0.467
+GAN cohens kappa score: 0.417
+
+-> test with 'LR'
+LR tn, fp: 129, 9
+LR fn, tp: 2, 7
+LR f1 score: 0.560
+LR cohens kappa score: 0.523
+LR average precision score: 0.600
+
+-> 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: 120, 18
+KNN fn, tp: 3, 6
+KNN f1 score: 0.364
+KNN cohens kappa score: 0.301
+
+
+------ Step 1/5: Slice 5/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 516 synthetic samples
+-> test with GAN.predict
+GAN tn, fp: 126, 11
+GAN fn, tp: 2, 4
+GAN f1 score: 0.381
+GAN cohens kappa score: 0.341
+
+-> test with 'LR'
+LR tn, fp: 128, 9
+LR fn, tp: 2, 4
+LR f1 score: 0.421
+LR cohens kappa score: 0.386
+LR average precision score: 0.477
+
+-> 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: 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 GAN.predict
+GAN tn, fp: 129, 9
+GAN fn, tp: 3, 6
+GAN f1 score: 0.500
+GAN cohens kappa score: 0.459
+
+-> test with 'LR'
+LR tn, fp: 119, 19
+LR fn, tp: 2, 7
+LR f1 score: 0.400
+LR cohens kappa score: 0.340
+LR average precision score: 0.625
+
+-> 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: 127, 11
+KNN fn, tp: 5, 4
+KNN f1 score: 0.333
+KNN cohens kappa score: 0.278
+
+
+------ Step 2/5: Slice 2/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 518 synthetic samples
+-> test with GAN.predict
+GAN tn, fp: 123, 15
+GAN fn, tp: 3, 6
+GAN f1 score: 0.400
+GAN cohens kappa score: 0.344
+
+-> test with 'LR'
+LR tn, fp: 130, 8
+LR fn, tp: 1, 8
+LR f1 score: 0.640
+LR cohens kappa score: 0.609
+LR average precision score: 0.784
+
+-> 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: 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 GAN.predict
+GAN tn, fp: 126, 12
+GAN fn, tp: 2, 7
+GAN f1 score: 0.500
+GAN cohens kappa score: 0.455
+
+-> test with 'LR'
+LR tn, fp: 131, 7
+LR fn, tp: 2, 7
+LR f1 score: 0.609
+LR cohens kappa score: 0.577
+LR average precision score: 0.731
+
+-> 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: 125, 13
+KNN fn, tp: 2, 7
+KNN f1 score: 0.483
+KNN cohens kappa score: 0.435
+
+
+------ Step 2/5: Slice 4/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 518 synthetic samples
+-> test with GAN.predict
+GAN tn, fp: 124, 14
+GAN fn, tp: 1, 8
+GAN f1 score: 0.516
+GAN cohens kappa score: 0.470
+
+-> 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: 0.715
+
+-> 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: 121, 17
+KNN fn, tp: 3, 6
+KNN f1 score: 0.375
+KNN cohens kappa score: 0.315
+
+
+------ Step 2/5: Slice 5/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 516 synthetic samples
+-> test with GAN.predict
+GAN tn, fp: 122, 15
+GAN fn, tp: 2, 4
+GAN f1 score: 0.320
+GAN cohens kappa score: 0.274
+
+-> test with 'LR'
+LR tn, fp: 125, 12
+LR fn, tp: 1, 5
+LR f1 score: 0.435
+LR cohens kappa score: 0.397
+LR average precision score: 0.579
+
+-> 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: 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 GAN.predict
+GAN tn, fp: 121, 17
+GAN fn, tp: 3, 6
+GAN f1 score: 0.375
+GAN cohens kappa score: 0.315
+
+-> test with 'LR'
+LR tn, fp: 131, 7
+LR fn, tp: 2, 7
+LR f1 score: 0.609
+LR cohens kappa score: 0.577
+LR average precision score: 0.631
+
+-> test with 'GB'
+GB tn, fp: 131, 7
+GB fn, tp: 8, 1
+GB f1 score: 0.118
+GB cohens kappa score: 0.064
+
+-> 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 3/5: Slice 2/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 518 synthetic samples
+-> test with GAN.predict
+GAN tn, fp: 125, 13
+GAN fn, tp: 0, 9
+GAN f1 score: 0.581
+GAN cohens kappa score: 0.541
+
+-> test with 'LR'
+LR tn, fp: 131, 7
+LR fn, tp: 0, 9
+LR f1 score: 0.720
+LR cohens kappa score: 0.696
+LR average precision score: 0.869
+
+-> test with 'GB'
+GB tn, fp: 132, 6
+GB fn, tp: 3, 6
+GB f1 score: 0.571
+GB cohens kappa score: 0.539
+
+-> test with 'KNN'
+KNN tn, fp: 117, 21
+KNN fn, tp: 1, 8
+KNN f1 score: 0.421
+KNN cohens kappa score: 0.361
+
+
+------ Step 3/5: Slice 3/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 518 synthetic samples
+-> test with GAN.predict
+GAN tn, fp: 127, 11
+GAN fn, tp: 4, 5
+GAN f1 score: 0.400
+GAN cohens kappa score: 0.349
+
+-> test with 'LR'
+LR tn, fp: 133, 5
+LR fn, tp: 4, 5
+LR f1 score: 0.526
+LR cohens kappa score: 0.494
+LR average precision score: 0.695
+
+-> 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: 127, 11
+KNN fn, tp: 4, 5
+KNN f1 score: 0.400
+KNN cohens kappa score: 0.349
+
+
+------ Step 3/5: Slice 4/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 518 synthetic samples
+-> test with GAN.predict
+GAN tn, fp: 122, 16
+GAN fn, tp: 3, 6
+GAN f1 score: 0.387
+GAN cohens kappa score: 0.329
+
+-> test with 'LR'
+LR tn, fp: 119, 19
+LR fn, tp: 2, 7
+LR f1 score: 0.400
+LR cohens kappa score: 0.340
+LR average precision score: 0.624
+
+-> test with 'GB'
+GB tn, fp: 129, 9
+GB fn, tp: 7, 2
+GB f1 score: 0.200
+GB cohens kappa score: 0.142
+
+-> test with 'KNN'
+KNN tn, fp: 119, 19
+KNN fn, tp: 5, 4
+KNN f1 score: 0.250
+KNN cohens kappa score: 0.178
+
+
+------ Step 3/5: Slice 5/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 516 synthetic samples
+-> test with GAN.predict
+GAN tn, fp: 118, 19
+GAN fn, tp: 1, 5
+GAN f1 score: 0.333
+GAN cohens kappa score: 0.285
+
+-> test with 'LR'
+LR tn, fp: 126, 11
+LR fn, tp: 1, 5
+LR f1 score: 0.455
+LR cohens kappa score: 0.419
+LR average precision score: 0.549
+
+-> test with 'GB'
+GB tn, fp: 131, 6
+GB fn, tp: 4, 2
+GB f1 score: 0.286
+GB cohens kappa score: 0.250
+
+-> test with 'KNN'
+KNN tn, fp: 117, 20
+KNN fn, tp: 2, 4
+KNN f1 score: 0.267
+KNN cohens kappa score: 0.214
+
+
+====== 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 GAN.predict
+GAN tn, fp: 126, 12
+GAN fn, tp: 3, 6
+GAN f1 score: 0.444
+GAN cohens kappa score: 0.395
+
+-> 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: 0.557
+
+-> 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: 127, 11
+KNN fn, tp: 4, 5
+KNN f1 score: 0.400
+KNN cohens kappa score: 0.349
+
+
+------ Step 4/5: Slice 2/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 518 synthetic samples
+-> test with GAN.predict
+GAN tn, fp: 120, 18
+GAN fn, tp: 2, 7
+GAN f1 score: 0.412
+GAN cohens kappa score: 0.354
+
+-> 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: 0.702
+
+-> test with 'GB'
+GB tn, fp: 127, 11
+GB fn, tp: 4, 5
+GB f1 score: 0.400
+GB cohens kappa score: 0.349
+
+-> 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 4/5: Slice 3/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 518 synthetic samples
+-> test with GAN.predict
+GAN tn, fp: 120, 18
+GAN fn, tp: 2, 7
+GAN f1 score: 0.412
+GAN cohens kappa score: 0.354
+
+-> test with 'LR'
+LR tn, fp: 124, 14
+LR fn, tp: 1, 8
+LR f1 score: 0.516
+LR cohens kappa score: 0.470
+LR average precision score: 0.728
+
+-> test with 'GB'
+GB tn, fp: 129, 9
+GB fn, tp: 6, 3
+GB f1 score: 0.286
+GB cohens kappa score: 0.232
+
+-> 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 4/5: Slice 4/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 518 synthetic samples
+-> test with GAN.predict
+GAN tn, fp: 118, 20
+GAN fn, tp: 0, 9
+GAN f1 score: 0.474
+GAN cohens kappa score: 0.419
+
+-> test with 'LR'
+LR tn, fp: 122, 16
+LR fn, tp: 0, 9
+LR f1 score: 0.529
+LR cohens kappa score: 0.483
+LR average precision score: 0.967
+
+-> 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: 120, 18
+KNN fn, tp: 2, 7
+KNN f1 score: 0.412
+KNN cohens kappa score: 0.354
+
+
+------ Step 4/5: Slice 5/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 516 synthetic samples
+-> test with GAN.predict
+GAN tn, fp: 131, 6
+GAN fn, tp: 2, 4
+GAN f1 score: 0.500
+GAN cohens kappa score: 0.472
+
+-> test with 'LR'
+LR tn, fp: 130, 7
+LR fn, tp: 1, 5
+LR f1 score: 0.556
+LR cohens kappa score: 0.529
+LR average precision score: 0.518
+
+-> 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: 122, 15
+KNN fn, tp: 2, 4
+KNN f1 score: 0.320
+KNN cohens kappa score: 0.274
+
+
+====== 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 GAN.predict
+GAN tn, fp: 123, 15
+GAN fn, tp: 3, 6
+GAN f1 score: 0.400
+GAN cohens kappa score: 0.344
+
+-> test with 'LR'
+LR tn, fp: 123, 15
+LR fn, tp: 2, 7
+LR f1 score: 0.452
+LR cohens kappa score: 0.399
+LR average precision score: 0.698
+
+-> 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: 117, 21
+KNN fn, tp: 5, 4
+KNN f1 score: 0.235
+KNN cohens kappa score: 0.160
+
+
+------ Step 5/5: Slice 2/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 518 synthetic samples
+-> test with GAN.predict
+GAN tn, fp: 121, 17
+GAN fn, tp: 4, 5
+GAN f1 score: 0.323
+GAN cohens kappa score: 0.258
+
+-> test with 'LR'
+LR tn, fp: 126, 12
+LR fn, tp: 0, 9
+LR f1 score: 0.600
+LR cohens kappa score: 0.562
+LR average precision score: 0.731
+
+-> 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: 4, 5
+KNN f1 score: 0.303
+KNN cohens kappa score: 0.235
+
+
+------ Step 5/5: Slice 3/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 518 synthetic samples
+-> test with GAN.predict
+GAN tn, fp: 126, 12
+GAN fn, tp: 4, 5
+GAN f1 score: 0.385
+GAN cohens kappa score: 0.331
+
+-> 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: 0.542
+
+-> 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: 6, 3
+KNN f1 score: 0.222
+KNN cohens kappa score: 0.153
+
+
+------ Step 5/5: Slice 4/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 518 synthetic samples
+-> test with GAN.predict
+GAN tn, fp: 126, 12
+GAN fn, tp: 1, 8
+GAN f1 score: 0.552
+GAN cohens kappa score: 0.510
+
+-> 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: 0.932
+
+-> test with 'GB'
+GB tn, fp: 135, 3
+GB fn, tp: 4, 5
+GB f1 score: 0.588
+GB cohens kappa score: 0.563
+
+-> 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 5/5: Slice 5/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 516 synthetic samples
+-> test with GAN.predict
+GAN tn, fp: 128, 9
+GAN fn, tp: 1, 5
+GAN f1 score: 0.500
+GAN cohens kappa score: 0.469
+
+-> test with 'LR'
+LR tn, fp: 128, 9
+LR fn, tp: 0, 6
+LR f1 score: 0.571
+LR cohens kappa score: 0.544
+LR average precision score: 0.802
+
+-> 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: 126, 11
+KNN fn, tp: 2, 4
+KNN f1 score: 0.381
+KNN cohens kappa score: 0.341
+
+### Exercise is done.
+
+-----[ LR ]-----
+maximum:
+LR tn, fp: 133, 19
+LR fn, tp: 4, 9
+LR f1 score: 0.720
+LR cohens kappa score: 0.696
+LR average precision score: 0.967
+
+
+average:
+LR tn, fp: 126.76, 11.04
+LR fn, tp: 1.6, 6.8
+LR f1 score: 0.523
+LR cohens kappa score: 0.483
+LR average precision score: 0.694
+
+
+minimum:
+LR tn, fp: 119, 5
+LR fn, tp: 0, 4
+LR f1 score: 0.400
+LR cohens kappa score: 0.340
+LR average precision score: 0.477
+
+
+-----[ GB ]-----
+maximum:
+GB tn, fp: 135, 11
+GB fn, tp: 8, 6
+GB f1 score: 0.588
+GB cohens kappa score: 0.563
+
+
+average:
+GB tn, fp: 131.48, 6.32
+GB fn, tp: 5.28, 3.12
+GB f1 score: 0.348
+GB cohens kappa score: 0.307
+
+
+minimum:
+GB tn, fp: 127, 3
+GB fn, tp: 3, 1
+GB f1 score: 0.118
+GB cohens kappa score: 0.064
+
+
+-----[ KNN ]-----
+maximum:
+KNN tn, fp: 129, 21
+KNN fn, tp: 6, 8
+KNN f1 score: 0.500
+KNN cohens kappa score: 0.459
+
+
+average:
+KNN tn, fp: 122.72, 15.08
+KNN fn, tp: 3.16, 5.24
+KNN f1 score: 0.366
+KNN cohens kappa score: 0.310
+
+
+minimum:
+KNN tn, fp: 117, 9
+KNN fn, tp: 1, 3
+KNN f1 score: 0.222
+KNN cohens kappa score: 0.153
+
+
+-----[ GAN ]-----
+maximum:
+GAN tn, fp: 131, 20
+GAN fn, tp: 4, 9
+GAN f1 score: 0.581
+GAN cohens kappa score: 0.541
+
+
+average:
+GAN tn, fp: 124.16, 13.64
+GAN fn, tp: 2.16, 6.24
+GAN f1 score: 0.442
+GAN cohens kappa score: 0.394
+
+
+minimum:
+GAN tn, fp: 118, 6
+GAN fn, tp: 0, 4
+GAN f1 score: 0.320
+GAN cohens kappa score: 0.258
+

data_result/convGAN-majority-5/folding_abalone_17_vs_7_8_9_10.csv

@@ -0,0 +1,123 @@
+LR
+Nr.;TN;TP;FN;FP;f1 score;cohens kappa score;average precision score
+1;415.000;12.000;0.000;41.000;0.369;0.342;0.449
+2;409.000;10.000;2.000;47.000;0.290;0.258;0.604
+3;426.000;8.000;4.000;30.000;0.320;0.292;0.341
+4;409.000;10.000;2.000;47.000;0.290;0.258;0.562
+5;413.000;7.000;3.000;43.000;0.233;0.205;0.285
+6;411.000;11.000;1.000;45.000;0.324;0.294;0.618
+7;393.000;12.000;0.000;63.000;0.276;0.242;0.569
+8;406.000;9.000;3.000;50.000;0.254;0.220;0.261
+9;421.000;9.000;3.000;35.000;0.321;0.293;0.527
+10;417.000;6.000;4.000;39.000;0.218;0.190;0.413
+11;411.000;9.000;3.000;45.000;0.273;0.241;0.529
+12;422.000;7.000;5.000;34.000;0.264;0.234;0.321
+13;419.000;11.000;1.000;37.000;0.367;0.340;0.504
+14;410.000;10.000;2.000;46.000;0.294;0.263;0.319
+15;416.000;9.000;1.000;40.000;0.305;0.279;0.581
+16;407.000;11.000;1.000;49.000;0.306;0.275;0.593
+17;391.000;11.000;1.000;65.000;0.250;0.215;0.639
+18;411.000;10.000;2.000;45.000;0.299;0.268;0.423
+19;412.000;11.000;1.000;44.000;0.328;0.299;0.474
+20;418.000;8.000;2.000;38.000;0.286;0.260;0.340
+21;406.000;9.000;3.000;50.000;0.254;0.220;0.373
+22;416.000;10.000;2.000;40.000;0.323;0.293;0.366
+23;420.000;10.000;2.000;36.000;0.345;0.317;0.366
+24;408.000;11.000;1.000;48.000;0.310;0.279;0.712
+25;415.000;9.000;1.000;41.000;0.300;0.274;0.369
+max;426.000;12.000;5.000;65.000;0.369;0.342;0.712
+avg;412.080;9.600;2.000;43.920;0.296;0.266;0.462
+min;391.000;6.000;0.000;30.000;0.218;0.190;0.261
+---
+GB
+Nr.;TN;TP;FN;FP;f1 score;cohens kappa score
+1;445.000;4.000;8.000;11.000;0.296;0.276
+2;443.000;4.000;8.000;13.000;0.276;0.253
+3;444.000;3.000;9.000;12.000;0.222;0.199
+4;441.000;7.000;5.000;15.000;0.412;0.392
+5;448.000;2.000;8.000;8.000;0.200;0.182
+6;441.000;2.000;10.000;15.000;0.138;0.111
+7;443.000;6.000;6.000;13.000;0.387;0.367
+8;439.000;1.000;11.000;17.000;0.067;0.037
+9;447.000;7.000;5.000;9.000;0.500;0.485
+10;442.000;4.000;6.000;14.000;0.286;0.265
+11;444.000;4.000;8.000;12.000;0.286;0.264
+12;443.000;3.000;9.000;13.000;0.214;0.191
+13;441.000;8.000;4.000;15.000;0.457;0.438
+14;447.000;1.000;11.000;9.000;0.091;0.069
+15;444.000;5.000;5.000;12.000;0.370;0.353
+16;440.000;6.000;6.000;16.000;0.353;0.331
+17;445.000;5.000;7.000;11.000;0.357;0.338
+18;441.000;4.000;8.000;15.000;0.258;0.234
+19;444.000;2.000;10.000;12.000;0.154;0.130
+20;447.000;5.000;5.000;9.000;0.417;0.402
+21;444.000;2.000;10.000;12.000;0.154;0.130
+22;441.000;4.000;8.000;15.000;0.258;0.234
+23;443.000;6.000;6.000;13.000;0.387;0.367
+24;441.000;5.000;7.000;15.000;0.312;0.290
+25;442.000;8.000;2.000;14.000;0.500;0.485
+max;448.000;8.000;11.000;17.000;0.500;0.485
+avg;443.200;4.320;7.280;12.800;0.294;0.273
+min;439.000;1.000;2.000;8.000;0.067;0.037
+---
+KNN
+Nr.;TN;TP;FN;FP;f1 score;cohens kappa score
+1;427.000;8.000;4.000;29.000;0.327;0.299
+2;411.000;11.000;1.000;45.000;0.324;0.294
+3;424.000;7.000;5.000;32.000;0.275;0.245
+4;408.000;10.000;2.000;48.000;0.286;0.254
+5;423.000;5.000;5.000;33.000;0.208;0.180
+6;423.000;8.000;4.000;33.000;0.302;0.273
+7;399.000;12.000;0.000;57.000;0.296;0.264
+8;418.000;9.000;3.000;38.000;0.305;0.275
+9;424.000;6.000;6.000;32.000;0.240;0.209
+10;419.000;8.000;2.000;37.000;0.291;0.265
+11;418.000;9.000;3.000;38.000;0.305;0.275
+12;424.000;10.000;2.000;32.000;0.370;0.344
+13;419.000;7.000;5.000;37.000;0.250;0.219
+14;419.000;6.000;6.000;37.000;0.218;0.186
+15;423.000;8.000;2.000;33.000;0.314;0.289
+16;411.000;11.000;1.000;45.000;0.324;0.294
+17;418.000;11.000;1.000;38.000;0.361;0.333
+18;421.000;6.000;6.000;35.000;0.226;0.194
+19;419.000;6.000;6.000;37.000;0.218;0.186
+20;422.000;6.000;4.000;34.000;0.240;0.213
+21;421.000;6.000;6.000;35.000;0.226;0.194
+22;415.000;8.000;4.000;41.000;0.262;0.231
+23;418.000;10.000;2.000;38.000;0.333;0.305
+24;427.000;10.000;2.000;29.000;0.392;0.367
+25;418.000;8.000;2.000;38.000;0.286;0.260
+max;427.000;12.000;6.000;57.000;0.392;0.367
+avg;418.760;8.240;3.360;37.240;0.287;0.258
+min;399.000;5.000;0.000;29.000;0.208;0.180
+---
+GAN
+Nr.;TN;TP;FN;FP;f1 score;cohens kappa score
+1;402.000;11.000;1.000;54.000;0.286;0.253
+2;398.000;11.000;1.000;58.000;0.272;0.238
+3;408.000;9.000;3.000;48.000;0.261;0.228
+4;393.000;11.000;1.000;63.000;0.256;0.221
+5;413.000;8.000;2.000;43.000;0.262;0.235
+6;431.000;10.000;2.000;25.000;0.426;0.403
+7;407.000;12.000;0.000;49.000;0.329;0.299
+8;390.000;11.000;1.000;66.000;0.247;0.212
+9;412.000;10.000;2.000;44.000;0.303;0.273
+10;392.000;7.000;3.000;64.000;0.173;0.141
+11;408.000;9.000;3.000;48.000;0.261;0.228
+12;407.000;8.000;4.000;49.000;0.232;0.198
+13;419.000;10.000;2.000;37.000;0.339;0.311
+14;416.000;9.000;3.000;40.000;0.295;0.265
+15;421.000;8.000;2.000;35.000;0.302;0.277
+16;393.000;11.000;1.000;63.000;0.256;0.221
+17;399.000;12.000;0.000;57.000;0.296;0.264
+18;394.000;9.000;3.000;62.000;0.217;0.181
+19;395.000;10.000;2.000;61.000;0.241;0.206
+20;429.000;6.000;4.000;27.000;0.279;0.255
+21;411.000;8.000;4.000;45.000;0.246;0.213
+22;424.000;9.000;3.000;32.000;0.340;0.312
+23;428.000;9.000;3.000;28.000;0.367;0.342
+24;430.000;10.000;2.000;26.000;0.417;0.393
+25;414.000;8.000;2.000;42.000;0.267;0.239
+max;431.000;12.000;4.000;66.000;0.426;0.403
+avg;409.360;9.440;2.160;46.640;0.287;0.256
+min;390.000;6.000;0.000;25.000;0.173;0.141

data_result/convGAN-majority-5/folding_abalone_17_vs_7_8_9_10.log

@@ -0,0 +1,873 @@
+
+
+///////////////////////////////////////////
+// Running convGAN-majority-5 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 GAN.predict
+GAN tn, fp: 402, 54
+GAN fn, tp: 1, 11
+GAN f1 score: 0.286
+GAN cohens kappa score: 0.253
+
+-> test with 'LR'
+LR tn, fp: 415, 41
+LR fn, tp: 0, 12
+LR f1 score: 0.369
+LR cohens kappa score: 0.342
+LR average precision score: 0.449
+
+-> test with 'GB'
+GB tn, fp: 445, 11
+GB fn, tp: 8, 4
+GB f1 score: 0.296
+GB cohens kappa score: 0.276
+
+-> test with 'KNN'
+KNN tn, fp: 427, 29
+KNN fn, tp: 4, 8
+KNN f1 score: 0.327
+KNN cohens kappa score: 0.299
+
+
+------ Step 1/5: Slice 2/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 1778 synthetic samples
+-> test with GAN.predict
+GAN tn, fp: 398, 58
+GAN fn, tp: 1, 11
+GAN f1 score: 0.272
+GAN cohens kappa score: 0.238
+
+-> test with 'LR'
+LR tn, fp: 409, 47
+LR fn, tp: 2, 10
+LR f1 score: 0.290
+LR cohens kappa score: 0.258
+LR average precision score: 0.604
+
+-> test with 'GB'
+GB tn, fp: 443, 13
+GB fn, tp: 8, 4
+GB f1 score: 0.276
+GB cohens kappa score: 0.253
+
+-> test with 'KNN'
+KNN tn, fp: 411, 45
+KNN fn, tp: 1, 11
+KNN f1 score: 0.324
+KNN cohens kappa score: 0.294
+
+
+------ Step 1/5: Slice 3/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 1778 synthetic samples
+-> test with GAN.predict
+GAN tn, fp: 408, 48
+GAN fn, tp: 3, 9
+GAN f1 score: 0.261
+GAN cohens kappa score: 0.228
+
+-> test with 'LR'
+LR tn, fp: 426, 30
+LR fn, tp: 4, 8
+LR f1 score: 0.320
+LR cohens kappa score: 0.292
+LR average precision score: 0.341
+
+-> test with 'GB'
+GB tn, fp: 444, 12
+GB fn, tp: 9, 3
+GB f1 score: 0.222
+GB cohens kappa score: 0.199
+
+-> test with 'KNN'
+KNN tn, fp: 424, 32
+KNN fn, tp: 5, 7
+KNN f1 score: 0.275
+KNN cohens kappa score: 0.245
+
+
+------ Step 1/5: Slice 4/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 1778 synthetic samples
+-> test with GAN.predict
+GAN tn, fp: 393, 63
+GAN fn, tp: 1, 11
+GAN f1 score: 0.256
+GAN cohens kappa score: 0.221
+
+-> test with 'LR'
+LR tn, fp: 409, 47
+LR fn, tp: 2, 10
+LR f1 score: 0.290
+LR cohens kappa score: 0.258
+LR average precision score: 0.562
+
+-> test with 'GB'
+GB tn, fp: 441, 15
+GB fn, tp: 5, 7
+GB f1 score: 0.412
+GB cohens kappa score: 0.392
+
+-> test with 'KNN'
+KNN tn, fp: 408, 48
+KNN fn, tp: 2, 10
+KNN f1 score: 0.286
+KNN cohens kappa score: 0.254
+
+
+------ Step 1/5: Slice 5/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 1776 synthetic samples
+-> test with GAN.predict
+GAN tn, fp: 413, 43
+GAN fn, tp: 2, 8
+GAN f1 score: 0.262
+GAN cohens kappa score: 0.235
+
+-> test with 'LR'
+LR tn, fp: 413, 43
+LR fn, tp: 3, 7
+LR f1 score: 0.233
+LR cohens kappa score: 0.205
+LR average precision score: 0.285
+
+-> test with 'GB'
+GB tn, fp: 448, 8
+GB fn, tp: 8, 2
+GB f1 score: 0.200
+GB cohens kappa score: 0.182
+
+-> test with 'KNN'
+KNN tn, fp: 423, 33
+KNN fn, tp: 5, 5
+KNN f1 score: 0.208
+KNN cohens kappa score: 0.180
+
+
+====== 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 GAN.predict
+GAN tn, fp: 431, 25
+GAN fn, tp: 2, 10
+GAN f1 score: 0.426
+GAN cohens kappa score: 0.403
+
+-> 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: 0.618
+
+-> test with 'GB'
+GB tn, fp: 441, 15
+GB fn, tp: 10, 2
+GB f1 score: 0.138
+GB cohens kappa score: 0.111
+
+-> test with 'KNN'
+KNN tn, fp: 423, 33
+KNN fn, tp: 4, 8
+KNN f1 score: 0.302
+KNN cohens kappa score: 0.273
+
+
+------ Step 2/5: Slice 2/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 1778 synthetic samples
+-> test with GAN.predict
+GAN tn, fp: 407, 49
+GAN fn, tp: 0, 12
+GAN f1 score: 0.329
+GAN cohens kappa score: 0.299
+
+-> 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: 0.569
+
+-> test with 'GB'
+GB tn, fp: 443, 13
+GB fn, tp: 6, 6
+GB f1 score: 0.387
+GB cohens kappa score: 0.367
+
+-> test with 'KNN'
+KNN tn, fp: 399, 57
+KNN fn, tp: 0, 12
+KNN f1 score: 0.296
+KNN cohens kappa score: 0.264
+
+
+------ Step 2/5: Slice 3/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 1778 synthetic samples
+-> test with GAN.predict
+GAN tn, fp: 390, 66
+GAN fn, tp: 1, 11
+GAN f1 score: 0.247
+GAN cohens kappa score: 0.212
+
+-> test with 'LR'
+LR tn, fp: 406, 50
+LR fn, tp: 3, 9
+LR f1 score: 0.254
+LR cohens kappa score: 0.220
+LR average precision score: 0.261
+
+-> test with 'GB'
+GB tn, fp: 439, 17
+GB fn, tp: 11, 1
+GB f1 score: 0.067
+GB cohens kappa score: 0.037
+
+-> 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 2/5: Slice 4/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 1778 synthetic samples
+-> test with GAN.predict
+GAN tn, fp: 412, 44
+GAN fn, tp: 2, 10
+GAN f1 score: 0.303
+GAN cohens kappa score: 0.273
+
+-> test with 'LR'
+LR tn, fp: 421, 35
+LR fn, tp: 3, 9
+LR f1 score: 0.321
+LR cohens kappa score: 0.293
+LR average precision score: 0.527
+
+-> test with 'GB'
+GB tn, fp: 447, 9
+GB fn, tp: 5, 7
+GB f1 score: 0.500
+GB cohens kappa score: 0.485
+
+-> test with 'KNN'
+KNN tn, fp: 424, 32
+KNN fn, tp: 6, 6
+KNN f1 score: 0.240
+KNN cohens kappa score: 0.209
+
+
+------ Step 2/5: Slice 5/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 1776 synthetic samples
+-> test with GAN.predict
+GAN tn, fp: 392, 64
+GAN fn, tp: 3, 7
+GAN f1 score: 0.173
+GAN cohens kappa score: 0.141
+
+-> test with 'LR'
+LR tn, fp: 417, 39
+LR fn, tp: 4, 6
+LR f1 score: 0.218
+LR cohens kappa score: 0.190
+LR average precision score: 0.413
+
+-> test with 'GB'
+GB tn, fp: 442, 14
+GB fn, tp: 6, 4
+GB f1 score: 0.286
+GB cohens kappa score: 0.265
+
+-> test with 'KNN'
+KNN tn, fp: 419, 37
+KNN fn, tp: 2, 8
+KNN f1 score: 0.291
+KNN cohens kappa score: 0.265
+
+
+====== 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 GAN.predict
+GAN tn, fp: 408, 48
+GAN fn, tp: 3, 9
+GAN f1 score: 0.261
+GAN cohens kappa score: 0.228
+
+-> test with 'LR'
+LR tn, fp: 411, 45
+LR fn, tp: 3, 9
+LR f1 score: 0.273
+LR cohens kappa score: 0.241
+LR average precision score: 0.529
+
+-> test with 'GB'
+GB tn, fp: 444, 12
+GB fn, tp: 8, 4
+GB f1 score: 0.286
+GB cohens kappa score: 0.264
+
+-> 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 3/5: Slice 2/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 1778 synthetic samples
+-> test with GAN.predict
+GAN tn, fp: 407, 49
+GAN fn, tp: 4, 8
+GAN f1 score: 0.232
+GAN cohens kappa score: 0.198
+
+-> test with 'LR'
+LR tn, fp: 422, 34
+LR fn, tp: 5, 7
+LR f1 score: 0.264
+LR cohens kappa score: 0.234
+LR average precision score: 0.321
+
+-> 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: 424, 32
+KNN fn, tp: 2, 10
+KNN f1 score: 0.370
+KNN cohens kappa score: 0.344
+
+
+------ Step 3/5: Slice 3/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 1778 synthetic samples
+-> test with GAN.predict
+GAN tn, fp: 419, 37
+GAN fn, tp: 2, 10
+GAN f1 score: 0.339
+GAN cohens kappa score: 0.311
+
+-> 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: 0.504
+
+-> test with 'GB'
+GB tn, fp: 441, 15
+GB fn, tp: 4, 8
+GB f1 score: 0.457
+GB cohens kappa score: 0.438
+
+-> test with 'KNN'
+KNN tn, fp: 419, 37
+KNN fn, tp: 5, 7
+KNN f1 score: 0.250
+KNN cohens kappa score: 0.219
+
+
+------ Step 3/5: Slice 4/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 1778 synthetic samples
+-> test with GAN.predict
+GAN tn, fp: 416, 40
+GAN fn, tp: 3, 9
+GAN f1 score: 0.295
+GAN cohens kappa score: 0.265
+
+-> 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: 0.319
+
+-> test with 'GB'
+GB tn, fp: 447, 9
+GB fn, tp: 11, 1
+GB f1 score: 0.091
+GB cohens kappa score: 0.069
+
+-> test with 'KNN'
+KNN tn, fp: 419, 37
+KNN fn, tp: 6, 6
+KNN f1 score: 0.218
+KNN cohens kappa score: 0.186
+
+
+------ Step 3/5: Slice 5/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 1776 synthetic samples
+-> test with GAN.predict
+GAN tn, fp: 421, 35
+GAN fn, tp: 2, 8
+GAN f1 score: 0.302
+GAN cohens kappa score: 0.277
+
+-> 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: 0.581
+
+-> test with 'GB'
+GB tn, fp: 444, 12
+GB fn, tp: 5, 5
+GB f1 score: 0.370
+GB cohens kappa score: 0.353
+
+-> test with 'KNN'
+KNN tn, fp: 423, 33
+KNN fn, tp: 2, 8
+KNN f1 score: 0.314
+KNN cohens kappa score: 0.289
+
+
+====== 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 GAN.predict
+GAN tn, fp: 393, 63
+GAN fn, tp: 1, 11
+GAN f1 score: 0.256
+GAN cohens kappa score: 0.221
+
+-> 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: 0.593
+
+-> 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: 411, 45
+KNN fn, tp: 1, 11
+KNN f1 score: 0.324
+KNN cohens kappa score: 0.294
+
+
+------ Step 4/5: Slice 2/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 1778 synthetic samples
+-> test with GAN.predict
+GAN tn, fp: 399, 57
+GAN fn, tp: 0, 12
+GAN f1 score: 0.296
+GAN cohens kappa score: 0.264
+
+-> test with 'LR'
+LR tn, fp: 391, 65
+LR fn, tp: 1, 11
+LR f1 score: 0.250
+LR cohens kappa score: 0.215
+LR average precision score: 0.639
+
+-> 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: 418, 38
+KNN fn, tp: 1, 11
+KNN f1 score: 0.361
+KNN cohens kappa score: 0.333
+
+
+------ Step 4/5: Slice 3/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 1778 synthetic samples
+-> test with GAN.predict
+GAN tn, fp: 394, 62
+GAN fn, tp: 3, 9
+GAN f1 score: 0.217
+GAN cohens kappa score: 0.181
+
+-> test with 'LR'
+LR tn, fp: 411, 45
+LR fn, tp: 2, 10
+LR f1 score: 0.299
+LR cohens kappa score: 0.268
+LR average precision score: 0.423
+
+-> 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: 421, 35
+KNN fn, tp: 6, 6
+KNN f1 score: 0.226
+KNN cohens kappa score: 0.194
+
+
+------ Step 4/5: Slice 4/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 1778 synthetic samples
+-> test with GAN.predict
+GAN tn, fp: 395, 61
+GAN fn, tp: 2, 10
+GAN f1 score: 0.241
+GAN cohens kappa score: 0.206
+
+-> test with 'LR'
+LR tn, fp: 412, 44
+LR fn, tp: 1, 11
+LR f1 score: 0.328
+LR cohens kappa score: 0.299
+LR average precision score: 0.474
+
+-> test with 'GB'
+GB tn, fp: 444, 12
+GB fn, tp: 10, 2
+GB f1 score: 0.154
+GB cohens kappa score: 0.130
+
+-> test with 'KNN'
+KNN tn, fp: 419, 37
+KNN fn, tp: 6, 6
+KNN f1 score: 0.218
+KNN cohens kappa score: 0.186
+
+
+------ Step 4/5: Slice 5/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 1776 synthetic samples
+-> test with GAN.predict
+GAN tn, fp: 429, 27
+GAN fn, tp: 4, 6
+GAN f1 score: 0.279
+GAN cohens kappa score: 0.255
+
+-> test with 'LR'
+LR tn, fp: 418, 38
+LR fn, tp: 2, 8
+LR f1 score: 0.286
+LR cohens kappa score: 0.260
+LR average precision score: 0.340
+
+-> test with 'GB'
+GB tn, fp: 447, 9
+GB fn, tp: 5, 5
+GB f1 score: 0.417
+GB cohens kappa score: 0.402
+
+-> test with 'KNN'
+KNN tn, fp: 422, 34
+KNN fn, tp: 4, 6
+KNN f1 score: 0.240
+KNN cohens kappa score: 0.213
+
+
+====== 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 GAN.predict
+GAN tn, fp: 411, 45
+GAN fn, tp: 4, 8
+GAN f1 score: 0.246
+GAN cohens kappa score: 0.213
+
+-> test with 'LR'
+LR tn, fp: 406, 50
+LR fn, tp: 3, 9
+LR f1 score: 0.254
+LR cohens kappa score: 0.220
+LR average precision score: 0.373
+
+-> test with 'GB'
+GB tn, fp: 444, 12
+GB fn, tp: 10, 2
+GB f1 score: 0.154
+GB cohens kappa score: 0.130
+
+-> test with 'KNN'
+KNN tn, fp: 421, 35
+KNN fn, tp: 6, 6
+KNN f1 score: 0.226
+KNN cohens kappa score: 0.194
+
+
+------ Step 5/5: Slice 2/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 1778 synthetic samples
+-> test with GAN.predict
+GAN tn, fp: 424, 32
+GAN fn, tp: 3, 9
+GAN f1 score: 0.340
+GAN cohens kappa score: 0.312
+
+-> 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: 0.366
+
+-> 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: 415, 41
+KNN fn, tp: 4, 8
+KNN f1 score: 0.262
+KNN cohens kappa score: 0.231
+
+
+------ Step 5/5: Slice 3/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 1778 synthetic samples
+-> test with GAN.predict
+GAN tn, fp: 428, 28
+GAN fn, tp: 3, 9
+GAN f1 score: 0.367
+GAN cohens kappa score: 0.342
+
+-> test with 'LR'
+LR tn, fp: 420, 36
+LR fn, tp: 2, 10
+LR f1 score: 0.345
+LR cohens kappa score: 0.317
+LR average precision score: 0.366
+
+-> test with 'GB'
+GB tn, fp: 443, 13
+GB fn, tp: 6, 6
+GB f1 score: 0.387
+GB cohens kappa score: 0.367
+
+-> test with 'KNN'
+KNN tn, fp: 418, 38
+KNN fn, tp: 2, 10
+KNN f1 score: 0.333
+KNN cohens kappa score: 0.305
+
+
+------ Step 5/5: Slice 4/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 1778 synthetic samples
+-> test with GAN.predict
+GAN tn, fp: 430, 26
+GAN fn, tp: 2, 10
+GAN f1 score: 0.417
+GAN cohens kappa score: 0.393
+
+-> test with 'LR'
+LR tn, fp: 408, 48
+LR fn, tp: 1, 11
+LR f1 score: 0.310
+LR cohens kappa score: 0.279
+LR average precision score: 0.712
+
+-> test with 'GB'
+GB tn, fp: 441, 15
+GB fn, tp: 7, 5
+GB f1 score: 0.312
+GB cohens kappa score: 0.290
+
+-> test with 'KNN'
+KNN tn, fp: 427, 29
+KNN fn, tp: 2, 10
+KNN f1 score: 0.392
+KNN cohens kappa score: 0.367
+
+
+------ Step 5/5: Slice 5/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 1776 synthetic samples
+-> test with GAN.predict
+GAN tn, fp: 414, 42
+GAN fn, tp: 2, 8
+GAN f1 score: 0.267
+GAN cohens kappa score: 0.239
+
+-> test with 'LR'
+LR tn, fp: 415, 41
+LR fn, tp: 1, 9
+LR f1 score: 0.300
+LR cohens kappa score: 0.274
+LR average precision score: 0.369
+
+-> test with 'GB'
+GB tn, fp: 442, 14
+GB fn, tp: 2, 8
+GB f1 score: 0.500
+GB cohens kappa score: 0.485
+
+-> test with 'KNN'
+KNN tn, fp: 418, 38
+KNN fn, tp: 2, 8
+KNN f1 score: 0.286
+KNN cohens kappa score: 0.260
+
+### Exercise is done.
+
+-----[ LR ]-----
+maximum:
+LR tn, fp: 426, 65
+LR fn, tp: 5, 12
+LR f1 score: 0.369
+LR cohens kappa score: 0.342
+LR average precision score: 0.712
+
+
+average:
+LR tn, fp: 412.08, 43.92
+LR fn, tp: 2.0, 9.6
+LR f1 score: 0.296
+LR cohens kappa score: 0.266
+LR average precision score: 0.462
+
+
+minimum:
+LR tn, fp: 391, 30
+LR fn, tp: 0, 6
+LR f1 score: 0.218
+LR cohens kappa score: 0.190
+LR average precision score: 0.261
+
+
+-----[ GB ]-----
+maximum:
+GB tn, fp: 448, 17
+GB fn, tp: 11, 8
+GB f1 score: 0.500
+GB cohens kappa score: 0.485
+
+
+average:
+GB tn, fp: 443.2, 12.8
+GB fn, tp: 7.28, 4.32
+GB f1 score: 0.294
+GB cohens kappa score: 0.273
+
+
+minimum:
+GB tn, fp: 439, 8
+GB fn, tp: 2, 1
+GB f1 score: 0.067
+GB cohens kappa score: 0.037
+
+
+-----[ KNN ]-----
+maximum:
+KNN tn, fp: 427, 57
+KNN fn, tp: 6, 12
+KNN f1 score: 0.392
+KNN cohens kappa score: 0.367
+
+
+average:
+KNN tn, fp: 418.76, 37.24
+KNN fn, tp: 3.36, 8.24
+KNN f1 score: 0.287
+KNN cohens kappa score: 0.258
+
+
+minimum:
+KNN tn, fp: 399, 29
+KNN fn, tp: 0, 5
+KNN f1 score: 0.208
+KNN cohens kappa score: 0.180
+
+
+-----[ GAN ]-----
+maximum:
+GAN tn, fp: 431, 66
+GAN fn, tp: 4, 12
+GAN f1 score: 0.426
+GAN cohens kappa score: 0.403
+
+
+average:
+GAN tn, fp: 409.36, 46.64
+GAN fn, tp: 2.16, 9.44
+GAN f1 score: 0.287
+GAN cohens kappa score: 0.256
+
+
+minimum:
+GAN tn, fp: 390, 25
+GAN fn, tp: 0, 6
+GAN f1 score: 0.173
+GAN cohens kappa score: 0.141
+

data_result/convGAN-majority-5/folding_car-vgood.csv

@@ -0,0 +1,123 @@
+LR
+Nr.;TN;TP;FN;FP;f1 score;cohens kappa score;average precision score
+1;292.000;13.000;0.000;41.000;0.388;0.349;0.361
+2;294.000;12.000;1.000;39.000;0.375;0.335;0.304
+3;285.000;13.000;0.000;48.000;0.351;0.309;0.382
+4;293.000;13.000;0.000;40.000;0.394;0.355;0.372
+5;295.000;12.000;1.000;36.000;0.393;0.355;0.433
+6;296.000;13.000;0.000;37.000;0.413;0.375;0.283
+7;275.000;13.000;0.000;58.000;0.310;0.263;0.290
+8;295.000;11.000;2.000;38.000;0.355;0.314;0.337
+9;297.000;13.000;0.000;36.000;0.419;0.383;0.284
+10;288.000;13.000;0.000;43.000;0.377;0.336;0.532
+11;296.000;12.000;1.000;37.000;0.387;0.348;0.310
+12;301.000;13.000;0.000;32.000;0.448;0.414;0.439
+13;282.000;13.000;0.000;51.000;0.338;0.294;0.340
+14;289.000;13.000;0.000;44.000;0.371;0.330;0.407
+15;293.000;11.000;2.000;38.000;0.355;0.314;0.338
+16;301.000;13.000;0.000;32.000;0.448;0.414;0.359
+17;285.000;12.000;1.000;48.000;0.329;0.285;0.533
+18;288.000;13.000;0.000;45.000;0.366;0.325;0.276
+19;298.000;12.000;1.000;35.000;0.400;0.362;0.270
+20;292.000;13.000;0.000;39.000;0.400;0.361;0.359
+21;275.000;13.000;0.000;58.000;0.310;0.263;0.328
+22;296.000;10.000;3.000;37.000;0.333;0.292;0.340
+23;303.000;12.000;1.000;30.000;0.436;0.402;0.356
+24;288.000;13.000;0.000;45.000;0.366;0.325;0.274
+25;292.000;13.000;0.000;39.000;0.400;0.361;0.471
+max;303.000;13.000;3.000;58.000;0.448;0.414;0.533
+avg;291.560;12.480;0.520;41.040;0.379;0.339;0.359
+min;275.000;10.000;0.000;30.000;0.310;0.263;0.270
+---
+GB
+Nr.;TN;TP;FN;FP;f1 score;cohens kappa score
+1;333.000;13.000;0.000;0.000;1.000;1.000
+2;332.000;11.000;2.000;1.000;0.880;0.876
+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;329.000;13.000;0.000;2.000;0.929;0.926
+6;333.000;13.000;0.000;0.000;1.000;1.000
+7;330.000;13.000;0.000;3.000;0.897;0.892
+8;331.000;13.000;0.000;2.000;0.929;0.926
+9;333.000;10.000;3.000;0.000;0.870;0.865
+10;331.000;13.000;0.000;0.000;1.000;1.000
+11;333.000;11.000;2.000;0.000;0.917;0.914
+12;332.000;13.000;0.000;1.000;0.963;0.961
+13;332.000;12.000;1.000;1.000;0.923;0.920
+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;13.000;0.000;0.000;1.000;1.000
+17;332.000;12.000;1.000;1.000;0.923;0.920
+18;329.000;13.000;0.000;4.000;0.867;0.861
+19;332.000;13.000;0.000;1.000;0.963;0.961
+20;331.000;12.000;1.000;0.000;0.960;0.958
+21;332.000;11.000;2.000;1.000;0.880;0.876
+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;333.000;13.000;0.000;0.000;1.000;1.000
+25;331.000;12.000;1.000;0.000;0.960;0.958
+max;333.000;13.000;3.000;4.000;1.000;1.000
+avg;331.880;12.400;0.600;0.720;0.950;0.948
+min;329.000;10.000;0.000;0.000;0.867;0.861
+---
+KNN
+Nr.;TN;TP;FN;FP;f1 score;cohens kappa score
+1;327.000;13.000;0.000;6.000;0.813;0.804
+2;314.000;12.000;1.000;19.000;0.545;0.520
+3;323.000;13.000;0.000;10.000;0.722;0.708
+4;320.000;13.000;0.000;13.000;0.667;0.649
+5;317.000;13.000;0.000;14.000;0.650;0.631
+6;316.000;13.000;0.000;17.000;0.605;0.583
+7;313.000;13.000;0.000;20.000;0.565;0.540
+8;322.000;13.000;0.000;11.000;0.703;0.687
+9;327.000;12.000;1.000;6.000;0.774;0.764
+10;322.000;13.000;0.000;9.000;0.743;0.730
+11;325.000;10.000;3.000;8.000;0.645;0.629
+12;330.000;13.000;0.000;3.000;0.897;0.892
+13;314.000;13.000;0.000;19.000;0.578;0.554
+14;318.000;13.000;0.000;15.000;0.634;0.614
+15;324.000;11.000;2.000;7.000;0.710;0.696
+16;328.000;12.000;1.000;5.000;0.800;0.791
+17;316.000;13.000;0.000;17.000;0.605;0.583
+18;321.000;13.000;0.000;12.000;0.684;0.668
+19;322.000;13.000;0.000;11.000;0.703;0.687
+20;317.000;13.000;0.000;14.000;0.650;0.631
+21;323.000;13.000;0.000;10.000;0.722;0.708
+22;320.000;12.000;1.000;13.000;0.632;0.612
+23;321.000;12.000;1.000;12.000;0.649;0.631
+24;323.000;13.000;0.000;10.000;0.722;0.708
+25;326.000;12.000;1.000;5.000;0.800;0.791
+max;330.000;13.000;3.000;20.000;0.897;0.892
+avg;321.160;12.560;0.440;11.440;0.689;0.673
+min;313.000;10.000;0.000;3.000;0.545;0.520
+---
+GAN
+Nr.;TN;TP;FN;FP;f1 score;cohens kappa score
+1;323.000;12.000;1.000;10.000;0.686;0.670
+2;329.000;10.000;3.000;4.000;0.741;0.730
+3;326.000;10.000;3.000;7.000;0.667;0.652
+4;327.000;11.000;2.000;6.000;0.733;0.721
+5;326.000;11.000;2.000;5.000;0.759;0.748
+6;328.000;9.000;4.000;5.000;0.667;0.653
+7;325.000;12.000;1.000;8.000;0.727;0.714
+8;329.000;10.000;3.000;4.000;0.741;0.730
+9;323.000;12.000;1.000;10.000;0.686;0.670
+10;324.000;13.000;0.000;7.000;0.788;0.778
+11;329.000;7.000;6.000;4.000;0.583;0.568
+12;328.000;11.000;2.000;5.000;0.759;0.748
+13;320.000;13.000;0.000;13.000;0.667;0.649
+14;326.000;12.000;1.000;7.000;0.750;0.738
+15;327.000;11.000;2.000;4.000;0.786;0.777
+16;330.000;11.000;2.000;3.000;0.815;0.807
+17;327.000;10.000;3.000;6.000;0.690;0.676
+18;324.000;12.000;1.000;9.000;0.706;0.692
+19;327.000;6.000;7.000;6.000;0.480;0.461
+20;328.000;9.000;4.000;3.000;0.720;0.709
+21;324.000;12.000;1.000;9.000;0.706;0.692
+22;327.000;10.000;3.000;6.000;0.690;0.676
+23;327.000;9.000;4.000;6.000;0.643;0.628
+24;324.000;11.000;2.000;9.000;0.667;0.651
+25;326.000;12.000;1.000;5.000;0.800;0.791
+max;330.000;13.000;7.000;13.000;0.815;0.807
+avg;326.160;10.640;2.360;6.440;0.706;0.693
+min;320.000;6.000;0.000;3.000;0.480;0.461

data_result/convGAN-majority-5/folding_car-vgood.log

@@ -0,0 +1,873 @@
+
+
+///////////////////////////////////////////
+// Running convGAN-majority-5 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 GAN.predict
+GAN tn, fp: 323, 10
+GAN fn, tp: 1, 12
+GAN f1 score: 0.686
+GAN cohens kappa score: 0.670
+
+-> test with 'LR'
+LR tn, fp: 292, 41
+LR fn, tp: 0, 13
+LR f1 score: 0.388
+LR cohens kappa score: 0.349
+LR average precision score: 0.361
+
+-> 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 1/5: Slice 2/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 1278 synthetic samples
+-> test with GAN.predict
+GAN tn, fp: 329, 4
+GAN fn, tp: 3, 10
+GAN f1 score: 0.741
+GAN cohens kappa score: 0.730
+
+-> 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: 0.304
+
+-> test with 'GB'
+GB tn, fp: 332, 1
+GB fn, tp: 2, 11
+GB f1 score: 0.880
+GB cohens kappa score: 0.876
+
+-> test with 'KNN'
+KNN tn, fp: 314, 19
+KNN fn, tp: 1, 12
+KNN f1 score: 0.545
+KNN cohens kappa score: 0.520
+
+
+------ Step 1/5: Slice 3/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 1278 synthetic samples
+-> test with GAN.predict
+GAN tn, fp: 326, 7
+GAN fn, tp: 3, 10
+GAN f1 score: 0.667
+GAN cohens kappa score: 0.652
+
+-> 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: 0.382
+
+-> 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 GAN.predict
+GAN tn, fp: 327, 6
+GAN fn, tp: 2, 11
+GAN f1 score: 0.733
+GAN cohens kappa score: 0.721
+
+-> 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: 0.372
+
+-> 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 1/5: Slice 5/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 1280 synthetic samples
+-> test with GAN.predict
+GAN tn, fp: 326, 5
+GAN fn, tp: 2, 11
+GAN f1 score: 0.759
+GAN cohens kappa score: 0.748
+
+-> test with 'LR'
+LR tn, fp: 295, 36
+LR fn, tp: 1, 12
+LR f1 score: 0.393
+LR cohens kappa score: 0.355
+LR average precision score: 0.433
+
+-> 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: 317, 14
+KNN fn, tp: 0, 13
+KNN f1 score: 0.650
+KNN cohens kappa score: 0.631
+
+
+====== 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 GAN.predict
+GAN tn, fp: 328, 5
+GAN fn, tp: 4, 9
+GAN f1 score: 0.667
+GAN cohens kappa score: 0.653
+
+-> 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: 0.283
+
+-> 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: 316, 17
+KNN fn, tp: 0, 13
+KNN f1 score: 0.605
+KNN cohens kappa score: 0.583
+
+
+------ Step 2/5: Slice 2/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 1278 synthetic samples
+-> test with GAN.predict
+GAN tn, fp: 325, 8
+GAN fn, tp: 1, 12
+GAN f1 score: 0.727
+GAN cohens kappa score: 0.714
+
+-> test with 'LR'
+LR tn, fp: 275, 58
+LR fn, tp: 0, 13
+LR f1 score: 0.310
+LR cohens kappa score: 0.263
+LR average precision score: 0.290
+
+-> 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: 313, 20
+KNN fn, tp: 0, 13
+KNN f1 score: 0.565
+KNN cohens kappa score: 0.540
+
+
+------ Step 2/5: Slice 3/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 1278 synthetic samples
+-> test with GAN.predict
+GAN tn, fp: 329, 4
+GAN fn, tp: 3, 10
+GAN f1 score: 0.741
+GAN cohens kappa score: 0.730
+
+-> test with 'LR'
+LR tn, fp: 295, 38
+LR fn, tp: 2, 11
+LR f1 score: 0.355
+LR cohens kappa score: 0.314
+LR average precision score: 0.337
+
+-> 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: 322, 11
+KNN fn, tp: 0, 13
+KNN f1 score: 0.703
+KNN cohens kappa score: 0.687
+
+
+------ Step 2/5: Slice 4/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 1278 synthetic samples
+-> test with GAN.predict
+GAN tn, fp: 323, 10
+GAN fn, tp: 1, 12
+GAN f1 score: 0.686
+GAN cohens kappa score: 0.670
+
+-> 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: 0.284
+
+-> test with 'GB'
+GB tn, fp: 333, 0
+GB fn, tp: 3, 10
+GB f1 score: 0.870
+GB cohens kappa score: 0.865
+
+-> test with 'KNN'
+KNN tn, fp: 327, 6
+KNN fn, tp: 1, 12
+KNN f1 score: 0.774
+KNN cohens kappa score: 0.764
+
+
+------ Step 2/5: Slice 5/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 1280 synthetic samples
+-> test with GAN.predict
+GAN tn, fp: 324, 7
+GAN fn, tp: 0, 13
+GAN f1 score: 0.788
+GAN cohens kappa score: 0.778
+
+-> test with 'LR'
+LR tn, fp: 288, 43
+LR fn, tp: 0, 13
+LR f1 score: 0.377
+LR cohens kappa score: 0.336
+LR average precision score: 0.532
+
+-> 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: 322, 9
+KNN fn, tp: 0, 13
+KNN f1 score: 0.743
+KNN cohens kappa score: 0.730
+
+
+====== 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 GAN.predict
+GAN tn, fp: 329, 4
+GAN fn, tp: 6, 7
+GAN f1 score: 0.583
+GAN cohens kappa score: 0.568
+
+-> 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: 0.310
+
+-> 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: 325, 8
+KNN fn, tp: 3, 10
+KNN f1 score: 0.645
+KNN cohens kappa score: 0.629
+
+
+------ Step 3/5: Slice 2/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 1278 synthetic samples
+-> test with GAN.predict
+GAN tn, fp: 328, 5
+GAN fn, tp: 2, 11
+GAN f1 score: 0.759
+GAN cohens kappa score: 0.748
+
+-> test with 'LR'
+LR tn, fp: 301, 32
+LR fn, tp: 0, 13
+LR f1 score: 0.448
+LR cohens kappa score: 0.414
+LR average precision score: 0.439
+
+-> 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: 330, 3
+KNN fn, tp: 0, 13
+KNN f1 score: 0.897
+KNN cohens kappa score: 0.892
+
+
+------ Step 3/5: Slice 3/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 1278 synthetic samples
+-> test with GAN.predict
+GAN tn, fp: 320, 13
+GAN fn, tp: 0, 13
+GAN f1 score: 0.667
+GAN cohens kappa score: 0.649
+
+-> 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: 0.340
+
+-> 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: 314, 19
+KNN fn, tp: 0, 13
+KNN f1 score: 0.578
+KNN cohens kappa score: 0.554
+
+
+------ Step 3/5: Slice 4/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 1278 synthetic samples
+-> test with GAN.predict
+GAN tn, fp: 326, 7
+GAN fn, tp: 1, 12
+GAN f1 score: 0.750
+GAN cohens kappa score: 0.738
+
+-> 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: 0.407
+
+-> 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: 318, 15
+KNN fn, tp: 0, 13
+KNN f1 score: 0.634
+KNN cohens kappa score: 0.614
+
+
+------ Step 3/5: Slice 5/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 1280 synthetic samples
+-> test with GAN.predict
+GAN tn, fp: 327, 4
+GAN fn, tp: 2, 11
+GAN f1 score: 0.786
+GAN cohens kappa score: 0.777
+
+-> test with 'LR'
+LR tn, fp: 293, 38
+LR fn, tp: 2, 11
+LR f1 score: 0.355
+LR cohens kappa score: 0.314
+LR average precision score: 0.338
+
+-> 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: 324, 7
+KNN fn, tp: 2, 11
+KNN f1 score: 0.710
+KNN cohens kappa score: 0.696
+
+
+====== 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 GAN.predict
+GAN tn, fp: 330, 3
+GAN fn, tp: 2, 11
+GAN f1 score: 0.815
+GAN cohens kappa score: 0.807
+
+-> test with 'LR'
+LR tn, fp: 301, 32
+LR fn, tp: 0, 13
+LR f1 score: 0.448
+LR cohens kappa score: 0.414
+LR average precision score: 0.359
+
+-> 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: 328, 5
+KNN fn, tp: 1, 12
+KNN f1 score: 0.800
+KNN cohens kappa score: 0.791
+
+
+------ Step 4/5: Slice 2/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 1278 synthetic samples
+-> test with GAN.predict
+GAN tn, fp: 327, 6
+GAN fn, tp: 3, 10
+GAN f1 score: 0.690
+GAN cohens kappa score: 0.676
+
+-> test with 'LR'
+LR tn, fp: 285, 48
+LR fn, tp: 1, 12
+LR f1 score: 0.329
+LR cohens kappa score: 0.285
+LR average precision score: 0.533
+
+-> 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: 316, 17
+KNN fn, tp: 0, 13
+KNN f1 score: 0.605
+KNN cohens kappa score: 0.583
+
+
+------ Step 4/5: Slice 3/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 1278 synthetic samples
+-> test with GAN.predict
+GAN tn, fp: 324, 9
+GAN fn, tp: 1, 12
+GAN f1 score: 0.706
+GAN cohens kappa score: 0.692
+
+-> 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: 0.276
+
+-> test with 'GB'
+GB tn, fp: 329, 4
+GB fn, tp: 0, 13
+GB f1 score: 0.867
+GB cohens kappa score: 0.861
+
+-> test with 'KNN'
+KNN tn, fp: 321, 12
+KNN fn, tp: 0, 13
+KNN f1 score: 0.684
+KNN cohens kappa score: 0.668
+
+
+------ Step 4/5: Slice 4/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 1278 synthetic samples
+-> test with GAN.predict
+GAN tn, fp: 327, 6
+GAN fn, tp: 7, 6
+GAN f1 score: 0.480
+GAN cohens kappa score: 0.461
+
+-> test with 'LR'
+LR tn, fp: 298, 35
+LR fn, tp: 1, 12
+LR f1 score: 0.400
+LR cohens kappa score: 0.362
+LR average precision score: 0.270
+
+-> 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: 322, 11
+KNN fn, tp: 0, 13
+KNN f1 score: 0.703
+KNN cohens kappa score: 0.687
+
+
+------ Step 4/5: Slice 5/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 1280 synthetic samples
+-> test with GAN.predict
+GAN tn, fp: 328, 3
+GAN fn, tp: 4, 9
+GAN f1 score: 0.720
+GAN cohens kappa score: 0.709
+
+-> 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: 0.359
+
+-> 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: 317, 14
+KNN fn, tp: 0, 13
+KNN f1 score: 0.650
+KNN cohens kappa score: 0.631
+
+
+====== 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 GAN.predict
+GAN tn, fp: 324, 9
+GAN fn, tp: 1, 12
+GAN f1 score: 0.706
+GAN cohens kappa score: 0.692
+
+-> test with 'LR'
+LR tn, fp: 275, 58
+LR fn, tp: 0, 13
+LR f1 score: 0.310
+LR cohens kappa score: 0.263
+LR average precision score: 0.328
+
+-> test with 'GB'
+GB tn, fp: 332, 1
+GB fn, tp: 2, 11
+GB f1 score: 0.880
+GB cohens kappa score: 0.876
+
+-> 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 5/5: Slice 2/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 1278 synthetic samples
+-> test with GAN.predict
+GAN tn, fp: 327, 6
+GAN fn, tp: 3, 10
+GAN f1 score: 0.690
+GAN cohens kappa score: 0.676
+
+-> test with 'LR'
+LR tn, fp: 296, 37
+LR fn, tp: 3, 10
+LR f1 score: 0.333
+LR cohens kappa score: 0.292
+LR average precision score: 0.340
+
+-> 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: 1, 12
+KNN f1 score: 0.632
+KNN cohens kappa score: 0.612
+
+
+------ Step 5/5: Slice 3/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 1278 synthetic samples
+-> test with GAN.predict
+GAN tn, fp: 327, 6
+GAN fn, tp: 4, 9
+GAN f1 score: 0.643
+GAN cohens kappa score: 0.628
+
+-> test with 'LR'
+LR tn, fp: 303, 30
+LR fn, tp: 1, 12
+LR f1 score: 0.436
+LR cohens kappa score: 0.402
+LR average precision score: 0.356
+
+-> 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: 321, 12
+KNN fn, tp: 1, 12
+KNN f1 score: 0.649
+KNN cohens kappa score: 0.631
+
+
+------ Step 5/5: Slice 4/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 1278 synthetic samples
+-> test with GAN.predict
+GAN tn, fp: 324, 9
+GAN fn, tp: 2, 11
+GAN f1 score: 0.667
+GAN cohens kappa score: 0.651
+
+-> 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: 0.274
+
+-> 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 5/5: Slice 5/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 1280 synthetic samples
+-> test with GAN.predict
+GAN tn, fp: 326, 5
+GAN fn, tp: 1, 12
+GAN f1 score: 0.800
+GAN cohens kappa score: 0.791
+
+-> 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: 0.471
+
+-> 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: 1, 12
+KNN f1 score: 0.800
+KNN cohens kappa score: 0.791
+
+### Exercise is done.
+
+-----[ LR ]-----
+maximum:
+LR tn, fp: 303, 58
+LR fn, tp: 3, 13
+LR f1 score: 0.448
+LR cohens kappa score: 0.414
+LR average precision score: 0.533
+
+
+average:
+LR tn, fp: 291.56, 41.04
+LR fn, tp: 0.52, 12.48
+LR f1 score: 0.379
+LR cohens kappa score: 0.339
+LR average precision score: 0.359
+
+
+minimum:
+LR tn, fp: 275, 30
+LR fn, tp: 0, 10
+LR f1 score: 0.310
+LR cohens kappa score: 0.263
+LR average precision score: 0.270
+
+
+-----[ GB ]-----
+maximum:
+GB tn, fp: 333, 4
+GB fn, tp: 3, 13
+GB f1 score: 1.000
+GB cohens kappa score: 1.000
+
+
+average:
+GB tn, fp: 331.88, 0.72
+GB fn, tp: 0.6, 12.4
+GB f1 score: 0.950
+GB cohens kappa score: 0.948
+
+
+minimum:
+GB tn, fp: 329, 0
+GB fn, tp: 0, 10
+GB f1 score: 0.867
+GB cohens kappa score: 0.861
+
+
+-----[ KNN ]-----
+maximum:
+KNN tn, fp: 330, 20
+KNN fn, tp: 3, 13
+KNN f1 score: 0.897
+KNN cohens kappa score: 0.892
+
+
+average:
+KNN tn, fp: 321.16, 11.44
+KNN fn, tp: 0.44, 12.56
+KNN f1 score: 0.689
+KNN cohens kappa score: 0.673
+
+
+minimum:
+KNN tn, fp: 313, 3
+KNN fn, tp: 0, 10
+KNN f1 score: 0.545
+KNN cohens kappa score: 0.520
+
+
+-----[ GAN ]-----
+maximum:
+GAN tn, fp: 330, 13
+GAN fn, tp: 7, 13
+GAN f1 score: 0.815
+GAN cohens kappa score: 0.807
+
+
+average:
+GAN tn, fp: 326.16, 6.44
+GAN fn, tp: 2.36, 10.64
+GAN f1 score: 0.706
+GAN cohens kappa score: 0.693
+
+
+minimum:
+GAN tn, fp: 320, 3
+GAN fn, tp: 0, 6
+GAN f1 score: 0.480
+GAN cohens kappa score: 0.461
+

data_result/convGAN-majority-5/folding_car_good.csv

@@ -0,0 +1,123 @@
+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;0.060
+2;180.000;10.000;4.000;152.000;0.114;0.042;0.083
+3;179.000;10.000;4.000;153.000;0.113;0.042;0.056
+4;178.000;11.000;3.000;154.000;0.123;0.052;0.076
+5;182.000;11.000;2.000;149.000;0.127;0.062;0.060
+6;150.000;9.000;5.000;182.000;0.088;0.013;0.062
+7;173.000;11.000;3.000;159.000;0.120;0.048;0.071
+8;199.000;11.000;3.000;133.000;0.139;0.071;0.072
+9;185.000;5.000;9.000;147.000;0.060;-0.015;0.050
+10;195.000;8.000;5.000;136.000;0.102;0.035;0.072
+11;174.000;11.000;3.000;158.000;0.120;0.049;0.077
+12;198.000;9.000;5.000;134.000;0.115;0.044;0.069
+13;180.000;8.000;6.000;152.000;0.092;0.019;0.056
+14;185.000;13.000;1.000;147.000;0.149;0.081;0.070
+15;165.000;8.000;5.000;166.000;0.086;0.016;0.049
+16;176.000;11.000;3.000;156.000;0.122;0.051;0.065
+17;179.000;8.000;6.000;153.000;0.091;0.018;0.062
+18;168.000;10.000;4.000;164.000;0.106;0.034;0.061
+19;198.000;7.000;7.000;134.000;0.090;0.018;0.056
+20;168.000;11.000;2.000;163.000;0.118;0.051;0.079
+21;185.000;6.000;8.000;147.000;0.072;-0.002;0.051
+22;184.000;10.000;4.000;148.000;0.116;0.045;0.067
+23;164.000;10.000;4.000;168.000;0.104;0.032;0.075
+24;177.000;10.000;4.000;155.000;0.112;0.040;0.073
+25;183.000;9.000;4.000;148.000;0.106;0.039;0.062
+max;199.000;13.000;9.000;182.000;0.149;0.081;0.083
+avg;179.360;9.400;4.400;152.440;0.107;0.036;0.065
+min;150.000;5.000;1.000;133.000;0.060;-0.015;0.049
+---
+GB
+Nr.;TN;TP;FN;FP;f1 score;cohens kappa score
+1;330.000;13.000;1.000;2.000;0.897;0.892
+2;331.000;10.000;4.000;1.000;0.800;0.793
+3;331.000;10.000;4.000;1.000;0.800;0.793
+4;330.000;7.000;7.000;2.000;0.609;0.596
+5;326.000;11.000;2.000;5.000;0.759;0.748
+6;331.000;7.000;7.000;1.000;0.636;0.625
+7;331.000;12.000;2.000;1.000;0.889;0.884
+8;332.000;6.000;8.000;0.000;0.600;0.590
+9;330.000;10.000;4.000;2.000;0.769;0.760
+10;328.000;11.000;2.000;3.000;0.815;0.807
+11;331.000;11.000;3.000;1.000;0.846;0.840
+12;330.000;11.000;3.000;2.000;0.815;0.807
+13;330.000;5.000;9.000;2.000;0.476;0.462
+14;331.000;11.000;3.000;1.000;0.846;0.840
+15;327.000;7.000;6.000;4.000;0.583;0.568
+16;331.000;10.000;4.000;1.000;0.800;0.793
+17;330.000;5.000;9.000;2.000;0.476;0.462
+18;331.000;10.000;4.000;1.000;0.800;0.793
+19;330.000;8.000;6.000;2.000;0.667;0.655
+20;327.000;6.000;7.000;4.000;0.522;0.505
+21;330.000;7.000;7.000;2.000;0.609;0.596
+22;330.000;8.000;6.000;2.000;0.667;0.655
+23;327.000;12.000;2.000;5.000;0.774;0.764
+24;331.000;7.000;7.000;1.000;0.636;0.625
+25;331.000;10.000;3.000;0.000;0.870;0.865
+max;332.000;13.000;9.000;5.000;0.897;0.892
+avg;329.880;9.000;4.800;1.920;0.718;0.709
+min;326.000;5.000;1.000;0.000;0.476;0.462
+---
+KNN
+Nr.;TN;TP;FN;FP;f1 score;cohens kappa score
+1;327.000;14.000;0.000;5.000;0.848;0.841
+2;313.000;14.000;0.000;19.000;0.596;0.571
+3;308.000;14.000;0.000;24.000;0.538;0.509
+4;315.000;12.000;2.000;17.000;0.558;0.533
+5;308.000;12.000;1.000;23.000;0.500;0.471
+6;310.000;13.000;1.000;22.000;0.531;0.502
+7;322.000;14.000;0.000;10.000;0.737;0.723
+8;317.000;11.000;3.000;15.000;0.550;0.525
+9;295.000;13.000;1.000;37.000;0.406;0.366
+10;294.000;13.000;0.000;37.000;0.413;0.375
+11;314.000;13.000;1.000;18.000;0.578;0.553
+12;307.000;14.000;0.000;25.000;0.528;0.498
+13;314.000;13.000;1.000;18.000;0.578;0.553
+14;303.000;14.000;0.000;29.000;0.491;0.458
+15;290.000;13.000;0.000;41.000;0.388;0.348
+16;313.000;14.000;0.000;19.000;0.596;0.571
+17;307.000;13.000;1.000;25.000;0.500;0.469
+18;307.000;14.000;0.000;25.000;0.528;0.498
+19;318.000;14.000;0.000;14.000;0.667;0.648
+20;317.000;12.000;1.000;14.000;0.615;0.595
+21;311.000;10.000;4.000;21.000;0.444;0.412
+22;309.000;14.000;0.000;23.000;0.549;0.521
+23;300.000;14.000;0.000;32.000;0.467;0.431
+24;310.000;13.000;1.000;22.000;0.531;0.502
+25;304.000;13.000;0.000;27.000;0.491;0.460
+max;327.000;14.000;4.000;41.000;0.848;0.841
+avg;309.320;13.120;0.680;22.480;0.545;0.517
+min;290.000;10.000;0.000;5.000;0.388;0.348
+---
+GAN
+Nr.;TN;TP;FN;FP;f1 score;cohens kappa score
+1;328.000;11.000;3.000;4.000;0.759;0.748
+2;327.000;12.000;2.000;5.000;0.774;0.764
+3;323.000;12.000;2.000;9.000;0.686;0.670
+4;323.000;11.000;3.000;9.000;0.647;0.629
+5;322.000;10.000;3.000;9.000;0.625;0.607
+6;324.000;12.000;2.000;8.000;0.706;0.691
+7;328.000;12.000;2.000;4.000;0.800;0.791
+8;322.000;10.000;4.000;10.000;0.588;0.568
+9;323.000;11.000;3.000;9.000;0.647;0.629
+10;324.000;11.000;2.000;7.000;0.710;0.696
+11;324.000;12.000;2.000;8.000;0.706;0.691
+12;321.000;14.000;0.000;11.000;0.718;0.703
+13;323.000;14.000;0.000;9.000;0.757;0.744
+14;328.000;10.000;4.000;4.000;0.714;0.702
+15;323.000;12.000;1.000;8.000;0.727;0.714
+16;322.000;12.000;2.000;10.000;0.667;0.649
+17;326.000;7.000;7.000;6.000;0.519;0.499
+18;326.000;11.000;3.000;6.000;0.710;0.696
+19;327.000;13.000;1.000;5.000;0.813;0.804
+20;320.000;10.000;3.000;11.000;0.588;0.568
+21;318.000;11.000;3.000;14.000;0.564;0.540
+22;323.000;12.000;2.000;9.000;0.686;0.670
+23;325.000;11.000;3.000;7.000;0.688;0.673
+24;330.000;11.000;3.000;2.000;0.815;0.807
+25;321.000;13.000;0.000;10.000;0.722;0.708
+max;330.000;14.000;7.000;14.000;0.815;0.807
+avg;324.040;11.400;2.400;7.760;0.693;0.678
+min;318.000;7.000;0.000;2.000;0.519;0.499

data_result/convGAN-majority-5/folding_car_good.log

@@ -0,0 +1,873 @@
+
+
+///////////////////////////////////////////
+// Running convGAN-majority-5 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 GAN.predict
+GAN tn, fp: 328, 4
+GAN fn, tp: 3, 11
+GAN f1 score: 0.759
+GAN cohens kappa score: 0.748
+
+-> 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: 0.060
+
+-> test with 'GB'
+GB tn, fp: 330, 2
+GB fn, tp: 1, 13
+GB f1 score: 0.897
+GB cohens kappa score: 0.892
+
+-> test with 'KNN'
+KNN tn, fp: 327, 5
+KNN fn, tp: 0, 14
+KNN f1 score: 0.848
+KNN cohens kappa score: 0.841
+
+
+------ Step 1/5: Slice 2/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 1272 synthetic samples
+-> test with GAN.predict
+GAN tn, fp: 327, 5
+GAN fn, tp: 2, 12
+GAN f1 score: 0.774
+GAN cohens kappa score: 0.764
+
+-> test with 'LR'
+LR tn, fp: 180, 152
+LR fn, tp: 4, 10
+LR f1 score: 0.114
+LR cohens kappa score: 0.042
+LR average precision score: 0.083
+
+-> 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: 0, 14
+KNN f1 score: 0.596
+KNN cohens kappa score: 0.571
+
+
+------ Step 1/5: Slice 3/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 1272 synthetic samples
+-> test with GAN.predict
+GAN tn, fp: 323, 9
+GAN fn, tp: 2, 12
+GAN f1 score: 0.686
+GAN cohens kappa score: 0.670
+
+-> 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: 0.056
+
+-> 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: 308, 24
+KNN fn, tp: 0, 14
+KNN f1 score: 0.538
+KNN cohens kappa score: 0.509
+
+
+------ Step 1/5: Slice 4/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 1272 synthetic samples
+-> test with GAN.predict
+GAN tn, fp: 323, 9
+GAN fn, tp: 3, 11
+GAN f1 score: 0.647
+GAN cohens kappa score: 0.629
+
+-> test with 'LR'
+LR tn, fp: 178, 154
+LR fn, tp: 3, 11
+LR f1 score: 0.123
+LR cohens kappa score: 0.052
+LR average precision score: 0.076
+
+-> 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: 315, 17
+KNN fn, tp: 2, 12
+KNN f1 score: 0.558
+KNN cohens kappa score: 0.533
+
+
+------ Step 1/5: Slice 5/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 1272 synthetic samples
+-> test with GAN.predict
+GAN tn, fp: 322, 9
+GAN fn, tp: 3, 10
+GAN f1 score: 0.625
+GAN cohens kappa score: 0.607
+
+-> test with 'LR'
+LR tn, fp: 182, 149
+LR fn, tp: 2, 11
+LR f1 score: 0.127
+LR cohens kappa score: 0.062
+LR average precision score: 0.060
+
+-> test with 'GB'
+GB tn, fp: 326, 5
+GB fn, tp: 2, 11
+GB f1 score: 0.759
+GB cohens kappa score: 0.748
+
+-> test with 'KNN'
+KNN tn, fp: 308, 23
+KNN fn, tp: 1, 12
+KNN f1 score: 0.500
+KNN cohens kappa score: 0.471
+
+
+====== 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 GAN.predict
+GAN tn, fp: 324, 8
+GAN fn, tp: 2, 12
+GAN f1 score: 0.706
+GAN cohens kappa score: 0.691
+
+-> test with 'LR'
+LR tn, fp: 150, 182
+LR fn, tp: 5, 9
+LR f1 score: 0.088
+LR cohens kappa score: 0.013
+LR average precision score: 0.062
+
+-> 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: 310, 22
+KNN fn, tp: 1, 13
+KNN f1 score: 0.531
+KNN cohens kappa score: 0.502
+
+
+------ Step 2/5: Slice 2/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 1272 synthetic samples
+-> test with GAN.predict
+GAN tn, fp: 328, 4
+GAN fn, tp: 2, 12
+GAN f1 score: 0.800
+GAN cohens kappa score: 0.791
+
+-> test with 'LR'
+LR tn, fp: 173, 159
+LR fn, tp: 3, 11
+LR f1 score: 0.120
+LR cohens kappa score: 0.048
+LR average precision score: 0.071
+
+-> test with 'GB'
+GB tn, fp: 331, 1
+GB fn, tp: 2, 12
+GB f1 score: 0.889
+GB cohens kappa score: 0.884
+
+-> 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 2/5: Slice 3/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 1272 synthetic samples
+-> test with GAN.predict
+GAN tn, fp: 322, 10
+GAN fn, tp: 4, 10
+GAN f1 score: 0.588
+GAN cohens kappa score: 0.568
+
+-> test with 'LR'
+LR tn, fp: 199, 133
+LR fn, tp: 3, 11
+LR f1 score: 0.139
+LR cohens kappa score: 0.071
+LR average precision score: 0.072
+
+-> 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: 317, 15
+KNN fn, tp: 3, 11
+KNN f1 score: 0.550
+KNN cohens kappa score: 0.525
+
+
+------ Step 2/5: Slice 4/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 1272 synthetic samples
+-> test with GAN.predict
+GAN tn, fp: 323, 9
+GAN fn, tp: 3, 11
+GAN f1 score: 0.647
+GAN cohens kappa score: 0.629
+
+-> test with 'LR'
+LR tn, fp: 185, 147
+LR fn, tp: 9, 5
+LR f1 score: 0.060
+LR cohens kappa score: -0.015
+LR average precision score: 0.050
+
+-> 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: 295, 37
+KNN fn, tp: 1, 13
+KNN f1 score: 0.406
+KNN cohens kappa score: 0.366
+
+
+------ Step 2/5: Slice 5/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 1272 synthetic samples
+-> test with GAN.predict
+GAN tn, fp: 324, 7
+GAN fn, tp: 2, 11
+GAN f1 score: 0.710
+GAN cohens kappa score: 0.696
+
+-> test with 'LR'
+LR tn, fp: 195, 136
+LR fn, tp: 5, 8
+LR f1 score: 0.102
+LR cohens kappa score: 0.035
+LR average precision score: 0.072
+
+-> test with 'GB'
+GB tn, fp: 328, 3
+GB fn, tp: 2, 11
+GB f1 score: 0.815
+GB cohens kappa score: 0.807
+
+-> 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 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 GAN.predict
+GAN tn, fp: 324, 8
+GAN fn, tp: 2, 12
+GAN f1 score: 0.706
+GAN cohens kappa score: 0.691
+
+-> 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: 0.077
+
+-> 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: 314, 18
+KNN fn, tp: 1, 13
+KNN f1 score: 0.578
+KNN cohens kappa score: 0.553
+
+
+------ Step 3/5: Slice 2/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 1272 synthetic samples
+-> test with GAN.predict
+GAN tn, fp: 321, 11
+GAN fn, tp: 0, 14
+GAN f1 score: 0.718
+GAN cohens kappa score: 0.703
+
+-> test with 'LR'
+LR tn, fp: 198, 134
+LR fn, tp: 5, 9
+LR f1 score: 0.115
+LR cohens kappa score: 0.044
+LR average precision score: 0.069
+
+-> test with 'GB'
+GB tn, fp: 330, 2
+GB fn, tp: 3, 11
+GB f1 score: 0.815
+GB cohens kappa score: 0.807
+
+-> test with 'KNN'
+KNN tn, fp: 307, 25
+KNN fn, tp: 0, 14
+KNN f1 score: 0.528
+KNN cohens kappa score: 0.498
+
+
+------ Step 3/5: Slice 3/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 1272 synthetic samples
+-> test with GAN.predict
+GAN tn, fp: 323, 9
+GAN fn, tp: 0, 14
+GAN f1 score: 0.757
+GAN cohens kappa score: 0.744
+
+-> 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: 330, 2
+GB fn, tp: 9, 5
+GB f1 score: 0.476
+GB cohens kappa score: 0.462
+
+-> test with 'KNN'
+KNN tn, fp: 314, 18
+KNN fn, tp: 1, 13
+KNN f1 score: 0.578
+KNN cohens kappa score: 0.553
+
+
+------ Step 3/5: Slice 4/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 1272 synthetic samples
+-> test with GAN.predict
+GAN tn, fp: 328, 4
+GAN fn, tp: 4, 10
+GAN f1 score: 0.714
+GAN cohens kappa score: 0.702
+
+-> test with 'LR'
+LR tn, fp: 185, 147
+LR fn, tp: 1, 13
+LR f1 score: 0.149
+LR cohens kappa score: 0.081
+LR average precision score: 0.070
+
+-> 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: 303, 29
+KNN fn, tp: 0, 14
+KNN f1 score: 0.491
+KNN cohens kappa score: 0.458
+
+
+------ Step 3/5: Slice 5/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 1272 synthetic samples
+-> test with GAN.predict
+GAN tn, fp: 323, 8
+GAN fn, tp: 1, 12
+GAN f1 score: 0.727
+GAN cohens kappa score: 0.714
+
+-> test with 'LR'
+LR tn, fp: 165, 166
+LR fn, tp: 5, 8
+LR f1 score: 0.086
+LR cohens kappa score: 0.016
+LR average precision score: 0.049
+
+-> test with 'GB'
+GB tn, fp: 327, 4
+GB fn, tp: 6, 7
+GB f1 score: 0.583
+GB cohens kappa score: 0.568
+
+-> 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 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 GAN.predict
+GAN tn, fp: 322, 10
+GAN fn, tp: 2, 12
+GAN f1 score: 0.667
+GAN cohens kappa score: 0.649
+
+-> test with 'LR'
+LR tn, fp: 176, 156
+LR fn, tp: 3, 11
+LR f1 score: 0.122
+LR cohens kappa score: 0.051
+LR average precision score: 0.065
+
+-> 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: 0, 14
+KNN f1 score: 0.596
+KNN cohens kappa score: 0.571
+
+
+------ Step 4/5: Slice 2/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 1272 synthetic samples
+-> test with GAN.predict
+GAN tn, fp: 326, 6
+GAN fn, tp: 7, 7
+GAN f1 score: 0.519
+GAN cohens kappa score: 0.499
+
+-> 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: 0.062
+
+-> test with 'GB'
+GB tn, fp: 330, 2
+GB fn, tp: 9, 5
+GB f1 score: 0.476
+GB cohens kappa score: 0.462
+
+-> test with 'KNN'
+KNN tn, fp: 307, 25
+KNN fn, tp: 1, 13
+KNN f1 score: 0.500
+KNN cohens kappa score: 0.469
+
+
+------ Step 4/5: Slice 3/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 1272 synthetic samples
+-> test with GAN.predict
+GAN tn, fp: 326, 6
+GAN fn, tp: 3, 11
+GAN f1 score: 0.710
+GAN cohens kappa score: 0.696
+
+-> test with 'LR'
+LR tn, fp: 168, 164
+LR fn, tp: 4, 10
+LR f1 score: 0.106
+LR cohens kappa score: 0.034
+LR average precision score: 0.061
+
+-> 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: 307, 25
+KNN fn, tp: 0, 14
+KNN f1 score: 0.528
+KNN cohens kappa score: 0.498
+
+
+------ Step 4/5: Slice 4/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 1272 synthetic samples
+-> test with GAN.predict
+GAN tn, fp: 327, 5
+GAN fn, tp: 1, 13
+GAN f1 score: 0.813
+GAN cohens kappa score: 0.804
+
+-> test with 'LR'
+LR tn, fp: 198, 134
+LR fn, tp: 7, 7
+LR f1 score: 0.090
+LR cohens kappa score: 0.018
+LR average precision score: 0.056
+
+-> 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: 318, 14
+KNN fn, tp: 0, 14
+KNN f1 score: 0.667
+KNN cohens kappa score: 0.648
+
+
+------ Step 4/5: Slice 5/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 1272 synthetic samples
+-> test with GAN.predict
+GAN tn, fp: 320, 11
+GAN fn, tp: 3, 10
+GAN f1 score: 0.588
+GAN cohens kappa score: 0.568
+
+-> test with 'LR'
+LR tn, fp: 168, 163
+LR fn, tp: 2, 11
+LR f1 score: 0.118
+LR cohens kappa score: 0.051
+LR average precision score: 0.079
+
+-> test with 'GB'
+GB tn, fp: 327, 4
+GB fn, tp: 7, 6
+GB f1 score: 0.522
+GB cohens kappa score: 0.505
+
+-> test with 'KNN'
+KNN tn, fp: 317, 14
+KNN fn, tp: 1, 12
+KNN f1 score: 0.615
+KNN cohens kappa score: 0.595
+
+
+====== 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 GAN.predict
+GAN tn, fp: 318, 14
+GAN fn, tp: 3, 11
+GAN f1 score: 0.564
+GAN cohens kappa score: 0.540
+
+-> test with 'LR'
+LR tn, fp: 185, 147
+LR fn, tp: 8, 6
+LR f1 score: 0.072
+LR cohens kappa score: -0.002
+LR average precision score: 0.051
+
+-> 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: 311, 21
+KNN fn, tp: 4, 10
+KNN f1 score: 0.444
+KNN cohens kappa score: 0.412
+
+
+------ Step 5/5: Slice 2/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 1272 synthetic samples
+-> test with GAN.predict
+GAN tn, fp: 323, 9
+GAN fn, tp: 2, 12
+GAN f1 score: 0.686
+GAN cohens kappa score: 0.670
+
+-> test with 'LR'
+LR tn, fp: 184, 148
+LR fn, tp: 4, 10
+LR f1 score: 0.116
+LR cohens kappa score: 0.045
+LR average precision score: 0.067
+
+-> 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: 309, 23
+KNN fn, tp: 0, 14
+KNN f1 score: 0.549
+KNN cohens kappa score: 0.521
+
+
+------ Step 5/5: Slice 3/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 1272 synthetic samples
+-> test with GAN.predict
+GAN tn, fp: 325, 7
+GAN fn, tp: 3, 11
+GAN f1 score: 0.688
+GAN cohens kappa score: 0.673
+
+-> test with 'LR'
+LR tn, fp: 164, 168
+LR fn, tp: 4, 10
+LR f1 score: 0.104
+LR cohens kappa score: 0.032
+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: 300, 32
+KNN fn, tp: 0, 14
+KNN f1 score: 0.467
+KNN cohens kappa score: 0.431
+
+
+------ Step 5/5: Slice 4/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 1272 synthetic samples
+-> test with GAN.predict
+GAN tn, fp: 330, 2
+GAN fn, tp: 3, 11
+GAN f1 score: 0.815
+GAN cohens kappa score: 0.807
+
+-> test with 'LR'
+LR tn, fp: 177, 155
+LR fn, tp: 4, 10
+LR f1 score: 0.112
+LR cohens kappa score: 0.040
+LR average precision score: 0.073
+
+-> 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: 310, 22
+KNN fn, tp: 1, 13
+KNN f1 score: 0.531
+KNN cohens kappa score: 0.502
+
+
+------ Step 5/5: Slice 5/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 1272 synthetic samples
+-> test with GAN.predict
+GAN tn, fp: 321, 10
+GAN fn, tp: 0, 13
+GAN f1 score: 0.722
+GAN cohens kappa score: 0.708
+
+-> test with 'LR'
+LR tn, fp: 183, 148
+LR fn, tp: 4, 9
+LR f1 score: 0.106
+LR cohens kappa score: 0.039
+LR average precision score: 0.062
+
+-> test with 'GB'
+GB tn, fp: 331, 0
+GB fn, tp: 3, 10
+GB f1 score: 0.870
+GB cohens kappa score: 0.865
+
+-> test with 'KNN'
+KNN tn, fp: 304, 27
+KNN fn, tp: 0, 13
+KNN f1 score: 0.491
+KNN cohens kappa score: 0.460
+
+### Exercise is done.
+
+-----[ LR ]-----
+maximum:
+LR tn, fp: 199, 182
+LR fn, tp: 9, 13
+LR f1 score: 0.149
+LR cohens kappa score: 0.081
+LR average precision score: 0.083
+
+
+average:
+LR tn, fp: 179.36, 152.44
+LR fn, tp: 4.4, 9.4
+LR f1 score: 0.107
+LR cohens kappa score: 0.036
+LR average precision score: 0.065
+
+
+minimum:
+LR tn, fp: 150, 133
+LR fn, tp: 1, 5
+LR f1 score: 0.060
+LR cohens kappa score: -0.015
+LR average precision score: 0.049
+
+
+-----[ GB ]-----
+maximum:
+GB tn, fp: 332, 5
+GB fn, tp: 9, 13
+GB f1 score: 0.897
+GB cohens kappa score: 0.892
+
+
+average:
+GB tn, fp: 329.88, 1.92
+GB fn, tp: 4.8, 9.0
+GB f1 score: 0.718
+GB cohens kappa score: 0.709
+
+
+minimum:
+GB tn, fp: 326, 0
+GB fn, tp: 1, 5
+GB f1 score: 0.476
+GB cohens kappa score: 0.462
+
+
+-----[ KNN ]-----
+maximum:
+KNN tn, fp: 327, 41
+KNN fn, tp: 4, 14
+KNN f1 score: 0.848
+KNN cohens kappa score: 0.841
+
+
+average:
+KNN tn, fp: 309.32, 22.48
+KNN fn, tp: 0.68, 13.12
+KNN f1 score: 0.545
+KNN cohens kappa score: 0.517
+
+
+minimum:
+KNN tn, fp: 290, 5
+KNN fn, tp: 0, 10
+KNN f1 score: 0.388
+KNN cohens kappa score: 0.348
+
+
+-----[ GAN ]-----
+maximum:
+GAN tn, fp: 330, 14
+GAN fn, tp: 7, 14
+GAN f1 score: 0.815
+GAN cohens kappa score: 0.807
+
+
+average:
+GAN tn, fp: 324.04, 7.76
+GAN fn, tp: 2.4, 11.4
+GAN f1 score: 0.693
+GAN cohens kappa score: 0.678
+
+
+minimum:
+GAN tn, fp: 318, 2
+GAN fn, tp: 0, 7
+GAN f1 score: 0.519
+GAN cohens kappa score: 0.499
+