Added benchmark for big datasets.

data_result/Repeater/imblearn_mammography.csv

@@ -0,0 +1,92 @@
+LR
+Nr.;TN;TP;FN;FP;f1 score;cohens kappa score;average precision score
+1;1897.000;45.000;7.000;288.000;0.234;0.202;0.558
+2;1888.000;46.000;6.000;297.000;0.233;0.201;0.506
+3;1893.000;47.000;5.000;292.000;0.240;0.209;0.601
+4;1905.000;44.000;8.000;280.000;0.234;0.202;0.373
+5;1895.000;46.000;6.000;288.000;0.238;0.206;0.555
+6;1877.000;45.000;7.000;308.000;0.222;0.189;0.530
+7;1880.000;44.000;8.000;305.000;0.219;0.187;0.499
+8;1911.000;44.000;8.000;274.000;0.238;0.206;0.521
+9;1912.000;48.000;4.000;273.000;0.257;0.226;0.533
+10;1906.000;44.000;8.000;277.000;0.236;0.204;0.564
+11;1888.000;44.000;8.000;297.000;0.224;0.191;0.604
+12;1907.000;45.000;7.000;278.000;0.240;0.208;0.420
+13;1886.000;50.000;2.000;299.000;0.249;0.218;0.506
+14;1902.000;43.000;9.000;283.000;0.228;0.195;0.501
+15;1894.000;45.000;7.000;289.000;0.233;0.201;0.609
+16;1917.000;44.000;8.000;268.000;0.242;0.210;0.589
+17;1899.000;46.000;6.000;286.000;0.240;0.208;0.442
+18;1902.000;45.000;7.000;283.000;0.237;0.205;0.569
+19;1901.000;43.000;9.000;284.000;0.227;0.195;0.477
+20;1868.000;49.000;3.000;315.000;0.236;0.203;0.538
+21;1890.000;49.000;3.000;295.000;0.247;0.216;0.533
+22;1904.000;46.000;6.000;281.000;0.243;0.211;0.482
+23;1899.000;42.000;10.000;286.000;0.221;0.188;0.529
+24;1888.000;48.000;4.000;297.000;0.242;0.210;0.521
+25;1900.000;43.000;9.000;283.000;0.228;0.195;0.598
+max;1917.000;50.000;10.000;315.000;0.257;0.226;0.609
+avg;1896.360;45.400;6.600;288.240;0.236;0.203;0.526
+min;1868.000;42.000;2.000;268.000;0.219;0.187;0.373
+---
+GB
+Nr.;TN;TP;FN;FP;f1 score;cohens kappa score
+1;2110.000;42.000;10.000;75.000;0.497;0.480
+2;2119.000;42.000;10.000;66.000;0.525;0.510
+3;2116.000;43.000;9.000;69.000;0.524;0.509
+4;2106.000;42.000;10.000;79.000;0.486;0.468
+5;2111.000;46.000;6.000;72.000;0.541;0.526
+6;2100.000;42.000;10.000;85.000;0.469;0.451
+7;2107.000;46.000;6.000;78.000;0.523;0.507
+8;2129.000;43.000;9.000;56.000;0.570;0.556
+9;2116.000;48.000;4.000;69.000;0.568;0.554
+10;2126.000;41.000;11.000;57.000;0.547;0.532
+11;2106.000;44.000;8.000;79.000;0.503;0.486
+12;2123.000;40.000;12.000;62.000;0.519;0.504
+13;2110.000;48.000;4.000;75.000;0.549;0.533
+14;2113.000;42.000;10.000;72.000;0.506;0.490
+15;2117.000;43.000;9.000;66.000;0.534;0.519
+16;2124.000;35.000;17.000;61.000;0.473;0.457
+17;2116.000;44.000;8.000;69.000;0.533;0.518
+18;2110.000;45.000;7.000;75.000;0.523;0.507
+19;2115.000;43.000;9.000;70.000;0.521;0.505
+20;2118.000;46.000;6.000;65.000;0.564;0.550
+21;2121.000;48.000;4.000;64.000;0.585;0.572
+22;2107.000;45.000;7.000;78.000;0.514;0.498
+23;2114.000;41.000;11.000;71.000;0.500;0.484
+24;2109.000;43.000;9.000;76.000;0.503;0.486
+25;2115.000;42.000;10.000;68.000;0.519;0.503
+max;2129.000;48.000;17.000;85.000;0.585;0.572
+avg;2114.320;43.360;8.640;70.280;0.524;0.508
+min;2100.000;35.000;4.000;56.000;0.469;0.451
+---
+KNN
+Nr.;TN;TP;FN;FP;f1 score;cohens kappa score
+1;1452.000;43.000;9.000;733.000;0.104;0.063
+2;1432.000;44.000;8.000;753.000;0.104;0.063
+3;1450.000;44.000;8.000;735.000;0.106;0.065
+4;1430.000;45.000;7.000;755.000;0.106;0.065
+5;1508.000;40.000;12.000;675.000;0.104;0.064
+6;1459.000;45.000;7.000;726.000;0.109;0.069
+7;1442.000;44.000;8.000;743.000;0.105;0.064
+8;1467.000;42.000;10.000;718.000;0.103;0.063
+9;1454.000;47.000;5.000;731.000;0.113;0.073
+10;1446.000;40.000;12.000;737.000;0.097;0.055
+11;1475.000;47.000;5.000;710.000;0.116;0.076
+12;1446.000;42.000;10.000;739.000;0.101;0.060
+13;1447.000;45.000;7.000;738.000;0.108;0.067
+14;1470.000;41.000;11.000;715.000;0.101;0.061
+15;1448.000;43.000;9.000;735.000;0.104;0.063
+16;1491.000;40.000;12.000;694.000;0.102;0.061
+17;1500.000;45.000;7.000;685.000;0.115;0.075
+18;1417.000;45.000;7.000;768.000;0.104;0.063
+19;1413.000;42.000;10.000;772.000;0.097;0.056
+20;1464.000;42.000;10.000;719.000;0.103;0.062
+21;1469.000;43.000;9.000;716.000;0.106;0.065
+22;1448.000;45.000;7.000;737.000;0.108;0.067
+23;1464.000;41.000;11.000;721.000;0.101;0.060
+24;1470.000;45.000;7.000;715.000;0.111;0.070
+25;1432.000;40.000;12.000;751.000;0.095;0.054
+max;1508.000;47.000;12.000;772.000;0.116;0.076
+avg;1455.760;43.200;8.800;728.840;0.105;0.064
+min;1413.000;40.000;5.000;675.000;0.095;0.054

data_result/Repeater/imblearn_mammography.log

@@ -0,0 +1,702 @@
+
+
+///////////////////////////////////////////
+// Running Repeater on imblearn_mammography
+///////////////////////////////////////////
+
+Load 'data_input/imblearn_mammography'
+from imblearn
+non empty cut in data_input/imblearn_mammography! (7 points)
+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 8530 synthetic samples
+-> test with 'LR'
+LR tn, fp: 1897, 288
+LR fn, tp: 7, 45
+LR f1 score: 0.234
+LR cohens kappa score: 0.202
+LR average precision score: 0.558
+
+-> test with 'GB'
+GB tn, fp: 2110, 75
+GB fn, tp: 10, 42
+GB f1 score: 0.497
+GB cohens kappa score: 0.480
+
+-> test with 'KNN'
+KNN tn, fp: 1452, 733
+KNN fn, tp: 9, 43
+KNN f1 score: 0.104
+KNN cohens kappa score: 0.063
+
+
+------ Step 1/5: Slice 2/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 8530 synthetic samples
+-> test with 'LR'
+LR tn, fp: 1888, 297
+LR fn, tp: 6, 46
+LR f1 score: 0.233
+LR cohens kappa score: 0.201
+LR average precision score: 0.506
+
+-> test with 'GB'
+GB tn, fp: 2119, 66
+GB fn, tp: 10, 42
+GB f1 score: 0.525
+GB cohens kappa score: 0.510
+
+-> test with 'KNN'
+KNN tn, fp: 1432, 753
+KNN fn, tp: 8, 44
+KNN f1 score: 0.104
+KNN cohens kappa score: 0.063
+
+
+------ Step 1/5: Slice 3/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 8530 synthetic samples
+-> test with 'LR'
+LR tn, fp: 1893, 292
+LR fn, tp: 5, 47
+LR f1 score: 0.240
+LR cohens kappa score: 0.209
+LR average precision score: 0.601
+
+-> test with 'GB'
+GB tn, fp: 2116, 69
+GB fn, tp: 9, 43
+GB f1 score: 0.524
+GB cohens kappa score: 0.509
+
+-> test with 'KNN'
+KNN tn, fp: 1450, 735
+KNN fn, tp: 8, 44
+KNN f1 score: 0.106
+KNN cohens kappa score: 0.065
+
+
+------ Step 1/5: Slice 4/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 8530 synthetic samples
+-> test with 'LR'
+LR tn, fp: 1905, 280
+LR fn, tp: 8, 44
+LR f1 score: 0.234
+LR cohens kappa score: 0.202
+LR average precision score: 0.373
+
+-> test with 'GB'
+GB tn, fp: 2106, 79
+GB fn, tp: 10, 42
+GB f1 score: 0.486
+GB cohens kappa score: 0.468
+
+-> test with 'KNN'
+KNN tn, fp: 1430, 755
+KNN fn, tp: 7, 45
+KNN f1 score: 0.106
+KNN cohens kappa score: 0.065
+
+
+------ Step 1/5: Slice 5/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 8532 synthetic samples
+-> test with 'LR'
+LR tn, fp: 1895, 288
+LR fn, tp: 6, 46
+LR f1 score: 0.238
+LR cohens kappa score: 0.206
+LR average precision score: 0.555
+
+-> test with 'GB'
+GB tn, fp: 2111, 72
+GB fn, tp: 6, 46
+GB f1 score: 0.541
+GB cohens kappa score: 0.526
+
+-> test with 'KNN'
+KNN tn, fp: 1508, 675
+KNN fn, tp: 12, 40
+KNN f1 score: 0.104
+KNN cohens kappa score: 0.064
+
+
+====== Step 2/5 =======
+-> Shuffling data
+-> Spliting data to slices
+
+------ Step 2/5: Slice 1/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 8530 synthetic samples
+-> test with 'LR'
+LR tn, fp: 1877, 308
+LR fn, tp: 7, 45
+LR f1 score: 0.222
+LR cohens kappa score: 0.189
+LR average precision score: 0.530
+
+-> test with 'GB'
+GB tn, fp: 2100, 85
+GB fn, tp: 10, 42
+GB f1 score: 0.469
+GB cohens kappa score: 0.451
+
+-> test with 'KNN'
+KNN tn, fp: 1459, 726
+KNN fn, tp: 7, 45
+KNN f1 score: 0.109
+KNN cohens kappa score: 0.069
+
+
+------ Step 2/5: Slice 2/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 8530 synthetic samples
+-> test with 'LR'
+LR tn, fp: 1880, 305
+LR fn, tp: 8, 44
+LR f1 score: 0.219
+LR cohens kappa score: 0.187
+LR average precision score: 0.499
+
+-> test with 'GB'
+GB tn, fp: 2107, 78
+GB fn, tp: 6, 46
+GB f1 score: 0.523
+GB cohens kappa score: 0.507
+
+-> test with 'KNN'
+KNN tn, fp: 1442, 743
+KNN fn, tp: 8, 44
+KNN f1 score: 0.105
+KNN cohens kappa score: 0.064
+
+
+------ Step 2/5: Slice 3/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 8530 synthetic samples
+-> test with 'LR'
+LR tn, fp: 1911, 274
+LR fn, tp: 8, 44
+LR f1 score: 0.238
+LR cohens kappa score: 0.206
+LR average precision score: 0.521
+
+-> test with 'GB'
+GB tn, fp: 2129, 56
+GB fn, tp: 9, 43
+GB f1 score: 0.570
+GB cohens kappa score: 0.556
+
+-> test with 'KNN'
+KNN tn, fp: 1467, 718
+KNN fn, tp: 10, 42
+KNN f1 score: 0.103
+KNN cohens kappa score: 0.063
+
+
+------ Step 2/5: Slice 4/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 8530 synthetic samples
+-> test with 'LR'
+LR tn, fp: 1912, 273
+LR fn, tp: 4, 48
+LR f1 score: 0.257
+LR cohens kappa score: 0.226
+LR average precision score: 0.533
+
+-> test with 'GB'
+GB tn, fp: 2116, 69
+GB fn, tp: 4, 48
+GB f1 score: 0.568
+GB cohens kappa score: 0.554
+
+-> test with 'KNN'
+KNN tn, fp: 1454, 731
+KNN fn, tp: 5, 47
+KNN f1 score: 0.113
+KNN cohens kappa score: 0.073
+
+
+------ Step 2/5: Slice 5/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 8532 synthetic samples
+-> test with 'LR'
+LR tn, fp: 1906, 277
+LR fn, tp: 8, 44
+LR f1 score: 0.236
+LR cohens kappa score: 0.204
+LR average precision score: 0.564
+
+-> test with 'GB'
+GB tn, fp: 2126, 57
+GB fn, tp: 11, 41
+GB f1 score: 0.547
+GB cohens kappa score: 0.532
+
+-> test with 'KNN'
+KNN tn, fp: 1446, 737
+KNN fn, tp: 12, 40
+KNN f1 score: 0.097
+KNN cohens kappa score: 0.055
+
+
+====== Step 3/5 =======
+-> Shuffling data
+-> Spliting data to slices
+
+------ Step 3/5: Slice 1/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 8530 synthetic samples
+-> test with 'LR'
+LR tn, fp: 1888, 297
+LR fn, tp: 8, 44
+LR f1 score: 0.224
+LR cohens kappa score: 0.191
+LR average precision score: 0.604
+
+-> test with 'GB'
+GB tn, fp: 2106, 79
+GB fn, tp: 8, 44
+GB f1 score: 0.503
+GB cohens kappa score: 0.486
+
+-> test with 'KNN'
+KNN tn, fp: 1475, 710
+KNN fn, tp: 5, 47
+KNN f1 score: 0.116
+KNN cohens kappa score: 0.076
+
+
+------ Step 3/5: Slice 2/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 8530 synthetic samples
+-> test with 'LR'
+LR tn, fp: 1907, 278
+LR fn, tp: 7, 45
+LR f1 score: 0.240
+LR cohens kappa score: 0.208
+LR average precision score: 0.420
+
+-> test with 'GB'
+GB tn, fp: 2123, 62
+GB fn, tp: 12, 40
+GB f1 score: 0.519
+GB cohens kappa score: 0.504
+
+-> test with 'KNN'
+KNN tn, fp: 1446, 739
+KNN fn, tp: 10, 42
+KNN f1 score: 0.101
+KNN cohens kappa score: 0.060
+
+
+------ Step 3/5: Slice 3/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 8530 synthetic samples
+-> test with 'LR'
+LR tn, fp: 1886, 299
+LR fn, tp: 2, 50
+LR f1 score: 0.249
+LR cohens kappa score: 0.218
+LR average precision score: 0.506
+
+-> test with 'GB'
+GB tn, fp: 2110, 75
+GB fn, tp: 4, 48
+GB f1 score: 0.549
+GB cohens kappa score: 0.533
+
+-> test with 'KNN'
+KNN tn, fp: 1447, 738
+KNN fn, tp: 7, 45
+KNN f1 score: 0.108
+KNN cohens kappa score: 0.067
+
+
+------ Step 3/5: Slice 4/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 8530 synthetic samples
+-> test with 'LR'
+LR tn, fp: 1902, 283
+LR fn, tp: 9, 43
+LR f1 score: 0.228
+LR cohens kappa score: 0.195
+LR average precision score: 0.501
+
+-> test with 'GB'
+GB tn, fp: 2113, 72
+GB fn, tp: 10, 42
+GB f1 score: 0.506
+GB cohens kappa score: 0.490
+
+-> test with 'KNN'
+KNN tn, fp: 1470, 715
+KNN fn, tp: 11, 41
+KNN f1 score: 0.101
+KNN cohens kappa score: 0.061
+
+
+------ Step 3/5: Slice 5/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 8532 synthetic samples
+-> test with 'LR'
+LR tn, fp: 1894, 289
+LR fn, tp: 7, 45
+LR f1 score: 0.233
+LR cohens kappa score: 0.201
+LR average precision score: 0.609
+
+-> test with 'GB'
+GB tn, fp: 2117, 66
+GB fn, tp: 9, 43
+GB f1 score: 0.534
+GB cohens kappa score: 0.519
+
+-> test with 'KNN'
+KNN tn, fp: 1448, 735
+KNN fn, tp: 9, 43
+KNN f1 score: 0.104
+KNN cohens kappa score: 0.063
+
+
+====== Step 4/5 =======
+-> Shuffling data
+-> Spliting data to slices
+
+------ Step 4/5: Slice 1/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 8530 synthetic samples
+-> test with 'LR'
+LR tn, fp: 1917, 268
+LR fn, tp: 8, 44
+LR f1 score: 0.242
+LR cohens kappa score: 0.210
+LR average precision score: 0.589
+
+-> test with 'GB'
+GB tn, fp: 2124, 61
+GB fn, tp: 17, 35
+GB f1 score: 0.473
+GB cohens kappa score: 0.457
+
+-> test with 'KNN'
+KNN tn, fp: 1491, 694
+KNN fn, tp: 12, 40
+KNN f1 score: 0.102
+KNN cohens kappa score: 0.061
+
+
+------ Step 4/5: Slice 2/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 8530 synthetic samples
+-> test with 'LR'
+LR tn, fp: 1899, 286
+LR fn, tp: 6, 46
+LR f1 score: 0.240
+LR cohens kappa score: 0.208
+LR average precision score: 0.442
+
+-> test with 'GB'
+GB tn, fp: 2116, 69
+GB fn, tp: 8, 44
+GB f1 score: 0.533
+GB cohens kappa score: 0.518
+
+-> test with 'KNN'
+KNN tn, fp: 1500, 685
+KNN fn, tp: 7, 45
+KNN f1 score: 0.115
+KNN cohens kappa score: 0.075
+
+
+------ Step 4/5: Slice 3/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 8530 synthetic samples
+-> test with 'LR'
+LR tn, fp: 1902, 283
+LR fn, tp: 7, 45
+LR f1 score: 0.237
+LR cohens kappa score: 0.205
+LR average precision score: 0.569
+
+-> test with 'GB'
+GB tn, fp: 2110, 75
+GB fn, tp: 7, 45
+GB f1 score: 0.523
+GB cohens kappa score: 0.507
+
+-> test with 'KNN'
+KNN tn, fp: 1417, 768
+KNN fn, tp: 7, 45
+KNN f1 score: 0.104
+KNN cohens kappa score: 0.063
+
+
+------ Step 4/5: Slice 4/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 8530 synthetic samples
+-> test with 'LR'
+LR tn, fp: 1901, 284
+LR fn, tp: 9, 43
+LR f1 score: 0.227
+LR cohens kappa score: 0.195
+LR average precision score: 0.477
+
+-> test with 'GB'
+GB tn, fp: 2115, 70
+GB fn, tp: 9, 43
+GB f1 score: 0.521
+GB cohens kappa score: 0.505
+
+-> test with 'KNN'
+KNN tn, fp: 1413, 772
+KNN fn, tp: 10, 42
+KNN f1 score: 0.097
+KNN cohens kappa score: 0.056
+
+
+------ Step 4/5: Slice 5/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 8532 synthetic samples
+-> test with 'LR'
+LR tn, fp: 1868, 315
+LR fn, tp: 3, 49
+LR f1 score: 0.236
+LR cohens kappa score: 0.203
+LR average precision score: 0.538
+
+-> test with 'GB'
+GB tn, fp: 2118, 65
+GB fn, tp: 6, 46
+GB f1 score: 0.564
+GB cohens kappa score: 0.550
+
+-> test with 'KNN'
+KNN tn, fp: 1464, 719
+KNN fn, tp: 10, 42
+KNN f1 score: 0.103
+KNN cohens kappa score: 0.062
+
+
+====== Step 5/5 =======
+-> Shuffling data
+-> Spliting data to slices
+
+------ Step 5/5: Slice 1/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 8530 synthetic samples
+-> test with 'LR'
+LR tn, fp: 1890, 295
+LR fn, tp: 3, 49
+LR f1 score: 0.247
+LR cohens kappa score: 0.216
+LR average precision score: 0.533
+
+-> test with 'GB'
+GB tn, fp: 2121, 64
+GB fn, tp: 4, 48
+GB f1 score: 0.585
+GB cohens kappa score: 0.572
+
+-> test with 'KNN'
+KNN tn, fp: 1469, 716
+KNN fn, tp: 9, 43
+KNN f1 score: 0.106
+KNN cohens kappa score: 0.065
+
+
+------ Step 5/5: Slice 2/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 8530 synthetic samples
+-> test with 'LR'
+LR tn, fp: 1904, 281
+LR fn, tp: 6, 46
+LR f1 score: 0.243
+LR cohens kappa score: 0.211
+LR average precision score: 0.482
+
+-> test with 'GB'
+GB tn, fp: 2107, 78
+GB fn, tp: 7, 45
+GB f1 score: 0.514
+GB cohens kappa score: 0.498
+
+-> test with 'KNN'
+KNN tn, fp: 1448, 737
+KNN fn, tp: 7, 45
+KNN f1 score: 0.108
+KNN cohens kappa score: 0.067
+
+
+------ Step 5/5: Slice 3/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 8530 synthetic samples
+-> test with 'LR'
+LR tn, fp: 1899, 286
+LR fn, tp: 10, 42
+LR f1 score: 0.221
+LR cohens kappa score: 0.188
+LR average precision score: 0.529
+
+-> test with 'GB'
+GB tn, fp: 2114, 71
+GB fn, tp: 11, 41
+GB f1 score: 0.500
+GB cohens kappa score: 0.484
+
+-> test with 'KNN'
+KNN tn, fp: 1464, 721
+KNN fn, tp: 11, 41
+KNN f1 score: 0.101
+KNN cohens kappa score: 0.060
+
+
+------ Step 5/5: Slice 4/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 8530 synthetic samples
+-> test with 'LR'
+LR tn, fp: 1888, 297
+LR fn, tp: 4, 48
+LR f1 score: 0.242
+LR cohens kappa score: 0.210
+LR average precision score: 0.521
+
+-> test with 'GB'
+GB tn, fp: 2109, 76
+GB fn, tp: 9, 43
+GB f1 score: 0.503
+GB cohens kappa score: 0.486
+
+-> test with 'KNN'
+KNN tn, fp: 1470, 715
+KNN fn, tp: 7, 45
+KNN f1 score: 0.111
+KNN cohens kappa score: 0.070
+
+
+------ Step 5/5: Slice 5/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 8532 synthetic samples
+-> test with 'LR'
+LR tn, fp: 1900, 283
+LR fn, tp: 9, 43
+LR f1 score: 0.228
+LR cohens kappa score: 0.195
+LR average precision score: 0.598
+
+-> test with 'GB'
+GB tn, fp: 2115, 68
+GB fn, tp: 10, 42
+GB f1 score: 0.519
+GB cohens kappa score: 0.503
+
+-> test with 'KNN'
+KNN tn, fp: 1432, 751
+KNN fn, tp: 12, 40
+KNN f1 score: 0.095
+KNN cohens kappa score: 0.054
+
+### Exercise is done.
+
+-----[ LR ]-----
+maximum:
+LR tn, fp: 1917, 315
+LR fn, tp: 10, 50
+LR f1 score: 0.257
+LR cohens kappa score: 0.226
+LR average precision score: 0.609
+
+
+average:
+LR tn, fp: 1896.36, 288.24
+LR fn, tp: 6.6, 45.4
+LR f1 score: 0.236
+LR cohens kappa score: 0.203
+LR average precision score: 0.526
+
+
+minimum:
+LR tn, fp: 1868, 268
+LR fn, tp: 2, 42
+LR f1 score: 0.219
+LR cohens kappa score: 0.187
+LR average precision score: 0.373
+
+
+-----[ GB ]-----
+maximum:
+GB tn, fp: 2129, 85
+GB fn, tp: 17, 48
+GB f1 score: 0.585
+GB cohens kappa score: 0.572
+
+
+average:
+GB tn, fp: 2114.32, 70.28
+GB fn, tp: 8.64, 43.36
+GB f1 score: 0.524
+GB cohens kappa score: 0.508
+
+
+minimum:
+GB tn, fp: 2100, 56
+GB fn, tp: 4, 35
+GB f1 score: 0.469
+GB cohens kappa score: 0.451
+
+
+-----[ KNN ]-----
+maximum:
+KNN tn, fp: 1508, 772
+KNN fn, tp: 12, 47
+KNN f1 score: 0.116
+KNN cohens kappa score: 0.076
+
+
+average:
+KNN tn, fp: 1455.76, 728.84
+KNN fn, tp: 8.8, 43.2
+KNN f1 score: 0.105
+KNN cohens kappa score: 0.064
+
+
+minimum:
+KNN tn, fp: 1413, 675
+KNN fn, tp: 5, 40
+KNN f1 score: 0.095
+KNN cohens kappa score: 0.054
+

data_result/Repeater/imblearn_ozone_level.csv

@@ -0,0 +1,92 @@
+LR
+Nr.;TN;TP;FN;FP;f1 score;cohens kappa score;average precision score
+1;424.000;13.000;2.000;69.000;0.268;0.230;0.348
+2;431.000;11.000;4.000;62.000;0.250;0.211;0.211
+3;432.000;11.000;4.000;61.000;0.253;0.214;0.124
+4;424.000;10.000;5.000;69.000;0.213;0.172;0.213
+5;418.000;10.000;3.000;73.000;0.208;0.171;0.188
+6;421.000;10.000;5.000;72.000;0.206;0.164;0.257
+7;440.000;10.000;5.000;53.000;0.256;0.219;0.229
+8;427.000;14.000;1.000;66.000;0.295;0.258;0.350
+9;424.000;10.000;5.000;69.000;0.213;0.172;0.151
+10;415.000;10.000;3.000;76.000;0.202;0.165;0.203
+11;427.000;12.000;3.000;66.000;0.258;0.219;0.311
+12;424.000;11.000;4.000;69.000;0.232;0.191;0.138
+13;445.000;11.000;4.000;48.000;0.297;0.263;0.177
+14;427.000;10.000;5.000;66.000;0.220;0.179;0.171
+15;418.000;10.000;3.000;73.000;0.208;0.171;0.345
+16;420.000;12.000;3.000;73.000;0.240;0.200;0.276
+17;434.000;11.000;4.000;59.000;0.259;0.221;0.230
+18;436.000;12.000;3.000;57.000;0.286;0.249;0.211
+19;418.000;12.000;3.000;75.000;0.235;0.195;0.277
+20;419.000;10.000;3.000;72.000;0.211;0.174;0.214
+21;430.000;13.000;2.000;63.000;0.286;0.249;0.276
+22;418.000;12.000;3.000;75.000;0.235;0.195;0.167
+23;449.000;10.000;5.000;44.000;0.290;0.255;0.187
+24;425.000;12.000;3.000;68.000;0.253;0.214;0.233
+25;410.000;11.000;2.000;81.000;0.210;0.172;0.261
+max;449.000;14.000;5.000;81.000;0.297;0.263;0.350
+avg;426.240;11.120;3.480;66.360;0.243;0.205;0.230
+min;410.000;10.000;1.000;44.000;0.202;0.164;0.124
+---
+GB
+Nr.;TN;TP;FN;FP;f1 score;cohens kappa score
+1;480.000;7.000;8.000;13.000;0.400;0.379
+2;487.000;8.000;7.000;6.000;0.552;0.539
+3;477.000;6.000;9.000;16.000;0.324;0.300
+4;477.000;7.000;8.000;16.000;0.368;0.345
+5;480.000;6.000;7.000;11.000;0.400;0.382
+6;481.000;3.000;12.000;12.000;0.200;0.176
+7;479.000;9.000;6.000;14.000;0.474;0.454
+8;484.000;6.000;9.000;9.000;0.400;0.382
+9;481.000;4.000;11.000;12.000;0.258;0.235
+10;481.000;6.000;7.000;10.000;0.414;0.397
+11;478.000;8.000;7.000;15.000;0.421;0.400
+12;482.000;10.000;5.000;11.000;0.556;0.540
+13;484.000;6.000;9.000;9.000;0.400;0.382
+14;482.000;7.000;8.000;11.000;0.424;0.405
+15;476.000;5.000;8.000;15.000;0.303;0.281
+16;477.000;9.000;6.000;16.000;0.450;0.429
+17;483.000;6.000;9.000;10.000;0.387;0.368
+18;484.000;9.000;6.000;9.000;0.545;0.530
+19;480.000;8.000;7.000;13.000;0.444;0.425
+20;480.000;5.000;8.000;11.000;0.345;0.326
+21;480.000;9.000;6.000;13.000;0.486;0.468
+22;482.000;6.000;9.000;11.000;0.375;0.355
+23;483.000;5.000;10.000;10.000;0.333;0.313
+24;479.000;7.000;8.000;14.000;0.389;0.367
+25;482.000;5.000;8.000;9.000;0.370;0.353
+max;487.000;10.000;12.000;16.000;0.556;0.540
+avg;480.760;6.680;7.920;11.840;0.401;0.381
+min;476.000;3.000;5.000;6.000;0.200;0.176
+---
+KNN
+Nr.;TN;TP;FN;FP;f1 score;cohens kappa score
+1;434.000;4.000;11.000;59.000;0.103;0.058
+2;446.000;2.000;13.000;47.000;0.062;0.018
+3;426.000;4.000;11.000;67.000;0.093;0.047
+4;443.000;1.000;14.000;50.000;0.030;-0.016
+5;430.000;4.000;9.000;61.000;0.103;0.062
+6;431.000;3.000;12.000;62.000;0.075;0.028
+7;436.000;6.000;9.000;57.000;0.154;0.111
+8;430.000;2.000;13.000;63.000;0.050;0.002
+9;448.000;2.000;13.000;45.000;0.065;0.021
+10;431.000;6.000;7.000;60.000;0.152;0.114
+11;439.000;2.000;13.000;54.000;0.056;0.010
+12;439.000;2.000;13.000;54.000;0.056;0.010
+13;429.000;3.000;12.000;64.000;0.073;0.026
+14;436.000;2.000;13.000;57.000;0.054;0.007
+15;425.000;4.000;9.000;66.000;0.096;0.055
+16;419.000;4.000;11.000;74.000;0.086;0.038
+17;444.000;4.000;11.000;49.000;0.118;0.075
+18;449.000;1.000;14.000;44.000;0.033;-0.011
+19;425.000;4.000;11.000;68.000;0.092;0.045
+20;426.000;1.000;12.000;65.000;0.025;-0.019
+21;421.000;3.000;12.000;72.000;0.067;0.018
+22;429.000;3.000;12.000;64.000;0.073;0.026
+23;433.000;4.000;11.000;60.000;0.101;0.056
+24;447.000;4.000;11.000;46.000;0.123;0.081
+25;421.000;3.000;10.000;70.000;0.070;0.027
+max;449.000;6.000;14.000;74.000;0.154;0.114
+avg;433.480;3.120;11.480;59.120;0.080;0.036
+min;419.000;1.000;7.000;44.000;0.025;-0.019

data_result/Repeater/imblearn_ozone_level.log

@@ -0,0 +1,701 @@
+
+
+///////////////////////////////////////////
+// Running Repeater on imblearn_ozone_level
+///////////////////////////////////////////
+
+Load 'data_input/imblearn_ozone_level'
+from imblearn
+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 1912 synthetic samples
+-> test with 'LR'
+LR tn, fp: 424, 69
+LR fn, tp: 2, 13
+LR f1 score: 0.268
+LR cohens kappa score: 0.230
+LR average precision score: 0.348
+
+-> test with 'GB'
+GB tn, fp: 480, 13
+GB fn, tp: 8, 7
+GB f1 score: 0.400
+GB cohens kappa score: 0.379
+
+-> test with 'KNN'
+KNN tn, fp: 434, 59
+KNN fn, tp: 11, 4
+KNN f1 score: 0.103
+KNN cohens kappa score: 0.058
+
+
+------ Step 1/5: Slice 2/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 1912 synthetic samples
+-> test with 'LR'
+LR tn, fp: 431, 62
+LR fn, tp: 4, 11
+LR f1 score: 0.250
+LR cohens kappa score: 0.211
+LR average precision score: 0.211
+
+-> test with 'GB'
+GB tn, fp: 487, 6
+GB fn, tp: 7, 8
+GB f1 score: 0.552
+GB cohens kappa score: 0.539
+
+-> test with 'KNN'
+KNN tn, fp: 446, 47
+KNN fn, tp: 13, 2
+KNN f1 score: 0.062
+KNN cohens kappa score: 0.018
+
+
+------ Step 1/5: Slice 3/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 1912 synthetic samples
+-> test with 'LR'
+LR tn, fp: 432, 61
+LR fn, tp: 4, 11
+LR f1 score: 0.253
+LR cohens kappa score: 0.214
+LR average precision score: 0.124
+
+-> test with 'GB'
+GB tn, fp: 477, 16
+GB fn, tp: 9, 6
+GB f1 score: 0.324
+GB cohens kappa score: 0.300
+
+-> test with 'KNN'
+KNN tn, fp: 426, 67
+KNN fn, tp: 11, 4
+KNN f1 score: 0.093
+KNN cohens kappa score: 0.047
+
+
+------ Step 1/5: Slice 4/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 1912 synthetic samples
+-> test with 'LR'
+LR tn, fp: 424, 69
+LR fn, tp: 5, 10
+LR f1 score: 0.213
+LR cohens kappa score: 0.172
+LR average precision score: 0.213
+
+-> test with 'GB'
+GB tn, fp: 477, 16
+GB fn, tp: 8, 7
+GB f1 score: 0.368
+GB cohens kappa score: 0.345
+
+-> test with 'KNN'
+KNN tn, fp: 443, 50
+KNN fn, tp: 14, 1
+KNN f1 score: 0.030
+KNN cohens kappa score: -0.016
+
+
+------ Step 1/5: Slice 5/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 1912 synthetic samples
+-> test with 'LR'
+LR tn, fp: 418, 73
+LR fn, tp: 3, 10
+LR f1 score: 0.208
+LR cohens kappa score: 0.171
+LR average precision score: 0.188
+
+-> test with 'GB'
+GB tn, fp: 480, 11
+GB fn, tp: 7, 6
+GB f1 score: 0.400
+GB cohens kappa score: 0.382
+
+-> test with 'KNN'
+KNN tn, fp: 430, 61
+KNN fn, tp: 9, 4
+KNN f1 score: 0.103
+KNN cohens kappa score: 0.062
+
+
+====== Step 2/5 =======
+-> Shuffling data
+-> Spliting data to slices
+
+------ Step 2/5: Slice 1/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 1912 synthetic samples
+-> test with 'LR'
+LR tn, fp: 421, 72
+LR fn, tp: 5, 10
+LR f1 score: 0.206
+LR cohens kappa score: 0.164
+LR average precision score: 0.257
+
+-> test with 'GB'
+GB tn, fp: 481, 12
+GB fn, tp: 12, 3
+GB f1 score: 0.200
+GB cohens kappa score: 0.176
+
+-> test with 'KNN'
+KNN tn, fp: 431, 62
+KNN fn, tp: 12, 3
+KNN f1 score: 0.075
+KNN cohens kappa score: 0.028
+
+
+------ Step 2/5: Slice 2/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 1912 synthetic samples
+-> test with 'LR'
+LR tn, fp: 440, 53
+LR fn, tp: 5, 10
+LR f1 score: 0.256
+LR cohens kappa score: 0.219
+LR average precision score: 0.229
+
+-> test with 'GB'
+GB tn, fp: 479, 14
+GB fn, tp: 6, 9
+GB f1 score: 0.474
+GB cohens kappa score: 0.454
+
+-> test with 'KNN'
+KNN tn, fp: 436, 57
+KNN fn, tp: 9, 6
+KNN f1 score: 0.154
+KNN cohens kappa score: 0.111
+
+
+------ Step 2/5: Slice 3/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 1912 synthetic samples
+-> test with 'LR'
+LR tn, fp: 427, 66
+LR fn, tp: 1, 14
+LR f1 score: 0.295
+LR cohens kappa score: 0.258
+LR average precision score: 0.350
+
+-> test with 'GB'
+GB tn, fp: 484, 9
+GB fn, tp: 9, 6
+GB f1 score: 0.400
+GB cohens kappa score: 0.382
+
+-> test with 'KNN'
+KNN tn, fp: 430, 63
+KNN fn, tp: 13, 2
+KNN f1 score: 0.050
+KNN cohens kappa score: 0.002
+
+
+------ Step 2/5: Slice 4/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 1912 synthetic samples
+-> test with 'LR'
+LR tn, fp: 424, 69
+LR fn, tp: 5, 10
+LR f1 score: 0.213
+LR cohens kappa score: 0.172
+LR average precision score: 0.151
+
+-> test with 'GB'
+GB tn, fp: 481, 12
+GB fn, tp: 11, 4
+GB f1 score: 0.258
+GB cohens kappa score: 0.235
+
+-> test with 'KNN'
+KNN tn, fp: 448, 45
+KNN fn, tp: 13, 2
+KNN f1 score: 0.065
+KNN cohens kappa score: 0.021
+
+
+------ Step 2/5: Slice 5/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 1912 synthetic samples
+-> test with 'LR'
+LR tn, fp: 415, 76
+LR fn, tp: 3, 10
+LR f1 score: 0.202
+LR cohens kappa score: 0.165
+LR average precision score: 0.203
+
+-> test with 'GB'
+GB tn, fp: 481, 10
+GB fn, tp: 7, 6
+GB f1 score: 0.414
+GB cohens kappa score: 0.397
+
+-> test with 'KNN'
+KNN tn, fp: 431, 60
+KNN fn, tp: 7, 6
+KNN f1 score: 0.152
+KNN cohens kappa score: 0.114
+
+
+====== Step 3/5 =======
+-> Shuffling data
+-> Spliting data to slices
+
+------ Step 3/5: Slice 1/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 1912 synthetic samples
+-> test with 'LR'
+LR tn, fp: 427, 66
+LR fn, tp: 3, 12
+LR f1 score: 0.258
+LR cohens kappa score: 0.219
+LR average precision score: 0.311
+
+-> test with 'GB'
+GB tn, fp: 478, 15
+GB fn, tp: 7, 8
+GB f1 score: 0.421
+GB cohens kappa score: 0.400
+
+-> test with 'KNN'
+KNN tn, fp: 439, 54
+KNN fn, tp: 13, 2
+KNN f1 score: 0.056
+KNN cohens kappa score: 0.010
+
+
+------ Step 3/5: Slice 2/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 1912 synthetic samples
+-> test with 'LR'
+LR tn, fp: 424, 69
+LR fn, tp: 4, 11
+LR f1 score: 0.232
+LR cohens kappa score: 0.191
+LR average precision score: 0.138
+
+-> test with 'GB'
+GB tn, fp: 482, 11
+GB fn, tp: 5, 10
+GB f1 score: 0.556
+GB cohens kappa score: 0.540
+
+-> test with 'KNN'
+KNN tn, fp: 439, 54
+KNN fn, tp: 13, 2
+KNN f1 score: 0.056
+KNN cohens kappa score: 0.010
+
+
+------ Step 3/5: Slice 3/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 1912 synthetic samples
+-> test with 'LR'
+LR tn, fp: 445, 48
+LR fn, tp: 4, 11
+LR f1 score: 0.297
+LR cohens kappa score: 0.263
+LR average precision score: 0.177
+
+-> test with 'GB'
+GB tn, fp: 484, 9
+GB fn, tp: 9, 6
+GB f1 score: 0.400
+GB cohens kappa score: 0.382
+
+-> test with 'KNN'
+KNN tn, fp: 429, 64
+KNN fn, tp: 12, 3
+KNN f1 score: 0.073
+KNN cohens kappa score: 0.026
+
+
+------ Step 3/5: Slice 4/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 1912 synthetic samples
+-> test with 'LR'
+LR tn, fp: 427, 66
+LR fn, tp: 5, 10
+LR f1 score: 0.220
+LR cohens kappa score: 0.179
+LR average precision score: 0.171
+
+-> test with 'GB'
+GB tn, fp: 482, 11
+GB fn, tp: 8, 7
+GB f1 score: 0.424
+GB cohens kappa score: 0.405
+
+-> test with 'KNN'
+KNN tn, fp: 436, 57
+KNN fn, tp: 13, 2
+KNN f1 score: 0.054
+KNN cohens kappa score: 0.007
+
+
+------ Step 3/5: Slice 5/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 1912 synthetic samples
+-> test with 'LR'
+LR tn, fp: 418, 73
+LR fn, tp: 3, 10
+LR f1 score: 0.208
+LR cohens kappa score: 0.171
+LR average precision score: 0.345
+
+-> test with 'GB'
+GB tn, fp: 476, 15
+GB fn, tp: 8, 5
+GB f1 score: 0.303
+GB cohens kappa score: 0.281
+
+-> test with 'KNN'
+KNN tn, fp: 425, 66
+KNN fn, tp: 9, 4
+KNN f1 score: 0.096
+KNN cohens kappa score: 0.055
+
+
+====== Step 4/5 =======
+-> Shuffling data
+-> Spliting data to slices
+
+------ Step 4/5: Slice 1/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 1912 synthetic samples
+-> test with 'LR'
+LR tn, fp: 420, 73
+LR fn, tp: 3, 12
+LR f1 score: 0.240
+LR cohens kappa score: 0.200
+LR average precision score: 0.276
+
+-> test with 'GB'
+GB tn, fp: 477, 16
+GB fn, tp: 6, 9
+GB f1 score: 0.450
+GB cohens kappa score: 0.429
+
+-> test with 'KNN'
+KNN tn, fp: 419, 74
+KNN fn, tp: 11, 4
+KNN f1 score: 0.086
+KNN cohens kappa score: 0.038
+
+
+------ Step 4/5: Slice 2/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 1912 synthetic samples
+-> test with 'LR'
+LR tn, fp: 434, 59
+LR fn, tp: 4, 11
+LR f1 score: 0.259
+LR cohens kappa score: 0.221
+LR average precision score: 0.230
+
+-> test with 'GB'
+GB tn, fp: 483, 10
+GB fn, tp: 9, 6
+GB f1 score: 0.387
+GB cohens kappa score: 0.368
+
+-> test with 'KNN'
+KNN tn, fp: 444, 49
+KNN fn, tp: 11, 4
+KNN f1 score: 0.118
+KNN cohens kappa score: 0.075
+
+
+------ Step 4/5: Slice 3/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 1912 synthetic samples
+-> test with 'LR'
+LR tn, fp: 436, 57
+LR fn, tp: 3, 12
+LR f1 score: 0.286
+LR cohens kappa score: 0.249
+LR average precision score: 0.211
+
+-> test with 'GB'
+GB tn, fp: 484, 9
+GB fn, tp: 6, 9
+GB f1 score: 0.545
+GB cohens kappa score: 0.530
+
+-> test with 'KNN'
+KNN tn, fp: 449, 44
+KNN fn, tp: 14, 1
+KNN f1 score: 0.033
+KNN cohens kappa score: -0.011
+
+
+------ Step 4/5: Slice 4/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 1912 synthetic samples
+-> test with 'LR'
+LR tn, fp: 418, 75
+LR fn, tp: 3, 12
+LR f1 score: 0.235
+LR cohens kappa score: 0.195
+LR average precision score: 0.277
+
+-> test with 'GB'
+GB tn, fp: 480, 13
+GB fn, tp: 7, 8
+GB f1 score: 0.444
+GB cohens kappa score: 0.425
+
+-> test with 'KNN'
+KNN tn, fp: 425, 68
+KNN fn, tp: 11, 4
+KNN f1 score: 0.092
+KNN cohens kappa score: 0.045
+
+
+------ Step 4/5: Slice 5/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 1912 synthetic samples
+-> test with 'LR'
+LR tn, fp: 419, 72
+LR fn, tp: 3, 10
+LR f1 score: 0.211
+LR cohens kappa score: 0.174
+LR average precision score: 0.214
+
+-> test with 'GB'
+GB tn, fp: 480, 11
+GB fn, tp: 8, 5
+GB f1 score: 0.345
+GB cohens kappa score: 0.326
+
+-> test with 'KNN'
+KNN tn, fp: 426, 65
+KNN fn, tp: 12, 1
+KNN f1 score: 0.025
+KNN cohens kappa score: -0.019
+
+
+====== Step 5/5 =======
+-> Shuffling data
+-> Spliting data to slices
+
+------ Step 5/5: Slice 1/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 1912 synthetic samples
+-> test with 'LR'
+LR tn, fp: 430, 63
+LR fn, tp: 2, 13
+LR f1 score: 0.286
+LR cohens kappa score: 0.249
+LR average precision score: 0.276
+
+-> test with 'GB'
+GB tn, fp: 480, 13
+GB fn, tp: 6, 9
+GB f1 score: 0.486
+GB cohens kappa score: 0.468
+
+-> test with 'KNN'
+KNN tn, fp: 421, 72
+KNN fn, tp: 12, 3
+KNN f1 score: 0.067
+KNN cohens kappa score: 0.018
+
+
+------ Step 5/5: Slice 2/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 1912 synthetic samples
+-> test with 'LR'
+LR tn, fp: 418, 75
+LR fn, tp: 3, 12
+LR f1 score: 0.235
+LR cohens kappa score: 0.195
+LR average precision score: 0.167
+
+-> test with 'GB'
+GB tn, fp: 482, 11
+GB fn, tp: 9, 6
+GB f1 score: 0.375
+GB cohens kappa score: 0.355
+
+-> test with 'KNN'
+KNN tn, fp: 429, 64
+KNN fn, tp: 12, 3
+KNN f1 score: 0.073
+KNN cohens kappa score: 0.026
+
+
+------ Step 5/5: Slice 3/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 1912 synthetic samples
+-> test with 'LR'
+LR tn, fp: 449, 44
+LR fn, tp: 5, 10
+LR f1 score: 0.290
+LR cohens kappa score: 0.255
+LR average precision score: 0.187
+
+-> test with 'GB'
+GB tn, fp: 483, 10
+GB fn, tp: 10, 5
+GB f1 score: 0.333
+GB cohens kappa score: 0.313
+
+-> test with 'KNN'
+KNN tn, fp: 433, 60
+KNN fn, tp: 11, 4
+KNN f1 score: 0.101
+KNN cohens kappa score: 0.056
+
+
+------ Step 5/5: Slice 4/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 1912 synthetic samples
+-> test with 'LR'
+LR tn, fp: 425, 68
+LR fn, tp: 3, 12
+LR f1 score: 0.253
+LR cohens kappa score: 0.214
+LR average precision score: 0.233
+
+-> test with 'GB'
+GB tn, fp: 479, 14
+GB fn, tp: 8, 7
+GB f1 score: 0.389
+GB cohens kappa score: 0.367
+
+-> test with 'KNN'
+KNN tn, fp: 447, 46
+KNN fn, tp: 11, 4
+KNN f1 score: 0.123
+KNN cohens kappa score: 0.081
+
+
+------ Step 5/5: Slice 5/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 1912 synthetic samples
+-> test with 'LR'
+LR tn, fp: 410, 81
+LR fn, tp: 2, 11
+LR f1 score: 0.210
+LR cohens kappa score: 0.172
+LR average precision score: 0.261
+
+-> test with 'GB'
+GB tn, fp: 482, 9
+GB fn, tp: 8, 5
+GB f1 score: 0.370
+GB cohens kappa score: 0.353
+
+-> test with 'KNN'
+KNN tn, fp: 421, 70
+KNN fn, tp: 10, 3
+KNN f1 score: 0.070
+KNN cohens kappa score: 0.027
+
+### Exercise is done.
+
+-----[ LR ]-----
+maximum:
+LR tn, fp: 449, 81
+LR fn, tp: 5, 14
+LR f1 score: 0.297
+LR cohens kappa score: 0.263
+LR average precision score: 0.350
+
+
+average:
+LR tn, fp: 426.24, 66.36
+LR fn, tp: 3.48, 11.12
+LR f1 score: 0.243
+LR cohens kappa score: 0.205
+LR average precision score: 0.230
+
+
+minimum:
+LR tn, fp: 410, 44
+LR fn, tp: 1, 10
+LR f1 score: 0.202
+LR cohens kappa score: 0.164
+LR average precision score: 0.124
+
+
+-----[ GB ]-----
+maximum:
+GB tn, fp: 487, 16
+GB fn, tp: 12, 10
+GB f1 score: 0.556
+GB cohens kappa score: 0.540
+
+
+average:
+GB tn, fp: 480.76, 11.84
+GB fn, tp: 7.92, 6.68
+GB f1 score: 0.401
+GB cohens kappa score: 0.381
+
+
+minimum:
+GB tn, fp: 476, 6
+GB fn, tp: 5, 3
+GB f1 score: 0.200
+GB cohens kappa score: 0.176
+
+
+-----[ KNN ]-----
+maximum:
+KNN tn, fp: 449, 74
+KNN fn, tp: 14, 6
+KNN f1 score: 0.154
+KNN cohens kappa score: 0.114
+
+
+average:
+KNN tn, fp: 433.48, 59.12
+KNN fn, tp: 11.48, 3.12
+KNN f1 score: 0.080
+KNN cohens kappa score: 0.036
+
+
+minimum:
+KNN tn, fp: 419, 44
+KNN fn, tp: 7, 1
+KNN f1 score: 0.025
+KNN cohens kappa score: -0.019
+

data_result/Repeater/imblearn_protein_homo.csv

@@ -0,0 +1,92 @@
+LR
+Nr.;TN;TP;FN;FP;f1 score;cohens kappa score;average precision score
+1;27649.000;243.000;17.000;1242.000;0.279;0.267;0.863
+2;27695.000;246.000;14.000;1196.000;0.289;0.278;0.883
+3;27630.000;254.000;6.000;1261.000;0.286;0.275;0.887
+4;27663.000;243.000;17.000;1228.000;0.281;0.270;0.855
+5;27824.000;234.000;22.000;1067.000;0.301;0.290;0.812
+6;27706.000;248.000;12.000;1185.000;0.293;0.282;0.870
+7;27692.000;250.000;10.000;1199.000;0.293;0.282;0.892
+8;27688.000;242.000;18.000;1203.000;0.284;0.273;0.830
+9;27688.000;247.000;13.000;1203.000;0.289;0.278;0.864
+10;27631.000;243.000;13.000;1260.000;0.276;0.265;0.840
+11;27777.000;244.000;16.000;1114.000;0.302;0.291;0.868
+12;27678.000;247.000;13.000;1213.000;0.287;0.276;0.865
+13;27722.000;243.000;17.000;1169.000;0.291;0.280;0.834
+14;27635.000;246.000;14.000;1256.000;0.279;0.268;0.855
+15;27643.000;245.000;11.000;1248.000;0.280;0.269;0.882
+16;27672.000;247.000;13.000;1219.000;0.286;0.275;0.873
+17;27703.000;244.000;16.000;1188.000;0.288;0.278;0.835
+18;27707.000;243.000;17.000;1184.000;0.288;0.277;0.859
+19;27671.000;248.000;12.000;1220.000;0.287;0.276;0.878
+20;27674.000;240.000;16.000;1217.000;0.280;0.269;0.841
+21;27688.000;247.000;13.000;1203.000;0.289;0.278;0.866
+22;27724.000;245.000;15.000;1167.000;0.293;0.282;0.870
+23;27654.000;244.000;16.000;1237.000;0.280;0.269;0.854
+24;27677.000;248.000;12.000;1214.000;0.288;0.277;0.863
+25;27691.000;243.000;13.000;1200.000;0.286;0.275;0.851
+max;27824.000;254.000;22.000;1261.000;0.302;0.291;0.892
+avg;27687.280;244.960;14.240;1203.720;0.287;0.276;0.860
+min;27630.000;234.000;6.000;1067.000;0.276;0.265;0.812
+---
+GB
+Nr.;TN;TP;FN;FP;f1 score;cohens kappa score
+1;28386.000;241.000;19.000;505.000;0.479;0.472
+2;28384.000;246.000;14.000;507.000;0.486;0.479
+3;28342.000;249.000;11.000;549.000;0.471;0.463
+4;28406.000;241.000;19.000;485.000;0.489;0.482
+5;28481.000;230.000;26.000;410.000;0.513;0.507
+6;28416.000;243.000;17.000;475.000;0.497;0.490
+7;28359.000;243.000;17.000;532.000;0.470;0.462
+8;28423.000;241.000;19.000;468.000;0.497;0.491
+9;28399.000;244.000;16.000;492.000;0.490;0.483
+10;28376.000;236.000;20.000;515.000;0.469;0.462
+11;28417.000;241.000;19.000;474.000;0.494;0.488
+12;28404.000;242.000;18.000;487.000;0.489;0.483
+13;28409.000;239.000;21.000;482.000;0.487;0.480
+14;28359.000;248.000;12.000;532.000;0.477;0.470
+15;28384.000;240.000;16.000;507.000;0.479;0.472
+16;28378.000;245.000;15.000;513.000;0.481;0.474
+17;28382.000;242.000;18.000;509.000;0.479;0.472
+18;28415.000;240.000;20.000;476.000;0.492;0.485
+19;28427.000;245.000;15.000;464.000;0.506;0.499
+20;28383.000;240.000;16.000;508.000;0.478;0.471
+21;28385.000;245.000;15.000;506.000;0.485;0.478
+22;28424.000;241.000;19.000;467.000;0.498;0.491
+23;28444.000;242.000;18.000;447.000;0.510;0.504
+24;28386.000;245.000;15.000;505.000;0.485;0.478
+25;28373.000;242.000;14.000;518.000;0.476;0.469
+max;28481.000;249.000;26.000;549.000;0.513;0.507
+avg;28397.680;242.040;17.160;493.320;0.487;0.480
+min;28342.000;230.000;11.000;410.000;0.469;0.462
+---
+KNN
+Nr.;TN;TP;FN;FP;f1 score;cohens kappa score
+1;28546.000;164.000;96.000;345.000;0.427;0.420
+2;28529.000;179.000;81.000;362.000;0.447;0.440
+3;28493.000;150.000;110.000;398.000;0.371;0.364
+4;28499.000;166.000;94.000;392.000;0.406;0.399
+5;28504.000;143.000;113.000;387.000;0.364;0.356
+6;28549.000;160.000;100.000;342.000;0.420;0.413
+7;28527.000;167.000;93.000;364.000;0.422;0.415
+8;28497.000;161.000;99.000;394.000;0.395;0.388
+9;28502.000;170.000;90.000;389.000;0.415;0.408
+10;28518.000;159.000;97.000;373.000;0.404;0.396
+11;28501.000;169.000;91.000;390.000;0.413;0.405
+12;28539.000;152.000;108.000;352.000;0.398;0.391
+13;28523.000;159.000;101.000;368.000;0.404;0.397
+14;28495.000;161.000;99.000;396.000;0.394;0.387
+15;28505.000;165.000;91.000;386.000;0.409;0.402
+16;28514.000;164.000;96.000;377.000;0.409;0.402
+17;28527.000;155.000;105.000;364.000;0.398;0.391
+18;28501.000;171.000;89.000;390.000;0.417;0.409
+19;28523.000;167.000;93.000;368.000;0.420;0.413
+20;28492.000;167.000;89.000;399.000;0.406;0.399
+21;28548.000;160.000;100.000;343.000;0.419;0.412
+22;28520.000;160.000;100.000;371.000;0.405;0.397
+23;28515.000;154.000;106.000;376.000;0.390;0.382
+24;28522.000;165.000;95.000;369.000;0.416;0.409
+25;28520.000;165.000;91.000;371.000;0.417;0.410
+max;28549.000;179.000;113.000;399.000;0.447;0.440
+avg;28516.360;162.120;97.080;374.640;0.407;0.400
+min;28492.000;143.000;81.000;342.000;0.364;0.356

data_result/Repeater/imblearn_protein_homo.log

@@ -0,0 +1,701 @@
+
+
+///////////////////////////////////////////
+// Running Repeater on imblearn_protein_homo
+///////////////////////////////////////////
+
+Load 'data_input/imblearn_protein_homo'
+from imblearn
+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 114528 synthetic samples
+-> test with 'LR'
+LR tn, fp: 27649, 1242
+LR fn, tp: 17, 243
+LR f1 score: 0.279
+LR cohens kappa score: 0.267
+LR average precision score: 0.863
+
+-> test with 'GB'
+GB tn, fp: 28386, 505
+GB fn, tp: 19, 241
+GB f1 score: 0.479
+GB cohens kappa score: 0.472
+
+-> test with 'KNN'
+KNN tn, fp: 28546, 345
+KNN fn, tp: 96, 164
+KNN f1 score: 0.427
+KNN cohens kappa score: 0.420
+
+
+------ Step 1/5: Slice 2/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 114528 synthetic samples
+-> test with 'LR'
+LR tn, fp: 27695, 1196
+LR fn, tp: 14, 246
+LR f1 score: 0.289
+LR cohens kappa score: 0.278
+LR average precision score: 0.883
+
+-> test with 'GB'
+GB tn, fp: 28384, 507
+GB fn, tp: 14, 246
+GB f1 score: 0.486
+GB cohens kappa score: 0.479
+
+-> test with 'KNN'
+KNN tn, fp: 28529, 362
+KNN fn, tp: 81, 179
+KNN f1 score: 0.447
+KNN cohens kappa score: 0.440
+
+
+------ Step 1/5: Slice 3/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 114528 synthetic samples
+-> test with 'LR'
+LR tn, fp: 27630, 1261
+LR fn, tp: 6, 254
+LR f1 score: 0.286
+LR cohens kappa score: 0.275
+LR average precision score: 0.887
+
+-> test with 'GB'
+GB tn, fp: 28342, 549
+GB fn, tp: 11, 249
+GB f1 score: 0.471
+GB cohens kappa score: 0.463
+
+-> test with 'KNN'
+KNN tn, fp: 28493, 398
+KNN fn, tp: 110, 150
+KNN f1 score: 0.371
+KNN cohens kappa score: 0.364
+
+
+------ Step 1/5: Slice 4/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 114528 synthetic samples
+-> test with 'LR'
+LR tn, fp: 27663, 1228
+LR fn, tp: 17, 243
+LR f1 score: 0.281
+LR cohens kappa score: 0.270
+LR average precision score: 0.855
+
+-> test with 'GB'
+GB tn, fp: 28406, 485
+GB fn, tp: 19, 241
+GB f1 score: 0.489
+GB cohens kappa score: 0.482
+
+-> test with 'KNN'
+KNN tn, fp: 28499, 392
+KNN fn, tp: 94, 166
+KNN f1 score: 0.406
+KNN cohens kappa score: 0.399
+
+
+------ Step 1/5: Slice 5/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 114524 synthetic samples
+-> test with 'LR'
+LR tn, fp: 27824, 1067
+LR fn, tp: 22, 234
+LR f1 score: 0.301
+LR cohens kappa score: 0.290
+LR average precision score: 0.812
+
+-> test with 'GB'
+GB tn, fp: 28481, 410
+GB fn, tp: 26, 230
+GB f1 score: 0.513
+GB cohens kappa score: 0.507
+
+-> test with 'KNN'
+KNN tn, fp: 28504, 387
+KNN fn, tp: 113, 143
+KNN f1 score: 0.364
+KNN cohens kappa score: 0.356
+
+
+====== Step 2/5 =======
+-> Shuffling data
+-> Spliting data to slices
+
+------ Step 2/5: Slice 1/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 114528 synthetic samples
+-> test with 'LR'
+LR tn, fp: 27706, 1185
+LR fn, tp: 12, 248
+LR f1 score: 0.293
+LR cohens kappa score: 0.282
+LR average precision score: 0.870
+
+-> test with 'GB'
+GB tn, fp: 28416, 475
+GB fn, tp: 17, 243
+GB f1 score: 0.497
+GB cohens kappa score: 0.490
+
+-> test with 'KNN'
+KNN tn, fp: 28549, 342
+KNN fn, tp: 100, 160
+KNN f1 score: 0.420
+KNN cohens kappa score: 0.413
+
+
+------ Step 2/5: Slice 2/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 114528 synthetic samples
+-> test with 'LR'
+LR tn, fp: 27692, 1199
+LR fn, tp: 10, 250
+LR f1 score: 0.293
+LR cohens kappa score: 0.282
+LR average precision score: 0.892
+
+-> test with 'GB'
+GB tn, fp: 28359, 532
+GB fn, tp: 17, 243
+GB f1 score: 0.470
+GB cohens kappa score: 0.462
+
+-> test with 'KNN'
+KNN tn, fp: 28527, 364
+KNN fn, tp: 93, 167
+KNN f1 score: 0.422
+KNN cohens kappa score: 0.415
+
+
+------ Step 2/5: Slice 3/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 114528 synthetic samples
+-> test with 'LR'
+LR tn, fp: 27688, 1203
+LR fn, tp: 18, 242
+LR f1 score: 0.284
+LR cohens kappa score: 0.273
+LR average precision score: 0.830
+
+-> test with 'GB'
+GB tn, fp: 28423, 468
+GB fn, tp: 19, 241
+GB f1 score: 0.497
+GB cohens kappa score: 0.491
+
+-> test with 'KNN'
+KNN tn, fp: 28497, 394
+KNN fn, tp: 99, 161
+KNN f1 score: 0.395
+KNN cohens kappa score: 0.388
+
+
+------ Step 2/5: Slice 4/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 114528 synthetic samples
+-> test with 'LR'
+LR tn, fp: 27688, 1203
+LR fn, tp: 13, 247
+LR f1 score: 0.289
+LR cohens kappa score: 0.278
+LR average precision score: 0.864
+
+-> test with 'GB'
+GB tn, fp: 28399, 492
+GB fn, tp: 16, 244
+GB f1 score: 0.490
+GB cohens kappa score: 0.483
+
+-> test with 'KNN'
+KNN tn, fp: 28502, 389
+KNN fn, tp: 90, 170
+KNN f1 score: 0.415
+KNN cohens kappa score: 0.408
+
+
+------ Step 2/5: Slice 5/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 114524 synthetic samples
+-> test with 'LR'
+LR tn, fp: 27631, 1260
+LR fn, tp: 13, 243
+LR f1 score: 0.276
+LR cohens kappa score: 0.265
+LR average precision score: 0.840
+
+-> test with 'GB'
+GB tn, fp: 28376, 515
+GB fn, tp: 20, 236
+GB f1 score: 0.469
+GB cohens kappa score: 0.462
+
+-> test with 'KNN'
+KNN tn, fp: 28518, 373
+KNN fn, tp: 97, 159
+KNN f1 score: 0.404
+KNN cohens kappa score: 0.396
+
+
+====== Step 3/5 =======
+-> Shuffling data
+-> Spliting data to slices
+
+------ Step 3/5: Slice 1/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 114528 synthetic samples
+-> test with 'LR'
+LR tn, fp: 27777, 1114
+LR fn, tp: 16, 244
+LR f1 score: 0.302
+LR cohens kappa score: 0.291
+LR average precision score: 0.868
+
+-> test with 'GB'
+GB tn, fp: 28417, 474
+GB fn, tp: 19, 241
+GB f1 score: 0.494
+GB cohens kappa score: 0.488
+
+-> test with 'KNN'
+KNN tn, fp: 28501, 390
+KNN fn, tp: 91, 169
+KNN f1 score: 0.413
+KNN cohens kappa score: 0.405
+
+
+------ Step 3/5: Slice 2/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 114528 synthetic samples
+-> test with 'LR'
+LR tn, fp: 27678, 1213
+LR fn, tp: 13, 247
+LR f1 score: 0.287
+LR cohens kappa score: 0.276
+LR average precision score: 0.865
+
+-> test with 'GB'
+GB tn, fp: 28404, 487
+GB fn, tp: 18, 242
+GB f1 score: 0.489
+GB cohens kappa score: 0.483
+
+-> test with 'KNN'
+KNN tn, fp: 28539, 352
+KNN fn, tp: 108, 152
+KNN f1 score: 0.398
+KNN cohens kappa score: 0.391
+
+
+------ Step 3/5: Slice 3/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 114528 synthetic samples
+-> test with 'LR'
+LR tn, fp: 27722, 1169
+LR fn, tp: 17, 243
+LR f1 score: 0.291
+LR cohens kappa score: 0.280
+LR average precision score: 0.834
+
+-> test with 'GB'
+GB tn, fp: 28409, 482
+GB fn, tp: 21, 239
+GB f1 score: 0.487
+GB cohens kappa score: 0.480
+
+-> test with 'KNN'
+KNN tn, fp: 28523, 368
+KNN fn, tp: 101, 159
+KNN f1 score: 0.404
+KNN cohens kappa score: 0.397
+
+
+------ Step 3/5: Slice 4/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 114528 synthetic samples
+-> test with 'LR'
+LR tn, fp: 27635, 1256
+LR fn, tp: 14, 246
+LR f1 score: 0.279
+LR cohens kappa score: 0.268
+LR average precision score: 0.855
+
+-> test with 'GB'
+GB tn, fp: 28359, 532
+GB fn, tp: 12, 248
+GB f1 score: 0.477
+GB cohens kappa score: 0.470
+
+-> test with 'KNN'
+KNN tn, fp: 28495, 396
+KNN fn, tp: 99, 161
+KNN f1 score: 0.394
+KNN cohens kappa score: 0.387
+
+
+------ Step 3/5: Slice 5/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 114524 synthetic samples
+-> test with 'LR'
+LR tn, fp: 27643, 1248
+LR fn, tp: 11, 245
+LR f1 score: 0.280
+LR cohens kappa score: 0.269
+LR average precision score: 0.882
+
+-> test with 'GB'
+GB tn, fp: 28384, 507
+GB fn, tp: 16, 240
+GB f1 score: 0.479
+GB cohens kappa score: 0.472
+
+-> test with 'KNN'
+KNN tn, fp: 28505, 386
+KNN fn, tp: 91, 165
+KNN f1 score: 0.409
+KNN cohens kappa score: 0.402
+
+
+====== Step 4/5 =======
+-> Shuffling data
+-> Spliting data to slices
+
+------ Step 4/5: Slice 1/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 114528 synthetic samples
+-> test with 'LR'
+LR tn, fp: 27672, 1219
+LR fn, tp: 13, 247
+LR f1 score: 0.286
+LR cohens kappa score: 0.275
+LR average precision score: 0.873
+
+-> test with 'GB'
+GB tn, fp: 28378, 513
+GB fn, tp: 15, 245
+GB f1 score: 0.481
+GB cohens kappa score: 0.474
+
+-> test with 'KNN'
+KNN tn, fp: 28514, 377
+KNN fn, tp: 96, 164
+KNN f1 score: 0.409
+KNN cohens kappa score: 0.402
+
+
+------ Step 4/5: Slice 2/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 114528 synthetic samples
+-> test with 'LR'
+LR tn, fp: 27703, 1188
+LR fn, tp: 16, 244
+LR f1 score: 0.288
+LR cohens kappa score: 0.278
+LR average precision score: 0.835
+
+-> test with 'GB'
+GB tn, fp: 28382, 509
+GB fn, tp: 18, 242
+GB f1 score: 0.479
+GB cohens kappa score: 0.472
+
+-> test with 'KNN'
+KNN tn, fp: 28527, 364
+KNN fn, tp: 105, 155
+KNN f1 score: 0.398
+KNN cohens kappa score: 0.391
+
+
+------ Step 4/5: Slice 3/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 114528 synthetic samples
+-> test with 'LR'
+LR tn, fp: 27707, 1184
+LR fn, tp: 17, 243
+LR f1 score: 0.288
+LR cohens kappa score: 0.277
+LR average precision score: 0.859
+
+-> test with 'GB'
+GB tn, fp: 28415, 476
+GB fn, tp: 20, 240
+GB f1 score: 0.492
+GB cohens kappa score: 0.485
+
+-> test with 'KNN'
+KNN tn, fp: 28501, 390
+KNN fn, tp: 89, 171
+KNN f1 score: 0.417
+KNN cohens kappa score: 0.409
+
+
+------ Step 4/5: Slice 4/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 114528 synthetic samples
+-> test with 'LR'
+LR tn, fp: 27671, 1220
+LR fn, tp: 12, 248
+LR f1 score: 0.287
+LR cohens kappa score: 0.276
+LR average precision score: 0.878
+
+-> test with 'GB'
+GB tn, fp: 28427, 464
+GB fn, tp: 15, 245
+GB f1 score: 0.506
+GB cohens kappa score: 0.499
+
+-> test with 'KNN'
+KNN tn, fp: 28523, 368
+KNN fn, tp: 93, 167
+KNN f1 score: 0.420
+KNN cohens kappa score: 0.413
+
+
+------ Step 4/5: Slice 5/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 114524 synthetic samples
+-> test with 'LR'
+LR tn, fp: 27674, 1217
+LR fn, tp: 16, 240
+LR f1 score: 0.280
+LR cohens kappa score: 0.269
+LR average precision score: 0.841
+
+-> test with 'GB'
+GB tn, fp: 28383, 508
+GB fn, tp: 16, 240
+GB f1 score: 0.478
+GB cohens kappa score: 0.471
+
+-> test with 'KNN'
+KNN tn, fp: 28492, 399
+KNN fn, tp: 89, 167
+KNN f1 score: 0.406
+KNN cohens kappa score: 0.399
+
+
+====== Step 5/5 =======
+-> Shuffling data
+-> Spliting data to slices
+
+------ Step 5/5: Slice 1/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 114528 synthetic samples
+-> test with 'LR'
+LR tn, fp: 27688, 1203
+LR fn, tp: 13, 247
+LR f1 score: 0.289
+LR cohens kappa score: 0.278
+LR average precision score: 0.866
+
+-> test with 'GB'
+GB tn, fp: 28385, 506
+GB fn, tp: 15, 245
+GB f1 score: 0.485
+GB cohens kappa score: 0.478
+
+-> test with 'KNN'
+KNN tn, fp: 28548, 343
+KNN fn, tp: 100, 160
+KNN f1 score: 0.419
+KNN cohens kappa score: 0.412
+
+
+------ Step 5/5: Slice 2/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 114528 synthetic samples
+-> test with 'LR'
+LR tn, fp: 27724, 1167
+LR fn, tp: 15, 245
+LR f1 score: 0.293
+LR cohens kappa score: 0.282
+LR average precision score: 0.870
+
+-> test with 'GB'
+GB tn, fp: 28424, 467
+GB fn, tp: 19, 241
+GB f1 score: 0.498
+GB cohens kappa score: 0.491
+
+-> test with 'KNN'
+KNN tn, fp: 28520, 371
+KNN fn, tp: 100, 160
+KNN f1 score: 0.405
+KNN cohens kappa score: 0.397
+
+
+------ Step 5/5: Slice 3/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 114528 synthetic samples
+-> test with 'LR'
+LR tn, fp: 27654, 1237
+LR fn, tp: 16, 244
+LR f1 score: 0.280
+LR cohens kappa score: 0.269
+LR average precision score: 0.854
+
+-> test with 'GB'
+GB tn, fp: 28444, 447
+GB fn, tp: 18, 242
+GB f1 score: 0.510
+GB cohens kappa score: 0.504
+
+-> test with 'KNN'
+KNN tn, fp: 28515, 376
+KNN fn, tp: 106, 154
+KNN f1 score: 0.390
+KNN cohens kappa score: 0.382
+
+
+------ Step 5/5: Slice 4/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 114528 synthetic samples
+-> test with 'LR'
+LR tn, fp: 27677, 1214
+LR fn, tp: 12, 248
+LR f1 score: 0.288
+LR cohens kappa score: 0.277
+LR average precision score: 0.863
+
+-> test with 'GB'
+GB tn, fp: 28386, 505
+GB fn, tp: 15, 245
+GB f1 score: 0.485
+GB cohens kappa score: 0.478
+
+-> test with 'KNN'
+KNN tn, fp: 28522, 369
+KNN fn, tp: 95, 165
+KNN f1 score: 0.416
+KNN cohens kappa score: 0.409
+
+
+------ Step 5/5: Slice 5/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 114524 synthetic samples
+-> test with 'LR'
+LR tn, fp: 27691, 1200
+LR fn, tp: 13, 243
+LR f1 score: 0.286
+LR cohens kappa score: 0.275
+LR average precision score: 0.851
+
+-> test with 'GB'
+GB tn, fp: 28373, 518
+GB fn, tp: 14, 242
+GB f1 score: 0.476
+GB cohens kappa score: 0.469
+
+-> test with 'KNN'
+KNN tn, fp: 28520, 371
+KNN fn, tp: 91, 165
+KNN f1 score: 0.417
+KNN cohens kappa score: 0.410
+
+### Exercise is done.
+
+-----[ LR ]-----
+maximum:
+LR tn, fp: 27824, 1261
+LR fn, tp: 22, 254
+LR f1 score: 0.302
+LR cohens kappa score: 0.291
+LR average precision score: 0.892
+
+
+average:
+LR tn, fp: 27687.28, 1203.72
+LR fn, tp: 14.24, 244.96
+LR f1 score: 0.287
+LR cohens kappa score: 0.276
+LR average precision score: 0.860
+
+
+minimum:
+LR tn, fp: 27630, 1067
+LR fn, tp: 6, 234
+LR f1 score: 0.276
+LR cohens kappa score: 0.265
+LR average precision score: 0.812
+
+
+-----[ GB ]-----
+maximum:
+GB tn, fp: 28481, 549
+GB fn, tp: 26, 249
+GB f1 score: 0.513
+GB cohens kappa score: 0.507
+
+
+average:
+GB tn, fp: 28397.68, 493.32
+GB fn, tp: 17.16, 242.04
+GB f1 score: 0.487
+GB cohens kappa score: 0.480
+
+
+minimum:
+GB tn, fp: 28342, 410
+GB fn, tp: 11, 230
+GB f1 score: 0.469
+GB cohens kappa score: 0.462
+
+
+-----[ KNN ]-----
+maximum:
+KNN tn, fp: 28549, 399
+KNN fn, tp: 113, 179
+KNN f1 score: 0.447
+KNN cohens kappa score: 0.440
+
+
+average:
+KNN tn, fp: 28516.36, 374.64
+KNN fn, tp: 97.08, 162.12
+KNN f1 score: 0.407
+KNN cohens kappa score: 0.400
+
+
+minimum:
+KNN tn, fp: 28492, 342
+KNN fn, tp: 81, 143
+KNN f1 score: 0.364
+KNN cohens kappa score: 0.356
+

data_result/Repeater/imblearn_webpage.csv

@@ -0,0 +1,92 @@
+LR
+Nr.;TN;TP;FN;FP;f1 score;cohens kappa score;average precision score
+1;6389.000;176.000;21.000;371.000;0.473;0.450;0.761
+2;6449.000;173.000;24.000;311.000;0.508;0.487;0.766
+3;6426.000;184.000;13.000;334.000;0.515;0.494;0.827
+4;6412.000;179.000;18.000;348.000;0.494;0.473;0.753
+5;6472.000;164.000;29.000;287.000;0.509;0.489;0.757
+6;6406.000;182.000;15.000;354.000;0.497;0.475;0.793
+7;6457.000;172.000;25.000;303.000;0.512;0.492;0.792
+8;6439.000;170.000;27.000;321.000;0.494;0.473;0.756
+9;6381.000;176.000;21.000;379.000;0.468;0.445;0.746
+10;6410.000;175.000;18.000;349.000;0.488;0.466;0.801
+11;6383.000;171.000;26.000;377.000;0.459;0.436;0.729
+12;6462.000;176.000;21.000;298.000;0.525;0.505;0.795
+13;6446.000;171.000;26.000;314.000;0.501;0.481;0.723
+14;6382.000;186.000;11.000;378.000;0.489;0.466;0.809
+15;6422.000;172.000;21.000;337.000;0.490;0.469;0.757
+16;6420.000;171.000;26.000;340.000;0.483;0.461;0.751
+17;6452.000;171.000;26.000;308.000;0.506;0.485;0.749
+18;6427.000;182.000;15.000;333.000;0.511;0.490;0.800
+19;6418.000;178.000;19.000;342.000;0.497;0.475;0.740
+20;6392.000;177.000;16.000;367.000;0.480;0.458;0.796
+21;6435.000;175.000;22.000;325.000;0.502;0.481;0.761
+22;6431.000;167.000;30.000;329.000;0.482;0.460;0.728
+23;6360.000;171.000;26.000;400.000;0.445;0.421;0.731
+24;6430.000;180.000;17.000;330.000;0.509;0.488;0.819
+25;6409.000;175.000;18.000;350.000;0.487;0.466;0.764
+max;6472.000;186.000;30.000;400.000;0.525;0.505;0.827
+avg;6420.400;174.960;21.240;339.400;0.493;0.471;0.768
+min;6360.000;164.000;11.000;287.000;0.445;0.421;0.723
+---
+GB
+Nr.;TN;TP;FN;FP;f1 score;cohens kappa score
+1;6418.000;173.000;24.000;342.000;0.486;0.464
+2;6446.000;169.000;28.000;314.000;0.497;0.476
+3;6433.000;175.000;22.000;327.000;0.501;0.480
+4;6433.000;171.000;26.000;327.000;0.492;0.471
+5;6479.000;162.000;31.000;280.000;0.510;0.491
+6;6454.000;169.000;28.000;306.000;0.503;0.482
+7;6455.000;173.000;24.000;305.000;0.513;0.492
+8;6433.000;169.000;28.000;327.000;0.488;0.466
+9;6453.000;169.000;28.000;307.000;0.502;0.481
+10;6420.000;171.000;22.000;339.000;0.486;0.465
+11;6427.000;170.000;27.000;333.000;0.486;0.464
+12;6456.000;170.000;27.000;304.000;0.507;0.486
+13;6450.000;165.000;32.000;310.000;0.491;0.470
+14;6415.000;174.000;23.000;345.000;0.486;0.464
+15;6424.000;170.000;23.000;335.000;0.487;0.466
+16;6421.000;169.000;28.000;339.000;0.479;0.457
+17;6479.000;166.000;31.000;281.000;0.516;0.496
+18;6420.000;174.000;23.000;340.000;0.489;0.468
+19;6453.000;171.000;26.000;307.000;0.507;0.486
+20;6409.000;175.000;18.000;350.000;0.487;0.466
+21;6469.000;170.000;27.000;291.000;0.517;0.497
+22;6413.000;170.000;27.000;347.000;0.476;0.454
+23;6400.000;174.000;23.000;360.000;0.476;0.453
+24;6469.000;173.000;24.000;291.000;0.523;0.504
+25;6476.000;166.000;27.000;283.000;0.517;0.498
+max;6479.000;175.000;32.000;360.000;0.523;0.504
+avg;6440.200;170.320;25.880;319.600;0.497;0.476
+min;6400.000;162.000;18.000;280.000;0.476;0.453
+---
+KNN
+Nr.;TN;TP;FN;FP;f1 score;cohens kappa score
+1;6155.000;181.000;16.000;605.000;0.368;0.338
+2;6175.000;167.000;30.000;585.000;0.352;0.322
+3;6122.000;177.000;20.000;638.000;0.350;0.319
+4;6071.000;173.000;24.000;689.000;0.327;0.294
+5;6046.000;168.000;25.000;713.000;0.313;0.280
+6;6135.000;177.000;20.000;625.000;0.354;0.324
+7;6076.000;179.000;18.000;684.000;0.338;0.306
+8;6141.000;172.000;25.000;619.000;0.348;0.317
+9;6108.000;180.000;17.000;652.000;0.350;0.319
+10;6097.000;165.000;28.000;662.000;0.324;0.292
+11;6158.000;173.000;24.000;602.000;0.356;0.326
+12;6118.000;179.000;18.000;642.000;0.352;0.321
+13;6071.000;163.000;34.000;689.000;0.311;0.278
+14;6045.000;179.000;18.000;715.000;0.328;0.295
+15;6139.000;180.000;13.000;620.000;0.363;0.333
+16;6149.000;167.000;30.000;611.000;0.343;0.311
+17;6086.000;179.000;18.000;674.000;0.341;0.309
+18;6145.000;177.000;20.000;615.000;0.358;0.327
+19;6117.000;176.000;21.000;643.000;0.346;0.315
+20;6104.000;181.000;12.000;655.000;0.352;0.321
+21;6158.000;181.000;16.000;602.000;0.369;0.340
+22;6158.000;167.000;30.000;602.000;0.346;0.315
+23;6074.000;174.000;23.000;686.000;0.329;0.297
+24;6086.000;173.000;24.000;674.000;0.331;0.299
+25;6094.000;170.000;23.000;665.000;0.331;0.299
+max;6175.000;181.000;34.000;715.000;0.369;0.340
+avg;6113.120;174.320;21.880;646.680;0.343;0.312
+min;6045.000;163.000;12.000;585.000;0.311;0.278

data_result/Repeater/imblearn_webpage.log

@@ -0,0 +1,702 @@
+
+
+///////////////////////////////////////////
+// Running Repeater on imblearn_webpage
+///////////////////////////////////////////
+
+Load 'data_input/imblearn_webpage'
+from imblearn
+non empty cut in data_input/imblearn_webpage! (76 points)
+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 26255 synthetic samples
+-> test with 'LR'
+LR tn, fp: 6389, 371
+LR fn, tp: 21, 176
+LR f1 score: 0.473
+LR cohens kappa score: 0.450
+LR average precision score: 0.761
+
+-> test with 'GB'
+GB tn, fp: 6418, 342
+GB fn, tp: 24, 173
+GB f1 score: 0.486
+GB cohens kappa score: 0.464
+
+-> test with 'KNN'
+KNN tn, fp: 6155, 605
+KNN fn, tp: 16, 181
+KNN f1 score: 0.368
+KNN cohens kappa score: 0.338
+
+
+------ Step 1/5: Slice 2/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 26255 synthetic samples
+-> test with 'LR'
+LR tn, fp: 6449, 311
+LR fn, tp: 24, 173
+LR f1 score: 0.508
+LR cohens kappa score: 0.487
+LR average precision score: 0.766
+
+-> test with 'GB'
+GB tn, fp: 6446, 314
+GB fn, tp: 28, 169
+GB f1 score: 0.497
+GB cohens kappa score: 0.476
+
+-> test with 'KNN'
+KNN tn, fp: 6175, 585
+KNN fn, tp: 30, 167
+KNN f1 score: 0.352
+KNN cohens kappa score: 0.322
+
+
+------ Step 1/5: Slice 3/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 26255 synthetic samples
+-> test with 'LR'
+LR tn, fp: 6426, 334
+LR fn, tp: 13, 184
+LR f1 score: 0.515
+LR cohens kappa score: 0.494
+LR average precision score: 0.827
+
+-> test with 'GB'
+GB tn, fp: 6433, 327
+GB fn, tp: 22, 175
+GB f1 score: 0.501
+GB cohens kappa score: 0.480
+
+-> test with 'KNN'
+KNN tn, fp: 6122, 638
+KNN fn, tp: 20, 177
+KNN f1 score: 0.350
+KNN cohens kappa score: 0.319
+
+
+------ Step 1/5: Slice 4/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 26255 synthetic samples
+-> test with 'LR'
+LR tn, fp: 6412, 348
+LR fn, tp: 18, 179
+LR f1 score: 0.494
+LR cohens kappa score: 0.473
+LR average precision score: 0.753
+
+-> test with 'GB'
+GB tn, fp: 6433, 327
+GB fn, tp: 26, 171
+GB f1 score: 0.492
+GB cohens kappa score: 0.471
+
+-> test with 'KNN'
+KNN tn, fp: 6071, 689
+KNN fn, tp: 24, 173
+KNN f1 score: 0.327
+KNN cohens kappa score: 0.294
+
+
+------ Step 1/5: Slice 5/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 26252 synthetic samples
+-> test with 'LR'
+LR tn, fp: 6472, 287
+LR fn, tp: 29, 164
+LR f1 score: 0.509
+LR cohens kappa score: 0.489
+LR average precision score: 0.757
+
+-> test with 'GB'
+GB tn, fp: 6479, 280
+GB fn, tp: 31, 162
+GB f1 score: 0.510
+GB cohens kappa score: 0.491
+
+-> test with 'KNN'
+KNN tn, fp: 6046, 713
+KNN fn, tp: 25, 168
+KNN f1 score: 0.313
+KNN cohens kappa score: 0.280
+
+
+====== Step 2/5 =======
+-> Shuffling data
+-> Spliting data to slices
+
+------ Step 2/5: Slice 1/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 26255 synthetic samples
+-> test with 'LR'
+LR tn, fp: 6406, 354
+LR fn, tp: 15, 182
+LR f1 score: 0.497
+LR cohens kappa score: 0.475
+LR average precision score: 0.793
+
+-> test with 'GB'
+GB tn, fp: 6454, 306
+GB fn, tp: 28, 169
+GB f1 score: 0.503
+GB cohens kappa score: 0.482
+
+-> test with 'KNN'
+KNN tn, fp: 6135, 625
+KNN fn, tp: 20, 177
+KNN f1 score: 0.354
+KNN cohens kappa score: 0.324
+
+
+------ Step 2/5: Slice 2/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 26255 synthetic samples
+-> test with 'LR'
+LR tn, fp: 6457, 303
+LR fn, tp: 25, 172
+LR f1 score: 0.512
+LR cohens kappa score: 0.492
+LR average precision score: 0.792
+
+-> test with 'GB'
+GB tn, fp: 6455, 305
+GB fn, tp: 24, 173
+GB f1 score: 0.513
+GB cohens kappa score: 0.492
+
+-> test with 'KNN'
+KNN tn, fp: 6076, 684
+KNN fn, tp: 18, 179
+KNN f1 score: 0.338
+KNN cohens kappa score: 0.306
+
+
+------ Step 2/5: Slice 3/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 26255 synthetic samples
+-> test with 'LR'
+LR tn, fp: 6439, 321
+LR fn, tp: 27, 170
+LR f1 score: 0.494
+LR cohens kappa score: 0.473
+LR average precision score: 0.756
+
+-> test with 'GB'
+GB tn, fp: 6433, 327
+GB fn, tp: 28, 169
+GB f1 score: 0.488
+GB cohens kappa score: 0.466
+
+-> test with 'KNN'
+KNN tn, fp: 6141, 619
+KNN fn, tp: 25, 172
+KNN f1 score: 0.348
+KNN cohens kappa score: 0.317
+
+
+------ Step 2/5: Slice 4/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 26255 synthetic samples
+-> test with 'LR'
+LR tn, fp: 6381, 379
+LR fn, tp: 21, 176
+LR f1 score: 0.468
+LR cohens kappa score: 0.445
+LR average precision score: 0.746
+
+-> test with 'GB'
+GB tn, fp: 6453, 307
+GB fn, tp: 28, 169
+GB f1 score: 0.502
+GB cohens kappa score: 0.481
+
+-> test with 'KNN'
+KNN tn, fp: 6108, 652
+KNN fn, tp: 17, 180
+KNN f1 score: 0.350
+KNN cohens kappa score: 0.319
+
+
+------ Step 2/5: Slice 5/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 26252 synthetic samples
+-> test with 'LR'
+LR tn, fp: 6410, 349
+LR fn, tp: 18, 175
+LR f1 score: 0.488
+LR cohens kappa score: 0.466
+LR average precision score: 0.801
+
+-> test with 'GB'
+GB tn, fp: 6420, 339
+GB fn, tp: 22, 171
+GB f1 score: 0.486
+GB cohens kappa score: 0.465
+
+-> test with 'KNN'
+KNN tn, fp: 6097, 662
+KNN fn, tp: 28, 165
+KNN f1 score: 0.324
+KNN cohens kappa score: 0.292
+
+
+====== Step 3/5 =======
+-> Shuffling data
+-> Spliting data to slices
+
+------ Step 3/5: Slice 1/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 26255 synthetic samples
+-> test with 'LR'
+LR tn, fp: 6383, 377
+LR fn, tp: 26, 171
+LR f1 score: 0.459
+LR cohens kappa score: 0.436
+LR average precision score: 0.729
+
+-> test with 'GB'
+GB tn, fp: 6427, 333
+GB fn, tp: 27, 170
+GB f1 score: 0.486
+GB cohens kappa score: 0.464
+
+-> test with 'KNN'
+KNN tn, fp: 6158, 602
+KNN fn, tp: 24, 173
+KNN f1 score: 0.356
+KNN cohens kappa score: 0.326
+
+
+------ Step 3/5: Slice 2/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 26255 synthetic samples
+-> test with 'LR'
+LR tn, fp: 6462, 298
+LR fn, tp: 21, 176
+LR f1 score: 0.525
+LR cohens kappa score: 0.505
+LR average precision score: 0.795
+
+-> test with 'GB'
+GB tn, fp: 6456, 304
+GB fn, tp: 27, 170
+GB f1 score: 0.507
+GB cohens kappa score: 0.486
+
+-> test with 'KNN'
+KNN tn, fp: 6118, 642
+KNN fn, tp: 18, 179
+KNN f1 score: 0.352
+KNN cohens kappa score: 0.321
+
+
+------ Step 3/5: Slice 3/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 26255 synthetic samples
+-> test with 'LR'
+LR tn, fp: 6446, 314
+LR fn, tp: 26, 171
+LR f1 score: 0.501
+LR cohens kappa score: 0.481
+LR average precision score: 0.723
+
+-> test with 'GB'
+GB tn, fp: 6450, 310
+GB fn, tp: 32, 165
+GB f1 score: 0.491
+GB cohens kappa score: 0.470
+
+-> test with 'KNN'
+KNN tn, fp: 6071, 689
+KNN fn, tp: 34, 163
+KNN f1 score: 0.311
+KNN cohens kappa score: 0.278
+
+
+------ Step 3/5: Slice 4/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 26255 synthetic samples
+-> test with 'LR'
+LR tn, fp: 6382, 378
+LR fn, tp: 11, 186
+LR f1 score: 0.489
+LR cohens kappa score: 0.466
+LR average precision score: 0.809
+
+-> test with 'GB'
+GB tn, fp: 6415, 345
+GB fn, tp: 23, 174
+GB f1 score: 0.486
+GB cohens kappa score: 0.464
+
+-> test with 'KNN'
+KNN tn, fp: 6045, 715
+KNN fn, tp: 18, 179
+KNN f1 score: 0.328
+KNN cohens kappa score: 0.295
+
+
+------ Step 3/5: Slice 5/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 26252 synthetic samples
+-> test with 'LR'
+LR tn, fp: 6422, 337
+LR fn, tp: 21, 172
+LR f1 score: 0.490
+LR cohens kappa score: 0.469
+LR average precision score: 0.757
+
+-> test with 'GB'
+GB tn, fp: 6424, 335
+GB fn, tp: 23, 170
+GB f1 score: 0.487
+GB cohens kappa score: 0.466
+
+-> test with 'KNN'
+KNN tn, fp: 6139, 620
+KNN fn, tp: 13, 180
+KNN f1 score: 0.363
+KNN cohens kappa score: 0.333
+
+
+====== Step 4/5 =======
+-> Shuffling data
+-> Spliting data to slices
+
+------ Step 4/5: Slice 1/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 26255 synthetic samples
+-> test with 'LR'
+LR tn, fp: 6420, 340
+LR fn, tp: 26, 171
+LR f1 score: 0.483
+LR cohens kappa score: 0.461
+LR average precision score: 0.751
+
+-> test with 'GB'
+GB tn, fp: 6421, 339
+GB fn, tp: 28, 169
+GB f1 score: 0.479
+GB cohens kappa score: 0.457
+
+-> test with 'KNN'
+KNN tn, fp: 6149, 611
+KNN fn, tp: 30, 167
+KNN f1 score: 0.343
+KNN cohens kappa score: 0.311
+
+
+------ Step 4/5: Slice 2/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 26255 synthetic samples
+-> test with 'LR'
+LR tn, fp: 6452, 308
+LR fn, tp: 26, 171
+LR f1 score: 0.506
+LR cohens kappa score: 0.485
+LR average precision score: 0.749
+
+-> test with 'GB'
+GB tn, fp: 6479, 281
+GB fn, tp: 31, 166
+GB f1 score: 0.516
+GB cohens kappa score: 0.496
+
+-> test with 'KNN'
+KNN tn, fp: 6086, 674
+KNN fn, tp: 18, 179
+KNN f1 score: 0.341
+KNN cohens kappa score: 0.309
+
+
+------ Step 4/5: Slice 3/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 26255 synthetic samples
+-> test with 'LR'
+LR tn, fp: 6427, 333
+LR fn, tp: 15, 182
+LR f1 score: 0.511
+LR cohens kappa score: 0.490
+LR average precision score: 0.800
+
+-> test with 'GB'
+GB tn, fp: 6420, 340
+GB fn, tp: 23, 174
+GB f1 score: 0.489
+GB cohens kappa score: 0.468
+
+-> test with 'KNN'
+KNN tn, fp: 6145, 615
+KNN fn, tp: 20, 177
+KNN f1 score: 0.358
+KNN cohens kappa score: 0.327
+
+
+------ Step 4/5: Slice 4/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 26255 synthetic samples
+-> test with 'LR'
+LR tn, fp: 6418, 342
+LR fn, tp: 19, 178
+LR f1 score: 0.497
+LR cohens kappa score: 0.475
+LR average precision score: 0.740
+
+-> test with 'GB'
+GB tn, fp: 6453, 307
+GB fn, tp: 26, 171
+GB f1 score: 0.507
+GB cohens kappa score: 0.486
+
+-> test with 'KNN'
+KNN tn, fp: 6117, 643
+KNN fn, tp: 21, 176
+KNN f1 score: 0.346
+KNN cohens kappa score: 0.315
+
+
+------ Step 4/5: Slice 5/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 26252 synthetic samples
+-> test with 'LR'
+LR tn, fp: 6392, 367
+LR fn, tp: 16, 177
+LR f1 score: 0.480
+LR cohens kappa score: 0.458
+LR average precision score: 0.796
+
+-> test with 'GB'
+GB tn, fp: 6409, 350
+GB fn, tp: 18, 175
+GB f1 score: 0.487
+GB cohens kappa score: 0.466
+
+-> test with 'KNN'
+KNN tn, fp: 6104, 655
+KNN fn, tp: 12, 181
+KNN f1 score: 0.352
+KNN cohens kappa score: 0.321
+
+
+====== Step 5/5 =======
+-> Shuffling data
+-> Spliting data to slices
+
+------ Step 5/5: Slice 1/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 26255 synthetic samples
+-> test with 'LR'
+LR tn, fp: 6435, 325
+LR fn, tp: 22, 175
+LR f1 score: 0.502
+LR cohens kappa score: 0.481
+LR average precision score: 0.761
+
+-> test with 'GB'
+GB tn, fp: 6469, 291
+GB fn, tp: 27, 170
+GB f1 score: 0.517
+GB cohens kappa score: 0.497
+
+-> test with 'KNN'
+KNN tn, fp: 6158, 602
+KNN fn, tp: 16, 181
+KNN f1 score: 0.369
+KNN cohens kappa score: 0.340
+
+
+------ Step 5/5: Slice 2/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 26255 synthetic samples
+-> test with 'LR'
+LR tn, fp: 6431, 329
+LR fn, tp: 30, 167
+LR f1 score: 0.482
+LR cohens kappa score: 0.460
+LR average precision score: 0.728
+
+-> test with 'GB'
+GB tn, fp: 6413, 347
+GB fn, tp: 27, 170
+GB f1 score: 0.476
+GB cohens kappa score: 0.454
+
+-> test with 'KNN'
+KNN tn, fp: 6158, 602
+KNN fn, tp: 30, 167
+KNN f1 score: 0.346
+KNN cohens kappa score: 0.315
+
+
+------ Step 5/5: Slice 3/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 26255 synthetic samples
+-> test with 'LR'
+LR tn, fp: 6360, 400
+LR fn, tp: 26, 171
+LR f1 score: 0.445
+LR cohens kappa score: 0.421
+LR average precision score: 0.731
+
+-> test with 'GB'
+GB tn, fp: 6400, 360
+GB fn, tp: 23, 174
+GB f1 score: 0.476
+GB cohens kappa score: 0.453
+
+-> test with 'KNN'
+KNN tn, fp: 6074, 686
+KNN fn, tp: 23, 174
+KNN f1 score: 0.329
+KNN cohens kappa score: 0.297
+
+
+------ Step 5/5: Slice 4/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 26255 synthetic samples
+-> test with 'LR'
+LR tn, fp: 6430, 330
+LR fn, tp: 17, 180
+LR f1 score: 0.509
+LR cohens kappa score: 0.488
+LR average precision score: 0.819
+
+-> test with 'GB'
+GB tn, fp: 6469, 291
+GB fn, tp: 24, 173
+GB f1 score: 0.523
+GB cohens kappa score: 0.504
+
+-> test with 'KNN'
+KNN tn, fp: 6086, 674
+KNN fn, tp: 24, 173
+KNN f1 score: 0.331
+KNN cohens kappa score: 0.299
+
+
+------ Step 5/5: Slice 5/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 26252 synthetic samples
+-> test with 'LR'
+LR tn, fp: 6409, 350
+LR fn, tp: 18, 175
+LR f1 score: 0.487
+LR cohens kappa score: 0.466
+LR average precision score: 0.764
+
+-> test with 'GB'
+GB tn, fp: 6476, 283
+GB fn, tp: 27, 166
+GB f1 score: 0.517
+GB cohens kappa score: 0.498
+
+-> test with 'KNN'
+KNN tn, fp: 6094, 665
+KNN fn, tp: 23, 170
+KNN f1 score: 0.331
+KNN cohens kappa score: 0.299
+
+### Exercise is done.
+
+-----[ LR ]-----
+maximum:
+LR tn, fp: 6472, 400
+LR fn, tp: 30, 186
+LR f1 score: 0.525
+LR cohens kappa score: 0.505
+LR average precision score: 0.827
+
+
+average:
+LR tn, fp: 6420.4, 339.4
+LR fn, tp: 21.24, 174.96
+LR f1 score: 0.493
+LR cohens kappa score: 0.471
+LR average precision score: 0.768
+
+
+minimum:
+LR tn, fp: 6360, 287
+LR fn, tp: 11, 164
+LR f1 score: 0.445
+LR cohens kappa score: 0.421
+LR average precision score: 0.723
+
+
+-----[ GB ]-----
+maximum:
+GB tn, fp: 6479, 360
+GB fn, tp: 32, 175
+GB f1 score: 0.523
+GB cohens kappa score: 0.504
+
+
+average:
+GB tn, fp: 6440.2, 319.6
+GB fn, tp: 25.88, 170.32
+GB f1 score: 0.497
+GB cohens kappa score: 0.476
+
+
+minimum:
+GB tn, fp: 6400, 280
+GB fn, tp: 18, 162
+GB f1 score: 0.476
+GB cohens kappa score: 0.453
+
+
+-----[ KNN ]-----
+maximum:
+KNN tn, fp: 6175, 715
+KNN fn, tp: 34, 181
+KNN f1 score: 0.369
+KNN cohens kappa score: 0.340
+
+
+average:
+KNN tn, fp: 6113.12, 646.68
+KNN fn, tp: 21.88, 174.32
+KNN f1 score: 0.343
+KNN cohens kappa score: 0.312
+
+
+minimum:
+KNN tn, fp: 6045, 585
+KNN fn, tp: 12, 163
+KNN f1 score: 0.311
+KNN cohens kappa score: 0.278
+