Added Benchmark for CTAB-GAN.

data_result/CTAB-GAN/folding_abalone9-18.csv

@@ -0,0 +1,92 @@
+LR
+Nr.;TN;TP;FN;FP;f1 score;cohens kappa score;average precision score
+1;124.000;7.000;2.000;14.000;0.467;0.417;0.695
+2;132.000;5.000;4.000;6.000;0.500;0.464;0.521
+3;127.000;7.000;2.000;11.000;0.519;0.476;0.704
+4;134.000;6.000;3.000;4.000;0.632;0.606;0.653
+5;121.000;4.000;2.000;16.000;0.308;0.260;0.514
+6;133.000;7.000;2.000;5.000;0.667;0.642;0.760
+7;130.000;6.000;3.000;8.000;0.522;0.483;0.630
+8;122.000;6.000;3.000;16.000;0.387;0.329;0.522
+9;118.000;8.000;1.000;20.000;0.432;0.374;0.730
+10;128.000;4.000;2.000;9.000;0.421;0.386;0.717
+11;129.000;5.000;4.000;9.000;0.435;0.389;0.492
+12;119.000;8.000;1.000;19.000;0.444;0.388;0.734
+13;132.000;5.000;4.000;6.000;0.500;0.464;0.619
+14;123.000;7.000;2.000;15.000;0.452;0.399;0.696
+15;119.000;5.000;1.000;18.000;0.345;0.298;0.730
+16;125.000;6.000;3.000;13.000;0.429;0.377;0.617
+17;114.000;6.000;3.000;24.000;0.308;0.236;0.647
+18;133.000;6.000;3.000;5.000;0.600;0.571;0.741
+19;129.000;6.000;3.000;9.000;0.500;0.459;0.620
+20;131.000;5.000;1.000;6.000;0.588;0.565;0.595
+21;114.000;7.000;2.000;24.000;0.350;0.282;0.516
+22;127.000;7.000;2.000;11.000;0.519;0.476;0.727
+23;132.000;4.000;5.000;6.000;0.421;0.381;0.557
+24;129.000;7.000;2.000;9.000;0.560;0.523;0.750
+25;130.000;4.000;2.000;7.000;0.471;0.440;0.722
+max;134.000;8.000;5.000;24.000;0.667;0.642;0.760
+avg;126.200;5.920;2.480;11.600;0.471;0.427;0.648
+min;114.000;4.000;1.000;4.000;0.308;0.236;0.492
+---
+GB
+Nr.;TN;TP;FN;FP;f1 score;cohens kappa score
+1;133.000;5.000;4.000;5.000;0.526;0.494
+2;137.000;2.000;7.000;1.000;0.333;0.312
+3;137.000;3.000;6.000;1.000;0.462;0.440
+4;137.000;3.000;6.000;1.000;0.462;0.440
+5;133.000;1.000;5.000;4.000;0.182;0.149
+6;134.000;1.000;8.000;4.000;0.143;0.104
+7;133.000;3.000;6.000;5.000;0.353;0.313
+8;130.000;2.000;7.000;8.000;0.211;0.156
+9;134.000;4.000;5.000;4.000;0.471;0.438
+10;135.000;3.000;3.000;2.000;0.545;0.527
+11;134.000;1.000;8.000;4.000;0.143;0.104
+12;137.000;1.000;8.000;1.000;0.182;0.163
+13;134.000;2.000;7.000;4.000;0.267;0.229
+14;135.000;4.000;5.000;3.000;0.500;0.472
+15;133.000;3.000;3.000;4.000;0.462;0.436
+16;135.000;3.000;6.000;3.000;0.400;0.369
+17;132.000;5.000;4.000;6.000;0.500;0.464
+18;133.000;3.000;6.000;5.000;0.353;0.313
+19;133.000;3.000;6.000;5.000;0.353;0.313
+20;134.000;1.000;5.000;3.000;0.200;0.172
+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;136.000;3.000;6.000;2.000;0.429;0.402
+24;135.000;5.000;4.000;3.000;0.588;0.563
+25;136.000;3.000;3.000;1.000;0.600;0.586
+max;137.000;5.000;8.000;8.000;0.600;0.586
+avg;134.240;2.760;5.640;3.560;0.371;0.340
+min;130.000;1.000;3.000;1.000;0.143;0.104
+---
+KNN
+Nr.;TN;TP;FN;FP;f1 score;cohens kappa score
+1;136.000;1.000;8.000;2.000;0.167;0.140
+2;136.000;1.000;8.000;2.000;0.167;0.140
+3;136.000;0.000;9.000;2.000;0.000;-0.023
+4;136.000;0.000;9.000;2.000;0.000;-0.023
+5;137.000;0.000;6.000;0.000;0.000;0.000
+6;137.000;1.000;8.000;1.000;0.182;0.163
+7;138.000;2.000;7.000;0.000;0.364;0.349
+8;133.000;2.000;7.000;5.000;0.250;0.208
+9;138.000;0.000;9.000;0.000;0.000;0.000
+10;137.000;0.000;6.000;0.000;0.000;0.000
+11;138.000;0.000;9.000;0.000;0.000;0.000
+12;136.000;3.000;6.000;2.000;0.429;0.402
+13;138.000;2.000;7.000;0.000;0.364;0.349
+14;135.000;1.000;8.000;3.000;0.154;0.121
+15;136.000;0.000;6.000;1.000;0.000;-0.012
+16;138.000;0.000;9.000;0.000;0.000;0.000
+17;138.000;0.000;9.000;0.000;0.000;0.000
+18;138.000;0.000;9.000;0.000;0.000;0.000
+19;136.000;1.000;8.000;2.000;0.167;0.140
+20;137.000;1.000;5.000;0.000;0.286;0.277
+21;138.000;1.000;8.000;0.000;0.200;0.190
+22;138.000;0.000;9.000;0.000;0.000;0.000
+23;138.000;1.000;8.000;0.000;0.200;0.190
+24;138.000;2.000;7.000;0.000;0.364;0.349
+25;137.000;0.000;6.000;0.000;0.000;0.000
+max;138.000;3.000;9.000;5.000;0.429;0.402
+avg;136.920;0.760;7.640;0.880;0.132;0.118
+min;133.000;0.000;5.000;0.000;0.000;-0.023

data_result/CTAB-GAN/folding_abalone9-18.log

@@ -7,11 +7,720 @@
 Load 'data_input/folding_abalone9-18'
 from pickle file
 Data loaded.
-Traceback (most recent call last):
-  File "/benchmark/data/run_all_exercises.py", line 13, in <module>
-    runExercise(dataset, None, name, f)
-  File "/benchmark/data/library/analysis.py", line 164, in runExercise
-    gan = ganCreator(data)
-  File "/benchmark/data/library/analysis.py", line 268, in <lambda>
-    , ("CTAB-GAN",      lambda _data: CtabGan())
-NameError: name 'CtabGan' is not defined
+-> 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
+
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100%|██████████| 10/10 [00:13<00:00,  1.00s/it]
100%|██████████| 10/10 [00:13<00:00,  1.36s/it]
+-> create 518 synthetic samples
+-> test with 'LR'
+LR tn, fp: 124, 14
+LR fn, tp: 2, 7
+LR f1 score: 0.467
+LR cohens kappa score: 0.417
+LR average precision score: 0.695
+
+-> test with 'GB'
+GB tn, fp: 133, 5
+GB fn, tp: 4, 5
+GB f1 score: 0.526
+GB cohens kappa score: 0.494
+
+-> test with 'KNN'
+KNN tn, fp: 136, 2
+KNN fn, tp: 8, 1
+KNN f1 score: 0.167
+KNN cohens kappa score: 0.140
+
+
+------ Step 1/5: Slice 2/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+
  0%|          | 0/10 [00:00<?, ?it/s]
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100%|██████████| 10/10 [00:07<00:00,  1.32it/s]
+-> create 518 synthetic samples
+-> test with 'LR'
+LR tn, fp: 132, 6
+LR fn, tp: 4, 5
+LR f1 score: 0.500
+LR cohens kappa score: 0.464
+LR average precision score: 0.521
+
+-> test with 'GB'
+GB tn, fp: 137, 1
+GB fn, tp: 7, 2
+GB f1 score: 0.333
+GB cohens kappa score: 0.312
+
+-> test with 'KNN'
+KNN tn, fp: 136, 2
+KNN fn, tp: 8, 1
+KNN f1 score: 0.167
+KNN cohens kappa score: 0.140
+
+
+------ Step 1/5: Slice 3/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+
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+-> create 518 synthetic samples
+-> test with 'LR'
+LR tn, fp: 127, 11
+LR fn, tp: 2, 7
+LR f1 score: 0.519
+LR cohens kappa score: 0.476
+LR average precision score: 0.704
+
+-> test with 'GB'
+GB tn, fp: 137, 1
+GB fn, tp: 6, 3
+GB f1 score: 0.462
+GB cohens kappa score: 0.440
+
+-> test with 'KNN'
+KNN tn, fp: 136, 2
+KNN fn, tp: 9, 0
+KNN f1 score: 0.000
+KNN cohens kappa score: -0.023
+
+
+------ Step 1/5: Slice 4/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+
  0%|          | 0/10 [00:00<?, ?it/s]
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+-> create 518 synthetic samples
+-> test with 'LR'
+LR tn, fp: 134, 4
+LR fn, tp: 3, 6
+LR f1 score: 0.632
+LR cohens kappa score: 0.606
+LR average precision score: 0.653
+
+-> test with 'GB'
+GB tn, fp: 137, 1
+GB fn, tp: 6, 3
+GB f1 score: 0.462
+GB cohens kappa score: 0.440
+
+-> test with 'KNN'
+KNN tn, fp: 136, 2
+KNN fn, tp: 9, 0
+KNN f1 score: 0.000
+KNN cohens kappa score: -0.023
+
+
+------ Step 1/5: Slice 5/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+
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+-> create 516 synthetic samples
+-> test with 'LR'
+LR tn, fp: 121, 16
+LR fn, tp: 2, 4
+LR f1 score: 0.308
+LR cohens kappa score: 0.260
+LR average precision score: 0.514
+
+-> test with 'GB'
+GB tn, fp: 133, 4
+GB fn, tp: 5, 1
+GB f1 score: 0.182
+GB cohens kappa score: 0.149
+
+-> test with 'KNN'
+KNN tn, fp: 137, 0
+KNN fn, tp: 6, 0
+KNN f1 score: 0.000
+KNN cohens kappa score: 0.000
+
+
+====== Step 2/5 =======
+-> Shuffling data
+-> Spliting data to slices
+
+------ Step 2/5: Slice 1/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+
  0%|          | 0/10 [00:00<?, ?it/s]
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100%|██████████| 10/10 [00:08<00:00,  1.17it/s]
+-> create 518 synthetic samples
+-> test with 'LR'
+LR tn, fp: 133, 5
+LR fn, tp: 2, 7
+LR f1 score: 0.667
+LR cohens kappa score: 0.642
+LR average precision score: 0.760
+
+-> test with 'GB'
+GB tn, fp: 134, 4
+GB fn, tp: 8, 1
+GB f1 score: 0.143
+GB cohens kappa score: 0.104
+
+-> test with 'KNN'
+KNN tn, fp: 137, 1
+KNN fn, tp: 8, 1
+KNN f1 score: 0.182
+KNN cohens kappa score: 0.163
+
+
+------ Step 2/5: Slice 2/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+
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100%|██████████| 10/10 [00:10<00:00,  1.01s/it]
+-> create 518 synthetic samples
+-> test with 'LR'
+LR tn, fp: 130, 8
+LR fn, tp: 3, 6
+LR f1 score: 0.522
+LR cohens kappa score: 0.483
+LR average precision score: 0.630
+
+-> test with 'GB'
+GB tn, fp: 133, 5
+GB fn, tp: 6, 3
+GB f1 score: 0.353
+GB cohens kappa score: 0.313
+
+-> test with 'KNN'
+KNN tn, fp: 138, 0
+KNN fn, tp: 7, 2
+KNN f1 score: 0.364
+KNN cohens kappa score: 0.349
+
+
+------ Step 2/5: Slice 3/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+
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+-> create 518 synthetic samples
+-> test with 'LR'
+LR tn, fp: 122, 16
+LR fn, tp: 3, 6
+LR f1 score: 0.387
+LR cohens kappa score: 0.329
+LR average precision score: 0.522
+
+-> test with 'GB'
+GB tn, fp: 130, 8
+GB fn, tp: 7, 2
+GB f1 score: 0.211
+GB cohens kappa score: 0.156
+
+-> test with 'KNN'
+KNN tn, fp: 133, 5
+KNN fn, tp: 7, 2
+KNN f1 score: 0.250
+KNN cohens kappa score: 0.208
+
+
+------ Step 2/5: Slice 4/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+
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+-> create 518 synthetic samples
+-> test with 'LR'
+LR tn, fp: 118, 20
+LR fn, tp: 1, 8
+LR f1 score: 0.432
+LR cohens kappa score: 0.374
+LR average precision score: 0.730
+
+-> test with 'GB'
+GB tn, fp: 134, 4
+GB fn, tp: 5, 4
+GB f1 score: 0.471
+GB cohens kappa score: 0.438
+
+-> test with 'KNN'
+KNN tn, fp: 138, 0
+KNN fn, tp: 9, 0
+KNN f1 score: 0.000
+KNN cohens kappa score: 0.000
+
+
+------ Step 2/5: Slice 5/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+
  0%|          | 0/10 [00:00<?, ?it/s]
 10%|█         | 1/10 [00:00<00:08,  1.05it/s]
 20%|██        | 2/10 [00:01<00:06,  1.30it/s]
 30%|███       | 3/10 [00:02<00:06,  1.16it/s]
 40%|████      | 4/10 [00:03<00:05,  1.18it/s]
 50%|█████     | 5/10 [00:04<00:04,  1.18it/s]
 60%|██████    | 6/10 [00:05<00:03,  1.21it/s]
 70%|███████   | 7/10 [00:05<00:02,  1.23it/s]
 80%|████████  | 8/10 [00:06<00:01,  1.23it/s]
 90%|█████████ | 9/10 [00:07<00:00,  1.23it/s]
100%|██████████| 10/10 [00:08<00:00,  1.24it/s]
100%|██████████| 10/10 [00:08<00:00,  1.21it/s]
+-> create 516 synthetic samples
+-> 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: 0.717
+
+-> test with 'GB'
+GB tn, fp: 135, 2
+GB fn, tp: 3, 3
+GB f1 score: 0.545
+GB cohens kappa score: 0.527
+
+-> test with 'KNN'
+KNN tn, fp: 137, 0
+KNN fn, tp: 6, 0
+KNN f1 score: 0.000
+KNN cohens kappa score: 0.000
+
+
+====== Step 3/5 =======
+-> Shuffling data
+-> Spliting data to slices
+
+------ Step 3/5: Slice 1/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+
  0%|          | 0/10 [00:00<?, ?it/s]
 10%|█         | 1/10 [00:00<00:07,  1.29it/s]
 20%|██        | 2/10 [00:01<00:05,  1.42it/s]
 30%|███       | 3/10 [00:02<00:04,  1.41it/s]
 40%|████      | 4/10 [00:02<00:04,  1.31it/s]
 50%|█████     | 5/10 [00:03<00:04,  1.23it/s]
 60%|██████    | 6/10 [00:04<00:03,  1.22it/s]
 70%|███████   | 7/10 [00:05<00:02,  1.24it/s]
 80%|████████  | 8/10 [00:06<00:01,  1.12it/s]
 90%|█████████ | 9/10 [00:07<00:00,  1.10it/s]
100%|██████████| 10/10 [00:08<00:00,  1.14it/s]
100%|██████████| 10/10 [00:08<00:00,  1.20it/s]
+-> create 518 synthetic samples
+-> test with 'LR'
+LR tn, fp: 129, 9
+LR fn, tp: 4, 5
+LR f1 score: 0.435
+LR cohens kappa score: 0.389
+LR average precision score: 0.492
+
+-> test with 'GB'
+GB tn, fp: 134, 4
+GB fn, tp: 8, 1
+GB f1 score: 0.143
+GB cohens kappa score: 0.104
+
+-> test with 'KNN'
+KNN tn, fp: 138, 0
+KNN fn, tp: 9, 0
+KNN f1 score: 0.000
+KNN cohens kappa score: 0.000
+
+
+------ Step 3/5: Slice 2/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+
  0%|          | 0/10 [00:00<?, ?it/s]
 10%|█         | 1/10 [00:00<00:07,  1.13it/s]
 20%|██        | 2/10 [00:01<00:07,  1.07it/s]
 30%|███       | 3/10 [00:02<00:06,  1.08it/s]
 40%|████      | 4/10 [00:03<00:05,  1.04it/s]
 50%|█████     | 5/10 [00:04<00:04,  1.11it/s]
 60%|██████    | 6/10 [00:05<00:03,  1.16it/s]
 70%|███████   | 7/10 [00:06<00:02,  1.11it/s]
 80%|████████  | 8/10 [00:07<00:01,  1.17it/s]
 90%|█████████ | 9/10 [00:07<00:00,  1.15it/s]
100%|██████████| 10/10 [00:08<00:00,  1.23it/s]
100%|██████████| 10/10 [00:08<00:00,  1.15it/s]
+-> create 518 synthetic samples
+-> test with 'LR'
+LR tn, fp: 119, 19
+LR fn, tp: 1, 8
+LR f1 score: 0.444
+LR cohens kappa score: 0.388
+LR average precision score: 0.734
+
+-> test with 'GB'
+GB tn, fp: 137, 1
+GB fn, tp: 8, 1
+GB f1 score: 0.182
+GB cohens kappa score: 0.163
+
+-> test with 'KNN'
+KNN tn, fp: 136, 2
+KNN fn, tp: 6, 3
+KNN f1 score: 0.429
+KNN cohens kappa score: 0.402
+
+
+------ Step 3/5: Slice 3/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+
  0%|          | 0/10 [00:00<?, ?it/s]
 10%|█         | 1/10 [00:00<00:07,  1.15it/s]
 20%|██        | 2/10 [00:01<00:05,  1.38it/s]
 30%|███       | 3/10 [00:02<00:05,  1.40it/s]
 40%|████      | 4/10 [00:03<00:05,  1.17it/s]
 50%|█████     | 5/10 [00:04<00:04,  1.21it/s]
 60%|██████    | 6/10 [00:04<00:03,  1.17it/s]
 70%|███████   | 7/10 [00:05<00:02,  1.19it/s]
 80%|████████  | 8/10 [00:06<00:01,  1.20it/s]
 90%|█████████ | 9/10 [00:07<00:00,  1.28it/s]
100%|██████████| 10/10 [00:08<00:00,  1.26it/s]
100%|██████████| 10/10 [00:08<00:00,  1.24it/s]
+-> create 518 synthetic samples
+-> test with 'LR'
+LR tn, fp: 132, 6
+LR fn, tp: 4, 5
+LR f1 score: 0.500
+LR cohens kappa score: 0.464
+LR average precision score: 0.619
+
+-> test with 'GB'
+GB tn, fp: 134, 4
+GB fn, tp: 7, 2
+GB f1 score: 0.267
+GB cohens kappa score: 0.229
+
+-> test with 'KNN'
+KNN tn, fp: 138, 0
+KNN fn, tp: 7, 2
+KNN f1 score: 0.364
+KNN cohens kappa score: 0.349
+
+
+------ Step 3/5: Slice 4/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+
  0%|          | 0/10 [00:00<?, ?it/s]
 10%|█         | 1/10 [00:00<00:08,  1.09it/s]
 20%|██        | 2/10 [00:01<00:06,  1.21it/s]
 30%|███       | 3/10 [00:02<00:06,  1.16it/s]
 40%|████      | 4/10 [00:03<00:05,  1.14it/s]
 50%|█████     | 5/10 [00:04<00:04,  1.16it/s]
 60%|██████    | 6/10 [00:05<00:03,  1.18it/s]
 70%|███████   | 7/10 [00:05<00:02,  1.18it/s]
 80%|████████  | 8/10 [00:06<00:01,  1.17it/s]
 90%|█████████ | 9/10 [00:07<00:00,  1.17it/s]
100%|██████████| 10/10 [00:08<00:00,  1.19it/s]
100%|██████████| 10/10 [00:08<00:00,  1.17it/s]
+-> create 518 synthetic samples
+-> 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: 0.696
+
+-> 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: 135, 3
+KNN fn, tp: 8, 1
+KNN f1 score: 0.154
+KNN cohens kappa score: 0.121
+
+
+------ Step 3/5: Slice 5/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+
  0%|          | 0/10 [00:00<?, ?it/s]
 10%|█         | 1/10 [00:01<00:09,  1.01s/it]
 20%|██        | 2/10 [00:01<00:06,  1.18it/s]
 30%|███       | 3/10 [00:02<00:05,  1.26it/s]
 40%|████      | 4/10 [00:03<00:04,  1.25it/s]
 50%|█████     | 5/10 [00:04<00:04,  1.16it/s]
 60%|██████    | 6/10 [00:05<00:03,  1.21it/s]
 70%|███████   | 7/10 [00:05<00:02,  1.21it/s]
 80%|████████  | 8/10 [00:06<00:01,  1.17it/s]
 90%|█████████ | 9/10 [00:07<00:00,  1.14it/s]
100%|██████████| 10/10 [00:08<00:00,  1.10it/s]
100%|██████████| 10/10 [00:08<00:00,  1.15it/s]
+-> create 516 synthetic samples
+-> test with 'LR'
+LR tn, fp: 119, 18
+LR fn, tp: 1, 5
+LR f1 score: 0.345
+LR cohens kappa score: 0.298
+LR average precision score: 0.730
+
+-> 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: 136, 1
+KNN fn, tp: 6, 0
+KNN f1 score: 0.000
+KNN cohens kappa score: -0.012
+
+
+====== Step 4/5 =======
+-> Shuffling data
+-> Spliting data to slices
+
+------ Step 4/5: Slice 1/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+
  0%|          | 0/10 [00:00<?, ?it/s]
 10%|█         | 1/10 [00:00<00:08,  1.02it/s]
 20%|██        | 2/10 [00:01<00:07,  1.11it/s]
 30%|███       | 3/10 [00:02<00:05,  1.19it/s]
 40%|████      | 4/10 [00:03<00:04,  1.21it/s]
 50%|█████     | 5/10 [00:04<00:04,  1.19it/s]
 60%|██████    | 6/10 [00:05<00:03,  1.12it/s]
 70%|███████   | 7/10 [00:06<00:02,  1.15it/s]
 80%|████████  | 8/10 [00:06<00:01,  1.20it/s]
 90%|█████████ | 9/10 [00:07<00:00,  1.19it/s]
100%|██████████| 10/10 [00:08<00:00,  1.27it/s]
100%|██████████| 10/10 [00:08<00:00,  1.20it/s]
+-> create 518 synthetic samples
+-> test with 'LR'
+LR tn, fp: 125, 13
+LR fn, tp: 3, 6
+LR f1 score: 0.429
+LR cohens kappa score: 0.377
+LR average precision score: 0.617
+
+-> test with 'GB'
+GB tn, fp: 135, 3
+GB fn, tp: 6, 3
+GB f1 score: 0.400
+GB cohens kappa score: 0.369
+
+-> test with 'KNN'
+KNN tn, fp: 138, 0
+KNN fn, tp: 9, 0
+KNN f1 score: 0.000
+KNN cohens kappa score: 0.000
+
+
+------ Step 4/5: Slice 2/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+
  0%|          | 0/10 [00:00<?, ?it/s]
 10%|█         | 1/10 [00:01<00:09,  1.08s/it]
 20%|██        | 2/10 [00:01<00:07,  1.09it/s]
 30%|███       | 3/10 [00:02<00:06,  1.13it/s]
 40%|████      | 4/10 [00:03<00:05,  1.12it/s]
 50%|█████     | 5/10 [00:04<00:04,  1.07it/s]
 60%|██████    | 6/10 [00:05<00:03,  1.07it/s]
 70%|███████   | 7/10 [00:06<00:02,  1.15it/s]
 80%|████████  | 8/10 [00:07<00:01,  1.12it/s]
 90%|█████████ | 9/10 [00:08<00:00,  1.14it/s]
100%|██████████| 10/10 [00:09<00:00,  1.12it/s]
100%|██████████| 10/10 [00:09<00:00,  1.11it/s]
+-> create 518 synthetic samples
+-> test with 'LR'
+LR tn, fp: 114, 24
+LR fn, tp: 3, 6
+LR f1 score: 0.308
+LR cohens kappa score: 0.236
+LR average precision score: 0.647
+
+-> test with 'GB'
+GB tn, fp: 132, 6
+GB fn, tp: 4, 5
+GB f1 score: 0.500
+GB cohens kappa score: 0.464
+
+-> test with 'KNN'
+KNN tn, fp: 138, 0
+KNN fn, tp: 9, 0
+KNN f1 score: 0.000
+KNN cohens kappa score: 0.000
+
+
+------ Step 4/5: Slice 3/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+
  0%|          | 0/10 [00:00<?, ?it/s]
 10%|█         | 1/10 [00:00<00:06,  1.47it/s]
 20%|██        | 2/10 [00:01<00:05,  1.46it/s]
 30%|███       | 3/10 [00:02<00:05,  1.25it/s]
 40%|████      | 4/10 [00:03<00:04,  1.28it/s]
 50%|█████     | 5/10 [00:03<00:03,  1.28it/s]
 60%|██████    | 6/10 [00:04<00:03,  1.29it/s]
 70%|███████   | 7/10 [00:05<00:02,  1.26it/s]
 80%|████████  | 8/10 [00:06<00:01,  1.24it/s]
 90%|█████████ | 9/10 [00:06<00:00,  1.28it/s]
100%|██████████| 10/10 [00:07<00:00,  1.27it/s]
100%|██████████| 10/10 [00:07<00:00,  1.28it/s]
+-> create 518 synthetic samples
+-> test with 'LR'
+LR tn, fp: 133, 5
+LR fn, tp: 3, 6
+LR f1 score: 0.600
+LR cohens kappa score: 0.571
+LR average precision score: 0.741
+
+-> test with 'GB'
+GB tn, fp: 133, 5
+GB fn, tp: 6, 3
+GB f1 score: 0.353
+GB cohens kappa score: 0.313
+
+-> test with 'KNN'
+KNN tn, fp: 138, 0
+KNN fn, tp: 9, 0
+KNN f1 score: 0.000
+KNN cohens kappa score: 0.000
+
+
+------ Step 4/5: Slice 4/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+
  0%|          | 0/10 [00:00<?, ?it/s]
 10%|█         | 1/10 [00:00<00:07,  1.20it/s]
 20%|██        | 2/10 [00:01<00:06,  1.25it/s]
 30%|███       | 3/10 [00:02<00:05,  1.27it/s]
 40%|████      | 4/10 [00:03<00:04,  1.32it/s]
 50%|█████     | 5/10 [00:03<00:03,  1.27it/s]
 60%|██████    | 6/10 [00:04<00:03,  1.31it/s]
 70%|███████   | 7/10 [00:05<00:02,  1.36it/s]
 80%|████████  | 8/10 [00:06<00:01,  1.31it/s]
 90%|█████████ | 9/10 [00:07<00:00,  1.22it/s]
100%|██████████| 10/10 [00:07<00:00,  1.24it/s]
100%|██████████| 10/10 [00:07<00:00,  1.27it/s]
+-> create 518 synthetic samples
+-> test with 'LR'
+LR tn, fp: 129, 9
+LR fn, tp: 3, 6
+LR f1 score: 0.500
+LR cohens kappa score: 0.459
+LR average precision score: 0.620
+
+-> test with 'GB'
+GB tn, fp: 133, 5
+GB fn, tp: 6, 3
+GB f1 score: 0.353
+GB cohens kappa score: 0.313
+
+-> test with 'KNN'
+KNN tn, fp: 136, 2
+KNN fn, tp: 8, 1
+KNN f1 score: 0.167
+KNN cohens kappa score: 0.140
+
+
+------ Step 4/5: Slice 5/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+
  0%|          | 0/10 [00:00<?, ?it/s]
 10%|█         | 1/10 [00:00<00:07,  1.16it/s]
 20%|██        | 2/10 [00:01<00:05,  1.40it/s]
 30%|███       | 3/10 [00:02<00:04,  1.41it/s]
 40%|████      | 4/10 [00:02<00:04,  1.39it/s]
 50%|█████     | 5/10 [00:03<00:03,  1.42it/s]
 60%|██████    | 6/10 [00:04<00:03,  1.33it/s]
 70%|███████   | 7/10 [00:05<00:02,  1.39it/s]
 80%|████████  | 8/10 [00:05<00:01,  1.34it/s]
 90%|█████████ | 9/10 [00:06<00:00,  1.44it/s]
100%|██████████| 10/10 [00:07<00:00,  1.41it/s]
100%|██████████| 10/10 [00:07<00:00,  1.39it/s]
+-> create 516 synthetic samples
+-> test with 'LR'
+LR tn, fp: 131, 6
+LR fn, tp: 1, 5
+LR f1 score: 0.588
+LR cohens kappa score: 0.565
+LR average precision score: 0.595
+
+-> test with 'GB'
+GB tn, fp: 134, 3
+GB fn, tp: 5, 1
+GB f1 score: 0.200
+GB cohens kappa score: 0.172
+
+-> test with 'KNN'
+KNN tn, fp: 137, 0
+KNN fn, tp: 5, 1
+KNN f1 score: 0.286
+KNN cohens kappa score: 0.277
+
+
+====== Step 5/5 =======
+-> Shuffling data
+-> Spliting data to slices
+
+------ Step 5/5: Slice 1/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+
  0%|          | 0/10 [00:00<?, ?it/s]
 10%|█         | 1/10 [00:00<00:08,  1.10it/s]
 20%|██        | 2/10 [00:01<00:06,  1.17it/s]
 30%|███       | 3/10 [00:02<00:05,  1.19it/s]
 40%|████      | 4/10 [00:03<00:05,  1.12it/s]
 50%|█████     | 5/10 [00:04<00:04,  1.15it/s]
 60%|██████    | 6/10 [00:05<00:03,  1.16it/s]
 70%|███████   | 7/10 [00:06<00:02,  1.14it/s]
 80%|████████  | 8/10 [00:06<00:01,  1.14it/s]
 90%|█████████ | 9/10 [00:07<00:00,  1.16it/s]
100%|██████████| 10/10 [00:08<00:00,  1.18it/s]
100%|██████████| 10/10 [00:08<00:00,  1.16it/s]
+-> create 518 synthetic samples
+-> test with 'LR'
+LR tn, fp: 114, 24
+LR fn, tp: 2, 7
+LR f1 score: 0.350
+LR cohens kappa score: 0.282
+LR average precision score: 0.516
+
+-> 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: 138, 0
+KNN fn, tp: 8, 1
+KNN f1 score: 0.200
+KNN cohens kappa score: 0.190
+
+
+------ Step 5/5: Slice 2/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+
  0%|          | 0/10 [00:00<?, ?it/s]
 10%|█         | 1/10 [00:01<00:09,  1.08s/it]
 20%|██        | 2/10 [00:01<00:07,  1.08it/s]
 30%|███       | 3/10 [00:02<00:06,  1.10it/s]
 40%|████      | 4/10 [00:03<00:05,  1.09it/s]
 50%|█████     | 5/10 [00:04<00:04,  1.06it/s]
 60%|██████    | 6/10 [00:05<00:03,  1.07it/s]
 70%|███████   | 7/10 [00:06<00:02,  1.04it/s]
 80%|████████  | 8/10 [00:07<00:01,  1.06it/s]
 90%|█████████ | 9/10 [00:08<00:00,  1.04it/s]
100%|██████████| 10/10 [00:09<00:00,  1.06it/s]
100%|██████████| 10/10 [00:09<00:00,  1.06it/s]
+-> create 518 synthetic samples
+-> test with 'LR'
+LR tn, fp: 127, 11
+LR fn, tp: 2, 7
+LR f1 score: 0.519
+LR cohens kappa score: 0.476
+LR average precision score: 0.727
+
+-> 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: 138, 0
+KNN fn, tp: 9, 0
+KNN f1 score: 0.000
+KNN cohens kappa score: 0.000
+
+
+------ Step 5/5: Slice 3/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+
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 70%|███████   | 7/10 [00:05<00:02,  1.19it/s]
 80%|████████  | 8/10 [00:06<00:01,  1.15it/s]
 90%|█████████ | 9/10 [00:07<00:00,  1.16it/s]
100%|██████████| 10/10 [00:08<00:00,  1.10it/s]
100%|██████████| 10/10 [00:08<00:00,  1.14it/s]
+-> create 518 synthetic samples
+-> test with 'LR'
+LR tn, fp: 132, 6
+LR fn, tp: 5, 4
+LR f1 score: 0.421
+LR cohens kappa score: 0.381
+LR average precision score: 0.557
+
+-> test with 'GB'
+GB tn, fp: 136, 2
+GB fn, tp: 6, 3
+GB f1 score: 0.429
+GB cohens kappa score: 0.402
+
+-> test with 'KNN'
+KNN tn, fp: 138, 0
+KNN fn, tp: 8, 1
+KNN f1 score: 0.200
+KNN cohens kappa score: 0.190
+
+
+------ Step 5/5: Slice 4/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+
  0%|          | 0/10 [00:00<?, ?it/s]
 10%|█         | 1/10 [00:00<00:07,  1.13it/s]
 20%|██        | 2/10 [00:01<00:06,  1.26it/s]
 30%|███       | 3/10 [00:02<00:05,  1.18it/s]
 40%|████      | 4/10 [00:03<00:04,  1.24it/s]
 50%|█████     | 5/10 [00:03<00:03,  1.30it/s]
 60%|██████    | 6/10 [00:04<00:03,  1.31it/s]
 70%|███████   | 7/10 [00:05<00:02,  1.36it/s]
 80%|████████  | 8/10 [00:06<00:01,  1.42it/s]
 90%|█████████ | 9/10 [00:06<00:00,  1.43it/s]
100%|██████████| 10/10 [00:07<00:00,  1.41it/s]
100%|██████████| 10/10 [00:07<00:00,  1.34it/s]
+-> create 518 synthetic samples
+-> 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: 0.750
+
+-> 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: 138, 0
+KNN fn, tp: 7, 2
+KNN f1 score: 0.364
+KNN cohens kappa score: 0.349
+
+
+------ Step 5/5: Slice 5/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+
  0%|          | 0/10 [00:00<?, ?it/s]
 10%|█         | 1/10 [00:01<00:10,  1.11s/it]
 20%|██        | 2/10 [00:01<00:07,  1.11it/s]
 30%|███       | 3/10 [00:02<00:06,  1.15it/s]
 40%|████      | 4/10 [00:03<00:05,  1.14it/s]
 50%|█████     | 5/10 [00:04<00:04,  1.14it/s]
 60%|██████    | 6/10 [00:05<00:03,  1.22it/s]
 70%|███████   | 7/10 [00:05<00:02,  1.25it/s]
 80%|████████  | 8/10 [00:06<00:01,  1.29it/s]
 90%|█████████ | 9/10 [00:07<00:00,  1.28it/s]
100%|██████████| 10/10 [00:08<00:00,  1.29it/s]
100%|██████████| 10/10 [00:08<00:00,  1.22it/s]
+-> create 516 synthetic samples
+-> test with 'LR'
+LR tn, fp: 130, 7
+LR fn, tp: 2, 4
+LR f1 score: 0.471
+LR cohens kappa score: 0.440
+LR average precision score: 0.722
+
+-> test with 'GB'
+GB tn, fp: 136, 1
+GB fn, tp: 3, 3
+GB f1 score: 0.600
+GB cohens kappa score: 0.586
+
+-> test with 'KNN'
+KNN tn, fp: 137, 0
+KNN fn, tp: 6, 0
+KNN f1 score: 0.000
+KNN cohens kappa score: 0.000
+
+### Exercise is done.
+
+-----[ LR ]-----
+maximum:
+LR tn, fp: 134, 24
+LR fn, tp: 5, 8
+LR f1 score: 0.667
+LR cohens kappa score: 0.642
+LR average precision score: 0.760
+
+
+average:
+LR tn, fp: 126.2, 11.6
+LR fn, tp: 2.48, 5.92
+LR f1 score: 0.471
+LR cohens kappa score: 0.427
+LR average precision score: 0.648
+
+
+minimum:
+LR tn, fp: 114, 4
+LR fn, tp: 1, 4
+LR f1 score: 0.308
+LR cohens kappa score: 0.236
+LR average precision score: 0.492
+
+
+-----[ GB ]-----
+maximum:
+GB tn, fp: 137, 8
+GB fn, tp: 8, 5
+GB f1 score: 0.600
+GB cohens kappa score: 0.586
+
+
+average:
+GB tn, fp: 134.24, 3.56
+GB fn, tp: 5.64, 2.76
+GB f1 score: 0.371
+GB cohens kappa score: 0.340
+
+
+minimum:
+GB tn, fp: 130, 1
+GB fn, tp: 3, 1
+GB f1 score: 0.143
+GB cohens kappa score: 0.104
+
+
+-----[ KNN ]-----
+maximum:
+KNN tn, fp: 138, 5
+KNN fn, tp: 9, 3
+KNN f1 score: 0.429
+KNN cohens kappa score: 0.402
+
+
+average:
+KNN tn, fp: 136.92, 0.88
+KNN fn, tp: 7.64, 0.76
+KNN f1 score: 0.132
+KNN cohens kappa score: 0.118
+
+
+minimum:
+KNN tn, fp: 133, 0
+KNN fn, tp: 5, 0
+KNN f1 score: 0.000
+KNN cohens kappa score: -0.023
+

data_result/CTAB-GAN/folding_abalone_17_vs_7_8_9_10.csv

@@ -0,0 +1,92 @@
+LR
+Nr.;TN;TP;FN;FP;f1 score;cohens kappa score;average precision score
+1;402.000;10.000;2.000;54.000;0.263;0.230;0.419
+2;391.000;7.000;5.000;65.000;0.167;0.128;0.436
+3;403.000;5.000;7.000;53.000;0.143;0.105;0.123
+4;374.000;10.000;2.000;82.000;0.192;0.154;0.226
+5;398.000;6.000;4.000;58.000;0.162;0.130;0.103
+6;368.000;10.000;2.000;88.000;0.182;0.143;0.282
+7;414.000;5.000;7.000;42.000;0.169;0.134;0.205
+8;404.000;7.000;5.000;52.000;0.197;0.161;0.206
+9;356.000;9.000;3.000;100.000;0.149;0.108;0.330
+10;413.000;5.000;5.000;43.000;0.172;0.142;0.298
+11;438.000;8.000;4.000;18.000;0.421;0.400;0.467
+12;413.000;5.000;7.000;43.000;0.167;0.131;0.179
+13;380.000;7.000;5.000;76.000;0.147;0.107;0.238
+14;358.000;7.000;5.000;98.000;0.120;0.077;0.115
+15;396.000;8.000;2.000;60.000;0.205;0.174;0.399
+16;371.000;11.000;1.000;85.000;0.204;0.166;0.387
+17;345.000;10.000;2.000;111.000;0.150;0.109;0.270
+18;395.000;6.000;6.000;61.000;0.152;0.113;0.103
+19;395.000;10.000;2.000;61.000;0.241;0.206;0.277
+20;413.000;5.000;5.000;43.000;0.172;0.142;0.199
+21;414.000;6.000;6.000;42.000;0.200;0.166;0.197
+22;388.000;5.000;7.000;68.000;0.118;0.077;0.102
+23;430.000;7.000;5.000;26.000;0.311;0.284;0.323
+24;382.000;8.000;4.000;74.000;0.170;0.131;0.405
+25;376.000;7.000;3.000;80.000;0.144;0.110;0.148
+max;438.000;11.000;7.000;111.000;0.421;0.400;0.467
+avg;392.680;7.360;4.240;63.320;0.189;0.153;0.258
+min;345.000;5.000;1.000;18.000;0.118;0.077;0.102
+---
+GB
+Nr.;TN;TP;FN;FP;f1 score;cohens kappa score
+1;451.000;3.000;9.000;5.000;0.300;0.285
+2;448.000;2.000;10.000;8.000;0.182;0.162
+3;450.000;2.000;10.000;6.000;0.200;0.183
+4;452.000;2.000;10.000;4.000;0.222;0.209
+5;453.000;0.000;10.000;3.000;0.000;-0.010
+6;453.000;2.000;10.000;3.000;0.235;0.224
+7;454.000;3.000;9.000;2.000;0.353;0.343
+8;456.000;2.000;10.000;0.000;0.286;0.280
+9;454.000;1.000;11.000;2.000;0.133;0.124
+10;451.000;1.000;9.000;5.000;0.125;0.111
+11;454.000;4.000;8.000;2.000;0.444;0.435
+12;452.000;1.000;11.000;4.000;0.118;0.104
+13;454.000;2.000;10.000;2.000;0.250;0.240
+14;454.000;0.000;12.000;2.000;0.000;-0.007
+15;451.000;2.000;8.000;5.000;0.235;0.222
+16;452.000;3.000;9.000;4.000;0.316;0.303
+17;456.000;2.000;10.000;0.000;0.286;0.280
+18;454.000;1.000;11.000;2.000;0.133;0.124
+19;454.000;3.000;9.000;2.000;0.353;0.343
+20;453.000;3.000;7.000;3.000;0.375;0.365
+21;450.000;2.000;10.000;6.000;0.200;0.183
+22;455.000;1.000;11.000;1.000;0.143;0.137
+23;452.000;1.000;11.000;4.000;0.118;0.104
+24;448.000;1.000;11.000;8.000;0.095;0.075
+25;455.000;1.000;9.000;1.000;0.167;0.161
+max;456.000;4.000;12.000;8.000;0.444;0.435
+avg;452.640;1.800;9.800;3.360;0.211;0.199
+min;448.000;0.000;7.000;0.000;0.000;-0.010
+---
+KNN
+Nr.;TN;TP;FN;FP;f1 score;cohens kappa score
+1;454.000;0.000;12.000;2.000;0.000;-0.007
+2;455.000;2.000;10.000;1.000;0.267;0.259
+3;453.000;0.000;12.000;3.000;0.000;-0.010
+4;455.000;2.000;10.000;1.000;0.267;0.259
+5;453.000;2.000;8.000;3.000;0.267;0.256
+6;449.000;2.000;10.000;7.000;0.190;0.172
+7;456.000;1.000;11.000;0.000;0.154;0.150
+8;454.000;2.000;10.000;2.000;0.250;0.240
+9;456.000;0.000;12.000;0.000;0.000;0.000
+10;456.000;0.000;10.000;0.000;0.000;0.000
+11;455.000;0.000;12.000;1.000;0.000;-0.004
+12;455.000;3.000;9.000;1.000;0.375;0.367
+13;454.000;2.000;10.000;2.000;0.250;0.240
+14;454.000;0.000;12.000;2.000;0.000;-0.007
+15;456.000;0.000;10.000;0.000;0.000;0.000
+16;456.000;1.000;11.000;0.000;0.154;0.150
+17;455.000;1.000;11.000;1.000;0.143;0.137
+18;455.000;0.000;12.000;1.000;0.000;-0.004
+19;456.000;0.000;12.000;0.000;0.000;0.000
+20;451.000;0.000;10.000;5.000;0.000;-0.015
+21;456.000;0.000;12.000;0.000;0.000;0.000
+22;455.000;1.000;11.000;1.000;0.143;0.137
+23;456.000;0.000;12.000;0.000;0.000;0.000
+24;456.000;0.000;12.000;0.000;0.000;0.000
+25;455.000;2.000;8.000;1.000;0.308;0.301
+max;456.000;3.000;12.000;7.000;0.375;0.367
+avg;454.640;0.840;10.760;1.360;0.111;0.105
+min;449.000;0.000;8.000;0.000;0.000;-0.015

data_result/CTAB-GAN/folding_abalone_17_vs_7_8_9_10.log

@@ -7,11 +7,720 @@
 Load 'data_input/folding_abalone_17_vs_7_8_9_10'
 from pickle file
 Data loaded.
-Traceback (most recent call last):
-  File "/benchmark/data/run_all_exercises.py", line 13, in <module>
-    runExercise(dataset, None, name, f)
-  File "/benchmark/data/library/analysis.py", line 164, in runExercise
-    gan = ganCreator(data)
-  File "/benchmark/data/library/analysis.py", line 268, in <lambda>
-    , ("CTAB-GAN",      lambda _data: CtabGan())
-NameError: name 'CtabGan' is not defined
+-> 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
+
  0%|          | 0/10 [00:00<?, ?it/s]
 10%|█         | 1/10 [00:00<00:03,  2.32it/s]
 20%|██        | 2/10 [00:00<00:03,  2.41it/s]
 30%|███       | 3/10 [00:01<00:05,  1.40it/s]
 40%|████      | 4/10 [00:03<00:05,  1.16it/s]
 50%|█████     | 5/10 [00:03<00:04,  1.14it/s]
 60%|██████    | 6/10 [00:05<00:04,  1.05s/it]
 70%|███████   | 7/10 [00:08<00:05,  1.71s/it]
 80%|████████  | 8/10 [00:09<00:02,  1.44s/it]
 90%|█████████ | 9/10 [00:10<00:01,  1.25s/it]
100%|██████████| 10/10 [00:10<00:00,  1.09s/it]
100%|██████████| 10/10 [00:10<00:00,  1.08s/it]
+-> create 1778 synthetic samples
+-> test with 'LR'
+LR tn, fp: 402, 54
+LR fn, tp: 2, 10
+LR f1 score: 0.263
+LR cohens kappa score: 0.230
+LR average precision score: 0.419
+
+-> test with 'GB'
+GB tn, fp: 451, 5
+GB fn, tp: 9, 3
+GB f1 score: 0.300
+GB cohens kappa score: 0.285
+
+-> test with 'KNN'
+KNN tn, fp: 454, 2
+KNN fn, tp: 12, 0
+KNN f1 score: 0.000
+KNN cohens kappa score: -0.007
+
+
+------ Step 1/5: Slice 2/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+
  0%|          | 0/10 [00:00<?, ?it/s]
 10%|█         | 1/10 [00:00<00:07,  1.23it/s]
 20%|██        | 2/10 [00:01<00:06,  1.16it/s]
 30%|███       | 3/10 [00:02<00:05,  1.20it/s]
 40%|████      | 4/10 [00:03<00:04,  1.25it/s]
 50%|█████     | 5/10 [00:03<00:03,  1.31it/s]
 60%|██████    | 6/10 [00:04<00:03,  1.30it/s]
 70%|███████   | 7/10 [00:05<00:02,  1.37it/s]
 80%|████████  | 8/10 [00:06<00:01,  1.37it/s]
 90%|█████████ | 9/10 [00:06<00:00,  1.30it/s]
100%|██████████| 10/10 [00:07<00:00,  1.24it/s]
100%|██████████| 10/10 [00:07<00:00,  1.27it/s]
+-> create 1778 synthetic samples
+-> test with 'LR'
+LR tn, fp: 391, 65
+LR fn, tp: 5, 7
+LR f1 score: 0.167
+LR cohens kappa score: 0.128
+LR average precision score: 0.436
+
+-> test with 'GB'
+GB tn, fp: 448, 8
+GB fn, tp: 10, 2
+GB f1 score: 0.182
+GB cohens kappa score: 0.162
+
+-> test with 'KNN'
+KNN tn, fp: 455, 1
+KNN fn, tp: 10, 2
+KNN f1 score: 0.267
+KNN cohens kappa score: 0.259
+
+
+------ Step 1/5: Slice 3/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+
  0%|          | 0/10 [00:00<?, ?it/s]
 10%|█         | 1/10 [00:00<00:07,  1.24it/s]
 20%|██        | 2/10 [00:01<00:06,  1.27it/s]
 30%|███       | 3/10 [00:02<00:05,  1.25it/s]
 40%|████      | 4/10 [00:03<00:04,  1.30it/s]
 50%|█████     | 5/10 [00:03<00:03,  1.28it/s]
 60%|██████    | 6/10 [00:04<00:03,  1.27it/s]
 70%|███████   | 7/10 [00:05<00:02,  1.26it/s]
 80%|████████  | 8/10 [00:06<00:01,  1.28it/s]
 90%|█████████ | 9/10 [00:07<00:00,  1.25it/s]
100%|██████████| 10/10 [00:08<00:00,  1.05it/s]
100%|██████████| 10/10 [00:08<00:00,  1.19it/s]
+-> create 1778 synthetic samples
+-> test with 'LR'
+LR tn, fp: 403, 53
+LR fn, tp: 7, 5
+LR f1 score: 0.143
+LR cohens kappa score: 0.105
+LR average precision score: 0.123
+
+-> test with 'GB'
+GB tn, fp: 450, 6
+GB fn, tp: 10, 2
+GB f1 score: 0.200
+GB cohens kappa score: 0.183
+
+-> test with 'KNN'
+KNN tn, fp: 453, 3
+KNN fn, tp: 12, 0
+KNN f1 score: 0.000
+KNN cohens kappa score: -0.010
+
+
+------ Step 1/5: Slice 4/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+
  0%|          | 0/10 [00:00<?, ?it/s]
 10%|█         | 1/10 [00:00<00:06,  1.47it/s]
 20%|██        | 2/10 [00:01<00:05,  1.38it/s]
 30%|███       | 3/10 [00:02<00:05,  1.39it/s]
 40%|████      | 4/10 [00:03<00:05,  1.13it/s]
 50%|█████     | 5/10 [00:04<00:04,  1.08it/s]
 60%|██████    | 6/10 [00:05<00:03,  1.09it/s]
 70%|███████   | 7/10 [00:06<00:02,  1.13it/s]
 80%|████████  | 8/10 [00:06<00:01,  1.17it/s]
 90%|█████████ | 9/10 [00:07<00:00,  1.16it/s]
100%|██████████| 10/10 [00:08<00:00,  1.24it/s]
100%|██████████| 10/10 [00:08<00:00,  1.20it/s]
+-> create 1778 synthetic samples
+-> test with 'LR'
+LR tn, fp: 374, 82
+LR fn, tp: 2, 10
+LR f1 score: 0.192
+LR cohens kappa score: 0.154
+LR average precision score: 0.226
+
+-> test with 'GB'
+GB tn, fp: 452, 4
+GB fn, tp: 10, 2
+GB f1 score: 0.222
+GB cohens kappa score: 0.209
+
+-> test with 'KNN'
+KNN tn, fp: 455, 1
+KNN fn, tp: 10, 2
+KNN f1 score: 0.267
+KNN cohens kappa score: 0.259
+
+
+------ Step 1/5: Slice 5/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+
  0%|          | 0/10 [00:00<?, ?it/s]
 10%|█         | 1/10 [00:00<00:06,  1.49it/s]
 20%|██        | 2/10 [00:01<00:05,  1.43it/s]
 30%|███       | 3/10 [00:02<00:05,  1.26it/s]
 40%|████      | 4/10 [00:03<00:04,  1.24it/s]
 50%|█████     | 5/10 [00:04<00:04,  1.14it/s]
 60%|██████    | 6/10 [00:05<00:03,  1.13it/s]
 70%|███████   | 7/10 [00:06<00:02,  1.05it/s]
 80%|████████  | 8/10 [00:07<00:01,  1.07it/s]
 90%|█████████ | 9/10 [00:07<00:00,  1.13it/s]
100%|██████████| 10/10 [00:08<00:00,  1.16it/s]
100%|██████████| 10/10 [00:08<00:00,  1.16it/s]
+-> create 1776 synthetic samples
+-> test with 'LR'
+LR tn, fp: 398, 58
+LR fn, tp: 4, 6
+LR f1 score: 0.162
+LR cohens kappa score: 0.130
+LR average precision score: 0.103
+
+-> test with 'GB'
+GB tn, fp: 453, 3
+GB fn, tp: 10, 0
+GB f1 score: 0.000
+GB cohens kappa score: -0.010
+
+-> test with 'KNN'
+KNN tn, fp: 453, 3
+KNN fn, tp: 8, 2
+KNN f1 score: 0.267
+KNN cohens kappa score: 0.256
+
+
+====== Step 2/5 =======
+-> Shuffling data
+-> Spliting data to slices
+
+------ Step 2/5: Slice 1/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+
  0%|          | 0/10 [00:00<?, ?it/s]
 10%|█         | 1/10 [00:01<00:10,  1.15s/it]
 20%|██        | 2/10 [00:01<00:07,  1.13it/s]
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 40%|████      | 4/10 [00:03<00:04,  1.22it/s]
 50%|█████     | 5/10 [00:04<00:04,  1.22it/s]
 60%|██████    | 6/10 [00:04<00:03,  1.23it/s]
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 80%|████████  | 8/10 [00:07<00:01,  1.05it/s]
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100%|██████████| 10/10 [00:08<00:00,  1.09it/s]
100%|██████████| 10/10 [00:08<00:00,  1.12it/s]
+-> create 1778 synthetic samples
+-> test with 'LR'
+LR tn, fp: 368, 88
+LR fn, tp: 2, 10
+LR f1 score: 0.182
+LR cohens kappa score: 0.143
+LR average precision score: 0.282
+
+-> test with 'GB'
+GB tn, fp: 453, 3
+GB fn, tp: 10, 2
+GB f1 score: 0.235
+GB cohens kappa score: 0.224
+
+-> test with 'KNN'
+KNN tn, fp: 449, 7
+KNN fn, tp: 10, 2
+KNN f1 score: 0.190
+KNN cohens kappa score: 0.172
+
+
+------ Step 2/5: Slice 2/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+
  0%|          | 0/10 [00:00<?, ?it/s]
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 20%|██        | 2/10 [00:01<00:07,  1.05it/s]
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 50%|█████     | 5/10 [00:04<00:04,  1.15it/s]
 60%|██████    | 6/10 [00:05<00:03,  1.21it/s]
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100%|██████████| 10/10 [00:08<00:00,  1.23it/s]
100%|██████████| 10/10 [00:08<00:00,  1.18it/s]
+-> create 1778 synthetic samples
+-> test with 'LR'
+LR tn, fp: 414, 42
+LR fn, tp: 7, 5
+LR f1 score: 0.169
+LR cohens kappa score: 0.134
+LR average precision score: 0.205
+
+-> test with 'GB'
+GB tn, fp: 454, 2
+GB fn, tp: 9, 3
+GB f1 score: 0.353
+GB cohens kappa score: 0.343
+
+-> test with 'KNN'
+KNN tn, fp: 456, 0
+KNN fn, tp: 11, 1
+KNN f1 score: 0.154
+KNN cohens kappa score: 0.150
+
+
+------ Step 2/5: Slice 3/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+
  0%|          | 0/10 [00:00<?, ?it/s]
 10%|█         | 1/10 [00:00<00:07,  1.19it/s]
 20%|██        | 2/10 [00:01<00:06,  1.24it/s]
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 40%|████      | 4/10 [00:03<00:04,  1.28it/s]
 50%|█████     | 5/10 [00:04<00:04,  1.22it/s]
 60%|██████    | 6/10 [00:04<00:03,  1.29it/s]
 70%|███████   | 7/10 [00:05<00:02,  1.32it/s]
 80%|████████  | 8/10 [00:06<00:01,  1.33it/s]
 90%|█████████ | 9/10 [00:06<00:00,  1.34it/s]
100%|██████████| 10/10 [00:07<00:00,  1.34it/s]
100%|██████████| 10/10 [00:07<00:00,  1.30it/s]
+-> create 1778 synthetic samples
+-> test with 'LR'
+LR tn, fp: 404, 52
+LR fn, tp: 5, 7
+LR f1 score: 0.197
+LR cohens kappa score: 0.161
+LR average precision score: 0.206
+
+-> test with 'GB'
+GB tn, fp: 456, 0
+GB fn, tp: 10, 2
+GB f1 score: 0.286
+GB cohens kappa score: 0.280
+
+-> test with 'KNN'
+KNN tn, fp: 454, 2
+KNN fn, tp: 10, 2
+KNN f1 score: 0.250
+KNN cohens kappa score: 0.240
+
+
+------ Step 2/5: Slice 4/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+
  0%|          | 0/10 [00:00<?, ?it/s]
 10%|█         | 1/10 [00:00<00:07,  1.20it/s]
 20%|██        | 2/10 [00:01<00:06,  1.22it/s]
 30%|███       | 3/10 [00:02<00:05,  1.25it/s]
 40%|████      | 4/10 [00:03<00:04,  1.25it/s]
 50%|█████     | 5/10 [00:03<00:03,  1.28it/s]
 60%|██████    | 6/10 [00:05<00:03,  1.15it/s]
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 80%|████████  | 8/10 [00:06<00:01,  1.19it/s]
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100%|██████████| 10/10 [00:07<00:00,  1.34it/s]
100%|██████████| 10/10 [00:07<00:00,  1.25it/s]
+-> create 1778 synthetic samples
+-> test with 'LR'
+LR tn, fp: 356, 100
+LR fn, tp: 3, 9
+LR f1 score: 0.149
+LR cohens kappa score: 0.108
+LR average precision score: 0.330
+
+-> test with 'GB'
+GB tn, fp: 454, 2
+GB fn, tp: 11, 1
+GB f1 score: 0.133
+GB cohens kappa score: 0.124
+
+-> test with 'KNN'
+KNN tn, fp: 456, 0
+KNN fn, tp: 12, 0
+KNN f1 score: 0.000
+KNN cohens kappa score: 0.000
+
+
+------ Step 2/5: Slice 5/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+
  0%|          | 0/10 [00:00<?, ?it/s]
 10%|█         | 1/10 [00:00<00:08,  1.03it/s]
 20%|██        | 2/10 [00:01<00:06,  1.22it/s]
 30%|███       | 3/10 [00:02<00:05,  1.24it/s]
 40%|████      | 4/10 [00:03<00:05,  1.19it/s]
 50%|█████     | 5/10 [00:04<00:04,  1.23it/s]
 60%|██████    | 6/10 [00:04<00:03,  1.21it/s]
 70%|███████   | 7/10 [00:05<00:02,  1.15it/s]
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 90%|█████████ | 9/10 [00:07<00:00,  1.15it/s]
100%|██████████| 10/10 [00:08<00:00,  1.19it/s]
100%|██████████| 10/10 [00:08<00:00,  1.18it/s]
+-> create 1776 synthetic samples
+-> test with 'LR'
+LR tn, fp: 413, 43
+LR fn, tp: 5, 5
+LR f1 score: 0.172
+LR cohens kappa score: 0.142
+LR average precision score: 0.298
+
+-> test with 'GB'
+GB tn, fp: 451, 5
+GB fn, tp: 9, 1
+GB f1 score: 0.125
+GB cohens kappa score: 0.111
+
+-> test with 'KNN'
+KNN tn, fp: 456, 0
+KNN fn, tp: 10, 0
+KNN f1 score: 0.000
+KNN cohens kappa score: 0.000
+
+
+====== Step 3/5 =======
+-> Shuffling data
+-> Spliting data to slices
+
+------ Step 3/5: Slice 1/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+
  0%|          | 0/10 [00:00<?, ?it/s]
 10%|█         | 1/10 [00:00<00:08,  1.09it/s]
 20%|██        | 2/10 [00:01<00:06,  1.31it/s]
 30%|███       | 3/10 [00:02<00:05,  1.35it/s]
 40%|████      | 4/10 [00:02<00:04,  1.39it/s]
 50%|█████     | 5/10 [00:03<00:03,  1.32it/s]
 60%|██████    | 6/10 [00:04<00:03,  1.22it/s]
 70%|███████   | 7/10 [00:05<00:02,  1.22it/s]
 80%|████████  | 8/10 [00:06<00:01,  1.22it/s]
 90%|█████████ | 9/10 [00:07<00:00,  1.19it/s]
100%|██████████| 10/10 [00:08<00:00,  1.15it/s]
100%|██████████| 10/10 [00:08<00:00,  1.22it/s]
+-> create 1778 synthetic samples
+-> test with 'LR'
+LR tn, fp: 438, 18
+LR fn, tp: 4, 8
+LR f1 score: 0.421
+LR cohens kappa score: 0.400
+LR average precision score: 0.467
+
+-> test with 'GB'
+GB tn, fp: 454, 2
+GB fn, tp: 8, 4
+GB f1 score: 0.444
+GB cohens kappa score: 0.435
+
+-> test with 'KNN'
+KNN tn, fp: 455, 1
+KNN fn, tp: 12, 0
+KNN f1 score: 0.000
+KNN cohens kappa score: -0.004
+
+
+------ Step 3/5: Slice 2/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+
  0%|          | 0/10 [00:00<?, ?it/s]
 10%|█         | 1/10 [00:00<00:06,  1.37it/s]
 20%|██        | 2/10 [00:01<00:05,  1.35it/s]
 30%|███       | 3/10 [00:02<00:05,  1.27it/s]
 40%|████      | 4/10 [00:03<00:04,  1.33it/s]
 50%|█████     | 5/10 [00:03<00:03,  1.39it/s]
 60%|██████    | 6/10 [00:04<00:02,  1.41it/s]
 70%|███████   | 7/10 [00:05<00:02,  1.34it/s]
 80%|████████  | 8/10 [00:06<00:01,  1.28it/s]
 90%|█████████ | 9/10 [00:06<00:00,  1.32it/s]
100%|██████████| 10/10 [00:07<00:00,  1.32it/s]
100%|██████████| 10/10 [00:07<00:00,  1.33it/s]
+-> create 1778 synthetic samples
+-> test with 'LR'
+LR tn, fp: 413, 43
+LR fn, tp: 7, 5
+LR f1 score: 0.167
+LR cohens kappa score: 0.131
+LR average precision score: 0.179
+
+-> test with 'GB'
+GB tn, fp: 452, 4
+GB fn, tp: 11, 1
+GB f1 score: 0.118
+GB cohens kappa score: 0.104
+
+-> test with 'KNN'
+KNN tn, fp: 455, 1
+KNN fn, tp: 9, 3
+KNN f1 score: 0.375
+KNN cohens kappa score: 0.367
+
+
+------ Step 3/5: Slice 3/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+
  0%|          | 0/10 [00:00<?, ?it/s]
 10%|█         | 1/10 [00:00<00:07,  1.15it/s]
 20%|██        | 2/10 [00:01<00:06,  1.31it/s]
 30%|███       | 3/10 [00:02<00:05,  1.28it/s]
 40%|████      | 4/10 [00:03<00:04,  1.30it/s]
 50%|█████     | 5/10 [00:03<00:03,  1.26it/s]
 60%|██████    | 6/10 [00:04<00:02,  1.38it/s]
 70%|███████   | 7/10 [00:05<00:02,  1.39it/s]
 80%|████████  | 8/10 [00:05<00:01,  1.40it/s]
 90%|█████████ | 9/10 [00:06<00:00,  1.43it/s]
100%|██████████| 10/10 [00:07<00:00,  1.34it/s]
100%|██████████| 10/10 [00:07<00:00,  1.34it/s]
+-> create 1778 synthetic samples
+-> test with 'LR'
+LR tn, fp: 380, 76
+LR fn, tp: 5, 7
+LR f1 score: 0.147
+LR cohens kappa score: 0.107
+LR average precision score: 0.238
+
+-> test with 'GB'
+GB tn, fp: 454, 2
+GB fn, tp: 10, 2
+GB f1 score: 0.250
+GB cohens kappa score: 0.240
+
+-> test with 'KNN'
+KNN tn, fp: 454, 2
+KNN fn, tp: 10, 2
+KNN f1 score: 0.250
+KNN cohens kappa score: 0.240
+
+
+------ Step 3/5: Slice 4/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+
  0%|          | 0/10 [00:00<?, ?it/s]
 10%|█         | 1/10 [00:00<00:07,  1.16it/s]
 20%|██        | 2/10 [00:01<00:07,  1.11it/s]
 30%|███       | 3/10 [00:02<00:06,  1.07it/s]
 40%|████      | 4/10 [00:03<00:05,  1.11it/s]
 50%|█████     | 5/10 [00:04<00:04,  1.10it/s]
 60%|██████    | 6/10 [00:05<00:03,  1.14it/s]
 70%|███████   | 7/10 [00:06<00:02,  1.18it/s]
 80%|████████  | 8/10 [00:07<00:01,  1.16it/s]
 90%|█████████ | 9/10 [00:07<00:00,  1.16it/s]
100%|██████████| 10/10 [00:08<00:00,  1.21it/s]
100%|██████████| 10/10 [00:08<00:00,  1.16it/s]
+-> create 1778 synthetic samples
+-> test with 'LR'
+LR tn, fp: 358, 98
+LR fn, tp: 5, 7
+LR f1 score: 0.120
+LR cohens kappa score: 0.077
+LR average precision score: 0.115
+
+-> test with 'GB'
+GB tn, fp: 454, 2
+GB fn, tp: 12, 0
+GB f1 score: 0.000
+GB cohens kappa score: -0.007
+
+-> test with 'KNN'
+KNN tn, fp: 454, 2
+KNN fn, tp: 12, 0
+KNN f1 score: 0.000
+KNN cohens kappa score: -0.007
+
+
+------ Step 3/5: Slice 5/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+
  0%|          | 0/10 [00:00<?, ?it/s]
 10%|█         | 1/10 [00:00<00:08,  1.08it/s]
 20%|██        | 2/10 [00:01<00:06,  1.23it/s]
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 40%|████      | 4/10 [00:03<00:04,  1.27it/s]
 50%|█████     | 5/10 [00:03<00:03,  1.34it/s]
 60%|██████    | 6/10 [00:04<00:02,  1.38it/s]
 70%|███████   | 7/10 [00:05<00:02,  1.31it/s]
 80%|████████  | 8/10 [00:06<00:01,  1.32it/s]
 90%|█████████ | 9/10 [00:06<00:00,  1.33it/s]
100%|██████████| 10/10 [00:07<00:00,  1.38it/s]
100%|██████████| 10/10 [00:07<00:00,  1.32it/s]
+-> create 1776 synthetic samples
+-> test with 'LR'
+LR tn, fp: 396, 60
+LR fn, tp: 2, 8
+LR f1 score: 0.205
+LR cohens kappa score: 0.174
+LR average precision score: 0.399
+
+-> test with 'GB'
+GB tn, fp: 451, 5
+GB fn, tp: 8, 2
+GB f1 score: 0.235
+GB cohens kappa score: 0.222
+
+-> test with 'KNN'
+KNN tn, fp: 456, 0
+KNN fn, tp: 10, 0
+KNN f1 score: 0.000
+KNN cohens kappa score: 0.000
+
+
+====== Step 4/5 =======
+-> Shuffling data
+-> Spliting data to slices
+
+------ Step 4/5: Slice 1/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+
  0%|          | 0/10 [00:00<?, ?it/s]
 10%|█         | 1/10 [00:00<00:06,  1.29it/s]
 20%|██        | 2/10 [00:01<00:05,  1.35it/s]
 30%|███       | 3/10 [00:02<00:05,  1.36it/s]
 40%|████      | 4/10 [00:02<00:04,  1.35it/s]
 50%|█████     | 5/10 [00:03<00:03,  1.32it/s]
 60%|██████    | 6/10 [00:04<00:03,  1.23it/s]
 70%|███████   | 7/10 [00:05<00:02,  1.32it/s]
 80%|████████  | 8/10 [00:06<00:01,  1.28it/s]
 90%|█████████ | 9/10 [00:06<00:00,  1.32it/s]
100%|██████████| 10/10 [00:07<00:00,  1.25it/s]
100%|██████████| 10/10 [00:07<00:00,  1.29it/s]
+-> create 1778 synthetic samples
+-> test with 'LR'
+LR tn, fp: 371, 85
+LR fn, tp: 1, 11
+LR f1 score: 0.204
+LR cohens kappa score: 0.166
+LR average precision score: 0.387
+
+-> test with 'GB'
+GB tn, fp: 452, 4
+GB fn, tp: 9, 3
+GB f1 score: 0.316
+GB cohens kappa score: 0.303
+
+-> test with 'KNN'
+KNN tn, fp: 456, 0
+KNN fn, tp: 11, 1
+KNN f1 score: 0.154
+KNN cohens kappa score: 0.150
+
+
+------ Step 4/5: Slice 2/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+
  0%|          | 0/10 [00:00<?, ?it/s]
 10%|█         | 1/10 [00:00<00:07,  1.18it/s]
 20%|██        | 2/10 [00:01<00:06,  1.18it/s]
 30%|███       | 3/10 [00:02<00:05,  1.22it/s]
 40%|████      | 4/10 [00:03<00:04,  1.20it/s]
 50%|█████     | 5/10 [00:04<00:03,  1.26it/s]
 60%|██████    | 6/10 [00:04<00:03,  1.28it/s]
 70%|███████   | 7/10 [00:05<00:02,  1.31it/s]
 80%|████████  | 8/10 [00:06<00:01,  1.31it/s]
 90%|█████████ | 9/10 [00:07<00:00,  1.26it/s]
100%|██████████| 10/10 [00:07<00:00,  1.30it/s]
100%|██████████| 10/10 [00:07<00:00,  1.27it/s]
+-> create 1778 synthetic samples
+-> test with 'LR'
+LR tn, fp: 345, 111
+LR fn, tp: 2, 10
+LR f1 score: 0.150
+LR cohens kappa score: 0.109
+LR average precision score: 0.270
+
+-> test with 'GB'
+GB tn, fp: 456, 0
+GB fn, tp: 10, 2
+GB f1 score: 0.286
+GB cohens kappa score: 0.280
+
+-> test with 'KNN'
+KNN tn, fp: 455, 1
+KNN fn, tp: 11, 1
+KNN f1 score: 0.143
+KNN cohens kappa score: 0.137
+
+
+------ Step 4/5: Slice 3/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+
  0%|          | 0/10 [00:00<?, ?it/s]
 10%|█         | 1/10 [00:00<00:06,  1.29it/s]
 20%|██        | 2/10 [00:01<00:05,  1.37it/s]
 30%|███       | 3/10 [00:02<00:05,  1.34it/s]
 40%|████      | 4/10 [00:02<00:04,  1.39it/s]
 50%|█████     | 5/10 [00:03<00:03,  1.32it/s]
 60%|██████    | 6/10 [00:04<00:03,  1.27it/s]
 70%|███████   | 7/10 [00:05<00:02,  1.35it/s]
 80%|████████  | 8/10 [00:06<00:01,  1.31it/s]
 90%|█████████ | 9/10 [00:06<00:00,  1.32it/s]
100%|██████████| 10/10 [00:07<00:00,  1.32it/s]
100%|██████████| 10/10 [00:07<00:00,  1.32it/s]
+-> create 1778 synthetic samples
+-> test with 'LR'
+LR tn, fp: 395, 61
+LR fn, tp: 6, 6
+LR f1 score: 0.152
+LR cohens kappa score: 0.113
+LR average precision score: 0.103
+
+-> test with 'GB'
+GB tn, fp: 454, 2
+GB fn, tp: 11, 1
+GB f1 score: 0.133
+GB cohens kappa score: 0.124
+
+-> test with 'KNN'
+KNN tn, fp: 455, 1
+KNN fn, tp: 12, 0
+KNN f1 score: 0.000
+KNN cohens kappa score: -0.004
+
+
+------ Step 4/5: Slice 4/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+
  0%|          | 0/10 [00:00<?, ?it/s]
 10%|█         | 1/10 [00:00<00:07,  1.26it/s]
 20%|██        | 2/10 [00:01<00:05,  1.47it/s]
 30%|███       | 3/10 [00:02<00:04,  1.40it/s]
 40%|████      | 4/10 [00:02<00:04,  1.30it/s]
 50%|█████     | 5/10 [00:03<00:04,  1.18it/s]
 60%|██████    | 6/10 [00:04<00:03,  1.18it/s]
 70%|███████   | 7/10 [00:05<00:02,  1.21it/s]
 80%|████████  | 8/10 [00:06<00:01,  1.19it/s]
 90%|█████████ | 9/10 [00:07<00:00,  1.23it/s]
100%|██████████| 10/10 [00:07<00:00,  1.27it/s]
100%|██████████| 10/10 [00:07<00:00,  1.26it/s]
+-> create 1778 synthetic samples
+-> test with 'LR'
+LR tn, fp: 395, 61
+LR fn, tp: 2, 10
+LR f1 score: 0.241
+LR cohens kappa score: 0.206
+LR average precision score: 0.277
+
+-> test with 'GB'
+GB tn, fp: 454, 2
+GB fn, tp: 9, 3
+GB f1 score: 0.353
+GB cohens kappa score: 0.343
+
+-> test with 'KNN'
+KNN tn, fp: 456, 0
+KNN fn, tp: 12, 0
+KNN f1 score: 0.000
+KNN cohens kappa score: 0.000
+
+
+------ Step 4/5: Slice 5/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+
  0%|          | 0/10 [00:00<?, ?it/s]
 10%|█         | 1/10 [00:00<00:06,  1.39it/s]
 20%|██        | 2/10 [00:01<00:06,  1.28it/s]
 30%|███       | 3/10 [00:02<00:05,  1.27it/s]
 40%|████      | 4/10 [00:03<00:04,  1.26it/s]
 50%|█████     | 5/10 [00:03<00:04,  1.23it/s]
 60%|██████    | 6/10 [00:04<00:03,  1.19it/s]
 70%|███████   | 7/10 [00:05<00:02,  1.15it/s]
 80%|████████  | 8/10 [00:06<00:01,  1.14it/s]
 90%|█████████ | 9/10 [00:07<00:00,  1.17it/s]
100%|██████████| 10/10 [00:08<00:00,  1.20it/s]
100%|██████████| 10/10 [00:08<00:00,  1.21it/s]
+-> create 1776 synthetic samples
+-> test with 'LR'
+LR tn, fp: 413, 43
+LR fn, tp: 5, 5
+LR f1 score: 0.172
+LR cohens kappa score: 0.142
+LR average precision score: 0.199
+
+-> test with 'GB'
+GB tn, fp: 453, 3
+GB fn, tp: 7, 3
+GB f1 score: 0.375
+GB cohens kappa score: 0.365
+
+-> test with 'KNN'
+KNN tn, fp: 451, 5
+KNN fn, tp: 10, 0
+KNN f1 score: 0.000
+KNN cohens kappa score: -0.015
+
+
+====== Step 5/5 =======
+-> Shuffling data
+-> Spliting data to slices
+
+------ Step 5/5: Slice 1/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+
  0%|          | 0/10 [00:00<?, ?it/s]
 10%|█         | 1/10 [00:00<00:07,  1.27it/s]
 20%|██        | 2/10 [00:01<00:05,  1.40it/s]
 30%|███       | 3/10 [00:02<00:04,  1.40it/s]
 40%|████      | 4/10 [00:03<00:04,  1.31it/s]
 50%|█████     | 5/10 [00:03<00:03,  1.28it/s]
 60%|██████    | 6/10 [00:04<00:03,  1.32it/s]
 70%|███████   | 7/10 [00:05<00:02,  1.22it/s]
 80%|████████  | 8/10 [00:06<00:01,  1.24it/s]
 90%|█████████ | 9/10 [00:07<00:00,  1.22it/s]
100%|██████████| 10/10 [00:07<00:00,  1.21it/s]
100%|██████████| 10/10 [00:07<00:00,  1.26it/s]
+-> create 1778 synthetic samples
+-> test with 'LR'
+LR tn, fp: 414, 42
+LR fn, tp: 6, 6
+LR f1 score: 0.200
+LR cohens kappa score: 0.166
+LR average precision score: 0.197
+
+-> test with 'GB'
+GB tn, fp: 450, 6
+GB fn, tp: 10, 2
+GB f1 score: 0.200
+GB cohens kappa score: 0.183
+
+-> test with 'KNN'
+KNN tn, fp: 456, 0
+KNN fn, tp: 12, 0
+KNN f1 score: 0.000
+KNN cohens kappa score: 0.000
+
+
+------ Step 5/5: Slice 2/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+
  0%|          | 0/10 [00:00<?, ?it/s]
 10%|█         | 1/10 [00:00<00:07,  1.22it/s]
 20%|██        | 2/10 [00:01<00:05,  1.39it/s]
 30%|███       | 3/10 [00:02<00:05,  1.29it/s]
 40%|████      | 4/10 [00:03<00:04,  1.32it/s]
 50%|█████     | 5/10 [00:03<00:04,  1.21it/s]
 60%|██████    | 6/10 [00:04<00:03,  1.23it/s]
 70%|███████   | 7/10 [00:05<00:02,  1.27it/s]
 80%|████████  | 8/10 [00:06<00:01,  1.25it/s]
 90%|█████████ | 9/10 [00:07<00:00,  1.23it/s]
100%|██████████| 10/10 [00:08<00:00,  1.20it/s]
100%|██████████| 10/10 [00:08<00:00,  1.24it/s]
+-> create 1778 synthetic samples
+-> test with 'LR'
+LR tn, fp: 388, 68
+LR fn, tp: 7, 5
+LR f1 score: 0.118
+LR cohens kappa score: 0.077
+LR average precision score: 0.102
+
+-> test with 'GB'
+GB tn, fp: 455, 1
+GB fn, tp: 11, 1
+GB f1 score: 0.143
+GB cohens kappa score: 0.137
+
+-> test with 'KNN'
+KNN tn, fp: 455, 1
+KNN fn, tp: 11, 1
+KNN f1 score: 0.143
+KNN cohens kappa score: 0.137
+
+
+------ Step 5/5: Slice 3/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+
  0%|          | 0/10 [00:00<?, ?it/s]
 10%|█         | 1/10 [00:00<00:07,  1.25it/s]
 20%|██        | 2/10 [00:01<00:06,  1.21it/s]
 30%|███       | 3/10 [00:02<00:05,  1.21it/s]
 40%|████      | 4/10 [00:03<00:04,  1.21it/s]
 50%|█████     | 5/10 [00:04<00:04,  1.19it/s]
 60%|██████    | 6/10 [00:05<00:03,  1.08it/s]
 70%|███████   | 7/10 [00:06<00:02,  1.07it/s]
 80%|████████  | 8/10 [00:07<00:01,  1.08it/s]
 90%|█████████ | 9/10 [00:07<00:00,  1.10it/s]
100%|██████████| 10/10 [00:08<00:00,  1.15it/s]
100%|██████████| 10/10 [00:08<00:00,  1.14it/s]
+-> create 1778 synthetic samples
+-> test with 'LR'
+LR tn, fp: 430, 26
+LR fn, tp: 5, 7
+LR f1 score: 0.311
+LR cohens kappa score: 0.284
+LR average precision score: 0.323
+
+-> test with 'GB'
+GB tn, fp: 452, 4
+GB fn, tp: 11, 1
+GB f1 score: 0.118
+GB cohens kappa score: 0.104
+
+-> test with 'KNN'
+KNN tn, fp: 456, 0
+KNN fn, tp: 12, 0
+KNN f1 score: 0.000
+KNN cohens kappa score: 0.000
+
+
+------ Step 5/5: Slice 4/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+
  0%|          | 0/10 [00:00<?, ?it/s]
 10%|█         | 1/10 [00:00<00:08,  1.01it/s]
 20%|██        | 2/10 [00:01<00:06,  1.23it/s]
 30%|███       | 3/10 [00:02<00:05,  1.26it/s]
 40%|████      | 4/10 [00:03<00:05,  1.19it/s]
 50%|█████     | 5/10 [00:04<00:04,  1.21it/s]
 60%|██████    | 6/10 [00:05<00:03,  1.11it/s]
 70%|███████   | 7/10 [00:05<00:02,  1.16it/s]
 80%|████████  | 8/10 [00:06<00:01,  1.21it/s]
 90%|█████████ | 9/10 [00:07<00:00,  1.23it/s]
100%|██████████| 10/10 [00:08<00:00,  1.29it/s]
100%|██████████| 10/10 [00:08<00:00,  1.22it/s]
+-> create 1778 synthetic samples
+-> test with 'LR'
+LR tn, fp: 382, 74
+LR fn, tp: 4, 8
+LR f1 score: 0.170
+LR cohens kappa score: 0.131
+LR average precision score: 0.405
+
+-> test with 'GB'
+GB tn, fp: 448, 8
+GB fn, tp: 11, 1
+GB f1 score: 0.095
+GB cohens kappa score: 0.075
+
+-> test with 'KNN'
+KNN tn, fp: 456, 0
+KNN fn, tp: 12, 0
+KNN f1 score: 0.000
+KNN cohens kappa score: 0.000
+
+
+------ Step 5/5: Slice 5/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+
  0%|          | 0/10 [00:00<?, ?it/s]
 10%|█         | 1/10 [00:00<00:07,  1.16it/s]
 20%|██        | 2/10 [00:01<00:05,  1.40it/s]
 30%|███       | 3/10 [00:02<00:05,  1.29it/s]
 40%|████      | 4/10 [00:03<00:04,  1.22it/s]
 50%|█████     | 5/10 [00:04<00:04,  1.22it/s]
 60%|██████    | 6/10 [00:04<00:03,  1.27it/s]
 70%|███████   | 7/10 [00:05<00:02,  1.28it/s]
 80%|████████  | 8/10 [00:06<00:01,  1.27it/s]
 90%|█████████ | 9/10 [00:07<00:00,  1.21it/s]
100%|██████████| 10/10 [00:08<00:00,  1.22it/s]
100%|██████████| 10/10 [00:08<00:00,  1.24it/s]
+-> create 1776 synthetic samples
+-> test with 'LR'
+LR tn, fp: 376, 80
+LR fn, tp: 3, 7
+LR f1 score: 0.144
+LR cohens kappa score: 0.110
+LR average precision score: 0.148
+
+-> test with 'GB'
+GB tn, fp: 455, 1
+GB fn, tp: 9, 1
+GB f1 score: 0.167
+GB cohens kappa score: 0.161
+
+-> test with 'KNN'
+KNN tn, fp: 455, 1
+KNN fn, tp: 8, 2
+KNN f1 score: 0.308
+KNN cohens kappa score: 0.301
+
+### Exercise is done.
+
+-----[ LR ]-----
+maximum:
+LR tn, fp: 438, 111
+LR fn, tp: 7, 11
+LR f1 score: 0.421
+LR cohens kappa score: 0.400
+LR average precision score: 0.467
+
+
+average:
+LR tn, fp: 392.68, 63.32
+LR fn, tp: 4.24, 7.36
+LR f1 score: 0.189
+LR cohens kappa score: 0.153
+LR average precision score: 0.258
+
+
+minimum:
+LR tn, fp: 345, 18
+LR fn, tp: 1, 5
+LR f1 score: 0.118
+LR cohens kappa score: 0.077
+LR average precision score: 0.102
+
+
+-----[ GB ]-----
+maximum:
+GB tn, fp: 456, 8
+GB fn, tp: 12, 4
+GB f1 score: 0.444
+GB cohens kappa score: 0.435
+
+
+average:
+GB tn, fp: 452.64, 3.36
+GB fn, tp: 9.8, 1.8
+GB f1 score: 0.211
+GB cohens kappa score: 0.199
+
+
+minimum:
+GB tn, fp: 448, 0
+GB fn, tp: 7, 0
+GB f1 score: 0.000
+GB cohens kappa score: -0.010
+
+
+-----[ KNN ]-----
+maximum:
+KNN tn, fp: 456, 7
+KNN fn, tp: 12, 3
+KNN f1 score: 0.375
+KNN cohens kappa score: 0.367
+
+
+average:
+KNN tn, fp: 454.64, 1.36
+KNN fn, tp: 10.76, 0.84
+KNN f1 score: 0.111
+KNN cohens kappa score: 0.105
+
+
+minimum:
+KNN tn, fp: 449, 0
+KNN fn, tp: 8, 0
+KNN f1 score: 0.000
+KNN cohens kappa score: -0.015
+

data_result/CTAB-GAN/folding_car-vgood.csv

@@ -0,0 +1,92 @@
+LR
+Nr.;TN;TP;FN;FP;f1 score;cohens kappa score;average precision score
+1;295.000;13.000;0.000;38.000;0.406;0.368;0.325
+2;286.000;11.000;2.000;47.000;0.310;0.265;0.294
+3;276.000;13.000;0.000;57.000;0.313;0.267;0.378
+4;284.000;13.000;0.000;49.000;0.347;0.303;0.394
+5;286.000;13.000;0.000;45.000;0.366;0.324;0.388
+6;316.000;5.000;8.000;17.000;0.286;0.250;0.286
+7;272.000;13.000;0.000;61.000;0.299;0.251;0.397
+8;291.000;12.000;1.000;42.000;0.358;0.317;0.355
+9;318.000;6.000;7.000;15.000;0.353;0.321;0.355
+10;319.000;9.000;4.000;12.000;0.529;0.506;0.433
+11;302.000;12.000;1.000;31.000;0.429;0.394;0.291
+12;298.000;13.000;0.000;35.000;0.426;0.390;0.446
+13;316.000;7.000;6.000;17.000;0.378;0.347;0.329
+14;291.000;13.000;0.000;42.000;0.382;0.342;0.390
+15;275.000;13.000;0.000;56.000;0.317;0.271;0.429
+16;318.000;8.000;5.000;15.000;0.444;0.416;0.386
+17;288.000;13.000;0.000;45.000;0.366;0.325;0.386
+18;275.000;13.000;0.000;58.000;0.310;0.263;0.316
+19;317.000;6.000;7.000;16.000;0.343;0.310;0.288
+20;284.000;13.000;0.000;47.000;0.356;0.314;0.337
+21;277.000;13.000;0.000;56.000;0.317;0.271;0.265
+22;279.000;13.000;0.000;54.000;0.325;0.280;0.341
+23;318.000;9.000;4.000;15.000;0.486;0.460;0.379
+24;277.000;13.000;0.000;56.000;0.317;0.271;0.285
+25;297.000;12.000;1.000;34.000;0.407;0.370;0.463
+max;319.000;13.000;8.000;61.000;0.529;0.506;0.463
+avg;294.200;11.160;1.840;38.400;0.367;0.328;0.357
+min;272.000;5.000;0.000;12.000;0.286;0.250;0.265
+---
+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;333.000;13.000;0.000;0.000;1.000;1.000
+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;332.000;13.000;0.000;1.000;0.963;0.961
+7;333.000;13.000;0.000;0.000;1.000;1.000
+8;332.000;13.000;0.000;1.000;0.963;0.961
+9;333.000;12.000;1.000;0.000;0.960;0.959
+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;331.000;13.000;0.000;2.000;0.929;0.926
+13;331.000;13.000;0.000;2.000;0.929;0.926
+14;333.000;13.000;0.000;0.000;1.000;1.000
+15;331.000;13.000;0.000;0.000;1.000;1.000
+16;333.000;13.000;0.000;0.000;1.000;1.000
+17;333.000;12.000;1.000;0.000;0.960;0.959
+18;333.000;13.000;0.000;0.000;1.000;1.000
+19;332.000;13.000;0.000;1.000;0.963;0.961
+20;331.000;13.000;0.000;0.000;1.000;1.000
+21;333.000;13.000;0.000;0.000;1.000;1.000
+22;333.000;13.000;0.000;0.000;1.000;1.000
+23;333.000;12.000;1.000;0.000;0.960;0.959
+24;333.000;13.000;0.000;0.000;1.000;1.000
+25;329.000;13.000;0.000;2.000;0.929;0.926
+max;333.000;13.000;2.000;2.000;1.000;1.000
+avg;332.160;12.760;0.240;0.440;0.974;0.973
+min;329.000;11.000;0.000;0.000;0.917;0.914
+---
+KNN
+Nr.;TN;TP;FN;FP;f1 score;cohens kappa score
+1;321.000;13.000;0.000;12.000;0.684;0.668
+2;314.000;13.000;0.000;19.000;0.578;0.554
+3;323.000;13.000;0.000;10.000;0.722;0.708
+4;315.000;12.000;1.000;18.000;0.558;0.534
+5;322.000;13.000;0.000;9.000;0.743;0.730
+6;309.000;13.000;0.000;24.000;0.520;0.492
+7;316.000;10.000;3.000;17.000;0.500;0.473
+8;321.000;13.000;0.000;12.000;0.684;0.668
+9;321.000;13.000;0.000;12.000;0.684;0.668
+10;326.000;7.000;6.000;5.000;0.560;0.543
+11;321.000;13.000;0.000;12.000;0.684;0.668
+12;321.000;13.000;0.000;12.000;0.684;0.668
+13;307.000;13.000;0.000;26.000;0.500;0.470
+14;323.000;13.000;0.000;10.000;0.722;0.708
+15;323.000;13.000;0.000;8.000;0.765;0.753
+16;319.000;13.000;0.000;14.000;0.650;0.631
+17;320.000;10.000;3.000;13.000;0.556;0.533
+18;326.000;13.000;0.000;7.000;0.788;0.778
+19;317.000;13.000;0.000;16.000;0.619;0.598
+20;316.000;13.000;0.000;15.000;0.634;0.614
+21;318.000;12.000;1.000;15.000;0.600;0.579
+22;319.000;13.000;0.000;14.000;0.650;0.631
+23;315.000;13.000;0.000;18.000;0.591;0.568
+24;301.000;13.000;0.000;32.000;0.448;0.414
+25;321.000;13.000;0.000;10.000;0.722;0.708
+max;326.000;13.000;6.000;32.000;0.788;0.778
+avg;318.200;12.440;0.560;14.400;0.634;0.614
+min;301.000;7.000;0.000;5.000;0.448;0.414

data_result/CTAB-GAN/folding_car-vgood.log

@@ -7,11 +7,720 @@
 Load 'data_input/folding_car-vgood'
 from pickle file
 Data loaded.
-Traceback (most recent call last):
-  File "/benchmark/data/run_all_exercises.py", line 13, in <module>
-    runExercise(dataset, None, name, f)
-  File "/benchmark/data/library/analysis.py", line 164, in runExercise
-    gan = ganCreator(data)
-  File "/benchmark/data/library/analysis.py", line 268, in <lambda>
-    , ("CTAB-GAN",      lambda _data: CtabGan())
-NameError: name 'CtabGan' is not defined
+-> 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
+
  0%|          | 0/10 [00:00<?, ?it/s]
 10%|█         | 1/10 [00:00<00:08,  1.08it/s]
 20%|██        | 2/10 [00:01<00:06,  1.17it/s]
 30%|███       | 3/10 [00:02<00:05,  1.21it/s]
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 50%|█████     | 5/10 [00:04<00:03,  1.27it/s]
 60%|██████    | 6/10 [00:04<00:03,  1.26it/s]
 70%|███████   | 7/10 [00:05<00:02,  1.25it/s]
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100%|██████████| 10/10 [00:08<00:00,  1.26it/s]
100%|██████████| 10/10 [00:08<00:00,  1.25it/s]
+-> create 1278 synthetic samples
+-> test with 'LR'
+LR tn, fp: 295, 38
+LR fn, tp: 0, 13
+LR f1 score: 0.406
+LR cohens kappa score: 0.368
+LR average precision score: 0.325
+
+-> 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: 0, 13
+KNN f1 score: 0.684
+KNN cohens kappa score: 0.668
+
+
+------ Step 1/5: Slice 2/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+
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 10%|█         | 1/10 [00:00<00:08,  1.11it/s]
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 80%|████████  | 8/10 [00:06<00:01,  1.23it/s]
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100%|██████████| 10/10 [00:07<00:00,  1.15it/s]
100%|██████████| 10/10 [00:07<00:00,  1.25it/s]
+-> create 1278 synthetic samples
+-> test with 'LR'
+LR tn, fp: 286, 47
+LR fn, tp: 2, 11
+LR f1 score: 0.310
+LR cohens kappa score: 0.265
+LR average precision score: 0.294
+
+-> 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: 314, 19
+KNN fn, tp: 0, 13
+KNN f1 score: 0.578
+KNN cohens kappa score: 0.554
+
+
+------ Step 1/5: Slice 3/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+
  0%|          | 0/10 [00:00<?, ?it/s]
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100%|██████████| 10/10 [00:08<00:00,  1.24it/s]
100%|██████████| 10/10 [00:08<00:00,  1.18it/s]
+-> create 1278 synthetic samples
+-> test with 'LR'
+LR tn, fp: 276, 57
+LR fn, tp: 0, 13
+LR f1 score: 0.313
+LR cohens kappa score: 0.267
+LR average precision score: 0.378
+
+-> 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
+
  0%|          | 0/10 [00:00<?, ?it/s]
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 20%|██        | 2/10 [00:01<00:05,  1.35it/s]
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 60%|██████    | 6/10 [00:04<00:02,  1.39it/s]
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100%|██████████| 10/10 [00:07<00:00,  1.36it/s]
100%|██████████| 10/10 [00:07<00:00,  1.37it/s]
+-> create 1278 synthetic samples
+-> test with 'LR'
+LR tn, fp: 284, 49
+LR fn, tp: 0, 13
+LR f1 score: 0.347
+LR cohens kappa score: 0.303
+LR average precision score: 0.394
+
+-> 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: 315, 18
+KNN fn, tp: 1, 12
+KNN f1 score: 0.558
+KNN cohens kappa score: 0.534
+
+
+------ Step 1/5: Slice 5/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+
  0%|          | 0/10 [00:00<?, ?it/s]
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 20%|██        | 2/10 [00:01<00:06,  1.28it/s]
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 50%|█████     | 5/10 [00:03<00:03,  1.38it/s]
 60%|██████    | 6/10 [00:04<00:02,  1.39it/s]
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100%|██████████| 10/10 [00:07<00:00,  1.37it/s]
100%|██████████| 10/10 [00:07<00:00,  1.36it/s]
+-> create 1280 synthetic samples
+-> test with 'LR'
+LR tn, fp: 286, 45
+LR fn, tp: 0, 13
+LR f1 score: 0.366
+LR cohens kappa score: 0.324
+LR average precision score: 0.388
+
+-> 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: 322, 9
+KNN fn, tp: 0, 13
+KNN f1 score: 0.743
+KNN cohens kappa score: 0.730
+
+
+====== Step 2/5 =======
+-> Shuffling data
+-> Spliting data to slices
+
+------ Step 2/5: Slice 1/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+
  0%|          | 0/10 [00:00<?, ?it/s]
 10%|█         | 1/10 [00:00<00:07,  1.17it/s]
 20%|██        | 2/10 [00:01<00:06,  1.30it/s]
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 40%|████      | 4/10 [00:03<00:04,  1.33it/s]
 50%|█████     | 5/10 [00:03<00:03,  1.27it/s]
 60%|██████    | 6/10 [00:04<00:03,  1.27it/s]
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100%|██████████| 10/10 [00:08<00:00,  1.16it/s]
100%|██████████| 10/10 [00:08<00:00,  1.22it/s]
+-> create 1278 synthetic samples
+-> test with 'LR'
+LR tn, fp: 316, 17
+LR fn, tp: 8, 5
+LR f1 score: 0.286
+LR cohens kappa score: 0.250
+LR average precision score: 0.286
+
+-> test with 'GB'
+GB tn, fp: 332, 1
+GB fn, tp: 0, 13
+GB f1 score: 0.963
+GB cohens kappa score: 0.961
+
+-> test with 'KNN'
+KNN tn, fp: 309, 24
+KNN fn, tp: 0, 13
+KNN f1 score: 0.520
+KNN cohens kappa score: 0.492
+
+
+------ Step 2/5: Slice 2/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+
  0%|          | 0/10 [00:00<?, ?it/s]
 10%|█         | 1/10 [00:00<00:07,  1.22it/s]
 20%|██        | 2/10 [00:01<00:05,  1.42it/s]
 30%|███       | 3/10 [00:02<00:04,  1.42it/s]
 40%|████      | 4/10 [00:03<00:04,  1.30it/s]
 50%|█████     | 5/10 [00:03<00:03,  1.26it/s]
 60%|██████    | 6/10 [00:04<00:03,  1.24it/s]
 70%|███████   | 7/10 [00:05<00:02,  1.20it/s]
 80%|████████  | 8/10 [00:06<00:01,  1.19it/s]
 90%|█████████ | 9/10 [00:07<00:00,  1.22it/s]
100%|██████████| 10/10 [00:07<00:00,  1.25it/s]
100%|██████████| 10/10 [00:07<00:00,  1.26it/s]
+-> create 1278 synthetic samples
+-> test with 'LR'
+LR tn, fp: 272, 61
+LR fn, tp: 0, 13
+LR f1 score: 0.299
+LR cohens kappa score: 0.251
+LR average precision score: 0.397
+
+-> 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: 3, 10
+KNN f1 score: 0.500
+KNN cohens kappa score: 0.473
+
+
+------ Step 2/5: Slice 3/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+
  0%|          | 0/10 [00:00<?, ?it/s]
 10%|█         | 1/10 [00:00<00:08,  1.11it/s]
 20%|██        | 2/10 [00:01<00:06,  1.21it/s]
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 50%|█████     | 5/10 [00:04<00:04,  1.24it/s]
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 80%|████████  | 8/10 [00:06<00:01,  1.27it/s]
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100%|██████████| 10/10 [00:08<00:00,  1.22it/s]
100%|██████████| 10/10 [00:08<00:00,  1.23it/s]
+-> create 1278 synthetic samples
+-> test with 'LR'
+LR tn, fp: 291, 42
+LR fn, tp: 1, 12
+LR f1 score: 0.358
+LR cohens kappa score: 0.317
+LR average precision score: 0.355
+
+-> test with 'GB'
+GB tn, fp: 332, 1
+GB fn, tp: 0, 13
+GB f1 score: 0.963
+GB cohens kappa score: 0.961
+
+-> test with 'KNN'
+KNN tn, fp: 321, 12
+KNN fn, tp: 0, 13
+KNN f1 score: 0.684
+KNN cohens kappa score: 0.668
+
+
+------ Step 2/5: Slice 4/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+
  0%|          | 0/10 [00:00<?, ?it/s]
 10%|█         | 1/10 [00:00<00:08,  1.12it/s]
 20%|██        | 2/10 [00:01<00:06,  1.21it/s]
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 50%|█████     | 5/10 [00:04<00:04,  1.11it/s]
 60%|██████    | 6/10 [00:05<00:03,  1.18it/s]
 70%|███████   | 7/10 [00:06<00:02,  1.21it/s]
 80%|████████  | 8/10 [00:06<00:01,  1.18it/s]
 90%|█████████ | 9/10 [00:07<00:00,  1.12it/s]
100%|██████████| 10/10 [00:08<00:00,  1.14it/s]
100%|██████████| 10/10 [00:08<00:00,  1.14it/s]
+-> create 1278 synthetic samples
+-> test with 'LR'
+LR tn, fp: 318, 15
+LR fn, tp: 7, 6
+LR f1 score: 0.353
+LR cohens kappa score: 0.321
+LR average precision score: 0.355
+
+-> 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: 321, 12
+KNN fn, tp: 0, 13
+KNN f1 score: 0.684
+KNN cohens kappa score: 0.668
+
+
+------ Step 2/5: Slice 5/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+
  0%|          | 0/10 [00:00<?, ?it/s]
 10%|█         | 1/10 [00:00<00:08,  1.05it/s]
 20%|██        | 2/10 [00:01<00:06,  1.15it/s]
 30%|███       | 3/10 [00:02<00:05,  1.21it/s]
 40%|████      | 4/10 [00:03<00:04,  1.24it/s]
 50%|█████     | 5/10 [00:04<00:04,  1.17it/s]
 60%|██████    | 6/10 [00:05<00:03,  1.18it/s]
 70%|███████   | 7/10 [00:06<00:02,  1.09it/s]
 80%|████████  | 8/10 [00:07<00:01,  1.10it/s]
 90%|█████████ | 9/10 [00:07<00:00,  1.18it/s]
100%|██████████| 10/10 [00:08<00:00,  1.23it/s]
100%|██████████| 10/10 [00:08<00:00,  1.18it/s]
+-> create 1280 synthetic samples
+-> test with 'LR'
+LR tn, fp: 319, 12
+LR fn, tp: 4, 9
+LR f1 score: 0.529
+LR cohens kappa score: 0.506
+LR average precision score: 0.433
+
+-> 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: 326, 5
+KNN fn, tp: 6, 7
+KNN f1 score: 0.560
+KNN cohens kappa score: 0.543
+
+
+====== Step 3/5 =======
+-> Shuffling data
+-> Spliting data to slices
+
+------ Step 3/5: Slice 1/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+
  0%|          | 0/10 [00:00<?, ?it/s]
 10%|█         | 1/10 [00:00<00:07,  1.15it/s]
 20%|██        | 2/10 [00:01<00:06,  1.31it/s]
 30%|███       | 3/10 [00:02<00:05,  1.22it/s]
 40%|████      | 4/10 [00:03<00:04,  1.27it/s]
 50%|█████     | 5/10 [00:03<00:03,  1.29it/s]
 60%|██████    | 6/10 [00:04<00:03,  1.27it/s]
 70%|███████   | 7/10 [00:05<00:02,  1.20it/s]
 80%|████████  | 8/10 [00:06<00:01,  1.19it/s]
 90%|█████████ | 9/10 [00:07<00:00,  1.21it/s]
100%|██████████| 10/10 [00:08<00:00,  1.16it/s]
100%|██████████| 10/10 [00:08<00:00,  1.21it/s]
+-> create 1278 synthetic samples
+-> test with 'LR'
+LR tn, fp: 302, 31
+LR fn, tp: 1, 12
+LR f1 score: 0.429
+LR cohens kappa score: 0.394
+LR average precision score: 0.291
+
+-> 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: 321, 12
+KNN fn, tp: 0, 13
+KNN f1 score: 0.684
+KNN cohens kappa score: 0.668
+
+
+------ Step 3/5: Slice 2/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+
  0%|          | 0/10 [00:00<?, ?it/s]
 10%|█         | 1/10 [00:00<00:08,  1.10it/s]
 20%|██        | 2/10 [00:01<00:06,  1.33it/s]
 30%|███       | 3/10 [00:02<00:04,  1.40it/s]
 40%|████      | 4/10 [00:03<00:05,  1.17it/s]
 50%|█████     | 5/10 [00:04<00:04,  1.17it/s]
 60%|██████    | 6/10 [00:05<00:03,  1.15it/s]
 70%|███████   | 7/10 [00:05<00:02,  1.24it/s]
 80%|████████  | 8/10 [00:06<00:01,  1.27it/s]
 90%|█████████ | 9/10 [00:07<00:00,  1.24it/s]
100%|██████████| 10/10 [00:08<00:00,  1.25it/s]
100%|██████████| 10/10 [00:08<00:00,  1.24it/s]
+-> create 1278 synthetic samples
+-> test with 'LR'
+LR tn, fp: 298, 35
+LR fn, tp: 0, 13
+LR f1 score: 0.426
+LR cohens kappa score: 0.390
+LR average precision score: 0.446
+
+-> 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: 321, 12
+KNN fn, tp: 0, 13
+KNN f1 score: 0.684
+KNN cohens kappa score: 0.668
+
+
+------ Step 3/5: Slice 3/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+
  0%|          | 0/10 [00:00<?, ?it/s]
 10%|█         | 1/10 [00:00<00:08,  1.02it/s]
 20%|██        | 2/10 [00:01<00:06,  1.16it/s]
 30%|███       | 3/10 [00:02<00:05,  1.23it/s]
 40%|████      | 4/10 [00:03<00:04,  1.21it/s]
 50%|█████     | 5/10 [00:04<00:04,  1.18it/s]
 60%|██████    | 6/10 [00:04<00:03,  1.25it/s]
 70%|███████   | 7/10 [00:05<00:02,  1.28it/s]
 80%|████████  | 8/10 [00:06<00:01,  1.35it/s]
 90%|█████████ | 9/10 [00:06<00:00,  1.41it/s]
100%|██████████| 10/10 [00:07<00:00,  1.41it/s]
100%|██████████| 10/10 [00:07<00:00,  1.30it/s]
+-> create 1278 synthetic samples
+-> test with 'LR'
+LR tn, fp: 316, 17
+LR fn, tp: 6, 7
+LR f1 score: 0.378
+LR cohens kappa score: 0.347
+LR average precision score: 0.329
+
+-> 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: 307, 26
+KNN fn, tp: 0, 13
+KNN f1 score: 0.500
+KNN cohens kappa score: 0.470
+
+
+------ Step 3/5: Slice 4/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+
  0%|          | 0/10 [00:00<?, ?it/s]
 10%|█         | 1/10 [00:00<00:06,  1.36it/s]
 20%|██        | 2/10 [00:01<00:05,  1.36it/s]
 30%|███       | 3/10 [00:02<00:05,  1.32it/s]
 40%|████      | 4/10 [00:03<00:04,  1.32it/s]
 50%|█████     | 5/10 [00:03<00:03,  1.30it/s]
 60%|██████    | 6/10 [00:04<00:03,  1.33it/s]
 70%|███████   | 7/10 [00:05<00:02,  1.27it/s]
 80%|████████  | 8/10 [00:06<00:01,  1.25it/s]
 90%|█████████ | 9/10 [00:07<00:00,  1.12it/s]
100%|██████████| 10/10 [00:07<00:00,  1.21it/s]
100%|██████████| 10/10 [00:07<00:00,  1.25it/s]
+-> create 1278 synthetic samples
+-> test with 'LR'
+LR tn, fp: 291, 42
+LR fn, tp: 0, 13
+LR f1 score: 0.382
+LR cohens kappa score: 0.342
+LR average precision score: 0.390
+
+-> test with 'GB'
+GB tn, fp: 333, 0
+GB fn, tp: 0, 13
+GB f1 score: 1.000
+GB cohens kappa score: 1.000
+
+-> test with 'KNN'
+KNN tn, fp: 323, 10
+KNN fn, tp: 0, 13
+KNN f1 score: 0.722
+KNN cohens kappa score: 0.708
+
+
+------ Step 3/5: Slice 5/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+
  0%|          | 0/10 [00:00<?, ?it/s]
 10%|█         | 1/10 [00:00<00:08,  1.08it/s]
 20%|██        | 2/10 [00:01<00:06,  1.30it/s]
 30%|███       | 3/10 [00:02<00:05,  1.33it/s]
 40%|████      | 4/10 [00:03<00:04,  1.33it/s]
 50%|█████     | 5/10 [00:04<00:04,  1.21it/s]
 60%|██████    | 6/10 [00:04<00:03,  1.23it/s]
 70%|███████   | 7/10 [00:05<00:02,  1.22it/s]
 80%|████████  | 8/10 [00:06<00:01,  1.25it/s]
 90%|█████████ | 9/10 [00:07<00:00,  1.24it/s]
100%|██████████| 10/10 [00:08<00:00,  1.18it/s]
100%|██████████| 10/10 [00:08<00:00,  1.23it/s]
+-> create 1280 synthetic samples
+-> test with 'LR'
+LR tn, fp: 275, 56
+LR fn, tp: 0, 13
+LR f1 score: 0.317
+LR cohens kappa score: 0.271
+LR average precision score: 0.429
+
+-> 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: 323, 8
+KNN fn, tp: 0, 13
+KNN f1 score: 0.765
+KNN cohens kappa score: 0.753
+
+
+====== Step 4/5 =======
+-> Shuffling data
+-> Spliting data to slices
+
+------ Step 4/5: Slice 1/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+
  0%|          | 0/10 [00:00<?, ?it/s]
 10%|█         | 1/10 [00:00<00:07,  1.21it/s]
 20%|██        | 2/10 [00:01<00:06,  1.23it/s]
 30%|███       | 3/10 [00:02<00:05,  1.23it/s]
 40%|████      | 4/10 [00:03<00:04,  1.25it/s]
 50%|█████     | 5/10 [00:03<00:03,  1.27it/s]
 60%|██████    | 6/10 [00:04<00:03,  1.32it/s]
 70%|███████   | 7/10 [00:05<00:02,  1.34it/s]
 80%|████████  | 8/10 [00:06<00:01,  1.31it/s]
 90%|█████████ | 9/10 [00:06<00:00,  1.33it/s]
100%|██████████| 10/10 [00:07<00:00,  1.24it/s]
100%|██████████| 10/10 [00:07<00:00,  1.27it/s]
+-> create 1278 synthetic samples
+-> test with 'LR'
+LR tn, fp: 318, 15
+LR fn, tp: 5, 8
+LR f1 score: 0.444
+LR cohens kappa score: 0.416
+LR average precision score: 0.386
+
+-> 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: 319, 14
+KNN fn, tp: 0, 13
+KNN f1 score: 0.650
+KNN cohens kappa score: 0.631
+
+
+------ Step 4/5: Slice 2/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+
  0%|          | 0/10 [00:00<?, ?it/s]
 10%|█         | 1/10 [00:00<00:06,  1.33it/s]
 20%|██        | 2/10 [00:01<00:05,  1.44it/s]
 30%|███       | 3/10 [00:02<00:05,  1.34it/s]
 40%|████      | 4/10 [00:02<00:04,  1.33it/s]
 50%|█████     | 5/10 [00:03<00:03,  1.29it/s]
 60%|██████    | 6/10 [00:04<00:03,  1.22it/s]
 70%|███████   | 7/10 [00:05<00:02,  1.24it/s]
 80%|████████  | 8/10 [00:06<00:01,  1.26it/s]
 90%|█████████ | 9/10 [00:06<00:00,  1.32it/s]
100%|██████████| 10/10 [00:07<00:00,  1.34it/s]
100%|██████████| 10/10 [00:07<00:00,  1.31it/s]
+-> create 1278 synthetic samples
+-> test with 'LR'
+LR tn, fp: 288, 45
+LR fn, tp: 0, 13
+LR f1 score: 0.366
+LR cohens kappa score: 0.325
+LR average precision score: 0.386
+
+-> 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: 320, 13
+KNN fn, tp: 3, 10
+KNN f1 score: 0.556
+KNN cohens kappa score: 0.533
+
+
+------ Step 4/5: Slice 3/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+
  0%|          | 0/10 [00:00<?, ?it/s]
 10%|█         | 1/10 [00:00<00:07,  1.19it/s]
 20%|██        | 2/10 [00:01<00:06,  1.17it/s]
 30%|███       | 3/10 [00:02<00:05,  1.22it/s]
 40%|████      | 4/10 [00:03<00:04,  1.24it/s]
 50%|█████     | 5/10 [00:04<00:03,  1.28it/s]
 60%|██████    | 6/10 [00:04<00:03,  1.30it/s]
 70%|███████   | 7/10 [00:05<00:02,  1.37it/s]
 80%|████████  | 8/10 [00:06<00:01,  1.40it/s]
 90%|█████████ | 9/10 [00:06<00:00,  1.41it/s]
100%|██████████| 10/10 [00:07<00:00,  1.34it/s]
100%|██████████| 10/10 [00:07<00:00,  1.31it/s]
+-> create 1278 synthetic samples
+-> test with 'LR'
+LR tn, fp: 275, 58
+LR fn, tp: 0, 13
+LR f1 score: 0.310
+LR cohens kappa score: 0.263
+LR average precision score: 0.316
+
+-> 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: 326, 7
+KNN fn, tp: 0, 13
+KNN f1 score: 0.788
+KNN cohens kappa score: 0.778
+
+
+------ Step 4/5: Slice 4/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+
  0%|          | 0/10 [00:00<?, ?it/s]
 10%|█         | 1/10 [00:00<00:07,  1.16it/s]
 20%|██        | 2/10 [00:01<00:06,  1.15it/s]
 30%|███       | 3/10 [00:02<00:05,  1.25it/s]
 40%|████      | 4/10 [00:03<00:04,  1.30it/s]
 50%|█████     | 5/10 [00:03<00:03,  1.32it/s]
 60%|██████    | 6/10 [00:04<00:03,  1.31it/s]
 70%|███████   | 7/10 [00:05<00:02,  1.30it/s]
 80%|████████  | 8/10 [00:06<00:01,  1.23it/s]
 90%|█████████ | 9/10 [00:07<00:00,  1.20it/s]
100%|██████████| 10/10 [00:08<00:00,  1.23it/s]
100%|██████████| 10/10 [00:08<00:00,  1.25it/s]
+-> create 1278 synthetic samples
+-> test with 'LR'
+LR tn, fp: 317, 16
+LR fn, tp: 7, 6
+LR f1 score: 0.343
+LR cohens kappa score: 0.310
+LR average precision score: 0.288
+
+-> 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: 317, 16
+KNN fn, tp: 0, 13
+KNN f1 score: 0.619
+KNN cohens kappa score: 0.598
+
+
+------ Step 4/5: Slice 5/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+
  0%|          | 0/10 [00:00<?, ?it/s]
 10%|█         | 1/10 [00:00<00:08,  1.11it/s]
 20%|██        | 2/10 [00:01<00:06,  1.26it/s]
 30%|███       | 3/10 [00:02<00:05,  1.25it/s]
 40%|████      | 4/10 [00:03<00:04,  1.33it/s]
 50%|█████     | 5/10 [00:03<00:03,  1.32it/s]
 60%|██████    | 6/10 [00:04<00:03,  1.30it/s]
 70%|███████   | 7/10 [00:05<00:02,  1.30it/s]
 80%|████████  | 8/10 [00:06<00:01,  1.36it/s]
 90%|█████████ | 9/10 [00:06<00:00,  1.39it/s]
100%|██████████| 10/10 [00:07<00:00,  1.43it/s]
100%|██████████| 10/10 [00:07<00:00,  1.35it/s]
+-> create 1280 synthetic samples
+-> test with 'LR'
+LR tn, fp: 284, 47
+LR fn, tp: 0, 13
+LR f1 score: 0.356
+LR cohens kappa score: 0.314
+LR average precision score: 0.337
+
+-> 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: 316, 15
+KNN fn, tp: 0, 13
+KNN f1 score: 0.634
+KNN cohens kappa score: 0.614
+
+
+====== Step 5/5 =======
+-> Shuffling data
+-> Spliting data to slices
+
+------ Step 5/5: Slice 1/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+
  0%|          | 0/10 [00:00<?, ?it/s]
 10%|█         | 1/10 [00:00<00:06,  1.34it/s]
 20%|██        | 2/10 [00:01<00:05,  1.39it/s]
 30%|███       | 3/10 [00:02<00:05,  1.36it/s]
 40%|████      | 4/10 [00:02<00:04,  1.37it/s]
 50%|█████     | 5/10 [00:03<00:03,  1.36it/s]
 60%|██████    | 6/10 [00:04<00:03,  1.33it/s]
 70%|███████   | 7/10 [00:05<00:02,  1.31it/s]
 80%|████████  | 8/10 [00:06<00:01,  1.31it/s]
 90%|█████████ | 9/10 [00:06<00:00,  1.34it/s]
100%|██████████| 10/10 [00:07<00:00,  1.31it/s]
100%|██████████| 10/10 [00:07<00:00,  1.33it/s]
+-> create 1278 synthetic samples
+-> test with 'LR'
+LR tn, fp: 277, 56
+LR fn, tp: 0, 13
+LR f1 score: 0.317
+LR cohens kappa score: 0.271
+LR average precision score: 0.265
+
+-> 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: 318, 15
+KNN fn, tp: 1, 12
+KNN f1 score: 0.600
+KNN cohens kappa score: 0.579
+
+
+------ Step 5/5: Slice 2/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+
  0%|          | 0/10 [00:00<?, ?it/s]
 10%|█         | 1/10 [00:00<00:06,  1.39it/s]
 20%|██        | 2/10 [00:01<00:05,  1.41it/s]
 30%|███       | 3/10 [00:02<00:05,  1.28it/s]
 40%|████      | 4/10 [00:03<00:04,  1.26it/s]
 50%|█████     | 5/10 [00:03<00:04,  1.24it/s]
 60%|██████    | 6/10 [00:04<00:03,  1.16it/s]
 70%|███████   | 7/10 [00:05<00:02,  1.18it/s]
 80%|████████  | 8/10 [00:06<00:01,  1.27it/s]
 90%|█████████ | 9/10 [00:07<00:00,  1.27it/s]
100%|██████████| 10/10 [00:07<00:00,  1.28it/s]
100%|██████████| 10/10 [00:07<00:00,  1.26it/s]
+-> create 1278 synthetic samples
+-> test with 'LR'
+LR tn, fp: 279, 54
+LR fn, tp: 0, 13
+LR f1 score: 0.325
+LR cohens kappa score: 0.280
+LR average precision score: 0.341
+
+-> 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: 319, 14
+KNN fn, tp: 0, 13
+KNN f1 score: 0.650
+KNN cohens kappa score: 0.631
+
+
+------ Step 5/5: Slice 3/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+
  0%|          | 0/10 [00:00<?, ?it/s]
 10%|█         | 1/10 [00:00<00:07,  1.17it/s]
 20%|██        | 2/10 [00:01<00:07,  1.03it/s]
 30%|███       | 3/10 [00:02<00:06,  1.04it/s]
 40%|████      | 4/10 [00:03<00:05,  1.09it/s]
 50%|█████     | 5/10 [00:04<00:04,  1.09it/s]
 60%|██████    | 6/10 [00:05<00:03,  1.08it/s]
 70%|███████   | 7/10 [00:06<00:02,  1.05it/s]
 80%|████████  | 8/10 [00:07<00:01,  1.11it/s]
 90%|█████████ | 9/10 [00:08<00:00,  1.14it/s]
100%|██████████| 10/10 [00:08<00:00,  1.16it/s]
100%|██████████| 10/10 [00:09<00:00,  1.11it/s]
+-> create 1278 synthetic samples
+-> test with 'LR'
+LR tn, fp: 318, 15
+LR fn, tp: 4, 9
+LR f1 score: 0.486
+LR cohens kappa score: 0.460
+LR average precision score: 0.379
+
+-> 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: 315, 18
+KNN fn, tp: 0, 13
+KNN f1 score: 0.591
+KNN cohens kappa score: 0.568
+
+
+------ Step 5/5: Slice 4/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+
  0%|          | 0/10 [00:00<?, ?it/s]
 10%|█         | 1/10 [00:00<00:08,  1.09it/s]
 20%|██        | 2/10 [00:01<00:06,  1.20it/s]
 30%|███       | 3/10 [00:02<00:05,  1.25it/s]
 40%|████      | 4/10 [00:03<00:04,  1.25it/s]
 50%|█████     | 5/10 [00:04<00:04,  1.15it/s]
 60%|██████    | 6/10 [00:05<00:03,  1.17it/s]
 70%|███████   | 7/10 [00:05<00:02,  1.24it/s]
 80%|████████  | 8/10 [00:06<00:01,  1.24it/s]
 90%|█████████ | 9/10 [00:07<00:00,  1.32it/s]
100%|██████████| 10/10 [00:07<00:00,  1.33it/s]
100%|██████████| 10/10 [00:07<00:00,  1.25it/s]
+-> create 1278 synthetic samples
+-> test with 'LR'
+LR tn, fp: 277, 56
+LR fn, tp: 0, 13
+LR f1 score: 0.317
+LR cohens kappa score: 0.271
+LR average precision score: 0.285
+
+-> 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: 301, 32
+KNN fn, tp: 0, 13
+KNN f1 score: 0.448
+KNN cohens kappa score: 0.414
+
+
+------ Step 5/5: Slice 5/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+
  0%|          | 0/10 [00:00<?, ?it/s]
 10%|█         | 1/10 [00:00<00:08,  1.06it/s]
 20%|██        | 2/10 [00:01<00:07,  1.06it/s]
 30%|███       | 3/10 [00:02<00:06,  1.10it/s]
 40%|████      | 4/10 [00:03<00:04,  1.21it/s]
 50%|█████     | 5/10 [00:04<00:04,  1.23it/s]
 60%|██████    | 6/10 [00:04<00:03,  1.29it/s]
 70%|███████   | 7/10 [00:05<00:02,  1.24it/s]
 80%|████████  | 8/10 [00:06<00:01,  1.28it/s]
 90%|█████████ | 9/10 [00:07<00:00,  1.29it/s]
100%|██████████| 10/10 [00:08<00:00,  1.29it/s]
100%|██████████| 10/10 [00:08<00:00,  1.24it/s]
+-> create 1280 synthetic samples
+-> test with 'LR'
+LR tn, fp: 297, 34
+LR fn, tp: 1, 12
+LR f1 score: 0.407
+LR cohens kappa score: 0.370
+LR average precision score: 0.463
+
+-> 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: 321, 10
+KNN fn, tp: 0, 13
+KNN f1 score: 0.722
+KNN cohens kappa score: 0.708
+
+### Exercise is done.
+
+-----[ LR ]-----
+maximum:
+LR tn, fp: 319, 61
+LR fn, tp: 8, 13
+LR f1 score: 0.529
+LR cohens kappa score: 0.506
+LR average precision score: 0.463
+
+
+average:
+LR tn, fp: 294.2, 38.4
+LR fn, tp: 1.84, 11.16
+LR f1 score: 0.367
+LR cohens kappa score: 0.328
+LR average precision score: 0.357
+
+
+minimum:
+LR tn, fp: 272, 12
+LR fn, tp: 0, 5
+LR f1 score: 0.286
+LR cohens kappa score: 0.250
+LR average precision score: 0.265
+
+
+-----[ GB ]-----
+maximum:
+GB tn, fp: 333, 2
+GB fn, tp: 2, 13
+GB f1 score: 1.000
+GB cohens kappa score: 1.000
+
+
+average:
+GB tn, fp: 332.16, 0.44
+GB fn, tp: 0.24, 12.76
+GB f1 score: 0.974
+GB cohens kappa score: 0.973
+
+
+minimum:
+GB tn, fp: 329, 0
+GB fn, tp: 0, 11
+GB f1 score: 0.917
+GB cohens kappa score: 0.914
+
+
+-----[ KNN ]-----
+maximum:
+KNN tn, fp: 326, 32
+KNN fn, tp: 6, 13
+KNN f1 score: 0.788
+KNN cohens kappa score: 0.778
+
+
+average:
+KNN tn, fp: 318.2, 14.4
+KNN fn, tp: 0.56, 12.44
+KNN f1 score: 0.634
+KNN cohens kappa score: 0.614
+
+
+minimum:
+KNN tn, fp: 301, 5
+KNN fn, tp: 0, 7
+KNN f1 score: 0.448
+KNN cohens kappa score: 0.414
+

data_result/CTAB-GAN/folding_car_good.csv

@@ -0,0 +1,92 @@
+LR
+Nr.;TN;TP;FN;FP;f1 score;cohens kappa score;average precision score
+1;162.000;10.000;4.000;170.000;0.103;0.030;0.067
+2;197.000;10.000;4.000;135.000;0.126;0.056;0.096
+3;182.000;8.000;6.000;150.000;0.093;0.020;0.060
+4;168.000;10.000;4.000;164.000;0.106;0.034;0.086
+5;185.000;8.000;5.000;146.000;0.096;0.028;0.046
+6;164.000;9.000;5.000;168.000;0.094;0.021;0.078
+7;171.000;11.000;3.000;161.000;0.118;0.047;0.093
+8;181.000;10.000;4.000;151.000;0.114;0.043;0.081
+9;200.000;7.000;7.000;132.000;0.092;0.019;0.058
+10;186.000;9.000;4.000;145.000;0.108;0.041;0.093
+11;186.000;11.000;3.000;146.000;0.129;0.059;0.071
+12;183.000;10.000;4.000;149.000;0.116;0.045;0.060
+13;167.000;11.000;3.000;165.000;0.116;0.044;0.056
+14;170.000;11.000;3.000;162.000;0.118;0.046;0.074
+15;188.000;7.000;6.000;143.000;0.086;0.018;0.079
+16;177.000;13.000;1.000;155.000;0.143;0.074;0.078
+17;169.000;9.000;5.000;163.000;0.097;0.024;0.057
+18;167.000;9.000;5.000;165.000;0.096;0.023;0.053
+19;175.000;11.000;3.000;157.000;0.121;0.050;0.051
+20;166.000;12.000;1.000;165.000;0.126;0.060;0.075
+21;159.000;8.000;6.000;173.000;0.082;0.007;0.053
+22;185.000;9.000;5.000;147.000;0.106;0.034;0.079
+23;168.000;10.000;4.000;164.000;0.106;0.034;0.142
+24;193.000;7.000;7.000;139.000;0.087;0.015;0.071
+25;176.000;7.000;6.000;155.000;0.080;0.011;0.072
+max;200.000;13.000;7.000;173.000;0.143;0.074;0.142
+avg;177.000;9.480;4.320;154.800;0.106;0.035;0.073
+min;159.000;7.000;1.000;132.000;0.080;0.007;0.046
+---
+GB
+Nr.;TN;TP;FN;FP;f1 score;cohens kappa score
+1;332.000;13.000;1.000;0.000;0.963;0.961
+2;332.000;8.000;6.000;0.000;0.727;0.719
+3;331.000;10.000;4.000;1.000;0.800;0.793
+4;331.000;9.000;5.000;1.000;0.750;0.741
+5;327.000;11.000;2.000;4.000;0.786;0.777
+6;331.000;12.000;2.000;1.000;0.889;0.884
+7;331.000;13.000;1.000;1.000;0.929;0.926
+8;332.000;11.000;3.000;0.000;0.880;0.876
+9;331.000;9.000;5.000;1.000;0.750;0.741
+10;327.000;11.000;2.000;4.000;0.786;0.777
+11;332.000;11.000;3.000;0.000;0.880;0.876
+12;330.000;8.000;6.000;2.000;0.667;0.655
+13;330.000;4.000;10.000;2.000;0.400;0.385
+14;332.000;10.000;4.000;0.000;0.833;0.828
+15;329.000;10.000;3.000;2.000;0.800;0.792
+16;331.000;7.000;7.000;1.000;0.636;0.625
+17;330.000;8.000;6.000;2.000;0.667;0.655
+18;330.000;12.000;2.000;2.000;0.857;0.851
+19;331.000;8.000;6.000;1.000;0.696;0.686
+20;329.000;8.000;5.000;2.000;0.696;0.685
+21;331.000;9.000;5.000;1.000;0.750;0.741
+22;331.000;9.000;5.000;1.000;0.750;0.741
+23;327.000;12.000;2.000;5.000;0.774;0.764
+24;332.000;9.000;5.000;0.000;0.783;0.775
+25;331.000;10.000;3.000;0.000;0.870;0.865
+max;332.000;13.000;10.000;5.000;0.963;0.961
+avg;330.440;9.680;4.120;1.360;0.773;0.765
+min;327.000;4.000;1.000;0.000;0.400;0.385
+---
+KNN
+Nr.;TN;TP;FN;FP;f1 score;cohens kappa score
+1;294.000;14.000;0.000;38.000;0.424;0.385
+2;280.000;14.000;0.000;52.000;0.350;0.303
+3;291.000;14.000;0.000;41.000;0.406;0.365
+4;292.000;14.000;0.000;40.000;0.412;0.371
+5;292.000;12.000;1.000;39.000;0.375;0.335
+6;298.000;14.000;0.000;34.000;0.452;0.415
+7;307.000;14.000;0.000;25.000;0.528;0.498
+8;292.000;13.000;1.000;40.000;0.388;0.346
+9;261.000;13.000;1.000;71.000;0.265;0.211
+10;288.000;13.000;0.000;43.000;0.377;0.336
+11;283.000;14.000;0.000;49.000;0.364;0.319
+12;282.000;14.000;0.000;50.000;0.359;0.313
+13;297.000;14.000;0.000;35.000;0.444;0.407
+14;290.000;14.000;0.000;42.000;0.400;0.358
+15;270.000;13.000;0.000;61.000;0.299;0.251
+16;308.000;14.000;0.000;24.000;0.538;0.509
+17;267.000;14.000;0.000;65.000;0.301;0.249
+18;276.000;14.000;0.000;56.000;0.333;0.285
+19;300.000;14.000;0.000;32.000;0.467;0.431
+20;315.000;7.000;6.000;16.000;0.389;0.358
+21;278.000;14.000;0.000;54.000;0.341;0.294
+22;291.000;14.000;0.000;41.000;0.406;0.365
+23;305.000;10.000;4.000;27.000;0.392;0.354
+24;289.000;11.000;3.000;43.000;0.324;0.277
+25;294.000;13.000;0.000;37.000;0.413;0.375
+max;315.000;14.000;6.000;71.000;0.538;0.509
+avg;289.600;13.160;0.640;42.200;0.390;0.349
+min;261.000;7.000;0.000;16.000;0.265;0.211

data_result/CTAB-GAN/folding_car_good.log

@@ -7,11 +7,720 @@
 Load 'data_input/folding_car_good'
 from pickle file
 Data loaded.
-Traceback (most recent call last):
-  File "/benchmark/data/run_all_exercises.py", line 13, in <module>
-    runExercise(dataset, None, name, f)
-  File "/benchmark/data/library/analysis.py", line 164, in runExercise
-    gan = ganCreator(data)
-  File "/benchmark/data/library/analysis.py", line 268, in <lambda>
-    , ("CTAB-GAN",      lambda _data: CtabGan())
-NameError: name 'CtabGan' is not defined
+-> 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
+
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100%|██████████| 10/10 [00:08<00:00,  1.27it/s]
100%|██████████| 10/10 [00:08<00:00,  1.22it/s]
+-> create 1272 synthetic samples
+-> test with 'LR'
+LR tn, fp: 162, 170
+LR fn, tp: 4, 10
+LR f1 score: 0.103
+LR cohens kappa score: 0.030
+LR average precision score: 0.067
+
+-> test with 'GB'
+GB tn, fp: 332, 0
+GB fn, tp: 1, 13
+GB f1 score: 0.963
+GB cohens kappa score: 0.961
+
+-> test with 'KNN'
+KNN tn, fp: 294, 38
+KNN fn, tp: 0, 14
+KNN f1 score: 0.424
+KNN cohens kappa score: 0.385
+
+
+------ Step 1/5: Slice 2/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+
  0%|          | 0/10 [00:00<?, ?it/s]
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100%|██████████| 10/10 [00:08<00:00,  1.22it/s]
100%|██████████| 10/10 [00:08<00:00,  1.19it/s]
+-> create 1272 synthetic samples
+-> test with 'LR'
+LR tn, fp: 197, 135
+LR fn, tp: 4, 10
+LR f1 score: 0.126
+LR cohens kappa score: 0.056
+LR average precision score: 0.096
+
+-> test with 'GB'
+GB tn, fp: 332, 0
+GB fn, tp: 6, 8
+GB f1 score: 0.727
+GB cohens kappa score: 0.719
+
+-> test with 'KNN'
+KNN tn, fp: 280, 52
+KNN fn, tp: 0, 14
+KNN f1 score: 0.350
+KNN cohens kappa score: 0.303
+
+
+------ Step 1/5: Slice 3/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+
  0%|          | 0/10 [00:00<?, ?it/s]
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100%|██████████| 10/10 [00:08<00:00,  1.28it/s]
100%|██████████| 10/10 [00:08<00:00,  1.18it/s]
+-> create 1272 synthetic samples
+-> test with 'LR'
+LR tn, fp: 182, 150
+LR fn, tp: 6, 8
+LR f1 score: 0.093
+LR cohens kappa score: 0.020
+LR average precision score: 0.060
+
+-> 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: 291, 41
+KNN fn, tp: 0, 14
+KNN f1 score: 0.406
+KNN cohens kappa score: 0.365
+
+
+------ Step 1/5: Slice 4/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+
  0%|          | 0/10 [00:00<?, ?it/s]
 10%|█         | 1/10 [00:00<00:07,  1.16it/s]
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100%|██████████| 10/10 [00:07<00:00,  1.32it/s]
100%|██████████| 10/10 [00:07<00:00,  1.28it/s]
+-> create 1272 synthetic samples
+-> 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: 0.086
+
+-> test with 'GB'
+GB tn, fp: 331, 1
+GB fn, tp: 5, 9
+GB f1 score: 0.750
+GB cohens kappa score: 0.741
+
+-> test with 'KNN'
+KNN tn, fp: 292, 40
+KNN fn, tp: 0, 14
+KNN f1 score: 0.412
+KNN cohens kappa score: 0.371
+
+
+------ Step 1/5: Slice 5/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+
  0%|          | 0/10 [00:00<?, ?it/s]
 10%|█         | 1/10 [00:00<00:06,  1.38it/s]
 20%|██        | 2/10 [00:01<00:05,  1.43it/s]
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100%|██████████| 10/10 [00:07<00:00,  1.33it/s]
100%|██████████| 10/10 [00:07<00:00,  1.35it/s]
+-> create 1272 synthetic samples
+-> test with 'LR'
+LR tn, fp: 185, 146
+LR fn, tp: 5, 8
+LR f1 score: 0.096
+LR cohens kappa score: 0.028
+LR average precision score: 0.046
+
+-> test with 'GB'
+GB tn, fp: 327, 4
+GB fn, tp: 2, 11
+GB f1 score: 0.786
+GB cohens kappa score: 0.777
+
+-> test with 'KNN'
+KNN tn, fp: 292, 39
+KNN fn, tp: 1, 12
+KNN f1 score: 0.375
+KNN cohens kappa score: 0.335
+
+
+====== Step 2/5 =======
+-> Shuffling data
+-> Spliting data to slices
+
+------ Step 2/5: Slice 1/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+
  0%|          | 0/10 [00:00<?, ?it/s]
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100%|██████████| 10/10 [00:08<00:00,  1.30it/s]
100%|██████████| 10/10 [00:08<00:00,  1.24it/s]
+-> create 1272 synthetic samples
+-> test with 'LR'
+LR tn, fp: 164, 168
+LR fn, tp: 5, 9
+LR f1 score: 0.094
+LR cohens kappa score: 0.021
+LR average precision score: 0.078
+
+-> 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: 298, 34
+KNN fn, tp: 0, 14
+KNN f1 score: 0.452
+KNN cohens kappa score: 0.415
+
+
+------ Step 2/5: Slice 2/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+
  0%|          | 0/10 [00:00<?, ?it/s]
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100%|██████████| 10/10 [00:07<00:00,  1.37it/s]
100%|██████████| 10/10 [00:07<00:00,  1.35it/s]
+-> create 1272 synthetic samples
+-> test with 'LR'
+LR tn, fp: 171, 161
+LR fn, tp: 3, 11
+LR f1 score: 0.118
+LR cohens kappa score: 0.047
+LR average precision score: 0.093
+
+-> test with 'GB'
+GB tn, fp: 331, 1
+GB fn, tp: 1, 13
+GB f1 score: 0.929
+GB cohens kappa score: 0.926
+
+-> 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 2/5: Slice 3/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+
  0%|          | 0/10 [00:00<?, ?it/s]
 10%|█         | 1/10 [00:00<00:06,  1.30it/s]
 20%|██        | 2/10 [00:01<00:06,  1.33it/s]
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 50%|█████     | 5/10 [00:04<00:04,  1.16it/s]
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100%|██████████| 10/10 [00:08<00:00,  1.22it/s]
100%|██████████| 10/10 [00:08<00:00,  1.23it/s]
+-> create 1272 synthetic samples
+-> test with 'LR'
+LR tn, fp: 181, 151
+LR fn, tp: 4, 10
+LR f1 score: 0.114
+LR cohens kappa score: 0.043
+LR average precision score: 0.081
+
+-> test with 'GB'
+GB tn, fp: 332, 0
+GB fn, tp: 3, 11
+GB f1 score: 0.880
+GB cohens kappa score: 0.876
+
+-> test with 'KNN'
+KNN tn, fp: 292, 40
+KNN fn, tp: 1, 13
+KNN f1 score: 0.388
+KNN cohens kappa score: 0.346
+
+
+------ Step 2/5: Slice 4/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+
  0%|          | 0/10 [00:00<?, ?it/s]
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100%|██████████| 10/10 [00:08<00:00,  1.19it/s]
100%|██████████| 10/10 [00:08<00:00,  1.22it/s]
+-> create 1272 synthetic samples
+-> test with 'LR'
+LR tn, fp: 200, 132
+LR fn, tp: 7, 7
+LR f1 score: 0.092
+LR cohens kappa score: 0.019
+LR average precision score: 0.058
+
+-> test with 'GB'
+GB tn, fp: 331, 1
+GB fn, tp: 5, 9
+GB f1 score: 0.750
+GB cohens kappa score: 0.741
+
+-> test with 'KNN'
+KNN tn, fp: 261, 71
+KNN fn, tp: 1, 13
+KNN f1 score: 0.265
+KNN cohens kappa score: 0.211
+
+
+------ Step 2/5: Slice 5/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+
  0%|          | 0/10 [00:00<?, ?it/s]
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100%|██████████| 10/10 [00:07<00:00,  1.26it/s]
+-> create 1272 synthetic samples
+-> test with 'LR'
+LR tn, fp: 186, 145
+LR fn, tp: 4, 9
+LR f1 score: 0.108
+LR cohens kappa score: 0.041
+LR average precision score: 0.093
+
+-> test with 'GB'
+GB tn, fp: 327, 4
+GB fn, tp: 2, 11
+GB f1 score: 0.786
+GB cohens kappa score: 0.777
+
+-> test with 'KNN'
+KNN tn, fp: 288, 43
+KNN fn, tp: 0, 13
+KNN f1 score: 0.377
+KNN cohens kappa score: 0.336
+
+
+====== Step 3/5 =======
+-> Shuffling data
+-> Spliting data to slices
+
+------ Step 3/5: Slice 1/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+
  0%|          | 0/10 [00:00<?, ?it/s]
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100%|██████████| 10/10 [00:08<00:00,  1.21it/s]
100%|██████████| 10/10 [00:08<00:00,  1.24it/s]
+-> create 1272 synthetic samples
+-> test with 'LR'
+LR tn, fp: 186, 146
+LR fn, tp: 3, 11
+LR f1 score: 0.129
+LR cohens kappa score: 0.059
+LR average precision score: 0.071
+
+-> test with 'GB'
+GB tn, fp: 332, 0
+GB fn, tp: 3, 11
+GB f1 score: 0.880
+GB cohens kappa score: 0.876
+
+-> test with 'KNN'
+KNN tn, fp: 283, 49
+KNN fn, tp: 0, 14
+KNN f1 score: 0.364
+KNN cohens kappa score: 0.319
+
+
+------ Step 3/5: Slice 2/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+
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 70%|███████   | 7/10 [00:05<00:02,  1.21it/s]
 80%|████████  | 8/10 [00:06<00:01,  1.24it/s]
 90%|█████████ | 9/10 [00:07<00:00,  1.26it/s]
100%|██████████| 10/10 [00:07<00:00,  1.31it/s]
100%|██████████| 10/10 [00:07<00:00,  1.30it/s]
+-> create 1272 synthetic samples
+-> test with 'LR'
+LR tn, fp: 183, 149
+LR fn, tp: 4, 10
+LR f1 score: 0.116
+LR cohens kappa score: 0.045
+LR average precision score: 0.060
+
+-> 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: 282, 50
+KNN fn, tp: 0, 14
+KNN f1 score: 0.359
+KNN cohens kappa score: 0.313
+
+
+------ Step 3/5: Slice 3/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+
  0%|          | 0/10 [00:00<?, ?it/s]
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 20%|██        | 2/10 [00:01<00:05,  1.33it/s]
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 60%|██████    | 6/10 [00:04<00:03,  1.26it/s]
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100%|██████████| 10/10 [00:08<00:00,  1.21it/s]
100%|██████████| 10/10 [00:08<00:00,  1.23it/s]
+-> create 1272 synthetic samples
+-> test with 'LR'
+LR tn, fp: 167, 165
+LR fn, tp: 3, 11
+LR f1 score: 0.116
+LR cohens kappa score: 0.044
+LR average precision score: 0.056
+
+-> test with 'GB'
+GB tn, fp: 330, 2
+GB fn, tp: 10, 4
+GB f1 score: 0.400
+GB cohens kappa score: 0.385
+
+-> test with 'KNN'
+KNN tn, fp: 297, 35
+KNN fn, tp: 0, 14
+KNN f1 score: 0.444
+KNN cohens kappa score: 0.407
+
+
+------ Step 3/5: Slice 4/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+
  0%|          | 0/10 [00:00<?, ?it/s]
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 20%|██        | 2/10 [00:01<00:05,  1.46it/s]
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 40%|████      | 4/10 [00:03<00:04,  1.25it/s]
 50%|█████     | 5/10 [00:03<00:03,  1.25it/s]
 60%|██████    | 6/10 [00:04<00:03,  1.31it/s]
 70%|███████   | 7/10 [00:05<00:02,  1.24it/s]
 80%|████████  | 8/10 [00:06<00:01,  1.25it/s]
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100%|██████████| 10/10 [00:08<00:00,  1.15it/s]
100%|██████████| 10/10 [00:08<00:00,  1.22it/s]
+-> create 1272 synthetic samples
+-> test with 'LR'
+LR tn, fp: 170, 162
+LR fn, tp: 3, 11
+LR f1 score: 0.118
+LR cohens kappa score: 0.046
+LR average precision score: 0.074
+
+-> test with 'GB'
+GB tn, fp: 332, 0
+GB fn, tp: 4, 10
+GB f1 score: 0.833
+GB cohens kappa score: 0.828
+
+-> test with 'KNN'
+KNN tn, fp: 290, 42
+KNN fn, tp: 0, 14
+KNN f1 score: 0.400
+KNN cohens kappa score: 0.358
+
+
+------ Step 3/5: Slice 5/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+
  0%|          | 0/10 [00:00<?, ?it/s]
 10%|█         | 1/10 [00:00<00:07,  1.20it/s]
 20%|██        | 2/10 [00:01<00:05,  1.36it/s]
 30%|███       | 3/10 [00:02<00:05,  1.36it/s]
 40%|████      | 4/10 [00:03<00:04,  1.28it/s]
 50%|█████     | 5/10 [00:03<00:03,  1.28it/s]
 60%|██████    | 6/10 [00:04<00:03,  1.23it/s]
 70%|███████   | 7/10 [00:05<00:02,  1.17it/s]
 80%|████████  | 8/10 [00:06<00:01,  1.15it/s]
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100%|██████████| 10/10 [00:08<00:00,  1.17it/s]
100%|██████████| 10/10 [00:08<00:00,  1.21it/s]
+-> create 1272 synthetic samples
+-> test with 'LR'
+LR tn, fp: 188, 143
+LR fn, tp: 6, 7
+LR f1 score: 0.086
+LR cohens kappa score: 0.018
+LR average precision score: 0.079
+
+-> test with 'GB'
+GB tn, fp: 329, 2
+GB fn, tp: 3, 10
+GB f1 score: 0.800
+GB cohens kappa score: 0.792
+
+-> test with 'KNN'
+KNN tn, fp: 270, 61
+KNN fn, tp: 0, 13
+KNN f1 score: 0.299
+KNN cohens kappa score: 0.251
+
+
+====== Step 4/5 =======
+-> Shuffling data
+-> Spliting data to slices
+
+------ Step 4/5: Slice 1/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+
  0%|          | 0/10 [00:00<?, ?it/s]
 10%|█         | 1/10 [00:00<00:07,  1.21it/s]
 20%|██        | 2/10 [00:01<00:05,  1.52it/s]
 30%|███       | 3/10 [00:02<00:05,  1.26it/s]
 40%|████      | 4/10 [00:03<00:04,  1.25it/s]
 50%|█████     | 5/10 [00:03<00:04,  1.23it/s]
 60%|██████    | 6/10 [00:04<00:03,  1.22it/s]
 70%|███████   | 7/10 [00:05<00:02,  1.21it/s]
 80%|████████  | 8/10 [00:06<00:01,  1.16it/s]
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100%|██████████| 10/10 [00:08<00:00,  1.14it/s]
100%|██████████| 10/10 [00:08<00:00,  1.20it/s]
+-> create 1272 synthetic samples
+-> test with 'LR'
+LR tn, fp: 177, 155
+LR fn, tp: 1, 13
+LR f1 score: 0.143
+LR cohens kappa score: 0.074
+LR average precision score: 0.078
+
+-> 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: 308, 24
+KNN fn, tp: 0, 14
+KNN f1 score: 0.538
+KNN cohens kappa score: 0.509
+
+
+------ Step 4/5: Slice 2/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+
  0%|          | 0/10 [00:00<?, ?it/s]
 10%|█         | 1/10 [00:00<00:07,  1.23it/s]
 20%|██        | 2/10 [00:01<00:06,  1.23it/s]
 30%|███       | 3/10 [00:02<00:05,  1.33it/s]
 40%|████      | 4/10 [00:03<00:04,  1.23it/s]
 50%|█████     | 5/10 [00:04<00:04,  1.17it/s]
 60%|██████    | 6/10 [00:04<00:03,  1.23it/s]
 70%|███████   | 7/10 [00:05<00:02,  1.24it/s]
 80%|████████  | 8/10 [00:06<00:01,  1.21it/s]
 90%|█████████ | 9/10 [00:07<00:00,  1.20it/s]
100%|██████████| 10/10 [00:08<00:00,  1.23it/s]
100%|██████████| 10/10 [00:08<00:00,  1.23it/s]
+-> create 1272 synthetic samples
+-> test with 'LR'
+LR tn, fp: 169, 163
+LR fn, tp: 5, 9
+LR f1 score: 0.097
+LR cohens kappa score: 0.024
+LR average precision score: 0.057
+
+-> test with 'GB'
+GB tn, fp: 330, 2
+GB fn, tp: 6, 8
+GB f1 score: 0.667
+GB cohens kappa score: 0.655
+
+-> test with 'KNN'
+KNN tn, fp: 267, 65
+KNN fn, tp: 0, 14
+KNN f1 score: 0.301
+KNN cohens kappa score: 0.249
+
+
+------ Step 4/5: Slice 3/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+
  0%|          | 0/10 [00:00<?, ?it/s]
 10%|█         | 1/10 [00:00<00:07,  1.15it/s]
 20%|██        | 2/10 [00:01<00:05,  1.36it/s]
 30%|███       | 3/10 [00:02<00:05,  1.23it/s]
 40%|████      | 4/10 [00:03<00:05,  1.15it/s]
 50%|█████     | 5/10 [00:04<00:04,  1.16it/s]
 60%|██████    | 6/10 [00:05<00:03,  1.18it/s]
 70%|███████   | 7/10 [00:06<00:02,  1.11it/s]
 80%|████████  | 8/10 [00:06<00:01,  1.20it/s]
 90%|█████████ | 9/10 [00:07<00:00,  1.20it/s]
100%|██████████| 10/10 [00:08<00:00,  1.20it/s]
100%|██████████| 10/10 [00:08<00:00,  1.19it/s]
+-> create 1272 synthetic samples
+-> test with 'LR'
+LR tn, fp: 167, 165
+LR fn, tp: 5, 9
+LR f1 score: 0.096
+LR cohens kappa score: 0.023
+LR average precision score: 0.053
+
+-> test with 'GB'
+GB tn, fp: 330, 2
+GB fn, tp: 2, 12
+GB f1 score: 0.857
+GB cohens kappa score: 0.851
+
+-> test with 'KNN'
+KNN tn, fp: 276, 56
+KNN fn, tp: 0, 14
+KNN f1 score: 0.333
+KNN cohens kappa score: 0.285
+
+
+------ Step 4/5: Slice 4/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+
  0%|          | 0/10 [00:00<?, ?it/s]
 10%|█         | 1/10 [00:00<00:07,  1.17it/s]
 20%|██        | 2/10 [00:01<00:06,  1.31it/s]
 30%|███       | 3/10 [00:02<00:05,  1.29it/s]
 40%|████      | 4/10 [00:03<00:04,  1.21it/s]
 50%|█████     | 5/10 [00:03<00:03,  1.26it/s]
 60%|██████    | 6/10 [00:04<00:03,  1.23it/s]
 70%|███████   | 7/10 [00:05<00:02,  1.18it/s]
 80%|████████  | 8/10 [00:06<00:01,  1.21it/s]
 90%|█████████ | 9/10 [00:07<00:00,  1.18it/s]
100%|██████████| 10/10 [00:08<00:00,  1.27it/s]
100%|██████████| 10/10 [00:08<00:00,  1.24it/s]
+-> create 1272 synthetic samples
+-> test with 'LR'
+LR tn, fp: 175, 157
+LR fn, tp: 3, 11
+LR f1 score: 0.121
+LR cohens kappa score: 0.050
+LR average precision score: 0.051
+
+-> test with 'GB'
+GB tn, fp: 331, 1
+GB fn, tp: 6, 8
+GB f1 score: 0.696
+GB cohens kappa score: 0.686
+
+-> test with 'KNN'
+KNN tn, fp: 300, 32
+KNN fn, tp: 0, 14
+KNN f1 score: 0.467
+KNN cohens kappa score: 0.431
+
+
+------ Step 4/5: Slice 5/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+
  0%|          | 0/10 [00:00<?, ?it/s]
 10%|█         | 1/10 [00:00<00:07,  1.15it/s]
 20%|██        | 2/10 [00:01<00:06,  1.20it/s]
 30%|███       | 3/10 [00:02<00:06,  1.06it/s]
 40%|████      | 4/10 [00:03<00:05,  1.12it/s]
 50%|█████     | 5/10 [00:04<00:04,  1.23it/s]
 60%|██████    | 6/10 [00:05<00:03,  1.18it/s]
 70%|███████   | 7/10 [00:05<00:02,  1.23it/s]
 80%|████████  | 8/10 [00:06<00:01,  1.27it/s]
 90%|█████████ | 9/10 [00:07<00:00,  1.32it/s]
100%|██████████| 10/10 [00:08<00:00,  1.33it/s]
100%|██████████| 10/10 [00:08<00:00,  1.24it/s]
+-> create 1272 synthetic samples
+-> test with 'LR'
+LR tn, fp: 166, 165
+LR fn, tp: 1, 12
+LR f1 score: 0.126
+LR cohens kappa score: 0.060
+LR average precision score: 0.075
+
+-> test with 'GB'
+GB tn, fp: 329, 2
+GB fn, tp: 5, 8
+GB f1 score: 0.696
+GB cohens kappa score: 0.685
+
+-> test with 'KNN'
+KNN tn, fp: 315, 16
+KNN fn, tp: 6, 7
+KNN f1 score: 0.389
+KNN cohens kappa score: 0.358
+
+
+====== Step 5/5 =======
+-> Shuffling data
+-> Spliting data to slices
+
+------ Step 5/5: Slice 1/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+
  0%|          | 0/10 [00:00<?, ?it/s]
 10%|█         | 1/10 [00:00<00:08,  1.10it/s]
 20%|██        | 2/10 [00:01<00:06,  1.23it/s]
 30%|███       | 3/10 [00:02<00:05,  1.18it/s]
 40%|████      | 4/10 [00:03<00:05,  1.11it/s]
 50%|█████     | 5/10 [00:04<00:04,  1.12it/s]
 60%|██████    | 6/10 [00:05<00:03,  1.11it/s]
 70%|███████   | 7/10 [00:06<00:02,  1.16it/s]
 80%|████████  | 8/10 [00:06<00:01,  1.19it/s]
 90%|█████████ | 9/10 [00:07<00:00,  1.11it/s]
100%|██████████| 10/10 [00:08<00:00,  1.07it/s]
100%|██████████| 10/10 [00:08<00:00,  1.12it/s]
+-> create 1272 synthetic samples
+-> test with 'LR'
+LR tn, fp: 159, 173
+LR fn, tp: 6, 8
+LR f1 score: 0.082
+LR cohens kappa score: 0.007
+LR average precision score: 0.053
+
+-> test with 'GB'
+GB tn, fp: 331, 1
+GB fn, tp: 5, 9
+GB f1 score: 0.750
+GB cohens kappa score: 0.741
+
+-> test with 'KNN'
+KNN tn, fp: 278, 54
+KNN fn, tp: 0, 14
+KNN f1 score: 0.341
+KNN cohens kappa score: 0.294
+
+
+------ Step 5/5: Slice 2/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+
  0%|          | 0/10 [00:00<?, ?it/s]
 10%|█         | 1/10 [00:00<00:08,  1.12it/s]
 20%|██        | 2/10 [00:01<00:07,  1.05it/s]
 30%|███       | 3/10 [00:02<00:06,  1.10it/s]
 40%|████      | 4/10 [00:03<00:05,  1.09it/s]
 50%|█████     | 5/10 [00:04<00:05,  1.00s/it]
 60%|██████    | 6/10 [00:05<00:03,  1.02it/s]
 70%|███████   | 7/10 [00:06<00:03,  1.00s/it]
 80%|████████  | 8/10 [00:07<00:01,  1.00it/s]
 90%|█████████ | 9/10 [00:08<00:00,  1.05it/s]
100%|██████████| 10/10 [00:09<00:00,  1.14it/s]
100%|██████████| 10/10 [00:09<00:00,  1.07it/s]
+-> create 1272 synthetic samples
+-> test with 'LR'
+LR tn, fp: 185, 147
+LR fn, tp: 5, 9
+LR f1 score: 0.106
+LR cohens kappa score: 0.034
+LR average precision score: 0.079
+
+-> test with 'GB'
+GB tn, fp: 331, 1
+GB fn, tp: 5, 9
+GB f1 score: 0.750
+GB cohens kappa score: 0.741
+
+-> test with 'KNN'
+KNN tn, fp: 291, 41
+KNN fn, tp: 0, 14
+KNN f1 score: 0.406
+KNN cohens kappa score: 0.365
+
+
+------ Step 5/5: Slice 3/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+
  0%|          | 0/10 [00:00<?, ?it/s]
 10%|█         | 1/10 [00:00<00:07,  1.14it/s]
 20%|██        | 2/10 [00:01<00:06,  1.14it/s]
 30%|███       | 3/10 [00:02<00:06,  1.08it/s]
 40%|████      | 4/10 [00:03<00:05,  1.16it/s]
 50%|█████     | 5/10 [00:04<00:04,  1.21it/s]
 60%|██████    | 6/10 [00:05<00:03,  1.25it/s]
 70%|███████   | 7/10 [00:05<00:02,  1.28it/s]
 80%|████████  | 8/10 [00:06<00:01,  1.19it/s]
 90%|█████████ | 9/10 [00:07<00:00,  1.16it/s]
100%|██████████| 10/10 [00:08<00:00,  1.14it/s]
100%|██████████| 10/10 [00:08<00:00,  1.17it/s]
+-> create 1272 synthetic samples
+-> 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: 0.142
+
+-> 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: 305, 27
+KNN fn, tp: 4, 10
+KNN f1 score: 0.392
+KNN cohens kappa score: 0.354
+
+
+------ Step 5/5: Slice 4/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+
  0%|          | 0/10 [00:00<?, ?it/s]
 10%|█         | 1/10 [00:00<00:08,  1.12it/s]
 20%|██        | 2/10 [00:01<00:06,  1.18it/s]
 30%|███       | 3/10 [00:02<00:06,  1.13it/s]
 40%|████      | 4/10 [00:03<00:05,  1.14it/s]
 50%|█████     | 5/10 [00:04<00:04,  1.11it/s]
 60%|██████    | 6/10 [00:05<00:03,  1.14it/s]
 70%|███████   | 7/10 [00:06<00:02,  1.11it/s]
 80%|████████  | 8/10 [00:07<00:01,  1.12it/s]
 90%|█████████ | 9/10 [00:07<00:00,  1.12it/s]
100%|██████████| 10/10 [00:08<00:00,  1.14it/s]
100%|██████████| 10/10 [00:08<00:00,  1.13it/s]
+-> create 1272 synthetic samples
+-> test with 'LR'
+LR tn, fp: 193, 139
+LR fn, tp: 7, 7
+LR f1 score: 0.087
+LR cohens kappa score: 0.015
+LR average precision score: 0.071
+
+-> test with 'GB'
+GB tn, fp: 332, 0
+GB fn, tp: 5, 9
+GB f1 score: 0.783
+GB cohens kappa score: 0.775
+
+-> test with 'KNN'
+KNN tn, fp: 289, 43
+KNN fn, tp: 3, 11
+KNN f1 score: 0.324
+KNN cohens kappa score: 0.277
+
+
+------ Step 5/5: Slice 5/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+
  0%|          | 0/10 [00:00<?, ?it/s]
 10%|█         | 1/10 [00:00<00:07,  1.13it/s]
 20%|██        | 2/10 [00:01<00:07,  1.12it/s]
 30%|███       | 3/10 [00:02<00:05,  1.23it/s]
 40%|████      | 4/10 [00:03<00:04,  1.21it/s]
 50%|█████     | 5/10 [00:04<00:04,  1.19it/s]
 60%|██████    | 6/10 [00:05<00:03,  1.19it/s]
 70%|███████   | 7/10 [00:05<00:02,  1.21it/s]
 80%|████████  | 8/10 [00:06<00:01,  1.22it/s]
 90%|█████████ | 9/10 [00:07<00:00,  1.22it/s]
100%|██████████| 10/10 [00:08<00:00,  1.03s/it]
100%|██████████| 10/10 [00:08<00:00,  1.11it/s]
+-> create 1272 synthetic samples
+-> test with 'LR'
+LR tn, fp: 176, 155
+LR fn, tp: 6, 7
+LR f1 score: 0.080
+LR cohens kappa score: 0.011
+LR average precision score: 0.072
+
+-> 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: 294, 37
+KNN fn, tp: 0, 13
+KNN f1 score: 0.413
+KNN cohens kappa score: 0.375
+
+### Exercise is done.
+
+-----[ LR ]-----
+maximum:
+LR tn, fp: 200, 173
+LR fn, tp: 7, 13
+LR f1 score: 0.143
+LR cohens kappa score: 0.074
+LR average precision score: 0.142
+
+
+average:
+LR tn, fp: 177.0, 154.8
+LR fn, tp: 4.32, 9.48
+LR f1 score: 0.106
+LR cohens kappa score: 0.035
+LR average precision score: 0.073
+
+
+minimum:
+LR tn, fp: 159, 132
+LR fn, tp: 1, 7
+LR f1 score: 0.080
+LR cohens kappa score: 0.007
+LR average precision score: 0.046
+
+
+-----[ GB ]-----
+maximum:
+GB tn, fp: 332, 5
+GB fn, tp: 10, 13
+GB f1 score: 0.963
+GB cohens kappa score: 0.961
+
+
+average:
+GB tn, fp: 330.44, 1.36
+GB fn, tp: 4.12, 9.68
+GB f1 score: 0.773
+GB cohens kappa score: 0.765
+
+
+minimum:
+GB tn, fp: 327, 0
+GB fn, tp: 1, 4
+GB f1 score: 0.400
+GB cohens kappa score: 0.385
+
+
+-----[ KNN ]-----
+maximum:
+KNN tn, fp: 315, 71
+KNN fn, tp: 6, 14
+KNN f1 score: 0.538
+KNN cohens kappa score: 0.509
+
+
+average:
+KNN tn, fp: 289.6, 42.2
+KNN fn, tp: 0.64, 13.16
+KNN f1 score: 0.390
+KNN cohens kappa score: 0.349
+
+
+minimum:
+KNN tn, fp: 261, 16
+KNN fn, tp: 0, 7
+KNN f1 score: 0.265
+KNN cohens kappa score: 0.211
+