///////////////////////////////////////////
// Running CTAB-GAN on folding_shuttle-2_vs_5
///////////////////////////////////////////

Load 'data_input/folding_shuttle-2_vs_5'
from pickle file
Data loaded.
-> Shuffling data
### Start exercise for synthetic point generator

====== Step 1/5 =======
-> Shuffling data
-> Spliting data to slices

------ Step 1/5: Slice 1/5 -------
-> Reset the GAN
-> Train generator for synthetic samples

  0%|          | 0/10 [00:00<?, ?it/s]
 10%|█         | 1/10 [00:00<00:03,  2.33it/s]
 20%|██        | 2/10 [00:00<00:03,  2.43it/s]
 30%|███       | 3/10 [00:03<00:09,  1.30s/it]
 40%|████      | 4/10 [00:04<00:06,  1.12s/it]
 50%|█████     | 5/10 [00:04<00:05,  1.05s/it]
 60%|██████    | 6/10 [00:05<00:03,  1.02it/s]
 70%|███████   | 7/10 [00:06<00:02,  1.13it/s]
 80%|████████  | 8/10 [00:07<00:01,  1.18it/s]
 90%|█████████ | 9/10 [00:08<00:00,  1.19it/s]
100%|██████████| 10/10 [00:08<00:00,  1.24it/s]
100%|██████████| 10/10 [00:08<00:00,  1.13it/s]
-> create 2574 synthetic samples
-> test with 'LR'
LR tn, fp: 652, 2
LR fn, tp: 0, 10
LR f1 score: 0.909
LR cohens kappa score: 0.908
LR average precision score: 1.000

-> test with 'GB'
GB tn, fp: 654, 0
GB fn, tp: 0, 10
GB f1 score: 1.000
GB cohens kappa score: 1.000

-> test with 'KNN'
KNN tn, fp: 653, 1
KNN fn, tp: 0, 10
KNN f1 score: 0.952
KNN cohens kappa score: 0.952


------ 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,  1.25it/s]
 20%|██        | 2/10 [00:01<00:05,  1.39it/s]
 30%|███       | 3/10 [00:02<00:05,  1.31it/s]
 40%|████      | 4/10 [00:03<00:04,  1.28it/s]
 50%|█████     | 5/10 [00:03<00:03,  1.25it/s]
 60%|██████    | 6/10 [00:04<00:03,  1.26it/s]
 70%|███████   | 7/10 [00:05<00:02,  1.26it/s]
 80%|████████  | 8/10 [00:06<00:01,  1.27it/s]
 90%|█████████ | 9/10 [00:06<00:00,  1.31it/s]
100%|██████████| 10/10 [00:07<00:00,  1.33it/s]
100%|██████████| 10/10 [00:07<00:00,  1.30it/s]
-> create 2574 synthetic samples
-> test with 'LR'
LR tn, fp: 654, 0
LR fn, tp: 0, 10
LR f1 score: 1.000
LR cohens kappa score: 1.000
LR average precision score: 1.000

-> test with 'GB'
GB tn, fp: 654, 0
GB fn, tp: 0, 10
GB f1 score: 1.000
GB cohens kappa score: 1.000

-> test with 'KNN'
KNN tn, fp: 653, 1
KNN fn, tp: 2, 8
KNN f1 score: 0.842
KNN cohens kappa score: 0.840


------ Step 1/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples

  0%|          | 0/10 [00:00<?, ?it/s]
 10%|█         | 1/10 [00:02<00:22,  2.47s/it]
 20%|██        | 2/10 [00:03<00:12,  1.59s/it]
 30%|███       | 3/10 [00:04<00:09,  1.33s/it]
 40%|████      | 4/10 [00:05<00:06,  1.13s/it]
 50%|█████     | 5/10 [00:06<00:05,  1.15s/it]
 60%|██████    | 6/10 [00:07<00:04,  1.06s/it]
 70%|███████   | 7/10 [00:08<00:03,  1.05s/it]
 80%|████████  | 8/10 [00:09<00:02,  1.03s/it]
 90%|█████████ | 9/10 [00:10<00:00,  1.01it/s]
100%|██████████| 10/10 [00:11<00:00,  1.07it/s]
100%|██████████| 10/10 [00:11<00:00,  1.11s/it]
-> create 2574 synthetic samples
-> test with 'LR'
LR tn, fp: 653, 1
LR fn, tp: 0, 10
LR f1 score: 0.952
LR cohens kappa score: 0.952
LR average precision score: 1.000

-> test with 'GB'
GB tn, fp: 654, 0
GB fn, tp: 0, 10
GB f1 score: 1.000
GB cohens kappa score: 1.000

-> test with 'KNN'
KNN tn, fp: 654, 0
KNN fn, tp: 1, 9
KNN f1 score: 0.947
KNN cohens kappa score: 0.947


------ Step 1/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples

  0%|          | 0/10 [00:00<?, ?it/s]
 10%|█         | 1/10 [00:01<00:09,  1.06s/it]
 20%|██        | 2/10 [00:01<00:07,  1.10it/s]
 30%|███       | 3/10 [00:02<00:06,  1.07it/s]
 40%|████      | 4/10 [00:03<00:05,  1.05it/s]
 50%|█████     | 5/10 [00:04<00:04,  1.10it/s]
 60%|██████    | 6/10 [00:05<00:03,  1.06it/s]
 70%|███████   | 7/10 [00:06<00:02,  1.05it/s]
 80%|████████  | 8/10 [00:07<00:01,  1.06it/s]
 90%|█████████ | 9/10 [00:08<00:00,  1.03it/s]
100%|██████████| 10/10 [00:09<00:00,  1.02it/s]
100%|██████████| 10/10 [00:09<00:00,  1.05it/s]
-> create 2574 synthetic samples
-> test with 'LR'
LR tn, fp: 654, 0
LR fn, tp: 0, 10
LR f1 score: 1.000
LR cohens kappa score: 1.000
LR average precision score: 1.000

-> test with 'GB'
GB tn, fp: 654, 0
GB fn, tp: 0, 10
GB f1 score: 1.000
GB cohens kappa score: 1.000

-> test with 'KNN'
KNN tn, fp: 652, 2
KNN fn, tp: 0, 10
KNN f1 score: 0.909
KNN cohens kappa score: 0.908


------ 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<00:07,  1.23it/s]
 20%|██        | 2/10 [00:01<00:06,  1.16it/s]
 30%|███       | 3/10 [00:02<00:06,  1.09it/s]
 40%|████      | 4/10 [00:03<00:05,  1.07it/s]
 50%|█████     | 5/10 [00:04<00:04,  1.10it/s]
 60%|██████    | 6/10 [00:05<00:03,  1.10it/s]
 70%|███████   | 7/10 [00:06<00:02,  1.11it/s]
 80%|████████  | 8/10 [00:07<00:01,  1.08it/s]
 90%|█████████ | 9/10 [00:08<00:00,  1.11it/s]
100%|██████████| 10/10 [00:08<00:00,  1.18it/s]
100%|██████████| 10/10 [00:08<00:00,  1.13it/s]
-> create 2576 synthetic samples
-> test with 'LR'
LR tn, fp: 651, 0
LR fn, tp: 0, 9
LR f1 score: 1.000
LR cohens kappa score: 1.000
LR average precision score: 1.000

-> test with 'GB'
GB tn, fp: 651, 0
GB fn, tp: 0, 9
GB f1 score: 1.000
GB cohens kappa score: 1.000

-> test with 'KNN'
KNN tn, fp: 650, 1
KNN fn, tp: 0, 9
KNN f1 score: 0.947
KNN cohens kappa score: 0.947


====== 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<00:09,  1.03s/it]
 20%|██        | 2/10 [00:02<00:08,  1.02s/it]
 30%|███       | 3/10 [00:02<00:06,  1.03it/s]
 40%|████      | 4/10 [00:03<00:05,  1.11it/s]
 50%|█████     | 5/10 [00:04<00:04,  1.06it/s]
 60%|██████    | 6/10 [00:05<00:04,  1.00s/it]
 70%|███████   | 7/10 [00:07<00:03,  1.06s/it]
 80%|████████  | 8/10 [00:07<00:01,  1.01it/s]
 90%|█████████ | 9/10 [00:08<00:00,  1.04it/s]
100%|██████████| 10/10 [00:09<00:00,  1.07it/s]
100%|██████████| 10/10 [00:09<00:00,  1.03it/s]
-> create 2574 synthetic samples
-> test with 'LR'
LR tn, fp: 654, 0
LR fn, tp: 0, 10
LR f1 score: 1.000
LR cohens kappa score: 1.000
LR average precision score: 1.000

-> test with 'GB'
GB tn, fp: 654, 0
GB fn, tp: 0, 10
GB f1 score: 1.000
GB cohens kappa score: 1.000

-> test with 'KNN'
KNN tn, fp: 653, 1
KNN fn, tp: 2, 8
KNN f1 score: 0.842
KNN cohens kappa score: 0.840


------ 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<00:08,  1.08it/s]
 20%|██        | 2/10 [00:01<00:06,  1.18it/s]
 30%|███       | 3/10 [00:02<00:05,  1.31it/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,  1.08it/s]
 70%|███████   | 7/10 [00:06<00:02,  1.07it/s]
 80%|████████  | 8/10 [00:07<00:01,  1.13it/s]
 90%|█████████ | 9/10 [00:08<00:00,  1.11it/s]
100%|██████████| 10/10 [00:08<00:00,  1.12it/s]
100%|██████████| 10/10 [00:08<00:00,  1.12it/s]
-> create 2574 synthetic samples
-> test with 'LR'
LR tn, fp: 654, 0
LR fn, tp: 0, 10
LR f1 score: 1.000
LR cohens kappa score: 1.000
LR average precision score: 1.000

-> test with 'GB'
GB tn, fp: 654, 0
GB fn, tp: 0, 10
GB f1 score: 1.000
GB cohens kappa score: 1.000

-> test with 'KNN'
KNN tn, fp: 654, 0
KNN fn, tp: 0, 10
KNN f1 score: 1.000
KNN cohens kappa score: 1.000


------ Step 2/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples

  0%|          | 0/10 [00:00<?, ?it/s]
 10%|█         | 1/10 [00:01<00:09,  1.05s/it]
 20%|██        | 2/10 [00:01<00:07,  1.07it/s]
 30%|███       | 3/10 [00:02<00:06,  1.02it/s]
 40%|████      | 4/10 [00:03<00:05,  1.03it/s]
 50%|█████     | 5/10 [00:04<00:04,  1.01it/s]
 60%|██████    | 6/10 [00:05<00:03,  1.07it/s]
 70%|███████   | 7/10 [00:06<00:02,  1.09it/s]
 80%|████████  | 8/10 [00:07<00:01,  1.10it/s]
 90%|█████████ | 9/10 [00:08<00:00,  1.08it/s]
100%|██████████| 10/10 [00:09<00:00,  1.05it/s]
100%|██████████| 10/10 [00:09<00:00,  1.05it/s]
-> create 2574 synthetic samples
-> test with 'LR'
LR tn, fp: 654, 0
LR fn, tp: 0, 10
LR f1 score: 1.000
LR cohens kappa score: 1.000
LR average precision score: 1.000

-> test with 'GB'
GB tn, fp: 654, 0
GB fn, tp: 0, 10
GB f1 score: 1.000
GB cohens kappa score: 1.000

-> test with 'KNN'
KNN tn, fp: 652, 2
KNN fn, tp: 2, 8
KNN f1 score: 0.800
KNN cohens kappa score: 0.797


------ Step 2/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples

  0%|          | 0/10 [00:00<?, ?it/s]
 10%|█         | 1/10 [00:03<00:35,  3.93s/it]
 20%|██        | 2/10 [00:04<00:17,  2.14s/it]
 30%|███       | 3/10 [00:05<00:10,  1.53s/it]
 40%|████      | 4/10 [00:06<00:07,  1.24s/it]
 50%|█████     | 5/10 [00:07<00:05,  1.12s/it]
 60%|██████    | 6/10 [00:08<00:03,  1.01it/s]
 70%|███████   | 7/10 [00:08<00:02,  1.08it/s]
 80%|████████  | 8/10 [00:09<00:01,  1.12it/s]
 90%|█████████ | 9/10 [00:10<00:00,  1.16it/s]
100%|██████████| 10/10 [00:11<00:00,  1.16it/s]
100%|██████████| 10/10 [00:11<00:00,  1.13s/it]
-> create 2574 synthetic samples
-> test with 'LR'
LR tn, fp: 654, 0
LR fn, tp: 0, 10
LR f1 score: 1.000
LR cohens kappa score: 1.000
LR average precision score: 1.000

-> test with 'GB'
GB tn, fp: 654, 0
GB fn, tp: 0, 10
GB f1 score: 1.000
GB cohens kappa score: 1.000

-> test with 'KNN'
KNN tn, fp: 654, 0
KNN fn, tp: 0, 10
KNN f1 score: 1.000
KNN cohens kappa score: 1.000


------ Step 2/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples

  0%|          | 0/10 [00:00<?, ?it/s]
 10%|█         | 1/10 [00:01<00:09,  1.05s/it]
 20%|██        | 2/10 [00:02<00:07,  1.00it/s]
 30%|███       | 3/10 [00:02<00:06,  1.08it/s]
 40%|████      | 4/10 [00:03<00:05,  1.07it/s]
 50%|█████     | 5/10 [00:04<00:04,  1.10it/s]
 60%|██████    | 6/10 [00:05<00:03,  1.08it/s]
 70%|███████   | 7/10 [00:06<00:02,  1.06it/s]
 80%|████████  | 8/10 [00:07<00:01,  1.05it/s]
 90%|█████████ | 9/10 [00:08<00:01,  1.07s/it]
100%|██████████| 10/10 [00:09<00:00,  1.03s/it]
100%|██████████| 10/10 [00:09<00:00,  1.02it/s]
-> create 2576 synthetic samples
-> test with 'LR'
LR tn, fp: 651, 0
LR fn, tp: 0, 9
LR f1 score: 1.000
LR cohens kappa score: 1.000
LR average precision score: 1.000

-> test with 'GB'
GB tn, fp: 651, 0
GB fn, tp: 0, 9
GB f1 score: 1.000
GB cohens kappa score: 1.000

-> test with 'KNN'
KNN tn, fp: 649, 2
KNN fn, tp: 0, 9
KNN f1 score: 0.900
KNN cohens kappa score: 0.898


====== 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,  1.24it/s]
 20%|██        | 2/10 [00:01<00:06,  1.19it/s]
 30%|███       | 3/10 [00:02<00:06,  1.07it/s]
 40%|████      | 4/10 [00:03<00:05,  1.08it/s]
 50%|█████     | 5/10 [00:04<00:04,  1.18it/s]
 60%|██████    | 6/10 [00:05<00:03,  1.15it/s]
 70%|███████   | 7/10 [00:06<00:02,  1.08it/s]
 80%|████████  | 8/10 [00:07<00:01,  1.03it/s]
 90%|█████████ | 9/10 [00:08<00:01,  1.01s/it]
100%|██████████| 10/10 [00:09<00:00,  1.00s/it]
100%|██████████| 10/10 [00:09<00:00,  1.06it/s]
-> create 2574 synthetic samples
-> test with 'LR'
LR tn, fp: 654, 0
LR fn, tp: 0, 10
LR f1 score: 1.000
LR cohens kappa score: 1.000
LR average precision score: 1.000

-> test with 'GB'
GB tn, fp: 654, 0
GB fn, tp: 0, 10
GB f1 score: 1.000
GB cohens kappa score: 1.000

-> test with 'KNN'
KNN tn, fp: 653, 1
KNN fn, tp: 0, 10
KNN f1 score: 0.952
KNN cohens kappa score: 0.952


------ Step 3/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples

  0%|          | 0/10 [00:00<?, ?it/s]
 10%|█         | 1/10 [00:01<00:09,  1.02s/it]
 20%|██        | 2/10 [00:01<00:07,  1.14it/s]
 30%|███       | 3/10 [00:02<00:06,  1.04it/s]
 40%|████      | 4/10 [00:03<00:05,  1.01it/s]
 50%|█████     | 5/10 [00:04<00:04,  1.07it/s]
 60%|██████    | 6/10 [00:05<00:03,  1.14it/s]
 70%|███████   | 7/10 [00:06<00:02,  1.05it/s]
 80%|████████  | 8/10 [00:07<00:01,  1.04it/s]
 90%|█████████ | 9/10 [00:08<00:00,  1.05it/s]
100%|██████████| 10/10 [00:09<00:00,  1.04it/s]
100%|██████████| 10/10 [00:09<00:00,  1.05it/s]
-> create 2574 synthetic samples
-> test with 'LR'
LR tn, fp: 652, 2
LR fn, tp: 0, 10
LR f1 score: 0.909
LR cohens kappa score: 0.908
LR average precision score: 0.965

-> test with 'GB'
GB tn, fp: 654, 0
GB fn, tp: 0, 10
GB f1 score: 1.000
GB cohens kappa score: 1.000

-> test with 'KNN'
KNN tn, fp: 653, 1
KNN fn, tp: 1, 9
KNN f1 score: 0.900
KNN cohens kappa score: 0.898


------ Step 3/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples

  0%|          | 0/10 [00:00<?, ?it/s]
 10%|█         | 1/10 [00:01<00:09,  1.09s/it]
 20%|██        | 2/10 [00:01<00:06,  1.17it/s]
 30%|███       | 3/10 [00:02<00:06,  1.17it/s]
 40%|████      | 4/10 [00:03<00:05,  1.15it/s]
 50%|█████     | 5/10 [00:04<00:04,  1.20it/s]
 60%|██████    | 6/10 [00:05<00:03,  1.13it/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,  1.23it/s]
100%|██████████| 10/10 [00:08<00:00,  1.16it/s]
100%|██████████| 10/10 [00:08<00:00,  1.17it/s]
-> create 2574 synthetic samples
-> test with 'LR'
LR tn, fp: 654, 0
LR fn, tp: 0, 10
LR f1 score: 1.000
LR cohens kappa score: 1.000
LR average precision score: 1.000

-> test with 'GB'
GB tn, fp: 654, 0
GB fn, tp: 0, 10
GB f1 score: 1.000
GB cohens kappa score: 1.000

-> test with 'KNN'
KNN tn, fp: 654, 0
KNN fn, tp: 0, 10
KNN f1 score: 1.000
KNN cohens kappa score: 1.000


------ Step 3/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples

  0%|          | 0/10 [00:00<?, ?it/s]
 10%|█         | 1/10 [00:01<00:17,  1.92s/it]
 20%|██        | 2/10 [00:02<00:09,  1.20s/it]
 30%|███       | 3/10 [00:03<00:07,  1.06s/it]
 40%|████      | 4/10 [00:04<00:05,  1.02it/s]
 50%|█████     | 5/10 [00:05<00:04,  1.04it/s]
 60%|██████    | 6/10 [00:06<00:03,  1.09it/s]
 70%|███████   | 7/10 [00:07<00:02,  1.05it/s]
 80%|████████  | 8/10 [00:08<00:01,  1.02it/s]
 90%|█████████ | 9/10 [00:09<00:00,  1.02it/s]
100%|██████████| 10/10 [00:10<00:00,  1.04it/s]
100%|██████████| 10/10 [00:10<00:00,  1.01s/it]
-> create 2574 synthetic samples
-> test with 'LR'
LR tn, fp: 654, 0
LR fn, tp: 0, 10
LR f1 score: 1.000
LR cohens kappa score: 1.000
LR average precision score: 1.000

-> test with 'GB'
GB tn, fp: 654, 0
GB fn, tp: 0, 10
GB f1 score: 1.000
GB cohens kappa score: 1.000

-> test with 'KNN'
KNN tn, fp: 653, 1
KNN fn, tp: 2, 8
KNN f1 score: 0.842
KNN cohens kappa score: 0.840


------ Step 3/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples

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 10%|█         | 1/10 [00:00<00:08,  1.08it/s]
 20%|██        | 2/10 [00:01<00:06,  1.23it/s]
 30%|███       | 3/10 [00:02<00:05,  1.19it/s]
 40%|████      | 4/10 [00:03<00:05,  1.13it/s]
 50%|█████     | 5/10 [00:04<00:04,  1.13it/s]
 60%|██████    | 6/10 [00:05<00:03,  1.11it/s]
 70%|███████   | 7/10 [00:06<00:02,  1.11it/s]
 80%|████████  | 8/10 [00:07<00:01,  1.15it/s]
 90%|█████████ | 9/10 [00:07<00:00,  1.18it/s]
100%|██████████| 10/10 [00:08<00:00,  1.18it/s]
100%|██████████| 10/10 [00:08<00:00,  1.16it/s]
-> create 2576 synthetic samples
-> test with 'LR'
LR tn, fp: 651, 0
LR fn, tp: 0, 9
LR f1 score: 1.000
LR cohens kappa score: 1.000
LR average precision score: 1.000

-> test with 'GB'
GB tn, fp: 651, 0
GB fn, tp: 0, 9
GB f1 score: 1.000
GB cohens kappa score: 1.000

-> test with 'KNN'
KNN tn, fp: 649, 2
KNN fn, tp: 1, 8
KNN f1 score: 0.842
KNN cohens kappa score: 0.840


====== 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,  1.05it/s]
 20%|██        | 2/10 [00:01<00:07,  1.13it/s]
 30%|███       | 3/10 [00:02<00:06,  1.10it/s]
 40%|████      | 4/10 [00:03<00:05,  1.01it/s]
 50%|█████     | 5/10 [00:04<00:04,  1.05it/s]
 60%|██████    | 6/10 [00:05<00:03,  1.05it/s]
 70%|███████   | 7/10 [00:06<00:02,  1.11it/s]
 80%|████████  | 8/10 [00:07<00:01,  1.10it/s]
 90%|█████████ | 9/10 [00:08<00:00,  1.06it/s]
100%|██████████| 10/10 [00:09<00:00,  1.06it/s]
100%|██████████| 10/10 [00:09<00:00,  1.07it/s]
-> create 2574 synthetic samples
-> test with 'LR'
LR tn, fp: 654, 0
LR fn, tp: 0, 10
LR f1 score: 1.000
LR cohens kappa score: 1.000
LR average precision score: 1.000

-> test with 'GB'
GB tn, fp: 654, 0
GB fn, tp: 0, 10
GB f1 score: 1.000
GB cohens kappa score: 1.000

-> test with 'KNN'
KNN tn, fp: 654, 0
KNN fn, tp: 0, 10
KNN f1 score: 1.000
KNN cohens kappa score: 1.000


------ Step 4/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples

  0%|          | 0/10 [00:00<?, ?it/s]
 10%|█         | 1/10 [00:00<00:07,  1.13it/s]
 20%|██        | 2/10 [00:01<00:06,  1.22it/s]
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 40%|████      | 4/10 [00:03<00:05,  1.09it/s]
 50%|█████     | 5/10 [00:04<00:04,  1.14it/s]
 60%|██████    | 6/10 [00:06<00:04,  1.17s/it]
 70%|███████   | 7/10 [00:06<00:03,  1.05s/it]
 80%|████████  | 8/10 [00:07<00:02,  1.04s/it]
 90%|█████████ | 9/10 [00:09<00:01,  1.03s/it]
100%|██████████| 10/10 [00:10<00:00,  1.16s/it]
100%|██████████| 10/10 [00:10<00:00,  1.05s/it]
-> create 2574 synthetic samples
-> test with 'LR'
LR tn, fp: 654, 0
LR fn, tp: 0, 10
LR f1 score: 1.000
LR cohens kappa score: 1.000
LR average precision score: 1.000

-> test with 'GB'
GB tn, fp: 654, 0
GB fn, tp: 0, 10
GB f1 score: 1.000
GB cohens kappa score: 1.000

-> test with 'KNN'
KNN tn, fp: 651, 3
KNN fn, tp: 0, 10
KNN f1 score: 0.870
KNN cohens kappa score: 0.867


------ 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<00:08,  1.09it/s]
 20%|██        | 2/10 [00:01<00:07,  1.10it/s]
 30%|███       | 3/10 [00:02<00:06,  1.10it/s]
 40%|████      | 4/10 [00:03<00:05,  1.06it/s]
 50%|█████     | 5/10 [00:07<00:09,  1.94s/it]
 60%|██████    | 6/10 [00:08<00:06,  1.54s/it]
 70%|███████   | 7/10 [00:09<00:04,  1.35s/it]
 80%|████████  | 8/10 [00:10<00:02,  1.19s/it]
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100%|██████████| 10/10 [00:11<00:00,  1.05s/it]
100%|██████████| 10/10 [00:11<00:00,  1.18s/it]
-> create 2574 synthetic samples
-> test with 'LR'
LR tn, fp: 654, 0
LR fn, tp: 0, 10
LR f1 score: 1.000
LR cohens kappa score: 1.000
LR average precision score: 1.000

-> test with 'GB'
GB tn, fp: 654, 0
GB fn, tp: 0, 10
GB f1 score: 1.000
GB cohens kappa score: 1.000

-> test with 'KNN'
KNN tn, fp: 654, 0
KNN fn, tp: 1, 9
KNN f1 score: 0.947
KNN cohens kappa score: 0.947


------ Step 4/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples

  0%|          | 0/10 [00:00<?, ?it/s]
 10%|█         | 1/10 [00:01<00:10,  1.14s/it]
 20%|██        | 2/10 [00:01<00:07,  1.08it/s]
 30%|███       | 3/10 [00:02<00:06,  1.13it/s]
 40%|████      | 4/10 [00:03<00:05,  1.15it/s]
 50%|█████     | 5/10 [00:04<00:04,  1.06it/s]
 60%|██████    | 6/10 [00:05<00:03,  1.02it/s]
 70%|███████   | 7/10 [00:06<00:02,  1.12it/s]
 80%|████████  | 8/10 [00:07<00:01,  1.04it/s]
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100%|██████████| 10/10 [00:09<00:00,  1.07it/s]
100%|██████████| 10/10 [00:09<00:00,  1.07it/s]
-> create 2574 synthetic samples
-> test with 'LR'
LR tn, fp: 654, 0
LR fn, tp: 0, 10
LR f1 score: 1.000
LR cohens kappa score: 1.000
LR average precision score: 1.000

-> test with 'GB'
GB tn, fp: 654, 0
GB fn, tp: 0, 10
GB f1 score: 1.000
GB cohens kappa score: 1.000

-> test with 'KNN'
KNN tn, fp: 653, 1
KNN fn, tp: 2, 8
KNN f1 score: 0.842
KNN cohens kappa score: 0.840


------ 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,  1.08it/s]
 20%|██        | 2/10 [00:01<00:06,  1.21it/s]
 30%|███       | 3/10 [00:02<00:06,  1.12it/s]
 40%|████      | 4/10 [00:03<00:05,  1.08it/s]
 50%|█████     | 5/10 [00:04<00:04,  1.14it/s]
 60%|██████    | 6/10 [00:05<00:03,  1.18it/s]
 70%|███████   | 7/10 [00:06<00:02,  1.19it/s]
 80%|████████  | 8/10 [00:06<00:01,  1.18it/s]
 90%|█████████ | 9/10 [00:07<00:00,  1.18it/s]
100%|██████████| 10/10 [00:08<00:00,  1.18it/s]
100%|██████████| 10/10 [00:08<00:00,  1.16it/s]
-> create 2576 synthetic samples
-> test with 'LR'
LR tn, fp: 651, 0
LR fn, tp: 0, 9
LR f1 score: 1.000
LR cohens kappa score: 1.000
LR average precision score: 1.000

-> test with 'GB'
GB tn, fp: 651, 0
GB fn, tp: 0, 9
GB f1 score: 1.000
GB cohens kappa score: 1.000

-> test with 'KNN'
KNN tn, fp: 650, 1
KNN fn, tp: 0, 9
KNN f1 score: 0.947
KNN cohens kappa score: 0.947


====== 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 [00:01<00:17,  1.94s/it]
 20%|██        | 2/10 [00:02<00:10,  1.31s/it]
 30%|███       | 3/10 [00:03<00:07,  1.09s/it]
 40%|████      | 4/10 [00:04<00:05,  1.03it/s]
 50%|█████     | 5/10 [00:05<00:04,  1.11it/s]
 60%|██████    | 6/10 [00:06<00:03,  1.07it/s]
 70%|███████   | 7/10 [00:07<00:02,  1.09it/s]
 80%|████████  | 8/10 [00:08<00:01,  1.06it/s]
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100%|██████████| 10/10 [00:09<00:00,  1.16it/s]
100%|██████████| 10/10 [00:09<00:00,  1.03it/s]
-> create 2574 synthetic samples
-> test with 'LR'
LR tn, fp: 654, 0
LR fn, tp: 0, 10
LR f1 score: 1.000
LR cohens kappa score: 1.000
LR average precision score: 1.000

-> test with 'GB'
GB tn, fp: 654, 0
GB fn, tp: 0, 10
GB f1 score: 1.000
GB cohens kappa score: 1.000

-> test with 'KNN'
KNN tn, fp: 653, 1
KNN fn, tp: 0, 10
KNN f1 score: 0.952
KNN cohens kappa score: 0.952


------ 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,  1.20it/s]
 20%|██        | 2/10 [00:01<00:06,  1.19it/s]
 30%|███       | 3/10 [00:02<00:05,  1.18it/s]
 40%|████      | 4/10 [00:03<00:04,  1.25it/s]
 50%|█████     | 5/10 [00:03<00:03,  1.29it/s]
 60%|██████    | 6/10 [00:04<00:02,  1.35it/s]
 70%|███████   | 7/10 [00:05<00:02,  1.31it/s]
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100%|██████████| 10/10 [00:07<00:00,  1.25it/s]
100%|██████████| 10/10 [00:07<00:00,  1.26it/s]
-> create 2574 synthetic samples
-> test with 'LR'
LR tn, fp: 654, 0
LR fn, tp: 0, 10
LR f1 score: 1.000
LR cohens kappa score: 1.000
LR average precision score: 1.000

-> test with 'GB'
GB tn, fp: 654, 0
GB fn, tp: 0, 10
GB f1 score: 1.000
GB cohens kappa score: 1.000

-> test with 'KNN'
KNN tn, fp: 654, 0
KNN fn, tp: 0, 10
KNN f1 score: 1.000
KNN cohens kappa score: 1.000


------ 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<00:08,  1.05it/s]
 20%|██        | 2/10 [00:01<00:06,  1.18it/s]
 30%|███       | 3/10 [00:02<00:05,  1.20it/s]
 40%|████      | 4/10 [00:03<00:04,  1.23it/s]
 50%|█████     | 5/10 [00:04<00:04,  1.23it/s]
 60%|██████    | 6/10 [00:04<00:03,  1.24it/s]
 70%|███████   | 7/10 [00:05<00:02,  1.18it/s]
 80%|████████  | 8/10 [00:06<00:01,  1.13it/s]
 90%|█████████ | 9/10 [00:07<00:00,  1.14it/s]
100%|██████████| 10/10 [00:08<00:00,  1.17it/s]
100%|██████████| 10/10 [00:08<00:00,  1.18it/s]
-> create 2574 synthetic samples
-> test with 'LR'
LR tn, fp: 654, 0
LR fn, tp: 0, 10
LR f1 score: 1.000
LR cohens kappa score: 1.000
LR average precision score: 1.000

-> test with 'GB'
GB tn, fp: 654, 0
GB fn, tp: 0, 10
GB f1 score: 1.000
GB cohens kappa score: 1.000

-> test with 'KNN'
KNN tn, fp: 650, 4
KNN fn, tp: 2, 8
KNN f1 score: 0.727
KNN cohens kappa score: 0.723


------ 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,  1.15it/s]
 20%|██        | 2/10 [00:01<00:06,  1.20it/s]
 30%|███       | 3/10 [00:02<00:06,  1.16it/s]
 40%|████      | 4/10 [00:03<00:05,  1.13it/s]
 50%|█████     | 5/10 [00:04<00:04,  1.25it/s]
 60%|██████    | 6/10 [00:04<00:03,  1.27it/s]
 70%|███████   | 7/10 [00:05<00:02,  1.30it/s]
 80%|████████  | 8/10 [00:06<00:01,  1.30it/s]
 90%|█████████ | 9/10 [00:07<00:00,  1.27it/s]
100%|██████████| 10/10 [00:08<00:00,  1.21it/s]
100%|██████████| 10/10 [00:08<00:00,  1.23it/s]
-> create 2574 synthetic samples
-> test with 'LR'
LR tn, fp: 654, 0
LR fn, tp: 0, 10
LR f1 score: 1.000
LR cohens kappa score: 1.000
LR average precision score: 1.000

-> test with 'GB'
GB tn, fp: 654, 0
GB fn, tp: 0, 10
GB f1 score: 1.000
GB cohens kappa score: 1.000

-> test with 'KNN'
KNN tn, fp: 654, 0
KNN fn, tp: 0, 10
KNN f1 score: 1.000
KNN cohens kappa score: 1.000


------ Step 5/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples

  0%|          | 0/10 [00:00<?, ?it/s]
 10%|█         | 1/10 [00:01<00:11,  1.26s/it]
 20%|██        | 2/10 [00:02<00:08,  1.07s/it]
 30%|███       | 3/10 [00:03<00:07,  1.02s/it]
 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,  1.13it/s]
 70%|███████   | 7/10 [00:06<00:02,  1.13it/s]
 80%|████████  | 8/10 [00:07<00:01,  1.14it/s]
 90%|█████████ | 9/10 [00:08<00:00,  1.16it/s]
100%|██████████| 10/10 [00:09<00:00,  1.18it/s]
100%|██████████| 10/10 [00:09<00:00,  1.10it/s]
-> create 2576 synthetic samples
-> test with 'LR'
LR tn, fp: 651, 0
LR fn, tp: 0, 9
LR f1 score: 1.000
LR cohens kappa score: 1.000
LR average precision score: 1.000

-> test with 'GB'
GB tn, fp: 651, 0
GB fn, tp: 0, 9
GB f1 score: 1.000
GB cohens kappa score: 1.000

-> test with 'KNN'
KNN tn, fp: 650, 1
KNN fn, tp: 0, 9
KNN f1 score: 0.947
KNN cohens kappa score: 0.947

### Exercise is done.

-----[ LR ]-----
maximum:
LR tn, fp: 654, 2
LR fn, tp: 0, 10
LR f1 score: 1.000
LR cohens kappa score: 1.000
LR average precision score: 1.000


average:
LR tn, fp: 653.2, 0.2
LR fn, tp: 0.0, 9.8
LR f1 score: 0.991
LR cohens kappa score: 0.991
LR average precision score: 0.999


minimum:
LR tn, fp: 651, 0
LR fn, tp: 0, 9
LR f1 score: 0.909
LR cohens kappa score: 0.908
LR average precision score: 0.965


-----[ GB ]-----
maximum:
GB tn, fp: 654, 0
GB fn, tp: 0, 10
GB f1 score: 1.000
GB cohens kappa score: 1.000


average:
GB tn, fp: 653.4, 0.0
GB fn, tp: 0.0, 9.8
GB f1 score: 1.000
GB cohens kappa score: 1.000


minimum:
GB tn, fp: 651, 0
GB fn, tp: 0, 9
GB f1 score: 1.000
GB cohens kappa score: 1.000


-----[ KNN ]-----
maximum:
KNN tn, fp: 654, 4
KNN fn, tp: 2, 10
KNN f1 score: 1.000
KNN cohens kappa score: 1.000


average:
KNN tn, fp: 652.36, 1.04
KNN fn, tp: 0.64, 9.16
KNN f1 score: 0.916
KNN cohens kappa score: 0.915


minimum:
KNN tn, fp: 649, 0
KNN fn, tp: 0, 8
KNN f1 score: 0.727
KNN cohens kappa score: 0.723