ConvGeNBenchmarkResults/data_result/CTAB-GAN/imblearn_ozone_level.log

///////////////////////////////////////////
// Running CTAB-GAN on imblearn_ozone_level
///////////////////////////////////////////

Load 'data_input/imblearn_ozone_level'
from imblearn
Data loaded.
-> Shuffling data
### Start exercise for synthetic point generator

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

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

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-> create 1912 synthetic samples
-> test with 'LR'
LR tn, fp: 477, 16
LR fn, tp: 9, 6
LR f1 score: 0.324
LR cohens kappa score: 0.300
LR average precision score: 0.388

-> test with 'GB'
GB tn, fp: 489, 4
GB fn, tp: 12, 3
GB f1 score: 0.273
GB cohens kappa score: 0.259

-> test with 'KNN'
KNN tn, fp: 489, 4
KNN fn, tp: 15, 0
KNN f1 score: 0.000
KNN cohens kappa score: -0.013


------ Step 1/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples

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-> create 1912 synthetic samples
-> test with 'LR'
LR tn, fp: 470, 23
LR fn, tp: 9, 6
LR f1 score: 0.273
LR cohens kappa score: 0.243
LR average precision score: 0.166

-> test with 'GB'
GB tn, fp: 492, 1
GB fn, tp: 12, 3
GB f1 score: 0.316
GB cohens kappa score: 0.307

-> test with 'KNN'
KNN tn, fp: 482, 11
KNN fn, tp: 14, 1
KNN f1 score: 0.074
KNN cohens kappa score: 0.049


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

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100%|██████████| 10/10 [00:22<00:00,  2.25s/it]
-> create 1912 synthetic samples
-> test with 'LR'
LR tn, fp: 469, 24
LR fn, tp: 8, 7
LR f1 score: 0.304
LR cohens kappa score: 0.276
LR average precision score: 0.184

-> test with 'GB'
GB tn, fp: 491, 2
GB fn, tp: 14, 1
GB f1 score: 0.111
GB cohens kappa score: 0.102

-> test with 'KNN'
KNN tn, fp: 488, 5
KNN fn, tp: 15, 0
KNN f1 score: 0.000
KNN cohens kappa score: -0.015


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

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-> create 1912 synthetic samples
-> test with 'LR'
LR tn, fp: 477, 16
LR fn, tp: 12, 3
LR f1 score: 0.176
LR cohens kappa score: 0.148
LR average precision score: 0.116

-> test with 'GB'
GB tn, fp: 487, 6
GB fn, tp: 13, 2
GB f1 score: 0.174
GB cohens kappa score: 0.157

-> test with 'KNN'
KNN tn, fp: 488, 5
KNN fn, tp: 15, 0
KNN f1 score: 0.000
KNN cohens kappa score: -0.015


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

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-> create 1912 synthetic samples
-> test with 'LR'
LR tn, fp: 473, 18
LR fn, tp: 9, 4
LR f1 score: 0.229
LR cohens kappa score: 0.203
LR average precision score: 0.123

-> test with 'GB'
GB tn, fp: 488, 3
GB fn, tp: 13, 0
GB f1 score: 0.000
GB cohens kappa score: -0.010

-> test with 'KNN'
KNN tn, fp: 481, 10
KNN fn, tp: 13, 0
KNN f1 score: 0.000
KNN cohens kappa score: -0.023


====== Step 2/5 =======
-> Shuffling data
-> Spliting data to slices

------ Step 2/5: Slice 1/5 -------
-> Reset the GAN
-> Train generator for synthetic samples

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-> create 1912 synthetic samples
-> test with 'LR'
LR tn, fp: 479, 14
LR fn, tp: 10, 5
LR f1 score: 0.294
LR cohens kappa score: 0.270
LR average precision score: 0.253

-> test with 'GB'
GB tn, fp: 488, 5
GB fn, tp: 13, 2
GB f1 score: 0.182
GB cohens kappa score: 0.166

-> test with 'KNN'
KNN tn, fp: 482, 11
KNN fn, tp: 15, 0
KNN f1 score: 0.000
KNN cohens kappa score: -0.026


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

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-> create 1912 synthetic samples
-> test with 'LR'
LR tn, fp: 478, 15
LR fn, tp: 9, 6
LR f1 score: 0.333
LR cohens kappa score: 0.310
LR average precision score: 0.186

-> test with 'GB'
GB tn, fp: 491, 2
GB fn, tp: 13, 2
GB f1 score: 0.211
GB cohens kappa score: 0.201

-> test with 'KNN'
KNN tn, fp: 487, 6
KNN fn, tp: 15, 0
KNN f1 score: 0.000
KNN cohens kappa score: -0.017


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

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-> create 1912 synthetic samples
-> test with 'LR'
LR tn, fp: 477, 16
LR fn, tp: 8, 7
LR f1 score: 0.368
LR cohens kappa score: 0.345
LR average precision score: 0.261

-> test with 'GB'
GB tn, fp: 488, 5
GB fn, tp: 13, 2
GB f1 score: 0.182
GB cohens kappa score: 0.166

-> test with 'KNN'
KNN tn, fp: 478, 15
KNN fn, tp: 15, 0
KNN f1 score: 0.000
KNN cohens kappa score: -0.030


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

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-> create 1912 synthetic samples
-> test with 'LR'
LR tn, fp: 465, 28
LR fn, tp: 13, 2
LR f1 score: 0.089
LR cohens kappa score: 0.052
LR average precision score: 0.110

-> test with 'GB'
GB tn, fp: 490, 3
GB fn, tp: 13, 2
GB f1 score: 0.200
GB cohens kappa score: 0.188

-> test with 'KNN'
KNN tn, fp: 486, 7
KNN fn, tp: 15, 0
KNN f1 score: 0.000
KNN cohens kappa score: -0.019


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

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-> create 1912 synthetic samples
-> test with 'LR'
LR tn, fp: 471, 20
LR fn, tp: 9, 4
LR f1 score: 0.216
LR cohens kappa score: 0.189
LR average precision score: 0.146

-> test with 'GB'
GB tn, fp: 489, 2
GB fn, tp: 12, 1
GB f1 score: 0.125
GB cohens kappa score: 0.116

-> test with 'KNN'
KNN tn, fp: 485, 6
KNN fn, tp: 13, 0
KNN f1 score: 0.000
KNN cohens kappa score: -0.017


====== Step 3/5 =======
-> Shuffling data
-> Spliting data to slices

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

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-> create 1912 synthetic samples
-> test with 'LR'
LR tn, fp: 477, 16
LR fn, tp: 10, 5
LR f1 score: 0.278
LR cohens kappa score: 0.252
LR average precision score: 0.228

-> test with 'GB'
GB tn, fp: 488, 5
GB fn, tp: 13, 2
GB f1 score: 0.182
GB cohens kappa score: 0.166

-> test with 'KNN'
KNN tn, fp: 486, 7
KNN fn, tp: 15, 0
KNN f1 score: 0.000
KNN cohens kappa score: -0.019


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

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-> create 1912 synthetic samples
-> test with 'LR'
LR tn, fp: 477, 16
LR fn, tp: 12, 3
LR f1 score: 0.176
LR cohens kappa score: 0.148
LR average precision score: 0.106

-> test with 'GB'
GB tn, fp: 489, 4
GB fn, tp: 13, 2
GB f1 score: 0.190
GB cohens kappa score: 0.177

-> test with 'KNN'
KNN tn, fp: 490, 3
KNN fn, tp: 15, 0
KNN f1 score: 0.000
KNN cohens kappa score: -0.010


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

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100%|██████████| 10/10 [00:21<00:00,  2.17s/it]
-> create 1912 synthetic samples
-> test with 'LR'
LR tn, fp: 477, 16
LR fn, tp: 7, 8
LR f1 score: 0.410
LR cohens kappa score: 0.388
LR average precision score: 0.299

-> test with 'GB'
GB tn, fp: 489, 4
GB fn, tp: 11, 4
GB f1 score: 0.348
GB cohens kappa score: 0.334

-> test with 'KNN'
KNN tn, fp: 489, 4
KNN fn, tp: 14, 1
KNN f1 score: 0.100
KNN cohens kappa score: 0.087


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

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-> create 1912 synthetic samples
-> test with 'LR'
LR tn, fp: 477, 16
LR fn, tp: 11, 4
LR f1 score: 0.229
LR cohens kappa score: 0.202
LR average precision score: 0.143

-> test with 'GB'
GB tn, fp: 492, 1
GB fn, tp: 13, 2
GB f1 score: 0.222
GB cohens kappa score: 0.214

-> test with 'KNN'
KNN tn, fp: 486, 7
KNN fn, tp: 15, 0
KNN f1 score: 0.000
KNN cohens kappa score: -0.019


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

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 60%|██████    | 6/10 [00:15<00:09,  2.46s/it]
 70%|███████   | 7/10 [00:17<00:07,  2.37s/it]
 80%|████████  | 8/10 [00:19<00:04,  2.31s/it]
 90%|█████████ | 9/10 [00:21<00:02,  2.29s/it]
100%|██████████| 10/10 [00:24<00:00,  2.33s/it]
100%|██████████| 10/10 [00:24<00:00,  2.44s/it]
-> create 1912 synthetic samples
-> test with 'LR'
LR tn, fp: 465, 26
LR fn, tp: 9, 4
LR f1 score: 0.186
LR cohens kappa score: 0.156
LR average precision score: 0.213

-> test with 'GB'
GB tn, fp: 484, 7
GB fn, tp: 13, 0
GB f1 score: 0.000
GB cohens kappa score: -0.018

-> test with 'KNN'
KNN tn, fp: 486, 5
KNN fn, tp: 13, 0
KNN f1 score: 0.000
KNN cohens kappa score: -0.015


====== Step 4/5 =======
-> Shuffling data
-> Spliting data to slices

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

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100%|██████████| 10/10 [00:23<00:00,  2.31s/it]
100%|██████████| 10/10 [00:23<00:00,  2.31s/it]
-> create 1912 synthetic samples
-> test with 'LR'
LR tn, fp: 475, 18
LR fn, tp: 12, 3
LR f1 score: 0.167
LR cohens kappa score: 0.137
LR average precision score: 0.135

-> test with 'GB'
GB tn, fp: 491, 2
GB fn, tp: 13, 2
GB f1 score: 0.211
GB cohens kappa score: 0.201

-> test with 'KNN'
KNN tn, fp: 485, 8
KNN fn, tp: 15, 0
KNN f1 score: 0.000
KNN cohens kappa score: -0.021


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

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100%|██████████| 10/10 [00:22<00:00,  2.12s/it]
100%|██████████| 10/10 [00:22<00:00,  2.21s/it]
-> create 1912 synthetic samples
-> test with 'LR'
LR tn, fp: 476, 17
LR fn, tp: 12, 3
LR f1 score: 0.171
LR cohens kappa score: 0.142
LR average precision score: 0.152

-> test with 'GB'
GB tn, fp: 491, 2
GB fn, tp: 13, 2
GB f1 score: 0.211
GB cohens kappa score: 0.201

-> test with 'KNN'
KNN tn, fp: 489, 4
KNN fn, tp: 15, 0
KNN f1 score: 0.000
KNN cohens kappa score: -0.013


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

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 10%|█         | 1/10 [00:02<00:21,  2.36s/it]
 20%|██        | 2/10 [00:04<00:18,  2.28s/it]
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100%|██████████| 10/10 [00:21<00:00,  2.11s/it]
100%|██████████| 10/10 [00:21<00:00,  2.13s/it]
-> create 1912 synthetic samples
-> test with 'LR'
LR tn, fp: 469, 24
LR fn, tp: 9, 6
LR f1 score: 0.267
LR cohens kappa score: 0.237
LR average precision score: 0.147

-> test with 'GB'
GB tn, fp: 493, 0
GB fn, tp: 13, 2
GB f1 score: 0.235
GB cohens kappa score: 0.230

-> test with 'KNN'
KNN tn, fp: 484, 9
KNN fn, tp: 14, 1
KNN f1 score: 0.080
KNN cohens kappa score: 0.058


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

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 60%|██████    | 6/10 [00:13<00:09,  2.27s/it]
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100%|██████████| 10/10 [00:22<00:00,  2.14s/it]
100%|██████████| 10/10 [00:22<00:00,  2.21s/it]
-> create 1912 synthetic samples
-> test with 'LR'
LR tn, fp: 484, 9
LR fn, tp: 7, 8
LR f1 score: 0.500
LR cohens kappa score: 0.484
LR average precision score: 0.424

-> test with 'GB'
GB tn, fp: 491, 2
GB fn, tp: 13, 2
GB f1 score: 0.211
GB cohens kappa score: 0.201

-> test with 'KNN'
KNN tn, fp: 484, 9
KNN fn, tp: 15, 0
KNN f1 score: 0.000
KNN cohens kappa score: -0.023


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

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 50%|█████     | 5/10 [00:11<00:11,  2.28s/it]
 60%|██████    | 6/10 [00:13<00:09,  2.28s/it]
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100%|██████████| 10/10 [00:22<00:00,  2.24s/it]
100%|██████████| 10/10 [00:22<00:00,  2.25s/it]
-> create 1912 synthetic samples
-> test with 'LR'
LR tn, fp: 475, 16
LR fn, tp: 7, 6
LR f1 score: 0.343
LR cohens kappa score: 0.321
LR average precision score: 0.181

-> test with 'GB'
GB tn, fp: 488, 3
GB fn, tp: 10, 3
GB f1 score: 0.316
GB cohens kappa score: 0.304

-> test with 'KNN'
KNN tn, fp: 483, 8
KNN fn, tp: 13, 0
KNN f1 score: 0.000
KNN cohens kappa score: -0.020


====== 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 [00:02<00:21,  2.42s/it]
 20%|██        | 2/10 [00:04<00:18,  2.32s/it]
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100%|██████████| 10/10 [00:22<00:00,  2.19s/it]
100%|██████████| 10/10 [00:22<00:00,  2.22s/it]
-> create 1912 synthetic samples
-> test with 'LR'
LR tn, fp: 477, 16
LR fn, tp: 7, 8
LR f1 score: 0.410
LR cohens kappa score: 0.388
LR average precision score: 0.275

-> test with 'GB'
GB tn, fp: 491, 2
GB fn, tp: 12, 3
GB f1 score: 0.300
GB cohens kappa score: 0.290

-> test with 'KNN'
KNN tn, fp: 485, 8
KNN fn, tp: 15, 0
KNN f1 score: 0.000
KNN cohens kappa score: -0.021


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

  0%|          | 0/10 [00:00<?, ?it/s]
 10%|█         | 1/10 [00:02<00:21,  2.35s/it]
 20%|██        | 2/10 [00:04<00:18,  2.30s/it]
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100%|██████████| 10/10 [00:22<00:00,  2.31s/it]
100%|██████████| 10/10 [00:22<00:00,  2.26s/it]
-> create 1912 synthetic samples
-> test with 'LR'
LR tn, fp: 479, 14
LR fn, tp: 11, 4
LR f1 score: 0.242
LR cohens kappa score: 0.217
LR average precision score: 0.200

-> test with 'GB'
GB tn, fp: 489, 4
GB fn, tp: 14, 1
GB f1 score: 0.100
GB cohens kappa score: 0.087

-> test with 'KNN'
KNN tn, fp: 488, 5
KNN fn, tp: 14, 1
KNN f1 score: 0.095
KNN cohens kappa score: 0.080


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

  0%|          | 0/10 [00:00<?, ?it/s]
 10%|█         | 1/10 [00:02<00:19,  2.20s/it]
 20%|██        | 2/10 [00:04<00:18,  2.25s/it]
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 40%|████      | 4/10 [00:08<00:13,  2.21s/it]
 50%|█████     | 5/10 [00:10<00:10,  2.14s/it]
 60%|██████    | 6/10 [00:12<00:08,  2.07s/it]
 70%|███████   | 7/10 [00:14<00:06,  2.09s/it]
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100%|██████████| 10/10 [00:21<00:00,  2.24s/it]
100%|██████████| 10/10 [00:21<00:00,  2.19s/it]
-> create 1912 synthetic samples
-> test with 'LR'
LR tn, fp: 482, 11
LR fn, tp: 10, 5
LR f1 score: 0.323
LR cohens kappa score: 0.301
LR average precision score: 0.242

-> test with 'GB'
GB tn, fp: 493, 0
GB fn, tp: 14, 1
GB f1 score: 0.125
GB cohens kappa score: 0.122

-> test with 'KNN'
KNN tn, fp: 481, 12
KNN fn, tp: 15, 0
KNN f1 score: 0.000
KNN cohens kappa score: -0.027


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

  0%|          | 0/10 [00:00<?, ?it/s]
 10%|█         | 1/10 [00:02<00:20,  2.32s/it]
 20%|██        | 2/10 [00:04<00:18,  2.37s/it]
 30%|███       | 3/10 [00:06<00:16,  2.31s/it]
 40%|████      | 4/10 [00:09<00:13,  2.33s/it]
 50%|█████     | 5/10 [00:11<00:11,  2.34s/it]
 60%|██████    | 6/10 [00:14<00:09,  2.35s/it]
 70%|███████   | 7/10 [00:16<00:07,  2.35s/it]
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100%|██████████| 10/10 [00:23<00:00,  2.33s/it]
100%|██████████| 10/10 [00:23<00:00,  2.33s/it]
-> create 1912 synthetic samples
-> test with 'LR'
LR tn, fp: 475, 18
LR fn, tp: 10, 5
LR f1 score: 0.263
LR cohens kappa score: 0.236
LR average precision score: 0.203

-> test with 'GB'
GB tn, fp: 490, 3
GB fn, tp: 12, 3
GB f1 score: 0.286
GB cohens kappa score: 0.273

-> test with 'KNN'
KNN tn, fp: 486, 7
KNN fn, tp: 15, 0
KNN f1 score: 0.000
KNN cohens kappa score: -0.019


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

  0%|          | 0/10 [00:00<?, ?it/s]
 10%|█         | 1/10 [00:02<00:22,  2.54s/it]
 20%|██        | 2/10 [00:04<00:19,  2.47s/it]
 30%|███       | 3/10 [00:07<00:15,  2.28s/it]
 40%|████      | 4/10 [00:09<00:13,  2.22s/it]
 50%|█████     | 5/10 [00:11<00:10,  2.18s/it]
 60%|██████    | 6/10 [00:13<00:08,  2.22s/it]
 70%|███████   | 7/10 [00:15<00:06,  2.24s/it]
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100%|██████████| 10/10 [00:22<00:00,  2.17s/it]
100%|██████████| 10/10 [00:22<00:00,  2.22s/it]
-> create 1912 synthetic samples
-> test with 'LR'
LR tn, fp: 474, 17
LR fn, tp: 10, 3
LR f1 score: 0.182
LR cohens kappa score: 0.155
LR average precision score: 0.151

-> test with 'GB'
GB tn, fp: 488, 3
GB fn, tp: 10, 3
GB f1 score: 0.316
GB cohens kappa score: 0.304

-> test with 'KNN'
KNN tn, fp: 487, 4
KNN fn, tp: 13, 0
KNN f1 score: 0.000
KNN cohens kappa score: -0.012

### Exercise is done.

-----[ LR ]-----
maximum:
LR tn, fp: 484, 28
LR fn, tp: 13, 8
LR f1 score: 0.500
LR cohens kappa score: 0.484
LR average precision score: 0.424


average:
LR tn, fp: 475.0, 17.6
LR fn, tp: 9.6, 5.0
LR f1 score: 0.270
LR cohens kappa score: 0.244
LR average precision score: 0.201


minimum:
LR tn, fp: 465, 9
LR fn, tp: 7, 2
LR f1 score: 0.089
LR cohens kappa score: 0.052
LR average precision score: 0.106


-----[ GB ]-----
maximum:
GB tn, fp: 493, 7
GB fn, tp: 14, 4
GB f1 score: 0.348
GB cohens kappa score: 0.334


average:
GB tn, fp: 489.6, 3.0
GB fn, tp: 12.6, 2.0
GB f1 score: 0.201
GB cohens kappa score: 0.189


minimum:
GB tn, fp: 484, 0
GB fn, tp: 10, 0
GB f1 score: 0.000
GB cohens kappa score: -0.018


-----[ KNN ]-----
maximum:
KNN tn, fp: 490, 15
KNN fn, tp: 15, 1
KNN f1 score: 0.100
KNN cohens kappa score: 0.087


average:
KNN tn, fp: 485.4, 7.2
KNN fn, tp: 14.44, 0.16
KNN f1 score: 0.014
KNN cohens kappa score: -0.005


minimum:
KNN tn, fp: 478, 3
KNN fn, tp: 13, 0
KNN f1 score: 0.000
KNN cohens kappa score: -0.030