ConvGeNBenchmarkResults/data_result/CTAB-GAN/folding_hypothyroid.log

///////////////////////////////////////////
// Running CTAB-GAN on folding_hypothyroid
///////////////////////////////////////////

Load 'data_input/folding_hypothyroid'
from pickle file
non empty cut in data_input/folding_hypothyroid! (1 points)
Data loaded.
-> Shuffling data
### Start exercise for synthetic point generator

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

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

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-> create 2289 synthetic samples
-> test with 'LR'
LR tn, fp: 571, 32
LR fn, tp: 20, 11
LR f1 score: 0.297
LR cohens kappa score: 0.255
LR average precision score: 0.234

-> test with 'GB'
GB tn, fp: 599, 4
GB fn, tp: 7, 24
GB f1 score: 0.814
GB cohens kappa score: 0.804

-> test with 'KNN'
KNN tn, fp: 589, 14
KNN fn, tp: 9, 22
KNN f1 score: 0.657
KNN cohens kappa score: 0.638


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

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-> create 2289 synthetic samples
-> test with 'LR'
LR tn, fp: 557, 46
LR fn, tp: 24, 7
LR f1 score: 0.167
LR cohens kappa score: 0.112
LR average precision score: 0.126

-> test with 'GB'
GB tn, fp: 595, 8
GB fn, tp: 8, 23
GB f1 score: 0.742
GB cohens kappa score: 0.729

-> test with 'KNN'
KNN tn, fp: 587, 16
KNN fn, tp: 6, 25
KNN f1 score: 0.694
KNN cohens kappa score: 0.676


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

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-> create 2289 synthetic samples
-> test with 'LR'
LR tn, fp: 590, 13
LR fn, tp: 26, 5
LR f1 score: 0.204
LR cohens kappa score: 0.174
LR average precision score: 0.207

-> test with 'GB'
GB tn, fp: 598, 5
GB fn, tp: 5, 26
GB f1 score: 0.839
GB cohens kappa score: 0.830

-> test with 'KNN'
KNN tn, fp: 592, 11
KNN fn, tp: 14, 17
KNN f1 score: 0.576
KNN cohens kappa score: 0.556


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

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-> create 2289 synthetic samples
-> test with 'LR'
LR tn, fp: 596, 7
LR fn, tp: 24, 7
LR f1 score: 0.311
LR cohens kappa score: 0.289
LR average precision score: 0.328

-> test with 'GB'
GB tn, fp: 601, 2
GB fn, tp: 11, 20
GB f1 score: 0.755
GB cohens kappa score: 0.744

-> test with 'KNN'
KNN tn, fp: 590, 13
KNN fn, tp: 13, 18
KNN f1 score: 0.581
KNN cohens kappa score: 0.559


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

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-> create 2288 synthetic samples
-> test with 'LR'
LR tn, fp: 542, 58
LR fn, tp: 16, 11
LR f1 score: 0.229
LR cohens kappa score: 0.178
LR average precision score: 0.155

-> test with 'GB'
GB tn, fp: 596, 4
GB fn, tp: 6, 21
GB f1 score: 0.808
GB cohens kappa score: 0.799

-> test with 'KNN'
KNN tn, fp: 583, 17
KNN fn, tp: 11, 16
KNN f1 score: 0.533
KNN cohens kappa score: 0.510


====== 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 2289 synthetic samples
-> test with 'LR'
LR tn, fp: 568, 35
LR fn, tp: 24, 7
LR f1 score: 0.192
LR cohens kappa score: 0.144
LR average precision score: 0.155

-> test with 'GB'
GB tn, fp: 597, 6
GB fn, tp: 10, 21
GB f1 score: 0.724
GB cohens kappa score: 0.711

-> test with 'KNN'
KNN tn, fp: 594, 9
KNN fn, tp: 12, 19
KNN f1 score: 0.644
KNN cohens kappa score: 0.627


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

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-> create 2289 synthetic samples
-> test with 'LR'
LR tn, fp: 557, 46
LR fn, tp: 20, 11
LR f1 score: 0.250
LR cohens kappa score: 0.199
LR average precision score: 0.167

-> test with 'GB'
GB tn, fp: 597, 6
GB fn, tp: 5, 26
GB f1 score: 0.825
GB cohens kappa score: 0.816

-> test with 'KNN'
KNN tn, fp: 592, 11
KNN fn, tp: 9, 22
KNN f1 score: 0.688
KNN cohens kappa score: 0.671


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

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-> create 2289 synthetic samples
-> test with 'LR'
LR tn, fp: 576, 27
LR fn, tp: 22, 9
LR f1 score: 0.269
LR cohens kappa score: 0.228
LR average precision score: 0.257

-> test with 'GB'
GB tn, fp: 600, 3
GB fn, tp: 10, 21
GB f1 score: 0.764
GB cohens kappa score: 0.753

-> test with 'KNN'
KNN tn, fp: 593, 10
KNN fn, tp: 12, 19
KNN f1 score: 0.633
KNN cohens kappa score: 0.615


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

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-> create 2289 synthetic samples
-> test with 'LR'
LR tn, fp: 574, 29
LR fn, tp: 19, 12
LR f1 score: 0.333
LR cohens kappa score: 0.294
LR average precision score: 0.208

-> test with 'GB'
GB tn, fp: 599, 4
GB fn, tp: 9, 22
GB f1 score: 0.772
GB cohens kappa score: 0.761

-> test with 'KNN'
KNN tn, fp: 585, 18
KNN fn, tp: 14, 17
KNN f1 score: 0.515
KNN cohens kappa score: 0.489


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

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-> create 2288 synthetic samples
-> test with 'LR'
LR tn, fp: 562, 38
LR fn, tp: 22, 5
LR f1 score: 0.143
LR cohens kappa score: 0.095
LR average precision score: 0.149

-> test with 'GB'
GB tn, fp: 597, 3
GB fn, tp: 5, 22
GB f1 score: 0.846
GB cohens kappa score: 0.840

-> test with 'KNN'
KNN tn, fp: 585, 15
KNN fn, tp: 9, 18
KNN f1 score: 0.600
KNN cohens kappa score: 0.580


====== 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 2289 synthetic samples
-> test with 'LR'
LR tn, fp: 571, 32
LR fn, tp: 21, 10
LR f1 score: 0.274
LR cohens kappa score: 0.231
LR average precision score: 0.258

-> test with 'GB'
GB tn, fp: 602, 1
GB fn, tp: 8, 23
GB f1 score: 0.836
GB cohens kappa score: 0.829

-> test with 'KNN'
KNN tn, fp: 597, 6
KNN fn, tp: 14, 17
KNN f1 score: 0.630
KNN cohens kappa score: 0.614


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

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-> create 2289 synthetic samples
-> test with 'LR'
LR tn, fp: 594, 9
LR fn, tp: 25, 6
LR f1 score: 0.261
LR cohens kappa score: 0.237
LR average precision score: 0.327

-> test with 'GB'
GB tn, fp: 594, 9
GB fn, tp: 5, 26
GB f1 score: 0.788
GB cohens kappa score: 0.776

-> test with 'KNN'
KNN tn, fp: 587, 16
KNN fn, tp: 10, 21
KNN f1 score: 0.618
KNN cohens kappa score: 0.596


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

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-> create 2289 synthetic samples
-> test with 'LR'
LR tn, fp: 592, 11
LR fn, tp: 26, 5
LR f1 score: 0.213
LR cohens kappa score: 0.186
LR average precision score: 0.218

-> test with 'GB'
GB tn, fp: 599, 4
GB fn, tp: 5, 26
GB f1 score: 0.852
GB cohens kappa score: 0.845

-> test with 'KNN'
KNN tn, fp: 588, 15
KNN fn, tp: 9, 22
KNN f1 score: 0.647
KNN cohens kappa score: 0.627


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

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-> create 2289 synthetic samples
-> test with 'LR'
LR tn, fp: 565, 38
LR fn, tp: 17, 14
LR f1 score: 0.337
LR cohens kappa score: 0.294
LR average precision score: 0.252

-> test with 'GB'
GB tn, fp: 595, 8
GB fn, tp: 6, 25
GB f1 score: 0.781
GB cohens kappa score: 0.770

-> test with 'KNN'
KNN tn, fp: 590, 13
KNN fn, tp: 10, 21
KNN f1 score: 0.646
KNN cohens kappa score: 0.627


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

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100%|██████████| 10/10 [00:20<00:00,  1.98s/it]
100%|██████████| 10/10 [00:20<00:00,  2.00s/it]
-> create 2288 synthetic samples
-> test with 'LR'
LR tn, fp: 571, 29
LR fn, tp: 19, 8
LR f1 score: 0.250
LR cohens kappa score: 0.211
LR average precision score: 0.186

-> test with 'GB'
GB tn, fp: 598, 2
GB fn, tp: 5, 22
GB f1 score: 0.863
GB cohens kappa score: 0.857

-> test with 'KNN'
KNN tn, fp: 582, 18
KNN fn, tp: 7, 20
KNN f1 score: 0.615
KNN cohens kappa score: 0.595


====== 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 [00:19<00:00,  1.96s/it]
100%|██████████| 10/10 [00:19<00:00,  1.99s/it]
-> create 2289 synthetic samples
-> test with 'LR'
LR tn, fp: 592, 11
LR fn, tp: 29, 2
LR f1 score: 0.091
LR cohens kappa score: 0.064
LR average precision score: 0.203

-> test with 'GB'
GB tn, fp: 596, 7
GB fn, tp: 6, 25
GB f1 score: 0.794
GB cohens kappa score: 0.783

-> test with 'KNN'
KNN tn, fp: 593, 10
KNN fn, tp: 16, 15
KNN f1 score: 0.536
KNN cohens kappa score: 0.515


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

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100%|██████████| 10/10 [00:19<00:00,  1.91s/it]
100%|██████████| 10/10 [00:19<00:00,  1.90s/it]
-> create 2289 synthetic samples
-> test with 'LR'
LR tn, fp: 577, 26
LR fn, tp: 24, 7
LR f1 score: 0.219
LR cohens kappa score: 0.177
LR average precision score: 0.162

-> test with 'GB'
GB tn, fp: 600, 3
GB fn, tp: 7, 24
GB f1 score: 0.828
GB cohens kappa score: 0.819

-> test with 'KNN'
KNN tn, fp: 589, 14
KNN fn, tp: 13, 18
KNN f1 score: 0.571
KNN cohens kappa score: 0.549


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

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100%|██████████| 10/10 [00:18<00:00,  1.91s/it]
100%|██████████| 10/10 [00:18<00:00,  1.85s/it]
-> create 2289 synthetic samples
-> test with 'LR'
LR tn, fp: 575, 28
LR fn, tp: 18, 13
LR f1 score: 0.361
LR cohens kappa score: 0.323
LR average precision score: 0.280

-> test with 'GB'
GB tn, fp: 601, 2
GB fn, tp: 6, 25
GB f1 score: 0.862
GB cohens kappa score: 0.855

-> test with 'KNN'
KNN tn, fp: 593, 10
KNN fn, tp: 10, 21
KNN f1 score: 0.677
KNN cohens kappa score: 0.661


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

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100%|██████████| 10/10 [00:20<00:00,  2.00s/it]
-> create 2289 synthetic samples
-> test with 'LR'
LR tn, fp: 562, 41
LR fn, tp: 20, 11
LR f1 score: 0.265
LR cohens kappa score: 0.217
LR average precision score: 0.197

-> test with 'GB'
GB tn, fp: 601, 2
GB fn, tp: 5, 26
GB f1 score: 0.881
GB cohens kappa score: 0.876

-> test with 'KNN'
KNN tn, fp: 584, 19
KNN fn, tp: 15, 16
KNN f1 score: 0.485
KNN cohens kappa score: 0.457


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

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100%|██████████| 10/10 [00:20<00:00,  2.06s/it]
100%|██████████| 10/10 [00:20<00:00,  2.03s/it]
-> create 2288 synthetic samples
-> test with 'LR'
LR tn, fp: 549, 51
LR fn, tp: 15, 12
LR f1 score: 0.267
LR cohens kappa score: 0.220
LR average precision score: 0.197

-> test with 'GB'
GB tn, fp: 597, 3
GB fn, tp: 8, 19
GB f1 score: 0.776
GB cohens kappa score: 0.766

-> test with 'KNN'
KNN tn, fp: 584, 16
KNN fn, tp: 10, 17
KNN f1 score: 0.567
KNN cohens kappa score: 0.545


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

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

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100%|██████████| 10/10 [00:20<00:00,  1.98s/it]
100%|██████████| 10/10 [00:20<00:00,  2.05s/it]
-> create 2289 synthetic samples
-> test with 'LR'
LR tn, fp: 560, 43
LR fn, tp: 16, 15
LR f1 score: 0.337
LR cohens kappa score: 0.292
LR average precision score: 0.205

-> test with 'GB'
GB tn, fp: 599, 4
GB fn, tp: 8, 23
GB f1 score: 0.793
GB cohens kappa score: 0.783

-> test with 'KNN'
KNN tn, fp: 586, 17
KNN fn, tp: 9, 22
KNN f1 score: 0.629
KNN cohens kappa score: 0.607


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

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100%|██████████| 10/10 [00:23<00:00,  2.30s/it]
-> create 2289 synthetic samples
-> test with 'LR'
LR tn, fp: 575, 28
LR fn, tp: 27, 4
LR f1 score: 0.127
LR cohens kappa score: 0.081
LR average precision score: 0.152

-> test with 'GB'
GB tn, fp: 603, 0
GB fn, tp: 6, 25
GB f1 score: 0.893
GB cohens kappa score: 0.888

-> test with 'KNN'
KNN tn, fp: 591, 12
KNN fn, tp: 14, 17
KNN f1 score: 0.567
KNN cohens kappa score: 0.545


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

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100%|██████████| 10/10 [00:22<00:00,  2.21s/it]
-> create 2289 synthetic samples
-> test with 'LR'
LR tn, fp: 544, 59
LR fn, tp: 20, 11
LR f1 score: 0.218
LR cohens kappa score: 0.161
LR average precision score: 0.159

-> test with 'GB'
GB tn, fp: 598, 5
GB fn, tp: 11, 20
GB f1 score: 0.714
GB cohens kappa score: 0.701

-> test with 'KNN'
KNN tn, fp: 590, 13
KNN fn, tp: 14, 17
KNN f1 score: 0.557
KNN cohens kappa score: 0.535


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

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100%|██████████| 10/10 [00:22<00:00,  2.26s/it]
-> create 2289 synthetic samples
-> test with 'LR'
LR tn, fp: 573, 30
LR fn, tp: 20, 11
LR f1 score: 0.306
LR cohens kappa score: 0.265
LR average precision score: 0.190

-> test with 'GB'
GB tn, fp: 598, 5
GB fn, tp: 4, 27
GB f1 score: 0.857
GB cohens kappa score: 0.850

-> test with 'KNN'
KNN tn, fp: 593, 10
KNN fn, tp: 10, 21
KNN f1 score: 0.677
KNN cohens kappa score: 0.661


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

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100%|██████████| 10/10 [00:19<00:00,  1.95s/it]
100%|██████████| 10/10 [00:19<00:00,  1.99s/it]
-> create 2288 synthetic samples
-> test with 'LR'
LR tn, fp: 556, 44
LR fn, tp: 15, 12
LR f1 score: 0.289
LR cohens kappa score: 0.245
LR average precision score: 0.168

-> test with 'GB'
GB tn, fp: 594, 6
GB fn, tp: 10, 17
GB f1 score: 0.680
GB cohens kappa score: 0.667

-> test with 'KNN'
KNN tn, fp: 587, 13
KNN fn, tp: 7, 20
KNN f1 score: 0.667
KNN cohens kappa score: 0.650

### Exercise is done.

-----[ LR ]-----
maximum:
LR tn, fp: 596, 59
LR fn, tp: 29, 15
LR f1 score: 0.361
LR cohens kappa score: 0.323
LR average precision score: 0.328


average:
LR tn, fp: 569.96, 32.44
LR fn, tp: 21.16, 9.04
LR f1 score: 0.248
LR cohens kappa score: 0.207
LR average precision score: 0.206


minimum:
LR tn, fp: 542, 7
LR fn, tp: 15, 2
LR f1 score: 0.091
LR cohens kappa score: 0.064
LR average precision score: 0.126


-----[ GB ]-----
maximum:
GB tn, fp: 603, 9
GB fn, tp: 11, 27
GB f1 score: 0.893
GB cohens kappa score: 0.888


average:
GB tn, fp: 598.16, 4.24
GB fn, tp: 7.04, 23.16
GB f1 score: 0.803
GB cohens kappa score: 0.794


minimum:
GB tn, fp: 594, 0
GB fn, tp: 4, 17
GB f1 score: 0.680
GB cohens kappa score: 0.667


-----[ KNN ]-----
maximum:
KNN tn, fp: 597, 19
KNN fn, tp: 16, 25
KNN f1 score: 0.694
KNN cohens kappa score: 0.676


average:
KNN tn, fp: 588.96, 13.44
KNN fn, tp: 11.08, 19.12
KNN f1 score: 0.608
KNN cohens kappa score: 0.588


minimum:
KNN tn, fp: 582, 6
KNN fn, tp: 6, 15
KNN f1 score: 0.485
KNN cohens kappa score: 0.457