ConvGeNBenchmarkResults/data_result/CTAB-GAN/folding_flare-F.log

///////////////////////////////////////////
// Running CTAB-GAN on folding_flare-F
///////////////////////////////////////////

Load 'data_input/folding_flare-F'
from pickle file
non empty cut in data_input/folding_flare-F! (23 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 784 synthetic samples
-> test with 'LR'
LR tn, fp: 193, 12
LR fn, tp: 7, 2
LR f1 score: 0.174
LR cohens kappa score: 0.129
LR average precision score: 0.131

-> test with 'GB'
GB tn, fp: 200, 5
GB fn, tp: 8, 1
GB f1 score: 0.133
GB cohens kappa score: 0.103

-> test with 'KNN'
KNN tn, fp: 180, 25
KNN fn, tp: 5, 4
KNN f1 score: 0.211
KNN cohens kappa score: 0.156


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

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-> create 784 synthetic samples
-> test with 'LR'
LR tn, fp: 187, 18
LR fn, tp: 2, 7
LR f1 score: 0.412
LR cohens kappa score: 0.373
LR average precision score: 0.416

-> test with 'GB'
GB tn, fp: 202, 3
GB fn, tp: 7, 2
GB f1 score: 0.286
GB cohens kappa score: 0.264

-> test with 'KNN'
KNN tn, fp: 187, 18
KNN fn, tp: 5, 4
KNN f1 score: 0.258
KNN cohens kappa score: 0.211


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

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-> create 784 synthetic samples
-> test with 'LR'
LR tn, fp: 184, 21
LR fn, tp: 2, 7
LR f1 score: 0.378
LR cohens kappa score: 0.336
LR average precision score: 0.330

-> test with 'GB'
GB tn, fp: 205, 0
GB fn, tp: 9, 0
GB f1 score: 0.000
GB cohens kappa score: 0.000

-> test with 'KNN'
KNN tn, fp: 187, 18
KNN fn, tp: 2, 7
KNN f1 score: 0.412
KNN cohens kappa score: 0.373


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

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-> create 784 synthetic samples
-> test with 'LR'
LR tn, fp: 202, 3
LR fn, tp: 4, 5
LR f1 score: 0.588
LR cohens kappa score: 0.571
LR average precision score: 0.548

-> test with 'GB'
GB tn, fp: 204, 1
GB fn, tp: 7, 2
GB f1 score: 0.333
GB cohens kappa score: 0.319

-> test with 'KNN'
KNN tn, fp: 199, 6
KNN fn, tp: 8, 1
KNN f1 score: 0.125
KNN cohens kappa score: 0.092


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

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-> create 784 synthetic samples
-> test with 'LR'
LR tn, fp: 194, 9
LR fn, tp: 5, 2
LR f1 score: 0.222
LR cohens kappa score: 0.189
LR average precision score: 0.172

-> test with 'GB'
GB tn, fp: 200, 3
GB fn, tp: 6, 1
GB f1 score: 0.182
GB cohens kappa score: 0.161

-> test with 'KNN'
KNN tn, fp: 191, 12
KNN fn, tp: 5, 2
KNN f1 score: 0.190
KNN cohens kappa score: 0.153


====== 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 784 synthetic samples
-> test with 'LR'
LR tn, fp: 182, 23
LR fn, tp: 2, 7
LR f1 score: 0.359
LR cohens kappa score: 0.315
LR average precision score: 0.350

-> test with 'GB'
GB tn, fp: 203, 2
GB fn, tp: 7, 2
GB f1 score: 0.308
GB cohens kappa score: 0.289

-> test with 'KNN'
KNN tn, fp: 190, 15
KNN fn, tp: 3, 6
KNN f1 score: 0.400
KNN cohens kappa score: 0.362


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

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-> create 784 synthetic samples
-> test with 'LR'
LR tn, fp: 184, 21
LR fn, tp: 3, 6
LR f1 score: 0.333
LR cohens kappa score: 0.288
LR average precision score: 0.256

-> test with 'GB'
GB tn, fp: 203, 2
GB fn, tp: 7, 2
GB f1 score: 0.308
GB cohens kappa score: 0.289

-> test with 'KNN'
KNN tn, fp: 189, 16
KNN fn, tp: 7, 2
KNN f1 score: 0.148
KNN cohens kappa score: 0.098


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

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-> create 784 synthetic samples
-> test with 'LR'
LR tn, fp: 200, 5
LR fn, tp: 8, 1
LR f1 score: 0.133
LR cohens kappa score: 0.103
LR average precision score: 0.253

-> test with 'GB'
GB tn, fp: 205, 0
GB fn, tp: 8, 1
GB f1 score: 0.200
GB cohens kappa score: 0.193

-> test with 'KNN'
KNN tn, fp: 189, 16
KNN fn, tp: 7, 2
KNN f1 score: 0.148
KNN cohens kappa score: 0.098


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

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-> create 784 synthetic samples
-> test with 'LR'
LR tn, fp: 186, 19
LR fn, tp: 3, 6
LR f1 score: 0.353
LR cohens kappa score: 0.310
LR average precision score: 0.254

-> test with 'GB'
GB tn, fp: 204, 1
GB fn, tp: 8, 1
GB f1 score: 0.182
GB cohens kappa score: 0.169

-> test with 'KNN'
KNN tn, fp: 190, 15
KNN fn, tp: 5, 4
KNN f1 score: 0.286
KNN cohens kappa score: 0.242


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

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-> create 784 synthetic samples
-> test with 'LR'
LR tn, fp: 179, 24
LR fn, tp: 0, 7
LR f1 score: 0.368
LR cohens kappa score: 0.332
LR average precision score: 0.401

-> test with 'GB'
GB tn, fp: 201, 2
GB fn, tp: 6, 1
GB f1 score: 0.200
GB cohens kappa score: 0.184

-> test with 'KNN'
KNN tn, fp: 187, 16
KNN fn, tp: 2, 5
KNN f1 score: 0.357
KNN cohens kappa score: 0.323


====== 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 784 synthetic samples
-> test with 'LR'
LR tn, fp: 195, 10
LR fn, tp: 1, 8
LR f1 score: 0.593
LR cohens kappa score: 0.568
LR average precision score: 0.542

-> test with 'GB'
GB tn, fp: 205, 0
GB fn, tp: 9, 0
GB f1 score: 0.000
GB cohens kappa score: 0.000

-> test with 'KNN'
KNN tn, fp: 192, 13
KNN fn, tp: 3, 6
KNN f1 score: 0.429
KNN cohens kappa score: 0.394


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

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-> create 784 synthetic samples
-> test with 'LR'
LR tn, fp: 196, 9
LR fn, tp: 6, 3
LR f1 score: 0.286
LR cohens kappa score: 0.250
LR average precision score: 0.243

-> test with 'GB'
GB tn, fp: 200, 5
GB fn, tp: 6, 3
GB f1 score: 0.353
GB cohens kappa score: 0.326

-> test with 'KNN'
KNN tn, fp: 188, 17
KNN fn, tp: 6, 3
KNN f1 score: 0.207
KNN cohens kappa score: 0.158


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

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-> create 784 synthetic samples
-> test with 'LR'
LR tn, fp: 184, 21
LR fn, tp: 3, 6
LR f1 score: 0.333
LR cohens kappa score: 0.288
LR average precision score: 0.287

-> test with 'GB'
GB tn, fp: 204, 1
GB fn, tp: 9, 0
GB f1 score: 0.000
GB cohens kappa score: -0.008

-> test with 'KNN'
KNN tn, fp: 186, 19
KNN fn, tp: 4, 5
KNN f1 score: 0.303
KNN cohens kappa score: 0.258


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

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-> create 784 synthetic samples
-> test with 'LR'
LR tn, fp: 186, 19
LR fn, tp: 4, 5
LR f1 score: 0.303
LR cohens kappa score: 0.258
LR average precision score: 0.283

-> test with 'GB'
GB tn, fp: 205, 0
GB fn, tp: 9, 0
GB f1 score: 0.000
GB cohens kappa score: 0.000

-> test with 'KNN'
KNN tn, fp: 189, 16
KNN fn, tp: 4, 5
KNN f1 score: 0.333
KNN cohens kappa score: 0.292


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

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100%|██████████| 10/10 [00:08<00:00,  1.19it/s]
-> create 784 synthetic samples
-> test with 'LR'
LR tn, fp: 172, 31
LR fn, tp: 2, 5
LR f1 score: 0.233
LR cohens kappa score: 0.187
LR average precision score: 0.278

-> test with 'GB'
GB tn, fp: 199, 4
GB fn, tp: 6, 1
GB f1 score: 0.167
GB cohens kappa score: 0.143

-> test with 'KNN'
KNN tn, fp: 186, 17
KNN fn, tp: 5, 2
KNN f1 score: 0.154
KNN cohens kappa score: 0.111


====== 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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-> create 784 synthetic samples
-> test with 'LR'
LR tn, fp: 184, 21
LR fn, tp: 5, 4
LR f1 score: 0.235
LR cohens kappa score: 0.185
LR average precision score: 0.141

-> test with 'GB'
GB tn, fp: 199, 6
GB fn, tp: 9, 0
GB f1 score: 0.000
GB cohens kappa score: -0.035

-> test with 'KNN'
KNN tn, fp: 186, 19
KNN fn, tp: 7, 2
KNN f1 score: 0.133
KNN cohens kappa score: 0.079


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

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100%|██████████| 10/10 [00:08<00:00,  1.24it/s]
-> create 784 synthetic samples
-> test with 'LR'
LR tn, fp: 186, 19
LR fn, tp: 3, 6
LR f1 score: 0.353
LR cohens kappa score: 0.310
LR average precision score: 0.532

-> test with 'GB'
GB tn, fp: 203, 2
GB fn, tp: 8, 1
GB f1 score: 0.167
GB cohens kappa score: 0.149

-> test with 'KNN'
KNN tn, fp: 187, 18
KNN fn, tp: 4, 5
KNN f1 score: 0.312
KNN cohens kappa score: 0.268


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

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100%|██████████| 10/10 [00:08<00:00,  1.20it/s]
-> create 784 synthetic samples
-> test with 'LR'
LR tn, fp: 173, 32
LR fn, tp: 4, 5
LR f1 score: 0.217
LR cohens kappa score: 0.161
LR average precision score: 0.226

-> test with 'GB'
GB tn, fp: 202, 3
GB fn, tp: 7, 2
GB f1 score: 0.286
GB cohens kappa score: 0.264

-> test with 'KNN'
KNN tn, fp: 183, 22
KNN fn, tp: 4, 5
KNN f1 score: 0.278
KNN cohens kappa score: 0.229


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

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-> create 784 synthetic samples
-> test with 'LR'
LR tn, fp: 188, 17
LR fn, tp: 2, 7
LR f1 score: 0.424
LR cohens kappa score: 0.387
LR average precision score: 0.361

-> test with 'GB'
GB tn, fp: 201, 4
GB fn, tp: 5, 4
GB f1 score: 0.471
GB cohens kappa score: 0.449

-> test with 'KNN'
KNN tn, fp: 191, 14
KNN fn, tp: 4, 5
KNN f1 score: 0.357
KNN cohens kappa score: 0.318


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

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-> create 784 synthetic samples
-> test with 'LR'
LR tn, fp: 200, 3
LR fn, tp: 3, 4
LR f1 score: 0.571
LR cohens kappa score: 0.557
LR average precision score: 0.522

-> test with 'GB'
GB tn, fp: 202, 1
GB fn, tp: 6, 1
GB f1 score: 0.222
GB cohens kappa score: 0.211

-> test with 'KNN'
KNN tn, fp: 192, 11
KNN fn, tp: 3, 4
KNN f1 score: 0.364
KNN cohens kappa score: 0.333


====== 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 [00:07<00:00,  1.28it/s]
-> create 784 synthetic samples
-> test with 'LR'
LR tn, fp: 179, 26
LR fn, tp: 3, 6
LR f1 score: 0.293
LR cohens kappa score: 0.243
LR average precision score: 0.215

-> test with 'GB'
GB tn, fp: 202, 3
GB fn, tp: 8, 1
GB f1 score: 0.154
GB cohens kappa score: 0.131

-> test with 'KNN'
KNN tn, fp: 183, 22
KNN fn, tp: 3, 6
KNN f1 score: 0.324
KNN cohens kappa score: 0.278


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

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-> create 784 synthetic samples
-> test with 'LR'
LR tn, fp: 186, 19
LR fn, tp: 2, 7
LR f1 score: 0.400
LR cohens kappa score: 0.360
LR average precision score: 0.308

-> test with 'GB'
GB tn, fp: 204, 1
GB fn, tp: 8, 1
GB f1 score: 0.182
GB cohens kappa score: 0.169

-> test with 'KNN'
KNN tn, fp: 184, 21
KNN fn, tp: 5, 4
KNN f1 score: 0.235
KNN cohens kappa score: 0.185


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

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-> create 784 synthetic samples
-> test with 'LR'
LR tn, fp: 194, 11
LR fn, tp: 1, 8
LR f1 score: 0.571
LR cohens kappa score: 0.545
LR average precision score: 0.429

-> test with 'GB'
GB tn, fp: 203, 2
GB fn, tp: 7, 2
GB f1 score: 0.308
GB cohens kappa score: 0.289

-> test with 'KNN'
KNN tn, fp: 188, 17
KNN fn, tp: 4, 5
KNN f1 score: 0.323
KNN cohens kappa score: 0.280


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

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-> create 784 synthetic samples
-> test with 'LR'
LR tn, fp: 201, 4
LR fn, tp: 8, 1
LR f1 score: 0.143
LR cohens kappa score: 0.116
LR average precision score: 0.228

-> test with 'GB'
GB tn, fp: 203, 2
GB fn, tp: 9, 0
GB f1 score: 0.000
GB cohens kappa score: -0.016

-> test with 'KNN'
KNN tn, fp: 195, 10
KNN fn, tp: 8, 1
KNN f1 score: 0.100
KNN cohens kappa score: 0.056


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

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100%|██████████| 10/10 [00:07<00:00,  1.25it/s]
-> create 784 synthetic samples
-> test with 'LR'
LR tn, fp: 192, 11
LR fn, tp: 3, 4
LR f1 score: 0.364
LR cohens kappa score: 0.333
LR average precision score: 0.280

-> test with 'GB'
GB tn, fp: 199, 4
GB fn, tp: 6, 1
GB f1 score: 0.167
GB cohens kappa score: 0.143

-> test with 'KNN'
KNN tn, fp: 187, 16
KNN fn, tp: 5, 2
KNN f1 score: 0.160
KNN cohens kappa score: 0.118

### Exercise is done.

-----[ LR ]-----
maximum:
LR tn, fp: 202, 32
LR fn, tp: 8, 8
LR f1 score: 0.593
LR cohens kappa score: 0.571
LR average precision score: 0.548


average:
LR tn, fp: 188.28, 16.32
LR fn, tp: 3.44, 5.16
LR f1 score: 0.346
LR cohens kappa score: 0.308
LR average precision score: 0.319


minimum:
LR tn, fp: 172, 3
LR fn, tp: 0, 1
LR f1 score: 0.133
LR cohens kappa score: 0.103
LR average precision score: 0.131


-----[ GB ]-----
maximum:
GB tn, fp: 205, 6
GB fn, tp: 9, 4
GB f1 score: 0.471
GB cohens kappa score: 0.449


average:
GB tn, fp: 202.32, 2.28
GB fn, tp: 7.4, 1.2
GB f1 score: 0.184
GB cohens kappa score: 0.167


minimum:
GB tn, fp: 199, 0
GB fn, tp: 5, 0
GB f1 score: 0.000
GB cohens kappa score: -0.035


-----[ KNN ]-----
maximum:
KNN tn, fp: 199, 25
KNN fn, tp: 8, 7
KNN f1 score: 0.429
KNN cohens kappa score: 0.394


average:
KNN tn, fp: 188.24, 16.36
KNN fn, tp: 4.72, 3.88
KNN f1 score: 0.262
KNN cohens kappa score: 0.219


minimum:
KNN tn, fp: 180, 6
KNN fn, tp: 2, 1
KNN f1 score: 0.100
KNN cohens kappa score: 0.056