ConvGeNBenchmarkResults/data_result/convGAN/folding_yeast6.log

///////////////////////////////////////////
// Running convGAN on folding_yeast6
///////////////////////////////////////////

Load 'data_input/folding_yeast6'
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
-> create 1131 synthetic samples
-> test with 'LR'
LR tn, fp: 265, 25
LR fn, tp: 0, 7
LR f1 score: 0.359
LR cohens kappa score: 0.333
LR average precision score: 0.654

-> test with 'GB'
GB tn, fp: 287, 3
GB fn, tp: 4, 3
GB f1 score: 0.462
GB cohens kappa score: 0.450

-> test with 'KNN'
KNN tn, fp: 275, 15
KNN fn, tp: 1, 6
KNN f1 score: 0.429
KNN cohens kappa score: 0.408


------ Step 1/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1131 synthetic samples
-> test with 'LR'
LR tn, fp: 264, 26
LR fn, tp: 2, 5
LR f1 score: 0.263
LR cohens kappa score: 0.234
LR average precision score: 0.427

-> test with 'GB'
GB tn, fp: 286, 4
GB fn, tp: 3, 4
GB f1 score: 0.533
GB cohens kappa score: 0.521

-> test with 'KNN'
KNN tn, fp: 268, 22
KNN fn, tp: 2, 5
KNN f1 score: 0.294
KNN cohens kappa score: 0.267


------ Step 1/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1131 synthetic samples
-> test with 'LR'
LR tn, fp: 257, 33
LR fn, tp: 1, 6
LR f1 score: 0.261
LR cohens kappa score: 0.230
LR average precision score: 0.270

-> test with 'GB'
GB tn, fp: 290, 0
GB fn, tp: 5, 2
GB f1 score: 0.444
GB cohens kappa score: 0.439

-> test with 'KNN'
KNN tn, fp: 265, 25
KNN fn, tp: 1, 6
KNN f1 score: 0.316
KNN cohens kappa score: 0.288


------ Step 1/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1131 synthetic samples
-> test with 'LR'
LR tn, fp: 269, 21
LR fn, tp: 2, 5
LR f1 score: 0.303
LR cohens kappa score: 0.276
LR average precision score: 0.554

-> test with 'GB'
GB tn, fp: 287, 3
GB fn, tp: 4, 3
GB f1 score: 0.462
GB cohens kappa score: 0.450

-> test with 'KNN'
KNN tn, fp: 273, 17
KNN fn, tp: 1, 6
KNN f1 score: 0.400
KNN cohens kappa score: 0.378


------ Step 1/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1132 synthetic samples
-> test with 'LR'
LR tn, fp: 242, 47
LR fn, tp: 0, 7
LR f1 score: 0.230
LR cohens kappa score: 0.196
LR average precision score: 0.599

-> test with 'GB'
GB tn, fp: 289, 0
GB fn, tp: 3, 4
GB f1 score: 0.727
GB cohens kappa score: 0.722

-> test with 'KNN'
KNN tn, fp: 264, 25
KNN fn, tp: 0, 7
KNN f1 score: 0.359
KNN cohens kappa score: 0.333


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

------ Step 2/5: Slice 1/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1131 synthetic samples
-> test with 'LR'
LR tn, fp: 258, 32
LR fn, tp: 0, 7
LR f1 score: 0.304
LR cohens kappa score: 0.275
LR average precision score: 0.667

-> test with 'GB'
GB tn, fp: 287, 3
GB fn, tp: 4, 3
GB f1 score: 0.462
GB cohens kappa score: 0.450

-> test with 'KNN'
KNN tn, fp: 269, 21
KNN fn, tp: 1, 6
KNN f1 score: 0.353
KNN cohens kappa score: 0.328


------ Step 2/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1131 synthetic samples
-> test with 'LR'
LR tn, fp: 253, 37
LR fn, tp: 0, 7
LR f1 score: 0.275
LR cohens kappa score: 0.244
LR average precision score: 0.227

-> test with 'GB'
GB tn, fp: 288, 2
GB fn, tp: 4, 3
GB f1 score: 0.500
GB cohens kappa score: 0.490

-> test with 'KNN'
KNN tn, fp: 262, 28
KNN fn, tp: 0, 7
KNN f1 score: 0.333
KNN cohens kappa score: 0.306


------ Step 2/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1131 synthetic samples
-> test with 'LR'
LR tn, fp: 260, 30
LR fn, tp: 1, 6
LR f1 score: 0.279
LR cohens kappa score: 0.249
LR average precision score: 0.550

-> test with 'GB'
GB tn, fp: 288, 2
GB fn, tp: 4, 3
GB f1 score: 0.500
GB cohens kappa score: 0.490

-> test with 'KNN'
KNN tn, fp: 268, 22
KNN fn, tp: 1, 6
KNN f1 score: 0.343
KNN cohens kappa score: 0.317


------ Step 2/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1131 synthetic samples
-> test with 'LR'
LR tn, fp: 254, 36
LR fn, tp: 2, 5
LR f1 score: 0.208
LR cohens kappa score: 0.175
LR average precision score: 0.530

-> test with 'GB'
GB tn, fp: 287, 3
GB fn, tp: 5, 2
GB f1 score: 0.333
GB cohens kappa score: 0.320

-> test with 'KNN'
KNN tn, fp: 266, 24
KNN fn, tp: 2, 5
KNN f1 score: 0.278
KNN cohens kappa score: 0.249


------ Step 2/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1132 synthetic samples
-> test with 'LR'
LR tn, fp: 268, 21
LR fn, tp: 1, 6
LR f1 score: 0.353
LR cohens kappa score: 0.328
LR average precision score: 0.555

-> test with 'GB'
GB tn, fp: 288, 1
GB fn, tp: 6, 1
GB f1 score: 0.222
GB cohens kappa score: 0.214

-> test with 'KNN'
KNN tn, fp: 273, 16
KNN fn, tp: 3, 4
KNN f1 score: 0.296
KNN cohens kappa score: 0.271


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

------ Step 3/5: Slice 1/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1131 synthetic samples
-> test with 'LR'
LR tn, fp: 263, 27
LR fn, tp: 1, 6
LR f1 score: 0.300
LR cohens kappa score: 0.272
LR average precision score: 0.659

-> test with 'GB'
GB tn, fp: 288, 2
GB fn, tp: 3, 4
GB f1 score: 0.615
GB cohens kappa score: 0.607

-> test with 'KNN'
KNN tn, fp: 268, 22
KNN fn, tp: 1, 6
KNN f1 score: 0.343
KNN cohens kappa score: 0.317


------ Step 3/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1131 synthetic samples
-> test with 'LR'
LR tn, fp: 247, 43
LR fn, tp: 0, 7
LR f1 score: 0.246
LR cohens kappa score: 0.213
LR average precision score: 0.802

-> test with 'GB'
GB tn, fp: 290, 0
GB fn, tp: 4, 3
GB f1 score: 0.600
GB cohens kappa score: 0.594

-> test with 'KNN'
KNN tn, fp: 258, 32
KNN fn, tp: 0, 7
KNN f1 score: 0.304
KNN cohens kappa score: 0.275


------ Step 3/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1131 synthetic samples
-> test with 'LR'
LR tn, fp: 267, 23
LR fn, tp: 2, 5
LR f1 score: 0.286
LR cohens kappa score: 0.258
LR average precision score: 0.397

-> test with 'GB'
GB tn, fp: 288, 2
GB fn, tp: 5, 2
GB f1 score: 0.364
GB cohens kappa score: 0.353

-> test with 'KNN'
KNN tn, fp: 275, 15
KNN fn, tp: 2, 5
KNN f1 score: 0.370
KNN cohens kappa score: 0.348


------ Step 3/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1131 synthetic samples
-> test with 'LR'
LR tn, fp: 261, 29
LR fn, tp: 1, 6
LR f1 score: 0.286
LR cohens kappa score: 0.257
LR average precision score: 0.423

-> test with 'GB'
GB tn, fp: 285, 5
GB fn, tp: 3, 4
GB f1 score: 0.500
GB cohens kappa score: 0.486

-> test with 'KNN'
KNN tn, fp: 260, 30
KNN fn, tp: 1, 6
KNN f1 score: 0.279
KNN cohens kappa score: 0.249


------ Step 3/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1132 synthetic samples
-> test with 'LR'
LR tn, fp: 268, 21
LR fn, tp: 1, 6
LR f1 score: 0.353
LR cohens kappa score: 0.328
LR average precision score: 0.420

-> test with 'GB'
GB tn, fp: 288, 1
GB fn, tp: 7, 0
GB f1 score: 0.000
GB cohens kappa score: -0.006

-> test with 'KNN'
KNN tn, fp: 275, 14
KNN fn, tp: 1, 6
KNN f1 score: 0.444
KNN cohens kappa score: 0.424


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

------ Step 4/5: Slice 1/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1131 synthetic samples
-> test with 'LR'
LR tn, fp: 271, 19
LR fn, tp: 1, 6
LR f1 score: 0.375
LR cohens kappa score: 0.351
LR average precision score: 0.676

-> test with 'GB'
GB tn, fp: 289, 1
GB fn, tp: 2, 5
GB f1 score: 0.769
GB cohens kappa score: 0.764

-> test with 'KNN'
KNN tn, fp: 270, 20
KNN fn, tp: 1, 6
KNN f1 score: 0.364
KNN cohens kappa score: 0.339


------ Step 4/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1131 synthetic samples
-> test with 'LR'
LR tn, fp: 258, 32
LR fn, tp: 0, 7
LR f1 score: 0.304
LR cohens kappa score: 0.275
LR average precision score: 0.222

-> test with 'GB'
GB tn, fp: 286, 4
GB fn, tp: 5, 2
GB f1 score: 0.308
GB cohens kappa score: 0.292

-> test with 'KNN'
KNN tn, fp: 270, 20
KNN fn, tp: 2, 5
KNN f1 score: 0.312
KNN cohens kappa score: 0.286


------ Step 4/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1131 synthetic samples
-> test with 'LR'
LR tn, fp: 247, 43
LR fn, tp: 1, 6
LR f1 score: 0.214
LR cohens kappa score: 0.180
LR average precision score: 0.550

-> test with 'GB'
GB tn, fp: 287, 3
GB fn, tp: 3, 4
GB f1 score: 0.571
GB cohens kappa score: 0.561

-> test with 'KNN'
KNN tn, fp: 255, 35
KNN fn, tp: 0, 7
KNN f1 score: 0.286
KNN cohens kappa score: 0.256


------ Step 4/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1131 synthetic samples
-> test with 'LR'
LR tn, fp: 262, 28
LR fn, tp: 1, 6
LR f1 score: 0.293
LR cohens kappa score: 0.264
LR average precision score: 0.635

-> test with 'GB'
GB tn, fp: 288, 2
GB fn, tp: 4, 3
GB f1 score: 0.500
GB cohens kappa score: 0.490

-> test with 'KNN'
KNN tn, fp: 270, 20
KNN fn, tp: 2, 5
KNN f1 score: 0.312
KNN cohens kappa score: 0.286


------ Step 4/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1132 synthetic samples
-> test with 'LR'
LR tn, fp: 268, 21
LR fn, tp: 2, 5
LR f1 score: 0.303
LR cohens kappa score: 0.276
LR average precision score: 0.677

-> test with 'GB'
GB tn, fp: 288, 1
GB fn, tp: 4, 3
GB f1 score: 0.545
GB cohens kappa score: 0.537

-> test with 'KNN'
KNN tn, fp: 275, 14
KNN fn, tp: 2, 5
KNN f1 score: 0.385
KNN cohens kappa score: 0.363


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

------ Step 5/5: Slice 1/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1131 synthetic samples
-> test with 'LR'
LR tn, fp: 251, 39
LR fn, tp: 0, 7
LR f1 score: 0.264
LR cohens kappa score: 0.233
LR average precision score: 0.514

-> test with 'GB'
GB tn, fp: 287, 3
GB fn, tp: 3, 4
GB f1 score: 0.571
GB cohens kappa score: 0.561

-> test with 'KNN'
KNN tn, fp: 259, 31
KNN fn, tp: 1, 6
KNN f1 score: 0.273
KNN cohens kappa score: 0.243


------ Step 5/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1131 synthetic samples
-> test with 'LR'
LR tn, fp: 268, 22
LR fn, tp: 3, 4
LR f1 score: 0.242
LR cohens kappa score: 0.213
LR average precision score: 0.216

-> test with 'GB'
GB tn, fp: 288, 2
GB fn, tp: 4, 3
GB f1 score: 0.500
GB cohens kappa score: 0.490

-> test with 'KNN'
KNN tn, fp: 272, 18
KNN fn, tp: 3, 4
KNN f1 score: 0.276
KNN cohens kappa score: 0.249


------ Step 5/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1131 synthetic samples
-> test with 'LR'
LR tn, fp: 259, 31
LR fn, tp: 0, 7
LR f1 score: 0.311
LR cohens kappa score: 0.283
LR average precision score: 0.754

-> test with 'GB'
GB tn, fp: 289, 1
GB fn, tp: 1, 6
GB f1 score: 0.857
GB cohens kappa score: 0.854

-> test with 'KNN'
KNN tn, fp: 263, 27
KNN fn, tp: 0, 7
KNN f1 score: 0.341
KNN cohens kappa score: 0.315


------ Step 5/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1131 synthetic samples
-> test with 'LR'
LR tn, fp: 256, 34
LR fn, tp: 1, 6
LR f1 score: 0.255
LR cohens kappa score: 0.224
LR average precision score: 0.295

-> test with 'GB'
GB tn, fp: 289, 1
GB fn, tp: 6, 1
GB f1 score: 0.222
GB cohens kappa score: 0.214

-> test with 'KNN'
KNN tn, fp: 266, 24
KNN fn, tp: 1, 6
KNN f1 score: 0.324
KNN cohens kappa score: 0.297


------ Step 5/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1132 synthetic samples
-> test with 'LR'
LR tn, fp: 270, 19
LR fn, tp: 2, 5
LR f1 score: 0.323
LR cohens kappa score: 0.297
LR average precision score: 0.372

-> test with 'GB'
GB tn, fp: 288, 1
GB fn, tp: 5, 2
GB f1 score: 0.400
GB cohens kappa score: 0.391

-> test with 'KNN'
KNN tn, fp: 273, 16
KNN fn, tp: 2, 5
KNN f1 score: 0.357
KNN cohens kappa score: 0.334

### Exercise is done.

-----[ LR ]-----
maximum:
LR tn, fp: 271, 47
LR fn, tp: 3, 7
LR f1 score: 0.375
LR cohens kappa score: 0.351
LR average precision score: 0.802


average:
LR tn, fp: 260.24, 29.56
LR fn, tp: 1.0, 6.0
LR f1 score: 0.288
LR cohens kappa score: 0.259
LR average precision score: 0.506


minimum:
LR tn, fp: 242, 19
LR fn, tp: 0, 4
LR f1 score: 0.208
LR cohens kappa score: 0.175
LR average precision score: 0.216


-----[ GB ]-----
maximum:
GB tn, fp: 290, 5
GB fn, tp: 7, 6
GB f1 score: 0.857
GB cohens kappa score: 0.854


average:
GB tn, fp: 287.8, 2.0
GB fn, tp: 4.04, 2.96
GB f1 score: 0.479
GB cohens kappa score: 0.469


minimum:
GB tn, fp: 285, 0
GB fn, tp: 1, 0
GB f1 score: 0.000
GB cohens kappa score: -0.006


-----[ KNN ]-----
maximum:
KNN tn, fp: 275, 35
KNN fn, tp: 3, 7
KNN f1 score: 0.444
KNN cohens kappa score: 0.424


average:
KNN tn, fp: 267.68, 22.12
KNN fn, tp: 1.24, 5.76
KNN f1 score: 0.335
KNN cohens kappa score: 0.309


minimum:
KNN tn, fp: 255, 14
KNN fn, tp: 0, 4
KNN f1 score: 0.273
KNN cohens kappa score: 0.243