ConvGeNBenchmarkResults/data_result/SpheredNoise/folding_yeast6.log

///////////////////////////////////////////
// Running SpheredNoise 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
Train 1159/28 points
-> new disc
-> calc distances
-> statistics
trained 28 points min:0.04795831523312718 max:0.19924858845171275
-> create 1131 synthetic samples
-> test with 'LR'
LR tn, fp: 289, 1
LR fn, tp: 4, 3
LR f1 score: 0.545
LR cohens kappa score: 0.538
LR average precision score: 0.635

-> 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: 289, 1
KNN fn, tp: 5, 2
KNN f1 score: 0.400
KNN cohens kappa score: 0.391


------ Step 1/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Train 1159/28 points
-> new disc
-> calc distances
-> statistics
trained 28 points min:0.03464101615137758 max:0.22248595461286985
-> create 1131 synthetic samples
-> test with 'LR'
LR tn, fp: 285, 5
LR fn, tp: 5, 2
LR f1 score: 0.286
LR cohens kappa score: 0.268
LR average precision score: 0.431

-> 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: 287, 3
KNN fn, tp: 4, 3
KNN f1 score: 0.462
KNN cohens kappa score: 0.450


------ Step 1/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Train 1159/28 points
-> new disc
-> calc distances
-> statistics
trained 28 points min:0.042426406871192875 max:0.19924858845171275
-> create 1131 synthetic samples
-> test with 'LR'
LR tn, fp: 289, 1
LR fn, tp: 6, 1
LR f1 score: 0.222
LR cohens kappa score: 0.214
LR average precision score: 0.455

-> 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: 288, 2
KNN fn, tp: 6, 1
KNN f1 score: 0.200
KNN cohens kappa score: 0.189


------ Step 1/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Train 1159/28 points
-> new disc
-> calc distances
-> statistics
trained 28 points min:0.03464101615137758 max:0.19924858845171275
-> create 1131 synthetic samples
-> test with 'LR'
LR tn, fp: 289, 1
LR fn, tp: 5, 2
LR f1 score: 0.400
LR cohens kappa score: 0.391
LR average precision score: 0.618

-> 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: 290, 0
KNN fn, tp: 5, 2
KNN f1 score: 0.444
KNN cohens kappa score: 0.439


------ Step 1/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Train 1160/28 points
-> new disc
-> calc distances
-> statistics
trained 28 points min:0.03464101615137758 max:0.19924858845171275
-> create 1132 synthetic samples
-> test with 'LR'
LR tn, fp: 287, 2
LR fn, tp: 3, 4
LR f1 score: 0.615
LR cohens kappa score: 0.607
LR average precision score: 0.662

-> test with 'GB'
GB tn, fp: 288, 1
GB fn, tp: 3, 4
GB f1 score: 0.667
GB cohens kappa score: 0.660

-> test with 'KNN'
KNN tn, fp: 289, 0
KNN fn, tp: 1, 6
KNN f1 score: 0.923
KNN cohens kappa score: 0.921


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

------ Step 2/5: Slice 1/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Train 1159/28 points
-> new disc
-> calc distances
-> statistics
trained 28 points min:0.045825756949558344 max:0.15874507866387544
-> create 1131 synthetic samples
-> test with 'LR'
LR tn, fp: 289, 1
LR fn, tp: 3, 4
LR f1 score: 0.667
LR cohens kappa score: 0.660
LR average precision score: 0.677

-> 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: 288, 2
KNN fn, tp: 4, 3
KNN f1 score: 0.500
KNN cohens kappa score: 0.490


------ Step 2/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Train 1159/28 points
-> new disc
-> calc distances
-> statistics
trained 28 points min:0.03464101615137758 max:0.19924858845171275
-> create 1131 synthetic samples
-> test with 'LR'
LR tn, fp: 286, 4
LR fn, tp: 5, 2
LR f1 score: 0.308
LR cohens kappa score: 0.292
LR average precision score: 0.403

-> 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: 290, 0
KNN fn, tp: 5, 2
KNN f1 score: 0.444
KNN cohens kappa score: 0.439


------ Step 2/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Train 1159/28 points
-> new disc
-> calc distances
-> statistics
trained 28 points min:0.03464101615137758 max:0.19924858845171275
-> create 1131 synthetic samples
-> test with 'LR'
LR tn, fp: 289, 1
LR fn, tp: 5, 2
LR f1 score: 0.400
LR cohens kappa score: 0.391
LR average precision score: 0.511

-> test with 'GB'
GB tn, fp: 288, 2
GB fn, tp: 6, 1
GB f1 score: 0.200
GB cohens kappa score: 0.189

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


------ Step 2/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Train 1159/28 points
-> new disc
-> calc distances
-> statistics
trained 28 points min:0.03464101615137758 max:0.2054263858417414
-> create 1131 synthetic samples
-> test with 'LR'
LR tn, fp: 288, 2
LR fn, tp: 4, 3
LR f1 score: 0.500
LR cohens kappa score: 0.490
LR average precision score: 0.560

-> 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: 288, 2
KNN fn, tp: 5, 2
KNN f1 score: 0.364
KNN cohens kappa score: 0.353


------ Step 2/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Train 1160/28 points
-> new disc
-> calc distances
-> statistics
trained 28 points min:0.03464101615137758 max:0.19924858845171275
-> create 1132 synthetic samples
-> test with 'LR'
LR tn, fp: 288, 1
LR fn, tp: 5, 2
LR f1 score: 0.400
LR cohens kappa score: 0.391
LR average precision score: 0.507

-> 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: 289, 0
KNN fn, tp: 4, 3
KNN f1 score: 0.600
KNN cohens kappa score: 0.594


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

------ Step 3/5: Slice 1/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Train 1159/28 points
-> new disc
-> calc distances
-> statistics
trained 28 points min:0.042426406871192875 max:0.19924858845171275
-> create 1131 synthetic samples
-> test with 'LR'
LR tn, fp: 289, 1
LR fn, tp: 4, 3
LR f1 score: 0.545
LR cohens kappa score: 0.538
LR average precision score: 0.646

-> 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: 289, 1
KNN fn, tp: 3, 4
KNN f1 score: 0.667
KNN cohens kappa score: 0.660


------ Step 3/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Train 1159/28 points
-> new disc
-> calc distances
-> statistics
trained 28 points min:0.03464101615137758 max:0.15132745950421558
-> create 1131 synthetic samples
-> test with 'LR'
LR tn, fp: 290, 0
LR fn, tp: 3, 4
LR f1 score: 0.727
LR cohens kappa score: 0.723
LR average precision score: 0.791

-> test with 'GB'
GB tn, fp: 289, 1
GB fn, tp: 4, 3
GB f1 score: 0.545
GB cohens kappa score: 0.538

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


------ Step 3/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Train 1159/28 points
-> new disc
-> calc distances
-> statistics
trained 28 points min:0.03464101615137758 max:0.22248595461286985
-> create 1131 synthetic samples
-> test with 'LR'
LR tn, fp: 288, 2
LR fn, tp: 5, 2
LR f1 score: 0.364
LR cohens kappa score: 0.353
LR average precision score: 0.451

-> 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: 290, 0
KNN fn, tp: 4, 3
KNN f1 score: 0.600
KNN cohens kappa score: 0.594


------ Step 3/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Train 1159/28 points
-> new disc
-> calc distances
-> statistics
trained 28 points min:0.045825756949558344 max:0.19924858845171275
-> create 1131 synthetic samples
-> test with 'LR'
LR tn, fp: 285, 5
LR fn, tp: 3, 4
LR f1 score: 0.500
LR cohens kappa score: 0.486
LR average precision score: 0.453

-> test with 'GB'
GB tn, fp: 285, 5
GB fn, tp: 5, 2
GB f1 score: 0.286
GB cohens kappa score: 0.268

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


------ Step 3/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Train 1160/28 points
-> new disc
-> calc distances
-> statistics
trained 28 points min:0.03464101615137758 max:0.19924858845171275
-> create 1132 synthetic samples
-> test with 'LR'
LR tn, fp: 289, 0
LR fn, tp: 7, 0
LR f1 score: 0.000
LR cohens kappa score: 0.000
LR average precision score: 0.358

-> 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: 288, 1
KNN fn, tp: 6, 1
KNN f1 score: 0.222
KNN cohens kappa score: 0.214


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

------ Step 4/5: Slice 1/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Train 1159/28 points
-> new disc
-> calc distances
-> statistics
trained 28 points min:0.03464101615137758 max:0.19924858845171275
-> create 1131 synthetic samples
-> test with 'LR'
LR tn, fp: 290, 0
LR fn, tp: 5, 2
LR f1 score: 0.444
LR cohens kappa score: 0.439
LR average precision score: 0.767

-> test with 'GB'
GB tn, fp: 289, 1
GB fn, tp: 4, 3
GB f1 score: 0.545
GB cohens kappa score: 0.538

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


------ Step 4/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Train 1159/28 points
-> new disc
-> calc distances
-> statistics
trained 28 points min:0.03464101615137758 max:0.2054263858417414
-> create 1131 synthetic samples
-> test with 'LR'
LR tn, fp: 286, 4
LR fn, tp: 6, 1
LR f1 score: 0.167
LR cohens kappa score: 0.150
LR average precision score: 0.263

-> test with 'GB'
GB tn, fp: 286, 4
GB fn, tp: 6, 1
GB f1 score: 0.167
GB cohens kappa score: 0.150

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


------ Step 4/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Train 1159/28 points
-> new disc
-> calc distances
-> statistics
trained 28 points min:0.03464101615137758 max:0.19924858845171275
-> create 1131 synthetic samples
-> test with 'LR'
LR tn, fp: 285, 5
LR fn, tp: 3, 4
LR f1 score: 0.500
LR cohens kappa score: 0.486
LR average precision score: 0.550

-> 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: 289, 1
KNN fn, tp: 3, 4
KNN f1 score: 0.667
KNN cohens kappa score: 0.660


------ Step 4/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Train 1159/28 points
-> new disc
-> calc distances
-> statistics
trained 28 points min:0.03464101615137758 max:0.19924858845171275
-> create 1131 synthetic samples
-> test with 'LR'
LR tn, fp: 290, 0
LR fn, tp: 4, 3
LR f1 score: 0.600
LR cohens kappa score: 0.594
LR average precision score: 0.670

-> test with 'GB'
GB tn, fp: 289, 1
GB fn, tp: 4, 3
GB f1 score: 0.545
GB cohens kappa score: 0.538

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


------ Step 4/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Train 1160/28 points
-> new disc
-> calc distances
-> statistics
trained 28 points min:0.042426406871192875 max:0.1923538406167134
-> create 1132 synthetic samples
-> test with 'LR'
LR tn, fp: 289, 0
LR fn, tp: 5, 2
LR f1 score: 0.444
LR cohens kappa score: 0.439
LR average precision score: 0.587

-> 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: 289, 0
KNN fn, tp: 5, 2
KNN f1 score: 0.444
KNN cohens kappa score: 0.439


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

------ Step 5/5: Slice 1/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Train 1159/28 points
-> new disc
-> calc distances
-> statistics
trained 28 points min:0.03464101615137758 max:0.19924858845171275
-> create 1131 synthetic samples
-> test with 'LR'
LR tn, fp: 288, 2
LR fn, tp: 3, 4
LR f1 score: 0.615
LR cohens kappa score: 0.607
LR average precision score: 0.501

-> test with 'GB'
GB tn, fp: 285, 5
GB fn, tp: 4, 3
GB f1 score: 0.400
GB cohens kappa score: 0.385

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


------ Step 5/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Train 1159/28 points
-> new disc
-> calc distances
-> statistics
trained 28 points min:0.03464101615137758 max:0.19924858845171275
-> create 1131 synthetic samples
-> test with 'LR'
LR tn, fp: 289, 1
LR fn, tp: 6, 1
LR f1 score: 0.222
LR cohens kappa score: 0.214
LR average precision score: 0.236

-> 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: 290, 0
KNN fn, tp: 6, 1
KNN f1 score: 0.250
KNN cohens kappa score: 0.246


------ Step 5/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Train 1159/28 points
-> new disc
-> calc distances
-> statistics
trained 28 points min:0.042426406871192875 max:0.19924858845171275
-> create 1131 synthetic samples
-> test with 'LR'
LR tn, fp: 290, 0
LR fn, tp: 4, 3
LR f1 score: 0.600
LR cohens kappa score: 0.594
LR average precision score: 0.744

-> 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: 288, 2
KNN fn, tp: 4, 3
KNN f1 score: 0.500
KNN cohens kappa score: 0.490


------ Step 5/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Train 1159/28 points
-> new disc
-> calc distances
-> statistics
trained 28 points min:0.045825756949558344 max:0.1923538406167134
-> create 1131 synthetic samples
-> test with 'LR'
LR tn, fp: 288, 2
LR fn, tp: 5, 2
LR f1 score: 0.364
LR cohens kappa score: 0.353
LR average precision score: 0.536

-> test with 'GB'
GB tn, fp: 289, 1
GB fn, tp: 5, 2
GB f1 score: 0.400
GB cohens kappa score: 0.391

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


------ Step 5/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Train 1160/28 points
-> new disc
-> calc distances
-> statistics
trained 28 points min:0.03464101615137758 max:0.2054263858417414
-> create 1132 synthetic samples
-> test with 'LR'
LR tn, fp: 285, 4
LR fn, tp: 4, 3
LR f1 score: 0.429
LR cohens kappa score: 0.415
LR average precision score: 0.443

-> test with 'GB'
GB tn, fp: 287, 2
GB fn, tp: 5, 2
GB f1 score: 0.364
GB cohens kappa score: 0.353

-> test with 'KNN'
KNN tn, fp: 289, 0
KNN fn, tp: 4, 3
KNN f1 score: 0.600
KNN cohens kappa score: 0.594

### Exercise is done.

-----[ LR ]-----
maximum:
LR tn, fp: 290, 5
LR fn, tp: 7, 4
LR f1 score: 0.727
LR cohens kappa score: 0.723
LR average precision score: 0.791


average:
LR tn, fp: 288.0, 1.8
LR fn, tp: 4.48, 2.52
LR f1 score: 0.435
LR cohens kappa score: 0.425
LR average precision score: 0.538


minimum:
LR tn, fp: 285, 0
LR fn, tp: 3, 0
LR f1 score: 0.000
LR cohens kappa score: 0.000
LR average precision score: 0.236


-----[ GB ]-----
maximum:
GB tn, fp: 290, 5
GB fn, tp: 7, 5
GB f1 score: 0.769
GB cohens kappa score: 0.764


average:
GB tn, fp: 287.64, 2.16
GB fn, tp: 4.64, 2.36
GB f1 score: 0.399
GB cohens kappa score: 0.389


minimum:
GB tn, fp: 285, 0
GB fn, tp: 2, 0
GB f1 score: 0.000
GB cohens kappa score: -0.006


-----[ KNN ]-----
maximum:
KNN tn, fp: 290, 5
KNN fn, tp: 6, 6
KNN f1 score: 0.923
KNN cohens kappa score: 0.921


average:
KNN tn, fp: 288.64, 1.16
KNN fn, tp: 4.24, 2.76
KNN f1 score: 0.496
KNN cohens kappa score: 0.488


minimum:
KNN tn, fp: 285, 0
KNN fn, tp: 1, 1
KNN f1 score: 0.200
KNN cohens kappa score: 0.189