ConvGeNBenchmarkResults/data_result/SpheredNoise/folding_yeast5.log

///////////////////////////////////////////
// Running SpheredNoise on folding_yeast5
///////////////////////////////////////////

Load 'data_input/folding_yeast5'
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 1152/35 points
-> new disc
-> calc distances
-> statistics
trained 35 points min:0.042426406871192875 max:0.4011234224026316
-> create 1117 synthetic samples
-> test with 'LR'
LR tn, fp: 288, 0
LR fn, tp: 5, 4
LR f1 score: 0.615
LR cohens kappa score: 0.608
LR average precision score: 0.888

-> test with 'GB'
GB tn, fp: 287, 1
GB fn, tp: 6, 3
GB f1 score: 0.462
GB cohens kappa score: 0.451

-> test with 'KNN'
KNN tn, fp: 288, 0
KNN fn, tp: 4, 5
KNN f1 score: 0.714
KNN cohens kappa score: 0.708


------ Step 1/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Train 1152/35 points
-> new disc
-> calc distances
-> statistics
trained 35 points min:0.06164414002968982 max:0.2973213749463702
-> create 1117 synthetic samples
-> test with 'LR'
LR tn, fp: 282, 6
LR fn, tp: 4, 5
LR f1 score: 0.500
LR cohens kappa score: 0.483
LR average precision score: 0.723

-> test with 'GB'
GB tn, fp: 284, 4
GB fn, tp: 0, 9
GB f1 score: 0.818
GB cohens kappa score: 0.811

-> test with 'KNN'
KNN tn, fp: 286, 2
KNN fn, tp: 3, 6
KNN f1 score: 0.706
KNN cohens kappa score: 0.697


------ Step 1/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Train 1152/35 points
-> new disc
-> calc distances
-> statistics
trained 35 points min:0.045825756949558344 max:0.4011234224026316
-> create 1117 synthetic samples
-> test with 'LR'
LR tn, fp: 284, 4
LR fn, tp: 5, 4
LR f1 score: 0.471
LR cohens kappa score: 0.455
LR average precision score: 0.627

-> test with 'GB'
GB tn, fp: 286, 2
GB fn, tp: 3, 6
GB f1 score: 0.706
GB cohens kappa score: 0.697

-> test with 'KNN'
KNN tn, fp: 284, 4
KNN fn, tp: 3, 6
KNN f1 score: 0.632
KNN cohens kappa score: 0.619


------ Step 1/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Train 1152/35 points
-> new disc
-> calc distances
-> statistics
trained 35 points min:0.042426406871192875 max:0.4277849927241488
-> create 1117 synthetic samples
-> test with 'LR'
LR tn, fp: 288, 0
LR fn, tp: 6, 3
LR f1 score: 0.500
LR cohens kappa score: 0.492
LR average precision score: 0.698

-> test with 'GB'
GB tn, fp: 288, 0
GB fn, tp: 4, 5
GB f1 score: 0.714
GB cohens kappa score: 0.708

-> test with 'KNN'
KNN tn, fp: 288, 0
KNN fn, tp: 8, 1
KNN f1 score: 0.200
KNN cohens kappa score: 0.195


------ Step 1/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Train 1152/36 points
-> new disc
-> calc distances
-> statistics
trained 36 points min:0.042426406871192875 max:0.4011234224026316
-> create 1116 synthetic samples
-> test with 'LR'
LR tn, fp: 282, 6
LR fn, tp: 2, 6
LR f1 score: 0.600
LR cohens kappa score: 0.587
LR average precision score: 0.680

-> test with 'GB'
GB tn, fp: 286, 2
GB fn, tp: 2, 6
GB f1 score: 0.750
GB cohens kappa score: 0.743

-> test with 'KNN'
KNN tn, fp: 286, 2
KNN fn, tp: 3, 5
KNN f1 score: 0.667
KNN cohens kappa score: 0.658


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

------ Step 2/5: Slice 1/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Train 1152/35 points
-> new disc
-> calc distances
-> statistics
trained 35 points min:0.042426406871192875 max:0.4277849927241488
-> create 1117 synthetic samples
-> test with 'LR'
LR tn, fp: 285, 3
LR fn, tp: 2, 7
LR f1 score: 0.737
LR cohens kappa score: 0.728
LR average precision score: 0.707

-> test with 'GB'
GB tn, fp: 287, 1
GB fn, tp: 2, 7
GB f1 score: 0.824
GB cohens kappa score: 0.818

-> test with 'KNN'
KNN tn, fp: 287, 1
KNN fn, tp: 3, 6
KNN f1 score: 0.750
KNN cohens kappa score: 0.743


------ Step 2/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Train 1152/35 points
-> new disc
-> calc distances
-> statistics
trained 35 points min:0.042426406871192875 max:0.2973213749463702
-> create 1117 synthetic samples
-> test with 'LR'
LR tn, fp: 281, 7
LR fn, tp: 7, 2
LR f1 score: 0.222
LR cohens kappa score: 0.198
LR average precision score: 0.426

-> test with 'GB'
GB tn, fp: 284, 4
GB fn, tp: 6, 3
GB f1 score: 0.375
GB cohens kappa score: 0.358

-> test with 'KNN'
KNN tn, fp: 284, 4
KNN fn, tp: 7, 2
KNN f1 score: 0.267
KNN cohens kappa score: 0.248


------ Step 2/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Train 1152/35 points
-> new disc
-> calc distances
-> statistics
trained 35 points min:0.04795831523312724 max:0.4011234224026316
-> create 1117 synthetic samples
-> test with 'LR'
LR tn, fp: 286, 2
LR fn, tp: 6, 3
LR f1 score: 0.429
LR cohens kappa score: 0.416
LR average precision score: 0.762

-> test with 'GB'
GB tn, fp: 287, 1
GB fn, tp: 1, 8
GB f1 score: 0.889
GB cohens kappa score: 0.885

-> test with 'KNN'
KNN tn, fp: 287, 1
KNN fn, tp: 4, 5
KNN f1 score: 0.667
KNN cohens kappa score: 0.658


------ Step 2/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Train 1152/35 points
-> new disc
-> calc distances
-> statistics
trained 35 points min:0.042426406871192875 max:0.4011234224026316
-> create 1117 synthetic samples
-> test with 'LR'
LR tn, fp: 285, 3
LR fn, tp: 2, 7
LR f1 score: 0.737
LR cohens kappa score: 0.728
LR average precision score: 0.900

-> test with 'GB'
GB tn, fp: 287, 1
GB fn, tp: 2, 7
GB f1 score: 0.824
GB cohens kappa score: 0.818

-> test with 'KNN'
KNN tn, fp: 286, 2
KNN fn, tp: 3, 6
KNN f1 score: 0.706
KNN cohens kappa score: 0.697


------ Step 2/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Train 1152/36 points
-> new disc
-> calc distances
-> statistics
trained 36 points min:0.045825756949558344 max:0.4011234224026316
-> create 1116 synthetic samples
-> test with 'LR'
LR tn, fp: 286, 2
LR fn, tp: 4, 4
LR f1 score: 0.571
LR cohens kappa score: 0.561
LR average precision score: 0.640

-> test with 'GB'
GB tn, fp: 286, 2
GB fn, tp: 7, 1
GB f1 score: 0.182
GB cohens kappa score: 0.170

-> test with 'KNN'
KNN tn, fp: 288, 0
KNN fn, tp: 5, 3
KNN f1 score: 0.545
KNN cohens kappa score: 0.539


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

------ Step 3/5: Slice 1/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Train 1152/35 points
-> new disc
-> calc distances
-> statistics
trained 35 points min:0.042426406871192875 max:0.4011234224026316
-> create 1117 synthetic samples
-> test with 'LR'
LR tn, fp: 285, 3
LR fn, tp: 4, 5
LR f1 score: 0.588
LR cohens kappa score: 0.576
LR average precision score: 0.677

-> test with 'GB'
GB tn, fp: 286, 2
GB fn, tp: 3, 6
GB f1 score: 0.706
GB cohens kappa score: 0.697

-> test with 'KNN'
KNN tn, fp: 287, 1
KNN fn, tp: 5, 4
KNN f1 score: 0.571
KNN cohens kappa score: 0.562


------ Step 3/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Train 1152/35 points
-> new disc
-> calc distances
-> statistics
trained 35 points min:0.042426406871192875 max:0.4277849927241488
-> create 1117 synthetic samples
-> test with 'LR'
LR tn, fp: 283, 5
LR fn, tp: 3, 6
LR f1 score: 0.600
LR cohens kappa score: 0.586
LR average precision score: 0.650

-> test with 'GB'
GB tn, fp: 286, 2
GB fn, tp: 3, 6
GB f1 score: 0.706
GB cohens kappa score: 0.697

-> test with 'KNN'
KNN tn, fp: 286, 2
KNN fn, tp: 6, 3
KNN f1 score: 0.429
KNN cohens kappa score: 0.416


------ Step 3/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Train 1152/35 points
-> new disc
-> calc distances
-> statistics
trained 35 points min:0.042426406871192875 max:0.4011234224026316
-> create 1117 synthetic samples
-> test with 'LR'
LR tn, fp: 285, 3
LR fn, tp: 4, 5
LR f1 score: 0.588
LR cohens kappa score: 0.576
LR average precision score: 0.771

-> test with 'GB'
GB tn, fp: 288, 0
GB fn, tp: 5, 4
GB f1 score: 0.615
GB cohens kappa score: 0.608

-> test with 'KNN'
KNN tn, fp: 288, 0
KNN fn, tp: 7, 2
KNN f1 score: 0.364
KNN cohens kappa score: 0.357


------ Step 3/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Train 1152/35 points
-> new disc
-> calc distances
-> statistics
trained 35 points min:0.045825756949558344 max:0.4011234224026316
-> create 1117 synthetic samples
-> test with 'LR'
LR tn, fp: 286, 2
LR fn, tp: 6, 3
LR f1 score: 0.429
LR cohens kappa score: 0.416
LR average precision score: 0.738

-> test with 'GB'
GB tn, fp: 288, 0
GB fn, tp: 5, 4
GB f1 score: 0.615
GB cohens kappa score: 0.608

-> test with 'KNN'
KNN tn, fp: 288, 0
KNN fn, tp: 4, 5
KNN f1 score: 0.714
KNN cohens kappa score: 0.708


------ Step 3/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Train 1152/36 points
-> new disc
-> calc distances
-> statistics
trained 36 points min:0.04795831523312724 max:0.2973213749463702
-> create 1116 synthetic samples
-> test with 'LR'
LR tn, fp: 283, 5
LR fn, tp: 5, 3
LR f1 score: 0.375
LR cohens kappa score: 0.358
LR average precision score: 0.483

-> test with 'GB'
GB tn, fp: 284, 4
GB fn, tp: 1, 7
GB f1 score: 0.737
GB cohens kappa score: 0.728

-> test with 'KNN'
KNN tn, fp: 285, 3
KNN fn, tp: 3, 5
KNN f1 score: 0.625
KNN cohens kappa score: 0.615


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

------ Step 4/5: Slice 1/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Train 1152/35 points
-> new disc
-> calc distances
-> statistics
trained 35 points min:0.045825756949558344 max:0.2973213749463702
-> create 1117 synthetic samples
-> test with 'LR'
LR tn, fp: 284, 4
LR fn, tp: 3, 6
LR f1 score: 0.632
LR cohens kappa score: 0.619
LR average precision score: 0.738

-> test with 'GB'
GB tn, fp: 286, 2
GB fn, tp: 3, 6
GB f1 score: 0.706
GB cohens kappa score: 0.697

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


------ Step 4/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Train 1152/35 points
-> new disc
-> calc distances
-> statistics
trained 35 points min:0.042426406871192875 max:0.4011234224026316
-> create 1117 synthetic samples
-> test with 'LR'
LR tn, fp: 284, 4
LR fn, tp: 5, 4
LR f1 score: 0.471
LR cohens kappa score: 0.455
LR average precision score: 0.615

-> test with 'GB'
GB tn, fp: 287, 1
GB fn, tp: 2, 7
GB f1 score: 0.824
GB cohens kappa score: 0.818

-> test with 'KNN'
KNN tn, fp: 286, 2
KNN fn, tp: 6, 3
KNN f1 score: 0.429
KNN cohens kappa score: 0.416


------ Step 4/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Train 1152/35 points
-> new disc
-> calc distances
-> statistics
trained 35 points min:0.045825756949558344 max:0.4011234224026316
-> create 1117 synthetic samples
-> test with 'LR'
LR tn, fp: 282, 6
LR fn, tp: 4, 5
LR f1 score: 0.500
LR cohens kappa score: 0.483
LR average precision score: 0.703

-> test with 'GB'
GB tn, fp: 282, 6
GB fn, tp: 4, 5
GB f1 score: 0.500
GB cohens kappa score: 0.483

-> test with 'KNN'
KNN tn, fp: 283, 5
KNN fn, tp: 3, 6
KNN f1 score: 0.600
KNN cohens kappa score: 0.586


------ Step 4/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Train 1152/35 points
-> new disc
-> calc distances
-> statistics
trained 35 points min:0.042426406871192875 max:0.4277849927241488
-> create 1117 synthetic samples
-> test with 'LR'
LR tn, fp: 286, 2
LR fn, tp: 6, 3
LR f1 score: 0.429
LR cohens kappa score: 0.416
LR average precision score: 0.692

-> test with 'GB'
GB tn, fp: 288, 0
GB fn, tp: 4, 5
GB f1 score: 0.714
GB cohens kappa score: 0.708

-> test with 'KNN'
KNN tn, fp: 288, 0
KNN fn, tp: 6, 3
KNN f1 score: 0.500
KNN cohens kappa score: 0.492


------ Step 4/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Train 1152/36 points
-> new disc
-> calc distances
-> statistics
trained 36 points min:0.042426406871192875 max:0.4011234224026316
-> create 1116 synthetic samples
-> test with 'LR'
LR tn, fp: 286, 2
LR fn, tp: 4, 4
LR f1 score: 0.571
LR cohens kappa score: 0.561
LR average precision score: 0.756

-> test with 'GB'
GB tn, fp: 287, 1
GB fn, tp: 2, 6
GB f1 score: 0.800
GB cohens kappa score: 0.795

-> test with 'KNN'
KNN tn, fp: 288, 0
KNN fn, tp: 4, 4
KNN f1 score: 0.667
KNN cohens kappa score: 0.661


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

------ Step 5/5: Slice 1/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Train 1152/35 points
-> new disc
-> calc distances
-> statistics
trained 35 points min:0.042426406871192875 max:0.4011234224026316
-> create 1117 synthetic samples
-> test with 'LR'
LR tn, fp: 283, 5
LR fn, tp: 2, 7
LR f1 score: 0.667
LR cohens kappa score: 0.655
LR average precision score: 0.724

-> test with 'GB'
GB tn, fp: 286, 2
GB fn, tp: 1, 8
GB f1 score: 0.842
GB cohens kappa score: 0.837

-> test with 'KNN'
KNN tn, fp: 288, 0
KNN fn, tp: 2, 7
KNN f1 score: 0.875
KNN cohens kappa score: 0.872


------ Step 5/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Train 1152/35 points
-> new disc
-> calc distances
-> statistics
trained 35 points min:0.042426406871192875 max:0.4011234224026316
-> create 1117 synthetic samples
-> test with 'LR'
LR tn, fp: 288, 0
LR fn, tp: 6, 3
LR f1 score: 0.500
LR cohens kappa score: 0.492
LR average precision score: 0.769

-> test with 'GB'
GB tn, fp: 288, 0
GB fn, tp: 3, 6
GB f1 score: 0.800
GB cohens kappa score: 0.795

-> test with 'KNN'
KNN tn, fp: 288, 0
KNN fn, tp: 5, 4
KNN f1 score: 0.615
KNN cohens kappa score: 0.608


------ Step 5/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Train 1152/35 points
-> new disc
-> calc distances
-> statistics
trained 35 points min:0.042426406871192875 max:0.4011234224026316
-> create 1117 synthetic samples
-> test with 'LR'
LR tn, fp: 284, 4
LR fn, tp: 2, 7
LR f1 score: 0.700
LR cohens kappa score: 0.690
LR average precision score: 0.787

-> test with 'GB'
GB tn, fp: 288, 0
GB fn, tp: 2, 7
GB f1 score: 0.875
GB cohens kappa score: 0.872

-> test with 'KNN'
KNN tn, fp: 286, 2
KNN fn, tp: 5, 4
KNN f1 score: 0.533
KNN cohens kappa score: 0.522


------ Step 5/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Train 1152/35 points
-> new disc
-> calc distances
-> statistics
trained 35 points min:0.04795831523312724 max:0.4011234224026316
-> create 1117 synthetic samples
-> test with 'LR'
LR tn, fp: 286, 2
LR fn, tp: 6, 3
LR f1 score: 0.429
LR cohens kappa score: 0.416
LR average precision score: 0.649

-> test with 'GB'
GB tn, fp: 287, 1
GB fn, tp: 3, 6
GB f1 score: 0.750
GB cohens kappa score: 0.743

-> test with 'KNN'
KNN tn, fp: 287, 1
KNN fn, tp: 6, 3
KNN f1 score: 0.462
KNN cohens kappa score: 0.451


------ Step 5/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
Train 1152/36 points
-> new disc
-> calc distances
-> statistics
trained 36 points min:0.045825756949558344 max:0.2973213749463702
-> create 1116 synthetic samples
-> test with 'LR'
LR tn, fp: 284, 4
LR fn, tp: 5, 3
LR f1 score: 0.400
LR cohens kappa score: 0.384
LR average precision score: 0.511

-> test with 'GB'
GB tn, fp: 284, 4
GB fn, tp: 3, 5
GB f1 score: 0.588
GB cohens kappa score: 0.576

-> test with 'KNN'
KNN tn, fp: 286, 2
KNN fn, tp: 4, 4
KNN f1 score: 0.571
KNN cohens kappa score: 0.561

### Exercise is done.

-----[ LR ]-----
maximum:
LR tn, fp: 288, 7
LR fn, tp: 7, 7
LR f1 score: 0.737
LR cohens kappa score: 0.728
LR average precision score: 0.900


average:
LR tn, fp: 284.64, 3.36
LR fn, tp: 4.32, 4.48
LR f1 score: 0.530
LR cohens kappa score: 0.518
LR average precision score: 0.693


minimum:
LR tn, fp: 281, 0
LR fn, tp: 2, 2
LR f1 score: 0.222
LR cohens kappa score: 0.198
LR average precision score: 0.426


-----[ GB ]-----
maximum:
GB tn, fp: 288, 6
GB fn, tp: 7, 9
GB f1 score: 0.889
GB cohens kappa score: 0.885


average:
GB tn, fp: 286.28, 1.72
GB fn, tp: 3.08, 5.72
GB f1 score: 0.693
GB cohens kappa score: 0.685


minimum:
GB tn, fp: 282, 0
GB fn, tp: 0, 1
GB f1 score: 0.182
GB cohens kappa score: 0.170


-----[ KNN ]-----
maximum:
KNN tn, fp: 288, 5
KNN fn, tp: 8, 7
KNN f1 score: 0.875
KNN cohens kappa score: 0.872


average:
KNN tn, fp: 286.52, 1.48
KNN fn, tp: 4.56, 4.24
KNN f1 score: 0.572
KNN cohens kappa score: 0.563


minimum:
KNN tn, fp: 283, 0
KNN fn, tp: 2, 1
KNN f1 score: 0.200
KNN cohens kappa score: 0.195