ConvGeNBenchmarkResults/data_result/convGAN-proximary-5/folding_yeast5.log

///////////////////////////////////////////
// Running convGAN-proximary-5 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
-> create 1117 synthetic samples
-> test with GAN.predict
GAN tn, fp: 163, 125
GAN fn, tp: 5, 4
GAN f1 score: 0.058
GAN cohens kappa score: 0.001

-> test with 'LR'
LR tn, fp: 276, 12
LR fn, tp: 1, 8
LR f1 score: 0.552
LR cohens kappa score: 0.532
LR average precision score: 0.872

-> 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: 281, 7
KNN fn, tp: 1, 8
KNN f1 score: 0.667
KNN cohens kappa score: 0.654


------ Step 1/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1117 synthetic samples
-> test with GAN.predict
GAN tn, fp: 254, 34
GAN fn, tp: 2, 7
GAN f1 score: 0.280
GAN cohens kappa score: 0.242

-> test with 'LR'
LR tn, fp: 273, 15
LR fn, tp: 0, 9
LR f1 score: 0.545
LR cohens kappa score: 0.524
LR average precision score: 0.641

-> test with 'GB'
GB tn, fp: 284, 4
GB fn, tp: 1, 8
GB f1 score: 0.762
GB cohens kappa score: 0.753

-> test with 'KNN'
KNN tn, fp: 275, 13
KNN fn, tp: 0, 9
KNN f1 score: 0.581
KNN cohens kappa score: 0.562


------ Step 1/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1117 synthetic samples
-> test with GAN.predict
GAN tn, fp: 273, 15
GAN fn, tp: 1, 8
GAN f1 score: 0.500
GAN cohens kappa score: 0.477

-> test with 'LR'
LR tn, fp: 277, 11
LR fn, tp: 0, 9
LR f1 score: 0.621
LR cohens kappa score: 0.604
LR average precision score: 0.609

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

-> test with 'KNN'
KNN tn, fp: 276, 12
KNN fn, tp: 0, 9
KNN f1 score: 0.600
KNN cohens kappa score: 0.582


------ Step 1/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1117 synthetic samples
-> test with GAN.predict
GAN tn, fp: 181, 107
GAN fn, tp: 4, 5
GAN f1 score: 0.083
GAN cohens kappa score: 0.028

-> test with 'LR'
LR tn, fp: 280, 8
LR fn, tp: 0, 9
LR f1 score: 0.692
LR cohens kappa score: 0.680
LR average precision score: 0.764

-> 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: 285, 3
KNN fn, tp: 0, 9
KNN f1 score: 0.857
KNN cohens kappa score: 0.852


------ Step 1/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1116 synthetic samples
-> test with GAN.predict
GAN tn, fp: 265, 23
GAN fn, tp: 1, 7
GAN f1 score: 0.368
GAN cohens kappa score: 0.340

-> test with 'LR'
LR tn, fp: 273, 15
LR fn, tp: 0, 8
LR f1 score: 0.516
LR cohens kappa score: 0.496
LR average precision score: 0.695

-> 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: 275, 13
KNN fn, tp: 0, 8
KNN f1 score: 0.552
KNN cohens kappa score: 0.533


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

------ Step 2/5: Slice 1/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1117 synthetic samples
-> test with GAN.predict
GAN tn, fp: 280, 8
GAN fn, tp: 1, 8
GAN f1 score: 0.640
GAN cohens kappa score: 0.625

-> test with 'LR'
LR tn, fp: 275, 13
LR fn, tp: 0, 9
LR f1 score: 0.581
LR cohens kappa score: 0.562
LR average precision score: 0.703

-> 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: 281, 7
KNN fn, tp: 0, 9
KNN f1 score: 0.720
KNN cohens kappa score: 0.709


------ Step 2/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1117 synthetic samples
-> test with GAN.predict
GAN tn, fp: 247, 41
GAN fn, tp: 5, 4
GAN f1 score: 0.148
GAN cohens kappa score: 0.103

-> test with 'LR'
LR tn, fp: 272, 16
LR fn, tp: 1, 8
LR f1 score: 0.485
LR cohens kappa score: 0.461
LR average precision score: 0.351

-> 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: 274, 14
KNN fn, tp: 0, 9
KNN f1 score: 0.562
KNN cohens kappa score: 0.543


------ Step 2/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1117 synthetic samples
-> test with GAN.predict
GAN tn, fp: 284, 4
GAN fn, tp: 4, 5
GAN f1 score: 0.556
GAN cohens kappa score: 0.542

-> test with 'LR'
LR tn, fp: 281, 7
LR fn, tp: 0, 9
LR f1 score: 0.720
LR cohens kappa score: 0.709
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: 278, 10
KNN fn, tp: 0, 9
KNN f1 score: 0.643
KNN cohens kappa score: 0.628


------ Step 2/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1117 synthetic samples
-> test with GAN.predict
GAN tn, fp: 282, 6
GAN fn, tp: 2, 7
GAN f1 score: 0.636
GAN cohens kappa score: 0.623

-> test with 'LR'
LR tn, fp: 273, 15
LR fn, tp: 0, 9
LR f1 score: 0.545
LR cohens kappa score: 0.524
LR average precision score: 0.878

-> 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: 277, 11
KNN fn, tp: 0, 9
KNN f1 score: 0.621
KNN cohens kappa score: 0.604


------ Step 2/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1116 synthetic samples
-> test with GAN.predict
GAN tn, fp: 277, 11
GAN fn, tp: 4, 4
GAN f1 score: 0.348
GAN cohens kappa score: 0.324

-> test with 'LR'
LR tn, fp: 277, 11
LR fn, tp: 0, 8
LR f1 score: 0.593
LR cohens kappa score: 0.576
LR average precision score: 0.607

-> test with 'GB'
GB tn, fp: 286, 2
GB fn, tp: 5, 3
GB f1 score: 0.462
GB cohens kappa score: 0.450

-> test with 'KNN'
KNN tn, fp: 281, 7
KNN fn, tp: 0, 8
KNN f1 score: 0.696
KNN cohens kappa score: 0.685


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

------ Step 3/5: Slice 1/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1117 synthetic samples
-> test with GAN.predict
GAN tn, fp: 273, 15
GAN fn, tp: 2, 7
GAN f1 score: 0.452
GAN cohens kappa score: 0.427

-> test with 'LR'
LR tn, fp: 272, 16
LR fn, tp: 0, 9
LR f1 score: 0.529
LR cohens kappa score: 0.507
LR average precision score: 0.673

-> 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: 274, 14
KNN fn, tp: 0, 9
KNN f1 score: 0.562
KNN cohens kappa score: 0.543


------ Step 3/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1117 synthetic samples
-> test with GAN.predict
GAN tn, fp: 250, 38
GAN fn, tp: 2, 7
GAN f1 score: 0.259
GAN cohens kappa score: 0.220

-> test with 'LR'
LR tn, fp: 272, 16
LR fn, tp: 0, 9
LR f1 score: 0.529
LR cohens kappa score: 0.507
LR average precision score: 0.697

-> test with 'GB'
GB tn, fp: 285, 3
GB fn, tp: 3, 6
GB f1 score: 0.667
GB cohens kappa score: 0.656

-> test with 'KNN'
KNN tn, fp: 276, 12
KNN fn, tp: 0, 9
KNN f1 score: 0.600
KNN cohens kappa score: 0.582


------ Step 3/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1117 synthetic samples
-> test with GAN.predict
GAN tn, fp: 277, 11
GAN fn, tp: 3, 6
GAN f1 score: 0.462
GAN cohens kappa score: 0.439

-> test with 'LR'
LR tn, fp: 282, 6
LR fn, tp: 1, 8
LR f1 score: 0.696
LR cohens kappa score: 0.684
LR average precision score: 0.813

-> 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: 285, 3
KNN fn, tp: 0, 9
KNN f1 score: 0.857
KNN cohens kappa score: 0.852


------ Step 3/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1117 synthetic samples
-> test with GAN.predict
GAN tn, fp: 264, 24
GAN fn, tp: 6, 3
GAN f1 score: 0.167
GAN cohens kappa score: 0.127

-> test with 'LR'
LR tn, fp: 278, 10
LR fn, tp: 0, 9
LR f1 score: 0.643
LR cohens kappa score: 0.628
LR average precision score: 0.768

-> 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: 281, 7
KNN fn, tp: 1, 8
KNN f1 score: 0.667
KNN cohens kappa score: 0.654


------ Step 3/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1116 synthetic samples
-> test with GAN.predict
GAN tn, fp: 279, 9
GAN fn, tp: 3, 5
GAN f1 score: 0.455
GAN cohens kappa score: 0.435

-> test with 'LR'
LR tn, fp: 275, 13
LR fn, tp: 0, 8
LR f1 score: 0.552
LR cohens kappa score: 0.533
LR average precision score: 0.324

-> 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: 276, 12
KNN fn, tp: 0, 8
KNN f1 score: 0.571
KNN cohens kappa score: 0.554


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

------ Step 4/5: Slice 1/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1117 synthetic samples
-> test with GAN.predict
GAN tn, fp: 261, 27
GAN fn, tp: 4, 5
GAN f1 score: 0.244
GAN cohens kappa score: 0.206

-> test with 'LR'
LR tn, fp: 275, 13
LR fn, tp: 0, 9
LR f1 score: 0.581
LR cohens kappa score: 0.562
LR average precision score: 0.742

-> 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: 277, 11
KNN fn, tp: 0, 9
KNN f1 score: 0.621
KNN cohens kappa score: 0.604


------ Step 4/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1117 synthetic samples
-> test with GAN.predict
GAN tn, fp: 232, 56
GAN fn, tp: 3, 6
GAN f1 score: 0.169
GAN cohens kappa score: 0.123

-> test with 'LR'
LR tn, fp: 271, 17
LR fn, tp: 1, 8
LR f1 score: 0.471
LR cohens kappa score: 0.446
LR average precision score: 0.622

-> test with 'GB'
GB tn, fp: 286, 2
GB fn, tp: 2, 7
GB f1 score: 0.778
GB cohens kappa score: 0.771

-> test with 'KNN'
KNN tn, fp: 278, 10
KNN fn, tp: 0, 9
KNN f1 score: 0.643
KNN cohens kappa score: 0.628


------ Step 4/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1117 synthetic samples
-> test with GAN.predict
GAN tn, fp: 279, 9
GAN fn, tp: 3, 6
GAN f1 score: 0.500
GAN cohens kappa score: 0.480

-> test with 'LR'
LR tn, fp: 277, 11
LR fn, tp: 2, 7
LR f1 score: 0.519
LR cohens kappa score: 0.498
LR average precision score: 0.660

-> test with 'GB'
GB tn, fp: 283, 5
GB fn, tp: 4, 5
GB f1 score: 0.526
GB cohens kappa score: 0.511

-> test with 'KNN'
KNN tn, fp: 279, 9
KNN fn, tp: 0, 9
KNN f1 score: 0.667
KNN cohens kappa score: 0.653


------ Step 4/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1117 synthetic samples
-> test with GAN.predict
GAN tn, fp: 265, 23
GAN fn, tp: 5, 4
GAN f1 score: 0.222
GAN cohens kappa score: 0.185

-> test with 'LR'
LR tn, fp: 280, 8
LR fn, tp: 0, 9
LR f1 score: 0.692
LR cohens kappa score: 0.680
LR average precision score: 0.690

-> 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: 283, 5
KNN fn, tp: 1, 8
KNN f1 score: 0.727
KNN cohens kappa score: 0.717


------ Step 4/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1116 synthetic samples
-> test with GAN.predict
GAN tn, fp: 284, 4
GAN fn, tp: 2, 6
GAN f1 score: 0.667
GAN cohens kappa score: 0.656

-> test with 'LR'
LR tn, fp: 275, 13
LR fn, tp: 0, 8
LR f1 score: 0.552
LR cohens kappa score: 0.533
LR average precision score: 0.709

-> test with 'GB'
GB tn, fp: 285, 3
GB fn, tp: 1, 7
GB f1 score: 0.778
GB cohens kappa score: 0.771

-> test with 'KNN'
KNN tn, fp: 273, 15
KNN fn, tp: 0, 8
KNN f1 score: 0.516
KNN cohens kappa score: 0.496


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

------ Step 5/5: Slice 1/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1117 synthetic samples
-> test with GAN.predict
GAN tn, fp: 277, 11
GAN fn, tp: 3, 6
GAN f1 score: 0.462
GAN cohens kappa score: 0.439

-> test with 'LR'
LR tn, fp: 273, 15
LR fn, tp: 0, 9
LR f1 score: 0.545
LR cohens kappa score: 0.524
LR average precision score: 0.713

-> test with 'GB'
GB tn, fp: 283, 5
GB fn, tp: 0, 9
GB f1 score: 0.783
GB cohens kappa score: 0.774

-> test with 'KNN'
KNN tn, fp: 273, 15
KNN fn, tp: 0, 9
KNN f1 score: 0.545
KNN cohens kappa score: 0.524


------ Step 5/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1117 synthetic samples
-> test with GAN.predict
GAN tn, fp: 201, 87
GAN fn, tp: 5, 4
GAN f1 score: 0.080
GAN cohens kappa score: 0.026

-> test with 'LR'
LR tn, fp: 280, 8
LR fn, tp: 1, 8
LR f1 score: 0.640
LR cohens kappa score: 0.625
LR average precision score: 0.797

-> 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: 281, 7
KNN fn, tp: 0, 9
KNN f1 score: 0.720
KNN cohens kappa score: 0.709


------ Step 5/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1117 synthetic samples
-> test with GAN.predict
GAN tn, fp: 249, 39
GAN fn, tp: 1, 8
GAN f1 score: 0.286
GAN cohens kappa score: 0.247

-> test with 'LR'
LR tn, fp: 277, 11
LR fn, tp: 0, 9
LR f1 score: 0.621
LR cohens kappa score: 0.604
LR average precision score: 0.738

-> test with 'GB'
GB tn, fp: 286, 2
GB fn, tp: 2, 7
GB f1 score: 0.778
GB cohens kappa score: 0.771

-> test with 'KNN'
KNN tn, fp: 282, 6
KNN fn, tp: 0, 9
KNN f1 score: 0.750
KNN cohens kappa score: 0.740


------ Step 5/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1117 synthetic samples
-> test with GAN.predict
GAN tn, fp: 283, 5
GAN fn, tp: 4, 5
GAN f1 score: 0.526
GAN cohens kappa score: 0.511

-> test with 'LR'
LR tn, fp: 280, 8
LR fn, tp: 1, 8
LR f1 score: 0.640
LR cohens kappa score: 0.625
LR average precision score: 0.568

-> 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: 283, 5
KNN fn, tp: 0, 9
KNN f1 score: 0.783
KNN cohens kappa score: 0.774


------ Step 5/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1116 synthetic samples
-> test with GAN.predict
GAN tn, fp: 248, 40
GAN fn, tp: 1, 7
GAN f1 score: 0.255
GAN cohens kappa score: 0.218

-> test with 'LR'
LR tn, fp: 274, 14
LR fn, tp: 0, 8
LR f1 score: 0.533
LR cohens kappa score: 0.514
LR average precision score: 0.395

-> test with 'GB'
GB tn, fp: 282, 6
GB fn, tp: 3, 5
GB f1 score: 0.526
GB cohens kappa score: 0.511

-> test with 'KNN'
KNN tn, fp: 275, 13
KNN fn, tp: 2, 6
KNN f1 score: 0.444
KNN cohens kappa score: 0.422

### Exercise is done.

-----[ LR ]-----
maximum:
LR tn, fp: 282, 17
LR fn, tp: 2, 9
LR f1 score: 0.720
LR cohens kappa score: 0.709
LR average precision score: 0.878


average:
LR tn, fp: 275.92, 12.08
LR fn, tp: 0.32, 8.48
LR f1 score: 0.584
LR cohens kappa score: 0.566
LR average precision score: 0.672


minimum:
LR tn, fp: 271, 6
LR fn, tp: 0, 7
LR f1 score: 0.471
LR cohens kappa score: 0.446
LR average precision score: 0.324


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


average:
GB tn, fp: 285.68, 2.32
GB fn, tp: 2.48, 6.32
GB f1 score: 0.721
GB cohens kappa score: 0.713


minimum:
GB tn, fp: 282, 0
GB fn, tp: 0, 3
GB f1 score: 0.375
GB cohens kappa score: 0.358


-----[ KNN ]-----
maximum:
KNN tn, fp: 285, 15
KNN fn, tp: 2, 9
KNN f1 score: 0.857
KNN cohens kappa score: 0.852


average:
KNN tn, fp: 278.36, 9.64
KNN fn, tp: 0.2, 8.6
KNN f1 score: 0.647
KNN cohens kappa score: 0.632


minimum:
KNN tn, fp: 273, 3
KNN fn, tp: 0, 6
KNN f1 score: 0.444
KNN cohens kappa score: 0.422


-----[ GAN ]-----
maximum:
GAN tn, fp: 284, 125
GAN fn, tp: 6, 8
GAN f1 score: 0.667
GAN cohens kappa score: 0.656


average:
GAN tn, fp: 257.12, 30.88
GAN fn, tp: 3.04, 5.76
GAN f1 score: 0.353
GAN cohens kappa score: 0.322


minimum:
GAN tn, fp: 163, 4
GAN fn, tp: 1, 3
GAN f1 score: 0.058
GAN cohens kappa score: 0.001