Removed unused old data.

data_result/SpheredNoise/folding_abalone9-18.csv

@@ -1,92 +0,0 @@
-LR
-Nr.;TN;TP;FN;FP;f1 score;cohens kappa score;average precision score
-1;137.000;6.000;3.000;1.000;0.750;0.736;0.829
-2;135.000;4.000;5.000;3.000;0.500;0.472;0.499
-3;136.000;4.000;5.000;2.000;0.533;0.509;0.711
-4;137.000;3.000;6.000;1.000;0.462;0.440;0.619
-5;134.000;3.000;3.000;3.000;0.500;0.478;0.498
-6;136.000;4.000;5.000;2.000;0.533;0.509;0.667
-7;136.000;6.000;3.000;2.000;0.706;0.688;0.726
-8;136.000;3.000;6.000;2.000;0.429;0.402;0.537
-9;136.000;3.000;6.000;2.000;0.429;0.402;0.657
-10;134.000;4.000;2.000;3.000;0.615;0.597;0.510
-11;134.000;3.000;6.000;4.000;0.375;0.340;0.503
-12;137.000;5.000;4.000;1.000;0.667;0.649;0.766
-13;136.000;4.000;5.000;2.000;0.533;0.509;0.588
-14;135.000;6.000;3.000;3.000;0.667;0.645;0.630
-15;135.000;2.000;4.000;2.000;0.400;0.379;0.582
-16;134.000;4.000;5.000;4.000;0.471;0.438;0.524
-17;137.000;4.000;5.000;1.000;0.571;0.552;0.600
-18;137.000;5.000;4.000;1.000;0.667;0.649;0.610
-19;137.000;4.000;5.000;1.000;0.571;0.552;0.805
-20;135.000;2.000;4.000;2.000;0.400;0.379;0.439
-21;133.000;6.000;3.000;5.000;0.600;0.571;0.707
-22;136.000;5.000;4.000;2.000;0.625;0.604;0.713
-23;135.000;3.000;6.000;3.000;0.400;0.369;0.479
-24;137.000;4.000;5.000;1.000;0.571;0.552;0.711
-25;137.000;3.000;3.000;0.000;0.667;0.657;0.785
-max;137.000;6.000;6.000;5.000;0.750;0.736;0.829
-avg;135.680;4.000;4.400;2.120;0.546;0.523;0.628
-min;133.000;2.000;2.000;0.000;0.375;0.340;0.439
----
-GB
-Nr.;TN;TP;FN;FP;f1 score;cohens kappa score
-1;134.000;5.000;4.000;4.000;0.556;0.527
-2;136.000;3.000;6.000;2.000;0.429;0.402
-3;137.000;3.000;6.000;1.000;0.462;0.440
-4;137.000;4.000;5.000;1.000;0.571;0.552
-5;136.000;0.000;6.000;1.000;0.000;-0.012
-6;135.000;2.000;7.000;3.000;0.286;0.253
-7;136.000;4.000;5.000;2.000;0.533;0.509
-8;136.000;3.000;6.000;2.000;0.429;0.402
-9;136.000;4.000;5.000;2.000;0.533;0.509
-10;135.000;3.000;3.000;2.000;0.545;0.527
-11;135.000;1.000;8.000;3.000;0.154;0.121
-12;137.000;2.000;7.000;1.000;0.333;0.312
-13;138.000;2.000;7.000;0.000;0.364;0.349
-14;136.000;5.000;4.000;2.000;0.625;0.604
-15;133.000;2.000;4.000;4.000;0.333;0.304
-16;134.000;3.000;6.000;4.000;0.375;0.340
-17;132.000;4.000;5.000;6.000;0.421;0.381
-18;134.000;3.000;6.000;4.000;0.375;0.340
-19;137.000;2.000;7.000;1.000;0.333;0.312
-20;134.000;1.000;5.000;3.000;0.200;0.172
-21;135.000;3.000;6.000;3.000;0.400;0.369
-22;136.000;2.000;7.000;2.000;0.308;0.281
-23;136.000;3.000;6.000;2.000;0.429;0.402
-24;138.000;3.000;6.000;0.000;0.500;0.484
-25;136.000;3.000;3.000;1.000;0.600;0.586
-max;138.000;5.000;8.000;6.000;0.625;0.604
-avg;135.560;2.800;5.600;2.240;0.404;0.379
-min;132.000;0.000;3.000;0.000;0.000;-0.012
----
-KNN
-Nr.;TN;TP;FN;FP;f1 score;cohens kappa score
-1;138.000;0.000;9.000;0.000;0.000;0.000
-2;138.000;0.000;9.000;0.000;0.000;0.000
-3;138.000;0.000;9.000;0.000;0.000;0.000
-4;138.000;0.000;9.000;0.000;0.000;0.000
-5;137.000;0.000;6.000;0.000;0.000;0.000
-6;138.000;0.000;9.000;0.000;0.000;0.000
-7;138.000;0.000;9.000;0.000;0.000;0.000
-8;138.000;0.000;9.000;0.000;0.000;0.000
-9;138.000;0.000;9.000;0.000;0.000;0.000
-10;137.000;0.000;6.000;0.000;0.000;0.000
-11;138.000;0.000;9.000;0.000;0.000;0.000
-12;138.000;0.000;9.000;0.000;0.000;0.000
-13;138.000;0.000;9.000;0.000;0.000;0.000
-14;138.000;0.000;9.000;0.000;0.000;0.000
-15;137.000;0.000;6.000;0.000;0.000;0.000
-16;138.000;0.000;9.000;0.000;0.000;0.000
-17;138.000;0.000;9.000;0.000;0.000;0.000
-18;138.000;0.000;9.000;0.000;0.000;0.000
-19;138.000;0.000;9.000;0.000;0.000;0.000
-20;137.000;0.000;6.000;0.000;0.000;0.000
-21;138.000;0.000;9.000;0.000;0.000;0.000
-22;138.000;0.000;9.000;0.000;0.000;0.000
-23;138.000;0.000;9.000;0.000;0.000;0.000
-24;138.000;0.000;9.000;0.000;0.000;0.000
-25;137.000;0.000;6.000;0.000;0.000;0.000
-max;138.000;0.000;9.000;0.000;0.000;0.000
-avg;137.800;0.000;8.400;0.000;0.000;0.000
-min;137.000;0.000;6.000;0.000;0.000;0.000

data_result/SpheredNoise/folding_abalone9-18.log

@@ -1,826 +0,0 @@
-
-
-///////////////////////////////////////////
-// Running SpheredNoise on folding_abalone9-18
-///////////////////////////////////////////
-
-Load 'data_input/folding_abalone9-18'
-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 551/33 points
--> new disc
--> calc distances
--> statistics
-trained 33 points min:0.03211308144666282 max:0.3975704340113837
--> create 518 synthetic samples
--> test with 'LR'
-LR tn, fp: 137, 1
-LR fn, tp: 3, 6
-LR f1 score: 0.750
-LR cohens kappa score: 0.736
-LR average precision score: 0.829
-
--> test with 'GB'
-GB tn, fp: 134, 4
-GB fn, tp: 4, 5
-GB f1 score: 0.556
-GB cohens kappa score: 0.527
-
--> test with 'KNN'
-KNN tn, fp: 138, 0
-KNN fn, tp: 9, 0
-KNN f1 score: 0.000
-KNN cohens kappa score: 0.000
-
-
------- Step 1/5: Slice 2/5 -------
--> Reset the GAN
--> Train generator for synthetic samples
-Train 551/33 points
--> new disc
--> calc distances
--> statistics
-trained 33 points min:0.03311344137959691 max:0.6176281243596343
--> create 518 synthetic samples
--> test with 'LR'
-LR tn, fp: 135, 3
-LR fn, tp: 5, 4
-LR f1 score: 0.500
-LR cohens kappa score: 0.472
-LR average precision score: 0.499
-
--> test with 'GB'
-GB tn, fp: 136, 2
-GB fn, tp: 6, 3
-GB f1 score: 0.429
-GB cohens kappa score: 0.402
-
--> test with 'KNN'
-KNN tn, fp: 138, 0
-KNN fn, tp: 9, 0
-KNN f1 score: 0.000
-KNN cohens kappa score: 0.000
-
-
------- Step 1/5: Slice 3/5 -------
--> Reset the GAN
--> Train generator for synthetic samples
-Train 551/33 points
--> new disc
--> calc distances
--> statistics
-trained 33 points min:0.03211308144666282 max:0.6176281243596343
--> create 518 synthetic samples
--> test with 'LR'
-LR tn, fp: 136, 2
-LR fn, tp: 5, 4
-LR f1 score: 0.533
-LR cohens kappa score: 0.509
-LR average precision score: 0.711
-
--> test with 'GB'
-GB tn, fp: 137, 1
-GB fn, tp: 6, 3
-GB f1 score: 0.462
-GB cohens kappa score: 0.440
-
--> test with 'KNN'
-KNN tn, fp: 138, 0
-KNN fn, tp: 9, 0
-KNN f1 score: 0.000
-KNN cohens kappa score: 0.000
-
-
------- Step 1/5: Slice 4/5 -------
--> Reset the GAN
--> Train generator for synthetic samples
-Train 551/33 points
--> new disc
--> calc distances
--> statistics
-trained 33 points min:0.03211308144666282 max:0.6176281243596343
--> create 518 synthetic samples
--> test with 'LR'
-LR tn, fp: 137, 1
-LR fn, tp: 6, 3
-LR f1 score: 0.462
-LR cohens kappa score: 0.440
-LR average precision score: 0.619
-
--> test with 'GB'
-GB tn, fp: 137, 1
-GB fn, tp: 5, 4
-GB f1 score: 0.571
-GB cohens kappa score: 0.552
-
--> test with 'KNN'
-KNN tn, fp: 138, 0
-KNN fn, tp: 9, 0
-KNN f1 score: 0.000
-KNN cohens kappa score: 0.000
-
-
------- Step 1/5: Slice 5/5 -------
--> Reset the GAN
--> Train generator for synthetic samples
-Train 552/36 points
--> new disc
--> calc distances
--> statistics
-trained 36 points min:0.03211308144666282 max:0.6863390197271315
--> create 516 synthetic samples
--> test with 'LR'
-LR tn, fp: 134, 3
-LR fn, tp: 3, 3
-LR f1 score: 0.500
-LR cohens kappa score: 0.478
-LR average precision score: 0.498
-
--> test with 'GB'
-GB tn, fp: 136, 1
-GB fn, tp: 6, 0
-GB f1 score: 0.000
-GB cohens kappa score: -0.012
-
--> test with 'KNN'
-KNN tn, fp: 137, 0
-KNN fn, tp: 6, 0
-KNN f1 score: 0.000
-KNN cohens kappa score: 0.000
-
-
-====== Step 2/5 =======
--> Shuffling data
--> Spliting data to slices
-
------- Step 2/5: Slice 1/5 -------
--> Reset the GAN
--> Train generator for synthetic samples
-Train 551/33 points
--> new disc
--> calc distances
--> statistics
-trained 33 points min:0.03311344137959691 max:0.6176281243596343
--> create 518 synthetic samples
--> test with 'LR'
-LR tn, fp: 136, 2
-LR fn, tp: 5, 4
-LR f1 score: 0.533
-LR cohens kappa score: 0.509
-LR average precision score: 0.667
-
--> test with 'GB'
-GB tn, fp: 135, 3
-GB fn, tp: 7, 2
-GB f1 score: 0.286
-GB cohens kappa score: 0.253
-
--> test with 'KNN'
-KNN tn, fp: 138, 0
-KNN fn, tp: 9, 0
-KNN f1 score: 0.000
-KNN cohens kappa score: 0.000
-
-
------- Step 2/5: Slice 2/5 -------
--> Reset the GAN
--> Train generator for synthetic samples
-Train 551/33 points
--> new disc
--> calc distances
--> statistics
-trained 33 points min:0.03311344137959691 max:0.6176281243596343
--> create 518 synthetic samples
--> test with 'LR'
-LR tn, fp: 136, 2
-LR fn, tp: 3, 6
-LR f1 score: 0.706
-LR cohens kappa score: 0.688
-LR average precision score: 0.726
-
--> test with 'GB'
-GB tn, fp: 136, 2
-GB fn, tp: 5, 4
-GB f1 score: 0.533
-GB cohens kappa score: 0.509
-
--> test with 'KNN'
-KNN tn, fp: 138, 0
-KNN fn, tp: 9, 0
-KNN f1 score: 0.000
-KNN cohens kappa score: 0.000
-
-
------- Step 2/5: Slice 3/5 -------
--> Reset the GAN
--> Train generator for synthetic samples
-Train 551/33 points
--> new disc
--> calc distances
--> statistics
-trained 33 points min:0.03211308144666282 max:0.3752825602129679
--> create 518 synthetic samples
--> test with 'LR'
-LR tn, fp: 136, 2
-LR fn, tp: 6, 3
-LR f1 score: 0.429
-LR cohens kappa score: 0.402
-LR average precision score: 0.537
-
--> test with 'GB'
-GB tn, fp: 136, 2
-GB fn, tp: 6, 3
-GB f1 score: 0.429
-GB cohens kappa score: 0.402
-
--> test with 'KNN'
-KNN tn, fp: 138, 0
-KNN fn, tp: 9, 0
-KNN f1 score: 0.000
-KNN cohens kappa score: 0.000
-
-
------- Step 2/5: Slice 4/5 -------
--> Reset the GAN
--> Train generator for synthetic samples
-Train 551/33 points
--> new disc
--> calc distances
--> statistics
-trained 33 points min:0.03211308144666282 max:0.6176281243596343
--> create 518 synthetic samples
--> test with 'LR'
-LR tn, fp: 136, 2
-LR fn, tp: 6, 3
-LR f1 score: 0.429
-LR cohens kappa score: 0.402
-LR average precision score: 0.657
-
--> test with 'GB'
-GB tn, fp: 136, 2
-GB fn, tp: 5, 4
-GB f1 score: 0.533
-GB cohens kappa score: 0.509
-
--> test with 'KNN'
-KNN tn, fp: 138, 0
-KNN fn, tp: 9, 0
-KNN f1 score: 0.000
-KNN cohens kappa score: 0.000
-
-
------- Step 2/5: Slice 5/5 -------
--> Reset the GAN
--> Train generator for synthetic samples
-Train 552/36 points
--> new disc
--> calc distances
--> statistics
-trained 36 points min:0.03211308144666282 max:0.6176281243596343
--> create 516 synthetic samples
--> test with 'LR'
-LR tn, fp: 134, 3
-LR fn, tp: 2, 4
-LR f1 score: 0.615
-LR cohens kappa score: 0.597
-LR average precision score: 0.510
-
--> test with 'GB'
-GB tn, fp: 135, 2
-GB fn, tp: 3, 3
-GB f1 score: 0.545
-GB cohens kappa score: 0.527
-
--> test with 'KNN'
-KNN tn, fp: 137, 0
-KNN fn, tp: 6, 0
-KNN f1 score: 0.000
-KNN cohens kappa score: 0.000
-
-
-====== Step 3/5 =======
--> Shuffling data
--> Spliting data to slices
-
------- Step 3/5: Slice 1/5 -------
--> Reset the GAN
--> Train generator for synthetic samples
-Train 551/33 points
--> new disc
--> calc distances
--> statistics
-trained 33 points min:0.03211308144666282 max:0.6176281243596343
--> create 518 synthetic samples
--> test with 'LR'
-LR tn, fp: 134, 4
-LR fn, tp: 6, 3
-LR f1 score: 0.375
-LR cohens kappa score: 0.340
-LR average precision score: 0.503
-
--> test with 'GB'
-GB tn, fp: 135, 3
-GB fn, tp: 8, 1
-GB f1 score: 0.154
-GB cohens kappa score: 0.121
-
--> test with 'KNN'
-KNN tn, fp: 138, 0
-KNN fn, tp: 9, 0
-KNN f1 score: 0.000
-KNN cohens kappa score: 0.000
-
-
------- Step 3/5: Slice 2/5 -------
--> Reset the GAN
--> Train generator for synthetic samples
-Train 551/33 points
--> new disc
--> calc distances
--> statistics
-trained 33 points min:0.03211308144666282 max:0.6176281243596343
--> create 518 synthetic samples
--> test with 'LR'
-LR tn, fp: 137, 1
-LR fn, tp: 4, 5
-LR f1 score: 0.667
-LR cohens kappa score: 0.649
-LR average precision score: 0.766
-
--> test with 'GB'
-GB tn, fp: 137, 1
-GB fn, tp: 7, 2
-GB f1 score: 0.333
-GB cohens kappa score: 0.312
-
--> test with 'KNN'
-KNN tn, fp: 138, 0
-KNN fn, tp: 9, 0
-KNN f1 score: 0.000
-KNN cohens kappa score: 0.000
-
-
------- Step 3/5: Slice 3/5 -------
--> Reset the GAN
--> Train generator for synthetic samples
-Train 551/33 points
--> new disc
--> calc distances
--> statistics
-trained 33 points min:0.03311344137959691 max:0.6176281243596343
--> create 518 synthetic samples
--> test with 'LR'
-LR tn, fp: 136, 2
-LR fn, tp: 5, 4
-LR f1 score: 0.533
-LR cohens kappa score: 0.509
-LR average precision score: 0.588
-
--> test with 'GB'
-GB tn, fp: 138, 0
-GB fn, tp: 7, 2
-GB f1 score: 0.364
-GB cohens kappa score: 0.349
-
--> test with 'KNN'
-KNN tn, fp: 138, 0
-KNN fn, tp: 9, 0
-KNN f1 score: 0.000
-KNN cohens kappa score: 0.000
-
-
------- Step 3/5: Slice 4/5 -------
--> Reset the GAN
--> Train generator for synthetic samples
-Train 551/33 points
--> new disc
--> calc distances
--> statistics
-trained 33 points min:0.03211308144666282 max:0.6610300673948196
--> create 518 synthetic samples
--> test with 'LR'
-LR tn, fp: 135, 3
-LR fn, tp: 3, 6
-LR f1 score: 0.667
-LR cohens kappa score: 0.645
-LR average precision score: 0.630
-
--> test with 'GB'
-GB tn, fp: 136, 2
-GB fn, tp: 4, 5
-GB f1 score: 0.625
-GB cohens kappa score: 0.604
-
--> test with 'KNN'
-KNN tn, fp: 138, 0
-KNN fn, tp: 9, 0
-KNN f1 score: 0.000
-KNN cohens kappa score: 0.000
-
-
------- Step 3/5: Slice 5/5 -------
--> Reset the GAN
--> Train generator for synthetic samples
-Train 552/36 points
--> new disc
--> calc distances
--> statistics
-trained 36 points min:0.0358887168898527 max:0.4738359420727812
--> create 516 synthetic samples
--> test with 'LR'
-LR tn, fp: 135, 2
-LR fn, tp: 4, 2
-LR f1 score: 0.400
-LR cohens kappa score: 0.379
-LR average precision score: 0.582
-
--> test with 'GB'
-GB tn, fp: 133, 4
-GB fn, tp: 4, 2
-GB f1 score: 0.333
-GB cohens kappa score: 0.304
-
--> test with 'KNN'
-KNN tn, fp: 137, 0
-KNN fn, tp: 6, 0
-KNN f1 score: 0.000
-KNN cohens kappa score: 0.000
-
-
-====== Step 4/5 =======
--> Shuffling data
--> Spliting data to slices
-
------- Step 4/5: Slice 1/5 -------
--> Reset the GAN
--> Train generator for synthetic samples
-Train 551/33 points
--> new disc
--> calc distances
--> statistics
-trained 33 points min:0.03311344137959691 max:0.6176281243596343
--> create 518 synthetic samples
--> test with 'LR'
-LR tn, fp: 134, 4
-LR fn, tp: 5, 4
-LR f1 score: 0.471
-LR cohens kappa score: 0.438
-LR average precision score: 0.524
-
--> test with 'GB'
-GB tn, fp: 134, 4
-GB fn, tp: 6, 3
-GB f1 score: 0.375
-GB cohens kappa score: 0.340
-
--> test with 'KNN'
-KNN tn, fp: 138, 0
-KNN fn, tp: 9, 0
-KNN f1 score: 0.000
-KNN cohens kappa score: 0.000
-
-
------- Step 4/5: Slice 2/5 -------
--> Reset the GAN
--> Train generator for synthetic samples
-Train 551/33 points
--> new disc
--> calc distances
--> statistics
-trained 33 points min:0.03311344137959691 max:0.3975704340113837
--> create 518 synthetic samples
--> test with 'LR'
-LR tn, fp: 137, 1
-LR fn, tp: 5, 4
-LR f1 score: 0.571
-LR cohens kappa score: 0.552
-LR average precision score: 0.600
-
--> test with 'GB'
-GB tn, fp: 132, 6
-GB fn, tp: 5, 4
-GB f1 score: 0.421
-GB cohens kappa score: 0.381
-
--> test with 'KNN'
-KNN tn, fp: 138, 0
-KNN fn, tp: 9, 0
-KNN f1 score: 0.000
-KNN cohens kappa score: 0.000
-
-
------- Step 4/5: Slice 3/5 -------
--> Reset the GAN
--> Train generator for synthetic samples
-Train 551/33 points
--> new disc
--> calc distances
--> statistics
-trained 33 points min:0.03211308144666282 max:0.6176281243596343
--> create 518 synthetic samples
--> test with 'LR'
-LR tn, fp: 137, 1
-LR fn, tp: 4, 5
-LR f1 score: 0.667
-LR cohens kappa score: 0.649
-LR average precision score: 0.610
-
--> test with 'GB'
-GB tn, fp: 134, 4
-GB fn, tp: 6, 3
-GB f1 score: 0.375
-GB cohens kappa score: 0.340
-
--> test with 'KNN'
-KNN tn, fp: 138, 0
-KNN fn, tp: 9, 0
-KNN f1 score: 0.000
-KNN cohens kappa score: 0.000
-
-
------- Step 4/5: Slice 4/5 -------
--> Reset the GAN
--> Train generator for synthetic samples
-Train 551/33 points
--> new disc
--> calc distances
--> statistics
-trained 33 points min:0.03211308144666282 max:0.6176281243596343
--> create 518 synthetic samples
--> test with 'LR'
-LR tn, fp: 137, 1
-LR fn, tp: 5, 4
-LR f1 score: 0.571
-LR cohens kappa score: 0.552
-LR average precision score: 0.805
-
--> test with 'GB'
-GB tn, fp: 137, 1
-GB fn, tp: 7, 2
-GB f1 score: 0.333
-GB cohens kappa score: 0.312
-
--> test with 'KNN'
-KNN tn, fp: 138, 0
-KNN fn, tp: 9, 0
-KNN f1 score: 0.000
-KNN cohens kappa score: 0.000
-
-
------- Step 4/5: Slice 5/5 -------
--> Reset the GAN
--> Train generator for synthetic samples
-Train 552/36 points
--> new disc
--> calc distances
--> statistics
-trained 36 points min:0.03211308144666282 max:0.6610300673948196
--> create 516 synthetic samples
--> test with 'LR'
-LR tn, fp: 135, 2
-LR fn, tp: 4, 2
-LR f1 score: 0.400
-LR cohens kappa score: 0.379
-LR average precision score: 0.439
-
--> test with 'GB'
-GB tn, fp: 134, 3
-GB fn, tp: 5, 1
-GB f1 score: 0.200
-GB cohens kappa score: 0.172
-
--> test with 'KNN'
-KNN tn, fp: 137, 0
-KNN fn, tp: 6, 0
-KNN f1 score: 0.000
-KNN cohens kappa score: 0.000
-
-
-====== Step 5/5 =======
--> Shuffling data
--> Spliting data to slices
-
------- Step 5/5: Slice 1/5 -------
--> Reset the GAN
--> Train generator for synthetic samples
-Train 551/33 points
--> new disc
--> calc distances
--> statistics
-trained 33 points min:0.03211308144666282 max:0.4217019089356842
--> create 518 synthetic samples
--> test with 'LR'
-LR tn, fp: 133, 5
-LR fn, tp: 3, 6
-LR f1 score: 0.600
-LR cohens kappa score: 0.571
-LR average precision score: 0.707
-
--> test with 'GB'
-GB tn, fp: 135, 3
-GB fn, tp: 6, 3
-GB f1 score: 0.400
-GB cohens kappa score: 0.369
-
--> test with 'KNN'
-KNN tn, fp: 138, 0
-KNN fn, tp: 9, 0
-KNN f1 score: 0.000
-KNN cohens kappa score: 0.000
-
-
------- Step 5/5: Slice 2/5 -------
--> Reset the GAN
--> Train generator for synthetic samples
-Train 551/33 points
--> new disc
--> calc distances
--> statistics
-trained 33 points min:0.03211308144666282 max:0.6610300673948196
--> create 518 synthetic samples
--> test with 'LR'
-LR tn, fp: 136, 2
-LR fn, tp: 4, 5
-LR f1 score: 0.625
-LR cohens kappa score: 0.604
-LR average precision score: 0.713
-
--> test with 'GB'
-GB tn, fp: 136, 2
-GB fn, tp: 7, 2
-GB f1 score: 0.308
-GB cohens kappa score: 0.281
-
--> test with 'KNN'
-KNN tn, fp: 138, 0
-KNN fn, tp: 9, 0
-KNN f1 score: 0.000
-KNN cohens kappa score: 0.000
-
-
------- Step 5/5: Slice 3/5 -------
--> Reset the GAN
--> Train generator for synthetic samples
-Train 551/33 points
--> new disc
--> calc distances
--> statistics
-trained 33 points min:0.037393181196576475 max:0.6176281243596343
--> create 518 synthetic samples
--> test with 'LR'
-LR tn, fp: 135, 3
-LR fn, tp: 6, 3
-LR f1 score: 0.400
-LR cohens kappa score: 0.369
-LR average precision score: 0.479
-
--> test with 'GB'
-GB tn, fp: 136, 2
-GB fn, tp: 6, 3
-GB f1 score: 0.429
-GB cohens kappa score: 0.402
-
--> test with 'KNN'
-KNN tn, fp: 138, 0
-KNN fn, tp: 9, 0
-KNN f1 score: 0.000
-KNN cohens kappa score: 0.000
-
-
------- Step 5/5: Slice 4/5 -------
--> Reset the GAN
--> Train generator for synthetic samples
-Train 551/33 points
--> new disc
--> calc distances
--> statistics
-trained 33 points min:0.03211308144666282 max:0.6176281243596343
--> create 518 synthetic samples
--> test with 'LR'
-LR tn, fp: 137, 1
-LR fn, tp: 5, 4
-LR f1 score: 0.571
-LR cohens kappa score: 0.552
-LR average precision score: 0.711
-
--> test with 'GB'
-GB tn, fp: 138, 0
-GB fn, tp: 6, 3
-GB f1 score: 0.500
-GB cohens kappa score: 0.484
-
--> test with 'KNN'
-KNN tn, fp: 138, 0
-KNN fn, tp: 9, 0
-KNN f1 score: 0.000
-KNN cohens kappa score: 0.000
-
-
------- Step 5/5: Slice 5/5 -------
--> Reset the GAN
--> Train generator for synthetic samples
-Train 552/36 points
--> new disc
--> calc distances
--> statistics
-trained 36 points min:0.03311344137959691 max:0.6176281243596343
--> create 516 synthetic samples
--> test with 'LR'
-LR tn, fp: 137, 0
-LR fn, tp: 3, 3
-LR f1 score: 0.667
-LR cohens kappa score: 0.657
-LR average precision score: 0.785
-
--> test with 'GB'
-GB tn, fp: 136, 1
-GB fn, tp: 3, 3
-GB f1 score: 0.600
-GB cohens kappa score: 0.586
-
--> test with 'KNN'
-KNN tn, fp: 137, 0
-KNN fn, tp: 6, 0
-KNN f1 score: 0.000
-KNN cohens kappa score: 0.000
-
-### Exercise is done.
-
------[ LR ]-----
-maximum:
-LR tn, fp: 137, 5
-LR fn, tp: 6, 6
-LR f1 score: 0.750
-LR cohens kappa score: 0.736
-LR average precision score: 0.829
-
-
-average:
-LR tn, fp: 135.68, 2.12
-LR fn, tp: 4.4, 4.0
-LR f1 score: 0.546
-LR cohens kappa score: 0.523
-LR average precision score: 0.628
-
-
-minimum:
-LR tn, fp: 133, 0
-LR fn, tp: 2, 2
-LR f1 score: 0.375
-LR cohens kappa score: 0.340
-LR average precision score: 0.439
-
-
------[ GB ]-----
-maximum:
-GB tn, fp: 138, 6
-GB fn, tp: 8, 5
-GB f1 score: 0.625
-GB cohens kappa score: 0.604
-
-
-average:
-GB tn, fp: 135.56, 2.24
-GB fn, tp: 5.6, 2.8
-GB f1 score: 0.404
-GB cohens kappa score: 0.379
-
-
-minimum:
-GB tn, fp: 132, 0
-GB fn, tp: 3, 0
-GB f1 score: 0.000
-GB cohens kappa score: -0.012
-
-
------[ KNN ]-----
-maximum:
-KNN tn, fp: 138, 0
-KNN fn, tp: 9, 0
-KNN f1 score: 0.000
-KNN cohens kappa score: 0.000
-
-
-average:
-KNN tn, fp: 137.8, 0.0
-KNN fn, tp: 8.4, 0.0
-KNN f1 score: 0.000
-KNN cohens kappa score: 0.000
-
-
-minimum:
-KNN tn, fp: 137, 0
-KNN fn, tp: 6, 0
-KNN f1 score: 0.000
-KNN cohens kappa score: 0.000
-

data_result/SpheredNoise/folding_abalone_17_vs_7_8_9_10.csv

@@ -1,92 +0,0 @@
-LR
-Nr.;TN;TP;FN;FP;f1 score;cohens kappa score;average precision score
-1;450.000;3.000;9.000;6.000;0.286;0.270;0.324
-2;449.000;6.000;6.000;7.000;0.480;0.466;0.604
-3;454.000;2.000;10.000;2.000;0.250;0.240;0.329
-4;453.000;4.000;8.000;3.000;0.421;0.410;0.530
-5;452.000;2.000;8.000;4.000;0.250;0.238;0.301
-6;450.000;4.000;8.000;6.000;0.364;0.348;0.541
-7;450.000;5.000;7.000;6.000;0.435;0.421;0.538
-8;453.000;2.000;10.000;3.000;0.235;0.224;0.196
-9;454.000;4.000;8.000;2.000;0.444;0.435;0.431
-10;452.000;3.000;7.000;4.000;0.353;0.341;0.378
-11;454.000;6.000;6.000;2.000;0.600;0.592;0.552
-12;452.000;2.000;10.000;4.000;0.222;0.209;0.369
-13;452.000;3.000;9.000;4.000;0.316;0.303;0.452
-14;452.000;2.000;10.000;4.000;0.222;0.209;0.244
-15;451.000;5.000;5.000;5.000;0.500;0.489;0.529
-16;447.000;7.000;5.000;9.000;0.500;0.485;0.567
-17;453.000;5.000;7.000;3.000;0.500;0.490;0.541
-18;454.000;2.000;10.000;2.000;0.250;0.240;0.279
-19;454.000;2.000;10.000;2.000;0.250;0.240;0.415
-20;455.000;2.000;8.000;1.000;0.308;0.301;0.295
-21;452.000;4.000;8.000;4.000;0.400;0.387;0.397
-22;450.000;2.000;10.000;6.000;0.200;0.183;0.160
-23;450.000;3.000;9.000;6.000;0.286;0.270;0.398
-24;456.000;4.000;8.000;0.000;0.500;0.494;0.668
-25;453.000;4.000;6.000;3.000;0.471;0.461;0.472
-max;456.000;7.000;10.000;9.000;0.600;0.592;0.668
-avg;452.080;3.520;8.080;3.920;0.362;0.350;0.420
-min;447.000;2.000;5.000;0.000;0.200;0.183;0.160
----
-GB
-Nr.;TN;TP;FN;FP;f1 score;cohens kappa score
-1;454.000;1.000;11.000;2.000;0.133;0.124
-2;452.000;5.000;7.000;4.000;0.476;0.464
-3;454.000;1.000;11.000;2.000;0.133;0.124
-4;455.000;2.000;10.000;1.000;0.267;0.259
-5;452.000;2.000;8.000;4.000;0.250;0.238
-6;456.000;1.000;11.000;0.000;0.154;0.150
-7;453.000;4.000;8.000;3.000;0.421;0.410
-8;454.000;2.000;10.000;2.000;0.250;0.240
-9;455.000;3.000;9.000;1.000;0.375;0.367
-10;453.000;2.000;8.000;3.000;0.267;0.256
-11;454.000;3.000;9.000;2.000;0.353;0.343
-12;454.000;2.000;10.000;2.000;0.250;0.240
-13;454.000;2.000;10.000;2.000;0.250;0.240
-14;453.000;0.000;12.000;3.000;0.000;-0.010
-15;453.000;3.000;7.000;3.000;0.375;0.365
-16;455.000;2.000;10.000;1.000;0.267;0.259
-17;454.000;3.000;9.000;2.000;0.353;0.343
-18;454.000;0.000;12.000;2.000;0.000;-0.007
-19;452.000;1.000;11.000;4.000;0.118;0.104
-20;453.000;2.000;8.000;3.000;0.267;0.256
-21;452.000;2.000;10.000;4.000;0.222;0.209
-22;453.000;0.000;12.000;3.000;0.000;-0.010
-23;453.000;2.000;10.000;3.000;0.235;0.224
-24;455.000;1.000;11.000;1.000;0.143;0.137
-25;452.000;3.000;7.000;4.000;0.353;0.341
-max;456.000;5.000;12.000;4.000;0.476;0.464
-avg;453.560;1.960;9.640;2.440;0.236;0.227
-min;452.000;0.000;7.000;0.000;0.000;-0.010
----
-KNN
-Nr.;TN;TP;FN;FP;f1 score;cohens kappa score
-1;456.000;0.000;12.000;0.000;0.000;0.000
-2;456.000;0.000;12.000;0.000;0.000;0.000
-3;456.000;0.000;12.000;0.000;0.000;0.000
-4;456.000;0.000;12.000;0.000;0.000;0.000
-5;456.000;0.000;10.000;0.000;0.000;0.000
-6;456.000;0.000;12.000;0.000;0.000;0.000
-7;456.000;0.000;12.000;0.000;0.000;0.000
-8;456.000;0.000;12.000;0.000;0.000;0.000
-9;456.000;0.000;12.000;0.000;0.000;0.000
-10;456.000;0.000;10.000;0.000;0.000;0.000
-11;456.000;0.000;12.000;0.000;0.000;0.000
-12;456.000;0.000;12.000;0.000;0.000;0.000
-13;456.000;0.000;12.000;0.000;0.000;0.000
-14;456.000;0.000;12.000;0.000;0.000;0.000
-15;456.000;0.000;10.000;0.000;0.000;0.000
-16;456.000;0.000;12.000;0.000;0.000;0.000
-17;456.000;0.000;12.000;0.000;0.000;0.000
-18;456.000;0.000;12.000;0.000;0.000;0.000
-19;456.000;0.000;12.000;0.000;0.000;0.000
-20;456.000;0.000;10.000;0.000;0.000;0.000
-21;456.000;0.000;12.000;0.000;0.000;0.000
-22;456.000;0.000;12.000;0.000;0.000;0.000
-23;456.000;0.000;12.000;0.000;0.000;0.000
-24;456.000;0.000;12.000;0.000;0.000;0.000
-25;456.000;0.000;10.000;0.000;0.000;0.000
-max;456.000;0.000;12.000;0.000;0.000;0.000
-avg;456.000;0.000;11.600;0.000;0.000;0.000
-min;456.000;0.000;10.000;0.000;0.000;0.000

data_result/SpheredNoise/folding_abalone_17_vs_7_8_9_10.log

@@ -1,826 +0,0 @@
-
-
-///////////////////////////////////////////
-// Running SpheredNoise on folding_abalone_17_vs_7_8_9_10
-///////////////////////////////////////////
-
-Load 'data_input/folding_abalone_17_vs_7_8_9_10'
-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 1824/46 points
--> new disc
--> calc distances
--> statistics
-trained 46 points min:0.02728552729928453 max:0.41828518979280144
--> create 1778 synthetic samples
--> test with 'LR'
-LR tn, fp: 450, 6
-LR fn, tp: 9, 3
-LR f1 score: 0.286
-LR cohens kappa score: 0.270
-LR average precision score: 0.324
-
--> test with 'GB'
-GB tn, fp: 454, 2
-GB fn, tp: 11, 1
-GB f1 score: 0.133
-GB cohens kappa score: 0.124
-
--> test with 'KNN'
-KNN tn, fp: 456, 0
-KNN fn, tp: 12, 0
-KNN f1 score: 0.000
-KNN cohens kappa score: 0.000
-
-
------- Step 1/5: Slice 2/5 -------
--> Reset the GAN
--> Train generator for synthetic samples
-Train 1824/46 points
--> new disc
--> calc distances
--> statistics
-trained 46 points min:0.02728552729928453 max:0.6446939196238787
--> create 1778 synthetic samples
--> test with 'LR'
-LR tn, fp: 449, 7
-LR fn, tp: 6, 6
-LR f1 score: 0.480
-LR cohens kappa score: 0.466
-LR average precision score: 0.604
-
--> test with 'GB'
-GB tn, fp: 452, 4
-GB fn, tp: 7, 5
-GB f1 score: 0.476
-GB cohens kappa score: 0.464
-
--> test with 'KNN'
-KNN tn, fp: 456, 0
-KNN fn, tp: 12, 0
-KNN f1 score: 0.000
-KNN cohens kappa score: 0.000
-
-
------- Step 1/5: Slice 3/5 -------
--> Reset the GAN
--> Train generator for synthetic samples
-Train 1824/46 points
--> new disc
--> calc distances
--> statistics
-trained 46 points min:0.02728552729928453 max:0.6966004952625284
--> create 1778 synthetic samples
--> test with 'LR'
-LR tn, fp: 454, 2
-LR fn, tp: 10, 2
-LR f1 score: 0.250
-LR cohens kappa score: 0.240
-LR average precision score: 0.329
-
--> test with 'GB'
-GB tn, fp: 454, 2
-GB fn, tp: 11, 1
-GB f1 score: 0.133
-GB cohens kappa score: 0.124
-
--> test with 'KNN'
-KNN tn, fp: 456, 0
-KNN fn, tp: 12, 0
-KNN f1 score: 0.000
-KNN cohens kappa score: 0.000
-
-
------- Step 1/5: Slice 4/5 -------
--> Reset the GAN
--> Train generator for synthetic samples
-Train 1824/46 points
--> new disc
--> calc distances
--> statistics
-trained 46 points min:0.02923183196448697 max:0.6446939196238787
--> create 1778 synthetic samples
--> test with 'LR'
-LR tn, fp: 453, 3
-LR fn, tp: 8, 4
-LR f1 score: 0.421
-LR cohens kappa score: 0.410
-LR average precision score: 0.530
-
--> test with 'GB'
-GB tn, fp: 455, 1
-GB fn, tp: 10, 2
-GB f1 score: 0.267
-GB cohens kappa score: 0.259
-
--> test with 'KNN'
-KNN tn, fp: 456, 0
-KNN fn, tp: 12, 0
-KNN f1 score: 0.000
-KNN cohens kappa score: 0.000
-
-
------- Step 1/5: Slice 5/5 -------
--> Reset the GAN
--> Train generator for synthetic samples
-Train 1824/48 points
--> new disc
--> calc distances
--> statistics
-trained 48 points min:0.030983866769659363 max:0.6446939196238787
--> create 1776 synthetic samples
--> test with 'LR'
-LR tn, fp: 452, 4
-LR fn, tp: 8, 2
-LR f1 score: 0.250
-LR cohens kappa score: 0.238
-LR average precision score: 0.301
-
--> test with 'GB'
-GB tn, fp: 452, 4
-GB fn, tp: 8, 2
-GB f1 score: 0.250
-GB cohens kappa score: 0.238
-
--> test with 'KNN'
-KNN tn, fp: 456, 0
-KNN fn, tp: 10, 0
-KNN f1 score: 0.000
-KNN cohens kappa score: 0.000
-
-
-====== Step 2/5 =======
--> Shuffling data
--> Spliting data to slices
-
------- Step 2/5: Slice 1/5 -------
--> Reset the GAN
--> Train generator for synthetic samples
-Train 1824/46 points
--> new disc
--> calc distances
--> statistics
-trained 46 points min:0.031772629730634515 max:0.6446939196238787
--> create 1778 synthetic samples
--> test with 'LR'
-LR tn, fp: 450, 6
-LR fn, tp: 8, 4
-LR f1 score: 0.364
-LR cohens kappa score: 0.348
-LR average precision score: 0.541
-
--> test with 'GB'
-GB tn, fp: 456, 0
-GB fn, tp: 11, 1
-GB f1 score: 0.154
-GB cohens kappa score: 0.150
-
--> test with 'KNN'
-KNN tn, fp: 456, 0
-KNN fn, tp: 12, 0
-KNN f1 score: 0.000
-KNN cohens kappa score: 0.000
-
-
------- Step 2/5: Slice 2/5 -------
--> Reset the GAN
--> Train generator for synthetic samples
-Train 1824/46 points
--> new disc
--> calc distances
--> statistics
-trained 46 points min:0.02728552729928453 max:0.6446939196238787
--> create 1778 synthetic samples
--> test with 'LR'
-LR tn, fp: 450, 6
-LR fn, tp: 7, 5
-LR f1 score: 0.435
-LR cohens kappa score: 0.421
-LR average precision score: 0.538
-
--> test with 'GB'
-GB tn, fp: 453, 3
-GB fn, tp: 8, 4
-GB f1 score: 0.421
-GB cohens kappa score: 0.410
-
--> test with 'KNN'
-KNN tn, fp: 456, 0
-KNN fn, tp: 12, 0
-KNN f1 score: 0.000
-KNN cohens kappa score: 0.000
-
-
------- Step 2/5: Slice 3/5 -------
--> Reset the GAN
--> Train generator for synthetic samples
-Train 1824/46 points
--> new disc
--> calc distances
--> statistics
-trained 46 points min:0.02923183196448697 max:0.6446939196238787
--> create 1778 synthetic samples
--> test with 'LR'
-LR tn, fp: 453, 3
-LR fn, tp: 10, 2
-LR f1 score: 0.235
-LR cohens kappa score: 0.224
-LR average precision score: 0.196
-
--> test with 'GB'
-GB tn, fp: 454, 2
-GB fn, tp: 10, 2
-GB f1 score: 0.250
-GB cohens kappa score: 0.240
-
--> test with 'KNN'
-KNN tn, fp: 456, 0
-KNN fn, tp: 12, 0
-KNN f1 score: 0.000
-KNN cohens kappa score: 0.000
-
-
------- Step 2/5: Slice 4/5 -------
--> Reset the GAN
--> Train generator for synthetic samples
-Train 1824/46 points
--> new disc
--> calc distances
--> statistics
-trained 46 points min:0.02728552729928453 max:0.6966004952625284
--> create 1778 synthetic samples
--> test with 'LR'
-LR tn, fp: 454, 2
-LR fn, tp: 8, 4
-LR f1 score: 0.444
-LR cohens kappa score: 0.435
-LR average precision score: 0.431
-
--> test with 'GB'
-GB tn, fp: 455, 1
-GB fn, tp: 9, 3
-GB f1 score: 0.375
-GB cohens kappa score: 0.367
-
--> test with 'KNN'
-KNN tn, fp: 456, 0
-KNN fn, tp: 12, 0
-KNN f1 score: 0.000
-KNN cohens kappa score: 0.000
-
-
------- Step 2/5: Slice 5/5 -------
--> Reset the GAN
--> Train generator for synthetic samples
-Train 1824/48 points
--> new disc
--> calc distances
--> statistics
-trained 48 points min:0.02728552729928453 max:0.37681593649950645
--> create 1776 synthetic samples
--> test with 'LR'
-LR tn, fp: 452, 4
-LR fn, tp: 7, 3
-LR f1 score: 0.353
-LR cohens kappa score: 0.341
-LR average precision score: 0.378
-
--> test with 'GB'
-GB tn, fp: 453, 3
-GB fn, tp: 8, 2
-GB f1 score: 0.267
-GB cohens kappa score: 0.256
-
--> test with 'KNN'
-KNN tn, fp: 456, 0
-KNN fn, tp: 10, 0
-KNN f1 score: 0.000
-KNN cohens kappa score: 0.000
-
-
-====== Step 3/5 =======
--> Shuffling data
--> Spliting data to slices
-
------- Step 3/5: Slice 1/5 -------
--> Reset the GAN
--> Train generator for synthetic samples
-Train 1824/46 points
--> new disc
--> calc distances
--> statistics
-trained 46 points min:0.02728552729928453 max:0.35035874471746814
--> create 1778 synthetic samples
--> test with 'LR'
-LR tn, fp: 454, 2
-LR fn, tp: 6, 6
-LR f1 score: 0.600
-LR cohens kappa score: 0.592
-LR average precision score: 0.552
-
--> test with 'GB'
-GB tn, fp: 454, 2
-GB fn, tp: 9, 3
-GB f1 score: 0.353
-GB cohens kappa score: 0.343
-
--> test with 'KNN'
-KNN tn, fp: 456, 0
-KNN fn, tp: 12, 0
-KNN f1 score: 0.000
-KNN cohens kappa score: 0.000
-
-
------- Step 3/5: Slice 2/5 -------
--> Reset the GAN
--> Train generator for synthetic samples
-Train 1824/46 points
--> new disc
--> calc distances
--> statistics
-trained 46 points min:0.02728552729928453 max:0.6966004952625284
--> create 1778 synthetic samples
--> test with 'LR'
-LR tn, fp: 452, 4
-LR fn, tp: 10, 2
-LR f1 score: 0.222
-LR cohens kappa score: 0.209
-LR average precision score: 0.369
-
--> test with 'GB'
-GB tn, fp: 454, 2
-GB fn, tp: 10, 2
-GB f1 score: 0.250
-GB cohens kappa score: 0.240
-
--> test with 'KNN'
-KNN tn, fp: 456, 0
-KNN fn, tp: 12, 0
-KNN f1 score: 0.000
-KNN cohens kappa score: 0.000
-
-
------- Step 3/5: Slice 3/5 -------
--> Reset the GAN
--> Train generator for synthetic samples
-Train 1824/46 points
--> new disc
--> calc distances
--> statistics
-trained 46 points min:0.02728552729928453 max:0.6446939196238787
--> create 1778 synthetic samples
--> test with 'LR'
-LR tn, fp: 452, 4
-LR fn, tp: 9, 3
-LR f1 score: 0.316
-LR cohens kappa score: 0.303
-LR average precision score: 0.452
-
--> test with 'GB'
-GB tn, fp: 454, 2
-GB fn, tp: 10, 2
-GB f1 score: 0.250
-GB cohens kappa score: 0.240
-
--> test with 'KNN'
-KNN tn, fp: 456, 0
-KNN fn, tp: 12, 0
-KNN f1 score: 0.000
-KNN cohens kappa score: 0.000
-
-
------- Step 3/5: Slice 4/5 -------
--> Reset the GAN
--> Train generator for synthetic samples
-Train 1824/46 points
--> new disc
--> calc distances
--> statistics
-trained 46 points min:0.030983866769659363 max:0.6446939196238787
--> create 1778 synthetic samples
--> test with 'LR'
-LR tn, fp: 452, 4
-LR fn, tp: 10, 2
-LR f1 score: 0.222
-LR cohens kappa score: 0.209
-LR average precision score: 0.244
-
--> test with 'GB'
-GB tn, fp: 453, 3
-GB fn, tp: 12, 0
-GB f1 score: 0.000
-GB cohens kappa score: -0.010
-
--> test with 'KNN'
-KNN tn, fp: 456, 0
-KNN fn, tp: 12, 0
-KNN f1 score: 0.000
-KNN cohens kappa score: 0.000
-
-
------- Step 3/5: Slice 5/5 -------
--> Reset the GAN
--> Train generator for synthetic samples
-Train 1824/48 points
--> new disc
--> calc distances
--> statistics
-trained 48 points min:0.02923183196448697 max:0.6446939196238787
--> create 1776 synthetic samples
--> test with 'LR'
-LR tn, fp: 451, 5
-LR fn, tp: 5, 5
-LR f1 score: 0.500
-LR cohens kappa score: 0.489
-LR average precision score: 0.529
-
--> test with 'GB'
-GB tn, fp: 453, 3
-GB fn, tp: 7, 3
-GB f1 score: 0.375
-GB cohens kappa score: 0.365
-
--> test with 'KNN'
-KNN tn, fp: 456, 0
-KNN fn, tp: 10, 0
-KNN f1 score: 0.000
-KNN cohens kappa score: 0.000
-
-
-====== Step 4/5 =======
--> Shuffling data
--> Spliting data to slices
-
------- Step 4/5: Slice 1/5 -------
--> Reset the GAN
--> Train generator for synthetic samples
-Train 1824/46 points
--> new disc
--> calc distances
--> statistics
-trained 46 points min:0.02728552729928453 max:0.37681593649950645
--> create 1778 synthetic samples
--> test with 'LR'
-LR tn, fp: 447, 9
-LR fn, tp: 5, 7
-LR f1 score: 0.500
-LR cohens kappa score: 0.485
-LR average precision score: 0.567
-
--> test with 'GB'
-GB tn, fp: 455, 1
-GB fn, tp: 10, 2
-GB f1 score: 0.267
-GB cohens kappa score: 0.259
-
--> test with 'KNN'
-KNN tn, fp: 456, 0
-KNN fn, tp: 12, 0
-KNN f1 score: 0.000
-KNN cohens kappa score: 0.000
-
-
------- Step 4/5: Slice 2/5 -------
--> Reset the GAN
--> Train generator for synthetic samples
-Train 1824/46 points
--> new disc
--> calc distances
--> statistics
-trained 46 points min:0.02728552729928453 max:0.6446939196238787
--> create 1778 synthetic samples
--> test with 'LR'
-LR tn, fp: 453, 3
-LR fn, tp: 7, 5
-LR f1 score: 0.500
-LR cohens kappa score: 0.490
-LR average precision score: 0.541
-
--> test with 'GB'
-GB tn, fp: 454, 2
-GB fn, tp: 9, 3
-GB f1 score: 0.353
-GB cohens kappa score: 0.343
-
--> test with 'KNN'
-KNN tn, fp: 456, 0
-KNN fn, tp: 12, 0
-KNN f1 score: 0.000
-KNN cohens kappa score: 0.000
-
-
------- Step 4/5: Slice 3/5 -------
--> Reset the GAN
--> Train generator for synthetic samples
-Train 1824/46 points
--> new disc
--> calc distances
--> statistics
-trained 46 points min:0.02923183196448697 max:0.6966004952625284
--> create 1778 synthetic samples
--> test with 'LR'
-LR tn, fp: 454, 2
-LR fn, tp: 10, 2
-LR f1 score: 0.250
-LR cohens kappa score: 0.240
-LR average precision score: 0.279
-
--> test with 'GB'
-GB tn, fp: 454, 2
-GB fn, tp: 12, 0
-GB f1 score: 0.000
-GB cohens kappa score: -0.007
-
--> test with 'KNN'
-KNN tn, fp: 456, 0
-KNN fn, tp: 12, 0
-KNN f1 score: 0.000
-KNN cohens kappa score: 0.000
-
-
------- Step 4/5: Slice 4/5 -------
--> Reset the GAN
--> Train generator for synthetic samples
-Train 1824/46 points
--> new disc
--> calc distances
--> statistics
-trained 46 points min:0.02728552729928453 max:0.6446939196238787
--> create 1778 synthetic samples
--> test with 'LR'
-LR tn, fp: 454, 2
-LR fn, tp: 10, 2
-LR f1 score: 0.250
-LR cohens kappa score: 0.240
-LR average precision score: 0.415
-
--> test with 'GB'
-GB tn, fp: 452, 4
-GB fn, tp: 11, 1
-GB f1 score: 0.118
-GB cohens kappa score: 0.104
-
--> test with 'KNN'
-KNN tn, fp: 456, 0
-KNN fn, tp: 12, 0
-KNN f1 score: 0.000
-KNN cohens kappa score: 0.000
-
-
------- Step 4/5: Slice 5/5 -------
--> Reset the GAN
--> Train generator for synthetic samples
-Train 1824/48 points
--> new disc
--> calc distances
--> statistics
-trained 48 points min:0.02728552729928453 max:0.6446939196238787
--> create 1776 synthetic samples
--> test with 'LR'
-LR tn, fp: 455, 1
-LR fn, tp: 8, 2
-LR f1 score: 0.308
-LR cohens kappa score: 0.301
-LR average precision score: 0.295
-
--> test with 'GB'
-GB tn, fp: 453, 3
-GB fn, tp: 8, 2
-GB f1 score: 0.267
-GB cohens kappa score: 0.256
-
--> test with 'KNN'
-KNN tn, fp: 456, 0
-KNN fn, tp: 10, 0
-KNN f1 score: 0.000
-KNN cohens kappa score: 0.000
-
-
-====== Step 5/5 =======
--> Shuffling data
--> Spliting data to slices
-
------- Step 5/5: Slice 1/5 -------
--> Reset the GAN
--> Train generator for synthetic samples
-Train 1824/46 points
--> new disc
--> calc distances
--> statistics
-trained 46 points min:0.02728552729928453 max:0.6446939196238787
--> create 1778 synthetic samples
--> test with 'LR'
-LR tn, fp: 452, 4
-LR fn, tp: 8, 4
-LR f1 score: 0.400
-LR cohens kappa score: 0.387
-LR average precision score: 0.397
-
--> test with 'GB'
-GB tn, fp: 452, 4
-GB fn, tp: 10, 2
-GB f1 score: 0.222
-GB cohens kappa score: 0.209
-
--> test with 'KNN'
-KNN tn, fp: 456, 0
-KNN fn, tp: 12, 0
-KNN f1 score: 0.000
-KNN cohens kappa score: 0.000
-
-
------- Step 5/5: Slice 2/5 -------
--> Reset the GAN
--> Train generator for synthetic samples
-Train 1824/46 points
--> new disc
--> calc distances
--> statistics
-trained 46 points min:0.02728552729928453 max:0.6966004952625284
--> create 1778 synthetic samples
--> test with 'LR'
-LR tn, fp: 450, 6
-LR fn, tp: 10, 2
-LR f1 score: 0.200
-LR cohens kappa score: 0.183
-LR average precision score: 0.160
-
--> test with 'GB'
-GB tn, fp: 453, 3
-GB fn, tp: 12, 0
-GB f1 score: 0.000
-GB cohens kappa score: -0.010
-
--> test with 'KNN'
-KNN tn, fp: 456, 0
-KNN fn, tp: 12, 0
-KNN f1 score: 0.000
-KNN cohens kappa score: 0.000
-
-
------- Step 5/5: Slice 3/5 -------
--> Reset the GAN
--> Train generator for synthetic samples
-Train 1824/46 points
--> new disc
--> calc distances
--> statistics
-trained 46 points min:0.02923183196448697 max:0.6446939196238787
--> create 1778 synthetic samples
--> test with 'LR'
-LR tn, fp: 450, 6
-LR fn, tp: 9, 3
-LR f1 score: 0.286
-LR cohens kappa score: 0.270
-LR average precision score: 0.398
-
--> test with 'GB'
-GB tn, fp: 453, 3
-GB fn, tp: 10, 2
-GB f1 score: 0.235
-GB cohens kappa score: 0.224
-
--> test with 'KNN'
-KNN tn, fp: 456, 0
-KNN fn, tp: 12, 0
-KNN f1 score: 0.000
-KNN cohens kappa score: 0.000
-
-
------- Step 5/5: Slice 4/5 -------
--> Reset the GAN
--> Train generator for synthetic samples
-Train 1824/46 points
--> new disc
--> calc distances
--> statistics
-trained 46 points min:0.030983866769659363 max:0.19717631703630123
--> create 1778 synthetic samples
--> test with 'LR'
-LR tn, fp: 456, 0
-LR fn, tp: 8, 4
-LR f1 score: 0.500
-LR cohens kappa score: 0.494
-LR average precision score: 0.668
-
--> test with 'GB'
-GB tn, fp: 455, 1
-GB fn, tp: 11, 1
-GB f1 score: 0.143
-GB cohens kappa score: 0.137
-
--> test with 'KNN'
-KNN tn, fp: 456, 0
-KNN fn, tp: 12, 0
-KNN f1 score: 0.000
-KNN cohens kappa score: 0.000
-
-
------- Step 5/5: Slice 5/5 -------
--> Reset the GAN
--> Train generator for synthetic samples
-Train 1824/48 points
--> new disc
--> calc distances
--> statistics
-trained 48 points min:0.02728552729928453 max:0.6446939196238787
--> create 1776 synthetic samples
--> test with 'LR'
-LR tn, fp: 453, 3
-LR fn, tp: 6, 4
-LR f1 score: 0.471
-LR cohens kappa score: 0.461
-LR average precision score: 0.472
-
--> test with 'GB'
-GB tn, fp: 452, 4
-GB fn, tp: 7, 3
-GB f1 score: 0.353
-GB cohens kappa score: 0.341
-
--> test with 'KNN'
-KNN tn, fp: 456, 0
-KNN fn, tp: 10, 0
-KNN f1 score: 0.000
-KNN cohens kappa score: 0.000
-
-### Exercise is done.
-
------[ LR ]-----
-maximum:
-LR tn, fp: 456, 9
-LR fn, tp: 10, 7
-LR f1 score: 0.600
-LR cohens kappa score: 0.592
-LR average precision score: 0.668
-
-
-average:
-LR tn, fp: 452.08, 3.92
-LR fn, tp: 8.08, 3.52
-LR f1 score: 0.362
-LR cohens kappa score: 0.350
-LR average precision score: 0.420
-
-
-minimum:
-LR tn, fp: 447, 0
-LR fn, tp: 5, 2
-LR f1 score: 0.200
-LR cohens kappa score: 0.183
-LR average precision score: 0.160
-
-
------[ GB ]-----
-maximum:
-GB tn, fp: 456, 4
-GB fn, tp: 12, 5
-GB f1 score: 0.476
-GB cohens kappa score: 0.464
-
-
-average:
-GB tn, fp: 453.56, 2.44
-GB fn, tp: 9.64, 1.96
-GB f1 score: 0.236
-GB cohens kappa score: 0.227
-
-
-minimum:
-GB tn, fp: 452, 0
-GB fn, tp: 7, 0
-GB f1 score: 0.000
-GB cohens kappa score: -0.010
-
-
------[ KNN ]-----
-maximum:
-KNN tn, fp: 456, 0
-KNN fn, tp: 12, 0
-KNN f1 score: 0.000
-KNN cohens kappa score: 0.000
-
-
-average:
-KNN tn, fp: 456.0, 0.0
-KNN fn, tp: 11.6, 0.0
-KNN f1 score: 0.000
-KNN cohens kappa score: 0.000
-
-
-minimum:
-KNN tn, fp: 456, 0
-KNN fn, tp: 10, 0
-KNN f1 score: 0.000
-KNN cohens kappa score: 0.000
-

data_result/SpheredNoise/folding_car-vgood.csv

@@ -1,92 +0,0 @@
-LR
-Nr.;TN;TP;FN;FP;f1 score;cohens kappa score;average precision score
-1;315.000;1.000;12.000;18.000;0.062;0.019;0.184
-2;314.000;4.000;9.000;19.000;0.222;0.183;0.182
-3;314.000;6.000;7.000;19.000;0.316;0.280;0.272
-4;321.000;4.000;9.000;12.000;0.276;0.245;0.248
-5;320.000;5.000;8.000;11.000;0.345;0.316;0.222
-6;316.000;4.000;9.000;17.000;0.235;0.198;0.331
-7;307.000;5.000;8.000;26.000;0.227;0.184;0.167
-8;320.000;3.000;10.000;13.000;0.207;0.173;0.208
-9;322.000;4.000;9.000;11.000;0.286;0.256;0.307
-10;317.000;3.000;10.000;14.000;0.200;0.164;0.182
-11;318.000;2.000;11.000;15.000;0.133;0.095;0.192
-12;314.000;4.000;9.000;19.000;0.222;0.183;0.184
-13;309.000;7.000;6.000;24.000;0.318;0.280;0.246
-14;320.000;4.000;9.000;13.000;0.267;0.234;0.234
-15;322.000;3.000;10.000;9.000;0.240;0.211;0.286
-16;319.000;3.000;10.000;14.000;0.200;0.164;0.215
-17;314.000;6.000;7.000;19.000;0.316;0.280;0.243
-18;315.000;3.000;10.000;18.000;0.176;0.136;0.179
-19;320.000;4.000;9.000;13.000;0.267;0.234;0.243
-20;317.000;4.000;9.000;14.000;0.258;0.224;0.223
-21;310.000;6.000;7.000;23.000;0.286;0.247;0.230
-22;320.000;2.000;11.000;13.000;0.143;0.107;0.180
-23;321.000;6.000;7.000;12.000;0.387;0.359;0.284
-24;314.000;4.000;9.000;19.000;0.222;0.183;0.197
-25;317.000;4.000;9.000;14.000;0.258;0.224;0.205
-max;322.000;7.000;12.000;26.000;0.387;0.359;0.331
-avg;316.640;4.040;8.960;15.960;0.243;0.207;0.226
-min;307.000;1.000;6.000;9.000;0.062;0.019;0.167
----
-GB
-Nr.;TN;TP;FN;FP;f1 score;cohens kappa score
-1;333.000;13.000;0.000;0.000;1.000;1.000
-2;333.000;13.000;0.000;0.000;1.000;1.000
-3;333.000;12.000;1.000;0.000;0.960;0.959
-4;333.000;13.000;0.000;0.000;1.000;1.000
-5;329.000;13.000;0.000;2.000;0.929;0.926
-6;332.000;13.000;0.000;1.000;0.963;0.961
-7;328.000;13.000;0.000;5.000;0.839;0.831
-8;333.000;13.000;0.000;0.000;1.000;1.000
-9;333.000;10.000;3.000;0.000;0.870;0.865
-10;331.000;13.000;0.000;0.000;1.000;1.000
-11;333.000;10.000;3.000;0.000;0.870;0.865
-12;332.000;13.000;0.000;1.000;0.963;0.961
-13;331.000;13.000;0.000;2.000;0.929;0.926
-14;333.000;13.000;0.000;0.000;1.000;1.000
-15;331.000;9.000;4.000;0.000;0.818;0.812
-16;333.000;13.000;0.000;0.000;1.000;1.000
-17;332.000;12.000;1.000;1.000;0.923;0.920
-18;332.000;13.000;0.000;1.000;0.963;0.961
-19;333.000;13.000;0.000;0.000;1.000;1.000
-20;331.000;12.000;1.000;0.000;0.960;0.958
-21;333.000;13.000;0.000;0.000;1.000;1.000
-22;333.000;10.000;3.000;0.000;0.870;0.865
-23;333.000;13.000;0.000;0.000;1.000;1.000
-24;333.000;11.000;2.000;0.000;0.917;0.914
-25;329.000;12.000;1.000;2.000;0.889;0.884
-max;333.000;13.000;4.000;5.000;1.000;1.000
-avg;332.000;12.240;0.760;0.600;0.946;0.944
-min;328.000;9.000;0.000;0.000;0.818;0.812
----
-KNN
-Nr.;TN;TP;FN;FP;f1 score;cohens kappa score
-1;328.000;3.000;10.000;5.000;0.286;0.265
-2;332.000;5.000;8.000;1.000;0.526;0.515
-3;330.000;7.000;6.000;3.000;0.609;0.595
-4;333.000;5.000;8.000;0.000;0.556;0.546
-5;329.000;6.000;7.000;2.000;0.571;0.559
-6;332.000;5.000;8.000;1.000;0.526;0.515
-7;326.000;7.000;6.000;7.000;0.519;0.499
-8;330.000;5.000;8.000;3.000;0.476;0.461
-9;333.000;4.000;9.000;0.000;0.471;0.461
-10;330.000;5.000;8.000;1.000;0.526;0.515
-11;330.000;6.000;7.000;3.000;0.545;0.531
-12;330.000;5.000;8.000;3.000;0.476;0.461
-13;330.000;6.000;7.000;3.000;0.545;0.531
-14;329.000;5.000;8.000;4.000;0.455;0.437
-15;331.000;5.000;8.000;0.000;0.556;0.546
-16;333.000;2.000;11.000;0.000;0.267;0.259
-17;328.000;4.000;9.000;5.000;0.364;0.343
-18;330.000;7.000;6.000;3.000;0.609;0.595
-19;330.000;4.000;9.000;3.000;0.400;0.384
-20;329.000;6.000;7.000;2.000;0.571;0.559
-21;325.000;6.000;7.000;8.000;0.444;0.422
-22;332.000;4.000;9.000;1.000;0.444;0.433
-23;332.000;5.000;8.000;1.000;0.526;0.515
-24;330.000;9.000;4.000;3.000;0.720;0.710
-25;328.000;7.000;6.000;3.000;0.609;0.595
-max;333.000;9.000;11.000;8.000;0.720;0.710
-avg;330.000;5.320;7.680;2.600;0.504;0.490
-min;325.000;2.000;4.000;0.000;0.267;0.259

data_result/SpheredNoise/folding_car-vgood.log

@@ -1,826 +0,0 @@
-
-
-///////////////////////////////////////////
-// Running SpheredNoise on folding_car-vgood
-///////////////////////////////////////////
-
-Load 'data_input/folding_car-vgood'
-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 1330/52 points
--> new disc
--> calc distances
--> statistics
-trained 52 points min:1.0 max:1.0
--> create 1278 synthetic samples
--> test with 'LR'
-LR tn, fp: 315, 18
-LR fn, tp: 12, 1
-LR f1 score: 0.062
-LR cohens kappa score: 0.019
-LR average precision score: 0.184
-
--> test with 'GB'
-GB tn, fp: 333, 0
-GB fn, tp: 0, 13
-GB f1 score: 1.000
-GB cohens kappa score: 1.000
-
--> test with 'KNN'
-KNN tn, fp: 328, 5
-KNN fn, tp: 10, 3
-KNN f1 score: 0.286
-KNN cohens kappa score: 0.265
-
-
------- Step 1/5: Slice 2/5 -------
--> Reset the GAN
--> Train generator for synthetic samples
-Train 1330/52 points
--> new disc
--> calc distances
--> statistics
-trained 52 points min:1.0 max:1.0
--> create 1278 synthetic samples
--> test with 'LR'
-LR tn, fp: 314, 19
-LR fn, tp: 9, 4
-LR f1 score: 0.222
-LR cohens kappa score: 0.183
-LR average precision score: 0.182
-
--> test with 'GB'
-GB tn, fp: 333, 0
-GB fn, tp: 0, 13
-GB f1 score: 1.000
-GB cohens kappa score: 1.000
-
--> test with 'KNN'
-KNN tn, fp: 332, 1
-KNN fn, tp: 8, 5
-KNN f1 score: 0.526
-KNN cohens kappa score: 0.515
-
-
------- Step 1/5: Slice 3/5 -------
--> Reset the GAN
--> Train generator for synthetic samples
-Train 1330/52 points
--> new disc
--> calc distances
--> statistics
-trained 52 points min:1.0 max:1.0
--> create 1278 synthetic samples
--> test with 'LR'
-LR tn, fp: 314, 19
-LR fn, tp: 7, 6
-LR f1 score: 0.316
-LR cohens kappa score: 0.280
-LR average precision score: 0.272
-
--> test with 'GB'
-GB tn, fp: 333, 0
-GB fn, tp: 1, 12
-GB f1 score: 0.960
-GB cohens kappa score: 0.959
-
--> test with 'KNN'
-KNN tn, fp: 330, 3
-KNN fn, tp: 6, 7
-KNN f1 score: 0.609
-KNN cohens kappa score: 0.595
-
-
------- Step 1/5: Slice 4/5 -------
--> Reset the GAN
--> Train generator for synthetic samples
-Train 1330/52 points
--> new disc
--> calc distances
--> statistics
-trained 52 points min:1.0 max:1.0
--> create 1278 synthetic samples
--> test with 'LR'
-LR tn, fp: 321, 12
-LR fn, tp: 9, 4
-LR f1 score: 0.276
-LR cohens kappa score: 0.245
-LR average precision score: 0.248
-
--> test with 'GB'
-GB tn, fp: 333, 0
-GB fn, tp: 0, 13
-GB f1 score: 1.000
-GB cohens kappa score: 1.000
-
--> test with 'KNN'
-KNN tn, fp: 333, 0
-KNN fn, tp: 8, 5
-KNN f1 score: 0.556
-KNN cohens kappa score: 0.546
-
-
------- Step 1/5: Slice 5/5 -------
--> Reset the GAN
--> Train generator for synthetic samples
-Train 1332/52 points
--> new disc
--> calc distances
--> statistics
-trained 52 points min:1.0 max:1.4142135623730951
--> create 1280 synthetic samples
--> test with 'LR'
-LR tn, fp: 320, 11
-LR fn, tp: 8, 5
-LR f1 score: 0.345
-LR cohens kappa score: 0.316
-LR average precision score: 0.222
-
--> test with 'GB'
-GB tn, fp: 329, 2
-GB fn, tp: 0, 13
-GB f1 score: 0.929
-GB cohens kappa score: 0.926
-
--> test with 'KNN'
-KNN tn, fp: 329, 2
-KNN fn, tp: 7, 6
-KNN f1 score: 0.571
-KNN cohens kappa score: 0.559
-
-
-====== Step 2/5 =======
--> Shuffling data
--> Spliting data to slices
-
------- Step 2/5: Slice 1/5 -------
--> Reset the GAN
--> Train generator for synthetic samples
-Train 1330/52 points
--> new disc
--> calc distances
--> statistics
-trained 52 points min:1.0 max:1.0
--> create 1278 synthetic samples
--> test with 'LR'
-LR tn, fp: 316, 17
-LR fn, tp: 9, 4
-LR f1 score: 0.235
-LR cohens kappa score: 0.198
-LR average precision score: 0.331
-
--> test with 'GB'
-GB tn, fp: 332, 1
-GB fn, tp: 0, 13
-GB f1 score: 0.963
-GB cohens kappa score: 0.961
-
--> test with 'KNN'
-KNN tn, fp: 332, 1
-KNN fn, tp: 8, 5
-KNN f1 score: 0.526
-KNN cohens kappa score: 0.515
-
-
------- Step 2/5: Slice 2/5 -------
--> Reset the GAN
--> Train generator for synthetic samples
-Train 1330/52 points
--> new disc
--> calc distances
--> statistics
-trained 52 points min:1.0 max:1.4142135623730951
--> create 1278 synthetic samples
--> test with 'LR'
-LR tn, fp: 307, 26
-LR fn, tp: 8, 5
-LR f1 score: 0.227
-LR cohens kappa score: 0.184
-LR average precision score: 0.167
-
--> test with 'GB'
-GB tn, fp: 328, 5
-GB fn, tp: 0, 13
-GB f1 score: 0.839
-GB cohens kappa score: 0.831
-
--> test with 'KNN'
-KNN tn, fp: 326, 7
-KNN fn, tp: 6, 7
-KNN f1 score: 0.519
-KNN cohens kappa score: 0.499
-
-
------- Step 2/5: Slice 3/5 -------
--> Reset the GAN
--> Train generator for synthetic samples
-Train 1330/52 points
--> new disc
--> calc distances
--> statistics
-trained 52 points min:1.0 max:1.0
--> create 1278 synthetic samples
--> test with 'LR'
-LR tn, fp: 320, 13
-LR fn, tp: 10, 3
-LR f1 score: 0.207
-LR cohens kappa score: 0.173
-LR average precision score: 0.208
-
--> test with 'GB'
-GB tn, fp: 333, 0
-GB fn, tp: 0, 13
-GB f1 score: 1.000
-GB cohens kappa score: 1.000
-
--> test with 'KNN'
-KNN tn, fp: 330, 3
-KNN fn, tp: 8, 5
-KNN f1 score: 0.476
-KNN cohens kappa score: 0.461
-
-
------- Step 2/5: Slice 4/5 -------
--> Reset the GAN
--> Train generator for synthetic samples
-Train 1330/52 points
--> new disc
--> calc distances
--> statistics
-trained 52 points min:1.0 max:1.0
--> create 1278 synthetic samples
--> test with 'LR'
-LR tn, fp: 322, 11
-LR fn, tp: 9, 4
-LR f1 score: 0.286
-LR cohens kappa score: 0.256
-LR average precision score: 0.307
-
--> test with 'GB'
-GB tn, fp: 333, 0
-GB fn, tp: 3, 10
-GB f1 score: 0.870
-GB cohens kappa score: 0.865
-
--> test with 'KNN'
-KNN tn, fp: 333, 0
-KNN fn, tp: 9, 4
-KNN f1 score: 0.471
-KNN cohens kappa score: 0.461
-
-
------- Step 2/5: Slice 5/5 -------
--> Reset the GAN
--> Train generator for synthetic samples
-Train 1332/52 points
--> new disc
--> calc distances
--> statistics
-trained 52 points min:1.0 max:1.0
--> create 1280 synthetic samples
--> test with 'LR'
-LR tn, fp: 317, 14
-LR fn, tp: 10, 3
-LR f1 score: 0.200
-LR cohens kappa score: 0.164
-LR average precision score: 0.182
-
--> test with 'GB'
-GB tn, fp: 331, 0
-GB fn, tp: 0, 13
-GB f1 score: 1.000
-GB cohens kappa score: 1.000
-
--> test with 'KNN'
-KNN tn, fp: 330, 1
-KNN fn, tp: 8, 5
-KNN f1 score: 0.526
-KNN cohens kappa score: 0.515
-
-
-====== Step 3/5 =======
--> Shuffling data
--> Spliting data to slices
-
------- Step 3/5: Slice 1/5 -------
--> Reset the GAN
--> Train generator for synthetic samples
-Train 1330/52 points
--> new disc
--> calc distances
--> statistics
-trained 52 points min:1.0 max:1.0
--> create 1278 synthetic samples
--> test with 'LR'
-LR tn, fp: 318, 15
-LR fn, tp: 11, 2
-LR f1 score: 0.133
-LR cohens kappa score: 0.095
-LR average precision score: 0.192
-
--> test with 'GB'
-GB tn, fp: 333, 0
-GB fn, tp: 3, 10
-GB f1 score: 0.870
-GB cohens kappa score: 0.865
-
--> test with 'KNN'
-KNN tn, fp: 330, 3
-KNN fn, tp: 7, 6
-KNN f1 score: 0.545
-KNN cohens kappa score: 0.531
-
-
------- Step 3/5: Slice 2/5 -------
--> Reset the GAN
--> Train generator for synthetic samples
-Train 1330/52 points
--> new disc
--> calc distances
--> statistics
-trained 52 points min:1.0 max:1.0
--> create 1278 synthetic samples
--> test with 'LR'
-LR tn, fp: 314, 19
-LR fn, tp: 9, 4
-LR f1 score: 0.222
-LR cohens kappa score: 0.183
-LR average precision score: 0.184
-
--> test with 'GB'
-GB tn, fp: 332, 1
-GB fn, tp: 0, 13
-GB f1 score: 0.963
-GB cohens kappa score: 0.961
-
--> test with 'KNN'
-KNN tn, fp: 330, 3
-KNN fn, tp: 8, 5
-KNN f1 score: 0.476
-KNN cohens kappa score: 0.461
-
-
------- Step 3/5: Slice 3/5 -------
--> Reset the GAN
--> Train generator for synthetic samples
-Train 1330/52 points
--> new disc
--> calc distances
--> statistics
-trained 52 points min:1.0 max:1.0
--> create 1278 synthetic samples
--> test with 'LR'
-LR tn, fp: 309, 24
-LR fn, tp: 6, 7
-LR f1 score: 0.318
-LR cohens kappa score: 0.280
-LR average precision score: 0.246
-
--> test with 'GB'
-GB tn, fp: 331, 2
-GB fn, tp: 0, 13
-GB f1 score: 0.929
-GB cohens kappa score: 0.926
-
--> test with 'KNN'
-KNN tn, fp: 330, 3
-KNN fn, tp: 7, 6
-KNN f1 score: 0.545
-KNN cohens kappa score: 0.531
-
-
------- Step 3/5: Slice 4/5 -------
--> Reset the GAN
--> Train generator for synthetic samples
-Train 1330/52 points
--> new disc
--> calc distances
--> statistics
-trained 52 points min:1.0 max:1.0
--> create 1278 synthetic samples
--> test with 'LR'
-LR tn, fp: 320, 13
-LR fn, tp: 9, 4
-LR f1 score: 0.267
-LR cohens kappa score: 0.234
-LR average precision score: 0.234
-
--> test with 'GB'
-GB tn, fp: 333, 0
-GB fn, tp: 0, 13
-GB f1 score: 1.000
-GB cohens kappa score: 1.000
-
--> test with 'KNN'
-KNN tn, fp: 329, 4
-KNN fn, tp: 8, 5
-KNN f1 score: 0.455
-KNN cohens kappa score: 0.437
-
-
------- Step 3/5: Slice 5/5 -------
--> Reset the GAN
--> Train generator for synthetic samples
-Train 1332/52 points
--> new disc
--> calc distances
--> statistics
-trained 52 points min:1.0 max:1.0
--> create 1280 synthetic samples
--> test with 'LR'
-LR tn, fp: 322, 9
-LR fn, tp: 10, 3
-LR f1 score: 0.240
-LR cohens kappa score: 0.211
-LR average precision score: 0.286
-
--> test with 'GB'
-GB tn, fp: 331, 0
-GB fn, tp: 4, 9
-GB f1 score: 0.818
-GB cohens kappa score: 0.812
-
--> test with 'KNN'
-KNN tn, fp: 331, 0
-KNN fn, tp: 8, 5
-KNN f1 score: 0.556
-KNN cohens kappa score: 0.546
-
-
-====== Step 4/5 =======
--> Shuffling data
--> Spliting data to slices
-
------- Step 4/5: Slice 1/5 -------
--> Reset the GAN
--> Train generator for synthetic samples
-Train 1330/52 points
--> new disc
--> calc distances
--> statistics
-trained 52 points min:1.0 max:1.0
--> create 1278 synthetic samples
--> test with 'LR'
-LR tn, fp: 319, 14
-LR fn, tp: 10, 3
-LR f1 score: 0.200
-LR cohens kappa score: 0.164
-LR average precision score: 0.215
-
--> test with 'GB'
-GB tn, fp: 333, 0
-GB fn, tp: 0, 13
-GB f1 score: 1.000
-GB cohens kappa score: 1.000
-
--> test with 'KNN'
-KNN tn, fp: 333, 0
-KNN fn, tp: 11, 2
-KNN f1 score: 0.267
-KNN cohens kappa score: 0.259
-
-
------- Step 4/5: Slice 2/5 -------
--> Reset the GAN
--> Train generator for synthetic samples
-Train 1330/52 points
--> new disc
--> calc distances
--> statistics
-trained 52 points min:1.0 max:1.0
--> create 1278 synthetic samples
--> test with 'LR'
-LR tn, fp: 314, 19
-LR fn, tp: 7, 6
-LR f1 score: 0.316
-LR cohens kappa score: 0.280
-LR average precision score: 0.243
-
--> test with 'GB'
-GB tn, fp: 332, 1
-GB fn, tp: 1, 12
-GB f1 score: 0.923
-GB cohens kappa score: 0.920
-
--> test with 'KNN'
-KNN tn, fp: 328, 5
-KNN fn, tp: 9, 4
-KNN f1 score: 0.364
-KNN cohens kappa score: 0.343
-
-
------- Step 4/5: Slice 3/5 -------
--> Reset the GAN
--> Train generator for synthetic samples
-Train 1330/52 points
--> new disc
--> calc distances
--> statistics
-trained 52 points min:1.0 max:1.0
--> create 1278 synthetic samples
--> test with 'LR'
-LR tn, fp: 315, 18
-LR fn, tp: 10, 3
-LR f1 score: 0.176
-LR cohens kappa score: 0.136
-LR average precision score: 0.179
-
--> test with 'GB'
-GB tn, fp: 332, 1
-GB fn, tp: 0, 13
-GB f1 score: 0.963
-GB cohens kappa score: 0.961
-
--> test with 'KNN'
-KNN tn, fp: 330, 3
-KNN fn, tp: 6, 7
-KNN f1 score: 0.609
-KNN cohens kappa score: 0.595
-
-
------- Step 4/5: Slice 4/5 -------
--> Reset the GAN
--> Train generator for synthetic samples
-Train 1330/52 points
--> new disc
--> calc distances
--> statistics
-trained 52 points min:1.0 max:1.0
--> create 1278 synthetic samples
--> test with 'LR'
-LR tn, fp: 320, 13
-LR fn, tp: 9, 4
-LR f1 score: 0.267
-LR cohens kappa score: 0.234
-LR average precision score: 0.243
-
--> test with 'GB'
-GB tn, fp: 333, 0
-GB fn, tp: 0, 13
-GB f1 score: 1.000
-GB cohens kappa score: 1.000
-
--> test with 'KNN'
-KNN tn, fp: 330, 3
-KNN fn, tp: 9, 4
-KNN f1 score: 0.400
-KNN cohens kappa score: 0.384
-
-
------- Step 4/5: Slice 5/5 -------
--> Reset the GAN
--> Train generator for synthetic samples
-Train 1332/52 points
--> new disc
--> calc distances
--> statistics
-trained 52 points min:1.0 max:1.0
--> create 1280 synthetic samples
--> test with 'LR'
-LR tn, fp: 317, 14
-LR fn, tp: 9, 4
-LR f1 score: 0.258
-LR cohens kappa score: 0.224
-LR average precision score: 0.223
-
--> test with 'GB'
-GB tn, fp: 331, 0
-GB fn, tp: 1, 12
-GB f1 score: 0.960
-GB cohens kappa score: 0.958
-
--> test with 'KNN'
-KNN tn, fp: 329, 2
-KNN fn, tp: 7, 6
-KNN f1 score: 0.571
-KNN cohens kappa score: 0.559
-
-
-====== Step 5/5 =======
--> Shuffling data
--> Spliting data to slices
-
------- Step 5/5: Slice 1/5 -------
--> Reset the GAN
--> Train generator for synthetic samples
-Train 1330/52 points
--> new disc
--> calc distances
--> statistics
-trained 52 points min:1.0 max:1.0
--> create 1278 synthetic samples
--> test with 'LR'
-LR tn, fp: 310, 23
-LR fn, tp: 7, 6
-LR f1 score: 0.286
-LR cohens kappa score: 0.247
-LR average precision score: 0.230
-
--> test with 'GB'
-GB tn, fp: 333, 0
-GB fn, tp: 0, 13
-GB f1 score: 1.000
-GB cohens kappa score: 1.000
-
--> test with 'KNN'
-KNN tn, fp: 325, 8
-KNN fn, tp: 7, 6
-KNN f1 score: 0.444
-KNN cohens kappa score: 0.422
-
-
------- Step 5/5: Slice 2/5 -------
--> Reset the GAN
--> Train generator for synthetic samples
-Train 1330/52 points
--> new disc
--> calc distances
--> statistics
-trained 52 points min:1.0 max:1.0
--> create 1278 synthetic samples
--> test with 'LR'
-LR tn, fp: 320, 13
-LR fn, tp: 11, 2
-LR f1 score: 0.143
-LR cohens kappa score: 0.107
-LR average precision score: 0.180
-
--> test with 'GB'
-GB tn, fp: 333, 0
-GB fn, tp: 3, 10
-GB f1 score: 0.870
-GB cohens kappa score: 0.865
-
--> test with 'KNN'
-KNN tn, fp: 332, 1
-KNN fn, tp: 9, 4
-KNN f1 score: 0.444
-KNN cohens kappa score: 0.433
-
-
------- Step 5/5: Slice 3/5 -------
--> Reset the GAN
--> Train generator for synthetic samples
-Train 1330/52 points
--> new disc
--> calc distances
--> statistics
-trained 52 points min:1.0 max:1.4142135623730951
--> create 1278 synthetic samples
--> test with 'LR'
-LR tn, fp: 321, 12
-LR fn, tp: 7, 6
-LR f1 score: 0.387
-LR cohens kappa score: 0.359
-LR average precision score: 0.284
-
--> test with 'GB'
-GB tn, fp: 333, 0
-GB fn, tp: 0, 13
-GB f1 score: 1.000
-GB cohens kappa score: 1.000
-
--> test with 'KNN'
-KNN tn, fp: 332, 1
-KNN fn, tp: 8, 5
-KNN f1 score: 0.526
-KNN cohens kappa score: 0.515
-
-
------- Step 5/5: Slice 4/5 -------
--> Reset the GAN
--> Train generator for synthetic samples
-Train 1330/52 points
--> new disc
--> calc distances
--> statistics
-trained 52 points min:1.0 max:1.0
--> create 1278 synthetic samples
--> test with 'LR'
-LR tn, fp: 314, 19
-LR fn, tp: 9, 4
-LR f1 score: 0.222
-LR cohens kappa score: 0.183
-LR average precision score: 0.197
-
--> test with 'GB'
-GB tn, fp: 333, 0
-GB fn, tp: 2, 11
-GB f1 score: 0.917
-GB cohens kappa score: 0.914
-
--> test with 'KNN'
-KNN tn, fp: 330, 3
-KNN fn, tp: 4, 9
-KNN f1 score: 0.720
-KNN cohens kappa score: 0.710
-
-
------- Step 5/5: Slice 5/5 -------
--> Reset the GAN
--> Train generator for synthetic samples
-Train 1332/52 points
--> new disc
--> calc distances
--> statistics
-trained 52 points min:1.0 max:1.0
--> create 1280 synthetic samples
--> test with 'LR'
-LR tn, fp: 317, 14
-LR fn, tp: 9, 4
-LR f1 score: 0.258
-LR cohens kappa score: 0.224
-LR average precision score: 0.205
-
--> test with 'GB'
-GB tn, fp: 329, 2
-GB fn, tp: 1, 12
-GB f1 score: 0.889
-GB cohens kappa score: 0.884
-
--> test with 'KNN'
-KNN tn, fp: 328, 3
-KNN fn, tp: 6, 7
-KNN f1 score: 0.609
-KNN cohens kappa score: 0.595
-
-### Exercise is done.
-
------[ LR ]-----
-maximum:
-LR tn, fp: 322, 26
-LR fn, tp: 12, 7
-LR f1 score: 0.387
-LR cohens kappa score: 0.359
-LR average precision score: 0.331
-
-
-average:
-LR tn, fp: 316.64, 15.96
-LR fn, tp: 8.96, 4.04
-LR f1 score: 0.243
-LR cohens kappa score: 0.207
-LR average precision score: 0.226
-
-
-minimum:
-LR tn, fp: 307, 9
-LR fn, tp: 6, 1
-LR f1 score: 0.062
-LR cohens kappa score: 0.019
-LR average precision score: 0.167
-
-
------[ GB ]-----
-maximum:
-GB tn, fp: 333, 5
-GB fn, tp: 4, 13
-GB f1 score: 1.000
-GB cohens kappa score: 1.000
-
-
-average:
-GB tn, fp: 332.0, 0.6
-GB fn, tp: 0.76, 12.24
-GB f1 score: 0.946
-GB cohens kappa score: 0.944
-
-
-minimum:
-GB tn, fp: 328, 0
-GB fn, tp: 0, 9
-GB f1 score: 0.818
-GB cohens kappa score: 0.812
-
-
------[ KNN ]-----
-maximum:
-KNN tn, fp: 333, 8
-KNN fn, tp: 11, 9
-KNN f1 score: 0.720
-KNN cohens kappa score: 0.710
-
-
-average:
-KNN tn, fp: 330.0, 2.6
-KNN fn, tp: 7.68, 5.32
-KNN f1 score: 0.504
-KNN cohens kappa score: 0.490
-
-
-minimum:
-KNN tn, fp: 325, 0
-KNN fn, tp: 4, 2
-KNN f1 score: 0.267
-KNN cohens kappa score: 0.259
-

data_result/SpheredNoise/folding_car_good.csv

@@ -1,92 +0,0 @@
-LR
-Nr.;TN;TP;FN;FP;f1 score;cohens kappa score;average precision score
-1;313.000;0.000;14.000;19.000;0.000;-0.049;0.038
-2;314.000;0.000;14.000;18.000;0.000;-0.048;0.033
-3;313.000;0.000;14.000;19.000;0.000;-0.049;0.043
-4;311.000;0.000;14.000;21.000;0.000;-0.051;0.038
-5;311.000;0.000;13.000;20.000;0.000;-0.048;0.048
-6;312.000;0.000;14.000;20.000;0.000;-0.050;0.040
-7;317.000;0.000;14.000;15.000;0.000;-0.044;0.035
-8;317.000;0.000;14.000;15.000;0.000;-0.044;0.043
-9;314.000;0.000;14.000;18.000;0.000;-0.048;0.040
-10;307.000;0.000;13.000;24.000;0.000;-0.052;0.040
-11;312.000;0.000;14.000;20.000;0.000;-0.050;0.039
-12;315.000;0.000;14.000;17.000;0.000;-0.046;0.045
-13;317.000;0.000;14.000;15.000;0.000;-0.044;0.039
-14;318.000;0.000;14.000;14.000;0.000;-0.042;0.037
-15;304.000;0.000;13.000;27.000;0.000;-0.054;0.037
-16;309.000;0.000;14.000;23.000;0.000;-0.053;0.040
-17;319.000;0.000;14.000;13.000;0.000;-0.041;0.037
-18;317.000;0.000;14.000;15.000;0.000;-0.044;0.037
-19;307.000;0.000;14.000;25.000;0.000;-0.055;0.037
-20;313.000;0.000;13.000;18.000;0.000;-0.046;0.046
-21;309.000;0.000;14.000;23.000;0.000;-0.053;0.034
-22;316.000;0.000;14.000;16.000;0.000;-0.045;0.041
-23;316.000;0.000;14.000;16.000;0.000;-0.045;0.043
-24;311.000;0.000;14.000;21.000;0.000;-0.051;0.046
-25;311.000;0.000;13.000;20.000;0.000;-0.048;0.034
-max;319.000;0.000;14.000;27.000;0.000;-0.041;0.048
-avg;312.920;0.000;13.800;18.880;0.000;-0.048;0.040
-min;304.000;0.000;13.000;13.000;0.000;-0.055;0.033
----
-GB
-Nr.;TN;TP;FN;FP;f1 score;cohens kappa score
-1;332.000;7.000;7.000;0.000;0.667;0.657
-2;332.000;10.000;4.000;0.000;0.833;0.828
-3;331.000;9.000;5.000;1.000;0.750;0.741
-4;331.000;7.000;7.000;1.000;0.636;0.625
-5;328.000;9.000;4.000;3.000;0.720;0.709
-6;331.000;10.000;4.000;1.000;0.800;0.793
-7;332.000;11.000;3.000;0.000;0.880;0.876
-8;332.000;8.000;6.000;0.000;0.727;0.719
-9;332.000;10.000;4.000;0.000;0.833;0.828
-10;330.000;10.000;3.000;1.000;0.833;0.827
-11;332.000;11.000;3.000;0.000;0.880;0.876
-12;330.000;11.000;3.000;2.000;0.815;0.807
-13;330.000;11.000;3.000;2.000;0.815;0.807
-14;331.000;10.000;4.000;1.000;0.800;0.793
-15;328.000;8.000;5.000;3.000;0.667;0.655
-16;332.000;14.000;0.000;0.000;1.000;1.000
-17;332.000;6.000;8.000;0.000;0.600;0.590
-18;331.000;6.000;8.000;1.000;0.571;0.560
-19;331.000;12.000;2.000;1.000;0.889;0.884
-20;329.000;9.000;4.000;2.000;0.750;0.741
-21;332.000;13.000;1.000;0.000;0.963;0.961
-22;331.000;8.000;6.000;1.000;0.696;0.686
-23;331.000;10.000;4.000;1.000;0.800;0.793
-24;332.000;7.000;7.000;0.000;0.667;0.657
-25;330.000;13.000;0.000;1.000;0.963;0.961
-max;332.000;14.000;8.000;3.000;1.000;1.000
-avg;330.920;9.600;4.200;0.880;0.782;0.775
-min;328.000;6.000;0.000;0.000;0.571;0.560
----
-KNN
-Nr.;TN;TP;FN;FP;f1 score;cohens kappa score
-1;330.000;2.000;12.000;2.000;0.222;0.208
-2;331.000;3.000;11.000;1.000;0.333;0.321
-3;329.000;2.000;12.000;3.000;0.211;0.193
-4;329.000;0.000;14.000;3.000;0.000;-0.014
-5;330.000;2.000;11.000;1.000;0.250;0.239
-6;329.000;4.000;10.000;3.000;0.381;0.364
-7;328.000;6.000;8.000;4.000;0.500;0.483
-8;331.000;1.000;13.000;1.000;0.125;0.116
-9;331.000;2.000;12.000;1.000;0.235;0.224
-10;327.000;1.000;12.000;4.000;0.111;0.092
-11;330.000;3.000;11.000;2.000;0.316;0.301
-12;330.000;2.000;12.000;2.000;0.222;0.208
-13;328.000;2.000;12.000;4.000;0.200;0.180
-14;330.000;1.000;13.000;2.000;0.118;0.105
-15;329.000;4.000;9.000;2.000;0.421;0.407
-16;329.000;5.000;9.000;3.000;0.455;0.438
-17;330.000;2.000;12.000;2.000;0.222;0.208
-18;329.000;1.000;13.000;3.000;0.111;0.095
-19;327.000;3.000;11.000;5.000;0.273;0.251
-20;328.000;3.000;10.000;3.000;0.316;0.299
-21;329.000;2.000;12.000;3.000;0.211;0.193
-22;331.000;1.000;13.000;1.000;0.125;0.116
-23;329.000;3.000;11.000;3.000;0.300;0.283
-24;329.000;1.000;13.000;3.000;0.111;0.095
-25;328.000;2.000;11.000;3.000;0.222;0.206
-max;331.000;6.000;14.000;5.000;0.500;0.483
-avg;329.240;2.320;11.480;2.560;0.240;0.224
-min;327.000;0.000;8.000;1.000;0.000;-0.014

data_result/SpheredNoise/folding_car_good.log

@@ -1,826 +0,0 @@
-
-
-///////////////////////////////////////////
-// Running SpheredNoise on folding_car_good
-///////////////////////////////////////////
-
-Load 'data_input/folding_car_good'
-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 1327/55 points
--> new disc
--> calc distances
--> statistics
-trained 55 points min:1.0 max:1.0
--> create 1272 synthetic samples
--> test with 'LR'
-LR tn, fp: 313, 19
-LR fn, tp: 14, 0
-LR f1 score: 0.000
-LR cohens kappa score: -0.049
-LR average precision score: 0.038
-
--> test with 'GB'
-GB tn, fp: 332, 0
-GB fn, tp: 7, 7
-GB f1 score: 0.667
-GB cohens kappa score: 0.657
-
--> test with 'KNN'
-KNN tn, fp: 330, 2
-KNN fn, tp: 12, 2
-KNN f1 score: 0.222
-KNN cohens kappa score: 0.208
-
-
------- Step 1/5: Slice 2/5 -------
--> Reset the GAN
--> Train generator for synthetic samples
-Train 1327/55 points
--> new disc
--> calc distances
--> statistics
-trained 55 points min:1.0 max:1.0
--> create 1272 synthetic samples
--> test with 'LR'
-LR tn, fp: 314, 18
-LR fn, tp: 14, 0
-LR f1 score: 0.000
-LR cohens kappa score: -0.048
-LR average precision score: 0.033
-
--> test with 'GB'
-GB tn, fp: 332, 0
-GB fn, tp: 4, 10
-GB f1 score: 0.833
-GB cohens kappa score: 0.828
-
--> test with 'KNN'
-KNN tn, fp: 331, 1
-KNN fn, tp: 11, 3
-KNN f1 score: 0.333
-KNN cohens kappa score: 0.321
-
-
------- Step 1/5: Slice 3/5 -------
--> Reset the GAN
--> Train generator for synthetic samples
-Train 1327/55 points
--> new disc
--> calc distances
--> statistics
-trained 55 points min:1.0 max:1.0
--> create 1272 synthetic samples
--> test with 'LR'
-LR tn, fp: 313, 19
-LR fn, tp: 14, 0
-LR f1 score: 0.000
-LR cohens kappa score: -0.049
-LR average precision score: 0.043
-
--> test with 'GB'
-GB tn, fp: 331, 1
-GB fn, tp: 5, 9
-GB f1 score: 0.750
-GB cohens kappa score: 0.741
-
--> test with 'KNN'
-KNN tn, fp: 329, 3
-KNN fn, tp: 12, 2
-KNN f1 score: 0.211
-KNN cohens kappa score: 0.193
-
-
------- Step 1/5: Slice 4/5 -------
--> Reset the GAN
--> Train generator for synthetic samples
-Train 1327/55 points
--> new disc
--> calc distances
--> statistics
-trained 55 points min:1.0 max:1.0
--> create 1272 synthetic samples
--> test with 'LR'
-LR tn, fp: 311, 21
-LR fn, tp: 14, 0
-LR f1 score: 0.000
-LR cohens kappa score: -0.051
-LR average precision score: 0.038
-
--> test with 'GB'
-GB tn, fp: 331, 1
-GB fn, tp: 7, 7
-GB f1 score: 0.636
-GB cohens kappa score: 0.625
-
--> test with 'KNN'
-KNN tn, fp: 329, 3
-KNN fn, tp: 14, 0
-KNN f1 score: 0.000
-KNN cohens kappa score: -0.014
-
-
------- Step 1/5: Slice 5/5 -------
--> Reset the GAN
--> Train generator for synthetic samples
-Train 1328/56 points
--> new disc
--> calc distances
--> statistics
-trained 56 points min:1.0 max:1.0
--> create 1272 synthetic samples
--> test with 'LR'
-LR tn, fp: 311, 20
-LR fn, tp: 13, 0
-LR f1 score: 0.000
-LR cohens kappa score: -0.048
-LR average precision score: 0.048
-
--> test with 'GB'
-GB tn, fp: 328, 3
-GB fn, tp: 4, 9
-GB f1 score: 0.720
-GB cohens kappa score: 0.709
-
--> test with 'KNN'
-KNN tn, fp: 330, 1
-KNN fn, tp: 11, 2
-KNN f1 score: 0.250
-KNN cohens kappa score: 0.239
-
-
-====== Step 2/5 =======
--> Shuffling data
--> Spliting data to slices
-
------- Step 2/5: Slice 1/5 -------
--> Reset the GAN
--> Train generator for synthetic samples
-Train 1327/55 points
--> new disc
--> calc distances
--> statistics
-trained 55 points min:1.0 max:1.0
--> create 1272 synthetic samples
--> test with 'LR'
-LR tn, fp: 312, 20
-LR fn, tp: 14, 0
-LR f1 score: 0.000
-LR cohens kappa score: -0.050
-LR average precision score: 0.040
-
--> test with 'GB'
-GB tn, fp: 331, 1
-GB fn, tp: 4, 10
-GB f1 score: 0.800
-GB cohens kappa score: 0.793
-
--> test with 'KNN'
-KNN tn, fp: 329, 3
-KNN fn, tp: 10, 4
-KNN f1 score: 0.381
-KNN cohens kappa score: 0.364
-
-
------- Step 2/5: Slice 2/5 -------
--> Reset the GAN
--> Train generator for synthetic samples
-Train 1327/55 points
--> new disc
--> calc distances
--> statistics
-trained 55 points min:1.0 max:1.0
--> create 1272 synthetic samples
--> test with 'LR'
-LR tn, fp: 317, 15
-LR fn, tp: 14, 0
-LR f1 score: 0.000
-LR cohens kappa score: -0.044
-LR average precision score: 0.035
-
--> test with 'GB'
-GB tn, fp: 332, 0
-GB fn, tp: 3, 11
-GB f1 score: 0.880
-GB cohens kappa score: 0.876
-
--> test with 'KNN'
-KNN tn, fp: 328, 4
-KNN fn, tp: 8, 6
-KNN f1 score: 0.500
-KNN cohens kappa score: 0.483
-
-
------- Step 2/5: Slice 3/5 -------
--> Reset the GAN
--> Train generator for synthetic samples
-Train 1327/55 points
--> new disc
--> calc distances
--> statistics
-trained 55 points min:1.0 max:1.0
--> create 1272 synthetic samples
--> test with 'LR'
-LR tn, fp: 317, 15
-LR fn, tp: 14, 0
-LR f1 score: 0.000
-LR cohens kappa score: -0.044
-LR average precision score: 0.043
-
--> test with 'GB'
-GB tn, fp: 332, 0
-GB fn, tp: 6, 8
-GB f1 score: 0.727
-GB cohens kappa score: 0.719
-
--> test with 'KNN'
-KNN tn, fp: 331, 1
-KNN fn, tp: 13, 1
-KNN f1 score: 0.125
-KNN cohens kappa score: 0.116
-
-
------- Step 2/5: Slice 4/5 -------
--> Reset the GAN
--> Train generator for synthetic samples
-Train 1327/55 points
--> new disc
--> calc distances
--> statistics
-trained 55 points min:1.0 max:1.0
--> create 1272 synthetic samples
--> test with 'LR'
-LR tn, fp: 314, 18
-LR fn, tp: 14, 0
-LR f1 score: 0.000
-LR cohens kappa score: -0.048
-LR average precision score: 0.040
-
--> test with 'GB'
-GB tn, fp: 332, 0
-GB fn, tp: 4, 10
-GB f1 score: 0.833
-GB cohens kappa score: 0.828
-
--> test with 'KNN'
-KNN tn, fp: 331, 1
-KNN fn, tp: 12, 2
-KNN f1 score: 0.235
-KNN cohens kappa score: 0.224
-
-
------- Step 2/5: Slice 5/5 -------
--> Reset the GAN
--> Train generator for synthetic samples
-Train 1328/56 points
--> new disc
--> calc distances
--> statistics
-trained 56 points min:1.0 max:1.0
--> create 1272 synthetic samples
--> test with 'LR'
-LR tn, fp: 307, 24
-LR fn, tp: 13, 0
-LR f1 score: 0.000
-LR cohens kappa score: -0.052
-LR average precision score: 0.040
-
--> test with 'GB'
-GB tn, fp: 330, 1
-GB fn, tp: 3, 10
-GB f1 score: 0.833
-GB cohens kappa score: 0.827
-
--> test with 'KNN'
-KNN tn, fp: 327, 4
-KNN fn, tp: 12, 1
-KNN f1 score: 0.111
-KNN cohens kappa score: 0.092
-
-
-====== Step 3/5 =======
--> Shuffling data
--> Spliting data to slices
-
------- Step 3/5: Slice 1/5 -------
--> Reset the GAN
--> Train generator for synthetic samples
-Train 1327/55 points
--> new disc
--> calc distances
--> statistics
-trained 55 points min:1.0 max:1.4142135623730951
--> create 1272 synthetic samples
--> test with 'LR'
-LR tn, fp: 312, 20
-LR fn, tp: 14, 0
-LR f1 score: 0.000
-LR cohens kappa score: -0.050
-LR average precision score: 0.039
-
--> test with 'GB'
-GB tn, fp: 332, 0
-GB fn, tp: 3, 11
-GB f1 score: 0.880
-GB cohens kappa score: 0.876
-
--> test with 'KNN'
-KNN tn, fp: 330, 2
-KNN fn, tp: 11, 3
-KNN f1 score: 0.316
-KNN cohens kappa score: 0.301
-
-
------- Step 3/5: Slice 2/5 -------
--> Reset the GAN
--> Train generator for synthetic samples
-Train 1327/55 points
--> new disc
--> calc distances
--> statistics
-trained 55 points min:1.0 max:1.0
--> create 1272 synthetic samples
--> test with 'LR'
-LR tn, fp: 315, 17
-LR fn, tp: 14, 0
-LR f1 score: 0.000
-LR cohens kappa score: -0.046
-LR average precision score: 0.045
-
--> test with 'GB'
-GB tn, fp: 330, 2
-GB fn, tp: 3, 11
-GB f1 score: 0.815
-GB cohens kappa score: 0.807
-
--> test with 'KNN'
-KNN tn, fp: 330, 2
-KNN fn, tp: 12, 2
-KNN f1 score: 0.222
-KNN cohens kappa score: 0.208
-
-
------- Step 3/5: Slice 3/5 -------
--> Reset the GAN
--> Train generator for synthetic samples
-Train 1327/55 points
--> new disc
--> calc distances
--> statistics
-trained 55 points min:1.0 max:1.0
--> create 1272 synthetic samples
--> test with 'LR'
-LR tn, fp: 317, 15
-LR fn, tp: 14, 0
-LR f1 score: 0.000
-LR cohens kappa score: -0.044
-LR average precision score: 0.039
-
--> test with 'GB'
-GB tn, fp: 330, 2
-GB fn, tp: 3, 11
-GB f1 score: 0.815
-GB cohens kappa score: 0.807
-
--> test with 'KNN'
-KNN tn, fp: 328, 4
-KNN fn, tp: 12, 2
-KNN f1 score: 0.200
-KNN cohens kappa score: 0.180
-
-
------- Step 3/5: Slice 4/5 -------
--> Reset the GAN
--> Train generator for synthetic samples
-Train 1327/55 points
--> new disc
--> calc distances
--> statistics
-trained 55 points min:1.0 max:1.0
--> create 1272 synthetic samples
--> test with 'LR'
-LR tn, fp: 318, 14
-LR fn, tp: 14, 0
-LR f1 score: 0.000
-LR cohens kappa score: -0.042
-LR average precision score: 0.037
-
--> test with 'GB'
-GB tn, fp: 331, 1
-GB fn, tp: 4, 10
-GB f1 score: 0.800
-GB cohens kappa score: 0.793
-
--> test with 'KNN'
-KNN tn, fp: 330, 2
-KNN fn, tp: 13, 1
-KNN f1 score: 0.118
-KNN cohens kappa score: 0.105
-
-
------- Step 3/5: Slice 5/5 -------
--> Reset the GAN
--> Train generator for synthetic samples
-Train 1328/56 points
--> new disc
--> calc distances
--> statistics
-trained 56 points min:1.0 max:1.0
--> create 1272 synthetic samples
--> test with 'LR'
-LR tn, fp: 304, 27
-LR fn, tp: 13, 0
-LR f1 score: 0.000
-LR cohens kappa score: -0.054
-LR average precision score: 0.037
-
--> test with 'GB'
-GB tn, fp: 328, 3
-GB fn, tp: 5, 8
-GB f1 score: 0.667
-GB cohens kappa score: 0.655
-
--> test with 'KNN'
-KNN tn, fp: 329, 2
-KNN fn, tp: 9, 4
-KNN f1 score: 0.421
-KNN cohens kappa score: 0.407
-
-
-====== Step 4/5 =======
--> Shuffling data
--> Spliting data to slices
-
------- Step 4/5: Slice 1/5 -------
--> Reset the GAN
--> Train generator for synthetic samples
-Train 1327/55 points
--> new disc
--> calc distances
--> statistics
-trained 55 points min:1.0 max:1.0
--> create 1272 synthetic samples
--> test with 'LR'
-LR tn, fp: 309, 23
-LR fn, tp: 14, 0
-LR f1 score: 0.000
-LR cohens kappa score: -0.053
-LR average precision score: 0.040
-
--> test with 'GB'
-GB tn, fp: 332, 0
-GB fn, tp: 0, 14
-GB f1 score: 1.000
-GB cohens kappa score: 1.000
-
--> test with 'KNN'
-KNN tn, fp: 329, 3
-KNN fn, tp: 9, 5
-KNN f1 score: 0.455
-KNN cohens kappa score: 0.438
-
-
------- Step 4/5: Slice 2/5 -------
--> Reset the GAN
--> Train generator for synthetic samples
-Train 1327/55 points
--> new disc
--> calc distances
--> statistics
-trained 55 points min:1.0 max:1.0
--> create 1272 synthetic samples
--> test with 'LR'
-LR tn, fp: 319, 13
-LR fn, tp: 14, 0
-LR f1 score: 0.000
-LR cohens kappa score: -0.041
-LR average precision score: 0.037
-
--> test with 'GB'
-GB tn, fp: 332, 0
-GB fn, tp: 8, 6
-GB f1 score: 0.600
-GB cohens kappa score: 0.590
-
--> test with 'KNN'
-KNN tn, fp: 330, 2
-KNN fn, tp: 12, 2
-KNN f1 score: 0.222
-KNN cohens kappa score: 0.208
-
-
------- Step 4/5: Slice 3/5 -------
--> Reset the GAN
--> Train generator for synthetic samples
-Train 1327/55 points
--> new disc
--> calc distances
--> statistics
-trained 55 points min:1.0 max:1.0
--> create 1272 synthetic samples
--> test with 'LR'
-LR tn, fp: 317, 15
-LR fn, tp: 14, 0
-LR f1 score: 0.000
-LR cohens kappa score: -0.044
-LR average precision score: 0.037
-
--> test with 'GB'
-GB tn, fp: 331, 1
-GB fn, tp: 8, 6
-GB f1 score: 0.571
-GB cohens kappa score: 0.560
-
--> test with 'KNN'
-KNN tn, fp: 329, 3
-KNN fn, tp: 13, 1
-KNN f1 score: 0.111
-KNN cohens kappa score: 0.095
-
-
------- Step 4/5: Slice 4/5 -------
--> Reset the GAN
--> Train generator for synthetic samples
-Train 1327/55 points
--> new disc
--> calc distances
--> statistics
-trained 55 points min:1.0 max:1.0
--> create 1272 synthetic samples
--> test with 'LR'
-LR tn, fp: 307, 25
-LR fn, tp: 14, 0
-LR f1 score: 0.000
-LR cohens kappa score: -0.055
-LR average precision score: 0.037
-
--> test with 'GB'
-GB tn, fp: 331, 1
-GB fn, tp: 2, 12
-GB f1 score: 0.889
-GB cohens kappa score: 0.884
-
--> test with 'KNN'
-KNN tn, fp: 327, 5
-KNN fn, tp: 11, 3
-KNN f1 score: 0.273
-KNN cohens kappa score: 0.251
-
-
------- Step 4/5: Slice 5/5 -------
--> Reset the GAN
--> Train generator for synthetic samples
-Train 1328/56 points
--> new disc
--> calc distances
--> statistics
-trained 56 points min:1.0 max:1.0
--> create 1272 synthetic samples
--> test with 'LR'
-LR tn, fp: 313, 18
-LR fn, tp: 13, 0
-LR f1 score: 0.000
-LR cohens kappa score: -0.046
-LR average precision score: 0.046
-
--> test with 'GB'
-GB tn, fp: 329, 2
-GB fn, tp: 4, 9
-GB f1 score: 0.750
-GB cohens kappa score: 0.741
-
--> test with 'KNN'
-KNN tn, fp: 328, 3
-KNN fn, tp: 10, 3
-KNN f1 score: 0.316
-KNN cohens kappa score: 0.299
-
-
-====== Step 5/5 =======
--> Shuffling data
--> Spliting data to slices
-
------- Step 5/5: Slice 1/5 -------
--> Reset the GAN
--> Train generator for synthetic samples
-Train 1327/55 points
--> new disc
--> calc distances
--> statistics
-trained 55 points min:1.0 max:1.0
--> create 1272 synthetic samples
--> test with 'LR'
-LR tn, fp: 309, 23
-LR fn, tp: 14, 0
-LR f1 score: 0.000
-LR cohens kappa score: -0.053
-LR average precision score: 0.034
-
--> test with 'GB'
-GB tn, fp: 332, 0
-GB fn, tp: 1, 13
-GB f1 score: 0.963
-GB cohens kappa score: 0.961
-
--> test with 'KNN'
-KNN tn, fp: 329, 3
-KNN fn, tp: 12, 2
-KNN f1 score: 0.211
-KNN cohens kappa score: 0.193
-
-
------- Step 5/5: Slice 2/5 -------
--> Reset the GAN
--> Train generator for synthetic samples
-Train 1327/55 points
--> new disc
--> calc distances
--> statistics
-trained 55 points min:1.0 max:1.0
--> create 1272 synthetic samples
--> test with 'LR'
-LR tn, fp: 316, 16
-LR fn, tp: 14, 0
-LR f1 score: 0.000
-LR cohens kappa score: -0.045
-LR average precision score: 0.041
-
--> test with 'GB'
-GB tn, fp: 331, 1
-GB fn, tp: 6, 8
-GB f1 score: 0.696
-GB cohens kappa score: 0.686
-
--> test with 'KNN'
-KNN tn, fp: 331, 1
-KNN fn, tp: 13, 1
-KNN f1 score: 0.125
-KNN cohens kappa score: 0.116
-
-
------- Step 5/5: Slice 3/5 -------
--> Reset the GAN
--> Train generator for synthetic samples
-Train 1327/55 points
--> new disc
--> calc distances
--> statistics
-trained 55 points min:1.0 max:1.0
--> create 1272 synthetic samples
--> test with 'LR'
-LR tn, fp: 316, 16
-LR fn, tp: 14, 0
-LR f1 score: 0.000
-LR cohens kappa score: -0.045
-LR average precision score: 0.043
-
--> test with 'GB'
-GB tn, fp: 331, 1
-GB fn, tp: 4, 10
-GB f1 score: 0.800
-GB cohens kappa score: 0.793
-
--> test with 'KNN'
-KNN tn, fp: 329, 3
-KNN fn, tp: 11, 3
-KNN f1 score: 0.300
-KNN cohens kappa score: 0.283
-
-
------- Step 5/5: Slice 4/5 -------
--> Reset the GAN
--> Train generator for synthetic samples
-Train 1327/55 points
--> new disc
--> calc distances
--> statistics
-trained 55 points min:1.0 max:1.0
--> create 1272 synthetic samples
--> test with 'LR'
-LR tn, fp: 311, 21
-LR fn, tp: 14, 0
-LR f1 score: 0.000
-LR cohens kappa score: -0.051
-LR average precision score: 0.046
-
--> test with 'GB'
-GB tn, fp: 332, 0
-GB fn, tp: 7, 7
-GB f1 score: 0.667
-GB cohens kappa score: 0.657
-
--> test with 'KNN'
-KNN tn, fp: 329, 3
-KNN fn, tp: 13, 1
-KNN f1 score: 0.111
-KNN cohens kappa score: 0.095
-
-
------- Step 5/5: Slice 5/5 -------
--> Reset the GAN
--> Train generator for synthetic samples
-Train 1328/56 points
--> new disc
--> calc distances
--> statistics
-trained 56 points min:1.0 max:1.4142135623730951
--> create 1272 synthetic samples
--> test with 'LR'
-LR tn, fp: 311, 20
-LR fn, tp: 13, 0
-LR f1 score: 0.000
-LR cohens kappa score: -0.048
-LR average precision score: 0.034
-
--> test with 'GB'
-GB tn, fp: 330, 1
-GB fn, tp: 0, 13
-GB f1 score: 0.963
-GB cohens kappa score: 0.961
-
--> test with 'KNN'
-KNN tn, fp: 328, 3
-KNN fn, tp: 11, 2
-KNN f1 score: 0.222
-KNN cohens kappa score: 0.206
-
-### Exercise is done.
-
------[ LR ]-----
-maximum:
-LR tn, fp: 319, 27
-LR fn, tp: 14, 0
-LR f1 score: 0.000
-LR cohens kappa score: -0.041
-LR average precision score: 0.048
-
-
-average:
-LR tn, fp: 312.92, 18.88
-LR fn, tp: 13.8, 0.0
-LR f1 score: 0.000
-LR cohens kappa score: -0.048
-LR average precision score: 0.040
-
-
-minimum:
-LR tn, fp: 304, 13
-LR fn, tp: 13, 0
-LR f1 score: 0.000
-LR cohens kappa score: -0.055
-LR average precision score: 0.033
-
-
------[ GB ]-----
-maximum:
-GB tn, fp: 332, 3
-GB fn, tp: 8, 14
-GB f1 score: 1.000
-GB cohens kappa score: 1.000
-
-
-average:
-GB tn, fp: 330.92, 0.88
-GB fn, tp: 4.2, 9.6
-GB f1 score: 0.782
-GB cohens kappa score: 0.775
-
-
-minimum:
-GB tn, fp: 328, 0
-GB fn, tp: 0, 6
-GB f1 score: 0.571
-GB cohens kappa score: 0.560
-
-
------[ KNN ]-----
-maximum:
-KNN tn, fp: 331, 5
-KNN fn, tp: 14, 6
-KNN f1 score: 0.500
-KNN cohens kappa score: 0.483
-
-
-average:
-KNN tn, fp: 329.24, 2.56
-KNN fn, tp: 11.48, 2.32
-KNN f1 score: 0.240
-KNN cohens kappa score: 0.224
-
-
-minimum:
-KNN tn, fp: 327, 1
-KNN fn, tp: 8, 0
-KNN f1 score: 0.000
-KNN cohens kappa score: -0.014
-