Added CTAB-GAN, ProWras statistics.

data_result/CTAB-GAN/folding_abalone9-18.log

@@ -0,0 +1,17 @@
+
+
+///////////////////////////////////////////
+// Running CTAB-GAN on folding_abalone9-18
+///////////////////////////////////////////
+
+Load 'data_input/folding_abalone9-18'
+from pickle file
+Data loaded.
+Traceback (most recent call last):
+  File "/benchmark/data/run_all_exercises.py", line 13, in <module>
+    runExercise(dataset, None, name, f)
+  File "/benchmark/data/library/analysis.py", line 164, in runExercise
+    gan = ganCreator(data)
+  File "/benchmark/data/library/analysis.py", line 268, in <lambda>
+    , ("CTAB-GAN",      lambda _data: CtabGan())
+NameError: name 'CtabGan' is not defined

data_result/CTAB-GAN/folding_abalone_17_vs_7_8_9_10.log

@@ -0,0 +1,17 @@
+
+
+///////////////////////////////////////////
+// Running CTAB-GAN 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.
+Traceback (most recent call last):
+  File "/benchmark/data/run_all_exercises.py", line 13, in <module>
+    runExercise(dataset, None, name, f)
+  File "/benchmark/data/library/analysis.py", line 164, in runExercise
+    gan = ganCreator(data)
+  File "/benchmark/data/library/analysis.py", line 268, in <lambda>
+    , ("CTAB-GAN",      lambda _data: CtabGan())
+NameError: name 'CtabGan' is not defined

data_result/CTAB-GAN/folding_car-vgood.log

@@ -0,0 +1,17 @@
+
+
+///////////////////////////////////////////
+// Running CTAB-GAN on folding_car-vgood
+///////////////////////////////////////////
+
+Load 'data_input/folding_car-vgood'
+from pickle file
+Data loaded.
+Traceback (most recent call last):
+  File "/benchmark/data/run_all_exercises.py", line 13, in <module>
+    runExercise(dataset, None, name, f)
+  File "/benchmark/data/library/analysis.py", line 164, in runExercise
+    gan = ganCreator(data)
+  File "/benchmark/data/library/analysis.py", line 268, in <lambda>
+    , ("CTAB-GAN",      lambda _data: CtabGan())
+NameError: name 'CtabGan' is not defined

data_result/CTAB-GAN/folding_car_good.log

@@ -0,0 +1,17 @@
+
+
+///////////////////////////////////////////
+// Running CTAB-GAN on folding_car_good
+///////////////////////////////////////////
+
+Load 'data_input/folding_car_good'
+from pickle file
+Data loaded.
+Traceback (most recent call last):
+  File "/benchmark/data/run_all_exercises.py", line 13, in <module>
+    runExercise(dataset, None, name, f)
+  File "/benchmark/data/library/analysis.py", line 164, in runExercise
+    gan = ganCreator(data)
+  File "/benchmark/data/library/analysis.py", line 268, in <lambda>
+    , ("CTAB-GAN",      lambda _data: CtabGan())
+NameError: name 'CtabGan' is not defined

data_result/CTAB-GAN/folding_flare-F.log

@@ -0,0 +1,18 @@
+
+
+///////////////////////////////////////////
+// Running CTAB-GAN on folding_flare-F
+///////////////////////////////////////////
+
+Load 'data_input/folding_flare-F'
+from pickle file
+non empty cut in data_input/folding_flare-F! (23 points)
+Data loaded.
+Traceback (most recent call last):
+  File "/benchmark/data/run_all_exercises.py", line 13, in <module>
+    runExercise(dataset, None, name, f)
+  File "/benchmark/data/library/analysis.py", line 164, in runExercise
+    gan = ganCreator(data)
+  File "/benchmark/data/library/analysis.py", line 268, in <lambda>
+    , ("CTAB-GAN",      lambda _data: CtabGan())
+NameError: name 'CtabGan' is not defined

data_result/CTAB-GAN/folding_hypothyroid.log

@@ -0,0 +1,18 @@
+
+
+///////////////////////////////////////////
+// Running CTAB-GAN on folding_hypothyroid
+///////////////////////////////////////////
+
+Load 'data_input/folding_hypothyroid'
+from pickle file
+non empty cut in data_input/folding_hypothyroid! (1 points)
+Data loaded.
+Traceback (most recent call last):
+  File "/benchmark/data/run_all_exercises.py", line 13, in <module>
+    runExercise(dataset, None, name, f)
+  File "/benchmark/data/library/analysis.py", line 164, in runExercise
+    gan = ganCreator(data)
+  File "/benchmark/data/library/analysis.py", line 268, in <lambda>
+    , ("CTAB-GAN",      lambda _data: CtabGan())
+NameError: name 'CtabGan' is not defined

data_result/CTAB-GAN/folding_kddcup-guess_passwd_vs_satan.log

@@ -0,0 +1,17 @@
+
+
+///////////////////////////////////////////
+// Running CTAB-GAN on folding_kddcup-guess_passwd_vs_satan
+///////////////////////////////////////////
+
+Load 'data_input/folding_kddcup-guess_passwd_vs_satan'
+from pickle file
+Data loaded.
+Traceback (most recent call last):
+  File "/benchmark/data/run_all_exercises.py", line 13, in <module>
+    runExercise(dataset, None, name, f)
+  File "/benchmark/data/library/analysis.py", line 164, in runExercise
+    gan = ganCreator(data)
+  File "/benchmark/data/library/analysis.py", line 268, in <lambda>
+    , ("CTAB-GAN",      lambda _data: CtabGan())
+NameError: name 'CtabGan' is not defined

data_result/CTAB-GAN/folding_kr-vs-k-three_vs_eleven.log

@@ -0,0 +1,17 @@
+
+
+///////////////////////////////////////////
+// Running CTAB-GAN on folding_kr-vs-k-three_vs_eleven
+///////////////////////////////////////////
+
+Load 'data_input/folding_kr-vs-k-three_vs_eleven'
+from pickle file
+Data loaded.
+Traceback (most recent call last):
+  File "/benchmark/data/run_all_exercises.py", line 13, in <module>
+    runExercise(dataset, None, name, f)
+  File "/benchmark/data/library/analysis.py", line 164, in runExercise
+    gan = ganCreator(data)
+  File "/benchmark/data/library/analysis.py", line 268, in <lambda>
+    , ("CTAB-GAN",      lambda _data: CtabGan())
+NameError: name 'CtabGan' is not defined

data_result/CTAB-GAN/folding_kr-vs-k-zero-one_vs_draw.log

@@ -0,0 +1,17 @@
+
+
+///////////////////////////////////////////
+// Running CTAB-GAN on folding_kr-vs-k-zero-one_vs_draw
+///////////////////////////////////////////
+
+Load 'data_input/folding_kr-vs-k-zero-one_vs_draw'
+from pickle file
+Data loaded.
+Traceback (most recent call last):
+  File "/benchmark/data/run_all_exercises.py", line 13, in <module>
+    runExercise(dataset, None, name, f)
+  File "/benchmark/data/library/analysis.py", line 164, in runExercise
+    gan = ganCreator(data)
+  File "/benchmark/data/library/analysis.py", line 268, in <lambda>
+    , ("CTAB-GAN",      lambda _data: CtabGan())
+NameError: name 'CtabGan' is not defined

data_result/CTAB-GAN/folding_shuttle-2_vs_5.log

@@ -0,0 +1,17 @@
+
+
+///////////////////////////////////////////
+// Running CTAB-GAN on folding_shuttle-2_vs_5
+///////////////////////////////////////////
+
+Load 'data_input/folding_shuttle-2_vs_5'
+from pickle file
+Data loaded.
+Traceback (most recent call last):
+  File "/benchmark/data/run_all_exercises.py", line 13, in <module>
+    runExercise(dataset, None, name, f)
+  File "/benchmark/data/library/analysis.py", line 164, in runExercise
+    gan = ganCreator(data)
+  File "/benchmark/data/library/analysis.py", line 268, in <lambda>
+    , ("CTAB-GAN",      lambda _data: CtabGan())
+NameError: name 'CtabGan' is not defined

data_result/CTAB-GAN/folding_winequality-red-4.log

@@ -0,0 +1,17 @@
+
+
+///////////////////////////////////////////
+// Running CTAB-GAN on folding_winequality-red-4
+///////////////////////////////////////////
+
+Load 'data_input/folding_winequality-red-4'
+from pickle file
+Data loaded.
+Traceback (most recent call last):
+  File "/benchmark/data/run_all_exercises.py", line 13, in <module>
+    runExercise(dataset, None, name, f)
+  File "/benchmark/data/library/analysis.py", line 164, in runExercise
+    gan = ganCreator(data)
+  File "/benchmark/data/library/analysis.py", line 268, in <lambda>
+    , ("CTAB-GAN",      lambda _data: CtabGan())
+NameError: name 'CtabGan' is not defined

data_result/CTAB-GAN/folding_yeast4.log

@@ -0,0 +1,17 @@
+
+
+///////////////////////////////////////////
+// Running CTAB-GAN on folding_yeast4
+///////////////////////////////////////////
+
+Load 'data_input/folding_yeast4'
+from pickle file
+Data loaded.
+Traceback (most recent call last):
+  File "/benchmark/data/run_all_exercises.py", line 13, in <module>
+    runExercise(dataset, None, name, f)
+  File "/benchmark/data/library/analysis.py", line 164, in runExercise
+    gan = ganCreator(data)
+  File "/benchmark/data/library/analysis.py", line 268, in <lambda>
+    , ("CTAB-GAN",      lambda _data: CtabGan())
+NameError: name 'CtabGan' is not defined

data_result/CTAB-GAN/folding_yeast5.log

@@ -0,0 +1,17 @@
+
+
+///////////////////////////////////////////
+// Running CTAB-GAN on folding_yeast5
+///////////////////////////////////////////
+
+Load 'data_input/folding_yeast5'
+from pickle file
+Data loaded.
+Traceback (most recent call last):
+  File "/benchmark/data/run_all_exercises.py", line 13, in <module>
+    runExercise(dataset, None, name, f)
+  File "/benchmark/data/library/analysis.py", line 164, in runExercise
+    gan = ganCreator(data)
+  File "/benchmark/data/library/analysis.py", line 268, in <lambda>
+    , ("CTAB-GAN",      lambda _data: CtabGan())
+NameError: name 'CtabGan' is not defined

data_result/CTAB-GAN/folding_yeast6.log

@@ -0,0 +1,17 @@
+
+
+///////////////////////////////////////////
+// Running CTAB-GAN on folding_yeast6
+///////////////////////////////////////////
+
+Load 'data_input/folding_yeast6'
+from pickle file
+Data loaded.
+Traceback (most recent call last):
+  File "/benchmark/data/run_all_exercises.py", line 13, in <module>
+    runExercise(dataset, None, name, f)
+  File "/benchmark/data/library/analysis.py", line 164, in runExercise
+    gan = ganCreator(data)
+  File "/benchmark/data/library/analysis.py", line 268, in <lambda>
+    , ("CTAB-GAN",      lambda _data: CtabGan())
+NameError: name 'CtabGan' is not defined

data_result/CTAB-GAN/imblearn_mammography.log

@@ -0,0 +1,18 @@
+
+
+///////////////////////////////////////////
+// Running CTAB-GAN on imblearn_mammography
+///////////////////////////////////////////
+
+Load 'data_input/imblearn_mammography'
+from imblearn
+non empty cut in data_input/imblearn_mammography! (7 points)
+Data loaded.
+Traceback (most recent call last):
+  File "/benchmark/data/run_all_exercises.py", line 13, in <module>
+    runExercise(dataset, None, name, f)
+  File "/benchmark/data/library/analysis.py", line 164, in runExercise
+    gan = ganCreator(data)
+  File "/benchmark/data/library/analysis.py", line 268, in <lambda>
+    , ("CTAB-GAN",      lambda _data: CtabGan())
+NameError: name 'CtabGan' is not defined

data_result/CTAB-GAN/imblearn_ozone_level.log

@@ -0,0 +1,17 @@
+
+
+///////////////////////////////////////////
+// Running CTAB-GAN on imblearn_ozone_level
+///////////////////////////////////////////
+
+Load 'data_input/imblearn_ozone_level'
+from imblearn
+Data loaded.
+Traceback (most recent call last):
+  File "/benchmark/data/run_all_exercises.py", line 13, in <module>
+    runExercise(dataset, None, name, f)
+  File "/benchmark/data/library/analysis.py", line 164, in runExercise
+    gan = ganCreator(data)
+  File "/benchmark/data/library/analysis.py", line 268, in <lambda>
+    , ("CTAB-GAN",      lambda _data: CtabGan())
+NameError: name 'CtabGan' is not defined

data_result/CTAB-GAN/imblearn_protein_homo.log

@@ -0,0 +1,17 @@
+
+
+///////////////////////////////////////////
+// Running CTAB-GAN on imblearn_protein_homo
+///////////////////////////////////////////
+
+Load 'data_input/imblearn_protein_homo'
+from imblearn
+Data loaded.
+Traceback (most recent call last):
+  File "/benchmark/data/run_all_exercises.py", line 13, in <module>
+    runExercise(dataset, None, name, f)
+  File "/benchmark/data/library/analysis.py", line 164, in runExercise
+    gan = ganCreator(data)
+  File "/benchmark/data/library/analysis.py", line 268, in <lambda>
+    , ("CTAB-GAN",      lambda _data: CtabGan())
+NameError: name 'CtabGan' is not defined

data_result/CTAB-GAN/imblearn_webpage.log

@@ -0,0 +1,18 @@
+
+
+///////////////////////////////////////////
+// Running CTAB-GAN on imblearn_webpage
+///////////////////////////////////////////
+
+Load 'data_input/imblearn_webpage'
+from imblearn
+non empty cut in data_input/imblearn_webpage! (76 points)
+Data loaded.
+Traceback (most recent call last):
+  File "/benchmark/data/run_all_exercises.py", line 13, in <module>
+    runExercise(dataset, None, name, f)
+  File "/benchmark/data/library/analysis.py", line 164, in runExercise
+    gan = ganCreator(data)
+  File "/benchmark/data/library/analysis.py", line 268, in <lambda>
+    , ("CTAB-GAN",      lambda _data: CtabGan())
+NameError: name 'CtabGan' is not defined

data_result/CTAB-GAN/kaggle_creditcard.log

@@ -0,0 +1,16 @@
+
+
+///////////////////////////////////////////
+// Running CTAB-GAN on kaggle_creditcard
+///////////////////////////////////////////
+
+Load 'data_input/kaggle_creditcard'
+Data loaded.
+Traceback (most recent call last):
+  File "/benchmark/data/run_all_exercises.py", line 13, in <module>
+    runExercise(dataset, None, name, f)
+  File "/benchmark/data/library/analysis.py", line 164, in runExercise
+    gan = ganCreator(data)
+  File "/benchmark/data/library/analysis.py", line 268, in <lambda>
+    , ("CTAB-GAN",      lambda _data: CtabGan())
+NameError: name 'CtabGan' is not defined

data_result/ProWRAS/folding_abalone9-18.csv

@@ -0,0 +1,92 @@
+LR
+Nr.;TN;TP;FN;FP;f1 score;cohens kappa score;average precision score
+1;128.000;8.000;1.000;10.000;0.593;0.556;0.908
+2;132.000;5.000;4.000;6.000;0.500;0.464;0.569
+3;132.000;8.000;1.000;6.000;0.696;0.671;0.815
+4;132.000;5.000;4.000;6.000;0.500;0.464;0.599
+5;128.000;4.000;2.000;9.000;0.421;0.386;0.442
+6;129.000;8.000;1.000;9.000;0.615;0.582;0.610
+7;133.000;6.000;3.000;5.000;0.600;0.571;0.796
+8;134.000;7.000;2.000;4.000;0.700;0.678;0.728
+9;131.000;7.000;2.000;7.000;0.609;0.577;0.726
+10;128.000;6.000;0.000;9.000;0.571;0.544;0.573
+11;132.000;4.000;5.000;6.000;0.421;0.381;0.515
+12;133.000;9.000;0.000;5.000;0.783;0.765;0.906
+13;134.000;5.000;4.000;4.000;0.556;0.527;0.649
+14;120.000;8.000;1.000;18.000;0.457;0.403;0.699
+15;129.000;4.000;2.000;8.000;0.444;0.412;0.532
+16;130.000;4.000;5.000;8.000;0.381;0.334;0.500
+17;129.000;6.000;3.000;9.000;0.500;0.459;0.668
+18;133.000;7.000;2.000;5.000;0.667;0.642;0.654
+19;131.000;9.000;0.000;7.000;0.720;0.696;0.912
+20;131.000;5.000;1.000;6.000;0.588;0.565;0.602
+21;129.000;7.000;2.000;9.000;0.560;0.523;0.676
+22;127.000;8.000;1.000;11.000;0.571;0.533;0.710
+23;132.000;5.000;4.000;6.000;0.500;0.464;0.528
+24;135.000;7.000;2.000;3.000;0.737;0.719;0.925
+25;131.000;4.000;2.000;6.000;0.500;0.472;0.802
+max;135.000;9.000;5.000;18.000;0.783;0.765;0.925
+avg;130.520;6.240;2.160;7.280;0.568;0.535;0.682
+min;120.000;4.000;0.000;3.000;0.381;0.334;0.442
+---
+GB
+Nr.;TN;TP;FN;FP;f1 score;cohens kappa score
+1;132.000;5.000;4.000;6.000;0.500;0.464
+2;134.000;3.000;6.000;4.000;0.375;0.340
+3;134.000;4.000;5.000;4.000;0.471;0.438
+4;133.000;3.000;6.000;5.000;0.353;0.313
+5;131.000;2.000;4.000;6.000;0.286;0.250
+6;131.000;4.000;5.000;7.000;0.400;0.357
+7;134.000;3.000;6.000;4.000;0.375;0.340
+8;130.000;2.000;7.000;8.000;0.211;0.156
+9;133.000;4.000;5.000;5.000;0.444;0.408
+10;130.000;3.000;3.000;7.000;0.375;0.340
+11;131.000;3.000;6.000;7.000;0.316;0.269
+12;135.000;6.000;3.000;3.000;0.667;0.645
+13;134.000;3.000;6.000;4.000;0.375;0.340
+14;129.000;4.000;5.000;9.000;0.364;0.314
+15;131.000;3.000;3.000;6.000;0.400;0.368
+16;134.000;4.000;5.000;4.000;0.471;0.438
+17;127.000;5.000;4.000;11.000;0.400;0.349
+18;133.000;5.000;4.000;5.000;0.526;0.494
+19;129.000;3.000;6.000;9.000;0.286;0.232
+20;131.000;3.000;3.000;6.000;0.400;0.368
+21;133.000;2.000;7.000;5.000;0.250;0.208
+22;132.000;4.000;5.000;6.000;0.421;0.381
+23;134.000;4.000;5.000;4.000;0.471;0.438
+24;134.000;4.000;5.000;4.000;0.471;0.438
+25;134.000;2.000;4.000;3.000;0.364;0.338
+max;135.000;6.000;7.000;11.000;0.667;0.645
+avg;132.120;3.520;4.880;5.680;0.399;0.361
+min;127.000;2.000;3.000;3.000;0.211;0.156
+---
+KNN
+Nr.;TN;TP;FN;FP;f1 score;cohens kappa score
+1;124.000;5.000;4.000;14.000;0.357;0.299
+2;123.000;5.000;4.000;15.000;0.345;0.284
+3;128.000;5.000;4.000;10.000;0.417;0.368
+4;129.000;6.000;3.000;9.000;0.500;0.459
+5;130.000;4.000;2.000;7.000;0.471;0.440
+6;129.000;4.000;5.000;9.000;0.364;0.314
+7;133.000;6.000;3.000;5.000;0.600;0.571
+8;129.000;8.000;1.000;9.000;0.615;0.582
+9;124.000;4.000;5.000;14.000;0.296;0.234
+10;123.000;3.000;3.000;14.000;0.261;0.212
+11;130.000;2.000;7.000;8.000;0.211;0.156
+12;122.000;5.000;4.000;16.000;0.333;0.271
+13;130.000;4.000;5.000;8.000;0.381;0.334
+14;123.000;5.000;4.000;15.000;0.345;0.284
+15;127.000;4.000;2.000;10.000;0.400;0.363
+16;125.000;4.000;5.000;13.000;0.308;0.247
+17;124.000;6.000;3.000;14.000;0.414;0.360
+18;129.000;5.000;4.000;9.000;0.435;0.389
+19;128.000;7.000;2.000;10.000;0.538;0.498
+20;127.000;4.000;2.000;10.000;0.400;0.363
+21;125.000;3.000;6.000;13.000;0.240;0.175
+22;130.000;5.000;4.000;8.000;0.455;0.412
+23;126.000;3.000;6.000;12.000;0.250;0.188
+24;129.000;5.000;4.000;9.000;0.435;0.389
+25;131.000;4.000;2.000;6.000;0.500;0.472
+max;133.000;8.000;7.000;16.000;0.615;0.582
+avg;127.120;4.640;3.760;10.680;0.395;0.347
+min;122.000;2.000;1.000;5.000;0.211;0.156

data_result/ProWRAS/folding_abalone9-18.log

@@ -0,0 +1,701 @@
+
+
+///////////////////////////////////////////
+// Running ProWRAS 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
+-> create 518 synthetic samples
+-> test with 'LR'
+LR tn, fp: 128, 10
+LR fn, tp: 1, 8
+LR f1 score: 0.593
+LR cohens kappa score: 0.556
+LR average precision score: 0.908
+
+-> test with 'GB'
+GB tn, fp: 132, 6
+GB fn, tp: 4, 5
+GB f1 score: 0.500
+GB cohens kappa score: 0.464
+
+-> test with 'KNN'
+KNN tn, fp: 124, 14
+KNN fn, tp: 4, 5
+KNN f1 score: 0.357
+KNN cohens kappa score: 0.299
+
+
+------ Step 1/5: Slice 2/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 518 synthetic samples
+-> test with 'LR'
+LR tn, fp: 132, 6
+LR fn, tp: 4, 5
+LR f1 score: 0.500
+LR cohens kappa score: 0.464
+LR average precision score: 0.569
+
+-> 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: 123, 15
+KNN fn, tp: 4, 5
+KNN f1 score: 0.345
+KNN cohens kappa score: 0.284
+
+
+------ Step 1/5: Slice 3/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 518 synthetic samples
+-> test with 'LR'
+LR tn, fp: 132, 6
+LR fn, tp: 1, 8
+LR f1 score: 0.696
+LR cohens kappa score: 0.671
+LR average precision score: 0.815
+
+-> test with 'GB'
+GB tn, fp: 134, 4
+GB fn, tp: 5, 4
+GB f1 score: 0.471
+GB cohens kappa score: 0.438
+
+-> test with 'KNN'
+KNN tn, fp: 128, 10
+KNN fn, tp: 4, 5
+KNN f1 score: 0.417
+KNN cohens kappa score: 0.368
+
+
+------ Step 1/5: Slice 4/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 518 synthetic samples
+-> test with 'LR'
+LR tn, fp: 132, 6
+LR fn, tp: 4, 5
+LR f1 score: 0.500
+LR cohens kappa score: 0.464
+LR average precision score: 0.599
+
+-> test with 'GB'
+GB tn, fp: 133, 5
+GB fn, tp: 6, 3
+GB f1 score: 0.353
+GB cohens kappa score: 0.313
+
+-> test with 'KNN'
+KNN tn, fp: 129, 9
+KNN fn, tp: 3, 6
+KNN f1 score: 0.500
+KNN cohens kappa score: 0.459
+
+
+------ Step 1/5: Slice 5/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 516 synthetic samples
+-> test with 'LR'
+LR tn, fp: 128, 9
+LR fn, tp: 2, 4
+LR f1 score: 0.421
+LR cohens kappa score: 0.386
+LR average precision score: 0.442
+
+-> test with 'GB'
+GB tn, fp: 131, 6
+GB fn, tp: 4, 2
+GB f1 score: 0.286
+GB cohens kappa score: 0.250
+
+-> test with 'KNN'
+KNN tn, fp: 130, 7
+KNN fn, tp: 2, 4
+KNN f1 score: 0.471
+KNN cohens kappa score: 0.440
+
+
+====== Step 2/5 =======
+-> Shuffling data
+-> Spliting data to slices
+
+------ Step 2/5: Slice 1/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 518 synthetic samples
+-> test with 'LR'
+LR tn, fp: 129, 9
+LR fn, tp: 1, 8
+LR f1 score: 0.615
+LR cohens kappa score: 0.582
+LR average precision score: 0.610
+
+-> test with 'GB'
+GB tn, fp: 131, 7
+GB fn, tp: 5, 4
+GB f1 score: 0.400
+GB cohens kappa score: 0.357
+
+-> test with 'KNN'
+KNN tn, fp: 129, 9
+KNN fn, tp: 5, 4
+KNN f1 score: 0.364
+KNN cohens kappa score: 0.314
+
+
+------ Step 2/5: Slice 2/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> 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: 0.796
+
+-> 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: 133, 5
+KNN fn, tp: 3, 6
+KNN f1 score: 0.600
+KNN cohens kappa score: 0.571
+
+
+------ Step 2/5: Slice 3/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 518 synthetic samples
+-> test with 'LR'
+LR tn, fp: 134, 4
+LR fn, tp: 2, 7
+LR f1 score: 0.700
+LR cohens kappa score: 0.678
+LR average precision score: 0.728
+
+-> test with 'GB'
+GB tn, fp: 130, 8
+GB fn, tp: 7, 2
+GB f1 score: 0.211
+GB cohens kappa score: 0.156
+
+-> test with 'KNN'
+KNN tn, fp: 129, 9
+KNN fn, tp: 1, 8
+KNN f1 score: 0.615
+KNN cohens kappa score: 0.582
+
+
+------ Step 2/5: Slice 4/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 518 synthetic samples
+-> test with 'LR'
+LR tn, fp: 131, 7
+LR fn, tp: 2, 7
+LR f1 score: 0.609
+LR cohens kappa score: 0.577
+LR average precision score: 0.726
+
+-> test with 'GB'
+GB tn, fp: 133, 5
+GB fn, tp: 5, 4
+GB f1 score: 0.444
+GB cohens kappa score: 0.408
+
+-> test with 'KNN'
+KNN tn, fp: 124, 14
+KNN fn, tp: 5, 4
+KNN f1 score: 0.296
+KNN cohens kappa score: 0.234
+
+
+------ Step 2/5: Slice 5/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 516 synthetic samples
+-> test with 'LR'
+LR tn, fp: 128, 9
+LR fn, tp: 0, 6
+LR f1 score: 0.571
+LR cohens kappa score: 0.544
+LR average precision score: 0.573
+
+-> test with 'GB'
+GB tn, fp: 130, 7
+GB fn, tp: 3, 3
+GB f1 score: 0.375
+GB cohens kappa score: 0.340
+
+-> test with 'KNN'
+KNN tn, fp: 123, 14
+KNN fn, tp: 3, 3
+KNN f1 score: 0.261
+KNN cohens kappa score: 0.212
+
+
+====== Step 3/5 =======
+-> Shuffling data
+-> Spliting data to slices
+
+------ Step 3/5: Slice 1/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 518 synthetic samples
+-> test with 'LR'
+LR tn, fp: 132, 6
+LR fn, tp: 5, 4
+LR f1 score: 0.421
+LR cohens kappa score: 0.381
+LR average precision score: 0.515
+
+-> test with 'GB'
+GB tn, fp: 131, 7
+GB fn, tp: 6, 3
+GB f1 score: 0.316
+GB cohens kappa score: 0.269
+
+-> test with 'KNN'
+KNN tn, fp: 130, 8
+KNN fn, tp: 7, 2
+KNN f1 score: 0.211
+KNN cohens kappa score: 0.156
+
+
+------ Step 3/5: Slice 2/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 518 synthetic samples
+-> test with 'LR'
+LR tn, fp: 133, 5
+LR fn, tp: 0, 9
+LR f1 score: 0.783
+LR cohens kappa score: 0.765
+LR average precision score: 0.906
+
+-> test with 'GB'
+GB tn, fp: 135, 3
+GB fn, tp: 3, 6
+GB f1 score: 0.667
+GB cohens kappa score: 0.645
+
+-> test with 'KNN'
+KNN tn, fp: 122, 16
+KNN fn, tp: 4, 5
+KNN f1 score: 0.333
+KNN cohens kappa score: 0.271
+
+
+------ Step 3/5: Slice 3/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 518 synthetic samples
+-> test with 'LR'
+LR tn, fp: 134, 4
+LR fn, tp: 4, 5
+LR f1 score: 0.556
+LR cohens kappa score: 0.527
+LR average precision score: 0.649
+
+-> 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: 130, 8
+KNN fn, tp: 5, 4
+KNN f1 score: 0.381
+KNN cohens kappa score: 0.334
+
+
+------ Step 3/5: Slice 4/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 518 synthetic samples
+-> test with 'LR'
+LR tn, fp: 120, 18
+LR fn, tp: 1, 8
+LR f1 score: 0.457
+LR cohens kappa score: 0.403
+LR average precision score: 0.699
+
+-> test with 'GB'
+GB tn, fp: 129, 9
+GB fn, tp: 5, 4
+GB f1 score: 0.364
+GB cohens kappa score: 0.314
+
+-> test with 'KNN'
+KNN tn, fp: 123, 15
+KNN fn, tp: 4, 5
+KNN f1 score: 0.345
+KNN cohens kappa score: 0.284
+
+
+------ Step 3/5: Slice 5/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 516 synthetic samples
+-> test with 'LR'
+LR tn, fp: 129, 8
+LR fn, tp: 2, 4
+LR f1 score: 0.444
+LR cohens kappa score: 0.412
+LR average precision score: 0.532
+
+-> test with 'GB'
+GB tn, fp: 131, 6
+GB fn, tp: 3, 3
+GB f1 score: 0.400
+GB cohens kappa score: 0.368
+
+-> test with 'KNN'
+KNN tn, fp: 127, 10
+KNN fn, tp: 2, 4
+KNN f1 score: 0.400
+KNN cohens kappa score: 0.363
+
+
+====== Step 4/5 =======
+-> Shuffling data
+-> Spliting data to slices
+
+------ Step 4/5: Slice 1/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 518 synthetic samples
+-> test with 'LR'
+LR tn, fp: 130, 8
+LR fn, tp: 5, 4
+LR f1 score: 0.381
+LR cohens kappa score: 0.334
+LR average precision score: 0.500
+
+-> test with 'GB'
+GB tn, fp: 134, 4
+GB fn, tp: 5, 4
+GB f1 score: 0.471
+GB cohens kappa score: 0.438
+
+-> test with 'KNN'
+KNN tn, fp: 125, 13
+KNN fn, tp: 5, 4
+KNN f1 score: 0.308
+KNN cohens kappa score: 0.247
+
+
+------ Step 4/5: Slice 2/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 518 synthetic samples
+-> test with 'LR'
+LR tn, fp: 129, 9
+LR fn, tp: 3, 6
+LR f1 score: 0.500
+LR cohens kappa score: 0.459
+LR average precision score: 0.668
+
+-> test with 'GB'
+GB tn, fp: 127, 11
+GB fn, tp: 4, 5
+GB f1 score: 0.400
+GB cohens kappa score: 0.349
+
+-> test with 'KNN'
+KNN tn, fp: 124, 14
+KNN fn, tp: 3, 6
+KNN f1 score: 0.414
+KNN cohens kappa score: 0.360
+
+
+------ Step 4/5: Slice 3/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 518 synthetic samples
+-> test with 'LR'
+LR tn, fp: 133, 5
+LR fn, tp: 2, 7
+LR f1 score: 0.667
+LR cohens kappa score: 0.642
+LR average precision score: 0.654
+
+-> test with 'GB'
+GB tn, fp: 133, 5
+GB fn, tp: 4, 5
+GB f1 score: 0.526
+GB cohens kappa score: 0.494
+
+-> test with 'KNN'
+KNN tn, fp: 129, 9
+KNN fn, tp: 4, 5
+KNN f1 score: 0.435
+KNN cohens kappa score: 0.389
+
+
+------ Step 4/5: Slice 4/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 518 synthetic samples
+-> test with 'LR'
+LR tn, fp: 131, 7
+LR fn, tp: 0, 9
+LR f1 score: 0.720
+LR cohens kappa score: 0.696
+LR average precision score: 0.912
+
+-> test with 'GB'
+GB tn, fp: 129, 9
+GB fn, tp: 6, 3
+GB f1 score: 0.286
+GB cohens kappa score: 0.232
+
+-> test with 'KNN'
+KNN tn, fp: 128, 10
+KNN fn, tp: 2, 7
+KNN f1 score: 0.538
+KNN cohens kappa score: 0.498
+
+
+------ Step 4/5: Slice 5/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 516 synthetic samples
+-> test with 'LR'
+LR tn, fp: 131, 6
+LR fn, tp: 1, 5
+LR f1 score: 0.588
+LR cohens kappa score: 0.565
+LR average precision score: 0.602
+
+-> test with 'GB'
+GB tn, fp: 131, 6
+GB fn, tp: 3, 3
+GB f1 score: 0.400
+GB cohens kappa score: 0.368
+
+-> test with 'KNN'
+KNN tn, fp: 127, 10
+KNN fn, tp: 2, 4
+KNN f1 score: 0.400
+KNN cohens kappa score: 0.363
+
+
+====== Step 5/5 =======
+-> Shuffling data
+-> Spliting data to slices
+
+------ Step 5/5: Slice 1/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 518 synthetic samples
+-> test with 'LR'
+LR tn, fp: 129, 9
+LR fn, tp: 2, 7
+LR f1 score: 0.560
+LR cohens kappa score: 0.523
+LR average precision score: 0.676
+
+-> test with 'GB'
+GB tn, fp: 133, 5
+GB fn, tp: 7, 2
+GB f1 score: 0.250
+GB cohens kappa score: 0.208
+
+-> test with 'KNN'
+KNN tn, fp: 125, 13
+KNN fn, tp: 6, 3
+KNN f1 score: 0.240
+KNN cohens kappa score: 0.175
+
+
+------ Step 5/5: Slice 2/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 518 synthetic samples
+-> test with 'LR'
+LR tn, fp: 127, 11
+LR fn, tp: 1, 8
+LR f1 score: 0.571
+LR cohens kappa score: 0.533
+LR average precision score: 0.710
+
+-> 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: 130, 8
+KNN fn, tp: 4, 5
+KNN f1 score: 0.455
+KNN cohens kappa score: 0.412
+
+
+------ Step 5/5: Slice 3/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 518 synthetic samples
+-> test with 'LR'
+LR tn, fp: 132, 6
+LR fn, tp: 4, 5
+LR f1 score: 0.500
+LR cohens kappa score: 0.464
+LR average precision score: 0.528
+
+-> test with 'GB'
+GB tn, fp: 134, 4
+GB fn, tp: 5, 4
+GB f1 score: 0.471
+GB cohens kappa score: 0.438
+
+-> test with 'KNN'
+KNN tn, fp: 126, 12
+KNN fn, tp: 6, 3
+KNN f1 score: 0.250
+KNN cohens kappa score: 0.188
+
+
+------ Step 5/5: Slice 4/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 518 synthetic samples
+-> test with 'LR'
+LR tn, fp: 135, 3
+LR fn, tp: 2, 7
+LR f1 score: 0.737
+LR cohens kappa score: 0.719
+LR average precision score: 0.925
+
+-> test with 'GB'
+GB tn, fp: 134, 4
+GB fn, tp: 5, 4
+GB f1 score: 0.471
+GB cohens kappa score: 0.438
+
+-> test with 'KNN'
+KNN tn, fp: 129, 9
+KNN fn, tp: 4, 5
+KNN f1 score: 0.435
+KNN cohens kappa score: 0.389
+
+
+------ Step 5/5: Slice 5/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 516 synthetic samples
+-> test with 'LR'
+LR tn, fp: 131, 6
+LR fn, tp: 2, 4
+LR f1 score: 0.500
+LR cohens kappa score: 0.472
+LR average precision score: 0.802
+
+-> test with 'GB'
+GB tn, fp: 134, 3
+GB fn, tp: 4, 2
+GB f1 score: 0.364
+GB cohens kappa score: 0.338
+
+-> test with 'KNN'
+KNN tn, fp: 131, 6
+KNN fn, tp: 2, 4
+KNN f1 score: 0.500
+KNN cohens kappa score: 0.472
+
+### Exercise is done.
+
+-----[ LR ]-----
+maximum:
+LR tn, fp: 135, 18
+LR fn, tp: 5, 9
+LR f1 score: 0.783
+LR cohens kappa score: 0.765
+LR average precision score: 0.925
+
+
+average:
+LR tn, fp: 130.52, 7.28
+LR fn, tp: 2.16, 6.24
+LR f1 score: 0.568
+LR cohens kappa score: 0.535
+LR average precision score: 0.682
+
+
+minimum:
+LR tn, fp: 120, 3
+LR fn, tp: 0, 4
+LR f1 score: 0.381
+LR cohens kappa score: 0.334
+LR average precision score: 0.442
+
+
+-----[ GB ]-----
+maximum:
+GB tn, fp: 135, 11
+GB fn, tp: 7, 6
+GB f1 score: 0.667
+GB cohens kappa score: 0.645
+
+
+average:
+GB tn, fp: 132.12, 5.68
+GB fn, tp: 4.88, 3.52
+GB f1 score: 0.399
+GB cohens kappa score: 0.361
+
+
+minimum:
+GB tn, fp: 127, 3
+GB fn, tp: 3, 2
+GB f1 score: 0.211
+GB cohens kappa score: 0.156
+
+
+-----[ KNN ]-----
+maximum:
+KNN tn, fp: 133, 16
+KNN fn, tp: 7, 8
+KNN f1 score: 0.615
+KNN cohens kappa score: 0.582
+
+
+average:
+KNN tn, fp: 127.12, 10.68
+KNN fn, tp: 3.76, 4.64
+KNN f1 score: 0.395
+KNN cohens kappa score: 0.347
+
+
+minimum:
+KNN tn, fp: 122, 5
+KNN fn, tp: 1, 2
+KNN f1 score: 0.211
+KNN cohens kappa score: 0.156
+

data_result/ProWRAS/folding_abalone_17_vs_7_8_9_10.csv

@@ -0,0 +1,92 @@
+LR
+Nr.;TN;TP;FN;FP;f1 score;cohens kappa score;average precision score
+1;422.000;11.000;1.000;34.000;0.386;0.360;0.427
+2;416.000;11.000;1.000;40.000;0.349;0.321;0.602
+3;439.000;7.000;5.000;17.000;0.389;0.367;0.375
+4;419.000;9.000;3.000;37.000;0.310;0.281;0.612
+5;428.000;6.000;4.000;28.000;0.273;0.248;0.285
+6;423.000;12.000;0.000;33.000;0.421;0.397;0.699
+7;414.000;12.000;0.000;42.000;0.364;0.336;0.475
+8;427.000;6.000;6.000;29.000;0.255;0.226;0.222
+9;433.000;9.000;3.000;23.000;0.409;0.386;0.514
+10;424.000;7.000;3.000;32.000;0.286;0.260;0.383
+11;423.000;8.000;4.000;33.000;0.302;0.273;0.505
+12;425.000;6.000;6.000;31.000;0.245;0.214;0.320
+13;432.000;9.000;3.000;24.000;0.400;0.377;0.569
+14;424.000;9.000;3.000;32.000;0.340;0.312;0.293
+15;420.000;9.000;1.000;36.000;0.327;0.303;0.548
+16;418.000;10.000;2.000;38.000;0.333;0.305;0.570
+17;421.000;11.000;1.000;35.000;0.379;0.353;0.490
+18;426.000;10.000;2.000;30.000;0.385;0.359;0.504
+19;429.000;9.000;3.000;27.000;0.375;0.350;0.451
+20;435.000;5.000;5.000;21.000;0.278;0.255;0.387
+21;410.000;9.000;3.000;46.000;0.269;0.237;0.394
+22;431.000;9.000;3.000;25.000;0.391;0.367;0.312
+23;430.000;9.000;3.000;26.000;0.383;0.358;0.358
+24;421.000;10.000;2.000;35.000;0.351;0.323;0.670
+25;421.000;8.000;2.000;35.000;0.302;0.277;0.367
+max;439.000;12.000;6.000;46.000;0.421;0.397;0.699
+avg;424.440;8.840;2.760;31.560;0.340;0.314;0.453
+min;410.000;5.000;0.000;17.000;0.245;0.214;0.222
+---
+GB
+Nr.;TN;TP;FN;FP;f1 score;cohens kappa score
+1;442.000;6.000;6.000;14.000;0.375;0.354
+2;443.000;5.000;7.000;13.000;0.333;0.312
+3;446.000;2.000;10.000;10.000;0.167;0.145
+4;444.000;6.000;6.000;12.000;0.400;0.381
+5;450.000;3.000;7.000;6.000;0.316;0.302
+6;448.000;6.000;6.000;8.000;0.462;0.446
+7;447.000;9.000;3.000;9.000;0.600;0.587
+8;450.000;4.000;8.000;6.000;0.364;0.348
+9;447.000;7.000;5.000;9.000;0.500;0.485
+10;445.000;5.000;5.000;11.000;0.385;0.368
+11;445.000;5.000;7.000;11.000;0.357;0.338
+12;442.000;2.000;10.000;14.000;0.143;0.117
+13;447.000;5.000;7.000;9.000;0.385;0.367
+14;445.000;3.000;9.000;11.000;0.231;0.209
+15;446.000;5.000;5.000;10.000;0.400;0.384
+16;435.000;7.000;5.000;21.000;0.350;0.326
+17;438.000;9.000;3.000;18.000;0.462;0.442
+18;448.000;2.000;10.000;8.000;0.182;0.162
+19;451.000;3.000;9.000;5.000;0.300;0.285
+20;450.000;3.000;7.000;6.000;0.316;0.302
+21;442.000;3.000;9.000;14.000;0.207;0.182
+22;447.000;2.000;10.000;9.000;0.174;0.153
+23;444.000;6.000;6.000;12.000;0.400;0.381
+24;446.000;4.000;8.000;10.000;0.308;0.288
+25;444.000;5.000;5.000;12.000;0.370;0.353
+max;451.000;9.000;10.000;21.000;0.600;0.587
+avg;445.280;4.680;6.920;10.720;0.339;0.321
+min;435.000;2.000;3.000;5.000;0.143;0.117
+---
+KNN
+Nr.;TN;TP;FN;FP;f1 score;cohens kappa score
+1;440.000;5.000;7.000;16.000;0.303;0.280
+2;427.000;8.000;4.000;29.000;0.327;0.299
+3;446.000;3.000;9.000;10.000;0.240;0.219
+4;440.000;10.000;2.000;16.000;0.526;0.509
+5;442.000;4.000;6.000;14.000;0.286;0.265
+6;438.000;7.000;5.000;18.000;0.378;0.356
+7;429.000;9.000;3.000;27.000;0.375;0.350
+8;440.000;7.000;5.000;16.000;0.400;0.379
+9;437.000;5.000;7.000;19.000;0.278;0.252
+10;440.000;5.000;5.000;16.000;0.323;0.302
+11;439.000;5.000;7.000;17.000;0.294;0.270
+12;435.000;5.000;7.000;21.000;0.263;0.236
+13;441.000;6.000;6.000;15.000;0.364;0.342
+14;440.000;4.000;8.000;16.000;0.250;0.225
+15;431.000;8.000;2.000;25.000;0.372;0.351
+16;430.000;8.000;4.000;26.000;0.348;0.322
+17;431.000;8.000;4.000;25.000;0.356;0.330
+18;443.000;6.000;6.000;13.000;0.387;0.367
+19;441.000;7.000;5.000;15.000;0.412;0.392
+20;440.000;3.000;7.000;16.000;0.207;0.184
+21;437.000;5.000;7.000;19.000;0.278;0.252
+22;435.000;8.000;4.000;21.000;0.390;0.367
+23;440.000;9.000;3.000;16.000;0.486;0.468
+24;438.000;6.000;6.000;18.000;0.333;0.310
+25;438.000;4.000;6.000;18.000;0.250;0.227
+max;446.000;10.000;9.000;29.000;0.526;0.509
+avg;437.520;6.200;5.400;18.480;0.337;0.314
+min;427.000;3.000;2.000;10.000;0.207;0.184

data_result/ProWRAS/folding_abalone_17_vs_7_8_9_10.log

@@ -0,0 +1,701 @@
+
+
+///////////////////////////////////////////
+// Running ProWRAS 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
+-> create 1778 synthetic samples
+-> test with 'LR'
+LR tn, fp: 422, 34
+LR fn, tp: 1, 11
+LR f1 score: 0.386
+LR cohens kappa score: 0.360
+LR average precision score: 0.427
+
+-> test with 'GB'
+GB tn, fp: 442, 14
+GB fn, tp: 6, 6
+GB f1 score: 0.375
+GB cohens kappa score: 0.354
+
+-> test with 'KNN'
+KNN tn, fp: 440, 16
+KNN fn, tp: 7, 5
+KNN f1 score: 0.303
+KNN cohens kappa score: 0.280
+
+
+------ Step 1/5: Slice 2/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 1778 synthetic samples
+-> test with 'LR'
+LR tn, fp: 416, 40
+LR fn, tp: 1, 11
+LR f1 score: 0.349
+LR cohens kappa score: 0.321
+LR average precision score: 0.602
+
+-> test with 'GB'
+GB tn, fp: 443, 13
+GB fn, tp: 7, 5
+GB f1 score: 0.333
+GB cohens kappa score: 0.312
+
+-> test with 'KNN'
+KNN tn, fp: 427, 29
+KNN fn, tp: 4, 8
+KNN f1 score: 0.327
+KNN cohens kappa score: 0.299
+
+
+------ Step 1/5: Slice 3/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 1778 synthetic samples
+-> test with 'LR'
+LR tn, fp: 439, 17
+LR fn, tp: 5, 7
+LR f1 score: 0.389
+LR cohens kappa score: 0.367
+LR average precision score: 0.375
+
+-> test with 'GB'
+GB tn, fp: 446, 10
+GB fn, tp: 10, 2
+GB f1 score: 0.167
+GB cohens kappa score: 0.145
+
+-> test with 'KNN'
+KNN tn, fp: 446, 10
+KNN fn, tp: 9, 3
+KNN f1 score: 0.240
+KNN cohens kappa score: 0.219
+
+
+------ Step 1/5: Slice 4/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 1778 synthetic samples
+-> test with 'LR'
+LR tn, fp: 419, 37
+LR fn, tp: 3, 9
+LR f1 score: 0.310
+LR cohens kappa score: 0.281
+LR average precision score: 0.612
+
+-> test with 'GB'
+GB tn, fp: 444, 12
+GB fn, tp: 6, 6
+GB f1 score: 0.400
+GB cohens kappa score: 0.381
+
+-> test with 'KNN'
+KNN tn, fp: 440, 16
+KNN fn, tp: 2, 10
+KNN f1 score: 0.526
+KNN cohens kappa score: 0.509
+
+
+------ Step 1/5: Slice 5/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 1776 synthetic samples
+-> test with 'LR'
+LR tn, fp: 428, 28
+LR fn, tp: 4, 6
+LR f1 score: 0.273
+LR cohens kappa score: 0.248
+LR average precision score: 0.285
+
+-> test with 'GB'
+GB tn, fp: 450, 6
+GB fn, tp: 7, 3
+GB f1 score: 0.316
+GB cohens kappa score: 0.302
+
+-> test with 'KNN'
+KNN tn, fp: 442, 14
+KNN fn, tp: 6, 4
+KNN f1 score: 0.286
+KNN cohens kappa score: 0.265
+
+
+====== Step 2/5 =======
+-> Shuffling data
+-> Spliting data to slices
+
+------ Step 2/5: Slice 1/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 1778 synthetic samples
+-> test with 'LR'
+LR tn, fp: 423, 33
+LR fn, tp: 0, 12
+LR f1 score: 0.421
+LR cohens kappa score: 0.397
+LR average precision score: 0.699
+
+-> test with 'GB'
+GB tn, fp: 448, 8
+GB fn, tp: 6, 6
+GB f1 score: 0.462
+GB cohens kappa score: 0.446
+
+-> test with 'KNN'
+KNN tn, fp: 438, 18
+KNN fn, tp: 5, 7
+KNN f1 score: 0.378
+KNN cohens kappa score: 0.356
+
+
+------ Step 2/5: Slice 2/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 1778 synthetic samples
+-> test with 'LR'
+LR tn, fp: 414, 42
+LR fn, tp: 0, 12
+LR f1 score: 0.364
+LR cohens kappa score: 0.336
+LR average precision score: 0.475
+
+-> test with 'GB'
+GB tn, fp: 447, 9
+GB fn, tp: 3, 9
+GB f1 score: 0.600
+GB cohens kappa score: 0.587
+
+-> test with 'KNN'
+KNN tn, fp: 429, 27
+KNN fn, tp: 3, 9
+KNN f1 score: 0.375
+KNN cohens kappa score: 0.350
+
+
+------ Step 2/5: Slice 3/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 1778 synthetic samples
+-> test with 'LR'
+LR tn, fp: 427, 29
+LR fn, tp: 6, 6
+LR f1 score: 0.255
+LR cohens kappa score: 0.226
+LR average precision score: 0.222
+
+-> test with 'GB'
+GB tn, fp: 450, 6
+GB fn, tp: 8, 4
+GB f1 score: 0.364
+GB cohens kappa score: 0.348
+
+-> test with 'KNN'
+KNN tn, fp: 440, 16
+KNN fn, tp: 5, 7
+KNN f1 score: 0.400
+KNN cohens kappa score: 0.379
+
+
+------ Step 2/5: Slice 4/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 1778 synthetic samples
+-> test with 'LR'
+LR tn, fp: 433, 23
+LR fn, tp: 3, 9
+LR f1 score: 0.409
+LR cohens kappa score: 0.386
+LR average precision score: 0.514
+
+-> test with 'GB'
+GB tn, fp: 447, 9
+GB fn, tp: 5, 7
+GB f1 score: 0.500
+GB cohens kappa score: 0.485
+
+-> test with 'KNN'
+KNN tn, fp: 437, 19
+KNN fn, tp: 7, 5
+KNN f1 score: 0.278
+KNN cohens kappa score: 0.252
+
+
+------ Step 2/5: Slice 5/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 1776 synthetic samples
+-> test with 'LR'
+LR tn, fp: 424, 32
+LR fn, tp: 3, 7
+LR f1 score: 0.286
+LR cohens kappa score: 0.260
+LR average precision score: 0.383
+
+-> test with 'GB'
+GB tn, fp: 445, 11
+GB fn, tp: 5, 5
+GB f1 score: 0.385
+GB cohens kappa score: 0.368
+
+-> test with 'KNN'
+KNN tn, fp: 440, 16
+KNN fn, tp: 5, 5
+KNN f1 score: 0.323
+KNN cohens kappa score: 0.302
+
+
+====== Step 3/5 =======
+-> Shuffling data
+-> Spliting data to slices
+
+------ Step 3/5: Slice 1/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 1778 synthetic samples
+-> test with 'LR'
+LR tn, fp: 423, 33
+LR fn, tp: 4, 8
+LR f1 score: 0.302
+LR cohens kappa score: 0.273
+LR average precision score: 0.505
+
+-> test with 'GB'
+GB tn, fp: 445, 11
+GB fn, tp: 7, 5
+GB f1 score: 0.357
+GB cohens kappa score: 0.338
+
+-> test with 'KNN'
+KNN tn, fp: 439, 17
+KNN fn, tp: 7, 5
+KNN f1 score: 0.294
+KNN cohens kappa score: 0.270
+
+
+------ Step 3/5: Slice 2/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 1778 synthetic samples
+-> test with 'LR'
+LR tn, fp: 425, 31
+LR fn, tp: 6, 6
+LR f1 score: 0.245
+LR cohens kappa score: 0.214
+LR average precision score: 0.320
+
+-> test with 'GB'
+GB tn, fp: 442, 14
+GB fn, tp: 10, 2
+GB f1 score: 0.143
+GB cohens kappa score: 0.117
+
+-> test with 'KNN'
+KNN tn, fp: 435, 21
+KNN fn, tp: 7, 5
+KNN f1 score: 0.263
+KNN cohens kappa score: 0.236
+
+
+------ Step 3/5: Slice 3/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 1778 synthetic samples
+-> test with 'LR'
+LR tn, fp: 432, 24
+LR fn, tp: 3, 9
+LR f1 score: 0.400
+LR cohens kappa score: 0.377
+LR average precision score: 0.569
+
+-> test with 'GB'
+GB tn, fp: 447, 9
+GB fn, tp: 7, 5
+GB f1 score: 0.385
+GB cohens kappa score: 0.367
+
+-> test with 'KNN'
+KNN tn, fp: 441, 15
+KNN fn, tp: 6, 6
+KNN f1 score: 0.364
+KNN cohens kappa score: 0.342
+
+
+------ Step 3/5: Slice 4/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 1778 synthetic samples
+-> test with 'LR'
+LR tn, fp: 424, 32
+LR fn, tp: 3, 9
+LR f1 score: 0.340
+LR cohens kappa score: 0.312
+LR average precision score: 0.293
+
+-> test with 'GB'
+GB tn, fp: 445, 11
+GB fn, tp: 9, 3
+GB f1 score: 0.231
+GB cohens kappa score: 0.209
+
+-> test with 'KNN'
+KNN tn, fp: 440, 16
+KNN fn, tp: 8, 4
+KNN f1 score: 0.250
+KNN cohens kappa score: 0.225
+
+
+------ Step 3/5: Slice 5/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 1776 synthetic samples
+-> test with 'LR'
+LR tn, fp: 420, 36
+LR fn, tp: 1, 9
+LR f1 score: 0.327
+LR cohens kappa score: 0.303
+LR average precision score: 0.548
+
+-> test with 'GB'
+GB tn, fp: 446, 10
+GB fn, tp: 5, 5
+GB f1 score: 0.400
+GB cohens kappa score: 0.384
+
+-> test with 'KNN'
+KNN tn, fp: 431, 25
+KNN fn, tp: 2, 8
+KNN f1 score: 0.372
+KNN cohens kappa score: 0.351
+
+
+====== Step 4/5 =======
+-> Shuffling data
+-> Spliting data to slices
+
+------ Step 4/5: Slice 1/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 1778 synthetic samples
+-> test with 'LR'
+LR tn, fp: 418, 38
+LR fn, tp: 2, 10
+LR f1 score: 0.333
+LR cohens kappa score: 0.305
+LR average precision score: 0.570
+
+-> test with 'GB'
+GB tn, fp: 435, 21
+GB fn, tp: 5, 7
+GB f1 score: 0.350
+GB cohens kappa score: 0.326
+
+-> test with 'KNN'
+KNN tn, fp: 430, 26
+KNN fn, tp: 4, 8
+KNN f1 score: 0.348
+KNN cohens kappa score: 0.322
+
+
+------ Step 4/5: Slice 2/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 1778 synthetic samples
+-> test with 'LR'
+LR tn, fp: 421, 35
+LR fn, tp: 1, 11
+LR f1 score: 0.379
+LR cohens kappa score: 0.353
+LR average precision score: 0.490
+
+-> test with 'GB'
+GB tn, fp: 438, 18
+GB fn, tp: 3, 9
+GB f1 score: 0.462
+GB cohens kappa score: 0.442
+
+-> test with 'KNN'
+KNN tn, fp: 431, 25
+KNN fn, tp: 4, 8
+KNN f1 score: 0.356
+KNN cohens kappa score: 0.330
+
+
+------ Step 4/5: Slice 3/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 1778 synthetic samples
+-> test with 'LR'
+LR tn, fp: 426, 30
+LR fn, tp: 2, 10
+LR f1 score: 0.385
+LR cohens kappa score: 0.359
+LR average precision score: 0.504
+
+-> test with 'GB'
+GB tn, fp: 448, 8
+GB fn, tp: 10, 2
+GB f1 score: 0.182
+GB cohens kappa score: 0.162
+
+-> test with 'KNN'
+KNN tn, fp: 443, 13
+KNN fn, tp: 6, 6
+KNN f1 score: 0.387
+KNN cohens kappa score: 0.367
+
+
+------ Step 4/5: Slice 4/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 1778 synthetic samples
+-> test with 'LR'
+LR tn, fp: 429, 27
+LR fn, tp: 3, 9
+LR f1 score: 0.375
+LR cohens kappa score: 0.350
+LR average precision score: 0.451
+
+-> test with 'GB'
+GB tn, fp: 451, 5
+GB fn, tp: 9, 3
+GB f1 score: 0.300
+GB cohens kappa score: 0.285
+
+-> test with 'KNN'
+KNN tn, fp: 441, 15
+KNN fn, tp: 5, 7
+KNN f1 score: 0.412
+KNN cohens kappa score: 0.392
+
+
+------ Step 4/5: Slice 5/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 1776 synthetic samples
+-> test with 'LR'
+LR tn, fp: 435, 21
+LR fn, tp: 5, 5
+LR f1 score: 0.278
+LR cohens kappa score: 0.255
+LR average precision score: 0.387
+
+-> test with 'GB'
+GB tn, fp: 450, 6
+GB fn, tp: 7, 3
+GB f1 score: 0.316
+GB cohens kappa score: 0.302
+
+-> test with 'KNN'
+KNN tn, fp: 440, 16
+KNN fn, tp: 7, 3
+KNN f1 score: 0.207
+KNN cohens kappa score: 0.184
+
+
+====== Step 5/5 =======
+-> Shuffling data
+-> Spliting data to slices
+
+------ Step 5/5: Slice 1/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 1778 synthetic samples
+-> test with 'LR'
+LR tn, fp: 410, 46
+LR fn, tp: 3, 9
+LR f1 score: 0.269
+LR cohens kappa score: 0.237
+LR average precision score: 0.394
+
+-> test with 'GB'
+GB tn, fp: 442, 14
+GB fn, tp: 9, 3
+GB f1 score: 0.207
+GB cohens kappa score: 0.182
+
+-> test with 'KNN'
+KNN tn, fp: 437, 19
+KNN fn, tp: 7, 5
+KNN f1 score: 0.278
+KNN cohens kappa score: 0.252
+
+
+------ Step 5/5: Slice 2/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 1778 synthetic samples
+-> test with 'LR'
+LR tn, fp: 431, 25
+LR fn, tp: 3, 9
+LR f1 score: 0.391
+LR cohens kappa score: 0.367
+LR average precision score: 0.312
+
+-> test with 'GB'
+GB tn, fp: 447, 9
+GB fn, tp: 10, 2
+GB f1 score: 0.174
+GB cohens kappa score: 0.153
+
+-> test with 'KNN'
+KNN tn, fp: 435, 21
+KNN fn, tp: 4, 8
+KNN f1 score: 0.390
+KNN cohens kappa score: 0.367
+
+
+------ Step 5/5: Slice 3/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 1778 synthetic samples
+-> test with 'LR'
+LR tn, fp: 430, 26
+LR fn, tp: 3, 9
+LR f1 score: 0.383
+LR cohens kappa score: 0.358
+LR average precision score: 0.358
+
+-> test with 'GB'
+GB tn, fp: 444, 12
+GB fn, tp: 6, 6
+GB f1 score: 0.400
+GB cohens kappa score: 0.381
+
+-> test with 'KNN'
+KNN tn, fp: 440, 16
+KNN fn, tp: 3, 9
+KNN f1 score: 0.486
+KNN cohens kappa score: 0.468
+
+
+------ Step 5/5: Slice 4/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 1778 synthetic samples
+-> test with 'LR'
+LR tn, fp: 421, 35
+LR fn, tp: 2, 10
+LR f1 score: 0.351
+LR cohens kappa score: 0.323
+LR average precision score: 0.670
+
+-> test with 'GB'
+GB tn, fp: 446, 10
+GB fn, tp: 8, 4
+GB f1 score: 0.308
+GB cohens kappa score: 0.288
+
+-> test with 'KNN'
+KNN tn, fp: 438, 18
+KNN fn, tp: 6, 6
+KNN f1 score: 0.333
+KNN cohens kappa score: 0.310
+
+
+------ Step 5/5: Slice 5/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 1776 synthetic samples
+-> test with 'LR'
+LR tn, fp: 421, 35
+LR fn, tp: 2, 8
+LR f1 score: 0.302
+LR cohens kappa score: 0.277
+LR average precision score: 0.367
+
+-> test with 'GB'
+GB tn, fp: 444, 12
+GB fn, tp: 5, 5
+GB f1 score: 0.370
+GB cohens kappa score: 0.353
+
+-> test with 'KNN'
+KNN tn, fp: 438, 18
+KNN fn, tp: 6, 4
+KNN f1 score: 0.250
+KNN cohens kappa score: 0.227
+
+### Exercise is done.
+
+-----[ LR ]-----
+maximum:
+LR tn, fp: 439, 46
+LR fn, tp: 6, 12
+LR f1 score: 0.421
+LR cohens kappa score: 0.397
+LR average precision score: 0.699
+
+
+average:
+LR tn, fp: 424.44, 31.56
+LR fn, tp: 2.76, 8.84
+LR f1 score: 0.340
+LR cohens kappa score: 0.314
+LR average precision score: 0.453
+
+
+minimum:
+LR tn, fp: 410, 17
+LR fn, tp: 0, 5
+LR f1 score: 0.245
+LR cohens kappa score: 0.214
+LR average precision score: 0.222
+
+
+-----[ GB ]-----
+maximum:
+GB tn, fp: 451, 21
+GB fn, tp: 10, 9
+GB f1 score: 0.600
+GB cohens kappa score: 0.587
+
+
+average:
+GB tn, fp: 445.28, 10.72
+GB fn, tp: 6.92, 4.68
+GB f1 score: 0.339
+GB cohens kappa score: 0.321
+
+
+minimum:
+GB tn, fp: 435, 5
+GB fn, tp: 3, 2
+GB f1 score: 0.143
+GB cohens kappa score: 0.117
+
+
+-----[ KNN ]-----
+maximum:
+KNN tn, fp: 446, 29
+KNN fn, tp: 9, 10
+KNN f1 score: 0.526
+KNN cohens kappa score: 0.509
+
+
+average:
+KNN tn, fp: 437.52, 18.48
+KNN fn, tp: 5.4, 6.2
+KNN f1 score: 0.337
+KNN cohens kappa score: 0.314
+
+
+minimum:
+KNN tn, fp: 427, 10
+KNN fn, tp: 2, 3
+KNN f1 score: 0.207
+KNN cohens kappa score: 0.184
+

data_result/ProWRAS/folding_car-vgood.csv

@@ -0,0 +1,92 @@
+LR
+Nr.;TN;TP;FN;FP;f1 score;cohens kappa score;average precision score
+1;295.000;13.000;0.000;38.000;0.406;0.368;0.356
+2;303.000;10.000;3.000;30.000;0.377;0.340;0.302
+3;290.000;13.000;0.000;43.000;0.377;0.336;0.389
+4;296.000;12.000;1.000;37.000;0.387;0.348;0.364
+5;304.000;11.000;2.000;27.000;0.431;0.397;0.441
+6;306.000;8.000;5.000;27.000;0.333;0.295;0.282
+7;283.000;13.000;0.000;50.000;0.342;0.298;0.370
+8;298.000;11.000;2.000;35.000;0.373;0.334;0.333
+9;299.000;13.000;0.000;34.000;0.433;0.398;0.285
+10;295.000;12.000;1.000;36.000;0.393;0.355;0.555
+11;299.000;12.000;1.000;34.000;0.407;0.370;0.307
+12;303.000;13.000;0.000;30.000;0.464;0.431;0.437
+13;289.000;13.000;0.000;44.000;0.371;0.330;0.321
+14;297.000;13.000;0.000;36.000;0.419;0.383;0.390
+15;299.000;10.000;3.000;32.000;0.364;0.325;0.366
+16;301.000;12.000;1.000;32.000;0.421;0.385;0.418
+17;294.000;12.000;1.000;39.000;0.375;0.335;0.515
+18;291.000;13.000;0.000;42.000;0.382;0.342;0.303
+19;299.000;11.000;2.000;34.000;0.379;0.341;0.268
+20;301.000;12.000;1.000;30.000;0.436;0.402;0.320
+21;286.000;13.000;0.000;47.000;0.356;0.314;0.291
+22;305.000;10.000;3.000;28.000;0.392;0.356;0.361
+23;307.000;11.000;2.000;26.000;0.440;0.407;0.334
+24;290.000;13.000;0.000;43.000;0.377;0.336;0.294
+25;297.000;13.000;0.000;34.000;0.433;0.398;0.539
+max;307.000;13.000;5.000;50.000;0.464;0.431;0.555
+avg;297.080;11.880;1.120;35.520;0.395;0.357;0.366
+min;283.000;8.000;0.000;26.000;0.333;0.295;0.268
+---
+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;12.000;1.000;0.000;0.960;0.959
+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;12.000;1.000;2.000;0.889;0.884
+6;332.000;13.000;0.000;1.000;0.963;0.961
+7;332.000;13.000;0.000;1.000;0.963;0.961
+8;333.000;13.000;0.000;0.000;1.000;1.000
+9;333.000;12.000;1.000;0.000;0.960;0.959
+10;331.000;13.000;0.000;0.000;1.000;1.000
+11;333.000;11.000;2.000;0.000;0.917;0.914
+12;333.000;13.000;0.000;0.000;1.000;1.000
+13;333.000;13.000;0.000;0.000;1.000;1.000
+14;333.000;13.000;0.000;0.000;1.000;1.000
+15;331.000;11.000;2.000;0.000;0.917;0.914
+16;333.000;13.000;0.000;0.000;1.000;1.000
+17;333.000;12.000;1.000;0.000;0.960;0.959
+18;332.000;13.000;0.000;1.000;0.963;0.961
+19;333.000;12.000;1.000;0.000;0.960;0.959
+20;331.000;13.000;0.000;0.000;1.000;1.000
+21;333.000;13.000;0.000;0.000;1.000;1.000
+22;333.000;11.000;2.000;0.000;0.917;0.914
+23;333.000;13.000;0.000;0.000;1.000;1.000
+24;333.000;13.000;0.000;0.000;1.000;1.000
+25;331.000;13.000;0.000;0.000;1.000;1.000
+max;333.000;13.000;2.000;2.000;1.000;1.000
+avg;332.400;12.520;0.480;0.200;0.973;0.972
+min;329.000;11.000;0.000;0.000;0.889;0.884
+---
+KNN
+Nr.;TN;TP;FN;FP;f1 score;cohens kappa score
+1;330.000;13.000;0.000;3.000;0.897;0.892
+2;326.000;10.000;3.000;7.000;0.667;0.652
+3;325.000;13.000;0.000;8.000;0.765;0.753
+4;331.000;12.000;1.000;2.000;0.889;0.884
+5;329.000;11.000;2.000;2.000;0.846;0.840
+6;326.000;13.000;0.000;7.000;0.788;0.778
+7;321.000;13.000;0.000;12.000;0.684;0.668
+8;328.000;10.000;3.000;5.000;0.714;0.702
+9;327.000;11.000;2.000;6.000;0.733;0.721
+10;329.000;13.000;0.000;2.000;0.929;0.926
+11;330.000;9.000;4.000;3.000;0.720;0.710
+12;331.000;12.000;1.000;2.000;0.889;0.884
+13;327.000;12.000;1.000;6.000;0.774;0.764
+14;329.000;13.000;0.000;4.000;0.867;0.861
+15;329.000;9.000;4.000;2.000;0.750;0.741
+16;328.000;11.000;2.000;5.000;0.759;0.748
+17;329.000;12.000;1.000;4.000;0.828;0.820
+18;329.000;12.000;1.000;4.000;0.828;0.820
+19;328.000;12.000;1.000;5.000;0.800;0.791
+20;324.000;13.000;0.000;7.000;0.788;0.778
+21;328.000;13.000;0.000;5.000;0.839;0.831
+22;329.000;12.000;1.000;4.000;0.828;0.820
+23;330.000;11.000;2.000;3.000;0.815;0.807
+24;327.000;13.000;0.000;6.000;0.813;0.804
+25;330.000;12.000;1.000;1.000;0.923;0.920
+max;331.000;13.000;4.000;12.000;0.929;0.926
+avg;328.000;11.800;1.200;4.600;0.805;0.797
+min;321.000;9.000;0.000;1.000;0.667;0.652

data_result/ProWRAS/folding_car-vgood.log

@@ -0,0 +1,701 @@
+
+
+///////////////////////////////////////////
+// Running ProWRAS 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
+-> create 1278 synthetic samples
+-> test with 'LR'
+LR tn, fp: 295, 38
+LR fn, tp: 0, 13
+LR f1 score: 0.406
+LR cohens kappa score: 0.368
+LR average precision score: 0.356
+
+-> 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: 0, 13
+KNN f1 score: 0.897
+KNN cohens kappa score: 0.892
+
+
+------ Step 1/5: Slice 2/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 1278 synthetic samples
+-> test with 'LR'
+LR tn, fp: 303, 30
+LR fn, tp: 3, 10
+LR f1 score: 0.377
+LR cohens kappa score: 0.340
+LR average precision score: 0.302
+
+-> 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: 326, 7
+KNN fn, tp: 3, 10
+KNN f1 score: 0.667
+KNN cohens kappa score: 0.652
+
+
+------ Step 1/5: Slice 3/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 1278 synthetic samples
+-> test with 'LR'
+LR tn, fp: 290, 43
+LR fn, tp: 0, 13
+LR f1 score: 0.377
+LR cohens kappa score: 0.336
+LR average precision score: 0.389
+
+-> 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: 325, 8
+KNN fn, tp: 0, 13
+KNN f1 score: 0.765
+KNN cohens kappa score: 0.753
+
+
+------ Step 1/5: Slice 4/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 1278 synthetic samples
+-> test with 'LR'
+LR tn, fp: 296, 37
+LR fn, tp: 1, 12
+LR f1 score: 0.387
+LR cohens kappa score: 0.348
+LR average precision score: 0.364
+
+-> 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: 331, 2
+KNN fn, tp: 1, 12
+KNN f1 score: 0.889
+KNN cohens kappa score: 0.884
+
+
+------ Step 1/5: Slice 5/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 1280 synthetic samples
+-> test with 'LR'
+LR tn, fp: 304, 27
+LR fn, tp: 2, 11
+LR f1 score: 0.431
+LR cohens kappa score: 0.397
+LR average precision score: 0.441
+
+-> 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: 329, 2
+KNN fn, tp: 2, 11
+KNN f1 score: 0.846
+KNN cohens kappa score: 0.840
+
+
+====== Step 2/5 =======
+-> Shuffling data
+-> Spliting data to slices
+
+------ Step 2/5: Slice 1/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 1278 synthetic samples
+-> test with 'LR'
+LR tn, fp: 306, 27
+LR fn, tp: 5, 8
+LR f1 score: 0.333
+LR cohens kappa score: 0.295
+LR average precision score: 0.282
+
+-> 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: 326, 7
+KNN fn, tp: 0, 13
+KNN f1 score: 0.788
+KNN cohens kappa score: 0.778
+
+
+------ Step 2/5: Slice 2/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 1278 synthetic samples
+-> test with 'LR'
+LR tn, fp: 283, 50
+LR fn, tp: 0, 13
+LR f1 score: 0.342
+LR cohens kappa score: 0.298
+LR average precision score: 0.370
+
+-> 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: 321, 12
+KNN fn, tp: 0, 13
+KNN f1 score: 0.684
+KNN cohens kappa score: 0.668
+
+
+------ Step 2/5: Slice 3/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 1278 synthetic samples
+-> test with 'LR'
+LR tn, fp: 298, 35
+LR fn, tp: 2, 11
+LR f1 score: 0.373
+LR cohens kappa score: 0.334
+LR average precision score: 0.333
+
+-> 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: 3, 10
+KNN f1 score: 0.714
+KNN cohens kappa score: 0.702
+
+
+------ Step 2/5: Slice 4/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 1278 synthetic samples
+-> test with 'LR'
+LR tn, fp: 299, 34
+LR fn, tp: 0, 13
+LR f1 score: 0.433
+LR cohens kappa score: 0.398
+LR average precision score: 0.285
+
+-> 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: 327, 6
+KNN fn, tp: 2, 11
+KNN f1 score: 0.733
+KNN cohens kappa score: 0.721
+
+
+------ Step 2/5: Slice 5/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 1280 synthetic samples
+-> test with 'LR'
+LR tn, fp: 295, 36
+LR fn, tp: 1, 12
+LR f1 score: 0.393
+LR cohens kappa score: 0.355
+LR average precision score: 0.555
+
+-> 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: 329, 2
+KNN fn, tp: 0, 13
+KNN f1 score: 0.929
+KNN cohens kappa score: 0.926
+
+
+====== Step 3/5 =======
+-> Shuffling data
+-> Spliting data to slices
+
+------ Step 3/5: Slice 1/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 1278 synthetic samples
+-> test with 'LR'
+LR tn, fp: 299, 34
+LR fn, tp: 1, 12
+LR f1 score: 0.407
+LR cohens kappa score: 0.370
+LR average precision score: 0.307
+
+-> 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 3/5: Slice 2/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 1278 synthetic samples
+-> test with 'LR'
+LR tn, fp: 303, 30
+LR fn, tp: 0, 13
+LR f1 score: 0.464
+LR cohens kappa score: 0.431
+LR average precision score: 0.437
+
+-> 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: 331, 2
+KNN fn, tp: 1, 12
+KNN f1 score: 0.889
+KNN cohens kappa score: 0.884
+
+
+------ Step 3/5: Slice 3/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 1278 synthetic samples
+-> test with 'LR'
+LR tn, fp: 289, 44
+LR fn, tp: 0, 13
+LR f1 score: 0.371
+LR cohens kappa score: 0.330
+LR average precision score: 0.321
+
+-> 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: 327, 6
+KNN fn, tp: 1, 12
+KNN f1 score: 0.774
+KNN cohens kappa score: 0.764
+
+
+------ Step 3/5: Slice 4/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 1278 synthetic samples
+-> test with 'LR'
+LR tn, fp: 297, 36
+LR fn, tp: 0, 13
+LR f1 score: 0.419
+LR cohens kappa score: 0.383
+LR average precision score: 0.390
+
+-> 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: 0, 13
+KNN f1 score: 0.867
+KNN cohens kappa score: 0.861
+
+
+------ Step 3/5: Slice 5/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 1280 synthetic samples
+-> test with 'LR'
+LR tn, fp: 299, 32
+LR fn, tp: 3, 10
+LR f1 score: 0.364
+LR cohens kappa score: 0.325
+LR average precision score: 0.366
+
+-> test with 'GB'
+GB tn, fp: 331, 0
+GB fn, tp: 2, 11
+GB f1 score: 0.917
+GB cohens kappa score: 0.914
+
+-> test with 'KNN'
+KNN tn, fp: 329, 2
+KNN fn, tp: 4, 9
+KNN f1 score: 0.750
+KNN cohens kappa score: 0.741
+
+
+====== Step 4/5 =======
+-> Shuffling data
+-> Spliting data to slices
+
+------ Step 4/5: Slice 1/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 1278 synthetic samples
+-> test with 'LR'
+LR tn, fp: 301, 32
+LR fn, tp: 1, 12
+LR f1 score: 0.421
+LR cohens kappa score: 0.385
+LR average precision score: 0.418
+
+-> 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: 2, 11
+KNN f1 score: 0.759
+KNN cohens kappa score: 0.748
+
+
+------ Step 4/5: Slice 2/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 1278 synthetic samples
+-> test with 'LR'
+LR tn, fp: 294, 39
+LR fn, tp: 1, 12
+LR f1 score: 0.375
+LR cohens kappa score: 0.335
+LR average precision score: 0.515
+
+-> 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: 329, 4
+KNN fn, tp: 1, 12
+KNN f1 score: 0.828
+KNN cohens kappa score: 0.820
+
+
+------ Step 4/5: Slice 3/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 1278 synthetic samples
+-> test with 'LR'
+LR tn, fp: 291, 42
+LR fn, tp: 0, 13
+LR f1 score: 0.382
+LR cohens kappa score: 0.342
+LR average precision score: 0.303
+
+-> 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: 329, 4
+KNN fn, tp: 1, 12
+KNN f1 score: 0.828
+KNN cohens kappa score: 0.820
+
+
+------ Step 4/5: Slice 4/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 1278 synthetic samples
+-> test with 'LR'
+LR tn, fp: 299, 34
+LR fn, tp: 2, 11
+LR f1 score: 0.379
+LR cohens kappa score: 0.341
+LR average precision score: 0.268
+
+-> 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: 328, 5
+KNN fn, tp: 1, 12
+KNN f1 score: 0.800
+KNN cohens kappa score: 0.791
+
+
+------ Step 4/5: Slice 5/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 1280 synthetic samples
+-> test with 'LR'
+LR tn, fp: 301, 30
+LR fn, tp: 1, 12
+LR f1 score: 0.436
+LR cohens kappa score: 0.402
+LR average precision score: 0.320
+
+-> 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: 324, 7
+KNN fn, tp: 0, 13
+KNN f1 score: 0.788
+KNN cohens kappa score: 0.778
+
+
+====== Step 5/5 =======
+-> Shuffling data
+-> Spliting data to slices
+
+------ Step 5/5: Slice 1/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 1278 synthetic samples
+-> test with 'LR'
+LR tn, fp: 286, 47
+LR fn, tp: 0, 13
+LR f1 score: 0.356
+LR cohens kappa score: 0.314
+LR average precision score: 0.291
+
+-> 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: 0, 13
+KNN f1 score: 0.839
+KNN cohens kappa score: 0.831
+
+
+------ Step 5/5: Slice 2/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 1278 synthetic samples
+-> test with 'LR'
+LR tn, fp: 305, 28
+LR fn, tp: 3, 10
+LR f1 score: 0.392
+LR cohens kappa score: 0.356
+LR average precision score: 0.361
+
+-> 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: 329, 4
+KNN fn, tp: 1, 12
+KNN f1 score: 0.828
+KNN cohens kappa score: 0.820
+
+
+------ Step 5/5: Slice 3/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 1278 synthetic samples
+-> test with 'LR'
+LR tn, fp: 307, 26
+LR fn, tp: 2, 11
+LR f1 score: 0.440
+LR cohens kappa score: 0.407
+LR average precision score: 0.334
+
+-> 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: 2, 11
+KNN f1 score: 0.815
+KNN cohens kappa score: 0.807
+
+
+------ Step 5/5: Slice 4/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 1278 synthetic samples
+-> test with 'LR'
+LR tn, fp: 290, 43
+LR fn, tp: 0, 13
+LR f1 score: 0.377
+LR cohens kappa score: 0.336
+LR average precision score: 0.294
+
+-> 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: 327, 6
+KNN fn, tp: 0, 13
+KNN f1 score: 0.813
+KNN cohens kappa score: 0.804
+
+
+------ Step 5/5: Slice 5/5 -------
+-> Reset the GAN
+-> Train generator for synthetic samples
+-> create 1280 synthetic samples
+-> test with 'LR'
+LR tn, fp: 297, 34
+LR fn, tp: 0, 13
+LR f1 score: 0.433
+LR cohens kappa score: 0.398
+LR average precision score: 0.539
+
+-> 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: 1, 12
+KNN f1 score: 0.923
+KNN cohens kappa score: 0.920
+
+### Exercise is done.
+
+-----[ LR ]-----
+maximum:
+LR tn, fp: 307, 50
+LR fn, tp: 5, 13
+LR f1 score: 0.464
+LR cohens kappa score: 0.431
+LR average precision score: 0.555
+
+
+average:
+LR tn, fp: 297.08, 35.52
+LR fn, tp: 1.12, 11.88
+LR f1 score: 0.395
+LR cohens kappa score: 0.357
+LR average precision score: 0.366
+
+
+minimum:
+LR tn, fp: 283, 26
+LR fn, tp: 0, 8
+LR f1 score: 0.333
+LR cohens kappa score: 0.295
+LR average precision score: 0.268
+
+
+-----[ GB ]-----
+maximum:
+GB tn, fp: 333, 2
+GB fn, tp: 2, 13
+GB f1 score: 1.000
+GB cohens kappa score: 1.000
+
+
+average:
+GB tn, fp: 332.4, 0.2
+GB fn, tp: 0.48, 12.52
+GB f1 score: 0.973
+GB cohens kappa score: 0.972
+
+
+minimum:
+GB tn, fp: 329, 0
+GB fn, tp: 0, 11
+GB f1 score: 0.889
+GB cohens kappa score: 0.884
+
+
+-----[ KNN ]-----
+maximum:
+KNN tn, fp: 331, 12
+KNN fn, tp: 4, 13
+KNN f1 score: 0.929
+KNN cohens kappa score: 0.926
+
+
+average:
+KNN tn, fp: 328.0, 4.6
+KNN fn, tp: 1.2, 11.8
+KNN f1 score: 0.805
+KNN cohens kappa score: 0.797
+
+
+minimum:
+KNN tn, fp: 321, 1
+KNN fn, tp: 0, 9
+KNN f1 score: 0.667
+KNN cohens kappa score: 0.652
+