ConvGeNBenchmarkResults/data_result/convGAN-majority-5/folding_kddcup-guess_passwd_vs_satan.log

///////////////////////////////////////////
// Running convGAN-majority-5 on folding_kddcup-guess_passwd_vs_satan
///////////////////////////////////////////

Load 'data_input/folding_kddcup-guess_passwd_vs_satan'
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 1229 synthetic samples
-> test with GAN.predict
GAN tn, fp: 318, 0
GAN fn, tp: 0, 11
GAN f1 score: 1.000
GAN cohens kappa score: 1.000

-> test with 'LR'
LR tn, fp: 318, 0
LR fn, tp: 0, 11
LR f1 score: 1.000
LR cohens kappa score: 1.000
LR average precision score: 1.000

-> test with 'GB'
GB tn, fp: 318, 0
GB fn, tp: 0, 11
GB f1 score: 1.000
GB cohens kappa score: 1.000

-> test with 'KNN'
KNN tn, fp: 318, 0
KNN fn, tp: 0, 11
KNN f1 score: 1.000
KNN cohens kappa score: 1.000


------ Step 1/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1229 synthetic samples
-> test with GAN.predict
GAN tn, fp: 318, 0
GAN fn, tp: 0, 11
GAN f1 score: 1.000
GAN cohens kappa score: 1.000

-> test with 'LR'
LR tn, fp: 318, 0
LR fn, tp: 0, 11
LR f1 score: 1.000
LR cohens kappa score: 1.000
LR average precision score: 1.000

-> test with 'GB'
GB tn, fp: 318, 0
GB fn, tp: 0, 11
GB f1 score: 1.000
GB cohens kappa score: 1.000

-> test with 'KNN'
KNN tn, fp: 318, 0
KNN fn, tp: 0, 11
KNN f1 score: 1.000
KNN cohens kappa score: 1.000


------ Step 1/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1229 synthetic samples
-> test with GAN.predict
GAN tn, fp: 318, 0
GAN fn, tp: 0, 11
GAN f1 score: 1.000
GAN cohens kappa score: 1.000

-> test with 'LR'
LR tn, fp: 318, 0
LR fn, tp: 0, 11
LR f1 score: 1.000
LR cohens kappa score: 1.000
LR average precision score: 1.000

-> test with 'GB'
GB tn, fp: 318, 0
GB fn, tp: 0, 11
GB f1 score: 1.000
GB cohens kappa score: 1.000

-> test with 'KNN'
KNN tn, fp: 317, 1
KNN fn, tp: 0, 11
KNN f1 score: 0.957
KNN cohens kappa score: 0.955


------ Step 1/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1229 synthetic samples
-> test with GAN.predict
GAN tn, fp: 318, 0
GAN fn, tp: 0, 11
GAN f1 score: 1.000
GAN cohens kappa score: 1.000

-> test with 'LR'
LR tn, fp: 318, 0
LR fn, tp: 0, 11
LR f1 score: 1.000
LR cohens kappa score: 1.000
LR average precision score: 1.000

-> test with 'GB'
GB tn, fp: 318, 0
GB fn, tp: 0, 11
GB f1 score: 1.000
GB cohens kappa score: 1.000

-> test with 'KNN'
KNN tn, fp: 317, 1
KNN fn, tp: 0, 11
KNN f1 score: 0.957
KNN cohens kappa score: 0.955


------ Step 1/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1228 synthetic samples
-> test with GAN.predict
GAN tn, fp: 317, 0
GAN fn, tp: 0, 9
GAN f1 score: 1.000
GAN cohens kappa score: 1.000

-> test with 'LR'
LR tn, fp: 317, 0
LR fn, tp: 0, 9
LR f1 score: 1.000
LR cohens kappa score: 1.000
LR average precision score: 1.000

-> test with 'GB'
GB tn, fp: 317, 0
GB fn, tp: 0, 9
GB f1 score: 1.000
GB cohens kappa score: 1.000

-> test with 'KNN'
KNN tn, fp: 317, 0
KNN fn, tp: 0, 9
KNN f1 score: 1.000
KNN cohens kappa score: 1.000


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

------ Step 2/5: Slice 1/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1229 synthetic samples
-> test with GAN.predict
GAN tn, fp: 318, 0
GAN fn, tp: 0, 11
GAN f1 score: 1.000
GAN cohens kappa score: 1.000

-> test with 'LR'
LR tn, fp: 318, 0
LR fn, tp: 0, 11
LR f1 score: 1.000
LR cohens kappa score: 1.000
LR average precision score: 1.000

-> test with 'GB'
GB tn, fp: 318, 0
GB fn, tp: 0, 11
GB f1 score: 1.000
GB cohens kappa score: 1.000

-> test with 'KNN'
KNN tn, fp: 318, 0
KNN fn, tp: 0, 11
KNN f1 score: 1.000
KNN cohens kappa score: 1.000


------ Step 2/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1229 synthetic samples
-> test with GAN.predict
GAN tn, fp: 318, 0
GAN fn, tp: 0, 11
GAN f1 score: 1.000
GAN cohens kappa score: 1.000

-> test with 'LR'
LR tn, fp: 317, 1
LR fn, tp: 0, 11
LR f1 score: 0.957
LR cohens kappa score: 0.955
LR average precision score: 0.917

-> test with 'GB'
GB tn, fp: 318, 0
GB fn, tp: 0, 11
GB f1 score: 1.000
GB cohens kappa score: 1.000

-> test with 'KNN'
KNN tn, fp: 317, 1
KNN fn, tp: 0, 11
KNN f1 score: 0.957
KNN cohens kappa score: 0.955


------ Step 2/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1229 synthetic samples
-> test with GAN.predict
GAN tn, fp: 318, 0
GAN fn, tp: 0, 11
GAN f1 score: 1.000
GAN cohens kappa score: 1.000

-> test with 'LR'
LR tn, fp: 318, 0
LR fn, tp: 0, 11
LR f1 score: 1.000
LR cohens kappa score: 1.000
LR average precision score: 1.000

-> test with 'GB'
GB tn, fp: 318, 0
GB fn, tp: 0, 11
GB f1 score: 1.000
GB cohens kappa score: 1.000

-> test with 'KNN'
KNN tn, fp: 317, 1
KNN fn, tp: 0, 11
KNN f1 score: 0.957
KNN cohens kappa score: 0.955


------ Step 2/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1229 synthetic samples
-> test with GAN.predict
GAN tn, fp: 318, 0
GAN fn, tp: 0, 11
GAN f1 score: 1.000
GAN cohens kappa score: 1.000

-> test with 'LR'
LR tn, fp: 318, 0
LR fn, tp: 0, 11
LR f1 score: 1.000
LR cohens kappa score: 1.000
LR average precision score: 1.000

-> test with 'GB'
GB tn, fp: 318, 0
GB fn, tp: 0, 11
GB f1 score: 1.000
GB cohens kappa score: 1.000

-> test with 'KNN'
KNN tn, fp: 318, 0
KNN fn, tp: 0, 11
KNN f1 score: 1.000
KNN cohens kappa score: 1.000


------ Step 2/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1228 synthetic samples
-> test with GAN.predict
GAN tn, fp: 316, 1
GAN fn, tp: 0, 9
GAN f1 score: 0.947
GAN cohens kappa score: 0.946

-> test with 'LR'
LR tn, fp: 317, 0
LR fn, tp: 0, 9
LR f1 score: 1.000
LR cohens kappa score: 1.000
LR average precision score: 1.000

-> test with 'GB'
GB tn, fp: 317, 0
GB fn, tp: 0, 9
GB f1 score: 1.000
GB cohens kappa score: 1.000

-> test with 'KNN'
KNN tn, fp: 317, 0
KNN fn, tp: 0, 9
KNN f1 score: 1.000
KNN cohens kappa score: 1.000


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

------ Step 3/5: Slice 1/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1229 synthetic samples
-> test with GAN.predict
GAN tn, fp: 317, 1
GAN fn, tp: 0, 11
GAN f1 score: 0.957
GAN cohens kappa score: 0.955

-> test with 'LR'
LR tn, fp: 317, 1
LR fn, tp: 0, 11
LR f1 score: 0.957
LR cohens kappa score: 0.955
LR average precision score: 0.917

-> test with 'GB'
GB tn, fp: 318, 0
GB fn, tp: 0, 11
GB f1 score: 1.000
GB cohens kappa score: 1.000

-> test with 'KNN'
KNN tn, fp: 317, 1
KNN fn, tp: 0, 11
KNN f1 score: 0.957
KNN cohens kappa score: 0.955


------ Step 3/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1229 synthetic samples
-> test with GAN.predict
GAN tn, fp: 317, 1
GAN fn, tp: 0, 11
GAN f1 score: 0.957
GAN cohens kappa score: 0.955

-> test with 'LR'
LR tn, fp: 316, 2
LR fn, tp: 0, 11
LR f1 score: 0.917
LR cohens kappa score: 0.914
LR average precision score: 1.000

-> test with 'GB'
GB tn, fp: 318, 0
GB fn, tp: 0, 11
GB f1 score: 1.000
GB cohens kappa score: 1.000

-> test with 'KNN'
KNN tn, fp: 316, 2
KNN fn, tp: 0, 11
KNN f1 score: 0.917
KNN cohens kappa score: 0.914


------ Step 3/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1229 synthetic samples
-> test with GAN.predict
GAN tn, fp: 318, 0
GAN fn, tp: 0, 11
GAN f1 score: 1.000
GAN cohens kappa score: 1.000

-> test with 'LR'
LR tn, fp: 318, 0
LR fn, tp: 0, 11
LR f1 score: 1.000
LR cohens kappa score: 1.000
LR average precision score: 1.000

-> test with 'GB'
GB tn, fp: 318, 0
GB fn, tp: 0, 11
GB f1 score: 1.000
GB cohens kappa score: 1.000

-> test with 'KNN'
KNN tn, fp: 318, 0
KNN fn, tp: 0, 11
KNN f1 score: 1.000
KNN cohens kappa score: 1.000


------ Step 3/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1229 synthetic samples
-> test with GAN.predict
GAN tn, fp: 318, 0
GAN fn, tp: 0, 11
GAN f1 score: 1.000
GAN cohens kappa score: 1.000

-> test with 'LR'
LR tn, fp: 318, 0
LR fn, tp: 0, 11
LR f1 score: 1.000
LR cohens kappa score: 1.000
LR average precision score: 1.000

-> test with 'GB'
GB tn, fp: 318, 0
GB fn, tp: 0, 11
GB f1 score: 1.000
GB cohens kappa score: 1.000

-> test with 'KNN'
KNN tn, fp: 318, 0
KNN fn, tp: 0, 11
KNN f1 score: 1.000
KNN cohens kappa score: 1.000


------ Step 3/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1228 synthetic samples
-> test with GAN.predict
GAN tn, fp: 317, 0
GAN fn, tp: 0, 9
GAN f1 score: 1.000
GAN cohens kappa score: 1.000

-> test with 'LR'
LR tn, fp: 317, 0
LR fn, tp: 0, 9
LR f1 score: 1.000
LR cohens kappa score: 1.000
LR average precision score: 1.000

-> test with 'GB'
GB tn, fp: 317, 0
GB fn, tp: 0, 9
GB f1 score: 1.000
GB cohens kappa score: 1.000

-> test with 'KNN'
KNN tn, fp: 317, 0
KNN fn, tp: 0, 9
KNN f1 score: 1.000
KNN cohens kappa score: 1.000


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

------ Step 4/5: Slice 1/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1229 synthetic samples
-> test with GAN.predict
GAN tn, fp: 317, 1
GAN fn, tp: 0, 11
GAN f1 score: 0.957
GAN cohens kappa score: 0.955

-> test with 'LR'
LR tn, fp: 318, 0
LR fn, tp: 0, 11
LR f1 score: 1.000
LR cohens kappa score: 1.000
LR average precision score: 1.000

-> test with 'GB'
GB tn, fp: 318, 0
GB fn, tp: 0, 11
GB f1 score: 1.000
GB cohens kappa score: 1.000

-> test with 'KNN'
KNN tn, fp: 318, 0
KNN fn, tp: 0, 11
KNN f1 score: 1.000
KNN cohens kappa score: 1.000


------ Step 4/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1229 synthetic samples
-> test with GAN.predict
GAN tn, fp: 318, 0
GAN fn, tp: 0, 11
GAN f1 score: 1.000
GAN cohens kappa score: 1.000

-> test with 'LR'
LR tn, fp: 318, 0
LR fn, tp: 0, 11
LR f1 score: 1.000
LR cohens kappa score: 1.000
LR average precision score: 1.000

-> test with 'GB'
GB tn, fp: 318, 0
GB fn, tp: 0, 11
GB f1 score: 1.000
GB cohens kappa score: 1.000

-> test with 'KNN'
KNN tn, fp: 317, 1
KNN fn, tp: 0, 11
KNN f1 score: 0.957
KNN cohens kappa score: 0.955


------ Step 4/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1229 synthetic samples
-> test with GAN.predict
GAN tn, fp: 315, 3
GAN fn, tp: 0, 11
GAN f1 score: 0.880
GAN cohens kappa score: 0.875

-> test with 'LR'
LR tn, fp: 318, 0
LR fn, tp: 0, 11
LR f1 score: 1.000
LR cohens kappa score: 1.000
LR average precision score: 1.000

-> test with 'GB'
GB tn, fp: 318, 0
GB fn, tp: 0, 11
GB f1 score: 1.000
GB cohens kappa score: 1.000

-> test with 'KNN'
KNN tn, fp: 318, 0
KNN fn, tp: 0, 11
KNN f1 score: 1.000
KNN cohens kappa score: 1.000


------ Step 4/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1229 synthetic samples
-> test with GAN.predict
GAN tn, fp: 318, 0
GAN fn, tp: 0, 11
GAN f1 score: 1.000
GAN cohens kappa score: 1.000

-> test with 'LR'
LR tn, fp: 318, 0
LR fn, tp: 0, 11
LR f1 score: 1.000
LR cohens kappa score: 1.000
LR average precision score: 1.000

-> test with 'GB'
GB tn, fp: 318, 0
GB fn, tp: 0, 11
GB f1 score: 1.000
GB cohens kappa score: 1.000

-> test with 'KNN'
KNN tn, fp: 318, 0
KNN fn, tp: 0, 11
KNN f1 score: 1.000
KNN cohens kappa score: 1.000


------ Step 4/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1228 synthetic samples
-> test with GAN.predict
GAN tn, fp: 316, 1
GAN fn, tp: 0, 9
GAN f1 score: 0.947
GAN cohens kappa score: 0.946

-> test with 'LR'
LR tn, fp: 317, 0
LR fn, tp: 0, 9
LR f1 score: 1.000
LR cohens kappa score: 1.000
LR average precision score: 1.000

-> test with 'GB'
GB tn, fp: 317, 0
GB fn, tp: 0, 9
GB f1 score: 1.000
GB cohens kappa score: 1.000

-> test with 'KNN'
KNN tn, fp: 316, 1
KNN fn, tp: 0, 9
KNN f1 score: 0.947
KNN cohens kappa score: 0.946


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

------ Step 5/5: Slice 1/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1229 synthetic samples
-> test with GAN.predict
GAN tn, fp: 318, 0
GAN fn, tp: 0, 11
GAN f1 score: 1.000
GAN cohens kappa score: 1.000

-> test with 'LR'
LR tn, fp: 317, 1
LR fn, tp: 0, 11
LR f1 score: 0.957
LR cohens kappa score: 0.955
LR average precision score: 1.000

-> test with 'GB'
GB tn, fp: 318, 0
GB fn, tp: 0, 11
GB f1 score: 1.000
GB cohens kappa score: 1.000

-> test with 'KNN'
KNN tn, fp: 317, 1
KNN fn, tp: 0, 11
KNN f1 score: 0.957
KNN cohens kappa score: 0.955


------ Step 5/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1229 synthetic samples
-> test with GAN.predict
GAN tn, fp: 318, 0
GAN fn, tp: 0, 11
GAN f1 score: 1.000
GAN cohens kappa score: 1.000

-> test with 'LR'
LR tn, fp: 318, 0
LR fn, tp: 0, 11
LR f1 score: 1.000
LR cohens kappa score: 1.000
LR average precision score: 1.000

-> test with 'GB'
GB tn, fp: 318, 0
GB fn, tp: 0, 11
GB f1 score: 1.000
GB cohens kappa score: 1.000

-> test with 'KNN'
KNN tn, fp: 318, 0
KNN fn, tp: 0, 11
KNN f1 score: 1.000
KNN cohens kappa score: 1.000


------ Step 5/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1229 synthetic samples
-> test with GAN.predict
GAN tn, fp: 318, 0
GAN fn, tp: 0, 11
GAN f1 score: 1.000
GAN cohens kappa score: 1.000

-> test with 'LR'
LR tn, fp: 318, 0
LR fn, tp: 0, 11
LR f1 score: 1.000
LR cohens kappa score: 1.000
LR average precision score: 1.000

-> test with 'GB'
GB tn, fp: 318, 0
GB fn, tp: 0, 11
GB f1 score: 1.000
GB cohens kappa score: 1.000

-> test with 'KNN'
KNN tn, fp: 318, 0
KNN fn, tp: 0, 11
KNN f1 score: 1.000
KNN cohens kappa score: 1.000


------ Step 5/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1229 synthetic samples
-> test with GAN.predict
GAN tn, fp: 318, 0
GAN fn, tp: 0, 11
GAN f1 score: 1.000
GAN cohens kappa score: 1.000

-> test with 'LR'
LR tn, fp: 318, 0
LR fn, tp: 0, 11
LR f1 score: 1.000
LR cohens kappa score: 1.000
LR average precision score: 1.000

-> test with 'GB'
GB tn, fp: 318, 0
GB fn, tp: 0, 11
GB f1 score: 1.000
GB cohens kappa score: 1.000

-> test with 'KNN'
KNN tn, fp: 318, 0
KNN fn, tp: 0, 11
KNN f1 score: 1.000
KNN cohens kappa score: 1.000


------ Step 5/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples
-> create 1228 synthetic samples
-> test with GAN.predict
GAN tn, fp: 316, 1
GAN fn, tp: 0, 9
GAN f1 score: 0.947
GAN cohens kappa score: 0.946

-> test with 'LR'
LR tn, fp: 317, 0
LR fn, tp: 0, 9
LR f1 score: 1.000
LR cohens kappa score: 1.000
LR average precision score: 1.000

-> test with 'GB'
GB tn, fp: 317, 0
GB fn, tp: 0, 9
GB f1 score: 1.000
GB cohens kappa score: 1.000

-> test with 'KNN'
KNN tn, fp: 317, 0
KNN fn, tp: 0, 9
KNN f1 score: 1.000
KNN cohens kappa score: 1.000

### Exercise is done.

-----[ LR ]-----
maximum:
LR tn, fp: 318, 2
LR fn, tp: 0, 11
LR f1 score: 1.000
LR cohens kappa score: 1.000
LR average precision score: 1.000


average:
LR tn, fp: 317.6, 0.2
LR fn, tp: 0.0, 10.6
LR f1 score: 0.991
LR cohens kappa score: 0.991
LR average precision score: 0.993


minimum:
LR tn, fp: 316, 0
LR fn, tp: 0, 9
LR f1 score: 0.917
LR cohens kappa score: 0.914
LR average precision score: 0.917


-----[ GB ]-----
maximum:
GB tn, fp: 318, 0
GB fn, tp: 0, 11
GB f1 score: 1.000
GB cohens kappa score: 1.000


average:
GB tn, fp: 317.8, 0.0
GB fn, tp: 0.0, 10.6
GB f1 score: 1.000
GB cohens kappa score: 1.000


minimum:
GB tn, fp: 317, 0
GB fn, tp: 0, 9
GB f1 score: 1.000
GB cohens kappa score: 1.000


-----[ KNN ]-----
maximum:
KNN tn, fp: 318, 2
KNN fn, tp: 0, 11
KNN f1 score: 1.000
KNN cohens kappa score: 1.000


average:
KNN tn, fp: 317.4, 0.4
KNN fn, tp: 0.0, 10.6
KNN f1 score: 0.982
KNN cohens kappa score: 0.982


minimum:
KNN tn, fp: 316, 0
KNN fn, tp: 0, 9
KNN f1 score: 0.917
KNN cohens kappa score: 0.914


-----[ GAN ]-----
maximum:
GAN tn, fp: 318, 3
GAN fn, tp: 0, 11
GAN f1 score: 1.000
GAN cohens kappa score: 1.000


average:
GAN tn, fp: 317.44, 0.36
GAN fn, tp: 0.0, 10.6
GAN f1 score: 0.984
GAN cohens kappa score: 0.983


minimum:
GAN tn, fp: 315, 0
GAN fn, tp: 0, 9
GAN f1 score: 0.880
GAN cohens kappa score: 0.875