/////////////////////////////////////////// // Running SpheredNoise on folding_winequality-red-4 /////////////////////////////////////////// Load 'data_input/folding_winequality-red-4' 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 1236/42 points -> new disc -> calc distances -> statistics trained 42 points min:0.542887025540305 max:13.164740804983591 -> create 1194 synthetic samples -> test with 'LR' LR tn, fp: 309, 1 LR fn, tp: 11, 0 LR f1 score: 0.000 LR cohens kappa score: -0.006 LR average precision score: 0.160 -> test with 'GB' GB tn, fp: 308, 2 GB fn, tp: 10, 1 GB f1 score: 0.143 GB cohens kappa score: 0.130 -> test with 'KNN' KNN tn, fp: 310, 0 KNN fn, tp: 11, 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 1236/42 points -> new disc -> calc distances -> statistics trained 42 points min:0.542887025540305 max:13.364807372932841 -> create 1194 synthetic samples -> test with 'LR' LR tn, fp: 309, 1 LR fn, tp: 11, 0 LR f1 score: 0.000 LR cohens kappa score: -0.006 LR average precision score: 0.134 -> test with 'GB' GB tn, fp: 308, 2 GB fn, tp: 11, 0 GB f1 score: 0.000 GB cohens kappa score: -0.011 -> test with 'KNN' KNN tn, fp: 310, 0 KNN fn, tp: 11, 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 1236/42 points -> new disc -> calc distances -> statistics trained 42 points min:0.7107056774783781 max:13.164740804983591 -> create 1194 synthetic samples -> test with 'LR' LR tn, fp: 310, 0 LR fn, tp: 11, 0 LR f1 score: 0.000 LR cohens kappa score: 0.000 LR average precision score: 0.183 -> test with 'GB' GB tn, fp: 307, 3 GB fn, tp: 11, 0 GB f1 score: 0.000 GB cohens kappa score: -0.015 -> test with 'KNN' KNN tn, fp: 310, 0 KNN fn, tp: 11, 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 1236/42 points -> new disc -> calc distances -> statistics trained 42 points min:0.542887025540305 max:3.9618467065751037 -> create 1194 synthetic samples -> test with 'LR' LR tn, fp: 308, 2 LR fn, tp: 10, 1 LR f1 score: 0.143 LR cohens kappa score: 0.130 LR average precision score: 0.113 -> test with 'GB' GB tn, fp: 307, 3 GB fn, tp: 10, 1 GB f1 score: 0.133 GB cohens kappa score: 0.117 -> test with 'KNN' KNN tn, fp: 310, 0 KNN fn, tp: 11, 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 1240/44 points -> new disc -> calc distances -> statistics trained 44 points min:0.7107056774783781 max:13.164740804983591 -> create 1196 synthetic samples -> test with 'LR' LR tn, fp: 306, 0 LR fn, tp: 8, 1 LR f1 score: 0.200 LR cohens kappa score: 0.195 LR average precision score: 0.218 -> test with 'GB' GB tn, fp: 306, 0 GB fn, tp: 8, 1 GB f1 score: 0.200 GB cohens kappa score: 0.195 -> test with 'KNN' KNN tn, fp: 306, 0 KNN fn, tp: 9, 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 1236/42 points -> new disc -> calc distances -> statistics trained 42 points min:0.542887025540305 max:13.364807372932841 -> create 1194 synthetic samples -> test with 'LR' LR tn, fp: 309, 1 LR fn, tp: 10, 1 LR f1 score: 0.154 LR cohens kappa score: 0.145 LR average precision score: 0.145 -> test with 'GB' GB tn, fp: 307, 3 GB fn, tp: 10, 1 GB f1 score: 0.133 GB cohens kappa score: 0.117 -> test with 'KNN' KNN tn, fp: 310, 0 KNN fn, tp: 11, 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 1236/42 points -> new disc -> calc distances -> statistics trained 42 points min:0.9743119738564242 max:13.164740804983591 -> create 1194 synthetic samples -> test with 'LR' LR tn, fp: 310, 0 LR fn, tp: 11, 0 LR f1 score: 0.000 LR cohens kappa score: 0.000 LR average precision score: 0.135 -> test with 'GB' GB tn, fp: 307, 3 GB fn, tp: 11, 0 GB f1 score: 0.000 GB cohens kappa score: -0.015 -> test with 'KNN' KNN tn, fp: 310, 0 KNN fn, tp: 11, 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 1236/42 points -> new disc -> calc distances -> statistics trained 42 points min:0.6050000330578494 max:5.15715300079414 -> create 1194 synthetic samples -> test with 'LR' LR tn, fp: 308, 2 LR fn, tp: 11, 0 LR f1 score: 0.000 LR cohens kappa score: -0.011 LR average precision score: 0.126 -> test with 'GB' GB tn, fp: 307, 3 GB fn, tp: 11, 0 GB f1 score: 0.000 GB cohens kappa score: -0.015 -> test with 'KNN' KNN tn, fp: 310, 0 KNN fn, tp: 11, 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 1236/42 points -> new disc -> calc distances -> statistics trained 42 points min:0.542887025540305 max:13.164740804983591 -> create 1194 synthetic samples -> test with 'LR' LR tn, fp: 310, 0 LR fn, tp: 11, 0 LR f1 score: 0.000 LR cohens kappa score: 0.000 LR average precision score: 0.259 -> test with 'GB' GB tn, fp: 310, 0 GB fn, tp: 11, 0 GB f1 score: 0.000 GB cohens kappa score: 0.000 -> test with 'KNN' KNN tn, fp: 310, 0 KNN fn, tp: 11, 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 1240/44 points -> new disc -> calc distances -> statistics trained 44 points min:0.542887025540305 max:13.164740804983591 -> create 1196 synthetic samples -> test with 'LR' LR tn, fp: 305, 1 LR fn, tp: 9, 0 LR f1 score: 0.000 LR cohens kappa score: -0.006 LR average precision score: 0.091 -> test with 'GB' GB tn, fp: 301, 5 GB fn, tp: 8, 1 GB f1 score: 0.133 GB cohens kappa score: 0.113 -> test with 'KNN' KNN tn, fp: 306, 0 KNN fn, tp: 9, 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 1236/42 points -> new disc -> calc distances -> statistics trained 42 points min:0.542887025540305 max:13.164740804983591 -> create 1194 synthetic samples -> test with 'LR' LR tn, fp: 309, 1 LR fn, tp: 11, 0 LR f1 score: 0.000 LR cohens kappa score: -0.006 LR average precision score: 0.168 -> test with 'GB' GB tn, fp: 309, 1 GB fn, tp: 10, 1 GB f1 score: 0.154 GB cohens kappa score: 0.145 -> test with 'KNN' KNN tn, fp: 310, 0 KNN fn, tp: 11, 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 1236/42 points -> new disc -> calc distances -> statistics trained 42 points min:0.6050000330578494 max:13.164740804983591 -> create 1194 synthetic samples -> test with 'LR' LR tn, fp: 310, 0 LR fn, tp: 11, 0 LR f1 score: 0.000 LR cohens kappa score: 0.000 LR average precision score: 0.208 -> test with 'GB' GB tn, fp: 308, 2 GB fn, tp: 11, 0 GB f1 score: 0.000 GB cohens kappa score: -0.011 -> test with 'KNN' KNN tn, fp: 310, 0 KNN fn, tp: 11, 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 1236/42 points -> new disc -> calc distances -> statistics trained 42 points min:0.542887025540305 max:8.006020815261474 -> create 1194 synthetic samples -> test with 'LR' LR tn, fp: 308, 2 LR fn, tp: 11, 0 LR f1 score: 0.000 LR cohens kappa score: -0.011 LR average precision score: 0.079 -> test with 'GB' GB tn, fp: 305, 5 GB fn, tp: 11, 0 GB f1 score: 0.000 GB cohens kappa score: -0.022 -> test with 'KNN' KNN tn, fp: 310, 0 KNN fn, tp: 11, 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 1236/42 points -> new disc -> calc distances -> statistics trained 42 points min:0.7107056774783781 max:13.164740804983591 -> create 1194 synthetic samples -> test with 'LR' LR tn, fp: 309, 1 LR fn, tp: 11, 0 LR f1 score: 0.000 LR cohens kappa score: -0.006 LR average precision score: 0.169 -> test with 'GB' GB tn, fp: 309, 1 GB fn, tp: 11, 0 GB f1 score: 0.000 GB cohens kappa score: -0.006 -> test with 'KNN' KNN tn, fp: 310, 0 KNN fn, tp: 11, 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 1240/44 points -> new disc -> calc distances -> statistics trained 44 points min:0.542887025540305 max:13.364807372932841 -> create 1196 synthetic samples -> test with 'LR' LR tn, fp: 306, 0 LR fn, tp: 9, 0 LR f1 score: 0.000 LR cohens kappa score: 0.000 LR average precision score: 0.104 -> test with 'GB' GB tn, fp: 305, 1 GB fn, tp: 8, 1 GB f1 score: 0.182 GB cohens kappa score: 0.173 -> test with 'KNN' KNN tn, fp: 306, 0 KNN fn, tp: 9, 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 1236/42 points -> new disc -> calc distances -> statistics trained 42 points min:0.8812840168753769 max:13.164740804983591 -> create 1194 synthetic samples -> test with 'LR' LR tn, fp: 310, 0 LR fn, tp: 11, 0 LR f1 score: 0.000 LR cohens kappa score: 0.000 LR average precision score: 0.344 -> test with 'GB' GB tn, fp: 309, 1 GB fn, tp: 10, 1 GB f1 score: 0.154 GB cohens kappa score: 0.145 -> test with 'KNN' KNN tn, fp: 310, 0 KNN fn, tp: 11, 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 1236/42 points -> new disc -> calc distances -> statistics trained 42 points min:0.542887025540305 max:5.15715300079414 -> create 1194 synthetic samples -> test with 'LR' LR tn, fp: 309, 1 LR fn, tp: 11, 0 LR f1 score: 0.000 LR cohens kappa score: -0.006 LR average precision score: 0.184 -> test with 'GB' GB tn, fp: 309, 1 GB fn, tp: 10, 1 GB f1 score: 0.154 GB cohens kappa score: 0.145 -> test with 'KNN' KNN tn, fp: 310, 0 KNN fn, tp: 11, 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 1236/42 points -> new disc -> calc distances -> statistics trained 42 points min:0.542887025540305 max:13.164740804983591 -> create 1194 synthetic samples -> test with 'LR' LR tn, fp: 310, 0 LR fn, tp: 11, 0 LR f1 score: 0.000 LR cohens kappa score: 0.000 LR average precision score: 0.102 -> test with 'GB' GB tn, fp: 308, 2 GB fn, tp: 11, 0 GB f1 score: 0.000 GB cohens kappa score: -0.011 -> test with 'KNN' KNN tn, fp: 310, 0 KNN fn, tp: 11, 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 1236/42 points -> new disc -> calc distances -> statistics trained 42 points min:0.542887025540305 max:13.164740804983591 -> create 1194 synthetic samples -> test with 'LR' LR tn, fp: 306, 4 LR fn, tp: 10, 1 LR f1 score: 0.125 LR cohens kappa score: 0.106 LR average precision score: 0.143 -> test with 'GB' GB tn, fp: 308, 2 GB fn, tp: 11, 0 GB f1 score: 0.000 GB cohens kappa score: -0.011 -> test with 'KNN' KNN tn, fp: 310, 0 KNN fn, tp: 11, 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 1240/44 points -> new disc -> calc distances -> statistics trained 44 points min:0.542887025540305 max:13.364807372932841 -> create 1196 synthetic samples -> test with 'LR' LR tn, fp: 306, 0 LR fn, tp: 9, 0 LR f1 score: 0.000 LR cohens kappa score: 0.000 LR average precision score: 0.048 -> test with 'GB' GB tn, fp: 303, 3 GB fn, tp: 9, 0 GB f1 score: 0.000 GB cohens kappa score: -0.014 -> test with 'KNN' KNN tn, fp: 306, 0 KNN fn, tp: 9, 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 1236/42 points -> new disc -> calc distances -> statistics trained 42 points min:0.542887025540305 max:5.15715300079414 -> create 1194 synthetic samples -> test with 'LR' LR tn, fp: 309, 1 LR fn, tp: 11, 0 LR f1 score: 0.000 LR cohens kappa score: -0.006 LR average precision score: 0.084 -> test with 'GB' GB tn, fp: 308, 2 GB fn, tp: 11, 0 GB f1 score: 0.000 GB cohens kappa score: -0.011 -> test with 'KNN' KNN tn, fp: 310, 0 KNN fn, tp: 11, 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 1236/42 points -> new disc -> calc distances -> statistics trained 42 points min:0.6050000330578494 max:13.164740804983591 -> create 1194 synthetic samples -> test with 'LR' LR tn, fp: 310, 0 LR fn, tp: 11, 0 LR f1 score: 0.000 LR cohens kappa score: 0.000 LR average precision score: 0.096 -> test with 'GB' GB tn, fp: 308, 2 GB fn, tp: 10, 1 GB f1 score: 0.143 GB cohens kappa score: 0.130 -> test with 'KNN' KNN tn, fp: 310, 0 KNN fn, tp: 11, 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 1236/42 points -> new disc -> calc distances -> statistics trained 42 points min:0.542887025540305 max:13.164740804983591 -> create 1194 synthetic samples -> test with 'LR' LR tn, fp: 309, 1 LR fn, tp: 11, 0 LR f1 score: 0.000 LR cohens kappa score: -0.006 LR average precision score: 0.260 -> test with 'GB' GB tn, fp: 307, 3 GB fn, tp: 10, 1 GB f1 score: 0.133 GB cohens kappa score: 0.117 -> test with 'KNN' KNN tn, fp: 310, 0 KNN fn, tp: 11, 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 1236/42 points -> new disc -> calc distances -> statistics trained 42 points min:0.542887025540305 max:13.164740804983591 -> create 1194 synthetic samples -> test with 'LR' LR tn, fp: 309, 1 LR fn, tp: 10, 1 LR f1 score: 0.154 LR cohens kappa score: 0.145 LR average precision score: 0.192 -> test with 'GB' GB tn, fp: 309, 1 GB fn, tp: 10, 1 GB f1 score: 0.154 GB cohens kappa score: 0.145 -> test with 'KNN' KNN tn, fp: 310, 0 KNN fn, tp: 11, 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 1240/44 points -> new disc -> calc distances -> statistics trained 44 points min:0.6050000330578494 max:13.164740804983591 -> create 1196 synthetic samples -> test with 'LR' LR tn, fp: 305, 1 LR fn, tp: 9, 0 LR f1 score: 0.000 LR cohens kappa score: -0.006 LR average precision score: 0.167 -> test with 'GB' GB tn, fp: 301, 5 GB fn, tp: 9, 0 GB f1 score: 0.000 GB cohens kappa score: -0.021 -> test with 'KNN' KNN tn, fp: 306, 0 KNN fn, tp: 9, 0 KNN f1 score: 0.000 KNN cohens kappa score: 0.000 ### Exercise is done. -----[ LR ]----- maximum: LR tn, fp: 310, 4 LR fn, tp: 11, 1 LR f1 score: 0.200 LR cohens kappa score: 0.195 LR average precision score: 0.344 average: LR tn, fp: 308.36, 0.84 LR fn, tp: 10.4, 0.2 LR f1 score: 0.031 LR cohens kappa score: 0.026 LR average precision score: 0.156 minimum: LR tn, fp: 305, 0 LR fn, tp: 8, 0 LR f1 score: 0.000 LR cohens kappa score: -0.011 LR average precision score: 0.048 -----[ GB ]----- maximum: GB tn, fp: 310, 5 GB fn, tp: 11, 1 GB f1 score: 0.200 GB cohens kappa score: 0.195 average: GB tn, fp: 306.96, 2.24 GB fn, tp: 10.12, 0.48 GB f1 score: 0.073 GB cohens kappa score: 0.060 minimum: GB tn, fp: 301, 0 GB fn, tp: 8, 0 GB f1 score: 0.000 GB cohens kappa score: -0.022 -----[ KNN ]----- maximum: KNN tn, fp: 310, 0 KNN fn, tp: 11, 0 KNN f1 score: 0.000 KNN cohens kappa score: 0.000 average: KNN tn, fp: 309.2, 0.0 KNN fn, tp: 10.6, 0.0 KNN f1 score: 0.000 KNN cohens kappa score: 0.000 minimum: KNN tn, fp: 306, 0 KNN fn, tp: 9, 0 KNN f1 score: 0.000 KNN cohens kappa score: 0.000