/////////////////////////////////////////// // Running SpheredNoise on folding_yeast4 /////////////////////////////////////////// Load 'data_input/folding_yeast4' 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 1146/40 points -> new disc -> calc distances -> statistics trained 40 points min:0.026457513110645887 max:0.21118712081942884 -> create 1106 synthetic samples -> test with 'LR' LR tn, fp: 285, 2 LR fn, tp: 8, 3 LR f1 score: 0.375 LR cohens kappa score: 0.360 LR average precision score: 0.428 -> test with 'GB' GB tn, fp: 284, 3 GB fn, tp: 10, 1 GB f1 score: 0.133 GB cohens kappa score: 0.116 -> test with 'KNN' KNN tn, fp: 287, 0 KNN fn, tp: 10, 1 KNN f1 score: 0.167 KNN cohens kappa score: 0.162 ------ Step 1/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples Train 1146/40 points -> new disc -> calc distances -> statistics trained 40 points min:0.026457513110645887 max:0.20808652046684803 -> create 1106 synthetic samples -> test with 'LR' LR tn, fp: 286, 1 LR fn, tp: 7, 4 LR f1 score: 0.500 LR cohens kappa score: 0.488 LR average precision score: 0.656 -> test with 'GB' GB tn, fp: 283, 4 GB fn, tp: 6, 5 GB f1 score: 0.500 GB cohens kappa score: 0.483 -> test with 'KNN' KNN tn, fp: 287, 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 1146/40 points -> new disc -> calc distances -> statistics trained 40 points min:0.03872983346207415 max:0.25000000000000006 -> create 1106 synthetic samples -> test with 'LR' LR tn, fp: 285, 2 LR fn, tp: 10, 1 LR f1 score: 0.143 LR cohens kappa score: 0.129 LR average precision score: 0.390 -> test with 'GB' GB tn, fp: 286, 1 GB fn, tp: 10, 1 GB f1 score: 0.154 GB cohens kappa score: 0.144 -> test with 'KNN' KNN tn, fp: 287, 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 1146/40 points -> new disc -> calc distances -> statistics trained 40 points min:0.03872983346207415 max:0.20808652046684803 -> create 1106 synthetic samples -> test with 'LR' LR tn, fp: 284, 3 LR fn, tp: 9, 2 LR f1 score: 0.250 LR cohens kappa score: 0.232 LR average precision score: 0.213 -> test with 'GB' GB tn, fp: 282, 5 GB fn, tp: 9, 2 GB f1 score: 0.222 GB cohens kappa score: 0.199 -> test with 'KNN' KNN tn, fp: 285, 2 KNN fn, tp: 11, 0 KNN f1 score: 0.000 KNN cohens kappa score: -0.011 ------ Step 1/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples Train 1148/44 points -> new disc -> calc distances -> statistics trained 44 points min:0.026457513110645887 max:0.23043437243605827 -> create 1104 synthetic samples -> test with 'LR' LR tn, fp: 284, 1 LR fn, tp: 6, 1 LR f1 score: 0.222 LR cohens kappa score: 0.214 LR average precision score: 0.493 -> test with 'GB' GB tn, fp: 284, 1 GB fn, tp: 5, 2 GB f1 score: 0.400 GB cohens kappa score: 0.391 -> test with 'KNN' KNN tn, fp: 284, 1 KNN fn, tp: 7, 0 KNN f1 score: 0.000 KNN cohens kappa score: -0.006 ====== Step 2/5 ======= -> Shuffling data -> Spliting data to slices ------ Step 2/5: Slice 1/5 ------- -> Reset the GAN -> Train generator for synthetic samples Train 1146/40 points -> new disc -> calc distances -> statistics trained 40 points min:0.026457513110645887 max:0.20808652046684803 -> create 1106 synthetic samples -> test with 'LR' LR tn, fp: 286, 1 LR fn, tp: 10, 1 LR f1 score: 0.154 LR cohens kappa score: 0.144 LR average precision score: 0.286 -> test with 'GB' GB tn, fp: 281, 6 GB fn, tp: 10, 1 GB f1 score: 0.111 GB cohens kappa score: 0.085 -> test with 'KNN' KNN tn, fp: 287, 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 1146/40 points -> new disc -> calc distances -> statistics trained 40 points min:0.026457513110645887 max:0.22181073012818836 -> create 1106 synthetic samples -> test with 'LR' LR tn, fp: 284, 3 LR fn, tp: 5, 6 LR f1 score: 0.600 LR cohens kappa score: 0.586 LR average precision score: 0.473 -> test with 'GB' GB tn, fp: 285, 2 GB fn, tp: 7, 4 GB f1 score: 0.471 GB cohens kappa score: 0.456 -> test with 'KNN' KNN tn, fp: 286, 1 KNN fn, tp: 9, 2 KNN f1 score: 0.286 KNN cohens kappa score: 0.274 ------ Step 2/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples Train 1146/40 points -> new disc -> calc distances -> statistics trained 40 points min:0.026457513110645887 max:0.2366431913239846 -> create 1106 synthetic samples -> test with 'LR' LR tn, fp: 285, 2 LR fn, tp: 9, 2 LR f1 score: 0.267 LR cohens kappa score: 0.252 LR average precision score: 0.402 -> test with 'GB' GB tn, fp: 285, 2 GB fn, tp: 8, 3 GB f1 score: 0.375 GB cohens kappa score: 0.360 -> test with 'KNN' KNN tn, fp: 286, 1 KNN fn, tp: 11, 0 KNN f1 score: 0.000 KNN cohens kappa score: -0.006 ------ Step 2/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples Train 1146/40 points -> new disc -> calc distances -> statistics trained 40 points min:0.052915026221291794 max:0.20049937655763428 -> create 1106 synthetic samples -> test with 'LR' LR tn, fp: 286, 1 LR fn, tp: 10, 1 LR f1 score: 0.154 LR cohens kappa score: 0.144 LR average precision score: 0.388 -> test with 'GB' GB tn, fp: 285, 2 GB fn, tp: 8, 3 GB f1 score: 0.375 GB cohens kappa score: 0.360 -> test with 'KNN' KNN tn, fp: 286, 1 KNN fn, tp: 11, 0 KNN f1 score: 0.000 KNN cohens kappa score: -0.006 ------ Step 2/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples Train 1148/44 points -> new disc -> calc distances -> statistics trained 44 points min:0.026457513110645887 max:0.20808652046684803 -> create 1104 synthetic samples -> test with 'LR' LR tn, fp: 284, 1 LR fn, tp: 6, 1 LR f1 score: 0.222 LR cohens kappa score: 0.214 LR average precision score: 0.505 -> test with 'GB' GB tn, fp: 283, 2 GB fn, tp: 6, 1 GB f1 score: 0.200 GB cohens kappa score: 0.188 -> test with 'KNN' KNN tn, fp: 285, 0 KNN fn, tp: 7, 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 1146/40 points -> new disc -> calc distances -> statistics trained 40 points min:0.026457513110645887 max:0.20808652046684803 -> create 1106 synthetic samples -> test with 'LR' LR tn, fp: 285, 2 LR fn, tp: 8, 3 LR f1 score: 0.375 LR cohens kappa score: 0.360 LR average precision score: 0.408 -> test with 'GB' GB tn, fp: 285, 2 GB fn, tp: 9, 2 GB f1 score: 0.267 GB cohens kappa score: 0.252 -> test with 'KNN' KNN tn, fp: 286, 1 KNN fn, tp: 11, 0 KNN f1 score: 0.000 KNN cohens kappa score: -0.006 ------ Step 3/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples Train 1146/40 points -> new disc -> calc distances -> statistics trained 40 points min:0.026457513110645887 max:0.2366431913239846 -> create 1106 synthetic samples -> test with 'LR' LR tn, fp: 284, 3 LR fn, tp: 11, 0 LR f1 score: 0.000 LR cohens kappa score: -0.016 LR average precision score: 0.382 -> test with 'GB' GB tn, fp: 284, 3 GB fn, tp: 10, 1 GB f1 score: 0.133 GB cohens kappa score: 0.116 -> test with 'KNN' KNN tn, fp: 287, 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 1146/40 points -> new disc -> calc distances -> statistics trained 40 points min:0.04000000000000002 max:0.20808652046684803 -> create 1106 synthetic samples -> test with 'LR' LR tn, fp: 283, 4 LR fn, tp: 10, 1 LR f1 score: 0.125 LR cohens kappa score: 0.104 LR average precision score: 0.247 -> test with 'GB' GB tn, fp: 283, 4 GB fn, tp: 9, 2 GB f1 score: 0.235 GB cohens kappa score: 0.215 -> test with 'KNN' KNN tn, fp: 287, 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 1146/40 points -> new disc -> calc distances -> statistics trained 40 points min:0.03872983346207415 max:0.20808652046684803 -> create 1106 synthetic samples -> test with 'LR' LR tn, fp: 286, 1 LR fn, tp: 8, 3 LR f1 score: 0.400 LR cohens kappa score: 0.388 LR average precision score: 0.502 -> test with 'GB' GB tn, fp: 283, 4 GB fn, tp: 6, 5 GB f1 score: 0.500 GB cohens kappa score: 0.483 -> test with 'KNN' KNN tn, fp: 287, 0 KNN fn, tp: 10, 1 KNN f1 score: 0.167 KNN cohens kappa score: 0.162 ------ Step 3/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples Train 1148/44 points -> new disc -> calc distances -> statistics trained 44 points min:0.026457513110645887 max:0.19672315572906002 -> create 1104 synthetic samples -> test with 'LR' LR tn, fp: 285, 0 LR fn, tp: 5, 2 LR f1 score: 0.444 LR cohens kappa score: 0.438 LR average precision score: 0.444 -> test with 'GB' GB tn, fp: 281, 4 GB fn, tp: 4, 3 GB f1 score: 0.429 GB cohens kappa score: 0.415 -> test with 'KNN' KNN tn, fp: 285, 0 KNN fn, tp: 5, 2 KNN f1 score: 0.444 KNN cohens kappa score: 0.438 ====== Step 4/5 ======= -> Shuffling data -> Spliting data to slices ------ Step 4/5: Slice 1/5 ------- -> Reset the GAN -> Train generator for synthetic samples Train 1146/40 points -> new disc -> calc distances -> statistics trained 40 points min:0.03872983346207415 max:0.21000000000000002 -> create 1106 synthetic samples -> test with 'LR' LR tn, fp: 287, 0 LR fn, tp: 8, 3 LR f1 score: 0.429 LR cohens kappa score: 0.419 LR average precision score: 0.471 -> test with 'GB' GB tn, fp: 284, 3 GB fn, tp: 11, 0 GB f1 score: 0.000 GB cohens kappa score: -0.016 -> test with 'KNN' KNN tn, fp: 287, 0 KNN fn, tp: 10, 1 KNN f1 score: 0.167 KNN cohens kappa score: 0.162 ------ Step 4/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples Train 1146/40 points -> new disc -> calc distances -> statistics trained 40 points min:0.026457513110645887 max:0.21725560982400433 -> create 1106 synthetic samples -> test with 'LR' LR tn, fp: 287, 0 LR fn, tp: 11, 0 LR f1 score: 0.000 LR cohens kappa score: 0.000 LR average precision score: 0.436 -> test with 'GB' GB tn, fp: 286, 1 GB fn, tp: 7, 4 GB f1 score: 0.500 GB cohens kappa score: 0.488 -> test with 'KNN' KNN tn, fp: 286, 1 KNN fn, tp: 11, 0 KNN f1 score: 0.000 KNN cohens kappa score: -0.006 ------ Step 4/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples Train 1146/40 points -> new disc -> calc distances -> statistics trained 40 points min:0.04000000000000002 max:0.2366431913239847 -> create 1106 synthetic samples -> test with 'LR' LR tn, fp: 285, 2 LR fn, tp: 10, 1 LR f1 score: 0.143 LR cohens kappa score: 0.129 LR average precision score: 0.244 -> test with 'GB' GB tn, fp: 282, 5 GB fn, tp: 10, 1 GB f1 score: 0.118 GB cohens kappa score: 0.094 -> test with 'KNN' KNN tn, fp: 287, 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 1146/40 points -> new disc -> calc distances -> statistics trained 40 points min:0.026457513110645887 max:0.21118712081942884 -> create 1106 synthetic samples -> test with 'LR' LR tn, fp: 283, 4 LR fn, tp: 8, 3 LR f1 score: 0.333 LR cohens kappa score: 0.314 LR average precision score: 0.306 -> test with 'GB' GB tn, fp: 282, 5 GB fn, tp: 10, 1 GB f1 score: 0.118 GB cohens kappa score: 0.094 -> test with 'KNN' KNN tn, fp: 286, 1 KNN fn, tp: 10, 1 KNN f1 score: 0.154 KNN cohens kappa score: 0.144 ------ Step 4/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples Train 1148/44 points -> new disc -> calc distances -> statistics trained 44 points min:0.026457513110645887 max:0.2366431913239846 -> create 1104 synthetic samples -> test with 'LR' LR tn, fp: 283, 2 LR fn, tp: 4, 3 LR f1 score: 0.500 LR cohens kappa score: 0.490 LR average precision score: 0.586 -> test with 'GB' GB tn, fp: 283, 2 GB fn, tp: 5, 2 GB f1 score: 0.364 GB cohens kappa score: 0.352 -> test with 'KNN' KNN tn, fp: 285, 0 KNN fn, tp: 5, 2 KNN f1 score: 0.444 KNN cohens kappa score: 0.438 ====== Step 5/5 ======= -> Shuffling data -> Spliting data to slices ------ Step 5/5: Slice 1/5 ------- -> Reset the GAN -> Train generator for synthetic samples Train 1146/40 points -> new disc -> calc distances -> statistics trained 40 points min:0.03872983346207415 max:0.21118712081942884 -> create 1106 synthetic samples -> test with 'LR' LR tn, fp: 286, 1 LR fn, tp: 10, 1 LR f1 score: 0.154 LR cohens kappa score: 0.144 LR average precision score: 0.270 -> test with 'GB' GB tn, fp: 285, 2 GB fn, tp: 11, 0 GB f1 score: 0.000 GB cohens kappa score: -0.011 -> test with 'KNN' KNN tn, fp: 287, 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 1146/40 points -> new disc -> calc distances -> statistics trained 40 points min:0.026457513110645887 max:0.22561028345356957 -> create 1106 synthetic samples -> test with 'LR' LR tn, fp: 285, 2 LR fn, tp: 8, 3 LR f1 score: 0.375 LR cohens kappa score: 0.360 LR average precision score: 0.544 -> test with 'GB' GB tn, fp: 285, 2 GB fn, tp: 9, 2 GB f1 score: 0.267 GB cohens kappa score: 0.252 -> test with 'KNN' KNN tn, fp: 287, 0 KNN fn, tp: 9, 2 KNN f1 score: 0.308 KNN cohens kappa score: 0.300 ------ Step 5/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples Train 1146/40 points -> new disc -> calc distances -> statistics trained 40 points min:0.026457513110645887 max:0.23043437243605827 -> create 1106 synthetic samples -> test with 'LR' LR tn, fp: 287, 0 LR fn, tp: 10, 1 LR f1 score: 0.167 LR cohens kappa score: 0.162 LR average precision score: 0.548 -> test with 'GB' GB tn, fp: 286, 1 GB fn, tp: 8, 3 GB f1 score: 0.400 GB cohens kappa score: 0.388 -> test with 'KNN' KNN tn, fp: 287, 0 KNN fn, tp: 10, 1 KNN f1 score: 0.167 KNN cohens kappa score: 0.162 ------ Step 5/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples Train 1146/40 points -> new disc -> calc distances -> statistics trained 40 points min:0.026457513110645887 max:0.25000000000000006 -> create 1106 synthetic samples -> test with 'LR' LR tn, fp: 284, 3 LR fn, tp: 7, 4 LR f1 score: 0.444 LR cohens kappa score: 0.428 LR average precision score: 0.503 -> test with 'GB' GB tn, fp: 281, 6 GB fn, tp: 9, 2 GB f1 score: 0.211 GB cohens kappa score: 0.185 -> test with 'KNN' KNN tn, fp: 287, 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 1148/44 points -> new disc -> calc distances -> statistics trained 44 points min:0.026457513110645887 max:0.20049937655763428 -> create 1104 synthetic samples -> test with 'LR' LR tn, fp: 283, 2 LR fn, tp: 6, 1 LR f1 score: 0.200 LR cohens kappa score: 0.188 LR average precision score: 0.180 -> test with 'GB' GB tn, fp: 283, 2 GB fn, tp: 5, 2 GB f1 score: 0.364 GB cohens kappa score: 0.352 -> test with 'KNN' KNN tn, fp: 285, 0 KNN fn, tp: 7, 0 KNN f1 score: 0.000 KNN cohens kappa score: 0.000 ### Exercise is done. -----[ LR ]----- maximum: LR tn, fp: 287, 4 LR fn, tp: 11, 6 LR f1 score: 0.600 LR cohens kappa score: 0.586 LR average precision score: 0.656 average: LR tn, fp: 284.88, 1.72 LR fn, tp: 8.16, 2.04 LR f1 score: 0.279 LR cohens kappa score: 0.267 LR average precision score: 0.412 minimum: LR tn, fp: 283, 0 LR fn, tp: 4, 0 LR f1 score: 0.000 LR cohens kappa score: -0.016 LR average precision score: 0.180 -----[ GB ]----- maximum: GB tn, fp: 286, 6 GB fn, tp: 11, 5 GB f1 score: 0.500 GB cohens kappa score: 0.488 average: GB tn, fp: 283.64, 2.96 GB fn, tp: 8.08, 2.12 GB f1 score: 0.274 GB cohens kappa score: 0.258 minimum: GB tn, fp: 281, 1 GB fn, tp: 4, 0 GB f1 score: 0.000 GB cohens kappa score: -0.016 -----[ KNN ]----- maximum: KNN tn, fp: 287, 2 KNN fn, tp: 11, 2 KNN f1 score: 0.444 KNN cohens kappa score: 0.438 average: KNN tn, fp: 286.24, 0.36 KNN fn, tp: 9.68, 0.52 KNN f1 score: 0.092 KNN cohens kappa score: 0.088 minimum: KNN tn, fp: 284, 0 KNN fn, tp: 5, 0 KNN f1 score: 0.000 KNN cohens kappa score: -0.011