/////////////////////////////////////////// // Running convGAN-proximary-5 on folding_shuttle-2_vs_5 /////////////////////////////////////////// Load 'data_input/folding_shuttle-2_vs_5' 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 2574 synthetic samples -> test with GAN.predict GAN tn, fp: 644, 10 GAN fn, tp: 0, 10 GAN f1 score: 0.667 GAN cohens kappa score: 0.660 -> test with 'LR' LR tn, fp: 654, 0 LR fn, tp: 0, 10 LR f1 score: 1.000 LR cohens kappa score: 1.000 LR average precision score: 1.000 -> test with 'GB' GB tn, fp: 654, 0 GB fn, tp: 0, 10 GB f1 score: 1.000 GB cohens kappa score: 1.000 -> test with 'KNN' KNN tn, fp: 653, 1 KNN fn, tp: 0, 10 KNN f1 score: 0.952 KNN cohens kappa score: 0.952 ------ Step 1/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 2574 synthetic samples -> test with GAN.predict GAN tn, fp: 638, 16 GAN fn, tp: 0, 10 GAN f1 score: 0.556 GAN cohens kappa score: 0.546 -> test with 'LR' LR tn, fp: 654, 0 LR fn, tp: 0, 10 LR f1 score: 1.000 LR cohens kappa score: 1.000 LR average precision score: 1.000 -> test with 'GB' GB tn, fp: 654, 0 GB fn, tp: 0, 10 GB f1 score: 1.000 GB cohens kappa score: 1.000 -> test with 'KNN' KNN tn, fp: 653, 1 KNN fn, tp: 0, 10 KNN f1 score: 0.952 KNN cohens kappa score: 0.952 ------ Step 1/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 2574 synthetic samples -> test with GAN.predict GAN tn, fp: 633, 21 GAN fn, tp: 0, 10 GAN f1 score: 0.488 GAN cohens kappa score: 0.476 -> test with 'LR' LR tn, fp: 653, 1 LR fn, tp: 0, 10 LR f1 score: 0.952 LR cohens kappa score: 0.952 LR average precision score: 0.901 -> test with 'GB' GB tn, fp: 654, 0 GB fn, tp: 0, 10 GB f1 score: 1.000 GB cohens kappa score: 1.000 -> test with 'KNN' KNN tn, fp: 654, 0 KNN fn, tp: 0, 10 KNN f1 score: 1.000 KNN cohens kappa score: 1.000 ------ Step 1/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 2574 synthetic samples -> test with GAN.predict GAN tn, fp: 646, 8 GAN fn, tp: 0, 10 GAN f1 score: 0.714 GAN cohens kappa score: 0.709 -> test with 'LR' LR tn, fp: 654, 0 LR fn, tp: 0, 10 LR f1 score: 1.000 LR cohens kappa score: 1.000 LR average precision score: 1.000 -> test with 'GB' GB tn, fp: 654, 0 GB fn, tp: 0, 10 GB f1 score: 1.000 GB cohens kappa score: 1.000 -> test with 'KNN' KNN tn, fp: 652, 2 KNN fn, tp: 0, 10 KNN f1 score: 0.909 KNN cohens kappa score: 0.908 ------ Step 1/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 2576 synthetic samples -> test with GAN.predict GAN tn, fp: 649, 2 GAN fn, tp: 0, 9 GAN f1 score: 0.900 GAN cohens kappa score: 0.898 -> test with 'LR' LR tn, fp: 651, 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: 651, 0 GB fn, tp: 0, 9 GB f1 score: 1.000 GB cohens kappa score: 1.000 -> test with 'KNN' KNN tn, fp: 651, 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 2574 synthetic samples -> test with GAN.predict GAN tn, fp: 633, 21 GAN fn, tp: 0, 10 GAN f1 score: 0.488 GAN cohens kappa score: 0.476 -> test with 'LR' LR tn, fp: 654, 0 LR fn, tp: 0, 10 LR f1 score: 1.000 LR cohens kappa score: 1.000 LR average precision score: 1.000 -> test with 'GB' GB tn, fp: 654, 0 GB fn, tp: 0, 10 GB f1 score: 1.000 GB cohens kappa score: 1.000 -> test with 'KNN' KNN tn, fp: 653, 1 KNN fn, tp: 0, 10 KNN f1 score: 0.952 KNN cohens kappa score: 0.952 ------ Step 2/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 2574 synthetic samples -> test with GAN.predict GAN tn, fp: 645, 9 GAN fn, tp: 0, 10 GAN f1 score: 0.690 GAN cohens kappa score: 0.683 -> test with 'LR' LR tn, fp: 654, 0 LR fn, tp: 0, 10 LR f1 score: 1.000 LR cohens kappa score: 1.000 LR average precision score: 1.000 -> test with 'GB' GB tn, fp: 654, 0 GB fn, tp: 0, 10 GB f1 score: 1.000 GB cohens kappa score: 1.000 -> test with 'KNN' KNN tn, fp: 654, 0 KNN fn, tp: 0, 10 KNN f1 score: 1.000 KNN cohens kappa score: 1.000 ------ Step 2/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 2574 synthetic samples -> test with GAN.predict GAN tn, fp: 639, 15 GAN fn, tp: 0, 10 GAN f1 score: 0.571 GAN cohens kappa score: 0.562 -> test with 'LR' LR tn, fp: 654, 0 LR fn, tp: 0, 10 LR f1 score: 1.000 LR cohens kappa score: 1.000 LR average precision score: 1.000 -> test with 'GB' GB tn, fp: 654, 0 GB fn, tp: 0, 10 GB f1 score: 1.000 GB cohens kappa score: 1.000 -> test with 'KNN' KNN tn, fp: 652, 2 KNN fn, tp: 0, 10 KNN f1 score: 0.909 KNN cohens kappa score: 0.908 ------ Step 2/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 2574 synthetic samples -> test with GAN.predict GAN tn, fp: 646, 8 GAN fn, tp: 0, 10 GAN f1 score: 0.714 GAN cohens kappa score: 0.709 -> test with 'LR' LR tn, fp: 654, 0 LR fn, tp: 0, 10 LR f1 score: 1.000 LR cohens kappa score: 1.000 LR average precision score: 1.000 -> test with 'GB' GB tn, fp: 654, 0 GB fn, tp: 0, 10 GB f1 score: 1.000 GB cohens kappa score: 1.000 -> test with 'KNN' KNN tn, fp: 654, 0 KNN fn, tp: 0, 10 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 2576 synthetic samples -> test with GAN.predict GAN tn, fp: 643, 8 GAN fn, tp: 0, 9 GAN f1 score: 0.692 GAN cohens kappa score: 0.687 -> test with 'LR' LR tn, fp: 651, 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: 651, 0 GB fn, tp: 0, 9 GB f1 score: 1.000 GB cohens kappa score: 1.000 -> test with 'KNN' KNN tn, fp: 650, 1 KNN fn, tp: 0, 9 KNN f1 score: 0.947 KNN cohens kappa score: 0.947 ====== Step 3/5 ======= -> Shuffling data -> Spliting data to slices ------ Step 3/5: Slice 1/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 2574 synthetic samples -> test with GAN.predict GAN tn, fp: 636, 18 GAN fn, tp: 0, 10 GAN f1 score: 0.526 GAN cohens kappa score: 0.516 -> test with 'LR' LR tn, fp: 654, 0 LR fn, tp: 0, 10 LR f1 score: 1.000 LR cohens kappa score: 1.000 LR average precision score: 1.000 -> test with 'GB' GB tn, fp: 654, 0 GB fn, tp: 0, 10 GB f1 score: 1.000 GB cohens kappa score: 1.000 -> test with 'KNN' KNN tn, fp: 653, 1 KNN fn, tp: 0, 10 KNN f1 score: 0.952 KNN cohens kappa score: 0.952 ------ Step 3/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 2574 synthetic samples -> test with GAN.predict GAN tn, fp: 648, 6 GAN fn, tp: 0, 10 GAN f1 score: 0.769 GAN cohens kappa score: 0.765 -> test with 'LR' LR tn, fp: 654, 0 LR fn, tp: 0, 10 LR f1 score: 1.000 LR cohens kappa score: 1.000 LR average precision score: 1.000 -> test with 'GB' GB tn, fp: 654, 0 GB fn, tp: 0, 10 GB f1 score: 1.000 GB cohens kappa score: 1.000 -> test with 'KNN' KNN tn, fp: 653, 1 KNN fn, tp: 0, 10 KNN f1 score: 0.952 KNN cohens kappa score: 0.952 ------ Step 3/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 2574 synthetic samples -> test with GAN.predict GAN tn, fp: 640, 14 GAN fn, tp: 0, 10 GAN f1 score: 0.588 GAN cohens kappa score: 0.579 -> test with 'LR' LR tn, fp: 653, 1 LR fn, tp: 0, 10 LR f1 score: 0.952 LR cohens kappa score: 0.952 LR average precision score: 0.901 -> test with 'GB' GB tn, fp: 654, 0 GB fn, tp: 0, 10 GB f1 score: 1.000 GB cohens kappa score: 1.000 -> test with 'KNN' KNN tn, fp: 654, 0 KNN fn, tp: 0, 10 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 2574 synthetic samples -> test with GAN.predict GAN tn, fp: 647, 7 GAN fn, tp: 0, 10 GAN f1 score: 0.741 GAN cohens kappa score: 0.736 -> test with 'LR' LR tn, fp: 654, 0 LR fn, tp: 0, 10 LR f1 score: 1.000 LR cohens kappa score: 1.000 LR average precision score: 1.000 -> test with 'GB' GB tn, fp: 654, 0 GB fn, tp: 0, 10 GB f1 score: 1.000 GB cohens kappa score: 1.000 -> test with 'KNN' KNN tn, fp: 653, 1 KNN fn, tp: 0, 10 KNN f1 score: 0.952 KNN cohens kappa score: 0.952 ------ Step 3/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 2576 synthetic samples -> test with GAN.predict GAN tn, fp: 628, 23 GAN fn, tp: 0, 9 GAN f1 score: 0.439 GAN cohens kappa score: 0.427 -> test with 'LR' LR tn, fp: 650, 1 LR fn, tp: 0, 9 LR f1 score: 0.947 LR cohens kappa score: 0.947 LR average precision score: 0.989 -> test with 'GB' GB tn, fp: 650, 1 GB fn, tp: 0, 9 GB f1 score: 0.947 GB cohens kappa score: 0.947 -> test with 'KNN' KNN tn, fp: 649, 2 KNN fn, tp: 0, 9 KNN f1 score: 0.900 KNN cohens kappa score: 0.898 ====== Step 4/5 ======= -> Shuffling data -> Spliting data to slices ------ Step 4/5: Slice 1/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 2574 synthetic samples -> test with GAN.predict GAN tn, fp: 647, 7 GAN fn, tp: 0, 10 GAN f1 score: 0.741 GAN cohens kappa score: 0.736 -> test with 'LR' LR tn, fp: 654, 0 LR fn, tp: 0, 10 LR f1 score: 1.000 LR cohens kappa score: 1.000 LR average precision score: 1.000 -> test with 'GB' GB tn, fp: 654, 0 GB fn, tp: 0, 10 GB f1 score: 1.000 GB cohens kappa score: 1.000 -> test with 'KNN' KNN tn, fp: 654, 0 KNN fn, tp: 0, 10 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 2574 synthetic samples -> test with GAN.predict GAN tn, fp: 645, 9 GAN fn, tp: 0, 10 GAN f1 score: 0.690 GAN cohens kappa score: 0.683 -> test with 'LR' LR tn, fp: 654, 0 LR fn, tp: 0, 10 LR f1 score: 1.000 LR cohens kappa score: 1.000 LR average precision score: 1.000 -> test with 'GB' GB tn, fp: 654, 0 GB fn, tp: 0, 10 GB f1 score: 1.000 GB cohens kappa score: 1.000 -> test with 'KNN' KNN tn, fp: 652, 2 KNN fn, tp: 0, 10 KNN f1 score: 0.909 KNN cohens kappa score: 0.908 ------ Step 4/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 2574 synthetic samples -> test with GAN.predict GAN tn, fp: 651, 3 GAN fn, tp: 0, 10 GAN f1 score: 0.870 GAN cohens kappa score: 0.867 -> test with 'LR' LR tn, fp: 654, 0 LR fn, tp: 0, 10 LR f1 score: 1.000 LR cohens kappa score: 1.000 LR average precision score: 1.000 -> test with 'GB' GB tn, fp: 654, 0 GB fn, tp: 0, 10 GB f1 score: 1.000 GB cohens kappa score: 1.000 -> test with 'KNN' KNN tn, fp: 654, 0 KNN fn, tp: 0, 10 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 2574 synthetic samples -> test with GAN.predict GAN tn, fp: 651, 3 GAN fn, tp: 0, 10 GAN f1 score: 0.870 GAN cohens kappa score: 0.867 -> test with 'LR' LR tn, fp: 654, 0 LR fn, tp: 0, 10 LR f1 score: 1.000 LR cohens kappa score: 1.000 LR average precision score: 1.000 -> test with 'GB' GB tn, fp: 654, 0 GB fn, tp: 0, 10 GB f1 score: 1.000 GB cohens kappa score: 1.000 -> test with 'KNN' KNN tn, fp: 653, 1 KNN fn, tp: 0, 10 KNN f1 score: 0.952 KNN cohens kappa score: 0.952 ------ Step 4/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 2576 synthetic samples -> test with GAN.predict GAN tn, fp: 642, 9 GAN fn, tp: 0, 9 GAN f1 score: 0.667 GAN cohens kappa score: 0.660 -> test with 'LR' LR tn, fp: 651, 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: 651, 0 GB fn, tp: 0, 9 GB f1 score: 1.000 GB cohens kappa score: 1.000 -> test with 'KNN' KNN tn, fp: 650, 1 KNN fn, tp: 0, 9 KNN f1 score: 0.947 KNN cohens kappa score: 0.947 ====== Step 5/5 ======= -> Shuffling data -> Spliting data to slices ------ Step 5/5: Slice 1/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 2574 synthetic samples -> test with GAN.predict GAN tn, fp: 650, 4 GAN fn, tp: 0, 10 GAN f1 score: 0.833 GAN cohens kappa score: 0.830 -> test with 'LR' LR tn, fp: 654, 0 LR fn, tp: 0, 10 LR f1 score: 1.000 LR cohens kappa score: 1.000 LR average precision score: 1.000 -> test with 'GB' GB tn, fp: 654, 0 GB fn, tp: 0, 10 GB f1 score: 1.000 GB cohens kappa score: 1.000 -> test with 'KNN' KNN tn, fp: 653, 1 KNN fn, tp: 0, 10 KNN f1 score: 0.952 KNN cohens kappa score: 0.952 ------ Step 5/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 2574 synthetic samples -> test with GAN.predict GAN tn, fp: 645, 9 GAN fn, tp: 0, 10 GAN f1 score: 0.690 GAN cohens kappa score: 0.683 -> test with 'LR' LR tn, fp: 654, 0 LR fn, tp: 0, 10 LR f1 score: 1.000 LR cohens kappa score: 1.000 LR average precision score: 1.000 -> test with 'GB' GB tn, fp: 654, 0 GB fn, tp: 0, 10 GB f1 score: 1.000 GB cohens kappa score: 1.000 -> test with 'KNN' KNN tn, fp: 654, 0 KNN fn, tp: 0, 10 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 2574 synthetic samples -> test with GAN.predict GAN tn, fp: 647, 7 GAN fn, tp: 0, 10 GAN f1 score: 0.741 GAN cohens kappa score: 0.736 -> test with 'LR' LR tn, fp: 654, 0 LR fn, tp: 0, 10 LR f1 score: 1.000 LR cohens kappa score: 1.000 LR average precision score: 1.000 -> test with 'GB' GB tn, fp: 654, 0 GB fn, tp: 0, 10 GB f1 score: 1.000 GB cohens kappa score: 1.000 -> test with 'KNN' KNN tn, fp: 651, 3 KNN fn, tp: 0, 10 KNN f1 score: 0.870 KNN cohens kappa score: 0.867 ------ Step 5/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 2574 synthetic samples -> test with GAN.predict GAN tn, fp: 645, 9 GAN fn, tp: 0, 10 GAN f1 score: 0.690 GAN cohens kappa score: 0.683 -> test with 'LR' LR tn, fp: 654, 0 LR fn, tp: 0, 10 LR f1 score: 1.000 LR cohens kappa score: 1.000 LR average precision score: 1.000 -> test with 'GB' GB tn, fp: 654, 0 GB fn, tp: 0, 10 GB f1 score: 1.000 GB cohens kappa score: 1.000 -> test with 'KNN' KNN tn, fp: 654, 0 KNN fn, tp: 0, 10 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 2576 synthetic samples -> test with GAN.predict GAN tn, fp: 646, 5 GAN fn, tp: 0, 9 GAN f1 score: 0.783 GAN cohens kappa score: 0.779 -> test with 'LR' LR tn, fp: 651, 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: 651, 0 GB fn, tp: 0, 9 GB f1 score: 1.000 GB cohens kappa score: 1.000 -> test with 'KNN' KNN tn, fp: 650, 1 KNN fn, tp: 0, 9 KNN f1 score: 0.947 KNN cohens kappa score: 0.947 ### Exercise is done. -----[ LR ]----- maximum: LR tn, fp: 654, 1 LR fn, tp: 0, 10 LR f1 score: 1.000 LR cohens kappa score: 1.000 LR average precision score: 1.000 average: LR tn, fp: 653.28, 0.12 LR fn, tp: 0.0, 9.8 LR f1 score: 0.994 LR cohens kappa score: 0.994 LR average precision score: 0.992 minimum: LR tn, fp: 650, 0 LR fn, tp: 0, 9 LR f1 score: 0.947 LR cohens kappa score: 0.947 LR average precision score: 0.901 -----[ GB ]----- maximum: GB tn, fp: 654, 1 GB fn, tp: 0, 10 GB f1 score: 1.000 GB cohens kappa score: 1.000 average: GB tn, fp: 653.36, 0.04 GB fn, tp: 0.0, 9.8 GB f1 score: 0.998 GB cohens kappa score: 0.998 minimum: GB tn, fp: 650, 0 GB fn, tp: 0, 9 GB f1 score: 0.947 GB cohens kappa score: 0.947 -----[ KNN ]----- maximum: KNN tn, fp: 654, 3 KNN fn, tp: 0, 10 KNN f1 score: 1.000 KNN cohens kappa score: 1.000 average: KNN tn, fp: 652.52, 0.88 KNN fn, tp: 0.0, 9.8 KNN f1 score: 0.958 KNN cohens kappa score: 0.958 minimum: KNN tn, fp: 649, 0 KNN fn, tp: 0, 9 KNN f1 score: 0.870 KNN cohens kappa score: 0.867 -----[ GAN ]----- maximum: GAN tn, fp: 651, 23 GAN fn, tp: 0, 10 GAN f1 score: 0.900 GAN cohens kappa score: 0.898 average: GAN tn, fp: 643.36, 10.04 GAN fn, tp: 0.0, 9.8 GAN f1 score: 0.685 GAN cohens kappa score: 0.678 minimum: GAN tn, fp: 628, 2 GAN fn, tp: 0, 9 GAN f1 score: 0.439 GAN cohens kappa score: 0.427