/////////////////////////////////////////// // Running SimpleGAN 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. -> 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 Epoch 1/3 Epoch 2/3 Epoch 3/3 -> create 784 synthetic samples -> test with 'LR' LR tn, fp: 203, 2 LR fn, tp: 8, 1 LR f1 score: 0.167 LR cohens kappa score: 0.149 LR average precision score: 0.192 -> test with 'GB' GB tn, fp: 199, 6 GB fn, tp: 8, 1 GB f1 score: 0.125 GB cohens kappa score: 0.092 -> test with 'KNN' KNN tn, fp: 200, 5 KNN fn, tp: 9, 0 KNN f1 score: 0.000 KNN cohens kappa score: -0.031 ------ Step 1/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples Epoch 1/3 Epoch 2/3 Epoch 3/3 -> create 784 synthetic samples -> test with 'LR' LR tn, fp: 203, 2 LR fn, tp: 5, 4 LR f1 score: 0.533 LR cohens kappa score: 0.517 LR average precision score: 0.471 -> test with 'GB' GB tn, fp: 203, 2 GB fn, tp: 8, 1 GB f1 score: 0.167 GB cohens kappa score: 0.149 -> test with 'KNN' KNN tn, fp: 204, 1 KNN fn, tp: 9, 0 KNN f1 score: 0.000 KNN cohens kappa score: -0.008 ------ Step 1/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples Epoch 1/3 Epoch 2/3 Epoch 3/3 -> create 784 synthetic samples -> test with 'LR' LR tn, fp: 205, 0 LR fn, tp: 7, 2 LR f1 score: 0.364 LR cohens kappa score: 0.354 LR average precision score: 0.440 -> test with 'GB' GB tn, fp: 205, 0 GB fn, tp: 8, 1 GB f1 score: 0.200 GB cohens kappa score: 0.193 -> test with 'KNN' KNN tn, fp: 205, 0 KNN fn, tp: 9, 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 Epoch 1/3 Epoch 2/3 Epoch 3/3 -> create 784 synthetic samples -> test with 'LR' LR tn, fp: 205, 0 LR fn, tp: 6, 3 LR f1 score: 0.500 LR cohens kappa score: 0.489 LR average precision score: 0.744 -> test with 'GB' GB tn, fp: 204, 1 GB fn, tp: 7, 2 GB f1 score: 0.333 GB cohens kappa score: 0.319 -> test with 'KNN' KNN tn, fp: 205, 0 KNN fn, tp: 9, 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 Epoch 1/3 Epoch 2/3 Epoch 3/3 -> create 784 synthetic samples -> test with 'LR' LR tn, fp: 200, 3 LR fn, tp: 5, 2 LR f1 score: 0.333 LR cohens kappa score: 0.314 LR average precision score: 0.271 -> test with 'GB' GB tn, fp: 201, 2 GB fn, tp: 6, 1 GB f1 score: 0.200 GB cohens kappa score: 0.184 -> test with 'KNN' KNN tn, fp: 203, 0 KNN fn, tp: 6, 1 KNN f1 score: 0.250 KNN cohens kappa score: 0.244 ====== Step 2/5 ======= -> Shuffling data -> Spliting data to slices ------ Step 2/5: Slice 1/5 ------- -> Reset the GAN -> Train generator for synthetic samples Epoch 1/3 Epoch 2/3 Epoch 3/3 -> create 784 synthetic samples -> test with 'LR' LR tn, fp: 204, 1 LR fn, tp: 5, 4 LR f1 score: 0.571 LR cohens kappa score: 0.558 LR average precision score: 0.612 -> test with 'GB' GB tn, fp: 203, 2 GB fn, tp: 7, 2 GB f1 score: 0.308 GB cohens kappa score: 0.289 -> test with 'KNN' KNN tn, fp: 204, 1 KNN fn, tp: 8, 1 KNN f1 score: 0.182 KNN cohens kappa score: 0.169 ------ Step 2/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples Epoch 1/3 Epoch 2/3 Epoch 3/3 -> create 784 synthetic samples -> test with 'LR' LR tn, fp: 203, 2 LR fn, tp: 7, 2 LR f1 score: 0.308 LR cohens kappa score: 0.289 LR average precision score: 0.305 -> test with 'GB' GB tn, fp: 203, 2 GB fn, tp: 9, 0 GB f1 score: 0.000 GB cohens kappa score: -0.016 -> test with 'KNN' KNN tn, fp: 205, 0 KNN fn, tp: 9, 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 Epoch 1/3 Epoch 2/3 Epoch 3/3 -> create 784 synthetic samples -> test with 'LR' LR tn, fp: 205, 0 LR fn, tp: 8, 1 LR f1 score: 0.200 LR cohens kappa score: 0.193 LR average precision score: 0.437 -> test with 'GB' GB tn, fp: 205, 0 GB fn, tp: 8, 1 GB f1 score: 0.200 GB cohens kappa score: 0.193 -> test with 'KNN' KNN tn, fp: 203, 2 KNN fn, tp: 9, 0 KNN f1 score: 0.000 KNN cohens kappa score: -0.016 ------ Step 2/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples Epoch 1/3 Epoch 2/3 Epoch 3/3 -> create 784 synthetic samples -> test with 'LR' LR tn, fp: 205, 0 LR fn, tp: 8, 1 LR f1 score: 0.200 LR cohens kappa score: 0.193 LR average precision score: 0.360 -> test with 'GB' GB tn, fp: 204, 1 GB fn, tp: 8, 1 GB f1 score: 0.182 GB cohens kappa score: 0.169 -> test with 'KNN' KNN tn, fp: 204, 1 KNN fn, tp: 9, 0 KNN f1 score: 0.000 KNN cohens kappa score: -0.008 ------ Step 2/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples Epoch 1/3 Epoch 2/3 Epoch 3/3 -> create 784 synthetic samples -> test with 'LR' LR tn, fp: 200, 3 LR fn, tp: 4, 3 LR f1 score: 0.462 LR cohens kappa score: 0.444 LR average precision score: 0.391 -> test with 'GB' GB tn, fp: 201, 2 GB fn, tp: 6, 1 GB f1 score: 0.200 GB cohens kappa score: 0.184 -> test with 'KNN' KNN tn, fp: 202, 1 KNN fn, tp: 7, 0 KNN f1 score: 0.000 KNN cohens kappa score: -0.008 ====== Step 3/5 ======= -> Shuffling data -> Spliting data to slices ------ Step 3/5: Slice 1/5 ------- -> Reset the GAN -> Train generator for synthetic samples Epoch 1/3 Epoch 2/3 Epoch 3/3 -> create 784 synthetic samples -> test with 'LR' LR tn, fp: 205, 0 LR fn, tp: 4, 5 LR f1 score: 0.714 LR cohens kappa score: 0.705 LR average precision score: 0.834 -> test with 'GB' GB tn, fp: 205, 0 GB fn, tp: 9, 0 GB f1 score: 0.000 GB cohens kappa score: 0.000 -> test with 'KNN' KNN tn, fp: 205, 0 KNN fn, tp: 9, 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 Epoch 1/3 Epoch 2/3 Epoch 3/3 -> create 784 synthetic samples -> test with 'LR' LR tn, fp: 200, 5 LR fn, tp: 7, 2 LR f1 score: 0.250 LR cohens kappa score: 0.221 LR average precision score: 0.210 -> test with 'GB' GB tn, fp: 199, 6 GB fn, tp: 6, 3 GB f1 score: 0.333 GB cohens kappa score: 0.304 -> test with 'KNN' KNN tn, fp: 201, 4 KNN fn, tp: 9, 0 KNN f1 score: 0.000 KNN cohens kappa score: -0.027 ------ Step 3/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples Epoch 1/3 Epoch 2/3 Epoch 3/3 -> create 784 synthetic samples -> test with 'LR' LR tn, fp: 202, 3 LR fn, tp: 6, 3 LR f1 score: 0.400 LR cohens kappa score: 0.379 LR average precision score: 0.483 -> test with 'GB' GB tn, fp: 204, 1 GB fn, tp: 8, 1 GB f1 score: 0.182 GB cohens kappa score: 0.169 -> test with 'KNN' KNN tn, fp: 205, 0 KNN fn, tp: 9, 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 Epoch 1/3 Epoch 2/3 Epoch 3/3 -> create 784 synthetic samples -> test with 'LR' LR tn, fp: 204, 1 LR fn, tp: 8, 1 LR f1 score: 0.182 LR cohens kappa score: 0.169 LR average precision score: 0.330 -> test with 'GB' GB tn, fp: 204, 1 GB fn, tp: 8, 1 GB f1 score: 0.182 GB cohens kappa score: 0.169 -> test with 'KNN' KNN tn, fp: 205, 0 KNN fn, tp: 9, 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 Epoch 1/3 Epoch 2/3 Epoch 3/3 -> create 784 synthetic samples -> test with 'LR' LR tn, fp: 201, 2 LR fn, tp: 6, 1 LR f1 score: 0.200 LR cohens kappa score: 0.184 LR average precision score: 0.212 -> test with 'GB' GB tn, fp: 199, 4 GB fn, tp: 5, 2 GB f1 score: 0.308 GB cohens kappa score: 0.286 -> test with 'KNN' KNN tn, fp: 200, 3 KNN fn, tp: 7, 0 KNN f1 score: 0.000 KNN cohens kappa score: -0.020 ====== Step 4/5 ======= -> Shuffling data -> Spliting data to slices ------ Step 4/5: Slice 1/5 ------- -> Reset the GAN -> Train generator for synthetic samples Epoch 1/3 Epoch 2/3 Epoch 3/3 -> create 784 synthetic samples -> test with 'LR' LR tn, fp: 199, 6 LR fn, tp: 9, 0 LR f1 score: 0.000 LR cohens kappa score: -0.035 LR average precision score: 0.203 -> test with 'GB' GB tn, fp: 199, 6 GB fn, tp: 9, 0 GB f1 score: 0.000 GB cohens kappa score: -0.035 -> test with 'KNN' KNN tn, fp: 205, 0 KNN fn, tp: 9, 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 Epoch 1/3 Epoch 2/3 Epoch 3/3 -> create 784 synthetic samples -> test with 'LR' LR tn, fp: 205, 0 LR fn, tp: 6, 3 LR f1 score: 0.500 LR cohens kappa score: 0.489 LR average precision score: 0.564 -> test with 'GB' GB tn, fp: 205, 0 GB fn, tp: 8, 1 GB f1 score: 0.200 GB cohens kappa score: 0.193 -> test with 'KNN' KNN tn, fp: 205, 0 KNN fn, tp: 9, 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 Epoch 1/3 Epoch 2/3 Epoch 3/3 -> create 784 synthetic samples -> test with 'LR' LR tn, fp: 200, 5 LR fn, tp: 6, 3 LR f1 score: 0.353 LR cohens kappa score: 0.326 LR average precision score: 0.382 -> test with 'GB' GB tn, fp: 201, 4 GB fn, tp: 7, 2 GB f1 score: 0.267 GB cohens kappa score: 0.241 -> test with 'KNN' KNN tn, fp: 204, 1 KNN fn, tp: 9, 0 KNN f1 score: 0.000 KNN cohens kappa score: -0.008 ------ Step 4/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples Epoch 1/3 Epoch 2/3 Epoch 3/3 -> create 784 synthetic samples -> test with 'LR' LR tn, fp: 201, 4 LR fn, tp: 7, 2 LR f1 score: 0.267 LR cohens kappa score: 0.241 LR average precision score: 0.333 -> test with 'GB' GB tn, fp: 204, 1 GB fn, tp: 8, 1 GB f1 score: 0.182 GB cohens kappa score: 0.169 -> test with 'KNN' KNN tn, fp: 200, 5 KNN fn, tp: 9, 0 KNN f1 score: 0.000 KNN cohens kappa score: -0.031 ------ Step 4/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples Epoch 1/3 Epoch 2/3 Epoch 3/3 -> create 784 synthetic samples -> test with 'LR' LR tn, fp: 202, 1 LR fn, tp: 5, 2 LR f1 score: 0.400 LR cohens kappa score: 0.388 LR average precision score: 0.596 -> test with 'GB' GB tn, fp: 203, 0 GB fn, tp: 7, 0 GB f1 score: 0.000 GB cohens kappa score: 0.000 -> test with 'KNN' KNN tn, fp: 203, 0 KNN fn, tp: 7, 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 Epoch 1/3 Epoch 2/3 Epoch 3/3 -> create 784 synthetic samples -> test with 'LR' LR tn, fp: 205, 0 LR fn, tp: 8, 1 LR f1 score: 0.200 LR cohens kappa score: 0.193 LR average precision score: 0.288 -> test with 'GB' GB tn, fp: 204, 1 GB fn, tp: 8, 1 GB f1 score: 0.182 GB cohens kappa score: 0.169 -> test with 'KNN' KNN tn, fp: 205, 0 KNN fn, tp: 9, 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 Epoch 1/3 Epoch 2/3 Epoch 3/3 -> create 784 synthetic samples -> test with 'LR' LR tn, fp: 203, 2 LR fn, tp: 8, 1 LR f1 score: 0.167 LR cohens kappa score: 0.149 LR average precision score: 0.410 -> test with 'GB' GB tn, fp: 205, 0 GB fn, tp: 9, 0 GB f1 score: 0.000 GB cohens kappa score: 0.000 -> test with 'KNN' KNN tn, fp: 205, 0 KNN fn, tp: 9, 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 Epoch 1/3 Epoch 2/3 Epoch 3/3 -> create 784 synthetic samples -> test with 'LR' LR tn, fp: 201, 4 LR fn, tp: 4, 5 LR f1 score: 0.556 LR cohens kappa score: 0.536 LR average precision score: 0.473 -> test with 'GB' GB tn, fp: 205, 0 GB fn, tp: 7, 2 GB f1 score: 0.364 GB cohens kappa score: 0.354 -> test with 'KNN' KNN tn, fp: 204, 1 KNN fn, tp: 8, 1 KNN f1 score: 0.182 KNN cohens kappa score: 0.169 ------ Step 5/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples Epoch 1/3 Epoch 2/3 Epoch 3/3 -> create 784 synthetic samples -> test with 'LR' LR tn, fp: 201, 4 LR fn, tp: 8, 1 LR f1 score: 0.143 LR cohens kappa score: 0.116 LR average precision score: 0.248 -> test with 'GB' GB tn, fp: 203, 2 GB fn, tp: 9, 0 GB f1 score: 0.000 GB cohens kappa score: -0.016 -> test with 'KNN' KNN tn, fp: 204, 1 KNN fn, tp: 9, 0 KNN f1 score: 0.000 KNN cohens kappa score: -0.008 ------ Step 5/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples Epoch 1/3 Epoch 2/3 Epoch 3/3 -> create 784 synthetic samples -> test with 'LR' LR tn, fp: 202, 1 LR fn, tp: 6, 1 LR f1 score: 0.222 LR cohens kappa score: 0.211 LR average precision score: 0.342 -> test with 'GB' GB tn, fp: 199, 4 GB fn, tp: 6, 1 GB f1 score: 0.167 GB cohens kappa score: 0.143 -> test with 'KNN' KNN tn, fp: 199, 4 KNN fn, tp: 7, 0 KNN f1 score: 0.000 KNN cohens kappa score: -0.025 ### Exercise is done. -----[ LR ]----- maximum: LR tn, fp: 205, 6 LR fn, tp: 9, 5 LR f1 score: 0.714 LR cohens kappa score: 0.705 LR average precision score: 0.834 average: LR tn, fp: 202.56, 2.04 LR fn, tp: 6.44, 2.16 LR f1 score: 0.328 LR cohens kappa score: 0.311 LR average precision score: 0.405 minimum: LR tn, fp: 199, 0 LR fn, tp: 4, 0 LR f1 score: 0.000 LR cohens kappa score: -0.035 LR average precision score: 0.192 -----[ GB ]----- maximum: GB tn, fp: 205, 6 GB fn, tp: 9, 3 GB f1 score: 0.364 GB cohens kappa score: 0.354 average: GB tn, fp: 202.68, 1.92 GB fn, tp: 7.56, 1.04 GB f1 score: 0.171 GB cohens kappa score: 0.156 minimum: GB tn, fp: 199, 0 GB fn, tp: 5, 0 GB f1 score: 0.000 GB cohens kappa score: -0.035 -----[ KNN ]----- maximum: KNN tn, fp: 205, 5 KNN fn, tp: 9, 1 KNN f1 score: 0.250 KNN cohens kappa score: 0.244 average: KNN tn, fp: 203.4, 1.2 KNN fn, tp: 8.48, 0.12 KNN f1 score: 0.025 KNN cohens kappa score: 0.016 minimum: KNN tn, fp: 199, 0 KNN fn, tp: 6, 0 KNN f1 score: 0.000 KNN cohens kappa score: -0.031