/////////////////////////////////////////// // Running convGAN-majority-5 on folding_car-vgood /////////////////////////////////////////// Load 'data_input/folding_car-vgood' 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 1278 synthetic samples -> retrain GAN for predict Epoch 1/10 1/133 [..............................] - ETA: 15s - loss: 0.0268 46/133 [=========>....................] - ETA: 0s - loss: 0.0356  91/133 [===================>..........] - ETA: 0s - loss: 0.0346 133/133 [==============================] - 0s 1ms/step - loss: 0.0364 Epoch 2/10 1/133 [..............................] - ETA: 0s - loss: 0.0187 52/133 [==========>...................] - ETA: 0s - loss: 0.0311 103/133 [======================>.......] - ETA: 0s - loss: 0.0294 133/133 [==============================] - 0s 991us/step - loss: 0.0343 Epoch 3/10 1/133 [..............................] - ETA: 0s - loss: 0.0179 52/133 [==========>...................] - ETA: 0s - loss: 0.0295 103/133 [======================>.......] - ETA: 0s - loss: 0.0328 133/133 [==============================] - 0s 993us/step - loss: 0.0335 Epoch 4/10 1/133 [..............................] - ETA: 0s - loss: 0.0143 52/133 [==========>...................] - ETA: 0s - loss: 0.0274 103/133 [======================>.......] - ETA: 0s - loss: 0.0332 133/133 [==============================] - 0s 987us/step - loss: 0.0314 Epoch 5/10 1/133 [..............................] - ETA: 0s - loss: 0.0030 49/133 [==========>...................] - ETA: 0s - loss: 0.0247 97/133 [====================>.........] - ETA: 0s - loss: 0.0244 133/133 [==============================] - 0s 1ms/step - loss: 0.0293 Epoch 6/10 1/133 [..............................] - ETA: 0s - loss: 0.0294 49/133 [==========>...................] - ETA: 0s - loss: 0.0268 100/133 [=====================>........] - ETA: 0s - loss: 0.0285 133/133 [==============================] - 0s 1ms/step - loss: 0.0287 Epoch 7/10 1/133 [..............................] - ETA: 0s - loss: 0.0146 52/133 [==========>...................] - ETA: 0s - loss: 0.0214 103/133 [======================>.......] - ETA: 0s - loss: 0.0241 133/133 [==============================] - 0s 986us/step - loss: 0.0272 Epoch 8/10 1/133 [..............................] - ETA: 0s - loss: 0.0327 52/133 [==========>...................] - ETA: 0s - loss: 0.0311 103/133 [======================>.......] - ETA: 0s - loss: 0.0259 133/133 [==============================] - 0s 991us/step - loss: 0.0245 Epoch 9/10 1/133 [..............................] - ETA: 0s - loss: 0.0076 52/133 [==========>...................] - ETA: 0s - loss: 0.0305 103/133 [======================>.......] - ETA: 0s - loss: 0.0248 133/133 [==============================] - 0s 991us/step - loss: 0.0238 Epoch 10/10 1/133 [..............................] - ETA: 0s - loss: 0.1428 52/133 [==========>...................] - ETA: 0s - loss: 0.0211 103/133 [======================>.......] - ETA: 0s - loss: 0.0221 133/133 [==============================] - 0s 985us/step - loss: 0.0227 -> test with GAN.predict GAN tn, fp: 328, 5 GAN fn, tp: 1, 12 GAN f1 score: 0.800 GAN cohens kappa score: 0.791 -> test with 'LR' LR tn, fp: 285, 48 LR fn, tp: 0, 13 LR f1 score: 0.351 LR cohens kappa score: 0.309 LR average precision score: 0.356 -> test with 'RF' RF tn, fp: 332, 1 RF fn, tp: 1, 12 RF f1 score: 0.923 RF cohens kappa score: 0.920 -> test with 'GB' GB tn, fp: 331, 2 GB fn, tp: 0, 13 GB f1 score: 0.929 GB cohens kappa score: 0.926 -> test with 'KNN' KNN tn, fp: 318, 15 KNN fn, tp: 0, 13 KNN f1 score: 0.634 KNN cohens kappa score: 0.614 ------ Step 1/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1278 synthetic samples -> retrain GAN for predict Epoch 1/10 1/133 [..............................] - ETA: 16s - loss: 0.0152 50/133 [==========>...................] - ETA: 0s - loss: 0.0324  100/133 [=====================>........] - ETA: 0s - loss: 0.0349 133/133 [==============================] - 0s 1ms/step - loss: 0.0374 Epoch 2/10 1/133 [..............................] - ETA: 0s - loss: 0.0132 52/133 [==========>...................] - ETA: 0s - loss: 0.0386 102/133 [======================>.......] - ETA: 0s - loss: 0.0389 133/133 [==============================] - 0s 1ms/step - loss: 0.0355 Epoch 3/10 1/133 [..............................] - ETA: 0s - loss: 0.0095 52/133 [==========>...................] - ETA: 0s - loss: 0.0287 103/133 [======================>.......] - ETA: 0s - loss: 0.0339 133/133 [==============================] - 0s 995us/step - loss: 0.0333 Epoch 4/10 1/133 [..............................] - ETA: 0s - loss: 0.0117 52/133 [==========>...................] - ETA: 0s - loss: 0.0292 103/133 [======================>.......] - ETA: 0s - loss: 0.0314 133/133 [==============================] - 0s 997us/step - loss: 0.0316 Epoch 5/10 1/133 [..............................] - ETA: 0s - loss: 0.0252 52/133 [==========>...................] - ETA: 0s - loss: 0.0263 103/133 [======================>.......] - ETA: 0s - loss: 0.0297 133/133 [==============================] - 0s 992us/step - loss: 0.0290 Epoch 6/10 1/133 [..............................] - ETA: 0s - loss: 0.0155 52/133 [==========>...................] - ETA: 0s - loss: 0.0309 103/133 [======================>.......] - ETA: 0s - loss: 0.0299 133/133 [==============================] - 0s 995us/step - loss: 0.0278 Epoch 7/10 1/133 [..............................] - ETA: 0s - loss: 0.0017 52/133 [==========>...................] - ETA: 0s - loss: 0.0182 103/133 [======================>.......] - ETA: 0s - loss: 0.0256 133/133 [==============================] - 0s 988us/step - loss: 0.0248 Epoch 8/10 1/133 [..............................] - ETA: 0s - loss: 0.0040 52/133 [==========>...................] - ETA: 0s - loss: 0.0240 103/133 [======================>.......] - ETA: 0s - loss: 0.0255 133/133 [==============================] - 0s 994us/step - loss: 0.0244 Epoch 9/10 1/133 [..............................] - ETA: 0s - loss: 0.0314 52/133 [==========>...................] - ETA: 0s - loss: 0.0205 103/133 [======================>.......] - ETA: 0s - loss: 0.0232 133/133 [==============================] - 0s 991us/step - loss: 0.0229 Epoch 10/10 1/133 [..............................] - ETA: 0s - loss: 0.0137 52/133 [==========>...................] - ETA: 0s - loss: 0.0206 103/133 [======================>.......] - ETA: 0s - loss: 0.0200 133/133 [==============================] - 0s 992us/step - loss: 0.0207 -> test with GAN.predict GAN tn, fp: 331, 2 GAN fn, tp: 1, 12 GAN f1 score: 0.889 GAN cohens kappa score: 0.884 -> test with 'LR' LR tn, fp: 295, 38 LR fn, tp: 1, 12 LR f1 score: 0.381 LR cohens kappa score: 0.342 LR average precision score: 0.301 -> test with 'RF' RF tn, fp: 333, 0 RF fn, tp: 2, 11 RF f1 score: 0.917 RF cohens kappa score: 0.914 -> test with 'GB' GB tn, fp: 333, 0 GB fn, tp: 0, 13 GB f1 score: 1.000 GB cohens kappa score: 1.000 -> test with 'KNN' KNN tn, fp: 316, 17 KNN fn, tp: 2, 11 KNN f1 score: 0.537 KNN cohens kappa score: 0.512 ------ Step 1/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1278 synthetic samples -> retrain GAN for predict Epoch 1/10 1/133 [..............................] - ETA: 15s - loss: 0.0188 51/133 [==========>...................] - ETA: 0s - loss: 0.0372  102/133 [======================>.......] - ETA: 0s - loss: 0.0452 133/133 [==============================] - 0s 999us/step - loss: 0.0401 Epoch 2/10 1/133 [..............................] - ETA: 0s - loss: 0.0094 51/133 [==========>...................] - ETA: 0s - loss: 0.0412 101/133 [=====================>........] - ETA: 0s - loss: 0.0401 133/133 [==============================] - 0s 1ms/step - loss: 0.0381 Epoch 3/10 1/133 [..............................] - ETA: 0s - loss: 0.0052 52/133 [==========>...................] - ETA: 0s - loss: 0.0392 103/133 [======================>.......] - ETA: 0s - loss: 0.0363 133/133 [==============================] - 0s 990us/step - loss: 0.0373 Epoch 4/10 1/133 [..............................] - ETA: 0s - loss: 0.0295 44/133 [========>.....................] - ETA: 0s - loss: 0.0333 95/133 [====================>.........] - ETA: 0s - loss: 0.0304 133/133 [==============================] - 0s 1ms/step - loss: 0.0331 Epoch 5/10 1/133 [..............................] - ETA: 0s - loss: 0.0647 44/133 [========>.....................] - ETA: 0s - loss: 0.0360 93/133 [===================>..........] - ETA: 0s - loss: 0.0318 133/133 [==============================] - 0s 1ms/step - loss: 0.0329 Epoch 6/10 1/133 [..............................] - ETA: 0s - loss: 0.0033 50/133 [==========>...................] - ETA: 0s - loss: 0.0303 100/133 [=====================>........] - ETA: 0s - loss: 0.0294 133/133 [==============================] - 0s 1ms/step - loss: 0.0305 Epoch 7/10 1/133 [..............................] - ETA: 0s - loss: 0.0187 52/133 [==========>...................] - ETA: 0s - loss: 0.0222 103/133 [======================>.......] - ETA: 0s - loss: 0.0262 133/133 [==============================] - 0s 990us/step - loss: 0.0275 Epoch 8/10 1/133 [..............................] - ETA: 0s - loss: 0.0173 52/133 [==========>...................] - ETA: 0s - loss: 0.0192 103/133 [======================>.......] - ETA: 0s - loss: 0.0251 133/133 [==============================] - 0s 993us/step - loss: 0.0286 Epoch 9/10 1/133 [..............................] - ETA: 0s - loss: 0.0264 52/133 [==========>...................] - ETA: 0s - loss: 0.0254 103/133 [======================>.......] - ETA: 0s - loss: 0.0260 133/133 [==============================] - 0s 995us/step - loss: 0.0246 Epoch 10/10 1/133 [..............................] - ETA: 0s - loss: 0.0054 52/133 [==========>...................] - ETA: 0s - loss: 0.0284 103/133 [======================>.......] - ETA: 0s - loss: 0.0238 133/133 [==============================] - 0s 985us/step - loss: 0.0248 -> test with GAN.predict GAN tn, fp: 326, 7 GAN fn, tp: 2, 11 GAN f1 score: 0.710 GAN cohens kappa score: 0.696 -> test with 'LR' LR tn, fp: 283, 50 LR fn, tp: 0, 13 LR f1 score: 0.342 LR cohens kappa score: 0.298 LR average precision score: 0.410 -> test with 'RF' RF tn, fp: 333, 0 RF fn, tp: 1, 12 RF f1 score: 0.960 RF cohens kappa score: 0.959 -> test with 'GB' GB tn, fp: 332, 1 GB fn, tp: 1, 12 GB f1 score: 0.923 GB cohens kappa score: 0.920 -> test with 'KNN' KNN tn, fp: 314, 19 KNN fn, tp: 0, 13 KNN f1 score: 0.578 KNN cohens kappa score: 0.554 ------ Step 1/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1278 synthetic samples -> retrain GAN for predict Epoch 1/10 1/133 [..............................] - ETA: 16s - loss: 0.2278 52/133 [==========>...................] - ETA: 0s - loss: 0.0466  103/133 [======================>.......] - ETA: 0s - loss: 0.0358 133/133 [==============================] - 0s 990us/step - loss: 0.0365 Epoch 2/10 1/133 [..............................] - ETA: 0s - loss: 0.0211 52/133 [==========>...................] - ETA: 0s - loss: 0.0359 103/133 [======================>.......] - ETA: 0s - loss: 0.0381 133/133 [==============================] - 0s 989us/step - loss: 0.0369 Epoch 3/10 1/133 [..............................] - ETA: 0s - loss: 0.0125 52/133 [==========>...................] - ETA: 0s - loss: 0.0445 103/133 [======================>.......] - ETA: 0s - loss: 0.0359 133/133 [==============================] - 0s 991us/step - loss: 0.0333 Epoch 4/10 1/133 [..............................] - ETA: 0s - loss: 0.0052 52/133 [==========>...................] - ETA: 0s - loss: 0.0276 98/133 [=====================>........] - ETA: 0s - loss: 0.0318 133/133 [==============================] - 0s 1ms/step - loss: 0.0297 Epoch 5/10 1/133 [..............................] - ETA: 0s - loss: 0.0272 50/133 [==========>...................] - ETA: 0s - loss: 0.0264 100/133 [=====================>........] - ETA: 0s - loss: 0.0305 133/133 [==============================] - 0s 1ms/step - loss: 0.0294 Epoch 6/10 1/133 [..............................] - ETA: 0s - loss: 0.0357 52/133 [==========>...................] - ETA: 0s - loss: 0.0330 103/133 [======================>.......] - ETA: 0s - loss: 0.0296 133/133 [==============================] - 0s 988us/step - loss: 0.0278 Epoch 7/10 1/133 [..............................] - ETA: 0s - loss: 0.0096 52/133 [==========>...................] - ETA: 0s - loss: 0.0269 103/133 [======================>.......] - ETA: 0s - loss: 0.0265 133/133 [==============================] - 0s 985us/step - loss: 0.0262 Epoch 8/10 1/133 [..............................] - ETA: 0s - loss: 0.0108 52/133 [==========>...................] - ETA: 0s - loss: 0.0211 103/133 [======================>.......] - ETA: 0s - loss: 0.0207 133/133 [==============================] - 0s 986us/step - loss: 0.0244 Epoch 9/10 1/133 [..............................] - ETA: 0s - loss: 0.0141 52/133 [==========>...................] - ETA: 0s - loss: 0.0275 103/133 [======================>.......] - ETA: 0s - loss: 0.0243 133/133 [==============================] - 0s 988us/step - loss: 0.0244 Epoch 10/10 1/133 [..............................] - ETA: 0s - loss: 0.0455 51/133 [==========>...................] - ETA: 0s - loss: 0.0254 101/133 [=====================>........] - ETA: 0s - loss: 0.0220 133/133 [==============================] - 0s 1ms/step - loss: 0.0235 -> test with GAN.predict GAN tn, fp: 330, 3 GAN fn, tp: 3, 10 GAN f1 score: 0.769 GAN cohens kappa score: 0.760 -> test with 'LR' LR tn, fp: 293, 40 LR fn, tp: 0, 13 LR f1 score: 0.394 LR cohens kappa score: 0.355 LR average precision score: 0.359 -> test with 'RF' RF tn, fp: 333, 0 RF fn, tp: 3, 10 RF f1 score: 0.870 RF cohens kappa score: 0.865 -> test with 'GB' GB tn, fp: 333, 0 GB fn, tp: 0, 13 GB f1 score: 1.000 GB cohens kappa score: 1.000 -> test with 'KNN' KNN tn, fp: 324, 9 KNN fn, tp: 0, 13 KNN f1 score: 0.743 KNN cohens kappa score: 0.730 ------ Step 1/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1280 synthetic samples -> retrain GAN for predict Epoch 1/10 1/134 [..............................] - ETA: 18s - loss: 0.0495 50/134 [==========>...................] - ETA: 0s - loss: 0.0415  99/134 [=====================>........] - ETA: 0s - loss: 0.0391 134/134 [==============================] - 0s 1ms/step - loss: 0.0372 Epoch 2/10 1/134 [..............................] - ETA: 0s - loss: 0.0060 50/134 [==========>...................] - ETA: 0s - loss: 0.0300 99/134 [=====================>........] - ETA: 0s - loss: 0.0276 134/134 [==============================] - 0s 1ms/step - loss: 0.0350 Epoch 3/10 1/134 [..............................] - ETA: 0s - loss: 0.0172 45/134 [=========>....................] - ETA: 0s - loss: 0.0201 91/134 [===================>..........] - ETA: 0s - loss: 0.0236 134/134 [==============================] - 0s 1ms/step - loss: 0.0329 Epoch 4/10 1/134 [..............................] - ETA: 0s - loss: 0.0343 46/134 [=========>....................] - ETA: 0s - loss: 0.0291 95/134 [====================>.........] - ETA: 0s - loss: 0.0258 134/134 [==============================] - 0s 1ms/step - loss: 0.0307 Epoch 5/10 1/134 [..............................] - ETA: 0s - loss: 0.0343 50/134 [==========>...................] - ETA: 0s - loss: 0.0296 99/134 [=====================>........] - ETA: 0s - loss: 0.0315 134/134 [==============================] - 0s 1ms/step - loss: 0.0290 Epoch 6/10 1/134 [..............................] - ETA: 0s - loss: 0.0112 50/134 [==========>...................] - ETA: 0s - loss: 0.0356 99/134 [=====================>........] - ETA: 0s - loss: 0.0275 134/134 [==============================] - 0s 1ms/step - loss: 0.0275 Epoch 7/10 1/134 [..............................] - ETA: 0s - loss: 0.0619 49/134 [=========>....................] - ETA: 0s - loss: 0.0206 97/134 [====================>.........] - ETA: 0s - loss: 0.0268 134/134 [==============================] - 0s 1ms/step - loss: 0.0262 Epoch 8/10 1/134 [..............................] - ETA: 0s - loss: 0.0037 49/134 [=========>....................] - ETA: 0s - loss: 0.0339 98/134 [====================>.........] - ETA: 0s - loss: 0.0284 134/134 [==============================] - 0s 1ms/step - loss: 0.0240 Epoch 9/10 1/134 [..............................] - ETA: 0s - loss: 0.0018 50/134 [==========>...................] - ETA: 0s - loss: 0.0183 99/134 [=====================>........] - ETA: 0s - loss: 0.0238 134/134 [==============================] - 0s 1ms/step - loss: 0.0229 Epoch 10/10 1/134 [..............................] - ETA: 0s - loss: 0.0091 50/134 [==========>...................] - ETA: 0s - loss: 0.0228 99/134 [=====================>........] - ETA: 0s - loss: 0.0229 134/134 [==============================] - 0s 1ms/step - loss: 0.0221 -> test with GAN.predict GAN tn, fp: 329, 2 GAN fn, tp: 2, 11 GAN f1 score: 0.846 GAN cohens kappa score: 0.840 -> test with 'LR' LR tn, fp: 300, 31 LR fn, tp: 2, 11 LR f1 score: 0.400 LR cohens kappa score: 0.363 LR average precision score: 0.437 -> test with 'RF' RF tn, fp: 330, 1 RF fn, tp: 2, 11 RF f1 score: 0.880 RF cohens kappa score: 0.875 -> test with 'GB' GB tn, fp: 328, 3 GB fn, tp: 1, 12 GB f1 score: 0.857 GB cohens kappa score: 0.851 -> test with 'KNN' KNN tn, fp: 320, 11 KNN fn, tp: 0, 13 KNN f1 score: 0.703 KNN cohens kappa score: 0.687 ====== Step 2/5 ======= -> Shuffling data -> Spliting data to slices ------ Step 2/5: Slice 1/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1278 synthetic samples -> retrain GAN for predict Epoch 1/10 1/133 [..............................] - ETA: 15s - loss: 0.0494 46/133 [=========>....................] - ETA: 0s - loss: 0.0374  97/133 [====================>.........] - ETA: 0s - loss: 0.0382 133/133 [==============================] - 0s 1ms/step - loss: 0.0355 Epoch 2/10 1/133 [..............................] - ETA: 0s - loss: 0.0126 52/133 [==========>...................] - ETA: 0s - loss: 0.0286 103/133 [======================>.......] - ETA: 0s - loss: 0.0356 133/133 [==============================] - 0s 988us/step - loss: 0.0345 Epoch 3/10 1/133 [..............................] - ETA: 0s - loss: 0.0130 52/133 [==========>...................] - ETA: 0s - loss: 0.0344 103/133 [======================>.......] - ETA: 0s - loss: 0.0336 133/133 [==============================] - 0s 992us/step - loss: 0.0330 Epoch 4/10 1/133 [..............................] - ETA: 0s - loss: 0.0095 52/133 [==========>...................] - ETA: 0s - loss: 0.0360 103/133 [======================>.......] - ETA: 0s - loss: 0.0289 133/133 [==============================] - 0s 986us/step - loss: 0.0301 Epoch 5/10 1/133 [..............................] - ETA: 0s - loss: 0.0108 52/133 [==========>...................] - ETA: 0s - loss: 0.0375 103/133 [======================>.......] - ETA: 0s - loss: 0.0328 133/133 [==============================] - 0s 991us/step - loss: 0.0295 Epoch 6/10 1/133 [..............................] - ETA: 0s - loss: 0.1089 52/133 [==========>...................] - ETA: 0s - loss: 0.0316 103/133 [======================>.......] - ETA: 0s - loss: 0.0288 133/133 [==============================] - 0s 989us/step - loss: 0.0271 Epoch 7/10 1/133 [..............................] - ETA: 0s - loss: 0.0162 52/133 [==========>...................] - ETA: 0s - loss: 0.0173 103/133 [======================>.......] - ETA: 0s - loss: 0.0250 133/133 [==============================] - 0s 990us/step - loss: 0.0253 Epoch 8/10 1/133 [..............................] - ETA: 0s - loss: 0.0094 51/133 [==========>...................] - ETA: 0s - loss: 0.0170 102/133 [======================>.......] - ETA: 0s - loss: 0.0199 133/133 [==============================] - 0s 995us/step - loss: 0.0244 Epoch 9/10 1/133 [..............................] - ETA: 0s - loss: 0.0016 52/133 [==========>...................] - ETA: 0s - loss: 0.0234 92/133 [===================>..........] - ETA: 0s - loss: 0.0242 133/133 [==============================] - 0s 1ms/step - loss: 0.0234 Epoch 10/10 1/133 [..............................] - ETA: 0s - loss: 0.0233 50/133 [==========>...................] - ETA: 0s - loss: 0.0249 100/133 [=====================>........] - ETA: 0s - loss: 0.0227 133/133 [==============================] - 0s 1ms/step - loss: 0.0221 -> test with GAN.predict GAN tn, fp: 328, 5 GAN fn, tp: 3, 10 GAN f1 score: 0.714 GAN cohens kappa score: 0.702 -> test with 'LR' LR tn, fp: 292, 41 LR fn, tp: 0, 13 LR f1 score: 0.388 LR cohens kappa score: 0.349 LR average precision score: 0.292 -> test with 'RF' RF tn, fp: 333, 0 RF fn, tp: 2, 11 RF f1 score: 0.917 RF cohens kappa score: 0.914 -> test with 'GB' GB tn, fp: 333, 0 GB fn, tp: 2, 11 GB f1 score: 0.917 GB cohens kappa score: 0.914 -> test with 'KNN' KNN tn, fp: 315, 18 KNN fn, tp: 0, 13 KNN f1 score: 0.591 KNN cohens kappa score: 0.568 ------ Step 2/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1278 synthetic samples -> retrain GAN for predict Epoch 1/10 1/133 [..............................] - ETA: 15s - loss: 0.0733 51/133 [==========>...................] - ETA: 0s - loss: 0.0466  102/133 [======================>.......] - ETA: 0s - loss: 0.0403 133/133 [==============================] - 0s 1ms/step - loss: 0.0369 Epoch 2/10 1/133 [..............................] - ETA: 0s - loss: 0.0247 52/133 [==========>...................] - ETA: 0s - loss: 0.0333 103/133 [======================>.......] - ETA: 0s - loss: 0.0323 133/133 [==============================] - 0s 993us/step - loss: 0.0341 Epoch 3/10 1/133 [..............................] - ETA: 0s - loss: 0.0229 51/133 [==========>...................] - ETA: 0s - loss: 0.0360 102/133 [======================>.......] - ETA: 0s - loss: 0.0354 133/133 [==============================] - 0s 999us/step - loss: 0.0319 Epoch 4/10 1/133 [..............................] - ETA: 0s - loss: 0.0683 52/133 [==========>...................] - ETA: 0s - loss: 0.0310 103/133 [======================>.......] - ETA: 0s - loss: 0.0325 133/133 [==============================] - 0s 990us/step - loss: 0.0289 Epoch 5/10 1/133 [..............................] - ETA: 0s - loss: 0.2729 52/133 [==========>...................] - ETA: 0s - loss: 0.0301 103/133 [======================>.......] - ETA: 0s - loss: 0.0284 133/133 [==============================] - 0s 996us/step - loss: 0.0288 Epoch 6/10 1/133 [..............................] - ETA: 0s - loss: 0.0027 52/133 [==========>...................] - ETA: 0s - loss: 0.0279 103/133 [======================>.......] - ETA: 0s - loss: 0.0290 133/133 [==============================] - 0s 997us/step - loss: 0.0258 Epoch 7/10 1/133 [..............................] - ETA: 0s - loss: 0.0111 51/133 [==========>...................] - ETA: 0s - loss: 0.0244 102/133 [======================>.......] - ETA: 0s - loss: 0.0235 133/133 [==============================] - 0s 999us/step - loss: 0.0256 Epoch 8/10 1/133 [..............................] - ETA: 0s - loss: 0.0151 49/133 [==========>...................] - ETA: 0s - loss: 0.0196 95/133 [====================>.........] - ETA: 0s - loss: 0.0246 133/133 [==============================] - 0s 1ms/step - loss: 0.0234 Epoch 9/10 1/133 [..............................] - ETA: 0s - loss: 0.0078 47/133 [=========>....................] - ETA: 0s - loss: 0.0173 96/133 [====================>.........] - ETA: 0s - loss: 0.0235 133/133 [==============================] - 0s 1ms/step - loss: 0.0221 Epoch 10/10 1/133 [..............................] - ETA: 0s - loss: 0.0967 52/133 [==========>...................] - ETA: 0s - loss: 0.0192 103/133 [======================>.......] - ETA: 0s - loss: 0.0230 133/133 [==============================] - 0s 995us/step - loss: 0.0225 -> test with GAN.predict GAN tn, fp: 325, 8 GAN fn, tp: 1, 12 GAN f1 score: 0.727 GAN cohens kappa score: 0.714 -> test with 'LR' LR tn, fp: 277, 56 LR fn, tp: 0, 13 LR f1 score: 0.317 LR cohens kappa score: 0.271 LR average precision score: 0.265 -> test with 'RF' RF tn, fp: 331, 2 RF fn, tp: 1, 12 RF f1 score: 0.889 RF cohens kappa score: 0.884 -> test with 'GB' GB tn, fp: 327, 6 GB fn, tp: 0, 13 GB f1 score: 0.813 GB cohens kappa score: 0.804 -> test with 'KNN' KNN tn, fp: 315, 18 KNN fn, tp: 0, 13 KNN f1 score: 0.591 KNN cohens kappa score: 0.568 ------ Step 2/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1278 synthetic samples -> retrain GAN for predict Epoch 1/10 1/133 [..............................] - ETA: 15s - loss: 0.0073 50/133 [==========>...................] - ETA: 0s - loss: 0.0317  100/133 [=====================>........] - ETA: 0s - loss: 0.0306 133/133 [==============================] - 0s 1ms/step - loss: 0.0340 Epoch 2/10 1/133 [..............................] - ETA: 0s - loss: 0.0260 52/133 [==========>...................] - ETA: 0s - loss: 0.0335 101/133 [=====================>........] - ETA: 0s - loss: 0.0302 133/133 [==============================] - 0s 1ms/step - loss: 0.0311 Epoch 3/10 1/133 [..............................] - ETA: 0s - loss: 0.0165 52/133 [==========>...................] - ETA: 0s - loss: 0.0337 103/133 [======================>.......] - ETA: 0s - loss: 0.0296 133/133 [==============================] - 0s 990us/step - loss: 0.0287 Epoch 4/10 1/133 [..............................] - ETA: 0s - loss: 0.0105 52/133 [==========>...................] - ETA: 0s - loss: 0.0297 103/133 [======================>.......] - ETA: 0s - loss: 0.0256 133/133 [==============================] - 0s 1ms/step - loss: 0.0273 Epoch 5/10 1/133 [..............................] - ETA: 0s - loss: 0.0690 50/133 [==========>...................] - ETA: 0s - loss: 0.0193 99/133 [=====================>........] - ETA: 0s - loss: 0.0230 133/133 [==============================] - 0s 1ms/step - loss: 0.0247 Epoch 6/10 1/133 [..............................] - ETA: 0s - loss: 0.0103 52/133 [==========>...................] - ETA: 0s - loss: 0.0254 99/133 [=====================>........] - ETA: 0s - loss: 0.0236 133/133 [==============================] - 0s 1ms/step - loss: 0.0254 Epoch 7/10 1/133 [..............................] - ETA: 0s - loss: 0.0126 45/133 [=========>....................] - ETA: 0s - loss: 0.0184 94/133 [====================>.........] - ETA: 0s - loss: 0.0232 133/133 [==============================] - 0s 1ms/step - loss: 0.0217 Epoch 8/10 1/133 [..............................] - ETA: 0s - loss: 0.0023 52/133 [==========>...................] - ETA: 0s - loss: 0.0191 98/133 [=====================>........] - ETA: 0s - loss: 0.0213 133/133 [==============================] - 0s 1ms/step - loss: 0.0209 Epoch 9/10 1/133 [..............................] - ETA: 0s - loss: 0.0070 52/133 [==========>...................] - ETA: 0s - loss: 0.0173 103/133 [======================>.......] - ETA: 0s - loss: 0.0202 133/133 [==============================] - 0s 990us/step - loss: 0.0194 Epoch 10/10 1/133 [..............................] - ETA: 0s - loss: 0.0023 52/133 [==========>...................] - ETA: 0s - loss: 0.0158 103/133 [======================>.......] - ETA: 0s - loss: 0.0180 133/133 [==============================] - 0s 988us/step - loss: 0.0185 -> test with GAN.predict GAN tn, fp: 331, 2 GAN fn, tp: 1, 12 GAN f1 score: 0.889 GAN cohens kappa score: 0.884 -> test with 'LR' LR tn, fp: 294, 39 LR fn, tp: 2, 11 LR f1 score: 0.349 LR cohens kappa score: 0.308 LR average precision score: 0.325 -> test with 'RF' RF tn, fp: 332, 1 RF fn, tp: 2, 11 RF f1 score: 0.880 RF cohens kappa score: 0.876 -> test with 'GB' GB tn, fp: 331, 2 GB fn, tp: 0, 13 GB f1 score: 0.929 GB cohens kappa score: 0.926 -> test with 'KNN' KNN tn, fp: 322, 11 KNN fn, tp: 1, 12 KNN f1 score: 0.667 KNN cohens kappa score: 0.650 ------ Step 2/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1278 synthetic samples -> retrain GAN for predict Epoch 1/10 1/133 [..............................] - ETA: 17s - loss: 0.0307 51/133 [==========>...................] - ETA: 0s - loss: 0.0342  101/133 [=====================>........] - ETA: 0s - loss: 0.0359 133/133 [==============================] - 0s 1ms/step - loss: 0.0333 Epoch 2/10 1/133 [..............................] - ETA: 0s - loss: 0.0072 52/133 [==========>...................] - ETA: 0s - loss: 0.0346 102/133 [======================>.......] - ETA: 0s - loss: 0.0310 133/133 [==============================] - 0s 999us/step - loss: 0.0316 Epoch 3/10 1/133 [..............................] - ETA: 0s - loss: 0.0070 51/133 [==========>...................] - ETA: 0s - loss: 0.0185 102/133 [======================>.......] - ETA: 0s - loss: 0.0252 133/133 [==============================] - 0s 998us/step - loss: 0.0292 Epoch 4/10 1/133 [..............................] - ETA: 0s - loss: 0.0172 49/133 [==========>...................] - ETA: 0s - loss: 0.0210 96/133 [====================>.........] - ETA: 0s - loss: 0.0264 133/133 [==============================] - 0s 1ms/step - loss: 0.0296 Epoch 5/10 1/133 [..............................] - ETA: 0s - loss: 0.0157 49/133 [==========>...................] - ETA: 0s - loss: 0.0298 99/133 [=====================>........] - ETA: 0s - loss: 0.0289 133/133 [==============================] - 0s 1ms/step - loss: 0.0279 Epoch 6/10 1/133 [..............................] - ETA: 0s - loss: 0.0183 49/133 [==========>...................] - ETA: 0s - loss: 0.0288 100/133 [=====================>........] - ETA: 0s - loss: 0.0289 133/133 [==============================] - 0s 1ms/step - loss: 0.0250 Epoch 7/10 1/133 [..............................] - ETA: 0s - loss: 0.0468 52/133 [==========>...................] - ETA: 0s - loss: 0.0287 102/133 [======================>.......] - ETA: 0s - loss: 0.0244 133/133 [==============================] - 0s 996us/step - loss: 0.0240 Epoch 8/10 1/133 [..............................] - ETA: 0s - loss: 0.0033 52/133 [==========>...................] - ETA: 0s - loss: 0.0202 98/133 [=====================>........] - ETA: 0s - loss: 0.0223 133/133 [==============================] - 0s 1ms/step - loss: 0.0237 Epoch 9/10 1/133 [..............................] - ETA: 0s - loss: 0.0073 47/133 [=========>....................] - ETA: 0s - loss: 0.0192 97/133 [====================>.........] - ETA: 0s - loss: 0.0222 133/133 [==============================] - 0s 1ms/step - loss: 0.0222 Epoch 10/10 1/133 [..............................] - ETA: 0s - loss: 0.0056 52/133 [==========>...................] - ETA: 0s - loss: 0.0184 102/133 [======================>.......] - ETA: 0s - loss: 0.0196 133/133 [==============================] - 0s 996us/step - loss: 0.0209 -> test with GAN.predict GAN tn, fp: 325, 8 GAN fn, tp: 2, 11 GAN f1 score: 0.688 GAN cohens kappa score: 0.673 -> test with 'LR' LR tn, fp: 298, 35 LR fn, tp: 0, 13 LR f1 score: 0.426 LR cohens kappa score: 0.390 LR average precision score: 0.288 -> test with 'RF' RF tn, fp: 333, 0 RF fn, tp: 3, 10 RF f1 score: 0.870 RF cohens kappa score: 0.865 -> test with 'GB' GB tn, fp: 333, 0 GB fn, tp: 2, 11 GB f1 score: 0.917 GB cohens kappa score: 0.914 -> test with 'KNN' KNN tn, fp: 324, 9 KNN fn, tp: 2, 11 KNN f1 score: 0.667 KNN cohens kappa score: 0.651 ------ Step 2/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1280 synthetic samples -> retrain GAN for predict Epoch 1/10 1/134 [..............................] - ETA: 18s - loss: 0.0232 47/134 [=========>....................] - ETA: 0s - loss: 0.0327  95/134 [====================>.........] - ETA: 0s - loss: 0.0336 134/134 [==============================] - 0s 1ms/step - loss: 0.0350 Epoch 2/10 1/134 [..............................] - ETA: 0s - loss: 0.0444 49/134 [=========>....................] - ETA: 0s - loss: 0.0293 98/134 [====================>.........] - ETA: 0s - loss: 0.0361 134/134 [==============================] - 0s 1ms/step - loss: 0.0335 Epoch 3/10 1/134 [..............................] - ETA: 0s - loss: 0.0112 46/134 [=========>....................] - ETA: 0s - loss: 0.0346 91/134 [===================>..........] - ETA: 0s - loss: 0.0333 134/134 [==============================] - 0s 1ms/step - loss: 0.0319 Epoch 4/10 1/134 [..............................] - ETA: 0s - loss: 0.0095 49/134 [=========>....................] - ETA: 0s - loss: 0.0190 97/134 [====================>.........] - ETA: 0s - loss: 0.0233 134/134 [==============================] - 0s 1ms/step - loss: 0.0300 Epoch 5/10 1/134 [..............................] - ETA: 0s - loss: 0.0193 50/134 [==========>...................] - ETA: 0s - loss: 0.0220 99/134 [=====================>........] - ETA: 0s - loss: 0.0297 134/134 [==============================] - 0s 1ms/step - loss: 0.0294 Epoch 6/10 1/134 [..............................] - ETA: 0s - loss: 0.0067 49/134 [=========>....................] - ETA: 0s - loss: 0.0236 97/134 [====================>.........] - ETA: 0s - loss: 0.0266 134/134 [==============================] - 0s 1ms/step - loss: 0.0271 Epoch 7/10 1/134 [..............................] - ETA: 0s - loss: 0.0160 44/134 [========>.....................] - ETA: 0s - loss: 0.0280 86/134 [==================>...........] - ETA: 0s - loss: 0.0258 134/134 [==============================] - ETA: 0s - loss: 0.0271 134/134 [==============================] - 0s 1ms/step - loss: 0.0271 Epoch 8/10 1/134 [..............................] - ETA: 0s - loss: 0.0163 49/134 [=========>....................] - ETA: 0s - loss: 0.0187 97/134 [====================>.........] - ETA: 0s - loss: 0.0236 134/134 [==============================] - 0s 1ms/step - loss: 0.0238 Epoch 9/10 1/134 [..............................] - ETA: 0s - loss: 0.0108 49/134 [=========>....................] - ETA: 0s - loss: 0.0244 97/134 [====================>.........] - ETA: 0s - loss: 0.0245 134/134 [==============================] - 0s 1ms/step - loss: 0.0235 Epoch 10/10 1/134 [..............................] - ETA: 0s - loss: 0.0166 49/134 [=========>....................] - ETA: 0s - loss: 0.0280 97/134 [====================>.........] - ETA: 0s - loss: 0.0238 134/134 [==============================] - 0s 1ms/step - loss: 0.0216 -> test with GAN.predict GAN tn, fp: 325, 6 GAN fn, tp: 2, 11 GAN f1 score: 0.733 GAN cohens kappa score: 0.721 -> test with 'LR' LR tn, fp: 288, 43 LR fn, tp: 0, 13 LR f1 score: 0.377 LR cohens kappa score: 0.336 LR average precision score: 0.524 -> test with 'RF' RF tn, fp: 331, 0 RF fn, tp: 0, 13 RF f1 score: 1.000 RF cohens kappa score: 1.000 -> test with 'GB' GB tn, fp: 331, 0 GB fn, tp: 0, 13 GB f1 score: 1.000 GB cohens kappa score: 1.000 -> test with 'KNN' KNN tn, fp: 319, 12 KNN fn, tp: 0, 13 KNN f1 score: 0.684 KNN cohens kappa score: 0.668 ====== Step 3/5 ======= -> Shuffling data -> Spliting data to slices ------ Step 3/5: Slice 1/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1278 synthetic samples -> retrain GAN for predict Epoch 1/10 1/133 [..............................] - ETA: 17s - loss: 0.0205 42/133 [========>.....................] - ETA: 0s - loss: 0.0206  92/133 [===================>..........] - ETA: 0s - loss: 0.0276 133/133 [==============================] - 0s 1ms/step - loss: 0.0282 Epoch 2/10 1/133 [..............................] - ETA: 0s - loss: 0.0083 52/133 [==========>...................] - ETA: 0s - loss: 0.0231 103/133 [======================>.......] - ETA: 0s - loss: 0.0255 133/133 [==============================] - 0s 996us/step - loss: 0.0266 Epoch 3/10 1/133 [..............................] - ETA: 0s - loss: 0.0359 52/133 [==========>...................] - ETA: 0s - loss: 0.0214 103/133 [======================>.......] - ETA: 0s - loss: 0.0240 133/133 [==============================] - 0s 995us/step - loss: 0.0246 Epoch 4/10 1/133 [..............................] - ETA: 0s - loss: 0.0192 51/133 [==========>...................] - ETA: 0s - loss: 0.0265 102/133 [======================>.......] - ETA: 0s - loss: 0.0235 133/133 [==============================] - 0s 999us/step - loss: 0.0231 Epoch 5/10 1/133 [..............................] - ETA: 0s - loss: 0.0018 52/133 [==========>...................] - ETA: 0s - loss: 0.0206 103/133 [======================>.......] - ETA: 0s - loss: 0.0232 133/133 [==============================] - 0s 994us/step - loss: 0.0220 Epoch 6/10 1/133 [..............................] - ETA: 0s - loss: 0.0120 52/133 [==========>...................] - ETA: 0s - loss: 0.0167 103/133 [======================>.......] - ETA: 0s - loss: 0.0199 133/133 [==============================] - 0s 994us/step - loss: 0.0207 Epoch 7/10 1/133 [..............................] - ETA: 0s - loss: 0.0116 52/133 [==========>...................] - ETA: 0s - loss: 0.0172 103/133 [======================>.......] - ETA: 0s - loss: 0.0156 133/133 [==============================] - 0s 992us/step - loss: 0.0189 Epoch 8/10 1/133 [..............................] - ETA: 0s - loss: 0.0347 52/133 [==========>...................] - ETA: 0s - loss: 0.0224 103/133 [======================>.......] - ETA: 0s - loss: 0.0197 133/133 [==============================] - 0s 992us/step - loss: 0.0201 Epoch 9/10 1/133 [..............................] - ETA: 0s - loss: 0.0093 52/133 [==========>...................] - ETA: 0s - loss: 0.0169 103/133 [======================>.......] - ETA: 0s - loss: 0.0171 133/133 [==============================] - 0s 995us/step - loss: 0.0164 Epoch 10/10 1/133 [..............................] - ETA: 0s - loss: 0.0292 52/133 [==========>...................] - ETA: 0s - loss: 0.0145 103/133 [======================>.......] - ETA: 0s - loss: 0.0162 133/133 [==============================] - 0s 996us/step - loss: 0.0160 -> test with GAN.predict GAN tn, fp: 329, 4 GAN fn, tp: 2, 11 GAN f1 score: 0.786 GAN cohens kappa score: 0.777 -> test with 'LR' LR tn, fp: 294, 39 LR fn, tp: 1, 12 LR f1 score: 0.375 LR cohens kappa score: 0.335 LR average precision score: 0.266 -> test with 'RF' RF tn, fp: 332, 1 RF fn, tp: 4, 9 RF f1 score: 0.783 RF cohens kappa score: 0.775 -> test with 'GB' GB tn, fp: 331, 2 GB fn, tp: 3, 10 GB f1 score: 0.800 GB cohens kappa score: 0.793 -> test with 'KNN' KNN tn, fp: 324, 9 KNN fn, tp: 3, 10 KNN f1 score: 0.625 KNN cohens kappa score: 0.607 ------ Step 3/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1278 synthetic samples -> retrain GAN for predict Epoch 1/10 1/133 [..............................] - ETA: 15s - loss: 0.0160 51/133 [==========>...................] - ETA: 0s - loss: 0.0414  102/133 [======================>.......] - ETA: 0s - loss: 0.0388 133/133 [==============================] - 0s 1ms/step - loss: 0.0373 Epoch 2/10 1/133 [..............................] - ETA: 0s - loss: 0.0159 52/133 [==========>...................] - ETA: 0s - loss: 0.0299 103/133 [======================>.......] - ETA: 0s - loss: 0.0319 133/133 [==============================] - 0s 995us/step - loss: 0.0346 Epoch 3/10 1/133 [..............................] - ETA: 0s - loss: 0.0133 52/133 [==========>...................] - ETA: 0s - loss: 0.0417 103/133 [======================>.......] - ETA: 0s - loss: 0.0317 133/133 [==============================] - 0s 992us/step - loss: 0.0319 Epoch 4/10 1/133 [..............................] - ETA: 0s - loss: 0.0152 52/133 [==========>...................] - ETA: 0s - loss: 0.0325 102/133 [======================>.......] - ETA: 0s - loss: 0.0294 133/133 [==============================] - 0s 995us/step - loss: 0.0318 Epoch 5/10 1/133 [..............................] - ETA: 0s - loss: 0.0066 52/133 [==========>...................] - ETA: 0s - loss: 0.0355 103/133 [======================>.......] - ETA: 0s - loss: 0.0365 133/133 [==============================] - 0s 992us/step - loss: 0.0320 Epoch 6/10 1/133 [..............................] - ETA: 0s - loss: 0.0060 52/133 [==========>...................] - ETA: 0s - loss: 0.0212 103/133 [======================>.......] - ETA: 0s - loss: 0.0236 133/133 [==============================] - 0s 991us/step - loss: 0.0278 Epoch 7/10 1/133 [..............................] - ETA: 0s - loss: 0.0509 52/133 [==========>...................] - ETA: 0s - loss: 0.0240 103/133 [======================>.......] - ETA: 0s - loss: 0.0259 133/133 [==============================] - 0s 989us/step - loss: 0.0278 Epoch 8/10 1/133 [..............................] - ETA: 0s - loss: 0.0281 52/133 [==========>...................] - ETA: 0s - loss: 0.0278 100/133 [=====================>........] - ETA: 0s - loss: 0.0278 133/133 [==============================] - 0s 1ms/step - loss: 0.0259 Epoch 9/10 1/133 [..............................] - ETA: 0s - loss: 0.0106 47/133 [=========>....................] - ETA: 0s - loss: 0.0227 95/133 [====================>.........] - ETA: 0s - loss: 0.0261 133/133 [==============================] - 0s 1ms/step - loss: 0.0250 Epoch 10/10 1/133 [..............................] - ETA: 0s - loss: 0.0156 52/133 [==========>...................] - ETA: 0s - loss: 0.0317 103/133 [======================>.......] - ETA: 0s - loss: 0.0262 133/133 [==============================] - 0s 991us/step - loss: 0.0245 -> test with GAN.predict GAN tn, fp: 328, 5 GAN fn, tp: 1, 12 GAN f1 score: 0.800 GAN cohens kappa score: 0.791 -> test with 'LR' LR tn, fp: 296, 37 LR fn, tp: 0, 13 LR f1 score: 0.413 LR cohens kappa score: 0.375 LR average precision score: 0.396 -> test with 'RF' RF tn, fp: 333, 0 RF fn, tp: 2, 11 RF f1 score: 0.917 RF cohens kappa score: 0.914 -> test with 'GB' GB tn, fp: 331, 2 GB fn, tp: 0, 13 GB f1 score: 0.929 GB cohens kappa score: 0.926 -> test with 'KNN' KNN tn, fp: 318, 15 KNN fn, tp: 0, 13 KNN f1 score: 0.634 KNN cohens kappa score: 0.614 ------ Step 3/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1278 synthetic samples -> retrain GAN for predict Epoch 1/10 1/133 [..............................] - ETA: 16s - loss: 0.0648 51/133 [==========>...................] - ETA: 0s - loss: 0.0366  101/133 [=====================>........] - ETA: 0s - loss: 0.0382 133/133 [==============================] - 0s 1ms/step - loss: 0.0373 Epoch 2/10 1/133 [..............................] - ETA: 0s - loss: 0.0992 52/133 [==========>...................] - ETA: 0s - loss: 0.0252 103/133 [======================>.......] - ETA: 0s - loss: 0.0316 133/133 [==============================] - 0s 996us/step - loss: 0.0340 Epoch 3/10 1/133 [..............................] - ETA: 0s - loss: 0.0270 52/133 [==========>...................] - ETA: 0s - loss: 0.0232 102/133 [======================>.......] - ETA: 0s - loss: 0.0335 133/133 [==============================] - 0s 997us/step - loss: 0.0321 Epoch 4/10 1/133 [..............................] - ETA: 0s - loss: 0.0027 52/133 [==========>...................] - ETA: 0s - loss: 0.0282 103/133 [======================>.......] - ETA: 0s - loss: 0.0334 133/133 [==============================] - 0s 997us/step - loss: 0.0308 Epoch 5/10 1/133 [..............................] - ETA: 0s - loss: 0.0037 51/133 [==========>...................] - ETA: 0s - loss: 0.0285 94/133 [====================>.........] - ETA: 0s - loss: 0.0248 133/133 [==============================] - 0s 1ms/step - loss: 0.0288 Epoch 6/10 1/133 [..............................] - ETA: 0s - loss: 0.1930 48/133 [=========>....................] - ETA: 0s - loss: 0.0317 99/133 [=====================>........] - ETA: 0s - loss: 0.0254 133/133 [==============================] - 0s 1ms/step - loss: 0.0273 Epoch 7/10 1/133 [..............................] - ETA: 0s - loss: 0.0100 51/133 [==========>...................] - ETA: 0s - loss: 0.0272 102/133 [======================>.......] - ETA: 0s - loss: 0.0236 133/133 [==============================] - 0s 1000us/step - loss: 0.0251 Epoch 8/10 1/133 [..............................] - ETA: 0s - loss: 0.0122 52/133 [==========>...................] - ETA: 0s - loss: 0.0278 101/133 [=====================>........] - ETA: 0s - loss: 0.0248 133/133 [==============================] - 0s 1ms/step - loss: 0.0238 Epoch 9/10 1/133 [..............................] - ETA: 0s - loss: 0.0124 52/133 [==========>...................] - ETA: 0s - loss: 0.0175 103/133 [======================>.......] - ETA: 0s - loss: 0.0180 133/133 [==============================] - 0s 1ms/step - loss: 0.0220 Epoch 10/10 1/133 [..............................] - ETA: 0s - loss: 0.0070 52/133 [==========>...................] - ETA: 0s - loss: 0.0241 103/133 [======================>.......] - ETA: 0s - loss: 0.0206 133/133 [==============================] - 0s 991us/step - loss: 0.0215 -> test with GAN.predict GAN tn, fp: 327, 6 GAN fn, tp: 0, 13 GAN f1 score: 0.813 GAN cohens kappa score: 0.804 -> test with 'LR' LR tn, fp: 279, 54 LR fn, tp: 0, 13 LR f1 score: 0.325 LR cohens kappa score: 0.280 LR average precision score: 0.333 -> test with 'RF' RF tn, fp: 333, 0 RF fn, tp: 1, 12 RF f1 score: 0.960 RF cohens kappa score: 0.959 -> test with 'GB' GB tn, fp: 332, 1 GB fn, tp: 0, 13 GB f1 score: 0.963 GB cohens kappa score: 0.961 -> test with 'KNN' KNN tn, fp: 312, 21 KNN fn, tp: 0, 13 KNN f1 score: 0.553 KNN cohens kappa score: 0.528 ------ Step 3/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1278 synthetic samples -> retrain GAN for predict Epoch 1/10 1/133 [..............................] - ETA: 15s - loss: 0.0170 49/133 [==========>...................] - ETA: 0s - loss: 0.0297  94/133 [====================>.........] - ETA: 0s - loss: 0.0290 133/133 [==============================] - 0s 1ms/step - loss: 0.0334 Epoch 2/10 1/133 [..............................] - ETA: 0s - loss: 0.0048 51/133 [==========>...................] - ETA: 0s - loss: 0.0173 102/133 [======================>.......] - ETA: 0s - loss: 0.0294 133/133 [==============================] - 0s 996us/step - loss: 0.0320 Epoch 3/10 1/133 [..............................] - ETA: 0s - loss: 0.0085 52/133 [==========>...................] - ETA: 0s - loss: 0.0280 103/133 [======================>.......] - ETA: 0s - loss: 0.0307 133/133 [==============================] - 0s 993us/step - loss: 0.0300 Epoch 4/10 1/133 [..............................] - ETA: 0s - loss: 0.0102 52/133 [==========>...................] - ETA: 0s - loss: 0.0335 103/133 [======================>.......] - ETA: 0s - loss: 0.0310 133/133 [==============================] - 0s 992us/step - loss: 0.0297 Epoch 5/10 1/133 [..............................] - ETA: 0s - loss: 0.0054 50/133 [==========>...................] - ETA: 0s - loss: 0.0269 98/133 [=====================>........] - ETA: 0s - loss: 0.0271 133/133 [==============================] - 0s 1ms/step - loss: 0.0276 Epoch 6/10 1/133 [..............................] - ETA: 0s - loss: 0.0197 43/133 [========>.....................] - ETA: 0s - loss: 0.0305 87/133 [==================>...........] - ETA: 0s - loss: 0.0261 133/133 [==============================] - 0s 1ms/step - loss: 0.0261 Epoch 7/10 1/133 [..............................] - ETA: 0s - loss: 0.0125 52/133 [==========>...................] - ETA: 0s - loss: 0.0275 103/133 [======================>.......] - ETA: 0s - loss: 0.0249 133/133 [==============================] - 0s 992us/step - loss: 0.0252 Epoch 8/10 1/133 [..............................] - ETA: 0s - loss: 0.0400 52/133 [==========>...................] - ETA: 0s - loss: 0.0221 103/133 [======================>.......] - ETA: 0s - loss: 0.0217 133/133 [==============================] - 0s 995us/step - loss: 0.0231 Epoch 9/10 1/133 [..............................] - ETA: 0s - loss: 0.0032 52/133 [==========>...................] - ETA: 0s - loss: 0.0214 103/133 [======================>.......] - ETA: 0s - loss: 0.0209 133/133 [==============================] - 0s 994us/step - loss: 0.0224 Epoch 10/10 1/133 [..............................] - ETA: 0s - loss: 0.0268 52/133 [==========>...................] - ETA: 0s - loss: 0.0234 103/133 [======================>.......] - ETA: 0s - loss: 0.0217 133/133 [==============================] - 0s 995us/step - loss: 0.0211 -> test with GAN.predict GAN tn, fp: 328, 5 GAN fn, tp: 1, 12 GAN f1 score: 0.800 GAN cohens kappa score: 0.791 -> test with 'LR' LR tn, fp: 297, 36 LR fn, tp: 0, 13 LR f1 score: 0.419 LR cohens kappa score: 0.383 LR average precision score: 0.384 -> test with 'RF' RF tn, fp: 333, 0 RF fn, tp: 0, 13 RF f1 score: 1.000 RF cohens kappa score: 1.000 -> test with 'GB' GB tn, fp: 333, 0 GB fn, tp: 0, 13 GB f1 score: 1.000 GB cohens kappa score: 1.000 -> test with 'KNN' KNN tn, fp: 327, 6 KNN fn, tp: 0, 13 KNN f1 score: 0.813 KNN cohens kappa score: 0.804 ------ Step 3/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1280 synthetic samples -> retrain GAN for predict Epoch 1/10 1/134 [..............................] - ETA: 18s - loss: 0.0160 49/134 [=========>....................] - ETA: 0s - loss: 0.0298  98/134 [====================>.........] - ETA: 0s - loss: 0.0344 134/134 [==============================] - 0s 1ms/step - loss: 0.0344 Epoch 2/10 1/134 [..............................] - ETA: 0s - loss: 0.0185 49/134 [=========>....................] - ETA: 0s - loss: 0.0367 97/134 [====================>.........] - ETA: 0s - loss: 0.0354 134/134 [==============================] - 0s 1ms/step - loss: 0.0327 Epoch 3/10 1/134 [..............................] - ETA: 0s - loss: 0.0441 49/134 [=========>....................] - ETA: 0s - loss: 0.0272 97/134 [====================>.........] - ETA: 0s - loss: 0.0342 134/134 [==============================] - 0s 1ms/step - loss: 0.0311 Epoch 4/10 1/134 [..............................] - ETA: 0s - loss: 0.0054 49/134 [=========>....................] - ETA: 0s - loss: 0.0298 98/134 [====================>.........] - ETA: 0s - loss: 0.0321 134/134 [==============================] - 0s 1ms/step - loss: 0.0296 Epoch 5/10 1/134 [..............................] - ETA: 0s - loss: 0.0303 49/134 [=========>....................] - ETA: 0s - loss: 0.0332 98/134 [====================>.........] - ETA: 0s - loss: 0.0314 134/134 [==============================] - 0s 1ms/step - loss: 0.0266 Epoch 6/10 1/134 [..............................] - ETA: 0s - loss: 0.1425 49/134 [=========>....................] - ETA: 0s - loss: 0.0236 97/134 [====================>.........] - ETA: 0s - loss: 0.0277 134/134 [==============================] - 0s 1ms/step - loss: 0.0272 Epoch 7/10 1/134 [..............................] - ETA: 0s - loss: 0.0118 48/134 [=========>....................] - ETA: 0s - loss: 0.0280 93/134 [===================>..........] - ETA: 0s - loss: 0.0249 134/134 [==============================] - 0s 1ms/step - loss: 0.0249 Epoch 8/10 1/134 [..............................] - ETA: 0s - loss: 0.1274 49/134 [=========>....................] - ETA: 0s - loss: 0.0293 97/134 [====================>.........] - ETA: 0s - loss: 0.0257 134/134 [==============================] - 0s 1ms/step - loss: 0.0219 Epoch 9/10 1/134 [..............................] - ETA: 0s - loss: 0.0275 49/134 [=========>....................] - ETA: 0s - loss: 0.0272 97/134 [====================>.........] - ETA: 0s - loss: 0.0231 134/134 [==============================] - 0s 1ms/step - loss: 0.0228 Epoch 10/10 1/134 [..............................] - ETA: 0s - loss: 0.0030 48/134 [=========>....................] - ETA: 0s - loss: 0.0241 96/134 [====================>.........] - ETA: 0s - loss: 0.0209 134/134 [==============================] - 0s 1ms/step - loss: 0.0203 -> test with GAN.predict GAN tn, fp: 327, 4 GAN fn, tp: 2, 11 GAN f1 score: 0.786 GAN cohens kappa score: 0.777 -> test with 'LR' LR tn, fp: 295, 36 LR fn, tp: 2, 11 LR f1 score: 0.367 LR cohens kappa score: 0.327 LR average precision score: 0.382 -> test with 'RF' RF tn, fp: 331, 0 RF fn, tp: 3, 10 RF f1 score: 0.870 RF cohens kappa score: 0.865 -> test with 'GB' GB tn, fp: 331, 0 GB fn, tp: 2, 11 GB f1 score: 0.917 GB cohens kappa score: 0.914 -> test with 'KNN' KNN tn, fp: 325, 6 KNN fn, tp: 1, 12 KNN f1 score: 0.774 KNN cohens kappa score: 0.764 ====== Step 4/5 ======= -> Shuffling data -> Spliting data to slices ------ Step 4/5: Slice 1/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1278 synthetic samples -> retrain GAN for predict Epoch 1/10 1/133 [..............................] - ETA: 18s - loss: 0.0274 50/133 [==========>...................] - ETA: 0s - loss: 0.0367  100/133 [=====================>........] - ETA: 0s - loss: 0.0383 133/133 [==============================] - 0s 1ms/step - loss: 0.0373 Epoch 2/10 1/133 [..............................] - ETA: 0s - loss: 0.0863 52/133 [==========>...................] - ETA: 0s - loss: 0.0356 103/133 [======================>.......] - ETA: 0s - loss: 0.0356 133/133 [==============================] - 0s 998us/step - loss: 0.0352 Epoch 3/10 1/133 [..............................] - ETA: 0s - loss: 0.0370 51/133 [==========>...................] - ETA: 0s - loss: 0.0311 102/133 [======================>.......] - ETA: 0s - loss: 0.0288 133/133 [==============================] - 0s 999us/step - loss: 0.0331 Epoch 4/10 1/133 [..............................] - ETA: 0s - loss: 0.1278 51/133 [==========>...................] - ETA: 0s - loss: 0.0447 102/133 [======================>.......] - ETA: 0s - loss: 0.0343 133/133 [==============================] - 0s 1ms/step - loss: 0.0317 Epoch 5/10 1/133 [..............................] - ETA: 0s - loss: 0.0497 52/133 [==========>...................] - ETA: 0s - loss: 0.0392 103/133 [======================>.......] - ETA: 0s - loss: 0.0316 133/133 [==============================] - 0s 997us/step - loss: 0.0297 Epoch 6/10 1/133 [..............................] - ETA: 0s - loss: 0.0134 52/133 [==========>...................] - ETA: 0s - loss: 0.0259 102/133 [======================>.......] - ETA: 0s - loss: 0.0276 133/133 [==============================] - 0s 998us/step - loss: 0.0284 Epoch 7/10 1/133 [..............................] - ETA: 0s - loss: 0.0329 48/133 [=========>....................] - ETA: 0s - loss: 0.0290 93/133 [===================>..........] - ETA: 0s - loss: 0.0284 133/133 [==============================] - 0s 1ms/step - loss: 0.0271 Epoch 8/10 1/133 [..............................] - ETA: 0s - loss: 0.0867 51/133 [==========>...................] - ETA: 0s - loss: 0.0258 101/133 [=====================>........] - ETA: 0s - loss: 0.0280 133/133 [==============================] - 0s 1ms/step - loss: 0.0246 Epoch 9/10 1/133 [..............................] - ETA: 0s - loss: 0.0358 48/133 [=========>....................] - ETA: 0s - loss: 0.0209 98/133 [=====================>........] - ETA: 0s - loss: 0.0254 133/133 [==============================] - 0s 1ms/step - loss: 0.0241 Epoch 10/10 1/133 [..............................] - ETA: 0s - loss: 0.0235 52/133 [==========>...................] - ETA: 0s - loss: 0.0215 102/133 [======================>.......] - ETA: 0s - loss: 0.0219 133/133 [==============================] - 0s 1ms/step - loss: 0.0223 -> test with GAN.predict GAN tn, fp: 332, 1 GAN fn, tp: 2, 11 GAN f1 score: 0.880 GAN cohens kappa score: 0.876 -> test with 'LR' LR tn, fp: 295, 38 LR fn, tp: 1, 12 LR f1 score: 0.381 LR cohens kappa score: 0.342 LR average precision score: 0.333 -> test with 'RF' RF tn, fp: 333, 0 RF fn, tp: 1, 12 RF f1 score: 0.960 RF cohens kappa score: 0.959 -> test with 'GB' GB tn, fp: 333, 0 GB fn, tp: 1, 12 GB f1 score: 0.960 GB cohens kappa score: 0.959 -> test with 'KNN' KNN tn, fp: 318, 15 KNN fn, tp: 0, 13 KNN f1 score: 0.634 KNN cohens kappa score: 0.614 ------ Step 4/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1278 synthetic samples -> retrain GAN for predict Epoch 1/10 1/133 [..............................] - ETA: 18s - loss: 0.0107 51/133 [==========>...................] - ETA: 0s - loss: 0.0444  101/133 [=====================>........] - ETA: 0s - loss: 0.0395 133/133 [==============================] - 0s 1ms/step - loss: 0.0374 Epoch 2/10 1/133 [..............................] - ETA: 0s - loss: 0.0127 51/133 [==========>...................] - ETA: 0s - loss: 0.0274 102/133 [======================>.......] - ETA: 0s - loss: 0.0364 133/133 [==============================] - 0s 1ms/step - loss: 0.0352 Epoch 3/10 1/133 [..............................] - ETA: 0s - loss: 0.0412 52/133 [==========>...................] - ETA: 0s - loss: 0.0288 102/133 [======================>.......] - ETA: 0s - loss: 0.0319 133/133 [==============================] - 0s 1ms/step - loss: 0.0328 Epoch 4/10 1/133 [..............................] - ETA: 0s - loss: 0.0220 52/133 [==========>...................] - ETA: 0s - loss: 0.0277 102/133 [======================>.......] - ETA: 0s - loss: 0.0303 133/133 [==============================] - 0s 1ms/step - loss: 0.0305 Epoch 5/10 1/133 [..............................] - ETA: 0s - loss: 0.0339 51/133 [==========>...................] - ETA: 0s - loss: 0.0323 101/133 [=====================>........] - ETA: 0s - loss: 0.0303 133/133 [==============================] - 0s 1ms/step - loss: 0.0285 Epoch 6/10 1/133 [..............................] - ETA: 0s - loss: 0.0079 51/133 [==========>...................] - ETA: 0s - loss: 0.0273 102/133 [======================>.......] - ETA: 0s - loss: 0.0280 133/133 [==============================] - 0s 1ms/step - loss: 0.0279 Epoch 7/10 1/133 [..............................] - ETA: 0s - loss: 0.0072 49/133 [==========>...................] - ETA: 0s - loss: 0.0197 99/133 [=====================>........] - ETA: 0s - loss: 0.0292 133/133 [==============================] - 0s 1ms/step - loss: 0.0261 Epoch 8/10 1/133 [..............................] - ETA: 0s - loss: 0.0119 52/133 [==========>...................] - ETA: 0s - loss: 0.0223 103/133 [======================>.......] - ETA: 0s - loss: 0.0244 133/133 [==============================] - 0s 998us/step - loss: 0.0233 Epoch 9/10 1/133 [..............................] - ETA: 0s - loss: 0.0305 50/133 [==========>...................] - ETA: 0s - loss: 0.0204 96/133 [====================>.........] - ETA: 0s - loss: 0.0187 133/133 [==============================] - 0s 1ms/step - loss: 0.0226 Epoch 10/10 1/133 [..............................] - ETA: 0s - loss: 0.0181 49/133 [==========>...................] - ETA: 0s - loss: 0.0224 100/133 [=====================>........] - ETA: 0s - loss: 0.0235 133/133 [==============================] - 0s 1ms/step - loss: 0.0222 -> test with GAN.predict GAN tn, fp: 330, 3 GAN fn, tp: 1, 12 GAN f1 score: 0.857 GAN cohens kappa score: 0.851 -> test with 'LR' LR tn, fp: 290, 43 LR fn, tp: 1, 12 LR f1 score: 0.353 LR cohens kappa score: 0.311 LR average precision score: 0.521 -> test with 'RF' RF tn, fp: 333, 0 RF fn, tp: 1, 12 RF f1 score: 0.960 RF cohens kappa score: 0.959 -> test with 'GB' GB tn, fp: 333, 0 GB fn, tp: 1, 12 GB f1 score: 0.960 GB cohens kappa score: 0.959 -> test with 'KNN' KNN tn, fp: 325, 8 KNN fn, tp: 0, 13 KNN f1 score: 0.765 KNN cohens kappa score: 0.753 ------ Step 4/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1278 synthetic samples -> retrain GAN for predict Epoch 1/10 1/133 [..............................] - ETA: 18s - loss: 0.0276 50/133 [==========>...................] - ETA: 0s - loss: 0.0530  100/133 [=====================>........] - ETA: 0s - loss: 0.0441 133/133 [==============================] - 0s 1ms/step - loss: 0.0423 Epoch 2/10 1/133 [..............................] - ETA: 0s - loss: 0.0102 51/133 [==========>...................] - ETA: 0s - loss: 0.0385 100/133 [=====================>........] - ETA: 0s - loss: 0.0384 133/133 [==============================] - 0s 1ms/step - loss: 0.0393 Epoch 3/10 1/133 [..............................] - ETA: 0s - loss: 0.1107 51/133 [==========>...................] - ETA: 0s - loss: 0.0356 102/133 [======================>.......] - ETA: 0s - loss: 0.0372 133/133 [==============================] - 0s 1ms/step - loss: 0.0369 Epoch 4/10 1/133 [..............................] - ETA: 0s - loss: 0.0050 51/133 [==========>...................] - ETA: 0s - loss: 0.0333 101/133 [=====================>........] - ETA: 0s - loss: 0.0341 133/133 [==============================] - 0s 1ms/step - loss: 0.0350 Epoch 5/10 1/133 [..............................] - ETA: 0s - loss: 0.0274 51/133 [==========>...................] - ETA: 0s - loss: 0.0337 102/133 [======================>.......] - ETA: 0s - loss: 0.0346 133/133 [==============================] - 0s 1ms/step - loss: 0.0334 Epoch 6/10 1/133 [..............................] - ETA: 0s - loss: 0.0335 51/133 [==========>...................] - ETA: 0s - loss: 0.0265 101/133 [=====================>........] - ETA: 0s - loss: 0.0251 133/133 [==============================] - 0s 1ms/step - loss: 0.0300 Epoch 7/10 1/133 [..............................] - ETA: 0s - loss: 0.0270 51/133 [==========>...................] - ETA: 0s - loss: 0.0329 101/133 [=====================>........] - ETA: 0s - loss: 0.0277 133/133 [==============================] - 0s 1ms/step - loss: 0.0295 Epoch 8/10 1/133 [..............................] - ETA: 0s - loss: 0.0127 51/133 [==========>...................] - ETA: 0s - loss: 0.0191 102/133 [======================>.......] - ETA: 0s - loss: 0.0233 133/133 [==============================] - 0s 1ms/step - loss: 0.0265 Epoch 9/10 1/133 [..............................] - ETA: 0s - loss: 0.0179 49/133 [==========>...................] - ETA: 0s - loss: 0.0163 99/133 [=====================>........] - ETA: 0s - loss: 0.0249 133/133 [==============================] - 0s 1ms/step - loss: 0.0255 Epoch 10/10 1/133 [..............................] - ETA: 0s - loss: 0.0142 52/133 [==========>...................] - ETA: 0s - loss: 0.0179 103/133 [======================>.......] - ETA: 0s - loss: 0.0178 133/133 [==============================] - 0s 997us/step - loss: 0.0223 -> test with GAN.predict GAN tn, fp: 326, 7 GAN fn, tp: 1, 12 GAN f1 score: 0.750 GAN cohens kappa score: 0.738 -> test with 'LR' LR tn, fp: 288, 45 LR fn, tp: 0, 13 LR f1 score: 0.366 LR cohens kappa score: 0.325 LR average precision score: 0.311 -> test with 'RF' RF tn, fp: 332, 1 RF fn, tp: 0, 13 RF f1 score: 0.963 RF cohens kappa score: 0.961 -> test with 'GB' GB tn, fp: 330, 3 GB fn, tp: 0, 13 GB f1 score: 0.897 GB cohens kappa score: 0.892 -> test with 'KNN' KNN tn, fp: 318, 15 KNN fn, tp: 0, 13 KNN f1 score: 0.634 KNN cohens kappa score: 0.614 ------ Step 4/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1278 synthetic samples -> retrain GAN for predict Epoch 1/10 1/133 [..............................] - ETA: 18s - loss: 0.0858 51/133 [==========>...................] - ETA: 0s - loss: 0.0278  101/133 [=====================>........] - ETA: 0s - loss: 0.0291 133/133 [==============================] - 0s 1ms/step - loss: 0.0294 Epoch 2/10 1/133 [..............................] - ETA: 0s - loss: 0.0123 52/133 [==========>...................] - ETA: 0s - loss: 0.0234 103/133 [======================>.......] - ETA: 0s - loss: 0.0279 133/133 [==============================] - 0s 998us/step - loss: 0.0275 Epoch 3/10 1/133 [..............................] - ETA: 0s - loss: 0.0077 51/133 [==========>...................] - ETA: 0s - loss: 0.0314 101/133 [=====================>........] - ETA: 0s - loss: 0.0283 133/133 [==============================] - 0s 1ms/step - loss: 0.0265 Epoch 4/10 1/133 [..............................] - ETA: 0s - loss: 0.0622 52/133 [==========>...................] - ETA: 0s - loss: 0.0219 103/133 [======================>.......] - ETA: 0s - loss: 0.0273 133/133 [==============================] - 0s 996us/step - loss: 0.0254 Epoch 5/10 1/133 [..............................] - ETA: 0s - loss: 0.0890 52/133 [==========>...................] - ETA: 0s - loss: 0.0219 100/133 [=====================>........] - ETA: 0s - loss: 0.0220 133/133 [==============================] - 0s 1ms/step - loss: 0.0232 Epoch 6/10 1/133 [..............................] - ETA: 0s - loss: 0.0396 52/133 [==========>...................] - ETA: 0s - loss: 0.0305 102/133 [======================>.......] - ETA: 0s - loss: 0.0251 133/133 [==============================] - 0s 999us/step - loss: 0.0226 Epoch 7/10 1/133 [..............................] - ETA: 0s - loss: 0.0179 51/133 [==========>...................] - ETA: 0s - loss: 0.0205 100/133 [=====================>........] - ETA: 0s - loss: 0.0219 133/133 [==============================] - 0s 1ms/step - loss: 0.0211 Epoch 8/10 1/133 [..............................] - ETA: 0s - loss: 0.0068 52/133 [==========>...................] - ETA: 0s - loss: 0.0158 102/133 [======================>.......] - ETA: 0s - loss: 0.0185 133/133 [==============================] - 0s 1ms/step - loss: 0.0194 Epoch 9/10 1/133 [..............................] - ETA: 0s - loss: 0.0047 51/133 [==========>...................] - ETA: 0s - loss: 0.0152 102/133 [======================>.......] - ETA: 0s - loss: 0.0174 133/133 [==============================] - 0s 1ms/step - loss: 0.0185 Epoch 10/10 1/133 [..............................] - ETA: 0s - loss: 0.0047 52/133 [==========>...................] - ETA: 0s - loss: 0.0221 99/133 [=====================>........] - ETA: 0s - loss: 0.0187 133/133 [==============================] - 0s 1ms/step - loss: 0.0173 -> test with GAN.predict GAN tn, fp: 324, 9 GAN fn, tp: 6, 7 GAN f1 score: 0.483 GAN cohens kappa score: 0.460 -> test with 'LR' LR tn, fp: 295, 38 LR fn, tp: 1, 12 LR f1 score: 0.381 LR cohens kappa score: 0.342 LR average precision score: 0.290 -> test with 'RF' RF tn, fp: 332, 1 RF fn, tp: 6, 7 RF f1 score: 0.667 RF cohens kappa score: 0.657 -> test with 'GB' GB tn, fp: 332, 1 GB fn, tp: 0, 13 GB f1 score: 0.963 GB cohens kappa score: 0.961 -> test with 'KNN' KNN tn, fp: 317, 16 KNN fn, tp: 0, 13 KNN f1 score: 0.619 KNN cohens kappa score: 0.598 ------ Step 4/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1280 synthetic samples -> retrain GAN for predict Epoch 1/10 1/134 [..............................] - ETA: 18s - loss: 0.0120 49/134 [=========>....................] - ETA: 0s - loss: 0.0448  98/134 [====================>.........] - ETA: 0s - loss: 0.0400 134/134 [==============================] - 0s 1ms/step - loss: 0.0392 Epoch 2/10 1/134 [..............................] - ETA: 0s - loss: 0.0190 44/134 [========>.....................] - ETA: 0s - loss: 0.0351 87/134 [==================>...........] - ETA: 0s - loss: 0.0363 132/134 [============================>.] - ETA: 0s - loss: 0.0384 134/134 [==============================] - 0s 1ms/step - loss: 0.0385 Epoch 3/10 1/134 [..............................] - ETA: 0s - loss: 0.0110 49/134 [=========>....................] - ETA: 0s - loss: 0.0405 97/134 [====================>.........] - ETA: 0s - loss: 0.0358 134/134 [==============================] - 0s 1ms/step - loss: 0.0338 Epoch 4/10 1/134 [..............................] - ETA: 0s - loss: 0.0748 50/134 [==========>...................] - ETA: 0s - loss: 0.0365 98/134 [====================>.........] - ETA: 0s - loss: 0.0346 134/134 [==============================] - 0s 1ms/step - loss: 0.0334 Epoch 5/10 1/134 [..............................] - ETA: 0s - loss: 0.0118 50/134 [==========>...................] - ETA: 0s - loss: 0.0225 99/134 [=====================>........] - ETA: 0s - loss: 0.0284 134/134 [==============================] - 0s 1ms/step - loss: 0.0315 Epoch 6/10 1/134 [..............................] - ETA: 0s - loss: 0.0287 50/134 [==========>...................] - ETA: 0s - loss: 0.0306 99/134 [=====================>........] - ETA: 0s - loss: 0.0288 134/134 [==============================] - 0s 1ms/step - loss: 0.0292 Epoch 7/10 1/134 [..............................] - ETA: 0s - loss: 0.0230 49/134 [=========>....................] - ETA: 0s - loss: 0.0322 95/134 [====================>.........] - ETA: 0s - loss: 0.0307 134/134 [==============================] - 0s 1ms/step - loss: 0.0287 Epoch 8/10 1/134 [..............................] - ETA: 0s - loss: 0.0693 50/134 [==========>...................] - ETA: 0s - loss: 0.0307 99/134 [=====================>........] - ETA: 0s - loss: 0.0286 134/134 [==============================] - 0s 1ms/step - loss: 0.0247 Epoch 9/10 1/134 [..............................] - ETA: 0s - loss: 0.0054 50/134 [==========>...................] - ETA: 0s - loss: 0.0338 99/134 [=====================>........] - ETA: 0s - loss: 0.0255 134/134 [==============================] - 0s 1ms/step - loss: 0.0246 Epoch 10/10 1/134 [..............................] - ETA: 0s - loss: 0.0132 49/134 [=========>....................] - ETA: 0s - loss: 0.0196 97/134 [====================>.........] - ETA: 0s - loss: 0.0207 134/134 [==============================] - 0s 1ms/step - loss: 0.0227 -> test with GAN.predict GAN tn, fp: 326, 5 GAN fn, tp: 1, 12 GAN f1 score: 0.800 GAN cohens kappa score: 0.791 -> test with 'LR' LR tn, fp: 286, 45 LR fn, tp: 0, 13 LR f1 score: 0.366 LR cohens kappa score: 0.324 LR average precision score: 0.325 -> test with 'RF' RF tn, fp: 331, 0 RF fn, tp: 2, 11 RF f1 score: 0.917 RF cohens kappa score: 0.914 -> test with 'GB' GB tn, fp: 331, 0 GB fn, tp: 1, 12 GB f1 score: 0.960 GB cohens kappa score: 0.958 -> test with 'KNN' KNN tn, fp: 315, 16 KNN fn, tp: 0, 13 KNN f1 score: 0.619 KNN cohens kappa score: 0.598 ====== Step 5/5 ======= -> Shuffling data -> Spliting data to slices ------ Step 5/5: Slice 1/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1278 synthetic samples -> retrain GAN for predict Epoch 1/10 1/133 [..............................] - ETA: 15s - loss: 0.1865 51/133 [==========>...................] - ETA: 0s - loss: 0.0481  101/133 [=====================>........] - ETA: 0s - loss: 0.0434 133/133 [==============================] - 0s 1ms/step - loss: 0.0426 Epoch 2/10 1/133 [..............................] - ETA: 0s - loss: 0.0322 52/133 [==========>...................] - ETA: 0s - loss: 0.0361 102/133 [======================>.......] - ETA: 0s - loss: 0.0421 133/133 [==============================] - 0s 997us/step - loss: 0.0400 Epoch 3/10 1/133 [..............................] - ETA: 0s - loss: 0.0076 50/133 [==========>...................] - ETA: 0s - loss: 0.0323 95/133 [====================>.........] - ETA: 0s - loss: 0.0363 133/133 [==============================] - 0s 1ms/step - loss: 0.0376 Epoch 4/10 1/133 [..............................] - ETA: 0s - loss: 0.0176 52/133 [==========>...................] - ETA: 0s - loss: 0.0250 103/133 [======================>.......] - ETA: 0s - loss: 0.0291 133/133 [==============================] - 0s 998us/step - loss: 0.0332 Epoch 5/10 1/133 [..............................] - ETA: 0s - loss: 0.0387 51/133 [==========>...................] - ETA: 0s - loss: 0.0303 102/133 [======================>.......] - ETA: 0s - loss: 0.0332 133/133 [==============================] - 0s 1ms/step - loss: 0.0324 Epoch 6/10 1/133 [..............................] - ETA: 0s - loss: 0.1939 50/133 [==========>...................] - ETA: 0s - loss: 0.0307 101/133 [=====================>........] - ETA: 0s - loss: 0.0276 133/133 [==============================] - 0s 1ms/step - loss: 0.0290 Epoch 7/10 1/133 [..............................] - ETA: 0s - loss: 0.0143 52/133 [==========>...................] - ETA: 0s - loss: 0.0163 102/133 [======================>.......] - ETA: 0s - loss: 0.0271 133/133 [==============================] - 0s 1ms/step - loss: 0.0291 Epoch 8/10 1/133 [..............................] - ETA: 0s - loss: 0.0316 52/133 [==========>...................] - ETA: 0s - loss: 0.0231 100/133 [=====================>........] - ETA: 0s - loss: 0.0263 133/133 [==============================] - 0s 1ms/step - loss: 0.0264 Epoch 9/10 1/133 [..............................] - ETA: 0s - loss: 0.0513 47/133 [=========>....................] - ETA: 0s - loss: 0.0277 95/133 [====================>.........] - ETA: 0s - loss: 0.0259 133/133 [==============================] - 0s 1ms/step - loss: 0.0252 Epoch 10/10 1/133 [..............................] - ETA: 0s - loss: 0.0134 52/133 [==========>...................] - ETA: 0s - loss: 0.0252 103/133 [======================>.......] - ETA: 0s - loss: 0.0237 133/133 [==============================] - 0s 996us/step - loss: 0.0237 -> test with GAN.predict GAN tn, fp: 327, 6 GAN fn, tp: 2, 11 GAN f1 score: 0.733 GAN cohens kappa score: 0.721 -> test with 'LR' LR tn, fp: 272, 61 LR fn, tp: 0, 13 LR f1 score: 0.299 LR cohens kappa score: 0.251 LR average precision score: 0.333 -> test with 'RF' RF tn, fp: 333, 0 RF fn, tp: 0, 13 RF f1 score: 1.000 RF cohens kappa score: 1.000 -> test with 'GB' GB tn, fp: 333, 0 GB fn, tp: 2, 11 GB f1 score: 0.917 GB cohens kappa score: 0.914 -> test with 'KNN' KNN tn, fp: 320, 13 KNN fn, tp: 0, 13 KNN f1 score: 0.667 KNN cohens kappa score: 0.649 ------ Step 5/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1278 synthetic samples -> retrain GAN for predict Epoch 1/10 1/133 [..............................] - ETA: 17s - loss: 0.0615 42/133 [========>.....................] - ETA: 0s - loss: 0.0405  82/133 [=================>............] - ETA: 0s - loss: 0.0336 120/133 [==========================>...] - ETA: 0s - loss: 0.0305 133/133 [==============================] - 0s 1ms/step - loss: 0.0329 Epoch 2/10 1/133 [..............................] - ETA: 0s - loss: 0.0134 48/133 [=========>....................] - ETA: 0s - loss: 0.0244 94/133 [====================>.........] - ETA: 0s - loss: 0.0301 133/133 [==============================] - 0s 1ms/step - loss: 0.0308 Epoch 3/10 1/133 [..............................] - ETA: 0s - loss: 0.0225 46/133 [=========>....................] - ETA: 0s - loss: 0.0287 93/133 [===================>..........] - ETA: 0s - loss: 0.0253 133/133 [==============================] - 0s 1ms/step - loss: 0.0287 Epoch 4/10 1/133 [..............................] - ETA: 0s - loss: 0.0117 48/133 [=========>....................] - ETA: 0s - loss: 0.0264 95/133 [====================>.........] - ETA: 0s - loss: 0.0256 133/133 [==============================] - 0s 1ms/step - loss: 0.0270 Epoch 5/10 1/133 [..............................] - ETA: 0s - loss: 0.0594 49/133 [==========>...................] - ETA: 0s - loss: 0.0261 93/133 [===================>..........] - ETA: 0s - loss: 0.0252 133/133 [==============================] - 0s 1ms/step - loss: 0.0254 Epoch 6/10 1/133 [..............................] - ETA: 0s - loss: 0.0115 47/133 [=========>....................] - ETA: 0s - loss: 0.0208 91/133 [===================>..........] - ETA: 0s - loss: 0.0248 133/133 [==============================] - 0s 1ms/step - loss: 0.0246 Epoch 7/10 1/133 [..............................] - ETA: 0s - loss: 0.0192 49/133 [==========>...................] - ETA: 0s - loss: 0.0155 91/133 [===================>..........] - ETA: 0s - loss: 0.0240 133/133 [==============================] - 0s 1ms/step - loss: 0.0231 Epoch 8/10 1/133 [..............................] - ETA: 0s - loss: 0.0117 47/133 [=========>....................] - ETA: 0s - loss: 0.0234 92/133 [===================>..........] - ETA: 0s - loss: 0.0224 133/133 [==============================] - 0s 1ms/step - loss: 0.0223 Epoch 9/10 1/133 [..............................] - ETA: 0s - loss: 0.0076 48/133 [=========>....................] - ETA: 0s - loss: 0.0222 94/133 [====================>.........] - ETA: 0s - loss: 0.0211 133/133 [==============================] - 0s 1ms/step - loss: 0.0211 Epoch 10/10 1/133 [..............................] - ETA: 0s - loss: 0.0093 45/133 [=========>....................] - ETA: 0s - loss: 0.0207 90/133 [===================>..........] - ETA: 0s - loss: 0.0214 133/133 [==============================] - 0s 1ms/step - loss: 0.0198 -> test with GAN.predict GAN tn, fp: 325, 8 GAN fn, tp: 2, 11 GAN f1 score: 0.688 GAN cohens kappa score: 0.673 -> test with 'LR' LR tn, fp: 299, 34 LR fn, tp: 3, 10 LR f1 score: 0.351 LR cohens kappa score: 0.311 LR average precision score: 0.361 -> test with 'RF' RF tn, fp: 333, 0 RF fn, tp: 5, 8 RF f1 score: 0.762 RF cohens kappa score: 0.755 -> test with 'GB' GB tn, fp: 332, 1 GB fn, tp: 0, 13 GB f1 score: 0.963 GB cohens kappa score: 0.961 -> test with 'KNN' KNN tn, fp: 322, 11 KNN fn, tp: 1, 12 KNN f1 score: 0.667 KNN cohens kappa score: 0.650 ------ Step 5/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1278 synthetic samples -> retrain GAN for predict Epoch 1/10 1/133 [..............................] - ETA: 18s - loss: 0.0210 50/133 [==========>...................] - ETA: 0s - loss: 0.0284  100/133 [=====================>........] - ETA: 0s - loss: 0.0389 133/133 [==============================] - 0s 1ms/step - loss: 0.0380 Epoch 2/10 1/133 [..............................] - ETA: 0s - loss: 0.0275 51/133 [==========>...................] - ETA: 0s - loss: 0.0418 102/133 [======================>.......] - ETA: 0s - loss: 0.0383 133/133 [==============================] - 0s 997us/step - loss: 0.0370 Epoch 3/10 1/133 [..............................] - ETA: 0s - loss: 0.0055 50/133 [==========>...................] - ETA: 0s - loss: 0.0278 97/133 [====================>.........] - ETA: 0s - loss: 0.0307 133/133 [==============================] - 0s 1ms/step - loss: 0.0327 Epoch 4/10 1/133 [..............................] - ETA: 0s - loss: 0.0236 51/133 [==========>...................] - ETA: 0s - loss: 0.0304 102/133 [======================>.......] - ETA: 0s - loss: 0.0318 133/133 [==============================] - 0s 1ms/step - loss: 0.0324 Epoch 5/10 1/133 [..............................] - ETA: 0s - loss: 0.0083 52/133 [==========>...................] - ETA: 0s - loss: 0.0280 103/133 [======================>.......] - ETA: 0s - loss: 0.0288 133/133 [==============================] - 0s 1ms/step - loss: 0.0308 Epoch 6/10 1/133 [..............................] - ETA: 0s - loss: 0.0185 48/133 [=========>....................] - ETA: 0s - loss: 0.0246 98/133 [=====================>........] - ETA: 0s - loss: 0.0297 133/133 [==============================] - 0s 1ms/step - loss: 0.0295 Epoch 7/10 1/133 [..............................] - ETA: 0s - loss: 0.0452 47/133 [=========>....................] - ETA: 0s - loss: 0.0237 94/133 [====================>.........] - ETA: 0s - loss: 0.0270 133/133 [==============================] - 0s 1ms/step - loss: 0.0284 Epoch 8/10 1/133 [..............................] - ETA: 0s - loss: 0.0161 51/133 [==========>...................] - ETA: 0s - loss: 0.0226 101/133 [=====================>........] - ETA: 0s - loss: 0.0278 133/133 [==============================] - 0s 1ms/step - loss: 0.0282 Epoch 9/10 1/133 [..............................] - ETA: 0s - loss: 0.0053 51/133 [==========>...................] - ETA: 0s - loss: 0.0267 102/133 [======================>.......] - ETA: 0s - loss: 0.0251 133/133 [==============================] - 0s 1ms/step - loss: 0.0247 Epoch 10/10 1/133 [..............................] - ETA: 0s - loss: 0.0483 48/133 [=========>....................] - ETA: 0s - loss: 0.0284 93/133 [===================>..........] - ETA: 0s - loss: 0.0271 133/133 [==============================] - 0s 1ms/step - loss: 0.0233 -> test with GAN.predict GAN tn, fp: 329, 4 GAN fn, tp: 2, 11 GAN f1 score: 0.786 GAN cohens kappa score: 0.777 -> test with 'LR' LR tn, fp: 304, 29 LR fn, tp: 1, 12 LR f1 score: 0.444 LR cohens kappa score: 0.411 LR average precision score: 0.334 -> test with 'RF' RF tn, fp: 333, 0 RF fn, tp: 0, 13 RF f1 score: 1.000 RF cohens kappa score: 1.000 -> test with 'GB' GB tn, fp: 333, 0 GB fn, tp: 0, 13 GB f1 score: 1.000 GB cohens kappa score: 1.000 -> test with 'KNN' KNN tn, fp: 318, 15 KNN fn, tp: 0, 13 KNN f1 score: 0.634 KNN cohens kappa score: 0.614 ------ Step 5/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1278 synthetic samples -> retrain GAN for predict Epoch 1/10 1/133 [..............................] - ETA: 16s - loss: 0.0084 50/133 [==========>...................] - ETA: 0s - loss: 0.0390  100/133 [=====================>........] - ETA: 0s - loss: 0.0377 133/133 [==============================] - 0s 1ms/step - loss: 0.0357 Epoch 2/10 1/133 [..............................] - ETA: 0s - loss: 0.1887 52/133 [==========>...................] - ETA: 0s - loss: 0.0392 102/133 [======================>.......] - ETA: 0s - loss: 0.0355 133/133 [==============================] - 0s 1ms/step - loss: 0.0338 Epoch 3/10 1/133 [..............................] - ETA: 0s - loss: 0.0417 52/133 [==========>...................] - ETA: 0s - loss: 0.0310 102/133 [======================>.......] - ETA: 0s - loss: 0.0331 133/133 [==============================] - 0s 1ms/step - loss: 0.0312 Epoch 4/10 1/133 [..............................] - ETA: 0s - loss: 0.0075 45/133 [=========>....................] - ETA: 0s - loss: 0.0275 91/133 [===================>..........] - ETA: 0s - loss: 0.0320 133/133 [==============================] - 0s 1ms/step - loss: 0.0299 Epoch 5/10 1/133 [..............................] - ETA: 0s - loss: 0.0130 48/133 [=========>....................] - ETA: 0s - loss: 0.0381 97/133 [====================>.........] - ETA: 0s - loss: 0.0284 133/133 [==============================] - 0s 1ms/step - loss: 0.0280 Epoch 6/10 1/133 [..............................] - ETA: 0s - loss: 0.0120 51/133 [==========>...................] - ETA: 0s - loss: 0.0245 101/133 [=====================>........] - ETA: 0s - loss: 0.0250 133/133 [==============================] - 0s 1ms/step - loss: 0.0261 Epoch 7/10 1/133 [..............................] - ETA: 0s - loss: 0.0054 52/133 [==========>...................] - ETA: 0s - loss: 0.0259 103/133 [======================>.......] - ETA: 0s - loss: 0.0266 133/133 [==============================] - 0s 996us/step - loss: 0.0253 Epoch 8/10 1/133 [..............................] - ETA: 0s - loss: 0.0039 52/133 [==========>...................] - ETA: 0s - loss: 0.0241 103/133 [======================>.......] - ETA: 0s - loss: 0.0237 133/133 [==============================] - 0s 999us/step - loss: 0.0248 Epoch 9/10 1/133 [..............................] - ETA: 0s - loss: 0.0140 51/133 [==========>...................] - ETA: 0s - loss: 0.0206 102/133 [======================>.......] - ETA: 0s - loss: 0.0223 133/133 [==============================] - 0s 1ms/step - loss: 0.0217 Epoch 10/10 1/133 [..............................] - ETA: 0s - loss: 0.0164 50/133 [==========>...................] - ETA: 0s - loss: 0.0222 100/133 [=====================>........] - ETA: 0s - loss: 0.0211 133/133 [==============================] - 0s 1ms/step - loss: 0.0209 -> test with GAN.predict GAN tn, fp: 329, 4 GAN fn, tp: 2, 11 GAN f1 score: 0.786 GAN cohens kappa score: 0.777 -> test with 'LR' LR tn, fp: 284, 49 LR fn, tp: 0, 13 LR f1 score: 0.347 LR cohens kappa score: 0.303 LR average precision score: 0.281 -> test with 'RF' RF tn, fp: 332, 1 RF fn, tp: 1, 12 RF f1 score: 0.923 RF cohens kappa score: 0.920 -> test with 'GB' GB tn, fp: 330, 3 GB fn, tp: 0, 13 GB f1 score: 0.897 GB cohens kappa score: 0.892 -> test with 'KNN' KNN tn, fp: 323, 10 KNN fn, tp: 0, 13 KNN f1 score: 0.722 KNN cohens kappa score: 0.708 ------ Step 5/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1280 synthetic samples -> retrain GAN for predict Epoch 1/10 1/134 [..............................] - ETA: 23s - loss: 0.0069 48/134 [=========>....................] - ETA: 0s - loss: 0.0326  96/134 [====================>.........] - ETA: 0s - loss: 0.0338 134/134 [==============================] - 0s 1ms/step - loss: 0.0338 Epoch 2/10 1/134 [..............................] - ETA: 0s - loss: 0.0023 49/134 [=========>....................] - ETA: 0s - loss: 0.0380 97/134 [====================>.........] - ETA: 0s - loss: 0.0319 134/134 [==============================] - 0s 1ms/step - loss: 0.0316 Epoch 3/10 1/134 [..............................] - ETA: 0s - loss: 0.0068 49/134 [=========>....................] - ETA: 0s - loss: 0.0254 92/134 [===================>..........] - ETA: 0s - loss: 0.0274 134/134 [==============================] - 0s 1ms/step - loss: 0.0296 Epoch 4/10 1/134 [..............................] - ETA: 0s - loss: 0.0272 49/134 [=========>....................] - ETA: 0s - loss: 0.0304 97/134 [====================>.........] - ETA: 0s - loss: 0.0288 134/134 [==============================] - 0s 1ms/step - loss: 0.0274 Epoch 5/10 1/134 [..............................] - ETA: 0s - loss: 0.0095 49/134 [=========>....................] - ETA: 0s - loss: 0.0289 97/134 [====================>.........] - ETA: 0s - loss: 0.0257 134/134 [==============================] - 0s 1ms/step - loss: 0.0257 Epoch 6/10 1/134 [..............................] - ETA: 0s - loss: 0.0937 49/134 [=========>....................] - ETA: 0s - loss: 0.0275 97/134 [====================>.........] - ETA: 0s - loss: 0.0264 134/134 [==============================] - 0s 1ms/step - loss: 0.0247 Epoch 7/10 1/134 [..............................] - ETA: 0s - loss: 0.0455 49/134 [=========>....................] - ETA: 0s - loss: 0.0213 97/134 [====================>.........] - ETA: 0s - loss: 0.0214 134/134 [==============================] - 0s 1ms/step - loss: 0.0226 Epoch 8/10 1/134 [..............................] - ETA: 0s - loss: 0.0764 49/134 [=========>....................] - ETA: 0s - loss: 0.0209 97/134 [====================>.........] - ETA: 0s - loss: 0.0198 134/134 [==============================] - 0s 1ms/step - loss: 0.0214 Epoch 9/10 1/134 [..............................] - ETA: 0s - loss: 0.0617 47/134 [=========>....................] - ETA: 0s - loss: 0.0235 96/134 [====================>.........] - ETA: 0s - loss: 0.0218 134/134 [==============================] - 0s 1ms/step - loss: 0.0198 Epoch 10/10 1/134 [..............................] - ETA: 0s - loss: 0.0078 50/134 [==========>...................] - ETA: 0s - loss: 0.0259 97/134 [====================>.........] - ETA: 0s - loss: 0.0220 134/134 [==============================] - 0s 1ms/step - loss: 0.0194 -> test with GAN.predict GAN tn, fp: 329, 2 GAN fn, tp: 3, 10 GAN f1 score: 0.800 GAN cohens kappa score: 0.792 -> test with 'LR' LR tn, fp: 292, 39 LR fn, tp: 0, 13 LR f1 score: 0.400 LR cohens kappa score: 0.361 LR average precision score: 0.468 -> test with 'RF' RF tn, fp: 331, 0 RF fn, tp: 1, 12 RF f1 score: 0.960 RF cohens kappa score: 0.958 -> test with 'GB' GB tn, fp: 330, 1 GB fn, tp: 1, 12 GB f1 score: 0.923 GB cohens kappa score: 0.920 -> test with 'KNN' KNN tn, fp: 321, 10 KNN fn, tp: 0, 13 KNN f1 score: 0.722 KNN cohens kappa score: 0.708 ### Exercise is done. -----[ LR ]----- maximum: LR tn, fp: 304, 61 LR fn, tp: 3, 13 LR f1 score: 0.444 LR cohens kappa score: 0.411 LR average precision score: 0.524 average: LR tn, fp: 290.84, 41.76 LR fn, tp: 0.6, 12.4 LR f1 score: 0.373 LR cohens kappa score: 0.332 LR average precision score: 0.355 minimum: LR tn, fp: 272, 29 LR fn, tp: 0, 10 LR f1 score: 0.299 LR cohens kappa score: 0.251 LR average precision score: 0.265 -----[ RF ]----- maximum: RF tn, fp: 333, 2 RF fn, tp: 6, 13 RF f1 score: 1.000 RF cohens kappa score: 1.000 average: RF tn, fp: 332.24, 0.36 RF fn, tp: 1.76, 11.24 RF f1 score: 0.910 RF cohens kappa score: 0.907 minimum: RF tn, fp: 330, 0 RF fn, tp: 0, 7 RF f1 score: 0.667 RF cohens kappa score: 0.657 -----[ GB ]----- maximum: GB tn, fp: 333, 6 GB fn, tp: 3, 13 GB f1 score: 1.000 GB cohens kappa score: 1.000 average: GB tn, fp: 331.48, 1.12 GB fn, tp: 0.68, 12.32 GB f1 score: 0.933 GB cohens kappa score: 0.931 minimum: GB tn, fp: 327, 0 GB fn, tp: 0, 10 GB f1 score: 0.800 GB cohens kappa score: 0.793 -----[ KNN ]----- maximum: KNN tn, fp: 327, 21 KNN fn, tp: 3, 13 KNN f1 score: 0.813 KNN cohens kappa score: 0.804 average: KNN tn, fp: 319.6, 13.0 KNN fn, tp: 0.4, 12.6 KNN f1 score: 0.659 KNN cohens kappa score: 0.641 minimum: KNN tn, fp: 312, 6 KNN fn, tp: 0, 10 KNN f1 score: 0.537 KNN cohens kappa score: 0.512 -----[ GAN ]----- maximum: GAN tn, fp: 332, 9 GAN fn, tp: 6, 13 GAN f1 score: 0.889 GAN cohens kappa score: 0.884 average: GAN tn, fp: 327.76, 4.84 GAN fn, tp: 1.84, 11.16 GAN f1 score: 0.772 GAN cohens kappa score: 0.763 minimum: GAN tn, fp: 324, 1 GAN fn, tp: 0, 7 GAN f1 score: 0.483 GAN cohens kappa score: 0.460