/////////////////////////////////////////// // Running ProWRAS on imblearn_webpage /////////////////////////////////////////// Load 'data_input/imblearn_webpage' from imblearn non empty cut in data_input/imblearn_webpage! (76 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 -> create 26255 synthetic samples -> test with 'LR' LR tn, fp: 6554, 206 LR fn, tp: 32, 165 LR f1 score: 0.581 LR cohens kappa score: 0.565 LR average precision score: 0.774 -> test with 'GB' GB tn, fp: 6707, 53 GB fn, tp: 111, 86 GB f1 score: 0.512 GB cohens kappa score: 0.500 -> test with 'KNN' KNN tn, fp: 5975, 785 KNN fn, tp: 10, 187 KNN f1 score: 0.320 KNN cohens kappa score: 0.286 ------ Step 1/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 26255 synthetic samples -> test with 'LR' LR tn, fp: 6559, 201 LR fn, tp: 38, 159 LR f1 score: 0.571 LR cohens kappa score: 0.555 LR average precision score: 0.770 -> test with 'GB' GB tn, fp: 6710, 50 GB fn, tp: 108, 89 GB f1 score: 0.530 GB cohens kappa score: 0.518 -> test with 'KNN' KNN tn, fp: 6119, 641 KNN fn, tp: 17, 180 KNN f1 score: 0.354 KNN cohens kappa score: 0.323 ------ Step 1/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 26255 synthetic samples -> test with 'LR' LR tn, fp: 6575, 185 LR fn, tp: 23, 174 LR f1 score: 0.626 LR cohens kappa score: 0.612 LR average precision score: 0.837 -> test with 'GB' GB tn, fp: 6720, 40 GB fn, tp: 106, 91 GB f1 score: 0.555 GB cohens kappa score: 0.545 -> test with 'KNN' KNN tn, fp: 6033, 727 KNN fn, tp: 18, 179 KNN f1 score: 0.325 KNN cohens kappa score: 0.292 ------ Step 1/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 26255 synthetic samples -> test with 'LR' LR tn, fp: 6557, 203 LR fn, tp: 32, 165 LR f1 score: 0.584 LR cohens kappa score: 0.568 LR average precision score: 0.750 -> test with 'GB' GB tn, fp: 6699, 61 GB fn, tp: 115, 82 GB f1 score: 0.482 GB cohens kappa score: 0.470 -> test with 'KNN' KNN tn, fp: 6024, 736 KNN fn, tp: 20, 177 KNN f1 score: 0.319 KNN cohens kappa score: 0.286 ------ Step 1/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 26252 synthetic samples -> test with 'LR' LR tn, fp: 6583, 176 LR fn, tp: 43, 150 LR f1 score: 0.578 LR cohens kappa score: 0.563 LR average precision score: 0.737 -> test with 'GB' GB tn, fp: 6715, 44 GB fn, tp: 114, 79 GB f1 score: 0.500 GB cohens kappa score: 0.489 -> test with 'KNN' KNN tn, fp: 6076, 683 KNN fn, tp: 27, 166 KNN f1 score: 0.319 KNN cohens kappa score: 0.286 ====== Step 2/5 ======= -> Shuffling data -> Spliting data to slices ------ Step 2/5: Slice 1/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 26255 synthetic samples -> test with 'LR' LR tn, fp: 6602, 158 LR fn, tp: 36, 161 LR f1 score: 0.624 LR cohens kappa score: 0.610 LR average precision score: 0.782 -> test with 'GB' GB tn, fp: 6699, 61 GB fn, tp: 106, 91 GB f1 score: 0.521 GB cohens kappa score: 0.509 -> test with 'KNN' KNN tn, fp: 6112, 648 KNN fn, tp: 17, 180 KNN f1 score: 0.351 KNN cohens kappa score: 0.320 ------ Step 2/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 26255 synthetic samples -> test with 'LR' LR tn, fp: 6563, 197 LR fn, tp: 31, 166 LR f1 score: 0.593 LR cohens kappa score: 0.577 LR average precision score: 0.804 -> test with 'GB' GB tn, fp: 6694, 66 GB fn, tp: 102, 95 GB f1 score: 0.531 GB cohens kappa score: 0.518 -> test with 'KNN' KNN tn, fp: 5926, 834 KNN fn, tp: 16, 181 KNN f1 score: 0.299 KNN cohens kappa score: 0.264 ------ Step 2/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 26255 synthetic samples -> test with 'LR' LR tn, fp: 6564, 196 LR fn, tp: 42, 155 LR f1 score: 0.566 LR cohens kappa score: 0.549 LR average precision score: 0.736 -> test with 'GB' GB tn, fp: 6706, 54 GB fn, tp: 112, 85 GB f1 score: 0.506 GB cohens kappa score: 0.494 -> test with 'KNN' KNN tn, fp: 6148, 612 KNN fn, tp: 21, 176 KNN f1 score: 0.357 KNN cohens kappa score: 0.327 ------ Step 2/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 26255 synthetic samples -> test with 'LR' LR tn, fp: 6534, 226 LR fn, tp: 34, 163 LR f1 score: 0.556 LR cohens kappa score: 0.539 LR average precision score: 0.754 -> test with 'GB' GB tn, fp: 6716, 44 GB fn, tp: 120, 77 GB f1 score: 0.484 GB cohens kappa score: 0.473 -> test with 'KNN' KNN tn, fp: 5999, 761 KNN fn, tp: 19, 178 KNN f1 score: 0.313 KNN cohens kappa score: 0.280 ------ Step 2/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 26252 synthetic samples -> test with 'LR' LR tn, fp: 6559, 200 LR fn, tp: 31, 162 LR f1 score: 0.584 LR cohens kappa score: 0.568 LR average precision score: 0.770 -> test with 'GB' GB tn, fp: 6713, 46 GB fn, tp: 119, 74 GB f1 score: 0.473 GB cohens kappa score: 0.461 -> test with 'KNN' KNN tn, fp: 6025, 734 KNN fn, tp: 23, 170 KNN f1 score: 0.310 KNN cohens kappa score: 0.277 ====== Step 3/5 ======= -> Shuffling data -> Spliting data to slices ------ Step 3/5: Slice 1/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 26255 synthetic samples -> test with 'LR' LR tn, fp: 6551, 209 LR fn, tp: 34, 163 LR f1 score: 0.573 LR cohens kappa score: 0.557 LR average precision score: 0.756 -> test with 'GB' GB tn, fp: 6699, 61 GB fn, tp: 108, 89 GB f1 score: 0.513 GB cohens kappa score: 0.501 -> test with 'KNN' KNN tn, fp: 6038, 722 KNN fn, tp: 23, 174 KNN f1 score: 0.318 KNN cohens kappa score: 0.285 ------ Step 3/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 26255 synthetic samples -> test with 'LR' LR tn, fp: 6578, 182 LR fn, tp: 29, 168 LR f1 score: 0.614 LR cohens kappa score: 0.600 LR average precision score: 0.796 -> test with 'GB' GB tn, fp: 6703, 57 GB fn, tp: 108, 89 GB f1 score: 0.519 GB cohens kappa score: 0.507 -> test with 'KNN' KNN tn, fp: 6025, 735 KNN fn, tp: 17, 180 KNN f1 score: 0.324 KNN cohens kappa score: 0.291 ------ Step 3/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 26255 synthetic samples -> test with 'LR' LR tn, fp: 6591, 169 LR fn, tp: 44, 153 LR f1 score: 0.590 LR cohens kappa score: 0.575 LR average precision score: 0.727 -> test with 'GB' GB tn, fp: 6726, 34 GB fn, tp: 121, 76 GB f1 score: 0.495 GB cohens kappa score: 0.485 -> test with 'KNN' KNN tn, fp: 6124, 636 KNN fn, tp: 27, 170 KNN f1 score: 0.339 KNN cohens kappa score: 0.307 ------ Step 3/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 26255 synthetic samples -> test with 'LR' LR tn, fp: 6541, 219 LR fn, tp: 26, 171 LR f1 score: 0.583 LR cohens kappa score: 0.566 LR average precision score: 0.804 -> test with 'GB' GB tn, fp: 6695, 65 GB fn, tp: 107, 90 GB f1 score: 0.511 GB cohens kappa score: 0.499 -> test with 'KNN' KNN tn, fp: 5968, 792 KNN fn, tp: 18, 179 KNN f1 score: 0.307 KNN cohens kappa score: 0.272 ------ Step 3/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 26252 synthetic samples -> test with 'LR' LR tn, fp: 6569, 190 LR fn, tp: 33, 160 LR f1 score: 0.589 LR cohens kappa score: 0.574 LR average precision score: 0.787 -> test with 'GB' GB tn, fp: 6710, 49 GB fn, tp: 105, 88 GB f1 score: 0.533 GB cohens kappa score: 0.522 -> test with 'KNN' KNN tn, fp: 6042, 717 KNN fn, tp: 12, 181 KNN f1 score: 0.332 KNN cohens kappa score: 0.300 ====== Step 4/5 ======= -> Shuffling data -> Spliting data to slices ------ Step 4/5: Slice 1/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 26255 synthetic samples -> test with 'LR' LR tn, fp: 6563, 197 LR fn, tp: 48, 149 LR f1 score: 0.549 LR cohens kappa score: 0.532 LR average precision score: 0.728 -> test with 'GB' GB tn, fp: 6724, 36 GB fn, tp: 115, 82 GB f1 score: 0.521 GB cohens kappa score: 0.510 -> test with 'KNN' KNN tn, fp: 6102, 658 KNN fn, tp: 25, 172 KNN f1 score: 0.335 KNN cohens kappa score: 0.303 ------ Step 4/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 26255 synthetic samples -> test with 'LR' LR tn, fp: 6549, 211 LR fn, tp: 28, 169 LR f1 score: 0.586 LR cohens kappa score: 0.570 LR average precision score: 0.769 -> test with 'GB' GB tn, fp: 6704, 56 GB fn, tp: 110, 87 GB f1 score: 0.512 GB cohens kappa score: 0.500 -> test with 'KNN' KNN tn, fp: 6013, 747 KNN fn, tp: 18, 179 KNN f1 score: 0.319 KNN cohens kappa score: 0.285 ------ Step 4/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 26255 synthetic samples -> test with 'LR' LR tn, fp: 6577, 183 LR fn, tp: 27, 170 LR f1 score: 0.618 LR cohens kappa score: 0.604 LR average precision score: 0.786 -> test with 'GB' GB tn, fp: 6723, 37 GB fn, tp: 109, 88 GB f1 score: 0.547 GB cohens kappa score: 0.536 -> test with 'KNN' KNN tn, fp: 5999, 761 KNN fn, tp: 19, 178 KNN f1 score: 0.313 KNN cohens kappa score: 0.280 ------ Step 4/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 26255 synthetic samples -> test with 'LR' LR tn, fp: 6561, 199 LR fn, tp: 40, 157 LR f1 score: 0.568 LR cohens kappa score: 0.551 LR average precision score: 0.747 -> test with 'GB' GB tn, fp: 6700, 60 GB fn, tp: 125, 72 GB f1 score: 0.438 GB cohens kappa score: 0.425 -> test with 'KNN' KNN tn, fp: 5996, 764 KNN fn, tp: 17, 180 KNN f1 score: 0.316 KNN cohens kappa score: 0.282 ------ Step 4/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 26252 synthetic samples -> test with 'LR' LR tn, fp: 6574, 185 LR fn, tp: 29, 164 LR f1 score: 0.605 LR cohens kappa score: 0.591 LR average precision score: 0.795 -> test with 'GB' GB tn, fp: 6716, 43 GB fn, tp: 106, 87 GB f1 score: 0.539 GB cohens kappa score: 0.528 -> test with 'KNN' KNN tn, fp: 6021, 738 KNN fn, tp: 10, 183 KNN f1 score: 0.329 KNN cohens kappa score: 0.296 ====== Step 5/5 ======= -> Shuffling data -> Spliting data to slices ------ Step 5/5: Slice 1/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 26255 synthetic samples -> test with 'LR' LR tn, fp: 6548, 212 LR fn, tp: 36, 161 LR f1 score: 0.565 LR cohens kappa score: 0.548 LR average precision score: 0.750 -> test with 'GB' GB tn, fp: 6715, 45 GB fn, tp: 106, 91 GB f1 score: 0.547 GB cohens kappa score: 0.536 -> test with 'KNN' KNN tn, fp: 6080, 680 KNN fn, tp: 16, 181 KNN f1 score: 0.342 KNN cohens kappa score: 0.310 ------ Step 5/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 26255 synthetic samples -> test with 'LR' LR tn, fp: 6566, 194 LR fn, tp: 32, 165 LR f1 score: 0.594 LR cohens kappa score: 0.578 LR average precision score: 0.762 -> test with 'GB' GB tn, fp: 6683, 77 GB fn, tp: 122, 75 GB f1 score: 0.430 GB cohens kappa score: 0.415 -> test with 'KNN' KNN tn, fp: 6068, 692 KNN fn, tp: 22, 175 KNN f1 score: 0.329 KNN cohens kappa score: 0.296 ------ Step 5/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 26255 synthetic samples -> test with 'LR' LR tn, fp: 6538, 222 LR fn, tp: 39, 158 LR f1 score: 0.548 LR cohens kappa score: 0.530 LR average precision score: 0.741 -> test with 'GB' GB tn, fp: 6709, 51 GB fn, tp: 105, 92 GB f1 score: 0.541 GB cohens kappa score: 0.530 -> test with 'KNN' KNN tn, fp: 5967, 793 KNN fn, tp: 16, 181 KNN f1 score: 0.309 KNN cohens kappa score: 0.275 ------ Step 5/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 26255 synthetic samples -> test with 'LR' LR tn, fp: 6570, 190 LR fn, tp: 33, 164 LR f1 score: 0.595 LR cohens kappa score: 0.580 LR average precision score: 0.811 -> test with 'GB' GB tn, fp: 6714, 46 GB fn, tp: 113, 84 GB f1 score: 0.514 GB cohens kappa score: 0.503 -> test with 'KNN' KNN tn, fp: 6012, 748 KNN fn, tp: 19, 178 KNN f1 score: 0.317 KNN cohens kappa score: 0.284 ------ Step 5/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 26252 synthetic samples -> test with 'LR' LR tn, fp: 6572, 187 LR fn, tp: 42, 151 LR f1 score: 0.569 LR cohens kappa score: 0.553 LR average precision score: 0.726 -> test with 'GB' GB tn, fp: 6695, 64 GB fn, tp: 105, 88 GB f1 score: 0.510 GB cohens kappa score: 0.498 -> test with 'KNN' KNN tn, fp: 6127, 632 KNN fn, tp: 17, 176 KNN f1 score: 0.352 KNN cohens kappa score: 0.321 ### Exercise is done. -----[ LR ]----- maximum: LR tn, fp: 6602, 226 LR fn, tp: 48, 174 LR f1 score: 0.626 LR cohens kappa score: 0.612 LR average precision score: 0.837 average: LR tn, fp: 6563.92, 195.88 LR fn, tp: 34.48, 161.72 LR f1 score: 0.584 LR cohens kappa score: 0.569 LR average precision score: 0.768 minimum: LR tn, fp: 6534, 158 LR fn, tp: 23, 149 LR f1 score: 0.548 LR cohens kappa score: 0.530 LR average precision score: 0.726 -----[ GB ]----- maximum: GB tn, fp: 6726, 77 GB fn, tp: 125, 95 GB f1 score: 0.555 GB cohens kappa score: 0.545 average: GB tn, fp: 6707.8, 52.0 GB fn, tp: 111.12, 85.08 GB f1 score: 0.511 GB cohens kappa score: 0.499 minimum: GB tn, fp: 6683, 34 GB fn, tp: 102, 72 GB f1 score: 0.430 GB cohens kappa score: 0.415 -----[ KNN ]----- maximum: KNN tn, fp: 6148, 834 KNN fn, tp: 27, 187 KNN f1 score: 0.357 KNN cohens kappa score: 0.327 average: KNN tn, fp: 6040.76, 719.04 KNN fn, tp: 18.56, 177.64 KNN f1 score: 0.326 KNN cohens kappa score: 0.293 minimum: KNN tn, fp: 5926, 612 KNN fn, tp: 10, 166 KNN f1 score: 0.299 KNN cohens kappa score: 0.264