/////////////////////////////////////////// // Running convGAN-proximary-5 on folding_flare-F /////////////////////////////////////////// Load 'data_input/folding_flare-F' from pickle file non empty cut in data_input/folding_flare-F! (23 points) Data loaded. -> Shuffling data ### Start exercise for synthetic point generator ====== Step 1/5 ======= -> Shuffling data -> Spliting data to slices ------ Step 1/5: Slice 1/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 784 synthetic samples -> test with GAN.predict GAN tn, fp: 187, 18 GAN fn, tp: 4, 5 GAN f1 score: 0.312 GAN cohens kappa score: 0.268 -> test with 'LR' LR tn, fp: 172, 33 LR fn, tp: 6, 3 LR f1 score: 0.133 LR cohens kappa score: 0.071 LR average precision score: 0.088 -> test with 'GB' GB tn, fp: 203, 2 GB fn, tp: 9, 0 GB f1 score: 0.000 GB cohens kappa score: -0.016 -> test with 'KNN' KNN tn, fp: 176, 29 KNN fn, tp: 4, 5 KNN f1 score: 0.233 KNN cohens kappa score: 0.178 ------ Step 1/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 784 synthetic samples -> test with GAN.predict GAN tn, fp: 186, 19 GAN fn, tp: 3, 6 GAN f1 score: 0.353 GAN cohens kappa score: 0.310 -> test with 'LR' LR tn, fp: 157, 48 LR fn, tp: 1, 8 LR f1 score: 0.246 LR cohens kappa score: 0.187 LR average precision score: 0.405 -> test with 'GB' GB tn, fp: 203, 2 GB fn, tp: 8, 1 GB f1 score: 0.167 GB cohens kappa score: 0.149 -> test with 'KNN' KNN tn, fp: 169, 36 KNN fn, tp: 3, 6 KNN f1 score: 0.235 KNN cohens kappa score: 0.178 ------ Step 1/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 784 synthetic samples -> test with GAN.predict GAN tn, fp: 179, 26 GAN fn, tp: 5, 4 GAN f1 score: 0.205 GAN cohens kappa score: 0.150 -> test with 'LR' LR tn, fp: 176, 29 LR fn, tp: 4, 5 LR f1 score: 0.233 LR cohens kappa score: 0.178 LR average precision score: 0.277 -> test with 'GB' GB tn, fp: 204, 1 GB fn, tp: 8, 1 GB f1 score: 0.182 GB cohens kappa score: 0.169 -> test with 'KNN' KNN tn, fp: 177, 28 KNN fn, tp: 5, 4 KNN f1 score: 0.195 KNN cohens kappa score: 0.139 ------ Step 1/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 784 synthetic samples -> test with GAN.predict GAN tn, fp: 180, 25 GAN fn, tp: 6, 3 GAN f1 score: 0.162 GAN cohens kappa score: 0.105 -> test with 'LR' LR tn, fp: 181, 24 LR fn, tp: 0, 9 LR f1 score: 0.429 LR cohens kappa score: 0.388 LR average precision score: 0.680 -> test with 'GB' GB tn, fp: 204, 1 GB fn, tp: 8, 1 GB f1 score: 0.182 GB cohens kappa score: 0.169 -> test with 'KNN' KNN tn, fp: 186, 19 KNN fn, tp: 3, 6 KNN f1 score: 0.353 KNN cohens kappa score: 0.310 ------ Step 1/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 784 synthetic samples -> test with GAN.predict GAN tn, fp: 180, 23 GAN fn, tp: 5, 2 GAN f1 score: 0.125 GAN cohens kappa score: 0.077 -> test with 'LR' LR tn, fp: 175, 28 LR fn, tp: 2, 5 LR f1 score: 0.250 LR cohens kappa score: 0.206 LR average precision score: 0.267 -> test with 'GB' GB tn, fp: 199, 4 GB fn, tp: 6, 1 GB f1 score: 0.167 GB cohens kappa score: 0.143 -> test with 'KNN' KNN tn, fp: 175, 28 KNN fn, tp: 2, 5 KNN f1 score: 0.250 KNN cohens kappa score: 0.206 ====== Step 2/5 ======= -> Shuffling data -> Spliting data to slices ------ Step 2/5: Slice 1/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 784 synthetic samples -> test with GAN.predict GAN tn, fp: 141, 64 GAN fn, tp: 5, 4 GAN f1 score: 0.104 GAN cohens kappa score: 0.032 -> test with 'LR' LR tn, fp: 167, 38 LR fn, tp: 1, 8 LR f1 score: 0.291 LR cohens kappa score: 0.237 LR average precision score: 0.385 -> test with 'GB' GB tn, fp: 203, 2 GB fn, tp: 7, 2 GB f1 score: 0.308 GB cohens kappa score: 0.289 -> test with 'KNN' KNN tn, fp: 178, 27 KNN fn, tp: 2, 7 KNN f1 score: 0.326 KNN cohens kappa score: 0.278 ------ Step 2/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 784 synthetic samples -> test with GAN.predict GAN tn, fp: 146, 59 GAN fn, tp: 6, 3 GAN f1 score: 0.085 GAN cohens kappa score: 0.012 -> test with 'LR' LR tn, fp: 169, 36 LR fn, tp: 3, 6 LR f1 score: 0.235 LR cohens kappa score: 0.178 LR average precision score: 0.374 -> test with 'GB' GB tn, fp: 204, 1 GB fn, tp: 9, 0 GB f1 score: 0.000 GB cohens kappa score: -0.008 -> test with 'KNN' KNN tn, fp: 176, 29 KNN fn, tp: 2, 7 KNN f1 score: 0.311 KNN cohens kappa score: 0.261 ------ Step 2/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 784 synthetic samples -> test with GAN.predict GAN tn, fp: 179, 26 GAN fn, tp: 8, 1 GAN f1 score: 0.056 GAN cohens kappa score: -0.008 -> test with 'LR' LR tn, fp: 170, 35 LR fn, tp: 3, 6 LR f1 score: 0.240 LR cohens kappa score: 0.184 LR average precision score: 0.261 -> test with 'GB' GB tn, fp: 203, 2 GB fn, tp: 7, 2 GB f1 score: 0.308 GB cohens kappa score: 0.289 -> test with 'KNN' KNN tn, fp: 181, 24 KNN fn, tp: 4, 5 KNN f1 score: 0.263 KNN cohens kappa score: 0.213 ------ Step 2/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 784 synthetic samples -> test with GAN.predict GAN tn, fp: 150, 55 GAN fn, tp: 5, 4 GAN f1 score: 0.118 GAN cohens kappa score: 0.048 -> test with 'LR' LR tn, fp: 181, 24 LR fn, tp: 4, 5 LR f1 score: 0.263 LR cohens kappa score: 0.213 LR average precision score: 0.306 -> test with 'GB' GB tn, fp: 204, 1 GB fn, tp: 8, 1 GB f1 score: 0.182 GB cohens kappa score: 0.169 -> test with 'KNN' KNN tn, fp: 181, 24 KNN fn, tp: 3, 6 KNN f1 score: 0.308 KNN cohens kappa score: 0.260 ------ Step 2/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 784 synthetic samples -> test with GAN.predict GAN tn, fp: 96, 107 GAN fn, tp: 3, 4 GAN f1 score: 0.068 GAN cohens kappa score: 0.005 -> test with 'LR' LR tn, fp: 167, 36 LR fn, tp: 0, 7 LR f1 score: 0.280 LR cohens kappa score: 0.236 LR average precision score: 0.380 -> test with 'GB' GB tn, fp: 201, 2 GB fn, tp: 6, 1 GB f1 score: 0.200 GB cohens kappa score: 0.184 -> test with 'KNN' KNN tn, fp: 174, 29 KNN fn, tp: 1, 6 KNN f1 score: 0.286 KNN cohens kappa score: 0.244 ====== Step 3/5 ======= -> Shuffling data -> Spliting data to slices ------ Step 3/5: Slice 1/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 784 synthetic samples -> test with GAN.predict GAN tn, fp: 112, 93 GAN fn, tp: 4, 5 GAN f1 score: 0.093 GAN cohens kappa score: 0.018 -> test with 'LR' LR tn, fp: 186, 19 LR fn, tp: 2, 7 LR f1 score: 0.400 LR cohens kappa score: 0.360 LR average precision score: 0.737 -> test with 'GB' GB tn, fp: 205, 0 GB fn, tp: 9, 0 GB f1 score: 0.000 GB cohens kappa score: 0.000 -> test with 'KNN' KNN tn, fp: 196, 9 KNN fn, tp: 3, 6 KNN f1 score: 0.500 KNN cohens kappa score: 0.472 ------ Step 3/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 784 synthetic samples -> test with GAN.predict GAN tn, fp: 188, 17 GAN fn, tp: 4, 5 GAN f1 score: 0.323 GAN cohens kappa score: 0.280 -> test with 'LR' LR tn, fp: 167, 38 LR fn, tp: 2, 7 LR f1 score: 0.259 LR cohens kappa score: 0.203 LR average precision score: 0.189 -> test with 'GB' GB tn, fp: 203, 2 GB fn, tp: 6, 3 GB f1 score: 0.429 GB cohens kappa score: 0.411 -> test with 'KNN' KNN tn, fp: 164, 41 KNN fn, tp: 3, 6 KNN f1 score: 0.214 KNN cohens kappa score: 0.155 ------ Step 3/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 784 synthetic samples -> test with GAN.predict GAN tn, fp: 153, 52 GAN fn, tp: 6, 3 GAN f1 score: 0.094 GAN cohens kappa score: 0.023 -> test with 'LR' LR tn, fp: 171, 34 LR fn, tp: 3, 6 LR f1 score: 0.245 LR cohens kappa score: 0.189 LR average precision score: 0.442 -> test with 'GB' GB tn, fp: 204, 1 GB fn, tp: 8, 1 GB f1 score: 0.182 GB cohens kappa score: 0.169 -> test with 'KNN' KNN tn, fp: 174, 31 KNN fn, tp: 2, 7 KNN f1 score: 0.298 KNN cohens kappa score: 0.247 ------ Step 3/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 784 synthetic samples -> test with GAN.predict GAN tn, fp: 187, 18 GAN fn, tp: 7, 2 GAN f1 score: 0.138 GAN cohens kappa score: 0.085 -> test with 'LR' LR tn, fp: 183, 22 LR fn, tp: 4, 5 LR f1 score: 0.278 LR cohens kappa score: 0.229 LR average precision score: 0.370 -> test with 'GB' GB tn, fp: 205, 0 GB fn, tp: 9, 0 GB f1 score: 0.000 GB cohens kappa score: 0.000 -> test with 'KNN' KNN tn, fp: 181, 24 KNN fn, tp: 4, 5 KNN f1 score: 0.263 KNN cohens kappa score: 0.213 ------ Step 3/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 784 synthetic samples -> test with GAN.predict GAN tn, fp: 183, 20 GAN fn, tp: 4, 3 GAN f1 score: 0.200 GAN cohens kappa score: 0.157 -> test with 'LR' LR tn, fp: 164, 39 LR fn, tp: 1, 6 LR f1 score: 0.231 LR cohens kappa score: 0.184 LR average precision score: 0.257 -> test with 'GB' GB tn, fp: 198, 5 GB fn, tp: 5, 2 GB f1 score: 0.286 GB cohens kappa score: 0.261 -> test with 'KNN' KNN tn, fp: 181, 22 KNN fn, tp: 3, 4 KNN f1 score: 0.242 KNN cohens kappa score: 0.200 ====== Step 4/5 ======= -> Shuffling data -> Spliting data to slices ------ Step 4/5: Slice 1/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 784 synthetic samples -> test with GAN.predict GAN tn, fp: 66, 139 GAN fn, tp: 4, 5 GAN f1 score: 0.065 GAN cohens kappa score: -0.015 -> test with 'LR' LR tn, fp: 170, 35 LR fn, tp: 1, 8 LR f1 score: 0.308 LR cohens kappa score: 0.256 LR average precision score: 0.206 -> test with 'GB' GB tn, fp: 202, 3 GB fn, tp: 9, 0 GB f1 score: 0.000 GB cohens kappa score: -0.021 -> test with 'KNN' KNN tn, fp: 183, 22 KNN fn, tp: 4, 5 KNN f1 score: 0.278 KNN cohens kappa score: 0.229 ------ Step 4/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 784 synthetic samples -> test with GAN.predict GAN tn, fp: 156, 49 GAN fn, tp: 7, 2 GAN f1 score: 0.067 GAN cohens kappa score: -0.005 -> test with 'LR' LR tn, fp: 180, 25 LR fn, tp: 3, 6 LR f1 score: 0.300 LR cohens kappa score: 0.251 LR average precision score: 0.545 -> test with 'GB' GB tn, fp: 203, 2 GB fn, tp: 7, 2 GB f1 score: 0.308 GB cohens kappa score: 0.289 -> test with 'KNN' KNN tn, fp: 183, 22 KNN fn, tp: 5, 4 KNN f1 score: 0.229 KNN cohens kappa score: 0.177 ------ Step 4/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 784 synthetic samples -> test with GAN.predict GAN tn, fp: 190, 15 GAN fn, tp: 4, 5 GAN f1 score: 0.345 GAN cohens kappa score: 0.304 -> test with 'LR' LR tn, fp: 173, 32 LR fn, tp: 3, 6 LR f1 score: 0.255 LR cohens kappa score: 0.201 LR average precision score: 0.243 -> test with 'GB' GB tn, fp: 204, 1 GB fn, tp: 8, 1 GB f1 score: 0.182 GB cohens kappa score: 0.169 -> test with 'KNN' KNN tn, fp: 171, 34 KNN fn, tp: 4, 5 KNN f1 score: 0.208 KNN cohens kappa score: 0.150 ------ Step 4/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 784 synthetic samples -> test with GAN.predict GAN tn, fp: 188, 17 GAN fn, tp: 4, 5 GAN f1 score: 0.323 GAN cohens kappa score: 0.280 -> test with 'LR' LR tn, fp: 170, 35 LR fn, tp: 0, 9 LR f1 score: 0.340 LR cohens kappa score: 0.290 LR average precision score: 0.360 -> test with 'GB' GB tn, fp: 203, 2 GB fn, tp: 6, 3 GB f1 score: 0.429 GB cohens kappa score: 0.411 -> test with 'KNN' KNN tn, fp: 182, 23 KNN fn, tp: 2, 7 KNN f1 score: 0.359 KNN cohens kappa score: 0.315 ------ Step 4/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 784 synthetic samples -> test with GAN.predict GAN tn, fp: 178, 25 GAN fn, tp: 1, 6 GAN f1 score: 0.316 GAN cohens kappa score: 0.276 -> test with 'LR' LR tn, fp: 176, 27 LR fn, tp: 2, 5 LR f1 score: 0.256 LR cohens kappa score: 0.213 LR average precision score: 0.687 -> test with 'GB' GB tn, fp: 202, 1 GB fn, tp: 6, 1 GB f1 score: 0.222 GB cohens kappa score: 0.211 -> test with 'KNN' KNN tn, fp: 175, 28 KNN fn, tp: 3, 4 KNN f1 score: 0.205 KNN cohens kappa score: 0.159 ====== Step 5/5 ======= -> Shuffling data -> Spliting data to slices ------ Step 5/5: Slice 1/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 784 synthetic samples -> test with GAN.predict GAN tn, fp: 112, 93 GAN fn, tp: 3, 6 GAN f1 score: 0.111 GAN cohens kappa score: 0.037 -> test with 'LR' LR tn, fp: 178, 27 LR fn, tp: 4, 5 LR f1 score: 0.244 LR cohens kappa score: 0.191 LR average precision score: 0.199 -> test with 'GB' GB tn, fp: 202, 3 GB fn, tp: 8, 1 GB f1 score: 0.154 GB cohens kappa score: 0.131 -> test with 'KNN' KNN tn, fp: 179, 26 KNN fn, tp: 3, 6 KNN f1 score: 0.293 KNN cohens kappa score: 0.243 ------ Step 5/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 784 synthetic samples -> test with GAN.predict GAN tn, fp: 147, 58 GAN fn, tp: 3, 6 GAN f1 score: 0.164 GAN cohens kappa score: 0.098 -> test with 'LR' LR tn, fp: 174, 31 LR fn, tp: 2, 7 LR f1 score: 0.298 LR cohens kappa score: 0.247 LR average precision score: 0.485 -> test with 'GB' GB tn, fp: 205, 0 GB fn, tp: 9, 0 GB f1 score: 0.000 GB cohens kappa score: 0.000 -> test with 'KNN' KNN tn, fp: 169, 36 KNN fn, tp: 2, 7 KNN f1 score: 0.269 KNN cohens kappa score: 0.215 ------ Step 5/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 784 synthetic samples -> test with GAN.predict GAN tn, fp: 130, 75 GAN fn, tp: 4, 5 GAN f1 score: 0.112 GAN cohens kappa score: 0.040 -> test with 'LR' LR tn, fp: 174, 31 LR fn, tp: 0, 9 LR f1 score: 0.367 LR cohens kappa score: 0.321 LR average precision score: 0.489 -> test with 'GB' GB tn, fp: 205, 0 GB fn, tp: 8, 1 GB f1 score: 0.200 GB cohens kappa score: 0.193 -> test with 'KNN' KNN tn, fp: 176, 29 KNN fn, tp: 2, 7 KNN f1 score: 0.311 KNN cohens kappa score: 0.261 ------ Step 5/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 784 synthetic samples -> test with GAN.predict GAN tn, fp: 153, 52 GAN fn, tp: 5, 4 GAN f1 score: 0.123 GAN cohens kappa score: 0.055 -> test with 'LR' LR tn, fp: 176, 29 LR fn, tp: 4, 5 LR f1 score: 0.233 LR cohens kappa score: 0.178 LR average precision score: 0.236 -> test with 'GB' GB tn, fp: 203, 2 GB fn, tp: 9, 0 GB f1 score: 0.000 GB cohens kappa score: -0.016 -> test with 'KNN' KNN tn, fp: 185, 20 KNN fn, tp: 4, 5 KNN f1 score: 0.294 KNN cohens kappa score: 0.248 ------ Step 5/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 784 synthetic samples -> test with GAN.predict GAN tn, fp: 159, 44 GAN fn, tp: 5, 2 GAN f1 score: 0.075 GAN cohens kappa score: 0.019 -> test with 'LR' LR tn, fp: 163, 40 LR fn, tp: 2, 5 LR f1 score: 0.192 LR cohens kappa score: 0.143 LR average precision score: 0.449 -> test with 'GB' GB tn, fp: 202, 1 GB fn, tp: 5, 2 GB f1 score: 0.400 GB cohens kappa score: 0.388 -> test with 'KNN' KNN tn, fp: 174, 29 KNN fn, tp: 4, 3 KNN f1 score: 0.154 KNN cohens kappa score: 0.105 ### Exercise is done. -----[ LR ]----- maximum: LR tn, fp: 186, 48 LR fn, tp: 6, 9 LR f1 score: 0.429 LR cohens kappa score: 0.388 LR average precision score: 0.737 average: LR tn, fp: 172.8, 31.8 LR fn, tp: 2.28, 6.32 LR f1 score: 0.272 LR cohens kappa score: 0.221 LR average precision score: 0.373 minimum: LR tn, fp: 157, 19 LR fn, tp: 0, 3 LR f1 score: 0.133 LR cohens kappa score: 0.071 LR average precision score: 0.088 -----[ GB ]----- maximum: GB tn, fp: 205, 5 GB fn, tp: 9, 3 GB f1 score: 0.429 GB cohens kappa score: 0.411 average: GB tn, fp: 202.96, 1.64 GB fn, tp: 7.52, 1.08 GB f1 score: 0.179 GB cohens kappa score: 0.165 minimum: GB tn, fp: 198, 0 GB fn, tp: 5, 0 GB f1 score: 0.000 GB cohens kappa score: -0.021 -----[ KNN ]----- maximum: KNN tn, fp: 196, 41 KNN fn, tp: 5, 7 KNN f1 score: 0.500 KNN cohens kappa score: 0.472 average: KNN tn, fp: 177.84, 26.76 KNN fn, tp: 3.08, 5.52 KNN f1 score: 0.275 KNN cohens kappa score: 0.226 minimum: KNN tn, fp: 164, 9 KNN fn, tp: 1, 3 KNN f1 score: 0.154 KNN cohens kappa score: 0.105 -----[ GAN ]----- maximum: GAN tn, fp: 190, 139 GAN fn, tp: 8, 6 GAN f1 score: 0.353 GAN cohens kappa score: 0.310 average: GAN tn, fp: 157.04, 47.56 GAN fn, tp: 4.6, 4.0 GAN f1 score: 0.165 GAN cohens kappa score: 0.106 minimum: GAN tn, fp: 66, 15 GAN fn, tp: 1, 1 GAN f1 score: 0.056 GAN cohens kappa score: -0.015