/////////////////////////////////////////// // Running convGAN-proximary-5 on folding_yeast4 /////////////////////////////////////////// Load 'data_input/folding_yeast4' 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 1106 synthetic samples -> test with GAN.predict GAN tn, fp: 195, 92 GAN fn, tp: 6, 5 GAN f1 score: 0.093 GAN cohens kappa score: 0.028 -> test with 'LR' LR tn, fp: 238, 49 LR fn, tp: 2, 9 LR f1 score: 0.261 LR cohens kappa score: 0.212 LR average precision score: 0.398 -> test with 'GB' GB tn, fp: 287, 0 GB fn, tp: 10, 1 GB f1 score: 0.167 GB cohens kappa score: 0.162 -> test with 'KNN' KNN tn, fp: 257, 30 KNN fn, tp: 3, 8 KNN f1 score: 0.327 KNN cohens kappa score: 0.286 ------ Step 1/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1106 synthetic samples -> test with GAN.predict GAN tn, fp: 143, 144 GAN fn, tp: 3, 8 GAN f1 score: 0.098 GAN cohens kappa score: 0.031 -> test with 'LR' LR tn, fp: 229, 58 LR fn, tp: 1, 10 LR f1 score: 0.253 LR cohens kappa score: 0.202 LR average precision score: 0.678 -> test with 'GB' GB tn, fp: 284, 3 GB fn, tp: 7, 4 GB f1 score: 0.444 GB cohens kappa score: 0.428 -> test with 'KNN' KNN tn, fp: 265, 22 KNN fn, tp: 0, 11 KNN f1 score: 0.500 KNN cohens kappa score: 0.471 ------ Step 1/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1106 synthetic samples -> test with GAN.predict GAN tn, fp: 152, 135 GAN fn, tp: 5, 6 GAN f1 score: 0.079 GAN cohens kappa score: 0.011 -> test with 'LR' LR tn, fp: 234, 53 LR fn, tp: 1, 10 LR f1 score: 0.270 LR cohens kappa score: 0.221 LR average precision score: 0.260 -> test with 'GB' GB tn, fp: 285, 2 GB fn, tp: 10, 1 GB f1 score: 0.143 GB cohens kappa score: 0.129 -> test with 'KNN' KNN tn, fp: 258, 29 KNN fn, tp: 3, 8 KNN f1 score: 0.333 KNN cohens kappa score: 0.293 ------ Step 1/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1106 synthetic samples -> test with GAN.predict GAN tn, fp: 245, 42 GAN fn, tp: 7, 4 GAN f1 score: 0.140 GAN cohens kappa score: 0.086 -> test with 'LR' LR tn, fp: 250, 37 LR fn, tp: 6, 5 LR f1 score: 0.189 LR cohens kappa score: 0.138 LR average precision score: 0.177 -> test with 'GB' GB tn, fp: 281, 6 GB fn, tp: 8, 3 GB f1 score: 0.300 GB cohens kappa score: 0.276 -> test with 'KNN' KNN tn, fp: 260, 27 KNN fn, tp: 4, 7 KNN f1 score: 0.311 KNN cohens kappa score: 0.270 ------ Step 1/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1104 synthetic samples -> test with GAN.predict GAN tn, fp: 229, 56 GAN fn, tp: 3, 4 GAN f1 score: 0.119 GAN cohens kappa score: 0.080 -> test with 'LR' LR tn, fp: 237, 48 LR fn, tp: 1, 6 LR f1 score: 0.197 LR cohens kappa score: 0.161 LR average precision score: 0.406 -> test with 'GB' GB tn, fp: 284, 1 GB fn, tp: 5, 2 GB f1 score: 0.400 GB cohens kappa score: 0.391 -> test with 'KNN' KNN tn, fp: 258, 27 KNN fn, tp: 1, 6 KNN f1 score: 0.300 KNN cohens kappa score: 0.271 ====== Step 2/5 ======= -> Shuffling data -> Spliting data to slices ------ Step 2/5: Slice 1/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1106 synthetic samples -> test with GAN.predict GAN tn, fp: 177, 110 GAN fn, tp: 3, 8 GAN f1 score: 0.124 GAN cohens kappa score: 0.061 -> test with 'LR' LR tn, fp: 242, 45 LR fn, tp: 1, 10 LR f1 score: 0.303 LR cohens kappa score: 0.257 LR average precision score: 0.242 -> test with 'GB' GB tn, fp: 284, 3 GB fn, tp: 10, 1 GB f1 score: 0.133 GB cohens kappa score: 0.116 -> test with 'KNN' KNN tn, fp: 261, 26 KNN fn, tp: 4, 7 KNN f1 score: 0.318 KNN cohens kappa score: 0.278 ------ Step 2/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1106 synthetic samples -> test with GAN.predict GAN tn, fp: 270, 17 GAN fn, tp: 3, 8 GAN f1 score: 0.444 GAN cohens kappa score: 0.414 -> test with 'LR' LR tn, fp: 247, 40 LR fn, tp: 2, 9 LR f1 score: 0.300 LR cohens kappa score: 0.255 LR average precision score: 0.444 -> test with 'GB' GB tn, fp: 284, 3 GB fn, tp: 6, 5 GB f1 score: 0.526 GB cohens kappa score: 0.511 -> test with 'KNN' KNN tn, fp: 238, 49 KNN fn, tp: 3, 8 KNN f1 score: 0.235 KNN cohens kappa score: 0.185 ------ Step 2/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1106 synthetic samples -> test with GAN.predict GAN tn, fp: 249, 38 GAN fn, tp: 5, 6 GAN f1 score: 0.218 GAN cohens kappa score: 0.169 -> test with 'LR' LR tn, fp: 244, 43 LR fn, tp: 4, 7 LR f1 score: 0.230 LR cohens kappa score: 0.180 LR average precision score: 0.386 -> test with 'GB' GB tn, fp: 284, 3 GB fn, tp: 8, 3 GB f1 score: 0.353 GB cohens kappa score: 0.336 -> test with 'KNN' KNN tn, fp: 252, 35 KNN fn, tp: 3, 8 KNN f1 score: 0.296 KNN cohens kappa score: 0.252 ------ Step 2/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1106 synthetic samples -> test with GAN.predict GAN tn, fp: 272, 15 GAN fn, tp: 6, 5 GAN f1 score: 0.323 GAN cohens kappa score: 0.289 -> test with 'LR' LR tn, fp: 248, 39 LR fn, tp: 3, 8 LR f1 score: 0.276 LR cohens kappa score: 0.230 LR average precision score: 0.332 -> test with 'GB' GB tn, fp: 285, 2 GB fn, tp: 10, 1 GB f1 score: 0.143 GB cohens kappa score: 0.129 -> test with 'KNN' KNN tn, fp: 265, 22 KNN fn, tp: 2, 9 KNN f1 score: 0.429 KNN cohens kappa score: 0.396 ------ Step 2/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1104 synthetic samples -> test with GAN.predict GAN tn, fp: 190, 95 GAN fn, tp: 2, 5 GAN f1 score: 0.093 GAN cohens kappa score: 0.051 -> test with 'LR' LR tn, fp: 227, 58 LR fn, tp: 1, 6 LR f1 score: 0.169 LR cohens kappa score: 0.131 LR average precision score: 0.392 -> test with 'GB' GB tn, fp: 284, 1 GB fn, tp: 6, 1 GB f1 score: 0.222 GB cohens kappa score: 0.214 -> test with 'KNN' KNN tn, fp: 264, 21 KNN fn, tp: 2, 5 KNN f1 score: 0.303 KNN cohens kappa score: 0.276 ====== Step 3/5 ======= -> Shuffling data -> Spliting data to slices ------ Step 3/5: Slice 1/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1106 synthetic samples -> test with GAN.predict GAN tn, fp: 229, 58 GAN fn, tp: 6, 5 GAN f1 score: 0.135 GAN cohens kappa score: 0.077 -> test with 'LR' LR tn, fp: 241, 46 LR fn, tp: 2, 9 LR f1 score: 0.273 LR cohens kappa score: 0.225 LR average precision score: 0.429 -> test with 'GB' GB tn, fp: 284, 3 GB fn, tp: 10, 1 GB f1 score: 0.133 GB cohens kappa score: 0.116 -> test with 'KNN' KNN tn, fp: 264, 23 KNN fn, tp: 3, 8 KNN f1 score: 0.381 KNN cohens kappa score: 0.345 ------ Step 3/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1106 synthetic samples -> test with GAN.predict GAN tn, fp: 239, 48 GAN fn, tp: 7, 4 GAN f1 score: 0.127 GAN cohens kappa score: 0.070 -> test with 'LR' LR tn, fp: 241, 46 LR fn, tp: 2, 9 LR f1 score: 0.273 LR cohens kappa score: 0.225 LR average precision score: 0.384 -> test with 'GB' GB tn, fp: 286, 1 GB fn, tp: 10, 1 GB f1 score: 0.154 GB cohens kappa score: 0.144 -> test with 'KNN' KNN tn, fp: 264, 23 KNN fn, tp: 2, 9 KNN f1 score: 0.419 KNN cohens kappa score: 0.385 ------ Step 3/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1106 synthetic samples -> test with GAN.predict GAN tn, fp: 280, 7 GAN fn, tp: 7, 4 GAN f1 score: 0.364 GAN cohens kappa score: 0.339 -> test with 'LR' LR tn, fp: 246, 41 LR fn, tp: 3, 8 LR f1 score: 0.267 LR cohens kappa score: 0.220 LR average precision score: 0.232 -> test with 'GB' GB tn, fp: 282, 5 GB fn, tp: 9, 2 GB f1 score: 0.222 GB cohens kappa score: 0.199 -> test with 'KNN' KNN tn, fp: 253, 34 KNN fn, tp: 2, 9 KNN f1 score: 0.333 KNN cohens kappa score: 0.292 ------ Step 3/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1106 synthetic samples -> test with GAN.predict GAN tn, fp: 247, 40 GAN fn, tp: 4, 7 GAN f1 score: 0.241 GAN cohens kappa score: 0.193 -> test with 'LR' LR tn, fp: 242, 45 LR fn, tp: 2, 9 LR f1 score: 0.277 LR cohens kappa score: 0.230 LR average precision score: 0.527 -> test with 'GB' GB tn, fp: 284, 3 GB fn, tp: 9, 2 GB f1 score: 0.250 GB cohens kappa score: 0.232 -> test with 'KNN' KNN tn, fp: 262, 25 KNN fn, tp: 5, 6 KNN f1 score: 0.286 KNN cohens kappa score: 0.245 ------ Step 3/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1104 synthetic samples -> test with GAN.predict GAN tn, fp: 234, 51 GAN fn, tp: 5, 2 GAN f1 score: 0.067 GAN cohens kappa score: 0.025 -> test with 'LR' LR tn, fp: 246, 39 LR fn, tp: 2, 5 LR f1 score: 0.196 LR cohens kappa score: 0.161 LR average precision score: 0.394 -> test with 'GB' GB tn, fp: 284, 1 GB fn, tp: 5, 2 GB f1 score: 0.400 GB cohens kappa score: 0.391 -> test with 'KNN' KNN tn, fp: 259, 26 KNN fn, tp: 1, 6 KNN f1 score: 0.308 KNN cohens kappa score: 0.279 ====== Step 4/5 ======= -> Shuffling data -> Spliting data to slices ------ Step 4/5: Slice 1/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1106 synthetic samples -> test with GAN.predict GAN tn, fp: 206, 81 GAN fn, tp: 5, 6 GAN f1 score: 0.122 GAN cohens kappa score: 0.061 -> test with 'LR' LR tn, fp: 251, 36 LR fn, tp: 4, 7 LR f1 score: 0.259 LR cohens kappa score: 0.213 LR average precision score: 0.472 -> test with 'GB' GB tn, fp: 286, 1 GB fn, tp: 9, 2 GB f1 score: 0.286 GB cohens kappa score: 0.274 -> test with 'KNN' KNN tn, fp: 263, 24 KNN fn, tp: 4, 7 KNN f1 score: 0.333 KNN cohens kappa score: 0.295 ------ Step 4/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1106 synthetic samples -> test with GAN.predict GAN tn, fp: 262, 25 GAN fn, tp: 3, 8 GAN f1 score: 0.364 GAN cohens kappa score: 0.326 -> test with 'LR' LR tn, fp: 243, 44 LR fn, tp: 2, 9 LR f1 score: 0.281 LR cohens kappa score: 0.234 LR average precision score: 0.347 -> test with 'GB' GB tn, fp: 283, 4 GB fn, tp: 8, 3 GB f1 score: 0.333 GB cohens kappa score: 0.314 -> test with 'KNN' KNN tn, fp: 260, 27 KNN fn, tp: 4, 7 KNN f1 score: 0.311 KNN cohens kappa score: 0.270 ------ Step 4/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1106 synthetic samples -> test with GAN.predict GAN tn, fp: 242, 45 GAN fn, tp: 5, 6 GAN f1 score: 0.194 GAN cohens kappa score: 0.141 -> test with 'LR' LR tn, fp: 230, 57 LR fn, tp: 2, 9 LR f1 score: 0.234 LR cohens kappa score: 0.182 LR average precision score: 0.261 -> test with 'GB' GB tn, fp: 282, 5 GB fn, tp: 10, 1 GB f1 score: 0.118 GB cohens kappa score: 0.094 -> test with 'KNN' KNN tn, fp: 253, 34 KNN fn, tp: 1, 10 KNN f1 score: 0.364 KNN cohens kappa score: 0.324 ------ Step 4/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1106 synthetic samples -> test with GAN.predict GAN tn, fp: 269, 18 GAN fn, tp: 5, 6 GAN f1 score: 0.343 GAN cohens kappa score: 0.308 -> test with 'LR' LR tn, fp: 243, 44 LR fn, tp: 3, 8 LR f1 score: 0.254 LR cohens kappa score: 0.206 LR average precision score: 0.288 -> test with 'GB' GB tn, fp: 283, 4 GB fn, tp: 11, 0 GB f1 score: 0.000 GB cohens kappa score: -0.020 -> test with 'KNN' KNN tn, fp: 251, 36 KNN fn, tp: 3, 8 KNN f1 score: 0.291 KNN cohens kappa score: 0.246 ------ Step 4/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1104 synthetic samples -> test with GAN.predict GAN tn, fp: 226, 59 GAN fn, tp: 2, 5 GAN f1 score: 0.141 GAN cohens kappa score: 0.102 -> test with 'LR' LR tn, fp: 238, 47 LR fn, tp: 2, 5 LR f1 score: 0.169 LR cohens kappa score: 0.133 LR average precision score: 0.500 -> test with 'GB' GB tn, fp: 282, 3 GB fn, tp: 4, 3 GB f1 score: 0.462 GB cohens kappa score: 0.449 -> test with 'KNN' KNN tn, fp: 259, 26 KNN fn, tp: 2, 5 KNN f1 score: 0.263 KNN cohens kappa score: 0.233 ====== Step 5/5 ======= -> Shuffling data -> Spliting data to slices ------ Step 5/5: Slice 1/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1106 synthetic samples -> test with GAN.predict GAN tn, fp: 232, 55 GAN fn, tp: 7, 4 GAN f1 score: 0.114 GAN cohens kappa score: 0.056 -> test with 'LR' LR tn, fp: 245, 42 LR fn, tp: 3, 8 LR f1 score: 0.262 LR cohens kappa score: 0.215 LR average precision score: 0.205 -> test with 'GB' GB tn, fp: 284, 3 GB fn, tp: 11, 0 GB f1 score: 0.000 GB cohens kappa score: -0.016 -> test with 'KNN' KNN tn, fp: 261, 26 KNN fn, tp: 1, 10 KNN f1 score: 0.426 KNN cohens kappa score: 0.391 ------ Step 5/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1106 synthetic samples -> test with GAN.predict GAN tn, fp: 267, 20 GAN fn, tp: 6, 5 GAN f1 score: 0.278 GAN cohens kappa score: 0.239 -> test with 'LR' LR tn, fp: 229, 58 LR fn, tp: 2, 9 LR f1 score: 0.231 LR cohens kappa score: 0.179 LR average precision score: 0.486 -> test with 'GB' GB tn, fp: 286, 1 GB fn, tp: 8, 3 GB f1 score: 0.400 GB cohens kappa score: 0.388 -> test with 'KNN' KNN tn, fp: 250, 37 KNN fn, tp: 1, 10 KNN f1 score: 0.345 KNN cohens kappa score: 0.303 ------ Step 5/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1106 synthetic samples -> test with GAN.predict GAN tn, fp: 278, 9 GAN fn, tp: 8, 3 GAN f1 score: 0.261 GAN cohens kappa score: 0.231 -> test with 'LR' LR tn, fp: 253, 34 LR fn, tp: 3, 8 LR f1 score: 0.302 LR cohens kappa score: 0.259 LR average precision score: 0.542 -> test with 'GB' GB tn, fp: 287, 0 GB fn, tp: 9, 2 GB f1 score: 0.308 GB cohens kappa score: 0.300 -> test with 'KNN' KNN tn, fp: 262, 25 KNN fn, tp: 6, 5 KNN f1 score: 0.244 KNN cohens kappa score: 0.201 ------ Step 5/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1106 synthetic samples -> test with GAN.predict GAN tn, fp: 266, 21 GAN fn, tp: 6, 5 GAN f1 score: 0.270 GAN cohens kappa score: 0.230 -> test with 'LR' LR tn, fp: 240, 47 LR fn, tp: 1, 10 LR f1 score: 0.294 LR cohens kappa score: 0.248 LR average precision score: 0.546 -> test with 'GB' GB tn, fp: 283, 4 GB fn, tp: 9, 2 GB f1 score: 0.235 GB cohens kappa score: 0.215 -> test with 'KNN' KNN tn, fp: 260, 27 KNN fn, tp: 5, 6 KNN f1 score: 0.273 KNN cohens kappa score: 0.230 ------ Step 5/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1104 synthetic samples -> test with GAN.predict GAN tn, fp: 246, 39 GAN fn, tp: 5, 2 GAN f1 score: 0.083 GAN cohens kappa score: 0.044 -> test with 'LR' LR tn, fp: 234, 51 LR fn, tp: 3, 4 LR f1 score: 0.129 LR cohens kappa score: 0.090 LR average precision score: 0.144 -> test with 'GB' GB tn, fp: 281, 4 GB fn, tp: 5, 2 GB f1 score: 0.308 GB cohens kappa score: 0.292 -> test with 'KNN' KNN tn, fp: 268, 17 KNN fn, tp: 2, 5 KNN f1 score: 0.345 KNN cohens kappa score: 0.320 ### Exercise is done. -----[ LR ]----- maximum: LR tn, fp: 253, 58 LR fn, tp: 6, 10 LR f1 score: 0.303 LR cohens kappa score: 0.259 LR average precision score: 0.678 average: LR tn, fp: 240.72, 45.88 LR fn, tp: 2.32, 7.88 LR f1 score: 0.246 LR cohens kappa score: 0.200 LR average precision score: 0.379 minimum: LR tn, fp: 227, 34 LR fn, tp: 1, 4 LR f1 score: 0.129 LR cohens kappa score: 0.090 LR average precision score: 0.144 -----[ GB ]----- maximum: GB tn, fp: 287, 6 GB fn, tp: 11, 5 GB f1 score: 0.526 GB cohens kappa score: 0.511 average: GB tn, fp: 283.96, 2.64 GB fn, tp: 8.28, 1.92 GB f1 score: 0.258 GB cohens kappa score: 0.243 minimum: GB tn, fp: 281, 0 GB fn, tp: 4, 0 GB f1 score: 0.000 GB cohens kappa score: -0.020 -----[ KNN ]----- maximum: KNN tn, fp: 268, 49 KNN fn, tp: 6, 11 KNN f1 score: 0.500 KNN cohens kappa score: 0.471 average: KNN tn, fp: 258.68, 27.92 KNN fn, tp: 2.68, 7.52 KNN f1 score: 0.331 KNN cohens kappa score: 0.293 minimum: KNN tn, fp: 238, 17 KNN fn, tp: 0, 5 KNN f1 score: 0.235 KNN cohens kappa score: 0.185 -----[ GAN ]----- maximum: GAN tn, fp: 280, 144 GAN fn, tp: 8, 8 GAN f1 score: 0.444 GAN cohens kappa score: 0.414 average: GAN tn, fp: 233.8, 52.8 GAN fn, tp: 4.96, 5.24 GAN f1 score: 0.193 GAN cohens kappa score: 0.147 minimum: GAN tn, fp: 143, 7 GAN fn, tp: 2, 2 GAN f1 score: 0.067 GAN cohens kappa score: 0.011