/////////////////////////////////////////// // Running CTGAN on folding_winequality-red-4 /////////////////////////////////////////// Load 'folding_winequality-red-4' 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 1194 synthetic samples -> test with 'LR' LR tn, fp: 273, 37 LR fn, tp: 10, 1 LR f1 score: 0.041 LR cohens kappa score: -0.013 LR average precision score: 0.043 -> test with 'RF' RF tn, fp: 298, 12 RF fn, tp: 10, 1 RF f1 score: 0.083 RF cohens kappa score: 0.048 -> test with 'GB' GB tn, fp: 301, 9 GB fn, tp: 9, 2 GB f1 score: 0.182 GB cohens kappa score: 0.153 -> test with 'KNN' KNN tn, fp: 287, 23 KNN fn, tp: 11, 0 KNN f1 score: 0.000 KNN cohens kappa score: -0.049 ------ Step 1/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1194 synthetic samples -> test with 'LR' LR tn, fp: 284, 26 LR fn, tp: 5, 6 LR f1 score: 0.279 LR cohens kappa score: 0.240 LR average precision score: 0.151 -> test with 'RF' RF tn, fp: 301, 9 RF fn, tp: 8, 3 RF f1 score: 0.261 RF cohens kappa score: 0.233 -> test with 'GB' GB tn, fp: 301, 9 GB fn, tp: 9, 2 GB f1 score: 0.182 GB cohens kappa score: 0.153 -> test with 'KNN' KNN tn, fp: 278, 32 KNN fn, tp: 10, 1 KNN f1 score: 0.045 KNN cohens kappa score: -0.006 ------ Step 1/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1194 synthetic samples -> test with 'LR' LR tn, fp: 232, 78 LR fn, tp: 7, 4 LR f1 score: 0.086 LR cohens kappa score: 0.027 LR average precision score: 0.044 -> test with 'RF' RF tn, fp: 301, 9 RF fn, tp: 11, 0 RF f1 score: 0.000 RF cohens kappa score: -0.032 -> test with 'GB' GB tn, fp: 302, 8 GB fn, tp: 11, 0 GB f1 score: 0.000 GB cohens kappa score: -0.030 -> test with 'KNN' KNN tn, fp: 285, 25 KNN fn, tp: 9, 2 KNN f1 score: 0.105 KNN cohens kappa score: 0.059 ------ Step 1/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1194 synthetic samples -> test with 'LR' LR tn, fp: 271, 39 LR fn, tp: 8, 3 LR f1 score: 0.113 LR cohens kappa score: 0.062 LR average precision score: 0.108 -> test with 'RF' RF tn, fp: 297, 13 RF fn, tp: 8, 3 RF f1 score: 0.222 RF cohens kappa score: 0.189 -> test with 'GB' GB tn, fp: 302, 8 GB fn, tp: 7, 4 GB f1 score: 0.348 GB cohens kappa score: 0.324 -> test with 'KNN' KNN tn, fp: 289, 21 KNN fn, tp: 11, 0 KNN f1 score: 0.000 KNN cohens kappa score: -0.047 ------ Step 1/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1196 synthetic samples -> test with 'LR' LR tn, fp: 283, 23 LR fn, tp: 7, 2 LR f1 score: 0.118 LR cohens kappa score: 0.079 LR average precision score: 0.095 -> test with 'RF' RF tn, fp: 299, 7 RF fn, tp: 8, 1 RF f1 score: 0.118 RF cohens kappa score: 0.093 -> test with 'GB' GB tn, fp: 299, 7 GB fn, tp: 8, 1 GB f1 score: 0.118 GB cohens kappa score: 0.093 -> test with 'KNN' KNN tn, fp: 290, 16 KNN fn, tp: 8, 1 KNN f1 score: 0.077 KNN cohens kappa score: 0.041 ====== Step 2/5 ======= -> Shuffling data -> Spliting data to slices ------ Step 2/5: Slice 1/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1194 synthetic samples -> test with 'LR' LR tn, fp: 282, 28 LR fn, tp: 7, 4 LR f1 score: 0.186 LR cohens kappa score: 0.142 LR average precision score: 0.130 -> test with 'RF' RF tn, fp: 299, 11 RF fn, tp: 8, 3 RF f1 score: 0.240 RF cohens kappa score: 0.210 -> test with 'GB' GB tn, fp: 299, 11 GB fn, tp: 10, 1 GB f1 score: 0.087 GB cohens kappa score: 0.053 -> test with 'KNN' KNN tn, fp: 288, 22 KNN fn, tp: 9, 2 KNN f1 score: 0.114 KNN cohens kappa score: 0.071 ------ Step 2/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1194 synthetic samples -> test with 'LR' LR tn, fp: 251, 59 LR fn, tp: 8, 3 LR f1 score: 0.082 LR cohens kappa score: 0.025 LR average precision score: 0.049 -> test with 'RF' RF tn, fp: 300, 10 RF fn, tp: 10, 1 RF f1 score: 0.091 RF cohens kappa score: 0.059 -> test with 'GB' GB tn, fp: 308, 2 GB fn, tp: 11, 0 GB f1 score: 0.000 GB cohens kappa score: -0.011 -> test with 'KNN' KNN tn, fp: 279, 31 KNN fn, tp: 10, 1 KNN f1 score: 0.047 KNN cohens kappa score: -0.005 ------ Step 2/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1194 synthetic samples -> test with 'LR' LR tn, fp: 290, 20 LR fn, tp: 10, 1 LR f1 score: 0.062 LR cohens kappa score: 0.018 LR average precision score: 0.077 -> test with 'RF' RF tn, fp: 309, 1 RF fn, tp: 11, 0 RF f1 score: 0.000 RF cohens kappa score: -0.006 -> test with 'GB' GB tn, fp: 307, 3 GB fn, tp: 10, 1 GB f1 score: 0.133 GB cohens kappa score: 0.117 -> test with 'KNN' KNN tn, fp: 298, 12 KNN fn, tp: 10, 1 KNN f1 score: 0.083 KNN cohens kappa score: 0.048 ------ Step 2/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1194 synthetic samples -> test with 'LR' LR tn, fp: 271, 39 LR fn, tp: 8, 3 LR f1 score: 0.113 LR cohens kappa score: 0.062 LR average precision score: 0.157 -> test with 'RF' RF tn, fp: 300, 10 RF fn, tp: 9, 2 RF f1 score: 0.174 RF cohens kappa score: 0.143 -> test with 'GB' GB tn, fp: 301, 9 GB fn, tp: 10, 1 GB f1 score: 0.095 GB cohens kappa score: 0.065 -> test with 'KNN' KNN tn, fp: 272, 38 KNN fn, tp: 11, 0 KNN f1 score: 0.000 KNN cohens kappa score: -0.056 ------ Step 2/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1196 synthetic samples -> test with 'LR' LR tn, fp: 274, 32 LR fn, tp: 6, 3 LR f1 score: 0.136 LR cohens kappa score: 0.095 LR average precision score: 0.187 -> test with 'RF' RF tn, fp: 297, 9 RF fn, tp: 7, 2 RF f1 score: 0.200 RF cohens kappa score: 0.174 -> test with 'GB' GB tn, fp: 299, 7 GB fn, tp: 8, 1 GB f1 score: 0.118 GB cohens kappa score: 0.093 -> test with 'KNN' KNN tn, fp: 281, 25 KNN fn, tp: 8, 1 KNN f1 score: 0.057 KNN cohens kappa score: 0.015 ====== Step 3/5 ======= -> Shuffling data -> Spliting data to slices ------ Step 3/5: Slice 1/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1194 synthetic samples -> test with 'LR' LR tn, fp: 261, 49 LR fn, tp: 8, 3 LR f1 score: 0.095 LR cohens kappa score: 0.041 LR average precision score: 0.065 -> test with 'RF' RF tn, fp: 301, 9 RF fn, tp: 8, 3 RF f1 score: 0.261 RF cohens kappa score: 0.233 -> test with 'GB' GB tn, fp: 302, 8 GB fn, tp: 10, 1 GB f1 score: 0.100 GB cohens kappa score: 0.071 -> test with 'KNN' KNN tn, fp: 296, 14 KNN fn, tp: 10, 1 KNN f1 score: 0.077 KNN cohens kappa score: 0.039 ------ Step 3/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1194 synthetic samples -> test with 'LR' LR tn, fp: 255, 55 LR fn, tp: 8, 3 LR f1 score: 0.087 LR cohens kappa score: 0.031 LR average precision score: 0.071 -> test with 'RF' RF tn, fp: 294, 16 RF fn, tp: 10, 1 RF f1 score: 0.071 RF cohens kappa score: 0.031 -> test with 'GB' GB tn, fp: 298, 12 GB fn, tp: 10, 1 GB f1 score: 0.083 GB cohens kappa score: 0.048 -> test with 'KNN' KNN tn, fp: 266, 44 KNN fn, tp: 10, 1 KNN f1 score: 0.036 KNN cohens kappa score: -0.020 ------ Step 3/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1194 synthetic samples -> test with 'LR' LR tn, fp: 291, 19 LR fn, tp: 8, 3 LR f1 score: 0.182 LR cohens kappa score: 0.143 LR average precision score: 0.074 -> test with 'RF' RF tn, fp: 303, 7 RF fn, tp: 10, 1 RF f1 score: 0.105 RF cohens kappa score: 0.079 -> test with 'GB' GB tn, fp: 302, 8 GB fn, tp: 10, 1 GB f1 score: 0.100 GB cohens kappa score: 0.071 -> test with 'KNN' KNN tn, fp: 292, 18 KNN fn, tp: 9, 2 KNN f1 score: 0.129 KNN cohens kappa score: 0.089 ------ Step 3/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1194 synthetic samples -> test with 'LR' LR tn, fp: 269, 41 LR fn, tp: 7, 4 LR f1 score: 0.143 LR cohens kappa score: 0.093 LR average precision score: 0.133 -> test with 'RF' RF tn, fp: 304, 6 RF fn, tp: 9, 2 RF f1 score: 0.211 RF cohens kappa score: 0.187 -> test with 'GB' GB tn, fp: 305, 5 GB fn, tp: 9, 2 GB f1 score: 0.222 GB cohens kappa score: 0.201 -> test with 'KNN' KNN tn, fp: 278, 32 KNN fn, tp: 11, 0 KNN f1 score: 0.000 KNN cohens kappa score: -0.054 ------ Step 3/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1196 synthetic samples -> test with 'LR' LR tn, fp: 265, 41 LR fn, tp: 7, 2 LR f1 score: 0.077 LR cohens kappa score: 0.031 LR average precision score: 0.060 -> test with 'RF' RF tn, fp: 291, 15 RF fn, tp: 7, 2 RF f1 score: 0.154 RF cohens kappa score: 0.121 -> test with 'GB' GB tn, fp: 298, 8 GB fn, tp: 8, 1 GB f1 score: 0.111 GB cohens kappa score: 0.085 -> test with 'KNN' KNN tn, fp: 283, 23 KNN fn, tp: 9, 0 KNN f1 score: 0.000 KNN cohens kappa score: -0.043 ====== Step 4/5 ======= -> Shuffling data -> Spliting data to slices ------ Step 4/5: Slice 1/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1194 synthetic samples -> test with 'LR' LR tn, fp: 295, 15 LR fn, tp: 8, 3 LR f1 score: 0.207 LR cohens kappa score: 0.172 LR average precision score: 0.226 -> test with 'RF' RF tn, fp: 305, 5 RF fn, tp: 9, 2 RF f1 score: 0.222 RF cohens kappa score: 0.201 -> test with 'GB' GB tn, fp: 308, 2 GB fn, tp: 9, 2 GB f1 score: 0.267 GB cohens kappa score: 0.253 -> test with 'KNN' KNN tn, fp: 278, 32 KNN fn, tp: 11, 0 KNN f1 score: 0.000 KNN cohens kappa score: -0.054 ------ Step 4/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1194 synthetic samples -> test with 'LR' LR tn, fp: 276, 34 LR fn, tp: 6, 5 LR f1 score: 0.200 LR cohens kappa score: 0.155 LR average precision score: 0.163 -> test with 'RF' RF tn, fp: 296, 14 RF fn, tp: 9, 2 RF f1 score: 0.148 RF cohens kappa score: 0.112 -> test with 'GB' GB tn, fp: 302, 8 GB fn, tp: 9, 2 GB f1 score: 0.190 GB cohens kappa score: 0.163 -> test with 'KNN' KNN tn, fp: 287, 23 KNN fn, tp: 9, 2 KNN f1 score: 0.111 KNN cohens kappa score: 0.067 ------ Step 4/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1194 synthetic samples -> test with 'LR' LR tn, fp: 266, 44 LR fn, tp: 7, 4 LR f1 score: 0.136 LR cohens kappa score: 0.085 LR average precision score: 0.045 -> test with 'RF' RF tn, fp: 292, 18 RF fn, tp: 11, 0 RF f1 score: 0.000 RF cohens kappa score: -0.044 -> test with 'GB' GB tn, fp: 298, 12 GB fn, tp: 11, 0 GB f1 score: 0.000 GB cohens kappa score: -0.037 -> test with 'KNN' KNN tn, fp: 287, 23 KNN fn, tp: 10, 1 KNN f1 score: 0.057 KNN cohens kappa score: 0.011 ------ Step 4/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1194 synthetic samples -> test with 'LR' LR tn, fp: 289, 21 LR fn, tp: 8, 3 LR f1 score: 0.171 LR cohens kappa score: 0.131 LR average precision score: 0.145 -> test with 'RF' RF tn, fp: 302, 8 RF fn, tp: 8, 3 RF f1 score: 0.273 RF cohens kappa score: 0.247 -> test with 'GB' GB tn, fp: 305, 5 GB fn, tp: 9, 2 GB f1 score: 0.222 GB cohens kappa score: 0.201 -> test with 'KNN' KNN tn, fp: 288, 22 KNN fn, tp: 11, 0 KNN f1 score: 0.000 KNN cohens kappa score: -0.048 ------ Step 4/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1196 synthetic samples -> test with 'LR' LR tn, fp: 269, 37 LR fn, tp: 8, 1 LR f1 score: 0.043 LR cohens kappa score: -0.004 LR average precision score: 0.036 -> test with 'RF' RF tn, fp: 297, 9 RF fn, tp: 9, 0 RF f1 score: 0.000 RF cohens kappa score: -0.029 -> test with 'GB' GB tn, fp: 299, 7 GB fn, tp: 9, 0 GB f1 score: 0.000 GB cohens kappa score: -0.026 -> test with 'KNN' KNN tn, fp: 278, 28 KNN fn, tp: 9, 0 KNN f1 score: 0.000 KNN cohens kappa score: -0.045 ====== Step 5/5 ======= -> Shuffling data -> Spliting data to slices ------ Step 5/5: Slice 1/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1194 synthetic samples -> test with 'LR' LR tn, fp: 266, 44 LR fn, tp: 8, 3 LR f1 score: 0.103 LR cohens kappa score: 0.051 LR average precision score: 0.127 -> test with 'RF' RF tn, fp: 304, 6 RF fn, tp: 10, 1 RF f1 score: 0.111 RF cohens kappa score: 0.087 -> test with 'GB' GB tn, fp: 302, 8 GB fn, tp: 11, 0 GB f1 score: 0.000 GB cohens kappa score: -0.030 -> test with 'KNN' KNN tn, fp: 292, 18 KNN fn, tp: 9, 2 KNN f1 score: 0.129 KNN cohens kappa score: 0.089 ------ Step 5/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1194 synthetic samples -> test with 'LR' LR tn, fp: 282, 28 LR fn, tp: 7, 4 LR f1 score: 0.186 LR cohens kappa score: 0.142 LR average precision score: 0.185 -> test with 'RF' RF tn, fp: 306, 4 RF fn, tp: 9, 2 RF f1 score: 0.235 RF cohens kappa score: 0.216 -> test with 'GB' GB tn, fp: 305, 5 GB fn, tp: 8, 3 GB f1 score: 0.316 GB cohens kappa score: 0.295 -> test with 'KNN' KNN tn, fp: 288, 22 KNN fn, tp: 9, 2 KNN f1 score: 0.114 KNN cohens kappa score: 0.071 ------ Step 5/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1194 synthetic samples -> test with 'LR' LR tn, fp: 242, 68 LR fn, tp: 9, 2 LR f1 score: 0.049 LR cohens kappa score: -0.010 LR average precision score: 0.045 -> test with 'RF' RF tn, fp: 300, 10 RF fn, tp: 10, 1 RF f1 score: 0.091 RF cohens kappa score: 0.059 -> test with 'GB' GB tn, fp: 299, 11 GB fn, tp: 10, 1 GB f1 score: 0.087 GB cohens kappa score: 0.053 -> test with 'KNN' KNN tn, fp: 276, 34 KNN fn, tp: 11, 0 KNN f1 score: 0.000 KNN cohens kappa score: -0.055 ------ Step 5/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1194 synthetic samples -> test with 'LR' LR tn, fp: 266, 44 LR fn, tp: 6, 5 LR f1 score: 0.167 LR cohens kappa score: 0.117 LR average precision score: 0.161 -> test with 'RF' RF tn, fp: 301, 9 RF fn, tp: 7, 4 RF f1 score: 0.333 RF cohens kappa score: 0.308 -> test with 'GB' GB tn, fp: 303, 7 GB fn, tp: 8, 3 GB f1 score: 0.286 GB cohens kappa score: 0.262 -> test with 'KNN' KNN tn, fp: 281, 29 KNN fn, tp: 11, 0 KNN f1 score: 0.000 KNN cohens kappa score: -0.052 ------ Step 5/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1196 synthetic samples -> test with 'LR' LR tn, fp: 253, 53 LR fn, tp: 5, 4 LR f1 score: 0.121 LR cohens kappa score: 0.076 LR average precision score: 0.118 -> test with 'RF' RF tn, fp: 299, 7 RF fn, tp: 7, 2 RF f1 score: 0.222 RF cohens kappa score: 0.199 -> test with 'GB' GB tn, fp: 303, 3 GB fn, tp: 9, 0 GB f1 score: 0.000 GB cohens kappa score: -0.014 -> test with 'KNN' KNN tn, fp: 288, 18 KNN fn, tp: 9, 0 KNN f1 score: 0.000 KNN cohens kappa score: -0.040 ### Exercise is done. -----[ LR ]----- maximum: LR tn, fp: 295, 78 LR fn, tp: 10, 6 LR f1 score: 0.279 LR cohens kappa score: 0.240 LR average precision score: 0.226 average: LR tn, fp: 270.24, 38.96 LR fn, tp: 7.44, 3.16 LR f1 score: 0.127 LR cohens kappa score: 0.080 LR average precision score: 0.108 minimum: LR tn, fp: 232, 15 LR fn, tp: 5, 1 LR f1 score: 0.041 LR cohens kappa score: -0.013 LR average precision score: 0.036 -----[ RF ]----- maximum: RF tn, fp: 309, 18 RF fn, tp: 11, 4 RF f1 score: 0.333 RF cohens kappa score: 0.308 average: RF tn, fp: 299.84, 9.36 RF fn, tp: 8.92, 1.68 RF f1 score: 0.153 RF cohens kappa score: 0.125 minimum: RF tn, fp: 291, 1 RF fn, tp: 7, 0 RF f1 score: 0.000 RF cohens kappa score: -0.044 -----[ GB ]----- maximum: GB tn, fp: 308, 12 GB fn, tp: 11, 4 GB f1 score: 0.348 GB cohens kappa score: 0.324 average: GB tn, fp: 301.92, 7.28 GB fn, tp: 9.32, 1.28 GB f1 score: 0.130 GB cohens kappa score: 0.104 minimum: GB tn, fp: 298, 2 GB fn, tp: 7, 0 GB f1 score: 0.000 GB cohens kappa score: -0.037 -----[ KNN ]----- maximum: KNN tn, fp: 298, 44 KNN fn, tp: 11, 2 KNN f1 score: 0.129 KNN cohens kappa score: 0.089 average: KNN tn, fp: 284.2, 25.0 KNN fn, tp: 9.8, 0.8 KNN f1 score: 0.047 KNN cohens kappa score: 0.001 minimum: KNN tn, fp: 266, 12 KNN fn, tp: 8, 0 KNN f1 score: 0.000 KNN cohens kappa score: -0.056 wall time: 00:06:38s, process time: 00:48:37s