/////////////////////////////////////////// // Running convGAN-full on folding_winequality-red-4 /////////////////////////////////////////// Load 'data_input/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: 214, 96 LR fn, tp: 5, 6 LR f1 score: 0.106 LR cohens kappa score: 0.047 LR average precision score: 0.133 -> test with 'GB' GB tn, fp: 295, 15 GB fn, tp: 9, 2 GB f1 score: 0.143 GB cohens kappa score: 0.106 -> test with 'KNN' KNN tn, fp: 204, 106 KNN fn, tp: 7, 4 KNN f1 score: 0.066 KNN cohens kappa score: 0.004 ------ Step 1/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1194 synthetic samples -> test with 'LR' LR tn, fp: 222, 88 LR fn, tp: 4, 7 LR f1 score: 0.132 LR cohens kappa score: 0.075 LR average precision score: 0.119 -> test with 'GB' GB tn, fp: 292, 18 GB fn, tp: 7, 4 GB f1 score: 0.242 GB cohens kappa score: 0.206 -> test with 'KNN' KNN tn, fp: 228, 82 KNN fn, tp: 5, 6 KNN f1 score: 0.121 KNN cohens kappa score: 0.064 ------ Step 1/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1194 synthetic samples -> test with 'LR' LR tn, fp: 198, 112 LR fn, tp: 1, 10 LR f1 score: 0.150 LR cohens kappa score: 0.093 LR average precision score: 0.261 -> test with 'GB' GB tn, fp: 289, 21 GB fn, tp: 6, 5 GB f1 score: 0.270 GB cohens kappa score: 0.233 -> test with 'KNN' KNN tn, fp: 204, 106 KNN fn, tp: 7, 4 KNN f1 score: 0.066 KNN cohens kappa score: 0.004 ------ Step 1/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1194 synthetic samples -> test with 'LR' LR tn, fp: 248, 62 LR fn, tp: 5, 6 LR f1 score: 0.152 LR cohens kappa score: 0.099 LR average precision score: 0.148 -> test with 'GB' GB tn, fp: 292, 18 GB fn, tp: 11, 0 GB f1 score: 0.000 GB cohens kappa score: -0.044 -> test with 'KNN' KNN tn, fp: 231, 79 KNN fn, tp: 9, 2 KNN f1 score: 0.043 KNN cohens kappa score: -0.018 ------ Step 1/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1196 synthetic samples -> test with 'LR' LR tn, fp: 224, 82 LR fn, tp: 4, 5 LR f1 score: 0.104 LR cohens kappa score: 0.055 LR average precision score: 0.213 -> test with 'GB' GB tn, fp: 294, 12 GB fn, tp: 9, 0 GB f1 score: 0.000 GB cohens kappa score: -0.034 -> test with 'KNN' KNN tn, fp: 230, 76 KNN fn, tp: 8, 1 KNN f1 score: 0.023 KNN cohens kappa score: -0.029 ====== 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: 215, 95 LR fn, tp: 3, 8 LR f1 score: 0.140 LR cohens kappa score: 0.084 LR average precision score: 0.128 -> test with 'GB' GB tn, fp: 292, 18 GB fn, tp: 8, 3 GB f1 score: 0.187 GB cohens kappa score: 0.149 -> test with 'KNN' KNN tn, fp: 219, 91 KNN fn, tp: 6, 5 KNN f1 score: 0.093 KNN cohens kappa score: 0.034 ------ Step 2/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1194 synthetic samples -> test with 'LR' LR tn, fp: 217, 93 LR fn, tp: 3, 8 LR f1 score: 0.143 LR cohens kappa score: 0.086 LR average precision score: 0.132 -> 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: 228, 82 KNN fn, tp: 8, 3 KNN f1 score: 0.062 KNN cohens kappa score: 0.002 ------ Step 2/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1194 synthetic samples -> test with 'LR' LR tn, fp: 224, 86 LR fn, tp: 3, 8 LR f1 score: 0.152 LR cohens kappa score: 0.097 LR average precision score: 0.177 -> test with 'GB' GB tn, fp: 287, 23 GB fn, tp: 9, 2 GB f1 score: 0.111 GB cohens kappa score: 0.067 -> test with 'KNN' KNN tn, fp: 223, 87 KNN fn, tp: 8, 3 KNN f1 score: 0.059 KNN cohens kappa score: -0.002 ------ Step 2/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1194 synthetic samples -> test with 'LR' LR tn, fp: 233, 77 LR fn, tp: 5, 6 LR f1 score: 0.128 LR cohens kappa score: 0.071 LR average precision score: 0.269 -> test with 'GB' GB tn, fp: 298, 12 GB fn, tp: 9, 2 GB f1 score: 0.160 GB cohens kappa score: 0.126 -> test with 'KNN' KNN tn, fp: 218, 92 KNN fn, tp: 8, 3 KNN f1 score: 0.057 KNN cohens kappa score: -0.005 ------ Step 2/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1196 synthetic samples -> test with 'LR' LR tn, fp: 228, 78 LR fn, tp: 3, 6 LR f1 score: 0.129 LR cohens kappa score: 0.082 LR average precision score: 0.108 -> test with 'GB' GB tn, fp: 289, 17 GB fn, tp: 7, 2 GB f1 score: 0.143 GB cohens kappa score: 0.108 -> test with 'KNN' KNN tn, fp: 222, 84 KNN fn, tp: 6, 3 KNN f1 score: 0.062 KNN cohens kappa score: 0.011 ====== 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: 233, 77 LR fn, tp: 5, 6 LR f1 score: 0.128 LR cohens kappa score: 0.071 LR average precision score: 0.171 -> test with 'GB' GB tn, fp: 296, 14 GB fn, tp: 9, 2 GB f1 score: 0.148 GB cohens kappa score: 0.112 -> test with 'KNN' KNN tn, fp: 216, 94 KNN fn, tp: 7, 4 KNN f1 score: 0.073 KNN cohens kappa score: 0.013 ------ Step 3/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1194 synthetic samples -> test with 'LR' LR tn, fp: 214, 96 LR fn, tp: 2, 9 LR f1 score: 0.155 LR cohens kappa score: 0.099 LR average precision score: 0.254 -> test with 'GB' GB tn, fp: 288, 22 GB fn, tp: 9, 2 GB f1 score: 0.114 GB cohens kappa score: 0.071 -> test with 'KNN' KNN tn, fp: 230, 80 KNN fn, tp: 8, 3 KNN f1 score: 0.064 KNN cohens kappa score: 0.004 ------ Step 3/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1194 synthetic samples -> test with 'LR' LR tn, fp: 219, 91 LR fn, tp: 5, 6 LR f1 score: 0.111 LR cohens kappa score: 0.053 LR average precision score: 0.069 -> test with 'GB' GB tn, fp: 289, 21 GB fn, tp: 10, 1 GB f1 score: 0.061 GB cohens kappa score: 0.016 -> test with 'KNN' KNN tn, fp: 211, 99 KNN fn, tp: 6, 5 KNN f1 score: 0.087 KNN cohens kappa score: 0.027 ------ Step 3/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1194 synthetic samples -> test with 'LR' LR tn, fp: 212, 98 LR fn, tp: 2, 9 LR f1 score: 0.153 LR cohens kappa score: 0.096 LR average precision score: 0.193 -> test with 'GB' GB tn, fp: 289, 21 GB fn, tp: 10, 1 GB f1 score: 0.061 GB cohens kappa score: 0.016 -> test with 'KNN' KNN tn, fp: 228, 82 KNN fn, tp: 5, 6 KNN f1 score: 0.121 KNN cohens kappa score: 0.064 ------ Step 3/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1196 synthetic samples -> test with 'LR' LR tn, fp: 231, 75 LR fn, tp: 3, 6 LR f1 score: 0.133 LR cohens kappa score: 0.086 LR average precision score: 0.093 -> test with 'GB' GB tn, fp: 293, 13 GB fn, tp: 7, 2 GB f1 score: 0.167 GB cohens kappa score: 0.136 -> test with 'KNN' KNN tn, fp: 229, 77 KNN fn, tp: 7, 2 KNN f1 score: 0.045 KNN cohens kappa score: -0.006 ====== 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: 225, 85 LR fn, tp: 3, 8 LR f1 score: 0.154 LR cohens kappa score: 0.099 LR average precision score: 0.380 -> 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: 236, 74 KNN fn, tp: 9, 2 KNN f1 score: 0.046 KNN cohens kappa score: -0.015 ------ Step 4/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1194 synthetic samples -> test with 'LR' LR tn, fp: 215, 95 LR fn, tp: 3, 8 LR f1 score: 0.140 LR cohens kappa score: 0.084 LR average precision score: 0.174 -> test with 'GB' GB tn, fp: 291, 19 GB fn, tp: 10, 1 GB f1 score: 0.065 GB cohens kappa score: 0.021 -> test with 'KNN' KNN tn, fp: 215, 95 KNN fn, tp: 9, 2 KNN f1 score: 0.037 KNN cohens kappa score: -0.026 ------ Step 4/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1194 synthetic samples -> test with 'LR' LR tn, fp: 229, 81 LR fn, tp: 5, 6 LR f1 score: 0.122 LR cohens kappa score: 0.066 LR average precision score: 0.109 -> test with 'GB' GB tn, fp: 291, 19 GB fn, tp: 9, 2 GB f1 score: 0.125 GB cohens kappa score: 0.084 -> test with 'KNN' KNN tn, fp: 231, 79 KNN fn, tp: 8, 3 KNN f1 score: 0.065 KNN cohens kappa score: 0.004 ------ Step 4/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1194 synthetic samples -> test with 'LR' LR tn, fp: 214, 96 LR fn, tp: 1, 10 LR f1 score: 0.171 LR cohens kappa score: 0.116 LR average precision score: 0.152 -> test with 'GB' GB tn, fp: 293, 17 GB fn, tp: 9, 2 GB f1 score: 0.133 GB cohens kappa score: 0.094 -> test with 'KNN' KNN tn, fp: 215, 95 KNN fn, tp: 8, 3 KNN f1 score: 0.055 KNN cohens kappa score: -0.007 ------ Step 4/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1196 synthetic samples -> test with 'LR' LR tn, fp: 212, 94 LR fn, tp: 6, 3 LR f1 score: 0.057 LR cohens kappa score: 0.005 LR average precision score: 0.070 -> test with 'GB' GB tn, fp: 281, 25 GB fn, tp: 8, 1 GB f1 score: 0.057 GB cohens kappa score: 0.015 -> test with 'KNN' KNN tn, fp: 238, 68 KNN fn, tp: 7, 2 KNN f1 score: 0.051 KNN cohens kappa score: 0.000 ====== 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: 232, 78 LR fn, tp: 5, 6 LR f1 score: 0.126 LR cohens kappa score: 0.070 LR average precision score: 0.073 -> test with 'GB' GB tn, fp: 295, 15 GB fn, tp: 9, 2 GB f1 score: 0.143 GB cohens kappa score: 0.106 -> test with 'KNN' KNN tn, fp: 227, 83 KNN fn, tp: 7, 4 KNN f1 score: 0.082 KNN cohens kappa score: 0.022 ------ Step 5/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1194 synthetic samples -> test with 'LR' LR tn, fp: 228, 82 LR fn, tp: 6, 5 LR f1 score: 0.102 LR cohens kappa score: 0.044 LR average precision score: 0.106 -> test with 'GB' GB tn, fp: 289, 21 GB fn, tp: 9, 2 GB f1 score: 0.118 GB cohens kappa score: 0.075 -> test with 'KNN' KNN tn, fp: 228, 82 KNN fn, tp: 7, 4 KNN f1 score: 0.082 KNN cohens kappa score: 0.023 ------ Step 5/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1194 synthetic samples -> test with 'LR' LR tn, fp: 203, 107 LR fn, tp: 0, 11 LR f1 score: 0.171 LR cohens kappa score: 0.115 LR average precision score: 0.275 -> test with 'GB' GB tn, fp: 281, 29 GB fn, tp: 9, 2 GB f1 score: 0.095 GB cohens kappa score: 0.047 -> test with 'KNN' KNN tn, fp: 220, 90 KNN fn, tp: 7, 4 KNN f1 score: 0.076 KNN cohens kappa score: 0.016 ------ Step 5/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1194 synthetic samples -> test with 'LR' LR tn, fp: 221, 89 LR fn, tp: 4, 7 LR f1 score: 0.131 LR cohens kappa score: 0.074 LR average precision score: 0.193 -> test with 'GB' GB tn, fp: 294, 16 GB fn, tp: 9, 2 GB f1 score: 0.138 GB cohens kappa score: 0.100 -> test with 'KNN' KNN tn, fp: 234, 76 KNN fn, tp: 7, 4 KNN f1 score: 0.088 KNN cohens kappa score: 0.029 ------ Step 5/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1196 synthetic samples -> test with 'LR' LR tn, fp: 248, 58 LR fn, tp: 4, 5 LR f1 score: 0.139 LR cohens kappa score: 0.094 LR average precision score: 0.142 -> test with 'GB' GB tn, fp: 296, 10 GB fn, tp: 8, 1 GB f1 score: 0.100 GB cohens kappa score: 0.071 -> test with 'KNN' KNN tn, fp: 227, 79 KNN fn, tp: 7, 2 KNN f1 score: 0.044 KNN cohens kappa score: -0.007 ### Exercise is done. -----[ LR ]----- maximum: LR tn, fp: 248, 112 LR fn, tp: 6, 11 LR f1 score: 0.171 LR cohens kappa score: 0.116 LR average precision score: 0.380 average: LR tn, fp: 222.36, 86.84 LR fn, tp: 3.6, 7.0 LR f1 score: 0.133 LR cohens kappa score: 0.078 LR average precision score: 0.166 minimum: LR tn, fp: 198, 58 LR fn, tp: 0, 3 LR f1 score: 0.057 LR cohens kappa score: 0.005 LR average precision score: 0.069 -----[ GB ]----- maximum: GB tn, fp: 298, 29 GB fn, tp: 11, 5 GB f1 score: 0.270 GB cohens kappa score: 0.233 average: GB tn, fp: 291.6, 17.6 GB fn, tp: 8.8, 1.8 GB f1 score: 0.118 GB cohens kappa score: 0.079 minimum: GB tn, fp: 281, 10 GB fn, tp: 6, 0 GB f1 score: 0.000 GB cohens kappa score: -0.044 -----[ KNN ]----- maximum: KNN tn, fp: 238, 106 KNN fn, tp: 9, 6 KNN f1 score: 0.121 KNN cohens kappa score: 0.064 average: KNN tn, fp: 223.68, 85.52 KNN fn, tp: 7.24, 3.36 KNN f1 score: 0.067 KNN cohens kappa score: 0.008 minimum: KNN tn, fp: 204, 68 KNN fn, tp: 5, 1 KNN f1 score: 0.023 KNN cohens kappa score: -0.029