/////////////////////////////////////////// // Running convGAN 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: 200, 110 LR fn, tp: 5, 6 LR f1 score: 0.094 LR cohens kappa score: 0.034 LR average precision score: 0.094 -> 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: 237, 73 KNN fn, tp: 7, 4 KNN f1 score: 0.091 KNN cohens kappa score: 0.033 ------ Step 1/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1194 synthetic samples -> test with 'LR' LR tn, fp: 223, 87 LR fn, tp: 3, 8 LR f1 score: 0.151 LR cohens kappa score: 0.095 LR average precision score: 0.110 -> 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: 229, 81 KNN fn, tp: 6, 5 KNN f1 score: 0.103 KNN cohens kappa score: 0.045 ------ Step 1/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1194 synthetic samples -> test with 'LR' LR tn, fp: 179, 131 LR fn, tp: 1, 10 LR f1 score: 0.132 LR cohens kappa score: 0.073 LR average precision score: 0.234 -> test with 'GB' GB tn, fp: 290, 20 GB fn, tp: 6, 5 GB f1 score: 0.278 GB cohens kappa score: 0.242 -> test with 'KNN' KNN tn, fp: 211, 99 KNN fn, tp: 7, 4 KNN f1 score: 0.070 KNN cohens kappa score: 0.009 ------ Step 1/5: 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: 6, 5 LR f1 score: 0.108 LR cohens kappa score: 0.050 LR average precision score: 0.129 -> test with 'GB' GB tn, fp: 287, 23 GB fn, tp: 10, 1 GB f1 score: 0.057 GB cohens kappa score: 0.011 -> test with 'KNN' KNN tn, fp: 235, 75 KNN fn, tp: 6, 5 KNN f1 score: 0.110 KNN cohens kappa score: 0.053 ------ Step 1/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1196 synthetic samples -> test with 'LR' LR tn, fp: 235, 71 LR fn, tp: 4, 5 LR f1 score: 0.118 LR cohens kappa score: 0.070 LR average precision score: 0.212 -> 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: 232, 74 KNN fn, tp: 5, 4 KNN f1 score: 0.092 KNN cohens kappa score: 0.043 ====== 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: 207, 103 LR fn, tp: 2, 9 LR f1 score: 0.146 LR cohens kappa score: 0.090 LR average precision score: 0.136 -> test with 'GB' GB tn, fp: 289, 21 GB fn, tp: 7, 4 GB f1 score: 0.222 GB cohens kappa score: 0.183 -> test with 'KNN' KNN tn, fp: 214, 96 KNN fn, tp: 8, 3 KNN f1 score: 0.055 KNN cohens kappa score: -0.008 ------ Step 2/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1194 synthetic samples -> test with 'LR' LR tn, fp: 209, 101 LR fn, tp: 3, 8 LR f1 score: 0.133 LR cohens kappa score: 0.076 LR average precision score: 0.128 -> 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: 229, 81 KNN fn, tp: 9, 2 KNN f1 score: 0.043 KNN cohens kappa score: -0.019 ------ Step 2/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1194 synthetic samples -> test with 'LR' LR tn, fp: 230, 80 LR fn, tp: 4, 7 LR f1 score: 0.143 LR cohens kappa score: 0.087 LR average precision score: 0.120 -> test with 'GB' GB tn, fp: 292, 18 GB fn, tp: 9, 2 GB f1 score: 0.129 GB cohens kappa score: 0.089 -> test with 'KNN' KNN tn, fp: 241, 69 KNN fn, tp: 9, 2 KNN f1 score: 0.049 KNN cohens kappa score: -0.011 ------ Step 2/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1194 synthetic samples -> test with 'LR' LR tn, fp: 215, 95 LR fn, tp: 5, 6 LR f1 score: 0.107 LR cohens kappa score: 0.048 LR average precision score: 0.299 -> 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: 229, 81 KNN fn, tp: 6, 5 KNN f1 score: 0.103 KNN cohens kappa score: 0.045 ------ Step 2/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1196 synthetic samples -> test with 'LR' LR tn, fp: 226, 80 LR fn, tp: 3, 6 LR f1 score: 0.126 LR cohens kappa score: 0.079 LR average precision score: 0.136 -> test with 'GB' GB tn, fp: 291, 15 GB fn, tp: 7, 2 GB f1 score: 0.154 GB cohens kappa score: 0.121 -> test with 'KNN' KNN tn, fp: 225, 81 KNN fn, tp: 6, 3 KNN f1 score: 0.065 KNN cohens kappa score: 0.014 ====== 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: 230, 80 LR fn, tp: 4, 7 LR f1 score: 0.143 LR cohens kappa score: 0.087 LR average precision score: 0.152 -> 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: 222, 88 KNN fn, tp: 9, 2 KNN f1 score: 0.040 KNN cohens kappa score: -0.023 ------ Step 3/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1194 synthetic samples -> test with 'LR' LR tn, fp: 204, 106 LR fn, tp: 3, 8 LR f1 score: 0.128 LR cohens kappa score: 0.070 LR average precision score: 0.254 -> test with 'GB' GB tn, fp: 291, 19 GB fn, tp: 8, 3 GB f1 score: 0.182 GB cohens kappa score: 0.143 -> test with 'KNN' KNN tn, fp: 230, 80 KNN fn, tp: 9, 2 KNN f1 score: 0.043 KNN cohens kappa score: -0.019 ------ Step 3/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1194 synthetic samples -> test with 'LR' LR tn, fp: 238, 72 LR fn, tp: 5, 6 LR f1 score: 0.135 LR cohens kappa score: 0.080 LR average precision score: 0.070 -> test with 'GB' GB tn, fp: 286, 24 GB fn, tp: 8, 3 GB f1 score: 0.158 GB cohens kappa score: 0.115 -> test with 'KNN' KNN tn, fp: 230, 80 KNN fn, tp: 5, 6 KNN f1 score: 0.124 KNN cohens kappa score: 0.067 ------ Step 3/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1194 synthetic samples -> test with 'LR' LR tn, fp: 201, 109 LR fn, tp: 2, 9 LR f1 score: 0.140 LR cohens kappa score: 0.082 LR average precision score: 0.182 -> test with 'GB' GB tn, fp: 285, 25 GB fn, tp: 8, 3 GB f1 score: 0.154 GB cohens kappa score: 0.110 -> test with 'KNN' KNN tn, fp: 230, 80 KNN fn, tp: 4, 7 KNN f1 score: 0.143 KNN cohens kappa score: 0.087 ------ Step 3/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1196 synthetic samples -> test with 'LR' LR tn, fp: 226, 80 LR fn, tp: 2, 7 LR f1 score: 0.146 LR cohens kappa score: 0.099 LR average precision score: 0.151 -> test with 'GB' GB tn, fp: 293, 13 GB fn, tp: 6, 3 GB f1 score: 0.240 GB cohens kappa score: 0.211 -> test with 'KNN' KNN tn, fp: 221, 85 KNN fn, tp: 5, 4 KNN f1 score: 0.082 KNN cohens kappa score: 0.031 ====== 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: 224, 86 LR fn, tp: 3, 8 LR f1 score: 0.152 LR cohens kappa score: 0.097 LR average precision score: 0.378 -> 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: 234, 76 KNN fn, tp: 9, 2 KNN f1 score: 0.045 KNN cohens kappa score: -0.016 ------ Step 4/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1194 synthetic samples -> test with 'LR' LR tn, fp: 212, 98 LR fn, tp: 3, 8 LR f1 score: 0.137 LR cohens kappa score: 0.080 LR average precision score: 0.201 -> test with 'GB' GB tn, fp: 285, 25 GB fn, tp: 8, 3 GB f1 score: 0.154 GB cohens kappa score: 0.110 -> test with 'KNN' KNN tn, fp: 233, 77 KNN fn, tp: 8, 3 KNN f1 score: 0.066 KNN cohens kappa score: 0.006 ------ Step 4/5: 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: 5, 6 LR f1 score: 0.117 LR cohens kappa score: 0.059 LR average precision score: 0.104 -> 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: 226, 84 KNN fn, tp: 8, 3 KNN f1 score: 0.061 KNN cohens kappa score: 0.000 ------ Step 4/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1194 synthetic samples -> test with 'LR' LR tn, fp: 216, 94 LR fn, tp: 1, 10 LR f1 score: 0.174 LR cohens kappa score: 0.119 LR average precision score: 0.141 -> test with 'GB' GB tn, fp: 293, 17 GB fn, tp: 6, 5 GB f1 score: 0.303 GB cohens kappa score: 0.270 -> 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 4/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1196 synthetic samples -> test with 'LR' LR tn, fp: 209, 97 LR fn, tp: 5, 4 LR f1 score: 0.073 LR cohens kappa score: 0.021 LR average precision score: 0.058 -> test with 'GB' GB tn, fp: 275, 31 GB fn, tp: 8, 1 GB f1 score: 0.049 GB cohens kappa score: 0.004 -> test with 'KNN' KNN tn, fp: 230, 76 KNN fn, tp: 7, 2 KNN f1 score: 0.046 KNN cohens kappa score: -0.006 ====== 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: 244, 66 LR fn, tp: 5, 6 LR f1 score: 0.145 LR cohens kappa score: 0.091 LR average precision score: 0.104 -> test with 'GB' GB tn, fp: 295, 15 GB fn, tp: 10, 1 GB f1 score: 0.074 GB cohens kappa score: 0.035 -> 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 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1194 synthetic samples -> test with 'LR' LR tn, fp: 230, 80 LR fn, tp: 6, 5 LR f1 score: 0.104 LR cohens kappa score: 0.046 LR average precision score: 0.097 -> 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: 216, 94 KNN fn, tp: 6, 5 KNN f1 score: 0.091 KNN cohens kappa score: 0.031 ------ 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: 1, 10 LR f1 score: 0.156 LR cohens kappa score: 0.100 LR average precision score: 0.229 -> test with 'GB' GB tn, fp: 282, 28 GB fn, tp: 7, 4 GB f1 score: 0.186 GB cohens kappa score: 0.142 -> test with 'KNN' KNN tn, fp: 251, 59 KNN fn, tp: 6, 5 KNN f1 score: 0.133 KNN cohens kappa score: 0.079 ------ Step 5/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1194 synthetic samples -> test with 'LR' LR tn, fp: 235, 75 LR fn, tp: 5, 6 LR f1 score: 0.130 LR cohens kappa score: 0.075 LR average precision score: 0.192 -> 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: 238, 72 KNN fn, tp: 6, 5 KNN f1 score: 0.114 KNN cohens kappa score: 0.057 ------ Step 5/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1196 synthetic samples -> test with 'LR' LR tn, fp: 242, 64 LR fn, tp: 3, 6 LR f1 score: 0.152 LR cohens kappa score: 0.107 LR average precision score: 0.160 -> test with 'GB' GB tn, fp: 288, 18 GB fn, tp: 7, 2 GB f1 score: 0.138 GB cohens kappa score: 0.103 -> test with 'KNN' KNN tn, fp: 234, 72 KNN fn, tp: 8, 1 KNN f1 score: 0.024 KNN cohens kappa score: -0.028 ### Exercise is done. -----[ LR ]----- maximum: LR tn, fp: 244, 131 LR fn, tp: 6, 10 LR f1 score: 0.174 LR cohens kappa score: 0.119 LR average precision score: 0.378 average: LR tn, fp: 219.8, 89.4 LR fn, tp: 3.56, 7.04 LR f1 score: 0.132 LR cohens kappa score: 0.077 LR average precision score: 0.163 minimum: LR tn, fp: 179, 64 LR fn, tp: 1, 4 LR f1 score: 0.073 LR cohens kappa score: 0.021 LR average precision score: 0.058 -----[ GB ]----- maximum: GB tn, fp: 301, 31 GB fn, tp: 10, 5 GB f1 score: 0.303 GB cohens kappa score: 0.270 average: GB tn, fp: 291.0, 18.2 GB fn, tp: 8.2, 2.4 GB f1 score: 0.150 GB cohens kappa score: 0.112 minimum: GB tn, fp: 275, 9 GB fn, tp: 6, 0 GB f1 score: 0.000 GB cohens kappa score: -0.034 -----[ KNN ]----- maximum: KNN tn, fp: 251, 99 KNN fn, tp: 9, 7 KNN f1 score: 0.143 KNN cohens kappa score: 0.087 average: KNN tn, fp: 229.52, 79.68 KNN fn, tp: 6.92, 3.68 KNN f1 score: 0.078 KNN cohens kappa score: 0.021 minimum: KNN tn, fp: 211, 59 KNN fn, tp: 4, 1 KNN f1 score: 0.024 KNN cohens kappa score: -0.028