/////////////////////////////////////////// // Running ProWRAS 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: 227, 83 LR fn, tp: 6, 5 LR f1 score: 0.101 LR cohens kappa score: 0.043 LR average precision score: 0.130 -> test with 'GB' GB tn, fp: 295, 15 GB fn, tp: 11, 0 GB f1 score: 0.000 GB cohens kappa score: -0.041 -> test with 'KNN' KNN tn, fp: 264, 46 KNN fn, tp: 10, 1 KNN f1 score: 0.034 KNN cohens kappa score: -0.022 ------ Step 1/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1194 synthetic samples -> test with 'LR' LR tn, fp: 239, 71 LR fn, tp: 4, 7 LR f1 score: 0.157 LR cohens kappa score: 0.103 LR average precision score: 0.121 -> test with 'GB' GB tn, fp: 303, 7 GB fn, tp: 11, 0 GB f1 score: 0.000 GB cohens kappa score: -0.027 -> test with 'KNN' KNN tn, fp: 273, 37 KNN fn, tp: 7, 4 KNN f1 score: 0.154 KNN cohens kappa score: 0.106 ------ Step 1/5: 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: 0.262 -> 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: 253, 57 KNN fn, tp: 9, 2 KNN f1 score: 0.057 KNN cohens kappa score: -0.001 ------ Step 1/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1194 synthetic samples -> test with 'LR' LR tn, fp: 258, 52 LR fn, tp: 6, 5 LR f1 score: 0.147 LR cohens kappa score: 0.095 LR average precision score: 0.154 -> test with 'GB' GB tn, fp: 299, 11 GB fn, tp: 9, 2 GB f1 score: 0.167 GB cohens kappa score: 0.135 -> 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 1/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1196 synthetic samples -> test with 'LR' LR tn, fp: 233, 73 LR fn, tp: 4, 5 LR f1 score: 0.115 LR cohens kappa score: 0.067 LR average precision score: 0.211 -> 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: 269, 37 KNN fn, tp: 8, 1 KNN f1 score: 0.043 KNN cohens kappa score: -0.004 ====== 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: 230, 80 LR fn, tp: 3, 8 LR f1 score: 0.162 LR cohens kappa score: 0.107 LR average precision score: 0.137 -> test with 'GB' GB tn, fp: 304, 6 GB fn, tp: 9, 2 GB f1 score: 0.211 GB cohens kappa score: 0.187 -> test with 'KNN' KNN tn, fp: 258, 52 KNN fn, tp: 9, 2 KNN f1 score: 0.062 KNN cohens kappa score: 0.005 ------ Step 2/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1194 synthetic samples -> test with 'LR' LR tn, fp: 233, 77 LR fn, tp: 3, 8 LR f1 score: 0.167 LR cohens kappa score: 0.113 LR average precision score: 0.140 -> test with 'GB' GB tn, fp: 306, 4 GB fn, tp: 11, 0 GB f1 score: 0.000 GB cohens kappa score: -0.019 -> test with 'KNN' KNN tn, fp: 269, 41 KNN fn, tp: 11, 0 KNN f1 score: 0.000 KNN cohens kappa score: -0.057 ------ Step 2/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1194 synthetic samples -> test with 'LR' LR tn, fp: 241, 69 LR fn, tp: 3, 8 LR f1 score: 0.182 LR cohens kappa score: 0.130 LR average precision score: 0.159 -> 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: 284, 26 KNN fn, tp: 10, 1 KNN f1 score: 0.053 KNN cohens kappa score: 0.004 ------ Step 2/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1194 synthetic samples -> test with 'LR' LR tn, fp: 232, 78 LR fn, tp: 6, 5 LR f1 score: 0.106 LR cohens kappa score: 0.049 LR average precision score: 0.259 -> test with 'GB' GB tn, fp: 306, 4 GB fn, tp: 11, 0 GB f1 score: 0.000 GB cohens kappa score: -0.019 -> test with 'KNN' KNN tn, fp: 270, 40 KNN fn, tp: 8, 3 KNN f1 score: 0.111 KNN cohens kappa score: 0.060 ------ Step 2/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1196 synthetic samples -> test with 'LR' LR tn, fp: 236, 70 LR fn, tp: 3, 6 LR f1 score: 0.141 LR cohens kappa score: 0.095 LR average precision score: 0.153 -> test with 'GB' GB tn, fp: 300, 6 GB fn, tp: 8, 1 GB f1 score: 0.125 GB cohens kappa score: 0.103 -> test with 'KNN' KNN tn, fp: 270, 36 KNN fn, tp: 6, 3 KNN f1 score: 0.125 KNN cohens kappa score: 0.082 ====== 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: 238, 72 LR fn, tp: 5, 6 LR f1 score: 0.135 LR cohens kappa score: 0.080 LR average precision score: 0.177 -> test with 'GB' GB tn, fp: 306, 4 GB fn, tp: 11, 0 GB f1 score: 0.000 GB cohens kappa score: -0.019 -> test with 'KNN' KNN tn, fp: 277, 33 KNN fn, tp: 9, 2 KNN f1 score: 0.087 KNN cohens kappa score: 0.037 ------ Step 3/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1194 synthetic samples -> test with 'LR' LR tn, fp: 225, 85 LR fn, tp: 2, 9 LR f1 score: 0.171 LR cohens kappa score: 0.117 LR average precision score: 0.178 -> 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: 275, 35 KNN fn, tp: 10, 1 KNN f1 score: 0.043 KNN cohens kappa score: -0.010 ------ Step 3/5: Slice 3/5 ------- -> 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: 0.063 -> 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: 274, 36 KNN fn, tp: 8, 3 KNN f1 score: 0.120 KNN cohens kappa score: 0.070 ------ Step 3/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1194 synthetic samples -> test with 'LR' LR tn, fp: 219, 91 LR fn, tp: 2, 9 LR f1 score: 0.162 LR cohens kappa score: 0.107 LR average precision score: 0.193 -> test with 'GB' GB tn, fp: 303, 7 GB fn, tp: 10, 1 GB f1 score: 0.105 GB cohens kappa score: 0.079 -> test with 'KNN' KNN tn, fp: 265, 45 KNN fn, tp: 7, 4 KNN f1 score: 0.133 KNN cohens kappa score: 0.082 ------ Step 3/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1196 synthetic samples -> test with 'LR' LR tn, fp: 241, 65 LR fn, tp: 3, 6 LR f1 score: 0.150 LR cohens kappa score: 0.105 LR average precision score: 0.117 -> test with 'GB' GB tn, fp: 299, 7 GB fn, tp: 6, 3 GB f1 score: 0.316 GB cohens kappa score: 0.295 -> test with 'KNN' KNN tn, fp: 259, 47 KNN fn, tp: 8, 1 KNN f1 score: 0.035 KNN cohens kappa score: -0.014 ====== 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: 229, 81 LR fn, tp: 3, 8 LR f1 score: 0.160 LR cohens kappa score: 0.105 LR average precision score: 0.394 -> test with 'GB' GB tn, fp: 305, 5 GB fn, tp: 11, 0 GB f1 score: 0.000 GB cohens kappa score: -0.022 -> 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 4/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1194 synthetic samples -> test with 'LR' LR tn, fp: 234, 76 LR fn, tp: 3, 8 LR f1 score: 0.168 LR cohens kappa score: 0.115 LR average precision score: 0.175 -> test with 'GB' GB tn, fp: 306, 4 GB fn, tp: 8, 3 GB f1 score: 0.333 GB cohens kappa score: 0.315 -> test with 'KNN' KNN tn, fp: 258, 52 KNN fn, tp: 10, 1 KNN f1 score: 0.031 KNN cohens kappa score: -0.027 ------ Step 4/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1194 synthetic samples -> test with 'LR' LR tn, fp: 248, 62 LR fn, tp: 8, 3 LR f1 score: 0.079 LR cohens kappa score: 0.022 LR average precision score: 0.083 -> 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: 267, 43 KNN fn, tp: 8, 3 KNN f1 score: 0.105 KNN cohens kappa score: 0.053 ------ Step 4/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1194 synthetic samples -> test with 'LR' LR tn, fp: 225, 85 LR fn, tp: 2, 9 LR f1 score: 0.171 LR cohens kappa score: 0.117 LR average precision score: 0.134 -> test with 'GB' GB tn, fp: 305, 5 GB fn, tp: 10, 1 GB f1 score: 0.118 GB cohens kappa score: 0.096 -> test with 'KNN' KNN tn, fp: 262, 48 KNN fn, tp: 9, 2 KNN f1 score: 0.066 KNN cohens kappa score: 0.010 ------ Step 4/5: 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: 6, 3 LR f1 score: 0.065 LR cohens kappa score: 0.014 LR average precision score: 0.075 -> test with 'GB' GB tn, fp: 290, 16 GB fn, tp: 9, 0 GB f1 score: 0.000 GB cohens kappa score: -0.038 -> test with 'KNN' KNN tn, fp: 266, 40 KNN fn, tp: 8, 1 KNN f1 score: 0.040 KNN cohens kappa score: -0.007 ====== 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: 246, 64 LR fn, tp: 5, 6 LR f1 score: 0.148 LR cohens kappa score: 0.095 LR average precision score: 0.084 -> 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: 254, 56 KNN fn, tp: 9, 2 KNN f1 score: 0.058 KNN cohens kappa score: 0.000 ------ Step 5/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1194 synthetic samples -> test with 'LR' LR tn, fp: 241, 69 LR fn, tp: 6, 5 LR f1 score: 0.118 LR cohens kappa score: 0.062 LR average precision score: 0.089 -> test with 'GB' GB tn, fp: 300, 10 GB fn, tp: 9, 2 GB f1 score: 0.174 GB cohens kappa score: 0.143 -> test with 'KNN' KNN tn, fp: 259, 51 KNN fn, tp: 10, 1 KNN f1 score: 0.032 KNN cohens kappa score: -0.026 ------ Step 5/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1194 synthetic samples -> test with 'LR' LR tn, fp: 215, 95 LR fn, tp: 0, 11 LR f1 score: 0.188 LR cohens kappa score: 0.134 LR average precision score: 0.298 -> test with 'GB' GB tn, fp: 297, 13 GB fn, tp: 11, 0 GB f1 score: 0.000 GB cohens kappa score: -0.039 -> test with 'KNN' KNN tn, fp: 275, 35 KNN fn, tp: 7, 4 KNN f1 score: 0.160 KNN cohens kappa score: 0.113 ------ Step 5/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1194 synthetic samples -> test with 'LR' LR tn, fp: 227, 83 LR fn, tp: 5, 6 LR f1 score: 0.120 LR cohens kappa score: 0.063 LR average precision score: 0.189 -> test with 'GB' GB tn, fp: 303, 7 GB fn, tp: 9, 2 GB f1 score: 0.200 GB cohens kappa score: 0.175 -> test with 'KNN' KNN tn, fp: 267, 43 KNN fn, tp: 8, 3 KNN f1 score: 0.105 KNN cohens kappa score: 0.053 ------ Step 5/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1196 synthetic samples -> test with 'LR' LR tn, fp: 258, 48 LR fn, tp: 4, 5 LR f1 score: 0.161 LR cohens kappa score: 0.118 LR average precision score: 0.143 -> test with 'GB' GB tn, fp: 298, 8 GB fn, tp: 9, 0 GB f1 score: 0.000 GB cohens kappa score: -0.028 -> test with 'KNN' KNN tn, fp: 271, 35 KNN fn, tp: 7, 2 KNN f1 score: 0.087 KNN cohens kappa score: 0.043 ### Exercise is done. -----[ LR ]----- maximum: LR tn, fp: 258, 107 LR fn, tp: 8, 11 LR f1 score: 0.188 LR cohens kappa score: 0.134 LR average precision score: 0.394 average: LR tn, fp: 233.92, 75.28 LR fn, tp: 3.92, 6.68 LR f1 score: 0.143 LR cohens kappa score: 0.090 LR average precision score: 0.165 minimum: LR tn, fp: 203, 48 LR fn, tp: 0, 3 LR f1 score: 0.065 LR cohens kappa score: 0.014 LR average precision score: 0.063 -----[ GB ]----- maximum: GB tn, fp: 306, 16 GB fn, tp: 11, 3 GB f1 score: 0.333 GB cohens kappa score: 0.315 average: GB tn, fp: 301.36, 7.84 GB fn, tp: 9.64, 0.96 GB f1 score: 0.096 GB cohens kappa score: 0.070 minimum: GB tn, fp: 290, 3 GB fn, tp: 6, 0 GB f1 score: 0.000 GB cohens kappa score: -0.041 -----[ KNN ]----- maximum: KNN tn, fp: 284, 57 KNN fn, tp: 11, 4 KNN f1 score: 0.160 KNN cohens kappa score: 0.113 average: KNN tn, fp: 266.84, 42.36 KNN fn, tp: 8.64, 1.96 KNN f1 score: 0.072 KNN cohens kappa score: 0.020 minimum: KNN tn, fp: 253, 26 KNN fn, tp: 6, 0 KNN f1 score: 0.000 KNN cohens kappa score: -0.057