/////////////////////////////////////////// // Running convGAN-proxymary-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 GAN.predict GAN tn, fp: 183, 127 GAN fn, tp: 5, 6 GAN f1 score: 0.083 GAN cohens kappa score: 0.021 -> test with 'LR' LR tn, fp: 210, 100 LR fn, tp: 5, 6 LR f1 score: 0.103 LR cohens kappa score: 0.043 LR average precision score: 0.136 -> 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: 216, 94 KNN fn, tp: 6, 5 KNN f1 score: 0.091 KNN cohens kappa score: 0.031 ------ Step 1/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1194 synthetic samples -> test with GAN.predict GAN tn, fp: 184, 126 GAN fn, tp: 4, 7 GAN f1 score: 0.097 GAN cohens kappa score: 0.036 -> test with 'LR' LR tn, fp: 225, 85 LR fn, tp: 4, 7 LR f1 score: 0.136 LR cohens kappa score: 0.080 LR average precision score: 0.110 -> 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: 233, 77 KNN fn, tp: 6, 5 KNN f1 score: 0.108 KNN cohens kappa score: 0.050 ------ Step 1/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1194 synthetic samples -> test with GAN.predict GAN tn, fp: 152, 158 GAN fn, tp: 6, 5 GAN f1 score: 0.057 GAN cohens kappa score: -0.007 -> 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: 0.239 -> test with 'GB' GB tn, fp: 284, 26 GB fn, tp: 7, 4 GB f1 score: 0.195 GB cohens kappa score: 0.153 -> test with 'KNN' KNN tn, fp: 195, 115 KNN fn, tp: 7, 4 KNN f1 score: 0.062 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 GAN.predict GAN tn, fp: 230, 80 GAN fn, tp: 7, 4 GAN f1 score: 0.084 GAN cohens kappa score: 0.025 -> test with 'LR' LR tn, fp: 237, 73 LR fn, tp: 6, 5 LR f1 score: 0.112 LR cohens kappa score: 0.056 LR average precision score: 0.149 -> test with 'GB' GB tn, fp: 291, 19 GB fn, tp: 11, 0 GB f1 score: 0.000 GB cohens kappa score: -0.045 -> 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 1/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1196 synthetic samples -> test with GAN.predict GAN tn, fp: 191, 115 GAN fn, tp: 4, 5 GAN f1 score: 0.078 GAN cohens kappa score: 0.026 -> test with 'LR' LR tn, fp: 221, 85 LR fn, tp: 4, 5 LR f1 score: 0.101 LR cohens kappa score: 0.052 LR average precision score: 0.231 -> 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 ====== 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 GAN.predict GAN tn, fp: 207, 103 GAN fn, tp: 5, 6 GAN f1 score: 0.100 GAN cohens kappa score: 0.040 -> 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: 0.131 -> 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: 214, 96 KNN fn, tp: 6, 5 KNN f1 score: 0.089 KNN cohens kappa score: 0.029 ------ Step 2/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1194 synthetic samples -> test with GAN.predict GAN tn, fp: 257, 53 GAN fn, tp: 9, 2 GAN f1 score: 0.061 GAN cohens kappa score: 0.004 -> test with 'LR' LR tn, fp: 216, 94 LR fn, tp: 3, 8 LR f1 score: 0.142 LR cohens kappa score: 0.085 LR average precision score: 0.132 -> test with 'GB' GB tn, fp: 296, 14 GB fn, tp: 10, 1 GB f1 score: 0.077 GB cohens kappa score: 0.039 -> 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 GAN.predict GAN tn, fp: 256, 54 GAN fn, tp: 9, 2 GAN f1 score: 0.060 GAN cohens kappa score: 0.003 -> 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: 0.174 -> test with 'GB' GB tn, fp: 286, 24 GB fn, tp: 9, 2 GB f1 score: 0.108 GB cohens kappa score: 0.063 -> test with 'KNN' KNN tn, fp: 227, 83 KNN fn, tp: 10, 1 KNN f1 score: 0.021 KNN cohens kappa score: -0.042 ------ Step 2/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1194 synthetic samples -> test with GAN.predict GAN tn, fp: 196, 114 GAN fn, tp: 6, 5 GAN f1 score: 0.077 GAN cohens kappa score: 0.015 -> 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: 0.269 -> test with 'GB' GB tn, fp: 288, 22 GB fn, tp: 8, 3 GB f1 score: 0.167 GB cohens kappa score: 0.125 -> test with 'KNN' KNN tn, fp: 221, 89 KNN fn, tp: 7, 4 KNN f1 score: 0.077 KNN cohens kappa score: 0.017 ------ Step 2/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1196 synthetic samples -> test with GAN.predict GAN tn, fp: 197, 109 GAN fn, tp: 5, 4 GAN f1 score: 0.066 GAN cohens kappa score: 0.013 -> test with 'LR' LR tn, fp: 227, 79 LR fn, tp: 3, 6 LR f1 score: 0.128 LR cohens kappa score: 0.080 LR average precision score: 0.147 -> test with 'GB' GB tn, fp: 287, 19 GB fn, tp: 5, 4 GB f1 score: 0.250 GB cohens kappa score: 0.218 -> test with 'KNN' KNN tn, fp: 216, 90 KNN fn, tp: 7, 2 KNN f1 score: 0.040 KNN cohens kappa score: -0.013 ====== 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 GAN.predict GAN tn, fp: 180, 130 GAN fn, tp: 3, 8 GAN f1 score: 0.107 GAN cohens kappa score: 0.047 -> test with 'LR' LR tn, fp: 225, 85 LR fn, tp: 4, 7 LR f1 score: 0.136 LR cohens kappa score: 0.080 LR average precision score: 0.149 -> 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: 223, 87 KNN fn, tp: 9, 2 KNN f1 score: 0.040 KNN cohens kappa score: -0.022 ------ Step 3/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1194 synthetic samples -> test with GAN.predict GAN tn, fp: 229, 81 GAN fn, tp: 6, 5 GAN f1 score: 0.103 GAN cohens kappa score: 0.045 -> test with 'LR' LR tn, fp: 210, 100 LR fn, tp: 4, 7 LR f1 score: 0.119 LR cohens kappa score: 0.060 LR average precision score: 0.265 -> 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: 228, 82 KNN fn, tp: 8, 3 KNN f1 score: 0.062 KNN cohens kappa score: 0.002 ------ Step 3/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1194 synthetic samples -> test with GAN.predict GAN tn, fp: 230, 80 GAN fn, tp: 5, 6 GAN f1 score: 0.124 GAN cohens kappa score: 0.067 -> 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: 0.068 -> 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: 203, 107 KNN fn, tp: 7, 4 KNN f1 score: 0.066 KNN cohens kappa score: 0.003 ------ Step 3/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1194 synthetic samples -> test with GAN.predict GAN tn, fp: 222, 88 GAN fn, tp: 4, 7 GAN f1 score: 0.132 GAN cohens kappa score: 0.075 -> test with 'LR' LR tn, fp: 206, 104 LR fn, tp: 2, 9 LR f1 score: 0.145 LR cohens kappa score: 0.088 LR average precision score: 0.194 -> 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: 219, 91 KNN fn, tp: 7, 4 KNN f1 score: 0.075 KNN cohens kappa score: 0.015 ------ Step 3/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1196 synthetic samples -> test with GAN.predict GAN tn, fp: 201, 105 GAN fn, tp: 6, 3 GAN f1 score: 0.051 GAN cohens kappa score: -0.002 -> test with 'LR' LR tn, fp: 225, 81 LR fn, tp: 2, 7 LR f1 score: 0.144 LR cohens kappa score: 0.098 LR average precision score: 0.095 -> 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: 225, 81 KNN fn, tp: 7, 2 KNN f1 score: 0.043 KNN cohens kappa score: -0.009 ====== 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 GAN.predict GAN tn, fp: 171, 139 GAN fn, tp: 4, 7 GAN f1 score: 0.089 GAN cohens kappa score: 0.027 -> 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: 0.403 -> test with 'GB' GB tn, fp: 298, 12 GB fn, tp: 8, 3 GB f1 score: 0.231 GB cohens kappa score: 0.199 -> test with 'KNN' KNN tn, fp: 232, 78 KNN fn, tp: 10, 1 KNN f1 score: 0.022 KNN cohens kappa score: -0.040 ------ Step 4/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1194 synthetic samples -> test with GAN.predict GAN tn, fp: 199, 111 GAN fn, tp: 9, 2 GAN f1 score: 0.032 GAN cohens kappa score: -0.032 -> test with 'LR' LR tn, fp: 221, 89 LR fn, tp: 3, 8 LR f1 score: 0.148 LR cohens kappa score: 0.092 LR average precision score: 0.179 -> 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: 219, 91 KNN fn, tp: 10, 1 KNN f1 score: 0.019 KNN cohens kappa score: -0.045 ------ Step 4/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1194 synthetic samples -> test with GAN.predict GAN tn, fp: 188, 122 GAN fn, tp: 9, 2 GAN f1 score: 0.030 GAN cohens kappa score: -0.036 -> 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: 234, 76 KNN fn, tp: 8, 3 KNN f1 score: 0.067 KNN cohens kappa score: 0.007 ------ Step 4/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1194 synthetic samples -> test with GAN.predict GAN tn, fp: 215, 95 GAN fn, tp: 6, 5 GAN f1 score: 0.090 GAN cohens kappa score: 0.030 -> 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: 0.135 -> test with 'GB' GB tn, fp: 289, 21 GB fn, tp: 8, 3 GB f1 score: 0.171 GB cohens kappa score: 0.131 -> test with 'KNN' KNN tn, fp: 224, 86 KNN fn, tp: 6, 5 KNN f1 score: 0.098 KNN cohens kappa score: 0.039 ------ Step 4/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1196 synthetic samples -> test with GAN.predict GAN tn, fp: 216, 90 GAN fn, tp: 6, 3 GAN f1 score: 0.059 GAN cohens kappa score: 0.007 -> test with 'LR' LR tn, fp: 222, 84 LR fn, tp: 7, 2 LR f1 score: 0.042 LR cohens kappa score: -0.010 LR average precision score: 0.037 -> test with 'GB' GB tn, fp: 270, 36 GB fn, tp: 7, 2 GB f1 score: 0.085 GB cohens kappa score: 0.041 -> test with 'KNN' KNN tn, fp: 224, 82 KNN fn, tp: 6, 3 KNN f1 score: 0.064 KNN cohens kappa score: 0.013 ====== 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 GAN.predict GAN tn, fp: 187, 123 GAN fn, tp: 7, 4 GAN f1 score: 0.058 GAN cohens kappa score: -0.005 -> test with 'LR' LR tn, fp: 228, 82 LR fn, tp: 5, 6 LR f1 score: 0.121 LR cohens kappa score: 0.064 LR average precision score: 0.073 -> test with 'GB' GB tn, fp: 293, 17 GB fn, tp: 10, 1 GB f1 score: 0.069 GB cohens kappa score: 0.028 -> test with 'KNN' KNN tn, fp: 226, 84 KNN fn, tp: 9, 2 KNN f1 score: 0.041 KNN cohens kappa score: -0.021 ------ Step 5/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1194 synthetic samples -> test with GAN.predict GAN tn, fp: 232, 78 GAN fn, tp: 7, 4 GAN f1 score: 0.086 GAN cohens kappa score: 0.027 -> test with 'LR' LR tn, fp: 224, 86 LR fn, tp: 6, 5 LR f1 score: 0.098 LR cohens kappa score: 0.039 LR average precision score: 0.098 -> 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: 218, 92 KNN fn, tp: 7, 4 KNN f1 score: 0.075 KNN cohens kappa score: 0.014 ------ Step 5/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1194 synthetic samples -> test with GAN.predict GAN tn, fp: 212, 98 GAN fn, tp: 5, 6 GAN f1 score: 0.104 GAN cohens kappa score: 0.045 -> test with 'LR' LR tn, fp: 199, 111 LR fn, tp: 0, 11 LR f1 score: 0.165 LR cohens kappa score: 0.109 LR average precision score: 0.291 -> test with 'GB' GB tn, fp: 278, 32 GB fn, tp: 9, 2 GB f1 score: 0.089 GB cohens kappa score: 0.039 -> test with 'KNN' KNN tn, fp: 222, 88 KNN fn, tp: 7, 4 KNN f1 score: 0.078 KNN cohens kappa score: 0.018 ------ Step 5/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1194 synthetic samples -> test with GAN.predict GAN tn, fp: 199, 111 GAN fn, tp: 5, 6 GAN f1 score: 0.094 GAN cohens kappa score: 0.033 -> test with 'LR' LR tn, fp: 218, 92 LR fn, tp: 3, 8 LR f1 score: 0.144 LR cohens kappa score: 0.088 LR average precision score: 0.185 -> test with 'GB' GB tn, fp: 290, 20 GB fn, tp: 7, 4 GB f1 score: 0.229 GB cohens kappa score: 0.191 -> test with 'KNN' KNN tn, fp: 231, 79 KNN fn, tp: 6, 5 KNN f1 score: 0.105 KNN cohens kappa score: 0.048 ------ Step 5/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1196 synthetic samples -> test with GAN.predict GAN tn, fp: 218, 88 GAN fn, tp: 5, 4 GAN f1 score: 0.079 GAN cohens kappa score: 0.029 -> test with 'LR' LR tn, fp: 245, 61 LR fn, tp: 4, 5 LR f1 score: 0.133 LR cohens kappa score: 0.087 LR average precision score: 0.146 -> test with 'GB' GB tn, fp: 297, 9 GB fn, tp: 8, 1 GB f1 score: 0.105 GB cohens kappa score: 0.078 -> test with 'KNN' KNN tn, fp: 220, 86 KNN fn, tp: 6, 3 KNN f1 score: 0.061 KNN cohens kappa score: 0.010 ### Exercise is done. -----[ LR ]----- maximum: LR tn, fp: 245, 112 LR fn, tp: 7, 11 LR f1 score: 0.174 LR cohens kappa score: 0.119 LR average precision score: 0.403 average: LR tn, fp: 220.64, 88.56 LR fn, tp: 3.64, 6.96 LR f1 score: 0.130 LR cohens kappa score: 0.075 LR average precision score: 0.166 minimum: LR tn, fp: 198, 61 LR fn, tp: 0, 2 LR f1 score: 0.042 LR cohens kappa score: -0.010 LR average precision score: 0.037 -----[ GB ]----- maximum: GB tn, fp: 298, 36 GB fn, tp: 11, 4 GB f1 score: 0.250 GB cohens kappa score: 0.218 average: GB tn, fp: 289.28, 19.92 GB fn, tp: 8.4, 2.2 GB f1 score: 0.135 GB cohens kappa score: 0.095 minimum: GB tn, fp: 270, 9 GB fn, tp: 5, 0 GB f1 score: 0.000 GB cohens kappa score: -0.045 -----[ KNN ]----- maximum: KNN tn, fp: 234, 115 KNN fn, tp: 10, 5 KNN f1 score: 0.108 KNN cohens kappa score: 0.050 average: KNN tn, fp: 222.56, 86.64 KNN fn, tp: 7.48, 3.12 KNN f1 score: 0.061 KNN cohens kappa score: 0.002 minimum: KNN tn, fp: 195, 72 KNN fn, tp: 6, 1 KNN f1 score: 0.019 KNN cohens kappa score: -0.045 -----[ GAN ]----- maximum: GAN tn, fp: 257, 158 GAN fn, tp: 9, 8 GAN f1 score: 0.132 GAN cohens kappa score: 0.075 average: GAN tn, fp: 206.08, 103.12 GAN fn, tp: 5.88, 4.72 GAN f1 score: 0.080 GAN cohens kappa score: 0.021 minimum: GAN tn, fp: 152, 53 GAN fn, tp: 3, 2 GAN f1 score: 0.030 GAN cohens kappa score: -0.036