/////////////////////////////////////////// // Running convGAN-majority-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: 209, 101 GAN fn, tp: 5, 6 GAN f1 score: 0.102 GAN cohens kappa score: 0.042 -> test with 'LR' LR tn, fp: 208, 102 LR fn, tp: 4, 7 LR f1 score: 0.117 LR cohens kappa score: 0.058 LR average precision score: 0.121 -> 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: 221, 89 KNN fn, tp: 8, 3 KNN f1 score: 0.058 KNN cohens kappa score: -0.003 ------ 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: 252, 58 GAN fn, tp: 7, 4 GAN f1 score: 0.110 GAN cohens kappa score: 0.055 -> 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: 0.118 -> 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: 232, 78 KNN fn, tp: 6, 5 KNN f1 score: 0.106 KNN cohens kappa score: 0.049 ------ 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: 188, 122 GAN fn, tp: 7, 4 GAN f1 score: 0.058 GAN cohens kappa score: -0.005 -> test with 'LR' LR tn, fp: 194, 116 LR fn, tp: 2, 9 LR f1 score: 0.132 LR cohens kappa score: 0.074 LR average precision score: 0.239 -> test with 'GB' GB tn, fp: 281, 29 GB fn, tp: 6, 5 GB f1 score: 0.222 GB cohens kappa score: 0.180 -> test with 'KNN' KNN tn, fp: 202, 108 KNN fn, tp: 7, 4 KNN f1 score: 0.065 KNN cohens kappa score: 0.003 ------ 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: 259, 51 GAN fn, tp: 9, 2 GAN f1 score: 0.062 GAN cohens kappa score: 0.006 -> test with 'LR' LR tn, fp: 242, 68 LR fn, tp: 6, 5 LR f1 score: 0.119 LR cohens kappa score: 0.063 LR average precision score: 0.148 -> test with 'GB' GB tn, fp: 296, 14 GB fn, tp: 11, 0 GB f1 score: 0.000 GB cohens kappa score: -0.040 -> 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 1/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1196 synthetic samples -> test with GAN.predict GAN tn, fp: 213, 93 GAN fn, tp: 6, 3 GAN f1 score: 0.057 GAN cohens kappa score: 0.005 -> test with 'LR' LR tn, fp: 225, 81 LR fn, tp: 4, 5 LR f1 score: 0.105 LR cohens kappa score: 0.056 LR average precision score: 0.226 -> test with 'GB' GB tn, fp: 286, 20 GB fn, tp: 9, 0 GB f1 score: 0.000 GB cohens kappa score: -0.041 -> test with 'KNN' KNN tn, fp: 231, 75 KNN fn, tp: 7, 2 KNN f1 score: 0.047 KNN cohens kappa score: -0.005 ====== 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: 214, 96 GAN fn, tp: 4, 7 GAN f1 score: 0.123 GAN cohens kappa score: 0.065 -> test with 'LR' LR tn, fp: 210, 100 LR fn, tp: 3, 8 LR f1 score: 0.134 LR cohens kappa score: 0.077 LR average precision score: 0.127 -> test with 'GB' GB tn, fp: 286, 24 GB fn, tp: 7, 4 GB f1 score: 0.205 GB cohens kappa score: 0.164 -> 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 2/5: 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: 7, 4 GAN f1 score: 0.083 GAN cohens kappa score: 0.024 -> 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: 0.142 -> test with 'GB' GB tn, fp: 297, 13 GB fn, tp: 10, 1 GB f1 score: 0.080 GB cohens kappa score: 0.043 -> test with 'KNN' KNN tn, fp: 235, 75 KNN fn, tp: 10, 1 KNN f1 score: 0.023 KNN cohens kappa score: -0.039 ------ 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: 255, 55 GAN fn, tp: 9, 2 GAN f1 score: 0.059 GAN cohens kappa score: 0.001 -> test with 'LR' LR tn, fp: 219, 91 LR fn, tp: 3, 8 LR f1 score: 0.145 LR cohens kappa score: 0.089 LR average precision score: 0.169 -> test with 'GB' GB tn, fp: 285, 25 GB fn, tp: 9, 2 GB f1 score: 0.105 GB cohens kappa score: 0.059 -> test with 'KNN' KNN tn, fp: 232, 78 KNN fn, tp: 9, 2 KNN f1 score: 0.044 KNN cohens kappa score: -0.017 ------ 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: 243, 67 GAN fn, tp: 7, 4 GAN f1 score: 0.098 GAN cohens kappa score: 0.041 -> test with 'LR' LR tn, fp: 231, 79 LR fn, tp: 6, 5 LR f1 score: 0.105 LR cohens kappa score: 0.048 LR average precision score: 0.273 -> test with 'GB' GB tn, fp: 292, 18 GB fn, tp: 10, 1 GB f1 score: 0.067 GB cohens kappa score: 0.024 -> test with 'KNN' KNN tn, fp: 216, 94 KNN fn, tp: 8, 3 KNN f1 score: 0.056 KNN cohens kappa score: -0.006 ------ 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: 259, 47 GAN fn, tp: 6, 3 GAN f1 score: 0.102 GAN cohens kappa score: 0.056 -> 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: 0.120 -> test with 'GB' GB tn, fp: 290, 16 GB fn, tp: 6, 3 GB f1 score: 0.214 GB cohens kappa score: 0.183 -> test with 'KNN' KNN tn, fp: 213, 93 KNN fn, tp: 7, 2 KNN f1 score: 0.038 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 GAN.predict GAN tn, fp: 241, 69 GAN fn, tp: 7, 4 GAN f1 score: 0.095 GAN cohens kappa score: 0.038 -> 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: 0.185 -> 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: 232, 78 KNN fn, tp: 9, 2 KNN f1 score: 0.044 KNN cohens kappa score: -0.017 ------ 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: 225, 85 GAN fn, tp: 6, 5 GAN f1 score: 0.099 GAN cohens kappa score: 0.040 -> test with 'LR' LR tn, fp: 216, 94 LR fn, tp: 4, 7 LR f1 score: 0.125 LR cohens kappa score: 0.067 LR average precision score: 0.257 -> 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: 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: 218, 92 GAN fn, tp: 5, 6 GAN f1 score: 0.110 GAN cohens kappa score: 0.052 -> test with 'LR' LR tn, fp: 223, 87 LR fn, tp: 5, 6 LR f1 score: 0.115 LR cohens kappa score: 0.058 LR average precision score: 0.067 -> test with 'GB' GB tn, fp: 292, 18 GB fn, tp: 10, 1 GB f1 score: 0.067 GB cohens kappa score: 0.024 -> 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 3/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1194 synthetic samples -> test with GAN.predict GAN tn, fp: 240, 70 GAN fn, tp: 7, 4 GAN f1 score: 0.094 GAN cohens kappa score: 0.037 -> 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: 0.181 -> test with 'GB' GB tn, fp: 283, 27 GB fn, tp: 10, 1 GB f1 score: 0.051 GB cohens kappa score: 0.002 -> 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 3/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1196 synthetic samples -> test with GAN.predict GAN tn, fp: 259, 47 GAN fn, tp: 8, 1 GAN f1 score: 0.035 GAN cohens kappa score: -0.014 -> test with 'LR' LR tn, fp: 224, 82 LR fn, tp: 2, 7 LR f1 score: 0.143 LR cohens kappa score: 0.096 LR average precision score: 0.106 -> test with 'GB' GB tn, fp: 294, 12 GB fn, tp: 6, 3 GB f1 score: 0.250 GB cohens kappa score: 0.222 -> test with 'KNN' KNN tn, fp: 238, 68 KNN fn, tp: 5, 4 KNN f1 score: 0.099 KNN cohens kappa score: 0.051 ====== 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: 246, 64 GAN fn, tp: 6, 5 GAN f1 score: 0.125 GAN cohens kappa score: 0.070 -> test with 'LR' LR tn, fp: 231, 79 LR fn, tp: 3, 8 LR f1 score: 0.163 LR cohens kappa score: 0.109 LR average precision score: 0.407 -> 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: 229, 81 KNN fn, tp: 9, 2 KNN f1 score: 0.043 KNN cohens kappa score: -0.019 ------ 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: 6, 5 GAN f1 score: 0.079 GAN cohens kappa score: 0.017 -> 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: 0.182 -> 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: 205, 105 KNN fn, tp: 8, 3 KNN f1 score: 0.050 KNN cohens kappa score: -0.013 ------ 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: 245, 65 GAN fn, tp: 9, 2 GAN f1 score: 0.051 GAN cohens kappa score: -0.008 -> 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: 0.090 -> test with 'GB' GB tn, fp: 292, 18 GB fn, tp: 10, 1 GB f1 score: 0.067 GB cohens kappa score: 0.024 -> test with 'KNN' KNN tn, fp: 237, 73 KNN fn, tp: 9, 2 KNN f1 score: 0.047 KNN cohens kappa score: -0.014 ------ 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: 195, 115 GAN fn, tp: 4, 7 GAN f1 score: 0.105 GAN cohens kappa score: 0.045 -> test with 'LR' LR tn, fp: 212, 98 LR fn, tp: 1, 10 LR f1 score: 0.168 LR cohens kappa score: 0.113 LR average precision score: 0.142 -> 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: 218, 92 KNN fn, tp: 8, 3 KNN f1 score: 0.057 KNN cohens kappa score: -0.005 ------ 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: 252, 54 GAN fn, tp: 9, 0 GAN f1 score: 0.000 GAN cohens kappa score: -0.052 -> test with 'LR' LR tn, fp: 215, 91 LR fn, tp: 5, 4 LR f1 score: 0.077 LR cohens kappa score: 0.026 LR average precision score: 0.065 -> test with 'GB' GB tn, fp: 278, 28 GB fn, tp: 8, 1 GB f1 score: 0.053 GB cohens kappa score: 0.009 -> test with 'KNN' KNN tn, fp: 227, 79 KNN fn, tp: 8, 1 KNN f1 score: 0.022 KNN cohens kappa score: -0.030 ====== 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: 257, 53 GAN fn, tp: 9, 2 GAN f1 score: 0.061 GAN cohens kappa score: 0.004 -> test with 'LR' LR tn, fp: 236, 74 LR fn, tp: 5, 6 LR f1 score: 0.132 LR cohens kappa score: 0.076 LR average precision score: 0.075 -> test with 'GB' GB tn, fp: 292, 18 GB fn, tp: 10, 1 GB f1 score: 0.067 GB cohens kappa score: 0.024 -> test with 'KNN' KNN tn, fp: 227, 83 KNN fn, tp: 9, 2 KNN f1 score: 0.042 KNN cohens kappa score: -0.020 ------ 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: 235, 75 GAN fn, tp: 6, 5 GAN f1 score: 0.110 GAN cohens kappa score: 0.053 -> test with 'LR' LR tn, fp: 223, 87 LR fn, tp: 6, 5 LR f1 score: 0.097 LR cohens kappa score: 0.038 LR average precision score: 0.088 -> 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: 212, 98 KNN fn, tp: 7, 4 KNN f1 score: 0.071 KNN cohens kappa score: 0.010 ------ 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: 235, 75 GAN fn, tp: 6, 5 GAN f1 score: 0.110 GAN cohens kappa score: 0.053 -> test with 'LR' LR tn, fp: 202, 108 LR fn, tp: 0, 11 LR f1 score: 0.169 LR cohens kappa score: 0.114 LR average precision score: 0.294 -> test with 'GB' GB tn, fp: 286, 24 GB fn, tp: 7, 4 GB f1 score: 0.205 GB cohens kappa score: 0.164 -> test with 'KNN' KNN tn, fp: 225, 85 KNN fn, tp: 6, 5 KNN f1 score: 0.099 KNN cohens kappa score: 0.040 ------ 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: 255, 55 GAN fn, tp: 5, 6 GAN f1 score: 0.167 GAN cohens kappa score: 0.115 -> test with 'LR' LR tn, fp: 223, 87 LR fn, tp: 4, 7 LR f1 score: 0.133 LR cohens kappa score: 0.077 LR average precision score: 0.190 -> test with 'GB' GB tn, fp: 296, 14 GB fn, tp: 8, 3 GB f1 score: 0.214 GB cohens kappa score: 0.180 -> test with 'KNN' KNN tn, fp: 230, 80 KNN fn, tp: 7, 4 KNN f1 score: 0.084 KNN cohens kappa score: 0.025 ------ 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: 253, 53 GAN fn, tp: 5, 4 GAN f1 score: 0.121 GAN cohens kappa score: 0.076 -> test with 'LR' LR tn, fp: 238, 68 LR fn, tp: 3, 6 LR f1 score: 0.145 LR cohens kappa score: 0.099 LR average precision score: 0.147 -> test with 'GB' GB tn, fp: 288, 18 GB fn, tp: 8, 1 GB f1 score: 0.071 GB cohens kappa score: 0.034 -> test with 'KNN' KNN tn, fp: 214, 92 KNN fn, tp: 8, 1 KNN f1 score: 0.020 KNN cohens kappa score: -0.034 ### Exercise is done. -----[ LR ]----- maximum: LR tn, fp: 242, 116 LR fn, tp: 6, 11 LR f1 score: 0.169 LR cohens kappa score: 0.114 LR average precision score: 0.407 average: LR tn, fp: 221.12, 88.08 LR fn, tp: 3.6, 7.0 LR f1 score: 0.132 LR cohens kappa score: 0.077 LR average precision score: 0.166 minimum: LR tn, fp: 194, 68 LR fn, tp: 0, 4 LR f1 score: 0.077 LR cohens kappa score: 0.026 LR average precision score: 0.065 -----[ GB ]----- maximum: GB tn, fp: 301, 29 GB fn, tp: 11, 5 GB f1 score: 0.250 GB cohens kappa score: 0.222 average: GB tn, fp: 290.16, 19.04 GB fn, tp: 8.68, 1.92 GB f1 score: 0.119 GB cohens kappa score: 0.079 minimum: GB tn, fp: 278, 9 GB fn, tp: 6, 0 GB f1 score: 0.000 GB cohens kappa score: -0.041 -----[ KNN ]----- maximum: KNN tn, fp: 238, 108 KNN fn, tp: 10, 5 KNN f1 score: 0.106 KNN cohens kappa score: 0.051 average: KNN tn, fp: 223.16, 86.04 KNN fn, tp: 7.72, 2.88 KNN f1 score: 0.058 KNN cohens kappa score: -0.002 minimum: KNN tn, fp: 202, 68 KNN fn, tp: 5, 1 KNN f1 score: 0.020 KNN cohens kappa score: -0.039 -----[ GAN ]----- maximum: GAN tn, fp: 259, 122 GAN fn, tp: 9, 7 GAN f1 score: 0.167 GAN cohens kappa score: 0.115 average: GAN tn, fp: 235.04, 74.16 GAN fn, tp: 6.6, 4.0 GAN f1 score: 0.089 GAN cohens kappa score: 0.033 minimum: GAN tn, fp: 188, 47 GAN fn, tp: 4, 0 GAN f1 score: 0.000 GAN cohens kappa score: -0.052