/////////////////////////////////////////// // 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: 208, 102 GAN fn, tp: 4, 7 GAN f1 score: 0.117 GAN cohens kappa score: 0.058 -> test with 'LR' LR tn, fp: 212, 98 LR fn, tp: 4, 7 LR f1 score: 0.121 LR cohens kappa score: 0.063 LR average precision score: 0.130 -> test with 'GB' GB tn, fp: 287, 23 GB fn, tp: 11, 0 GB f1 score: 0.000 GB cohens kappa score: -0.049 -> test with 'KNN' KNN tn, fp: 210, 100 KNN fn, tp: 6, 5 KNN f1 score: 0.086 KNN cohens kappa score: 0.026 ------ 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: 6, 5 GAN f1 score: 0.135 GAN cohens kappa score: 0.082 -> test with 'LR' LR tn, fp: 228, 82 LR fn, tp: 3, 8 LR f1 score: 0.158 LR cohens kappa score: 0.104 LR average precision score: 0.118 -> 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: 243, 67 KNN fn, tp: 9, 2 KNN f1 score: 0.050 KNN cohens kappa score: -0.010 ------ 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: 252, 58 GAN fn, tp: 6, 5 GAN f1 score: 0.135 GAN cohens kappa score: 0.082 -> test with 'LR' LR tn, fp: 203, 107 LR fn, tp: 2, 9 LR f1 score: 0.142 LR cohens kappa score: 0.084 LR average precision score: 0.224 -> 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: 198, 112 KNN fn, tp: 8, 3 KNN f1 score: 0.048 KNN cohens kappa score: -0.016 ------ 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: 10, 1 GAN f1 score: 0.032 GAN cohens kappa score: -0.026 -> 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: 0.106 -> 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: 241, 69 KNN fn, tp: 10, 1 KNN f1 score: 0.025 KNN cohens kappa score: -0.037 ------ 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: 242, 64 GAN fn, tp: 4, 5 GAN f1 score: 0.128 GAN cohens kappa score: 0.082 -> test with 'LR' LR tn, fp: 227, 79 LR fn, tp: 4, 5 LR f1 score: 0.108 LR cohens kappa score: 0.059 LR average precision score: 0.227 -> test with 'GB' GB tn, fp: 287, 19 GB fn, tp: 7, 2 GB f1 score: 0.133 GB cohens kappa score: 0.097 -> test with 'KNN' KNN tn, fp: 237, 69 KNN fn, tp: 5, 4 KNN f1 score: 0.098 KNN cohens kappa score: 0.049 ====== 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: 252, 58 GAN fn, tp: 8, 3 GAN f1 score: 0.083 GAN cohens kappa score: 0.027 -> 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: 0.139 -> test with 'GB' GB tn, fp: 293, 17 GB fn, tp: 8, 3 GB f1 score: 0.194 GB cohens kappa score: 0.156 -> test with 'KNN' KNN tn, fp: 225, 85 KNN fn, tp: 8, 3 KNN f1 score: 0.061 KNN cohens kappa score: -0.000 ------ 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: 247, 63 GAN fn, tp: 7, 4 GAN f1 score: 0.103 GAN cohens kappa score: 0.046 -> test with 'LR' LR tn, fp: 209, 101 LR fn, tp: 4, 7 LR f1 score: 0.118 LR cohens kappa score: 0.059 LR average precision score: 0.132 -> 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: 225, 85 KNN fn, tp: 6, 5 KNN f1 score: 0.099 KNN cohens kappa score: 0.040 ------ 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: 277, 33 GAN fn, tp: 8, 3 GAN f1 score: 0.128 GAN cohens kappa score: 0.079 -> test with 'LR' LR tn, fp: 227, 83 LR fn, tp: 3, 8 LR f1 score: 0.157 LR cohens kappa score: 0.102 LR average precision score: 0.184 -> 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: 259, 51 KNN fn, tp: 9, 2 KNN f1 score: 0.062 KNN cohens kappa score: 0.006 ------ 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: 258, 52 GAN fn, tp: 10, 1 GAN f1 score: 0.031 GAN cohens kappa score: -0.027 -> test with 'LR' LR tn, fp: 226, 84 LR fn, tp: 4, 7 LR f1 score: 0.137 LR cohens kappa score: 0.081 LR average precision score: 0.287 -> 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: 231, 79 KNN fn, tp: 10, 1 KNN f1 score: 0.022 KNN cohens kappa score: -0.041 ------ 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: 260, 46 GAN fn, tp: 6, 3 GAN f1 score: 0.103 GAN cohens kappa score: 0.058 -> test with 'LR' LR tn, fp: 230, 76 LR fn, tp: 3, 6 LR f1 score: 0.132 LR cohens kappa score: 0.085 LR average precision score: 0.118 -> 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: 226, 80 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 GAN.predict GAN tn, fp: 248, 62 GAN fn, tp: 5, 6 GAN f1 score: 0.152 GAN cohens kappa score: 0.099 -> test with 'LR' LR tn, fp: 233, 77 LR fn, tp: 4, 7 LR f1 score: 0.147 LR cohens kappa score: 0.092 LR average precision score: 0.166 -> test with 'GB' GB tn, fp: 300, 10 GB fn, tp: 10, 1 GB f1 score: 0.091 GB cohens kappa score: 0.059 -> test with 'KNN' KNN tn, fp: 249, 61 KNN fn, tp: 9, 2 KNN f1 score: 0.054 KNN cohens kappa score: -0.005 ------ 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: 242, 68 GAN fn, tp: 5, 6 GAN f1 score: 0.141 GAN cohens kappa score: 0.087 -> test with 'LR' LR tn, fp: 213, 97 LR fn, tp: 3, 8 LR f1 score: 0.138 LR cohens kappa score: 0.081 LR average precision score: 0.277 -> test with 'GB' GB tn, fp: 290, 20 GB fn, tp: 8, 3 GB f1 score: 0.176 GB cohens kappa score: 0.136 -> test with 'KNN' KNN tn, fp: 228, 82 KNN fn, tp: 6, 5 KNN f1 score: 0.102 KNN cohens kappa score: 0.044 ------ 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: 235, 75 GAN fn, tp: 7, 4 GAN f1 score: 0.089 GAN cohens kappa score: 0.031 -> test with 'LR' LR tn, fp: 220, 90 LR fn, tp: 5, 6 LR f1 score: 0.112 LR cohens kappa score: 0.054 LR average precision score: 0.084 -> 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: 227, 83 KNN fn, tp: 7, 4 KNN f1 score: 0.082 KNN cohens kappa score: 0.022 ------ 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: 257, 53 GAN fn, tp: 6, 5 GAN f1 score: 0.145 GAN cohens kappa score: 0.093 -> test with 'LR' LR tn, fp: 220, 90 LR fn, tp: 2, 9 LR f1 score: 0.164 LR cohens kappa score: 0.109 LR average precision score: 0.159 -> 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: 233, 77 KNN fn, tp: 8, 3 KNN f1 score: 0.066 KNN cohens kappa score: 0.006 ------ 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: 251, 55 GAN fn, tp: 6, 3 GAN f1 score: 0.090 GAN cohens kappa score: 0.042 -> test with 'LR' LR tn, fp: 219, 87 LR fn, tp: 2, 7 LR f1 score: 0.136 LR cohens kappa score: 0.088 LR average precision score: 0.201 -> test with 'GB' GB tn, fp: 284, 22 GB fn, tp: 7, 2 GB f1 score: 0.121 GB cohens kappa score: 0.083 -> test with 'KNN' KNN tn, fp: 214, 92 KNN fn, tp: 6, 3 KNN f1 score: 0.058 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 GAN.predict GAN tn, fp: 272, 38 GAN fn, tp: 6, 5 GAN f1 score: 0.185 GAN cohens kappa score: 0.138 -> 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: 0.340 -> test with 'GB' GB tn, fp: 293, 17 GB fn, tp: 7, 4 GB f1 score: 0.250 GB cohens kappa score: 0.215 -> test with 'KNN' KNN tn, fp: 225, 85 KNN fn, tp: 5, 6 KNN f1 score: 0.118 KNN cohens kappa score: 0.060 ------ 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: 247, 63 GAN fn, tp: 8, 3 GAN f1 score: 0.078 GAN cohens kappa score: 0.020 -> test with 'LR' LR tn, fp: 214, 96 LR fn, tp: 3, 8 LR f1 score: 0.139 LR cohens kappa score: 0.082 LR average precision score: 0.185 -> test with 'GB' GB tn, fp: 285, 25 GB fn, tp: 11, 0 GB f1 score: 0.000 GB cohens kappa score: -0.050 -> 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 4/5: 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: 9, 2 GAN f1 score: 0.045 GAN cohens kappa score: -0.015 -> 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: 0.090 -> 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: 222, 88 KNN fn, tp: 9, 2 KNN f1 score: 0.040 KNN cohens kappa score: -0.023 ------ 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: 256, 54 GAN fn, tp: 7, 4 GAN f1 score: 0.116 GAN cohens kappa score: 0.062 -> test with 'LR' LR tn, fp: 218, 92 LR fn, tp: 1, 10 LR f1 score: 0.177 LR cohens kappa score: 0.123 LR average precision score: 0.156 -> test with 'GB' GB tn, fp: 295, 15 GB fn, tp: 8, 3 GB f1 score: 0.207 GB cohens kappa score: 0.172 -> test with 'KNN' KNN tn, fp: 251, 59 KNN fn, tp: 9, 2 KNN f1 score: 0.056 KNN cohens kappa score: -0.003 ------ 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: 239, 67 GAN fn, tp: 8, 1 GAN f1 score: 0.026 GAN cohens kappa score: -0.026 -> test with 'LR' LR tn, fp: 211, 95 LR fn, tp: 5, 4 LR f1 score: 0.074 LR cohens kappa score: 0.023 LR average precision score: 0.058 -> test with 'GB' GB tn, fp: 274, 32 GB fn, tp: 8, 1 GB f1 score: 0.048 GB cohens kappa score: 0.003 -> test with 'KNN' KNN tn, fp: 219, 87 KNN fn, tp: 8, 1 KNN f1 score: 0.021 KNN cohens kappa score: -0.033 ====== 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: 258, 52 GAN fn, tp: 8, 3 GAN f1 score: 0.091 GAN cohens kappa score: 0.036 -> test with 'LR' LR tn, fp: 221, 89 LR fn, tp: 5, 6 LR f1 score: 0.113 LR cohens kappa score: 0.055 LR average precision score: 0.077 -> 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: 220, 90 KNN fn, tp: 8, 3 KNN f1 score: 0.058 KNN cohens kappa score: -0.004 ------ 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: 9, 2 GAN f1 score: 0.045 GAN cohens kappa score: -0.015 -> test with 'LR' LR tn, fp: 220, 90 LR fn, tp: 5, 6 LR f1 score: 0.112 LR cohens kappa score: 0.054 LR average precision score: 0.101 -> 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: 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 GAN.predict GAN tn, fp: 245, 65 GAN fn, tp: 5, 6 GAN f1 score: 0.146 GAN cohens kappa score: 0.092 -> test with 'LR' LR tn, fp: 196, 114 LR fn, tp: 0, 11 LR f1 score: 0.162 LR cohens kappa score: 0.105 LR average precision score: 0.288 -> 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: 218, 92 KNN fn, tp: 7, 4 KNN f1 score: 0.075 KNN cohens kappa score: 0.014 ------ 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: 246, 64 GAN fn, tp: 6, 5 GAN f1 score: 0.125 GAN cohens kappa score: 0.070 -> 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: 0.188 -> 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: 219, 91 KNN fn, tp: 8, 3 KNN f1 score: 0.057 KNN cohens kappa score: -0.004 ------ 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: 255, 51 GAN fn, tp: 6, 3 GAN f1 score: 0.095 GAN cohens kappa score: 0.049 -> 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: 0.176 -> test with 'GB' GB tn, fp: 292, 14 GB fn, tp: 7, 2 GB f1 score: 0.160 GB cohens kappa score: 0.128 -> test with 'KNN' KNN tn, fp: 239, 67 KNN fn, tp: 7, 2 KNN f1 score: 0.051 KNN cohens kappa score: 0.001 ### Exercise is done. -----[ LR ]----- maximum: LR tn, fp: 241, 114 LR fn, tp: 6, 11 LR f1 score: 0.177 LR cohens kappa score: 0.123 LR average precision score: 0.340 average: LR tn, fp: 219.64, 89.56 LR fn, tp: 3.36, 7.24 LR f1 score: 0.134 LR cohens kappa score: 0.079 LR average precision score: 0.168 minimum: LR tn, fp: 196, 68 LR fn, tp: 0, 4 LR f1 score: 0.074 LR cohens kappa score: 0.023 LR average precision score: 0.058 -----[ GB ]----- maximum: GB tn, fp: 301, 32 GB fn, tp: 11, 4 GB f1 score: 0.250 GB cohens kappa score: 0.218 average: GB tn, fp: 290.16, 19.04 GB fn, tp: 8.84, 1.76 GB f1 score: 0.111 GB cohens kappa score: 0.071 minimum: GB tn, fp: 274, 9 GB fn, tp: 5, 0 GB f1 score: 0.000 GB cohens kappa score: -0.050 -----[ KNN ]----- maximum: KNN tn, fp: 259, 112 KNN fn, tp: 10, 6 KNN f1 score: 0.118 KNN cohens kappa score: 0.060 average: KNN tn, fp: 228.24, 80.96 KNN fn, tp: 7.56, 3.04 KNN f1 score: 0.063 KNN cohens kappa score: 0.005 minimum: KNN tn, fp: 198, 51 KNN fn, tp: 5, 1 KNN f1 score: 0.021 KNN cohens kappa score: -0.041 -----[ GAN ]----- maximum: GAN tn, fp: 277, 102 GAN fn, tp: 10, 7 GAN f1 score: 0.185 GAN cohens kappa score: 0.138 average: GAN tn, fp: 249.12, 60.08 GAN fn, tp: 6.8, 3.8 GAN f1 score: 0.103 GAN cohens kappa score: 0.049 minimum: GAN tn, fp: 208, 33 GAN fn, tp: 4, 1 GAN f1 score: 0.026 GAN cohens kappa score: -0.027