/////////////////////////////////////////// // Running convGAN-proximary-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: 177, 133 GAN fn, tp: 1, 10 GAN f1 score: 0.130 GAN cohens kappa score: 0.071 -> test with 'LR' LR tn, fp: 204, 106 LR fn, tp: 4, 7 LR f1 score: 0.113 LR cohens kappa score: 0.054 LR average precision score: 0.115 -> test with 'GB' GB tn, fp: 284, 26 GB fn, tp: 11, 0 GB f1 score: 0.000 GB cohens kappa score: -0.051 -> test with 'KNN' KNN tn, fp: 207, 103 KNN fn, tp: 6, 5 KNN f1 score: 0.084 KNN cohens kappa score: 0.023 ------ 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: 207, 103 GAN fn, tp: 7, 4 GAN f1 score: 0.068 GAN cohens kappa score: 0.006 -> 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: 0.109 -> 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: 232, 78 KNN fn, tp: 8, 3 KNN f1 score: 0.065 KNN cohens kappa score: 0.005 ------ 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: 193, 117 GAN fn, tp: 5, 6 GAN f1 score: 0.090 GAN cohens kappa score: 0.028 -> test with 'LR' LR tn, fp: 196, 114 LR fn, tp: 2, 9 LR f1 score: 0.134 LR cohens kappa score: 0.076 LR average precision score: 0.180 -> test with 'GB' GB tn, fp: 282, 28 GB fn, tp: 6, 5 GB f1 score: 0.227 GB cohens kappa score: 0.185 -> test with 'KNN' KNN tn, fp: 211, 99 KNN fn, tp: 8, 3 KNN f1 score: 0.053 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 GAN.predict GAN tn, fp: 228, 82 GAN fn, tp: 8, 3 GAN f1 score: 0.062 GAN cohens kappa score: 0.002 -> test with 'LR' LR tn, fp: 225, 85 LR fn, tp: 6, 5 LR f1 score: 0.099 LR cohens kappa score: 0.040 LR average precision score: 0.097 -> test with 'GB' GB tn, fp: 294, 16 GB fn, tp: 10, 1 GB f1 score: 0.071 GB cohens kappa score: 0.031 -> 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 1/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1196 synthetic samples -> test with GAN.predict GAN tn, fp: 228, 78 GAN fn, tp: 4, 5 GAN f1 score: 0.109 GAN cohens kappa score: 0.060 -> test with 'LR' LR tn, fp: 226, 80 LR fn, tp: 4, 5 LR f1 score: 0.106 LR cohens kappa score: 0.058 LR average precision score: 0.226 -> test with 'GB' GB tn, fp: 286, 20 GB fn, tp: 6, 3 GB f1 score: 0.188 GB cohens kappa score: 0.153 -> test with 'KNN' KNN tn, fp: 235, 71 KNN fn, tp: 5, 4 KNN f1 score: 0.095 KNN cohens kappa score: 0.047 ====== 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: 240, 70 GAN fn, tp: 6, 5 GAN f1 score: 0.116 GAN cohens kappa score: 0.060 -> 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: 0.158 -> test with 'GB' GB tn, fp: 291, 19 GB fn, tp: 7, 4 GB f1 score: 0.235 GB cohens kappa score: 0.198 -> 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 2/5: Slice 2/5 ------- -> 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: 214, 96 LR fn, tp: 4, 7 LR f1 score: 0.123 LR cohens kappa score: 0.065 LR average precision score: 0.142 -> test with 'GB' GB tn, fp: 294, 16 GB fn, tp: 10, 1 GB f1 score: 0.071 GB cohens kappa score: 0.031 -> test with 'KNN' KNN tn, fp: 225, 85 KNN fn, tp: 9, 2 KNN f1 score: 0.041 KNN cohens kappa score: -0.021 ------ 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: 218, 92 GAN fn, tp: 7, 4 GAN f1 score: 0.075 GAN cohens kappa score: 0.014 -> test with 'LR' LR tn, fp: 208, 102 LR fn, tp: 3, 8 LR f1 score: 0.132 LR cohens kappa score: 0.075 LR average precision score: 0.198 -> test with 'GB' GB tn, fp: 284, 26 GB fn, tp: 9, 2 GB f1 score: 0.103 GB cohens kappa score: 0.056 -> test with 'KNN' KNN tn, fp: 230, 80 KNN fn, tp: 8, 3 KNN f1 score: 0.064 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 GAN.predict GAN tn, fp: 214, 96 GAN fn, tp: 7, 4 GAN f1 score: 0.072 GAN cohens kappa score: 0.011 -> test with 'LR' LR tn, fp: 225, 85 LR fn, tp: 5, 6 LR f1 score: 0.118 LR cohens kappa score: 0.060 LR average precision score: 0.310 -> test with 'GB' GB tn, fp: 294, 16 GB fn, tp: 10, 1 GB f1 score: 0.071 GB cohens kappa score: 0.031 -> 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 2/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1196 synthetic samples -> test with GAN.predict GAN tn, fp: 183, 123 GAN fn, tp: 4, 5 GAN f1 score: 0.073 GAN cohens kappa score: 0.021 -> test with 'LR' LR tn, fp: 232, 74 LR fn, tp: 3, 6 LR f1 score: 0.135 LR cohens kappa score: 0.088 LR average precision score: 0.102 -> test with 'GB' GB tn, fp: 289, 17 GB fn, tp: 6, 3 GB f1 score: 0.207 GB cohens kappa score: 0.174 -> test with 'KNN' KNN tn, fp: 227, 79 KNN fn, tp: 6, 3 KNN f1 score: 0.066 KNN cohens kappa score: 0.015 ====== 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: 247, 63 GAN fn, tp: 7, 4 GAN f1 score: 0.103 GAN cohens kappa score: 0.046 -> test with 'LR' LR tn, fp: 231, 79 LR fn, tp: 4, 7 LR f1 score: 0.144 LR cohens kappa score: 0.089 LR average precision score: 0.167 -> 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: 241, 69 KNN fn, tp: 9, 2 KNN f1 score: 0.049 KNN cohens kappa score: -0.011 ------ 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: 197, 113 GAN fn, tp: 5, 6 GAN f1 score: 0.092 GAN cohens kappa score: 0.032 -> test with 'LR' LR tn, fp: 212, 98 LR fn, tp: 2, 9 LR f1 score: 0.153 LR cohens kappa score: 0.096 LR average precision score: 0.263 -> 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: 232, 78 KNN fn, tp: 7, 4 KNN f1 score: 0.086 KNN cohens kappa score: 0.027 ------ 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: 222, 88 GAN fn, tp: 6, 5 GAN f1 score: 0.096 GAN cohens kappa score: 0.037 -> test with 'LR' LR tn, fp: 221, 89 LR fn, tp: 6, 5 LR f1 score: 0.095 LR cohens kappa score: 0.036 LR average precision score: 0.075 -> 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: 239, 71 KNN fn, tp: 9, 2 KNN f1 score: 0.048 KNN cohens kappa score: -0.013 ------ 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: 206, 104 GAN fn, tp: 3, 8 GAN f1 score: 0.130 GAN cohens kappa score: 0.072 -> test with 'LR' LR tn, fp: 209, 101 LR fn, tp: 2, 9 LR f1 score: 0.149 LR cohens kappa score: 0.092 LR average precision score: 0.200 -> 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: 220, 90 KNN fn, tp: 7, 4 KNN f1 score: 0.076 KNN cohens kappa score: 0.016 ------ 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: 189, 117 GAN fn, tp: 3, 6 GAN f1 score: 0.091 GAN cohens kappa score: 0.040 -> test with 'LR' LR tn, fp: 216, 90 LR fn, tp: 2, 7 LR f1 score: 0.132 LR cohens kappa score: 0.084 LR average precision score: 0.199 -> 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: 220, 86 KNN fn, tp: 8, 1 KNN f1 score: 0.021 KNN cohens kappa score: -0.033 ====== 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: 214, 96 GAN fn, tp: 4, 7 GAN f1 score: 0.123 GAN cohens kappa score: 0.065 -> 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: 0.360 -> test with 'GB' GB tn, fp: 297, 13 GB fn, tp: 8, 3 GB f1 score: 0.222 GB cohens kappa score: 0.189 -> test with 'KNN' KNN tn, fp: 218, 92 KNN fn, tp: 9, 2 KNN f1 score: 0.038 KNN cohens kappa score: -0.025 ------ 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: 210, 100 GAN fn, tp: 5, 6 GAN f1 score: 0.103 GAN cohens kappa score: 0.043 -> test with 'LR' LR tn, fp: 206, 104 LR fn, tp: 3, 8 LR f1 score: 0.130 LR cohens kappa score: 0.072 LR average precision score: 0.194 -> test with 'GB' GB tn, fp: 288, 22 GB fn, tp: 10, 1 GB f1 score: 0.059 GB cohens kappa score: 0.013 -> 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 4/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1194 synthetic samples -> test with GAN.predict GAN tn, fp: 205, 105 GAN fn, tp: 9, 2 GAN f1 score: 0.034 GAN cohens kappa score: -0.030 -> 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: 0.093 -> test with 'GB' GB tn, fp: 294, 16 GB fn, tp: 10, 1 GB f1 score: 0.071 GB cohens kappa score: 0.031 -> 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: 233, 77 GAN fn, tp: 6, 5 GAN f1 score: 0.108 GAN cohens kappa score: 0.050 -> test with 'LR' LR tn, fp: 213, 97 LR fn, tp: 1, 10 LR f1 score: 0.169 LR cohens kappa score: 0.114 LR average precision score: 0.157 -> 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: 232, 78 KNN fn, tp: 7, 4 KNN f1 score: 0.086 KNN cohens kappa score: 0.027 ------ 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: 153, 153 GAN fn, tp: 2, 7 GAN f1 score: 0.083 GAN cohens kappa score: 0.030 -> test with 'LR' LR tn, fp: 201, 105 LR fn, tp: 5, 4 LR f1 score: 0.068 LR cohens kappa score: 0.016 LR average precision score: 0.054 -> test with 'GB' GB tn, fp: 268, 38 GB fn, tp: 8, 1 GB f1 score: 0.042 GB cohens kappa score: -0.005 -> test with 'KNN' KNN tn, fp: 208, 98 KNN fn, tp: 8, 1 KNN f1 score: 0.019 KNN cohens kappa score: -0.036 ====== 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: 219, 91 GAN fn, tp: 9, 2 GAN f1 score: 0.038 GAN cohens kappa score: -0.024 -> test with 'LR' LR tn, fp: 231, 79 LR fn, tp: 5, 6 LR f1 score: 0.125 LR cohens kappa score: 0.068 LR average precision score: 0.072 -> 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: 241, 69 KNN fn, tp: 10, 1 KNN f1 score: 0.025 KNN cohens kappa score: -0.037 ------ 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: 251, 59 GAN fn, tp: 9, 2 GAN f1 score: 0.056 GAN cohens kappa score: -0.003 -> test with 'LR' LR tn, fp: 233, 77 LR fn, tp: 5, 6 LR f1 score: 0.128 LR cohens kappa score: 0.071 LR average precision score: 0.097 -> 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: 229, 81 KNN fn, tp: 8, 3 KNN f1 score: 0.063 KNN cohens kappa score: 0.003 ------ 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: 240, 70 GAN fn, tp: 7, 4 GAN f1 score: 0.094 GAN cohens kappa score: 0.037 -> test with 'LR' LR tn, fp: 204, 106 LR fn, tp: 0, 11 LR f1 score: 0.172 LR cohens kappa score: 0.117 LR average precision score: 0.289 -> 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: 229, 81 KNN fn, tp: 7, 4 KNN f1 score: 0.083 KNN cohens kappa score: 0.024 ------ 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: 221, 89 GAN fn, tp: 8, 3 GAN f1 score: 0.058 GAN cohens kappa score: -0.003 -> 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: 0.193 -> test with 'GB' GB tn, fp: 292, 18 GB fn, tp: 7, 4 GB f1 score: 0.242 GB cohens kappa score: 0.206 -> test with 'KNN' KNN tn, fp: 240, 70 KNN fn, tp: 9, 2 KNN f1 score: 0.048 KNN cohens kappa score: -0.012 ------ 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: 254, 52 GAN fn, tp: 5, 4 GAN f1 score: 0.123 GAN cohens kappa score: 0.078 -> test with 'LR' LR tn, fp: 243, 63 LR fn, tp: 3, 6 LR f1 score: 0.154 LR cohens kappa score: 0.109 LR average precision score: 0.193 -> 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: 251, 55 KNN fn, tp: 7, 2 KNN f1 score: 0.061 KNN cohens kappa score: 0.012 ### Exercise is done. -----[ LR ]----- maximum: LR tn, fp: 243, 114 LR fn, tp: 6, 11 LR f1 score: 0.172 LR cohens kappa score: 0.117 LR average precision score: 0.360 average: LR tn, fp: 218.0, 91.2 LR fn, tp: 3.44, 7.16 LR f1 score: 0.131 LR cohens kappa score: 0.076 LR average precision score: 0.170 minimum: LR tn, fp: 196, 63 LR fn, tp: 0, 4 LR f1 score: 0.068 LR cohens kappa score: 0.016 LR average precision score: 0.054 -----[ GB ]----- maximum: GB tn, fp: 297, 38 GB fn, tp: 11, 5 GB f1 score: 0.242 GB cohens kappa score: 0.206 average: GB tn, fp: 290.2, 19.0 GB fn, tp: 8.72, 1.88 GB f1 score: 0.119 GB cohens kappa score: 0.079 minimum: GB tn, fp: 268, 9 GB fn, tp: 6, 0 GB f1 score: 0.000 GB cohens kappa score: -0.051 -----[ KNN ]----- maximum: KNN tn, fp: 251, 103 KNN fn, tp: 10, 5 KNN f1 score: 0.095 KNN cohens kappa score: 0.047 average: KNN tn, fp: 228.24, 80.96 KNN fn, tp: 7.84, 2.76 KNN f1 score: 0.058 KNN cohens kappa score: -0.000 minimum: KNN tn, fp: 207, 55 KNN fn, tp: 5, 1 KNN f1 score: 0.019 KNN cohens kappa score: -0.037 -----[ GAN ]----- maximum: GAN tn, fp: 254, 153 GAN fn, tp: 9, 10 GAN f1 score: 0.130 GAN cohens kappa score: 0.078 average: GAN tn, fp: 214.28, 94.92 GAN fn, tp: 5.64, 4.96 GAN f1 score: 0.090 GAN cohens kappa score: 0.032 minimum: GAN tn, fp: 153, 52 GAN fn, tp: 1, 2 GAN f1 score: 0.034 GAN cohens kappa score: -0.030