/////////////////////////////////////////// // Running convGAN-proximary-5 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: 192, 118 GAN fn, tp: 7, 4 GAN f1 score: 0.060 GAN cohens kappa score: -0.003 -> test with 'LR' LR tn, fp: 212, 98 LR fn, tp: 6, 5 LR f1 score: 0.088 LR cohens kappa score: 0.027 LR average precision score: 0.099 -> 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: 210, 100 KNN fn, tp: 7, 4 KNN f1 score: 0.070 KNN cohens kappa score: 0.008 ------ 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: 219, 91 GAN fn, tp: 3, 8 GAN f1 score: 0.145 GAN cohens kappa score: 0.089 -> 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: 0.096 -> 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: 238, 72 KNN fn, tp: 6, 5 KNN f1 score: 0.114 KNN cohens kappa score: 0.057 ------ 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: 239, 71 GAN fn, tp: 9, 2 GAN f1 score: 0.048 GAN cohens kappa score: -0.013 -> test with 'LR' LR tn, fp: 201, 109 LR fn, tp: 4, 7 LR f1 score: 0.110 LR cohens kappa score: 0.051 LR average precision score: 0.192 -> test with 'GB' GB tn, fp: 295, 15 GB fn, tp: 7, 4 GB f1 score: 0.267 GB cohens kappa score: 0.233 -> test with 'KNN' KNN tn, fp: 235, 75 KNN fn, tp: 8, 3 KNN f1 score: 0.067 KNN cohens kappa score: 0.008 ------ 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: 186, 124 GAN fn, tp: 4, 7 GAN f1 score: 0.099 GAN cohens kappa score: 0.038 -> test with 'LR' LR tn, fp: 230, 80 LR fn, tp: 5, 6 LR f1 score: 0.124 LR cohens kappa score: 0.067 LR average precision score: 0.130 -> 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: 224, 86 KNN fn, tp: 6, 5 KNN f1 score: 0.098 KNN cohens kappa score: 0.039 ------ 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: 219, 87 GAN fn, tp: 3, 6 GAN f1 score: 0.118 GAN cohens kappa score: 0.069 -> 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: 0.223 -> test with 'GB' GB tn, fp: 287, 19 GB fn, tp: 8, 1 GB f1 score: 0.069 GB cohens kappa score: 0.031 -> test with 'KNN' KNN tn, fp: 235, 71 KNN fn, tp: 6, 3 KNN f1 score: 0.072 KNN cohens kappa score: 0.022 ====== 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: 215, 95 GAN fn, tp: 6, 5 GAN f1 score: 0.090 GAN cohens kappa score: 0.030 -> 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: 0.132 -> test with 'GB' GB tn, fp: 290, 20 GB fn, tp: 9, 2 GB f1 score: 0.121 GB cohens kappa score: 0.079 -> test with 'KNN' KNN tn, fp: 221, 89 KNN fn, tp: 10, 1 KNN f1 score: 0.020 KNN cohens kappa score: -0.044 ------ 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: 116, 194 GAN fn, tp: 7, 4 GAN f1 score: 0.038 GAN cohens kappa score: -0.029 -> test with 'LR' LR tn, fp: 227, 83 LR fn, tp: 4, 7 LR f1 score: 0.139 LR cohens kappa score: 0.083 LR average precision score: 0.171 -> test with 'GB' GB tn, fp: 297, 13 GB fn, tp: 9, 2 GB f1 score: 0.154 GB cohens kappa score: 0.119 -> 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 2/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1194 synthetic samples -> test with GAN.predict GAN tn, fp: 278, 32 GAN fn, tp: 9, 2 GAN f1 score: 0.089 GAN cohens kappa score: 0.039 -> 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: 0.126 -> 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: 241, 69 KNN fn, tp: 10, 1 KNN f1 score: 0.025 KNN cohens kappa score: -0.037 ------ 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: 230, 80 GAN fn, tp: 3, 8 GAN f1 score: 0.162 GAN cohens kappa score: 0.107 -> test with 'LR' LR tn, fp: 213, 97 LR fn, tp: 6, 5 LR f1 score: 0.088 LR cohens kappa score: 0.028 LR average precision score: 0.288 -> 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: 238, 72 KNN fn, tp: 7, 4 KNN f1 score: 0.092 KNN cohens kappa score: 0.034 ------ 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: 231, 75 GAN fn, tp: 5, 4 GAN f1 score: 0.091 GAN cohens kappa score: 0.042 -> test with 'LR' LR tn, fp: 217, 89 LR fn, tp: 3, 6 LR f1 score: 0.115 LR cohens kappa score: 0.067 LR average precision score: 0.116 -> test with 'GB' GB tn, fp: 287, 19 GB fn, tp: 6, 3 GB f1 score: 0.194 GB cohens kappa score: 0.159 -> test with 'KNN' KNN tn, fp: 229, 77 KNN fn, tp: 5, 4 KNN f1 score: 0.089 KNN cohens kappa score: 0.039 ====== 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: 207, 103 GAN fn, tp: 5, 6 GAN f1 score: 0.100 GAN cohens kappa score: 0.040 -> 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: 0.209 -> test with 'GB' GB tn, fp: 301, 9 GB fn, tp: 11, 0 GB f1 score: 0.000 GB cohens kappa score: -0.032 -> 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: 164, 146 GAN fn, tp: 5, 6 GAN f1 score: 0.074 GAN cohens kappa score: 0.010 -> test with 'LR' LR tn, fp: 215, 95 LR fn, tp: 2, 9 LR f1 score: 0.157 LR cohens kappa score: 0.101 LR average precision score: 0.215 -> 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: 235, 75 KNN fn, tp: 9, 2 KNN f1 score: 0.045 KNN cohens kappa score: -0.015 ------ 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: 190, 120 GAN fn, tp: 7, 4 GAN f1 score: 0.059 GAN cohens kappa score: -0.004 -> 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: 0.071 -> test with 'GB' GB tn, fp: 283, 27 GB fn, tp: 9, 2 GB f1 score: 0.100 GB cohens kappa score: 0.053 -> test with 'KNN' KNN tn, fp: 221, 89 KNN fn, tp: 6, 5 KNN f1 score: 0.095 KNN cohens kappa score: 0.036 ------ 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: 173, 137 GAN fn, tp: 3, 8 GAN f1 score: 0.103 GAN cohens kappa score: 0.042 -> test with 'LR' LR tn, fp: 197, 113 LR fn, tp: 2, 9 LR f1 score: 0.135 LR cohens kappa score: 0.077 LR average precision score: 0.169 -> 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: 229, 81 KNN fn, tp: 5, 6 KNN f1 score: 0.122 KNN cohens kappa score: 0.066 ------ 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: 223, 83 GAN fn, tp: 5, 4 GAN f1 score: 0.083 GAN cohens kappa score: 0.033 -> test with 'LR' LR tn, fp: 236, 70 LR fn, tp: 2, 7 LR f1 score: 0.163 LR cohens kappa score: 0.118 LR average precision score: 0.094 -> test with 'GB' GB tn, fp: 292, 14 GB fn, tp: 6, 3 GB f1 score: 0.231 GB cohens kappa score: 0.201 -> test with 'KNN' KNN tn, fp: 223, 83 KNN fn, tp: 6, 3 KNN f1 score: 0.063 KNN cohens kappa score: 0.012 ====== 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: 251, 59 GAN fn, tp: 7, 4 GAN f1 score: 0.108 GAN cohens kappa score: 0.053 -> 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: 0.392 -> test with 'GB' GB tn, fp: 300, 10 GB fn, tp: 8, 3 GB f1 score: 0.250 GB cohens kappa score: 0.221 -> test with 'KNN' KNN tn, fp: 224, 86 KNN fn, tp: 9, 2 KNN f1 score: 0.040 KNN cohens kappa score: -0.022 ------ 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: 206, 104 GAN fn, tp: 5, 6 GAN f1 score: 0.099 GAN cohens kappa score: 0.039 -> 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: 0.194 -> 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: 207, 103 KNN fn, tp: 7, 4 KNN f1 score: 0.068 KNN cohens kappa score: 0.006 ------ 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: 281, 29 GAN fn, tp: 9, 2 GAN f1 score: 0.095 GAN cohens kappa score: 0.047 -> 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: 0.107 -> 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: 240, 70 KNN fn, tp: 8, 3 KNN f1 score: 0.071 KNN cohens kappa score: 0.013 ------ 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: 203, 107 GAN fn, tp: 4, 7 GAN f1 score: 0.112 GAN cohens kappa score: 0.053 -> 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: 0.147 -> 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: 240, 70 KNN fn, tp: 8, 3 KNN f1 score: 0.071 KNN cohens kappa score: 0.013 ------ 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: 203, 103 GAN fn, tp: 5, 4 GAN f1 score: 0.069 GAN cohens kappa score: 0.017 -> test with 'LR' LR tn, fp: 213, 93 LR fn, tp: 6, 3 LR f1 score: 0.057 LR cohens kappa score: 0.005 LR average precision score: 0.034 -> test with 'GB' GB tn, fp: 279, 27 GB fn, tp: 9, 0 GB f1 score: 0.000 GB cohens kappa score: -0.045 -> 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 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: 271, 39 GAN fn, tp: 11, 0 GAN f1 score: 0.000 GAN cohens kappa score: -0.056 -> test with 'LR' LR tn, fp: 245, 65 LR fn, tp: 5, 6 LR f1 score: 0.146 LR cohens kappa score: 0.092 LR average precision score: 0.096 -> 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: 236, 74 KNN fn, tp: 9, 2 KNN f1 score: 0.046 KNN cohens kappa score: -0.015 ------ 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: 201, 109 GAN fn, tp: 6, 5 GAN f1 score: 0.080 GAN cohens kappa score: 0.019 -> 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: 0.085 -> test with 'GB' GB tn, fp: 285, 25 GB fn, tp: 10, 1 GB f1 score: 0.054 GB cohens kappa score: 0.006 -> 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: 230, 80 GAN fn, tp: 5, 6 GAN f1 score: 0.124 GAN cohens kappa score: 0.067 -> test with 'LR' LR tn, fp: 201, 109 LR fn, tp: 0, 11 LR f1 score: 0.168 LR cohens kappa score: 0.112 LR average precision score: 0.295 -> test with 'GB' GB tn, fp: 281, 29 GB fn, tp: 8, 3 GB f1 score: 0.140 GB cohens kappa score: 0.093 -> test with 'KNN' KNN tn, fp: 230, 80 KNN fn, tp: 6, 5 KNN f1 score: 0.104 KNN cohens kappa score: 0.046 ------ 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: 200, 110 GAN fn, tp: 5, 6 GAN f1 score: 0.094 GAN cohens kappa score: 0.034 -> 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: 0.187 -> 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: 226, 84 KNN fn, tp: 7, 4 KNN f1 score: 0.081 KNN cohens kappa score: 0.021 ------ 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: 187, 119 GAN fn, tp: 4, 5 GAN f1 score: 0.075 GAN cohens kappa score: 0.023 -> test with 'LR' LR tn, fp: 244, 62 LR fn, tp: 4, 5 LR f1 score: 0.132 LR cohens kappa score: 0.086 LR average precision score: 0.148 -> 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: 226, 80 KNN fn, tp: 7, 2 KNN f1 score: 0.044 KNN cohens kappa score: -0.008 ### Exercise is done. -----[ LR ]----- maximum: LR tn, fp: 245, 113 LR fn, tp: 6, 11 LR f1 score: 0.168 LR cohens kappa score: 0.118 LR average precision score: 0.392 average: LR tn, fp: 219.16, 90.04 LR fn, tp: 3.68, 6.92 LR f1 score: 0.129 LR cohens kappa score: 0.073 LR average precision score: 0.161 minimum: LR tn, fp: 197, 62 LR fn, tp: 0, 3 LR f1 score: 0.057 LR cohens kappa score: 0.005 LR average precision score: 0.034 -----[ GB ]----- maximum: GB tn, fp: 301, 29 GB fn, tp: 11, 4 GB f1 score: 0.267 GB cohens kappa score: 0.233 average: GB tn, fp: 291.08, 18.12 GB fn, tp: 8.68, 1.92 GB f1 score: 0.126 GB cohens kappa score: 0.087 minimum: GB tn, fp: 279, 9 GB fn, tp: 6, 0 GB f1 score: 0.000 GB cohens kappa score: -0.045 -----[ KNN ]----- maximum: KNN tn, fp: 241, 103 KNN fn, tp: 10, 6 KNN f1 score: 0.122 KNN cohens kappa score: 0.066 average: KNN tn, fp: 228.44, 80.76 KNN fn, tp: 7.28, 3.32 KNN f1 score: 0.070 KNN cohens kappa score: 0.012 minimum: KNN tn, fp: 207, 69 KNN fn, tp: 5, 1 KNN f1 score: 0.020 KNN cohens kappa score: -0.044 -----[ GAN ]----- maximum: GAN tn, fp: 281, 194 GAN fn, tp: 11, 8 GAN f1 score: 0.162 GAN cohens kappa score: 0.107 average: GAN tn, fp: 212.6, 96.6 GAN fn, tp: 5.68, 4.92 GAN f1 score: 0.089 GAN cohens kappa score: 0.031 minimum: GAN tn, fp: 116, 29 GAN fn, tp: 3, 0 GAN f1 score: 0.000 GAN cohens kappa score: -0.056