/////////////////////////////////////////// // Running Repeater on folding_winequality-red-4 /////////////////////////////////////////// Load '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 'LR' LR tn, fp: 186, 124 LR fn, tp: 1, 10 LR f1 score: 0.138 LR cohens kappa score: 0.080 LR average precision score: 0.093 -> test with 'RF' RF tn, fp: 310, 0 RF fn, tp: 11, 0 RF f1 score: 0.000 RF cohens kappa score: 0.000 -> test with 'GB' GB tn, fp: 304, 6 GB fn, tp: 10, 1 GB f1 score: 0.111 GB cohens kappa score: 0.087 -> test with 'KNN' KNN tn, fp: 261, 49 KNN fn, tp: 10, 1 KNN f1 score: 0.033 KNN cohens kappa score: -0.025 ------ Step 1/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1194 synthetic samples -> test with 'LR' LR tn, fp: 200, 110 LR fn, tp: 3, 8 LR f1 score: 0.124 LR cohens kappa score: 0.065 LR average precision score: 0.111 -> test with 'RF' RF tn, fp: 307, 3 RF fn, tp: 11, 0 RF f1 score: 0.000 RF cohens kappa score: -0.015 -> 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: 266, 44 KNN fn, tp: 7, 4 KNN f1 score: 0.136 KNN cohens kappa score: 0.085 ------ Step 1/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1194 synthetic samples -> test with 'LR' LR tn, fp: 168, 142 LR fn, tp: 1, 10 LR f1 score: 0.123 LR cohens kappa score: 0.063 LR average precision score: 0.148 -> test with 'RF' RF tn, fp: 309, 1 RF fn, tp: 11, 0 RF f1 score: 0.000 RF cohens kappa score: -0.006 -> test with 'GB' GB tn, fp: 298, 12 GB fn, tp: 7, 4 GB f1 score: 0.296 GB cohens kappa score: 0.267 -> test with 'KNN' KNN tn, fp: 264, 46 KNN fn, tp: 8, 3 KNN f1 score: 0.100 KNN cohens kappa score: 0.047 ------ Step 1/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1194 synthetic samples -> test with 'LR' LR tn, fp: 216, 94 LR fn, tp: 6, 5 LR f1 score: 0.091 LR cohens kappa score: 0.031 LR average precision score: 0.119 -> test with 'RF' RF tn, fp: 307, 3 RF fn, tp: 11, 0 RF f1 score: 0.000 RF cohens kappa score: -0.015 -> 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: 265, 45 KNN fn, tp: 8, 3 KNN f1 score: 0.102 KNN cohens kappa score: 0.049 ------ Step 1/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1196 synthetic samples -> test with 'LR' LR tn, fp: 209, 97 LR fn, tp: 4, 5 LR f1 score: 0.090 LR cohens kappa score: 0.040 LR average precision score: 0.226 -> test with 'RF' RF tn, fp: 306, 0 RF fn, tp: 9, 0 RF f1 score: 0.000 RF cohens kappa score: 0.000 -> test with 'GB' GB tn, fp: 296, 10 GB fn, tp: 8, 1 GB f1 score: 0.100 GB cohens kappa score: 0.071 -> test with 'KNN' KNN tn, fp: 267, 39 KNN fn, tp: 8, 1 KNN f1 score: 0.041 KNN cohens kappa score: -0.006 ====== 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 'LR' LR tn, fp: 185, 125 LR fn, tp: 2, 9 LR f1 score: 0.124 LR cohens kappa score: 0.065 LR average precision score: 0.130 -> test with 'RF' RF tn, fp: 307, 3 RF fn, tp: 11, 0 RF f1 score: 0.000 RF cohens kappa score: -0.015 -> 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: 268, 42 KNN fn, tp: 10, 1 KNN f1 score: 0.037 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 'LR' LR tn, fp: 191, 119 LR fn, tp: 3, 8 LR f1 score: 0.116 LR cohens kappa score: 0.056 LR average precision score: 0.141 -> test with 'RF' RF tn, fp: 309, 1 RF fn, tp: 11, 0 RF f1 score: 0.000 RF cohens kappa score: -0.006 -> test with 'GB' GB tn, fp: 305, 5 GB fn, tp: 10, 1 GB f1 score: 0.118 GB cohens kappa score: 0.096 -> test with 'KNN' KNN tn, fp: 270, 40 KNN fn, tp: 10, 1 KNN f1 score: 0.038 KNN cohens kappa score: -0.016 ------ Step 2/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1194 synthetic samples -> test with 'LR' LR tn, fp: 192, 118 LR fn, tp: 3, 8 LR f1 score: 0.117 LR cohens kappa score: 0.057 LR average precision score: 0.164 -> test with 'RF' RF tn, fp: 309, 1 RF fn, tp: 11, 0 RF f1 score: 0.000 RF cohens kappa score: -0.006 -> test with 'GB' GB tn, fp: 299, 11 GB fn, tp: 10, 1 GB f1 score: 0.087 GB cohens kappa score: 0.053 -> test with 'KNN' KNN tn, fp: 263, 47 KNN fn, tp: 10, 1 KNN f1 score: 0.034 KNN cohens kappa score: -0.023 ------ Step 2/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1194 synthetic samples -> 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: 0.277 -> test with 'RF' RF tn, fp: 310, 0 RF fn, tp: 11, 0 RF f1 score: 0.000 RF cohens kappa score: 0.000 -> test with 'GB' GB tn, fp: 298, 12 GB fn, tp: 7, 4 GB f1 score: 0.296 GB cohens kappa score: 0.267 -> test with 'KNN' KNN tn, fp: 271, 39 KNN fn, tp: 7, 4 KNN f1 score: 0.148 KNN cohens kappa score: 0.099 ------ Step 2/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1196 synthetic samples -> test with 'LR' LR tn, fp: 206, 100 LR fn, tp: 3, 6 LR f1 score: 0.104 LR cohens kappa score: 0.055 LR average precision score: 0.090 -> test with 'RF' RF tn, fp: 306, 0 RF fn, tp: 8, 1 RF f1 score: 0.200 RF cohens kappa score: 0.195 -> test with 'GB' GB tn, fp: 296, 10 GB fn, tp: 7, 2 GB f1 score: 0.190 GB cohens kappa score: 0.163 -> test with 'KNN' KNN tn, fp: 262, 44 KNN fn, tp: 7, 2 KNN f1 score: 0.073 KNN cohens kappa score: 0.026 ====== 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 'LR' LR tn, fp: 215, 95 LR fn, tp: 5, 6 LR f1 score: 0.107 LR cohens kappa score: 0.048 LR average precision score: 0.118 -> test with 'RF' RF tn, fp: 308, 2 RF fn, tp: 11, 0 RF f1 score: 0.000 RF cohens kappa score: -0.011 -> test with 'GB' GB tn, fp: 300, 10 GB fn, tp: 11, 0 GB f1 score: 0.000 GB cohens kappa score: -0.034 -> test with 'KNN' KNN tn, fp: 275, 35 KNN fn, tp: 9, 2 KNN f1 score: 0.083 KNN cohens kappa score: 0.032 ------ Step 3/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1194 synthetic samples -> test with 'LR' LR tn, fp: 193, 117 LR fn, tp: 2, 9 LR f1 score: 0.131 LR cohens kappa score: 0.073 LR average precision score: 0.224 -> test with 'RF' RF tn, fp: 308, 2 RF fn, tp: 11, 0 RF f1 score: 0.000 RF cohens kappa score: -0.011 -> 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: 267, 43 KNN fn, tp: 10, 1 KNN f1 score: 0.036 KNN cohens kappa score: -0.020 ------ Step 3/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1194 synthetic samples -> test with 'LR' LR tn, fp: 202, 108 LR fn, tp: 6, 5 LR f1 score: 0.081 LR cohens kappa score: 0.019 LR average precision score: 0.077 -> test with 'RF' RF tn, fp: 310, 0 RF fn, tp: 11, 0 RF f1 score: 0.000 RF cohens kappa score: 0.000 -> 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: 263, 47 KNN fn, tp: 8, 3 KNN f1 score: 0.098 KNN cohens kappa score: 0.045 ------ Step 3/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1194 synthetic samples -> test with 'LR' LR tn, fp: 193, 117 LR fn, tp: 2, 9 LR f1 score: 0.131 LR cohens kappa score: 0.073 LR average precision score: 0.149 -> test with 'RF' RF tn, fp: 309, 1 RF fn, tp: 11, 0 RF f1 score: 0.000 RF cohens kappa score: -0.006 -> test with 'GB' GB tn, fp: 304, 6 GB fn, tp: 9, 2 GB f1 score: 0.211 GB cohens kappa score: 0.187 -> test with 'KNN' KNN tn, fp: 273, 37 KNN fn, tp: 9, 2 KNN f1 score: 0.080 KNN cohens kappa score: 0.028 ------ Step 3/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1196 synthetic samples -> test with 'LR' LR tn, fp: 198, 108 LR fn, tp: 2, 7 LR f1 score: 0.113 LR cohens kappa score: 0.063 LR average precision score: 0.108 -> test with 'RF' RF tn, fp: 305, 1 RF fn, tp: 9, 0 RF f1 score: 0.000 RF cohens kappa score: -0.006 -> test with 'GB' GB tn, fp: 296, 10 GB fn, tp: 8, 1 GB f1 score: 0.100 GB cohens kappa score: 0.071 -> test with 'KNN' KNN tn, fp: 258, 48 KNN fn, tp: 7, 2 KNN f1 score: 0.068 KNN cohens kappa score: 0.020 ====== 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 '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: 0.312 -> test with 'RF' RF tn, fp: 310, 0 RF fn, tp: 11, 0 RF f1 score: 0.000 RF cohens kappa score: 0.000 -> test with 'GB' GB tn, fp: 299, 11 GB fn, tp: 11, 0 GB f1 score: 0.000 GB cohens kappa score: -0.035 -> test with 'KNN' KNN tn, fp: 272, 38 KNN fn, tp: 11, 0 KNN f1 score: 0.000 KNN cohens kappa score: -0.056 ------ Step 4/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1194 synthetic samples -> test with 'LR' LR tn, fp: 183, 127 LR fn, tp: 3, 8 LR f1 score: 0.110 LR cohens kappa score: 0.049 LR average precision score: 0.203 -> test with 'RF' RF tn, fp: 309, 1 RF fn, tp: 11, 0 RF f1 score: 0.000 RF cohens kappa score: -0.006 -> test with 'GB' GB tn, fp: 299, 11 GB fn, tp: 9, 2 GB f1 score: 0.167 GB cohens kappa score: 0.135 -> test with 'KNN' KNN tn, fp: 271, 39 KNN fn, tp: 8, 3 KNN f1 score: 0.113 KNN cohens kappa score: 0.062 ------ Step 4/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1194 synthetic samples -> 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: 0.081 -> test with 'RF' RF tn, fp: 310, 0 RF fn, tp: 11, 0 RF f1 score: 0.000 RF cohens kappa score: 0.000 -> test with 'GB' GB tn, fp: 298, 12 GB fn, tp: 9, 2 GB f1 score: 0.160 GB cohens kappa score: 0.126 -> test with 'KNN' KNN tn, fp: 276, 34 KNN fn, tp: 11, 0 KNN f1 score: 0.000 KNN cohens kappa score: -0.055 ------ Step 4/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1194 synthetic samples -> test with 'LR' LR tn, fp: 200, 110 LR fn, tp: 1, 10 LR f1 score: 0.153 LR cohens kappa score: 0.096 LR average precision score: 0.120 -> test with 'RF' RF tn, fp: 310, 0 RF fn, tp: 11, 0 RF f1 score: 0.000 RF cohens kappa score: 0.000 -> test with 'GB' GB tn, fp: 298, 12 GB fn, tp: 9, 2 GB f1 score: 0.160 GB cohens kappa score: 0.126 -> test with 'KNN' KNN tn, fp: 266, 44 KNN fn, tp: 9, 2 KNN f1 score: 0.070 KNN cohens kappa score: 0.016 ------ Step 4/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1196 synthetic samples -> test with 'LR' LR tn, fp: 182, 124 LR fn, tp: 5, 4 LR f1 score: 0.058 LR cohens kappa score: 0.005 LR average precision score: 0.056 -> test with 'RF' RF tn, fp: 306, 0 RF fn, tp: 9, 0 RF f1 score: 0.000 RF cohens kappa score: 0.000 -> test with 'GB' GB tn, fp: 278, 28 GB fn, tp: 9, 0 GB f1 score: 0.000 GB cohens kappa score: -0.045 -> test with 'KNN' KNN tn, fp: 248, 58 KNN fn, tp: 8, 1 KNN f1 score: 0.029 KNN cohens kappa score: -0.021 ====== 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 'LR' LR tn, fp: 194, 116 LR fn, tp: 5, 6 LR f1 score: 0.090 LR cohens kappa score: 0.029 LR average precision score: 0.071 -> test with 'RF' RF tn, fp: 309, 1 RF fn, tp: 11, 0 RF f1 score: 0.000 RF cohens kappa score: -0.006 -> test with 'GB' GB tn, fp: 303, 7 GB fn, tp: 10, 1 GB f1 score: 0.105 GB cohens kappa score: 0.079 -> test with 'KNN' KNN tn, fp: 270, 40 KNN fn, tp: 9, 2 KNN f1 score: 0.075 KNN cohens kappa score: 0.022 ------ Step 5/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1194 synthetic samples -> test with 'LR' LR tn, fp: 201, 109 LR fn, tp: 5, 6 LR f1 score: 0.095 LR cohens kappa score: 0.035 LR average precision score: 0.086 -> test with 'RF' RF tn, fp: 310, 0 RF fn, tp: 11, 0 RF f1 score: 0.000 RF cohens kappa score: 0.000 -> 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: 256, 54 KNN fn, tp: 8, 3 KNN f1 score: 0.088 KNN cohens kappa score: 0.033 ------ Step 5/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1194 synthetic samples -> test with 'LR' LR tn, fp: 170, 140 LR fn, tp: 0, 11 LR f1 score: 0.136 LR cohens kappa score: 0.077 LR average precision score: 0.235 -> test with 'RF' RF tn, fp: 310, 0 RF fn, tp: 11, 0 RF f1 score: 0.000 RF cohens kappa score: 0.000 -> 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: 272, 38 KNN fn, tp: 8, 3 KNN f1 score: 0.115 KNN cohens kappa score: 0.065 ------ Step 5/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1194 synthetic samples -> test with 'LR' LR tn, fp: 194, 116 LR fn, tp: 3, 8 LR f1 score: 0.119 LR cohens kappa score: 0.059 LR average precision score: 0.174 -> test with 'RF' RF tn, fp: 309, 1 RF fn, tp: 10, 1 RF f1 score: 0.154 RF cohens kappa score: 0.145 -> 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: 269, 41 KNN fn, tp: 9, 2 KNN f1 score: 0.074 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 'LR' LR tn, fp: 221, 85 LR fn, tp: 3, 6 LR f1 score: 0.120 LR cohens kappa score: 0.072 LR average precision score: 0.126 -> test with 'RF' RF tn, fp: 306, 0 RF fn, tp: 9, 0 RF f1 score: 0.000 RF cohens kappa score: 0.000 -> test with 'GB' GB tn, fp: 291, 15 GB fn, tp: 9, 0 GB f1 score: 0.000 GB cohens kappa score: -0.037 -> test with 'KNN' KNN tn, fp: 267, 39 KNN fn, tp: 8, 1 KNN f1 score: 0.041 KNN cohens kappa score: -0.006 ### Exercise is done. -----[ LR ]----- maximum: LR tn, fp: 221, 142 LR fn, tp: 6, 11 LR f1 score: 0.153 LR cohens kappa score: 0.096 LR average precision score: 0.312 average: LR tn, fp: 196.72, 112.48 LR fn, tp: 3.16, 7.44 LR f1 score: 0.113 LR cohens kappa score: 0.056 LR average precision score: 0.146 minimum: LR tn, fp: 168, 85 LR fn, tp: 0, 4 LR f1 score: 0.058 LR cohens kappa score: 0.005 LR average precision score: 0.056 -----[ RF ]----- maximum: RF tn, fp: 310, 3 RF fn, tp: 11, 1 RF f1 score: 0.200 RF cohens kappa score: 0.195 average: RF tn, fp: 308.36, 0.84 RF fn, tp: 10.52, 0.08 RF f1 score: 0.014 RF cohens kappa score: 0.009 minimum: RF tn, fp: 305, 0 RF fn, tp: 8, 0 RF f1 score: 0.000 RF cohens kappa score: -0.015 -----[ GB ]----- maximum: GB tn, fp: 305, 28 GB fn, tp: 11, 4 GB f1 score: 0.296 GB cohens kappa score: 0.267 average: GB tn, fp: 296.48, 12.72 GB fn, tp: 9.0, 1.6 GB f1 score: 0.127 GB cohens kappa score: 0.094 minimum: GB tn, fp: 278, 5 GB fn, tp: 7, 0 GB f1 score: 0.000 GB cohens kappa score: -0.045 -----[ KNN ]----- maximum: KNN tn, fp: 276, 58 KNN fn, tp: 11, 4 KNN f1 score: 0.148 KNN cohens kappa score: 0.099 average: KNN tn, fp: 266.4, 42.8 KNN fn, tp: 8.68, 1.92 KNN f1 score: 0.069 KNN cohens kappa score: 0.016 minimum: KNN tn, fp: 248, 34 KNN fn, tp: 7, 0 KNN f1 score: 0.000 KNN cohens kappa score: -0.056 wall time: 00:00:30s, process time: 00:01:14s