/////////////////////////////////////////// // Running ProWRAS 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: 225, 85 LR fn, tp: 6, 5 LR f1 score: 0.099 LR cohens kappa score: 0.040 LR average precision score: 0.126 -> 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: 294, 16 GB fn, tp: 11, 0 GB f1 score: 0.000 GB cohens kappa score: -0.042 -> 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 1/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1194 synthetic samples -> test with 'LR' LR tn, fp: 234, 76 LR fn, tp: 4, 7 LR f1 score: 0.149 LR cohens kappa score: 0.094 LR average precision score: 0.120 -> 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: 303, 7 GB fn, tp: 10, 1 GB f1 score: 0.105 GB cohens kappa score: 0.079 -> test with 'KNN' KNN tn, fp: 272, 38 KNN fn, tp: 7, 4 KNN f1 score: 0.151 KNN cohens kappa score: 0.102 ------ Step 1/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1194 synthetic samples -> test with 'LR' LR tn, fp: 209, 101 LR fn, tp: 1, 10 LR f1 score: 0.164 LR cohens kappa score: 0.108 LR average precision score: 0.266 -> 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: 302, 8 GB fn, tp: 9, 2 GB f1 score: 0.190 GB cohens kappa score: 0.163 -> test with 'KNN' KNN tn, fp: 254, 56 KNN fn, tp: 9, 2 KNN f1 score: 0.058 KNN cohens kappa score: 0.000 ------ Step 1/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1194 synthetic samples -> test with 'LR' LR tn, fp: 260, 50 LR fn, tp: 6, 5 LR f1 score: 0.152 LR cohens kappa score: 0.100 LR average precision score: 0.153 -> test with 'RF' RF tn, fp: 307, 3 RF fn, tp: 10, 1 RF f1 score: 0.133 RF cohens kappa score: 0.117 -> 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: 268, 42 KNN fn, tp: 10, 1 KNN f1 score: 0.037 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 'LR' LR tn, fp: 232, 74 LR fn, tp: 4, 5 LR f1 score: 0.114 LR cohens kappa score: 0.066 LR average precision score: 0.215 -> 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: 303, 3 GB fn, tp: 9, 0 GB f1 score: 0.000 GB cohens kappa score: -0.014 -> test with 'KNN' KNN tn, fp: 268, 38 KNN fn, tp: 8, 1 KNN f1 score: 0.042 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 'LR' LR tn, fp: 224, 86 LR fn, tp: 3, 8 LR f1 score: 0.152 LR cohens kappa score: 0.097 LR average precision score: 0.136 -> 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: 304, 6 GB fn, tp: 9, 2 GB f1 score: 0.211 GB cohens kappa score: 0.187 -> test with 'KNN' KNN tn, fp: 258, 52 KNN fn, tp: 9, 2 KNN f1 score: 0.062 KNN cohens kappa score: 0.005 ------ Step 2/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1194 synthetic samples -> test with 'LR' LR tn, fp: 235, 75 LR fn, tp: 3, 8 LR f1 score: 0.170 LR cohens kappa score: 0.117 LR average precision score: 0.155 -> 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: 307, 3 GB fn, tp: 11, 0 GB f1 score: 0.000 GB cohens kappa score: -0.015 -> test with 'KNN' KNN tn, fp: 270, 40 KNN fn, tp: 11, 0 KNN f1 score: 0.000 KNN cohens kappa score: -0.057 ------ Step 2/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1194 synthetic samples -> test with 'LR' LR tn, fp: 242, 68 LR fn, tp: 3, 8 LR f1 score: 0.184 LR cohens kappa score: 0.132 LR average precision score: 0.159 -> test with 'RF' RF tn, fp: 305, 5 RF fn, tp: 11, 0 RF f1 score: 0.000 RF cohens kappa score: -0.022 -> 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: 285, 25 KNN fn, tp: 10, 1 KNN f1 score: 0.054 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 '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: 0.259 -> 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: 307, 3 GB fn, tp: 10, 1 GB f1 score: 0.133 GB cohens kappa score: 0.117 -> test with 'KNN' KNN tn, fp: 270, 40 KNN fn, tp: 8, 3 KNN f1 score: 0.111 KNN cohens kappa score: 0.060 ------ Step 2/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1196 synthetic samples -> test with 'LR' LR tn, fp: 239, 67 LR fn, tp: 3, 6 LR f1 score: 0.146 LR cohens kappa score: 0.101 LR average precision score: 0.172 -> 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: 301, 5 GB fn, tp: 8, 1 GB f1 score: 0.133 GB cohens kappa score: 0.113 -> test with 'KNN' KNN tn, fp: 271, 35 KNN fn, tp: 5, 4 KNN f1 score: 0.167 KNN cohens kappa score: 0.126 ====== 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: 241, 69 LR fn, tp: 5, 6 LR f1 score: 0.140 LR cohens kappa score: 0.085 LR average precision score: 0.178 -> 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: 308, 2 GB fn, tp: 11, 0 GB f1 score: 0.000 GB cohens kappa score: -0.011 -> test with 'KNN' KNN tn, fp: 276, 34 KNN fn, tp: 9, 2 KNN f1 score: 0.085 KNN cohens kappa score: 0.034 ------ Step 3/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1194 synthetic samples -> test with 'LR' LR tn, fp: 230, 80 LR fn, tp: 2, 9 LR f1 score: 0.180 LR cohens kappa score: 0.127 LR average precision score: 0.182 -> 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: 275, 35 KNN fn, tp: 11, 0 KNN f1 score: 0.000 KNN cohens kappa score: -0.055 ------ Step 3/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1194 synthetic samples -> 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: 0.063 -> test with 'RF' RF tn, fp: 306, 4 RF fn, tp: 11, 0 RF f1 score: 0.000 RF cohens kappa score: -0.019 -> 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: 272, 38 KNN fn, tp: 8, 3 KNN f1 score: 0.115 KNN cohens kappa score: 0.065 ------ Step 3/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1194 synthetic samples -> test with 'LR' LR tn, fp: 218, 92 LR fn, tp: 2, 9 LR f1 score: 0.161 LR cohens kappa score: 0.105 LR average precision score: 0.198 -> 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: 302, 8 GB fn, tp: 10, 1 GB f1 score: 0.100 GB cohens kappa score: 0.071 -> 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 3/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1196 synthetic samples -> test with 'LR' LR tn, fp: 242, 64 LR fn, tp: 3, 6 LR f1 score: 0.152 LR cohens kappa score: 0.107 LR average precision score: 0.116 -> test with 'RF' RF tn, fp: 304, 2 RF fn, tp: 9, 0 RF f1 score: 0.000 RF cohens kappa score: -0.010 -> test with 'GB' GB tn, fp: 299, 7 GB fn, tp: 6, 3 GB f1 score: 0.316 GB cohens kappa score: 0.295 -> test with 'KNN' KNN tn, fp: 258, 48 KNN fn, tp: 8, 1 KNN f1 score: 0.034 KNN cohens kappa score: -0.014 ====== 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: 229, 81 LR fn, tp: 3, 8 LR f1 score: 0.160 LR cohens kappa score: 0.105 LR average precision score: 0.394 -> 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: 307, 3 GB fn, tp: 11, 0 GB f1 score: 0.000 GB cohens kappa score: -0.015 -> 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 4/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1194 synthetic samples -> test with 'LR' LR tn, fp: 233, 77 LR fn, tp: 3, 8 LR f1 score: 0.167 LR cohens kappa score: 0.113 LR average precision score: 0.168 -> 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: 307, 3 GB fn, tp: 8, 3 GB f1 score: 0.353 GB cohens kappa score: 0.337 -> test with 'KNN' KNN tn, fp: 260, 50 KNN fn, tp: 10, 1 KNN f1 score: 0.032 KNN cohens kappa score: -0.026 ------ Step 4/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1194 synthetic samples -> test with 'LR' LR tn, fp: 251, 59 LR fn, tp: 8, 3 LR f1 score: 0.082 LR cohens kappa score: 0.025 LR average precision score: 0.083 -> 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: 300, 10 GB fn, tp: 10, 1 GB f1 score: 0.091 GB cohens kappa score: 0.059 -> test with 'KNN' KNN tn, fp: 267, 43 KNN fn, tp: 8, 3 KNN f1 score: 0.105 KNN cohens kappa score: 0.053 ------ Step 4/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1194 synthetic samples -> test with 'LR' LR tn, fp: 225, 85 LR fn, tp: 2, 9 LR f1 score: 0.171 LR cohens kappa score: 0.117 LR average precision score: 0.134 -> 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: 304, 6 GB fn, tp: 10, 1 GB f1 score: 0.111 GB cohens kappa score: 0.087 -> test with 'KNN' KNN tn, fp: 262, 48 KNN fn, tp: 9, 2 KNN f1 score: 0.066 KNN cohens kappa score: 0.010 ------ Step 4/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1196 synthetic samples -> test with 'LR' LR tn, fp: 226, 80 LR fn, tp: 6, 3 LR f1 score: 0.065 LR cohens kappa score: 0.014 LR average precision score: 0.075 -> test with 'RF' RF tn, fp: 301, 5 RF fn, tp: 9, 0 RF f1 score: 0.000 RF cohens kappa score: -0.021 -> test with 'GB' GB tn, fp: 290, 16 GB fn, tp: 8, 1 GB f1 score: 0.077 GB cohens kappa score: 0.041 -> 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 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: 246, 64 LR fn, tp: 5, 6 LR f1 score: 0.148 LR cohens kappa score: 0.095 LR average precision score: 0.089 -> 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: 300, 10 GB fn, tp: 11, 0 GB f1 score: 0.000 GB cohens kappa score: -0.034 -> test with 'KNN' KNN tn, fp: 254, 56 KNN fn, tp: 9, 2 KNN f1 score: 0.058 KNN cohens kappa score: 0.000 ------ Step 5/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1194 synthetic samples -> 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: 0.094 -> 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: 299, 11 GB fn, tp: 10, 1 GB f1 score: 0.087 GB cohens kappa score: 0.053 -> 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 5/5: 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: 0, 11 LR f1 score: 0.183 LR cohens kappa score: 0.129 LR average precision score: 0.300 -> 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: 301, 9 GB fn, tp: 11, 0 GB f1 score: 0.000 GB cohens kappa score: -0.032 -> test with 'KNN' KNN tn, fp: 275, 35 KNN fn, tp: 7, 4 KNN f1 score: 0.160 KNN cohens kappa score: 0.113 ------ Step 5/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1194 synthetic samples -> 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: 0.191 -> 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: 305, 5 GB fn, tp: 8, 3 GB f1 score: 0.316 GB cohens kappa score: 0.295 -> test with 'KNN' KNN tn, fp: 266, 44 KNN fn, tp: 8, 3 KNN f1 score: 0.103 KNN cohens kappa score: 0.051 ------ Step 5/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1196 synthetic samples -> test with 'LR' LR tn, fp: 252, 54 LR fn, tp: 4, 5 LR f1 score: 0.147 LR cohens kappa score: 0.103 LR average precision score: 0.131 -> 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: 297, 9 GB fn, tp: 9, 0 GB f1 score: 0.000 GB cohens kappa score: -0.029 -> test with 'KNN' KNN tn, fp: 270, 36 KNN fn, tp: 7, 2 KNN f1 score: 0.085 KNN cohens kappa score: 0.041 ### Exercise is done. -----[ LR ]----- maximum: LR tn, fp: 260, 101 LR fn, tp: 8, 11 LR f1 score: 0.184 LR cohens kappa score: 0.132 LR average precision score: 0.394 average: LR tn, fp: 234.08, 75.12 LR fn, tp: 3.92, 6.68 LR f1 score: 0.143 LR cohens kappa score: 0.090 LR average precision score: 0.166 minimum: LR tn, fp: 209, 50 LR fn, tp: 0, 3 LR f1 score: 0.065 LR cohens kappa score: 0.014 LR average precision score: 0.063 -----[ RF ]----- maximum: RF tn, fp: 310, 5 RF fn, tp: 11, 1 RF f1 score: 0.133 RF cohens kappa score: 0.117 average: RF tn, fp: 307.64, 1.56 RF fn, tp: 10.56, 0.04 RF f1 score: 0.005 RF cohens kappa score: -0.002 minimum: RF tn, fp: 301, 0 RF fn, tp: 9, 0 RF f1 score: 0.000 RF cohens kappa score: -0.022 -----[ GB ]----- maximum: GB tn, fp: 308, 16 GB fn, tp: 11, 3 GB f1 score: 0.353 GB cohens kappa score: 0.337 average: GB tn, fp: 301.4, 7.8 GB fn, tp: 9.64, 0.96 GB f1 score: 0.099 GB cohens kappa score: 0.073 minimum: GB tn, fp: 290, 2 GB fn, tp: 6, 0 GB f1 score: 0.000 GB cohens kappa score: -0.042 -----[ KNN ]----- maximum: KNN tn, fp: 285, 56 KNN fn, tp: 11, 4 KNN f1 score: 0.167 KNN cohens kappa score: 0.126 average: KNN tn, fp: 267.0, 42.2 KNN fn, tp: 8.64, 1.96 KNN f1 score: 0.072 KNN cohens kappa score: 0.020 minimum: KNN tn, fp: 254, 25 KNN fn, tp: 5, 0 KNN f1 score: 0.000 KNN cohens kappa score: -0.057 wall time: 00:14:42s, process time: 03:24:05s