/////////////////////////////////////////// // Running ProWRAS 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 'LR' LR tn, fp: 227, 83 LR fn, tp: 6, 5 LR f1 score: 0.101 LR cohens kappa score: 0.043 LR average precision score: 0.128 -> 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: 11, 0 GB f1 score: 0.000 GB cohens kappa score: -0.041 -> test with 'KNN' KNN tn, fp: 264, 46 KNN fn, tp: 10, 1 KNN f1 score: 0.034 KNN cohens kappa score: -0.022 ------ Step 1/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1194 synthetic samples -> test with 'LR' LR tn, fp: 237, 73 LR fn, tp: 4, 7 LR f1 score: 0.154 LR cohens kappa score: 0.100 LR average precision score: 0.121 -> 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: 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: 207, 103 LR fn, tp: 1, 10 LR f1 score: 0.161 LR cohens kappa score: 0.105 LR average precision score: 0.265 -> 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: 298, 12 GB fn, tp: 9, 2 GB f1 score: 0.160 GB cohens kappa score: 0.126 -> test with 'KNN' KNN tn, fp: 253, 57 KNN fn, tp: 9, 2 KNN f1 score: 0.057 KNN cohens kappa score: -0.001 ------ Step 1/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1194 synthetic samples -> test with 'LR' LR tn, fp: 259, 51 LR fn, tp: 6, 5 LR f1 score: 0.149 LR cohens kappa score: 0.098 LR average precision score: 0.152 -> 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: 9, 2 GB f1 score: 0.167 GB cohens kappa score: 0.135 -> test with 'KNN' KNN tn, fp: 266, 44 KNN fn, tp: 10, 1 KNN f1 score: 0.036 KNN cohens kappa score: -0.020 ------ 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: 0.208 -> 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: 266, 40 KNN fn, tp: 8, 1 KNN f1 score: 0.040 KNN cohens kappa score: -0.007 ====== 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: 226, 84 LR fn, tp: 3, 8 LR f1 score: 0.155 LR cohens kappa score: 0.100 LR average precision score: 0.138 -> 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: 302, 8 GB fn, tp: 9, 2 GB f1 score: 0.190 GB cohens kappa score: 0.163 -> 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 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: 3, 8 LR f1 score: 0.168 LR cohens kappa score: 0.115 LR average precision score: 0.140 -> 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: 11, 0 GB f1 score: 0.000 GB cohens kappa score: -0.022 -> 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: 238, 72 LR fn, tp: 3, 8 LR f1 score: 0.176 LR cohens kappa score: 0.123 LR average precision score: 0.158 -> 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: 301, 9 GB fn, tp: 10, 1 GB f1 score: 0.095 GB cohens kappa score: 0.065 -> 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: 232, 78 LR fn, tp: 6, 5 LR f1 score: 0.106 LR cohens kappa score: 0.049 LR average precision score: 0.262 -> 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: 10, 1 GB f1 score: 0.133 GB cohens kappa score: 0.117 -> 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 2/5: 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: 0.139 -> 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: 299, 7 GB fn, tp: 8, 1 GB f1 score: 0.118 GB cohens kappa score: 0.093 -> 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: 240, 70 LR fn, tp: 5, 6 LR f1 score: 0.138 LR cohens kappa score: 0.083 LR average precision score: 0.185 -> 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: 11, 0 GB f1 score: 0.000 GB cohens kappa score: -0.022 -> test with 'KNN' KNN tn, fp: 277, 33 KNN fn, tp: 9, 2 KNN f1 score: 0.087 KNN cohens kappa score: 0.037 ------ Step 3/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1194 synthetic samples -> test with 'LR' LR tn, fp: 224, 86 LR fn, tp: 2, 9 LR f1 score: 0.170 LR cohens kappa score: 0.115 LR average precision score: 0.180 -> 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: 275, 35 KNN fn, tp: 10, 1 KNN f1 score: 0.043 KNN cohens kappa score: -0.010 ------ Step 3/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1194 synthetic samples -> test with 'LR' LR tn, fp: 238, 72 LR fn, tp: 5, 6 LR f1 score: 0.135 LR cohens kappa score: 0.080 LR average precision score: 0.064 -> 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: 302, 8 GB fn, tp: 10, 1 GB f1 score: 0.100 GB cohens kappa score: 0.071 -> 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: 217, 93 LR fn, tp: 2, 9 LR f1 score: 0.159 LR cohens kappa score: 0.104 LR average precision score: 0.193 -> 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: 303, 7 GB fn, tp: 10, 1 GB f1 score: 0.105 GB cohens kappa score: 0.079 -> 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: 244, 62 LR fn, tp: 3, 6 LR f1 score: 0.156 LR cohens kappa score: 0.111 LR average precision score: 0.106 -> 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: 261, 45 KNN fn, tp: 8, 1 KNN f1 score: 0.036 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 'LR' LR tn, fp: 231, 79 LR fn, tp: 3, 8 LR f1 score: 0.163 LR cohens kappa score: 0.109 LR average precision score: 0.395 -> 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: 11, 0 GB f1 score: 0.000 GB cohens kappa score: -0.025 -> test with 'KNN' KNN tn, fp: 269, 41 KNN fn, tp: 10, 1 KNN f1 score: 0.038 KNN cohens kappa score: -0.018 ------ Step 4/5: 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: 0.166 -> 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: 258, 52 KNN fn, tp: 10, 1 KNN f1 score: 0.031 KNN cohens kappa score: -0.027 ------ Step 4/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1194 synthetic samples -> test with 'LR' LR tn, fp: 249, 61 LR fn, tp: 7, 4 LR f1 score: 0.105 LR cohens kappa score: 0.050 LR average precision score: 0.082 -> 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: 264, 46 KNN fn, tp: 8, 3 KNN f1 score: 0.100 KNN cohens kappa score: 0.047 ------ 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: 0.137 -> 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: 302, 8 GB fn, tp: 10, 1 GB f1 score: 0.100 GB cohens kappa score: 0.071 -> test with 'KNN' KNN tn, fp: 261, 49 KNN fn, tp: 9, 2 KNN f1 score: 0.065 KNN cohens kappa score: 0.009 ------ Step 4/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1196 synthetic samples -> test with 'LR' LR tn, fp: 224, 82 LR fn, tp: 6, 3 LR f1 score: 0.064 LR cohens kappa score: 0.013 LR average precision score: 0.074 -> test with 'RF' RF tn, fp: 302, 4 RF fn, tp: 9, 0 RF f1 score: 0.000 RF cohens kappa score: -0.018 -> 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: 245, 65 LR fn, tp: 5, 6 LR f1 score: 0.146 LR cohens kappa score: 0.092 LR average precision score: 0.089 -> 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: 253, 57 KNN fn, tp: 9, 2 KNN f1 score: 0.057 KNN cohens kappa score: -0.001 ------ Step 5/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1194 synthetic samples -> test with 'LR' LR tn, fp: 240, 70 LR fn, tp: 6, 5 LR f1 score: 0.116 LR cohens kappa score: 0.060 LR average precision score: 0.103 -> 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: 259, 51 KNN fn, tp: 10, 1 KNN f1 score: 0.032 KNN cohens kappa score: -0.026 ------ Step 5/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1194 synthetic samples -> test with 'LR' LR tn, fp: 211, 99 LR fn, tp: 0, 11 LR f1 score: 0.182 LR cohens kappa score: 0.127 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: 276, 34 KNN fn, tp: 7, 4 KNN f1 score: 0.163 KNN cohens kappa score: 0.116 ------ 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: 0.189 -> 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: 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: 254, 52 LR fn, tp: 4, 5 LR f1 score: 0.152 LR cohens kappa score: 0.107 LR average precision score: 0.125 -> 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: 9, 0 GB f1 score: 0.000 GB cohens kappa score: -0.031 -> test with 'KNN' KNN tn, fp: 271, 35 KNN fn, tp: 7, 2 KNN f1 score: 0.087 KNN cohens kappa score: 0.043 ### Exercise is done. -----[ LR ]----- maximum: LR tn, fp: 259, 103 LR fn, tp: 7, 11 LR f1 score: 0.182 LR cohens kappa score: 0.127 LR average precision score: 0.395 average: LR tn, fp: 233.52, 75.68 LR fn, tp: 3.88, 6.72 LR f1 score: 0.143 LR cohens kappa score: 0.090 LR average precision score: 0.164 minimum: LR tn, fp: 207, 51 LR fn, tp: 0, 3 LR f1 score: 0.064 LR cohens kappa score: 0.013 LR average precision score: 0.064 -----[ RF ]----- maximum: RF tn, fp: 310, 5 RF fn, tp: 11, 0 RF f1 score: 0.000 RF cohens kappa score: 0.000 average: RF tn, fp: 307.52, 1.68 RF fn, tp: 10.6, 0.0 RF f1 score: 0.000 RF cohens kappa score: -0.008 minimum: RF tn, fp: 302, 0 RF fn, tp: 9, 0 RF f1 score: 0.000 RF cohens kappa score: -0.022 -----[ GB ]----- maximum: GB tn, fp: 307, 16 GB fn, tp: 11, 3 GB f1 score: 0.353 GB cohens kappa score: 0.337 average: GB tn, fp: 301.04, 8.16 GB fn, tp: 9.68, 0.92 GB f1 score: 0.093 GB cohens kappa score: 0.065 minimum: GB tn, fp: 290, 3 GB fn, tp: 6, 0 GB f1 score: 0.000 GB cohens kappa score: -0.041 -----[ KNN ]----- maximum: KNN tn, fp: 285, 57 KNN fn, tp: 11, 4 KNN f1 score: 0.167 KNN cohens kappa score: 0.126 average: KNN tn, fp: 266.88, 42.32 KNN fn, tp: 8.6, 2.0 KNN f1 score: 0.074 KNN cohens kappa score: 0.022 minimum: KNN tn, fp: 253, 25 KNN fn, tp: 5, 0 KNN f1 score: 0.000 KNN cohens kappa score: -0.057