/////////////////////////////////////////// // Running CTAB-GAN 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 0%| | 0/10 [00:00 create 1194 synthetic samples -> test with 'LR' LR tn, fp: 223, 87 LR fn, tp: 2, 9 LR f1 score: 0.168 LR cohens kappa score: 0.114 LR average precision score: 0.088 -> 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: 278, 32 KNN fn, tp: 11, 0 KNN f1 score: 0.000 KNN cohens kappa score: -0.054 ------ Step 1/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples 0%| | 0/10 [00:00 create 1194 synthetic samples -> test with 'LR' LR tn, fp: 277, 33 LR fn, tp: 6, 5 LR f1 score: 0.204 LR cohens kappa score: 0.159 LR average precision score: 0.112 -> 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: 306, 4 GB fn, tp: 10, 1 GB f1 score: 0.125 GB cohens kappa score: 0.106 -> 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 1/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples 0%| | 0/10 [00:00 create 1194 synthetic samples -> test with 'LR' LR tn, fp: 287, 23 LR fn, tp: 6, 5 LR f1 score: 0.256 LR cohens kappa score: 0.218 LR average precision score: 0.296 -> 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: 280, 30 KNN fn, tp: 11, 0 KNN f1 score: 0.000 KNN cohens kappa score: -0.053 ------ Step 1/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples 0%| | 0/10 [00:00 create 1194 synthetic samples -> test with 'LR' LR tn, fp: 269, 41 LR fn, tp: 9, 2 LR f1 score: 0.074 LR cohens kappa score: 0.021 LR average precision score: 0.059 -> 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: 304, 6 GB fn, tp: 11, 0 GB f1 score: 0.000 GB cohens kappa score: -0.025 -> test with 'KNN' KNN tn, fp: 271, 39 KNN fn, tp: 9, 2 KNN f1 score: 0.077 KNN cohens kappa score: 0.024 ------ Step 1/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples 0%| | 0/10 [00:00 create 1196 synthetic samples -> test with 'LR' LR tn, fp: 291, 15 LR fn, tp: 6, 3 LR f1 score: 0.222 LR cohens kappa score: 0.191 LR average precision score: 0.157 -> 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: 304, 2 GB fn, tp: 8, 1 GB f1 score: 0.167 GB cohens kappa score: 0.155 -> test with 'KNN' KNN tn, fp: 277, 29 KNN fn, tp: 7, 2 KNN f1 score: 0.100 KNN cohens kappa score: 0.058 ====== Step 2/5 ======= -> Shuffling data -> Spliting data to slices ------ Step 2/5: Slice 1/5 ------- -> Reset the GAN -> Train generator for synthetic samples 0%| | 0/10 [00:00 create 1194 synthetic samples -> test with 'LR' LR tn, fp: 286, 24 LR fn, tp: 8, 3 LR f1 score: 0.158 LR cohens kappa score: 0.115 LR average precision score: 0.129 -> 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: 309, 1 GB fn, tp: 10, 1 GB f1 score: 0.154 GB cohens kappa score: 0.145 -> test with 'KNN' KNN tn, fp: 288, 22 KNN fn, tp: 9, 2 KNN f1 score: 0.114 KNN cohens kappa score: 0.071 ------ Step 2/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples 0%| | 0/10 [00:00 create 1194 synthetic samples -> test with 'LR' LR tn, fp: 247, 63 LR fn, tp: 6, 5 LR f1 score: 0.127 LR cohens kappa score: 0.072 LR average precision score: 0.073 -> 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: 11, 0 GB f1 score: 0.000 GB cohens kappa score: -0.027 -> test with 'KNN' KNN tn, fp: 286, 24 KNN fn, tp: 9, 2 KNN f1 score: 0.108 KNN cohens kappa score: 0.063 ------ Step 2/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples 0%| | 0/10 [00:00 create 1194 synthetic samples -> test with 'LR' LR tn, fp: 258, 52 LR fn, tp: 7, 4 LR f1 score: 0.119 LR cohens kappa score: 0.066 LR average precision score: 0.092 -> 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: 11, 0 GB f1 score: 0.000 GB cohens kappa score: -0.030 -> 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 2/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples 0%| | 0/10 [00:00 create 1194 synthetic samples -> test with 'LR' LR tn, fp: 228, 82 LR fn, tp: 5, 6 LR f1 score: 0.121 LR cohens kappa score: 0.064 LR average precision score: 0.202 -> 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: 306, 4 GB fn, tp: 10, 1 GB f1 score: 0.125 GB cohens kappa score: 0.106 -> test with 'KNN' KNN tn, fp: 273, 37 KNN fn, tp: 11, 0 KNN f1 score: 0.000 KNN cohens kappa score: -0.056 ------ Step 2/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples 0%| | 0/10 [00:00 create 1196 synthetic samples -> test with 'LR' LR tn, fp: 248, 58 LR fn, tp: 6, 3 LR f1 score: 0.086 LR cohens kappa score: 0.038 LR average precision score: 0.042 -> 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: 269, 37 KNN fn, tp: 8, 1 KNN f1 score: 0.043 KNN cohens kappa score: -0.004 ====== Step 3/5 ======= -> Shuffling data -> Spliting data to slices ------ Step 3/5: Slice 1/5 ------- -> Reset the GAN -> Train generator for synthetic samples 0%| | 0/10 [00:00 create 1194 synthetic samples -> test with 'LR' LR tn, fp: 291, 19 LR fn, tp: 7, 4 LR f1 score: 0.235 LR cohens kappa score: 0.198 LR average precision score: 0.188 -> 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: 286, 24 KNN fn, tp: 10, 1 KNN f1 score: 0.056 KNN cohens kappa score: 0.008 ------ Step 3/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples 0%| | 0/10 [00:00 create 1194 synthetic samples -> test with 'LR' LR tn, fp: 286, 24 LR fn, tp: 7, 4 LR f1 score: 0.205 LR cohens kappa score: 0.164 LR average precision score: 0.202 -> 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: 9, 2 GB f1 score: 0.222 GB cohens kappa score: 0.201 -> test with 'KNN' KNN tn, fp: 279, 31 KNN fn, tp: 10, 1 KNN f1 score: 0.047 KNN cohens kappa score: -0.005 ------ Step 3/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples 0%| | 0/10 [00:00 create 1194 synthetic samples -> test with 'LR' LR tn, fp: 293, 17 LR fn, tp: 11, 0 LR f1 score: 0.000 LR cohens kappa score: -0.043 LR average precision score: 0.054 -> 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: 271, 39 KNN fn, tp: 9, 2 KNN f1 score: 0.077 KNN cohens kappa score: 0.024 ------ Step 3/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples 0%| | 0/10 [00:00 create 1194 synthetic samples -> test with 'LR' LR tn, fp: 254, 56 LR fn, tp: 6, 5 LR f1 score: 0.139 LR cohens kappa score: 0.086 LR average precision score: 0.076 -> 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: 308, 2 GB fn, tp: 10, 1 GB f1 score: 0.143 GB cohens kappa score: 0.130 -> test with 'KNN' KNN tn, fp: 281, 29 KNN fn, tp: 10, 1 KNN f1 score: 0.049 KNN cohens kappa score: -0.001 ------ Step 3/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples 0%| | 0/10 [00:00 create 1196 synthetic samples -> test with 'LR' LR tn, fp: 294, 12 LR fn, tp: 8, 1 LR f1 score: 0.091 LR cohens kappa score: 0.059 LR average precision score: 0.084 -> 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: 305, 1 GB fn, tp: 8, 1 GB f1 score: 0.182 GB cohens kappa score: 0.173 -> test with 'KNN' KNN tn, fp: 286, 20 KNN fn, tp: 6, 3 KNN f1 score: 0.188 KNN cohens kappa score: 0.153 ====== Step 4/5 ======= -> Shuffling data -> Spliting data to slices ------ Step 4/5: Slice 1/5 ------- -> Reset the GAN -> Train generator for synthetic samples 0%| | 0/10 [00:00 create 1194 synthetic samples -> test with 'LR' LR tn, fp: 263, 47 LR fn, tp: 5, 6 LR f1 score: 0.188 LR cohens kappa score: 0.139 LR average precision score: 0.292 -> 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: 309, 1 GB fn, tp: 11, 0 GB f1 score: 0.000 GB cohens kappa score: -0.006 -> test with 'KNN' KNN tn, fp: 269, 41 KNN fn, tp: 8, 3 KNN f1 score: 0.109 KNN cohens kappa score: 0.057 ------ Step 4/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples 0%| | 0/10 [00:00 create 1194 synthetic samples -> test with 'LR' LR tn, fp: 241, 69 LR fn, tp: 6, 5 LR f1 score: 0.118 LR cohens kappa score: 0.062 LR average precision score: 0.083 -> test with 'RF' RF tn, fp: 304, 6 RF fn, tp: 11, 0 RF f1 score: 0.000 RF cohens kappa score: -0.025 -> 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: 280, 30 KNN fn, tp: 11, 0 KNN f1 score: 0.000 KNN cohens kappa score: -0.053 ------ Step 4/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples 0%| | 0/10 [00:00 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: 0.053 -> 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: 272, 38 KNN fn, tp: 11, 0 KNN f1 score: 0.000 KNN cohens kappa score: -0.056 ------ Step 4/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples 0%| | 0/10 [00:00 create 1194 synthetic samples -> test with 'LR' LR tn, fp: 227, 83 LR fn, tp: 2, 9 LR f1 score: 0.175 LR cohens kappa score: 0.121 LR average precision score: 0.087 -> 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: 308, 2 GB fn, tp: 9, 2 GB f1 score: 0.267 GB cohens kappa score: 0.253 -> 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 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples 0%| | 0/10 [00:00 create 1196 synthetic samples -> test with 'LR' LR tn, fp: 239, 67 LR fn, tp: 7, 2 LR f1 score: 0.051 LR cohens kappa score: 0.001 LR average precision score: 0.029 -> test with 'RF' RF tn, fp: 305, 1 RF fn, tp: 8, 1 RF f1 score: 0.182 RF cohens kappa score: 0.173 -> test with 'GB' GB tn, fp: 300, 6 GB fn, tp: 8, 1 GB f1 score: 0.125 GB cohens kappa score: 0.103 -> test with 'KNN' KNN tn, fp: 265, 41 KNN fn, tp: 9, 0 KNN f1 score: 0.000 KNN cohens kappa score: -0.049 ====== Step 5/5 ======= -> Shuffling data -> Spliting data to slices ------ Step 5/5: Slice 1/5 ------- -> Reset the GAN -> Train generator for synthetic samples 0%| | 0/10 [00:00 create 1194 synthetic samples -> 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: 0.076 -> 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: 274, 36 KNN fn, tp: 9, 2 KNN f1 score: 0.082 KNN cohens kappa score: 0.030 ------ Step 5/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples 0%| | 0/10 [00:00 create 1194 synthetic samples -> test with 'LR' LR tn, fp: 240, 70 LR fn, tp: 7, 4 LR f1 score: 0.094 LR cohens kappa score: 0.037 LR average precision score: 0.062 -> 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: 279, 31 KNN fn, tp: 10, 1 KNN f1 score: 0.047 KNN cohens kappa score: -0.005 ------ Step 5/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples 0%| | 0/10 [00:00 create 1194 synthetic samples -> test with 'LR' LR tn, fp: 300, 10 LR fn, tp: 7, 4 LR f1 score: 0.320 LR cohens kappa score: 0.293 LR average precision score: 0.250 -> 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: 289, 21 KNN fn, tp: 11, 0 KNN f1 score: 0.000 KNN cohens kappa score: -0.047 ------ Step 5/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples 0%| | 0/10 [00:00 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: 0.184 -> 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: 305, 5 GB fn, tp: 10, 1 GB f1 score: 0.118 GB cohens kappa score: 0.096 -> test with 'KNN' KNN tn, fp: 282, 28 KNN fn, tp: 11, 0 KNN f1 score: 0.000 KNN cohens kappa score: -0.052 ------ Step 5/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples 0%| | 0/10 [00:00 create 1196 synthetic samples -> test with 'LR' LR tn, fp: 269, 37 LR fn, tp: 6, 3 LR f1 score: 0.122 LR cohens kappa score: 0.080 LR average precision score: 0.095 -> 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: 301, 5 GB fn, tp: 9, 0 GB f1 score: 0.000 GB cohens kappa score: -0.021 -> test with 'KNN' KNN tn, fp: 272, 34 KNN fn, tp: 9, 0 KNN f1 score: 0.000 KNN cohens kappa score: -0.047 ### Exercise is done. -----[ LR ]----- maximum: LR tn, fp: 300, 97 LR fn, tp: 11, 9 LR f1 score: 0.320 LR cohens kappa score: 0.293 LR average precision score: 0.296 average: LR tn, fp: 260.56, 48.64 LR fn, tp: 6.32, 4.28 LR f1 score: 0.144 LR cohens kappa score: 0.097 LR average precision score: 0.123 minimum: LR tn, fp: 213, 10 LR fn, tp: 2, 0 LR f1 score: 0.000 LR cohens kappa score: -0.043 LR average precision score: 0.029 -----[ RF ]----- maximum: RF tn, fp: 310, 6 RF fn, tp: 11, 1 RF f1 score: 0.182 RF cohens kappa score: 0.173 average: RF tn, fp: 307.72, 1.48 RF fn, tp: 10.56, 0.04 RF f1 score: 0.007 RF cohens kappa score: -0.000 minimum: RF tn, fp: 302, 0 RF fn, tp: 8, 0 RF f1 score: 0.000 RF cohens kappa score: -0.025 -----[ GB ]----- maximum: GB tn, fp: 309, 9 GB fn, tp: 11, 2 GB f1 score: 0.267 GB cohens kappa score: 0.253 average: GB tn, fp: 304.96, 4.24 GB fn, tp: 9.96, 0.64 GB f1 score: 0.084 GB cohens kappa score: 0.065 minimum: GB tn, fp: 297, 1 GB fn, tp: 8, 0 GB f1 score: 0.000 GB cohens kappa score: -0.032 -----[ KNN ]----- maximum: KNN tn, fp: 289, 43 KNN fn, tp: 11, 3 KNN f1 score: 0.188 KNN cohens kappa score: 0.153 average: KNN tn, fp: 276.72, 32.48 KNN fn, tp: 9.64, 0.96 KNN f1 score: 0.045 KNN cohens kappa score: -0.005 minimum: KNN tn, fp: 265, 20 KNN fn, tp: 6, 0 KNN f1 score: 0.000 KNN cohens kappa score: -0.056