/////////////////////////////////////////// // Running ctGAN 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: 283, 27 LR fn, tp: 6, 5 LR f1 score: 0.233 LR cohens kappa score: 0.191 LR average precision score: 0.097 -> 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: 292, 18 KNN fn, tp: 11, 0 KNN f1 score: 0.000 KNN cohens kappa score: -0.044 ------ Step 1/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1194 synthetic samples -> test with 'LR' LR tn, fp: 274, 36 LR fn, tp: 7, 4 LR f1 score: 0.157 LR cohens kappa score: 0.109 LR average precision score: 0.156 -> 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: 293, 17 KNN fn, tp: 11, 0 KNN f1 score: 0.000 KNN cohens kappa score: -0.043 ------ Step 1/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1194 synthetic samples -> test with 'LR' LR tn, fp: 218, 92 LR fn, tp: 7, 4 LR f1 score: 0.075 LR cohens kappa score: 0.014 LR average precision score: 0.052 -> 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: 295, 15 KNN fn, tp: 11, 0 KNN f1 score: 0.000 KNN cohens kappa score: -0.041 ------ Step 1/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1194 synthetic samples -> test with 'LR' LR tn, fp: 269, 41 LR fn, tp: 7, 4 LR f1 score: 0.143 LR cohens kappa score: 0.093 LR average precision score: 0.083 -> test with 'GB' GB tn, fp: 301, 9 GB fn, tp: 9, 2 GB f1 score: 0.182 GB cohens kappa score: 0.153 -> test with 'KNN' KNN tn, fp: 278, 32 KNN fn, tp: 7, 4 KNN f1 score: 0.170 KNN cohens kappa score: 0.124 ------ Step 1/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1196 synthetic samples -> test with 'LR' LR tn, fp: 267, 39 LR fn, tp: 7, 2 LR f1 score: 0.080 LR cohens kappa score: 0.035 LR average precision score: 0.048 -> 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: 286, 20 KNN fn, tp: 9, 0 KNN f1 score: 0.000 KNN cohens kappa score: -0.041 ====== 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: 267, 43 LR fn, tp: 6, 5 LR f1 score: 0.169 LR cohens kappa score: 0.120 LR average precision score: 0.125 -> 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: 297, 13 KNN fn, tp: 10, 1 KNN f1 score: 0.080 KNN cohens kappa score: 0.043 ------ Step 2/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1194 synthetic samples -> test with 'LR' LR tn, fp: 291, 19 LR fn, tp: 9, 2 LR f1 score: 0.125 LR cohens kappa score: 0.084 LR average precision score: 0.055 -> 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: 293, 17 KNN fn, tp: 11, 0 KNN f1 score: 0.000 KNN cohens kappa score: -0.043 ------ Step 2/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1194 synthetic samples -> test with 'LR' LR tn, fp: 290, 20 LR fn, tp: 10, 1 LR f1 score: 0.062 LR cohens kappa score: 0.018 LR average precision score: 0.066 -> 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: 284, 26 KNN fn, tp: 10, 1 KNN f1 score: 0.053 KNN cohens kappa score: 0.004 ------ Step 2/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1194 synthetic samples -> test with 'LR' LR tn, fp: 289, 21 LR fn, tp: 8, 3 LR f1 score: 0.171 LR cohens kappa score: 0.131 LR average precision score: 0.109 -> 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: 296, 14 KNN fn, tp: 10, 1 KNN f1 score: 0.077 KNN cohens kappa score: 0.039 ------ Step 2/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1196 synthetic samples -> test with 'LR' LR tn, fp: 266, 40 LR fn, tp: 8, 1 LR f1 score: 0.040 LR cohens kappa score: -0.007 LR average precision score: 0.037 -> 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: 280, 26 KNN fn, tp: 9, 0 KNN f1 score: 0.000 KNN cohens kappa score: -0.044 ====== 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: 275, 35 LR fn, tp: 10, 1 LR f1 score: 0.043 LR cohens kappa score: -0.010 LR average precision score: 0.036 -> 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: 294, 16 KNN fn, tp: 10, 1 KNN f1 score: 0.071 KNN cohens kappa score: 0.031 ------ Step 3/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1194 synthetic samples -> test with 'LR' LR tn, fp: 294, 16 LR fn, tp: 8, 3 LR f1 score: 0.200 LR cohens kappa score: 0.164 LR average precision score: 0.155 -> test with 'GB' GB tn, fp: 301, 9 GB fn, tp: 9, 2 GB f1 score: 0.182 GB cohens kappa score: 0.153 -> 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 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1194 synthetic samples -> test with 'LR' LR tn, fp: 274, 36 LR fn, tp: 8, 3 LR f1 score: 0.120 LR cohens kappa score: 0.070 LR average precision score: 0.073 -> 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: 281, 29 KNN fn, tp: 9, 2 KNN f1 score: 0.095 KNN cohens kappa score: 0.047 ------ Step 3/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1194 synthetic samples -> test with 'LR' LR tn, fp: 214, 96 LR fn, tp: 7, 4 LR f1 score: 0.072 LR cohens kappa score: 0.011 LR average precision score: 0.045 -> 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: 290, 20 KNN fn, tp: 11, 0 KNN f1 score: 0.000 KNN cohens kappa score: -0.046 ------ Step 3/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1196 synthetic samples -> test with 'LR' LR tn, fp: 264, 42 LR fn, tp: 7, 2 LR f1 score: 0.075 LR cohens kappa score: 0.029 LR average precision score: 0.042 -> 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: 290, 16 KNN fn, tp: 9, 0 KNN f1 score: 0.000 KNN cohens kappa score: -0.038 ====== 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: 276, 34 LR fn, tp: 6, 5 LR f1 score: 0.200 LR cohens kappa score: 0.155 LR average precision score: 0.174 -> 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: 283, 27 KNN fn, tp: 10, 1 KNN f1 score: 0.051 KNN cohens kappa score: 0.002 ------ Step 4/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1194 synthetic samples -> test with 'LR' LR tn, fp: 283, 27 LR fn, tp: 10, 1 LR f1 score: 0.051 LR cohens kappa score: 0.002 LR average precision score: 0.056 -> 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: 280, 30 KNN fn, tp: 8, 3 KNN f1 score: 0.136 KNN cohens kappa score: 0.090 ------ Step 4/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1194 synthetic samples -> test with 'LR' LR tn, fp: 265, 45 LR fn, tp: 8, 3 LR f1 score: 0.102 LR cohens kappa score: 0.049 LR average precision score: 0.062 -> 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: 293, 17 KNN fn, tp: 11, 0 KNN f1 score: 0.000 KNN cohens kappa score: -0.043 ------ Step 4/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> 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: 0.061 -> 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: 302, 8 KNN fn, tp: 11, 0 KNN f1 score: 0.000 KNN cohens kappa score: -0.030 ------ Step 4/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1196 synthetic samples -> test with 'LR' LR tn, fp: 235, 71 LR fn, tp: 8, 1 LR f1 score: 0.025 LR cohens kappa score: -0.027 LR average precision score: 0.033 -> test with 'GB' GB tn, fp: 293, 13 GB fn, tp: 8, 1 GB f1 score: 0.087 GB cohens kappa score: 0.054 -> test with 'KNN' KNN tn, fp: 282, 24 KNN fn, tp: 9, 0 KNN f1 score: 0.000 KNN cohens kappa score: -0.043 ====== 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: 235, 75 LR fn, tp: 7, 4 LR f1 score: 0.089 LR cohens kappa score: 0.031 LR average precision score: 0.046 -> test with 'GB' GB tn, fp: 296, 14 GB fn, tp: 10, 1 GB f1 score: 0.077 GB cohens kappa score: 0.039 -> test with 'KNN' KNN tn, fp: 289, 21 KNN fn, tp: 8, 3 KNN f1 score: 0.171 KNN cohens kappa score: 0.131 ------ Step 5/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1194 synthetic samples -> test with 'LR' LR tn, fp: 288, 22 LR fn, tp: 10, 1 LR f1 score: 0.059 LR cohens kappa score: 0.013 LR average precision score: 0.058 -> 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: 290, 20 KNN fn, tp: 9, 2 KNN f1 score: 0.121 KNN cohens kappa score: 0.079 ------ Step 5/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1194 synthetic samples -> test with 'LR' LR tn, fp: 261, 49 LR fn, tp: 4, 7 LR f1 score: 0.209 LR cohens kappa score: 0.161 LR average precision score: 0.203 -> test with 'GB' GB tn, fp: 299, 11 GB fn, tp: 7, 4 GB f1 score: 0.308 GB cohens kappa score: 0.279 -> test with 'KNN' KNN tn, fp: 294, 16 KNN fn, tp: 11, 0 KNN f1 score: 0.000 KNN cohens kappa score: -0.042 ------ Step 5/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1194 synthetic samples -> test with 'LR' LR tn, fp: 182, 128 LR fn, tp: 4, 7 LR f1 score: 0.096 LR cohens kappa score: 0.035 LR average precision score: 0.106 -> 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: 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 -> create 1196 synthetic samples -> test with 'LR' LR tn, fp: 247, 59 LR fn, tp: 8, 1 LR f1 score: 0.029 LR cohens kappa score: -0.022 LR average precision score: 0.035 -> 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: 289, 17 KNN fn, tp: 9, 0 KNN f1 score: 0.000 KNN cohens kappa score: -0.039 ### Exercise is done. -----[ LR ]----- maximum: LR tn, fp: 294, 128 LR fn, tp: 10, 7 LR f1 score: 0.233 LR cohens kappa score: 0.191 LR average precision score: 0.203 average: LR tn, fp: 263.04, 46.16 LR fn, tp: 7.56, 3.04 LR f1 score: 0.108 LR cohens kappa score: 0.059 LR average precision score: 0.080 minimum: LR tn, fp: 182, 16 LR fn, tp: 4, 1 LR f1 score: 0.025 LR cohens kappa score: -0.027 LR average precision score: 0.033 -----[ GB ]----- maximum: GB tn, fp: 308, 14 GB fn, tp: 11, 4 GB f1 score: 0.308 GB cohens kappa score: 0.279 average: GB tn, fp: 302.28, 6.92 GB fn, tp: 9.56, 1.04 GB f1 score: 0.106 GB cohens kappa score: 0.081 minimum: GB tn, fp: 293, 2 GB fn, tp: 7, 0 GB f1 score: 0.000 GB cohens kappa score: -0.035 -----[ KNN ]----- maximum: KNN tn, fp: 302, 32 KNN fn, tp: 11, 4 KNN f1 score: 0.171 KNN cohens kappa score: 0.131 average: KNN tn, fp: 288.76, 20.44 KNN fn, tp: 9.8, 0.8 KNN f1 score: 0.043 KNN cohens kappa score: 0.000 minimum: KNN tn, fp: 278, 8 KNN fn, tp: 7, 0 KNN f1 score: 0.000 KNN cohens kappa score: -0.052