{ "cells": [ { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "|--- AT <= 16.96\n", "| |--- TEY <= 118.41\n", "| | |--- AT <= 10.84\n", "| | | |--- AT <= 7.75\n", "| | | | |--- AFDP <= 3.08\n", "| | | | | |--- AFDP <= 2.68\n", "| | | | | | |--- AFDP <= 2.54\n", "| | | | | | | |--- value: [102.29]\n", "| | | | | | |--- AFDP > 2.54\n", "| | | | | | | |--- value: [93.81]\n", "| | | | | |--- AFDP > 2.68\n", "| | | | | | |--- AP <= 1004.50\n", "| | | | | | | |--- value: [65.69]\n", "| | | | | | |--- AP > 1004.50\n", "| | | | | | | |--- value: [84.70]\n", "| | | | |--- AFDP > 3.08\n", "| | | | | |--- AT <= 6.44\n", "| | | | | | |--- AP <= 1027.60\n", "| | | | | | | |--- value: [105.70]\n", "| | | | | | |--- AP > 1027.60\n", "| | | | | | | |--- value: [94.80]\n", "| | | | | |--- AT > 6.44\n", "| | | | | | |--- AP <= 1022.35\n", "| | | | | | | |--- value: [92.55]\n", "| | | | | | |--- AP > 1022.35\n", "| | | | | | | |--- value: [104.67]\n", "| | | |--- AT > 7.75\n", "| | | | |--- TEY <= 105.73\n", "| | | | | |--- AP <= 1019.95\n", "| | | | | | |--- AFDP <= 3.30\n", "| | | | | | | |--- value: [100.50]\n", "| | | | | | |--- AFDP > 3.30\n", "| | | | | | | |--- value: [118.67]\n", "| | | | | |--- AP > 1019.95\n", "| | | | | | |--- AT <= 9.77\n", "| | | | | | | |--- value: [109.96]\n", "| | | | | | |--- AT > 9.77\n", "| | | | | | | |--- value: [115.86]\n", "| | | | |--- TEY > 105.73\n", "| | | | | |--- TEY <= 112.78\n", "| | | | | | |--- AFDP <= 3.07\n", "| | | | | | | |--- value: [86.03]\n", "| | | | | | |--- AFDP > 3.07\n", "| | | | | | | |--- value: [92.86]\n", "| | | | | |--- TEY > 112.78\n", "| | | | | | |--- AT <= 8.93\n", "| | | | | | | |--- value: [87.68]\n", "| | | | | | |--- AT > 8.93\n", "| | | | | | | |--- value: [75.77]\n", "| | |--- AT > 10.84\n", "| | | |--- TEY <= 105.88\n", "| | | | |--- AFDP <= 3.11\n", "| | | | | |--- AP <= 1017.55\n", "| | | | | | |--- AP <= 1009.20\n", "| | | | | | | |--- value: [76.47]\n", "| | | | | | |--- AP > 1009.20\n", "| | | | | | | |--- value: [85.34]\n", "| | | | | |--- AP > 1017.55\n", "| | | | | | |--- AT <= 13.47\n", "| | | | | | | |--- value: [99.92]\n", "| | | | | | |--- AT > 13.47\n", "| | | | | | | |--- value: [89.17]\n", "| | | | |--- AFDP > 3.11\n", "| | | | | |--- AFDP <= 3.43\n", "| | | | | | |--- AT <= 15.98\n", "| | | | | | | |--- value: [112.88]\n", "| | | | | | |--- AT > 15.98\n", "| | | | | | | |--- value: [90.33]\n", "| | | | | |--- AFDP > 3.43\n", "| | | | | | |--- AP <= 1019.55\n", "| | | | | | | |--- value: [94.65]\n", "| | | | | | |--- AP > 1019.55\n", "| | | | | | | |--- value: [75.20]\n", "| | | |--- TEY > 105.88\n", "| | | | |--- AT <= 14.18\n", "| | | | | |--- AT <= 12.96\n", "| | | | | | |--- AFDP <= 3.28\n", "| | | | | | | |--- value: [77.01]\n", "| | | | | | |--- AFDP > 3.28\n", "| | | | | | | |--- value: [81.37]\n", "| | | | | |--- AT > 12.96\n", "| | | | | | |--- AFDP <= 3.18\n", "| | | | | | | |--- value: [71.14]\n", "| | | | | | |--- AFDP > 3.18\n", "| | | | | | | |--- value: [77.02]\n", "| | | | |--- AT > 14.18\n", "| | | | | |--- AFDP <= 3.23\n", "| | | | | | |--- AFDP <= 2.64\n", "| | | | | | | |--- value: [69.02]\n", "| | | | | | |--- AFDP > 2.64\n", "| | | | | | | |--- value: [63.94]\n", "| | | | | |--- AFDP > 3.23\n", "| | | | | | |--- AT <= 15.63\n", "| | | | | | | |--- value: [73.26]\n", "| | | | | | |--- AT > 15.63\n", "| | | | | | | |--- value: [68.00]\n", "| |--- TEY > 118.41\n", "| | |--- TEY <= 154.69\n", "| | | |--- AT <= 10.44\n", "| | | | |--- AFDP <= 3.69\n", "| | | | | |--- AFDP <= 3.08\n", "| | | | | | |--- AT <= 7.32\n", "| | | | | | | |--- value: [81.58]\n", "| | | | | | |--- AT > 7.32\n", "| | | | | | | |--- value: [75.62]\n", "| | | | | |--- AFDP > 3.08\n", "| | | | | | |--- AT <= 7.40\n", "| | | | | | | |--- value: [72.62]\n", "| | | | | | |--- AT > 7.40\n", "| | | | | | | |--- value: [68.88]\n", "| | | | |--- AFDP > 3.69\n", "| | | | | |--- TEY <= 140.11\n", "| | | | | | |--- TEY <= 129.48\n", "| | | | | | | |--- value: [88.91]\n", "| | | | | | |--- TEY > 129.48\n", "| | | | | | | |--- value: [80.23]\n", "| | | | | |--- TEY > 140.11\n", "| | | | | | |--- AP <= 1026.90\n", "| | | | | | | |--- value: [75.41]\n", "| | | | | | |--- AP > 1026.90\n", "| | | | | | | |--- value: [65.58]\n", "| | | |--- AT > 10.44\n", "| | | | |--- AFDP <= 3.85\n", "| | | | | |--- AFDP <= 3.22\n", "| | | | | | |--- AP <= 1011.45\n", "| | | | | | | |--- value: [72.39]\n", "| | | | | | |--- AP > 1011.45\n", "| | | | | | | |--- value: [67.36]\n", "| | | | | |--- AFDP > 3.22\n", "| | | | | | |--- TEY <= 132.02\n", "| | | | | | | |--- value: [60.59]\n", "| | | | | | |--- TEY > 132.02\n", "| | | | | | | |--- value: [65.98]\n", "| | | | |--- AFDP > 3.85\n", "| | | | | |--- AT <= 13.81\n", "| | | | | | |--- TEY <= 134.82\n", "| | | | | | | |--- value: [74.76]\n", "| | | | | | |--- TEY > 134.82\n", "| | | | | | | |--- value: [72.19]\n", "| | | | | |--- AT > 13.81\n", "| | | | | | |--- TEY <= 131.55\n", "| | | | | | | |--- value: [65.92]\n", "| | | | | | |--- TEY > 131.55\n", "| | | | | | | |--- value: [70.24]\n", "| | |--- TEY > 154.69\n", "| | | |--- AP <= 1021.95\n", "| | | | |--- AFDP <= 4.02\n", "| | | | | |--- AP <= 1010.05\n", "| | | | | | |--- AT <= 14.50\n", "| | | | | | | |--- value: [66.77]\n", "| | | | | | |--- AT > 14.50\n", "| | | | | | | |--- value: [72.38]\n", "| | | | | |--- AP > 1010.05\n", "| | | | | | |--- AT <= 14.17\n", "| | | | | | | |--- value: [62.56]\n", "| | | | | | |--- AT > 14.17\n", "| | | | | | | |--- value: [57.23]\n", "| | | | |--- AFDP > 4.02\n", "| | | | | |--- AP <= 1003.35\n", "| | | | | | |--- TEY <= 161.49\n", "| | | | | | | |--- value: [71.07]\n", "| | | | | | |--- TEY > 161.49\n", "| | | | | | | |--- value: [77.45]\n", "| | | | | |--- AP > 1003.35\n", "| | | | | | |--- AT <= 12.80\n", "| | | | | | | |--- value: [67.79]\n", "| | | | | | |--- AT > 12.80\n", "| | | | | | | |--- value: [66.01]\n", "| | | |--- AP > 1021.95\n", "| | | | |--- AP <= 1028.65\n", "| | | | | |--- AFDP <= 4.64\n", "| | | | | | |--- AFDP <= 4.46\n", "| | | | | | | |--- value: [62.99]\n", "| | | | | | |--- AFDP > 4.46\n", "| | | | | | | |--- value: [55.18]\n", "| | | | | |--- AFDP > 4.64\n", "| | | | | | |--- AFDP <= 4.68\n", "| | | | | | | |--- value: [80.15]\n", "| | | | | | |--- AFDP > 4.68\n", "| | | | | | | |--- value: [64.14]\n", "| | | | |--- AP > 1028.65\n", "| | | | | |--- AFDP <= 4.31\n", "| | | | | | |--- AT <= 7.17\n", "| | | | | | | |--- value: [59.32]\n", "| | | | | | |--- AT > 7.17\n", "| | | | | | | |--- value: [68.59]\n", "| | | | | |--- AFDP > 4.31\n", "| | | | | | |--- AFDP <= 5.00\n", "| | | | | | | |--- value: [50.78]\n", "| | | | | | |--- AFDP > 5.00\n", "| | | | | | | |--- value: [60.61]\n", "|--- AT > 16.96\n", "| |--- TEY <= 131.89\n", "| | |--- AT <= 20.42\n", "| | | |--- TEY <= 106.33\n", "| | | | |--- AFDP <= 2.25\n", "| | | | | |--- AT <= 17.35\n", "| | | | | | |--- value: [110.76]\n", "| | | | | |--- AT > 17.35\n", "| | | | | | |--- value: [115.68]\n", "| | | | |--- AFDP > 2.25\n", "| | | | | |--- AFDP <= 2.65\n", "| | | | | | |--- TEY <= 100.91\n", "| | | | | | | |--- value: [79.89]\n", "| | | | | | |--- TEY > 100.91\n", "| | | | | | | |--- value: [64.11]\n", "| | | | | |--- AFDP > 2.65\n", "| | | | | | |--- AT <= 19.34\n", "| | | | | | | |--- value: [82.01]\n", "| | | | | | |--- AT > 19.34\n", "| | | | | | | |--- value: [72.42]\n", "| | | |--- TEY > 106.33\n", "| | | | |--- AFDP <= 3.91\n", "| | | | | |--- TEY <= 127.09\n", "| | | | | | |--- AT <= 18.32\n", "| | | | | | | |--- value: [62.18]\n", "| | | | | | |--- AT > 18.32\n", "| | | | | | | |--- value: [58.25]\n", "| | | | | |--- TEY > 127.09\n", "| | | | | | |--- AFDP <= 3.23\n", "| | | | | | | |--- value: [75.86]\n", "| | | | | | |--- AFDP > 3.23\n", "| | | | | | | |--- value: [53.85]\n", "| | | | |--- AFDP > 3.91\n", "| | | | | |--- TEY <= 116.94\n", "| | | | | | |--- AFDP <= 4.41\n", "| | | | | | | |--- value: [69.74]\n", "| | | | | | |--- AFDP > 4.41\n", "| | | | | | | |--- value: [64.69]\n", "| | | | | |--- TEY > 116.94\n", "| | | | | | |--- AFDP <= 4.54\n", "| | | | | | | |--- value: [59.59]\n", "| | | | | | |--- AFDP > 4.54\n", "| | | | | | | |--- value: [68.11]\n", "| | |--- AT > 20.42\n", "| | | |--- AT <= 22.75\n", "| | | | |--- AFDP <= 4.25\n", "| | | | | |--- TEY <= 103.75\n", "| | | | | | |--- AT <= 22.49\n", "| | | | | | | |--- value: [65.69]\n", "| | | | | | |--- AT > 22.49\n", "| | | | | | | |--- value: [45.74]\n", "| | | | | |--- TEY > 103.75\n", "| | | | | | |--- TEY <= 127.48\n", "| | | | | | | |--- value: [55.44]\n", "| | | | | | |--- TEY > 127.48\n", "| | | | | | | |--- value: [52.86]\n", "| | | | |--- AFDP > 4.25\n", "| | | | | |--- AFDP <= 4.97\n", "| | | | | | |--- TEY <= 121.69\n", "| | | | | | | |--- value: [64.04]\n", "| | | | | | |--- TEY > 121.69\n", "| | | | | | | |--- value: [58.09]\n", "| | | | | |--- AFDP > 4.97\n", "| | | | | | |--- AFDP <= 5.14\n", "| | | | | | | |--- value: [67.76]\n", "| | | | | | |--- AFDP > 5.14\n", "| | | | | | | |--- value: [82.86]\n", "| | | |--- AT > 22.75\n", "| | | | |--- AFDP <= 4.34\n", "| | | | | |--- TEY <= 130.00\n", "| | | | | | |--- AP <= 1013.35\n", "| | | | | | | |--- value: [50.91]\n", "| | | | | | |--- AP > 1013.35\n", "| | | | | | | |--- value: [53.61]\n", "| | | | | |--- TEY > 130.00\n", "| | | | | | |--- AT <= 28.02\n", "| | | | | | | |--- value: [54.92]\n", "| | | | | | |--- AT > 28.02\n", "| | | | | | | |--- value: [58.59]\n", "| | | | |--- AFDP > 4.34\n", "| | | | | |--- AP <= 1015.20\n", "| | | | | | |--- AFDP <= 4.74\n", "| | | | | | | |--- value: [56.41]\n", "| | | | | | |--- AFDP > 4.74\n", "| | | | | | | |--- value: [61.15]\n", "| | | | | |--- AP > 1015.20\n", "| | | | | | |--- AT <= 24.31\n", "| | | | | | | |--- value: [56.77]\n", "| | | | | | |--- AT > 24.31\n", "| | | | | | | |--- value: [75.10]\n", "| |--- TEY > 131.89\n", "| | |--- TEY <= 136.65\n", "| | | |--- AFDP <= 3.45\n", "| | | | |--- AP <= 1019.80\n", "| | | | | |--- AP <= 1003.00\n", "| | | | | | |--- AFDP <= 3.25\n", "| | | | | | | |--- value: [72.68]\n", "| | | | | | |--- AFDP > 3.25\n", "| | | | | | | |--- value: [81.19]\n", "| | | | | |--- AP > 1003.00\n", "| | | | | | |--- AP <= 1015.95\n", "| | | | | | | |--- value: [69.30]\n", "| | | | | | |--- AP > 1015.95\n", "| | | | | | | |--- value: [63.88]\n", "| | | | |--- AP > 1019.80\n", "| | | | | |--- AFDP <= 3.31\n", "| | | | | | |--- AT <= 18.05\n", "| | | | | | | |--- value: [82.82]\n", "| | | | | | |--- AT > 18.05\n", "| | | | | | | |--- value: [91.66]\n", "| | | | | |--- AFDP > 3.31\n", "| | | | | | |--- AT <= 20.69\n", "| | | | | | | |--- value: [67.32]\n", "| | | | | | |--- AT > 20.69\n", "| | | | | | | |--- value: [90.61]\n", "| | | |--- AFDP > 3.45\n", "| | | | |--- AFDP <= 4.76\n", "| | | | | |--- AP <= 999.62\n", "| | | | | | |--- AFDP <= 4.15\n", "| | | | | | | |--- value: [63.25]\n", "| | | | | | |--- AFDP > 4.15\n", "| | | | | | | |--- value: [76.43]\n", "| | | | | |--- AP > 999.62\n", "| | | | | | |--- TEY <= 132.22\n", "| | | | | | | |--- value: [58.26]\n", "| | | | | | |--- TEY > 132.22\n", "| | | | | | | |--- value: [64.09]\n", "| | | | |--- AFDP > 4.76\n", "| | | | | |--- AT <= 24.07\n", "| | | | | | |--- AP <= 1014.35\n", "| | | | | | | |--- value: [70.08]\n", "| | | | | | |--- AP > 1014.35\n", "| | | | | | | |--- value: [74.16]\n", "| | | | | |--- AT > 24.07\n", "| | | | | | |--- AT <= 29.80\n", "| | | | | | | |--- value: [63.82]\n", "| | | | | | |--- AT > 29.80\n", "| | | | | | | |--- value: [68.33]\n", "| | |--- TEY > 136.65\n", "| | | |--- AT <= 21.70\n", "| | | | |--- AP <= 1020.65\n", "| | | | | |--- AFDP <= 5.60\n", "| | | | | | |--- AFDP <= 4.18\n", "| | | | | | | |--- value: [64.12]\n", "| | | | | | |--- AFDP > 4.18\n", "| | | | | | | |--- value: [59.09]\n", "| | | | | |--- AFDP > 5.60\n", "| | | | | | |--- TEY <= 152.04\n", "| | | | | | | |--- value: [71.03]\n", "| | | | | | |--- TEY > 152.04\n", "| | | | | | | |--- value: [67.11]\n", "| | | | |--- AP > 1020.65\n", "| | | | | |--- AFDP <= 4.18\n", "| | | | | | |--- TEY <= 151.01\n", "| | | | | | | |--- value: [97.00]\n", "| | | | | | |--- TEY > 151.01\n", "| | | | | | | |--- value: [76.43]\n", "| | | | | |--- AFDP > 4.18\n", "| | | | | | |--- TEY <= 150.69\n", "| | | | | | | |--- value: [70.50]\n", "| | | | | | |--- TEY > 150.69\n", "| | | | | | | |--- value: [56.02]\n", "| | | |--- AT > 21.70\n", "| | | | |--- AFDP <= 5.60\n", "| | | | | |--- AP <= 1019.05\n", "| | | | | | |--- TEY <= 143.73\n", "| | | | | | | |--- value: [59.74]\n", "| | | | | | |--- TEY > 143.73\n", "| | | | | | | |--- value: [55.51]\n", "| | | | | |--- AP > 1019.05\n", "| | | | | | |--- AFDP <= 4.11\n", "| | | | | | | |--- value: [89.38]\n", "| | | | | | |--- AFDP > 4.11\n", "| | | | | | | |--- value: [65.24]\n", "| | | | |--- AFDP > 5.60\n", "| | | | | |--- AP <= 1014.35\n", "| | | | | | |--- AT <= 25.35\n", "| | | | | | | |--- value: [67.08]\n", "| | | | | | |--- AT > 25.35\n", "| | | | | | | |--- value: [59.91]\n", "| | | | | |--- AP > 1014.35\n", "| | | | | | |--- AFDP <= 5.82\n", "| | | | | | | |--- value: [58.17]\n", "| | | | | | |--- AFDP > 5.82\n", "| | | | | | | |--- value: [72.44]\n", "\n" ] } ], "source": [ "import pickle\n", "import pandas as pd\n", "from sklearn import tree\n", "\n", "data = pd.read_csv(\"data-turbine/clear-data-nox.csv\")\n", "model = pickle.load(open(\"data-turbine/tree-nox.model.sav\", \"rb\"))\n", "features = (\n", " data\n", " .drop([\"NOX\"], axis=1)\n", " .columns.values.tolist()\n", ")\n", "\n", "rules = tree.export_text(model, feature_names=features)\n", "print(rules)" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "126" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "[if (AT > 16.958) and (TEY <= 131.885) and (AT > 20.418) and (AT <= 22.746) and (AFDP <= 4.251) and (TEY <= 103.75) and (AT > 22.491) -> 45.744,\n", " if (AT <= 16.958) and (TEY > 118.405) and (TEY > 154.69) and (AP > 1021.95) and (AP > 1028.65) and (AFDP > 4.31) and (AFDP <= 4.999) -> 50.781,\n", " if (AT <= 16.958) and (TEY <= 118.405) and (AT <= 10.837) and (AT <= 7.752) and (AFDP <= 3.081) and (AFDP > 2.684) and (AP <= 1004.5) -> 65.69,\n", " if (AT <= 16.958) and (TEY <= 118.405) and (AT <= 10.837) and (AT <= 7.752) and (AFDP <= 3.081) and (AFDP > 2.684) and (AP > 1004.5) -> 84.697,\n", " if (AT <= 16.958) and (TEY <= 118.405) and (AT <= 10.837) and (AT <= 7.752) and (AFDP > 3.081) and (AT <= 6.44) and (AP <= 1027.6) -> 105.703,\n", " if (AT <= 16.958) and (TEY <= 118.405) and (AT <= 10.837) and (AT <= 7.752) and (AFDP > 3.081) and (AT <= 6.44) and (AP > 1027.6) -> 94.802,\n", " if (AT <= 16.958) and (TEY <= 118.405) and (AT <= 10.837) and (AT <= 7.752) and (AFDP > 3.081) and (AT > 6.44) and (AP <= 1022.35) -> 92.554,\n", " if (AT <= 16.958) and (TEY <= 118.405) and (AT <= 10.837) and (AT <= 7.752) and (AFDP > 3.081) and (AT > 6.44) and (AP > 1022.35) -> 104.667,\n", " if (AT <= 16.958) and (TEY <= 118.405) and (AT <= 10.837) and (AT > 7.752) and (TEY <= 105.73) and (AP <= 1019.95) and (AFDP <= 3.304) -> 100.502,\n", " if (AT <= 16.958) and (TEY <= 118.405) and (AT <= 10.837) and (AT > 7.752) and (TEY <= 105.73) and (AP <= 1019.95) and (AFDP > 3.304) -> 118.67,\n", " if (AT <= 16.958) and (TEY <= 118.405) and (AT <= 10.837) and (AT > 7.752) and (TEY <= 105.73) and (AP > 1019.95) and (AT <= 9.768) -> 109.956,\n", " if (AT <= 16.958) and (TEY <= 118.405) and (AT <= 10.837) and (AT > 7.752) and (TEY <= 105.73) and (AP > 1019.95) and (AT > 9.768) -> 115.858,\n", " if (AT <= 16.958) and (TEY <= 118.405) and (AT <= 10.837) and (AT > 7.752) and (TEY > 105.73) and (TEY <= 112.785) and (AFDP <= 3.07) -> 86.027,\n", " if (AT <= 16.958) and (TEY <= 118.405) and (AT <= 10.837) and (AT > 7.752) and (TEY > 105.73) and (TEY <= 112.785) and (AFDP > 3.07) -> 92.86,\n", " if (AT <= 16.958) and (TEY <= 118.405) and (AT <= 10.837) and (AT > 7.752) and (TEY > 105.73) and (TEY > 112.785) and (AT <= 8.934) -> 87.682,\n", " if (AT <= 16.958) and (TEY <= 118.405) and (AT <= 10.837) and (AT > 7.752) and (TEY > 105.73) and (TEY > 112.785) and (AT > 8.934) -> 75.774,\n", " if (AT <= 16.958) and (TEY <= 118.405) and (AT > 10.837) and (TEY <= 105.88) and (AFDP <= 3.107) and (AP <= 1017.55) and (AP <= 1009.2) -> 76.472,\n", " if (AT <= 16.958) and (TEY <= 118.405) and (AT > 10.837) and (TEY <= 105.88) and (AFDP <= 3.107) and (AP <= 1017.55) and (AP > 1009.2) -> 85.342,\n", " if (AT <= 16.958) and (TEY <= 118.405) and (AT > 10.837) and (TEY <= 105.88) and (AFDP <= 3.107) and (AP > 1017.55) and (AT <= 13.467) -> 99.921,\n", " if (AT <= 16.958) and (TEY <= 118.405) and (AT > 10.837) and (TEY <= 105.88) and (AFDP <= 3.107) and (AP > 1017.55) and (AT > 13.467) -> 89.167,\n", " if (AT <= 16.958) and (TEY <= 118.405) and (AT > 10.837) and (TEY <= 105.88) and (AFDP > 3.107) and (AFDP <= 3.427) and (AT <= 15.977) -> 112.884,\n", " if (AT <= 16.958) and (TEY <= 118.405) and (AT > 10.837) and (TEY <= 105.88) and (AFDP > 3.107) and (AFDP <= 3.427) and (AT > 15.977) -> 90.334,\n", " if (AT <= 16.958) and (TEY <= 118.405) and (AT > 10.837) and (TEY <= 105.88) and (AFDP > 3.107) and (AFDP > 3.427) and (AP <= 1019.55) -> 94.654,\n", " if (AT <= 16.958) and (TEY <= 118.405) and (AT > 10.837) and (TEY <= 105.88) and (AFDP > 3.107) and (AFDP > 3.427) and (AP > 1019.55) -> 75.199,\n", " if (AT <= 16.958) and (TEY <= 118.405) and (AT > 10.837) and (TEY > 105.88) and (AT <= 14.178) and (AT <= 12.964) and (AFDP <= 3.276) -> 77.011,\n", " if (AT <= 16.958) and (TEY <= 118.405) and (AT > 10.837) and (TEY > 105.88) and (AT <= 14.178) and (AT <= 12.964) and (AFDP > 3.276) -> 81.372,\n", " if (AT <= 16.958) and (TEY <= 118.405) and (AT > 10.837) and (TEY > 105.88) and (AT <= 14.178) and (AT > 12.964) and (AFDP <= 3.181) -> 71.136,\n", " if (AT <= 16.958) and (TEY <= 118.405) and (AT > 10.837) and (TEY > 105.88) and (AT <= 14.178) and (AT > 12.964) and (AFDP > 3.181) -> 77.019,\n", " if (AT <= 16.958) and (TEY <= 118.405) and (AT > 10.837) and (TEY > 105.88) and (AT > 14.178) and (AFDP <= 3.225) and (AFDP <= 2.637) -> 69.023,\n", " if (AT <= 16.958) and (TEY <= 118.405) and (AT > 10.837) and (TEY > 105.88) and (AT > 14.178) and (AFDP <= 3.225) and (AFDP > 2.637) -> 63.935,\n", " if (AT <= 16.958) and (TEY <= 118.405) and (AT > 10.837) and (TEY > 105.88) and (AT > 14.178) and (AFDP > 3.225) and (AT <= 15.626) -> 73.26,\n", " if (AT <= 16.958) and (TEY <= 118.405) and (AT > 10.837) and (TEY > 105.88) and (AT > 14.178) and (AFDP > 3.225) and (AT > 15.626) -> 68.002,\n", " if (AT <= 16.958) and (TEY > 118.405) and (TEY <= 154.69) and (AT <= 10.438) and (AFDP <= 3.694) and (AFDP <= 3.077) and (AT <= 7.324) -> 81.582,\n", " if (AT <= 16.958) and (TEY > 118.405) and (TEY <= 154.69) and (AT <= 10.438) and (AFDP <= 3.694) and (AFDP <= 3.077) and (AT > 7.324) -> 75.621,\n", " if (AT <= 16.958) and (TEY > 118.405) and (TEY <= 154.69) and (AT <= 10.438) and (AFDP <= 3.694) and (AFDP > 3.077) and (AT <= 7.404) -> 72.624,\n", " if (AT <= 16.958) and (TEY > 118.405) and (TEY <= 154.69) and (AT <= 10.438) and (AFDP <= 3.694) and (AFDP > 3.077) and (AT > 7.404) -> 68.878,\n", " if (AT <= 16.958) and (TEY > 118.405) and (TEY <= 154.69) and (AT <= 10.438) and (AFDP > 3.694) and (TEY <= 140.105) and (TEY <= 129.48) -> 88.915,\n", " if (AT <= 16.958) and (TEY > 118.405) and (TEY <= 154.69) and (AT <= 10.438) and (AFDP > 3.694) and (TEY <= 140.105) and (TEY > 129.48) -> 80.231,\n", " if (AT <= 16.958) and (TEY > 118.405) and (TEY <= 154.69) and (AT <= 10.438) and (AFDP > 3.694) and (TEY > 140.105) and (AP <= 1026.9) -> 75.408,\n", " if (AT <= 16.958) and (TEY > 118.405) and (TEY <= 154.69) and (AT <= 10.438) and (AFDP > 3.694) and (TEY > 140.105) and (AP > 1026.9) -> 65.576,\n", " if (AT <= 16.958) and (TEY > 118.405) and (TEY <= 154.69) and (AT > 10.438) and (AFDP <= 3.846) and (AFDP <= 3.223) and (AP <= 1011.45) -> 72.391,\n", " if (AT <= 16.958) and (TEY > 118.405) and (TEY <= 154.69) and (AT > 10.438) and (AFDP <= 3.846) and (AFDP <= 3.223) and (AP > 1011.45) -> 67.358,\n", " if (AT <= 16.958) and (TEY > 118.405) and (TEY <= 154.69) and (AT > 10.438) and (AFDP <= 3.846) and (AFDP > 3.223) and (TEY <= 132.02) -> 60.585,\n", " if (AT <= 16.958) and (TEY > 118.405) and (TEY <= 154.69) and (AT > 10.438) and (AFDP <= 3.846) and (AFDP > 3.223) and (TEY > 132.02) -> 65.977,\n", " if (AT <= 16.958) and (TEY > 118.405) and (TEY <= 154.69) and (AT > 10.438) and (AFDP > 3.846) and (AT <= 13.809) and (TEY <= 134.82) -> 74.765,\n", " if (AT <= 16.958) and (TEY > 118.405) and (TEY <= 154.69) and (AT > 10.438) and (AFDP > 3.846) and (AT <= 13.809) and (TEY > 134.82) -> 72.188,\n", " if (AT <= 16.958) and (TEY > 118.405) and (TEY <= 154.69) and (AT > 10.438) and (AFDP > 3.846) and (AT > 13.809) and (TEY <= 131.55) -> 65.916,\n", " if (AT <= 16.958) and (TEY > 118.405) and (TEY <= 154.69) and (AT > 10.438) and (AFDP > 3.846) and (AT > 13.809) and (TEY > 131.55) -> 70.244,\n", " if (AT <= 16.958) and (TEY > 118.405) and (TEY > 154.69) and (AP <= 1021.95) and (AFDP <= 4.022) and (AP <= 1010.05) and (AT <= 14.497) -> 66.765,\n", " if (AT <= 16.958) and (TEY > 118.405) and (TEY > 154.69) and (AP <= 1021.95) and (AFDP <= 4.022) and (AP <= 1010.05) and (AT > 14.497) -> 72.379,\n", " if (AT <= 16.958) and (TEY > 118.405) and (TEY > 154.69) and (AP <= 1021.95) and (AFDP <= 4.022) and (AP > 1010.05) and (AT <= 14.172) -> 62.562,\n", " if (AT <= 16.958) and (TEY > 118.405) and (TEY > 154.69) and (AP <= 1021.95) and (AFDP <= 4.022) and (AP > 1010.05) and (AT > 14.172) -> 57.232,\n", " if (AT <= 16.958) and (TEY > 118.405) and (TEY > 154.69) and (AP <= 1021.95) and (AFDP > 4.022) and (AP <= 1003.35) and (TEY <= 161.49) -> 71.066,\n", " if (AT <= 16.958) and (TEY > 118.405) and (TEY > 154.69) and (AP <= 1021.95) and (AFDP > 4.022) and (AP <= 1003.35) and (TEY > 161.49) -> 77.453,\n", " if (AT <= 16.958) and (TEY > 118.405) and (TEY > 154.69) and (AP <= 1021.95) and (AFDP > 4.022) and (AP > 1003.35) and (AT <= 12.797) -> 67.79,\n", " if (AT <= 16.958) and (TEY > 118.405) and (TEY > 154.69) and (AP <= 1021.95) and (AFDP > 4.022) and (AP > 1003.35) and (AT > 12.797) -> 66.014,\n", " if (AT <= 16.958) and (TEY > 118.405) and (TEY > 154.69) and (AP > 1021.95) and (AP <= 1028.65) and (AFDP <= 4.639) and (AFDP <= 4.464) -> 62.992,\n", " if (AT <= 16.958) and (TEY > 118.405) and (TEY > 154.69) and (AP > 1021.95) and (AP <= 1028.65) and (AFDP <= 4.639) and (AFDP > 4.464) -> 55.182,\n", " if (AT <= 16.958) and (TEY > 118.405) and (TEY > 154.69) and (AP > 1021.95) and (AP <= 1028.65) and (AFDP > 4.639) and (AFDP <= 4.678) -> 80.146,\n", " if (AT <= 16.958) and (TEY > 118.405) and (TEY > 154.69) and (AP > 1021.95) and (AP <= 1028.65) and (AFDP > 4.639) and (AFDP > 4.678) -> 64.14,\n", " if (AT <= 16.958) and (TEY > 118.405) and (TEY > 154.69) and (AP > 1021.95) and (AP > 1028.65) and (AFDP <= 4.31) and (AT <= 7.169) -> 59.322,\n", " if (AT <= 16.958) and (TEY > 118.405) and (TEY > 154.69) and (AP > 1021.95) and (AP > 1028.65) and (AFDP <= 4.31) and (AT > 7.169) -> 68.586,\n", " if (AT <= 16.958) and (TEY <= 118.405) and (AT <= 10.837) and (AT <= 7.752) and (AFDP <= 3.081) and (AFDP <= 2.684) and (AFDP > 2.544) -> 93.814,\n", " if (AT <= 16.958) and (TEY > 118.405) and (TEY > 154.69) and (AP > 1021.95) and (AP > 1028.65) and (AFDP > 4.31) and (AFDP > 4.999) -> 60.608,\n", " if (AT > 16.958) and (TEY <= 131.885) and (AT <= 20.418) and (TEY <= 106.33) and (AFDP <= 2.245) and (AT <= 17.352) -> 110.76,\n", " if (AT > 16.958) and (TEY <= 131.885) and (AT <= 20.418) and (TEY <= 106.33) and (AFDP <= 2.245) and (AT > 17.352) -> 115.68,\n", " if (AT > 16.958) and (TEY <= 131.885) and (AT <= 20.418) and (TEY <= 106.33) and (AFDP > 2.245) and (AFDP <= 2.65) and (TEY <= 100.905) -> 79.894,\n", " if (AT > 16.958) and (TEY <= 131.885) and (AT <= 20.418) and (TEY <= 106.33) and (AFDP > 2.245) and (AFDP <= 2.65) and (TEY > 100.905) -> 64.112,\n", " if (AT > 16.958) and (TEY <= 131.885) and (AT <= 20.418) and (TEY <= 106.33) and (AFDP > 2.245) and (AFDP > 2.65) and (AT <= 19.339) -> 82.01,\n", " if (AT > 16.958) and (TEY <= 131.885) and (AT <= 20.418) and (TEY <= 106.33) and (AFDP > 2.245) and (AFDP > 2.65) and (AT > 19.339) -> 72.418,\n", " if (AT > 16.958) and (TEY <= 131.885) and (AT <= 20.418) and (TEY > 106.33) and (AFDP <= 3.911) and (TEY <= 127.095) and (AT <= 18.319) -> 62.177,\n", " if (AT > 16.958) and (TEY <= 131.885) and (AT <= 20.418) and (TEY > 106.33) and (AFDP <= 3.911) and (TEY <= 127.095) and (AT > 18.319) -> 58.251,\n", " if (AT > 16.958) and (TEY <= 131.885) and (AT <= 20.418) and (TEY > 106.33) and (AFDP <= 3.911) and (TEY > 127.095) and (AFDP <= 3.235) -> 75.858,\n", " if (AT > 16.958) and (TEY <= 131.885) and (AT <= 20.418) and (TEY > 106.33) and (AFDP <= 3.911) and (TEY > 127.095) and (AFDP > 3.235) -> 53.854,\n", " if (AT > 16.958) and (TEY <= 131.885) and (AT <= 20.418) and (TEY > 106.33) and (AFDP > 3.911) and (TEY <= 116.94) and (AFDP <= 4.411) -> 69.744,\n", " if (AT > 16.958) and (TEY <= 131.885) and (AT <= 20.418) and (TEY > 106.33) and (AFDP > 3.911) and (TEY <= 116.94) and (AFDP > 4.411) -> 64.687,\n", " if (AT > 16.958) and (TEY <= 131.885) and (AT <= 20.418) and (TEY > 106.33) and (AFDP > 3.911) and (TEY > 116.94) and (AFDP <= 4.543) -> 59.585,\n", " if (AT > 16.958) and (TEY <= 131.885) and (AT <= 20.418) and (TEY > 106.33) and (AFDP > 3.911) and (TEY > 116.94) and (AFDP > 4.543) -> 68.111,\n", " if (AT > 16.958) and (TEY <= 131.885) and (AT > 20.418) and (AT <= 22.746) and (AFDP <= 4.251) and (TEY <= 103.75) and (AT <= 22.491) -> 65.687,\n", " if (AT <= 16.958) and (TEY <= 118.405) and (AT <= 10.837) and (AT <= 7.752) and (AFDP <= 3.081) and (AFDP <= 2.684) and (AFDP <= 2.544) -> 102.293,\n", " if (AT > 16.958) and (TEY <= 131.885) and (AT > 20.418) and (AT <= 22.746) and (AFDP <= 4.251) and (TEY > 103.75) and (TEY <= 127.485) -> 55.441,\n", " if (AT > 16.958) and (TEY <= 131.885) and (AT > 20.418) and (AT <= 22.746) and (AFDP <= 4.251) and (TEY > 103.75) and (TEY > 127.485) -> 52.864,\n", " if (AT > 16.958) and (TEY <= 131.885) and (AT > 20.418) and (AT <= 22.746) and (AFDP > 4.251) and (AFDP <= 4.966) and (TEY <= 121.69) -> 64.037,\n", " if (AT > 16.958) and (TEY <= 131.885) and (AT > 20.418) and (AT <= 22.746) and (AFDP > 4.251) and (AFDP <= 4.966) and (TEY > 121.69) -> 58.089,\n", " if (AT > 16.958) and (TEY <= 131.885) and (AT > 20.418) and (AT <= 22.746) and (AFDP > 4.251) and (AFDP > 4.966) and (AFDP <= 5.136) -> 67.764,\n", " if (AT > 16.958) and (TEY <= 131.885) and (AT > 20.418) and (AT <= 22.746) and (AFDP > 4.251) and (AFDP > 4.966) and (AFDP > 5.136) -> 82.857,\n", " if (AT > 16.958) and (TEY <= 131.885) and (AT > 20.418) and (AT > 22.746) and (AFDP <= 4.344) and (TEY <= 129.995) and (AP <= 1013.35) -> 50.906,\n", " if (AT > 16.958) and (TEY <= 131.885) and (AT > 20.418) and (AT > 22.746) and (AFDP <= 4.344) and (TEY <= 129.995) and (AP > 1013.35) -> 53.606,\n", " if (AT > 16.958) and (TEY <= 131.885) and (AT > 20.418) and (AT > 22.746) and (AFDP <= 4.344) and (TEY > 129.995) and (AT <= 28.021) -> 54.921,\n", " if (AT > 16.958) and (TEY <= 131.885) and (AT > 20.418) and (AT > 22.746) and (AFDP <= 4.344) and (TEY > 129.995) and (AT > 28.021) -> 58.587,\n", " if (AT > 16.958) and (TEY <= 131.885) and (AT > 20.418) and (AT > 22.746) and (AFDP > 4.344) and (AP <= 1015.2) and (AFDP <= 4.739) -> 56.411,\n", " if (AT > 16.958) and (TEY <= 131.885) and (AT > 20.418) and (AT > 22.746) and (AFDP > 4.344) and (AP <= 1015.2) and (AFDP > 4.739) -> 61.151,\n", " if (AT > 16.958) and (TEY <= 131.885) and (AT > 20.418) and (AT > 22.746) and (AFDP > 4.344) and (AP > 1015.2) and (AT <= 24.312) -> 56.769,\n", " if (AT > 16.958) and (TEY <= 131.885) and (AT > 20.418) and (AT > 22.746) and (AFDP > 4.344) and (AP > 1015.2) and (AT > 24.312) -> 75.097,\n", " if (AT > 16.958) and (TEY > 131.885) and (TEY <= 136.645) and (AFDP <= 3.449) and (AP <= 1019.8) and (AP <= 1003.0) and (AFDP <= 3.245) -> 72.684,\n", " if (AT > 16.958) and (TEY > 131.885) and (TEY <= 136.645) and (AFDP <= 3.449) and (AP <= 1019.8) and (AP <= 1003.0) and (AFDP > 3.245) -> 81.186,\n", " if (AT > 16.958) and (TEY > 131.885) and (TEY <= 136.645) and (AFDP <= 3.449) and (AP <= 1019.8) and (AP > 1003.0) and (AP <= 1015.95) -> 69.3,\n", " if (AT > 16.958) and (TEY > 131.885) and (TEY <= 136.645) and (AFDP <= 3.449) and (AP <= 1019.8) and (AP > 1003.0) and (AP > 1015.95) -> 63.882,\n", " if (AT > 16.958) and (TEY > 131.885) and (TEY <= 136.645) and (AFDP <= 3.449) and (AP > 1019.8) and (AFDP <= 3.309) and (AT <= 18.052) -> 82.823,\n", " if (AT > 16.958) and (TEY > 131.885) and (TEY <= 136.645) and (AFDP <= 3.449) and (AP > 1019.8) and (AFDP <= 3.309) and (AT > 18.052) -> 91.663,\n", " if (AT > 16.958) and (TEY > 131.885) and (TEY <= 136.645) and (AFDP <= 3.449) and (AP > 1019.8) and (AFDP > 3.309) and (AT <= 20.688) -> 67.318,\n", " if (AT > 16.958) and (TEY > 131.885) and (TEY <= 136.645) and (AFDP <= 3.449) and (AP > 1019.8) and (AFDP > 3.309) and (AT > 20.688) -> 90.607,\n", " if (AT > 16.958) and (TEY > 131.885) and (TEY <= 136.645) and (AFDP > 3.449) and (AFDP <= 4.756) and (AP <= 999.625) and (AFDP <= 4.154) -> 63.255,\n", " if (AT > 16.958) and (TEY > 131.885) and (TEY <= 136.645) and (AFDP > 3.449) and (AFDP <= 4.756) and (AP <= 999.625) and (AFDP > 4.154) -> 76.434,\n", " if (AT > 16.958) and (TEY > 131.885) and (TEY <= 136.645) and (AFDP > 3.449) and (AFDP <= 4.756) and (AP > 999.625) and (TEY <= 132.215) -> 58.258,\n", " if (AT > 16.958) and (TEY > 131.885) and (TEY <= 136.645) and (AFDP > 3.449) and (AFDP <= 4.756) and (AP > 999.625) and (TEY > 132.215) -> 64.093,\n", " if (AT > 16.958) and (TEY > 131.885) and (TEY <= 136.645) and (AFDP > 3.449) and (AFDP > 4.756) and (AT <= 24.071) and (AP <= 1014.35) -> 70.079,\n", " if (AT > 16.958) and (TEY > 131.885) and (TEY <= 136.645) and (AFDP > 3.449) and (AFDP > 4.756) and (AT <= 24.071) and (AP > 1014.35) -> 74.157,\n", " if (AT > 16.958) and (TEY > 131.885) and (TEY <= 136.645) and (AFDP > 3.449) and (AFDP > 4.756) and (AT > 24.071) and (AT <= 29.795) -> 63.817,\n", " if (AT > 16.958) and (TEY > 131.885) and (TEY <= 136.645) and (AFDP > 3.449) and (AFDP > 4.756) and (AT > 24.071) and (AT > 29.795) -> 68.328,\n", " if (AT > 16.958) and (TEY > 131.885) and (TEY > 136.645) and (AT <= 21.703) and (AP <= 1020.65) and (AFDP <= 5.596) and (AFDP <= 4.179) -> 64.124,\n", " if (AT > 16.958) and (TEY > 131.885) and (TEY > 136.645) and (AT <= 21.703) and (AP <= 1020.65) and (AFDP <= 5.596) and (AFDP > 4.179) -> 59.091,\n", " if (AT > 16.958) and (TEY > 131.885) and (TEY > 136.645) and (AT <= 21.703) and (AP <= 1020.65) and (AFDP > 5.596) and (TEY <= 152.045) -> 71.027,\n", " if (AT > 16.958) and (TEY > 131.885) and (TEY > 136.645) and (AT <= 21.703) and (AP <= 1020.65) and (AFDP > 5.596) and (TEY > 152.045) -> 67.105,\n", " if (AT > 16.958) and (TEY > 131.885) and (TEY > 136.645) and (AT <= 21.703) and (AP > 1020.65) and (AFDP <= 4.18) and (TEY <= 151.015) -> 97.001,\n", " if (AT > 16.958) and (TEY > 131.885) and (TEY > 136.645) and (AT <= 21.703) and (AP > 1020.65) and (AFDP <= 4.18) and (TEY > 151.015) -> 76.428,\n", " if (AT > 16.958) and (TEY > 131.885) and (TEY > 136.645) and (AT <= 21.703) and (AP > 1020.65) and (AFDP > 4.18) and (TEY <= 150.695) -> 70.498,\n", " if (AT > 16.958) and (TEY > 131.885) and (TEY > 136.645) and (AT <= 21.703) and (AP > 1020.65) and (AFDP > 4.18) and (TEY > 150.695) -> 56.017,\n", " if (AT > 16.958) and (TEY > 131.885) and (TEY > 136.645) and (AT > 21.703) and (AFDP <= 5.597) and (AP <= 1019.05) and (TEY <= 143.725) -> 59.741,\n", " if (AT > 16.958) and (TEY > 131.885) and (TEY > 136.645) and (AT > 21.703) and (AFDP <= 5.597) and (AP <= 1019.05) and (TEY > 143.725) -> 55.512,\n", " if (AT > 16.958) and (TEY > 131.885) and (TEY > 136.645) and (AT > 21.703) and (AFDP <= 5.597) and (AP > 1019.05) and (AFDP <= 4.114) -> 89.381,\n", " if (AT > 16.958) and (TEY > 131.885) and (TEY > 136.645) and (AT > 21.703) and (AFDP <= 5.597) and (AP > 1019.05) and (AFDP > 4.114) -> 65.239,\n", " if (AT > 16.958) and (TEY > 131.885) and (TEY > 136.645) and (AT > 21.703) and (AFDP > 5.597) and (AP <= 1014.35) and (AT <= 25.345) -> 67.084,\n", " if (AT > 16.958) and (TEY > 131.885) and (TEY > 136.645) and (AT > 21.703) and (AFDP > 5.597) and (AP <= 1014.35) and (AT > 25.345) -> 59.906,\n", " if (AT > 16.958) and (TEY > 131.885) and (TEY > 136.645) and (AT > 21.703) and (AFDP > 5.597) and (AP > 1014.35) and (AFDP <= 5.819) -> 58.17,\n", " if (AT > 16.958) and (TEY > 131.885) and (TEY > 136.645) and (AT > 21.703) and (AFDP > 5.597) and (AP > 1014.35) and (AFDP > 5.819) -> 72.443]" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from src.rules import get_rules\n", "\n", "\n", "rules = get_rules(model, features)\n", "display(len(rules))\n", "rules" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "126" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "[if (AT > 16.958) and (AT <= 22.746) and (TEY <= 131.885) and (AFDP <= 4.251) -> 45.744,\n", " if (AT <= 16.958) and (TEY > 118.405) and (AP > 1021.95) and (AFDP > 4.31) and (AFDP <= 4.999) -> 50.781,\n", " if (AT <= 16.958) and (TEY <= 118.405) and (AFDP <= 3.081) and (AFDP > 2.684) and (AP <= 1004.5) -> 65.69,\n", " if (AT <= 16.958) and (TEY <= 118.405) and (AFDP <= 3.081) and (AFDP > 2.684) and (AP > 1004.5) -> 84.697,\n", " if (AT <= 16.958) and (TEY <= 118.405) and (AFDP > 3.081) and (AP <= 1027.6) -> 105.703,\n", " if (AT <= 16.958) and (TEY <= 118.405) and (AFDP > 3.081) and (AP > 1027.6) -> 94.802,\n", " if (AT <= 16.958) and (AT > 6.44) and (TEY <= 118.405) and (AFDP > 3.081) and (AP <= 1022.35) -> 92.554,\n", " if (AT <= 16.958) and (AT > 6.44) and (TEY <= 118.405) and (AFDP > 3.081) and (AP > 1022.35) -> 104.667,\n", " if (AT <= 16.958) and (AT > 7.752) and (TEY <= 118.405) and (AP <= 1019.95) and (AFDP <= 3.304) -> 100.502,\n", " if (AT <= 16.958) and (AT > 7.752) and (TEY <= 118.405) and (AP <= 1019.95) and (AFDP > 3.304) -> 118.67,\n", " if (AT <= 16.958) and (AT > 7.752) and (TEY <= 118.405) and (AP > 1019.95) -> 109.956,\n", " if (AT <= 16.958) and (AT > 7.752) and (TEY <= 118.405) and (AP > 1019.95) -> 115.858,\n", " if (AT <= 16.958) and (AT > 7.752) and (TEY <= 118.405) and (TEY > 105.73) and (AFDP <= 3.07) -> 86.027,\n", " if (AT <= 16.958) and (AT > 7.752) and (TEY <= 118.405) and (TEY > 105.73) and (AFDP > 3.07) -> 92.86,\n", " if (AT <= 16.958) and (AT > 7.752) and (TEY <= 118.405) and (TEY > 105.73) -> 87.682,\n", " if (AT <= 16.958) and (AT > 7.752) and (TEY <= 118.405) and (TEY > 105.73) -> 75.774,\n", " if (AT <= 16.958) and (AT > 10.837) and (TEY <= 118.405) and (AFDP <= 3.107) and (AP <= 1017.55) -> 76.472,\n", " if (AT <= 16.958) and (AT > 10.837) and (TEY <= 118.405) and (AFDP <= 3.107) and (AP <= 1017.55) and (AP > 1009.2) -> 85.342,\n", " if (AT <= 16.958) and (AT > 10.837) and (TEY <= 118.405) and (AFDP <= 3.107) and (AP > 1017.55) -> 99.921,\n", " if (AT <= 16.958) and (AT > 10.837) and (TEY <= 118.405) and (AFDP <= 3.107) and (AP > 1017.55) -> 89.167,\n", " if (AT <= 16.958) and (AT > 10.837) and (TEY <= 118.405) and (AFDP > 3.107) and (AFDP <= 3.427) -> 112.884,\n", " if (AT <= 16.958) and (AT > 10.837) and (TEY <= 118.405) and (AFDP > 3.107) and (AFDP <= 3.427) -> 90.334,\n", " if (AT <= 16.958) and (AT > 10.837) and (TEY <= 118.405) and (AFDP > 3.107) and (AP <= 1019.55) -> 94.654,\n", " if (AT <= 16.958) and (AT > 10.837) and (TEY <= 118.405) and (AFDP > 3.107) and (AP > 1019.55) -> 75.199,\n", " if (AT <= 16.958) and (AT > 10.837) and (TEY <= 118.405) and (TEY > 105.88) and (AFDP <= 3.276) -> 77.011,\n", " if (AT <= 16.958) and (AT > 10.837) and (TEY <= 118.405) and (TEY > 105.88) and (AFDP > 3.276) -> 81.372,\n", " if (AT <= 16.958) and (AT > 10.837) and (TEY <= 118.405) and (TEY > 105.88) and (AFDP <= 3.181) -> 71.136,\n", " if (AT <= 16.958) and (AT > 10.837) and (TEY <= 118.405) and (TEY > 105.88) and (AFDP > 3.181) -> 77.019,\n", " if (AT <= 16.958) and (AT > 10.837) and (TEY <= 118.405) and (TEY > 105.88) and (AFDP <= 3.225) -> 69.023,\n", " if (AT <= 16.958) and (AT > 10.837) and (TEY <= 118.405) and (TEY > 105.88) and (AFDP <= 3.225) and (AFDP > 2.637) -> 63.935,\n", " if (AT <= 16.958) and (AT > 10.837) and (TEY <= 118.405) and (TEY > 105.88) and (AFDP > 3.225) -> 73.26,\n", " if (AT <= 16.958) and (AT > 10.837) and (TEY <= 118.405) and (TEY > 105.88) and (AFDP > 3.225) -> 68.002,\n", " if (AT <= 16.958) and (TEY > 118.405) and (TEY <= 154.69) and (AFDP <= 3.694) -> 81.582,\n", " if (AT <= 16.958) and (AT > 7.324) and (TEY > 118.405) and (TEY <= 154.69) and (AFDP <= 3.694) -> 75.621,\n", " if (AT <= 16.958) and (TEY > 118.405) and (TEY <= 154.69) and (AFDP <= 3.694) and (AFDP > 3.077) -> 72.624,\n", " if (AT <= 16.958) and (AT > 7.404) and (TEY > 118.405) and (TEY <= 154.69) and (AFDP <= 3.694) and (AFDP > 3.077) -> 68.878,\n", " if (AT <= 16.958) and (TEY > 118.405) and (TEY <= 154.69) and (AFDP > 3.694) -> 88.915,\n", " if (AT <= 16.958) and (TEY > 118.405) and (TEY <= 154.69) and (AFDP > 3.694) -> 80.231,\n", " if (AT <= 16.958) and (TEY > 118.405) and (TEY <= 154.69) and (AFDP > 3.694) and (AP <= 1026.9) -> 75.408,\n", " if (AT <= 16.958) and (TEY > 118.405) and (TEY <= 154.69) and (AFDP > 3.694) and (AP > 1026.9) -> 65.576,\n", " if (AT <= 16.958) and (AT > 10.438) and (TEY > 118.405) and (TEY <= 154.69) and (AFDP <= 3.846) and (AP <= 1011.45) -> 72.391,\n", " if (AT <= 16.958) and (AT > 10.438) and (TEY > 118.405) and (TEY <= 154.69) and (AFDP <= 3.846) and (AP > 1011.45) -> 67.358,\n", " if (AT <= 16.958) and (AT > 10.438) and (TEY > 118.405) and (TEY <= 154.69) and (AFDP <= 3.846) and (AFDP > 3.223) -> 60.585,\n", " if (AT <= 16.958) and (AT > 10.438) and (TEY > 118.405) and (TEY <= 154.69) and (AFDP <= 3.846) and (AFDP > 3.223) -> 65.977,\n", " if (AT <= 16.958) and (AT > 10.438) and (TEY > 118.405) and (TEY <= 154.69) and (AFDP > 3.846) -> 74.765,\n", " if (AT <= 16.958) and (AT > 10.438) and (TEY > 118.405) and (TEY <= 154.69) and (AFDP > 3.846) -> 72.188,\n", " if (AT <= 16.958) and (AT > 10.438) and (TEY > 118.405) and (TEY <= 154.69) and (AFDP > 3.846) -> 65.916,\n", " if (AT <= 16.958) and (AT > 10.438) and (TEY > 118.405) and (TEY <= 154.69) and (AFDP > 3.846) -> 70.244,\n", " if (AT <= 16.958) and (TEY > 118.405) and (AP <= 1021.95) and (AFDP <= 4.022) -> 66.765,\n", " if (AT <= 16.958) and (AT > 14.497) and (TEY > 118.405) and (AP <= 1021.95) and (AFDP <= 4.022) -> 72.379,\n", " if (AT <= 16.958) and (TEY > 118.405) and (AP <= 1021.95) and (AP > 1010.05) and (AFDP <= 4.022) -> 62.562,\n", " if (AT <= 16.958) and (AT > 14.172) and (TEY > 118.405) and (AP <= 1021.95) and (AP > 1010.05) and (AFDP <= 4.022) -> 57.232,\n", " if (AT <= 16.958) and (TEY > 118.405) and (TEY <= 161.49) and (AP <= 1021.95) and (AFDP > 4.022) -> 71.066,\n", " if (AT <= 16.958) and (TEY > 118.405) and (AP <= 1021.95) and (AFDP > 4.022) -> 77.453,\n", " if (AT <= 16.958) and (TEY > 118.405) and (AP <= 1021.95) and (AP > 1003.35) and (AFDP > 4.022) -> 67.79,\n", " if (AT <= 16.958) and (AT > 12.797) and (TEY > 118.405) and (AP <= 1021.95) and (AP > 1003.35) and (AFDP > 4.022) -> 66.014,\n", " if (AT <= 16.958) and (TEY > 118.405) and (AP > 1021.95) and (AP <= 1028.65) and (AFDP <= 4.639) -> 62.992,\n", " if (AT <= 16.958) and (TEY > 118.405) and (AP > 1021.95) and (AP <= 1028.65) and (AFDP <= 4.639) and (AFDP > 4.464) -> 55.182,\n", " if (AT <= 16.958) and (TEY > 118.405) and (AP > 1021.95) and (AP <= 1028.65) and (AFDP > 4.639) and (AFDP <= 4.678) -> 80.146,\n", " if (AT <= 16.958) and (TEY > 118.405) and (AP > 1021.95) and (AP <= 1028.65) and (AFDP > 4.639) -> 64.14,\n", " if (AT <= 16.958) and (TEY > 118.405) and (AP > 1021.95) and (AFDP <= 4.31) -> 59.322,\n", " if (AT <= 16.958) and (AT > 7.169) and (TEY > 118.405) and (AP > 1021.95) and (AFDP <= 4.31) -> 68.586,\n", " if (AT <= 16.958) and (TEY <= 118.405) and (AFDP <= 3.081) and (AFDP > 2.544) -> 93.814,\n", " if (AT <= 16.958) and (TEY > 118.405) and (AP > 1021.95) and (AFDP > 4.31) -> 60.608,\n", " if (AT > 16.958) and (AT <= 20.418) and (TEY <= 131.885) and (AFDP <= 2.245) -> 110.76,\n", " if (AT > 16.958) and (AT <= 20.418) and (TEY <= 131.885) and (AFDP <= 2.245) -> 115.68,\n", " if (AT > 16.958) and (AT <= 20.418) and (TEY <= 131.885) and (AFDP > 2.245) and (AFDP <= 2.65) -> 79.894,\n", " if (AT > 16.958) and (AT <= 20.418) and (TEY <= 131.885) and (TEY > 100.905) and (AFDP > 2.245) and (AFDP <= 2.65) -> 64.112,\n", " if (AT > 16.958) and (AT <= 20.418) and (TEY <= 131.885) and (AFDP > 2.245) -> 82.01,\n", " if (AT > 16.958) and (AT <= 20.418) and (TEY <= 131.885) and (AFDP > 2.245) -> 72.418,\n", " if (AT > 16.958) and (AT <= 20.418) and (TEY <= 131.885) and (TEY > 106.33) and (AFDP <= 3.911) -> 62.177,\n", " if (AT > 16.958) and (AT <= 20.418) and (TEY <= 131.885) and (TEY > 106.33) and (AFDP <= 3.911) -> 58.251,\n", " if (AT > 16.958) and (AT <= 20.418) and (TEY <= 131.885) and (TEY > 106.33) and (AFDP <= 3.911) -> 75.858,\n", " if (AT > 16.958) and (AT <= 20.418) and (TEY <= 131.885) and (TEY > 106.33) and (AFDP <= 3.911) and (AFDP > 3.235) -> 53.854,\n", " if (AT > 16.958) and (AT <= 20.418) and (TEY <= 131.885) and (TEY > 106.33) and (AFDP > 3.911) and (AFDP <= 4.411) -> 69.744,\n", " if (AT > 16.958) and (AT <= 20.418) and (TEY <= 131.885) and (TEY > 106.33) and (AFDP > 3.911) -> 64.687,\n", " if (AT > 16.958) and (AT <= 20.418) and (TEY <= 131.885) and (TEY > 106.33) and (AFDP > 3.911) and (AFDP <= 4.543) -> 59.585,\n", " if (AT > 16.958) and (AT <= 20.418) and (TEY <= 131.885) and (TEY > 106.33) and (AFDP > 3.911) -> 68.111,\n", " if (AT > 16.958) and (AT <= 22.746) and (TEY <= 131.885) and (AFDP <= 4.251) -> 65.687,\n", " if (AT <= 16.958) and (TEY <= 118.405) and (AFDP <= 3.081) -> 102.293,\n", " if (AT > 16.958) and (AT <= 22.746) and (TEY <= 131.885) and (TEY > 103.75) and (AFDP <= 4.251) -> 55.441,\n", " if (AT > 16.958) and (AT <= 22.746) and (TEY <= 131.885) and (TEY > 103.75) and (AFDP <= 4.251) -> 52.864,\n", " if (AT > 16.958) and (AT <= 22.746) and (TEY <= 131.885) and (AFDP > 4.251) and (AFDP <= 4.966) -> 64.037,\n", " if (AT > 16.958) and (AT <= 22.746) and (TEY <= 131.885) and (TEY > 121.69) and (AFDP > 4.251) and (AFDP <= 4.966) -> 58.089,\n", " if (AT > 16.958) and (AT <= 22.746) and (TEY <= 131.885) and (AFDP > 4.251) and (AFDP <= 5.136) -> 67.764,\n", " if (AT > 16.958) and (AT <= 22.746) and (TEY <= 131.885) and (AFDP > 4.251) -> 82.857,\n", " if (AT > 16.958) and (TEY <= 131.885) and (AFDP <= 4.344) and (AP <= 1013.35) -> 50.906,\n", " if (AT > 16.958) and (TEY <= 131.885) and (AFDP <= 4.344) and (AP > 1013.35) -> 53.606,\n", " if (AT > 16.958) and (AT <= 28.021) and (TEY <= 131.885) and (TEY > 129.995) and (AFDP <= 4.344) -> 54.921,\n", " if (AT > 16.958) and (TEY <= 131.885) and (TEY > 129.995) and (AFDP <= 4.344) -> 58.587,\n", " if (AT > 16.958) and (TEY <= 131.885) and (AFDP > 4.344) and (AFDP <= 4.739) and (AP <= 1015.2) -> 56.411,\n", " if (AT > 16.958) and (TEY <= 131.885) and (AFDP > 4.344) and (AP <= 1015.2) -> 61.151,\n", " if (AT > 16.958) and (AT <= 24.312) and (TEY <= 131.885) and (AFDP > 4.344) and (AP > 1015.2) -> 56.769,\n", " if (AT > 16.958) and (TEY <= 131.885) and (AFDP > 4.344) and (AP > 1015.2) -> 75.097,\n", " if (AT > 16.958) and (TEY > 131.885) and (TEY <= 136.645) and (AFDP <= 3.449) and (AP <= 1019.8) -> 72.684,\n", " if (AT > 16.958) and (TEY > 131.885) and (TEY <= 136.645) and (AFDP <= 3.449) and (AFDP > 3.245) and (AP <= 1019.8) -> 81.186,\n", " if (AT > 16.958) and (TEY > 131.885) and (TEY <= 136.645) and (AFDP <= 3.449) and (AP <= 1019.8) and (AP > 1003.0) -> 69.3,\n", " if (AT > 16.958) and (TEY > 131.885) and (TEY <= 136.645) and (AFDP <= 3.449) and (AP <= 1019.8) and (AP > 1003.0) -> 63.882,\n", " if (AT > 16.958) and (AT <= 18.052) and (TEY > 131.885) and (TEY <= 136.645) and (AFDP <= 3.449) and (AP > 1019.8) -> 82.823,\n", " if (AT > 16.958) and (TEY > 131.885) and (TEY <= 136.645) and (AFDP <= 3.449) and (AP > 1019.8) -> 91.663,\n", " if (AT > 16.958) and (AT <= 20.688) and (TEY > 131.885) and (TEY <= 136.645) and (AFDP <= 3.449) and (AFDP > 3.309) and (AP > 1019.8) -> 67.318,\n", " if (AT > 16.958) and (TEY > 131.885) and (TEY <= 136.645) and (AFDP <= 3.449) and (AFDP > 3.309) and (AP > 1019.8) -> 90.607,\n", " if (AT > 16.958) and (TEY > 131.885) and (TEY <= 136.645) and (AFDP > 3.449) and (AFDP <= 4.756) and (AP <= 999.625) -> 63.255,\n", " if (AT > 16.958) and (TEY > 131.885) and (TEY <= 136.645) and (AFDP > 3.449) and (AFDP <= 4.756) and (AP <= 999.625) -> 76.434,\n", " if (AT > 16.958) and (TEY > 131.885) and (TEY <= 136.645) and (AFDP > 3.449) and (AFDP <= 4.756) and (AP > 999.625) -> 58.258,\n", " if (AT > 16.958) and (TEY > 131.885) and (TEY <= 136.645) and (AFDP > 3.449) and (AFDP <= 4.756) and (AP > 999.625) -> 64.093,\n", " if (AT > 16.958) and (AT <= 24.071) and (TEY > 131.885) and (TEY <= 136.645) and (AFDP > 3.449) and (AP <= 1014.35) -> 70.079,\n", " if (AT > 16.958) and (AT <= 24.071) and (TEY > 131.885) and (TEY <= 136.645) and (AFDP > 3.449) and (AP > 1014.35) -> 74.157,\n", " if (AT > 16.958) and (AT <= 29.795) and (TEY > 131.885) and (TEY <= 136.645) and (AFDP > 3.449) -> 63.817,\n", " if (AT > 16.958) and (TEY > 131.885) and (TEY <= 136.645) and (AFDP > 3.449) -> 68.328,\n", " if (AT > 16.958) and (AT <= 21.703) and (TEY > 131.885) and (AP <= 1020.65) and (AFDP <= 5.596) -> 64.124,\n", " if (AT > 16.958) and (AT <= 21.703) and (TEY > 131.885) and (AP <= 1020.65) and (AFDP <= 5.596) and (AFDP > 4.179) -> 59.091,\n", " if (AT > 16.958) and (AT <= 21.703) and (TEY > 131.885) and (TEY <= 152.045) and (AP <= 1020.65) and (AFDP > 5.596) -> 71.027,\n", " if (AT > 16.958) and (AT <= 21.703) and (TEY > 131.885) and (AP <= 1020.65) and (AFDP > 5.596) -> 67.105,\n", " if (AT > 16.958) and (AT <= 21.703) and (TEY > 131.885) and (TEY <= 151.015) and (AP > 1020.65) and (AFDP <= 4.18) -> 97.001,\n", " if (AT > 16.958) and (AT <= 21.703) and (TEY > 131.885) and (AP > 1020.65) and (AFDP <= 4.18) -> 76.428,\n", " if (AT > 16.958) and (AT <= 21.703) and (TEY > 131.885) and (TEY <= 150.695) and (AP > 1020.65) and (AFDP > 4.18) -> 70.498,\n", " if (AT > 16.958) and (AT <= 21.703) and (TEY > 131.885) and (AP > 1020.65) and (AFDP > 4.18) -> 56.017,\n", " if (AT > 16.958) and (TEY > 131.885) and (TEY <= 143.725) and (AFDP <= 5.597) and (AP <= 1019.05) -> 59.741,\n", " if (AT > 16.958) and (TEY > 131.885) and (AFDP <= 5.597) and (AP <= 1019.05) -> 55.512,\n", " if (AT > 16.958) and (TEY > 131.885) and (AFDP <= 5.597) and (AP > 1019.05) -> 89.381,\n", " if (AT > 16.958) and (TEY > 131.885) and (AFDP <= 5.597) and (AFDP > 4.114) and (AP > 1019.05) -> 65.239,\n", " if (AT > 16.958) and (AT <= 25.345) and (TEY > 131.885) and (AFDP > 5.597) and (AP <= 1014.35) -> 67.084,\n", " if (AT > 16.958) and (TEY > 131.885) and (AFDP > 5.597) and (AP <= 1014.35) -> 59.906,\n", " if (AT > 16.958) and (TEY > 131.885) and (AFDP > 5.597) and (AFDP <= 5.819) and (AP > 1014.35) -> 58.17,\n", " if (AT > 16.958) and (TEY > 131.885) and (AFDP > 5.597) and (AP > 1014.35) -> 72.443]" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from src.rules import normalise_rules\n", "\n", "\n", "rules = normalise_rules(rules)\n", "display(len(rules))\n", "rules" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "106" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "[if (AT <= 16.958) and (TEY > 118.405) and (AP > 1021.95) and (AFDP > 4.31) and (AFDP <= 4.999) -> 50.781,\n", " if (AT <= 16.958) and (TEY <= 118.405) and (AFDP <= 3.081) and (AFDP > 2.684) and (AP <= 1004.5) -> 65.69,\n", " if (AT <= 16.958) and (TEY <= 118.405) and (AFDP <= 3.081) and (AFDP > 2.684) and (AP > 1004.5) -> 84.697,\n", " if (AT <= 16.958) and (TEY <= 118.405) and (AFDP > 3.081) and (AP <= 1027.6) -> 105.703,\n", " if (AT <= 16.958) and (TEY <= 118.405) and (AFDP > 3.081) and (AP > 1027.6) -> 94.802,\n", " if (AT <= 16.958) and (AT > 6.44) and (TEY <= 118.405) and (AFDP > 3.081) and (AP <= 1022.35) -> 92.554,\n", " if (AT <= 16.958) and (AT > 6.44) and (TEY <= 118.405) and (AFDP > 3.081) and (AP > 1022.35) -> 104.667,\n", " if (AT <= 16.958) and (AT > 7.752) and (TEY <= 118.405) and (AP <= 1019.95) and (AFDP <= 3.304) -> 100.502,\n", " if (AT <= 16.958) and (AT > 7.752) and (TEY <= 118.405) and (AP <= 1019.95) and (AFDP > 3.304) -> 118.67,\n", " if (AT <= 16.958) and (AT > 7.752) and (TEY <= 118.405) and (AP > 1019.95) -> 112.907,\n", " if (AT <= 16.958) and (AT > 7.752) and (TEY <= 118.405) and (TEY > 105.73) and (AFDP <= 3.07) -> 86.027,\n", " if (AT <= 16.958) and (AT > 7.752) and (TEY <= 118.405) and (TEY > 105.73) and (AFDP > 3.07) -> 92.86,\n", " if (AT <= 16.958) and (AT > 7.752) and (TEY <= 118.405) and (TEY > 105.73) -> 81.728,\n", " if (AT <= 16.958) and (AT > 10.837) and (TEY <= 118.405) and (AFDP <= 3.107) and (AP <= 1017.55) -> 76.472,\n", " if (AT <= 16.958) and (AT > 10.837) and (TEY <= 118.405) and (AFDP <= 3.107) and (AP <= 1017.55) and (AP > 1009.2) -> 85.342,\n", " if (AT <= 16.958) and (AT > 10.837) and (TEY <= 118.405) and (AFDP <= 3.107) and (AP > 1017.55) -> 94.544,\n", " if (AT <= 16.958) and (AT > 10.837) and (TEY <= 118.405) and (AFDP > 3.107) and (AFDP <= 3.427) -> 101.609,\n", " if (AT <= 16.958) and (AT > 10.837) and (TEY <= 118.405) and (AFDP > 3.107) and (AP <= 1019.55) -> 94.654,\n", " if (AT <= 16.958) and (AT > 10.837) and (TEY <= 118.405) and (AFDP > 3.107) and (AP > 1019.55) -> 75.199,\n", " if (AT <= 16.958) and (AT > 10.837) and (TEY <= 118.405) and (TEY > 105.88) and (AFDP <= 3.276) -> 77.011,\n", " if (AT <= 16.958) and (AT > 10.837) and (TEY <= 118.405) and (TEY > 105.88) and (AFDP > 3.276) -> 81.372,\n", " if (AT <= 16.958) and (AT > 10.837) and (TEY <= 118.405) and (TEY > 105.88) and (AFDP <= 3.181) -> 71.136,\n", " if (AT <= 16.958) and (AT > 10.837) and (TEY <= 118.405) and (TEY > 105.88) and (AFDP > 3.181) -> 77.019,\n", " if (AT <= 16.958) and (AT > 10.837) and (TEY <= 118.405) and (TEY > 105.88) and (AFDP <= 3.225) -> 69.023,\n", " if (AT <= 16.958) and (AT > 10.837) and (TEY <= 118.405) and (TEY > 105.88) and (AFDP <= 3.225) and (AFDP > 2.637) -> 63.935,\n", " if (AT <= 16.958) and (AT > 10.837) and (TEY <= 118.405) and (TEY > 105.88) and (AFDP > 3.225) -> 70.631,\n", " if (AT <= 16.958) and (TEY > 118.405) and (TEY <= 154.69) and (AFDP <= 3.694) -> 81.582,\n", " if (AT <= 16.958) and (AT > 7.324) and (TEY > 118.405) and (TEY <= 154.69) and (AFDP <= 3.694) -> 75.621,\n", " if (AT <= 16.958) and (TEY > 118.405) and (TEY <= 154.69) and (AFDP <= 3.694) and (AFDP > 3.077) -> 72.624,\n", " if (AT <= 16.958) and (AT > 7.404) and (TEY > 118.405) and (TEY <= 154.69) and (AFDP <= 3.694) and (AFDP > 3.077) -> 68.878,\n", " if (AT <= 16.958) and (TEY > 118.405) and (TEY <= 154.69) and (AFDP > 3.694) -> 84.573,\n", " if (AT <= 16.958) and (TEY > 118.405) and (TEY <= 154.69) and (AFDP > 3.694) and (AP <= 1026.9) -> 75.408,\n", " if (AT <= 16.958) and (TEY > 118.405) and (TEY <= 154.69) and (AFDP > 3.694) and (AP > 1026.9) -> 65.576,\n", " if (AT <= 16.958) and (AT > 10.438) and (TEY > 118.405) and (TEY <= 154.69) and (AFDP <= 3.846) and (AP <= 1011.45) -> 72.391,\n", " if (AT <= 16.958) and (AT > 10.438) and (TEY > 118.405) and (TEY <= 154.69) and (AFDP <= 3.846) and (AP > 1011.45) -> 67.358,\n", " if (AT <= 16.958) and (AT > 10.438) and (TEY > 118.405) and (TEY <= 154.69) and (AFDP <= 3.846) and (AFDP > 3.223) -> 63.281,\n", " if (AT <= 16.958) and (AT > 10.438) and (TEY > 118.405) and (TEY <= 154.69) and (AFDP > 3.846) -> 70.778,\n", " if (AT <= 16.958) and (TEY > 118.405) and (AP <= 1021.95) and (AFDP <= 4.022) -> 66.765,\n", " if (AT <= 16.958) and (AT > 14.497) and (TEY > 118.405) and (AP <= 1021.95) and (AFDP <= 4.022) -> 72.379,\n", " if (AT <= 16.958) and (TEY > 118.405) and (AP <= 1021.95) and (AP > 1010.05) and (AFDP <= 4.022) -> 62.562,\n", " if (AT <= 16.958) and (AT > 14.172) and (TEY > 118.405) and (AP <= 1021.95) and (AP > 1010.05) and (AFDP <= 4.022) -> 57.232,\n", " if (AT <= 16.958) and (TEY > 118.405) and (TEY <= 161.49) and (AP <= 1021.95) and (AFDP > 4.022) -> 71.066,\n", " if (AT <= 16.958) and (TEY > 118.405) and (AP <= 1021.95) and (AFDP > 4.022) -> 77.453,\n", " if (AT <= 16.958) and (TEY > 118.405) and (AP <= 1021.95) and (AP > 1003.35) and (AFDP > 4.022) -> 67.79,\n", " if (AT <= 16.958) and (AT > 12.797) and (TEY > 118.405) and (AP <= 1021.95) and (AP > 1003.35) and (AFDP > 4.022) -> 66.014,\n", " if (AT <= 16.958) and (TEY > 118.405) and (AP > 1021.95) and (AP <= 1028.65) and (AFDP <= 4.639) -> 62.992,\n", " if (AT <= 16.958) and (TEY > 118.405) and (AP > 1021.95) and (AP <= 1028.65) and (AFDP <= 4.639) and (AFDP > 4.464) -> 55.182,\n", " if (AT <= 16.958) and (TEY > 118.405) and (AP > 1021.95) and (AP <= 1028.65) and (AFDP > 4.639) and (AFDP <= 4.678) -> 80.146,\n", " if (AT <= 16.958) and (TEY > 118.405) and (AP > 1021.95) and (AP <= 1028.65) and (AFDP > 4.639) -> 64.14,\n", " if (AT <= 16.958) and (TEY > 118.405) and (AP > 1021.95) and (AFDP <= 4.31) -> 59.322,\n", " if (AT <= 16.958) and (AT > 7.169) and (TEY > 118.405) and (AP > 1021.95) and (AFDP <= 4.31) -> 68.586,\n", " if (AT <= 16.958) and (TEY <= 118.405) and (AFDP <= 3.081) and (AFDP > 2.544) -> 93.814,\n", " if (AT <= 16.958) and (TEY > 118.405) and (AP > 1021.95) and (AFDP > 4.31) -> 60.608,\n", " if (AT > 16.958) and (AT <= 20.418) and (TEY <= 131.885) and (AFDP <= 2.245) -> 113.22,\n", " if (AT > 16.958) and (AT <= 20.418) and (TEY <= 131.885) and (AFDP > 2.245) and (AFDP <= 2.65) -> 79.894,\n", " if (AT > 16.958) and (AT <= 20.418) and (TEY <= 131.885) and (TEY > 100.905) and (AFDP > 2.245) and (AFDP <= 2.65) -> 64.112,\n", " if (AT > 16.958) and (AT <= 20.418) and (TEY <= 131.885) and (AFDP > 2.245) -> 77.214,\n", " if (AT > 16.958) and (AT <= 20.418) and (TEY <= 131.885) and (TEY > 106.33) and (AFDP <= 3.911) -> 65.429,\n", " if (AT > 16.958) and (AT <= 20.418) and (TEY <= 131.885) and (TEY > 106.33) and (AFDP <= 3.911) and (AFDP > 3.235) -> 53.854,\n", " if (AT > 16.958) and (AT <= 20.418) and (TEY <= 131.885) and (TEY > 106.33) and (AFDP > 3.911) and (AFDP <= 4.411) -> 69.744,\n", " if (AT > 16.958) and (AT <= 20.418) and (TEY <= 131.885) and (TEY > 106.33) and (AFDP > 3.911) and (AFDP <= 4.543) -> 59.585,\n", " if (AT > 16.958) and (AT <= 20.418) and (TEY <= 131.885) and (TEY > 106.33) and (AFDP > 3.911) -> 66.399,\n", " if (AT > 16.958) and (AT <= 22.746) and (TEY <= 131.885) and (AFDP <= 4.251) -> 55.716,\n", " if (AT <= 16.958) and (TEY <= 118.405) and (AFDP <= 3.081) -> 102.293,\n", " if (AT > 16.958) and (AT <= 22.746) and (TEY <= 131.885) and (TEY > 103.75) and (AFDP <= 4.251) -> 54.153,\n", " if (AT > 16.958) and (AT <= 22.746) and (TEY <= 131.885) and (AFDP > 4.251) and (AFDP <= 4.966) -> 64.037,\n", " if (AT > 16.958) and (AT <= 22.746) and (TEY <= 131.885) and (TEY > 121.69) and (AFDP > 4.251) and (AFDP <= 4.966) -> 58.089,\n", " if (AT > 16.958) and (AT <= 22.746) and (TEY <= 131.885) and (AFDP > 4.251) and (AFDP <= 5.136) -> 67.764,\n", " if (AT > 16.958) and (AT <= 22.746) and (TEY <= 131.885) and (AFDP > 4.251) -> 82.857,\n", " if (AT > 16.958) and (TEY <= 131.885) and (AFDP <= 4.344) and (AP <= 1013.35) -> 50.906,\n", " if (AT > 16.958) and (TEY <= 131.885) and (AFDP <= 4.344) and (AP > 1013.35) -> 53.606,\n", " if (AT > 16.958) and (AT <= 28.021) and (TEY <= 131.885) and (TEY > 129.995) and (AFDP <= 4.344) -> 54.921,\n", " if (AT > 16.958) and (TEY <= 131.885) and (TEY > 129.995) and (AFDP <= 4.344) -> 58.587,\n", " if (AT > 16.958) and (TEY <= 131.885) and (AFDP > 4.344) and (AFDP <= 4.739) and (AP <= 1015.2) -> 56.411,\n", " if (AT > 16.958) and (TEY <= 131.885) and (AFDP > 4.344) and (AP <= 1015.2) -> 61.151,\n", " if (AT > 16.958) and (AT <= 24.312) and (TEY <= 131.885) and (AFDP > 4.344) and (AP > 1015.2) -> 56.769,\n", " if (AT > 16.958) and (TEY <= 131.885) and (AFDP > 4.344) and (AP > 1015.2) -> 75.097,\n", " if (AT > 16.958) and (TEY > 131.885) and (TEY <= 136.645) and (AFDP <= 3.449) and (AP <= 1019.8) -> 72.684,\n", " if (AT > 16.958) and (TEY > 131.885) and (TEY <= 136.645) and (AFDP <= 3.449) and (AFDP > 3.245) and (AP <= 1019.8) -> 81.186,\n", " if (AT > 16.958) and (TEY > 131.885) and (TEY <= 136.645) and (AFDP <= 3.449) and (AP <= 1019.8) and (AP > 1003.0) -> 66.591,\n", " if (AT > 16.958) and (AT <= 18.052) and (TEY > 131.885) and (TEY <= 136.645) and (AFDP <= 3.449) and (AP > 1019.8) -> 82.823,\n", " if (AT > 16.958) and (TEY > 131.885) and (TEY <= 136.645) and (AFDP <= 3.449) and (AP > 1019.8) -> 91.663,\n", " if (AT > 16.958) and (AT <= 20.688) and (TEY > 131.885) and (TEY <= 136.645) and (AFDP <= 3.449) and (AFDP > 3.309) and (AP > 1019.8) -> 67.318,\n", " if (AT > 16.958) and (TEY > 131.885) and (TEY <= 136.645) and (AFDP <= 3.449) and (AFDP > 3.309) and (AP > 1019.8) -> 90.607,\n", " if (AT > 16.958) and (TEY > 131.885) and (TEY <= 136.645) and (AFDP > 3.449) and (AFDP <= 4.756) and (AP <= 999.625) -> 69.844,\n", " if (AT > 16.958) and (TEY > 131.885) and (TEY <= 136.645) and (AFDP > 3.449) and (AFDP <= 4.756) and (AP > 999.625) -> 61.175,\n", " if (AT > 16.958) and (AT <= 24.071) and (TEY > 131.885) and (TEY <= 136.645) and (AFDP > 3.449) and (AP <= 1014.35) -> 70.079,\n", " if (AT > 16.958) and (AT <= 24.071) and (TEY > 131.885) and (TEY <= 136.645) and (AFDP > 3.449) and (AP > 1014.35) -> 74.157,\n", " if (AT > 16.958) and (AT <= 29.795) and (TEY > 131.885) and (TEY <= 136.645) and (AFDP > 3.449) -> 63.817,\n", " if (AT > 16.958) and (TEY > 131.885) and (TEY <= 136.645) and (AFDP > 3.449) -> 68.328,\n", " if (AT > 16.958) and (AT <= 21.703) and (TEY > 131.885) and (AP <= 1020.65) and (AFDP <= 5.596) -> 64.124,\n", " if (AT > 16.958) and (AT <= 21.703) and (TEY > 131.885) and (AP <= 1020.65) and (AFDP <= 5.596) and (AFDP > 4.179) -> 59.091,\n", " if (AT > 16.958) and (AT <= 21.703) and (TEY > 131.885) and (TEY <= 152.045) and (AP <= 1020.65) and (AFDP > 5.596) -> 71.027,\n", " if (AT > 16.958) and (AT <= 21.703) and (TEY > 131.885) and (AP <= 1020.65) and (AFDP > 5.596) -> 67.105,\n", " if (AT > 16.958) and (AT <= 21.703) and (TEY > 131.885) and (TEY <= 151.015) and (AP > 1020.65) and (AFDP <= 4.18) -> 97.001,\n", " if (AT > 16.958) and (AT <= 21.703) and (TEY > 131.885) and (AP > 1020.65) and (AFDP <= 4.18) -> 76.428,\n", " if (AT > 16.958) and (AT <= 21.703) and (TEY > 131.885) and (TEY <= 150.695) and (AP > 1020.65) and (AFDP > 4.18) -> 70.498,\n", " if (AT > 16.958) and (AT <= 21.703) and (TEY > 131.885) and (AP > 1020.65) and (AFDP > 4.18) -> 56.017,\n", " if (AT > 16.958) and (TEY > 131.885) and (TEY <= 143.725) and (AFDP <= 5.597) and (AP <= 1019.05) -> 59.741,\n", " if (AT > 16.958) and (TEY > 131.885) and (AFDP <= 5.597) and (AP <= 1019.05) -> 55.512,\n", " if (AT > 16.958) and (TEY > 131.885) and (AFDP <= 5.597) and (AP > 1019.05) -> 89.381,\n", " if (AT > 16.958) and (TEY > 131.885) and (AFDP <= 5.597) and (AFDP > 4.114) and (AP > 1019.05) -> 65.239,\n", " if (AT > 16.958) and (AT <= 25.345) and (TEY > 131.885) and (AFDP > 5.597) and (AP <= 1014.35) -> 67.084,\n", " if (AT > 16.958) and (TEY > 131.885) and (AFDP > 5.597) and (AP <= 1014.35) -> 59.906,\n", " if (AT > 16.958) and (TEY > 131.885) and (AFDP > 5.597) and (AFDP <= 5.819) and (AP > 1014.35) -> 58.17,\n", " if (AT > 16.958) and (TEY > 131.885) and (AFDP > 5.597) and (AP > 1014.35) -> 72.443]" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from src.rules import delete_same_rules\n", "\n", "\n", "rules = delete_same_rules(rules)\n", "display(len(rules))\n", "rules" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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ATAPAFDPTEY
448026.63501009.74.4137147.37
2488320.22801016.24.6238154.71
2155715.63301018.54.0899154.26
170516.65401020.24.5755132.60
2138821.00201004.34.1101153.48
...............
2572517.51901015.93.6809125.22
501421.97801014.43.1246110.81
225844.71031003.03.2741127.67
5016.77581008.35.1192166.46
2082817.67301020.73.0370127.08
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29386 rows × 4 columns

\n", "
" ], "text/plain": [ " AT AP AFDP TEY\n", "4480 26.6350 1009.7 4.4137 147.37\n", "24883 20.2280 1016.2 4.6238 154.71\n", "21557 15.6330 1018.5 4.0899 154.26\n", "1705 16.6540 1020.2 4.5755 132.60\n", "21388 21.0020 1004.3 4.1101 153.48\n", "... ... ... ... ...\n", "25725 17.5190 1015.9 3.6809 125.22\n", "5014 21.9780 1014.4 3.1246 110.81\n", "22584 4.7103 1003.0 3.2741 127.67\n", "501 6.7758 1008.3 5.1192 166.46\n", "20828 17.6730 1020.7 3.0370 127.08\n", "\n", "[29386 rows x 4 columns]" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "4480 52.970\n", "24883 58.801\n", "21557 78.066\n", "1705 73.955\n", "21388 79.989\n", " ... \n", "25725 53.424\n", "5014 58.462\n", "22584 65.275\n", "501 65.469\n", "20828 77.008\n", "Name: NOX, Length: 29386, dtype: float64" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
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ATAPAFDPTEY
1824623.45301006.23.7535132.47
2034328.70901011.26.0321145.91
292421.83301017.03.9663139.04
1177.81671022.24.6605164.73
571319.91201013.13.6710126.90
...............
219179.57911017.52.9617130.74
1309922.61501012.14.2739133.44
2670428.40201004.44.0643123.17
418231.74001012.24.5323148.16
298223.71301013.53.7112134.75
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7347 rows × 4 columns

\n", "
" ], "text/plain": [ " AT AP AFDP TEY\n", "18246 23.4530 1006.2 3.7535 132.47\n", "20343 28.7090 1011.2 6.0321 145.91\n", "2924 21.8330 1017.0 3.9663 139.04\n", "117 7.8167 1022.2 4.6605 164.73\n", "5713 19.9120 1013.1 3.6710 126.90\n", "... ... ... ... ...\n", "21917 9.5791 1017.5 2.9617 130.74\n", "13099 22.6150 1012.1 4.2739 133.44\n", "26704 28.4020 1004.4 4.0643 123.17\n", "4182 31.7400 1012.2 4.5323 148.16\n", "2982 23.7130 1013.5 3.7112 134.75\n", "\n", "[7347 rows x 4 columns]" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "18246 58.948\n", "20343 62.909\n", "2924 61.083\n", "117 66.369\n", "5713 56.675\n", " ... \n", "21917 71.316\n", "13099 63.308\n", "26704 49.210\n", "4182 59.452\n", "2982 66.807\n", "Name: NOX, Length: 7347, dtype: float64" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from sklearn.model_selection import train_test_split\n", "\n", "random_state = 9\n", "\n", "y = data[\"NOX\"]\n", "X = data.drop([\"NOX\"], axis=1).copy()\n", "X_train, X_test, y_train, y_test = train_test_split(\n", " X, y, test_size=0.2, random_state=random_state\n", ")\n", "display(X_train, y_train, X_test, y_test)" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[if (AT = -6.235) and (TEY = 179.5) and (AP = 1036.6) and (AFDP = 4.654) -> 50.781,\n", " if (AT = -6.235) and (TEY = 100.02) and (AFDP = 2.883) and (AP = 985.85) -> 65.69,\n", " if (AT = -6.235) and (TEY = 100.02) and (AFDP = 2.883) and (AP = 1036.6) -> 84.697,\n", " if (AT = -6.235) and (TEY = 100.02) and (AFDP = 7.611) and (AP = 985.85) -> 105.703,\n", " if (AT = -6.235) and (TEY = 100.02) and (AFDP = 7.611) and (AP = 1036.6) -> 94.802,\n", " if (AT = 11.699) and (TEY = 100.02) and (AFDP = 7.611) and (AP = 985.85) -> 92.554,\n", " if (AT = 11.699) and (TEY = 100.02) and (AFDP = 7.611) and (AP = 1036.6) -> 104.667,\n", " if (AT = 12.355) and (TEY = 100.02) and (AP = 985.85) and (AFDP = 2.099) -> 100.502,\n", " if (AT = 12.355) and (TEY = 100.02) and (AP = 985.85) and (AFDP = 7.611) -> 118.67,\n", " if (AT = 12.355) and (TEY = 100.02) and (AP = 1036.6) -> 112.907,\n", " if (AT = 12.355) and (TEY = 112.068) and (AFDP = 2.099) -> 86.027,\n", " if (AT = 12.355) and (TEY = 112.068) and (AFDP = 7.611) -> 92.86,\n", " if (AT = 12.355) and (TEY = 112.068) -> 81.728,\n", " if (AT = 13.898) and (TEY = 100.02) and (AFDP = 2.099) and (AP = 985.85) -> 76.472,\n", " if (AT = 13.898) and (TEY = 100.02) and (AFDP = 2.099) and (AP = 1013.375) -> 85.342,\n", " if (AT = 13.898) and (TEY = 100.02) and (AFDP = 2.099) and (AP = 1036.6) -> 94.544,\n", " if (AT = 13.898) and (TEY = 100.02) and (AFDP = 3.267) -> 101.609,\n", " if (AT = 13.898) and (TEY = 100.02) and (AFDP = 7.611) and (AP = 985.85) -> 94.654,\n", " if (AT = 13.898) and (TEY = 100.02) and (AFDP = 7.611) and (AP = 1036.6) -> 75.199,\n", " if (AT = 13.898) and (TEY = 112.142) and (AFDP = 2.099) -> 77.011,\n", " if (AT = 13.898) and (TEY = 112.142) and (AFDP = 7.611) -> 81.372,\n", " if (AT = 13.898) and (TEY = 112.142) and (AFDP = 2.099) -> 71.136,\n", " if (AT = 13.898) and (TEY = 112.142) and (AFDP = 7.611) -> 77.019,\n", " if (AT = 13.898) and (TEY = 112.142) and (AFDP = 2.099) -> 69.023,\n", " if (AT = 13.898) and (TEY = 112.142) and (AFDP = 2.931) -> 63.935,\n", " if (AT = 13.898) and (TEY = 112.142) and (AFDP = 7.611) -> 70.631,\n", " if (AT = -6.235) and (TEY = 136.547) and (AFDP = 2.099) -> 81.582,\n", " if (AT = 12.141) and (TEY = 136.547) and (AFDP = 2.099) -> 75.621,\n", " if (AT = -6.235) and (TEY = 136.547) and (AFDP = 3.386) -> 72.624,\n", " if (AT = 12.181) and (TEY = 136.547) and (AFDP = 3.386) -> 68.878,\n", " if (AT = -6.235) and (TEY = 136.547) and (AFDP = 7.611) -> 84.573,\n", " if (AT = -6.235) and (TEY = 136.547) and (AFDP = 7.611) and (AP = 985.85) -> 75.408,\n", " if (AT = -6.235) and (TEY = 136.547) and (AFDP = 7.611) and (AP = 1036.6) -> 65.576,\n", " if (AT = 13.698) and (TEY = 136.547) and (AFDP = 2.099) and (AP = 985.85) -> 72.391,\n", " if (AT = 13.698) and (TEY = 136.547) and (AFDP = 2.099) and (AP = 1036.6) -> 67.358,\n", " if (AT = 13.698) and (TEY = 136.547) and (AFDP = 3.534) -> 63.281,\n", " if (AT = 13.698) and (TEY = 136.547) and (AFDP = 7.611) -> 70.778,\n", " if (AT = -6.235) and (TEY = 179.5) and (AP = 985.85) and (AFDP = 2.099) -> 66.765,\n", " if (AT = 15.728) and (TEY = 179.5) and (AP = 985.85) and (AFDP = 2.099) -> 72.379,\n", " if (AT = -6.235) and (TEY = 179.5) and (AP = 1016.0) and (AFDP = 2.099) -> 62.562,\n", " if (AT = 15.565) and (TEY = 179.5) and (AP = 1016.0) and (AFDP = 2.099) -> 57.232,\n", " if (AT = -6.235) and (TEY = 139.948) and (AP = 985.85) and (AFDP = 7.611) -> 71.066,\n", " if (AT = -6.235) and (TEY = 179.5) and (AP = 985.85) and (AFDP = 7.611) -> 77.453,\n", " if (AT = -6.235) and (TEY = 179.5) and (AP = 1012.65) and (AFDP = 7.611) -> 67.79,\n", " if (AT = 14.878) and (TEY = 179.5) and (AP = 1012.65) and (AFDP = 7.611) -> 66.014,\n", " if (AT = -6.235) and (TEY = 179.5) and (AP = 1025.3) and (AFDP = 2.099) -> 62.992,\n", " if (AT = -6.235) and (TEY = 179.5) and (AP = 1025.3) and (AFDP = 4.551) -> 55.182,\n", " if (AT = -6.235) and (TEY = 179.5) and (AP = 1025.3) and (AFDP = 4.658) -> 80.146,\n", " if (AT = -6.235) and (TEY = 179.5) and (AP = 1025.3) and (AFDP = 7.611) -> 64.14,\n", " if (AT = -6.235) and (TEY = 179.5) and (AP = 1036.6) and (AFDP = 2.099) -> 59.322,\n", " if (AT = 12.064) and (TEY = 179.5) and (AP = 1036.6) and (AFDP = 2.099) -> 68.586,\n", " if (AT = -6.235) and (TEY = 100.02) and (AFDP = 2.812) -> 93.814,\n", " if (AT = -6.235) and (TEY = 179.5) and (AP = 1036.6) and (AFDP = 7.611) -> 60.608,\n", " if (AT = 18.688) and (TEY = 100.02) and (AFDP = 2.099) -> 113.22,\n", " if (AT = 18.688) and (TEY = 100.02) and (AFDP = 2.448) -> 79.894,\n", " if (AT = 18.688) and (TEY = 116.395) and (AFDP = 2.448) -> 64.112,\n", " if (AT = 18.688) and (TEY = 100.02) and (AFDP = 7.611) -> 77.214,\n", " if (AT = 18.688) and (TEY = 119.108) and (AFDP = 2.099) -> 65.429,\n", " if (AT = 18.688) and (TEY = 119.108) and (AFDP = 3.573) -> 53.854,\n", " if (AT = 18.688) and (TEY = 119.108) and (AFDP = 4.161) -> 69.744,\n", " if (AT = 18.688) and (TEY = 119.108) and (AFDP = 4.227) -> 59.585,\n", " if (AT = 18.688) and (TEY = 119.108) and (AFDP = 7.611) -> 66.399,\n", " if (AT = 19.852) and (TEY = 100.02) and (AFDP = 2.099) -> 55.716,\n", " if (AT = -6.235) and (TEY = 100.02) and (AFDP = 2.099) -> 102.293,\n", " if (AT = 19.852) and (TEY = 117.818) and (AFDP = 2.099) -> 54.153,\n", " if (AT = 19.852) and (TEY = 100.02) and (AFDP = 4.609) -> 64.037,\n", " if (AT = 19.852) and (TEY = 126.788) and (AFDP = 4.609) -> 58.089,\n", " if (AT = 19.852) and (TEY = 100.02) and (AFDP = 4.694) -> 67.764,\n", " if (AT = 19.852) and (TEY = 100.02) and (AFDP = 7.611) -> 82.857,\n", " if (AT = 37.103) and (TEY = 100.02) and (AFDP = 2.099) and (AP = 985.85) -> 50.906,\n", " if (AT = 37.103) and (TEY = 100.02) and (AFDP = 2.099) and (AP = 1036.6) -> 53.606,\n", " if (AT = 22.49) and (TEY = 130.94) and (AFDP = 2.099) -> 54.921,\n", " if (AT = 37.103) and (TEY = 130.94) and (AFDP = 2.099) -> 58.587,\n", " if (AT = 37.103) and (TEY = 100.02) and (AFDP = 4.541) and (AP = 985.85) -> 56.411,\n", " if (AT = 37.103) and (TEY = 100.02) and (AFDP = 7.611) and (AP = 985.85) -> 61.151,\n", " if (AT = 20.635) and (TEY = 100.02) and (AFDP = 7.611) and (AP = 1036.6) -> 56.769,\n", " if (AT = 37.103) and (TEY = 100.02) and (AFDP = 7.611) and (AP = 1036.6) -> 75.097,\n", " if (AT = 37.103) and (TEY = 134.265) and (AFDP = 2.099) and (AP = 985.85) -> 72.684,\n", " if (AT = 37.103) and (TEY = 134.265) and (AFDP = 3.347) and (AP = 985.85) -> 81.186,\n", " if (AT = 37.103) and (TEY = 134.265) and (AFDP = 2.099) and (AP = 1011.4) -> 66.591,\n", " if (AT = 17.505) and (TEY = 134.265) and (AFDP = 2.099) and (AP = 1036.6) -> 82.823,\n", " if (AT = 37.103) and (TEY = 134.265) and (AFDP = 2.099) and (AP = 1036.6) -> 91.663,\n", " if (AT = 18.823) and (TEY = 134.265) and (AFDP = 3.379) and (AP = 1036.6) -> 67.318,\n", " if (AT = 37.103) and (TEY = 134.265) and (AFDP = 3.379) and (AP = 1036.6) -> 90.607,\n", " if (AT = 37.103) and (TEY = 134.265) and (AFDP = 4.103) and (AP = 985.85) -> 69.844,\n", " if (AT = 37.103) and (TEY = 134.265) and (AFDP = 4.103) and (AP = 1036.6) -> 61.175,\n", " if (AT = 20.515) and (TEY = 134.265) and (AFDP = 7.611) and (AP = 985.85) -> 70.079,\n", " if (AT = 20.515) and (TEY = 134.265) and (AFDP = 7.611) and (AP = 1036.6) -> 74.157,\n", " if (AT = 23.377) and (TEY = 134.265) and (AFDP = 7.611) -> 63.817,\n", " if (AT = 37.103) and (TEY = 134.265) and (AFDP = 7.611) -> 68.328,\n", " if (AT = 19.331) and (TEY = 179.5) and (AP = 985.85) and (AFDP = 2.099) -> 64.124,\n", " if (AT = 19.331) and (TEY = 179.5) and (AP = 985.85) and (AFDP = 4.887) -> 59.091,\n", " if (AT = 19.331) and (TEY = 141.965) and (AP = 985.85) and (AFDP = 7.611) -> 71.027,\n", " if (AT = 19.331) and (TEY = 179.5) and (AP = 985.85) and (AFDP = 7.611) -> 67.105,\n", " if (AT = 19.331) and (TEY = 141.45) and (AP = 1036.6) and (AFDP = 2.099) -> 97.001,\n", " if (AT = 19.331) and (TEY = 179.5) and (AP = 1036.6) and (AFDP = 2.099) -> 76.428,\n", " if (AT = 19.331) and (TEY = 141.29) and (AP = 1036.6) and (AFDP = 7.611) -> 70.498,\n", " if (AT = 19.331) and (TEY = 179.5) and (AP = 1036.6) and (AFDP = 7.611) -> 56.017,\n", " if (AT = 37.103) and (TEY = 137.805) and (AFDP = 2.099) and (AP = 985.85) -> 59.741,\n", " if (AT = 37.103) and (TEY = 179.5) and (AFDP = 2.099) and (AP = 985.85) -> 55.512,\n", " if (AT = 37.103) and (TEY = 179.5) and (AFDP = 2.099) and (AP = 1036.6) -> 89.381,\n", " if (AT = 37.103) and (TEY = 179.5) and (AFDP = 4.855) and (AP = 1036.6) -> 65.239,\n", " if (AT = 21.152) and (TEY = 179.5) and (AFDP = 7.611) and (AP = 985.85) -> 67.084,\n", " if (AT = 37.103) and (TEY = 179.5) and (AFDP = 7.611) and (AP = 985.85) -> 59.906,\n", " if (AT = 37.103) and (TEY = 179.5) and (AFDP = 5.708) and (AP = 1036.6) -> 58.17,\n", " if (AT = 37.103) and (TEY = 179.5) and (AFDP = 7.611) and (AP = 1036.6) -> 72.443]" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from src.rules import simplify_rules\n", "\n", "rules = simplify_rules(X_train, rules)\n", "rules" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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countmeanstdmin25%50%75%max
AT36733.017.7127267.447451-6.234811.781017.801023.665037.1030
AP36733.01013.0701656.463346985.85001008.80001012.60001017.00001036.6000
AFDP36733.03.9255180.7739362.08743.35563.93774.37697.6106
TEY36733.0133.50640415.618634100.0200124.4500133.7300144.0800179.5000
NOX36733.065.29306711.67835725.905057.162063.849071.5480119.9100
\n", "
" ], "text/plain": [ " count mean std min 25% 50% \\\n", "AT 36733.0 17.712726 7.447451 -6.2348 11.7810 17.8010 \n", "AP 36733.0 1013.070165 6.463346 985.8500 1008.8000 1012.6000 \n", "AFDP 36733.0 3.925518 0.773936 2.0874 3.3556 3.9377 \n", "TEY 36733.0 133.506404 15.618634 100.0200 124.4500 133.7300 \n", "NOX 36733.0 65.293067 11.678357 25.9050 57.1620 63.8490 \n", "\n", " 75% max \n", "AT 23.6650 37.1030 \n", "AP 1017.0000 1036.6000 \n", "AFDP 4.3769 7.6106 \n", "TEY 144.0800 179.5000 \n", "NOX 71.5480 119.9100 " ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data.describe().transpose()" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import matplotlib.pyplot as plt\n", "\n", "data.hist(bins=30, figsize=(10, 10))\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/Users/user/Projects/python/fuzzy-rules-generator/.venv/lib/python3.12/site-packages/skfuzzy/control/fuzzyvariable.py:125: UserWarning: FigureCanvasAgg is non-interactive, and thus cannot be shown\n", " fig.show()\n" ] }, { "data": { "image/png": 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", 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", 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", 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", 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import numpy as np\n", "from skfuzzy import control as ctrl\n", "import skfuzzy as fuzz\n", "\n", "AT = ctrl.Antecedent(np.arange(-6.2348, 37.103, 0.1), \"AT\")\n", "AP = ctrl.Antecedent(np.arange(985.85, 1036.6, 0.1), \"AP\")\n", "# AH = ctrl.Antecedent(np.arange(24.085, 100.2, 0.001), \"AH\")\n", "AFDP = ctrl.Antecedent(np.arange(2.0874, 7.6106, 0.1), \"AFDP\")\n", "TEY = ctrl.Antecedent(np.arange(100.02, 179.5, 0.1), \"TEY\")\n", "# TIT = ctrl.Antecedent(np.arange(1000.8, 1100.9, 0.1), \"TIT\")\n", "# TAT = ctrl.Antecedent(np.arange(511.04, 550.61, 0.01), \"TAT\")\n", "NOX = ctrl.Consequent(np.arange(25.905, 119.91, 0.1), \"NOX\")\n", "\n", "AT.automf(3, variable_type=\"quant\")\n", "AT.view()\n", "AP.automf(3, variable_type=\"quant\")\n", "AP.view()\n", "# AH.automf(3, variable_type=\"quant\")\n", "AFDP.automf(3, variable_type=\"quant\")\n", "AFDP.view()\n", "TEY.automf(5, variable_type=\"quant\")\n", "TEY.view()\n", "# TIT.automf(5, variable_type=\"quant\")\n", "# TAT.automf(5, variable_type=\"quant\")\n", "# NOX.automf(names=[str(i) for i in range(1, 16)], variable_type=\"quant\")\n", "NOX.automf(5, variable_type=\"quant\")\n", "NOX.view()" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "72" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "[IF ((AT[low] AND TEY[lower]) AND AFDP[low]) AND AP[low] THEN NOX[average]\n", " \tAND aggregation function : fmin\n", " \tOR aggregation function : fmax,\n", " IF ((AT[low] AND TEY[lower]) AND AFDP[low]) AND AP[high] THEN NOX[high]\n", " \tAND aggregation function : fmin\n", " \tOR aggregation function : fmax,\n", " IF ((AT[low] AND TEY[lower]) AND AFDP[high]) AND AP[low] THEN NOX[high]\n", " \tAND aggregation function : fmin\n", " \tOR aggregation function : fmax,\n", " IF ((AT[low] AND TEY[lower]) AND AFDP[high]) AND AP[high] THEN NOX[high]\n", " \tAND aggregation function : fmin\n", " \tOR aggregation function : fmax,\n", " IF ((AT[average] AND TEY[lower]) AND AFDP[high]) AND AP[high] THEN NOX[high]\n", " \tAND aggregation function : fmin\n", " \tOR aggregation function : fmax,\n", " IF ((AT[average] AND TEY[lower]) AND AP[low]) AND AFDP[low] THEN NOX[high]\n", " \tAND aggregation function : fmin\n", " \tOR aggregation function : fmax,\n", " IF ((AT[average] AND TEY[lower]) AND AP[low]) AND AFDP[high] THEN NOX[higher]\n", " \tAND aggregation function : fmin\n", " \tOR aggregation function : fmax,\n", " IF (AT[average] AND TEY[lower]) AND AP[high] THEN NOX[higher]\n", " \tAND aggregation function : fmin\n", " \tOR aggregation function : fmax,\n", " IF (AT[average] AND TEY[low]) AND AFDP[low] THEN NOX[high]\n", " \tAND aggregation function : fmin\n", " \tOR aggregation function : fmax,\n", " IF (AT[average] AND TEY[low]) AND AFDP[high] THEN NOX[high]\n", " \tAND aggregation function : fmin\n", " \tOR aggregation function : fmax,\n", " IF AT[average] AND TEY[low] THEN NOX[average]\n", " \tAND aggregation function : fmin\n", " \tOR aggregation function : fmax,\n", " IF ((AT[average] AND TEY[lower]) AND AFDP[low]) AND AP[low] THEN NOX[average]\n", " \tAND aggregation function : fmin\n", " \tOR aggregation function : fmax,\n", " IF ((AT[average] AND TEY[lower]) AND AFDP[low]) AND AP[average] THEN NOX[high]\n", " \tAND aggregation function : fmin\n", " \tOR aggregation function : fmax,\n", " IF ((AT[average] AND TEY[lower]) AND AFDP[low]) AND AP[high] THEN NOX[high]\n", " \tAND aggregation function : fmin\n", " \tOR aggregation function : fmax,\n", " IF (AT[average] AND TEY[lower]) AND AFDP[low] THEN NOX[high]\n", " \tAND aggregation function : fmin\n", " \tOR aggregation function : fmax,\n", " IF ((AT[average] AND TEY[lower]) AND AFDP[high]) AND AP[low] THEN NOX[high]\n", " \tAND aggregation function : fmin\n", " \tOR aggregation function : fmax,\n", " IF (AT[low] AND TEY[average]) AND AFDP[low] THEN NOX[average]\n", " \tAND aggregation function : fmin\n", " \tOR aggregation function : fmax,\n", " IF (AT[average] AND TEY[average]) AND AFDP[low] THEN NOX[average]\n", " \tAND aggregation function : fmin\n", " \tOR aggregation function : fmax,\n", " IF (AT[low] AND TEY[average]) AND AFDP[high] THEN NOX[average]\n", " \tAND aggregation function : fmin\n", " \tOR aggregation function : fmax,\n", " IF ((AT[low] AND TEY[average]) AND AFDP[high]) AND AP[low] THEN NOX[average]\n", " \tAND aggregation function : fmin\n", " \tOR aggregation function : fmax,\n", " IF ((AT[low] AND TEY[average]) AND AFDP[high]) AND AP[high] THEN NOX[average]\n", " \tAND aggregation function : fmin\n", " \tOR aggregation function : fmax,\n", " IF ((AT[average] AND TEY[average]) AND AFDP[low]) AND AP[low] THEN NOX[average]\n", " \tAND aggregation function : fmin\n", " \tOR aggregation function : fmax,\n", " IF (AT[average] AND TEY[average]) AND AFDP[average] THEN NOX[average]\n", " \tAND aggregation function : fmin\n", " \tOR aggregation function : fmax,\n", " IF ((AT[low] AND TEY[higher]) AND AP[low]) AND AFDP[low] THEN NOX[average]\n", " \tAND aggregation function : fmin\n", " \tOR aggregation function : fmax,\n", " IF ((AT[low] AND TEY[higher]) AND AP[average]) AND AFDP[low] THEN NOX[average]\n", " \tAND aggregation function : fmin\n", " \tOR aggregation function : fmax,\n", " IF ((AT[average] AND TEY[higher]) AND AP[average]) AND AFDP[low] THEN NOX[low]\n", " \tAND aggregation function : fmin\n", " \tOR aggregation function : fmax,\n", " IF ((AT[low] AND TEY[average]) AND AP[low]) AND AFDP[high] THEN NOX[average]\n", " \tAND aggregation function : fmin\n", " \tOR aggregation function : fmax,\n", " IF ((AT[low] AND TEY[higher]) AND AP[low]) AND AFDP[high] THEN NOX[average]\n", " \tAND aggregation function : fmin\n", " \tOR aggregation function : fmax,\n", " IF ((AT[low] AND TEY[higher]) AND AP[average]) AND AFDP[high] THEN NOX[average]\n", " \tAND aggregation function : fmin\n", " \tOR aggregation function : fmax,\n", " IF ((AT[average] AND TEY[higher]) AND AP[average]) AND AFDP[high] THEN NOX[average]\n", " \tAND aggregation function : fmin\n", " \tOR aggregation function : fmax,\n", " IF ((AT[low] AND TEY[higher]) AND AP[high]) AND AFDP[low] THEN NOX[average]\n", " \tAND aggregation function : fmin\n", " \tOR aggregation function : fmax,\n", " IF ((AT[low] AND TEY[higher]) AND AP[high]) AND AFDP[average] THEN NOX[low]\n", " \tAND aggregation function : fmin\n", " \tOR aggregation function : fmax,\n", " IF ((AT[low] AND TEY[higher]) AND AP[high]) AND AFDP[high] THEN NOX[average]\n", " \tAND aggregation function : fmin\n", " \tOR aggregation function : fmax,\n", " IF (AT[average] AND TEY[lower]) AND AFDP[low] THEN NOX[average]\n", " \tAND aggregation function : fmin\n", " \tOR aggregation function : fmax,\n", " IF (AT[average] AND TEY[low]) AND AFDP[average] THEN NOX[low]\n", " \tAND aggregation function : fmin\n", " \tOR aggregation function : fmax,\n", " IF (AT[average] AND TEY[low]) AND AFDP[high] THEN NOX[average]\n", " \tAND aggregation function : fmin\n", " \tOR aggregation function : fmax,\n", " IF (AT[low] AND TEY[lower]) AND AFDP[low] THEN NOX[high]\n", " \tAND aggregation function : fmin\n", " \tOR aggregation function : fmax,\n", " IF (AT[average] AND TEY[low]) AND AFDP[low] THEN NOX[low]\n", " \tAND aggregation function : fmin\n", " \tOR aggregation function : fmax,\n", " IF (AT[average] AND TEY[low]) AND AFDP[average] THEN NOX[low]\n", " \tAND aggregation function : fmin\n", " \tOR aggregation function : fmax,\n", " IF (AT[average] AND TEY[lower]) AND AFDP[average] THEN NOX[average]\n", " \tAND aggregation function : fmin\n", " \tOR aggregation function : fmax,\n", " IF (AT[average] AND TEY[lower]) AND AFDP[high] THEN NOX[average]\n", " \tAND aggregation function : fmin\n", " \tOR aggregation function : fmax,\n", " IF ((AT[high] AND TEY[lower]) AND AFDP[low]) AND AP[low] THEN NOX[low]\n", " \tAND aggregation function : fmin\n", " \tOR aggregation function : fmax,\n", " IF ((AT[high] AND TEY[lower]) AND AFDP[low]) AND AP[high] THEN NOX[low]\n", " \tAND aggregation function : fmin\n", " \tOR aggregation function : fmax,\n", " IF (AT[high] AND TEY[average]) AND AFDP[low] THEN NOX[low]\n", " \tAND aggregation function : fmin\n", " \tOR aggregation function : fmax,\n", " IF ((AT[high] AND TEY[lower]) AND AFDP[average]) AND AP[low] THEN NOX[low]\n", " \tAND aggregation function : fmin\n", " \tOR aggregation function : fmax,\n", " IF ((AT[high] AND TEY[lower]) AND AFDP[high]) AND AP[low] THEN NOX[low]\n", " \tAND aggregation function : fmin\n", " \tOR aggregation function : fmax,\n", " IF ((AT[average] AND TEY[lower]) AND AFDP[high]) AND AP[high] THEN NOX[low]\n", " \tAND aggregation function : fmin\n", " \tOR aggregation function : fmax,\n", " IF ((AT[high] AND TEY[lower]) AND AFDP[high]) AND AP[high] THEN NOX[average]\n", " \tAND aggregation function : fmin\n", " \tOR aggregation function : fmax,\n", " IF ((AT[high] AND TEY[average]) AND AFDP[low]) AND AP[low] THEN NOX[average]\n", " \tAND aggregation function : fmin\n", " \tOR aggregation function : fmax,\n", " IF ((AT[high] AND TEY[average]) AND AFDP[low]) AND AP[average] THEN NOX[average]\n", " \tAND aggregation function : fmin\n", " \tOR aggregation function : fmax,\n", " IF ((AT[average] AND TEY[average]) AND AFDP[low]) AND AP[high] THEN NOX[average]\n", " \tAND aggregation function : fmin\n", " \tOR aggregation function : fmax,\n", " IF ((AT[high] AND TEY[average]) AND AFDP[low]) AND AP[high] THEN NOX[high]\n", " \tAND aggregation function : fmin\n", " \tOR aggregation function : fmax,\n", " IF ((AT[high] AND TEY[average]) AND AFDP[average]) AND AP[low] THEN NOX[average]\n", " \tAND aggregation function : fmin\n", " \tOR aggregation function : fmax,\n", " IF ((AT[high] AND TEY[average]) AND AFDP[average]) AND AP[high] THEN NOX[average]\n", " \tAND aggregation function : fmin\n", " \tOR aggregation function : fmax,\n", " IF ((AT[average] AND TEY[average]) AND AFDP[high]) AND AP[low] THEN NOX[average]\n", " \tAND aggregation function : fmin\n", " \tOR aggregation function : fmax,\n", " IF ((AT[average] AND TEY[average]) AND AFDP[high]) AND AP[high] THEN NOX[average]\n", " \tAND aggregation function : fmin\n", " \tOR aggregation function : fmax,\n", " IF (AT[average] AND TEY[average]) AND AFDP[high] THEN NOX[average]\n", " \tAND aggregation function : fmin\n", " \tOR aggregation function : fmax,\n", " IF (AT[high] AND TEY[average]) AND AFDP[high] THEN NOX[average]\n", " \tAND aggregation function : fmin\n", " \tOR aggregation function : fmax,\n", " IF ((AT[average] AND TEY[higher]) AND AP[low]) AND AFDP[low] THEN NOX[average]\n", " \tAND aggregation function : fmin\n", " \tOR aggregation function : fmax,\n", " IF ((AT[average] AND TEY[higher]) AND AP[low]) AND AFDP[average] THEN NOX[low]\n", " \tAND aggregation function : fmin\n", " \tOR aggregation function : fmax,\n", " IF ((AT[average] AND TEY[average]) AND AP[low]) AND AFDP[high] THEN NOX[average]\n", " \tAND aggregation function : fmin\n", " \tOR aggregation function : fmax,\n", " IF ((AT[average] AND TEY[higher]) AND AP[low]) AND AFDP[high] THEN NOX[average]\n", " \tAND aggregation function : fmin\n", " \tOR aggregation function : fmax,\n", " IF ((AT[average] AND TEY[average]) AND AP[high]) AND AFDP[low] THEN NOX[high]\n", " \tAND aggregation function : fmin\n", " \tOR aggregation function : fmax,\n", " IF ((AT[average] AND TEY[higher]) AND AP[high]) AND AFDP[low] THEN NOX[average]\n", " \tAND aggregation function : fmin\n", " \tOR aggregation function : fmax,\n", " IF ((AT[average] AND TEY[average]) AND AP[high]) AND AFDP[high] THEN NOX[average]\n", " \tAND aggregation function : fmin\n", " \tOR aggregation function : fmax,\n", " IF ((AT[average] AND TEY[higher]) AND AP[high]) AND AFDP[high] THEN NOX[low]\n", " \tAND aggregation function : fmin\n", " \tOR aggregation function : fmax,\n", " IF ((AT[high] AND TEY[higher]) AND AFDP[low]) AND AP[low] THEN NOX[low]\n", " \tAND aggregation function : fmin\n", " \tOR aggregation function : fmax,\n", " IF ((AT[high] AND TEY[higher]) AND AFDP[low]) AND AP[high] THEN NOX[high]\n", " \tAND aggregation function : fmin\n", " \tOR aggregation function : fmax,\n", " IF ((AT[average] AND TEY[higher]) AND AFDP[high]) AND AP[low] THEN NOX[average]\n", " \tAND aggregation function : fmin\n", " \tOR aggregation function : fmax,\n", " IF ((AT[high] AND TEY[higher]) AND AFDP[high]) AND AP[low] THEN NOX[low]\n", " \tAND aggregation function : fmin\n", " \tOR aggregation function : fmax,\n", " IF ((AT[high] AND TEY[higher]) AND AFDP[average]) AND AP[high] THEN NOX[low]\n", " \tAND aggregation function : fmin\n", " \tOR aggregation function : fmax,\n", " IF ((AT[high] AND TEY[higher]) AND AFDP[high]) AND AP[high] THEN NOX[average]\n", " \tAND aggregation function : fmin\n", " \tOR aggregation function : fmax]" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from src.rules import get_fuzzy_rules\n", "\n", "fuzzy_variables = {\n", " \"AT\": AT,\n", " \"AP\": AP,\n", " # \"AH\": AH,\n", " \"AFDP\": AFDP,\n", " \"TEY\": TEY,\n", " # \"TIT\": TIT,\n", " # \"TAT\": TAT,\n", " \"consequent\": NOX,\n", "}\n", "fuzzy_rules = get_fuzzy_rules(rules, fuzzy_variables)\n", "\n", "fuzzy_cntrl = ctrl.ControlSystem(fuzzy_rules)\n", "\n", "sim = ctrl.ControlSystemSimulation(fuzzy_cntrl, lenient=False)\n", "\n", "display(len(fuzzy_rules))\n", "fuzzy_rules" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "=============\n", " Antecedents \n", "=============\n", "Antecedent: AT = 4.5878\n", " - low : 0.5001108545034625\n", " - average : 0.4998891454965375\n", " - high : 0.0\n", "Antecedent: TEY = 134.67\n", " - lower : 0.0\n", " - low : 0.25440806045330117\n", " - average : 0.745591939546699\n", " - high : 0.0\n", " - higher : 0.0\n", "Antecedent: AFDP = 3.5758\n", " - low : 0.4587636363636368\n", " - average : 0.5412363636363632\n", " - high : 0.0\n", "Antecedent: AP = 1018.7\n", " - low : 0.0\n", " - average : 0.7041420118346117\n", " - high : 0.2958579881653883\n", "\n", "=======\n", " Rules \n", "=======\n", "RULE #0:\n", " IF ((AT[low] AND TEY[lower]) AND AFDP[low]) AND AP[low] THEN NOX[average]\n", "\tAND aggregation function : fmin\n", "\tOR aggregation function : fmax\n", "\n", " Aggregation (IF-clause):\n", " - AT[low] : 0.5001108545034625\n", " - TEY[lower] : 0.0\n", " - AFDP[low] : 0.4587636363636368\n", " - AP[low] : 0.0\n", " ((AT[low] AND TEY[lower]) AND AFDP[low]) AND AP[low] = 0.0\n", " Activation (THEN-clause):\n", " NOX[average] : 0.0\n", "\n", "RULE #1:\n", " IF ((AT[low] AND TEY[lower]) AND AFDP[low]) AND AP[high] THEN NOX[high]\n", "\tAND aggregation function : fmin\n", "\tOR aggregation function : fmax\n", "\n", " Aggregation (IF-clause):\n", " - AT[low] : 0.5001108545034625\n", " - TEY[lower] : 0.0\n", " - AFDP[low] : 0.4587636363636368\n", " - AP[high] : 0.2958579881653883\n", " ((AT[low] AND TEY[lower]) AND AFDP[low]) AND AP[high] = 0.0\n", " Activation (THEN-clause):\n", " NOX[high] : 0.0\n", "\n", "RULE #2:\n", " IF ((AT[low] AND TEY[lower]) AND AFDP[high]) AND AP[low] THEN NOX[high]\n", "\tAND aggregation function : fmin\n", "\tOR aggregation function : fmax\n", "\n", " Aggregation (IF-clause):\n", " - AT[low] : 0.5001108545034625\n", " - TEY[lower] : 0.0\n", " - AFDP[high] : 0.0\n", " - AP[low] : 0.0\n", " ((AT[low] AND TEY[lower]) AND AFDP[high]) AND AP[low] = 0.0\n", " Activation (THEN-clause):\n", " NOX[high] : 0.0\n", "\n", "RULE #3:\n", " IF ((AT[low] AND TEY[lower]) AND AFDP[high]) AND AP[high] THEN NOX[high]\n", "\tAND aggregation function : fmin\n", "\tOR aggregation function : fmax\n", "\n", " Aggregation (IF-clause):\n", " - AT[low] : 0.5001108545034625\n", " - TEY[lower] : 0.0\n", " - AFDP[high] : 0.0\n", " - AP[high] : 0.2958579881653883\n", " ((AT[low] AND TEY[lower]) AND AFDP[high]) AND AP[high] = 0.0\n", " Activation (THEN-clause):\n", " NOX[high] : 0.0\n", "\n", "RULE #4:\n", " IF ((AT[average] AND TEY[lower]) AND AFDP[high]) AND AP[high] THEN NOX[high]\n", "\tAND aggregation function : fmin\n", "\tOR aggregation function : fmax\n", "\n", " Aggregation (IF-clause):\n", " - AT[average] : 0.4998891454965375\n", " - TEY[lower] : 0.0\n", " - AFDP[high] : 0.0\n", " - AP[high] : 0.2958579881653883\n", " ((AT[average] AND TEY[lower]) AND AFDP[high]) AND AP[high] = 0.0\n", " Activation (THEN-clause):\n", " NOX[high] : 0.0\n", "\n", "RULE #5:\n", " IF ((AT[average] AND TEY[lower]) AND AP[low]) AND AFDP[low] THEN NOX[high]\n", "\tAND aggregation function : fmin\n", "\tOR aggregation function : fmax\n", "\n", " Aggregation (IF-clause):\n", " - AT[average] : 0.4998891454965375\n", " - TEY[lower] : 0.0\n", " - AP[low] : 0.0\n", " - AFDP[low] : 0.4587636363636368\n", " ((AT[average] AND TEY[lower]) AND AP[low]) AND AFDP[low] = 0.0\n", " Activation (THEN-clause):\n", " NOX[high] : 0.0\n", "\n", "RULE #6:\n", " IF ((AT[average] AND TEY[lower]) AND AP[low]) AND AFDP[high] THEN NOX[higher]\n", "\tAND aggregation function : fmin\n", "\tOR aggregation function : fmax\n", "\n", " Aggregation (IF-clause):\n", " - AT[average] : 0.4998891454965375\n", " - TEY[lower] : 0.0\n", " - AP[low] : 0.0\n", " - AFDP[high] : 0.0\n", " ((AT[average] AND TEY[lower]) AND AP[low]) AND AFDP[high] = 0.0\n", " Activation (THEN-clause):\n", " NOX[higher] : 0.0\n", "\n", "RULE #7:\n", " IF (AT[average] AND TEY[lower]) AND AP[high] THEN NOX[higher]\n", "\tAND aggregation function : fmin\n", "\tOR aggregation function : fmax\n", "\n", " Aggregation (IF-clause):\n", " - AT[average] : 0.4998891454965375\n", " - TEY[lower] : 0.0\n", " - AP[high] : 0.2958579881653883\n", " (AT[average] AND TEY[lower]) AND AP[high] = 0.0\n", " Activation (THEN-clause):\n", " NOX[higher] : 0.0\n", "\n", "RULE #8:\n", " IF (AT[average] AND TEY[low]) AND AFDP[low] THEN NOX[high]\n", "\tAND aggregation function : fmin\n", "\tOR aggregation function : fmax\n", "\n", " Aggregation (IF-clause):\n", " - AT[average] : 0.4998891454965375\n", " - TEY[low] : 0.25440806045330117\n", " - AFDP[low] : 0.4587636363636368\n", " (AT[average] AND TEY[low]) AND AFDP[low] = 0.25440806045330117\n", " Activation (THEN-clause):\n", " NOX[high] : 0.25440806045330117\n", "\n", "RULE #9:\n", " IF (AT[average] AND TEY[low]) AND AFDP[high] THEN NOX[high]\n", "\tAND aggregation function : fmin\n", "\tOR aggregation function : fmax\n", "\n", " Aggregation (IF-clause):\n", " - AT[average] : 0.4998891454965375\n", " - TEY[low] : 0.25440806045330117\n", " - AFDP[high] : 0.0\n", " (AT[average] AND TEY[low]) AND AFDP[high] = 0.0\n", " Activation (THEN-clause):\n", " NOX[high] : 0.0\n", "\n", "RULE #10:\n", " IF AT[average] AND TEY[low] THEN NOX[average]\n", "\tAND aggregation function : fmin\n", "\tOR aggregation function : fmax\n", "\n", " Aggregation (IF-clause):\n", " - AT[average] : 0.4998891454965375\n", " - TEY[low] : 0.25440806045330117\n", " AT[average] AND TEY[low] = 0.25440806045330117\n", " Activation (THEN-clause):\n", " NOX[average] : 0.25440806045330117\n", "\n", "RULE #11:\n", " IF ((AT[average] AND TEY[lower]) AND AFDP[low]) AND AP[low] THEN NOX[average]\n", "\tAND aggregation function : fmin\n", "\tOR aggregation function : fmax\n", "\n", " Aggregation (IF-clause):\n", " - AT[average] : 0.4998891454965375\n", " - TEY[lower] : 0.0\n", " - AFDP[low] : 0.4587636363636368\n", " - AP[low] : 0.0\n", " ((AT[average] AND TEY[lower]) AND AFDP[low]) AND AP[low] = 0.0\n", " Activation (THEN-clause):\n", " NOX[average] : 0.0\n", "\n", "RULE #12:\n", " IF ((AT[average] AND TEY[lower]) AND AFDP[low]) AND AP[average] THEN NOX[high]\n", "\tAND aggregation function : fmin\n", "\tOR aggregation function : fmax\n", "\n", " Aggregation (IF-clause):\n", " - AT[average] : 0.4998891454965375\n", " - TEY[lower] : 0.0\n", " - AFDP[low] : 0.4587636363636368\n", " - AP[average] : 0.7041420118346117\n", " ((AT[average] AND TEY[lower]) AND AFDP[low]) AND AP[average] = 0.0\n", " Activation (THEN-clause):\n", " NOX[high] : 0.0\n", "\n", "RULE #13:\n", " IF ((AT[average] AND TEY[lower]) AND AFDP[low]) AND AP[high] THEN NOX[high]\n", "\tAND aggregation function : fmin\n", "\tOR aggregation function : fmax\n", "\n", " Aggregation (IF-clause):\n", " - AT[average] : 0.4998891454965375\n", " - TEY[lower] : 0.0\n", " - AFDP[low] : 0.4587636363636368\n", " - AP[high] : 0.2958579881653883\n", " ((AT[average] AND TEY[lower]) AND AFDP[low]) AND AP[high] = 0.0\n", " Activation (THEN-clause):\n", " NOX[high] : 0.0\n", "\n", "RULE #14:\n", " IF (AT[average] AND TEY[lower]) AND AFDP[low] THEN NOX[high]\n", "\tAND aggregation function : fmin\n", "\tOR aggregation function : fmax\n", "\n", " Aggregation (IF-clause):\n", " - AT[average] : 0.4998891454965375\n", " - TEY[lower] : 0.0\n", " - AFDP[low] : 0.4587636363636368\n", " (AT[average] AND TEY[lower]) AND AFDP[low] = 0.0\n", " Activation (THEN-clause):\n", " NOX[high] : 0.0\n", "\n", "RULE #15:\n", " IF ((AT[average] AND TEY[lower]) AND AFDP[high]) AND AP[low] THEN NOX[high]\n", "\tAND aggregation function : fmin\n", "\tOR aggregation function : fmax\n", "\n", " Aggregation (IF-clause):\n", " - AT[average] : 0.4998891454965375\n", " - TEY[lower] : 0.0\n", " - AFDP[high] : 0.0\n", " - AP[low] : 0.0\n", " ((AT[average] AND TEY[lower]) AND AFDP[high]) AND AP[low] = 0.0\n", " Activation (THEN-clause):\n", " NOX[high] : 0.0\n", "\n", "RULE #16:\n", " IF (AT[low] AND TEY[average]) AND AFDP[low] THEN NOX[average]\n", "\tAND aggregation function : fmin\n", "\tOR aggregation function : fmax\n", "\n", " Aggregation (IF-clause):\n", " - AT[low] : 0.5001108545034625\n", " - TEY[average] : 0.745591939546699\n", " - AFDP[low] : 0.4587636363636368\n", " (AT[low] AND TEY[average]) AND AFDP[low] = 0.4587636363636368\n", " Activation (THEN-clause):\n", " NOX[average] : 0.4587636363636368\n", "\n", "RULE #17:\n", " IF (AT[average] AND TEY[average]) AND AFDP[low] THEN NOX[average]\n", "\tAND aggregation function : fmin\n", "\tOR aggregation function : fmax\n", "\n", " Aggregation (IF-clause):\n", " - AT[average] : 0.4998891454965375\n", " - TEY[average] : 0.745591939546699\n", " - AFDP[low] : 0.4587636363636368\n", " (AT[average] AND TEY[average]) AND AFDP[low] = 0.4587636363636368\n", " Activation (THEN-clause):\n", " NOX[average] : 0.4587636363636368\n", "\n", "RULE #18:\n", " IF (AT[low] AND TEY[average]) AND AFDP[high] THEN NOX[average]\n", "\tAND aggregation function : fmin\n", "\tOR aggregation function : fmax\n", "\n", " Aggregation (IF-clause):\n", " - AT[low] : 0.5001108545034625\n", " - TEY[average] : 0.745591939546699\n", " - AFDP[high] : 0.0\n", " (AT[low] AND TEY[average]) AND AFDP[high] = 0.0\n", " Activation (THEN-clause):\n", " NOX[average] : 0.0\n", "\n", "RULE #19:\n", " IF ((AT[low] AND TEY[average]) AND AFDP[high]) AND AP[low] THEN NOX[average]\n", "\tAND aggregation function : fmin\n", "\tOR aggregation function : fmax\n", "\n", " Aggregation (IF-clause):\n", " - AT[low] : 0.5001108545034625\n", " - TEY[average] : 0.745591939546699\n", " - AFDP[high] : 0.0\n", " - AP[low] : 0.0\n", " ((AT[low] AND TEY[average]) AND AFDP[high]) AND AP[low] = 0.0\n", " Activation (THEN-clause):\n", " NOX[average] : 0.0\n", "\n", "RULE #20:\n", " IF ((AT[low] AND TEY[average]) AND AFDP[high]) AND AP[high] THEN NOX[average]\n", "\tAND aggregation function : fmin\n", "\tOR aggregation function : fmax\n", "\n", " Aggregation (IF-clause):\n", " - AT[low] : 0.5001108545034625\n", " - TEY[average] : 0.745591939546699\n", " - AFDP[high] : 0.0\n", " - AP[high] : 0.2958579881653883\n", " ((AT[low] AND TEY[average]) AND AFDP[high]) AND AP[high] = 0.0\n", " Activation (THEN-clause):\n", " NOX[average] : 0.0\n", "\n", "RULE #21:\n", " IF ((AT[average] AND TEY[average]) AND AFDP[low]) AND AP[low] THEN NOX[average]\n", "\tAND aggregation function : fmin\n", "\tOR aggregation function : fmax\n", "\n", " Aggregation (IF-clause):\n", " - AT[average] : 0.4998891454965375\n", " - TEY[average] : 0.745591939546699\n", " - AFDP[low] : 0.4587636363636368\n", " - AP[low] : 0.0\n", " ((AT[average] AND TEY[average]) AND AFDP[low]) AND AP[low] = 0.0\n", " Activation (THEN-clause):\n", " NOX[average] : 0.0\n", "\n", "RULE #22:\n", " IF (AT[average] AND TEY[average]) AND AFDP[average] THEN NOX[average]\n", "\tAND aggregation function : fmin\n", "\tOR aggregation function : fmax\n", "\n", " Aggregation (IF-clause):\n", " - AT[average] : 0.4998891454965375\n", " - TEY[average] : 0.745591939546699\n", " - AFDP[average] : 0.5412363636363632\n", " (AT[average] AND TEY[average]) AND AFDP[average] = 0.4998891454965375\n", " Activation (THEN-clause):\n", " NOX[average] : 0.4998891454965375\n", "\n", "RULE #23:\n", " IF ((AT[low] AND TEY[higher]) AND AP[low]) AND AFDP[low] THEN NOX[average]\n", "\tAND aggregation function : fmin\n", "\tOR aggregation function : fmax\n", "\n", " Aggregation (IF-clause):\n", " - AT[low] : 0.5001108545034625\n", " - TEY[higher] : 0.0\n", " - AP[low] : 0.0\n", " - AFDP[low] : 0.4587636363636368\n", " ((AT[low] AND TEY[higher]) AND AP[low]) AND AFDP[low] = 0.0\n", " Activation (THEN-clause):\n", " NOX[average] : 0.0\n", "\n", "RULE #24:\n", " IF ((AT[low] AND TEY[higher]) AND AP[average]) AND AFDP[low] THEN NOX[average]\n", "\tAND aggregation function : fmin\n", "\tOR aggregation function : fmax\n", "\n", " Aggregation (IF-clause):\n", " - AT[low] : 0.5001108545034625\n", " - TEY[higher] : 0.0\n", " - AP[average] : 0.7041420118346117\n", " - AFDP[low] : 0.4587636363636368\n", " ((AT[low] AND TEY[higher]) AND AP[average]) AND AFDP[low] = 0.0\n", " Activation (THEN-clause):\n", " NOX[average] : 0.0\n", "\n", "RULE #25:\n", " IF ((AT[average] AND TEY[higher]) AND AP[average]) AND AFDP[low] THEN NOX[low]\n", "\tAND aggregation function : fmin\n", "\tOR aggregation function : fmax\n", "\n", " Aggregation (IF-clause):\n", " - AT[average] : 0.4998891454965375\n", " - TEY[higher] : 0.0\n", " - AP[average] : 0.7041420118346117\n", " - AFDP[low] : 0.4587636363636368\n", " ((AT[average] AND TEY[higher]) AND AP[average]) AND AFDP[low] = 0.0\n", " Activation (THEN-clause):\n", " NOX[low] : 0.0\n", "\n", "RULE #26:\n", " IF ((AT[low] AND TEY[average]) AND AP[low]) AND AFDP[high] THEN NOX[average]\n", "\tAND aggregation function : fmin\n", "\tOR aggregation function : fmax\n", "\n", " Aggregation (IF-clause):\n", " - AT[low] : 0.5001108545034625\n", " - TEY[average] : 0.745591939546699\n", " - AP[low] : 0.0\n", " - AFDP[high] : 0.0\n", " ((AT[low] AND TEY[average]) AND AP[low]) AND AFDP[high] = 0.0\n", " Activation (THEN-clause):\n", " NOX[average] : 0.0\n", "\n", "RULE #27:\n", " IF ((AT[low] AND TEY[higher]) AND AP[low]) AND AFDP[high] THEN NOX[average]\n", "\tAND aggregation function : fmin\n", "\tOR aggregation function : fmax\n", "\n", " Aggregation (IF-clause):\n", " - AT[low] : 0.5001108545034625\n", " - TEY[higher] : 0.0\n", " - AP[low] : 0.0\n", " - AFDP[high] : 0.0\n", " ((AT[low] AND TEY[higher]) AND AP[low]) AND AFDP[high] = 0.0\n", " Activation (THEN-clause):\n", " NOX[average] : 0.0\n", "\n", "RULE #28:\n", " IF ((AT[low] AND TEY[higher]) AND AP[average]) AND AFDP[high] THEN NOX[average]\n", "\tAND aggregation function : fmin\n", "\tOR aggregation function : fmax\n", "\n", " Aggregation (IF-clause):\n", " - AT[low] : 0.5001108545034625\n", " - TEY[higher] : 0.0\n", " - AP[average] : 0.7041420118346117\n", " - AFDP[high] : 0.0\n", " ((AT[low] AND TEY[higher]) AND AP[average]) AND AFDP[high] = 0.0\n", " Activation (THEN-clause):\n", " NOX[average] : 0.0\n", "\n", "RULE #29:\n", " IF ((AT[average] AND TEY[higher]) AND AP[average]) AND AFDP[high] THEN NOX[average]\n", "\tAND aggregation function : fmin\n", "\tOR aggregation function : fmax\n", "\n", " Aggregation (IF-clause):\n", " - AT[average] : 0.4998891454965375\n", " - TEY[higher] : 0.0\n", " - AP[average] : 0.7041420118346117\n", " - AFDP[high] : 0.0\n", " ((AT[average] AND TEY[higher]) AND AP[average]) AND AFDP[high] = 0.0\n", " Activation (THEN-clause):\n", " NOX[average] : 0.0\n", "\n", "RULE #30:\n", " IF ((AT[low] AND TEY[higher]) AND AP[high]) AND AFDP[low] THEN NOX[average]\n", "\tAND aggregation function : fmin\n", "\tOR aggregation function : fmax\n", "\n", " Aggregation (IF-clause):\n", " - AT[low] : 0.5001108545034625\n", " - TEY[higher] : 0.0\n", " - AP[high] : 0.2958579881653883\n", " - AFDP[low] : 0.4587636363636368\n", " ((AT[low] AND TEY[higher]) AND AP[high]) AND AFDP[low] = 0.0\n", " Activation (THEN-clause):\n", " NOX[average] : 0.0\n", "\n", "RULE #31:\n", " IF ((AT[low] AND TEY[higher]) AND AP[high]) AND AFDP[average] THEN NOX[low]\n", "\tAND aggregation function : fmin\n", "\tOR aggregation function : fmax\n", "\n", " Aggregation (IF-clause):\n", " - AT[low] : 0.5001108545034625\n", " - TEY[higher] : 0.0\n", " - AP[high] : 0.2958579881653883\n", " - AFDP[average] : 0.5412363636363632\n", " ((AT[low] AND TEY[higher]) AND AP[high]) AND AFDP[average] = 0.0\n", " Activation (THEN-clause):\n", " NOX[low] : 0.0\n", "\n", "RULE #32:\n", " IF ((AT[low] AND TEY[higher]) AND AP[high]) AND AFDP[high] THEN NOX[average]\n", "\tAND aggregation function : fmin\n", "\tOR aggregation function : fmax\n", "\n", " Aggregation (IF-clause):\n", " - AT[low] : 0.5001108545034625\n", " - TEY[higher] : 0.0\n", " - AP[high] : 0.2958579881653883\n", " - AFDP[high] : 0.0\n", " ((AT[low] AND TEY[higher]) AND AP[high]) AND AFDP[high] = 0.0\n", " Activation (THEN-clause):\n", " NOX[average] : 0.0\n", "\n", "RULE #33:\n", " IF (AT[average] AND TEY[lower]) AND AFDP[low] THEN NOX[average]\n", "\tAND aggregation function : fmin\n", "\tOR aggregation function : fmax\n", "\n", " Aggregation (IF-clause):\n", " - AT[average] : 0.4998891454965375\n", " - TEY[lower] : 0.0\n", " - AFDP[low] : 0.4587636363636368\n", " (AT[average] AND TEY[lower]) AND AFDP[low] = 0.0\n", " Activation (THEN-clause):\n", " NOX[average] : 0.0\n", "\n", "RULE #34:\n", " IF (AT[average] AND TEY[low]) AND AFDP[average] THEN NOX[low]\n", "\tAND aggregation function : fmin\n", "\tOR aggregation function : fmax\n", "\n", " Aggregation (IF-clause):\n", " - AT[average] : 0.4998891454965375\n", " - TEY[low] : 0.25440806045330117\n", " - AFDP[average] : 0.5412363636363632\n", " (AT[average] AND TEY[low]) AND AFDP[average] = 0.25440806045330117\n", " Activation (THEN-clause):\n", " NOX[low] : 0.25440806045330117\n", "\n", "RULE #35:\n", " IF (AT[average] AND TEY[low]) AND AFDP[high] THEN NOX[average]\n", "\tAND aggregation function : fmin\n", "\tOR aggregation function : fmax\n", "\n", " Aggregation (IF-clause):\n", " - AT[average] : 0.4998891454965375\n", " - TEY[low] : 0.25440806045330117\n", " - AFDP[high] : 0.0\n", " (AT[average] AND TEY[low]) AND AFDP[high] = 0.0\n", " Activation (THEN-clause):\n", " NOX[average] : 0.0\n", "\n", "RULE #36:\n", " IF (AT[low] AND TEY[lower]) AND AFDP[low] THEN NOX[high]\n", "\tAND aggregation function : fmin\n", "\tOR aggregation function : fmax\n", "\n", " Aggregation (IF-clause):\n", " - AT[low] : 0.5001108545034625\n", " - TEY[lower] : 0.0\n", " - AFDP[low] : 0.4587636363636368\n", " (AT[low] AND TEY[lower]) AND AFDP[low] = 0.0\n", " Activation (THEN-clause):\n", " NOX[high] : 0.0\n", "\n", "RULE #37:\n", " IF (AT[average] AND TEY[low]) AND AFDP[low] THEN NOX[low]\n", "\tAND aggregation function : fmin\n", "\tOR aggregation function : fmax\n", "\n", " Aggregation (IF-clause):\n", " - AT[average] : 0.4998891454965375\n", " - TEY[low] : 0.25440806045330117\n", " - AFDP[low] : 0.4587636363636368\n", " (AT[average] AND TEY[low]) AND AFDP[low] = 0.25440806045330117\n", " Activation (THEN-clause):\n", " NOX[low] : 0.25440806045330117\n", "\n", "RULE #38:\n", " IF (AT[average] AND TEY[low]) AND AFDP[average] THEN NOX[low]\n", "\tAND aggregation function : fmin\n", "\tOR aggregation function : fmax\n", "\n", " Aggregation (IF-clause):\n", " - AT[average] : 0.4998891454965375\n", " - TEY[low] : 0.25440806045330117\n", " - AFDP[average] : 0.5412363636363632\n", " (AT[average] AND TEY[low]) AND AFDP[average] = 0.25440806045330117\n", " Activation (THEN-clause):\n", " NOX[low] : 0.25440806045330117\n", "\n", "RULE #39:\n", " IF (AT[average] AND TEY[lower]) AND AFDP[average] THEN NOX[average]\n", "\tAND aggregation function : fmin\n", "\tOR aggregation function : fmax\n", "\n", " Aggregation (IF-clause):\n", " - AT[average] : 0.4998891454965375\n", " - TEY[lower] : 0.0\n", " - AFDP[average] : 0.5412363636363632\n", " (AT[average] AND TEY[lower]) AND AFDP[average] = 0.0\n", " Activation (THEN-clause):\n", " NOX[average] : 0.0\n", "\n", "RULE #40:\n", " IF (AT[average] AND TEY[lower]) AND AFDP[high] THEN NOX[average]\n", "\tAND aggregation function : fmin\n", "\tOR aggregation function : fmax\n", "\n", " Aggregation (IF-clause):\n", " - AT[average] : 0.4998891454965375\n", " - TEY[lower] : 0.0\n", " - AFDP[high] : 0.0\n", " (AT[average] AND TEY[lower]) AND AFDP[high] = 0.0\n", " Activation (THEN-clause):\n", " NOX[average] : 0.0\n", "\n", "RULE #41:\n", " IF ((AT[high] AND TEY[lower]) AND AFDP[low]) AND AP[low] THEN NOX[low]\n", "\tAND aggregation function : fmin\n", "\tOR aggregation function : fmax\n", "\n", " Aggregation (IF-clause):\n", " - AT[high] : 0.0\n", " - TEY[lower] : 0.0\n", " - AFDP[low] : 0.4587636363636368\n", " - AP[low] : 0.0\n", " ((AT[high] AND TEY[lower]) AND AFDP[low]) AND AP[low] = 0.0\n", " Activation (THEN-clause):\n", " NOX[low] : 0.0\n", "\n", "RULE #42:\n", " IF ((AT[high] AND TEY[lower]) AND AFDP[low]) AND AP[high] THEN NOX[low]\n", "\tAND aggregation function : fmin\n", "\tOR aggregation function : fmax\n", "\n", " Aggregation (IF-clause):\n", " - AT[high] : 0.0\n", " - TEY[lower] : 0.0\n", " - AFDP[low] : 0.4587636363636368\n", " - AP[high] : 0.2958579881653883\n", " ((AT[high] AND TEY[lower]) AND AFDP[low]) AND AP[high] = 0.0\n", " Activation (THEN-clause):\n", " NOX[low] : 0.0\n", "\n", "RULE #43:\n", " IF (AT[high] AND TEY[average]) AND AFDP[low] THEN NOX[low]\n", "\tAND aggregation function : fmin\n", "\tOR aggregation function : fmax\n", "\n", " Aggregation (IF-clause):\n", " - AT[high] : 0.0\n", " - TEY[average] : 0.745591939546699\n", " - AFDP[low] : 0.4587636363636368\n", " (AT[high] AND TEY[average]) AND AFDP[low] = 0.0\n", " Activation (THEN-clause):\n", " NOX[low] : 0.0\n", "\n", "RULE #44:\n", " IF ((AT[high] AND TEY[lower]) AND AFDP[average]) AND AP[low] THEN NOX[low]\n", "\tAND aggregation function : fmin\n", "\tOR aggregation function : fmax\n", "\n", " Aggregation (IF-clause):\n", " - AT[high] : 0.0\n", " - TEY[lower] : 0.0\n", " - AFDP[average] : 0.5412363636363632\n", " - AP[low] : 0.0\n", " ((AT[high] AND TEY[lower]) AND AFDP[average]) AND AP[low] = 0.0\n", " Activation (THEN-clause):\n", " NOX[low] : 0.0\n", "\n", "RULE #45:\n", " IF ((AT[high] AND TEY[lower]) AND AFDP[high]) AND AP[low] THEN NOX[low]\n", "\tAND aggregation function : fmin\n", "\tOR aggregation function : fmax\n", "\n", " Aggregation (IF-clause):\n", " - AT[high] : 0.0\n", " - TEY[lower] : 0.0\n", " - AFDP[high] : 0.0\n", " - AP[low] : 0.0\n", " ((AT[high] AND TEY[lower]) AND AFDP[high]) AND AP[low] = 0.0\n", " Activation (THEN-clause):\n", " NOX[low] : 0.0\n", "\n", "RULE #46:\n", " IF ((AT[average] AND TEY[lower]) AND AFDP[high]) AND AP[high] THEN NOX[low]\n", "\tAND aggregation function : fmin\n", "\tOR aggregation function : fmax\n", "\n", " Aggregation (IF-clause):\n", " - AT[average] : 0.4998891454965375\n", " - TEY[lower] : 0.0\n", " - AFDP[high] : 0.0\n", " - AP[high] : 0.2958579881653883\n", " ((AT[average] AND TEY[lower]) AND AFDP[high]) AND AP[high] = 0.0\n", " Activation (THEN-clause):\n", " NOX[low] : 0.0\n", "\n", "RULE #47:\n", " IF ((AT[high] AND TEY[lower]) AND AFDP[high]) AND AP[high] THEN NOX[average]\n", "\tAND aggregation function : fmin\n", "\tOR aggregation function : fmax\n", "\n", " Aggregation (IF-clause):\n", " - AT[high] : 0.0\n", " - TEY[lower] : 0.0\n", " - AFDP[high] : 0.0\n", " - AP[high] : 0.2958579881653883\n", " ((AT[high] AND TEY[lower]) AND AFDP[high]) AND AP[high] = 0.0\n", " Activation (THEN-clause):\n", " NOX[average] : 0.0\n", "\n", "RULE #48:\n", " IF ((AT[high] AND TEY[average]) AND AFDP[low]) AND AP[low] THEN NOX[average]\n", "\tAND aggregation function : fmin\n", "\tOR aggregation function : fmax\n", "\n", " Aggregation (IF-clause):\n", " - AT[high] : 0.0\n", " - TEY[average] : 0.745591939546699\n", " - AFDP[low] : 0.4587636363636368\n", " - AP[low] : 0.0\n", " ((AT[high] AND TEY[average]) AND AFDP[low]) AND AP[low] = 0.0\n", " Activation (THEN-clause):\n", " NOX[average] : 0.0\n", "\n", "RULE #49:\n", " IF ((AT[high] AND TEY[average]) AND AFDP[low]) AND AP[average] THEN NOX[average]\n", "\tAND aggregation function : fmin\n", "\tOR aggregation function : fmax\n", "\n", " Aggregation (IF-clause):\n", " - AT[high] : 0.0\n", " - TEY[average] : 0.745591939546699\n", " - AFDP[low] : 0.4587636363636368\n", " - AP[average] : 0.7041420118346117\n", " ((AT[high] AND TEY[average]) AND AFDP[low]) AND AP[average] = 0.0\n", " Activation (THEN-clause):\n", " NOX[average] : 0.0\n", "\n", "RULE #50:\n", " IF ((AT[average] AND TEY[average]) AND AFDP[low]) AND AP[high] THEN NOX[average]\n", "\tAND aggregation function : fmin\n", "\tOR aggregation function : fmax\n", "\n", " Aggregation (IF-clause):\n", " - AT[average] : 0.4998891454965375\n", " - TEY[average] : 0.745591939546699\n", " - AFDP[low] : 0.4587636363636368\n", " - AP[high] : 0.2958579881653883\n", " ((AT[average] AND TEY[average]) AND AFDP[low]) AND AP[high] = 0.2958579881653883\n", " Activation (THEN-clause):\n", " NOX[average] : 0.2958579881653883\n", "\n", "RULE #51:\n", " IF ((AT[high] AND TEY[average]) AND AFDP[low]) AND AP[high] THEN NOX[high]\n", "\tAND aggregation function : fmin\n", "\tOR aggregation function : fmax\n", "\n", " Aggregation (IF-clause):\n", " - AT[high] : 0.0\n", " - TEY[average] : 0.745591939546699\n", " - AFDP[low] : 0.4587636363636368\n", " - AP[high] : 0.2958579881653883\n", " ((AT[high] AND TEY[average]) AND AFDP[low]) AND AP[high] = 0.0\n", " Activation (THEN-clause):\n", " NOX[high] : 0.0\n", "\n", "RULE #52:\n", " IF ((AT[high] AND TEY[average]) AND AFDP[average]) AND AP[low] THEN NOX[average]\n", "\tAND aggregation function : fmin\n", "\tOR aggregation function : fmax\n", "\n", " Aggregation (IF-clause):\n", " - AT[high] : 0.0\n", " - TEY[average] : 0.745591939546699\n", " - AFDP[average] : 0.5412363636363632\n", " - AP[low] : 0.0\n", " ((AT[high] AND TEY[average]) AND AFDP[average]) AND AP[low] = 0.0\n", " Activation (THEN-clause):\n", " NOX[average] : 0.0\n", "\n", "RULE #53:\n", " IF ((AT[high] AND TEY[average]) AND AFDP[average]) AND AP[high] THEN NOX[average]\n", "\tAND aggregation function : fmin\n", "\tOR aggregation function : fmax\n", "\n", " Aggregation (IF-clause):\n", " - AT[high] : 0.0\n", " - TEY[average] : 0.745591939546699\n", " - AFDP[average] : 0.5412363636363632\n", " - AP[high] : 0.2958579881653883\n", " ((AT[high] AND TEY[average]) AND AFDP[average]) AND AP[high] = 0.0\n", " Activation (THEN-clause):\n", " NOX[average] : 0.0\n", "\n", "RULE #54:\n", " IF ((AT[average] AND TEY[average]) AND AFDP[high]) AND AP[low] THEN NOX[average]\n", "\tAND aggregation function : fmin\n", "\tOR aggregation function : fmax\n", "\n", " Aggregation (IF-clause):\n", " - AT[average] : 0.4998891454965375\n", " - TEY[average] : 0.745591939546699\n", " - AFDP[high] : 0.0\n", " - AP[low] : 0.0\n", " ((AT[average] AND TEY[average]) AND AFDP[high]) AND AP[low] = 0.0\n", " Activation (THEN-clause):\n", " NOX[average] : 0.0\n", "\n", "RULE #55:\n", " IF ((AT[average] AND TEY[average]) AND AFDP[high]) AND AP[high] THEN NOX[average]\n", "\tAND aggregation function : fmin\n", "\tOR aggregation function : fmax\n", "\n", " Aggregation (IF-clause):\n", " - AT[average] : 0.4998891454965375\n", " - TEY[average] : 0.745591939546699\n", " - AFDP[high] : 0.0\n", " - AP[high] : 0.2958579881653883\n", " ((AT[average] AND TEY[average]) AND AFDP[high]) AND AP[high] = 0.0\n", " Activation (THEN-clause):\n", " NOX[average] : 0.0\n", "\n", "RULE #56:\n", " IF (AT[average] AND TEY[average]) AND AFDP[high] THEN NOX[average]\n", "\tAND aggregation function : fmin\n", "\tOR aggregation function : fmax\n", "\n", " Aggregation (IF-clause):\n", " - AT[average] : 0.4998891454965375\n", " - TEY[average] : 0.745591939546699\n", " - AFDP[high] : 0.0\n", " (AT[average] AND TEY[average]) AND AFDP[high] = 0.0\n", " Activation (THEN-clause):\n", " NOX[average] : 0.0\n", "\n", "RULE #57:\n", " IF (AT[high] AND TEY[average]) AND AFDP[high] THEN NOX[average]\n", "\tAND aggregation function : fmin\n", "\tOR aggregation function : fmax\n", "\n", " Aggregation (IF-clause):\n", " - AT[high] : 0.0\n", " - TEY[average] : 0.745591939546699\n", " - AFDP[high] : 0.0\n", " (AT[high] AND TEY[average]) AND AFDP[high] = 0.0\n", " Activation (THEN-clause):\n", " NOX[average] : 0.0\n", "\n", "RULE #58:\n", " IF ((AT[average] AND TEY[higher]) AND AP[low]) AND AFDP[low] THEN NOX[average]\n", "\tAND aggregation function : fmin\n", "\tOR aggregation function : fmax\n", "\n", " Aggregation (IF-clause):\n", " - AT[average] : 0.4998891454965375\n", " - TEY[higher] : 0.0\n", " - AP[low] : 0.0\n", " - AFDP[low] : 0.4587636363636368\n", " ((AT[average] AND TEY[higher]) AND AP[low]) AND AFDP[low] = 0.0\n", " Activation (THEN-clause):\n", " NOX[average] : 0.0\n", "\n", "RULE #59:\n", " IF ((AT[average] AND TEY[higher]) AND AP[low]) AND AFDP[average] THEN NOX[low]\n", "\tAND aggregation function : fmin\n", "\tOR aggregation function : fmax\n", "\n", " Aggregation (IF-clause):\n", " - AT[average] : 0.4998891454965375\n", " - TEY[higher] : 0.0\n", " - AP[low] : 0.0\n", " - AFDP[average] : 0.5412363636363632\n", " ((AT[average] AND TEY[higher]) AND AP[low]) AND AFDP[average] = 0.0\n", " Activation (THEN-clause):\n", " NOX[low] : 0.0\n", "\n", "RULE #60:\n", " IF ((AT[average] AND TEY[average]) AND AP[low]) AND AFDP[high] THEN NOX[average]\n", "\tAND aggregation function : fmin\n", "\tOR aggregation function : fmax\n", "\n", " Aggregation (IF-clause):\n", " - AT[average] : 0.4998891454965375\n", " - TEY[average] : 0.745591939546699\n", " - AP[low] : 0.0\n", " - AFDP[high] : 0.0\n", " ((AT[average] AND TEY[average]) AND AP[low]) AND AFDP[high] = 0.0\n", " Activation (THEN-clause):\n", " NOX[average] : 0.0\n", "\n", "RULE #61:\n", " IF ((AT[average] AND TEY[higher]) AND AP[low]) AND AFDP[high] THEN NOX[average]\n", "\tAND aggregation function : fmin\n", "\tOR aggregation function : fmax\n", "\n", " Aggregation (IF-clause):\n", " - AT[average] : 0.4998891454965375\n", " - TEY[higher] : 0.0\n", " - AP[low] : 0.0\n", " - AFDP[high] : 0.0\n", " ((AT[average] AND TEY[higher]) AND AP[low]) AND AFDP[high] = 0.0\n", " Activation (THEN-clause):\n", " NOX[average] : 0.0\n", "\n", "RULE #62:\n", " IF ((AT[average] AND TEY[average]) AND AP[high]) AND AFDP[low] THEN NOX[high]\n", "\tAND aggregation function : fmin\n", "\tOR aggregation function : fmax\n", "\n", " Aggregation (IF-clause):\n", " - AT[average] : 0.4998891454965375\n", " - TEY[average] : 0.745591939546699\n", " - AP[high] : 0.2958579881653883\n", " - AFDP[low] : 0.4587636363636368\n", " ((AT[average] AND TEY[average]) AND AP[high]) AND AFDP[low] = 0.2958579881653883\n", " Activation (THEN-clause):\n", " NOX[high] : 0.2958579881653883\n", "\n", "RULE #63:\n", " IF ((AT[average] AND TEY[higher]) AND AP[high]) AND AFDP[low] THEN NOX[average]\n", "\tAND aggregation function : fmin\n", "\tOR aggregation function : fmax\n", "\n", " Aggregation (IF-clause):\n", " - AT[average] : 0.4998891454965375\n", " - TEY[higher] : 0.0\n", " - AP[high] : 0.2958579881653883\n", " - AFDP[low] : 0.4587636363636368\n", " ((AT[average] AND TEY[higher]) AND AP[high]) AND AFDP[low] = 0.0\n", " Activation (THEN-clause):\n", " NOX[average] : 0.0\n", "\n", "RULE #64:\n", " IF ((AT[average] AND TEY[average]) AND AP[high]) AND AFDP[high] THEN NOX[average]\n", "\tAND aggregation function : fmin\n", "\tOR aggregation function : fmax\n", "\n", " Aggregation (IF-clause):\n", " - AT[average] : 0.4998891454965375\n", " - TEY[average] : 0.745591939546699\n", " - AP[high] : 0.2958579881653883\n", " - AFDP[high] : 0.0\n", " ((AT[average] AND TEY[average]) AND AP[high]) AND AFDP[high] = 0.0\n", " Activation (THEN-clause):\n", " NOX[average] : 0.0\n", "\n", "RULE #65:\n", " IF ((AT[average] AND TEY[higher]) AND AP[high]) AND AFDP[high] THEN NOX[low]\n", "\tAND aggregation function : fmin\n", "\tOR aggregation function : fmax\n", "\n", " Aggregation (IF-clause):\n", " - AT[average] : 0.4998891454965375\n", " - TEY[higher] : 0.0\n", " - AP[high] : 0.2958579881653883\n", " - AFDP[high] : 0.0\n", " ((AT[average] AND TEY[higher]) AND AP[high]) AND AFDP[high] = 0.0\n", " Activation (THEN-clause):\n", " NOX[low] : 0.0\n", "\n", "RULE #66:\n", " IF ((AT[high] AND TEY[higher]) AND AFDP[low]) AND AP[low] THEN NOX[low]\n", "\tAND aggregation function : fmin\n", "\tOR aggregation function : fmax\n", "\n", " Aggregation (IF-clause):\n", " - AT[high] : 0.0\n", " - TEY[higher] : 0.0\n", " - AFDP[low] : 0.4587636363636368\n", " - AP[low] : 0.0\n", " ((AT[high] AND TEY[higher]) AND AFDP[low]) AND AP[low] = 0.0\n", " Activation (THEN-clause):\n", " NOX[low] : 0.0\n", "\n", "RULE #67:\n", " IF ((AT[high] AND TEY[higher]) AND AFDP[low]) AND AP[high] THEN NOX[high]\n", "\tAND aggregation function : fmin\n", "\tOR aggregation function : fmax\n", "\n", " Aggregation (IF-clause):\n", " - AT[high] : 0.0\n", " - TEY[higher] : 0.0\n", " - AFDP[low] : 0.4587636363636368\n", " - AP[high] : 0.2958579881653883\n", " ((AT[high] AND TEY[higher]) AND AFDP[low]) AND AP[high] = 0.0\n", " Activation (THEN-clause):\n", " NOX[high] : 0.0\n", "\n", "RULE #68:\n", " IF ((AT[average] AND TEY[higher]) AND AFDP[high]) AND AP[low] THEN NOX[average]\n", "\tAND aggregation function : fmin\n", "\tOR aggregation function : fmax\n", "\n", " Aggregation (IF-clause):\n", " - AT[average] : 0.4998891454965375\n", " - TEY[higher] : 0.0\n", " - AFDP[high] : 0.0\n", " - AP[low] : 0.0\n", " ((AT[average] AND TEY[higher]) AND AFDP[high]) AND AP[low] = 0.0\n", " Activation (THEN-clause):\n", " NOX[average] : 0.0\n", "\n", "RULE #69:\n", " IF ((AT[high] AND TEY[higher]) AND AFDP[high]) AND AP[low] THEN NOX[low]\n", "\tAND aggregation function : fmin\n", "\tOR aggregation function : fmax\n", "\n", " Aggregation (IF-clause):\n", " - AT[high] : 0.0\n", " - TEY[higher] : 0.0\n", " - AFDP[high] : 0.0\n", " - AP[low] : 0.0\n", " ((AT[high] AND TEY[higher]) AND AFDP[high]) AND AP[low] = 0.0\n", " Activation (THEN-clause):\n", " NOX[low] : 0.0\n", "\n", "RULE #70:\n", " IF ((AT[high] AND TEY[higher]) AND AFDP[average]) AND AP[high] THEN NOX[low]\n", "\tAND aggregation function : fmin\n", "\tOR aggregation function : fmax\n", "\n", " Aggregation (IF-clause):\n", " - AT[high] : 0.0\n", " - TEY[higher] : 0.0\n", " - AFDP[average] : 0.5412363636363632\n", " - AP[high] : 0.2958579881653883\n", " ((AT[high] AND TEY[higher]) AND AFDP[average]) AND AP[high] = 0.0\n", " Activation (THEN-clause):\n", " NOX[low] : 0.0\n", "\n", "RULE #71:\n", " IF ((AT[high] AND TEY[higher]) AND AFDP[high]) AND AP[high] THEN NOX[average]\n", "\tAND aggregation function : fmin\n", "\tOR aggregation function : fmax\n", "\n", " Aggregation (IF-clause):\n", " - AT[high] : 0.0\n", " - TEY[higher] : 0.0\n", " - AFDP[high] : 0.0\n", " - AP[high] : 0.2958579881653883\n", " ((AT[high] AND TEY[higher]) AND AFDP[high]) AND AP[high] = 0.0\n", " Activation (THEN-clause):\n", " NOX[average] : 0.0\n", "\n", "\n", "==============================\n", " Intermediaries and Conquests \n", "==============================\n", "Consequent: NOX = 73.82267305642429\n", " lower:\n", " Accumulate using accumulation_max : None\n", " low:\n", " Accumulate using accumulation_max : 0.25440806045330117\n", " average:\n", " Accumulate using accumulation_max : 0.4998891454965375\n", " high:\n", " Accumulate using accumulation_max : 0.2958579881653883\n", " higher:\n", " Accumulate using accumulation_max : 0.0\n", "\n" ] }, { "data": { "text/plain": [ "np.float64(73.82267305642429)" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sim.input[\"AT\"] = 4.5878\n", "sim.input[\"AP\"] = 1018.7\n", "sim.input[\"AFDP\"] = 3.5758\n", "sim.input[\"TEY\"] = 134.67\n", "# sim.input[\"AH\"] = 83.675\n", "# sim.input[\"TIT\"] = 1086.2\n", "# sim.input[\"TAT\"] = 549.83\n", "\n", "# sim.input[\"AT\"] = 31.6890\n", "# sim.input[\"AP\"] = 1010.9\n", "# sim.input[\"AFDP\"] = 4.1312\n", "# sim.input[\"TEY\"] = 128.03\n", "\n", "# sim.input[\"AT\"] = 10.9920\n", "# sim.input[\"AP\"] = 1022.4\n", "# sim.input[\"AFDP\"] = 3.2764\n", "# sim.input[\"TEY\"] = 107.81\n", "\n", "sim.compute()\n", "sim.print_state()\n", "display(sim.output[\"NOX\"])\n", "NOX.view(sim=sim)" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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ATAPAFDPTEYRealInferredRMSE
448026.6351009.74.4137147.3752.97067.08667014.117
2488320.2281016.24.6238154.7158.80172.90500014.104
2155715.6331018.54.0899154.2678.06684.0184385.952
170516.6541020.24.5755132.6073.95566.9578716.997
2138821.0021004.34.1101153.4879.98962.21227717.777
........................
1095424.9431008.74.3612134.0358.37370.22975811.857
1382621.0671008.63.7510110.8256.01870.83143214.813
1171925.3981011.24.2003134.5763.88472.2646468.381
2598426.9711012.94.5755142.6650.04771.83428221.787
82614.1981017.85.1631156.3071.73172.9050001.174
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1000 rows × 7 columns

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" ], "text/plain": [ " AT AP AFDP TEY Real Inferred RMSE\n", "4480 26.635 1009.7 4.4137 147.37 52.970 67.086670 14.117\n", "24883 20.228 1016.2 4.6238 154.71 58.801 72.905000 14.104\n", "21557 15.633 1018.5 4.0899 154.26 78.066 84.018438 5.952\n", "1705 16.654 1020.2 4.5755 132.60 73.955 66.957871 6.997\n", "21388 21.002 1004.3 4.1101 153.48 79.989 62.212277 17.777\n", "... ... ... ... ... ... ... ...\n", "10954 24.943 1008.7 4.3612 134.03 58.373 70.229758 11.857\n", "13826 21.067 1008.6 3.7510 110.82 56.018 70.831432 14.813\n", "11719 25.398 1011.2 4.2003 134.57 63.884 72.264646 8.381\n", "25984 26.971 1012.9 4.5755 142.66 50.047 71.834282 21.787\n", "826 14.198 1017.8 5.1631 156.30 71.731 72.905000 1.174\n", "\n", "[1000 rows x 7 columns]" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from sklearn import metrics\n", "import math\n", "\n", "\n", "def fuzzy_pred(row):\n", " sim.input[\"AT\"] = row[\"AT\"]\n", " sim.input[\"AP\"] = row[\"AP\"]\n", " # sim.input[\"AH\"] = row[\"AH\"]\n", " sim.input[\"AFDP\"] = row[\"AFDP\"]\n", " sim.input[\"TEY\"] = row[\"TEY\"]\n", " # sim.input[\"TIT\"] = row[\"TIT\"]\n", " # sim.input[\"TAT\"] = row[\"TAT\"]\n", " sim.compute()\n", " return sim.output[\"NOX\"]\n", "\n", "\n", "def rmse(row):\n", " return math.sqrt(metrics.mean_squared_error([row[\"Real\"]], [row[\"Inferred\"]]))\n", "\n", "result_train = X_train.copy()\n", "result_train[\"Real\"] = y_train\n", "result_train = result_train[:1000]\n", "\n", "result_train[\"Inferred\"] = result_train.apply(fuzzy_pred, axis=1)\n", "result_train[\"RMSE\"] = result_train.apply(rmse, axis=1)\n", "result_train = result_train.round({\"RMSE\": 3})\n", "result_train" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/var/folders/yr/lls0svhd1fl_wljz9zn7kgzh0000gn/T/ipykernel_20346/3258912175.py:1: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", " result_train.sort_values(by=\"RMSE\", ascending=False)[result_train[\"RMSE\"] < 10]\n" ] }, { "data": { "text/html": [ "
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ATAPAFDPTEYRealInferredRMSE
3586913.84201016.42.9790119.3862.92672.9112719.985
278823.74101013.24.4428155.6659.27769.2547449.978
2500916.08801013.23.1244109.3663.78373.7581939.975
6534.87031021.43.7381134.4585.45575.4841369.971
239109.42401013.03.6061134.8362.95072.9050009.955
........................
1328914.92101013.64.1546134.3672.53272.4665290.065
80515.06101019.65.1283156.4272.95672.9050000.051
295305.47791025.54.1873164.3561.08761.0355760.051
306108.65341028.33.0518126.8772.87272.9050000.033
13758.27171020.15.5121165.0660.38060.4038210.024
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493 rows × 7 columns

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" ], "text/plain": [ " AT AP AFDP TEY Real Inferred RMSE\n", "35869 13.8420 1016.4 2.9790 119.38 62.926 72.911271 9.985\n", "2788 23.7410 1013.2 4.4428 155.66 59.277 69.254744 9.978\n", "25009 16.0880 1013.2 3.1244 109.36 63.783 73.758193 9.975\n", "653 4.8703 1021.4 3.7381 134.45 85.455 75.484136 9.971\n", "23910 9.4240 1013.0 3.6061 134.83 62.950 72.905000 9.955\n", "... ... ... ... ... ... ... ...\n", "13289 14.9210 1013.6 4.1546 134.36 72.532 72.466529 0.065\n", "805 15.0610 1019.6 5.1283 156.42 72.956 72.905000 0.051\n", "29530 5.4779 1025.5 4.1873 164.35 61.087 61.035576 0.051\n", "30610 8.6534 1028.3 3.0518 126.87 72.872 72.905000 0.033\n", "1375 8.2717 1020.1 5.5121 165.06 60.380 60.403821 0.024\n", "\n", "[493 rows x 7 columns]" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "result_train.sort_values(by=\"RMSE\", ascending=False)[result_train[\"RMSE\"] < 10]" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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ATAPAFDPTEYRealInferredRMSE
1824623.45301006.23.7535132.4758.94872.80370713.856
2034328.70901011.26.0321145.9162.90972.9050009.996
292421.83301017.03.9663139.0461.08371.52468910.442
1177.81671022.24.6605164.7366.36953.69982212.669
571319.91201013.13.6710126.9056.67571.03346114.358
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219179.57911017.52.9617130.7471.31672.9050001.589
1309922.61501012.14.2739133.4463.30870.6037307.296
2670428.40201004.44.0643123.1749.21070.45110521.241
418231.74001012.24.5323148.1659.45268.7905369.339
298223.71301013.53.7112134.7566.80770.3844213.577
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7347 rows × 7 columns

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" ], "text/plain": [ " AT AP AFDP TEY Real Inferred RMSE\n", "18246 23.4530 1006.2 3.7535 132.47 58.948 72.803707 13.856\n", "20343 28.7090 1011.2 6.0321 145.91 62.909 72.905000 9.996\n", "2924 21.8330 1017.0 3.9663 139.04 61.083 71.524689 10.442\n", "117 7.8167 1022.2 4.6605 164.73 66.369 53.699822 12.669\n", "5713 19.9120 1013.1 3.6710 126.90 56.675 71.033461 14.358\n", "... ... ... ... ... ... ... ...\n", "21917 9.5791 1017.5 2.9617 130.74 71.316 72.905000 1.589\n", "13099 22.6150 1012.1 4.2739 133.44 63.308 70.603730 7.296\n", "26704 28.4020 1004.4 4.0643 123.17 49.210 70.451105 21.241\n", "4182 31.7400 1012.2 4.5323 148.16 59.452 68.790536 9.339\n", "2982 23.7130 1013.5 3.7112 134.75 66.807 70.384421 3.577\n", "\n", "[7347 rows x 7 columns]" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "result_test = X_test.copy()\n", "result_test[\"Real\"] = y_test\n", "# result_test = result_test[:items_count]\n", "\n", "result_test[\"Inferred\"] = result_test.apply(fuzzy_pred, axis=1)\n", "result_test[\"RMSE\"] = result_test.apply(rmse, axis=1)\n", "result_test = result_test.round({\"RMSE\": 3})\n", "result_test" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "result_test.hist(bins=30, figsize=(10, 10))\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{'RMSE_train': 12.98734738523384,\n", " 'RMSE_test': 13.28470315504043,\n", " 'RMAE_train': 3.2974956651470366,\n", " 'RMAE_test': 3.3117541879627588,\n", " 'MAE_train': 10.873477661663498,\n", " 'MAE_test': 10.967715801488872,\n", " 'R2_train': -0.28702863717588256,\n", " 'R2_test': -0.26174697467774677}" ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ "rmetrics = {}\n", "rmetrics[\"RMSE_train\"] = math.sqrt(\n", " metrics.mean_squared_error(result_train[\"Real\"], result_train[\"Inferred\"])\n", ")\n", "rmetrics[\"RMSE_test\"] = math.sqrt(\n", " metrics.mean_squared_error(result_test[\"Real\"], result_test[\"Inferred\"])\n", ")\n", "rmetrics[\"RMAE_train\"] = math.sqrt(\n", " metrics.mean_absolute_error(result_train[\"Real\"], result_train[\"Inferred\"])\n", ")\n", "rmetrics[\"RMAE_test\"] = math.sqrt(\n", " metrics.mean_absolute_error(result_test[\"Real\"], result_test[\"Inferred\"])\n", ")\n", "rmetrics[\"MAE_train\"] = float(\n", " metrics.mean_absolute_error(result_train[\"Real\"], result_train[\"Inferred\"])\n", ")\n", "rmetrics[\"MAE_test\"] = float(\n", " metrics.mean_absolute_error(result_test[\"Real\"], result_test[\"Inferred\"])\n", ")\n", "rmetrics[\"R2_train\"] = metrics.r2_score(result_train[\"Real\"], result_train[\"Inferred\"])\n", "rmetrics[\"R2_test\"] = metrics.r2_score(result_test[\"Real\"], result_test[\"Inferred\"])\n", "\n", "rmetrics" ] } ], "metadata": { "kernelspec": { "display_name": ".venv", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.12.9" } }, "nbformat": 4, "nbformat_minor": 2 }