IdealRMT-DecisionTrees/CompressedTreeParser.ipynb

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 "cells": [
  {
   "cell_type": "code",
   "execution_count": 138,
   "id": "938dec51",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import argparse\n",
    "from sklearn.tree import DecisionTreeClassifier, plot_tree, _tree\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.tree import export_graphviz\n",
    "import pydotplus\n",
    "from matplotlib import pyplot as plt\n",
    "from labels import mac_to_label\n",
    "import json\n",
    "import math"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "id": "442624c7",
   "metadata": {},
   "outputs": [],
   "source": [
    "Set1 = pd.read_csv('data/combined/data.csv').values.tolist()\n",
    "X = [i[0:3] for i in Set1]\n",
    "Y =[i[3] for i in Set1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "id": "12ad454d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'0': 20, '1': 20, '2': 9, '3': 20, '4': 0, '5': 13, '6': 20, '7': 0, '8': 12, '9': 4, '10': 20, '11': 4, '12': 1, '13': 16, '14': 20, '15': 2, '16': 20, '17': 0, '18': 20, '19': 20, '20': 20, '21': 20, '22': 20, '23': 1, '24': 2, '25': 20, '26': 13, '27': 11, '28': 20, '29': 20}\n"
     ]
    }
   ],
   "source": [
    "predict_Yt = []\n",
    "index=0\n",
    "\n",
    "with open('compressed_tree.json', 'r') as file:\n",
    "    data = json.load(file)\n",
    "    classes = data[\"classes\"]\n",
    "    for x in X:\n",
    "        counter = 0\n",
    "        class_set = []\n",
    "        paths_set = []\n",
    "        features = [\"protocol\", \"src\", \"dst\"]\n",
    "        for feature in features:\n",
    "            if feature in data[\"layers\"]:\n",
    "                for node in data['layers'][feature]:\n",
    "                    if node['min'] is None:\n",
    "                        if x[counter] <= node['max']:\n",
    "                            class_set.append(node['classes'])\n",
    "                            paths_set.append(node[\"paths\"])\n",
    "                            break #is this an issue?\n",
    "                        else:\n",
    "                            continue\n",
    "                    elif node['max'] is None:\n",
    "                        if node['min'] < x[counter]:\n",
    "                            class_set.append(node['classes'])\n",
    "                            paths_set.append(node[\"paths\"])\n",
    "                            break #is this an issue?\n",
    "                        else:\n",
    "                            continue\n",
    "                    elif node['min'] < x[counter] and x[counter] <= node['max']:\n",
    "                        class_set.append(node['classes'])\n",
    "                        paths_set.append(node[\"paths\"])\n",
    "                        break #is this an issue?\n",
    "\n",
    "            counter += 1\n",
    "        result = set(class_set[0])\n",
    "        paths = set(paths_set[0])\n",
    "        for s in class_set[1:]:\n",
    "            result.intersection_update(s)\n",
    "        for s in paths_set[1:]:\n",
    "            paths.intersection_update(s)\n",
    "\n",
    "        #predict_Yt.append(list(result))\n",
    "        #print(result)\n",
    "        if len(paths) != 1:\n",
    "            print(paths)\n",
    "            print(x)\n",
    "            print(result)\n",
    "        assert len(paths) == 1\n",
    "        path = list(paths)[0]\n",
    "        pred = data[\"path_to_class\"][str(path)]\n",
    "        pred_class = classes[pred]\n",
    "        predict_Yt.append(pred_class)\n",
    "        \n",
    "        index += 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "id": "8b4c56b6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8410252791654538\n"
     ]
    }
   ],
   "source": [
    "correct = 0\n",
    "for i in range(len(Y)):\n",
    "    prediction = predict_Yt[i]\n",
    "    if prediction != None and Y[i] == prediction:\n",
    "        correct += 1\n",
    "\n",
    "print(correct / len(Y))"
   ]
  }
 ],
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