{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 17,
   "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\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "442624c7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[6, 40234, 5228], [6, 40234, 5228], [6, 443, 46330], [6, 3063, 443], [1, 0, 0], [17, 61725, 53], [6, 5228, 40234], [6, 443, 3063], [0, 0, 0], [0, 0, 0], [6, 40234, 5228], [17, 4500, 45966], [17, 53, 61725], [1, 0, 0], [6, 46330, 443], [6, 443, 46330], [0, 0, 0], [1, 0, 0], [6, 3063, 443], [6, 443, 3063]]\n"
     ]
    }
   ],
   "source": [
    "Set1 = pd.read_csv('data.csv').values.tolist()\n",
    "X = [i[0:3] for i in Set1]\n",
    "Y =[i[3] for i in Set1]\n",
    "\n",
    "print(X[0:20])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "f18850b1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Amazon Echo', 'Belkin Motion Sensor', 'Belkin Switch', 'Blipcare BP Meter', 'Dropcam', 'HP Printer', 'NEST Smoke Sensor', 'Netatmo Camera', 'Netatmo Weather station', 'Pixstart photo frame', 'Samsung Smart Cam', 'Smart Things', 'TP-Link Camera', 'TP-Link Plug', 'Triby Speaker', 'Withings', 'Withings Scale', 'other']\n"
     ]
    }
   ],
   "source": [
    "classes  = [\n",
    "        \"Amazon Echo\",\n",
    "        \"Belkin Motion Sensor\",\n",
    "        \"Belkin Switch\",\n",
    "        \"Blipcare BP Meter\",\n",
    "        \"Dropcam\",\n",
    "        \"HP Printer\",\n",
    "        \"NEST Smoke Sensor\",\n",
    "        \"Netatmo Camera\",\n",
    "        \"Netatmo Weather station\",\n",
    "        \"Pixstart photo frame\",\n",
    "        \"Samsung Smart Cam\",\n",
    "        \"Smart Things\",\n",
    "        \"TP-Link Camera\",\n",
    "        \"TP-Link Plug\",\n",
    "        \"Triby Speaker\",\n",
    "        \"Withings\",\n",
    "        \"Withings Scale\",\n",
    "        \"other\"\n",
    "    ]\n",
    "\n",
    "print(classes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "12ad454d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1, 11, 17], [1, 11, 17], [1, 4, 11, 15, 17], [17], [8, 17], [], [1, 11, 17, 15], [11], [8, 17], [8, 17], [1, 11, 17], [], [], [8, 17], [17]]\n",
      "['other', 'other', 'Dropcam', 'other', 'Netatmo Camera', 'other', 'Triby Speaker', 'Smart Things', 'other', 'Belkin Switch', 'other', 'Netatmo Camera', 'Netatmo Camera', 'other', 'other']\n",
      "947072\n",
      "947072\n",
      "The accuracy was:  0.0\n"
     ]
    }
   ],
   "source": [
    "predict_Yt = []\n",
    "with open('compressed_tree.json', 'r') as file:\n",
    "    data = json.load(file)\n",
    "    #print(data['layers']['dst'])\n",
    "    for x in X:\n",
    "        counter = 0\n",
    "        class_set = []\n",
    "        for feature in reversed(data['layers']): #Have to reverse this list due to structure of the data.csv file and how it aligns with the compressed tree layers\n",
    "            #print(\"The feature is: \", feature)\n",
    "\n",
    "            for node in data['layers'][feature]:\n",
    "                #print(\"The node is: \", node)\n",
    "                #print(f\"The min is: {node['min']}, the max is {node['max']}\")\n",
    "                if node['min'] is None:\n",
    "                    if x[counter] < node['max']:\n",
    "                        class_set.append(node['classes'])\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",
    "                        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",
    "                    break #is this an issue?\n",
    "\n",
    "            counter += 1\n",
    "        #print(\"The list of classes is: \", class_set)\n",
    "        result = set(class_set[0])\n",
    "        for s in class_set[1:]:\n",
    "            result.intersection_update(s)\n",
    "\n",
    "        #print(\"The result was: \", result)\n",
    "        predict_Yt.append(list(result))\n",
    "        #print(predict_Yt)\n",
    "\n",
    "        \n",
    "\n",
    "print(predict_Yt[0:15])\n",
    "print(Y[0:15])\n",
    "\n",
    "\n",
    "print(len(predict_Yt))\n",
    "print(len(Y))\n",
    "\n",
    "\n",
    "counter = 0\n",
    "for index in range(len(Y)):\n",
    "    for possible_class_index in predict_Yt[index]:\n",
    "        if Y[index] == classes[possible_class_index]:\n",
    "            counter += 1\n",
    "\n",
    "\n",
    "print(\"The accuracy was: \", counter / len(Y))\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n"
   ]
  }
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