{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "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": 2,
   "id": "442624c7",
   "metadata": {},
   "outputs": [],
   "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]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "12ad454d",
   "metadata": {},
   "outputs": [],
   "source": [
    "predict_Yt = []\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",
    "        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",
    "            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",
    "                        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",
    "        result = set(class_set[0])\n",
    "        for s in class_set[1:]:\n",
    "            result.intersection_update(s)\n",
    "\n",
    "        #predict_Yt.append(list(result))\n",
    "        #print(result)\n",
    "        if len(result) == 1:\n",
    "            prediction = list(result)[0]\n",
    "            pred_class = classes[prediction]\n",
    "            predict_Yt.append(pred_class)\n",
    "        else:\n",
    "            predict_Yt.append(None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "8b4c56b6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8448217242194891\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))"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.12.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}