visual-comparison/Compare-Classification-Performance.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "aeff7acd",
   "metadata": {},
   "source": [
    "## Comparison of Binary Classification Results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "3b985b3f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LaTeX Table for Tuesday:\n",
      "\\begin{tabular}{rrrrr}\n",
      "\\toprule\n",
      "Precision & Recall & Accuracy & F1 Score & ROC AUC \\\\\n",
      "\\midrule\n",
      "0.9984 & 0.7723 & 0.9929 & 0.8709 & 0.8861 \\\\\n",
      "1.0000 & 1.0000 & 1.0000 & 1.0000 & 1.0000 \\\\\n",
      "1.0000 & 0.9986 & 1.0000 & 0.9993 & 0.9993 \\\\\n",
      "1.0000 & 0.9986 & 1.0000 & 0.9993 & 0.9993 \\\\\n",
      "0.9991 & 0.9997 & 1.0000 & 0.9994 & 0.9998 \\\\\n",
      "\\bottomrule\n",
      "\\end{tabular}\n",
      "\n",
      "\n",
      "LaTeX Table for Wednesday:\n",
      "\\begin{tabular}{rrrrr}\n",
      "\\toprule\n",
      "Precision & Recall & Accuracy & F1 Score & ROC AUC \\\\\n",
      "\\midrule\n",
      "0.9792 & 0.9958 & 0.9908 & 0.9874 & 0.9918 \\\\\n",
      "0.9999 & 0.9998 & 0.9999 & 0.9999 & 0.9999 \\\\\n",
      "1.0000 & 0.9999 & 1.0000 & 1.0000 & 1.0000 \\\\\n",
      "1.0000 & 0.9997 & 0.9999 & 0.9998 & 0.9999 \\\\\n",
      "0.9998 & 0.9995 & 0.9998 & 0.9996 & 0.9997 \\\\\n",
      "\\bottomrule\n",
      "\\end{tabular}\n",
      "\n",
      "\n",
      "LaTeX Table for Thursday:\n",
      "\\begin{tabular}{rrrrr}\n",
      "\\toprule\n",
      "Precision & Recall & Accuracy & F1 Score & ROC AUC \\\\\n",
      "\\midrule\n",
      "0.9966 & 0.8902 & 0.9995 & 0.9404 & 0.9451 \\\\\n",
      "1.0000 & 0.8806 & 0.9999 & 0.9365 & 0.9403 \\\\\n",
      "0.9685 & 0.9987 & 0.9965 & 0.9834 & 0.9975 \\\\\n",
      "0.9834 & 0.9995 & 0.9969 & 0.9914 & 0.9979 \\\\\n",
      "0.9840 & 0.9027 & 0.9835 & 0.9416 & 0.9501 \\\\\n",
      "\\bottomrule\n",
      "\\end{tabular}\n",
      "\n",
      "\n",
      "LaTeX Table for Friday:\n",
      "\\begin{tabular}{rrrrr}\n",
      "\\toprule\n",
      "Precision & Recall & Accuracy & F1 Score & ROC AUC \\\\\n",
      "\\midrule\n",
      "0.9992 & 0.9962 & 0.9981 & 0.9977 & 0.9978 \\\\\n",
      "0.9976 & 0.9997 & 0.9987 & 0.9986 & 0.9988 \\\\\n",
      "0.9978 & 0.9997 & 0.9988 & 0.9987 & 0.9989 \\\\\n",
      "1.0000 & 0.9999 & 1.0000 & 1.0000 & 1.0000 \\\\\n",
      "1.0000 & 0.9993 & 0.9998 & 0.9996 & 0.9996 \\\\\n",
      "\\bottomrule\n",
      "\\end{tabular}\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import json\n",
    "\n",
    "# File paths\n",
    "file_paths = [\n",
    "    '../1-CICIDS-2017/datasets/no_feat_sel/binary-class/model_performance_metrics.json',\n",
    "    '../2-WTMC-2021/datasets/no_feat_sel/binary-class/model_performance_metrics.json',\n",
    "    '../3-CRiSIS-2022/datasets/no_feat_sel/binary-class/model_performance_metrics.json',\n",
    "    '../4-NFS-2023/NFS-2023-nTE/no_feat_sel/binary-class/model_performance_metrics.json',\n",
    "    '../4-NFS-2023/NFS-2023-TE/no_feat_sel/binary-class/model_performance_metrics.json'\n",
    "]\n",
    "\n",
    "# Function to read JSON and return DataFrame\n",
    "def read_json_to_df(file_path):\n",
    "    with open(file_path, 'r') as file:\n",
    "        json_data = json.load(file)\n",
    "    return pd.DataFrame(json_data['data'], columns=json_data['columns'], index=json_data['index'])\n",
    "\n",
    "# Days of the week\n",
    "days = [\"Tuesday\", \"Wednesday\", \"Thursday\", \"Friday\"]\n",
    "\n",
    "# Iterate over each day and construct the DataFrame\n",
    "for day in days:\n",
    "    # List to hold data for the current day from each file\n",
    "    day_data_list = []\n",
    "\n",
    "    # Iterate through each JSON file\n",
    "    for path in file_paths:\n",
    "        df = read_json_to_df(path)\n",
    "        if day in df.index:\n",
    "            # Append the row corresponding to the current day to the list\n",
    "            day_data_list.append(df.loc[day].to_frame().T)\n",
    "\n",
    "    # Concatenate all rows into a single DataFrame\n",
    "    day_data = pd.concat(day_data_list, ignore_index=True)\n",
    "    \n",
    "    # Convert to LaTeX table\n",
    "    latex_table = day_data.to_latex(float_format=\"%.4f\", index=False)\n",
    "    print(f\"LaTeX Table for {day}:\\n{latex_table}\\n\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4f3e22ba",
   "metadata": {},
   "source": [
    "## Comparison of Multi-class Classification Results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "f9f89202",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LaTeX Table for Tuesday:\n",
      "\\begin{tabular}{rrrrr}\n",
      "\\toprule\n",
      "Precision & Recall & Accuracy & F1 Score & ROC AUC \\\\\n",
      "\\midrule\n",
      "0.9935 & 0.9935 & 0.9935 & 0.9925 & 0.9949 \\\\\n",
      "0.9999 & 0.9999 & 0.9999 & 0.9999 & 1.0000 \\\\\n",
      "0.9999 & 0.9999 & 0.9999 & 0.9999 & 0.9998 \\\\\n",
      "1.0000 & 1.0000 & 1.0000 & 1.0000 & 1.0000 \\\\\n",
      "1.0000 & 1.0000 & 1.0000 & 1.0000 & 1.0000 \\\\\n",
      "\\bottomrule\n",
      "\\end{tabular}\n",
      "\n",
      "\n",
      "LaTeX Table for Wednesday:\n",
      "\\begin{tabular}{rrrrr}\n",
      "\\toprule\n",
      "Precision & Recall & Accuracy & F1 Score & ROC AUC \\\\\n",
      "\\midrule\n",
      "0.9907 & 0.9906 & 0.9906 & 0.9906 & 0.9989 \\\\\n",
      "0.9999 & 0.9999 & 0.9999 & 0.9999 & 1.0000 \\\\\n",
      "0.9999 & 0.9999 & 0.9999 & 0.9999 & 1.0000 \\\\\n",
      "0.9997 & 0.9997 & 0.9997 & 0.9997 & 1.0000 \\\\\n",
      "0.9997 & 0.9997 & 0.9997 & 0.9997 & 0.9999 \\\\\n",
      "\\bottomrule\n",
      "\\end{tabular}\n",
      "\n",
      "\n",
      "LaTeX Table for Thursday:\n",
      "\\begin{tabular}{rrrrr}\n",
      "\\toprule\n",
      "Precision & Recall & Accuracy & F1 Score & ROC AUC \\\\\n",
      "\\midrule\n",
      "0.9979 & 0.9981 & 0.9981 & 0.9979 & 0.9840 \\\\\n",
      "0.9999 & 0.9999 & 0.9999 & 0.9999 & 0.9925 \\\\\n",
      "0.9966 & 0.9965 & 0.9965 & 0.9965 & 0.9986 \\\\\n",
      "0.9970 & 0.9969 & 0.9969 & 0.9969 & 0.9990 \\\\\n",
      "0.9839 & 0.9839 & 0.9839 & 0.9836 & 0.9954 \\\\\n",
      "\\bottomrule\n",
      "\\end{tabular}\n",
      "\n",
      "\n",
      "LaTeX Table for Friday:\n",
      "\\begin{tabular}{rrrrr}\n",
      "\\toprule\n",
      "Precision & Recall & Accuracy & F1 Score & ROC AUC \\\\\n",
      "\\midrule\n",
      "0.9977 & 0.9979 & 0.9979 & 0.9977 & 0.9999 \\\\\n",
      "0.9987 & 0.9987 & 0.9987 & 0.9987 & 0.9998 \\\\\n",
      "0.9988 & 0.9988 & 0.9988 & 0.9988 & 0.9998 \\\\\n",
      "1.0000 & 1.0000 & 1.0000 & 1.0000 & 1.0000 \\\\\n",
      "0.9998 & 0.9998 & 0.9998 & 0.9998 & 1.0000 \\\\\n",
      "\\bottomrule\n",
      "\\end{tabular}\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import json\n",
    "\n",
    "# File paths\n",
    "file_paths = [\n",
    "    '../1-CICIDS-2017/datasets/no_feat_sel/multi-class/model_performance_metrics.json',\n",
    "    '../2-WTMC-2021/datasets/no_feat_sel/multi-class/model_performance_metrics.json',\n",
    "    '../3-CRiSIS-2022/datasets/no_feat_sel/multi-class/model_performance_metrics.json',\n",
    "    '../4-NFS-2023/NFS-2023-nTE/no_feat_sel/multi-class/model_performance_metrics.json',\n",
    "    '../4-NFS-2023/NFS-2023-TE/no_feat_sel/multi-class/model_performance_metrics.json'  \n",
    "]\n",
    "\n",
    "# Function to read JSON and return DataFrame\n",
    "def read_json_to_df(file_path):\n",
    "    with open(file_path, 'r') as file:\n",
    "        json_data = json.load(file)\n",
    "    return pd.DataFrame(json_data['data'], columns=json_data['columns'], index=json_data['index'])\n",
    "\n",
    "# Days of the week\n",
    "days = [\"Tuesday\", \"Wednesday\", \"Thursday\", \"Friday\"]\n",
    "\n",
    "# Iterate over each day and construct the DataFrame\n",
    "for day in days:\n",
    "    # List to hold data for the current day from each file\n",
    "    day_data_list = []\n",
    "\n",
    "    # Iterate through each JSON file\n",
    "    for path in file_paths:\n",
    "        df = read_json_to_df(path)\n",
    "        if day in df.index:\n",
    "            # Append the row corresponding to the current day to the list\n",
    "            day_data_list.append(df.loc[day].to_frame().T)\n",
    "\n",
    "    # Concatenate all rows into a single DataFrame\n",
    "    day_data = pd.concat(day_data_list, ignore_index=True)\n",
    "    \n",
    "    # Convert to LaTeX table\n",
    "    latex_table = day_data.to_latex(float_format=\"%.4f\", index=False)\n",
    "    print(f\"LaTeX Table for {day}:\\n{latex_table}\\n\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "604ff93f",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.11.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}