ALMA_EdgeIIoT.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "73cf10d0",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import time\n",
    "\n",
    "from sklearn.utils import shuffle\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "from collections import Counter\n",
    "\n",
    "from river import evaluate\n",
    "from river import metrics\n",
    "from AdaptiveALMA import AdaptiveALMA\n",
    "\n",
    "from river import stream, compose, drift\n",
    "from river import preprocessing\n",
    "from river import optim\n",
    "from river import linear_model as ln\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "f6480439",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Original dataset shape  Counter({1: 49396, 0: 24301})\n"
     ]
    }
   ],
   "source": [
    "#Loading the dataset\n",
    "df= pd.read_csv(\"/Users/promisea/ALMA/EdgeIIoT/Edge_DDoS_Attack.csv\")\n",
    "print('Original dataset shape  %s' % Counter(df['Attack_type']))\n",
    "X = df.drop(['Attack_type'], axis=1)\n",
    "y = df['Attack_type']\n",
    "X, y = shuffle(X, y, random_state=42)\n",
    "dataset = stream.iter_pandas(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "bac804c2-6d12-4dd7-b41a-d7c74e557a0f",
   "metadata": {},
   "outputs": [],
   "source": [
    "ALMA = compose.Pipeline(\n",
    "    preprocessing.StandardScaler(),\n",
    "    ln.ALMAClassifier()\n",
    ")\n",
    "\n",
    "#Passing the evalaution metrics into a variable\n",
    "report = metrics.ClassificationReport()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "75f50d95-d1f6-49d0-ac24-e8166a50a980",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[50,000]            Precision   Recall   F1       Support  \n",
      "                                                  \n",
      "       0      74.16%   75.14%   74.65%     16575  \n",
      "       1      87.59%   87.02%   87.30%     33425  \n",
      "                                                  \n",
      "   Macro      80.88%   81.08%   80.98%            \n",
      "   Micro      83.08%   83.08%   83.08%            \n",
      "Weighted      83.14%   83.08%   83.11%            \n",
      "\n",
      "                 83.08% accuracy                   – 00:00:05 – 14.02 KB\n",
      "[73,697]            Precision   Recall   F1       Support  \n",
      "                                                  \n",
      "       0      74.50%   75.28%   74.89%     24301  \n",
      "       1      87.77%   87.33%   87.55%     49396  \n",
      "                                                  \n",
      "   Macro      81.14%   81.30%   81.22%            \n",
      "   Micro      83.35%   83.35%   83.35%            \n",
      "Weighted      83.40%   83.35%   83.38%            \n",
      "\n",
      "                 83.35% accuracy                   – 00:00:08 – 14.02 KB\n",
      "CPU times: user 8.01 s, sys: 62.3 ms, total: 8.07 s\n",
      "Wall time: 8.33 s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "           Precision   Recall   F1       Support  \n",
       "                                                  \n",
       "       0      74.50%   75.28%   74.89%     24301  \n",
       "       1      87.77%   87.33%   87.55%     49396  \n",
       "                                                  \n",
       "   Macro      81.14%   81.30%   81.22%            \n",
       "   Micro      83.35%   83.35%   83.35%            \n",
       "Weighted      83.40%   83.35%   83.38%            \n",
       "\n",
       "                 83.35% accuracy                  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "evaluate.progressive_val_score(dataset, ALMA, report, print_every=50000, show_time=True, show_memory=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "2b0c52d4-776f-4e33-9a23-38a8341e5c67",
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset = stream.iter_pandas(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "56f5c2a3-f3dc-423b-a72d-803ffe15c44c",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = compose.Pipeline(\n",
    "    preprocessing.StandardScaler(),\n",
    "    AdaptiveALMA()\n",
    ")\n",
    "\n",
    "#Passing the evalaution metrics into a variable\n",
    "report = metrics.ClassificationReport()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "6ee1ab5a-3656-4e7a-9da0-1bbc1cb22ab9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[50,000]            Precision   Recall   F1       Support  \n",
      "                                                  \n",
      "       0      80.13%   70.73%   75.14%     16575  \n",
      "       1      86.28%   91.30%   88.72%     33425  \n",
      "                                                  \n",
      "   Macro      83.21%   81.01%   81.93%            \n",
      "   Micro      84.48%   84.48%   84.48%            \n",
      "Weighted      84.24%   84.48%   84.22%            \n",
      "\n",
      "                 84.48% accuracy                   – 00:00:05 – 21.05 KB\n",
      "[73,697]            Precision   Recall   F1       Support  \n",
      "                                                  \n",
      "       0      79.86%   70.93%   75.13%     24301  \n",
      "       1      86.44%   91.20%   88.76%     49396  \n",
      "                                                  \n",
      "   Macro      83.15%   81.06%   81.94%            \n",
      "   Micro      84.52%   84.52%   84.52%            \n",
      "Weighted      84.27%   84.52%   84.26%            \n",
      "\n",
      "                 84.52% accuracy                   – 00:00:08 – 21.47 KB\n",
      "CPU times: user 8.41 s, sys: 46 ms, total: 8.46 s\n",
      "Wall time: 8.46 s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "           Precision   Recall   F1       Support  \n",
       "                                                  \n",
       "       0      79.86%   70.93%   75.13%     24301  \n",
       "       1      86.44%   91.20%   88.76%     49396  \n",
       "                                                  \n",
       "   Macro      83.15%   81.06%   81.94%            \n",
       "   Micro      84.52%   84.52%   84.52%            \n",
       "Weighted      84.27%   84.52%   84.26%            \n",
       "\n",
       "                 84.52% accuracy                  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "evaluate.progressive_val_score(dataset, model, report, print_every=50000, show_time=True, show_memory=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "a869f334-6edd-4f0c-8ac5-fae631d38446",
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset = stream.iter_pandas(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "8964bd8e-b80c-4537-bc70-2947760fe7ff",
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_performance(dataset, metric, models):\n",
    "    metric_name = metric.__class__.__name__\n",
    "    \n",
    "    # Creating a single plot for accuracy\n",
    "    fig, ax = plt.subplots(figsize=(10, 4), dpi=300)\n",
    "    threshold_reached_step = None  # To store the step where accuracy reaches 75%\n",
    "\n",
    "    for model_name, model in models.items():\n",
    "        steps = []\n",
    "        accuracies = []\n",
    "\n",
    "        for checkpoint in evaluate.iter_progressive_val_score(\n",
    "            dataset, model, metric, measure_time=False, measure_memory=False, step=20\n",
    "        ):\n",
    "            current_step = checkpoint[\"Step\"]\n",
    "            current_accuracy = checkpoint[metric_name].get()\n",
    "            steps.append(current_step)\n",
    "            accuracies.append(current_accuracy)\n",
    "\n",
    "            # Check if the accuracy reaches 75% for the first time\n",
    "            if current_accuracy >= 0.75 and threshold_reached_step is None:\n",
    "                threshold_reached_step = current_step\n",
    "\n",
    "        ax.plot(steps, accuracies, label=model_name)\n",
    "\n",
    "        # Draw a vertical red line and annotate if the threshold is reached\n",
    "        if threshold_reached_step:\n",
    "            ax.axvline(x=threshold_reached_step, color='red', linestyle='--')\n",
    "            ax.annotate(f'{threshold_reached_step} samples', xy=(threshold_reached_step, 0.75),\n",
    "                        xytext=(threshold_reached_step + 50, 0.80),\n",
    "                        arrowprops=dict(facecolor='blue', shrink=0.05),\n",
    "                        horizontalalignment='left', verticalalignment='top')\n",
    "\n",
    "    ax.set_ylabel('Accuracy', fontweight='bold')\n",
    "    ax.set_xlabel('#Samples', fontweight='bold')\n",
    "    ax.grid(True)\n",
    "    ax.legend(\n",
    "        loc='upper center', bbox_to_anchor=(0.5, 1.15),\n",
    "        ncol=1, fancybox=True, shadow=True\n",
    "    )\n",
    "    \n",
    "    # Apply bold formatting directly to tick labels\n",
    "    for label in ax.get_xticklabels() + ax.get_yticklabels():\n",
    "        label.set_fontweight('bold')\n",
    "\n",
    "    plt.tight_layout()\n",
    "    plt.savefig('DDoS_EdgeIIoT.png')\n",
    "    plt.close()\n",
    "\n",
    "    return fig"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "d24d21e2-c476-4c0b-b567-b922de646c01",
   "metadata": {},
   "outputs": [
    {
     "data": {
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EGIYUTaS1tSFuB+aSaRUHPCoMuFXdZHcFHFbozwaaioMeBT12UKq8wKfB+T4Zhv25iCbSLUIQ9vnSpv23R2meV0GPS/GUmTmOW3k+t/xup0YUBeR0GookUopHk0qZUibHYofx6mNKmWY2yDesyK+CTKe6gdYcenP14oQiYCDFU/Ydb+KZ799Eysp2fY0m02qMbQ9zJtKm/B6XBoU8Kgx45HM7s+HF5uCTHVxwqjGWVDJlqS66PXAZS9rf03k+lwoDbhX4PfK5HTmBz+aJY4mUaQdwk3ZI1ut2alDImw29dYdl2cGIaDbgm87Wkso85pS5PZjS3D02HLcDvAGPS06Hsh0qrRbdKq1sN0t7OWVaqo0klE5bmQlk2yeS+dz25LGmeEo1ETtMGk/ZE+AaYna32ny/PVHLDiCn1JQNItuh4eZ/TofkddkdaxuiSTsEnXAqZdohuKJP37V/juT7sq/tuRxG9ndKS1aLj1uG9raPG4ZUGPBoUJ5XAbdT7mxgzshO2EumrezPg2gyrXgqnf2ZkDatbIAxmrC7PjYHxLP/WnQPbb5LUl1TUrFUOvu3Q3d5MiHn5uCsMzPhbXC+V/l+t2oyd2ZqeW7TVDYEm9uFdHswFxgIpqXs931/aA50N4fBW9aRSJlqnmYfT5mZu1p2/650XcXr2b0vZVpKtXguNfXT82pHf3Z39HqHaUmNsVSXtu0Pzb/vyLJkf+baL6ax/0oacC077fcFe/J//10DsWtKZBpAhHvwOnL2tei9jP09lBqw1+D/3+GjCJqjzxA0BwAAAPpBJJHSu6tr9OrSSi1YXa2apkS2k0HLbqdBj1OThubLYRhavKkhGyqX1OaP0tVVTVpd1ZQz9vLnW3Xfa6uyy2V5Xh0wvEClIa/KC/wqCXk0aWi+BoW8Glrot2/TlrnFmGFIry/bpmVbG7WqMqy0Zak6nNAnG+vU0OrFxd5S1eJVis8qGiRJ9RXJ7MddtWRzg5ZsbtCf3l4jSRqS79OWhliP65s2qkijSgLZEEjA45KZCd4vXFuj99d1HoQHAAD9Z111ROuqI23Gl27ZNd5ybA7PVNRtDwY0Voa1ojI8gFW1lcrcfWVHkmlL762t2eGxOltG30ukTSWipuqj9mSPgTSqJKBBIa+2hePaVBfNCbMZhpTvswPnLodhh51NUw2xVKed2lwOQ2V5XjmdhhpjqUz4pmeau2qm0uZOdZjrqi0NsZy/WWq68H3Xu1xyGJbchhRfML/dLXxuR3YiQHNwcHxZSKNKgnbnVIcht8uh4oBHMppve5/O3go7mjDlMJQNSjoy3WGbu/IXZroHNsaS2bB+Q8y+44GZCanWR5PZ50teprOpy2lPGhpa6NewQr+GFvo0tNAvj9ORnbyQMk01xlKKp0x5XY7sbcH9meBpyrQ7ejaHH5NpU1saYmqKp7N3WGiIprS5PqqtDfFsV9ZIwu5u63M55XQY2YkbzeFIj9PuxurMTNqxu6oacjqauy06NbzIr1ElQQ0t9MnvtruWmaa9vjjoUXVTXKm0papwXMlM59GGWFI+t1OJlCnLsju5JtKmQl6XIolUNnDrMAy5HYZcTodSaVOWlO2cak/ActvBT58787m0v45FQY9Kgh6V5fnkdtmTnAIeVzawalmW6iJJxVOmapoSSmUmN7QMzdrL279pto9tf04Zhv3zsCmeynQrtjv/Jk1LPpcjG0ZoDkLneV1KmnYw1Q5G20HfgoA7tyOuYXfxrWmyP1euzB06fG6ngl6nkmlLjbGkIol0JgBtaUt9VFXhRDbg13KqS85t6A2p0G8Hfwv8bqUyoYvmO3A0xe3nmd/tlMtpqCqc0LbGuMKZjsaxTBDYYRiqiyRV2Rjr07vZ7Izm27939PqGfat4V/b5WhTwKN/vyt6pw7Tsr2VFbVTxTFg5mkwPQDfcgbD9mbMtnNC2cELLt+5av9v1BzuMtH05nZlQ1huvzQEAAADomr3jbzAMFILmAAAAA6A+klRDLKnN9THVNCU0dVShyvLs2aWptKm11U16d02NAh6npo8q1ojiwABX3DEzE5apiyTlchoanLmF5J58i3bLsrSxNqq11U1qjKW075A8jSwOZG8B1nK7lZVhPbFwg/62cENuaLwDTYm0Fq6t7bVaKxvj+u+Syg7Xe10OJdLmHheW7q03sj5YV6sP1vXe1wN218lhRX6FvC6VhjwyDPvWlcOL/Rpa4NeQAp8+q6jX0i2Nend1da9NcnAYUnmBX8OK/Bpe6Fc8Zeq9tTXa1ti9wJXLYai80JcJtPhVHPCoOOSRwzBU2RBXyrSDLRtqImqIpeRxOuRwSH63SxuqG7V6S51qE1Layr1GGoY0OM9n11dkB2aGFfnlczm1uT6qJVsaFY6ltL4moqKAWz633cGuOZRVURdVVTiuhmiy3zrj9FR+pmNnPHPrYEvSsEK/gl6nAm6XxpWFNLE8T/uU5ammKaFXlm7V6m1N2e66XpdDo0oCGlkcVHHQLbfToVXbwvpoQ51MUxpa6JNlSQ2xpH07a79bsqR42tS2hri2heN2Z9BEWg6HoQmD8zSxPE/RhKktDVGtrYrItKztt2bN3MI46HFpv/I8bayNavW2sJriaXndDhUGPHIYUlHAo011US3b2tjm2up3O2XJatN5JuixO9U133I8z7f95ZpIIq36aFJN8RQBTQDAbq2jySCSshNwW07C7YqUaWlTfe+G2FJ2YrdXj7mrMi1D8U4eaixpKpaM50xSGMgAZWN8e1ewrQ1xLd7UvQnK/WXLrlnWTnM5DFniDes92Y6+to2xVJvupgAAAACAXQt/t6MvETQHgO5KpaSNG9tfN3y45OLSCqBjry7dqttfWNruG7PFQY/qIokO388uL/Bp2qgi+d1OTR9dJL/HpbGlQU0emp+9xa1lWVpd1aS1VU1Kpk1NGJynMaXB7PqWUmlTFXVRhbwuFQc9em1ZpRaurdVnFfVasTWcDerOmDBIkwf75aw3tDUq1cQNff7GOi2pjOrjDXUyLavNm01Bj1NTRxXp/MNH6UuTBqsqnNCj767TysqwSoIenXzgUB0yuliptKn1NRGZljS6JCCX0+4Otr4moqDHpeFF/pzaU2mzX2+VbVmW4ilTG2ujenPFNv3ro01aV93UYeepIfk+fWGfUq2stIOOu4O98bZle4vCgFsBt1M+j1Mhr329mFier4DXpYDbqU11UW2qj2p9TUTLtjSqLM+nAr9b28JxrWzVWXVEsV8z9hmkI8aVqCjgkdflkNfllNftUMjrksthqCGWlN/jUlmeV59srNfG2ojqo0ml0pbGlYVUludVyOtSeYFvh9/HJ0weIsn+HmyIpbSxNqKmeFojiv1qiqdV05RQMm0qz+dSMm1lu+a17JjXPNbc5W/soJAK/O4259pcH1VTPCUj011ufU2TQl63EplOiMu2NmpNVVijS4I6Zr8yjSoJyrmTE2kaGhr02muv2Z324pJv+CTlhYKaVJ6nkcXBnbodeUeqwnE99u56vbq0Um6n3XlyW2Nci9bXKZk2NaIoIK/bIa/LIY/LqXyfS6UhrwzD/vw1T0AJeOxAe8o05XI45HU7NCTfp/ICXzYMVhtJyLKkkpBXDdGklm5pUCSRVlHAviV70OvSgcMLNb4spETaVFmeV8MK/e3+bOrMVw4o77XPT3+wMrdwdxiGYsm0/G6nHA5DpmmpqimuWMJU0jQ1JN+noLdrf0OYpv1zadW2sDbWRuR1O1Ua9KowYD+3q5sSaowllTYtFQU8mVs92x0WiwIeDSnwKZpIa2tjTG6HQ9FkWpvqoool0yoNeTUoz6uSkEe1TUnVRBIaUxJU0OtUZWNcAY9T+T77PCsrw6qoi6oxZt/aPpk2FY6l1BCzJzrEEmmt3BbOdmZ1OuwJJWnL7vq4O01uKg56NLokIK/LngzgcTqUTJtatL5WhpTtblobSXbacRcAAGB3xWRHAAAAAAB2fQTN0ZcMy9qd3t4DesfixYs1ZcqU7PJnn32myZMnD2BF6EvNgZ5ms2bNUn5+/s4fcO1aacyY9tetWSONHr3zxwZ2UjyV1t8WbtD66ogG5/t04VGj23RWRu+zLEtrqpq0uT4m07Kyobm6SEJrq5tUEvRq0tB8hbwuLVhdrd+8skKL1tf1eh3N3XWbA2Ob2+nmtu/gPAW9ThX43dp/eKFWVjbq9WXbBrTrbUnQk70tdnfk+Vw6eGSRnIZ9u+SUaSnodWp0SVDfOGykyvJ9ne6fTJuKxNMqCLgVS6a1eFODVmxt1JqqJn2+uUFV4YRqmxLc2hX9xuUwFPK5VNfJrbOHZW4Jn+ezbxsuSaUhT7ab9pShBXI7HfK4HJpUni+/x9njuqrDcW2uj6kk5FF5gb/Hx0Mf/F4K7MIsy1J9NCmvy5lzTYokUqqojcrhMFQS9MjhMLS1PibTUubOH5ZKQ16ZlpRImWqM2RMaosm0EilT4XhKm+tjSqZMNcSSWlPVpIZoKttx3u1yyDQtJVKmSkIeDc73qSmeUlU4LsMw5DSM7AQUS/ZEgEEhryYMydOwQr9GlwRVmudRUzylkNetwfneLk2KME174l19NKk11U3aXBfVloaYfG6ngh6nioNeFQXd2bB+yrRUF0moLpJUOJ7KTswxLUtW5vOXSJnaVB9TJJ5SVVNCAbdTQa9LHpehxlhKQ/J9Kgp6tHxro+Yt2yZJcjsNJdM7/1Kfy2HfnabA77YnLHlciiTsz5/f7cxOSHEYhoqDHhUFPcrzuVQdTiiSSGlrQ1wbayNKmZYK/G57YkU4oYq66E7XBAAAAAAAdi+uzMR8t9Mht9OQ22nfyS9tWkpblv2/ack07aYE8VRaljp+/cXtNORyOORzO9QQS+UE6ZyO5rsR2v87DEMOw8i+ftE81rze5TCyjVBMy5Jl2f+nTEsN0aR8mbsN+t3ObFMOqWWTk9ymJ2nTUlPcbvYQS6b3lpsjSbLvAOl2Omi+gC4xDMlpGHI47Ndom793nQ77Y5fDoaDXaX9PJdKyLMnpaLFPZj9Hy/8dajtmGHI57etNfdRuymJmrjv2NckhhyGlLUumaX//N69v1nxdaN5mR9qLXrZ3KUikTDkdhooCHpnNTVlkX0csy8581DTZTencTvv1V5fDISv7mrFkycr833x9ylzHTFOJZFKZITldLqUt5Tx+wzBkWdYueZ3yOO07cBuGFPK45HU75cs0S/K5nW3+Ny372huOp9QUT9n7Zo6Vtiyl05aSpqVU2lQqbWUnUKctS07D/hmVSJmKJtPZYwYyjbuaczaRRErhuH13p92lcdtTVxyh6aOLB7oM9JGBzrvSdhcAgN3c55sadPXji7R6W1N27Nbnl0iSZh+7j2ZMGKSpIwtlGHa32bqmpIpDHjtcY1n6YF2tFq6tVVmeV185oFw+d8/DiXu6+auq9H8fVGj+qqp2Q939LWVa2lDTeXhn2dbG7MevZYJIA6060+W0uxpjKb2xvP3H8OtXVugL40s1YXBetttxYcCttCkt2dygZVsatWpbmG5cu6migFtel3O3nATgdhqaWJ6v/YcVyO92yuk0NLwooENHF2vcoKBcTofSpqXV28JqjKfkyrxolu9za1ihX46d7KDdEyUhr0pC3n4/L4A9g2EYKgx42owHPC7tMzgvZ6w5fL07czgMFQTcKgi4NbIkMCA1mKYlI/PGal0koZWVYfv3/0hSpmVPtnMY9hsaAY9LKdOU2+lQeYFfg/K8Ksv3KuRx9cnPnIZYUvNXVmnh2lrVNiVU1ZRQJPMifSSZUnHQq2gipQ01UaVMM3MnBfv3NcOQSoJ2x/2GzF0UIgMwWTLgccrtdMjMvJMTTab5nbKPODJvVLudDvk99ps8sWRajbGUfG6nXA47MLCtMZ7zxpjLYXT4NQl47Em3zZMoCvxuBb32nVFiybRiybQcmTeZ0qalSCKlaCKtoqBHAY9Tm+piaojab9Z5nA45MxMFPU6HCgNuBT0u1UQSqo8kVd0UV2VjPPMmqf1GqctpZMMMLoeRnSBYFPCoMZbU5vpY5nc/l6rCCaVNS8OK/CoNebJvaPkyd5PxuZ3yuZ2KJ9NatrVRdZGkIolUjyaZAAAA7Ak8LodSaVOmJfkzYaHm3yf9Hldm8u72jx0O+26P8aQ9kTmeMuV2Gook0jIk5fvdmd8VTfndzmyAyZf5uCGaVGMsJbfTUMDjUtDrVMDjUsjrktflyDRYsRRJpNUQS6ohmlI8lZbL4VBjzP7d0u+2f08NZN6v2R56bRFmzYTd6qJJpdJ2yCltWkqm7YBaV/4uaR3EdbYM4mZCeS1Duk5HbkjX6TAU9LoU9LgU9LoU8tqf23A8LZfDyN69rCDgVqHfrTyfW6ZlKZ5MqyETzkqlzWzAOJUJGKfM7cvxzO/8bqchGYbCsWT2TnGezO/PHpcdtgt6XfK5ndm7Gub73fK6HArHU9m/F+3H0fzYtwcUPS6HBuV5s3c+LPC7VZrnlTdzp0HTlJoSmdBaZsJ7VTihyoaYIom0XM7tgUh35mN35m+E5t/7/W6nmhLp7PmaA9cel0M+l32HSJ/HKUNSKm3ZdxSU/XV2Oew6Uqb9tU2lLTkM+2/QxlhKjZnnUvP7H83bJE1TyZQlr9uRDUlnQ9aZz4HDkIJee11zJjKQed42ZMKYDsf254jTYago6FFJ0CO3yyFD9t9qnkwAL5yw/1Zpiqfkz4SzfW6nPE5Ht15baHkXyJQpHfGFo5Wfnyd3i7+lWjYBME378bod3TtPf4kl09pSH9Om+qjiSTtE3xzubIzZd9ZMm6YaM4F5M3NXyaDXpUTKlGnZk/dDmee51+1Q0ONSwOPMfn08Tkf2znsp01JVY9xuYmCaSqQspUwz+zpGczAzkTIVy9QTT5nZyQBm5hpjmpYSaVNN8ZScjsz3nNOhlGnK53aq0O9WcdAjj8tuehX0uLJ3cLQs+5oUS6UVjtnPT7NFwNXQ9muNwzAUTdphWo/LkNfllMdlT0Soz1znmr8flAnTtpwQ0ByiNTNh2XSm7mTKtP9Pm0pkPk6kti9blhTyZT6PmedT2rRUG0lmJ0M0P2aX037OGVLOtdDQ9mtjy/+NFuvta23zmJF57B3sqxb7tvjfMJQNzFra/ppfMm0Hpz1OR/b1AX/mNQKf2yFfpjlXJJFSNGn/vPE4HbLU4nqbtrJ3bG1KpOR1OTIB59zPWyJlKmXa15migN1owu9xZZ/jhpR97jQ3z0iZlnyZu+AGPfbz1+HYfj3p7h1O91amaSmaTCvgcXbrc9a20dGMDhsdNQeso4m04im7wUs8+2/7cu7/aaXS9te4+bWplpOJml/H87udCseTmZC//XPa3c5z1t/8vM1cowzDUCpt2t9v/XhttyyrS5/nRMq+PpqZ7dv7PDWPtRxPpE3Fk+ns91bu9m232T4hYvvnNW3ar1+6nc0/F+2fw3WRhEzLypms0Xz3W6AvEDQHAGA3ZFmWlmxu1HV//0hLtzR2uN2vX1mhX7+yot11QY+zTTfrOU9+rMPGFOsL40s1c98y7T+8oFfr3p1FE2kt2dKgu/+7osOQM3YNb62s0lsrqwa6jF4V8Dg1Y59BOnJ8iQoDHpXlebVia6NeXLxFK7aGJUmjS4Pyu516fTd4fhYHPcr3uXTcxMEaXRrMdKxN6fPN9Vq0vk7bGuOSpENGF+nQMcU6Ymyppo4qVMCz/c+XZNpUfTSp5VsbNX9ltV5fvk2fbarPvkC+I+UFvl6dKDK6JKCg16W1VU2Kp0xNHlagC48cpRMnlyuSSKkw4Mm+eNcRp8NoE74EAKCrWr4AXxjw7FKdS/J9bp04pVwnTinv8j4NsaRcDkM+lzPnsTU0NOg//31NluxA8owZM7S+0dKi9bVavKlBNU0JFfjdGpTn1djSoBoyneaTaVOD8rxKpiyF40k5DENel0PFQY8dash0pRmS77O71/hcGpTnVXm+XzKkfJ8r500Hy7LUEE1pS0NMLqehIfk+WZKSKTPbld6SVBWOK5JIy8x0Y4om0qoKx+VxOhRPpbWlIaaapoTC8bQaokmtr4moNpKQmelklDTtN4KbBTxODS/yy7KkWMoOR+f73Mr3u+RzOdUYT8k0LQ3K86o+mpTTYdjBGrf9mEpD3hb/PCoJeTNvrCbU/GneUh9X2rLkczns7kEuh/J8bpXm2ROWt9THtKkuppWVjVq1rUmJlB0YaYzZkxqG5Ps0rMivfYfkyZF5k6gw4FYybWXDO3led/YN3uY3yJsD2Dv6nalZOJ7S2ip7wvXIkoDyfW7VNiW0sTYqZ2byh9tpqMBvT1bck1mWpW3huGqaEtk3/aIJU7WRhGLJtNKmpc31MW2qiyqeMuX32G/iVYUT2lwfzek+F46n1BBNZr93unPL3c7C/nsqt9NQWZ5PeT5XzhuBHpfDvhNEJsQUTaRV4HdrSIG9rcvhUHVTXE1x+2+FtGlpa0NMadNSOJ5SbVNCsaSpmszdtrZlJi/43E75PXZYwO20O3t5XPb3TTJtaUvmjmsel0ORRFq1TYm97msC7O4MQwp5Xcr3uTPhEmlzXUxNiVS3Og86HXYXx+aA6K44Ianl5DKX01AwE9BNmaYi2S6W28NYOR8bueP5fpe8LntyWjQTDI5nJpPFUnZIuPmOPD6XQ/GUqZDPDvRFE+lsUDiQCSUHvHYQOeB1ZX+XMi079BOJp5Xvd8lpJrX088VyGVJa0qh9JiqccmhzXTT7e2O6eTJo5jG3/H3SyATStn9sv95V1ZRQTTiRE5ZLZkInzZPWvK7mCXku+dyObPDT6TCyQTyv2/5Z5HTY+zgyk+CaQ7fNYz63Pfkt6HXJ3RxSyZxne3DWkQ3ppkw7mGQHfKVE2tS2xrgchrJBoUgira0NMW1piCkcS6koaP8euT1kqNxuptmPlZ3853U5s1+P5tCSy+FQY+Z3+exjdm4PQntdTpmZb5RdMXzaV9KZjtDpTCjbMLaHq5tDi0BfyPe5e7V5gB2gtv/+7uy4Dochr2PX/RvP53ZqdGlQo0uD/XbOkLd/o195rb4+hmHI47Kv33tCQ4ndHw2EdmeOzOSuvtQ8Cap50tWuovluE/2pq78n2Z+zto11gL0JQXMAAHYjlmXplSWVum/eSn24vq5Hx2odMm/27poavbumRne+vFxOh6G3bzpGQwp8PTrX7iqZNrW2qkl/WbBWT76/cbe5JRJ2bYUBt/YdnKcRxQGNKQ3K5TBUlu9VSdCrZVsa9cJnm7VsS6O8bqe+NHGwzpg2XAcML2hzt4HDx5bo/CNGtzl+OJ7SK0u2qj6alNvp0IaaiJZvbdTKyrCqwgmF46ku1Tm2NKgDRxQq5HVpZHFABwwv0PDigPJ8Ln22sV5bG+2QtiFD00cXKRxPKRxLqTjo0brqiBwOI/vm1IjigMryvNpcH9OQAl/2lmM94XY6siGpI8eV6voT9lVdJKEVlWEt3dygWNLuNmNZlrxup0aXBBVLplUS8mjy0AINyst9oc2+FZ/dveH9tbX6cH2tEilTtRE7zN7cTSeWNO032JwOFfrdOnRMsU6YPFjjBoVkGEY2pONxbX+Mfs+u+6I3AAC7qs7emGz5o9Xndmr66Px+D9YbxvYu9jlavZdX2gt3B2nuaJZMmwp5XQMeFBk7KKSxg0L6wj6lA1pHyOvSlGG5k6OLMgGyvY1h2GHnsrze/dvdygTq6qN218TG2PbbyDeH/4KZQKDP7VSe16V4ylRlQ1ymZSnkc8nlMNQQTWnJlgat2NqoRMpUUdCj8gK/hhf5VeB3qz5q332hJpJQRW1UDsO+u47badhhS78dYHE6pGjCDrdJ9vlrmxLaFrbDbR6XQ2V5Po0vC6nA75Zh2HfECsfszpbNnegdhjKTTlJyOKRCvycbIKyoi2pbY1yGoXa70TcvOx12l7aSoGeXDrI1dxasboprU11UTodDQY9TdQ2NeuudhYqbhhJp6fBpB2pseYncTsPuThqzO3faXxv7/2girXDCnuARjqeyt56OJNKqiyTsCTx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      "text/plain": [
       "<Figure size 3000x1200 with 1 Axes>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plot_performance(\n",
    "    dataset,\n",
    "    metrics.Accuracy(),\n",
    "    {\n",
    "        \"Real-time Performance of Adaptive ALMA on DDoS attack type of the EdgeIIoT Dataset\": (\n",
    "            preprocessing.StandardScaler() | AdaptiveALMA()\n",
    "        )\n",
    "    }\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "feda38d4-7fa4-4f8d-8c4c-a98b01e1bd68",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Original dataset shape  Counter({0: 24301, 1: 21148})\n"
     ]
    }
   ],
   "source": [
    "#Loading the dataset\n",
    "df= pd.read_csv(\"/Users/promisea/ALMA/EdgeIIoT/Edge_IG_Attack.csv\")\n",
    "print('Original dataset shape  %s' % Counter(df['Attack_type']))\n",
    "X = df.drop(['Attack_type'], axis=1)\n",
    "y = df['Attack_type']\n",
    "X, y = shuffle(X, y, random_state=42)\n",
    "dataset = stream.iter_pandas(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "1d28bd18-25dd-4934-9525-1b92c42209f5",
   "metadata": {},
   "outputs": [],
   "source": [
    "ALMA = compose.Pipeline(\n",
    "    preprocessing.StandardScaler(),\n",
    "    ln.ALMAClassifier()\n",
    ")\n",
    "\n",
    "#Passing the evalaution metrics into a variable\n",
    "report = metrics.ClassificationReport()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "0173b62c-3a18-47a4-88ef-66887b58a3c0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[10,000]            Precision   Recall   F1       Support  \n",
      "                                                  \n",
      "       0      87.17%   85.43%   86.29%      5386  \n",
      "       1      83.38%   85.33%   84.34%      4614  \n",
      "                                                  \n",
      "   Macro      85.27%   85.38%   85.32%            \n",
      "   Micro      85.38%   85.38%   85.38%            \n",
      "Weighted      85.42%   85.38%   85.39%            \n",
      "\n",
      "                 85.38% accuracy                   – 00:00:01 – 14.02 KB\n",
      "[20,000]            Precision   Recall   F1       Support  \n",
      "                                                  \n",
      "       0      87.93%   86.93%   87.42%     10700  \n",
      "       1      85.15%   86.27%   85.71%      9300  \n",
      "                                                  \n",
      "   Macro      86.54%   86.60%   86.57%            \n",
      "   Micro      86.62%   86.62%   86.62%            \n",
      "Weighted      86.64%   86.62%   86.63%            \n",
      "\n",
      "                 86.62% accuracy                   – 00:00:02 – 14.02 KB\n",
      "[30,000]            Precision   Recall   F1       Support  \n",
      "                                                  \n",
      "       0      88.02%   87.28%   87.65%     16072  \n",
      "       1      85.47%   86.29%   85.88%     13928  \n",
      "                                                  \n",
      "   Macro      86.74%   86.79%   86.76%            \n",
      "   Micro      86.82%   86.82%   86.82%            \n",
      "Weighted      86.83%   86.82%   86.83%            \n",
      "\n",
      "                 86.82% accuracy                   – 00:00:03 – 14.02 KB\n",
      "[40,000]            Precision   Recall   F1       Support  \n",
      "                                                  \n",
      "       0      88.16%   87.51%   87.84%     21390  \n",
      "       1      85.77%   86.50%   86.13%     18610  \n",
      "                                                  \n",
      "   Macro      86.97%   87.00%   86.98%            \n",
      "   Micro      87.04%   87.04%   87.04%            \n",
      "Weighted      87.05%   87.04%   87.04%            \n",
      "\n",
      "                 87.04% accuracy                   – 00:00:04 – 14.02 KB\n",
      "[45,449]            Precision   Recall   F1       Support  \n",
      "                                                  \n",
      "       0      88.12%   87.56%   87.84%     24301  \n",
      "       1      85.81%   86.43%   86.12%     21148  \n",
      "                                                  \n",
      "   Macro      86.97%   87.00%   86.98%            \n",
      "   Micro      87.04%   87.04%   87.04%            \n",
      "Weighted      87.05%   87.04%   87.04%            \n",
      "\n",
      "                 87.04% accuracy                   – 00:00:04 – 14.02 KB\n",
      "CPU times: user 4.89 s, sys: 24.8 ms, total: 4.91 s\n",
      "Wall time: 4.93 s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "           Precision   Recall   F1       Support  \n",
       "                                                  \n",
       "       0      88.12%   87.56%   87.84%     24301  \n",
       "       1      85.81%   86.43%   86.12%     21148  \n",
       "                                                  \n",
       "   Macro      86.97%   87.00%   86.98%            \n",
       "   Micro      87.04%   87.04%   87.04%            \n",
       "Weighted      87.05%   87.04%   87.04%            \n",
       "\n",
       "                 87.04% accuracy                  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "evaluate.progressive_val_score(dataset, ALMA, report, print_every=10000, show_time=True, show_memory=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "bf69d9ea-b5e9-42b8-95d2-e415bd184dd9",
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset = stream.iter_pandas(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "b8a00909-d827-485a-99f9-02423fcd4d70",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = compose.Pipeline(\n",
    "    preprocessing.StandardScaler(),\n",
    "    AdaptiveALMA()\n",
    ")\n",
    "\n",
    "#Passing the evalaution metrics into a variable\n",
    "report = metrics.ClassificationReport()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "be9a25d6-923d-4e40-9b6a-96bf3ac57745",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[10,000]            Precision   Recall   F1       Support  \n",
      "                                                  \n",
      "       0      89.91%   93.28%   91.56%      5386  \n",
      "       1      91.80%   87.78%   89.74%      4614  \n",
      "                                                  \n",
      "   Macro      90.85%   90.53%   90.65%            \n",
      "   Micro      90.74%   90.74%   90.74%            \n",
      "Weighted      90.78%   90.74%   90.72%            \n",
      "\n",
      "                 90.74% accuracy                   – 00:00:01 – 20.9 KB\n",
      "[20,000]            Precision   Recall   F1       Support  \n",
      "                                                  \n",
      "       0      90.65%   93.05%   91.83%     10700  \n",
      "       1      91.75%   88.96%   90.33%      9300  \n",
      "                                                  \n",
      "   Macro      91.20%   91.00%   91.08%            \n",
      "   Micro      91.14%   91.14%   91.14%            \n",
      "Weighted      91.16%   91.14%   91.13%            \n",
      "\n",
      "                 91.14% accuracy                   – 00:00:02 – 21.02 KB\n",
      "[30,000]            Precision   Recall   F1       Support  \n",
      "                                                  \n",
      "       0      90.62%   92.79%   91.69%     16072  \n",
      "       1      91.45%   88.91%   90.16%     13928  \n",
      "                                                  \n",
      "   Macro      91.03%   90.85%   90.93%            \n",
      "   Micro      90.99%   90.99%   90.99%            \n",
      "Weighted      91.00%   90.99%   90.98%            \n",
      "\n",
      "                 90.99% accuracy                   – 00:00:03 – 20.92 KB\n",
      "[40,000]            Precision   Recall   F1       Support  \n",
      "                                                  \n",
      "       0      90.65%   92.82%   91.73%     21390  \n",
      "       1      91.52%   89.00%   90.24%     18610  \n",
      "                                                  \n",
      "   Macro      91.09%   90.91%   90.98%            \n",
      "   Micro      91.05%   91.05%   91.05%            \n",
      "Weighted      91.06%   91.05%   91.04%            \n",
      "\n",
      "                 91.05% accuracy                   – 00:00:04 – 20.9 KB\n",
      "[45,449]            Precision   Recall   F1       Support  \n",
      "                                                  \n",
      "       0      90.64%   92.87%   91.74%     24301  \n",
      "       1      91.57%   88.98%   90.26%     21148  \n",
      "                                                  \n",
      "   Macro      91.10%   90.93%   91.00%            \n",
      "   Micro      91.06%   91.06%   91.06%            \n",
      "Weighted      91.07%   91.06%   91.05%            \n",
      "\n",
      "                 91.06% accuracy                   – 00:00:05 – 21.05 KB\n",
      "CPU times: user 5.23 s, sys: 21.6 ms, total: 5.25 s\n",
      "Wall time: 5.29 s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "           Precision   Recall   F1       Support  \n",
       "                                                  \n",
       "       0      90.64%   92.87%   91.74%     24301  \n",
       "       1      91.57%   88.98%   90.26%     21148  \n",
       "                                                  \n",
       "   Macro      91.10%   90.93%   91.00%            \n",
       "   Micro      91.06%   91.06%   91.06%            \n",
       "Weighted      91.07%   91.06%   91.05%            \n",
       "\n",
       "                 91.06% accuracy                  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "evaluate.progressive_val_score(dataset, model, report, print_every=10000, show_time=True, show_memory=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "3d0cb36d-11f9-48c2-a3fc-f9d5fd012893",
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset = stream.iter_pandas(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "b8bbca4e-0297-4e98-acee-909d83d0c79f",
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_performance(dataset, metric, models):\n",
    "    metric_name = metric.__class__.__name__\n",
    "    \n",
    "    # Creating a single plot for accuracy\n",
    "    fig, ax = plt.subplots(figsize=(10, 4), dpi=300)\n",
    "    threshold_reached_step = None  # To store the step where accuracy reaches 75%\n",
    "\n",
    "    for model_name, model in models.items():\n",
    "        steps = []\n",
    "        accuracies = []\n",
    "\n",
    "        for checkpoint in evaluate.iter_progressive_val_score(\n",
    "            dataset, model, metric, measure_time=False, measure_memory=False, step=20\n",
    "        ):\n",
    "            current_step = checkpoint[\"Step\"]\n",
    "            current_accuracy = checkpoint[metric_name].get()\n",
    "            steps.append(current_step)\n",
    "            accuracies.append(current_accuracy)\n",
    "\n",
    "            # Check if the accuracy reaches 75% for the first time\n",
    "            if current_accuracy >= 0.75 and threshold_reached_step is None:\n",
    "                threshold_reached_step = current_step\n",
    "\n",
    "        ax.plot(steps, accuracies, label=model_name)\n",
    "\n",
    "        # Draw a vertical red line and annotate if the threshold is reached\n",
    "        if threshold_reached_step:\n",
    "            ax.axvline(x=threshold_reached_step, color='red', linestyle='--')\n",
    "            ax.annotate(f'{threshold_reached_step} samples', xy=(threshold_reached_step, 0.75),\n",
    "                        xytext=(threshold_reached_step + 50, 0.80),\n",
    "                        arrowprops=dict(facecolor='blue', shrink=0.05),\n",
    "                        horizontalalignment='left', verticalalignment='top')\n",
    "\n",
    "    ax.set_ylabel('Accuracy', fontweight='bold')\n",
    "    ax.set_xlabel('#Samples', fontweight='bold')\n",
    "    ax.grid(True)\n",
    "    ax.legend(\n",
    "        loc='upper center', bbox_to_anchor=(0.5, 1.15),\n",
    "        ncol=1, fancybox=True, shadow=True\n",
    "    )\n",
    "    \n",
    "    # Apply bold formatting directly to tick labels\n",
    "    for label in ax.get_xticklabels() + ax.get_yticklabels():\n",
    "        label.set_fontweight('bold')\n",
    "\n",
    "    plt.tight_layout()\n",
    "    plt.savefig('Information_EdgeIIoT.png')\n",
    "    plt.close()\n",
    "\n",
    "    return fig"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "d60b513e-e4c2-4a88-8deb-9baae28a1ebb",
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 3000x1200 with 1 Axes>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plot_performance(\n",
    "    dataset,\n",
    "    metrics.Accuracy(),\n",
    "    {\n",
    "        \"Real-time Performance of Adaptive ALMA on Information Gathering attack type of the EdgeIIoT Dataset\": (\n",
    "            preprocessing.StandardScaler() | AdaptiveALMA()\n",
    "        )\n",
    "    }\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "7a70d95c-796a-4257-b121-868b04cd6fae",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Original dataset shape  Counter({1: 30632, 0: 24301})\n"
     ]
    }
   ],
   "source": [
    "#Loading the dataset\n",
    "df= pd.read_csv(\"/Users/promisea/ALMA/EdgeIIoT/Edge_Injection_Attack.csv\")\n",
    "print('Original dataset shape  %s' % Counter(df['Attack_type']))\n",
    "X = df.drop(['Attack_type'], axis=1)\n",
    "y = df['Attack_type']\n",
    "X, y = shuffle(X, y, random_state=42)\n",
    "dataset = stream.iter_pandas(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "13c4a4ec-eee2-41c7-8ef5-ddfd4c5a2e13",
   "metadata": {},
   "outputs": [],
   "source": [
    "ALMA = compose.Pipeline(\n",
    "    preprocessing.StandardScaler(),\n",
    "    ln.ALMAClassifier()\n",
    ")\n",
    "\n",
    "#Passing the evalaution metrics into a variable\n",
    "report = metrics.ClassificationReport()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "4741c5ae-723e-4e6d-b842-70a21df2afb7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[50,000]            Precision   Recall   F1       Support  \n",
      "                                                  \n",
      "       0      63.23%   61.96%   62.59%     22191  \n",
      "       1      70.12%   71.25%   70.68%     27809  \n",
      "                                                  \n",
      "   Macro      66.68%   66.60%   66.64%            \n",
      "   Micro      67.13%   67.13%   67.13%            \n",
      "Weighted      67.06%   67.13%   67.09%            \n",
      "\n",
      "                 67.13% accuracy                   – 00:00:05 – 14.02 KB\n",
      "[54,933]            Precision   Recall   F1       Support  \n",
      "                                                  \n",
      "       0      63.21%   61.96%   62.58%     24301  \n",
      "       1      70.29%   71.39%   70.84%     30632  \n",
      "                                                  \n",
      "   Macro      66.75%   66.68%   66.71%            \n",
      "   Micro      67.22%   67.22%   67.22%            \n",
      "Weighted      67.16%   67.22%   67.18%            \n",
      "\n",
      "                 67.22% accuracy                   – 00:00:06 – 14.02 KB\n",
      "CPU times: user 6.08 s, sys: 54.8 ms, total: 6.13 s\n",
      "Wall time: 6.13 s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "           Precision   Recall   F1       Support  \n",
       "                                                  \n",
       "       0      63.21%   61.96%   62.58%     24301  \n",
       "       1      70.29%   71.39%   70.84%     30632  \n",
       "                                                  \n",
       "   Macro      66.75%   66.68%   66.71%            \n",
       "   Micro      67.22%   67.22%   67.22%            \n",
       "Weighted      67.16%   67.22%   67.18%            \n",
       "\n",
       "                 67.22% accuracy                  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "evaluate.progressive_val_score(dataset, ALMA, report, print_every=50000, show_time=True, show_memory=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "4c2cb7fa-3a85-4382-91fd-d145293a3a96",
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset = stream.iter_pandas(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "cbeb5e96-e789-489d-8a6a-df7eec9b706b",
   "metadata": {},
   "outputs": [],
   "source": [
    "Model = compose.Pipeline(\n",
    "    preprocessing.StandardScaler(),\n",
    "    AdaptiveALMA()\n",
    ")\n",
    "\n",
    "#Passing the evalaution metrics into a variable\n",
    "report = metrics.ClassificationReport()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "98425d44-b3d8-4135-88b7-69ed46a00bb8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[50,000]            Precision   Recall   F1       Support  \n",
      "                                                  \n",
      "       0      53.70%   86.78%   66.34%     22191  \n",
      "       1      79.25%   40.29%   53.42%     27809  \n",
      "                                                  \n",
      "   Macro      66.47%   63.53%   59.88%            \n",
      "   Micro      60.92%   60.92%   60.92%            \n",
      "Weighted      67.91%   60.92%   59.16%            \n",
      "\n",
      "                 60.92% accuracy                   – 00:00:06 – 21.05 KB\n",
      "[54,933]            Precision   Recall   F1       Support  \n",
      "                                                  \n",
      "       0      53.69%   86.23%   66.18%     24301  \n",
      "       1      78.96%   41.00%   53.97%     30632  \n",
      "                                                  \n",
      "   Macro      66.33%   63.62%   60.08%            \n",
      "   Micro      61.01%   61.01%   61.01%            \n",
      "Weighted      67.78%   61.01%   59.37%            \n",
      "\n",
      "                 61.01% accuracy                   – 00:00:07 – 21.13 KB\n",
      "CPU times: user 7.09 s, sys: 72.8 ms, total: 7.16 s\n",
      "Wall time: 7.14 s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "           Precision   Recall   F1       Support  \n",
       "                                                  \n",
       "       0      53.69%   86.23%   66.18%     24301  \n",
       "       1      78.96%   41.00%   53.97%     30632  \n",
       "                                                  \n",
       "   Macro      66.33%   63.62%   60.08%            \n",
       "   Micro      61.01%   61.01%   61.01%            \n",
       "Weighted      67.78%   61.01%   59.37%            \n",
       "\n",
       "                 61.01% accuracy                  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "evaluate.progressive_val_score(dataset, model, report, print_every=50000, show_time=True, show_memory=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "c819c579-e065-40f9-8ce2-6adda132af48",
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset = stream.iter_pandas(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "6c9dd91b-3f20-4222-be0e-2785ab0d692d",
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_performance(dataset, metric, models):\n",
    "    metric_name = metric.__class__.__name__\n",
    "    \n",
    "    # Creating a single plot for accuracy\n",
    "    fig, ax = plt.subplots(figsize=(10, 4), dpi=300)\n",
    "    threshold_reached_step = None  # To store the step where accuracy reaches 75%\n",
    "\n",
    "    for model_name, model in models.items():\n",
    "        steps = []\n",
    "        accuracies = []\n",
    "\n",
    "        for checkpoint in evaluate.iter_progressive_val_score(\n",
    "            dataset, model, metric, measure_time=False, measure_memory=False, step=20\n",
    "        ):\n",
    "            current_step = checkpoint[\"Step\"]\n",
    "            current_accuracy = checkpoint[metric_name].get()\n",
    "            steps.append(current_step)\n",
    "            accuracies.append(current_accuracy)\n",
    "\n",
    "            # Check if the accuracy reaches 75% for the first time\n",
    "            if current_accuracy >= 0.75 and threshold_reached_step is None:\n",
    "                threshold_reached_step = current_step\n",
    "\n",
    "        ax.plot(steps, accuracies, label=model_name)\n",
    "\n",
    "        # Draw a vertical red line and annotate if the threshold is reached\n",
    "        if threshold_reached_step:\n",
    "            ax.axvline(x=threshold_reached_step, color='red', linestyle='--')\n",
    "            ax.annotate(f'{threshold_reached_step} samples', xy=(threshold_reached_step, 0.75),\n",
    "                        xytext=(threshold_reached_step + 50, 0.80),\n",
    "                        arrowprops=dict(facecolor='blue', shrink=0.05),\n",
    "                        horizontalalignment='left', verticalalignment='top')\n",
    "\n",
    "    ax.set_ylabel('Accuracy', fontweight='bold')\n",
    "    ax.set_xlabel('#Samples', fontweight='bold')\n",
    "    ax.grid(True)\n",
    "    ax.legend(\n",
    "        loc='upper center', bbox_to_anchor=(0.5, 1.15),\n",
    "        ncol=1, fancybox=True, shadow=True\n",
    "    )\n",
    "    \n",
    "    # Apply bold formatting directly to tick labels\n",
    "    for label in ax.get_xticklabels() + ax.get_yticklabels():\n",
    "        label.set_fontweight('bold')\n",
    "\n",
    "    plt.tight_layout()\n",
    "    plt.savefig('Injection_EdgeIIoT.png')\n",
    "    plt.close()\n",
    "\n",
    "    return fig"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "b9499902-e24b-41e6-9c93-5ad8495a0231",
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 3000x1200 with 1 Axes>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plot_performance(\n",
    "    dataset,\n",
    "    metrics.Accuracy(),\n",
    "    {\n",
    "        \"Real-time Performance of Adaptive ALMA on Injection attack type of the EdgeIIoT Dataset\": (\n",
    "            preprocessing.StandardScaler() | AdaptiveALMA()\n",
    "        )\n",
    "    }\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "18cd9d88-75b0-4c09-b759-278f2addfaea",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Original dataset shape  Counter({1: 31109, 0: 24301})\n"
     ]
    }
   ],
   "source": [
    "#Loading the dataset\n",
    "df= pd.read_csv(\"/Users/promisea/ALMA/EdgeIIoT/Edge_Malware_Attack.csv\")\n",
    "print('Original dataset shape  %s' % Counter(df['Attack_type']))\n",
    "X = df.drop(['Attack_type'], axis=1)\n",
    "y = df['Attack_type']\n",
    "X, y = shuffle(X, y, random_state=42)\n",
    "dataset = stream.iter_pandas(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "1981fa8d-5ae5-473a-b816-ab3267851ee3",
   "metadata": {},
   "outputs": [],
   "source": [
    "ALMA = compose.Pipeline(\n",
    "    preprocessing.StandardScaler(),\n",
    "    ln.ALMAClassifier()\n",
    ")\n",
    "\n",
    "#Passing the evalaution metrics into a variable\n",
    "report = metrics.ClassificationReport()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "56ced06d-9d7f-4467-bbf8-b6e9fa91d28a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[50,000]            Precision   Recall   F1       Support  \n",
      "                                                  \n",
      "       0      65.57%   63.33%   64.43%     21998  \n",
      "       1      71.95%   73.87%   72.90%     28002  \n",
      "                                                  \n",
      "   Macro      68.76%   68.60%   68.66%            \n",
      "   Micro      69.24%   69.24%   69.24%            \n",
      "Weighted      69.14%   69.24%   69.17%            \n",
      "\n",
      "                 69.24% accuracy                   – 00:00:05 – 14.02 KB\n",
      "[55,410]            Precision   Recall   F1       Support  \n",
      "                                                  \n",
      "       0      65.49%   63.45%   64.46%     24301  \n",
      "       1      72.13%   73.88%   72.99%     31109  \n",
      "                                                  \n",
      "   Macro      68.81%   68.67%   68.72%            \n",
      "   Micro      69.31%   69.31%   69.31%            \n",
      "Weighted      69.22%   69.31%   69.25%            \n",
      "\n",
      "                 69.31% accuracy                   – 00:00:06 – 14.02 KB\n",
      "CPU times: user 6.08 s, sys: 51.5 ms, total: 6.13 s\n",
      "Wall time: 6.13 s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "           Precision   Recall   F1       Support  \n",
       "                                                  \n",
       "       0      65.49%   63.45%   64.46%     24301  \n",
       "       1      72.13%   73.88%   72.99%     31109  \n",
       "                                                  \n",
       "   Macro      68.81%   68.67%   68.72%            \n",
       "   Micro      69.31%   69.31%   69.31%            \n",
       "Weighted      69.22%   69.31%   69.25%            \n",
       "\n",
       "                 69.31% accuracy                  "
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "evaluate.progressive_val_score(dataset, ALMA, report, print_every=50000, show_time=True, show_memory=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "2f79e896-95cd-412d-9974-7c41fa97a8b8",
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset = stream.iter_pandas(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "636fac49-cd43-42e8-bef4-7627f85757b8",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = compose.Pipeline(\n",
    "    preprocessing.StandardScaler(),\n",
    "    AdaptiveALMA()\n",
    ")\n",
    "\n",
    "#Passing the evalaution metrics into a variable\n",
    "report = metrics.ClassificationReport()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "3747670b-ebf4-4513-bda4-574eef614874",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[50,000]            Precision   Recall   F1       Support  \n",
      "                                                  \n",
      "       0      84.70%   45.81%   59.46%     21998  \n",
      "       1      68.72%   93.50%   79.21%     28002  \n",
      "                                                  \n",
      "   Macro      76.71%   69.66%   69.34%            \n",
      "   Micro      72.52%   72.52%   72.52%            \n",
      "Weighted      75.75%   72.52%   70.53%            \n",
      "\n",
      "                 72.52% accuracy                   – 00:00:05 – 21.03 KB\n",
      "[55,410]            Precision   Recall   F1       Support  \n",
      "                                                  \n",
      "       0      84.79%   45.74%   59.42%     24301  \n",
      "       1      68.83%   93.59%   79.32%     31109  \n",
      "                                                  \n",
      "   Macro      76.81%   69.66%   69.37%            \n",
      "   Micro      72.60%   72.60%   72.60%            \n",
      "Weighted      75.83%   72.60%   70.59%            \n",
      "\n",
      "                 72.60% accuracy                   – 00:00:06 – 21.02 KB\n",
      "CPU times: user 6.53 s, sys: 47.5 ms, total: 6.58 s\n",
      "Wall time: 6.57 s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "           Precision   Recall   F1       Support  \n",
       "                                                  \n",
       "       0      84.79%   45.74%   59.42%     24301  \n",
       "       1      68.83%   93.59%   79.32%     31109  \n",
       "                                                  \n",
       "   Macro      76.81%   69.66%   69.37%            \n",
       "   Micro      72.60%   72.60%   72.60%            \n",
       "Weighted      75.83%   72.60%   70.59%            \n",
       "\n",
       "                 72.60% accuracy                  "
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "evaluate.progressive_val_score(dataset, model, report, print_every=50000, show_time=True, show_memory=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "392bf2ff-0123-4984-9264-dfa87bffb220",
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset = stream.iter_pandas(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "c6815540-8065-4929-94a2-301128738561",
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_performance(dataset, metric, models):\n",
    "    metric_name = metric.__class__.__name__\n",
    "    \n",
    "    # Creating a single plot for accuracy\n",
    "    fig, ax = plt.subplots(figsize=(10, 4), dpi=300)\n",
    "    threshold_reached_step = None  # To store the step where accuracy reaches 75%\n",
    "\n",
    "    for model_name, model in models.items():\n",
    "        steps = []\n",
    "        accuracies = []\n",
    "\n",
    "        for checkpoint in evaluate.iter_progressive_val_score(\n",
    "            dataset, model, metric, measure_time=False, measure_memory=False, step=20\n",
    "        ):\n",
    "            current_step = checkpoint[\"Step\"]\n",
    "            current_accuracy = checkpoint[metric_name].get()\n",
    "            steps.append(current_step)\n",
    "            accuracies.append(current_accuracy)\n",
    "\n",
    "            # Check if the accuracy reaches 75% for the first time\n",
    "            if current_accuracy >= 0.75 and threshold_reached_step is None:\n",
    "                threshold_reached_step = current_step\n",
    "\n",
    "        ax.plot(steps, accuracies, label=model_name)\n",
    "\n",
    "        # Draw a vertical red line and annotate if the threshold is reached\n",
    "        if threshold_reached_step:\n",
    "            ax.axvline(x=threshold_reached_step, color='red', linestyle='--')\n",
    "            ax.annotate(f'{threshold_reached_step} samples', xy=(threshold_reached_step, 0.75),\n",
    "                        xytext=(threshold_reached_step + 50, 0.80),\n",
    "                        arrowprops=dict(facecolor='blue', shrink=0.05),\n",
    "                        horizontalalignment='left', verticalalignment='top')\n",
    "\n",
    "    ax.set_ylabel('Accuracy', fontweight='bold')\n",
    "    ax.set_xlabel('#Samples', fontweight='bold')\n",
    "    ax.grid(True)\n",
    "    ax.legend(\n",
    "        loc='upper center', bbox_to_anchor=(0.5, 1.15),\n",
    "        ncol=1, fancybox=True, shadow=True\n",
    "    )\n",
    "    \n",
    "    # Apply bold formatting directly to tick labels\n",
    "    for label in ax.get_xticklabels() + ax.get_yticklabels():\n",
    "        label.set_fontweight('bold')\n",
    "\n",
    "    plt.tight_layout()\n",
    "    plt.savefig('Malware_EdgeIIoT.png')\n",
    "    plt.close()\n",
    "\n",
    "    return fig"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "adf1a3c7-e75a-4691-a3b0-f64913f64ad6",
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 3000x1200 with 1 Axes>"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plot_performance(\n",
    "    dataset,\n",
    "    metrics.Accuracy(),\n",
    "    {\n",
    "        \"Real-time Performance of Adaptive ALMA on Malware attack type of the EdgeIIoT Dataset\": (\n",
    "            preprocessing.StandardScaler() | AdaptiveALMA()\n",
    "        )\n",
    "    }\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "bce1bcc3-2b19-4b8c-b9df-2d31693fd04e",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Loading the dataset\n",
    "df = pd.read_csv(\"/Users/promisea/ALMA/EdgeIIoT/Edge_Benign_Only.csv\")\n",
    "df1= pd.read_csv(\"/Users/promisea/ALMA/EdgeIIoT/Edge_DDoS_Only.csv\")\n",
    "df2= pd.read_csv(\"/Users/promisea/ALMA/EdgeIIoT/Edge_Malware_Only.csv\")\n",
    "X = df.drop(['Attack_type'], axis=1)\n",
    "y = df['Attack_type']\n",
    "X1 = df1.drop(['Attack_type'], axis=1)\n",
    "y1 = df1['Attack_type']\n",
    "X2 = df2.drop(['Attack_type'], axis=1)\n",
    "y2 = df2['Attack_type']\n",
    "X3 = pd.concat([X, X1, X2])\n",
    "y3 = pd.concat([y, y1, y2])\n",
    "X3, y3 = shuffle(X3, y3, random_state=42)\n",
    "dataset = stream.iter_pandas(X3, y3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "1ee3b197-2691-4d28-ad98-821e826f9fbc",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = compose.Pipeline(\n",
    "    preprocessing.StandardScaler(),\n",
    "    AdaptiveALMA()\n",
    ")\n",
    "\n",
    "#Passing the evalaution metrics into a variable\n",
    "report = metrics.ClassificationReport()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "46dee632-8e39-4dee-8e23-d0b4fe4d5ac3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[50,000]            Precision   Recall   F1       Support  \n",
      "                                                  \n",
      "       0      80.04%   37.67%   51.23%     11426  \n",
      "       1      84.04%   97.22%   90.15%     38574  \n",
      "                                                  \n",
      "   Macro      82.04%   67.44%   70.69%            \n",
      "   Micro      83.61%   83.61%   83.61%            \n",
      "Weighted      83.13%   83.61%   81.26%            \n",
      "\n",
      "                 83.61% accuracy                   – 00:00:05 – 21.05 KB\n",
      "[100,000]            Precision   Recall   F1       Support  \n",
      "                                                  \n",
      "       0      80.70%   37.26%   50.99%     23148  \n",
      "       1      83.74%   97.32%   90.02%     76852  \n",
      "                                                  \n",
      "   Macro      82.22%   67.29%   70.50%            \n",
      "   Micro      83.41%   83.41%   83.41%            \n",
      "Weighted      83.04%   83.41%   80.98%            \n",
      "\n",
      "                 83.41% accuracy                   – 00:00:11 – 21.05 KB\n",
      "[104,806]            Precision   Recall   F1       Support  \n",
      "                                                  \n",
      "       0      80.75%   37.31%   51.04%     24301  \n",
      "       1      83.72%   97.32%   90.01%     80505  \n",
      "                                                  \n",
      "   Macro      82.24%   67.31%   70.52%            \n",
      "   Micro      83.40%   83.40%   83.40%            \n",
      "Weighted      83.03%   83.40%   80.97%            \n",
      "\n",
      "                 83.40% accuracy                   – 00:00:12 – 21.56 KB\n",
      "CPU times: user 12.2 s, sys: 72.4 ms, total: 12.3 s\n",
      "Wall time: 12.3 s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "           Precision   Recall   F1       Support  \n",
       "                                                  \n",
       "       0      80.75%   37.31%   51.04%     24301  \n",
       "       1      83.72%   97.32%   90.01%     80505  \n",
       "                                                  \n",
       "   Macro      82.24%   67.31%   70.52%            \n",
       "   Micro      83.40%   83.40%   83.40%            \n",
       "Weighted      83.03%   83.40%   80.97%            \n",
       "\n",
       "                 83.40% accuracy                  "
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "evaluate.progressive_val_score(dataset, model, report, print_every=50000, show_time=True, show_memory=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "0959cf2f-c4bb-41de-8544-8dbcb5d1e80e",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Loading the dataset\n",
    "df = pd.read_csv(\"/Users/promisea/ALMA/EdgeIIoT/Edge_Benign_Only.csv\")\n",
    "df1= pd.read_csv(\"/Users/promisea/ALMA/EdgeIIoT/Edge_DDoS_Only.csv\")\n",
    "df2= pd.read_csv(\"/Users/promisea/ALMA/EdgeIIoT/Edge_Injection_Only.csv\")\n",
    "X = df.drop(['Attack_type'], axis=1)\n",
    "y = df['Attack_type']\n",
    "X1 = df1.drop(['Attack_type'], axis=1)\n",
    "y1 = df1['Attack_type']\n",
    "X2 = df2.drop(['Attack_type'], axis=1)\n",
    "y2 = df2['Attack_type']\n",
    "X3 = pd.concat([X, X1, X2])\n",
    "y3 = pd.concat([y, y1, y2])\n",
    "X3, y3 = shuffle(X3, y3, random_state=42)\n",
    "dataset = stream.iter_pandas(X3, y3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "1e7148eb-38c5-4960-8538-6a46dcdeaca0",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = compose.Pipeline(\n",
    "    preprocessing.StandardScaler(),\n",
    "    AdaptiveALMA()\n",
    ")\n",
    "\n",
    "#Passing the evalaution metrics into a variable\n",
    "report = metrics.ClassificationReport()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "128c50f6-bce5-481e-bd14-44c0c338900b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[50,000]            Precision   Recall   F1       Support  \n",
      "                                                  \n",
      "       0      74.95%   52.60%   61.81%     11455  \n",
      "       1      87.06%   94.77%   90.75%     38545  \n",
      "                                                  \n",
      "   Macro      81.00%   73.69%   76.28%            \n",
      "   Micro      85.11%   85.11%   85.11%            \n",
      "Weighted      84.28%   85.11%   84.12%            \n",
      "\n",
      "                 85.11% accuracy                   – 00:00:05 – 20.95 KB\n",
      "[100,000]            Precision   Recall   F1       Support  \n",
      "                                                  \n",
      "       0      76.29%   52.65%   62.30%     23257  \n",
      "       1      86.88%   95.04%   90.78%     76743  \n",
      "                                                  \n",
      "   Macro      81.59%   73.85%   76.54%            \n",
      "   Micro      85.18%   85.18%   85.18%            \n",
      "Weighted      84.42%   85.18%   84.16%            \n",
      "\n",
      "                 85.18% accuracy                   – 00:00:11 – 20.9 KB\n",
      "[104,329]            Precision   Recall   F1       Support  \n",
      "                                                  \n",
      "       0      76.43%   52.66%   62.35%     24301  \n",
      "       1      86.86%   95.07%   90.78%     80028  \n",
      "                                                  \n",
      "   Macro      81.65%   73.86%   76.57%            \n",
      "   Micro      85.19%   85.19%   85.19%            \n",
      "Weighted      84.43%   85.19%   84.16%            \n",
      "\n",
      "                 85.19% accuracy                   – 00:00:12 – 21.02 KB\n",
      "CPU times: user 12.1 s, sys: 67.3 ms, total: 12.2 s\n",
      "Wall time: 12.3 s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "           Precision   Recall   F1       Support  \n",
       "                                                  \n",
       "       0      76.43%   52.66%   62.35%     24301  \n",
       "       1      86.86%   95.07%   90.78%     80028  \n",
       "                                                  \n",
       "   Macro      81.65%   73.86%   76.57%            \n",
       "   Micro      85.19%   85.19%   85.19%            \n",
       "Weighted      84.43%   85.19%   84.16%            \n",
       "\n",
       "                 85.19% accuracy                  "
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "evaluate.progressive_val_score(dataset, model, report, print_every=50000, show_time=True, show_memory=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "7c790960-5e32-4877-83bf-43bdf3c6d616",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Loading the dataset\n",
    "df = pd.read_csv(\"/Users/promisea/ALMA/EdgeIIoT/Edge_Benign_Only.csv\")\n",
    "df1= pd.read_csv(\"/Users/promisea/ALMA/EdgeIIoT/Edge_DDoS_Only.csv\")\n",
    "df2= pd.read_csv(\"/Users/promisea/ALMA/EdgeIIoT/Edge_IG_Only.csv\")\n",
    "X = df.drop(['Attack_type'], axis=1)\n",
    "y = df['Attack_type']\n",
    "X1 = df1.drop(['Attack_type'], axis=1)\n",
    "y1 = df1['Attack_type']\n",
    "X2 = df2.drop(['Attack_type'], axis=1)\n",
    "y2 = df2['Attack_type']\n",
    "X3 = pd.concat([X, X1, X2])\n",
    "y3 = pd.concat([y, y1, y2])\n",
    "X3, y3 = shuffle(X3, y3, random_state=42)\n",
    "dataset = stream.iter_pandas(X3, y3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "06abecf6-5e1d-4251-947b-03c205523e96",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = compose.Pipeline(\n",
    "    preprocessing.StandardScaler(),\n",
    "    AdaptiveALMA()\n",
    ")\n",
    "\n",
    "#Passing the evalaution metrics into a variable\n",
    "report = metrics.ClassificationReport()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "759b8559-3540-4b27-b3ed-73ebee12dae0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[50,000]            Precision   Recall   F1       Support  \n",
      "                                                  \n",
      "       0      64.27%   59.91%   62.01%     12641  \n",
      "       1      86.74%   88.73%   87.72%     37359  \n",
      "                                                  \n",
      "   Macro      75.50%   74.32%   74.87%            \n",
      "   Micro      81.44%   81.44%   81.44%            \n",
      "Weighted      81.06%   81.44%   81.22%            \n",
      "\n",
      "                 81.44% accuracy                   – 00:00:05 – 20.95 KB\n",
      "[94,845]            Precision   Recall   F1       Support  \n",
      "                                                  \n",
      "       0      65.34%   59.85%   62.47%     24301  \n",
      "       1      86.56%   89.06%   87.79%     70544  \n",
      "                                                  \n",
      "   Macro      75.95%   74.45%   75.13%            \n",
      "   Micro      81.58%   81.58%   81.58%            \n",
      "Weighted      81.12%   81.58%   81.30%            \n",
      "\n",
      "                 81.58% accuracy                   – 00:00:11 – 21.08 KB\n",
      "CPU times: user 11.1 s, sys: 65.3 ms, total: 11.1 s\n",
      "Wall time: 11.1 s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "           Precision   Recall   F1       Support  \n",
       "                                                  \n",
       "       0      65.34%   59.85%   62.47%     24301  \n",
       "       1      86.56%   89.06%   87.79%     70544  \n",
       "                                                  \n",
       "   Macro      75.95%   74.45%   75.13%            \n",
       "   Micro      81.58%   81.58%   81.58%            \n",
       "Weighted      81.12%   81.58%   81.30%            \n",
       "\n",
       "                 81.58% accuracy                  "
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "evaluate.progressive_val_score(dataset, model, report, print_every=50000, show_time=True, show_memory=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "5d0aabe3-8f8f-49cd-a4c7-e65e773fedcd",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Loading the dataset\n",
    "df = pd.read_csv(\"/Users/promisea/ALMA/EdgeIIoT/Edge_Benign_Only.csv\")\n",
    "df1= pd.read_csv(\"/Users/promisea/ALMA/EdgeIIoT/Edge_Malware_Only.csv\")\n",
    "df2= pd.read_csv(\"/Users/promisea/ALMA/EdgeIIoT/Edge_Injection_Only.csv\")\n",
    "X = df.drop(['Attack_type'], axis=1)\n",
    "y = df['Attack_type']\n",
    "X1 = df1.drop(['Attack_type'], axis=1)\n",
    "y1 = df1['Attack_type']\n",
    "X2 = df2.drop(['Attack_type'], axis=1)\n",
    "y2 = df2['Attack_type']\n",
    "X3 = pd.concat([X, X1, X2])\n",
    "y3 = pd.concat([y, y1, y2])\n",
    "X3, y3 = shuffle(X3, y3, random_state=42)\n",
    "dataset = stream.iter_pandas(X3, y3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "4ba6641b-e3e9-4c10-a8f2-44375f0e0d75",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = compose.Pipeline(\n",
    "    preprocessing.StandardScaler(),\n",
    "    AdaptiveALMA()\n",
    ")\n",
    "\n",
    "#Passing the evalaution metrics into a variable\n",
    "report = metrics.ClassificationReport()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "56586ba8-33dd-4079-92b5-783682e509fd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[50,000]            Precision   Recall   F1       Support  \n",
      "                                                  \n",
      "       0      66.22%   49.58%   56.70%     13927  \n",
      "       1      82.26%   90.23%   86.06%     36073  \n",
      "                                                  \n",
      "   Macro      74.24%   69.91%   71.38%            \n",
      "   Micro      78.91%   78.91%   78.91%            \n",
      "Weighted      77.79%   78.91%   77.88%            \n",
      "\n",
      "                 78.91% accuracy                   – 00:00:06 – 21.05 KB\n",
      "[86,042]            Precision   Recall   F1       Support  \n",
      "                                                  \n",
      "       0      66.96%   49.92%   57.20%     24301  \n",
      "       1      82.08%   90.31%   86.00%     61741  \n",
      "                                                  \n",
      "   Macro      74.52%   70.12%   71.60%            \n",
      "   Micro      78.90%   78.90%   78.90%            \n",
      "Weighted      77.81%   78.90%   77.87%            \n",
      "\n",
      "                 78.90% accuracy                   – 00:00:10 – 21.08 KB\n",
      "CPU times: user 10 s, sys: 68.8 ms, total: 10.1 s\n",
      "Wall time: 10.2 s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "           Precision   Recall   F1       Support  \n",
       "                                                  \n",
       "       0      66.96%   49.92%   57.20%     24301  \n",
       "       1      82.08%   90.31%   86.00%     61741  \n",
       "                                                  \n",
       "   Macro      74.52%   70.12%   71.60%            \n",
       "   Micro      78.90%   78.90%   78.90%            \n",
       "Weighted      77.81%   78.90%   77.87%            \n",
       "\n",
       "                 78.90% accuracy                  "
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "evaluate.progressive_val_score(dataset, model, report, print_every=50000, show_time=True, show_memory=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "1b3c3ff0-635f-4ce9-85f2-7c83c3c20d9b",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Loading the dataset\n",
    "df = pd.read_csv(\"/Users/promisea/ALMA/EdgeIIoT/Edge_Benign_Only.csv\")\n",
    "df1= pd.read_csv(\"/Users/promisea/ALMA/EdgeIIoT/Edge_Malware_Only.csv\")\n",
    "df2= pd.read_csv(\"/Users/promisea/ALMA/EdgeIIoT/Edge_IG_Only.csv\")\n",
    "X = df.drop(['Attack_type'], axis=1)\n",
    "y = df['Attack_type']\n",
    "X1 = df1.drop(['Attack_type'], axis=1)\n",
    "y1 = df1['Attack_type']\n",
    "X2 = df2.drop(['Attack_type'], axis=1)\n",
    "y2 = df2['Attack_type']\n",
    "X3 = pd.concat([X, X1, X2])\n",
    "y3 = pd.concat([y, y1, y2])\n",
    "X3, y3 = shuffle(X3, y3, random_state=42)\n",
    "dataset = stream.iter_pandas(X3, y3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "c4b99b47-3ab5-4924-add4-52d8ec19ec38",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = compose.Pipeline(\n",
    "    preprocessing.StandardScaler(),\n",
    "    AdaptiveALMA()\n",
    ")\n",
    "\n",
    "#Passing the evalaution metrics into a variable\n",
    "report = metrics.ClassificationReport()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "fd8c1315-c838-41c3-a830-81e1d65d72b0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[50,000]            Precision   Recall   F1       Support  \n",
      "                                                  \n",
      "       0      70.68%   54.05%   61.26%     15747  \n",
      "       1      80.94%   89.69%   85.09%     34253  \n",
      "                                                  \n",
      "   Macro      75.81%   71.87%   73.18%            \n",
      "   Micro      78.47%   78.47%   78.47%            \n",
      "Weighted      77.71%   78.47%   77.59%            \n",
      "\n",
      "                 78.47% accuracy                   – 00:00:05 – 21.03 KB\n",
      "[76,558]            Precision   Recall   F1       Support  \n",
      "                                                  \n",
      "       0      71.08%   53.31%   60.92%     24301  \n",
      "       1      80.55%   89.91%   84.97%     52257  \n",
      "                                                  \n",
      "   Macro      75.81%   71.61%   72.95%            \n",
      "   Micro      78.29%   78.29%   78.29%            \n",
      "Weighted      77.54%   78.29%   77.34%            \n",
      "\n",
      "                 78.29% accuracy                   – 00:00:08 – 21.08 KB\n",
      "CPU times: user 8.87 s, sys: 52 ms, total: 8.92 s\n",
      "Wall time: 8.91 s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "           Precision   Recall   F1       Support  \n",
       "                                                  \n",
       "       0      71.08%   53.31%   60.92%     24301  \n",
       "       1      80.55%   89.91%   84.97%     52257  \n",
       "                                                  \n",
       "   Macro      75.81%   71.61%   72.95%            \n",
       "   Micro      78.29%   78.29%   78.29%            \n",
       "Weighted      77.54%   78.29%   77.34%            \n",
       "\n",
       "                 78.29% accuracy                  "
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "evaluate.progressive_val_score(dataset, model, report, print_every=50000, show_time=True, show_memory=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "db39340d-d95f-4955-b16a-3d4fda098b6d",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Loading the dataset\n",
    "df = pd.read_csv(\"/Users/promisea/ALMA/EdgeIIoT/Edge_Benign_Only.csv\")\n",
    "df1= pd.read_csv(\"/Users/promisea/ALMA/EdgeIIoT/Edge_Injection_Only.csv\")\n",
    "df2= pd.read_csv(\"/Users/promisea/ALMA/EdgeIIoT/Edge_IG_Only.csv\")\n",
    "X = df.drop(['Attack_type'], axis=1)\n",
    "y = df['Attack_type']\n",
    "X1 = df1.drop(['Attack_type'], axis=1)\n",
    "y1 = df1['Attack_type']\n",
    "X2 = df2.drop(['Attack_type'], axis=1)\n",
    "y2 = df2['Attack_type']\n",
    "X3 = pd.concat([X, X1, X2])\n",
    "y3 = pd.concat([y, y1, y2])\n",
    "X3, y3 = shuffle(X3, y3, random_state=42)\n",
    "dataset = stream.iter_pandas(X3, y3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "a88d1fb8-e6e7-4e96-a378-72bef0d721eb",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = compose.Pipeline(\n",
    "    preprocessing.StandardScaler(),\n",
    "    AdaptiveALMA()\n",
    ")\n",
    "\n",
    "#Passing the evalaution metrics into a variable\n",
    "report = metrics.ClassificationReport()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "6cc18722-a1c6-4cd5-af0d-4d33b958db9b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[50,000]            Precision   Recall   F1       Support  \n",
      "                                                  \n",
      "       0      57.27%   71.64%   63.66%     15843  \n",
      "       1      85.11%   75.21%   79.86%     34157  \n",
      "                                                  \n",
      "   Macro      71.19%   73.43%   71.76%            \n",
      "   Micro      74.08%   74.08%   74.08%            \n",
      "Weighted      76.29%   74.08%   74.72%            \n",
      "\n",
      "                 74.08% accuracy                   – 00:00:05 – 20.95 KB\n",
      "[76,081]            Precision   Recall   F1       Support  \n",
      "                                                  \n",
      "       0      57.71%   70.93%   63.64%     24301  \n",
      "       1      84.71%   75.60%   79.90%     51780  \n",
      "                                                  \n",
      "   Macro      71.21%   73.27%   71.77%            \n",
      "   Micro      74.11%   74.11%   74.11%            \n",
      "Weighted      76.09%   74.11%   74.71%            \n",
      "\n",
      "                 74.11% accuracy                   – 00:00:08 – 21 KB\n",
      "CPU times: user 8.92 s, sys: 68.2 ms, total: 8.99 s\n",
      "Wall time: 8.95 s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "           Precision   Recall   F1       Support  \n",
       "                                                  \n",
       "       0      57.71%   70.93%   63.64%     24301  \n",
       "       1      84.71%   75.60%   79.90%     51780  \n",
       "                                                  \n",
       "   Macro      71.21%   73.27%   71.77%            \n",
       "   Micro      74.11%   74.11%   74.11%            \n",
       "Weighted      76.09%   74.11%   74.71%            \n",
       "\n",
       "                 74.11% accuracy                  "
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "evaluate.progressive_val_score(dataset, model, report, print_every=50000, show_time=True, show_memory=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "173bb62c-d46c-4bac-a5ff-ada6c0b4bdd1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Original dataset shape  Counter({1: 49396, 0: 24301})\n"
     ]
    }
   ],
   "source": [
    "#Loading the dataset\n",
    "df= pd.read_csv(\"/Users/promisea/ALMA/EdgeIIoT/Edge_DDoS_Attack.csv\")\n",
    "print('Original dataset shape  %s' % Counter(df['Attack_type']))\n",
    "X = df.drop(['Attack_type'], axis=1)\n",
    "y = df['Attack_type']\n",
    "X, y = shuffle(X, y, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "bf14a97d-e4fe-4316-b4f6-97ec3751c306",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.95, random_state=100, stratify=df['Attack_type'])\n",
    "\n",
    "model = compose.Pipeline(\n",
    "    preprocessing.StandardScaler(),\n",
    "    AdaptiveALMA()\n",
    ")\n",
    "\n",
    "dataset = stream.iter_pandas(X_train, y_train)\n",
    "\n",
    "#Passing the evalaution metrics into a variable\n",
    "report = metrics.ClassificationReport()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "9a298e52-d315-4fbc-88b9-f686c45980a2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 422 ms, sys: 3.63 ms, total: 425 ms\n",
      "Wall time: 423 ms\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "           Precision   Recall   F1       Support  \n",
       "                                                  \n",
       "       0      68.86%   70.60%   69.72%      1187  \n",
       "       1      85.85%   84.82%   85.33%      2497  \n",
       "                                                  \n",
       "   Macro      77.36%   77.71%   77.53%            \n",
       "   Micro      80.24%   80.24%   80.24%            \n",
       "Weighted      80.38%   80.24%   80.30%            \n",
       "\n",
       "                 80.24% accuracy                  "
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "evaluate.progressive_val_score(dataset, model, report, print_every=5000, show_time=True, show_memory=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "12e06603-f5cf-41cf-86b3-db7a81cd16b9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Printing the results for 95% unseen samples\n",
      "Accuracy: 0.8282747489751903\n",
      "Precision: 0.8546904238873972\n",
      "Recall: 0.8959679310859506\n",
      "F1_Score: 0.8748425512424138\n"
     ]
    }
   ],
   "source": [
    "# Initialize metrics\n",
    "Acc = metrics.Accuracy()\n",
    "Pre = metrics.Precision()\n",
    "Rec = metrics.Recall()\n",
    "F1 = metrics.F1()\n",
    "\n",
    "# Iterate over the test stream and update metrics\n",
    "for xi, yi in stream.iter_pandas(X_test, y_test):\n",
    "    y_pred = model.predict_one(xi)\n",
    "    Acc.update(yi, y_pred)\n",
    "    Pre.update(yi, y_pred)\n",
    "    Rec.update(yi, y_pred)\n",
    "    F1.update(yi, y_pred)\n",
    "\n",
    "# Retrieve final metric values\n",
    "Accuracy = Acc.get()\n",
    "Precision = Pre.get()\n",
    "Recall = Rec.get()\n",
    "F1_Score = F1.get()\n",
    "\n",
    "# Output the results\n",
    "print(\"Printing the results for 95% unseen samples\")\n",
    "print(\"Accuracy:\", Accuracy)\n",
    "print(\"Precision:\", Precision)\n",
    "print(\"Recall:\", Recall)\n",
    "print(\"F1_Score:\", F1_Score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "3736904b-0e7e-41d8-9b2a-63bd5d95322e",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.90, random_state=100, stratify=df['Attack_type'])\n",
    "\n",
    "model = compose.Pipeline(\n",
    "    preprocessing.StandardScaler(),\n",
    "    AdaptiveALMA()\n",
    ")\n",
    "\n",
    "dataset = stream.iter_pandas(X_train, y_train)\n",
    "\n",
    "#Passing the evalaution metrics into a variable\n",
    "report = metrics.ClassificationReport()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "b750480b-51da-47af-b8ad-4cf0fffc5e3a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[5,000]            Precision   Recall   F1       Support  \n",
      "                                                  \n",
      "       0      72.86%   76.66%   74.71%      1607  \n",
      "       1      88.67%   86.47%   87.56%      3393  \n",
      "                                                  \n",
      "   Macro      80.76%   81.57%   81.13%            \n",
      "   Micro      83.32%   83.32%   83.32%            \n",
      "Weighted      83.59%   83.32%   83.43%            \n",
      "\n",
      "                 83.32% accuracy                   – 00:00:00 – 20.92 KB\n",
      "[7,369]            Precision   Recall   F1       Support  \n",
      "                                                  \n",
      "       0      74.12%   77.37%   75.71%      2373  \n",
      "       1      89.02%   87.17%   88.09%      4996  \n",
      "                                                  \n",
      "   Macro      81.57%   82.27%   81.90%            \n",
      "   Micro      84.01%   84.01%   84.01%            \n",
      "Weighted      84.22%   84.01%   84.10%            \n",
      "\n",
      "                 84.01% accuracy                   – 00:00:00 – 20.9 KB\n",
      "CPU times: user 842 ms, sys: 6 ms, total: 848 ms\n",
      "Wall time: 844 ms\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "           Precision   Recall   F1       Support  \n",
       "                                                  \n",
       "       0      74.12%   77.37%   75.71%      2373  \n",
       "       1      89.02%   87.17%   88.09%      4996  \n",
       "                                                  \n",
       "   Macro      81.57%   82.27%   81.90%            \n",
       "   Micro      84.01%   84.01%   84.01%            \n",
       "Weighted      84.22%   84.01%   84.10%            \n",
       "\n",
       "                 84.01% accuracy                  "
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "evaluate.progressive_val_score(dataset, model, report, print_every=5000, show_time=True, show_memory=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "35f4ed3c-3827-474b-b7d8-cf38ded8aa5f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Printing the results for 90% unseen samples\n",
      "Accuracy: 0.8566819442769268\n",
      "Precision: 0.8931806913958624\n",
      "Recall: 0.8926576576576577\n",
      "F1_Score: 0.8929190979340798\n"
     ]
    }
   ],
   "source": [
    "# Initialize metrics\n",
    "Acc = metrics.Accuracy()\n",
    "Pre = metrics.Precision()\n",
    "Rec = metrics.Recall()\n",
    "F1 = metrics.F1()\n",
    "\n",
    "# Iterate over the test stream and update metrics\n",
    "for xi, yi in stream.iter_pandas(X_test, y_test):\n",
    "    y_pred = model.predict_one(xi)\n",
    "    Acc.update(yi, y_pred)\n",
    "    Pre.update(yi, y_pred)\n",
    "    Rec.update(yi, y_pred)\n",
    "    F1.update(yi, y_pred)\n",
    "\n",
    "# Retrieve final metric values\n",
    "Accuracy = Acc.get()\n",
    "Precision = Pre.get()\n",
    "Recall = Rec.get()\n",
    "F1_Score = F1.get()\n",
    "\n",
    "# Output the results\n",
    "print(\"Printing the results for 90% unseen samples\")\n",
    "print(\"Accuracy:\", Accuracy)\n",
    "print(\"Precision:\", Precision)\n",
    "print(\"Recall:\", Recall)\n",
    "print(\"F1_Score:\", F1_Score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "7b085ab5-dc11-4383-b8f8-273d55b44aed",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.85, random_state=100, stratify=df['Attack_type'])\n",
    "\n",
    "model = compose.Pipeline(\n",
    "    preprocessing.StandardScaler(),\n",
    "    AdaptiveALMA()\n",
    ")\n",
    "\n",
    "dataset = stream.iter_pandas(X_train, y_train)\n",
    "\n",
    "#Passing the evalaution metrics into a variable\n",
    "report = metrics.ClassificationReport()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "747dd9bb-2846-4d0a-970c-8660a8b9ef6a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[5,000]            Precision   Recall   F1       Support  \n",
      "                                                  \n",
      "       0      68.29%   76.04%   71.96%      1603  \n",
      "       1      88.06%   83.34%   85.63%      3397  \n",
      "                                                  \n",
      "   Macro      78.17%   79.69%   78.80%            \n",
      "   Micro      81.00%   81.00%   81.00%            \n",
      "Weighted      81.72%   81.00%   81.25%            \n",
      "\n",
      "                 81.00% accuracy                   – 00:00:00 – 21.03 KB\n",
      "[10,000]            Precision   Recall   F1       Support  \n",
      "                                                  \n",
      "       0      68.66%   75.24%   71.80%      3211  \n",
      "       1      87.73%   83.75%   85.70%      6789  \n",
      "                                                  \n",
      "   Macro      78.19%   79.50%   78.75%            \n",
      "   Micro      81.02%   81.02%   81.02%            \n",
      "Weighted      81.61%   81.02%   81.23%            \n",
      "\n",
      "                 81.02% accuracy                   – 00:00:01 – 20.92 KB\n",
      "[11,054]            Precision   Recall   F1       Support  \n",
      "                                                  \n",
      "       0      68.55%   74.82%   71.55%      3571  \n",
      "       1      87.44%   83.62%   85.48%      7483  \n",
      "                                                  \n",
      "   Macro      77.99%   79.22%   78.52%            \n",
      "   Micro      80.78%   80.78%   80.78%            \n",
      "Weighted      81.33%   80.78%   80.98%            \n",
      "\n",
      "                 80.78% accuracy                   – 00:00:01 – 21.47 KB\n",
      "CPU times: user 1.29 s, sys: 9.83 ms, total: 1.3 s\n",
      "Wall time: 1.29 s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "           Precision   Recall   F1       Support  \n",
       "                                                  \n",
       "       0      68.55%   74.82%   71.55%      3571  \n",
       "       1      87.44%   83.62%   85.48%      7483  \n",
       "                                                  \n",
       "   Macro      77.99%   79.22%   78.52%            \n",
       "   Micro      80.78%   80.78%   80.78%            \n",
       "Weighted      81.33%   80.78%   80.98%            \n",
       "\n",
       "                 80.78% accuracy                  "
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "evaluate.progressive_val_score(dataset, model, report, print_every=5000, show_time=True, show_memory=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "43b627d3-5ccd-4fc1-afef-c4dee32e0ca2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Printing the results for 85% unseen samples\n",
      "Accuracy: 0.8162284692623278\n",
      "Precision: 0.8720597234120671\n",
      "Recall: 0.8500465249445279\n",
      "F1_Score: 0.8609124299246085\n"
     ]
    }
   ],
   "source": [
    "# Initialize metrics\n",
    "Acc = metrics.Accuracy()\n",
    "Pre = metrics.Precision()\n",
    "Rec = metrics.Recall()\n",
    "F1 = metrics.F1()\n",
    "\n",
    "# Iterate over the test stream and update metrics\n",
    "for xi, yi in stream.iter_pandas(X_test, y_test):\n",
    "    y_pred = model.predict_one(xi)\n",
    "    Acc.update(yi, y_pred)\n",
    "    Pre.update(yi, y_pred)\n",
    "    Rec.update(yi, y_pred)\n",
    "    F1.update(yi, y_pred)\n",
    "\n",
    "# Retrieve final metric values\n",
    "Accuracy = Acc.get()\n",
    "Precision = Pre.get()\n",
    "Recall = Rec.get()\n",
    "F1_Score = F1.get()\n",
    "\n",
    "# Output the results\n",
    "print(\"Printing the results for 85% unseen samples\")\n",
    "print(\"Accuracy:\", Accuracy)\n",
    "print(\"Precision:\", Precision)\n",
    "print(\"Recall:\", Recall)\n",
    "print(\"F1_Score:\", F1_Score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "bdce514d-34cb-4fce-952f-73021ae2ee26",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Original dataset shape  Counter({1: 31109, 0: 24301})\n"
     ]
    }
   ],
   "source": [
    "#Loading the dataset\n",
    "df= pd.read_csv(\"/Users/promisea/ALMA/EdgeIIoT/Edge_Malware_Attack.csv\")\n",
    "print('Original dataset shape  %s' % Counter(df['Attack_type']))\n",
    "X = df.drop(['Attack_type'], axis=1)\n",
    "y = df['Attack_type']\n",
    "X, y = shuffle(X, y, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "9112e649-08b1-42a3-81b0-d60532022611",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.95, random_state=100, stratify=df['Attack_type'])\n",
    "\n",
    "model = compose.Pipeline(\n",
    "    preprocessing.StandardScaler(),\n",
    "    AdaptiveALMA()\n",
    ")\n",
    "\n",
    "dataset = stream.iter_pandas(X_train, y_train)\n",
    "\n",
    "#Passing the evalaution metrics into a variable\n",
    "report = metrics.ClassificationReport()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "414f9fd2-32c3-4d42-8aeb-ee4463f02c21",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 326 ms, sys: 3.61 ms, total: 330 ms\n",
      "Wall time: 327 ms\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "           Precision   Recall   F1       Support  \n",
       "                                                  \n",
       "       0      69.31%   46.15%   55.40%      1233  \n",
       "       1      65.93%   83.60%   73.72%      1537  \n",
       "                                                  \n",
       "   Macro      67.62%   64.88%   64.56%            \n",
       "   Micro      66.93%   66.93%   66.93%            \n",
       "Weighted      67.43%   66.93%   65.57%            \n",
       "\n",
       "                 66.93% accuracy                  "
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "evaluate.progressive_val_score(dataset, model, report, print_every=5000, show_time=True, show_memory=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "12fa6278-4b44-41ed-bce1-0936c9fd6395",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Printing the results for 95% unseen samples\n",
      "Accuracy: 0.6908434650455927\n",
      "Precision: 0.6708551752492548\n",
      "Recall: 0.8828283511429731\n",
      "F1_Score: 0.7623817311061791\n"
     ]
    }
   ],
   "source": [
    "# Initialize metrics\n",
    "Acc = metrics.Accuracy()\n",
    "Pre = metrics.Precision()\n",
    "Rec = metrics.Recall()\n",
    "F1 = metrics.F1()\n",
    "\n",
    "# Iterate over the test stream and update metrics\n",
    "for xi, yi in stream.iter_pandas(X_test, y_test):\n",
    "    y_pred = model.predict_one(xi)\n",
    "    Acc.update(yi, y_pred)\n",
    "    Pre.update(yi, y_pred)\n",
    "    Rec.update(yi, y_pred)\n",
    "    F1.update(yi, y_pred)\n",
    "\n",
    "# Retrieve final metric values\n",
    "Accuracy = Acc.get()\n",
    "Precision = Pre.get()\n",
    "Recall = Rec.get()\n",
    "F1_Score = F1.get()\n",
    "\n",
    "# Output the results\n",
    "print(\"Printing the results for 95% unseen samples\")\n",
    "print(\"Accuracy:\", Accuracy)\n",
    "print(\"Precision:\", Precision)\n",
    "print(\"Recall:\", Recall)\n",
    "print(\"F1_Score:\", F1_Score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "9d1ccf1e-ad48-4259-b6ba-fa895e5e5a6a",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.90, random_state=100, stratify=df['Attack_type'])\n",
    "\n",
    "model = compose.Pipeline(\n",
    "    preprocessing.StandardScaler(),\n",
    "    AdaptiveALMA()\n",
    ")\n",
    "\n",
    "dataset = stream.iter_pandas(X_train, y_train)\n",
    "\n",
    "#Passing the evalaution metrics into a variable\n",
    "report = metrics.ClassificationReport()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "5c7bc4f6-3377-4d95-a426-9cd33f608fb7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[5,000]            Precision   Recall   F1       Support  \n",
      "                                                  \n",
      "       0      81.06%   44.22%   57.23%      2207  \n",
      "       1      67.57%   91.84%   77.86%      2793  \n",
      "                                                  \n",
      "   Macro      74.32%   68.03%   67.54%            \n",
      "   Micro      70.82%   70.82%   70.82%            \n",
      "Weighted      73.53%   70.82%   68.75%            \n",
      "\n",
      "                 70.82% accuracy                   – 00:00:00 – 21 KB\n",
      "[5,541]            Precision   Recall   F1       Support  \n",
      "                                                  \n",
      "       0      81.19%   43.88%   56.97%      2420  \n",
      "       1      67.92%   92.12%   78.19%      3121  \n",
      "                                                  \n",
      "   Macro      74.56%   68.00%   67.58%            \n",
      "   Micro      71.05%   71.05%   71.05%            \n",
      "Weighted      73.72%   71.05%   68.92%            \n",
      "\n",
      "                 71.05% accuracy                   – 00:00:00 – 21.52 KB\n",
      "CPU times: user 652 ms, sys: 6.68 ms, total: 659 ms\n",
      "Wall time: 655 ms\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "           Precision   Recall   F1       Support  \n",
       "                                                  \n",
       "       0      81.19%   43.88%   56.97%      2420  \n",
       "       1      67.92%   92.12%   78.19%      3121  \n",
       "                                                  \n",
       "   Macro      74.56%   68.00%   67.58%            \n",
       "   Micro      71.05%   71.05%   71.05%            \n",
       "Weighted      73.72%   71.05%   68.92%            \n",
       "\n",
       "                 71.05% accuracy                  "
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "evaluate.progressive_val_score(dataset, model, report, print_every=5000, show_time=True, show_memory=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "150a7ece-575b-4253-b595-c924f7d225f5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Printing the results for 90% unseen samples\n",
      "Accuracy: 0.7173594818424271\n",
      "Precision: 0.6813092161929372\n",
      "Recall: 0.9326497070172931\n",
      "F1_Score: 0.7874089380250675\n"
     ]
    }
   ],
   "source": [
    "# Initialize metrics\n",
    "Acc = metrics.Accuracy()\n",
    "Pre = metrics.Precision()\n",
    "Rec = metrics.Recall()\n",
    "F1 = metrics.F1()\n",
    "\n",
    "# Iterate over the test stream and update metrics\n",
    "for xi, yi in stream.iter_pandas(X_test, y_test):\n",
    "    y_pred = model.predict_one(xi)\n",
    "    Acc.update(yi, y_pred)\n",
    "    Pre.update(yi, y_pred)\n",
    "    Rec.update(yi, y_pred)\n",
    "    F1.update(yi, y_pred)\n",
    "\n",
    "# Retrieve final metric values\n",
    "Accuracy = Acc.get()\n",
    "Precision = Pre.get()\n",
    "Recall = Rec.get()\n",
    "F1_Score = F1.get()\n",
    "\n",
    "# Output the results\n",
    "print(\"Printing the results for 90% unseen samples\")\n",
    "print(\"Accuracy:\", Accuracy)\n",
    "print(\"Precision:\", Precision)\n",
    "print(\"Recall:\", Recall)\n",
    "print(\"F1_Score:\", F1_Score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "537ffe0a-07cf-48d7-9ddd-f454dea49242",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.85, random_state=100, stratify=df['Attack_type'])\n",
    "\n",
    "model = compose.Pipeline(\n",
    "    preprocessing.StandardScaler(),\n",
    "    AdaptiveALMA()\n",
    ")\n",
    "\n",
    "dataset = stream.iter_pandas(X_train, y_train)\n",
    "\n",
    "#Passing the evalaution metrics into a variable\n",
    "report = metrics.ClassificationReport()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "63e6b3cb-47f1-4b3d-b2de-70f21a640991",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[5,000]            Precision   Recall   F1       Support  \n",
      "                                                  \n",
      "       0      80.61%   45.41%   58.10%      2224  \n",
      "       1      67.60%   91.25%   77.66%      2776  \n",
      "                                                  \n",
      "   Macro      74.10%   68.33%   67.88%            \n",
      "   Micro      70.86%   70.86%   70.86%            \n",
      "Weighted      73.39%   70.86%   68.96%            \n",
      "\n",
      "                 70.86% accuracy                   – 00:00:00 – 20.92 KB\n",
      "[8,311]            Precision   Recall   F1       Support  \n",
      "                                                  \n",
      "       0      82.84%   45.15%   58.44%      3626  \n",
      "       1      68.60%   92.76%   78.87%      4685  \n",
      "                                                  \n",
      "   Macro      75.72%   68.96%   68.66%            \n",
      "   Micro      71.99%   71.99%   71.99%            \n",
      "Weighted      74.82%   71.99%   69.96%            \n",
      "\n",
      "                 71.99% accuracy                   – 00:00:00 – 21.08 KB\n",
      "CPU times: user 981 ms, sys: 9.32 ms, total: 990 ms\n",
      "Wall time: 983 ms\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "           Precision   Recall   F1       Support  \n",
       "                                                  \n",
       "       0      82.84%   45.15%   58.44%      3626  \n",
       "       1      68.60%   92.76%   78.87%      4685  \n",
       "                                                  \n",
       "   Macro      75.72%   68.96%   68.66%            \n",
       "   Micro      71.99%   71.99%   71.99%            \n",
       "Weighted      74.82%   71.99%   69.96%            \n",
       "\n",
       "                 71.99% accuracy                  "
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "evaluate.progressive_val_score(dataset, model, report, print_every=5000, show_time=True, show_memory=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "d24d1799-46cb-4a5a-88f8-ff9124ce8394",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Printing the results for 85% unseen samples\n",
      "Accuracy: 0.7240918066200981\n",
      "Precision: 0.6841152759878253\n",
      "Recall: 0.9441795337571904\n",
      "F1_Score: 0.7933792313930007\n"
     ]
    }
   ],
   "source": [
    "# Initialize metrics\n",
    "Acc = metrics.Accuracy()\n",
    "Pre = metrics.Precision()\n",
    "Rec = metrics.Recall()\n",
    "F1 = metrics.F1()\n",
    "\n",
    "# Iterate over the test stream and update metrics\n",
    "for xi, yi in stream.iter_pandas(X_test, y_test):\n",
    "    y_pred = model.predict_one(xi)\n",
    "    Acc.update(yi, y_pred)\n",
    "    Pre.update(yi, y_pred)\n",
    "    Rec.update(yi, y_pred)\n",
    "    F1.update(yi, y_pred)\n",
    "\n",
    "# Retrieve final metric values\n",
    "Accuracy = Acc.get()\n",
    "Precision = Pre.get()\n",
    "Recall = Rec.get()\n",
    "F1_Score = F1.get()\n",
    "\n",
    "# Output the results\n",
    "print(\"Printing the results for 85% unseen samples\")\n",
    "print(\"Accuracy:\", Accuracy)\n",
    "print(\"Precision:\", Precision)\n",
    "print(\"Recall:\", Recall)\n",
    "print(\"F1_Score:\", F1_Score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "f69f0ed9-586c-4895-b40c-64a0378acae5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Original dataset shape  Counter({0: 24301, 1: 21148})\n"
     ]
    }
   ],
   "source": [
    "#Loading the dataset\n",
    "df= pd.read_csv(\"/Users/promisea/ALMA/EdgeIIoT/Edge_IG_Attack.csv\")\n",
    "print('Original dataset shape  %s' % Counter(df['Attack_type']))\n",
    "X = df.drop(['Attack_type'], axis=1)\n",
    "y = df['Attack_type']\n",
    "X, y = shuffle(X, y, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "40e3554c-aeed-419b-bdca-84c3878f7e00",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.95, random_state=100, stratify=df['Attack_type'])\n",
    "\n",
    "model = compose.Pipeline(\n",
    "    preprocessing.StandardScaler(),\n",
    "    AdaptiveALMA()\n",
    ")\n",
    "\n",
    "dataset = stream.iter_pandas(X_train, y_train)\n",
    "\n",
    "#Passing the evalaution metrics into a variable\n",
    "report = metrics.ClassificationReport()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "95af62fe-b075-4d05-9ab7-97544fb35607",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 269 ms, sys: 2.45 ms, total: 272 ms\n",
      "Wall time: 270 ms\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "           Precision   Recall   F1       Support  \n",
       "                                                  \n",
       "       0      88.40%   78.16%   82.97%      1209  \n",
       "       1      78.05%   88.33%   82.88%      1063  \n",
       "                                                  \n",
       "   Macro      83.23%   83.25%   82.92%            \n",
       "   Micro      82.92%   82.92%   82.92%            \n",
       "Weighted      83.56%   82.92%   82.93%            \n",
       "\n",
       "                 82.92% accuracy                  "
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "evaluate.progressive_val_score(dataset, model, report, print_every=5000, show_time=True, show_memory=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "e46e91a8-3839-4794-ada5-bff0b399b66f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Printing the results for 95% unseen samples\n",
      "Accuracy: 0.8474882460569284\n",
      "Precision: 0.7949659150498165\n",
      "Recall: 0.9057505601194922\n",
      "F1_Score: 0.8467499825455561\n"
     ]
    }
   ],
   "source": [
    "# Initialize metrics\n",
    "Acc = metrics.Accuracy()\n",
    "Pre = metrics.Precision()\n",
    "Rec = metrics.Recall()\n",
    "F1 = metrics.F1()\n",
    "\n",
    "# Iterate over the test stream and update metrics\n",
    "for xi, yi in stream.iter_pandas(X_test, y_test):\n",
    "    y_pred = model.predict_one(xi)\n",
    "    Acc.update(yi, y_pred)\n",
    "    Pre.update(yi, y_pred)\n",
    "    Rec.update(yi, y_pred)\n",
    "    F1.update(yi, y_pred)\n",
    "\n",
    "# Retrieve final metric values\n",
    "Accuracy = Acc.get()\n",
    "Precision = Pre.get()\n",
    "Recall = Rec.get()\n",
    "F1_Score = F1.get()\n",
    "\n",
    "# Output the results\n",
    "print(\"Printing the results for 95% unseen samples\")\n",
    "print(\"Accuracy:\", Accuracy)\n",
    "print(\"Precision:\", Precision)\n",
    "print(\"Recall:\", Recall)\n",
    "print(\"F1_Score:\", F1_Score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "a410edb0-0559-4903-af1a-1561909622d7",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.90, random_state=100, stratify=df['Attack_type'])\n",
    "\n",
    "model = compose.Pipeline(\n",
    "    preprocessing.StandardScaler(),\n",
    "    AdaptiveALMA()\n",
    ")\n",
    "\n",
    "dataset = stream.iter_pandas(X_train, y_train)\n",
    "\n",
    "#Passing the evalaution metrics into a variable\n",
    "report = metrics.ClassificationReport()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "64c09339-004e-4a09-8dfb-41f844dcd621",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 517 ms, sys: 3.54 ms, total: 520 ms\n",
      "Wall time: 518 ms\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "           Precision   Recall   F1       Support  \n",
       "                                                  \n",
       "       0      89.30%   87.78%   88.53%      2414  \n",
       "       1      86.41%   88.08%   87.24%      2130  \n",
       "                                                  \n",
       "   Macro      87.85%   87.93%   87.88%            \n",
       "   Micro      87.92%   87.92%   87.92%            \n",
       "Weighted      87.94%   87.92%   87.92%            \n",
       "\n",
       "                 87.92% accuracy                  "
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "evaluate.progressive_val_score(dataset, model, report, print_every=5000, show_time=True, show_memory=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "487c6836-1314-4715-872d-61ea77470adb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Printing the results for 90% unseen samples\n",
      "Accuracy: 0.8988387727661655\n",
      "Precision: 0.8884711779448622\n",
      "Recall: 0.894731307182669\n",
      "F1_Score: 0.8915902541262771\n"
     ]
    }
   ],
   "source": [
    "# Initialize metrics\n",
    "Acc = metrics.Accuracy()\n",
    "Pre = metrics.Precision()\n",
    "Rec = metrics.Recall()\n",
    "F1 = metrics.F1()\n",
    "\n",
    "# Iterate over the test stream and update metrics\n",
    "for xi, yi in stream.iter_pandas(X_test, y_test):\n",
    "    y_pred = model.predict_one(xi)\n",
    "    Acc.update(yi, y_pred)\n",
    "    Pre.update(yi, y_pred)\n",
    "    Rec.update(yi, y_pred)\n",
    "    F1.update(yi, y_pred)\n",
    "\n",
    "# Retrieve final metric values\n",
    "Accuracy = Acc.get()\n",
    "Precision = Pre.get()\n",
    "Recall = Rec.get()\n",
    "F1_Score = F1.get()\n",
    "\n",
    "# Output the results\n",
    "print(\"Printing the results for 90% unseen samples\")\n",
    "print(\"Accuracy:\", Accuracy)\n",
    "print(\"Precision:\", Precision)\n",
    "print(\"Recall:\", Recall)\n",
    "print(\"F1_Score:\", F1_Score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "bfce9b4a-8c00-4e1b-a1c3-5ae94df202e8",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.85, random_state=100, stratify=df['Attack_type'])\n",
    "\n",
    "model = compose.Pipeline(\n",
    "    preprocessing.StandardScaler(),\n",
    "    AdaptiveALMA()\n",
    ")\n",
    "\n",
    "dataset = stream.iter_pandas(X_train, y_train)\n",
    "\n",
    "#Passing the evalaution metrics into a variable\n",
    "report = metrics.ClassificationReport()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "b98df2fd-7272-4943-b909-6d7b7f589f8a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[5,000]            Precision   Recall   F1       Support  \n",
      "                                                  \n",
      "       0      87.90%   82.02%   84.86%      2631  \n",
      "       1      81.41%   87.46%   84.33%      2369  \n",
      "                                                  \n",
      "   Macro      84.66%   84.74%   84.60%            \n",
      "   Micro      84.60%   84.60%   84.60%            \n",
      "Weighted      84.83%   84.60%   84.61%            \n",
      "\n",
      "                 84.60% accuracy                   – 00:00:00 – 20.95 KB\n",
      "[6,817]            Precision   Recall   F1       Support  \n",
      "                                                  \n",
      "       0      88.61%   81.62%   84.97%      3640  \n",
      "       1      80.69%   87.98%   84.17%      3177  \n",
      "                                                  \n",
      "   Macro      84.65%   84.80%   84.57%            \n",
      "   Micro      84.58%   84.58%   84.58%            \n",
      "Weighted      84.92%   84.58%   84.60%            \n",
      "\n",
      "                 84.58% accuracy                   – 00:00:00 – 21.44 KB\n",
      "CPU times: user 800 ms, sys: 5.02 ms, total: 805 ms\n",
      "Wall time: 807 ms\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "           Precision   Recall   F1       Support  \n",
       "                                                  \n",
       "       0      88.61%   81.62%   84.97%      3640  \n",
       "       1      80.69%   87.98%   84.17%      3177  \n",
       "                                                  \n",
       "   Macro      84.65%   84.80%   84.57%            \n",
       "   Micro      84.58%   84.58%   84.58%            \n",
       "Weighted      84.92%   84.58%   84.60%            \n",
       "\n",
       "                 84.58% accuracy                  "
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "evaluate.progressive_val_score(dataset, model, report, print_every=5000, show_time=True, show_memory=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "a1b07628-85bf-402b-8ede-219ed1ad8df2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Printing the results for 85% unseen samples\n",
      "Accuracy: 0.8526610064195486\n",
      "Precision: 0.8101227458705864\n",
      "Recall: 0.8924378164821101\n",
      "F1_Score: 0.8492904045753018\n"
     ]
    }
   ],
   "source": [
    "# Initialize metrics\n",
    "Acc = metrics.Accuracy()\n",
    "Pre = metrics.Precision()\n",
    "Rec = metrics.Recall()\n",
    "F1 = metrics.F1()\n",
    "\n",
    "# Iterate over the test stream and update metrics\n",
    "for xi, yi in stream.iter_pandas(X_test, y_test):\n",
    "    y_pred = model.predict_one(xi)\n",
    "    Acc.update(yi, y_pred)\n",
    "    Pre.update(yi, y_pred)\n",
    "    Rec.update(yi, y_pred)\n",
    "    F1.update(yi, y_pred)\n",
    "\n",
    "# Retrieve final metric values\n",
    "Accuracy = Acc.get()\n",
    "Precision = Pre.get()\n",
    "Recall = Rec.get()\n",
    "F1_Score = F1.get()\n",
    "\n",
    "# Output the results\n",
    "print(\"Printing the results for 85% unseen samples\")\n",
    "print(\"Accuracy:\", Accuracy)\n",
    "print(\"Precision:\", Precision)\n",
    "print(\"Recall:\", Recall)\n",
    "print(\"F1_Score:\", F1_Score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "117c6cc1-8fbd-4ff8-90b7-4bb325f8c9d5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Original dataset shape  Counter({1: 30632, 0: 24301})\n"
     ]
    }
   ],
   "source": [
    "#Loading the dataset\n",
    "df= pd.read_csv(\"/Users/promisea/ALMA/EdgeIIoT/Edge_Injection_Attack.csv\")\n",
    "print('Original dataset shape  %s' % Counter(df['Attack_type']))\n",
    "X = df.drop(['Attack_type'], axis=1)\n",
    "y = df['Attack_type']\n",
    "X, y = shuffle(X, y, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "22722d54-d0fc-42b4-92b3-024177f421d4",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.95, random_state=100, stratify=df['Attack_type'])\n",
    "\n",
    "model = compose.Pipeline(\n",
    "    preprocessing.StandardScaler(),\n",
    "    AdaptiveALMA()\n",
    ")\n",
    "\n",
    "dataset = stream.iter_pandas(X_train, y_train)\n",
    "\n",
    "#Passing the evalaution metrics into a variable\n",
    "report = metrics.ClassificationReport()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "cb91046a-bdb6-4b91-9e39-87aa701a4355",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 332 ms, sys: 4.8 ms, total: 337 ms\n",
      "Wall time: 344 ms\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "           Precision   Recall   F1       Support  \n",
       "                                                  \n",
       "       0      78.43%   47.43%   59.11%      1204  \n",
       "       1      68.63%   89.82%   77.81%      1542  \n",
       "                                                  \n",
       "   Macro      73.53%   68.62%   68.46%            \n",
       "   Micro      71.23%   71.23%   71.23%            \n",
       "Weighted      72.93%   71.23%   69.61%            \n",
       "\n",
       "                 71.23% accuracy                  "
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "evaluate.progressive_val_score(dataset, model, report, print_every=5000, show_time=True, show_memory=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "8d94445e-1267-452c-9d65-ba36c074225c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Printing the results for 95% unseen samples\n",
      "Accuracy: 0.7375016766627704\n",
      "Precision: 0.6907707181635638\n",
      "Recall: 0.9578893090409075\n",
      "F1_Score: 0.8026905183712858\n"
     ]
    }
   ],
   "source": [
    "# Initialize metrics\n",
    "Acc = metrics.Accuracy()\n",
    "Pre = metrics.Precision()\n",
    "Rec = metrics.Recall()\n",
    "F1 = metrics.F1()\n",
    "\n",
    "# Iterate over the test stream and update metrics\n",
    "for xi, yi in stream.iter_pandas(X_test, y_test):\n",
    "    y_pred = model.predict_one(xi)\n",
    "    Acc.update(yi, y_pred)\n",
    "    Pre.update(yi, y_pred)\n",
    "    Rec.update(yi, y_pred)\n",
    "    F1.update(yi, y_pred)\n",
    "\n",
    "# Retrieve final metric values\n",
    "Accuracy = Acc.get()\n",
    "Precision = Pre.get()\n",
    "Recall = Rec.get()\n",
    "F1_Score = F1.get()\n",
    "\n",
    "# Output the results\n",
    "print(\"Printing the results for 95% unseen samples\")\n",
    "print(\"Accuracy:\", Accuracy)\n",
    "print(\"Precision:\", Precision)\n",
    "print(\"Recall:\", Recall)\n",
    "print(\"F1_Score:\", F1_Score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "d1af08f5-0868-48e5-b17e-16f31eee4a07",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.90, random_state=100, stratify=df['Attack_type'])\n",
    "\n",
    "model = compose.Pipeline(\n",
    "    preprocessing.StandardScaler(),\n",
    "    AdaptiveALMA()\n",
    ")\n",
    "\n",
    "dataset = stream.iter_pandas(X_train, y_train)\n",
    "\n",
    "#Passing the evalaution metrics into a variable\n",
    "report = metrics.ClassificationReport()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "36531838-70d7-4d9c-b616-b5dc87940413",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[5,000]            Precision   Recall   F1       Support  \n",
      "                                                  \n",
      "       0      73.10%   58.97%   65.28%      2208  \n",
      "       1      71.85%   82.84%   76.96%      2792  \n",
      "                                                  \n",
      "   Macro      72.48%   70.91%   71.12%            \n",
      "   Micro      72.30%   72.30%   72.30%            \n",
      "Weighted      72.41%   72.30%   71.80%            \n",
      "\n",
      "                 72.30% accuracy                   – 00:00:00 – 20.95 KB\n",
      "[5,493]            Precision   Recall   F1       Support  \n",
      "                                                  \n",
      "       0      73.01%   58.76%   65.12%      2408  \n",
      "       1      72.07%   83.05%   77.17%      3085  \n",
      "                                                  \n",
      "   Macro      72.54%   70.90%   71.14%            \n",
      "   Micro      72.40%   72.40%   72.40%            \n",
      "Weighted      72.48%   72.40%   71.89%            \n",
      "\n",
      "                 72.40% accuracy                   – 00:00:01 – 21.08 KB\n",
      "CPU times: user 776 ms, sys: 18.5 ms, total: 794 ms\n",
      "Wall time: 1.04 s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "           Precision   Recall   F1       Support  \n",
       "                                                  \n",
       "       0      73.01%   58.76%   65.12%      2408  \n",
       "       1      72.07%   83.05%   77.17%      3085  \n",
       "                                                  \n",
       "   Macro      72.54%   70.90%   71.14%            \n",
       "   Micro      72.40%   72.40%   72.40%            \n",
       "Weighted      72.48%   72.40%   71.89%            \n",
       "\n",
       "                 72.40% accuracy                  "
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "evaluate.progressive_val_score(dataset, model, report, print_every=5000, show_time=True, show_memory=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "ce29f7b1-79e8-4b78-97f8-bffaf847fb0a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Printing the results for 90% unseen samples\n",
      "Accuracy: 0.7254045307443365\n",
      "Precision: 0.722291169451074\n",
      "Recall: 0.8239735724398302\n",
      "F1_Score: 0.7697890524316625\n"
     ]
    }
   ],
   "source": [
    "# Initialize metrics\n",
    "Acc = metrics.Accuracy()\n",
    "Pre = metrics.Precision()\n",
    "Rec = metrics.Recall()\n",
    "F1 = metrics.F1()\n",
    "\n",
    "# Iterate over the test stream and update metrics\n",
    "for xi, yi in stream.iter_pandas(X_test, y_test):\n",
    "    y_pred = model.predict_one(xi)\n",
    "    Acc.update(yi, y_pred)\n",
    "    Pre.update(yi, y_pred)\n",
    "    Rec.update(yi, y_pred)\n",
    "    F1.update(yi, y_pred)\n",
    "\n",
    "# Retrieve final metric values\n",
    "Accuracy = Acc.get()\n",
    "Precision = Pre.get()\n",
    "Recall = Rec.get()\n",
    "F1_Score = F1.get()\n",
    "\n",
    "# Output the results\n",
    "print(\"Printing the results for 90% unseen samples\")\n",
    "print(\"Accuracy:\", Accuracy)\n",
    "print(\"Precision:\", Precision)\n",
    "print(\"Recall:\", Recall)\n",
    "print(\"F1_Score:\", F1_Score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "ce1baae4-b77c-496e-8ab4-8b03e1a7306e",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.85, random_state=100, stratify=df['Attack_type'])\n",
    "\n",
    "model = compose.Pipeline(\n",
    "    preprocessing.StandardScaler(),\n",
    "    AdaptiveALMA()\n",
    ")\n",
    "\n",
    "dataset = stream.iter_pandas(X_train, y_train)\n",
    "\n",
    "#Passing the evalaution metrics into a variable\n",
    "report = metrics.ClassificationReport()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "f23fc48b-c859-4524-b87f-ad3cf5f55fb6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[5,000]            Precision   Recall   F1       Support  \n",
      "                                                  \n",
      "       0      76.31%   57.08%   65.31%      2218  \n",
      "       1      71.51%   85.87%   78.03%      2782  \n",
      "                                                  \n",
      "   Macro      73.91%   71.48%   71.67%            \n",
      "   Micro      73.10%   73.10%   73.10%            \n",
      "Weighted      73.64%   73.10%   72.39%            \n",
      "\n",
      "                 73.10% accuracy                   – 00:00:00 – 21.03 KB\n",
      "[8,239]            Precision   Recall   F1       Support  \n",
      "                                                  \n",
      "       0      76.55%   56.15%   64.79%      3640  \n",
      "       1      71.34%   86.39%   78.15%      4599  \n",
      "                                                  \n",
      "   Macro      73.95%   71.27%   71.47%            \n",
      "   Micro      73.03%   73.03%   73.03%            \n",
      "Weighted      73.64%   73.03%   72.24%            \n",
      "\n",
      "                 73.03% accuracy                   – 00:00:00 – 21.49 KB\n",
      "CPU times: user 964 ms, sys: 8.77 ms, total: 973 ms\n",
      "Wall time: 966 ms\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "           Precision   Recall   F1       Support  \n",
       "                                                  \n",
       "       0      76.55%   56.15%   64.79%      3640  \n",
       "       1      71.34%   86.39%   78.15%      4599  \n",
       "                                                  \n",
       "   Macro      73.95%   71.27%   71.47%            \n",
       "   Micro      73.03%   73.03%   73.03%            \n",
       "Weighted      73.64%   73.03%   72.24%            \n",
       "\n",
       "                 73.03% accuracy                  "
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "evaluate.progressive_val_score(dataset, model, report, print_every=5000, show_time=True, show_memory=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "1d1c69a6-cc0f-43b4-8391-c7c5158ff6cb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Printing the results for 85% unseen samples\n",
      "Accuracy: 0.7364757784726089\n",
      "Precision: 0.7168720379146919\n",
      "Recall: 0.8715476510582721\n",
      "F1_Score: 0.7866789175320285\n"
     ]
    }
   ],
   "source": [
    "# Initialize metrics\n",
    "Acc = metrics.Accuracy()\n",
    "Pre = metrics.Precision()\n",
    "Rec = metrics.Recall()\n",
    "F1 = metrics.F1()\n",
    "\n",
    "# Iterate over the test stream and update metrics\n",
    "for xi, yi in stream.iter_pandas(X_test, y_test):\n",
    "    y_pred = model.predict_one(xi)\n",
    "    Acc.update(yi, y_pred)\n",
    "    Pre.update(yi, y_pred)\n",
    "    Rec.update(yi, y_pred)\n",
    "    F1.update(yi, y_pred)\n",
    "\n",
    "# Retrieve final metric values\n",
    "Accuracy = Acc.get()\n",
    "Precision = Pre.get()\n",
    "Recall = Rec.get()\n",
    "F1_Score = F1.get()\n",
    "\n",
    "# Output the results\n",
    "print(\"Printing the results for 85% unseen samples\")\n",
    "print(\"Accuracy:\", Accuracy)\n",
    "print(\"Precision:\", Precision)\n",
    "print(\"Recall:\", Recall)\n",
    "print(\"F1_Score:\", F1_Score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "291d7f58-b503-42ff-a4ad-b5979e414332",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Original dataset shape  Counter({1: 133499, 0: 24301})\n"
     ]
    }
   ],
   "source": [
    "#Loading the dataset\n",
    "df= pd.read_csv(\"/Users/promisea/ALMA/EdgeIIoT/EdgeIIoT.csv\")\n",
    "print('Original dataset shape  %s' % Counter(df['Attack_type']))\n",
    "X = df.drop(['Attack_type'], axis=1)\n",
    "y = df['Attack_type']\n",
    "X, y = shuffle(X, y, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "87de9c1a-c68d-4e94-b257-4c7979c9b7a3",
   "metadata": {},
   "outputs": [],
   "source": [
    "ALMA = compose.Pipeline(\n",
    "    preprocessing.StandardScaler(),\n",
    "    ln.ALMAClassifier()\n",
    ")\n",
    "\n",
    "dataset = stream.iter_pandas(X, y)\n",
    "\n",
    "#Passing the evalaution metrics into a variable\n",
    "report = metrics.ClassificationReport()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "eddd64a7-aa4f-4499-ba54-8d222e012184",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[5,000]            Precision   Recall   F1       Support  \n",
      "                                                  \n",
      "       0      28.84%   55.33%   37.91%       788  \n",
      "       1      89.91%   74.45%   81.45%      4212  \n",
      "                                                  \n",
      "   Macro      59.37%   64.89%   59.68%            \n",
      "   Micro      71.44%   71.44%   71.44%            \n",
      "Weighted      80.28%   71.44%   74.59%            \n",
      "\n",
      "                 71.44% accuracy                   – 00:00:00 – 14.02 KB\n",
      "[10,000]            Precision   Recall   F1       Support  \n",
      "                                                  \n",
      "       0      33.11%   52.29%   40.54%      1597  \n",
      "       1      89.81%   79.92%   84.58%      8403  \n",
      "                                                  \n",
      "   Macro      61.46%   66.10%   62.56%            \n",
      "   Micro      75.51%   75.51%   75.51%            \n",
      "Weighted      80.75%   75.51%   77.55%            \n",
      "\n",
      "                 75.51% accuracy                   – 00:00:01 – 14.02 KB\n",
      "[15,000]            Precision   Recall   F1       Support  \n",
      "                                                  \n",
      "       0      35.37%   51.04%   41.78%      2359  \n",
      "       1      90.04%   82.60%   86.16%     12641  \n",
      "                                                  \n",
      "   Macro      62.70%   66.82%   63.97%            \n",
      "   Micro      77.63%   77.63%   77.63%            \n",
      "Weighted      81.44%   77.63%   79.18%            \n",
      "\n",
      "                 77.63% accuracy                   – 00:00:01 – 14.02 KB\n",
      "[20,000]            Precision   Recall   F1       Support  \n",
      "                                                  \n",
      "       0      37.45%   49.87%   42.78%      3138  \n",
      "       1      90.06%   84.50%   87.19%     16862  \n",
      "                                                  \n",
      "   Macro      63.75%   67.19%   64.98%            \n",
      "   Micro      79.06%   79.06%   79.06%            \n",
      "Weighted      81.80%   79.06%   80.22%            \n",
      "\n",
      "                 79.06% accuracy                   – 00:00:02 – 14.02 KB\n",
      "[25,000]            Precision   Recall   F1       Support  \n",
      "                                                  \n",
      "       0      38.63%   48.91%   43.17%      3917  \n",
      "       1      90.01%   85.56%   87.73%     21083  \n",
      "                                                  \n",
      "   Macro      64.32%   67.24%   65.45%            \n",
      "   Micro      79.82%   79.82%   79.82%            \n",
      "Weighted      81.96%   79.82%   80.75%            \n",
      "\n",
      "                 79.82% accuracy                   – 00:00:02 – 14.02 KB\n",
      "[30,000]            Precision   Recall   F1       Support  \n",
      "                                                  \n",
      "       0      40.13%   48.83%   44.06%      4743  \n",
      "       1      89.98%   86.32%   88.11%     25257  \n",
      "                                                  \n",
      "   Macro      65.06%   67.58%   66.08%            \n",
      "   Micro      80.39%   80.39%   80.39%            \n",
      "Weighted      82.10%   80.39%   81.15%            \n",
      "\n",
      "                 80.39% accuracy                   – 00:00:03 – 14.02 KB\n",
      "[35,000]            Precision   Recall   F1       Support  \n",
      "                                                  \n",
      "       0      41.07%   48.84%   44.62%      5526  \n",
      "       1      90.06%   86.86%   88.43%     29474  \n",
      "                                                  \n",
      "   Macro      65.57%   67.85%   66.53%            \n",
      "   Micro      80.86%   80.86%   80.86%            \n",
      "Weighted      82.32%   80.86%   81.51%            \n",
      "\n",
      "                 80.86% accuracy                   – 00:00:03 – 14.02 KB\n",
      "[40,000]            Precision   Recall   F1       Support  \n",
      "                                                  \n",
      "       0      41.36%   48.59%   44.68%      6298  \n",
      "       1      90.07%   87.13%   88.57%     33702  \n",
      "                                                  \n",
      "   Macro      65.71%   67.86%   66.63%            \n",
      "   Micro      81.06%   81.06%   81.06%            \n",
      "Weighted      82.40%   81.06%   81.66%            \n",
      "\n",
      "                 81.06% accuracy                   – 00:00:04 – 14.02 KB\n",
      "[45,000]            Precision   Recall   F1       Support  \n",
      "                                                  \n",
      "       0      41.70%   48.22%   44.72%      7034  \n",
      "       1      90.12%   87.51%   88.79%     37966  \n",
      "                                                  \n",
      "   Macro      65.91%   67.87%   66.76%            \n",
      "   Micro      81.37%   81.37%   81.37%            \n",
      "Weighted      82.55%   81.37%   81.91%            \n",
      "\n",
      "                 81.37% accuracy                   – 00:00:04 – 14.02 KB\n",
      "[50,000]            Precision   Recall   F1       Support  \n",
      "                                                  \n",
      "       0      42.17%   48.05%   44.92%      7789  \n",
      "       1      90.16%   87.84%   88.99%     42211  \n",
      "                                                  \n",
      "   Macro      66.17%   67.95%   66.95%            \n",
      "   Micro      81.64%   81.64%   81.64%            \n",
      "Weighted      82.69%   81.64%   82.12%            \n",
      "\n",
      "                 81.64% accuracy                   – 00:00:05 – 14.02 KB\n",
      "[55,000]            Precision   Recall   F1       Support  \n",
      "                                                  \n",
      "       0      42.48%   47.86%   45.01%      8534  \n",
      "       1      90.19%   88.10%   89.13%     46466  \n",
      "                                                  \n",
      "   Macro      66.34%   67.98%   67.07%            \n",
      "   Micro      81.85%   81.85%   81.85%            \n",
      "Weighted      82.79%   81.85%   82.29%            \n",
      "\n",
      "                 81.85% accuracy                   – 00:00:06 – 14.02 KB\n",
      "[60,000]            Precision   Recall   F1       Support  \n",
      "                                                  \n",
      "       0      42.66%   47.64%   45.01%      9306  \n",
      "       1      90.18%   88.25%   89.20%     50694  \n",
      "                                                  \n",
      "   Macro      66.42%   67.94%   67.11%            \n",
      "   Micro      81.95%   81.95%   81.95%            \n",
      "Weighted      82.81%   81.95%   82.35%            \n",
      "\n",
      "                 81.95% accuracy                   – 00:00:07 – 14.02 KB\n",
      "[65,000]            Precision   Recall   F1       Support  \n",
      "                                                  \n",
      "       0      42.88%   47.45%   45.05%     10094  \n",
      "       1      90.15%   88.38%   89.25%     54906  \n",
      "                                                  \n",
      "   Macro      66.51%   67.92%   67.15%            \n",
      "   Micro      82.02%   82.02%   82.02%            \n",
      "Weighted      82.81%   82.02%   82.39%            \n",
      "\n",
      "                 82.02% accuracy                   – 00:00:07 – 14.02 KB\n",
      "[70,000]            Precision   Recall   F1       Support  \n",
      "                                                  \n",
      "       0      43.27%   47.51%   45.29%     10879  \n",
      "       1      90.16%   88.54%   89.34%     59121  \n",
      "                                                  \n",
      "   Macro      66.72%   68.02%   67.32%            \n",
      "   Micro      82.16%   82.16%   82.16%            \n",
      "Weighted      82.88%   82.16%   82.50%            \n",
      "\n",
      "                 82.16% accuracy                   – 00:00:08 – 14.02 KB\n",
      "[75,000]            Precision   Recall   F1       Support  \n",
      "                                                  \n",
      "       0      43.47%   47.47%   45.38%     11624  \n",
      "       1      90.20%   88.68%   89.43%     63376  \n",
      "                                                  \n",
      "   Macro      66.84%   68.07%   67.41%            \n",
      "   Micro      82.29%   82.29%   82.29%            \n",
      "Weighted      82.96%   82.29%   82.61%            \n",
      "\n",
      "                 82.29% accuracy                   – 00:00:09 – 14.02 KB\n",
      "[80,000]            Precision   Recall   F1       Support  \n",
      "                                                  \n",
      "       0      43.60%   47.32%   45.39%     12400  \n",
      "       1      90.18%   88.77%   89.47%     67600  \n",
      "                                                  \n",
      "   Macro      66.89%   68.05%   67.43%            \n",
      "   Micro      82.35%   82.35%   82.35%            \n",
      "Weighted      82.96%   82.35%   82.64%            \n",
      "\n",
      "                 82.35% accuracy                   – 00:00:10 – 14.02 KB\n",
      "[85,000]            Precision   Recall   F1       Support  \n",
      "                                                  \n",
      "       0      43.75%   47.24%   45.43%     13183  \n",
      "       1      90.17%   88.85%   89.51%     71817  \n",
      "                                                  \n",
      "   Macro      66.96%   68.04%   67.47%            \n",
      "   Micro      82.40%   82.40%   82.40%            \n",
      "Weighted      82.97%   82.40%   82.67%            \n",
      "\n",
      "                 82.40% accuracy                   – 00:00:10 – 14.02 KB\n",
      "[90,000]            Precision   Recall   F1       Support  \n",
      "                                                  \n",
      "       0      43.84%   47.12%   45.42%     13946  \n",
      "       1      90.17%   88.93%   89.55%     76054  \n",
      "                                                  \n",
      "   Macro      67.01%   68.03%   67.48%            \n",
      "   Micro      82.45%   82.45%   82.45%            \n",
      "Weighted      82.99%   82.45%   82.71%            \n",
      "\n",
      "                 82.45% accuracy                   – 00:00:11 – 14.02 KB\n",
      "[95,000]            Precision   Recall   F1       Support  \n",
      "                                                  \n",
      "       0      43.88%   46.97%   45.37%     14721  \n",
      "       1      90.15%   88.99%   89.56%     80279  \n",
      "                                                  \n",
      "   Macro      67.01%   67.98%   67.47%            \n",
      "   Micro      82.47%   82.47%   82.47%            \n",
      "Weighted      82.98%   82.47%   82.72%            \n",
      "\n",
      "                 82.47% accuracy                   – 00:00:11 – 14.02 KB\n",
      "[100,000]            Precision   Recall   F1       Support  \n",
      "                                                  \n",
      "       0      44.03%   46.98%   45.46%     15443  \n",
      "       1      90.20%   89.09%   89.64%     84557  \n",
      "                                                  \n",
      "   Macro      67.12%   68.04%   67.55%            \n",
      "   Micro      82.59%   82.59%   82.59%            \n",
      "Weighted      83.07%   82.59%   82.82%            \n",
      "\n",
      "                 82.59% accuracy                   – 00:00:12 – 14.02 KB\n",
      "[105,000]            Precision   Recall   F1       Support  \n",
      "                                                  \n",
      "       0      44.20%   46.98%   45.55%     16242  \n",
      "       1      90.18%   89.15%   89.66%     88758  \n",
      "                                                  \n",
      "   Macro      67.19%   68.06%   67.60%            \n",
      "   Micro      82.62%   82.62%   82.62%            \n",
      "Weighted      83.07%   82.62%   82.84%            \n",
      "\n",
      "                 82.62% accuracy                   – 00:00:13 – 14.02 KB\n",
      "[110,000]            Precision   Recall   F1       Support  \n",
      "                                                  \n",
      "       0      44.27%   46.92%   45.55%     16994  \n",
      "       1      90.19%   89.21%   89.70%     93006  \n",
      "                                                  \n",
      "   Macro      67.23%   68.06%   67.63%            \n",
      "   Micro      82.67%   82.67%   82.67%            \n",
      "Weighted      83.10%   82.67%   82.88%            \n",
      "\n",
      "                 82.67% accuracy                   – 00:00:13 – 14.02 KB\n",
      "[115,000]            Precision   Recall   F1       Support  \n",
      "                                                  \n",
      "       0      44.39%   46.92%   45.62%     17749  \n",
      "       1      90.21%   89.27%   89.74%     97251  \n",
      "                                                  \n",
      "   Macro      67.30%   68.09%   67.68%            \n",
      "   Micro      82.74%   82.74%   82.74%            \n",
      "Weighted      83.14%   82.74%   82.93%            \n",
      "\n",
      "                 82.74% accuracy                   – 00:00:14 – 14.02 KB\n",
      "[120,000]            Precision   Recall   F1       Support  \n",
      "                                                  \n",
      "       0      44.52%   46.95%   45.70%     18497  \n",
      "       1      90.24%   89.34%   89.79%    101503  \n",
      "                                                  \n",
      "   Macro      67.38%   68.14%   67.74%            \n",
      "   Micro      82.81%   82.81%   82.81%            \n",
      "Weighted      83.19%   82.81%   82.99%            \n",
      "\n",
      "                 82.81% accuracy                   – 00:00:14 – 14.02 KB\n",
      "[125,000]            Precision   Recall   F1       Support  \n",
      "                                                  \n",
      "       0      44.49%   46.82%   45.63%     19263  \n",
      "       1      90.22%   89.36%   89.79%    105737  \n",
      "                                                  \n",
      "   Macro      67.36%   68.09%   67.71%            \n",
      "   Micro      82.80%   82.80%   82.80%            \n",
      "Weighted      83.17%   82.80%   82.98%            \n",
      "\n",
      "                 82.80% accuracy                   – 00:00:15 – 14.02 KB\n",
      "[130,000]            Precision   Recall   F1       Support  \n",
      "                                                  \n",
      "       0      44.54%   46.78%   45.63%     20030  \n",
      "       1      90.22%   89.39%   89.80%    109970  \n",
      "                                                  \n",
      "   Macro      67.38%   68.09%   67.72%            \n",
      "   Micro      82.83%   82.83%   82.83%            \n",
      "Weighted      83.18%   82.83%   83.00%            \n",
      "\n",
      "                 82.83% accuracy                   – 00:00:15 – 14.02 KB\n",
      "[135,000]            Precision   Recall   F1       Support  \n",
      "                                                  \n",
      "       0      44.62%   46.77%   45.67%     20804  \n",
      "       1      90.22%   89.43%   89.82%    114196  \n",
      "                                                  \n",
      "   Macro      67.42%   68.10%   67.74%            \n",
      "   Micro      82.85%   82.85%   82.85%            \n",
      "Weighted      83.19%   82.85%   83.02%            \n",
      "\n",
      "                 82.85% accuracy                   – 00:00:16 – 14.02 KB\n",
      "[140,000]            Precision   Recall   F1       Support  \n",
      "                                                  \n",
      "       0      44.70%   46.75%   45.70%     21580  \n",
      "       1      90.21%   89.46%   89.84%    118420  \n",
      "                                                  \n",
      "   Macro      67.46%   68.11%   67.77%            \n",
      "   Micro      82.88%   82.88%   82.88%            \n",
      "Weighted      83.20%   82.88%   83.03%            \n",
      "\n",
      "                 82.88% accuracy                   – 00:00:16 – 14.02 KB\n",
      "[145,000]            Precision   Recall   F1       Support  \n",
      "                                                  \n",
      "       0      44.74%   46.71%   45.71%     22369  \n",
      "       1      90.20%   89.48%   89.84%    122631  \n",
      "                                                  \n",
      "   Macro      67.47%   68.09%   67.77%            \n",
      "   Micro      82.88%   82.88%   82.88%            \n",
      "Weighted      83.19%   82.88%   83.03%            \n",
      "\n",
      "                 82.88% accuracy                   – 00:00:17 – 14.02 KB\n",
      "[150,000]            Precision   Recall   F1       Support  \n",
      "                                                  \n",
      "       0      44.73%   46.62%   45.66%     23106  \n",
      "       1      90.21%   89.51%   89.86%    126894  \n",
      "                                                  \n",
      "   Macro      67.47%   68.07%   67.76%            \n",
      "   Micro      82.91%   82.91%   82.91%            \n",
      "Weighted      83.20%   82.91%   83.05%            \n",
      "\n",
      "                 82.91% accuracy                   – 00:00:18 – 14.02 KB\n",
      "[155,000]            Precision   Recall   F1       Support  \n",
      "                                                  \n",
      "       0      44.85%   46.68%   45.75%     23862  \n",
      "       1      90.22%   89.56%   89.89%    131138  \n",
      "                                                  \n",
      "   Macro      67.54%   68.12%   67.82%            \n",
      "   Micro      82.96%   82.96%   82.96%            \n",
      "Weighted      83.24%   82.96%   83.09%            \n",
      "\n",
      "                 82.96% accuracy                   – 00:00:18 – 14.02 KB\n",
      "[157,800]            Precision   Recall   F1       Support  \n",
      "                                                  \n",
      "       0      44.91%   46.69%   45.78%     24301  \n",
      "       1      90.23%   89.58%   89.90%    133499  \n",
      "                                                  \n",
      "   Macro      67.57%   68.13%   67.84%            \n",
      "   Micro      82.97%   82.97%   82.97%            \n",
      "Weighted      83.25%   82.97%   83.11%            \n",
      "\n",
      "                 82.97% accuracy                   – 00:00:18 – 14.02 KB\n",
      "CPU times: user 17.3 s, sys: 163 ms, total: 17.4 s\n",
      "Wall time: 1min 18s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "           Precision   Recall   F1       Support  \n",
       "                                                  \n",
       "       0      44.91%   46.69%   45.78%     24301  \n",
       "       1      90.23%   89.58%   89.90%    133499  \n",
       "                                                  \n",
       "   Macro      67.57%   68.13%   67.84%            \n",
       "   Micro      82.97%   82.97%   82.97%            \n",
       "Weighted      83.25%   82.97%   83.11%            \n",
       "\n",
       "                 82.97% accuracy                  "
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "evaluate.progressive_val_score(dataset, ALMA, report, print_every=5000, show_time=True, show_memory=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "217a57f9-f34d-4387-b253-6a2f20889cb7",
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset = stream.iter_pandas(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "97c71d24-6c00-4c32-80a3-29e764e35318",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = compose.Pipeline(\n",
    "    preprocessing.StandardScaler(),\n",
    "    AdaptiveALMA()\n",
    ")\n",
    "\n",
    "dataset = stream.iter_pandas(X, y)\n",
    "\n",
    "#Passing the evalaution metrics into a variable\n",
    "report = metrics.ClassificationReport()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "20f9e0e1-4271-41e6-a602-0972088ac7d1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[5,000]            Precision   Recall   F1       Support  \n",
      "                                                  \n",
      "       0      45.08%   48.22%   46.60%       788  \n",
      "       1      90.19%   89.01%   89.59%      4212  \n",
      "                                                  \n",
      "   Macro      67.63%   68.62%   68.09%            \n",
      "   Micro      82.58%   82.58%   82.58%            \n",
      "Weighted      83.08%   82.58%   82.82%            \n",
      "\n",
      "                 82.58% accuracy                   – 00:00:00 – 20.98 KB\n",
      "[10,000]            Precision   Recall   F1       Support  \n",
      "                                                  \n",
      "       0      46.78%   46.46%   46.62%      1597  \n",
      "       1      89.84%   89.96%   89.90%      8403  \n",
      "                                                  \n",
      "   Macro      68.31%   68.21%   68.26%            \n",
      "   Micro      83.01%   83.01%   83.01%            \n",
      "Weighted      82.96%   83.01%   82.99%            \n",
      "\n",
      "                 83.01% accuracy                   – 00:00:01 – 21.02 KB\n",
      "[15,000]            Precision   Recall   F1       Support  \n",
      "                                                  \n",
      "       0      47.04%   46.55%   46.79%      2359  \n",
      "       1      90.04%   90.22%   90.13%     12641  \n",
      "                                                  \n",
      "   Macro      68.54%   68.38%   68.46%            \n",
      "   Micro      83.35%   83.35%   83.35%            \n",
      "Weighted      83.28%   83.35%   83.32%            \n",
      "\n",
      "                 83.35% accuracy                   – 00:00:03 – 21.02 KB\n",
      "[20,000]            Precision   Recall   F1       Support  \n",
      "                                                  \n",
      "       0      47.65%   46.43%   47.03%      3138  \n",
      "       1      90.08%   90.51%   90.29%     16862  \n",
      "                                                  \n",
      "   Macro      68.86%   68.47%   68.66%            \n",
      "   Micro      83.59%   83.59%   83.59%            \n",
      "Weighted      83.42%   83.59%   83.50%            \n",
      "\n",
      "                 83.59% accuracy                   – 00:00:04 – 21.08 KB\n",
      "[25,000]            Precision   Recall   F1       Support  \n",
      "                                                  \n",
      "       0      47.98%   46.03%   46.98%      3917  \n",
      "       1      90.05%   90.73%   90.39%     21083  \n",
      "                                                  \n",
      "   Macro      69.01%   68.38%   68.69%            \n",
      "   Micro      83.72%   83.72%   83.72%            \n",
      "Weighted      83.46%   83.72%   83.59%            \n",
      "\n",
      "                 83.72% accuracy                   – 00:00:05 – 21.56 KB\n",
      "[30,000]            Precision   Recall   F1       Support  \n",
      "                                                  \n",
      "       0      49.03%   46.49%   47.73%      4743  \n",
      "       1      90.05%   90.93%   90.48%     25257  \n",
      "                                                  \n",
      "   Macro      69.54%   68.71%   69.11%            \n",
      "   Micro      83.90%   83.90%   83.90%            \n",
      "Weighted      83.56%   83.90%   83.72%            \n",
      "\n",
      "                 83.90% accuracy                   – 00:00:05 – 21.02 KB\n",
      "[35,000]            Precision   Recall   F1       Support  \n",
      "                                                  \n",
      "       0      49.51%   46.58%   48.00%      5526  \n",
      "       1      90.09%   91.09%   90.59%     29474  \n",
      "                                                  \n",
      "   Macro      69.80%   68.84%   69.30%            \n",
      "   Micro      84.07%   84.07%   84.07%            \n",
      "Weighted      83.69%   84.07%   83.87%            \n",
      "\n",
      "                 84.07% accuracy                   – 00:00:06 – 20.9 KB\n",
      "[40,000]            Precision   Recall   F1       Support  \n",
      "                                                  \n",
      "       0      49.59%   46.54%   48.02%      6298  \n",
      "       1      90.12%   91.16%   90.64%     33702  \n",
      "                                                  \n",
      "   Macro      69.86%   68.85%   69.33%            \n",
      "   Micro      84.14%   84.14%   84.14%            \n",
      "Weighted      83.74%   84.14%   83.93%            \n",
      "\n",
      "                 84.14% accuracy                   – 00:00:07 – 20.95 KB\n",
      "[45,000]            Precision   Recall   F1       Support  \n",
      "                                                  \n",
      "       0      49.65%   46.62%   48.09%      7034  \n",
      "       1      90.22%   91.24%   90.73%     37966  \n",
      "                                                  \n",
      "   Macro      69.94%   68.93%   69.41%            \n",
      "   Micro      84.27%   84.27%   84.27%            \n",
      "Weighted      83.88%   84.27%   84.06%            \n",
      "\n",
      "                 84.27% accuracy                   – 00:00:07 – 20.92 KB\n",
      "[50,000]            Precision   Recall   F1       Support  \n",
      "                                                  \n",
      "       0      49.50%   46.27%   47.83%      7789  \n",
      "       1      90.20%   91.29%   90.74%     42211  \n",
      "                                                  \n",
      "   Macro      69.85%   68.78%   69.29%            \n",
      "   Micro      84.28%   84.28%   84.28%            \n",
      "Weighted      83.86%   84.28%   84.06%            \n",
      "\n",
      "                 84.28% accuracy                   – 00:00:08 – 20.9 KB\n",
      "[55,000]            Precision   Recall   F1       Support  \n",
      "                                                  \n",
      "       0      49.39%   46.17%   47.73%      8534  \n",
      "       1      90.23%   91.31%   90.77%     46466  \n",
      "                                                  \n",
      "   Macro      69.81%   68.74%   69.25%            \n",
      "   Micro      84.31%   84.31%   84.31%            \n",
      "Weighted      83.89%   84.31%   84.09%            \n",
      "\n",
      "                 84.31% accuracy                   – 00:00:08 – 20.98 KB\n",
      "[60,000]            Precision   Recall   F1       Support  \n",
      "                                                  \n",
      "       0      49.52%   46.36%   47.89%      9306  \n",
      "       1      90.27%   91.32%   90.79%     50694  \n",
      "                                                  \n",
      "   Macro      69.89%   68.84%   69.34%            \n",
      "   Micro      84.35%   84.35%   84.35%            \n",
      "Weighted      83.95%   84.35%   84.14%            \n",
      "\n",
      "                 84.35% accuracy                   – 00:00:09 – 20.95 KB\n",
      "[65,000]            Precision   Recall   F1       Support  \n",
      "                                                  \n",
      "       0      49.57%   46.35%   47.91%     10094  \n",
      "       1      90.25%   91.33%   90.79%     54906  \n",
      "                                                  \n",
      "   Macro      69.91%   68.84%   69.35%            \n",
      "   Micro      84.35%   84.35%   84.35%            \n",
      "Weighted      83.94%   84.35%   84.13%            \n",
      "\n",
      "                 84.35% accuracy                   – 00:00:09 – 21.49 KB\n",
      "[70,000]            Precision   Recall   F1       Support  \n",
      "                                                  \n",
      "       0      49.61%   46.25%   47.87%     10879  \n",
      "       1      90.23%   91.36%   90.79%     59121  \n",
      "                                                  \n",
      "   Macro      69.92%   68.80%   69.33%            \n",
      "   Micro      84.35%   84.35%   84.35%            \n",
      "Weighted      83.92%   84.35%   84.12%            \n",
      "\n",
      "                 84.35% accuracy                   – 00:00:10 – 20.95 KB\n",
      "[75,000]            Precision   Recall   F1       Support  \n",
      "                                                  \n",
      "       0      49.94%   46.50%   48.16%     11624  \n",
      "       1      90.31%   91.45%   90.88%     63376  \n",
      "                                                  \n",
      "   Macro      70.13%   68.98%   69.52%            \n",
      "   Micro      84.49%   84.49%   84.49%            \n",
      "Weighted      84.05%   84.49%   84.26%            \n",
      "\n",
      "                 84.49% accuracy                   – 00:00:11 – 20.9 KB\n",
      "[80,000]            Precision   Recall   F1       Support  \n",
      "                                                  \n",
      "       0      49.97%   46.36%   48.10%     12400  \n",
      "       1      90.29%   91.49%   90.88%     67600  \n",
      "                                                  \n",
      "   Macro      70.13%   68.92%   69.49%            \n",
      "   Micro      84.49%   84.49%   84.49%            \n",
      "Weighted      84.04%   84.49%   84.25%            \n",
      "\n",
      "                 84.49% accuracy                   – 00:00:11 – 20.9 KB\n",
      "[85,000]            Precision   Recall   F1       Support  \n",
      "                                                  \n",
      "       0      49.98%   46.28%   48.06%     13183  \n",
      "       1      90.27%   91.50%   90.88%     71817  \n",
      "                                                  \n",
      "   Macro      70.12%   68.89%   69.47%            \n",
      "   Micro      84.48%   84.48%   84.48%            \n",
      "Weighted      84.02%   84.48%   84.24%            \n",
      "\n",
      "                 84.48% accuracy                   – 00:00:12 – 20.9 KB\n",
      "[90,000]            Precision   Recall   F1       Support  \n",
      "                                                  \n",
      "       0      50.02%   46.21%   48.04%     13946  \n",
      "       1      90.27%   91.53%   90.90%     76054  \n",
      "                                                  \n",
      "   Macro      70.14%   68.87%   69.47%            \n",
      "   Micro      84.51%   84.51%   84.51%            \n",
      "Weighted      84.03%   84.51%   84.26%            \n",
      "\n",
      "                 84.51% accuracy                   – 00:00:12 – 20.9 KB\n",
      "[95,000]            Precision   Recall   F1       Support  \n",
      "                                                  \n",
      "       0      49.90%   46.15%   47.95%     14721  \n",
      "       1      90.26%   91.50%   90.88%     80279  \n",
      "                                                  \n",
      "   Macro      70.08%   68.83%   69.42%            \n",
      "   Micro      84.48%   84.48%   84.48%            \n",
      "Weighted      84.01%   84.48%   84.23%            \n",
      "\n",
      "                 84.48% accuracy                   – 00:00:13 – 20.9 KB\n",
      "[100,000]            Precision   Recall   F1       Support  \n",
      "                                                  \n",
      "       0      49.83%   46.09%   47.88%     15443  \n",
      "       1      90.29%   91.53%   90.90%     84557  \n",
      "                                                  \n",
      "   Macro      70.06%   68.81%   69.39%            \n",
      "   Micro      84.51%   84.51%   84.51%            \n",
      "Weighted      84.04%   84.51%   84.26%            \n",
      "\n",
      "                 84.51% accuracy                   – 00:00:13 – 20.98 KB\n",
      "[105,000]            Precision   Recall   F1       Support  \n",
      "                                                  \n",
      "       0      50.01%   46.14%   48.00%     16242  \n",
      "       1      90.28%   91.56%   90.92%     88758  \n",
      "                                                  \n",
      "   Macro      70.15%   68.85%   69.46%            \n",
      "   Micro      84.54%   84.54%   84.54%            \n",
      "Weighted      84.05%   84.54%   84.28%            \n",
      "\n",
      "                 84.54% accuracy                   – 00:00:14 – 21.51 KB\n",
      "[110,000]            Precision   Recall   F1       Support  \n",
      "                                                  \n",
      "       0      50.07%   46.10%   48.01%     16994  \n",
      "       1      90.29%   91.60%   90.94%     93006  \n",
      "                                                  \n",
      "   Macro      70.18%   68.85%   69.47%            \n",
      "   Micro      84.57%   84.57%   84.57%            \n",
      "Weighted      84.08%   84.57%   84.31%            \n",
      "\n",
      "                 84.57% accuracy                   – 00:00:14 – 21.05 KB\n",
      "[115,000]            Precision   Recall   F1       Support  \n",
      "                                                  \n",
      "       0      50.13%   46.20%   48.09%     17749  \n",
      "       1      90.32%   91.61%   90.96%     97251  \n",
      "                                                  \n",
      "   Macro      70.23%   68.91%   69.52%            \n",
      "   Micro      84.60%   84.60%   84.60%            \n",
      "Weighted      84.12%   84.60%   84.34%            \n",
      "\n",
      "                 84.60% accuracy                   – 00:00:16 – 20.95 KB\n",
      "[120,000]            Precision   Recall   F1       Support  \n",
      "                                                  \n",
      "       0      50.08%   46.13%   48.02%     18497  \n",
      "       1      90.32%   91.62%   90.97%    101503  \n",
      "                                                  \n",
      "   Macro      70.20%   68.88%   69.50%            \n",
      "   Micro      84.61%   84.61%   84.61%            \n",
      "Weighted      84.12%   84.61%   84.35%            \n",
      "\n",
      "                 84.61% accuracy                   – 00:00:17 – 20.98 KB\n",
      "[125,000]            Precision   Recall   F1       Support  \n",
      "                                                  \n",
      "       0      50.11%   46.06%   48.00%     19263  \n",
      "       1      90.32%   91.65%   90.98%    105737  \n",
      "                                                  \n",
      "   Macro      70.21%   68.85%   69.49%            \n",
      "   Micro      84.62%   84.62%   84.62%            \n",
      "Weighted      84.12%   84.62%   84.35%            \n",
      "\n",
      "                 84.62% accuracy                   – 00:00:17 – 20.98 KB\n",
      "[130,000]            Precision   Recall   F1       Support  \n",
      "                                                  \n",
      "       0      50.06%   46.03%   47.96%     20030  \n",
      "       1      90.31%   91.64%   90.97%    109970  \n",
      "                                                  \n",
      "   Macro      70.18%   68.83%   69.46%            \n",
      "   Micro      84.61%   84.61%   84.61%            \n",
      "Weighted      84.11%   84.61%   84.34%            \n",
      "\n",
      "                 84.61% accuracy                   – 00:00:18 – 21 KB\n",
      "[135,000]            Precision   Recall   F1       Support  \n",
      "                                                  \n",
      "       0      50.05%   45.90%   47.89%     20804  \n",
      "       1      90.29%   91.65%   90.97%    114196  \n",
      "                                                  \n",
      "   Macro      70.17%   68.78%   69.43%            \n",
      "   Micro      84.60%   84.60%   84.60%            \n",
      "Weighted      84.09%   84.60%   84.33%            \n",
      "\n",
      "                 84.60% accuracy                   – 00:00:18 – 20.98 KB\n",
      "[140,000]            Precision   Recall   F1       Support  \n",
      "                                                  \n",
      "       0      50.05%   45.84%   47.85%     21580  \n",
      "       1      90.28%   91.66%   90.97%    118420  \n",
      "                                                  \n",
      "   Macro      70.16%   68.75%   69.41%            \n",
      "   Micro      84.60%   84.60%   84.60%            \n",
      "Weighted      84.08%   84.60%   84.32%            \n",
      "\n",
      "                 84.60% accuracy                   – 00:00:19 – 21.08 KB\n",
      "[145,000]            Precision   Recall   F1       Support  \n",
      "                                                  \n",
      "       0      50.08%   45.82%   47.86%     22369  \n",
      "       1      90.27%   91.67%   90.96%    122631  \n",
      "                                                  \n",
      "   Macro      70.17%   68.75%   69.41%            \n",
      "   Micro      84.60%   84.60%   84.60%            \n",
      "Weighted      84.07%   84.60%   84.31%            \n",
      "\n",
      "                 84.60% accuracy                   – 00:00:20 – 21.49 KB\n",
      "[150,000]            Precision   Recall   F1       Support  \n",
      "                                                  \n",
      "       0      50.05%   45.79%   47.83%     23106  \n",
      "       1      90.28%   91.68%   90.97%    126894  \n",
      "                                                  \n",
      "   Macro      70.16%   68.74%   69.40%            \n",
      "   Micro      84.61%   84.61%   84.61%            \n",
      "Weighted      84.08%   84.61%   84.33%            \n",
      "\n",
      "                 84.61% accuracy                   – 00:00:20 – 20.98 KB\n",
      "[155,000]            Precision   Recall   F1       Support  \n",
      "                                                  \n",
      "       0      49.97%   45.72%   47.75%     23862  \n",
      "       1      90.27%   91.67%   90.97%    131138  \n",
      "                                                  \n",
      "   Macro      70.12%   68.70%   69.36%            \n",
      "   Micro      84.60%   84.60%   84.60%            \n",
      "Weighted      84.07%   84.60%   84.31%            \n",
      "\n",
      "                 84.60% accuracy                   – 00:00:21 – 20.99 KB\n",
      "[157,800]            Precision   Recall   F1       Support  \n",
      "                                                  \n",
      "       0      49.93%   45.69%   47.72%     24301  \n",
      "       1      90.26%   91.66%   90.96%    133499  \n",
      "                                                  \n",
      "   Macro      70.10%   68.68%   69.34%            \n",
      "   Micro      84.58%   84.58%   84.58%            \n",
      "Weighted      84.05%   84.58%   84.30%            \n",
      "\n",
      "                 84.58% accuracy                   – 00:00:21 – 21.56 KB\n",
      "CPU times: user 20.5 s, sys: 122 ms, total: 20.6 s\n",
      "Wall time: 21.5 s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "           Precision   Recall   F1       Support  \n",
       "                                                  \n",
       "       0      49.93%   45.69%   47.72%     24301  \n",
       "       1      90.26%   91.66%   90.96%    133499  \n",
       "                                                  \n",
       "   Macro      70.10%   68.68%   69.34%            \n",
       "   Micro      84.58%   84.58%   84.58%            \n",
       "Weighted      84.05%   84.58%   84.30%            \n",
       "\n",
       "                 84.58% accuracy                  "
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "evaluate.progressive_val_score(dataset, model, report, print_every=5000, show_time=True, show_memory=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "7f12378e-bf13-4ad1-88ab-7b68f8b79053",
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset = stream.iter_pandas(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "9f7931f2-e047-4304-be7b-3ac5f83be8db",
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_performance(dataset, metric, models):\n",
    "    metric_name = metric.__class__.__name__\n",
    "    \n",
    "    # Creating a single plot for accuracy\n",
    "    fig, ax = plt.subplots(figsize=(10, 4), dpi=300)\n",
    "    threshold_reached_step = None  # To store the step where accuracy reaches 75%\n",
    "\n",
    "    for model_name, model in models.items():\n",
    "        steps = []\n",
    "        accuracies = []\n",
    "\n",
    "        for checkpoint in evaluate.iter_progressive_val_score(\n",
    "            dataset, model, metric, measure_time=False, measure_memory=False, step=20\n",
    "        ):\n",
    "            current_step = checkpoint[\"Step\"]\n",
    "            current_accuracy = checkpoint[metric_name].get()\n",
    "            steps.append(current_step)\n",
    "            accuracies.append(current_accuracy)\n",
    "\n",
    "            # Check if the accuracy reaches 75% for the first time\n",
    "            if current_accuracy >= 0.75 and threshold_reached_step is None:\n",
    "                threshold_reached_step = current_step\n",
    "\n",
    "        ax.plot(steps, accuracies, label=model_name)\n",
    "\n",
    "        # Draw a vertical red line and annotate if the threshold is reached\n",
    "        if threshold_reached_step:\n",
    "            ax.axvline(x=threshold_reached_step, color='red', linestyle='--')\n",
    "            ax.annotate(f'{threshold_reached_step} samples', xy=(threshold_reached_step, 0.75),\n",
    "                        xytext=(threshold_reached_step + 50, 0.80),\n",
    "                        arrowprops=dict(facecolor='blue', shrink=0.05),\n",
    "                        horizontalalignment='left', verticalalignment='top')\n",
    "\n",
    "    ax.set_ylabel('Accuracy', fontweight='bold')\n",
    "    ax.set_xlabel('#Samples', fontweight='bold')\n",
    "    ax.grid(True)\n",
    "    ax.legend(\n",
    "        loc='upper center', bbox_to_anchor=(0.5, 1.15),\n",
    "        ncol=1, fancybox=True, shadow=True\n",
    "    )\n",
    "    \n",
    "    # Apply bold formatting directly to tick labels\n",
    "    for label in ax.get_xticklabels() + ax.get_yticklabels():\n",
    "        label.set_fontweight('bold')\n",
    "\n",
    "    plt.tight_layout()\n",
    "    plt.savefig('Performance_ALMA_EdgeIIoT.png')\n",
    "    plt.close()\n",
    "\n",
    "    return fig"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "ed431f3a-e8e2-4fdd-8972-9098b71ca959",
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 3000x1200 with 1 Axes>"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plot_performance(\n",
    "    dataset,\n",
    "    metrics.Accuracy(),\n",
    "    {\n",
    "        \"Real-time Performance of Adaptive ALMA the EdgeIIoT Dataset\": (\n",
    "            preprocessing.StandardScaler() | AdaptiveALMA()\n",
    "        )\n",
    "    }\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2942b789-038a-49d8-a5f6-da39e2b3c3b3",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "river",
   "language": "python",
   "name": "river"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.18"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}